From f0e95fc3586253f5f6af84ba662445579c1b96c7 Mon Sep 17 00:00:00 2001 From: kwliu Date: Sat, 3 Apr 2021 19:09:25 -0700 Subject: [PATCH 01/20] some manual attempt to upgrade to tf2 and sonnet 2.0.0 --- dnc/access.py | 25 ++++++++++++++++++------- dnc/addressing.py | 38 +++++++++++++++++++++++++++++--------- dnc/dnc.py | 29 +++++++++++++++++++---------- dnc/repeat_copy.py | 16 +++++++++++----- dnc/util.py | 11 +++++++++++ train.py | 7 ++++--- 6 files changed, 92 insertions(+), 34 deletions(-) diff --git a/dnc/access.py b/dnc/access.py index 211d454..182993a 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -102,6 +102,10 @@ def __init__(self, self._num_reads = num_reads self._num_writes = num_writes + self._memory_dtype = tf.float32 + self._read_dtype = tf.float32 + self._write_dtype = tf.float32 + self._write_content_weights_mod = addressing.CosineWeights( num_writes, word_size, name='write_content_weights') self._read_content_weights_mod = addressing.CosineWeights( @@ -110,6 +114,9 @@ def __init__(self, self._linkage = addressing.TemporalLinkage(memory_size, num_writes) self._freeness = addressing.Freeness(memory_size) + def __call__(inputs, prev_state): + return self._build(inputs, prev_state) + def _build(self, inputs, prev_state): """Connects the MemoryAccess module into the graph. @@ -302,15 +309,19 @@ def _read_weights(self, inputs, memory, prev_read_weights, link): return read_weights - @property - def state_size(self): + # memory access states sizes indpendent of batch size + def initial_state(self, batch_size=None): """Returns a tuple of the shape of the state tensors.""" return AccessState( - memory=tf.TensorShape([self._memory_size, self._word_size]), - read_weights=tf.TensorShape([self._num_reads, self._memory_size]), - write_weights=tf.TensorShape([self._num_writes, self._memory_size]), - linkage=self._linkage.state_size, - usage=self._freeness.state_size) + memory=tf.zeros([self._memory_size, self._word_size], dtype=self._memory_dtype), + read_weights=tf.zeros([self._num_reads, self._memory_size], dtype=self._read_dtype), + write_weights=tf.zeros([self._num_writes, self._memory_size], dtype=self._write_dtype), + linkage=self._linkage.initial_state(batch_size), + usage=self._freeness.initial_state(batch_size)) + + @property + def state_size(self): + return util.state_size_from_initial_state(self.initial_state()) @property def output_size(self): diff --git a/dnc/addressing.py b/dnc/addressing.py index 97365b1..0d1d388 100644 --- a/dnc/addressing.py +++ b/dnc/addressing.py @@ -55,7 +55,7 @@ def weighted_softmax(activations, strengths, strengths_op): return softmax(sharp_activations) -class CosineWeights(snt.AbstractModule): +class CosineWeights(snt.Module): """Cosine-weighted attention. Calculates the cosine similarity between a query and each word in memory, then @@ -129,6 +129,11 @@ def __init__(self, memory_size, num_writes, name='temporal_linkage'): super(TemporalLinkage, self).__init__(name=name) self._memory_size = memory_size self._num_writes = num_writes + self._link_dtype = tf.float32 + self._precedence_dtype = tf.float32 + + def __call__(inputs, prev_state): + return self._build(inputs, prev_state) def _build(self, write_weights, prev_state): """Calculate the updated linkage state given the write weights. @@ -239,14 +244,21 @@ def _precedence_weights(self, prev_precedence_weights, write_weights): write_sum = tf.reduce_sum(write_weights, 2, keepdims=True) return (1 - write_sum) * prev_precedence_weights + write_weights - @property - def state_size(self): + # addressing state size is independent of batch size + def initial_state(self, batch_size=None): """Returns a `TemporalLinkageState` tuple of the state tensors' shapes.""" return TemporalLinkageState( - link=tf.TensorShape( - [self._num_writes, self._memory_size, self._memory_size]), - precedence_weights=tf.TensorShape([self._num_writes, - self._memory_size]),) + link=tf.zeros( + [self._num_writes, self._memory_size, self._memory_size], + dtype=self._link_dtype), + precedence_weights=tf.zeros( + [self._num_writes, self._memory_size], + dtype=self._precedence_dtype) + ) + + @property + def state_size(self): + return util.state_size_from_initial_state(self.initial_state()) class Freeness(snt.RNNCore): @@ -275,6 +287,10 @@ def __init__(self, memory_size, name='freeness'): """ super(Freeness, self).__init__(name=name) self._memory_size = memory_size + self._dtype = tf.float32 + + def __call__(inputs, prev_state): + return self._build(inputs, prev_state) def _build(self, write_weights, free_gate, read_weights, prev_usage): """Calculates the new memory usage u_t. @@ -404,7 +420,11 @@ def _allocation(self, usage): # corresponds to the original indexing of `usage`. return util.batch_gather(sorted_allocation, inverse_indices) + # freeness size is independent of batch size + def initial_state(self, batch_size=None): + """Returns the shape of the state tensor.""" + return tf.zeros([self._memory_size], dtype=self._dtype) + @property def state_size(self): - """Returns the shape of the state tensor.""" - return tf.TensorShape([self._memory_size]) + return util.state_size_from_initial_state(self.initial_state()) diff --git a/dnc/dnc.py b/dnc/dnc.py index db14b2a..a5efdd4 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -25,9 +25,9 @@ import collections import numpy as np import sonnet as snt -import tensorflow as tf +import tensorflow.compat.v1 as tf -from dnc import access +from dnc import access, util DNCState = collections.namedtuple('DNCState', ('access_output', 'access_state', 'controller_state')) @@ -43,6 +43,7 @@ def __init__(self, access_config, controller_config, output_size, + batch_size, clip_value=None, name='dnc'): """Initializes the DNC core. @@ -61,19 +62,21 @@ def __init__(self, """ super(DNC, self).__init__(name=name) - with self._enter_variable_scope(): - self._controller = snt.LSTM(**controller_config) - self._access = access.MemoryAccess(**access_config) + #with self._enter_variable_scope(): + self._controller = snt.LSTM(**controller_config) + self._access = access.MemoryAccess(**access_config) self._access_output_size = np.prod(self._access.output_size.as_list()) self._output_size = output_size + self._batch_size = batch_size self._clip_value = clip_value or 0 self._output_size = tf.TensorShape([output_size]) self._state_size = DNCState( access_output=self._access_output_size, access_state=self._access.state_size, - controller_state=self._controller.state_size) + controller_state=util.state_size_from_initial_state( + self._controller.initial_state(batch_size))) def _clip_if_enabled(self, x): if self._clip_value > 0: @@ -81,6 +84,9 @@ def _clip_if_enabled(self, x): else: return x + def __call__(self, inputs, prev_state): + return self._build(inputs, prev_state) + def _build(self, inputs, prev_state): """Connects the DNC core into the graph. @@ -102,7 +108,7 @@ def _build(self, inputs, prev_state): prev_access_state = prev_state.access_state prev_controller_state = prev_state.controller_state - batch_flatten = snt.BatchFlatten() + batch_flatten = tf.layers.Flatten() controller_input = tf.concat( [batch_flatten(inputs), batch_flatten(prev_access_output)], 1) @@ -126,12 +132,15 @@ def _build(self, inputs, prev_state): access_state=access_state, controller_state=controller_state) + def get_initial_state(self): + return self.initial_state(self._batch_size) + def initial_state(self, batch_size, dtype=tf.float32): return DNCState( - controller_state=self._controller.initial_state(batch_size, dtype), - access_state=self._access.initial_state(batch_size, dtype), + controller_state=self._controller.initial_state(batch_size), + access_state=self._access.initial_state(batch_size), access_output=tf.zeros( - [batch_size] + self._access.output_size.as_list(), dtype)) + [batch_size] + self._access.output_size.as_list(), dtype=dtype)) @property def state_size(self): diff --git a/dnc/repeat_copy.py b/dnc/repeat_copy.py index ad52579..d77d141 100644 --- a/dnc/repeat_copy.py +++ b/dnc/repeat_copy.py @@ -112,7 +112,7 @@ def _readable(datum): return '\n' + '\n\n\n\n'.join(batch_strings) -class RepeatCopy(snt.AbstractModule): +class RepeatCopy(snt.Module): """Sequence data generator for the task of repeating a random binary pattern. When called, an instance of this class will return a tuple of tensorflow ops @@ -249,6 +249,11 @@ def target_size(self): def batch_size(self): return self._batch_size + def __call__(self): + self._build() + return self.datasettensor + + @snt.once def _build(self): """Implements build method which adds ops to graph.""" @@ -266,9 +271,9 @@ def _build(self): num_repeats_channel_idx = full_obs_size - 1 # Samples each batch index's sequence length and the number of repeats. - sub_seq_length_batch = tf.random_uniform( + sub_seq_length_batch = tf.random.uniform( [batch_size], minval=min_length, maxval=max_length + 1, dtype=tf.int32) - num_repeats_batch = tf.random_uniform( + num_repeats_batch = tf.random.uniform( [batch_size], minval=min_reps, maxval=max_reps + 1, dtype=tf.int32) # Pads all the batches to have the same total sequence length. @@ -292,7 +297,7 @@ def _build(self): # The observation pattern is a sequence of random binary vectors. obs_pattern_shape = [sub_seq_len, num_bits] obs_pattern = tf.cast( - tf.random_uniform( + tf.random.uniform( obs_pattern_shape, minval=0, maxval=2, dtype=tf.int32), tf.float32) @@ -374,7 +379,8 @@ def _build(self): targ = tf.reshape(tf.concat(targ_tensors, 1), targ_batch_shape) mask = tf.transpose( tf.reshape(tf.concat(mask_tensors, 0), mask_batch_trans_shape)) - return DatasetTensors(obs, targ, mask) + + self.datasettensor = DatasetTensors(obs, targ, mask) def cost(self, logits, targ, mask): return masked_sigmoid_cross_entropy( diff --git a/dnc/util.py b/dnc/util.py index 5009c77..65cea8a 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -70,3 +70,14 @@ def reduce_prod(x, axis, name=None): idx2 = tf.zeros([size], tf.float32) indices = tf.stack([idx1, idx2], 1) return tf.gather_nd(cp, tf.cast(indices, tf.int32)) + +# tf2 and sonnet2 compatibility +def state_size_from_initial_state(initial_state): + if isinstance(initial_state, tf.Tensor): + return initial_state.shape + + state_size_dict = {} + #import ipdb; ipdb.set_trace() + for field, value in initial_state._asdict().items(): + state_size_dict[field] = state_size_from_initial_state(value) + return type(initial_state)(**state_size_dict) diff --git a/train.py b/train.py index 036daef..2e96e9c 100644 --- a/train.py +++ b/train.py @@ -18,7 +18,7 @@ from __future__ import division from __future__ import print_function -import tensorflow as tf +import tensorflow.compat.v1 as tf import sonnet as snt from dnc import dnc @@ -80,8 +80,9 @@ def run_model(input_sequence, output_size): } clip_value = FLAGS.clip_value - dnc_core = dnc.DNC(access_config, controller_config, output_size, clip_value) - initial_state = dnc_core.initial_state(FLAGS.batch_size) + dnc_core = dnc.DNC( + access_config, controller_config, output_size, FLAGS.batch_size, clip_value) + initial_state = dnc_core.get_initial_state() output_sequence, _ = tf.nn.dynamic_rnn( cell=dnc_core, inputs=input_sequence, From 0c2a5e22ea94b2356581fb32e3c9c9b0a77fc3af Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 4 Apr 2021 12:40:20 -0700 Subject: [PATCH 02/20] upgrade with auto upgrade script --- dnc/access.py | 12 +-- dnc/access_test.py | 18 ++-- dnc/addressing.py | 26 ++--- dnc/addressing_test.py | 32 +++--- dnc/dnc.py | 2 +- dnc/repeat_copy.py | 22 ++-- dnc/util.py | 18 ++-- report.txt | 238 +++++++++++++++++++++++++++++++++++++++++ train.py | 26 ++--- 9 files changed, 316 insertions(+), 78 deletions(-) create mode 100644 report.txt diff --git a/dnc/access.py b/dnc/access.py index 211d454..62cd3fa 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -49,14 +49,14 @@ def _erase_and_write(memory, address, reset_weights, values): Returns: 3-D tensor of shape `[batch_size, num_writes, word_size]`. """ - with tf.name_scope('erase_memory', values=[memory, address, reset_weights]): + with tf.compat.v1.name_scope('erase_memory', values=[memory, address, reset_weights]): expand_address = tf.expand_dims(address, 3) reset_weights = tf.expand_dims(reset_weights, 2) weighted_resets = expand_address * reset_weights reset_gate = util.reduce_prod(1 - weighted_resets, 1) memory *= reset_gate - with tf.name_scope('additive_write', values=[memory, address, values]): + with tf.compat.v1.name_scope('additive_write', values=[memory, address, values]): add_matrix = tf.matmul(address, values, adjoint_a=True) memory += add_matrix @@ -236,7 +236,7 @@ def _write_weights(self, inputs, memory, usage): tensor of shape `[batch_size, num_writes, memory_size]` indicating where to write to (if anywhere) for each write head. """ - with tf.name_scope('write_weights', values=[inputs, memory, usage]): + with tf.compat.v1.name_scope('write_weights', values=[inputs, memory, usage]): # c_t^{w, i} - The content-based weights for each write head. write_content_weights = self._write_content_weights_mod( memory, inputs['write_content_keys'], @@ -278,7 +278,7 @@ def _read_weights(self, inputs, memory, prev_read_weights, link): A tensor of shape `[batch_size, num_reads, memory_size]` containing the read weights for each read head. """ - with tf.name_scope( + with tf.compat.v1.name_scope( 'read_weights', values=[inputs, memory, prev_read_weights, link]): # c_t^{r, i} - The content weightings for each read head. content_weights = self._read_content_weights_mod( @@ -297,8 +297,8 @@ def _read_weights(self, inputs, memory, prev_read_weights, link): read_weights = ( tf.expand_dims(content_mode, 2) * content_weights + tf.reduce_sum( - tf.expand_dims(forward_mode, 3) * forward_weights, 2) + - tf.reduce_sum(tf.expand_dims(backward_mode, 3) * backward_weights, 2)) + input_tensor=tf.expand_dims(forward_mode, 3) * forward_weights, axis=2) + + tf.reduce_sum(input_tensor=tf.expand_dims(backward_mode, 3) * backward_weights, axis=2)) return read_weights diff --git a/dnc/access_test.py b/dnc/access_test.py index 20fe7c2..b258909 100644 --- a/dnc/access_test.py +++ b/dnc/access_test.py @@ -42,7 +42,7 @@ def setUp(self): self.initial_state = self.module.initial_state(BATCH_SIZE) def testBuildAndTrain(self): - inputs = tf.random_normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE]) + inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE]) output, _ = rnn.dynamic_rnn( cell=self.module, @@ -51,9 +51,9 @@ def testBuildAndTrain(self): time_major=True) targets = np.random.rand(TIME_STEPS, BATCH_SIZE, NUM_READS, WORD_SIZE) - loss = tf.reduce_mean(tf.square(output - targets)) - train_op = tf.train.GradientDescentOptimizer(1).minimize(loss) - init = tf.global_variables_initializer() + loss = tf.reduce_mean(input_tensor=tf.square(output - targets)) + train_op = tf.compat.v1.train.GradientDescentOptimizer(1).minimize(loss) + init = tf.compat.v1.global_variables_initializer() with self.test_session(): init.run() @@ -61,8 +61,8 @@ def testBuildAndTrain(self): def testValidReadMode(self): inputs = self.module._read_inputs( - tf.random_normal([BATCH_SIZE, INPUT_SIZE])) - init = tf.global_variables_initializer() + tf.random.normal([BATCH_SIZE, INPUT_SIZE])) + init = tf.compat.v1.global_variables_initializer() with self.test_session() as sess: init.run() @@ -145,7 +145,7 @@ def testReadWeights(self): def testGradients(self): inputs = tf.constant(np.random.randn(BATCH_SIZE, INPUT_SIZE), tf.float32) output, _ = self.module(inputs, self.initial_state) - loss = tf.reduce_sum(output) + loss = tf.reduce_sum(input_tensor=output) tensors_to_check = [ inputs, self.initial_state.memory, self.initial_state.read_weights, @@ -154,8 +154,8 @@ def testGradients(self): ] shapes = [x.get_shape().as_list() for x in tensors_to_check] with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - err = tf.test.compute_gradient_error(tensors_to_check, shapes, loss, [1]) + sess.run(tf.compat.v1.global_variables_initializer()) + err = tf.compat.v1.test.compute_gradient_error(tensors_to_check, shapes, loss, [1]) self.assertLess(err, 0.1) diff --git a/dnc/addressing.py b/dnc/addressing.py index 97365b1..8cab265 100644 --- a/dnc/addressing.py +++ b/dnc/addressing.py @@ -32,7 +32,7 @@ def _vector_norms(m): - squared_norms = tf.reduce_sum(m * m, axis=2, keepdims=True) + squared_norms = tf.reduce_sum(input_tensor=m * m, axis=2, keepdims=True) return tf.sqrt(squared_norms + _EPSILON) @@ -170,7 +170,7 @@ def directional_read_weights(self, link, prev_read_weights, forward): Returns: tensor of shape `[batch_size, num_reads, num_writes, memory_size]` """ - with tf.name_scope('directional_read_weights'): + with tf.compat.v1.name_scope('directional_read_weights'): # We calculate the forward and backward directions for each pair of # read and write heads; hence we need to tile the read weights and do a # sort of "outer product" to get this. @@ -178,7 +178,7 @@ def directional_read_weights(self, link, prev_read_weights, forward): 1) result = tf.matmul(expanded_read_weights, link, adjoint_b=forward) # Swap dimensions 1, 2 so order is [batch, reads, writes, memory]: - return tf.transpose(result, perm=[0, 2, 1, 3]) + return tf.transpose(a=result, perm=[0, 2, 1, 3]) def _link(self, prev_link, prev_precedence_weights, write_weights): """Calculates the new link graphs. @@ -201,8 +201,8 @@ def _link(self, prev_link, prev_precedence_weights, write_weights): A tensor of shape `[batch_size, num_writes, memory_size, memory_size]` containing the new link graphs for each write head. """ - with tf.name_scope('link'): - batch_size = tf.shape(prev_link)[0] + with tf.compat.v1.name_scope('link'): + batch_size = tf.shape(input=prev_link)[0] write_weights_i = tf.expand_dims(write_weights, 3) write_weights_j = tf.expand_dims(write_weights, 2) prev_precedence_weights_j = tf.expand_dims(prev_precedence_weights, 2) @@ -211,7 +211,7 @@ def _link(self, prev_link, prev_precedence_weights, write_weights): link = prev_link_scale * prev_link + new_link # Return the link with the diagonal set to zero, to remove self-looping # edges. - return tf.matrix_set_diag( + return tf.linalg.set_diag( link, tf.zeros( [batch_size, self._num_writes, self._memory_size], @@ -235,8 +235,8 @@ def _precedence_weights(self, prev_precedence_weights, write_weights): A tensor of shape `[batch_size, num_writes, memory_size]` containing the new precedence weights. """ - with tf.name_scope('precedence_weights'): - write_sum = tf.reduce_sum(write_weights, 2, keepdims=True) + with tf.compat.v1.name_scope('precedence_weights'): + write_sum = tf.reduce_sum(input_tensor=write_weights, axis=2, keepdims=True) return (1 - write_sum) * prev_precedence_weights + write_weights @property @@ -326,7 +326,7 @@ def write_allocation_weights(self, usage, write_gates, num_writes): freeness-based write locations. Note that this isn't scaled by `write_gate`; this scaling must be applied externally. """ - with tf.name_scope('write_allocation_weights'): + with tf.compat.v1.name_scope('write_allocation_weights'): # expand gatings over memory locations write_gates = tf.expand_dims(write_gates, -1) @@ -349,7 +349,7 @@ def _usage_after_write(self, prev_usage, write_weights): Returns: New usage, a tensor of shape `[batch_size, memory_size]`. """ - with tf.name_scope('usage_after_write'): + with tf.compat.v1.name_scope('usage_after_write'): # Calculate the aggregated effect of all write heads write_weights = 1 - util.reduce_prod(1 - write_weights, 1) return prev_usage + (1 - prev_usage) * write_weights @@ -367,7 +367,7 @@ def _usage_after_read(self, prev_usage, free_gate, read_weights): Returns: New usage, a tensor of shape `[batch_size, memory_size]`. """ - with tf.name_scope('usage_after_read'): + with tf.compat.v1.name_scope('usage_after_read'): free_gate = tf.expand_dims(free_gate, -1) free_read_weights = free_gate * read_weights phi = util.reduce_prod(1 - free_read_weights, 1, name='phi') @@ -388,7 +388,7 @@ def _allocation(self, usage): Returns: Tensor of shape `[batch_size, memory_size]` corresponding to allocation. """ - with tf.name_scope('allocation'): + with tf.compat.v1.name_scope('allocation'): # Ensure values are not too small prior to cumprod. usage = _EPSILON + (1 - _EPSILON) * usage @@ -396,7 +396,7 @@ def _allocation(self, usage): sorted_nonusage, indices = tf.nn.top_k( nonusage, k=self._memory_size, name='sort') sorted_usage = 1 - sorted_nonusage - prod_sorted_usage = tf.cumprod(sorted_usage, axis=1, exclusive=True) + prod_sorted_usage = tf.math.cumprod(sorted_usage, axis=1, exclusive=True) sorted_allocation = sorted_nonusage * prod_sorted_usage inverse_indices = util.batch_invert_permutation(indices) diff --git a/dnc/addressing_test.py b/dnc/addressing_test.py index a8a8ac4..993d064 100644 --- a/dnc/addressing_test.py +++ b/dnc/addressing_test.py @@ -36,9 +36,9 @@ def testValues(self): activations_data = np.random.randn(batch_size, num_heads, memory_size) weights_data = np.ones((batch_size, num_heads)) - activations = tf.placeholder(tf.float32, + activations = tf.compat.v1.placeholder(tf.float32, [batch_size, num_heads, memory_size]) - weights = tf.placeholder(tf.float32, [batch_size, num_heads]) + weights = tf.compat.v1.placeholder(tf.float32, [batch_size, num_heads]) # Run weighted softmax with identity placed on weights. Output should be # equal to a standalone softmax. observed = addressing.weighted_softmax(activations, weights, tf.identity) @@ -62,9 +62,9 @@ def testShape(self): word_size = 2 module = addressing.CosineWeights(num_heads, word_size) - mem = tf.placeholder(tf.float32, [batch_size, memory_size, word_size]) - keys = tf.placeholder(tf.float32, [batch_size, num_heads, word_size]) - strengths = tf.placeholder(tf.float32, [batch_size, num_heads]) + mem = tf.compat.v1.placeholder(tf.float32, [batch_size, memory_size, word_size]) + keys = tf.compat.v1.placeholder(tf.float32, [batch_size, num_heads, word_size]) + strengths = tf.compat.v1.placeholder(tf.float32, [batch_size, num_heads]) weights = module(mem, keys, strengths) self.assertTrue(weights.get_shape().is_compatible_with( [batch_size, num_heads, memory_size])) @@ -88,9 +88,9 @@ def testValues(self): strengths_data = np.random.randn(batch_size, num_heads) module = addressing.CosineWeights(num_heads, word_size) - mem = tf.placeholder(tf.float32, [batch_size, memory_size, word_size]) - keys = tf.placeholder(tf.float32, [batch_size, num_heads, word_size]) - strengths = tf.placeholder(tf.float32, [batch_size, num_heads]) + mem = tf.compat.v1.placeholder(tf.float32, [batch_size, memory_size, word_size]) + keys = tf.compat.v1.placeholder(tf.float32, [batch_size, num_heads, word_size]) + strengths = tf.compat.v1.placeholder(tf.float32, [batch_size, num_heads]) weights = module(mem, keys, strengths) with self.test_session() as sess: @@ -124,8 +124,8 @@ def testDivideByZero(self): word_size = 2 module = addressing.CosineWeights(num_heads, word_size) - keys = tf.random_normal([batch_size, num_heads, word_size]) - strengths = tf.random_normal([batch_size, num_heads]) + keys = tf.random.normal([batch_size, num_heads, word_size]) + strengths = tf.random.normal([batch_size, num_heads]) # First row of memory is non-zero to concentrate attention on this location. # Remaining rows are all zero. @@ -135,7 +135,7 @@ def testDivideByZero(self): mem = tf.concat((first_row_ones, remaining_zeros), 1) output = module(mem, keys, strengths) - gradients = tf.gradients(output, [mem, keys, strengths]) + gradients = tf.gradients(ys=output, xs=[mem, keys, strengths]) with self.test_session() as sess: output, gradients = sess.run([output, gradients]) @@ -155,11 +155,11 @@ def testModule(self): module = addressing.TemporalLinkage( memory_size=memory_size, num_writes=num_writes) - prev_link_in = tf.placeholder( + prev_link_in = tf.compat.v1.placeholder( tf.float32, (batch_size, num_writes, memory_size, memory_size)) - prev_precedence_weights_in = tf.placeholder( + prev_precedence_weights_in = tf.compat.v1.placeholder( tf.float32, (batch_size, num_writes, memory_size)) - write_weights_in = tf.placeholder(tf.float32, + write_weights_in = tf.compat.v1.placeholder(tf.float32, (batch_size, num_writes, memory_size)) state = addressing.TemporalLinkageState( @@ -376,7 +376,7 @@ def testWriteAllocationWeightsGradient(self): weights = module.write_allocation_weights(usage, write_gates, num_writes) with self.test_session(): - err = tf.test.compute_gradient_error( + err = tf.compat.v1.test.compute_gradient_error( [usage, write_gates], [usage.get_shape().as_list(), write_gates.get_shape().as_list()], weights, @@ -407,7 +407,7 @@ def testAllocationGradient(self): module = addressing.Freeness(memory_size) allocation = module._allocation(usage) with self.test_session(): - err = tf.test.compute_gradient_error( + err = tf.compat.v1.test.compute_gradient_error( usage, usage.get_shape().as_list(), allocation, diff --git a/dnc/dnc.py b/dnc/dnc.py index db14b2a..264669a 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -110,7 +110,7 @@ def _build(self, inputs, prev_state): controller_input, prev_controller_state) controller_output = self._clip_if_enabled(controller_output) - controller_state = tf.contrib.framework.nest.map_structure(self._clip_if_enabled, controller_state) + controller_state = tf.nest.map_structure(self._clip_if_enabled, controller_state) access_output, access_state = self._access(controller_output, prev_access_state) diff --git a/dnc/repeat_copy.py b/dnc/repeat_copy.py index ad52579..2d9b553 100644 --- a/dnc/repeat_copy.py +++ b/dnc/repeat_copy.py @@ -50,18 +50,18 @@ def masked_sigmoid_cross_entropy(logits, A `Tensor` representing the log-probability of the target. """ xent = tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=logits) - loss_time_batch = tf.reduce_sum(xent, axis=2) - loss_batch = tf.reduce_sum(loss_time_batch * mask, axis=0) + loss_time_batch = tf.reduce_sum(input_tensor=xent, axis=2) + loss_batch = tf.reduce_sum(input_tensor=loss_time_batch * mask, axis=0) - batch_size = tf.cast(tf.shape(logits)[1], dtype=loss_time_batch.dtype) + batch_size = tf.cast(tf.shape(input=logits)[1], dtype=loss_time_batch.dtype) if time_average: - mask_count = tf.reduce_sum(mask, axis=0) + mask_count = tf.reduce_sum(input_tensor=mask, axis=0) loss_batch /= (mask_count + np.finfo(np.float32).eps) - loss = tf.reduce_sum(loss_batch) / batch_size + loss = tf.reduce_sum(input_tensor=loss_batch) / batch_size if log_prob_in_bits: - loss /= tf.log(2.) + loss /= tf.math.log(2.) return loss @@ -266,14 +266,14 @@ def _build(self): num_repeats_channel_idx = full_obs_size - 1 # Samples each batch index's sequence length and the number of repeats. - sub_seq_length_batch = tf.random_uniform( + sub_seq_length_batch = tf.random.uniform( [batch_size], minval=min_length, maxval=max_length + 1, dtype=tf.int32) - num_repeats_batch = tf.random_uniform( + num_repeats_batch = tf.random.uniform( [batch_size], minval=min_reps, maxval=max_reps + 1, dtype=tf.int32) # Pads all the batches to have the same total sequence length. total_length_batch = sub_seq_length_batch * (num_repeats_batch + 1) + 3 - max_length_batch = tf.reduce_max(total_length_batch) + max_length_batch = tf.reduce_max(input_tensor=total_length_batch) residual_length_batch = max_length_batch - total_length_batch obs_batch_shape = [max_length_batch, batch_size, full_obs_size] @@ -292,7 +292,7 @@ def _build(self): # The observation pattern is a sequence of random binary vectors. obs_pattern_shape = [sub_seq_len, num_bits] obs_pattern = tf.cast( - tf.random_uniform( + tf.random.uniform( obs_pattern_shape, minval=0, maxval=2, dtype=tf.int32), tf.float32) @@ -373,7 +373,7 @@ def _build(self): obs = tf.reshape(tf.concat(obs_tensors, 1), obs_batch_shape) targ = tf.reshape(tf.concat(targ_tensors, 1), targ_batch_shape) mask = tf.transpose( - tf.reshape(tf.concat(mask_tensors, 0), mask_batch_trans_shape)) + a=tf.reshape(tf.concat(mask_tensors, 0), mask_batch_trans_shape)) return DatasetTensors(obs, targ, mask) def cost(self, logits, targ, mask): diff --git a/dnc/util.py b/dnc/util.py index 5009c77..aae932f 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -24,26 +24,26 @@ def batch_invert_permutation(permutations): """Returns batched `tf.invert_permutation` for every row in `permutations`.""" - with tf.name_scope('batch_invert_permutation', values=[permutations]): + with tf.compat.v1.name_scope('batch_invert_permutation', values=[permutations]): perm = tf.cast(permutations, tf.float32) dim = int(perm.get_shape()[-1]) - size = tf.cast(tf.shape(perm)[0], tf.float32) - delta = tf.cast(tf.shape(perm)[-1], tf.float32) + size = tf.cast(tf.shape(input=perm)[0], tf.float32) + delta = tf.cast(tf.shape(input=perm)[-1], tf.float32) rg = tf.range(0, size * delta, delta, dtype=tf.float32) rg = tf.expand_dims(rg, 1) rg = tf.tile(rg, [1, dim]) perm = tf.add(perm, rg) flat = tf.reshape(perm, [-1]) - perm = tf.invert_permutation(tf.cast(flat, tf.int32)) + perm = tf.math.invert_permutation(tf.cast(flat, tf.int32)) perm = tf.reshape(perm, [-1, dim]) return tf.subtract(perm, tf.cast(rg, tf.int32)) def batch_gather(values, indices): """Returns batched `tf.gather` for every row in the input.""" - with tf.name_scope('batch_gather', values=[values, indices]): + with tf.compat.v1.name_scope('batch_gather', values=[values, indices]): idx = tf.expand_dims(indices, -1) - size = tf.shape(indices)[0] + size = tf.shape(input=indices)[0] rg = tf.range(size, dtype=tf.int32) rg = tf.expand_dims(rg, -1) rg = tf.tile(rg, [1, int(indices.get_shape()[-1])]) @@ -63,9 +63,9 @@ def reduce_prod(x, axis, name=None): Uses tf.cumprod and tf.gather_nd as a workaround to the poor performance of calculating tf.reduce_prod's gradient on CPU. """ - with tf.name_scope(name, 'util_reduce_prod', values=[x]): - cp = tf.cumprod(x, axis, reverse=True) - size = tf.shape(cp)[0] + with tf.compat.v1.name_scope(name, 'util_reduce_prod', values=[x]): + cp = tf.math.cumprod(x, axis, reverse=True) + size = tf.shape(input=cp)[0] idx1 = tf.range(tf.cast(size, tf.float32), dtype=tf.float32) idx2 = tf.zeros([size], tf.float32) indices = tf.stack([idx1, idx2], 1) diff --git a/report.txt b/report.txt new file mode 100644 index 0000000..50ceedb --- /dev/null +++ b/report.txt @@ -0,0 +1,238 @@ +TensorFlow 2.0 Upgrade Script +----------------------------- +Converted 10 files +Detected 21 issues that require attention +-------------------------------------------------------------------------------- +-------------------------------------------------------------------------------- +File: dnc/train.py +-------------------------------------------------------------------------------- +dnc/train.py:27:8: ERROR: Using member tf.flags.FLAGS in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:30:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:31:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:32:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:33:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:34:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:35:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:39:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:40:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:41:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:45:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:46:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:47:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:50:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:53:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:55:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:59:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:61:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:63:0: ERROR: Using member tf.flags.DEFINE_string in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:65:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +dnc/train.py:115:16: WARNING: tf.get_variable requires manual check. tf.get_variable returns ResourceVariables by default in 2.0, which have well-defined semantics and are stricter about shapes. You can disable this behavior by passing use_resource=False, or by calling tf.compat.v1.disable_resource_variables(). +================================================================================ +Detailed log follows: + +================================================================================ +================================================================================ +Input tree: 'dnc/' +================================================================================ +-------------------------------------------------------------------------------- +Processing file 'dnc/addressing_test.py' + outputting to 'dncV2/dnc/addressing_test.py' +-------------------------------------------------------------------------------- + +39:18: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +41:14: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +65:10: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +66:11: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +67:16: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +91:10: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +92:11: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +93:16: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +127:11: INFO: Renamed 'tf.random_normal' to 'tf.random.normal' +128:16: INFO: Renamed 'tf.random_normal' to 'tf.random.normal' +138:16: INFO: Added keywords to args of function 'tf.gradients' +158:19: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +160:33: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +162:23: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' +379:12: INFO: Renamed 'tf.test.compute_gradient_error' to 'tf.compat.v1.test.compute_gradient_error' +410:12: INFO: Renamed 'tf.test.compute_gradient_error' to 'tf.compat.v1.test.compute_gradient_error' +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/access.py' + outputting to 'dncV2/dnc/access.py' +-------------------------------------------------------------------------------- + +52:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +52:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +59:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +59:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +239:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +239:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +281:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +281:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +299:62: INFO: Added keywords to args of function 'tf.reduce_sum' +301:10: INFO: Added keywords to args of function 'tf.reduce_sum' +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/dnc.py' + outputting to 'dncV2/dnc/dnc.py' +-------------------------------------------------------------------------------- + +113:23: INFO: Renamed 'tf.contrib.framework.nest.map_structure' to 'tf.nest.map_structure' +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/train.py' + outputting to 'dncV2/dnc/train.py' +-------------------------------------------------------------------------------- + +27:8: ERROR: Using member tf.flags.FLAGS in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +30:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +31:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +32:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +33:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +34:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +35:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +39:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +40:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +41:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +45:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +46:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +47:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +50:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +53:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +55:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +59:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +61:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +63:0: ERROR: Using member tf.flags.DEFINE_string in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +65:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. +85:23: INFO: Renamed 'tf.nn.dynamic_rnn' to 'tf.compat.v1.nn.dynamic_rnn' +111:24: INFO: Renamed 'tf.trainable_variables' to 'tf.compat.v1.trainable_variables' +113:6: INFO: Added keywords to args of function 'tf.gradients' +115:16: WARNING: tf.get_variable requires manual check. tf.get_variable returns ResourceVariables by default in 2.0, which have well-defined semantics and are stricter about shapes. You can disable this behavior by passing use_resource=False, or by calling tf.compat.v1.disable_resource_variables(). +115:16: INFO: Renamed 'tf.get_variable' to 'tf.compat.v1.get_variable' +119:18: INFO: tf.zeros_initializer requires manual check. Initializers no longer have the dtype argument in the constructor or partition_info argument in the __call__ method. +The calls have been converted to compat.v1 for safety (even though they may already have been correct). +119:18: INFO: Renamed 'tf.zeros_initializer' to 'tf.compat.v1.zeros_initializer' +121:19: INFO: Renamed 'tf.GraphKeys' to 'tf.compat.v1.GraphKeys' +121:50: INFO: Renamed 'tf.GraphKeys' to 'tf.compat.v1.GraphKeys' +123:14: INFO: Renamed 'tf.train.RMSPropOptimizer' to 'tf.compat.v1.train.RMSPropOptimizer' +128:10: INFO: Renamed 'tf.train.Saver' to 'tf.compat.v1.train.Saver' +132:8: INFO: Renamed 'tf.train.CheckpointSaverHook' to 'tf.estimator.CheckpointSaverHook' +141:7: INFO: Renamed 'tf.train.SingularMonitoredSession' to 'tf.compat.v1.train.SingularMonitoredSession' +155:8: INFO: Renamed 'tf.logging.info' to 'tf.compat.v1.logging.info' +162:2: INFO: Renamed 'tf.logging.set_verbosity' to 'tf.compat.v1.logging.set_verbosity' +167:2: INFO: Renamed 'tf.app.run' to 'tf.compat.v1.app.run' +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/repeat_copy.py' + outputting to 'dncV2/dnc/repeat_copy.py' +-------------------------------------------------------------------------------- + +53:20: INFO: Added keywords to args of function 'tf.reduce_sum' +54:15: INFO: Added keywords to args of function 'tf.reduce_sum' +56:23: INFO: Added keywords to args of function 'tf.shape' +59:17: INFO: Added keywords to args of function 'tf.reduce_sum' +62:9: INFO: Added keywords to args of function 'tf.reduce_sum' +64:12: INFO: Renamed 'tf.log' to 'tf.math.log' +269:27: INFO: Renamed 'tf.random_uniform' to 'tf.random.uniform' +271:24: INFO: Renamed 'tf.random_uniform' to 'tf.random.uniform' +276:23: INFO: Added keywords to args of function 'tf.reduce_max' +295:10: INFO: Renamed 'tf.random_uniform' to 'tf.random.uniform' +375:11: INFO: Added keywords to args of function 'tf.transpose' +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/util_test.py' + outputting to 'dncV2/dnc/util_test.py' +-------------------------------------------------------------------------------- + + +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/addressing.py' + outputting to 'dncV2/dnc/addressing.py' +-------------------------------------------------------------------------------- + +35:18: INFO: Added keywords to args of function 'tf.reduce_sum' +173:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +173:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +181:13: INFO: Added keywords to args of function 'tf.transpose' +204:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +204:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +205:19: INFO: Added keywords to args of function 'tf.shape' +214:13: INFO: Renamed 'tf.matrix_set_diag' to 'tf.linalg.set_diag' +238:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +238:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +239:18: INFO: Added keywords to args of function 'tf.reduce_sum' +329:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +329:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +352:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +352:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +370:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +370:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +391:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +391:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +399:26: INFO: Renamed 'tf.cumprod' to 'tf.math.cumprod' +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/__init__.py' + outputting to 'dncV2/dnc/__init__.py' +-------------------------------------------------------------------------------- + + +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/util.py' + outputting to 'dncV2/dnc/util.py' +-------------------------------------------------------------------------------- + +27:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +27:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +30:19: INFO: Added keywords to args of function 'tf.shape' +31:20: INFO: Added keywords to args of function 'tf.shape' +37:11: INFO: Renamed 'tf.invert_permutation' to 'tf.math.invert_permutation' +44:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +44:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +46:11: INFO: Added keywords to args of function 'tf.shape' +66:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. + +66:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' +67:9: INFO: Renamed 'tf.cumprod' to 'tf.math.cumprod' +68:11: INFO: Added keywords to args of function 'tf.shape' +-------------------------------------------------------------------------------- + +-------------------------------------------------------------------------------- +Processing file 'dnc/access_test.py' + outputting to 'dncV2/dnc/access_test.py' +-------------------------------------------------------------------------------- + +45:13: INFO: Renamed 'tf.random_normal' to 'tf.random.normal' +54:11: INFO: Added keywords to args of function 'tf.reduce_mean' +55:15: INFO: Renamed 'tf.train.GradientDescentOptimizer' to 'tf.compat.v1.train.GradientDescentOptimizer' +56:11: INFO: Renamed 'tf.global_variables_initializer' to 'tf.compat.v1.global_variables_initializer' +64:8: INFO: Renamed 'tf.random_normal' to 'tf.random.normal' +65:11: INFO: Renamed 'tf.global_variables_initializer' to 'tf.compat.v1.global_variables_initializer' +148:11: INFO: Added keywords to args of function 'tf.reduce_sum' +157:15: INFO: Renamed 'tf.global_variables_initializer' to 'tf.compat.v1.global_variables_initializer' +158:12: INFO: Renamed 'tf.test.compute_gradient_error' to 'tf.compat.v1.test.compute_gradient_error' +-------------------------------------------------------------------------------- + diff --git a/train.py b/train.py index 036daef..3448a32 100644 --- a/train.py +++ b/train.py @@ -82,7 +82,7 @@ def run_model(input_sequence, output_size): dnc_core = dnc.DNC(access_config, controller_config, output_size, clip_value) initial_state = dnc_core.initial_state(FLAGS.batch_size) - output_sequence, _ = tf.nn.dynamic_rnn( + output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( cell=dnc_core, inputs=input_sequence, time_major=True, @@ -108,28 +108,28 @@ def train(num_training_iterations, report_interval): dataset_tensors.mask) # Set up optimizer with global norm clipping. - trainable_variables = tf.trainable_variables() + trainable_variables = tf.compat.v1.trainable_variables() grads, _ = tf.clip_by_global_norm( - tf.gradients(train_loss, trainable_variables), FLAGS.max_grad_norm) + tf.gradients(ys=train_loss, xs=trainable_variables), FLAGS.max_grad_norm) - global_step = tf.get_variable( + global_step = tf.compat.v1.get_variable( name="global_step", shape=[], dtype=tf.int64, - initializer=tf.zeros_initializer(), + initializer=tf.compat.v1.zeros_initializer(), trainable=False, - collections=[tf.GraphKeys.GLOBAL_VARIABLES, tf.GraphKeys.GLOBAL_STEP]) + collections=[tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, tf.compat.v1.GraphKeys.GLOBAL_STEP]) - optimizer = tf.train.RMSPropOptimizer( + optimizer = tf.compat.v1.train.RMSPropOptimizer( FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon) train_step = optimizer.apply_gradients( zip(grads, trainable_variables), global_step=global_step) - saver = tf.train.Saver() + saver = tf.compat.v1.train.Saver() if FLAGS.checkpoint_interval > 0: hooks = [ - tf.train.CheckpointSaverHook( + tf.estimator.CheckpointSaverHook( checkpoint_dir=FLAGS.checkpoint_dir, save_steps=FLAGS.checkpoint_interval, saver=saver) @@ -138,7 +138,7 @@ def train(num_training_iterations, report_interval): hooks = [] # Train. - with tf.train.SingularMonitoredSession( + with tf.compat.v1.train.SingularMonitoredSession( hooks=hooks, checkpoint_dir=FLAGS.checkpoint_dir) as sess: start_iteration = sess.run(global_step) @@ -152,16 +152,16 @@ def train(num_training_iterations, report_interval): dataset_tensors_np, output_np = sess.run([dataset_tensors, output]) dataset_string = dataset.to_human_readable(dataset_tensors_np, output_np) - tf.logging.info("%d: Avg training loss %f.\n%s", + tf.compat.v1.logging.info("%d: Avg training loss %f.\n%s", train_iteration, total_loss / report_interval, dataset_string) total_loss = 0 def main(unused_argv): - tf.logging.set_verbosity(3) # Print INFO log messages. + tf.compat.v1.logging.set_verbosity(3) # Print INFO log messages. train(FLAGS.num_training_iterations, FLAGS.report_interval) if __name__ == "__main__": - tf.app.run() + tf.compat.v1.app.run() From 0168414a177eded469c449c065a5843a3021a0cc Mon Sep 17 00:00:00 2001 From: kwliu Date: Sat, 1 May 2021 15:10:47 -0700 Subject: [PATCH 03/20] organize tests and rewrite training loop in TF2 with metrics --- dnc/dnc.py | 7 +- tests/access_test.py | 4 - tests/addressing_test.py | 5 +- tests/util_test.py | 6 +- train.py | 208 ++++++++++++++++++++------------------- 5 files changed, 115 insertions(+), 115 deletions(-) diff --git a/dnc/dnc.py b/dnc/dnc.py index 2628d7d..877c17f 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -78,6 +78,9 @@ def __init__(self, access_state=self._access.state_size, controller_state=util.state_size_from_initial_state( self._controller.initial_state(batch_size))) + self._output_linear = snt.Linear( + output_size=self._output_size.as_list()[0], + name='output_linear') def _clip_if_enabled(self, x): if self._clip_value > 0: @@ -123,9 +126,7 @@ def _build(self, inputs, prev_state): prev_access_state) output = tf.concat([controller_output, batch_flatten(access_output)], 1) - output = snt.Linear( - output_size=self._output_size.as_list()[0], - name='output_linear')(output) + output = self._output_linear(output) output = self._clip_if_enabled(output) return output, DNCState( diff --git a/tests/access_test.py b/tests/access_test.py index 3660172..9684160 100644 --- a/tests/access_test.py +++ b/tests/access_test.py @@ -167,7 +167,3 @@ def evaluate_module(inputs, memory, read_weights, precedence_weights, link): sum([tf.norm(numerical[i] - theoretical[i]) for i in range(2)]), 0.01 ) - - -if __name__ == '__main__': - tf.test.main() diff --git a/tests/addressing_test.py b/tests/addressing_test.py index 3f9a875..22e9153 100644 --- a/tests/addressing_test.py +++ b/tests/addressing_test.py @@ -22,7 +22,7 @@ import sonnet as snt import tensorflow as tf -import addressing, util +from dnc import addressing, util class WeightedSoftmaxTest(tf.test.TestCase): @@ -382,6 +382,3 @@ def testAllocationGradient(self): sum([tf.norm(numerical[i] - theoretical[i]) for i in range(1)]), 0.01 ) - -if __name__ == '__main__': - tf.test.main() diff --git a/tests/util_test.py b/tests/util_test.py index 29c98e5..5f19ddf 100644 --- a/tests/util_test.py +++ b/tests/util_test.py @@ -21,7 +21,7 @@ import numpy as np import tensorflow as tf -import util +from dnc import util class BatchInvertPermutation(tf.test.TestCase): @@ -53,7 +53,3 @@ def test(self): result = util.batch_gather(tf.constant(values), tf.constant(indexs)) result = result.numpy() self.assertAllEqual(target, result) - - -if __name__ == '__main__': - tf.test.main() diff --git a/train.py b/train.py index cc4195d..f9e5110 100644 --- a/train.py +++ b/train.py @@ -18,65 +18,85 @@ from __future__ import division from __future__ import print_function -import tensorflow.compat.v1 as tf +import tensorflow.compat.v1 as tf1 +import tensorflow as tf import sonnet as snt +import datetime from dnc import dnc from dnc import repeat_copy -FLAGS = tf.flags.FLAGS +FLAGS = tf1.flags.FLAGS # Model parameters -tf.flags.DEFINE_integer("hidden_size", 64, "Size of LSTM hidden layer.") -tf.flags.DEFINE_integer("memory_size", 16, "The number of memory slots.") -tf.flags.DEFINE_integer("word_size", 16, "The width of each memory slot.") -tf.flags.DEFINE_integer("num_write_heads", 1, "Number of memory write heads.") -tf.flags.DEFINE_integer("num_read_heads", 4, "Number of memory read heads.") -tf.flags.DEFINE_integer("clip_value", 20, +tf1.flags.DEFINE_integer("hidden_size", 64, "Size of LSTM hidden layer.") +tf1.flags.DEFINE_integer("memory_size", 16, "The number of memory slots.") +tf1.flags.DEFINE_integer("word_size", 16, "The width of each memory slot.") +tf1.flags.DEFINE_integer("num_write_heads", 1, "Number of memory write heads.") +tf1.flags.DEFINE_integer("num_read_heads", 4, "Number of memory read heads.") +tf1.flags.DEFINE_integer("clip_value", 20, "Maximum absolute value of controller and dnc outputs.") # Optimizer parameters. -tf.flags.DEFINE_float("max_grad_norm", 50, "Gradient clipping norm limit.") -tf.flags.DEFINE_float("learning_rate", 1e-4, "Optimizer learning rate.") -tf.flags.DEFINE_float("optimizer_epsilon", 1e-10, +tf1.flags.DEFINE_float("max_grad_norm", 50, "Gradient clipping norm limit.") +tf1.flags.DEFINE_float("learning_rate", 1e-4, "Optimizer learning rate.") +tf1.flags.DEFINE_float("optimizer_epsilon", 1e-10, "Epsilon used for RMSProp optimizer.") # Task parameters -tf.flags.DEFINE_integer("batch_size", 16, "Batch size for training.") -tf.flags.DEFINE_integer("num_bits", 4, "Dimensionality of each vector to copy") -tf.flags.DEFINE_integer( +tf1.flags.DEFINE_integer("batch_size", 16, "Batch size for training.") +tf1.flags.DEFINE_integer("num_bits", 4, "Dimensionality of each vector to copy") +tf1.flags.DEFINE_integer( "min_length", 1, "Lower limit on number of vectors in the observation pattern to copy") -tf.flags.DEFINE_integer( +tf1.flags.DEFINE_integer( "max_length", 2, "Upper limit on number of vectors in the observation pattern to copy") -tf.flags.DEFINE_integer("min_repeats", 1, +tf1.flags.DEFINE_integer("min_repeats", 1, "Lower limit on number of copy repeats.") -tf.flags.DEFINE_integer("max_repeats", 2, +tf1.flags.DEFINE_integer("max_repeats", 2, "Upper limit on number of copy repeats.") # Training options. -tf.flags.DEFINE_integer("num_training_iterations", 100000, +tf1.flags.DEFINE_integer("num_training_iterations", 10000, "Number of iterations to train for.") -tf.flags.DEFINE_integer("report_interval", 100, +tf1.flags.DEFINE_integer("report_interval", 100, "Iterations between reports (samples, valid loss).") -tf.flags.DEFINE_string("checkpoint_dir", "/tmp/tf/dnc", +tf1.flags.DEFINE_string("checkpoint_dir", "./logs/dnc/checkpoint", "Checkpointing directory.") -tf.flags.DEFINE_integer("checkpoint_interval", -1, +tf1.flags.DEFINE_integer("checkpoint_interval", 2000, "Checkpointing step interval.") @tf.function -def run_model(input_sequence, rnn_model): +def train_step(x, y, rnn_model, loss, optimizer): """Runs model on input sequence.""" initial_state = rnn_model.get_initial_state() - import ipdb; ipdb.set_trace() - output_sequence, _ = tf.nn.dynamic_rnn( - cell=rnn_model, - inputs=input_sequence, - time_major=True, - initial_state=initial_state) + with tf.GradientTape() as tape: + output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( + cell=rnn_model, + inputs=x, + time_major=True, + initial_state=initial_state) + loss_value = loss(output_sequence, y) + grads = tape.gradient(loss_value, rnn_model.trainable_variables) + grads, _ = tf.clip_by_global_norm(grads, FLAGS.max_grad_norm) + optimizer.apply_gradients(zip(grads, rnn_model.trainable_variables)) + + return loss_value - return output_sequence +@tf.function +def test_step(x, y, rnn_model, loss, mask): + initial_state = rnn_model.get_initial_state() + output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( + cell=rnn_model, + inputs=x, + time_major=True, + initial_state=initial_state) + loss_value = loss(output_sequence, y) + # Used for visualization. + output = tf.round( + tf.expand_dims(mask, -1) * tf.sigmoid(output_sequence)) + return loss_value, output def train(num_training_iterations, report_interval): @@ -101,81 +121,71 @@ def train(num_training_iterations, report_interval): dnc_core = dnc.DNC( access_config, controller_config, dataset.target_size, FLAGS.batch_size, clip_value) - - import ipdb; ipdb.set_trace() - # Set up logging. - from datetime import datetime - import tensorflow as tf2 - stamp = datetime.now().strftime("%Y%m%d-%H%M%S") - logdir = 'logs/func/%s' % stamp - writer = tf2.summary.create_file_writer(logdir) - - tf2.summary.trace_on(graph=True, profiler=True) - output_logits = run_model(dataset_tensors.observations, dnc_core) - with writer.as_default(): - tf2.summary.trace_export( - name="my_func_trace", - step=0, - profiler_outdir=logdir) - - # Used for visualization. - output = tf.round( - tf.expand_dims(dataset_tensors.mask, -1) * tf.sigmoid(output_logits)) - - train_loss = dataset.cost(output_logits, dataset_tensors.target, - dataset_tensors.mask) - - # Set up optimizer with global norm clipping. - trainable_variables = dnc_core.trainable_variables - import ipdb; ipdb.set_trace() - with tf.GradientTape() as gtape: - grads = gtape.gradient(train_loss, trainable_variables) - grads, _ = tf.clip_by_global_norm(grads, FLAGS.max_grad_norm) - - global_step = tf.compat.v1.get_variable( - name="global_step", - shape=[], - dtype=tf.int64, - initializer=tf.compat.v1.zeros_initializer(), - trainable=False, - collections=[tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, tf.compat.v1.GraphKeys.GLOBAL_STEP]) - + loss_fn = lambda pred, target: dataset.cost( + pred, target, dataset_tensors.mask) optimizer = tf.compat.v1.train.RMSPropOptimizer( FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon) - train_step = optimizer.apply_gradients( - zip(grads, trainable_variables), global_step=global_step) - - saver = tf.compat.v1.train.Saver() - if FLAGS.checkpoint_interval > 0: - hooks = [ - tf.estimator.CheckpointSaverHook( - checkpoint_dir=FLAGS.checkpoint_dir, - save_steps=FLAGS.checkpoint_interval, - saver=saver) - ] - else: - hooks = [] + #saver = tf.train.Checkpoint() + + # Set up logging and metrics + train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32) + test_loss = tf.keras.metrics.Mean('test_loss', dtype=tf.float32) + + current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + train_log_dir = 'logs/dnc/' + current_time + '/train' + test_log_dir = 'logs/dnc/' + current_time + '/test' + train_summary_writer = tf.summary.create_file_writer(train_log_dir) + test_summary_writer = tf.summary.create_file_writer(test_log_dir) + + # Test once to initialize + graph_log_dir = 'logs/dnc/' + current_time + '/graph' + graph_writer = tf.summary.create_file_writer(graph_log_dir) + with graph_writer.as_default(): + tf.summary.trace_on(graph=True, profiler=True) + test_step( + dataset_tensors.observations, dataset_tensors.target, dnc_core, loss_fn, dataset_tensors.mask + ) + tf.summary.trace_export( + name="dnc_trace", + step=0, + profiler_outdir=graph_log_dir) + return + + # Set up model checkpointing + checkpoint = tf.train.Checkpoint(model=dnc_core, optimizer=optimizer) # Train. - with tf.compat.v1.train.SingularMonitoredSession( - hooks=hooks, checkpoint_dir=FLAGS.checkpoint_dir) as sess: - - start_iteration = sess.run(global_step) - total_loss = 0 - - for train_iteration in range(start_iteration, num_training_iterations): - _, loss = sess.run([train_step, train_loss]) - total_loss += loss - - if (train_iteration + 1) % report_interval == 0: - dataset_tensors_np, output_np = sess.run([dataset_tensors, output]) - dataset_string = dataset.to_human_readable(dataset_tensors_np, - output_np) - tf.compat.v1.logging.info("%d: Avg training loss %f.\n%s", - train_iteration, total_loss / report_interval, - dataset_string) - total_loss = 0 + for epoch in range(0, num_training_iterations): + loss_value = train_step( + dataset_tensors.observations, dataset_tensors.target, dnc_core, loss_fn, optimizer, + ) + train_loss(loss_value) + with train_summary_writer.as_default(): + tf.summary.scalar('loss', train_loss.result(), step=epoch) + + if (epoch) % report_interval == 0: + loss_value, output = test_step( + dataset_tensors.observations, dataset_tensors.target, dnc_core, loss_fn, dataset_tensors.mask + ) + test_loss(loss_value) + #dataset_string = dataset.to_human_readable(dataset_tensors_np,output_np) + with test_summary_writer.as_default(): + tf.summary.scalar('loss', test_loss.result(), step=epoch) + + template = 'Epoch {}, Loss: {}, Test Loss: {}' + print(template.format( + epoch + 1, + train_loss.result(), + test_loss.result(), + )) + + # reset metrics every epoch + train_loss.reset_states() + test_loss.reset_states() + + if (epoch) % FLAGS.checkpoint_interval == 0: + checkpoint.save(FLAGS.checkpoint_dir) def main(unused_argv): From b4ad854aa09a760b2d425af72462feb6009be74f Mon Sep 17 00:00:00 2001 From: kwliu Date: Fri, 21 May 2021 19:56:43 -0700 Subject: [PATCH 04/20] propagate typing and implement makefile + requirements.txt --- Makefile | 30 ++++++++++++++++++++++++++++ dnc/access.py | 9 +++++---- dnc/addressing.py | 10 +++++----- dnc/dnc.py | 23 +++++++++++++--------- dnc/repeat_copy.py | 14 ++++++++----- dnc/util.py | 4 ++-- requirements.txt | 47 ++++++++++++++++++++++++++++++++++++++++++++ tests/access_test.py | 33 ++++++++++++++++++------------- 8 files changed, 131 insertions(+), 39 deletions(-) create mode 100644 Makefile create mode 100644 requirements.txt diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..e834d28 --- /dev/null +++ b/Makefile @@ -0,0 +1,30 @@ + +all: install run + +install: venv + : # Activate venv and install smthing inside + mkdir tmp + . venv/bin/activate && TMPDIR=tmp pip install -r requirements.txt + rm -r tmp/ + +venv: + : # Create venv if it doesn't exist + : # test -d venv || virtualenv -p python3 --no-site-packages venv + test -d venv || python -m venv venv + +test: venv + python -m pytest + +run: + : # Run your app here, e.g + : # determine if we are in venv, + : # see https://stackoverflow.com/q/1871549 + bash -c ". venv/bin/activate && pip -V" + +clean: + rm -rf venv + find -iname "*.pyc" -delete + rm -rf logs + rm -rf .pytest_cache + rm -rf tmp/ + diff --git a/dnc/access.py b/dnc/access.py index f2c8e80..da1bae3 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -85,7 +85,8 @@ def __init__(self, word_size=20, num_reads=1, num_writes=1, - name='memory_access'): + name='memory_access', + dtype=tf.float32): """Creates a MemoryAccess module. Args: @@ -101,15 +102,15 @@ def __init__(self, self._num_reads = num_reads self._num_writes = num_writes - self._dtype = tf.float64 + self._dtype = dtype self._write_content_weights_mod = addressing.CosineWeights( num_writes, word_size, name='write_content_weights') self._read_content_weights_mod = addressing.CosineWeights( num_reads, word_size, name='read_content_weights') - self._linkage = addressing.TemporalLinkage(memory_size, num_writes) - self._freeness = addressing.Freeness(memory_size) + self._linkage = addressing.TemporalLinkage(memory_size, num_writes, dtype=dtype) + self._freeness = addressing.Freeness(memory_size, dtype=dtype) self.initialize() diff --git a/dnc/addressing.py b/dnc/addressing.py index 963b178..536e661 100644 --- a/dnc/addressing.py +++ b/dnc/addressing.py @@ -121,7 +121,7 @@ class TemporalLinkage(snt.RNNCore): forward and backward directions in the link graphs. """ - def __init__(self, memory_size, num_writes, name='temporal_linkage'): + def __init__(self, memory_size, num_writes, name='temporal_linkage', dtype=tf.float32): """Construct a TemporalLinkage module. Args: @@ -132,7 +132,7 @@ def __init__(self, memory_size, num_writes, name='temporal_linkage'): super(TemporalLinkage, self).__init__(name=name) self._memory_size = memory_size self._num_writes = num_writes - self._dtype = tf.float64 + self._dtype = dtype def __call__(self, write_weights, prev_state): return self._build(write_weights, prev_state) @@ -277,7 +277,7 @@ class Freeness(snt.RNNCore): to write to for a number of write heads. """ - def __init__(self, memory_size, name='freeness'): + def __init__(self, memory_size, name='freeness', dtype=tf.float32): """Creates a Freeness module. Args: @@ -286,7 +286,7 @@ def __init__(self, memory_size, name='freeness'): """ super(Freeness, self).__init__(name=name) self._memory_size = memory_size - self._dtype = tf.float64 + self._dtype = dtype def __call__(self, write_weights, free_gate, read_weights, prev_usage): return self._build(write_weights, free_gate, read_weights, prev_usage) @@ -415,7 +415,7 @@ def _allocation(self, usage): sorted_allocation = sorted_nonusage * prod_sorted_usage inverse_indices = tf.cast( util.batch_invert_permutation(indices), - tf.int64 + tf.int32 ) # This final line "unsorts" sorted_allocation, so that the indexing diff --git a/dnc/dnc.py b/dnc/dnc.py index 877c17f..7a92879 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -45,7 +45,8 @@ def __init__(self, output_size, batch_size, clip_value=None, - name='dnc'): + name='dnc', + dtype=tf.float32): """Initializes the DNC core. Args: @@ -62,10 +63,13 @@ def __init__(self, """ super(DNC, self).__init__(name=name) + self._dtype = dtype + #with self._enter_variable_scope(): #with tf.variable_scope(name): - self._controller = snt.LSTM(**controller_config, dtype=tf.float64) - self._access = access.MemoryAccess(**access_config) + #self._controller = snt.LSTM(**controller_config, dtype=tf.float64) + self._controller = tf.keras.layers.LSTMCell(**controller_config, dtype=dtype) + self._access = access.MemoryAccess(**access_config, dtype=dtype) self._access_output_size = np.prod(self._access.output_size.as_list()) self._output_size = output_size @@ -76,8 +80,8 @@ def __init__(self, self._state_size = DNCState( access_output=self._access_output_size, access_state=self._access.state_size, - controller_state=util.state_size_from_initial_state( - self._controller.initial_state(batch_size))) + controller_state=self._controller.state_size, + ) self._output_linear = snt.Linear( output_size=self._output_size.as_list()[0], name='output_linear') @@ -134,12 +138,13 @@ def _build(self, inputs, prev_state): access_state=access_state, controller_state=controller_state) - def get_initial_state(self): - return self.initial_state(self._batch_size) + def get_initial_state(self, batch_size=None): + return self.initial_state(batch_size or self._batch_size) - def initial_state(self, batch_size, dtype=tf.float64): + def initial_state(self, batch_size): return DNCState( - controller_state=self._controller.initial_state(batch_size), + #controller_state=self._controller.initial_state(batch_size), + controller_state=self._controller.get_initial_state(batch_size=batch_size, dtype=self._dtype), access_state=self._access.initial_state(batch_size), access_output=tf.zeros( [batch_size] + self._access.output_size.as_list(), dtype=dtype)) diff --git a/dnc/repeat_copy.py b/dnc/repeat_copy.py index d8e3d8d..393fb11 100644 --- a/dnc/repeat_copy.py +++ b/dnc/repeat_copy.py @@ -185,7 +185,7 @@ def __init__( log_prob_in_bits=False, time_average_cost=False, name='repeat_copy', - dtype=tf.float64): + dtype=tf.float32): """Creates an instance of RepeatCopy task. Args: @@ -252,10 +252,9 @@ def batch_size(self): return self._batch_size def __call__(self): - self._build() - return self.datasettensor + return self._build() + #return self.datasettensor - @snt.once def _build(self): """Implements build method which adds ops to graph.""" @@ -381,7 +380,7 @@ def _build(self): targ = tf.cast(tf.reshape(tf.concat(targ_tensors, 1), targ_batch_shape), dtype=self._dtype) mask = tf.cast(tf.transpose( a=tf.reshape(tf.concat(mask_tensors, 0), mask_batch_trans_shape)), dtype=self._dtype) - self.datasettensor = DatasetTensors(obs, targ, mask) + return DatasetTensors(obs, targ, mask) def cost(self, logits, targ, mask): return masked_sigmoid_cross_entropy( @@ -392,6 +391,11 @@ def cost(self, logits, targ, mask): log_prob_in_bits=self.log_prob_in_bits) def to_human_readable(self, data, model_output=None, whole_batch=False): + data = DatasetTensors( + observations=data.observations.numpy(), + target=data.target.numpy(), + mask=data.mask.numpy() + ) obs = data.observations unnormalised_num_reps_flag = self._unnormalise(obs[:,:,-1:]).round() obs = np.concatenate([obs[:,:,:-1], unnormalised_num_reps_flag], axis=2) diff --git a/dnc/util.py b/dnc/util.py index d10a275..65c7ee0 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -42,9 +42,9 @@ def batch_invert_permutation(permutations): def batch_gather(values, indices): """Returns batched `tf.gather` for every row in the input.""" with tf.compat.v1.name_scope('batch_gather', values=[values, indices]): - idx = tf.expand_dims(indices, -1) + idx = tf.expand_dims(tf.cast(indices, tf.int32), -1) size = tf.shape(input=indices)[0] - rg = tf.range(tf.cast(size, tf.int64), dtype=tf.int64) + rg = tf.range(tf.cast(size, tf.int32), dtype=tf.int32) rg = tf.expand_dims(rg, -1) rg = tf.tile(rg, [1, int(indices.get_shape()[-1])]) rg = tf.expand_dims(rg, -1) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..5417974 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,47 @@ +absl-py==0.12.0 +astunparse==1.6.3 +attrs==21.2.0 +cachetools==4.2.2 +certifi==2020.12.5 +chardet==4.0.0 +dm-sonnet==2.0.0 +dm-tree==0.1.6 +flatbuffers==1.12 +gast==0.4.0 +google-auth==1.30.0 +google-auth-oauthlib==0.4.4 +google-pasta==0.2.0 +grpcio==1.34.1 +h5py==3.1.0 +idna==2.10 +iniconfig==1.1.1 +keras-nightly==2.5.0.dev2021032900 +Keras-Preprocessing==1.1.2 +Markdown==3.3.4 +numpy==1.19.5 +oauthlib==3.1.0 +opt-einsum==3.3.0 +packaging==20.9 +pluggy==0.13.1 +protobuf==3.17.0 +py==1.10.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pyparsing==2.4.7 +pytest==6.2.4 +requests==2.25.1 +requests-oauthlib==1.3.0 +rsa==4.7.2 +six==1.15.0 +tabulate==0.8.9 +tensorboard==2.5.0 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.0 +tensorflow==2.5.0 +tensorflow-estimator==2.5.0 +termcolor==1.1.0 +toml==0.10.2 +typing-extensions==3.7.4.3 +urllib3==1.26.4 +Werkzeug==2.0.0 +wrapt==1.12.1 diff --git a/tests/access_test.py b/tests/access_test.py index 9684160..9c13e46 100644 --- a/tests/access_test.py +++ b/tests/access_test.py @@ -32,6 +32,7 @@ TIME_STEPS = 4 INPUT_SIZE = 10 +DTYPE=tf.float32 class MemoryAccessTest(tf.test.TestCase): @@ -41,7 +42,7 @@ def setUp(self): self.initial_state = self.module.initial_state(BATCH_SIZE) def testBuildAndTrain(self): - inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=tf.float64) + inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) targets = np.random.rand(TIME_STEPS, BATCH_SIZE, NUM_READS, WORD_SIZE) loss = lambda outputs, targets: tf.reduce_mean(input_tensor=tf.square(outputs - targets)) @@ -59,7 +60,7 @@ def testBuildAndTrain(self): def testValidReadMode(self): inputs = self.module._read_inputs( - tf.random.normal([BATCH_SIZE, INPUT_SIZE], dtype=tf.float64)) + tf.random.normal([BATCH_SIZE, INPUT_SIZE], dtype=DTYPE)) init = tf.compat.v1.global_variables_initializer() # Check that the read modes for each read head constitute a probability @@ -84,15 +85,15 @@ def testWriteWeights(self): write_gate[:, 0] = 1 inputs = { - 'allocation_gate': tf.constant(allocation_gate), - 'write_gate': tf.constant(write_gate), - 'write_content_keys': tf.constant(write_content_keys), - 'write_content_strengths': tf.constant(write_content_strengths) + 'allocation_gate': tf.constant(allocation_gate, dtype=DTYPE), + 'write_gate': tf.constant(write_gate, dtype=DTYPE), + 'write_content_keys': tf.constant(write_content_keys, dtype=DTYPE), + 'write_content_strengths': tf.constant(write_content_strengths, dtype=DTYPE) } weights = self.module._write_weights(inputs, - tf.constant(memory), - tf.constant(usage)) + tf.constant(memory, dtype=DTYPE), + tf.constant(usage, dtype=DTYPE)) weights = weights.numpy() @@ -118,16 +119,20 @@ def testReadWeights(self): read_content_keys = np.random.rand(BATCH_SIZE, NUM_READS, WORD_SIZE) read_content_keys[0, 0] = memory[0, 3] read_content_strengths = tf.constant( - 100., shape=[BATCH_SIZE, NUM_READS], dtype=tf.float64) + 100., shape=[BATCH_SIZE, NUM_READS], dtype=DTYPE) read_mode = np.random.rand(BATCH_SIZE, NUM_READS, 1 + 2 * NUM_WRITES) read_mode[0, 0, :] = util.one_hot(1 + 2 * NUM_WRITES, 2 * NUM_WRITES) inputs = { - 'read_content_keys': tf.constant(read_content_keys), + 'read_content_keys': tf.constant(read_content_keys, dtype=DTYPE), 'read_content_strengths': read_content_strengths, - 'read_mode': tf.constant(read_mode), + 'read_mode': tf.constant(read_mode, dtype=DTYPE), } - read_weights = self.module._read_weights(inputs, memory, prev_read_weights, - link) + read_weights = self.module._read_weights( + inputs, + tf.cast(memory, dtype=DTYPE), + tf.cast(prev_read_weights, dtype=DTYPE), + tf.cast(link, dtype=DTYPE), + ) read_weights = read_weights.numpy() @@ -136,7 +141,7 @@ def testReadWeights(self): read_weights[0, 0, :], util.one_hot(MEMORY_SIZE, 3), atol=1e-3) def testGradients(self): - inputs = tf.constant(np.random.randn(BATCH_SIZE, INPUT_SIZE), tf.float64) + inputs = tf.constant(np.random.randn(BATCH_SIZE, INPUT_SIZE), dtype=DTYPE) def evaluate_module(inputs, memory, read_weights, precedence_weights, link): initial_state = access.AccessState( memory=memory, From 6b56cfb1664d1ec5334c26b71849febe78f6a254 Mon Sep 17 00:00:00 2001 From: kwliu Date: Fri, 21 May 2021 20:21:30 -0700 Subject: [PATCH 05/20] remove tf1 name scopes --- dnc/access.py | 95 +++++++++++++++----------------- dnc/addressing.py | 136 ++++++++++++++++++++-------------------------- dnc/dnc.py | 2 +- dnc/util.py | 42 +++++++------- 4 files changed, 126 insertions(+), 149 deletions(-) diff --git a/dnc/access.py b/dnc/access.py index da1bae3..a125392 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -48,16 +48,14 @@ def _erase_and_write(memory, address, reset_weights, values): Returns: 3-D tensor of shape `[batch_size, num_writes, word_size]`. """ - with tf.compat.v1.name_scope('erase_memory', values=[memory, address, reset_weights]): - expand_address = tf.expand_dims(address, 3) - reset_weights = tf.expand_dims(reset_weights, 2) - weighted_resets = expand_address * reset_weights - reset_gate = util.reduce_prod(1 - weighted_resets, 1) - memory *= reset_gate + expand_address = tf.expand_dims(address, 3) + reset_weights = tf.expand_dims(reset_weights, 2) + weighted_resets = expand_address * reset_weights + reset_gate = util.reduce_prod(1 - weighted_resets, 1) + memory *= reset_gate - with tf.compat.v1.name_scope('additive_write', values=[memory, address, values]): - add_matrix = tf.matmul(address, values, adjoint_a=True) - memory += add_matrix + add_matrix = tf.matmul(address, values, adjoint_a=True) + memory += add_matrix return memory @@ -271,24 +269,23 @@ def _write_weights(self, inputs, memory, usage): tensor of shape `[batch_size, num_writes, memory_size]` indicating where to write to (if anywhere) for each write head. """ - with tf.compat.v1.name_scope('write_weights', values=[inputs, memory, usage]): - # c_t^{w, i} - The content-based weights for each write head. - write_content_weights = self._write_content_weights_mod( - memory, inputs['write_content_keys'], - inputs['write_content_strengths']) - - # a_t^i - The allocation weights for each write head. - write_allocation_weights = self._freeness.write_allocation_weights( - usage=usage, - write_gates=(inputs['allocation_gate'] * inputs['write_gate']), - num_writes=self._num_writes) - - # Expands gates over memory locations. - allocation_gate = tf.expand_dims(inputs['allocation_gate'], -1) - write_gate = tf.expand_dims(inputs['write_gate'], -1) - - # w_t^{w, i} - The write weightings for each write head. - return write_gate * (allocation_gate * write_allocation_weights + + # c_t^{w, i} - The content-based weights for each write head. + write_content_weights = self._write_content_weights_mod( + memory, inputs['write_content_keys'], + inputs['write_content_strengths']) + + # a_t^i - The allocation weights for each write head. + write_allocation_weights = self._freeness.write_allocation_weights( + usage=usage, + write_gates=(inputs['allocation_gate'] * inputs['write_gate']), + num_writes=self._num_writes) + + # Expands gates over memory locations. + allocation_gate = tf.expand_dims(inputs['allocation_gate'], -1) + write_gate = tf.expand_dims(inputs['write_gate'], -1) + + # w_t^{w, i} - The write weightings for each write head. + return write_gate * (allocation_gate * write_allocation_weights + (1 - allocation_gate) * write_content_weights) def _read_weights(self, inputs, memory, prev_read_weights, link): @@ -313,29 +310,27 @@ def _read_weights(self, inputs, memory, prev_read_weights, link): A tensor of shape `[batch_size, num_reads, memory_size]` containing the read weights for each read head. """ - with tf.compat.v1.name_scope( - 'read_weights', values=[inputs, memory, prev_read_weights, link]): - # c_t^{r, i} - The content weightings for each read head. - content_weights = self._read_content_weights_mod( - memory, inputs['read_content_keys'], inputs['read_content_strengths']) - - # Calculates f_t^i and b_t^i. - forward_weights = self._linkage.directional_read_weights( - link, prev_read_weights, forward=True) - backward_weights = self._linkage.directional_read_weights( - link, prev_read_weights, forward=False) - - backward_mode = inputs['read_mode'][:, :, :self._num_writes] - forward_mode = ( - inputs['read_mode'][:, :, self._num_writes:2 * self._num_writes]) - content_mode = inputs['read_mode'][:, :, 2 * self._num_writes] - - read_weights = ( - tf.expand_dims(content_mode, 2) * content_weights + tf.reduce_sum( - input_tensor=tf.expand_dims(forward_mode, 3) * forward_weights, axis=2) + - tf.reduce_sum(input_tensor=tf.expand_dims(backward_mode, 3) * backward_weights, axis=2)) - - return read_weights + # c_t^{r, i} - The content weightings for each read head. + content_weights = self._read_content_weights_mod( + memory, inputs['read_content_keys'], inputs['read_content_strengths']) + + # Calculates f_t^i and b_t^i. + forward_weights = self._linkage.directional_read_weights( + link, prev_read_weights, forward=True) + backward_weights = self._linkage.directional_read_weights( + link, prev_read_weights, forward=False) + + backward_mode = inputs['read_mode'][:, :, :self._num_writes] + forward_mode = ( + inputs['read_mode'][:, :, self._num_writes:2 * self._num_writes]) + content_mode = inputs['read_mode'][:, :, 2 * self._num_writes] + + read_weights = ( + tf.expand_dims(content_mode, 2) * content_weights + tf.reduce_sum( + input_tensor=tf.expand_dims(forward_mode, 3) * forward_weights, axis=2) + + tf.reduce_sum(input_tensor=tf.expand_dims(backward_mode, 3) * backward_weights, axis=2)) + + return read_weights def initial_state(self, batch_size): return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) diff --git a/dnc/addressing.py b/dnc/addressing.py index 536e661..bc64a82 100644 --- a/dnc/addressing.py +++ b/dnc/addressing.py @@ -81,9 +81,6 @@ def __init__(self, self._strength_op = strength_op def __call__(self, memory, keys, strengths): - return self._build(memory, keys, strengths) - - def _build(self, memory, keys, strengths): """Connects the CosineWeights module into the graph. Args: @@ -135,9 +132,6 @@ def __init__(self, memory_size, num_writes, name='temporal_linkage', dtype=tf.fl self._dtype = dtype def __call__(self, write_weights, prev_state): - return self._build(write_weights, prev_state) - - def _build(self, write_weights, prev_state): """Calculate the updated linkage state given the write weights. Args: @@ -177,15 +171,14 @@ def directional_read_weights(self, link, prev_read_weights, forward): Returns: tensor of shape `[batch_size, num_reads, num_writes, memory_size]` """ - with tf.compat.v1.name_scope('directional_read_weights'): - # We calculate the forward and backward directions for each pair of - # read and write heads; hence we need to tile the read weights and do a - # sort of "outer product" to get this. - expanded_read_weights = tf.stack([prev_read_weights] * self._num_writes, - 1) - result = tf.matmul(expanded_read_weights, link, adjoint_b=forward) - # Swap dimensions 1, 2 so order is [batch, reads, writes, memory]: - return tf.transpose(a=result, perm=[0, 2, 1, 3]) + # We calculate the forward and backward directions for each pair of + # read and write heads; hence we need to tile the read weights and do a + # sort of "outer product" to get this. + expanded_read_weights = tf.stack([prev_read_weights] * self._num_writes, + 1) + result = tf.matmul(expanded_read_weights, link, adjoint_b=forward) + # Swap dimensions 1, 2 so order is [batch, reads, writes, memory]: + return tf.transpose(a=result, perm=[0, 2, 1, 3]) def _link(self, prev_link, prev_precedence_weights, write_weights): """Calculates the new link graphs. @@ -208,21 +201,20 @@ def _link(self, prev_link, prev_precedence_weights, write_weights): A tensor of shape `[batch_size, num_writes, memory_size, memory_size]` containing the new link graphs for each write head. """ - with tf.compat.v1.name_scope('link'): - batch_size = tf.shape(input=prev_link)[0] - write_weights_i = tf.expand_dims(write_weights, 3) - write_weights_j = tf.expand_dims(write_weights, 2) - prev_precedence_weights_j = tf.expand_dims(prev_precedence_weights, 2) - prev_link_scale = 1 - write_weights_i - write_weights_j - new_link = write_weights_i * prev_precedence_weights_j - link = prev_link_scale * prev_link + new_link - # Return the link with the diagonal set to zero, to remove self-looping - # edges. - return tf.linalg.set_diag( - link, - tf.zeros( - [batch_size, self._num_writes, self._memory_size], - dtype=link.dtype)) + batch_size = tf.shape(input=prev_link)[0] + write_weights_i = tf.expand_dims(write_weights, 3) + write_weights_j = tf.expand_dims(write_weights, 2) + prev_precedence_weights_j = tf.expand_dims(prev_precedence_weights, 2) + prev_link_scale = 1 - write_weights_i - write_weights_j + new_link = write_weights_i * prev_precedence_weights_j + link = prev_link_scale * prev_link + new_link + # Return the link with the diagonal set to zero, to remove self-looping + # edges. + return tf.linalg.set_diag( + link, + tf.zeros( + [batch_size, self._num_writes, self._memory_size], + dtype=link.dtype)) def _precedence_weights(self, prev_precedence_weights, write_weights): """Calculates the new precedence weights given the current write weights. @@ -242,12 +234,11 @@ def _precedence_weights(self, prev_precedence_weights, write_weights): A tensor of shape `[batch_size, num_writes, memory_size]` containing the new precedence weights. """ - with tf.compat.v1.name_scope('precedence_weights'): - write_sum = tf.reduce_sum(input_tensor=write_weights, axis=2, keepdims=True) - return (1 - write_sum) * prev_precedence_weights + write_weights + write_sum = tf.reduce_sum(input_tensor=write_weights, axis=2, keepdims=True) + return (1 - write_sum) * prev_precedence_weights + write_weights def initial_state(self, batch_size): - return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) + return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) @property @@ -289,9 +280,6 @@ def __init__(self, memory_size, name='freeness', dtype=tf.float32): self._dtype = dtype def __call__(self, write_weights, free_gate, read_weights, prev_usage): - return self._build(write_weights, free_gate, read_weights, prev_usage) - - def _build(self, write_weights, free_gate, read_weights, prev_usage): """Calculates the new memory usage u_t. Memory that was written to in the previous time step will have its usage @@ -341,21 +329,20 @@ def write_allocation_weights(self, usage, write_gates, num_writes): freeness-based write locations. Note that this isn't scaled by `write_gate`; this scaling must be applied externally. """ - with tf.compat.v1.name_scope('write_allocation_weights'): - # expand gatings over memory locations - write_gates = tf.expand_dims(write_gates, -1) + # expand gatings over memory locations + write_gates = tf.expand_dims(write_gates, -1) - allocation_weights = [] - for i in range(num_writes): - allocation_weights.append(self._allocation(usage)) - # update usage to take into account writing to this new allocation - usage += ((1 - usage) * write_gates[:, i, :] * allocation_weights[i]) + allocation_weights = [] + for i in range(num_writes): + allocation_weights.append(self._allocation(usage)) + # update usage to take into account writing to this new allocation + usage += ((1 - usage) * write_gates[:, i, :] * allocation_weights[i]) - # Pack the allocation weights for the write heads into one tensor. - return tf.stack(allocation_weights, axis=1) + # Pack the allocation weights for the write heads into one tensor. + return tf.stack(allocation_weights, axis=1) def _usage_after_write(self, prev_usage, write_weights): - """Calcualtes the new usage after writing to memory. + """Calculates the new usage after writing to memory. Args: prev_usage: tensor of shape `[batch_size, memory_size]`. @@ -364,10 +351,9 @@ def _usage_after_write(self, prev_usage, write_weights): Returns: New usage, a tensor of shape `[batch_size, memory_size]`. """ - with tf.compat.v1.name_scope('usage_after_write'): - # Calculate the aggregated effect of all write heads - write_weights = 1 - util.reduce_prod(1 - write_weights, 1) - return prev_usage + (1 - prev_usage) * write_weights + # Calculate the aggregated effect of all write heads + write_weights = 1 - util.reduce_prod(1 - write_weights, 1) + return prev_usage + (1 - prev_usage) * write_weights def _usage_after_read(self, prev_usage, free_gate, read_weights): """Calcualtes the new usage after reading and freeing from memory. @@ -382,11 +368,10 @@ def _usage_after_read(self, prev_usage, free_gate, read_weights): Returns: New usage, a tensor of shape `[batch_size, memory_size]`. """ - with tf.compat.v1.name_scope('usage_after_read'): - free_gate = tf.expand_dims(free_gate, -1) - free_read_weights = free_gate * read_weights - phi = util.reduce_prod(1 - free_read_weights, 1, name='phi') - return prev_usage * phi + free_gate = tf.expand_dims(free_gate, -1) + free_read_weights = free_gate * read_weights + phi = util.reduce_prod(1 - free_read_weights, 1, name='phi') + return prev_usage * phi def _allocation(self, usage): r"""Computes allocation by sorting `usage`. @@ -403,28 +388,27 @@ def _allocation(self, usage): Returns: Tensor of shape `[batch_size, memory_size]` corresponding to allocation. """ - with tf.compat.v1.name_scope('allocation'): - # Ensure values are not too small prior to cumprod. - usage = _EPSILON + (1 - _EPSILON) * usage - - nonusage = 1 - usage - sorted_nonusage, indices = tf.nn.top_k( - nonusage, k=self._memory_size, name='sort') - sorted_usage = 1 - sorted_nonusage - prod_sorted_usage = tf.math.cumprod(sorted_usage, axis=1, exclusive=True) - sorted_allocation = sorted_nonusage * prod_sorted_usage - inverse_indices = tf.cast( - util.batch_invert_permutation(indices), - tf.int32 - ) - - # This final line "unsorts" sorted_allocation, so that the indexing - # corresponds to the original indexing of `usage`. - return util.batch_gather(sorted_allocation, inverse_indices) + # Ensure values are not too small prior to cumprod. + usage = _EPSILON + (1 - _EPSILON) * usage + + nonusage = 1 - usage + sorted_nonusage, indices = tf.nn.top_k( + nonusage, k=self._memory_size, name='sort') + sorted_usage = 1 - sorted_nonusage + prod_sorted_usage = tf.math.cumprod(sorted_usage, axis=1, exclusive=True) + sorted_allocation = sorted_nonusage * prod_sorted_usage + inverse_indices = tf.cast( + util.batch_invert_permutation(indices), + tf.int32 + ) + + # This final line "unsorts" sorted_allocation, so that the indexing + # corresponds to the original indexing of `usage`. + return util.batch_gather(sorted_allocation, inverse_indices) # freeness size is independent of batch size def initial_state(self, batch_size): - return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) + return tf.zeros([self._memory_size], dtype=self._dtype) @property def state_size(self): diff --git a/dnc/dnc.py b/dnc/dnc.py index 7a92879..07889ef 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -147,7 +147,7 @@ def initial_state(self, batch_size): controller_state=self._controller.get_initial_state(batch_size=batch_size, dtype=self._dtype), access_state=self._access.initial_state(batch_size), access_output=tf.zeros( - [batch_size] + self._access.output_size.as_list(), dtype=dtype)) + [batch_size] + self._access.output_size.as_list(), dtype=self._dtype)) @property def state_size(self): diff --git a/dnc/util.py b/dnc/util.py index 65c7ee0..71cb9d9 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -24,32 +24,30 @@ def batch_invert_permutation(permutations): """Returns batched `tf.invert_permutation` for every row in `permutations`.""" - with tf.compat.v1.name_scope('batch_invert_permutation', values=[permutations]): - perm = tf.cast(permutations, tf.float32) - dim = int(perm.get_shape()[-1]) - size = tf.cast(tf.shape(input=perm)[0], tf.float32) - delta = tf.cast(tf.shape(input=perm)[-1], tf.float32) - rg = tf.range(0, size * delta, delta, dtype=tf.float32) - rg = tf.expand_dims(rg, 1) - rg = tf.tile(rg, [1, dim]) - perm = tf.add(perm, rg) - flat = tf.reshape(perm, [-1]) - perm = tf.math.invert_permutation(tf.cast(flat, tf.int32)) - perm = tf.reshape(perm, [-1, dim]) - return tf.subtract(perm, tf.cast(rg, tf.int32)) + perm = tf.cast(permutations, tf.float32) + dim = int(perm.get_shape()[-1]) + size = tf.cast(tf.shape(input=perm)[0], tf.float32) + delta = tf.cast(tf.shape(input=perm)[-1], tf.float32) + rg = tf.range(0, size * delta, delta, dtype=tf.float32) + rg = tf.expand_dims(rg, 1) + rg = tf.tile(rg, [1, dim]) + perm = tf.add(perm, rg) + flat = tf.reshape(perm, [-1]) + perm = tf.math.invert_permutation(tf.cast(flat, tf.int32)) + perm = tf.reshape(perm, [-1, dim]) + return tf.subtract(perm, tf.cast(rg, tf.int32)) def batch_gather(values, indices): """Returns batched `tf.gather` for every row in the input.""" - with tf.compat.v1.name_scope('batch_gather', values=[values, indices]): - idx = tf.expand_dims(tf.cast(indices, tf.int32), -1) - size = tf.shape(input=indices)[0] - rg = tf.range(tf.cast(size, tf.int32), dtype=tf.int32) - rg = tf.expand_dims(rg, -1) - rg = tf.tile(rg, [1, int(indices.get_shape()[-1])]) - rg = tf.expand_dims(rg, -1) - gidx = tf.concat([rg, idx], -1) - return tf.gather_nd(values, gidx) + idx = tf.expand_dims(tf.cast(indices, tf.int32), -1) + size = tf.shape(input=indices)[0] + rg = tf.range(tf.cast(size, tf.int32), dtype=tf.int32) + rg = tf.expand_dims(rg, -1) + rg = tf.tile(rg, [1, int(indices.get_shape()[-1])]) + rg = tf.expand_dims(rg, -1) + gidx = tf.concat([rg, idx], -1) + return tf.gather_nd(values, gidx) def one_hot(length, index): From af570661c3d596010299d53f91bfb646785e6aeb Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 23 May 2021 09:53:00 -0700 Subject: [PATCH 06/20] clean up + deterministic tests --- dnc/access.py | 110 +++++++++++++++------------------------ dnc/dnc.py | 26 ++++----- tests/access_test.py | 18 ++++--- tests/addressing_test.py | 8 +-- tests/dnc_test.py | 97 ++++++++++++++++++++++++++++++++++ tests/util_test.py | 2 + 6 files changed, 167 insertions(+), 94 deletions(-) create mode 100644 tests/dnc_test.py diff --git a/dnc/access.py b/dnc/access.py index a125392..8fae411 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -19,6 +19,7 @@ from __future__ import print_function import collections +import numpy as np import sonnet as snt import tensorflow as tf @@ -110,51 +111,7 @@ def __init__(self, self._linkage = addressing.TemporalLinkage(memory_size, num_writes, dtype=dtype) self._freeness = addressing.Freeness(memory_size, dtype=dtype) - self.initialize() - - @snt.once - def initialize(self): - def _linear(first_dim, second_dim, name, activation=None): - """Returns a linear transformation of `inputs`, followed by a reshape.""" - linear = snt.Linear(first_dim * second_dim, name=name) - def call(inputs): - output = linear(inputs) - if activation is not None: - output = activation(output, name=name + '_activation') - return tf.reshape(output, [-1, first_dim, second_dim]) - return call - - # v_t^i - The vectors to write to memory, for each write head `i`. - self.write_vectors = _linear(self._num_writes, self._word_size, 'write_vectors') - - # e_t^i - Amount to erase the memory by before writing, for each write head. - self.erase_vectors = _linear(self._num_writes, self._word_size, 'erase_vectors', - tf.sigmoid) - - # f_t^j - Amount that the memory at the locations read from at the previous - # time step can be declared unused, for each read head `j`. - self.free_gate = snt.Linear(self._num_reads, name='free_gate') - - # g_t^{a, i} - Interpolation between writing to unallocated memory and - # content-based lookup, for each write head `i`. Note: `a` is simply used to - # identify this gate with allocation vs writing (as defined below). - self.allocation_gate = snt.Linear(self._num_writes, name='allocation_gate') - - # g_t^{w, i} - Overall gating of write amount for each write head. - self.write_gate = snt.Linear(self._num_writes, name='write_gate') - - # \pi_t^j - Mixing between "backwards" and "forwards" positions (for - # each write head), and content-based lookup, for each read head. - self.num_read_modes = 1 + 2 * self._num_writes - self.read_mode = _linear(self._num_reads, self.num_read_modes, name='read_mode') - - # Parameters for the (read / write) "weights by content matching" modules. - self.write_keys = _linear(self._num_writes, self._word_size, 'write_keys') - self.write_strengths = snt.Linear(self._num_writes, name='write_strengths') - - self.read_keys = _linear(self._num_reads, self._word_size, 'read_keys') - self.read_strengths = snt.Linear(self._num_reads, name='read_strengths') - + self._linear_layers = {} def __call__(self, inputs, prev_state): """Connects the MemoryAccess module into the graph. @@ -207,46 +164,61 @@ def __call__(self, inputs, prev_state): def _read_inputs(self, inputs): """Applies transformations to `inputs` to get control for this module.""" + def _linear(dims, name, activation=None): + """Returns a linear transformation of `inputs`, followed by a reshape.""" + linear = self._linear_layers.get(name) + if not linear: + linear = snt.Linear(np.prod(dims), name=name) + self._linear_layers[name] = linear + + linear = linear(inputs) + if activation is not None: + linear = activation(linear, name=name + '_activation') + return tf.reshape(linear, [-1, *dims]) + # v_t^i - The vectors to write to memory, for each write head `i`. - write_vectors = self.write_vectors(inputs) + write_vectors = _linear([self._num_writes, self._word_size], 'write_vectors') # e_t^i - Amount to erase the memory by before writing, for each write head. - erase_vectors = self.erase_vectors(inputs) + erase_vectors = _linear([self._num_writes, self._word_size], 'erase_vectors', + tf.sigmoid) # f_t^j - Amount that the memory at the locations read from at the previous # time step can be declared unused, for each read head `j`. - free_gate = tf.sigmoid(self.free_gate(inputs)) + free_gate = _linear([self._num_reads], 'free_gate', tf.sigmoid) # g_t^{a, i} - Interpolation between writing to unallocated memory and # content-based lookup, for each write head `i`. Note: `a` is simply used to # identify this gate with allocation vs writing (as defined below). - allocation_gate = tf.sigmoid(self.allocation_gate(inputs)) + allocation_gate = _linear([self._num_writes], 'allocation_gate', tf.sigmoid) # g_t^{w, i} - Overall gating of write amount for each write head. - write_gate = tf.sigmoid(self.write_gate(inputs)) + write_gate = _linear([self._num_writes], 'write_gate', tf.sigmoid) # \pi_t^j - Mixing between "backwards" and "forwards" positions (for # each write head), and content-based lookup, for each read head. - read_mode = snt.BatchApply(tf.nn.softmax)(self.read_mode(inputs)) + num_read_modes = 1 + 2 * self._num_writes + read_mode = snt.BatchApply(tf.nn.softmax)( + _linear([self._num_reads, num_read_modes], name='read_mode')) # Parameters for the (read / write) "weights by content matching" modules. - write_keys = self.write_keys(inputs) - write_strengths = self.write_strengths(inputs) + write_keys = _linear([self._num_writes, self._word_size], 'write_keys') + write_strengths = _linear([self._num_writes], name='write_strengths') - read_keys = self.read_keys(inputs) - read_strengths = self.read_strengths(inputs) + read_keys = _linear([self._num_reads, self._word_size], 'read_keys') + read_strengths = _linear([self._num_reads], name='read_strengths') result = { - 'read_content_keys': read_keys, - 'read_content_strengths': read_strengths, - 'write_content_keys': write_keys, - 'write_content_strengths': write_strengths, - 'write_vectors': write_vectors, - 'erase_vectors': erase_vectors, - 'free_gate': free_gate, - 'allocation_gate': allocation_gate, - 'write_gate': write_gate, - 'read_mode': read_mode, + 'read_content_keys': read_keys, + 'read_content_strengths': read_strengths, + 'write_content_keys': write_keys, + 'write_content_strengths': write_strengths, + 'write_vectors': write_vectors, + 'erase_vectors': erase_vectors, + 'free_gate': free_gate, + 'allocation_gate': allocation_gate, + 'write_gate': write_gate, + 'read_mode': read_mode, } return result @@ -332,11 +304,13 @@ def _read_weights(self, inputs, memory, prev_read_weights, link): return read_weights - def initial_state(self, batch_size): + # keras uses get_initial_state + def get_initial_state(self, batch_size): return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) - def get_initial_state(self, batch_size): - return self.initial_state(batch_size) + # snt.RNNCore uses initial_state + def initial_state(self, batch_size): + return self.get_initial_state(batch_size) @property def state_size(self): diff --git a/dnc/dnc.py b/dnc/dnc.py index 07889ef..0406a7c 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -25,12 +25,12 @@ import collections import numpy as np import sonnet as snt -import tensorflow.compat.v1 as tf +import tensorflow as tf from dnc import access, util -DNCState = collections.namedtuple('DNCState', ('access_output', 'access_state', - 'controller_state')) +DNCState = collections.namedtuple('DNCState', + ('access_output', 'access_state', 'controller_state')) class DNC(snt.RNNCore): @@ -64,9 +64,8 @@ def __init__(self, super(DNC, self).__init__(name=name) self._dtype = dtype - - #with self._enter_variable_scope(): - #with tf.variable_scope(name): + # dm-sonnet=2.0.0 LSTM is not integrated with TF2 tracing. + # Use keras to allow for Tensorboard visualization #self._controller = snt.LSTM(**controller_config, dtype=tf.float64) self._controller = tf.keras.layers.LSTMCell(**controller_config, dtype=dtype) self._access = access.MemoryAccess(**access_config, dtype=dtype) @@ -93,9 +92,6 @@ def _clip_if_enabled(self, x): return x def __call__(self, inputs, prev_state): - return self._build(inputs, prev_state) - - def _build(self, inputs, prev_state): """Connects the DNC core into the graph. Args: @@ -111,12 +107,11 @@ def _build(self, inputs, prev_state): is a `DNCState` tuple containing the fields `access_output`, `access_state`, and `controller_state`. """ - #import ipdb; ipdb.set_trace() prev_access_output = prev_state.access_output prev_access_state = prev_state.access_state prev_controller_state = prev_state.controller_state - batch_flatten = tf.layers.Flatten() + batch_flatten = tf.keras.layers.Flatten() controller_input = tf.concat( [batch_flatten(inputs), batch_flatten(prev_access_output)], 1) @@ -138,14 +133,13 @@ def _build(self, inputs, prev_state): access_state=access_state, controller_state=controller_state) - def get_initial_state(self, batch_size=None): - return self.initial_state(batch_size or self._batch_size) + def initial_state(self, batch_size=None): + return self.get_initial_state(batch_size) - def initial_state(self, batch_size): + def get_initial_state(self, batch_size=None): return DNCState( - #controller_state=self._controller.initial_state(batch_size), controller_state=self._controller.get_initial_state(batch_size=batch_size, dtype=self._dtype), - access_state=self._access.initial_state(batch_size), + access_state=self._access.get_initial_state(batch_size), access_output=tf.zeros( [batch_size] + self._access.output_size.as_list(), dtype=self._dtype)) diff --git a/tests/access_test.py b/tests/access_test.py index 9c13e46..d2e7667 100644 --- a/tests/access_test.py +++ b/tests/access_test.py @@ -20,7 +20,6 @@ import numpy as np import tensorflow as tf -from tensorflow.python.ops import rnn from dnc import access, addressing, util @@ -34,12 +33,17 @@ DTYPE=tf.float32 +# set seeds for determinism +np.random.seed(42) +from tensorflow.python.framework import random_seed +random_seed.set_seed(42) + class MemoryAccessTest(tf.test.TestCase): def setUp(self): self.module = access.MemoryAccess(MEMORY_SIZE, WORD_SIZE, NUM_READS, NUM_WRITES) - self.initial_state = self.module.initial_state(BATCH_SIZE) + self.initial_state = self.module.get_initial_state(BATCH_SIZE) def testBuildAndTrain(self): inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) @@ -47,7 +51,7 @@ def testBuildAndTrain(self): loss = lambda outputs, targets: tf.reduce_mean(input_tensor=tf.square(outputs - targets)) with tf.GradientTape() as tape: - outputs, _ = rnn.dynamic_rnn( + outputs, _ = tf.compat.v1.nn.dynamic_rnn( cell=self.module, inputs=inputs, initial_state=self.initial_state, @@ -61,7 +65,6 @@ def testBuildAndTrain(self): def testValidReadMode(self): inputs = self.module._read_inputs( tf.random.normal([BATCH_SIZE, INPUT_SIZE], dtype=DTYPE)) - init = tf.compat.v1.global_variables_initializer() # Check that the read modes for each read head constitute a probability # distribution. @@ -158,7 +161,9 @@ def evaluate_module(inputs, memory, read_weights, precedence_weights, link): return loss tensors_to_check = [ - inputs, self.initial_state.memory, self.initial_state.read_weights, + inputs, + self.initial_state.memory, + self.initial_state.read_weights, self.initial_state.linkage.precedence_weights, self.initial_state.linkage.link ] @@ -170,5 +175,6 @@ def evaluate_module(inputs, memory, read_weights, precedence_weights, link): ) self.assertLess( sum([tf.norm(numerical[i] - theoretical[i]) for i in range(2)]), - 0.01 + 0.02, + tensors_to_check ) diff --git a/tests/addressing_test.py b/tests/addressing_test.py index 22e9153..6a36bab 100644 --- a/tests/addressing_test.py +++ b/tests/addressing_test.py @@ -24,6 +24,10 @@ from dnc import addressing, util +# set seeds for determinism +np.random.seed(42) +from tensorflow.python.framework import random_seed +random_seed.set_seed(42) class WeightedSoftmaxTest(tf.test.TestCase): @@ -115,13 +119,9 @@ def testDivideByZero(self): mem = tf.Variable(tf.concat((first_row_ones, remaining_zeros), 1)) with tf.GradientTape() as gtape: - #gtape.watch(mem) - #gtape.watch(keys) - #gtape.watch(strengths) output = module(mem, keys, strengths) gradients = gtape.gradient(target=output, sources=[mem, keys, strengths]) - #import ipdb; ipdb.set_trace() self.assertFalse(np.any(np.isnan(output))) self.assertFalse(np.any(np.isnan(gradients[0]))) self.assertFalse(np.any(np.isnan(gradients[1]))) diff --git a/tests/dnc_test.py b/tests/dnc_test.py new file mode 100644 index 0000000..7da4857 --- /dev/null +++ b/tests/dnc_test.py @@ -0,0 +1,97 @@ +# Copyright 2017 Google Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for DNCCore""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import datetime +import numpy as np +import tensorflow as tf + +from dnc import dnc, access, addressing +from dnc import repeat_copy + +# set seeds for determinism +np.random.seed(42) +from tensorflow.python.framework import random_seed +random_seed.set_seed(42) + +DTYPE = tf.float32 + +# Model parameters +HIDDEN_SIZE = 64 +MEMORY_SIZE = 16 +WORD_SIZE = 16 +NUM_WRITE_HEADS = 1 +NUM_READ_HEADS = 4 +CLIP_VALUE = 20 + +# Optimizer parameters. +MAX_GRAD_NORM = 50 +LEARNING_RATE = 1e-4 +OPTIMIZER_EPSILON = 1e-10 + +# Task parameters +BATCH_SIZE = 16 +TIME_STEPS = 4 +INPUT_SIZE = 4 +OUTPUT_SIZE = 4 + + +class DNCCoreTest(tf.test.TestCase): + + def setUp(self): + access_config = { + "memory_size": MEMORY_SIZE, + "word_size": WORD_SIZE, + "num_reads": NUM_READ_HEADS, + "num_writes": NUM_WRITE_HEADS, + } + controller_config = { + #"hidden_size": FLAGS.hidden_size, + "units": HIDDEN_SIZE, + } + + self.module = dnc.DNC( + access_config, + controller_config, + OUTPUT_SIZE, + BATCH_SIZE, + CLIP_VALUE, + name='dnc_test', + dtype=DTYPE, + ) + self.initial_state = self.module.get_initial_state(BATCH_SIZE) + + def testBuildAndTrain(self): + inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) + targets = np.random.rand(TIME_STEPS, BATCH_SIZE, OUTPUT_SIZE) + loss = lambda outputs, targets: tf.reduce_mean(input_tensor=tf.square(outputs - targets)) + optimizer = tf.compat.v1.train.RMSPropOptimizer( + LEARNING_RATE, epsilon=OPTIMIZER_EPSILON) + + with tf.GradientTape() as tape: + outputs, _ = tf.compat.v1.nn.dynamic_rnn( + cell=self.module, + inputs=inputs, + initial_state=self.initial_state, + time_major=True) + loss_value = loss(outputs, targets) + gradients = tape.gradient(loss_value, self.module.trainable_variables) + + grads, _ = tf.clip_by_global_norm(gradients, MAX_GRAD_NORM) + optimizer.apply_gradients(zip(gradients, self.module.trainable_variables)) diff --git a/tests/util_test.py b/tests/util_test.py index 5f19ddf..f5d139e 100644 --- a/tests/util_test.py +++ b/tests/util_test.py @@ -23,6 +23,8 @@ from dnc import util +# set seeds for determinism +np.random.seed(42) class BatchInvertPermutation(tf.test.TestCase): From 71d83f4720fb1e680d01b0ce6ad2e78082e62b15 Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 23 May 2021 10:05:35 -0700 Subject: [PATCH 07/20] update repeat copy train script --- train.py | 195 ++++++++++++++++++++++++++++++++++++------------------- 1 file changed, 127 insertions(+), 68 deletions(-) diff --git a/train.py b/train.py index f9e5110..ea757e0 100644 --- a/train.py +++ b/train.py @@ -18,57 +18,85 @@ from __future__ import division from __future__ import print_function -import tensorflow.compat.v1 as tf1 -import tensorflow as tf -import sonnet as snt +import argparse import datetime +import tensorflow as tf from dnc import dnc from dnc import repeat_copy -FLAGS = tf1.flags.FLAGS +parser = argparse.ArgumentParser(description='Train DNC for repeat copy task.') # Model parameters -tf1.flags.DEFINE_integer("hidden_size", 64, "Size of LSTM hidden layer.") -tf1.flags.DEFINE_integer("memory_size", 16, "The number of memory slots.") -tf1.flags.DEFINE_integer("word_size", 16, "The width of each memory slot.") -tf1.flags.DEFINE_integer("num_write_heads", 1, "Number of memory write heads.") -tf1.flags.DEFINE_integer("num_read_heads", 4, "Number of memory read heads.") -tf1.flags.DEFINE_integer("clip_value", 20, - "Maximum absolute value of controller and dnc outputs.") +parser.add_argument("--hidden_size", default=64, type=int, help= + "Size of LSTM hidden layer.") +parser.add_argument("--memory_size", default=16, type=int, help= + "The number of memory slots.") +parser.add_argument("--word_size", default=16, type=int, help= + "The width of each memory slot.") +parser.add_argument("--num_write_heads", default=1, type=int, help= + "Number of memory write heads.") +parser.add_argument("--num_read_heads", default=4, type=int, help= + "Number of memory read heads.") +parser.add_argument("--clip_value", default=20, type=int, help= + "Maximum absolute value of controller and dnc outputs.") # Optimizer parameters. -tf1.flags.DEFINE_float("max_grad_norm", 50, "Gradient clipping norm limit.") -tf1.flags.DEFINE_float("learning_rate", 1e-4, "Optimizer learning rate.") -tf1.flags.DEFINE_float("optimizer_epsilon", 1e-10, +parser.add_argument("--max_grad_norm", default=50, type=float, help= + "Gradient clipping norm limit.") +parser.add_argument("--learning_rate", default=1e-4, type=float, help= + "Optimizer learning rate.") +parser.add_argument("--optimizer_epsilon", default=1e-10, type=float, help= "Epsilon used for RMSProp optimizer.") # Task parameters -tf1.flags.DEFINE_integer("batch_size", 16, "Batch size for training.") -tf1.flags.DEFINE_integer("num_bits", 4, "Dimensionality of each vector to copy") -tf1.flags.DEFINE_integer( - "min_length", 1, - "Lower limit on number of vectors in the observation pattern to copy") -tf1.flags.DEFINE_integer( - "max_length", 2, - "Upper limit on number of vectors in the observation pattern to copy") -tf1.flags.DEFINE_integer("min_repeats", 1, - "Lower limit on number of copy repeats.") -tf1.flags.DEFINE_integer("max_repeats", 2, - "Upper limit on number of copy repeats.") +parser.add_argument("--batch_size", default=16, type=int, help= + "Batch size for training.") +parser.add_argument("--num_bits", default=4, type=int, help= + "Dimensionality of each vector to copy") +parser.add_argument("--min_length", default=1, type=int, help= + "Lower limit on number of vectors in the observation pattern to copy") +parser.add_argument("--max_length", default=3, type=int, help= + "Upper limit on number of vectors in the observation pattern to copy") +parser.add_argument("--min_repeats", default=1, type=int, help= + "Lower limit on number of copy repeats.") +parser.add_argument("--max_repeats", default=7, type=int, help= + "Upper limit on number of copy repeats.") # Training options. -tf1.flags.DEFINE_integer("num_training_iterations", 10000, - "Number of iterations to train for.") -tf1.flags.DEFINE_integer("report_interval", 100, - "Iterations between reports (samples, valid loss).") -tf1.flags.DEFINE_string("checkpoint_dir", "./logs/dnc/checkpoint", - "Checkpointing directory.") -tf1.flags.DEFINE_integer("checkpoint_interval", 2000, - "Checkpointing step interval.") +parser.add_argument("--epochs", default=10000, type=int, help= + "Number of epochs to train for.") +parser.add_argument("--log_dir", default="./logs/dnc/", type=str, help= + "Logging directory.") +parser.add_argument("--report_interval", default=100, type=int, help= + "Epochs between reports (samples, valid loss).") +parser.add_argument("--checkpoint_dir", default="./checkpoints/repeat_copy", type=str, help= + "Checkpointing directory.") +parser.add_argument("--checkpoint_interval", default=2000, type=int, help= + "Checkpointing step interval.") + +FLAGS = parser.parse_args() + + +def train_step(dataset_tensors, rnn_model, optimizer, loss_fn): + return train_step_graphed( + dataset_tensors.observations, + dataset_tensors.target, + dataset_tensors.mask, + rnn_model, + optimizer, + loss_fn, + ) @tf.function -def train_step(x, y, rnn_model, loss, optimizer): +def train_step_graphed( + x, + y, + mask, + rnn_model, + optimizer, + loss_fn, +): """Runs model on input sequence.""" initial_state = rnn_model.get_initial_state() with tf.GradientTape() as tape: @@ -77,22 +105,43 @@ def train_step(x, y, rnn_model, loss, optimizer): inputs=x, time_major=True, initial_state=initial_state) - loss_value = loss(output_sequence, y) + # Unable to migrate to tf.keras.layers.RNN due to contraints on RNN state structure + """output_sequence = tf.keras.layers.RNN( + cell=rnn_model, + time_major=True, + inputs=x, + initial_state=initial_state, + )""" + loss_value = loss_fn(output_sequence, y, mask) grads = tape.gradient(loss_value, rnn_model.trainable_variables) grads, _ = tf.clip_by_global_norm(grads, FLAGS.max_grad_norm) optimizer.apply_gradients(zip(grads, rnn_model.trainable_variables)) - return loss_value +def test_step(dataset_tensors, rnn_model, optimizer, loss_fn): + return test_step_graphed( + dataset_tensors.observations, + dataset_tensors.target, + dataset_tensors.mask, + rnn_model, + loss_fn, + ) + @tf.function -def test_step(x, y, rnn_model, loss, mask): +def test_step_graphed( + x, + y, + mask, + rnn_model, + loss_fn, +): initial_state = rnn_model.get_initial_state() output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( cell=rnn_model, inputs=x, time_major=True, initial_state=initial_state) - loss_value = loss(output_sequence, y) + loss_value = loss_fn(output_sequence, y, mask) # Used for visualization. output = tf.round( tf.expand_dims(mask, -1) * tf.sigmoid(output_sequence)) @@ -105,8 +154,8 @@ def train(num_training_iterations, report_interval): dataset = repeat_copy.RepeatCopy(FLAGS.num_bits, FLAGS.batch_size, FLAGS.min_length, FLAGS.max_length, FLAGS.min_repeats, FLAGS.max_repeats, - dtype=tf.float64) - dataset_tensors = dataset() + dtype=tf.float32) + dataset_tensor = dataset() access_config = { "memory_size": FLAGS.memory_size, @@ -115,16 +164,16 @@ def train(num_training_iterations, report_interval): "num_writes": FLAGS.num_write_heads, } controller_config = { - "hidden_size": FLAGS.hidden_size, + #"hidden_size": FLAGS.hidden_size, + "units": FLAGS.hidden_size, } clip_value = FLAGS.clip_value dnc_core = dnc.DNC( access_config, controller_config, dataset.target_size, FLAGS.batch_size, clip_value) - loss_fn = lambda pred, target: dataset.cost( - pred, target, dataset_tensors.mask) optimizer = tf.compat.v1.train.RMSPropOptimizer( FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon) + loss_fn = dataset.cost #saver = tf.train.Checkpoint() @@ -133,64 +182,74 @@ def train(num_training_iterations, report_interval): test_loss = tf.keras.metrics.Mean('test_loss', dtype=tf.float32) current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") - train_log_dir = 'logs/dnc/' + current_time + '/train' - test_log_dir = 'logs/dnc/' + current_time + '/test' + train_log_dir = FLAGS.log_dir + current_time + '/train' + test_log_dir = FLAGS.log_dir + current_time + '/test' train_summary_writer = tf.summary.create_file_writer(train_log_dir) test_summary_writer = tf.summary.create_file_writer(test_log_dir) # Test once to initialize - graph_log_dir = 'logs/dnc/' + current_time + '/graph' + graph_log_dir = FLAGS.log_dir + current_time + '/graph' graph_writer = tf.summary.create_file_writer(graph_log_dir) with graph_writer.as_default(): tf.summary.trace_on(graph=True, profiler=True) - test_step( - dataset_tensors.observations, dataset_tensors.target, dnc_core, loss_fn, dataset_tensors.mask - ) + test_step(dataset_tensor, dnc_core, optimizer, loss_fn) tf.summary.trace_export( name="dnc_trace", step=0, profiler_outdir=graph_log_dir) - return # Set up model checkpointing checkpoint = tf.train.Checkpoint(model=dnc_core, optimizer=optimizer) + manager = tf.train.CheckpointManager(checkpoint, FLAGS.checkpoint_dir, max_to_keep=10) + + checkpoint.restore(manager.latest_checkpoint) + if manager.latest_checkpoint: + print("Restored from {}".format(manager.latest_checkpoint)) + else: + print("Initializing from scratch.") # Train. - for epoch in range(0, num_training_iterations): - loss_value = train_step( - dataset_tensors.observations, dataset_tensors.target, dnc_core, loss_fn, optimizer, + for epoch in range(num_training_iterations): + dataset_tensor = dataset() + train_loss_value = train_step( + dataset_tensor, dnc_core, optimizer, loss_fn ) - train_loss(loss_value) - with train_summary_writer.as_default(): - tf.summary.scalar('loss', train_loss.result(), step=epoch) + train_loss(train_loss_value) if (epoch) % report_interval == 0: - loss_value, output = test_step( - dataset_tensors.observations, dataset_tensors.target, dnc_core, loss_fn, dataset_tensors.mask + dataset_tensor = dataset() + test_loss_value, output = test_step( + dataset_tensor, dnc_core, optimizer, loss_fn ) - test_loss(loss_value) - #dataset_string = dataset.to_human_readable(dataset_tensors_np,output_np) + test_loss(test_loss_value) with test_summary_writer.as_default(): tf.summary.scalar('loss', test_loss.result(), step=epoch) + with train_summary_writer.as_default(): + tf.summary.scalar('loss', train_loss.result(), step=epoch) template = 'Epoch {}, Loss: {}, Test Loss: {}' print(template.format( - epoch + 1, + epoch, train_loss.result(), test_loss.result(), )) - # reset metrics every epoch - train_loss.reset_states() - test_loss.reset_states() + dataset_string = dataset.to_human_readable(dataset_tensor,output.numpy()) + print(dataset_string) + + # reset metrics every epoch + train_loss.reset_states() + test_loss.reset_states() - if (epoch) % FLAGS.checkpoint_interval == 0: - checkpoint.save(FLAGS.checkpoint_dir) + if (1 + epoch) % FLAGS.checkpoint_interval == 0: + manager.save() + # At the end, checkpoint as well + manager.save() def main(unused_argv): tf.compat.v1.logging.set_verbosity(3) # Print INFO log messages. - train(FLAGS.num_training_iterations, FLAGS.report_interval) + train(FLAGS.epochs, FLAGS.report_interval) if __name__ == "__main__": From cf07bf046699636fdebca47ca7d57507ee11a46c Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 23 May 2021 13:02:35 -0700 Subject: [PATCH 08/20] WIP, can't handle complex stats structure --- dnc/access.py | 37 ++++++++++++++++++++++------- dnc/addressing.py | 21 +++++++++++------ dnc/dnc.py | 51 ++++++++++++++++++++++++++-------------- dnc/util.py | 5 ++++ tests/access_test.py | 39 ++++++++++++++++-------------- tests/addressing_test.py | 24 ++++++++++--------- tests/dnc_test.py | 2 +- train.py | 7 +++--- 8 files changed, 122 insertions(+), 64 deletions(-) diff --git a/dnc/access.py b/dnc/access.py index 8fae411..31aaefa 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -28,6 +28,11 @@ AccessState = collections.namedtuple('AccessState', ( 'memory', 'read_weights', 'write_weights', 'linkage', 'usage')) +MEMORY = 0 +READ_WEIGHTS = 1 +WRITE_WEIGHTS = 2 +LINKAGE = 3 +USAGE = 4 def _erase_and_write(memory, address, reset_weights, values): """Module to erase and write in the external memory. @@ -113,6 +118,9 @@ def __init__(self, self._linear_layers = {} + def call(self, inputs, prev_state): + return self.__call__(inputs, prev_state) + def __call__(self, inputs, prev_state): """Connects the MemoryAccess module into the graph. @@ -126,6 +134,13 @@ def __call__(self, inputs, prev_state): `[batch_size, num_reads, word_size]`, and `next_state` is the new `AccessState` named tuple at the current time t. """ + prev_state = AccessState( + memory=prev_state[MEMORY], + read_weights=prev_state[READ_WEIGHTS], + write_weights=prev_state[WRITE_WEIGHTS], + linkage=prev_state[LINKAGE], + usage=prev_state[USAGE], + ) #import ipdb; ipdb.set_trace() inputs = self._read_inputs(inputs) @@ -144,7 +159,7 @@ def __call__(self, inputs, prev_state): reset_weights=inputs['erase_vectors'], values=inputs['write_vectors']) - linkage_state = self._linkage(write_weights, prev_state.linkage) + linkage_state = addressing.TemporalLinkageState(*self._linkage(write_weights, prev_state.linkage)) # Read from memory. read_weights = self._read_weights( @@ -154,12 +169,12 @@ def __call__(self, inputs, prev_state): link=linkage_state.link) read_words = tf.matmul(read_weights, memory) - return (read_words, AccessState( + return (read_words, list(AccessState( memory=memory, read_weights=read_weights, write_weights=write_weights, - linkage=linkage_state, - usage=usage)) + linkage=list(linkage_state), + usage=usage))) def _read_inputs(self, inputs): """Applies transformations to `inputs` to get control for this module.""" @@ -304,23 +319,29 @@ def _read_weights(self, inputs, memory, prev_read_weights, link): return read_weights + """ # keras uses get_initial_state def get_initial_state(self, batch_size): return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) - +""" # snt.RNNCore uses initial_state def initial_state(self, batch_size): return self.get_initial_state(batch_size) - @property def state_size(self): """Returns a tuple of the shape of the state tensors.""" - return AccessState( + """return list(AccessState( memory=tf.TensorShape([self._memory_size, self._word_size]), read_weights=tf.TensorShape([self._num_reads, self._memory_size]), write_weights=tf.TensorShape([self._num_writes, self._memory_size]), linkage=self._linkage.state_size, - usage=self._freeness.state_size) + usage=self._freeness.state_size))""" + return tuple(AccessState( + memory=[self._memory_size, self._word_size], + read_weights=[self._num_reads, self._memory_size], + write_weights=[self._num_writes, self._memory_size], + linkage=self._linkage.state_size, + usage=self._freeness.state_size)) @property def output_size(self): diff --git a/dnc/addressing.py b/dnc/addressing.py index bc64a82..7ff6a60 100644 --- a/dnc/addressing.py +++ b/dnc/addressing.py @@ -30,6 +30,8 @@ TemporalLinkageState = collections.namedtuple('TemporalLinkageState', ('link', 'precedence_weights')) +LINK = 0 +PRECEDENCE_WEIGHTS = 1 def _vector_norms(m): squared_norms = tf.compat.v1.reduce_sum(input_tensor=m * m, axis=2, keepdims=True) @@ -146,12 +148,12 @@ def __call__(self, write_weights, prev_state): A `TemporalLinkageState` tuple `next_state`, which contains the updated link and precedence weights. """ - link = self._link(prev_state.link, prev_state.precedence_weights, + link = self._link(prev_state[LINK], prev_state[PRECEDENCE_WEIGHTS], write_weights) - precedence_weights = self._precedence_weights(prev_state.precedence_weights, + precedence_weights = self._precedence_weights(prev_state[PRECEDENCE_WEIGHTS], write_weights) - return TemporalLinkageState( - link=link, precedence_weights=precedence_weights) + return list(TemporalLinkageState( + link=link, precedence_weights=precedence_weights)) def directional_read_weights(self, link, prev_read_weights, forward): """Calculates the forward or the backward read weights. @@ -244,12 +246,16 @@ def initial_state(self, batch_size): @property def state_size(self): """Returns a `TemporalLinkageState` tuple of the state tensors' shapes.""" - return TemporalLinkageState( + """return list(TemporalLinkageState( link=tf.TensorShape( [self._num_writes, self._memory_size, self._memory_size]), precedence_weights=tf.TensorShape( [self._num_writes, self._memory_size]) - ) + ))""" + return list(TemporalLinkageState( + link=(self._num_writes, self._memory_size, self._memory_size), + precedence_weights=(self._num_writes, self._memory_size), + )) class Freeness(snt.RNNCore): """Memory usage that is increased by writing and decreased by reading. @@ -413,4 +419,5 @@ def initial_state(self, batch_size): @property def state_size(self): """Returns the shape of the state tensor.""" - return tf.TensorShape([self._memory_size]) + #return tf.TensorShape([self._memory_size]) + return (self._memory_size,) diff --git a/dnc/dnc.py b/dnc/dnc.py index 0406a7c..61c7e65 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -32,13 +32,15 @@ DNCState = collections.namedtuple('DNCState', ('access_output', 'access_state', 'controller_state')) +ACCESS_OUTPUT = 0 +ACCESS_STATE = 1 +CONTROLLER_STATE = 2 class DNC(snt.RNNCore): """DNC core module. Contains controller and memory access module. """ - def __init__(self, access_config, controller_config, @@ -76,11 +78,11 @@ def __init__(self, self._clip_value = clip_value or 0 self._output_size = tf.TensorShape([output_size]) - self._state_size = DNCState( - access_output=self._access_output_size, - access_state=self._access.state_size, - controller_state=self._controller.state_size, - ) + self._state_size = [ + self._access_output_size, + self._access.state_size, + self._controller.state_size, + ] self._output_linear = snt.Linear( output_size=self._output_size.as_list()[0], name='output_linear') @@ -90,6 +92,9 @@ def _clip_if_enabled(self, x): return tf.clip_by_value(x, -self._clip_value, self._clip_value) else: return x + + def call(self, inputs, prev_state): + return self.__call__(inputs, prev_state) def __call__(self, inputs, prev_state): """Connects the DNC core into the graph. @@ -107,9 +112,9 @@ def __call__(self, inputs, prev_state): is a `DNCState` tuple containing the fields `access_output`, `access_state`, and `controller_state`. """ - prev_access_output = prev_state.access_output - prev_access_state = prev_state.access_state - prev_controller_state = prev_state.controller_state + prev_access_output = prev_state[ACCESS_OUTPUT] + prev_access_state = prev_state[ACCESS_STATE] + prev_controller_state = prev_state[CONTROLLER_STATE] batch_flatten = tf.keras.layers.Flatten() controller_input = tf.concat( @@ -128,21 +133,33 @@ def __call__(self, inputs, prev_state): output = self._output_linear(output) output = self._clip_if_enabled(output) - return output, DNCState( - access_output=access_output, - access_state=access_state, - controller_state=controller_state) + return output, [ + access_output, + access_state, + controller_state, + ] def initial_state(self, batch_size=None): return self.get_initial_state(batch_size) def get_initial_state(self, batch_size=None): - return DNCState( + return list(DNCState( controller_state=self._controller.get_initial_state(batch_size=batch_size, dtype=self._dtype), - access_state=self._access.get_initial_state(batch_size), + access_state=self._access.get_initial_state(batch_size=batch_size, dtype=self._dtype), access_output=tf.zeros( - [batch_size] + self._access.output_size.as_list(), dtype=self._dtype)) - + [batch_size] + self._access.output_size.as_list(), dtype=self._dtype))) + + """def initial_state(self, batch_size): + return [ + #controller_state + self._controller.get_initial_state(batch_size=batch_size, dtype=self._dtype), + #access_state + self._access.initial_state(batch_size), + #access_output + tf.zeros( + [batch_size] + self._access.output_size.as_list(), dtype=self._dtype) + ]""" + @property def state_size(self): return self._state_size diff --git a/dnc/util.py b/dnc/util.py index 71cb9d9..d3db0b8 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -84,6 +84,11 @@ def state_size_from_initial_state(initial_state): def initial_state_from_state_size(state_size, batch_size, dtype): if isinstance(state_size, tf.TensorShape): return tf.zeros(batch_size + state_size, dtype=dtype) + elif isinstance(state_size, list): + return [ + initial_state_from_state_size(s, batch_size, dtype) + for s in state_size + ] initial_state_dict = {} for field, value in state_size._asdict().items(): diff --git a/tests/access_test.py b/tests/access_test.py index d2e7667..b65c3f2 100644 --- a/tests/access_test.py +++ b/tests/access_test.py @@ -41,21 +41,24 @@ class MemoryAccessTest(tf.test.TestCase): def setUp(self): - self.module = access.MemoryAccess(MEMORY_SIZE, WORD_SIZE, NUM_READS, - NUM_WRITES) - self.initial_state = self.module.get_initial_state(BATCH_SIZE) + self.cell = access.MemoryAccess( + MEMORY_SIZE, WORD_SIZE, NUM_READS, NUM_WRITES) + + self.module = tf.keras.layers.RNN( + cell=self.cell, + time_major=True) def testBuildAndTrain(self): inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) targets = np.random.rand(TIME_STEPS, BATCH_SIZE, NUM_READS, WORD_SIZE) loss = lambda outputs, targets: tf.reduce_mean(input_tensor=tf.square(outputs - targets)) - + print(self.module.get_initial_state(inputs)) + import ipdb; ipdb.set_trace() with tf.GradientTape() as tape: - outputs, _ = tf.compat.v1.nn.dynamic_rnn( - cell=self.module, + outputs, _ = self.module( inputs=inputs, - initial_state=self.initial_state, - time_major=True) + #initial_state=self.initial_state, + ) loss_value = loss(outputs, targets) gradients = tape.gradient(loss_value, self.module.trainable_variables) @@ -63,7 +66,7 @@ def testBuildAndTrain(self): optimizer.apply_gradients(zip(gradients, self.module.trainable_variables)) def testValidReadMode(self): - inputs = self.module._read_inputs( + inputs = self.cell._read_inputs( tf.random.normal([BATCH_SIZE, INPUT_SIZE], dtype=DTYPE)) # Check that the read modes for each read head constitute a probability @@ -94,7 +97,7 @@ def testWriteWeights(self): 'write_content_strengths': tf.constant(write_content_strengths, dtype=DTYPE) } - weights = self.module._write_weights(inputs, + weights = self.cell._write_weights(inputs, tf.constant(memory, dtype=DTYPE), tf.constant(usage, dtype=DTYPE)) @@ -130,7 +133,7 @@ def testReadWeights(self): 'read_content_strengths': read_content_strengths, 'read_mode': tf.constant(read_mode, dtype=DTYPE), } - read_weights = self.module._read_weights( + read_weights = self.cell._read_weights( inputs, tf.cast(memory, dtype=DTYPE), tf.cast(prev_read_weights, dtype=DTYPE), @@ -145,16 +148,18 @@ def testReadWeights(self): def testGradients(self): inputs = tf.constant(np.random.randn(BATCH_SIZE, INPUT_SIZE), dtype=DTYPE) + initial_state = self.module.get_initial_state(inputs) + def evaluate_module(inputs, memory, read_weights, precedence_weights, link): initial_state = access.AccessState( memory=memory, read_weights=read_weights, - write_weights=self.initial_state.write_weights, + write_weights=initial_state[access.WRITE_WEIGHTS], linkage=addressing.TemporalLinkageState( precedence_weights=precedence_weights, link=link ), - usage=self.initial_state.usage + usage=initial_state[access.USAGE], ) output, _ = self.module(inputs, initial_state) loss = tf.reduce_sum(input_tensor=output) @@ -162,10 +167,10 @@ def evaluate_module(inputs, memory, read_weights, precedence_weights, link): tensors_to_check = [ inputs, - self.initial_state.memory, - self.initial_state.read_weights, - self.initial_state.linkage.precedence_weights, - self.initial_state.linkage.link + initial_state[access.MEMORY], + initial_state[access.READ_WEIGHTS], + initial_state[access.LINKAGE][addressing.PRECEDENCE_WEIGHTS], + initial_state[access.LINKAGE][addressing.LINK], ] theoretical, numerical = tf.test.compute_gradient( diff --git a/tests/addressing_test.py b/tests/addressing_test.py index 6a36bab..eba29e0 100644 --- a/tests/addressing_test.py +++ b/tests/addressing_test.py @@ -155,8 +155,8 @@ def testModule(self): write_weights[0, 0, :] = util.one_hot(memory_size, 1) write_weights[0, 1, :] = util.one_hot(memory_size, 2) - prev_link_in = state.link - prev_precedence_weights_in = state.precedence_weights + prev_link_in = state[addressing.LINK] + prev_precedence_weights_in = state[addressing.PRECEDENCE_WEIGHTS] write_weights_in = write_weights state = module( @@ -167,35 +167,37 @@ def testModule(self): ) ) + result_link = state[addressing.LINK] + # link should be bounded in range [0, 1] - self.assertGreaterEqual(tf.math.reduce_min(state.link), 0) - self.assertLessEqual(tf.math.reduce_max(state.link), 1) + self.assertGreaterEqual(tf.math.reduce_min(result_link), 0) + self.assertLessEqual(tf.math.reduce_max(result_link), 1) # link diagonal should be zero self.assertAllEqual( - tf.linalg.diag_part(state.link), + tf.linalg.diag_part(result_link), np.zeros([batch_size, num_writes, memory_size])) # link rows and columns should sum to at most 1 self.assertLessEqual( - tf.math.reduce_max(tf.math.reduce_sum(state.link, axis=2)), 1) + tf.math.reduce_max(tf.math.reduce_sum(result_link, axis=2)), 1) self.assertLessEqual( - tf.math.reduce_max(tf.math.reduce_sum(state.link, axis=3)), 1) + tf.math.reduce_max(tf.math.reduce_sum(result_link, axis=3)), 1) # records our transitions in batch 0: head 0: 0->1, and head 1: 3->2 - self.assertAllEqual(state.link[0, 0, :, 0], util.one_hot(memory_size, 1)) - self.assertAllEqual(state.link[0, 1, :, 3], util.one_hot(memory_size, 2)) + self.assertAllEqual(result_link[0, 0, :, 0], util.one_hot(memory_size, 1)) + self.assertAllEqual(result_link[0, 1, :, 3], util.one_hot(memory_size, 2)) # Now test calculation of forward and backward read weights prev_read_weights = np.random.rand(batch_size, num_reads, memory_size) prev_read_weights[0, 5, :] = util.one_hot(memory_size, 0) # read 5, posn 0 prev_read_weights[0, 6, :] = util.one_hot(memory_size, 2) # read 6, posn 2 forward_read_weights = module.directional_read_weights( - tf.constant(state.link), + tf.constant(result_link), tf.constant(prev_read_weights, dtype=tf.float64), forward=True) backward_read_weights = module.directional_read_weights( - tf.constant(state.link), + tf.constant(result_link), tf.constant(prev_read_weights, dtype=tf.float64), forward=False) diff --git a/tests/dnc_test.py b/tests/dnc_test.py index 7da4857..3956b31 100644 --- a/tests/dnc_test.py +++ b/tests/dnc_test.py @@ -88,7 +88,7 @@ def testBuildAndTrain(self): outputs, _ = tf.compat.v1.nn.dynamic_rnn( cell=self.module, inputs=inputs, - initial_state=self.initial_state, + #initial_state=self.initial_state, time_major=True) loss_value = loss(outputs, targets) gradients = tape.gradient(loss_value, self.module.trainable_variables) diff --git a/train.py b/train.py index ea757e0..c70fed3 100644 --- a/train.py +++ b/train.py @@ -100,18 +100,19 @@ def train_step_graphed( """Runs model on input sequence.""" initial_state = rnn_model.get_initial_state() with tf.GradientTape() as tape: - output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( + """output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( cell=rnn_model, inputs=x, time_major=True, initial_state=initial_state) # Unable to migrate to tf.keras.layers.RNN due to contraints on RNN state structure - """output_sequence = tf.keras.layers.RNN( + """ + output_sequence = tf.keras.layers.RNN( cell=rnn_model, time_major=True, inputs=x, initial_state=initial_state, - )""" + ) loss_value = loss_fn(output_sequence, y, mask) grads = tape.gradient(loss_value, rnn_model.trainable_variables) grads, _ = tf.clip_by_global_norm(grads, FLAGS.max_grad_norm) From 2059de86037dd0bf77c09e084fb3168e51fe44b7 Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 30 May 2021 14:17:46 -0700 Subject: [PATCH 09/20] migrated to keras.layers.RNN for rnn evaluation --- dnc/access.py | 22 +- dnc/addressing.py | 9 +- dnc/dnc.py | 31 +- dnc/util.py | 15 +- interactive.ipynb | 973 +++++++++++++++++++++++++++++++++++++++++++ tests/access_test.py | 28 +- tests/dnc_test.py | 12 +- train.py | 26 +- 8 files changed, 1039 insertions(+), 77 deletions(-) create mode 100644 interactive.ipynb diff --git a/dnc/access.py b/dnc/access.py index 31aaefa..362f814 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -134,13 +134,7 @@ def __call__(self, inputs, prev_state): `[batch_size, num_reads, word_size]`, and `next_state` is the new `AccessState` named tuple at the current time t. """ - prev_state = AccessState( - memory=prev_state[MEMORY], - read_weights=prev_state[READ_WEIGHTS], - write_weights=prev_state[WRITE_WEIGHTS], - linkage=prev_state[LINKAGE], - usage=prev_state[USAGE], - ) + prev_state = AccessState(*prev_state) #import ipdb; ipdb.set_trace() inputs = self._read_inputs(inputs) @@ -319,28 +313,22 @@ def _read_weights(self, inputs, memory, prev_read_weights, link): return read_weights - """ # keras uses get_initial_state - def get_initial_state(self, batch_size): + def get_initial_state(self, batch_size=None, inputs=None, dtype=None): return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) -""" + # snt.RNNCore uses initial_state def initial_state(self, batch_size): return self.get_initial_state(batch_size) + @property def state_size(self): """Returns a tuple of the shape of the state tensors.""" - """return list(AccessState( + return list(AccessState( memory=tf.TensorShape([self._memory_size, self._word_size]), read_weights=tf.TensorShape([self._num_reads, self._memory_size]), write_weights=tf.TensorShape([self._num_writes, self._memory_size]), linkage=self._linkage.state_size, - usage=self._freeness.state_size))""" - return tuple(AccessState( - memory=[self._memory_size, self._word_size], - read_weights=[self._num_reads, self._memory_size], - write_weights=[self._num_writes, self._memory_size], - linkage=self._linkage.state_size, usage=self._freeness.state_size)) @property diff --git a/dnc/addressing.py b/dnc/addressing.py index 7ff6a60..2b2ee3d 100644 --- a/dnc/addressing.py +++ b/dnc/addressing.py @@ -246,15 +246,11 @@ def initial_state(self, batch_size): @property def state_size(self): """Returns a `TemporalLinkageState` tuple of the state tensors' shapes.""" - """return list(TemporalLinkageState( + return list(TemporalLinkageState( link=tf.TensorShape( [self._num_writes, self._memory_size, self._memory_size]), precedence_weights=tf.TensorShape( [self._num_writes, self._memory_size]) - ))""" - return list(TemporalLinkageState( - link=(self._num_writes, self._memory_size, self._memory_size), - precedence_weights=(self._num_writes, self._memory_size), )) class Freeness(snt.RNNCore): @@ -419,5 +415,4 @@ def initial_state(self, batch_size): @property def state_size(self): """Returns the shape of the state tensor.""" - #return tf.TensorShape([self._memory_size]) - return (self._memory_size,) + return tf.TensorShape([self._memory_size]) diff --git a/dnc/dnc.py b/dnc/dnc.py index 61c7e65..a7094b1 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -72,19 +72,18 @@ def __init__(self, self._controller = tf.keras.layers.LSTMCell(**controller_config, dtype=dtype) self._access = access.MemoryAccess(**access_config, dtype=dtype) - self._access_output_size = np.prod(self._access.output_size.as_list()) self._output_size = output_size self._batch_size = batch_size self._clip_value = clip_value or 0 self._output_size = tf.TensorShape([output_size]) - self._state_size = [ - self._access_output_size, - self._access.state_size, - self._controller.state_size, - ] + self._state_size = list(DNCState( + access_output=self._access.output_size, + access_state=self._access.state_size, + controller_state=[tf.TensorShape([i]) for i in self._controller.state_size], + )) self._output_linear = snt.Linear( - output_size=self._output_size.as_list()[0], + output_size=output_size, name='output_linear') def _clip_if_enabled(self, x): @@ -112,40 +111,38 @@ def __call__(self, inputs, prev_state): is a `DNCState` tuple containing the fields `access_output`, `access_state`, and `controller_state`. """ - prev_access_output = prev_state[ACCESS_OUTPUT] - prev_access_state = prev_state[ACCESS_STATE] - prev_controller_state = prev_state[CONTROLLER_STATE] + prev_state = DNCState(*prev_state) batch_flatten = tf.keras.layers.Flatten() controller_input = tf.concat( - [batch_flatten(inputs), batch_flatten(prev_access_output)], 1) + [batch_flatten(inputs), batch_flatten(prev_state.access_output)], 1) controller_output, controller_state = self._controller( - controller_input, prev_controller_state) + controller_input, prev_state.controller_state) controller_output = self._clip_if_enabled(controller_output) controller_state = tf.nest.map_structure(self._clip_if_enabled, controller_state) access_output, access_state = self._access(controller_output, - prev_access_state) + prev_state.access_state) output = tf.concat([controller_output, batch_flatten(access_output)], 1) output = self._output_linear(output) output = self._clip_if_enabled(output) - return output, [ + return output, list(DNCState( access_output, access_state, controller_state, - ] + )) def initial_state(self, batch_size=None): return self.get_initial_state(batch_size) - def get_initial_state(self, batch_size=None): + def get_initial_state(self, batch_size=None, inputs=None, dtype=None): return list(DNCState( controller_state=self._controller.get_initial_state(batch_size=batch_size, dtype=self._dtype), - access_state=self._access.get_initial_state(batch_size=batch_size, dtype=self._dtype), + access_state=self._access.get_initial_state(batch_size=batch_size), access_output=tf.zeros( [batch_size] + self._access.output_size.as_list(), dtype=self._dtype))) diff --git a/dnc/util.py b/dnc/util.py index d3db0b8..76ebbaf 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -83,14 +83,17 @@ def state_size_from_initial_state(initial_state): def initial_state_from_state_size(state_size, batch_size, dtype): if isinstance(state_size, tf.TensorShape): - return tf.zeros(batch_size + state_size, dtype=dtype) + return tf.zeros([batch_size] + state_size.as_list(), dtype=dtype) elif isinstance(state_size, list): return [ initial_state_from_state_size(s, batch_size, dtype) for s in state_size ] - - initial_state_dict = {} - for field, value in state_size._asdict().items(): - initial_state_dict[field] = initial_state_from_state_size(value, batch_size, dtype) - return type(state_size)(**initial_state_dict) + # Not used anymore since migration off of namedtuple state representation + elif isinstance(state_size, namedtuple): + initial_state_dict = {} + for field, value in state_size._asdict().items(): + initial_state_dict[field] = initial_state_from_state_size(value, batch_size, dtype) + return type(state_size)(**initial_state_dict) + + raise NotImplemented(f"Cannot parse initial_state from state_size of type {type(state)}: {state}") diff --git a/interactive.ipynb b/interactive.ipynb new file mode 100644 index 0000000..758b98a --- /dev/null +++ b/interactive.ipynb @@ -0,0 +1,973 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "474c9cfa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Enabling eager execution\n", + "INFO:tensorflow:Enabling v2 tensorshape\n", + "INFO:tensorflow:Enabling resource variables\n", + "INFO:tensorflow:Enabling tensor equality\n", + "INFO:tensorflow:Enabling control flow v2\n" + ] + } + ], + "source": [ + "# Copyright 2017 Google Inc.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "# ==============================================================================\n", + "\"\"\"Example script to train the DNC on a repeated copy task.\"\"\"\n", + "\n", + "from __future__ import absolute_import\n", + "from __future__ import division\n", + "from __future__ import print_function\n", + "\n", + "import argparse\n", + "import datetime\n", + "import tensorflow as tf\n", + "\n", + "from dnc import dnc, access\n", + "from dnc import repeat_copy\n", + "\n", + "from collections import namedtuple\n", + "\n", + "flags_dict = {\n", + " # Model parameters\n", + " \"hidden_size\": 64, # Size of LSTM hidden layer.\n", + " \"memory_size\": 16, # The number of memory slots.\n", + " \"word_size\": 16, #\"The width of each memory slot.\"\n", + " \"num_write_heads\": 1, #\"Number of memory write heads.\"\n", + " \"num_read_heads\": 4, #\"Number of memory read heads.\"\n", + " \"clip_value\": 20,#\"Maximum absolute value of controller and dnc outputs.\"\n", + "\n", + " # Optimizer parameters.\n", + " \"max_grad_norm\": 50, #\"Gradient clipping norm limit.\"\n", + " \"learning_rate\": 1e-4, #\"Optimizer learning rate.\"\n", + " \"optimizer_epsilon\": 1e-10, #\"Epsilon used for RMSProp optimizer.\"\n", + "\n", + " # Task parameters\n", + " \"batch_size\": 16, #\"Batch size for training.\"\n", + " \"num_bits\": 4, #\"Dimensionality of each vector to copy\"\n", + " \"min_length\": 1,#\"Lower limit on number of vectors in the observation pattern to copy\"\n", + " \"max_length\": 2,#\"Upper limit on number of vectors in the observation pattern to copy\"\n", + " \"min_repeats\": 1,#\"Lower limit on number of copy repeats.\"\n", + " \"max_repeats\": 2, #\"Upper limit on number of copy repeats.\"\n", + "\n", + " \"checkpoint_dir\": \"./checkpoints/repeat_copy\", #\"Checkpointing directory.\"\n", + "}\n", + "\n", + "flags_schema = namedtuple('flags_schema', list(flags_dict.keys()))\n", + "FLAGS = flags_schema(**flags_dict)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3112d2e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restored from ./checkpoints/repeat_copy/ckpt-90045\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def load_model():\n", + " \"\"\"Trains the DNC and periodically reports the loss.\"\"\"\n", + " access_config = {\n", + " \"memory_size\": FLAGS.memory_size,\n", + " \"word_size\": FLAGS.word_size,\n", + " \"num_reads\": FLAGS.num_read_heads,\n", + " \"num_writes\": FLAGS.num_write_heads,\n", + " }\n", + " controller_config = {\n", + " #\"hidden_size\": FLAGS.hidden_size,\n", + " \"units\": FLAGS.hidden_size,\n", + " }\n", + " clip_value = FLAGS.clip_value\n", + "\n", + " dnc_cell = dnc.DNC(\n", + " access_config, controller_config, dataset.target_size, FLAGS.batch_size, clip_value)\n", + " dnc_core = tf.keras.layers.RNN(\n", + " cell=dnc_cell,\n", + " time_major=True,\n", + " return_sequences=True,\n", + " return_state=True,\n", + " )\n", + " optimizer = tf.compat.v1.train.RMSPropOptimizer(\n", + " FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon)\n", + "\n", + " # Set up model checkpointing\n", + " checkpoint = tf.train.Checkpoint(model=dnc_core, optimizer=optimizer)\n", + " manager = tf.train.CheckpointManager(checkpoint, FLAGS.checkpoint_dir, max_to_keep=10)\n", + "\n", + " checkpoint.restore(manager.latest_checkpoint)\n", + " if manager.latest_checkpoint:\n", + " print(\"Restored from {}\".format(manager.latest_checkpoint))\n", + " else:\n", + " print(\"Initializing from scratch.\")\n", + " return dnc_core\n", + "\n", + "\n", + "def get_inputs(x, num_reps):\n", + " if len(x[0]) > FLAGS.num_bits:\n", + " print(f\"Max input sequence length is {FLAGS.num_bits}\")\n", + " return\n", + " sub_seq_len = len(x)\n", + " num_bits = FLAGS.num_bits\n", + " \n", + " # We reserve one dimension for the num-repeats and one for the start-marker.\n", + " full_obs_size = num_bits + 2\n", + " start_end_flag_idx = full_obs_size - 2\n", + " num_repeats_channel_idx = full_obs_size - 1\n", + " \n", + " obs_pattern = tf.cast(x, tf.float32)\n", + " obs_flag_channel_pad = tf.zeros([sub_seq_len, 2])\n", + " obs_start_flag = tf.one_hot(\n", + " [start_end_flag_idx], full_obs_size, on_value=1., off_value=0.)\n", + " num_reps_flag = tf.one_hot(\n", + " [num_repeats_channel_idx],\n", + " full_obs_size,\n", + " on_value=tf.cast(num_reps / 10.0, tf.float32),\n", + " off_value=0.)\n", + " # note the concatenation dimensions.\n", + " obs = tf.concat([obs_pattern, obs_flag_channel_pad], 1)\n", + " obs = tf.concat([obs_start_flag, obs], 0)\n", + " obs = tf.concat([obs, num_reps_flag], 0)\n", + " # add padding\n", + " obs = tf.concat([\n", + " obs,\n", + " tf.zeros((sub_seq_len * num_reps + 1, full_obs_size))\n", + " ], 0)\n", + " obs = tf.reshape(obs, [sub_seq_len * (num_reps + 1) + 3, 1, full_obs_size])\n", + " return obs\n", + "\n", + "dataset = repeat_copy.RepeatCopy(FLAGS.num_bits, FLAGS.batch_size,\n", + " FLAGS.min_length, FLAGS.max_length,\n", + " FLAGS.min_repeats, FLAGS.max_repeats,\n", + " dtype=tf.float32)\n", + "dataset_tensor = dataset()\n", + "\n", + "dnc_core = load_model()\n", + "\n", + "x = get_inputs([[1,1,1,1]], 2)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8daa62a5", + "metadata": {}, + "outputs": [], + "source": [ + "def read_weights_from_dnc_state(dnc_state):\n", + " return dnc_state[dnc.ACCESS_STATE][access.READ_WEIGHTS]\n", + "def write_weights_from_dnc_state(dnc_state):\n", + " return dnc_state[dnc.ACCESS_STATE][access.WRITE_WEIGHTS]\n", + "def memory_from_dnc_state(dnc_state):\n", + " return dnc_state[dnc.ACCESS_STATE][access.MEMORY]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5e67e26a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([[0. 0. 0. 0. 1. 0.]], shape=(1, 6), dtype=float32)\n", + "tf.Tensor([[1. 1. 1. 1. 0. 0.]], shape=(1, 6), dtype=float32)\n", + "tf.Tensor([[0. 0. 0. 0. 0. 0.2]], shape=(1, 6), dtype=float32)\n", + "tf.Tensor([[0. 0. 0. 0. 0. 0.]], shape=(1, 6), dtype=float32)\n", + "tf.Tensor([[0. 0. 0. 0. 0. 0.]], shape=(1, 6), dtype=float32)\n", + "tf.Tensor([[0. 0. 0. 0. 0. 0.]], shape=(1, 6), dtype=float32)\n" + ] + } + ], + "source": [ + "for i in x:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6500f979", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model(\n", + " x,\n", + " mask,\n", + " rnn_model,\n", + "):\n", + " output_sequence = []\n", + " output_states = []\n", + " input_state = rnn_model.get_initial_state(inputs=x)\n", + " for input_seq in x:\n", + " #print(tf.expand_dims(input_seq, axis=0))\n", + " #print(input_state)\n", + " output = rnn_model(\n", + " inputs=tf.expand_dims(input_seq, axis=0),\n", + " initial_state=input_state,\n", + " )\n", + " output_sequence.append(tf.round(tf.sigmoid(output[0])))\n", + " input_state = output[1:]\n", + " output_states.append(input_state)\n", + " return output_sequence, output_states\n", + "\n", + "get_outputs = lambda x: evaluate_model(x, None, dnc_core)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d960721d", + "metadata": {}, + "outputs": [], + "source": [ + "y = get_outputs(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cf911c67", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b29aad3a", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "def visualize_states(states):\n", + " memory = [memory_from_dnc_state(state)[0] for state in states]\n", + " read_weights = [tf.transpose(read_weights_from_dnc_state(state)[0]) for state in states]\n", + " write_weights = [tf.transpose(write_weights_from_dnc_state(state)[0]) for state in states]\n", + " \n", + " memory_color_range = {\n", + " 'vmin': np.min(memory),\n", + " 'vmax': np.max(memory)\n", + " }\n", + " read_weights_color_range = {\n", + " 'vmin': np.min(read_weights),\n", + " 'vmax': np.max(read_weights),\n", + " }\n", + " write_weights_color_range = {\n", + " 'vmin': np.min(write_weights),\n", + " 'vmax': np.max(write_weights),\n", + " }\n", + "\n", + " for i in range(len(states)):\n", + " print(f'Timestep {i}')\n", + " fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(18,5))\n", + " ax1.set_title('Memory')\n", + " ax2.set_title('Read Weights')\n", + " ax3.set_title('Write Weights')\n", + "\n", + " seaborn.heatmap(memory[i], ax=ax1, **memory_color_range)\n", + " seaborn.heatmap(read_weights[i], ax=ax2, **read_weights_color_range)\n", + " seaborn.heatmap(write_weights[i], ax=ax3, **write_weights_color_range)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "89115c0e", + "metadata": {}, + "outputs": [], + "source": [ + "def debug_model(x, num_repeats):\n", + " inputs = get_inputs(x, num_repeats)\n", + " print(f\"Input Sequence:\\n {inputs}\")\n", + " output_sequence, states = evaluate_model(inputs, None, dnc_core)\n", + " print(\"Output Sequence:\")\n", + " print(\"Reading input phase:\")\n", + " for i, output in enumerate(output_sequence):\n", + " if i == len(x) + 2:\n", + " print(\"Ouput printing phase:\")\n", + " print(output)\n", + " visualize_states(states)\n", + " return output_sequence, states" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "eeb76634", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input Sequence:\n", + " [[[0. 0. 0. 0. 1. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[1. 1. 1. 1. 0. 0. ]]\n", + "\n", + " [[0. 1. 0. 1. 0. 0. ]]\n", + "\n", + " [[1. 0. 1. 0. 0. 0. ]]\n", + "\n", + " [[1. 1. 1. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0.3]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]\n", + "\n", + " [[0. 0. 0. 0. 0. 0. ]]]\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._controller.kernel\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._controller.recurrent_kernel\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._controller.bias\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._output_linear.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._output_linear.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_vectors.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_vectors.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.erase_vectors.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.erase_vectors.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.free_gate.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.free_gate.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.allocation_gate.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.allocation_gate.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_gate.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_gate.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_mode.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_mode.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_keys.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_keys.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_strengths.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_strengths.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_keys.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_keys.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_strengths.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_strengths.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._controller.kernel\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._controller.recurrent_kernel\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._controller.bias\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._output_linear.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._output_linear.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_vectors.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_vectors.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.erase_vectors.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.erase_vectors.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.free_gate.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.free_gate.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.allocation_gate.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.allocation_gate.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_gate.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_gate.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_mode.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_mode.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_keys.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_keys.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_strengths.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_strengths.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_keys.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_keys.b\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_strengths.w\n", + "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_strengths.b\n", + "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output Sequence:\n", + "Reading input phase:\n", + "tf.Tensor([[[1. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 0. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 0. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "Ouput printing phase:\n", + "tf.Tensor([[[1. 1. 1. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 1. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 1. 1. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 1. 1. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 1. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 1. 1. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 1. 1. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[1. 1. 1. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", + "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", + "Timestep 0\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 1\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 3\n" + ] + }, + { + "data": { + "image/png": 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7v367CABDL6FPpABgKCWUwxQDAFSi7G2W8z/8x6fZ/XxJr7L9CmUTLba1/YWIeFO5XgLAcOOW9wDQrJRymGIAgGrUMCUqIo6VdKwk5TMD/pZCAAB0kND0VAAYSgnlMMUAANVIaEoUAAwtshgAmpVQDlMMAFCNmqugEXGBpAtqPQkApC6hT6QAYCgllMMUAwBUo5XO9VEAMLTIYgBoVkI5XPpueLaPrLIjABIXrXIP9IUsBrAFcnjgyGEAW0hoTFy6GCDphOl22F5he6XtlWeuvbWPUwBIRqtV7oF+FcridRseHGSfADSFHG5CoRxutR4ZZJ8ANCWhMXHHywRsXz3dLkmLp2vXfquwy3Z9TZTuHQCgkizefuEeZDEAlFRFDs+eu4QcBjCjdFszYLGkAyXdN2m7JV1cS48ApImppnUiiwEUQxbXhRwGUExCOdytGHC2pIURceXkHbYvqKNDABLFVNM6kcUAiiGL60IOAygmoRzuWAyIiKM67Du8+u4ASFZCwZcashhAYWRxLchhAIUllMPcWhBAJSLSuY0KAAwrshgAmpVSDlMMAFCNhKqgADC0yGIAaFZCOUwxAEA1ElosBQCGFlkMAM1KKIcpBgCoRkJVUAAYWmQxADQroRyuvRgwf96G0m3vXDu/dNtNcum2u+8y+a4xxfzsFzuWPuccl/+l2WHhr0u3/dWD5f6N+/kVnzNWvvW225R/rQ89PK9Uu/WbZpU+512eW7rtXlvfX7rtptZY6balJVQFHUV2+UxMzSw18PsPzBRkMQA0K6EcZmYAgGokVAUFgKFFFgNAsxLKYYoBAKqRUBUUAIYWWQwAzUoohykGAKhGQlVQABhaZDEANCuhHKYYAKAaCQUfAAwtshgAmpVQDlMMAFCNhKZEAcDQIosBoFkJ5XDXJZdtP8X2S20vnLT9oPq6BSA5rVa5B7oihwEURg7XhiwGUEhCY+KOxQDbfy3p65L+StI1tg9p2/2PdXYMQGKiVe6BjshhAD0hh2tBFgMoLKExcbfLBN4q6dkR8bDt3SWdbnv3iPi4pGlvWm17haQVknT8Dk/XH27z+Kr6C2Cm4tOlupTKYWnLLJ4/byfNm7Oo9s4CaBhZXJe+x8SetUhjYwsG0lkADUooh7sVA8Yi4mFJiojbbO+nLPwerw7BFxHjksYl6SfLXhnVdBUARlKpHM6fvzmLH7PNnmQxAJTX95h49twl5DCAGaXbmgG/tL33xDd5CB4saUdJT6+xXwBSk9CUqMSQwwCKI4frQhYDKCahMXG3mQF/Imlj+4aI2CjpT2z/V229ApCemqZE2d5K0oWS5inLrNMj4n21nGxmIocBFJfQ9NTEkMUAikkohzsWAyJidYd9P6i+OwCSVV/wrZP0kvw6zTmSvm/7mxFxaV0nnEnIYQA9SWgQmhKyGEBhCeVw11sLAkAhEeUeXQ8bMXGdpqQ5+YPrLgFgKjXkMACgBzWNiaXsVqa2b7B9k+1jptj/ONvn2/6x7attv6LT8bpdJgAAxdRYBbU9S9KPJO0h6RMRcVltJwOAlCX0iRQADKX6Lp2dJekTkg6QtFrS5bbPiojr2p72d5K+HBH/YXsvSedI2n26Y1IMAFCNksHXftul3Hi++vJmEbFJ0t62t5N0hu2nRcQ1ZbsKAEOLYgAANKu+HN5X0k0RcYsk2T5N0iGS2osBIWnb/OtFkm7vdECKAQCqUXIV1PbbLhV47v22z5d0kCSKAQAwGXcHAIBm1ZfDSyStavt+taTnTnrO+yWda/uvJC2QtH+nA9ZeDFi7bk7ptlt5U4U9Ke7uOxeWatdUfx96ZF7ptna5awVnlT6j1IqOt0bv6MGHtirddlMf5y3rsbG+dNuHHi3/c21EfVOidpK0IS8EbK1satRJtZwMQ2ETfwxhlNWXxQdJ+riyIcCnIuLESfsfJ+mzkrbLn3NMRJxTS2cAYCarcbZsAW+QdEpEfMT270r6fD6jdspOMTMAQDXqW4RqF0mfza+TGlN2HdTZdZ0MAJJWQxbXcZ0qAAytkjlcYLbsGkm7tX2/NN/W7ihlM2gVEZfkt+jeUdJdUx2QYgCAatT0aVREXC3pWbUcHACGTT1ZXPl1qgAwtOpbM+BySXvaXqasCHCYpMMnPecXkl4q6RTbvyNpK0l3T3dAigEAqsGiVQDQvBJZXGBqauXXqQLA0KrvA7KNto+W9G1ll2N9JiKutf0BSSsj4ixJ75T0Sdt/o6xI++aI6acqUAwAUA2u0waA5pXI4l4Wcu2gp+tUAWBo1Rh7+Vos50zadnzb19dJen7R41EMAFCJaNW2ZgAAoKCasrjy61QBYFilNCbuWgywva+kiIjL8wVhDpL0U1aIBbAFLhOoDTkMoLB6srjy61RTRBYDKCShMXHHYoDt90l6uaTZts9Tdn3Y+ZKOsf2siPjQAPoIIAXMBK0FOQygJzVkcR3XqaaGLAZQWEJj4m4zA/5Q0t6S5km6U9LSiHjQ9j9LukzSlMHXvhDNMYv21qHzl1XWYQAzVEJTohJTKoelLbN4/rydNG/Oovp7C6BZNWVx1depJqjvMbFnLdLY2ILB9BZAcxIaE4912b8xIjZFxFpJN0fEg5IUEY9KmrbkERHjEbE8IpZTCACAvpTK4fw5m7OYQgAA9KXvMTGFAAAzTbeZAettz8+D79kTG20vUpdBKIARk9D1UYkhhwEURxbXhSwGUExCOdytGPCiiFgnSZNuDTNH0hG19QpAehIKvsSQwwCKI4vrQhYDKCahHO5YDJgIvSm23yPpnlp6BCBNw7NO1IxCDgPoCVlcC7IYQGEJ5XDXWwsCQCEJVUEBYGiRxQDQrIRymGIAgGoktHIqAAwtshgAmpVQDlMMAFCNhO6pCgBDiywGgGYllMMUAwBUI6EqKAAMLbIYAJqVUA7P6GLAWB9t+6nHtMJ9tB681PqL+jURQZHQ9VGjKBJazAZAeWQxADQrpRye0cUAAAlJqAoKAEOLLAaAZiWUwxQDAFQjoeujAGBokcUA0KyEcphiAIBqJFQFBYChRRYDQLMSymGKAQCqkdD1UQAwtMhiAGhWQjlMMQBANRKqggLA0CKLAaBZCeVwzwv22/5cHR0BkLholXugZ+QwgGmRwwNDFgOYUkJj4o4zA2yfNXmTpN+3vZ0kRcSrauoXgNTUVAW1vZukz0larOyuieMR8fFaTjYDkcMAepLQJ1IpIYsBFJZQDne7TGCppOskfUrZINySlkv6SKdGtldIWiFJxyzaW4fOX9Z/TwHMaDXeU3WjpHdGxBW2t5H0I9vnRcR1dZ1whimVw9KWWbz13J00b862NXYTwEyQ0v2tE9P3mNizFmlsbEHN3QTQtJRyuNtlAssl/UjScZIeiIgLJD0aEd+LiO9N1ygixiNieUQspxAAoB8RcUdEXJF//ZCk6yUtabZXA1Uqh6Uts5hCAAD0pe8xMYUAADNNx5kBEdGS9FHbX8n//5fd2gAYUQOYEmV7d0nPknRZ7SebIchhAD1JaHpqSshiAIUllMOFQiwiVkt6ne0/kPRgvV0CkKSSwdc+hTI3HhHjUzxvoaSvSnp7RIxcDpHDAApJaBCaIrIYQFcJ5XBPFc2I+Iakb9TUFwApK7kKav6H/2/98d/O9hxlhYBTI+JrpU40JMhhAB1xd4CBIIsBTCuhHGZ6E4Bq1Hc3AUv6tKTrI+JfajkJAAyLhD6RAoChlFAOUwwAUImoL/ieL+mPJf3E9pX5tvdGxDl1nRAAUlVjFgMACkgphykGAKhGTcEXEd9XdgsnAEA3CQ1CAWAoJZTDFAMAVCOhe6oCwNAiiwGgWQnlMMUAANVIqAoKAEOLLAaAZiWUwxQDAFQjoeADgKFFFgNAsxLKYYoBACoRkU7wAcCwIosBoFkp5TDFAADVSKgKCgBDiywGgGYllMMUAwBUI6HgA4ChRRYDQLMSymGKAQAqkdI9VQFgWJHFANCslHK4p2KA7RdI2lfSNRFxbj1dApCkhIIvdWQxgGmRxQNBDgOYVkI5PNZpp+0ftn39Vkn/JmkbSe+zfUzNfQOQklbJB7oiiwEURg7XghwGUFhCY+KOxQBJc9q+XiHpgIg4QdLLJL1xuka2V9heaXvlmWtvraCbAGa6aEWpBwrpO4vXbXiw7j4CmAHI4dr0ncOt1iN19xHADJDSmLjbZQJjtrdXVjRwRNwtSRHxiO2N0zWKiHFJ45J02a6v4V0GGAUMKOvUdxZvv3APfkDAKCCL69J3Ds+eu4QfDjAKEsrhbsWARZJ+JMmSwvYuEXGH7YX5NgBA/chiAGgWOQxg6HQsBkTE7tPsakl6deW9AZAurjutDVkMoDCyuBbkMIDCEsrhUrcWjIi1klgMAMBmXHc6eGQxgMnI4sEihwFMllIOlyoGAMBvSagKCgBDiywGgGYllMMUAwBUIqUqKAAMK7IYAJqVUg5TDABQjYSqoAAwtMhiAGhWQjlMMQBAJSKh4AOAYUUWA0CzUsrhsaY7AGBItEo+AADVIYcBoFk1joltH2T7Bts32T5mmue83vZ1tq+1/cVOx2NmAIBKpFQFBYBhRRYDQLPqymHbsyR9QtIBklZLutz2WRFxXdtz9pR0rKTnR8R9th/b6ZgUAwBUgwEoADSPLAaAZtWXw/tKuikibpEk26dJOkTSdW3PeaukT0TEfZIUEXd1OiCXCQCoRLTKPQAA1akrh6uemgoAw6rGMfESSavavl+db2v3JElPsv0D25faPqjTAZkZAKASNU6J+oykgyXdFRFPq+csADAc6sjiOqamAsCwKpvDtldIWtG2aTwixns8zGxJe0raT9JSSRfafnpE3D/dkwGgbzV+yn+KpH+T9LnazgAAQ6KmLK58aioADKuyOZz/4d/pj/81knZr+35pvq3dakmXRcQGSbfa/pmy4sDlUx2w42UCtp9re9v8661tn2D7f2yfZHtR55cDYKSEyz26HTbiQkn31v8CZiZyGEBPashh1TA1NTVkMYDCahoTK/uDfk/by2zPlXSYpLMmPedMZbMCZHtHZdl8y3QH7LZmwGckrc2//rikRZJOyredXKTHAEZD2eujbK+wvbLtsaL72UYKOQygsAZzuH1q6hskfdL2dhW+tKaRxQAKqWvNgIjYKOloSd+WdL2kL0fEtbY/YPtV+dO+LelXtq+TdL6kd0XEr6Y7ZrfLBMbyk0rS8ojYJ//6+7avnK5R+/UOxyzaW4fOX9blNABSF61CFc3fbtd9StSoK5XD0pZZvPXcnTRvzrb19RLAjFAmi5uYmpqgvsfEnrVIY2ML6u0lgMaVHRMXOnbEOZLOmbTt+LavQ9I78kdX3WYGXGP7yPzrq2wvlyTbT5K0oUMnxyNieUQspxAAjAbuJlCbUjksbZnFFAKA0VBTDlc+NTVBfY+JKQQAoyGlMXG3YsCfSnqx7Zsl7SXpEtu3SPpkvg8AUC9yGECj6piamiCyGMDQ6XiZQEQ8IOnN+YIpy/Lnr46IXw6icwDSEcUWPumZ7S8p+7RpR9urJb0vIj5dy8lmIHIYQC/qyuKqp6amhiwGUFRdOVyHQrcWjIgHJV1Vc18AJKyu6U0R8YZ6jpwWchhAEVx+VS+yGEA3KeVwoWIAAHRT52IpAIBiyGIAaFZKOUwxAEAlIpruAQCALAaAZqWUwxQDAFQipSooAAwrshgAmpVSDlMMAFCJlIIPAIYVWQwAzUophykGAEOoiQhKaUrUKLLTeWPqlxv5LwB146daDFkMAM1KKYcpBgCoREpVUAAYVmQxADQrpRymGACgEindUxUAhhVZDADNSimHKQYAqERK91QFgGFFFgNAs1LKYYoBACrRSqgKCgDDiiwGgGallMMUAwBUIqUpUQAwrMhiAGhWSjk81mmn7b+2vdugOgMgXdFyqQe6I4sBFEUO14McBlBUSmPijsUASR+UdJnti2z/H9s7DaJTANITUe6BQshiAIWQw7UhhwEUktKYuFsx4BZJS5UF4LMlXWf7W7aPsL3NdI1sr7C90vbKM9feWmF3AcxUKVVBE9R3Fq/b8MCg+gqgQeRwbfrO4VbrkUH1FUCDUhoTdysGRES0IuLciDhK0q6S/l3SQcpCcbpG4xGxPCKWHzp/WYXdBTBTtcKlHiik7yyeN2fRoPoKoEHkcG36zuGxsQWD6iuABqU0Ju62gOAWvYqIDZLOknSW7fm19QoA0I4sBoBmkcMAhk63YsAfTbcjItZW3BcACUtp5dQEkcUACiGLa0MOAygkpRzuWAyIiJ8NqiMA0sYiVPUhiwEURRbXgxwGUFRKOdxtZgAAFMJ1pwDQPLIYAJqVUg5TDABQiZSmRAHAsCKLAaBZKeUwxQAAlUhpShQADCuyGACalVIOUwwAUImUpkQBwLAiiwGgWSnlcO3FgD2e9avSbS/60ZLSbVulW0oHvqXcP8sZnyr/z3lnHz+JI5+5qnTbS3+4a6l2T1z0QOlzrn10Tum2T3zJw6Xb3vi/25Rqt2Dr9aXP+ZX125du+8oo/1p32b38z6esOqdE2T5I0sclzZL0qYg4sbaTDamFc7ZqugsD01JCJfk+bWr1826Xltmz+PyiiJSmp44afjLAaEgph3lnBVCJuqqgtmdJ+oSkAyStlnS57bMi4rpaTggACUvpEykAGEYp5TDFAACVqPGz2H0l3RQRt0iS7dMkHSKJYgAATDI682IAYGZKKYcpBgCoRI1V0CWS2q+FWS3puXWdDABSltInUgAwjFLKYYoBACpR9voo2yskrWjbNB4R45V0CgBGTErXqgLAMEophykGAKhE2WXM8j/8O/3xv0bSbm3fL823AQAmGZ0lJQFgZkophzsWA2zPlXSYpNsj4ju2D5f0e5KuV/bp3YYB9BFAAqK+dZIvl7Sn7WXKigCHSTq8rpPNNOQwgF7UmMUjjSwGUFRKOdxtZsDJ+XPm2z5C0kJJX5P0UmWLeh1Rb/cApKJV02opEbHR9tGSvq3s1oKfiYhr6znbjEQOAyisriwGWQygmJRyuFsx4OkR8Qzbs5V9IrdrRGyy/QVJV03XqP0a4I88bU8d8bhdKuswgJmpVWMVNCLOkXRObSeY2UrlsLRlFm8/f1ctnPeY+nsLoFF1ZvGI63tMPDZrkcbGFgymtwAak1IOj3Xbn0+L2kbSfEmL8u3zJM2ZrlFEjEfE8ohYTiEAGA0hl3qgq1I5LG2ZxRQCgNFADtem7zExhQBgNKQ0Ju42M+DTkn6qbGrucZK+YvsWSc+TdFrNfQMAkMMAMBOQxQCGTsdiQER81PZ/51/fbvtzkvaX9MmI+OEgOgggDSmtnJoSchhAL8jiepDFAIpKKYe73lowIm5v+/p+SafX2SEAaWKqaX3IYQBFkcX1IYsBFJFSDnctBgBAESlVQQFgWJHFANCslHKYYgCASqQUfAAwrMhiAGhWSjlMMQBAJVKaEgUAw4osBoBmpZTDFAMAVKKVTu4BwNAiiwGgWSnlcO3FgFuv2r502x20vsKeFHfjZ8ud9/GtuaXP+bj15X9rbv1x+X/j7byhVLv7H96q9DlbUf613vzdhaXbbmyNlWp338Nblz7nC1rl/n0laf2sWaXbrrq5/O/EziXbtRKqgo6iRzc1k6dNiIimuzAws8fK50RqNrU2Nd2FJJDFM9foJBMw2lLKYWYGAKgEgxwAaB5ZDADNSimHKQYAqERKi6UAwLAiiwGgWSnlMMUAAJVoOZ0pUQAwrMhiAGhWSjlMMQBAJVKaEgUAw4osBoBmpZTD5VZUA4BJWiUfAIDqkMMA0Kw6x8S2D7J9g+2bbB/T4XmvtR22l3c6XteZAbafIOk1knaTtEnSzyR9MSIeLNhnACMgpduopIYcBlBUXVls+yBJH5c0S9KnIuLEaZ73WkmnS3pORKyspzfNIIsBFFFjDs+S9AlJB0haLely22dFxHWTnreNpLdJuqzbMTvODLD915L+U9JWkp4jaZ6yALzU9n69vwQAw6oll3qgM3IYQC/qyOG2AejLJe0l6Q2295rieYUHoKkhiwEUVeOYeF9JN0XELRGxXtJpkg6Z4nkflHSSpF93O2C3ywTeKunlEfEPkvaX9NSIOE7SQZI+Ol0j2ytsr7S98muP3NatDwCGQJR8oKtSOSxtmcW/Xn9//T0F0LiacrjyAWiC+h4Tt1qPDKirAJpUdkzcnhf5Y8WkQy+RtKrt+9X5ts1s7yNpt4j4RpG+FllAcLayqVDzJC2UpIj4he050zWIiHFJ45K0cumhjPeBEcBlArXqOYfz52zO4p0WPZksBkZATVk81QD0ue1PaB+A2n5XLb1oXl9j4tlzl5DDwAgom8PteVGG7TFJ/yLpzUXbdCsGfErZtQiXSXqhsmqvbO8k6d5y3QQA9IAcBlCr/NOn9k+gxvNBadH2PQ9AE0QWA2jaGmWXJ01Ymm+bsI2kp0m6wNntDXeWdJbtV023hkvHYkBEfNz2dyT9jqSPRMRP8+13S3pR2VcBYPiwInU9yGEAvSiTxQU+jap8AJoashhAUTWOiS+XtKftZcoy+DBJh0/sjIgHJO048b3tCyT9bacc7nqZQERcK+na8n0GMAqY+1gfchhAUTVlceUD0BSRxQCKqGtMHBEbbR8t6dvK7uzymYi41vYHJK2MiLN6PWaRNQMAoKsm1gyw/TpJ71f2Sc2+wzbwBIBe1ZHFdQxAAWBY1TkmjohzJJ0zadvx0zx3v27HoxgAoBINXSZwjbJ7Pv9XM6cHgJmlriyuegAKAMMqpUtnKQYAqEQTwRcR10tSfo0qAIy8lAahADCMUsphigEAKhH8PQ4AjSOLAaBZKeVw7cWATa2x0m3Lt+yvIrNxU7kz99Pffpaa6Off2C533lZDv+X9vNYmzC7579uvJn4+Zf+b63ZLq3z15p2naHpcRHy95GlHzqZWSnXq/mxsbWq6CwMzy2llYj8iWKa0iNH5Lx0AZqaUcpiZAQAqUTb4ut3SKiL2L3loABg5KQ1CAWAYpZTDFAMAVILP7ACgeWQxADQrpRymGACgEg3dWvDVkv5V0k6SvmH7yog4cPA9AYCZoYksBgD8Rko5TDEAQCUaupvAGZLOaODUADAjpTQ9FQCGUUo5TDEAQCVSCj4AGFZkMQA0K6UcphgAoBIpXR8FAMOKLAaAZqWUwxQDAFQipeujAGBYkcUA0KyUcrjjDYptL7J9ou2f2r7X9q9sX59v265DuxW2V9peeebaWyvvNICZp1Xyge6qyOJ1Gx4YYI8BNIUcrkcVOdxqPTLAHgNoSkpj4o7FAElflnSfpP0i4jERsYOk38+3fXm6RhExHhHLI2L5ofOXVddbADNWlHygkL6zeN6cRQPqKoAmkcO16TuHx8YWDKirAJqU0pi4WzFg94g4KSLunNgQEXdGxEmSHl9v1wCkpKUo9UAhZDGAQsjh2pDDAApJaUzcrRjwc9vvtr14YoPtxbbfI2lVvV0DAOTIYgBoFjkMYOh0Kwb8kaQdJH0vvz7qXkkXSHqMpNfV3DcACUnp+qgEkcUACiGHa0MOAygkpTFxx7sJRMR9kt6TP7Zg+0hJJ9fULwCJYaJpfchiAEWRxfUghwEUlVIOd5sZ0MkJlfUCQPJSqoIOGbIYwGbkcCPIYQCbpTQm7jgzwPbV0+2StHiafQBGUEr3VE0NWQygKLK4HuQwgKJSyuGOxQBl4XagstumtLOki2vpEYAksSJ1rchiAIWQxbUhhwEUklIOdysGnC1pYURcOXmH7Qvq6BCA/jURQenEXpL6zuIIfkJIG7/BxfDvVBvGxAAKSSmHuy0geFSHfYdX3x0AqeK60/qQxQCKIovrQQ4DKCqlHO42MwAACklpShQADCuyGACalVIOUwwAUIl0Yg8AhhdZDADNSimHKQYAqERKU6IAYFiRxQDQrJRymGIAgEqkNCUKAIYVWQwAzUophykGAKhEOrEHAMOLLAaAZqWUwxQDAFQipSlRADCsyGIAaFZKOTxWtqHtb3bYt8L2Stsrz1x7a9lTAEhIlPwf+lM0i9dteHCQ3QLQEHJ48IrmcKv1yCC7BaAhKY2JO84MsL3PdLsk7T1du4gYlzQuSZft+hreZYARkFIVNDVVZPH2C/cgi4ERQBbXo4ocnj13CTkMjICUcrjbZQKXS/qesqCbbLvKewMgWU0slmL7w5JeKWm9pJslHRkR9w+8I/UjiwEUktLCVYkhhwEUklIOdysGXC/pzyLixsk7bK+qp0sAUNh5ko6NiI22T5J0rKT3NNynOpDFANAschjA0Om2ZsD7Ozznr6rtCoCURclHX+eMODciNubfXippaZ+HnKneL7IYQAGDzuER8n6RwwAKaGJMXFbHmQERcXqH3dtX3BcACSs7Jcr2Ckkr2jaN59dY9uotkv67VCdmOLIYQFEpTU9NCTkMoKiUcrifWwueIOnkqjoCIG1lF0tpX1xpKra/I2nnKXYdFxFfz59znKSNkk4t2Y2UkcUANktp4aohQg4D2CylHO52N4Grp9slaXH13QGQqrpuiRIR+3fab/vNkg6W9NKISKcU2wOyGEBR3CqwHuQwgKJSyuFuMwMWSzpQ0n2TtlvSxbX0CECSmqiC2j5I0rslvTgi1jbQhUEhiwEUktInUokhhwEUklIOdysGnC1pYURcOXmH7Qvq6BCANDVUBf03SfMknWdbki6NiD9voiM1I4sBFJLSJ1KJIYcBFJJSDndbQPCoDvsOr747AFLVRBU0IvZo4LQDRxYDKCqlT6RSQg4DKCqlHO5nAUEA2Kw1nJfrA0BSyGIAaFZKOUwxAEAl0ok9ABheZDEANCulHKYYAKASKd1TFQCGFVkMAM1KKYcpBgCoREqLpQDAsCKLAaBZKeUwxQAAlUhpsRQAGFZkMQA0K6UcphgAoBIpTYkCgGFFFgNAs1LK4bFOO21va/ufbH/e9uGT9v17h3YrbK+0vfLMtbdW1VcAM1iU/B+6qyKL1214sP6OAmgcOVyPKnK41Xqk/o4CaFxKY+KOxQBJJ0uypK9KOsz2V23Py/c9b7pGETEeEcsjYvmh85dV1FUAM1mr5AOF9J3F8+ZsO4h+AmgYOVybvnN4bGzBIPoJoGF1joltH2T7Bts32T5miv3vsH2d7att/6/tx3c6XrdiwBMj4piIODMiXiXpCknftb1Dwf4CGBERUeqBQshiAIXUlcNVD0ATRA4DKKSuMbHtWZI+IenlkvaS9Abbe0162o8lLY+IZ0g6XdL/7XTMbmsGzLM9FhGt/IV9yPYaSRdKWti1xwCAKpDFABrTNgA9QNJqSZfbPisirmt72sQAdK3tv1A2AP2jwfe2NuQwgKbtK+mmiLhFkmyfJukQSZuzOCLOb3v+pZLe1OmA3WYG/I+kl7RviIhTJL1T0vqivQYw/FqKUg8UQhYDKKSmHN48AI2I9ZImBqCbRcT5EbE2//ZSSUsrfWHNI4cBFFLjmHiJpFVt36/Ot03nKEnf7HTAjjMDIuLd02z/lu1/7NQWwGjhutP6kMUAiiqTxbZXSFrRtmk8Isbbvp9qAPrcDofsOgBNDTkMoKiyY+ICWdzLsd4kabmkF3d6Xj+3FjxB2WIqAMCK1M0hiwFsViaL88FmqQHnZEUHoEOGHAawWdkxcYEsXiNpt7bvl+bbtmB7f0nHSXpxRKzrdM6OxQDbV0+3S9LiTm0BjBam/NeHLAZQVE1ZXPkANDXkMICiahwTXy5pT9vLlGXwYZIm3+r0WZL+S9JBEXFXtwN2mxmwWNKBku6btN2SLi7YaQAjgDsD1IosBlBITVlc+QA0QeQwgELqGhNHxEbbR0v6tqRZkj4TEdfa/oCklRFxlqQPK1vU9Cu2JekX+R1QptStGHC2pIURceXkHbYvKPUqAAwl1gyoFVkMoJA6sriOAWiCyGEAhdQ5Jo6IcySdM2nb8W1f79/L8botIHhUh32HT7cPwOhhzYD6kMUAiqori6segKaGHAZQVEpj4n4WEASAzVgzAACaRxYDQLNSymGKAQAqwZoBANA8shgAmpVSDlMMAFCJlKqgADCsyGIAaFZKOUwxAEAlUro+CgCGFVkMAM1KKYcpBgCoRKuBKVG2PyjpEGULt94l6c0RcfvAOwIAM0QTWQwA+I2Ucnis6Q4AGA5R8tGnD0fEMyJib2W3fTq+y/MBYKg1kMMAgDYNjYlL6VgMsL2z7f+w/QnbO9h+v+2f2P6y7V06tFthe6XtlWeuvbX6XgOYcVqKUo9+RMSDbd8u0JCOa6vI4nUbHpzuaQCGyKBzeFRUkcOt1iOD7DKAhjQxJi6r28yAUyRdJ2mVpPMlPSrpFZIukvSf0zWKiPGIWB4Ryw+dv6yirgKYyZoKPtsfsr1K0hs1vDMDTlGfWTxvzraD6CeAhqUyAE3QKeozh8fGFgyinwAaNkzFgMUR8a8RcaKk7SLipIhYFRH/KunxA+gfgERERKlH+6cm+WNF+3Ftf8f2NVM8DsnPe1xE7CbpVElHN/HaB4AsBlBImRxGIeQwgELKjomb0G0BwfZiwecm7ZtVcV8AjKCIGJc03mH//gUPdaqkcyS9r4p+zTBkMQA0ixwGMHS6FQO+bnthRDwcEX83sdH2HpJuqLdrAFLSxPQm23tGxI35t4dI+unAOzEYZDGAQpj2XxtyGEAhKeVwx2JAREx5/W1E3GT7G/V0CUCKGrqn6om2n6zs1oI/l/TnTXSibmQxgKJSur91SshhAEWllMPdZgZ0coKkk6vqCIC0NXGtU0S8duAnnXnIYgCbsQZAI8hhAJullMMdiwG2r55ul6TF1XcHQKpSmhKVGrIYQFFkcT3IYQBFpZTD3WYGLJZ0oKT7Jm23pItr6RGAJKVUBU0QWQygELK4NuQwgEJSyuFuxYCzJS2MiCsn77B9QZET7Lrkgd57lbv/nvml225quXTbPQ4rd/XEh/57bulz/rT1UOm2n3lu+bbn/GBpqXYve+qq0uecvV35n82s7cr/G1/1P+Xus/7U37279Dn/4ortS7f9vVhYuu0Rz19Tum1ZKVVBE9R3Fr9hx30q7tLMtXuUz4nUfO7XNzXdhYG57fN/2nQXkkAW16bvHH7k2q9U3CUAM1FKOdxtAcGjOuw7vPruAEhVSoulpIYsBlAUWVwPchhAUSnlcD8LCALAZq2EpkQBwLAiiwGgWSnlMMUAAJVIqQoKAMOKLAaAZqWUwxQDAFQipSooAAwrshgAmpVSDlMMAFCJlKqgADCsyGIAaFZKOUwxAEAlUqqCAsCwIosBoFkp5XDPxQDbj42Iu+roDIB0pVQFHQZkMYCpkMWDQw4DmEpKOTzWaaftx0x67CDph7a3t/2YDu1W2F5pe+Wpdw/+fucABq8VUeqB7qrI4useumWAPQbQFHK4HlXk8KdO+/oAewygKSmNibvNDLhH0s8nbVsi6QpJIekJUzWKiHFJ45K06jkv5V0GGAEpVUET1HcW/5/dX88PCBgBZHFt+s7hdTdezA8HGAEp5XC3YsC7JB0g6V0R8RNJsn1rRCyrvWcAkhLRaroLw4wsBlAIWVwbchhAISnlcMfLBCLiI5L+VNLxtv/F9jZSQqUOABgCZDEANIscBjCMui4gGBGrJb3O9qsknSdpfu29ApCcFmOiWpHFAIogi+tDDgMoIqUc7jgzoF1EnCXp9yXtL0m2j6yrUwDSExGlHugNWQygE3K4fuQwgE5SGhMXLgZIUkQ8GhHX5N+eUEN/ACSqpSj1QO/IYgDTIYcHgxwGMJ2UxsQdLxOwffV0uyQtrr47AFLFp0v1IYsBFEUW14McBlBUSjncbc2AxZIOlHTfpO2WdHEtPQKQJO5VXSuyGEAhZHFtyGEAhaSUw92KAWdLWhgRV07eYfuCIie48/Zte+9Vbv2mWaXb9uOm09aXavfydRtKn/PA2Kp025svKf/vtHvr1+XOee0Opc/p0i2lObM2lW87Vu42HzdeVv61rtg0p3TbrcceLd32posWlW67vGS7lO6pmqC+s/jbD99UcZdmru3nLGi6CwNz/4aHm+7CwDz5zZ9tugsD84t7/7Z0W7K4Nn3n8JJnv7naHgGo1T0P/qxUu5RyuGMxICKO6rDv8Oq7AyBVKU2JSg1ZDKAosrge5DCAolLK4Z4WEASA6TS5WIrtd9oO2ztWckAASFQqi1YBwLAamgUEAaCopqqgtneT9DJJv2ikAwAwg6T0iRQADKOUcphiAIBKNLhYykclvVvS15vqAADMFCktXAUAwyilHKYYAKASTVRBbR8iaU1EXGX3szQlAAyHlD6RAoBhlFIOUwwAUImy1zrZXiFpRdum8YgYb9v/HUk7T9H0OEnvVXaJAABA5bMYAFCNlHKYYgCASpStguZ/+I932L//VNttP13SMkkTswKWSrrC9r4RcWepzgBA4lL6RAoAhlFKOdzxbgK2D2r7epHtT9u+2vYXbS/u0G6F7ZW2V57xyG0VdhfATNWKKPUoKyJ+EhGPjYjdI2J3Sasl7TOMhYAqsvjBX98zmM4CaNQgc3iUVJHDv17/wGA6C6BRgx4T96PbrQX/se3rj0i6Q9IrJV0u6b+maxQR4xGxPCKWv3rB7n13EsDMFyX/h0L6zuJtt+Kui8AoIIdr03cObzV3Uc1dBDATpDQm7uUygeURsXf+9UdtH1FDfwAkqulPl/LZAaOALAYwraazeESQwwCmlVIOdysGPNb2OyRZ0ra2Hb+5CKLbrAIAIySl66MSRBYDKIQsrg05DKCQlHK4W3h9UtI2khZK+qykHSXJ9s6Srqy1ZwCACWQxADSLHAYwdDrODIiIE6bZfqft8+vpEoAUcd1pfchiAEWRxfUghwEUlVIO9zOtacpQBDCaIqLUA30jiwFsRg43ghwGsFlKY+KOMwNsXz3dLknT3kYFwOhhQFkfshhAUWRxPchhAEWllMPdFhBcLOlASfdN2m5JF9fSIwBJSif2kkQWAyiELK4NOQygkJRyuFsx4GxJCyPiysk7bF9Q5ATPWXOGO+23vSIixoscq4p2KbZNrb9NtU2tv/20baq/nWxcv6bjf+voS99ZfPM9Vwz851PX79pMxGsdTim+VrK4Nn3n8D0P/oyfzRBKMSdQr5Ry2E1PY7C9MiKWD6pdim1T629TbVPrbz9tm+ov0ItR+l3jtQ6nUXqtAMohJ5Ay7osKAAAAAMCIoRgAAAAAAMCImQnFgLLX2PRzbU5qbVPrb1NtU+tvP22b6i/Qi1H6XeO1DqdReq0AyiEnkKzG1wwAAAAAAACDNRNmBgAAAAAAgAFqrBhg+yDbN9i+yfYxPbT7jO27bF9T4py72T7f9nW2r7X9th7abmX7h7avytue0OO5Z9n+se2ze2x3m+2f2L7S9soe225n+3TbP7V9ve3fLdjuyfn5Jh4P2n57wbZ/k//7XGP7S7a36qG/b8vbXdvtfFP9Hth+jO3zbN+Y///2PbR9XX7elu1pV4Sdpu2H83/jq22fYXu7gu0+mLe50va5tnctes62fe+0HbZ37KG/77e9pu3n+4rpXi9QVtmMT00/70mp6ec9NDX9vucDGA2j8l6H4dVIMcD2LEmfkPRySXtJeoPtvQo2P0XSQSVPvVHSOyNiL0nPk/SXPZx3naSXRMQzJe0t6SDbz+vh3G+TdH0vnW3z+xGxd4nblnxc0rci4imSnln0/BFxQ36+vSU9W9JaSWd0a2d7iaS/lrQ8Ip4maZakw4qc0/bTJL1V0r55Xw+2vUeHJqfot38PjpH0vxGxp6T/zb8v2vYaSa+RdGGXrk7V9jxJT4uIZ0j6maRjC7b7cEQ8I/93PlvS8T2cU7Z3k/QySb/osb+S9NGJn3FEnNOhPdCzPjM+Naeo/HtSavp5D01Nv+/5AIbciL3XYUg1NTNgX0k3RcQtEbFe0mmSDinSMCIulHRvmZNGxB0RcUX+9UPK/jheUrBtRMTD+bdz8kehBRdsL5X0B5I+1XOnS7K9SNKLJH1akiJifUTcX+JQL5V0c0T8vODzZ0va2vZsSfMl3V6w3e9Iuiwi1kbERknfU/bH+ZSm+T04RNJn868/K+nQom0j4vqIuKFbJ6dpe27eZ0m6VNLSgu0ebPt2gab5ferwO/9RSe+erl2XtkCdSmd8akbpv7F+3kNT0897PoCRMTLvdRheTRUDlkha1fb9ag14QGF7d0nPknRZD21m2b5S0l2SzouIom0/puyPtlZvvZSUDT7Otf0j2yt6aLdM0t2STs4vT/iU7QUlzn+YpC8V6mjEGkn/rOyT6jskPRAR5xY8zzWSXmh7B9vzJb1C0m499nVxRNyRf32npMU9tq/CWyR9s+iTbX/I9ipJb9T0MwOmaneIpDURcVXvXZQkHZ1fovCZ6S6nAPrQeMajXmXeQ1PTx3s+gNHAex2SN5ILCNpeKOmrkt4+6dPZjiJiUz6le6mkffOp7d3OdbCkuyLiRyW7+4KI2EfZFKS/tP2igu1mS9pH0n9ExLMkPaLpp81PyfZcSa+S9JWCz99eWUV0maRdJS2w/aYibSPiekknSTpX0rckXSlpUy/9nXS80IA/xbF9nLJptKcWbRMRx0XEbnmbowueZ76k96qH4sEk/yHpicqmvt4h6SMljwNgBJV9D01Nmfd8AABS0lQxYI22/NR3ab6tdrbnKBvEnBoRXytzjHy6/fkqdp3o8yW9yvZtyqYPvcT2F3o415r8/+9Sdt3+vgWbrpa0uu2TjNOVFQd68XJJV0TELws+f39Jt0bE3RGxQdLXJP1e0ZNFxKcj4tkR8SJJ9ym7/r4Xv7S9iyTl/39Xj+1Ls/1mSQdLemOUu1/nqZJeW/C5T1RWcLkq/71aKukK2zsXaRwRv8wHuS1Jn1Tx3ymgqMYyHvWq4j00NT2+5wMYHbzXIXlNFQMul7Sn7WX5p8+HSTqr7pPatrJr6K+PiH/pse1OE6vE295a0gGSftqtXUQcGxFLI2J3Za/zuxFR6NNy2wtsbzPxtbLF4gqtWB0Rd0paZfvJ+aaXSrquSNs2b1DBSwRyv5D0PNvz83/rl6qHRRNtPzb//8cpWy/giz2cW8p+h47Ivz5C0td7bF+K7YOUXQbyqohY20O7Pdu+PUQFfp8kKSJ+EhGPjYjd89+r1ZL2yX/mRc67S9u3r1bB3ymgB41kPOrVz3toasq+5wMYKbzXIXmzmzhpRGy0fbSkbytbcf4zEXFtkba2vyRpP0k72l4t6X0R8emCp36+pD+W9JP8OkBJem/B1dR3kfTZfOXQMUlfjoiebhNYwmJJZ2TjL82W9MWI+FYP7f9K0ql5QN0i6ciiDfPiwwGS/qxom4i4zPbpkq5QNl3+x5LGe+jvV23vIGmDpL/stODhVL8Hkk6U9GXbR0n6uaTX99D2Xkn/KmknSd+wfWVEHFiw7bGS5kk6L/9ZXRoRf16g3SvyYk0r7+8WbTq1Lfo7P81597O9t7LLKG5TDz9joIh+Mj41fb4npaaf99DUNPGeDyAho/Reh+HlcjOaAQAAAABAqkZyAUEAAAAAAEYZxQAAAAAAAEYMxQAAAAAAAEYMxQAAAAAAAEYMxQAAAAAAAEYMxQAAAAAAAEYMxQAAAAAAAEYMxQAAAAAAAEbM/wd9MaT+iNVjDAAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 4\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 5\n" + ] + }, + { + "data": { + "image/png": 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stpmzdd1dANAPCeUwMwMAlKNgFdT2cknLWzaNRcRYdt8Rku6LiB/YPqjXLgLAwEvoEykAGEgJ5TDFAAClKHqZ5ewP/7Fp7n6+pFfafrmkrSRta/uzEXFssV4CwGDjkvcAUK+UcphiAIByVDAlKiLeJeldkpTNDPg7CgEA0EZC01MBYCAllMMUAwCUI6EpUQAwsMhiAKhXQjlMMQBAOSqugkbEFZKuqPQgAJC6hD6RAoCBlFAOUwwAUI5GOudHAcDAIosBoF4J5XDh657ZPr7MjgBIXDSK3dATshjAFsjhviOHAWwhoTFx8YugS++d7g7by22vsL3igrV39XAIAMloNIrd0KtcWTw+/mg/+wSgLuRwHXLl8PqNj/SzTwDqktCYuO1pArZvnO4uSYuna9d6qbCrd311FO4dAKCULJ671W5kMQAUVEYOb7dgb3IYwIzSac2AxZIOlfTgpO2W9N1KegQgTUw1rRJZDCAfsrgq5DCAfBLK4U7FgIskLYiI6yffYfuKKjoEIFFMNa0SWQwgH7K4KuQwgHwSyuG2xYCIOKHNfceU3x0AyUoo+FJDFgPIjSyuBDkMILeEcphLCwIoRUQ6l1EBgEFFFgNAvVLKYYoBAMqRUBUUAAYWWQwA9UoohykGAChHQoulAMDAIosBoF4J5TDFAADlSKgKCgADiywGgHollMOVFwPmz91YuO29a+cVbjsuF2771KUPFWr38C/mFj5m9NDfbeavL9x2wyPFfsbrNhV/6cweKf4PZLtt1xVuu+bRYr+fDeOjhY/54Ejxn9O+8x4t3Ha8MVK4bWEJVUGH0Y5bb1t3F/rm52t+WXcX+qaR0ICjV9++79a6u5AGsnjGWrOh+BgGQEISymFmBgAoxxD9UQIAMxZZDAD1SiiHKQYAKEdCVVAAGFhkMQDUK6EcphgAoBwJVUEBYGCRxQBQr4RymGIAgHIkFHwAMLDIYgCoV0I5TDEAQDkSmhIFAAOLLAaAeiWUwx2XHLf9ZNsvtb1g0vbDqusWgOQ0GsVu6IgcBpAbOVwZshhALgmNidsWA2z/jaSvSPprSTfZPrLl7n+qsmMAEhONYje0RQ4D6Ao5XAmyGEBuCY2JO50m8CZJz4qIR23vKek823tGxMckebpGtpdLWi5Jp+zwdP3hNnuU1V8AMxWfLlWlUA5LW2bxwq130fy521XeWQA1I4ur0vOY2KMLNTIyvy+dBVCjhHK4UzFgJCIelaSI+Jntg9QMvz3UJvgiYkzSmCT9aK9XRDldBYChVCiHs8dvzuIl2z2VLAaA4noeE8+as4QcBjCjdFoz4Je295/4JgvBIyTtKOnpFfYLQGoSmhKVGHIYQH7kcFXIYgD5JDQm7jQz4E8kbWrdEBGbJP2J7f+qrFcA0lPRlCjbW0m6UtJcNTPrvIh4TyUHm5nIYQD5JTQ9NTFkMYB8EsrhtsWAiFjV5r7vlN8dAMmqLvjWS3pJdp7mbEnftv21iLi6qgPOJOQwgK4kNAhNCVkMILeEcrjjpQUBIJeIYreOu42YOE9T0uzsxnmXADCVCnIYANCFisbEUvNSprZvs3277ZOmuH9325fb/qHtG22/vN3+Op0mAAD5VFgFtT0q6QeS9pZ0RkRcU9nBACBlCX0iBQADqbpTZ0clnSHpEEmrJF1r+8KIuKXlYX8v6QsR8Z+295V0iaQ9p9snxQAA5SgYfK2XXcqMZasvbxYR45L2t71I0vm2nxYRNxXtKgAMLIoBAFCv6nL4QEm3R8SdkmT7XElHSmotBoSkbbOvF0q6u90OKQYAKEfBVVBbL7uU47EP2b5c0mGSKAYAwGRcHQAA6lVdDi+RtLLl+1WSnjPpMadKutT2X0uaL+ngdjusvBiwdv3swm238niJPcnv/nsXFGpXV3/XPDa3cFu72LmCo4WPKDWi7aXR23pkzVaF2473cNyiHhcbCrdds67477UW1U2J2knSxqwQsLWaU6NOr+RgA8zu/+u/LsE50Bhm1WXxYZI+puYQ4MyIOG3S/btL+rSkRdljToqISyrpDADMZBXOls3h9ZLOjogP2X6epP/OZtRO2SlmBgAoR3V/gO0i6dPZeVIjap4HdVFVBwOApFWQxVWcpwoAA6tgDueYLbta0m4t3y/NtrU6Qc0ZtIqI72WX6N5R0n1T7ZBiAIByVPRpVETcKOmASnYOAIOmmiwu/TxVABhY1a0ZcK2kfWzvpWYR4GhJx0x6zC8kvVTS2bafImkrSfdPt0OKAQDKwaJVAFC/AlmcY2pq6eepAsDAqu4Dsk22T5T0DTVPx/pURNxs+32SVkTEhZLeJukTtv9WzSLtG6PN+ZMUAwCUg0WrAKB+BbK4m4Vc2+jqPFUAGFgVxl62Fsslk7ad0vL1LZKen3d/FAMAlCIaLNoGAHWrKItLP08VAAZVSmPijsUA2wdKioi4NlsQ5jBJP2aFWABb4DSBypDDAHKrJotLP081RWQxgFwSGhO3LQbYfo+kwyXNsn2ZmueHXS7pJNsHRMQH+tBHAClgJmglyGEAXakgi6s4TzU1ZDGA3BIaE3eaGfCHkvaXNFfSvZKWRsQjtv9F0jWSpgy+1oVoTlq4v46at1dpHQYwQyU0JSoxhXJY2jKLF83bRfPnbl99bwHUq6IsLvs81QT1PCb26EKNjMzvT28B1CehMfFIh/s3RcR4RKyVdEdEPCJJEbFO0rQlj4gYi4hlEbGMQgAA9KRQDmeP2ZzFFAIAoCc9j4kpBACYaTrNDNhge14WfM+a2Gh7oToMQgEMmYTOj0oMOQwgP7K4KmQxgHwSyuFOxYAXRcR6SZp0aZjZko6rrFcA0pNQ8CWGHAaQH1lcFbIYQD4J5XDbYsBE6E2x/VeSflVJjwCkaXDWiZpRyGEAXSGLK0EWA8gtoRzueGlBAMgloSooAAwsshgA6pVQDlMMAFCOhFZOBYCBRRYDQL0SymGKAQDKkdA1VQFgYJHFAFCvhHKYYgCAciRUBQWAgUUWA0C9EsrhGV0MGOmhbS/1mEa4h9b9l1p/Ub06IigSOj9qGI2InACGAVkMAPVKKYdndDEAQEISqoICwMAiiwGgXgnlMMUAAOVI6PwoABhYZDEA1CuhHKYYAKAcCVVBAWBgkcUAUK+EcphiAIByJHR+FAAMLLIYAOqVUA5TDABQjoSqoAAwsMhiAKhXQjnc9YL9tj9TRUcAJC4axW7oGjkMYFrkcN+QxQCmlNCYuO3MANsXTt4k6fdtL5KkiHhlRf0CkJqKqqC2d5P0GUmL1bxq4lhEfKySg81A5DCAriT0iVRKyGIAuSWUw51OE1gq6RZJZ6o5CLekZZI+1K6R7eWSlkvSSQv311Hz9uq9pwBmtAqvqbpJ0tsi4jrb20j6ge3LIuKWqg44wxTKYWnLLN5u3q5aMHf7CrsJYCZI6frWiel5TOzRhRoZmV9xNwHULaUc7nSawDJJP5B0sqSHI+IKSesi4lsR8a3pGkXEWEQsi4hlFAIA9CIi7omI67Kv10i6VdKSenvVV4VyWNoyiykEAEBPeh4TUwgAMNO0nRkQEQ1JH7H9xez/v+zUBsCQ6sOUKNt7SjpA0jWVH2yGIIcBdCWh6akpIYsB5JZQDucKsYhYJem1tv9A0iPVdglAkgoGX+sUysxYRIxN8bgFkr4k6S0RMXQ5RA4DyCWhQWiKyGIAHSWUw11VNCPiYkkXV9QXACkruApq9of/7/zx38r2bDULAedExJcLHWhAkMMA2uLqAH1BFgOYVkI5zPQmAOWo7moClvRJSbdGxIcrOQgADIqEPpECgIGUUA5TDABQiqgu+J4v6Y8l/cj29dm2d0fEJVUdEABSVWEWAwBySCmHKQYAKEdFwRcR31bzEk4AgE4SGoQCwEBKKIcpBgAoR0LXVAWAgUUWA0C9EsphigHAAKrlY/SEqqDDaENjU91d6JvmMhPDIYJ/d5iELAaAeiWUwxQDAJQjoeADgIFFFgNAvRLKYYoBAErBJ5QAUD+yGADqlVIOUwwAUI6EqqAAMLDIYgCoV0I5TDEAQDkSCj4AGFhkMQDUK6EcphgAoBQpXVMVAAYVWQwA9Uoph7sqBth+gaQDJd0UEZdW0yUASUoo+FJHFgOYFlncF+QwgGkllMMj7e60/f2Wr98k6d8lbSPpPbZPqrhvAFLSKHhDR2QxgNzI4UqQwwByS2hM3LYYIGl2y9fLJR0SEe+V9DJJb5iuke3ltlfYXnHB2rtK6CaAmS4aUeiGXHrO4rUbHqq4iwBmAnK4Mj3ncKPxWNV9BDADpDQm7nSawIjt7dQsGjgi7pekiHjM9qbpGkXEmKQxSbpm11fzLgMMAwaUVeo5i3de9BR+QcAwIIur0nMOz5qzhF8OMAwSyuFOxYCFkn4gyZLC9i4RcY/tBdk2AED1yGIAqBc5DGDgtC0GRMSe09zVkPSq0nsDIF2cd1oZshhAbmRxJchhALkllMOFLi0YEWslsRgAgM0477T/yGIAk5HF/UUOA5gspRwuVAwAgN+RUBUUAAYWWQwA9UoohykGAChFSlVQABhUZDEA1CulHKYYAKAcCVVBAWBgkcUAUK+EcphiAIBSRELBBwCDiiwGgHqllMOVFwNmj44XbrvTDo8WbjsyWnx6hkeKtT3q7nWFj/mEuTsUbvtf+z1cuO3F1+1WqN1r/7lYO0mKtWsLt/3hqasLt501Uuxf5n7HFv8X/Zpziz/XJaPzC7f91z/cWLhtYQkF3zBat2lD3V3om9ctfnbdXeibcaUzFbFX94wXHxMMFbIYAOpVYQ7bPkzSxySNSjozIk6b4jGvk3SqpJB0Q0QcM93+mBkAoBQpVUEBYFCRxQBQr6py2PaopDMkHSJplaRrbV8YEbe0PGYfSe+S9PyIeND249rtk2IAgHIwAAWA+pHFAFCv6nL4QEm3R8SdkmT7XElHSrql5TFvknRGRDwoSRFxX7sdjlTUUQBDJhrFbgCA8lSVw7YPs32b7dttnzTNY15n+xbbN9v+XJnPCwBSUeGYeImklS3fr8q2tXqipCfa/o7tq7PTCqbFzAAApahwStSnJB0h6b6IeFo1RwGAwVBFFlcxNRUABlXRHLa9XNLylk1jETHW5W5mSdpH0kGSlkq60vbTI+Kh6R4MAD2r8FP+syX9u6TPVHYEABgQFWVx6VNTAWBQFc3h7A//dn/8r5bUuor70mxbq1WSromIjZLusv0TNYsD1061w7anCdh+ju1ts6+3tv1e21+1fbrthe2fDoChEi5267TbiCslPVD9E5iZyGEAXakgh1XB1NTUkMUAcqtoTKzmH/T72N7L9hxJR0u6cNJjLlBzVoBs76hmNt853Q47rRnwKUkT10b7mKSFkk7Ptp2Vp8cAhkPR86NsL7e9ouW2vPPRhgo5DCC3GnO4dWrq6yV9wvaiEp9a3chiALlUtWZARGySdKKkb0i6VdIXIuJm2++z/crsYd+Q9Gvbt0i6XNLbI+LX0+2z02kCI9lBJWlZRDwz+/rbtq+frlHr+Q4nL9pPr16wZ4fDAEhdNHJVNH+3XecpUcOuUA5LW2bxVnN21JzZ21bXSwAzQpEsrmNqaoJ6HhN7dKFGRuZX20sAtSs6Js6174hLJF0yadspLV+HpLdmt446zQy4yfbx2dc32F4mSbafKGljm06ORcSyiFhGIQAYDlxNoDKFcljaMospBADDoaIcLn1qaoJ6HhNTCACGQ0pj4k7FgD+T9GLbd0jaV9L3bN8p6RPZfQCAapHDAGpVxdTUBJHFAAZO29MEIuJhSW/MFkzZK3v8qoj4ZT86ByAdkW/hk67Z/ryanzbtaHuVpPdExCcrOdgMRA4D6EZVWVz21NTUkMUA8qoqh6uQ69KCEfGIpBsq7guAhFU1vSkiXl/NntNCDgPIg9OvqkUWA+gkpRzOVQwAgE6qXCwFAJAPWQwA9UophykGAChFRN09AACQxQBQr5RymGIAgFKkVAUFgEFFFgNAvVLKYYoBAEqRUvABwKAiiwGgXinlcOXFgI3jo4Xb3nN/8eti97Juw9azNhVq9+9aVPyg64s3vf0HOxRuu3esK9Tuh+/8SeFj9mLOaPHf7HjBlT1v/GynK3BO7+RYULjt3PHxwm1v/lzxf3fP+Zdi7VKaEjWMFszequ4u9M0Xfnlt3V3om0YjoVWKekTE5EMWA0C9UsphZgYAKEVKVVAAGFRkMQDUK6UcphgAoBQpXVMVAAYVWQwA9UophykGAChFStdUBYBBRRYDQL1SymGKAQBK0UioCgoAg4osBoB6pZTDFAMAlCKlKVEAMKjIYgCoV0o53HaZdNt/Y3u3fnUGQLqi4UI3dEYWA8iLHK4GOQwgr5TGxJ2umfZ+SdfYvsr2/2d7p350CkB6IordkAtZDCAXcrgy5DCAXFIaE3cqBtwpaamaAfgsSbfY/rrt42xvM10j28ttr7C94oK1d5XYXQAzVUpV0AT1nMWPrX+wX30FUCNyuDI953Cj8Vi/+gqgRimNiTsVAyIiGhFxaUScIGlXSf8h6TA1Q3G6RmMRsSwilh01b68SuwtgpmqEC92QS89ZPH/udv3qK4AakcOV6TmHR0bm96uvAGqU0pi40wKCW/QqIjZKulDShbbnVdYrAEArshgA6kUOAxg4nYoBfzTdHRGxtuS+AEhYSiunJogsBpALWVwZchhALinlcNtiQET8pF8dAZA2FqGqDlkMIC+yuBrkMIC8UsrhTjMDACAXzjsFgPqRxQBQr5RymGIAgFKkNCUKAAYVWQwA9UophykGAChFSlOiAGBQkcUAUK+UcphiAIBSpDQlCgAGFVkMAPVKKYcrLwbsfcCvC7e96gdLCrdtFG4pHfqnxX4s559Z/Md5bw+/ieOfsbJw26u/v2uhdk9Y+HDhY65dN7tw2ye85NHCbX/6v9sUajd/6w2Fj/nFDcWv7f6KKP5cd9mz+O+nqCqnRNk+TNLHJI1KOjMiTqvsYANq3qyt6u5C31hr6u5C34yOjNbdhb7Z1BivuwtJSGl6KgAMopRymJkBAEpRVRXU9qikMyQdImmVpGttXxgRt1RyQABIWEqfSAHAIEophykGAChFhadHHSjp9oi4U5JsnyvpSEkUAwBgkoROVQWAgZRSDlMMAFCKCqugSyS1nguzStJzqjoYAKQspU+kAGAQpZTDFAMAlKLo+VG2l0ta3rJpLCLGSukUAAyZlM5VBYBBlFIOUwwAUIqii3Zmf/i3++N/taTdWr5fmm0DAEzSywLKAIDepZTDbYsBtudIOlrS3RHxTdvHSPo9Sbeq+endxj70EUACQpVVQa+VtI/tvdQsAhwt6ZiqDjbTkMMAulFhFg81shhAXinlcKeZAWdlj5ln+zhJCyR9WdJL1VzU67hquwcgFY2KVkuJiE22T5T0DTUvLfipiLi5mqPNSOQwgNyqymKQxQDySSmHOxUDnh4R+9mepeYncrtGxLjtz0q6YbpGrecAf+hp++i43XcprcMAZqZGhVXQiLhE0iWVHWBmK5TD0pZZvNOC3bVwqx2r7y2AWlWZxUOu5zGxRxdqZGR+f3oLoDYp5fBIp/uzaVHbSJonaWG2fa6k2dM1ioixiFgWEcsoBADDIeRCN3RUKIelLbOYQgAwHMjhyvQ8JqYQAAyHlMbEnWYGfFLSj9WcmnuypC/avlPScyWdW3HfAADkMADMBGQxgIHTthgQER+x/T/Z13fb/oykgyV9IiK+348OAkhDSiunpoQcBtANsrgaZDGAvFLK4Y6XFoyIu1u+fkjSeVV2CECamGpaHXIYQF5kcXXIYgB5pJTDHYsBAJBHSlVQABhUZDEA1CulHKYYAKAUKQUfAAwqshgA6pVSDlMMAFCKlKZEAcCgIosBoF4p5TDFAAClaKSTewAwsMhiAKhXSjlceTHgrhu2K9x2B20osSf5/fTTxY67R2NO4WPuvqH4q+auHxb/GS/yxkLtHnp0q8LHbETx53rH/y0o3HZTY6RQuwcf3brwMV/QKPbzlaQNo6OF2668o/hrYueC7RoJVUGH0frxevK0DqGouwt9EzE8zxX5kMUAUK+UcpiZAQBKwZ8kAFA/shgA6pVSDlMMAFCKlBZLAYBBRRYDQL1SymGKAQBK0XA6U6IAYFCRxQBQr5RymGIAgFKkNCUKAAYVWQwA9Uoph4utqAYAkzQK3gAA5SGHAaBeVY6JbR9m+zbbt9s+qc3jXmM7bC9rt7+OMwNsP17SqyXtJmlc0k8kfS4iHsnZZwBDIKXLqKSGHAaQV1VZbPswSR+TNCrpzIg4bZrHvUbSeZKeHRErqulNPchiAHlUmMOjks6QdIikVZKutX1hRNwy6XHbSHqzpGs67bPtzADbfyPp45K2kvRsSXPVDMCrbR/U/VMAMKgacqEb2iOHAXSjihxuGYAeLmlfSa+3ve8Uj8s9AE0NWQwgrwrHxAdKuj0i7oyIDZLOlXTkFI97v6TTJf2m0w47nSbwJkmHR8Q/SjpY0lMj4mRJh0n6yHSNbC+3vcL2ii8/9rNOfQAwAKLgDR0VymFpyyx+dP0DfegqgLpVlMOlD0AT1POYuNF4rE9dBVCnomPi1rzIbssn7XqJpJUt36/Ktm1m+5mSdouIi/P0Nc8CgrPUnAo1V9ICSYqIX9iePV2DiBiTNCZJK5YexXgfGAKcJlCprnM4e8zmLN59+6eTxcAQqCiLpxqAPqf1Aa0DUNtvr6QX9etpTDxrzhJyGBgCRXO4NS+KsD0i6cOS3pi3TadiwJlqnotwjaQXqlntle2dJPExEwBUjxwGUKns06fWT6DGskFp3vZdD0ATRBYDqNtqNU9PmrA02zZhG0lPk3SFm5c33FnShbZfOd0aLm2LARHxMdvflPQUSR+KiB9n2++X9KKizwLA4GFF6mqQwwC6USSLc3waVfoANDVkMYC8KhwTXytpH9t7qZnBR0s6ZuLOiHhY0o4T39u+QtLftcvhjqcJRMTNkm4u3mcAw4C5j9UhhwHkVVEWlz4ATRFZDCCPqsbEEbHJ9omSvqHmlV0+FRE3236fpBURcWG3+8yzZgAAdFTHmgG2XyvpVDU/qTlw0AaeANCtKrK4igEoAAyqKsfEEXGJpEsmbTtlmsce1Gl/FAMAlKKm0wRuUvOaz/9Vz+EBYGapKovLHoACwKBK6dRZigEASlFH8EXErZKUnaMKAEMvpUEoAAyilHKYYgCAUgR/jwNA7chiAKhXSjlceTFgvDFSuG3xlr1VZDaNFztyL/3tZamJXn7GdrHjNmp6lffyXOswq+DPt1d1/H6K/pvrdEmrbPXmnadoenJEfKXgYTHAIoZnOctheq7IJ6VPpABgEKWUw8wMAFCKosHX6ZJWEXFwwV0DwNBJaRAKAIMopRymGACgFHw+CQD1I4sBoF4p5TDFAAClqOnSgq+S9G+SdpJ0se3rI+LQ/vcEAGaGOrIYAPBbKeUwxQAApajpagLnSzq/hkMDwIyU0vRUABhEKeUwxQAApUgp+ABgUJHFAFCvlHKYYgCAUqR0fhQADCqyGADqlVIOUwwAUIqUzo8CgEFFFgNAvVLK4bYXbbe90PZptn9s+wHbv7Z9a7ZtUZt2y22vsL3igrV3ld5pADNPo+ANnZWRxY+uf6CPPQZQF3K4GmXkcKPxWB97DKAuKY2J2xYDJH1B0oOSDoqI7SNiB0m/n237wnSNImIsIpZFxLKj5u1VXm8BzFhR8IZces7iBXO371NXAdSJHK5Mzzk8MjK/T10FUKeUxsSdigF7RsTpEXHvxIaIuDciTpe0R7VdA5CShqLQDbmQxQByIYcrQw4DyCWlMXGnYsDPbb/D9uKJDbYX236npJXVdg0AkCGLAaBe5DCAgdOpGPBHknaQ9K3s/KgHJF0haXtJr624bwASktL5UQkiiwHkQg5XhhwGkEtKY+K2VxOIiAclvTO7bcH28ZLOqqhfABLDRNPqkMUA8iKLq0EOA8grpRzuNDOgnfeW1gsAyUupCjpgyGIAm5HDtSCHAWyW0pi47cwA2zdOd5ekxdPcB2AIpXRN1dSQxQDyIourQQ4DyCulHG5bDFAz3A5V87IprSzpu5X0CECSWJG6UmQxgFzI4sqQwwBySSmHOxUDLpK0ICKun3yH7SvyHOCexlbd9yrzjMX3F267w4HFSzLXXLhdoXZ/tPa6wsd836LnFG57yPxfF2771bU7FGp34nlHFj5m/Lj4z+mMvy++YO8LN60t1O4Zf1n8NfzaMyePGfJb6DmF2479wfrCbYtKJ/aS1HMWj8fwTAZ+984vrrsLfXP4xnV1d6Fvdlj0WN1dSAJZXJmec3jtHZeU3CUAM1FKOdxpAcET2tx3TPndAZCq4flTs//IYgB5kcXVIIcB5JVSDneaGQAAuaQ0JQoABhVZDAD1SimHKQYAKEU6sQcAg4ssBoB6pZTDFAMAlCKlKVEAMKjIYgCoV0o5TDEAQClSmhIFAIOKLAaAeqWUwxQDAJQindgDgMFFFgNAvVLKYYoBAEqR0pQoABhUZDEA1CulHB4p2tD219rct9z2CtsrLl17e9FDAEhIFPwPvcmbxY+tf7Cf3QJQE3K4//Lm8JnnnNfPbgGoSUpj4rYzA2w/c7q7JO0/XbuIGJM0JkkX7HwM7zLAEEipCpqaMrJ4yXZPJYuBIUAWV6OMHN6w8gZyGBgCKeVwp9MErpX0LTWDbrJFpfcGQLLqWCzF9gclvULSBkl3SDo+Ih7qe0eqRxYDyCWlhasSQw4DyCWlHO5UDLhV0p9HxE8n32F7ZTVdAoDcLpP0rojYZPt0Se+S9M6a+1QFshgA6kUOAxg4ndYMOLXNY/663K4ASFkUvPV0zIhLI2JT9u3Vkpb2uMuZ6lSRxQBy6HcOD5FTRQ4DyKGOMXFRbWcGRES7lU62K7kvABJWdEqU7eWSlrdsGsvOsezWn0r6n0KdmOHIYgB5pTQ9NSXkMIC8UsrhXi4t+F5JZ5XVEQBpK7pYSuviSlOx/U1JO09x18kR8ZXsMSdL2iTpnILdSBlZDGCzlBauGiDkMIDNUsrhTlcTuHG6uyQtLr87AFJV1SVRIuLgdvfbfqOkIyS9NCLSKcV2gSwGkBeXCqwGOQwgr5RyuNPMgMWSDpU0+QLVlvTdSnoEIEl1VEFtHybpHZJeHBFra+hCv5DFAHJJ6ROpxJDDAHJJKYc7FQMukrQgIq6ffIftK/IcYJeR33Tfq8w9921buO3dX53qyi/57DCnWJ/P0wGFjzmyYX3htmtibuG2z9tU7Lne9Oris7Ejiv9uXthD26I1ups+vq7wMd82vk3htlt5vHDb2y4s3FTL/qNYu5qqoP8uaa6ky2xL0tUR8Rd1dKRiPWfxr9etKblLM9eZvqHuLvTNh9cPcg1sS7PWjNbdhb55oIe2KX0ilZiec3jeE15ecpcAVGnThtWF2qWUw50WEDyhzX3HlN8dAKmqowoaEXvXcNi+I4sB5JXSJ1IpIYcB5JVSDveygCAAbNYYzNP1ASApZDEA1CulHKYYAKAU6cQeAAwushgA6pVSDlMMAFCKlK6pCgCDiiwGgHqllMMUAwCUIqXFUgBgUJHFAFCvlHKYYgCAUqS0WAoADCqyGADqlVIOUwwAUIqUpkQBwKAiiwGgXinl8Ei7O21va/ufbf+37WMm3Tft1chtL7e9wvaKC9beVVZfAcxgUfA/dFZGFo+PP1p9RwHUjhyuRhk53Gg8Vn1HAdQupTFx22KApLMkWdKXJB1t+0u252b3PXe6RhExFhHLImLZUfP2KqmrAGayRsEbcuk5i0dHF/SjnwBqRg5XpuccHhmZ349+AqhZlWNi24fZvs327bZPmuL+t9q+xfaNtv/X9h7t9tepGPCEiDgpIi6IiFdKuk7S/9neIWd/AQyJiCh0Qy5kMYBcqsrhsgegCSKHAeRS1ZjY9qikMyQdLmlfSa+3ve+kh/1Q0rKI2E/SeZL+X7t9dlozYK7tkYhoZE/sA7ZXS7pSEh8zAUB/kMUAatMyAD1E0ipJ19q+MCJuaXnYxAB0re2/VHMA+kf9721lyGEAdTtQ0u0Rcack2T5X0pGSNmdxRFze8virJR3bboedZgZ8VdJLWjdExNmS3iZpQ95eAxh8DUWhG3IhiwHkUlEObx6ARsQGSRMD0M0i4vKIWJt9e7WkpaU+sfqRwwByqXBMvETSypbvV2XbpnOCpK+122HbmQER8Y5ptn/d9j+1awtguHDeaXXIYgB5Fcli28slLW/ZNBYRYy3fTzUAfU6bXXYcgKaGHAaQV9ExcY4s7mZfx0paJunF7R7Xy6UF36vmYioAwIrU9SGLAWxWJIuzwWahAedkeQegA4YcBrBZ0TFxjixeLWm3lu+XZtu2YPtgSSdLenFErG93zLbFANs3TneXpMXt2gIYLkz5rw5ZDCCvirK49AFoashhAHlVOCa+VtI+tvdSM4OPljT5UqcHSPovSYdFxH2ddthpZsBiSYdKenDSdkv6bs5OAxgCXBmgUmQxgFwqyuLSB6AJIocB5FLVmDgiNtk+UdI3JI1K+lRE3Gz7fZJWRMSFkj6o5qKmX7QtSb/IroAypU7FgIskLYiI6yffYfuKQs+iCxEu3LaX85cbBY871/WcNT3e6LQO5PTsYi/WDeOjhY/Zi9kjxX/G4wV/r0XbSdLsHl4Ts3p5rj28JopizYBK9ZzFi7Yanutb37/24bq70DeNxvD8y6PcmE8Vr4gqBqAJqnVMDCAdVb4zR8Qlki6ZtO2Ulq8P7mZ/nRYQPKHNfcdMdx+A4cOaAdUhiwHkVVUWlz0ATQ05DCCvlMbEvSwgCACbsWYAANSPLAaAeqWUwxQDAJSCNQMAoH5kMQDUK6UcphgAoBQpVUEBYFCRxQBQr5RymGIAgFKkdH4UAAwqshgA6pVSDlMMAFCKRg1Tomy/X9KRai7cep+kN0bE3X3vCADMEHVkMQDgt1LK4f5ffwzAQIqCtx59MCL2i4j91bzs0ykdHg8AA62GHAYAtKhpTFxI22KA7Z1t/6ftM2zvYPtU2z+y/QXbu7Rpt9z2CtsrLlh7V/m9BjDjNBSFbr2IiEdavp2vAR3XlpHFazc81MceA6hLv3N4WJSRw43GY/3sMoCa1DEmLqrTzICzJd0iaaWkyyWtk/RySVdJ+vh0jSJiLCKWRcSyo+btVVJXAcxkdQWf7Q/YXinpDRrcmQFnq8csnjdnUR+6CaBuqQxAE3S2eszhkZH5/egngJoNUjFgcUT8W0ScJmlRRJweESsj4t8k7dGH/gFIREQUurV+apLdlrfu1/Y3bd80xe3I7LgnR8Ruks6RdGIdz70PyGIAuRTJYeRCDgPIpeiYuA6dFhBsLRZ8ZtJ9oyX3BcAQiogxSWNt7j84567OkXSJpPeU0a8ZhiwGgHqRwwAGTqdiwFdsL4iIRyPi7yc22t5b0m3Vdg1ASuqY3mR7n4j4afbtkZJ+3PdO9AdZDCAXpv1XhhwGkEtKOdy2GBARU55/GxG32764mi4BSFFN11Q9zfaT1Ly04M8l/UUdnagaWQwgr5Sub50SchhAXinlcKeZAe28V9JZZXUEQNrqONcpIl7T94POPGQxgM1YA6AW5DCAzVLK4bbFANs3TneXpMXldwdAqlKaEpUashhAXmRxNchhAHmllMOdZgYslnSopAcnbbek71bSIwBJSqkKmiCyGEAuZHFlyGEAuaSUw52KARdJWhAR10++w/YVeQ6w65KHu+9V5qFfzSvcdrzhwm33PrrY2RMf+J85hY/548aawm0/9ZzibS/5ztJC7V721JWFjzlrUfHfzei2swu3veHihYXaPfV59xc+5l9et13htr8XCwq3Pe75qwu3LSqlKmiCes7iWSPDs9j147fdpe4u9M14NOruQt/cv+6huruQBLK4Mj3nMIDhkFIOd1pA8IQ29x1TfncApCqlxVJSQxYDyIssrgY5DCCvlHK4lwUEAWCzRkJTogBgUJHFAFCvlHKYYgCAUqRUBQWAQUUWA0C9UsphigEASpFSFRQABhVZDAD1SimHKQYAKEVKVVAAGFRkMQDUK6UcphgAoBQpVUEBYFCRxQBQr5RyuOtigO3HRcR9VXQGQLpSqoIOArIYwFTI4v4hhwFMJaUcblsMsL395E2Svm/7AEmOiAemabdc0nJJ+uc9nqQ37LSkjL4CmMFSqoKmpowsXjRvF82fO3k3AAYNWVyNMnLYows1MjK/2o4CqF1KOdxpZsCvJP180rYlkq6TFJIeP1WjiBiTNCZJK5/90nR+GgAKS6kKmqCes3jp9k/jFwQMAbK4Mj3n8Kw5S/jlAEMgpRzuVAx4u6RDJL09In4kSbbvioi9Ku8ZgKRENOruwiAjiwHkQhZXhhwGkEtKOTzS7s6I+JCkP5N0iu0P295GSqjUAQADgCwGgHqRwwAGUccFBCNilaTX2n6lpMskzau8VwCS02BMVCmyGEAeZHF1yGEAeaSUw21nBrSKiAsl/b6kgyXJ9vFVdQpAeiKi0A3dIYsBtEMOV48cBtBOSmPi3MUASYqIdRFxU/bteyvoD4BENRSFbugeWQxgOuRwf5DDAKaT0pi406UFb5zuLkmLy+8OgFTx6VJ1yGIAeZHF1SCHAeSVUg53WjNgsaRDJT04abslfbeSHgFIUkrXVE0QWQwgF7K4MuQwgFxSyuFOxYCLJC2IiOsn32H7ijwHuPfubbvvVWbD+Gjhtr24/dwNhdodvn5j4WMeGlsVbnvH94r/nPZs/KbYMW/eofAxXbilNHt0vHjbkWKX+fjpNcWf6/Lx2YXbbj2yrnDb269aWLjtsoLtUrqmaoJ6zuK1G9eX3KWZa82G4v92UrNhfFPdXeib8Ubx/B8mZHFles5hAMMhpRxuWwyIiBPa3HdM+d0BkKqUpkSlhiwGkBdZXA1yGEBeKeVwVwsIAsB06lwsxfbbbIftHUvZIQAkKpVFqwBgUA3MAoIAkFddVVDbu0l6maRf1NIBAJhBUvpECgAGUUo5TDEAQClqXCzlI5LeIekrdXUAAGaKlBauAoBBlFIOUwwAUIo6qqC2j5S0OiJusHtZmhIABkNKn0gBwCBKKYcpBgAoRdFznWwvl7S8ZdNYRIy13P9NSTtP0fRkSe9W8xQBAICKZzEAoBwp5TDFAAClKFoFzf7wH2tz/8FTbbf9dEl7SZqYFbBU0nW2D4yIewt1BgASl9InUgAwiFLK4bZXE7B9WMvXC21/0vaNtj9ne3Gbdsttr7C94vzHflZidwHMVI2IQreiIuJHEfG4iNgzIvaUtErSMwexEFBGFq/f+HB/OgugVv3M4WFSRg43Go/1p7MAatXvMXEvOl1a8J9avv6QpHskvULStZL+a7pGETEWEcsiYtmr5u/ZcycBzHxR8D/k0nMWz529sOIuApgJyOHK9JzDIyPzK+4igJkgpTFxN6cJLIuI/bOvP2L7uAr6AyBRdX+6lM0OGAZkMYBp1Z3FQ4IcBjCtlHK4UzHgcbbfKsmStrXt+O1JEJ1mFQAYIimdH5UgshhALmRxZchhALmklMOdwusTkraRtEDSpyXtKEm2d5Z0faU9AwBMIIsBoF7kMICB03ZmQES8d5rt99q+vJouAUgR551WhywGkBdZXA1yGEBeKeVwL9OapgxFAMMpIgrd0DOyGMBm5HAtyGEAm6U0Jm47M8D2jdPdJWnay6gAGD4MKKtDFgPIiyyuBjkMIK+UcrjTAoKLJR0q6cFJ2y3pu5X0CECS0om9JJHFAHIhiytDDgPIJaUc7lQMuEjSgoi4fvIdtq/Ic4Bnrz7f7e63vTwixvLsq4x2KbZNrb91tU2tv720rau/7WzasLrtv3X0pOcsfmDNT/v++6nqtTYT8VwHU4rPlSyuTM85zO9mMKWYE6hWSv/WXfc0BtsrImJZv9ql2Da1/tbVNrX+9tK2rv4C3Rim1xrPdTAN03MFUAw5gZRxXVQAAAAAAIYMxQAAAAAAAIbMTCgGFD3Hppdzc1Jrm1p/62qbWn97aVtXf4FuDNNrjec6mIbpuQIohpxAsmpfMwAAAAAAAPTXTJgZAAAAAAAA+qi2YoDtw2zfZvt22yd10e5Ttu+zfVOBY+5m+3Lbt9i+2fabu2i7le3v274ha/veLo89avuHti/qst3PbP/I9vW2V3TZdpHt82z/2Pattp+Xs92TsuNN3B6x/Zacbf82+/ncZPvztrfqor9vztrd3Ol4U70ObG9v+zLbP83+v10XbV+bHbdhe9oVYadp+8HsZ3yj7fNtL8rZ7v1Zm+ttX2p717zHbLnvbbbD9o5d9PdU26tbfr8vn+75AkUVzfjU9PKelJpe3kNT0+t7PoDhMCzvdRhctRQDbI9KOkPS4ZL2lfR62/vmbH62pMMKHnqTpLdFxL6Snivpr7o47npJL4mIZ0jaX9Jhtp/bxbHfLOnWbjrb4vcjYv8Cly35mKSvR8STJT0j7/Ej4rbsePtLepaktZLO79TO9hJJfyNpWUQ8TdKopKPzHNP20yS9SdKBWV+PsL13myZn63dfBydJ+t+I2EfS/2bf5217k6RXS7qyQ1enanuZpKdFxH6SfiLpXTnbfTAi9st+zhdJOqWLY8r2bpJeJukXXfZXkj4y8TuOiEvatAe61mPGp+ZsFX9PSk0v76Gp6fU9H8CAG7L3OgyoumYGHCjp9oi4MyI2SDpX0pF5GkbElZIeKHLQiLgnIq7Lvl6j5h/HS3K2jYh4NPt2dnbLteCC7aWS/kDSmV13uiDbCyW9SNInJSkiNkTEQwV29VJJd0TEz3M+fpakrW3PkjRP0t052z1F0jURsTYiNkn6lpp/nE9pmtfBkZI+nX39aUlH5W0bEbdGxG2dOjlN20uzPkvS1ZKW5mz3SMu38zXN66nNa/4jkt4xXbsObYEqFc741AzTv7Fe3kNT08t7PoChMTTvdRhcdRUDlkha2fL9KvV5QGF7T0kHSLqmizajtq+XdJ+kyyIib9uPqvlHW6O7XkpqDj4utf0D28u7aLeXpPslnZWdnnCm7fkFjn+0pM/n6mjEakn/ouYn1fdIejgiLs15nJskvdD2DrbnSXq5pN267OviiLgn+/peSYu7bF+GP5X0tbwPtv0B2yslvUHTzwyYqt2RklZHxA3dd1GSdGJ2isKnpjudAuhB7RmPahV5D01ND+/5AIYD73VI3lAuIGh7gaQvSXrLpE9n24qI8WxK91JJB2ZT2zsd6whJ90XEDwp29wUR8Uw1pyD9le0X5Ww3S9IzJf1nRBwg6TFNP21+SrbnSHqlpC/mfPx2alZE95K0q6T5to/N0zYibpV0uqRLJX1d0vWSxrvp76T9hfr8KY7tk9WcRntO3jYRcXJE7Ja1OTHnceZJere6KB5M8p+SnqDm1Nd7JH2o4H4ADKGi76GpKfKeDwBASuoqBqzWlp/6Ls22Vc72bDUHMedExJeL7CObbn+58p0n+nxJr7T9MzWnD73E9me7ONbq7P/3qXne/oE5m66StKrlk4zz1CwOdONwSddFxC9zPv5gSXdFxP0RsVHSlyX9Xt6DRcQnI+JZEfEiSQ+qef59N35pexdJyv5/X5ftC7P9RklHSHpDFLte5zmSXpPzsU9Qs+ByQ/a6WirpOts752kcEb/MBrkNSZ9Q/tcUkFdtGY9qlfEempou3/MBDA/e65C8uooB10rax/Ze2afPR0u6sOqD2raa59DfGhEf7rLtThOrxNveWtIhkn7cqV1EvCsilkbEnmo+z/+LiFyfltueb3ubia/VXCwu14rVEXGvpJW2n5RteqmkW/K0bfF65TxFIPMLSc+1PS/7Wb9UXSyaaPtx2f93V3O9gM91cWyp+Ro6Lvv6OElf6bJ9IbYPU/M0kFdGxNou2u3T8u2RyvF6kqSI+FFEPC4i9sxeV6skPTP7nec57i4t375KOV9TQBdqyXhUq5f30NQUfc8HMFR4r0PyZtVx0IjYZPtESd9Qc8X5T0XEzXna2v68pIMk7Wh7laT3RMQncx76+ZL+WNKPsvMAJendOVdT30XSp7OVQ0ckfSEiurpMYAGLJZ3fHH9plqTPRcTXu2j/15LOyQLqTknH522YFR8OkfTnedtExDW2z5N0nZrT5X8oaayL/n7J9g6SNkr6q3YLHk71OpB0mqQv2D5B0s8lva6Ltg9I+jdJO0m62Pb1EXFozrbvkjRX0mXZ7+rqiPiLHO1enhVrGll/t2jTrm3e1/w0xz3I9v5qnkbxM3XxOwby6CXjU9Pje1JqenkPTU0d7/kAEjJM73UYXC42oxkAAAAAAKRqKBcQBAAAAABgmFEMAAAAAABgyFAMAAAAAABgyFAMAAAAAABgyFAMAAAAAABgyFAMAAAAAABgyFAMAAAAAABgyFAMAAAAAABgyPz/Mx2+zp2zLu4AAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 6\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 7\n" + ] + }, + { + "data": { + "image/png": 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t79i0IiJWFPe9SNIdEfED2wcM2kUAGHoUTQCgWRnlMMUAAJVIvcxy8Yf/iinufqakw2y/UNJWkraz/dmIOCqtlwAw3LjkPQA0K6ccphgAoBo1TImKiLdLerskFTMD/o5CAAB0kdH0VAAYShnlMMUAANXIaEoUAAwtshgAmpVRDlMMAFCNmqugEXGRpItqPQgA5C6jT6QAYChllMMUAwBUo5XP+VEAMLTIYgBoVkY5nHzdM9tHV9kRAJmLVtoNAyGLAWyBHJ525DCALWQ0Jk6/CLp00lR32F5ue6XtlWevvXmAQwDIRquVdsOgSmVxq/XAdPYJQFPI4SaQwwB+K6MxcdfTBGxfPdVdkhZP1a7zUmGX7fqySO4dAKCSLJ49dwlZDACJyGEAw6jXmgGLJR0s6e4J2y3pe7X0CECemGpaJ7IYQDlkcV3IYQDlZJTDvYoB50paEBFXTrzD9kV1dAhApphqWieyGEA5ZHFdyGEA5WSUw12LARFxTJf7jqy+OwCylVHw5YYsBlAaWVwLchhAaRnlMJcWBFCJiHwuowIAw4osBoBm5ZTDFAMAVCOjKigADC2yGACalVEOUwwAUI2MFksBgKFFFgNAszLKYYoBAKqRURUUAIYWWQwAzcooh2svBsyftyG57e1rt0luu0lObvu4pXcmtbv3FzsmHzMG6O+28x9Kbrv+3rSf8bqN6S+dOWPp/0C2325dctv77p+X1G79plnJx7x7LP3ntM829ye33dQaS26bLKMqKDAs5s/ZqukuTJv716fn/0ghiwGgWRnlMDMDAFQjoyooAAwtshgAmpVRDlMMAFCNjKqgADC0yGIAaFZGOUwxAEA1MqqCAsDQIosBoFkZ5TDFAADVyCj4AGBokcUA0KyMcphiAIBqZDQlCgCGFlkMAM3KKId7Ljlu+7G2n297wYTth9TXLQDZabXSbuiJHAZQGjlcG7IYQCkZjYm7FgNs/42kr0r6a0nX2D684+731dkxAJmJVtoNXZHDAPpCDteCLAZQWkZj4l6nCbxe0lMi4n7be0g60/YeEfFRSZ6qke3lkpZL0ok7PEF/tO0jquovgJmKT5fqkpTD0pZZ7FkLNTY2v/bOAmgYWVyXgcfE5DAwIjLK4V7FgLGIuF+SIuIW2weoHX6PUJfgi4gVklZI0o/3fHFU01UAGElJOVw8fnMWz567hCwGgHQDj4nJYQAzTa81A35pe9/xb4oQfJGkHSU9ocZ+AchNRlOiMkMOAyiPHK4LWQygnIzGxL1mBvyppI2dGyJio6Q/tf0ftfUKQH5qmhJleytJF0uap3ZmnRkR76zlYDMTOQygvIymp2aGLAZQTkY53LUYEBGru9z3v9V3B0C26gu+hyQ9rzhPc46k79r+RkRcVtcBZxJyGEBfMhqE5oQsBlBaRjnc89KCAFBKRNqt524jxs/TlDSnuHHeJQBMpoYcBgD0oaYxsdS+lKntG2zfaPv4Se7f3faFtn9k+2rbL+y2v16nCQBAOTVWQW3PkvQDSXtJOjUiLq/tYACQs4w+kQKAoVTfqbOzJJ0q6SBJqyVdYfuciLiu42F/L+mLEfHvtveRdJ6kPabaJ8UAANVIDL7Oyy4VVhSrL28WEZsk7Wt7kaSzbD8+Iq5J7SoADC2KAQDQrPpyeH9JN0bETZJk+wuSDpfUWQwISdsVXy+UdGu3HVIMAFCNxFVQOy+7VOKx99i+UNIhkigGAMBEXB0AAJpVXw4vkbSq4/vVkp424THvknS+7b+WNF/Sgd12WHsxYO1Dc5LbbuVNFfakvDtvX5DUrqn+3vfAvOS2dtq5grOSjyi1ouul0bu6976tkttuGuC4qR4e65Pb3rcu/ffaiPqmRO0kaUNRCNha7alRp9RyMCAzc2eNTk3fnv4Mz1J9WXyIpI+qPQQ4LSJOnnD/7pI+LWlR8ZjjI+K8WjoDADNZjbNlS3iVpNMj4oO2nyHpP4sZtZN2anRGEQDqVd8iVLtI+nRxntSY2udBnVvXwQAgazVkcR3nqQLA0ErM4RKzZddI2q3j+6XFtk7HqD2DVhFxaXGJ7h0l3THZDikGAKhGTZ9GRcTVkvarZecAMGzqyeLKz1MFgKFV35oBV0ja2/aeahcBjpB05ITH/ELS8yWdbvv3JG0l6c6pdkgxAEA1WLQKAJqXkMUlpqZWfp4qAAyt+j4g22j7WEnfUvt0rE9GxLW23y1pZUScI+k4SR+3/bdqF2lfGzH1VAWKAQCqwaJVANC8hCzuZyHXLvo6TxUAhlaNsVesxXLehG0ndnx9naRnlt0fxQAAlYhWbWsGAABKqimLKz9PFQCGVU5j4p7FANv7S4qIuKJYEOYQST9hhVgAW+A0gdqQwwBKqyeLKz9PNUdkMYBSMhoTdy0G2H6npEMlzbZ9gdrnh10o6Xjb+0XEe6ehjwBywEzQWpDDAPpSQxbXcZ5qbshiAKVlNCbuNTPgjyTtK2mepNslLY2Ie21/QNLlkiYNvs6FaI5fuK9ess2elXUYwAyV0ZSozCTlsLRlFnvWQo2Nza+/twCaVVMWV32eaoYGHhOTw8CIyGhMPNbj/o0RsSki1kr6WUTcK0kRsU7SlCWPiFgREcsiYhmFAAAYSFIOF4/ZnMUMQAFgIAOPiclhADNNr5kB621vUwTfU8Y32l6oHoNQACMmo/OjMkMOAyiPLK4LWQygnIxyuFcx4DkR8ZAkTbg0zBxJr6mtVwDyk1HwZYYcBlAeWVwXshhAORnlcNdiwHjoTbL9V5J+VUuPAORpeNaJmlHIYQB9IYtrQRYDKC2jHO55aUEAKCWjKigADC2yGACalVEOUwwAUI2MVk4FgKFFFgNAszLKYYoBAKqR0TVVAWBokcUA0KyMcphiAIBqZFQFBYChRRYDQLMyyuHaiwG/3jQ3ue1z3+DkthtuuD257d9e+rCkdvt6q+RjXjM26bo0pTx3ffp1az83566kdmcet3vyMVf9+8+T215/z/bJbf/g0DuS2t1+6ZzkY35xXdprSZJWe31y2w8cti65barI6PyoUbT2pm823YVps+G09zXdhWnziA/9oOkuTJtfve4JTXchC2TxzLX2lvOb7gKAaZBTDjMzAEA1MqqCAsDQIosBoFkZ5TDFAADVyOj8KAAYWmQxADQroxymGACgGhlVQQFgaJHFANCsjHKYYgCAamR0fhQADC2yGACalVEOUwwAUI2MqqAAMLTIYgBoVkY5PNZvA9ufqaMjADIXrbQb+kYOA5gSOTxtyGIAk8poTNx1ZoDtcyZukvQHthdJUkQcVlO/AOSmpiqo7d0kfUbSYkkhaUVEfLSWg81A5DCAvmT0iVROyGIApWWUw71OE1gq6TpJp6k9CLekZZI+2K2R7eWSlkvSsdsu06FbP2rwngKY0Wq8pupGScdFxA9tbyvpB7YviIjr6jrgDJOUw9KWWXzqySfqda/+oxq7CWAmyOn61pkZeEx86inv1OuOekXN3QTQtJxyuFcxYJmkN0o6QdJbIuJK2+si4jvdGkXECkkrJOm8xUfkUxoBMONExG2Sbiu+vs/29ZKWqD0oGwVJOSxtmcXrV/+YLAaAdAOPidffei05DGBG6VoMiIiWpA/b/lLx/1/2agNgRE3DlCjbe0jaT9LltR9shiCHAfQlo+mpOSGLAZSWUQ6XCrGIWC3pFbb/UNK99XYJQJYSg69zCmVhRfFJysTHLZD0ZUlvioiRyyFyGEApGQ1Cc0QWA+gpoxzuq6IZEV+X9PWa+gIgZ4mroHZOoZyK7TlqFwLOiIivJB1oSJDDALri6gDTgiwGMKWMcpjpTQCqUd/VBCzpE5Kuj4gP1XIQABgWGX0iBQBDKaMcphgAoBJRX/A9U9KfSPqx7SuLbe+IiPPqOiAA5KrGLAYAlJBTDlMMAFCNmoIvIr6r9iWcAAC9ZDQIBYChlFEOUwwAUI2MrqkKAEOLLAaAZmWUw46ot3Jx+a4vSz7AxtZY8nEH+RVsPXtjUruHNjZTW5k9lv5sN0VeH7jOaeC5tgb4GW2M9NfwvLFNyW0H8bRbv5L0hO/7y0OT/q1v+2/fyOtFmKnZc5fkU6Ye0Ci9oB629bZNd2Ha3PPQA013Ydo89OCq5JdxShaTw9NjlHIYGAYb168Z+jExMwMAVCOjKVEAMLTIYgBoVkY5TDEAQCXqnmUEAOiNLAaAZuWUwxQDAFQjoyooAAwtshgAmpVRDlMMAFCNjIIPAIYWWQwAzcoohykGAKhETtdUBYBhRRYDQLNyyuG+igG2nyVpf0nXRMT59XQJQJYyCr7ckcUApkQWTwtyGMCUMsrhrtc9s/39jq9fL+lfJW0r6Z22j6+5bwBy0kq8oSeyGEBp5HAtyGEApWU0Ju51EfQ5HV8vl3RQRJwk6QWSXj1VI9vLba+0vfLstTdX0E0AM120IumGUgbO4lZrdK7RDowycrg25DCAUnIaE/c6TWDM9vZqFw0cEXdKUkQ8YHvjVI0iYoWkFZJ0+a4v410GGAUMKOs0cBbPnruEXxAwCsjiupDDAMrJKId7FQMWSvqBJEsK27tExG22FxTbAAD1I4sBoFnkMICh07UYEBF7THFXS9JLK+8NgHxx3mltyGIApZHFtSCHAZSWUQ4nXVowItZKYjEAAJtx3un0I4sBTEQWTy9yGMBEOeVwUjEAAH5HRlVQABhaZDEANCujHKYYAKASOVVBAWBYkcUA0KyccphiAIBqZFQFBYChRRYDQLMyymGKAQAqERkFHwAMK7IYAJqVUw7XXgy4rbVVctsDj7w//cBj6Vd5+aczFyS1O+PB65OPedLcfZLbPsYPJLc9Y+6cpHYfuOgtycfccMY/J7d9x8fXJ7c9bF3alJ0nP+uXycf8y5WLktv+YuO9yW3P/8ulyW2TZRR8o2jW2FjTXZg2W82e23QXps1d6+5rugvTZmyEXsMDIYsBoFk15rDtQyR9VNIsSadFxMmTPOaVkt4lKSRdFRFHTrU/ZgYAqEROVVAAGFZkMQA0q64ctj1L0qmSDpK0WtIVts+JiOs6HrO3pLdLemZE3G374d32STEAQDUYgAJA88hiAGhWfTm8v6QbI+ImSbL9BUmHS7qu4zGvl3RqRNwtSRFxR7cdMucOQCWilXYDAFSnrhy2fYjtG2zfaPv4KR7zStvX2b7W9ueqfF4AkIsax8RLJK3q+H51sa3ToyU92vb/2r6sOK1gSswMAFCJGqdEfVLSiyTdERGPr+coADAc6sjiOqamAsCwSs1h28slLe/YtCIiVvS5m9mS9pZ0gKSlki62/YSIuGeqBwPAwGr8lP90Sf8q6TO1HQEAhkRNWVz51FQAGFapOVz84d/tj/81knbr+H5psa3TakmXR8QGSTfb/j+1iwNXTLbDrqcJ2H6a7e2Kr7e2fZLtr9k+xfbC7k8HwEgJp9167TbiYkl31f8EZiZyGEBfashh1TA1NTdkMYDSahoTq/0H/d6297Q9V9IRks6Z8Jiz1Z4VINs7qp3NN021w15rBnxS0tri649KWijplGLbp8r0GMBoSD0/yvZy2ys7bst7H22kkMMASmswhzunpr5K0sdtL6rwqTWNLAZQSl1rBkTERknHSvqWpOslfTEirrX9btuHFQ/7lqRf275O0oWS3hIRv55qn71OExgrDipJyyLiycXX37V95VSNOs93eMO2T9ULttmrx2EA5C5apSqav9uu95SoUZeUw9KWWTxr9iLNmrWgvl4CmBFSsriJqakZGnhM7FkLNTY2v95eAmhc6pi41L4jzpN03oRtJ3Z8HZLeXNx66jUz4BrbRxdfX2V7mSTZfrSkDV06uSIilkXEMgoBwGjgagK1ScphacssphAAjIaacrjyqakZGnhMTCEAGA05jYl7FQNeJ+m5tn8maR9Jl9q+SdLHi/sAAPUihwE0qo6pqRkiiwEMna6nCUTEbyS9tlgwZc/i8asj4pfT0TkA+YhyC5/0zfbn1f60aUfbqyW9MyI+UcvBZiByGEA/6sriqqem5oYsBlBWXTlch1KXFoyIeyVdVXNfAGSsrulNEfGqevacF3IYQBmcflUvshhALznlcKliAAD0UudiKQCAcshiAGhWTjlMMQBAJSKa7gEAgCwGgGbllMMUAwBUIqcqKAAMK7IYAJqVUw5TDABQiZyCDwCGFVkMAM3KKYdrLwbsMvZgcturPjc3ue0g6zYcNvuBpHYHz35k+kFb6T+n1lj6C+6IhzYmtVv5jH9MPuYgjhpL/81umpX2c7r+ezskH/MN0evqnVObN7Youe01p96f3PZpJ6S1y2lK1Cja1MpoNZsBrdvwUNNdmDYPn7+o6S5Mm1+tu7fpLmSBLAaAZuWUw8wMAFCJnKqgADCsyGIAaFZOOUwxAEAlcrqmKgAMK7IYAJqVUw5TDABQiZyuqQoAw4osBoBm5ZTDFAMAVKKVURUUAIYVWQwAzcophykGAKhETlOiAGBYkcUA0KyccrjrUue2/8b2btPVGQD5ipaTbuiNLAZQFjlcD3IYQFk5jYl7XffsPZIut32J7b+0vdN0dApAfiLSbiiFLAZQCjlcG3IYQCk5jYl7FQNukrRU7QB8iqTrbH/T9mtsbztVI9vLba+0vfLstTdX2F0AM1VOVdAMDZzFrdYD09VXAA0ih2tDDgMoJacxca9iQEREKyLOj4hjJO0q6d8kHaJ2KE7VaEVELIuIZS/ZZs8KuwtgpmqFk24oZeAsHhubP119BdAgcrg25DCAUnIaE/daQHCLXkXEBknnSDrH9ja19QoA0IksBoBmkcMAhk6vYsAfT3VHRKytuC8AMpbTyqkZIosBlEIW14YcBlBKTjnctRgQEf83XR0BkDcWoaoPWQygLLK4HuQwgLJyyuFeMwMAoBTOOwWA5pHFANCsnHKYYgCASuQ0JQoAhhVZDADNyimHKQYAqEROU6IAYFiRxQDQrJxymGIAgErkNCUKAIYVWQwAzcoph2svBuy136+T217ygyXJbVvJLaWD/yztx3LWaek/ztsH+E0c/aRVyW0v+/6uSe0etfA3ycdcu25OcttHPe/+5LY//e9tk9rN33p98jG/tH775LYvjvTnusse6b+fVHVOibJ9iKSPSpol6bSIOLm2gyF7dj5vwoPadesdmu7CtLnjgXua7kIWcpqeCgDDKKccZmYAgErUVQW1PUvSqZIOkrRa0hW2z4mI62o5IABkLKdPpABgGOWUwxQDAFSixtOj9pd0Y0TcJEm2vyDpcEkUAwBggoxOVQWAoZRTDlMMAFCJGqugSyR1nguzWtLT6joYAOQsp0+kAGAY5ZTDFAMAVCL1/CjbyyUt79i0IiJWVNIpABgxOZ2rCgDDKKccphgAoBKpi3YWf/h3++N/jaTdOr5fWmwDAEwwyALKAIDB5ZTDXYsBtudKOkLSrRHxbdtHSvp9Sder/endhmnoI4AMhGqrgl4haW/be6pdBDhC0pF1HWymIYcB9KPGLB5pZDGAsnLK4V4zAz5VPGYb26+RtEDSVyQ9X+1FvV5Tb/cA5KJV02opEbHR9rGSvqX2pQU/GRHX1nO0GYkcBlBaXVkMshhAOTnlcK9iwBMi4om2Z6v9idyuEbHJ9mclXTVVo85zgD/4+L31mt13qazDAGamVo1V0Ig4T9J5tR1gZkvKYWnLLPashRobm19/bwE0qs4sHnEDj4nJYWA05JTDY73uL6ZFbStpG0kLi+3zJM2ZqlFErIiIZRGxjEIAMBpCTrqhp6QclrbMYgagwGggh2sz8JiYHAZGQ05j4l4zAz4h6SdqT809QdKXbN8k6emSvlBz3wAA5DAAzARkMYCh07UYEBEftv1fxde32v6MpAMlfTwivj8dHQSQh5xWTs0JOQygH2RxPchiAGXllMM9Ly0YEbd2fH2PpDPr7BCAPDHVtD7kMICyyOL6kMUAysgph3sWAwCgjJyqoAAwrMhiAGhWTjlMMQBAJXIKPgAYVmQxADQrpxymGACgEjlNiQKAYUUWA0CzcsphigEAKtHKJ/cAYGiRxQDQrJxyuPZiwM1XbZ/cdgetr7An5f3002nHfURrbvIxd1+f/qq5+UfpP+NF3pDU7p77t0o+ZivSn+vP/mdBctuNrbGkdnffv3XyMZ/VSvv5StL6WbOS2676WfprYufEdq2MqqAYbhHRdBemzT0bHmi6C9PGJmPKIIsBoFk55TAzAwBUYnT+/AKAmYssBoBm5ZTDFAMAVCKnxVIAYFiRxQDQrJxymGIAgEq0mMILAI0jiwGgWTnlMMUAAJXIaUoUAAwrshgAmpVTDqetqAYAE7QSbwCA6pDDANCsOsfEtg+xfYPtG20f3+VxL7cdtpd121/PmQG2HynpZZJ2k7RJ0v9J+lxE3FuyzwBGQE6XUckNOQygrLqy2PYhkj4qaZak0yLi5Cke93JJZ0p6akSsrKc3zSCLAZRRYw7PknSqpIMkrZZ0he1zIuK6CY/bVtIbJV3ea59dZwbY/htJH5O0laSnSpqndgBeZvuA/p8CgGHVkpNu6I4cBtCPOnK4YwB6qKR9JL3K9j6TPK70ADQ3ZDGAsmocE+8v6caIuCki1kv6gqTDJ3nceySdIunBXjvsdZrA6yUdGhH/IOlASY+LiBMkHSLpw1M1sr3c9krbK7/ywC29+gBgCETiDT0l5bC0ZRa3WqNzPXpglNWUw5UPQDM08JiYHAZGQ+qYuDMvitvyCbteImlVx/eri22b2X6ypN0i4utl+lpmAcHZak+FmidpgSRFxC9sz5mqQUSskLRCklYufQnjfWAEcJpArfrO4eIxm7N49twlZDEwAmrK4skGoE/rfEDnANT2W2rpRfMGGhOTw8BoSM3hzrxIYXtM0ockvbZsm17FgNPUPhfhcknPVrvaK9s7SborrZsAgD6QwwBqVXz61PkJ1IpiUFq2fd8D0AyRxQCatkbt05PGLS22jdtW0uMlXeT25Q13lnSO7cOmWsOlazEgIj5q+9uSfk/SByPiJ8X2OyU9J/VZABg+rEhdD3IYQD9SsrjEp1GVD0BzQxYDKKvGMfEVkva2vafaGXyEpCPH74yI30jacfx72xdJ+rtuOdzzNIGIuFbStel9BjAKmPtYH3IYQFk1ZXHlA9AckcUAyqhrTBwRG20fK+lbal/Z5ZMRca3td0taGRHn9LvPMmsGAEBPTawZYPsVkt6l9ic1+w/bwBMA+lVHFtcxAAWAYVXnmDgizpN03oRtJ07x2AN67Y9iAIBKNHSawDVqX/P5P5o5PADMLHVlcdUDUAAYVjmdOksxAEAlmgi+iLhekopzVAFg5OU0CAWAYZRTDlMMAFCJ4O9xAGgcWQwAzcoph2svBmxqjSW3nTOWXldpDfBbeGhDXjWSQX7Gm5T2c2rqJ7Rh06zktk38uxwbYAmRTQO8hsc8/cv5pf5r7XVJq2L15p0naXpCRHw18bAjJ6P3pYFtN2+bprswbZbM277pLkyb3XfaoekuZCGnT6RGzVaz5zbdBQDTIKcczuuvXgAzVmrw9bqkVUQcmLhrABg5OQ1CAWAY5ZTDFAMAVIJLCwJA88hiAGhWTjlMMQBAJRq6tOBLJf2LpJ0kfd32lRFx8PT3BABmhiayGADwWznlMMUAAJVo6GoCZ0k6q4FDA8CMlNP0VAAYRjnlMMUAAJXIKfgAYFiRxQDQrJxymGIAgErkdH4UAAwrshgAmpVTDlMMAFCJnM6PAoBhRRYDQLNyyuGuF6i3vdD2ybZ/Yvsu27+2fX2xbVGXdsttr7S98uy1N1feaQAzTyvxht6qyOJW64Fp7DGAppDD9agihzdsvG8aewygKTmNibsWAyR9UdLdkg6IiIdFxA6S/qDY9sWpGkXEiohYFhHLXrLNntX1FsCMFYk3lDJwFo+NzZ+mrgJoEjlcm4FzeM7sbaepqwCalNOYuFcxYI+IOCUibh/fEBG3R8Qpkh5Rb9cA5KSlSLqhFLIYQCnkcG3IYQCl5DQm7lUM+Lntt9pePL7B9mLbb5O0qt6uAQAKZDEANIscBjB0ehUD/ljSDpK+U5wfdZekiyQ9TNIrau4bgIzkdH5UhshiAKWQw7UhhwGUktOYuOvVBCLibklvK25bsH20pE/V1C8AmWGiaX3IYgBlkcX1IIcBlJVTDveaGdDNSZX1AkD2cqqCDhmyGMBm5HAjyGEAm+U0Ju46M8D21VPdJWnxFPcBGEE5XVM1N2QxgLLI4nqQwwDKyimHuxYD1A63g9W+bEonS/peLT0CkCVWpK4VWQygFLK4NuQwgFJyyuFexYBzJS2IiCsn3mH7ojIHuK21Vf+9Kjxp8Z3JbXfYP70kc/k52ye1++O1P0w+5rsXPS257UHzf53c9mtrd0hqd+yZhycfM36S/nM69e/TF+x99sa1Se2e9Ib01/ArTps4Zihvoecmt13xhw8lt02VT+xlaeAsHqXfz/pNG5vuwrT57h3XN92FaZPRBy2NGqV/69Ns4Bx+cOP6irsEYCbKKYd7LSB4TJf7jqy+OwByxXmn9SGLAZRFFteDHAZQVk453GtmAACUktOUKAAYVmQxADQrpxymGACgEvnEHgAML7IYAJqVUw5TDABQiZymRAHAsCKLAaBZOeUwxQAAlchpShQADCuyGACalVMOUwwAUIl8Yg8AhhdZDADNyimHKQYAqEROU6IAYFiRxQDQrJxyeCy1oe1vdLlvue2Vtleev/bG1EMAyEgk/ofBlM3iVuuB6ewWgIaQw9OPHAbQKacxcdeZAbafPNVdkvadql1ErJC0QpLO3vlI3mWAEZBTFTQ3VWTx7LlLyGJgBJDF9SCHAZSVUw73Ok3gCknfUTvoJlpUeW8AZKuJxVJsv1/SiyWtl/QzSUdHxD3T3pH6kcUASslp4arMkMMASskph3sVA66X9OcR8dOJd9heVU+XAKC0CyS9PSI22j5F0tslva3hPtWBLAaAZpHDAIZOrzUD3tXlMX9dbVcA5CwSbwMdM+L8iNhYfHuZpKUD7nKmepfIYgAlTHcOj5B3iRwGUEITY+JUXWcGRMSZXe7evuK+AMhY6pQo28slLe/YtKI4x7Jffybpv5I6McORxQDKyml6ak7IYQBl5ZTDg1xa8CRJn6qqIwDylrpYSufiSpOx/W1JO09y1wkR8dXiMSdI2ijpjMRu5IwsBrBZTgtXDRFyGMBmOeVwr6sJXD3VXZIWV98dALmq65IoEXFgt/ttv1bSiyQ9PyLyKcX2gSwGUBaXCqwHOQygrJxyuNfMgMWSDpZ094TtlvS9WnoEIEtNVEFtHyLprZKeGxFrG+jCdCGLAZSS0ydSmSGHAZSSUw73KgacK2lBRFw58Q7bF5U5wC5jD/bfq8Jtd2yX3PbWr0125Zdydpib1ucztV/yMcfWP5Tc9r6Yl9z2GRvTnus1L0ufjR2R/rt59gBtU2t013xsXfIxj9u0bXLbrbwpue0N5yQ31bJ/S2vXUBX0XyXNk3SBbUm6LCL+oomO1GzgLB4lD25c33QXps1Ws+c23YVpM0q/10Hk9IlUZshhAKXklMO9FhA8pst9R1bfHQC5aqIKGhF7NXDYaUcWAygrp0+kckIOAygrpxweZAFBANisNZyn6wNAVshiAGhWTjlMMQBAJfKJPQAYXmQxADQrpxymGACgEjldUxUAhhVZDADNyimHKQYAqEROi6UAwLAiiwGgWTnlMMUAAJXIabEUABhWZDEANCunHKYYAKASOU2JAoBhRRYDQLNyyuGxbnfa3s72P9r+T9tHTrhvyquR215ue6XtlWevvbmqvgKYwSLxP/RWRRa3Wg/U31EAjSOH60EOAygrpzFx12KApE9JsqQvSzrC9pdtzyvue/pUjSJiRUQsi4hlL9lmz4q6CmAmayXeUMrAWTw2Nn86+gmgYeRwbchhAKXUOSa2fYjtG2zfaPv4Se5/s+3rbF9t+79tP6Lb/noVAx4VEcdHxNkRcZikH0r6H9s7lOwvgBEREUk3lEIWAyilrhyuegCaIXIYQCl1jYltz5J0qqRDJe0j6VW295nwsB9JWhYRT5R0pqR/6rbPXmsGzLM9FhGt4om91/YaSRdLWtCzxwCAKpDFABrTMQA9SNJqSVfYPicirut42PgAdK3tN6g9AP3j6e9tbchhAE3bX9KNEXGTJNn+gqTDJW3O4oi4sOPxl0k6qtsOe80M+Jqk53VuiIjTJR0naX3ZXgMYfi1F0g2lkMUASqkphzcPQCNivaTxAehmEXFhRKwtvr1M0tJKn1jzyGEApdQ4Jl4iaVXH96uLbVM5RtI3uu2w68yAiHjrFNu/aft93doCGC2cd1ofshhAWSlZbHu5pOUdm1ZExIqO7ycbgD6tyy57DkBzQw4DKCt1TFwii/vZ11GSlkl6brfHDXJpwZPUXkwFAFiRujlkMYDNUrK4GGwmDTgnKjsAHTLkMIDNUsfEJbJ4jaTdOr5fWmzbgu0DJZ0g6bkR8VC3Y3YtBti+eqq7JC3u1hbAaGHKf33IYgBl1ZTFlQ9Ac0MOAyirxjHxFZL2tr2n2hl8hKSJlzrdT9J/SDokIu7otcNeMwMWSzpY0t0TtlvS90p2GsAI4MoAtSKLAZRSUxZXPgDNEDkMoJS6xsQRsdH2sZK+JWmWpE9GxLW23y1pZUScI+n9ai9q+iXbkvSL4gook+pVDDhX0oKIuHLiHbYvSnoWfYhwcttBzl9uJR53nps5a3pTq9c6kFOz016s6zfNSj7mIOaMpf+MNyX+XlPbSdKcAV4Tswd5rgO8JlKxZkCtGs1izFwL5m7VdBemzYbWxqa7kIU6sriOAWiGyGEApdQ5Jo6I8ySdN2HbiR1fH9jP/notIHhMl/uOnOo+AKOHNQPqQxYDKKuuLK56AJobchhAWTmNiQdZQBAANmPNAABoHlkMAM3KKYcpBgCoBGsGAEDzyGIAaFZOOUwxAEAlcqqCAsCwIosBoFk55TDFAACVyOn8KAAYVmQxADQrpxymGACgEq0GpkTZfo+kw9VeuPUOSa+NiFunvSMAMEM0kcUAgN/KKYen//pjAIZSJN4G9P6IeGJE7Kv2ZZ9O7PF4ABhqDeQwAKBDQ2PiJF2LAbZ3tv3vtk+1vYPtd9n+se0v2t6lS7vltlfaXnn22pur7zWAGaelSLoNIiLu7fh2voZ0XFtFFrdaD0xnlwE0ZLpzeFSQwwDKamJMnKrXzIDTJV0naZWkCyWtk/RCSZdI+thUjSJiRUQsi4hlL9lmz4q6CmAmayr4bL/X9ipJr9bwzgw4XQNm8djY/OnoJ4CG5TIAzdDpIocBlDBMxYDFEfEvEXGypEURcUpErIqIf5H0iGnoH4BMRETSrfNTk+K2vHO/tr9t+5pJbocXxz0hInaTdIakY5t47tOALAZQSkoOoxRyGEApqWPiJvRaQLCzWPCZCffNqrgvAEZQRKyQtKLL/QeW3NUZks6T9M4q+jXDkMUA0CxyGMDQ6VUM+KrtBRFxf0T8/fhG23tJuqHergHISRPTm2zvHRE/Lb49XNJPpr0T04MsBlAK0/5rQw4DKCWnHO5aDIiISc+/jYgbbX+9ni4ByFFD11Q92fZj1L604M8l/UUTnagbWQygrJyub50TchhAWTnlcK+ZAd2cJOlTVXUEQN6aONcpIl4+7QedechiAJuxBkAjyGEAm+WUw12LAbavnuouSYur7w6AXOU0JSo3ZDGAssjiepDDAMrKKYd7zQxYLOlgSXdP2G5J36ulRwCylFMVNENkMYBSyOLakMMASskph3sVA86VtCAirpx4h+2Lyhxgl11/03+vCvfetXVy21bLyW0f+Ue9rrg4ufecOSf5mD+NB5LbfmLZvcltv37p0qR2hzxpVfIxZy1I/92MLUg/s+XqbyxKave4Z9yZfMw3/HD75LZPjwXJbY9+5prktqlyqoJmaOAsHiXz52zVdBemzeyx0VnEfFOr1XQXskAW14YcBlBKTjncawHBY7rcd2T13QGQq5wWS8kNWQygLLK4HuQwgLJyyuFBFhAEgM1aGU2JAoBhRRYDQLNyymGKAQAqkVMVFACGFVkMAM3KKYcpBgCoRE5VUAAYVmQxADQrpxymGACgEjlVQQFgWJHFANCsnHKYYgCASuRUBQWAYUUWA0CzcsrhvosBth8eEXfU0RkA+cqpCjoMyGIAkyGLpw85DGAyOeVw12KA7YdN3CTp+7b3k+SIuGuKdsslLZekf9z9MTpypyVV9BXADJZTFTQ3VWSxZy3U2Nj8ejsKoHFkcT3IYQBl5ZTDvWYG/ErSzydsWyLph5JC0iMnaxQRKyStkKRfLHt+Pj8NAMlyqoJmaOAsnj13Cb8gYASQxbUhhwGUklMO9yoGvEXSQZLeEhE/liTbN0fEnrX3DEBWIlpNd2GYkcUASiGLa0MOAyglpxwe63ZnRHxQ0usknWj7Q7a3lTIqdQDAECCLAaBZ5DCAYdRzAcGIWC3pFbYPk3SBpG1q7xWA7LQYE9WKLAZQBllcH3IYQBk55XDXmQGdIuIcSX8g6UBJsn10XZ0CkJ+ISLqhP2QxgG7I4fqRwwC6yWlMXLoYIEkRsS4irim+PamG/gDIVEuRdEP/yGIAUyGHpwc5DGAqOY2Je11a8Oqp7pK0uPruAMgVny7VhywGUBZZXA9yGEBZOeVwrzUDFks6WNLdE7Zb0vdq6RGALOV0TdUMkcUASiGLa0MOAyglpxzuVQw4V9KCiLhy4h22LypzgF/etl3/vSqs3zQrue0gbvzS+qR2f/jQxuRjtmJectufXb4oue2erQfTjnnVDsnHHMScWZvS246lXebjp5enP9flm+Ykt916bF1y2xsvWZjcdlliu5yuqZqhgbN4lKzbmJbhOVo0b37TXZg2broDmSCLa0MOAyglpxzuWgyIiGO63Hdk9d0BkKucpkTlhiwGUBZZXA9yGEBZOeVwXwsIAsBUmlwsxfZxtsP2jpXsEAAylcuiVQAwrIZmAUEAKKupKqjt3SS9QNIvGukAAMwgOX0iBQDDKKccphgAoBINLpbyYUlvlfTVpjoAADNFTgtXAcAwyimHKQYAqEQTVVDbh0taExFX2SwvBgA5fSIFAMMopxymGACgEqnnOtleLml5x6YVEbGi4/5vS9p5kqYnSHqH2qcIAACUnsUAgGrklMMUAwBUIrUKWvzhv6LL/QdOtt32EyTtKWl8VsBSST+0vX9E3J7UGQDIXE6fSAHAMMoph7teTcD2IR1fL7T9CdtX2/6c7cVd2i23vdL2yrMeuKXC7gKYqVoRSbdUEfHjiHh4ROwREXtIWi3pycNYCKgii1utB6answAaNZ05PErIYQBlTfeYeBC9Li34vo6vPyjpNkkvlnSFpP+YqlFErIiIZRGx7KXz9xi4kwBmvkj8D6UMnMVjY/Nr7iKAmYAcrg05DKCUnMbE/ZwmsCwi9i2+/rDt19TQHwCZavrTpWJ2wCggiwFMqeksHhHkMIAp5ZTDvYoBD7f9ZkmWtJ1tx29Pgug1qwDACMnp/KgMkcUASiGLa0MOAyglpxzuFV4fl7StpAWSPi1pR0myvbOkK2vtGQBgHFkMAM0ihwEMna4zAyLipCm23277wnq6BCBHnHdaH7IYQFlkcT3IYQBl5ZTDg0xrmjQUAYymiEi6YWBkMYDNyOFGkMMANstpTNx1ZoDtq6e6S9KUl1EBMHoYUNaHLAZQFllcD3IYQFk55XCvBQQXSzpY0t0TtlvS92rpEYAs5RN7WSKLAZRCFteGHAZQSk453KsYcK6kBRFx5cQ7bF9U5gBPXXOWu91ve3lErCizryra5dg2t/421Ta3/g7Stqn+drNx/Zqu/9YxkIGzuInfT12vtZmI5zqccnyuZHFtssxh1C/HnEC9cvq37qanMdheGRHLpqtdjm1z629TbXPr7yBtm+ov0I9Req3xXIfTKD1XAGnICeSM66ICAAAAADBiKAYAAAAAADBiZkIxIPUcm0HOzcmtbW79baptbv0dpG1T/QX6MUqvNZ7rcBql5wogDTmBbDW+ZgAAAAAAAJheM2FmAAAAAAAAmEaNFQNsH2L7Bts32j6+j3aftH2H7WsSjrmb7QttX2f7Wttv7KPtVra/b/uqou1JfR57lu0f2T63z3a32P6x7Sttr+yz7SLbZ9r+ie3rbT+jZLvHFMcbv91r+00l2/5t8fO5xvbnbW/VR3/fWLS7ttfxJnsd2H6Y7Qts/7T4//Z9tH1FcdyW7SlXhJ2i7fuLn/HVts+yvahku/cUba60fb7tXcses+O+42yH7R376O+7bK/p+P2+cKrnC6RKzfjcDPKelJtB3kNzM+h7PoDRMCrvdRhejRQDbM+SdKqkQyXtI+lVtvcp2fx0SYckHnqjpOMiYh9JT5f0V30c9yFJz4uIJ0naV9Ihtp/ex7HfKOn6fjrb4Q8iYt+Ey5Z8VNI3I+Kxkp5U9vgRcUNxvH0lPUXSWkln9Wpne4mkv5G0LCIeL2mWpCPKHNP24yW9XtL+RV9fZHuvLk1O1+++Do6X9N8Rsbek/y6+L9v2Gkkvk3Rxj65O1vYCSY+PiCdK+j9Jby/Z7v0R8cTi53yupBP7OKZs7ybpBZJ+0Wd/JenD47/jiDivS3ugbwNmfG5OV/p7Um4GeQ/NzaDv+QCG3Ii912FINTUzYH9JN0bETRGxXtIXJB1epmFEXCzprpSDRsRtEfHD4uv71P7jeEnJthER9xffzilupRZcsL1U0h9KOq3vTieyvVDScyR9QpIiYn1E3JOwq+dL+llE/Lzk42dL2tr2bEnbSLq1ZLvfk3R5RKyNiI2SvqP2H+eTmuJ1cLikTxdff1rSS8q2jYjrI+KGXp2cou35RZ8l6TJJS0u2u7fj2/ma4vXU5TX/YUlvnapdj7ZAnZIzPjej9G9skPfQ3Azyng9gZIzMex2GV1PFgCWSVnV8v1rTPKCwvYek/SRd3kebWbavlHSHpAsiomzbj6j9R1urv15Kag8+zrf9A9vL+2i3p6Q7JX2qOD3hNNvzE45/hKTPl+poxBpJH1D7k+rbJP0mIs4veZxrJD3b9g62t5H0Qkm79dnXxRFxW/H17ZIW99m+Cn8m6RtlH2z7vbZXSXq1pp4ZMFm7wyWtiYir+u+iJOnY4hSFT051OgUwgMYzHvVKeQ/NzQDv+QBGA+91yN5ILiBoe4GkL0t604RPZ7uKiE3FlO6lkvYvprb3OtaLJN0RET9I7O6zIuLJak9B+ivbzynZbrakJ0v694jYT9IDmnra/KRsz5V0mKQvlXz89mpXRPeUtKuk+baPKtM2Iq6XdIqk8yV9U9KVkjb1098J+wtN86c4tk9QexrtGWXbRMQJEbFb0ebYksfZRtI71EfxYIJ/l/Qotae+3ibpg4n7ATCCUt9Dc5Pyng8AQE6aKgas0Zaf+i4tttXO9hy1BzFnRMRXUvZRTLe/UOXOE32mpMNs36L29KHn2f5sH8daU/z/DrXP29+/ZNPVklZ3fJJxptrFgX4cKumHEfHLko8/UNLNEXFnRGyQ9BVJv1/2YBHxiYh4SkQ8R9Ldap9/349f2t5Fkor/39Fn+2S2XyvpRZJeHWnX6zxD0stLPvZRahdcripeV0sl/dD2zmUaR8Qvi0FuS9LHVf41BZTVWMajXlW8h+amz/d8AKOD9zpkr6liwBWS9ra9Z/Hp8xGSzqn7oLat9jn010fEh/psu9P4KvG2t5Z0kKSf9GoXEW+PiKURsYfaz/N/IqLUp+W259vedvxrtReLK7VidUTcLmmV7ccUm54v6boybTu8SiVPESj8QtLTbW9T/Kyfrz4WTbT98OL/u6u9XsDn+ji21H4Nvab4+jWSvtpn+yS2D1H7NJDDImJtH+327vj2cJV4PUlSRPw4Ih4eEXsUr6vVkp5c/M7LHHeXjm9fqpKvKaAPjWQ86jXIe2huUt/zAYwU3uuQvdlNHDQiNto+VtK31F5x/pMRcW2ZtrY/L+kASTvaXi3pnRHxiZKHfqakP5H04+I8QEl6R8nV1HeR9Oli5dAxSV+MiL4uE5hgsaSz2uMvzZb0uYj4Zh/t/1rSGUVA3STp6LINi+LDQZL+vGybiLjc9pmSfqj2dPkfSVrRR3+/bHsHSRsk/VW3BQ8nex1IOlnSF20fI+nnkl7ZR9u7JP2LpJ0kfd32lRFxcMm2b5c0T9IFxe/qsoj4ixLtXlgUa1pFf7do061t2df8FMc9wPa+ap9GcYv6+B0DZQyS8bkZ8D0pN4O8h+amifd8ABkZpfc6DC+nzWgGAAAAAAC5GskFBAEAAAAAGGUUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDH/H7OmumawDDTIAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 8\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 10\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 11\n" + ] + }, + { + "data": { + "image/png": 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0PCKWF/cdKumuiPiR7QP7LREABl5Gn0gBwEDKKIdpBgCoROpllos//JdPcvezJB1m+6WS5krayvYXIuKotCoBYLBxyXsAaFZOOUwzAEA1algSFRHHSTpOkoqVAX9LIwAAOshoeSoADKSMcphmAIBqZLQkCgAGFlkMAM3KKIdpBgCoRs1d0Ii4UNKFte4EAHKX0SdSADCQMsphmgEAqjFEZ3AHgGmLLAaAZmWUw8nXPbP95ioLAZC5GE27oS9kMYDNkMNTjhwGsJmM5sTpF0GXTprsDtvLbK+wveLra3/Rxy4AZGN0NO2GfpXK4o0bH5zKmgA0hRxuQqkcHh1dM5U1AWhKRnPijocJ2L5msrskLZpsXPulwi7Z+VWRXB0AoJIsnjt3V7IYABJVkcMzZy8mhwFMK93OGbBI0sGS7h233ZIuqaUiAHliqWmdyGIA5ZDFdSGHAZSTUQ53awacI2lBRFw1/g7bF9ZREIBMsdS0TmQxgHLI4rqQwwDKySiHOzYDIuLoDvcdWX05ALKVUfDlhiwGUBpZXAtyGEBpGeUwlxYEUImIfC6jAgCDiiwGgGbllMM0AwBUI6MuKAAMLLIYAJqVUQ7TDABQjYxOlgIAA4ssBoBmZZTDNAMAVCOjLigADCyyGACalVEO194MWDBnXfLYOx+alzw25OSxj9vl7qRxD6zcPnmf/dS71fyHk8f++rdpP+OHNqT/6sweSf8PZJutHkoeu2bN7KRxD/fxWu/3jOSxO89/MHnsho0jyWOTZdQFHUYbR/M5fg3lPXrhzk2XMGVuvHdV0yXkgSwGgGZllMOsDABQjYy6oAAwsMhiAGhWRjlMMwBANTLqggLAwCKLAaBZGeUwzQAA1cioCwoAA4ssBoBmZZTDNAMAVCOj4AOAgUUWA0CzMsphmgEAqpHRkigAGFhkMQA0K6Mc7nrKcduPs/1C2wvGbT+kvrIAZGd0NO2GrshhAKWRw7UhiwGUktGcuGMzwPZfS/q6pL+SdK3tw9vu/sc6CwOQmRhNu6EjchhAT8jhWpDFAErLaE7c7TCBt0h6akQ8aHt3SWfY3j0iPi7Jkw2yvUzSMkl637ZP0qu33K2qegFMV3y6VJekHJY2z+KRGQs1MjK/9mIBNIwsrkvfc2KTw8BwyCiHuzUDRiLiQUmKiF/aPlCt8NtNHYIvIpZLWi5J1+z+8qimVAAYSkk5XDx+UxbPmr2YLAaAdH3PiWeSwwCmmW7nDLjT9n5j3xQheKik7SU9qca6AOQmoyVRmSGHAZRHDteFLAZQTkZz4m4rA/5Y0ob2DRGxQdIf2/7v2qoCkJ+alkTZnivpIklz1MqsMyLi/bXsbHoihwGUl9Hy1MyQxQDKySiHOzYDImJVh/t+UH05ALJVX/A9IukFxXGasyR93/Y3I+KyunY4nZDDAHqS0SQ0J2QxgNIyyuGulxYEgFIi0m5dnzZi7DhNSbOKG8ddAsBEashhAEAPapoTS61Lmdq+0fZNto+d4P5dbV9g+8e2r7H90k7P1+0wAQAop8YuqO0Zkn4kaS9Jn4iIy2vbGQDkLKNPpABgINV36OwMSZ+Q9CJJqyRdYfvsiLi+7WF/J+n0iPhP2/tIOlfS7pM9J80AANVIDL72yy4VlhdnX94kIjZK2s/21pLOtP3EiLg2tVQAGFg0AwCgWfXl8AGSboqIWyTJ9pclHS6pvRkQkrYqvl4o6bZOT0gzAEA1Es+C2n7ZpRKPvc/2BZIOkUQzAADG4+oAANCs+nJ4saSVbd+vkvT0cY85UdJ5tv9K0nxJB3V6wtqbAR/TnOSxfzHrkeSxez7t3uSxp63YJWnc3pufZLYns/s4BPrr67ZNHrtuTtp+X77pEO7e7f2G9F+7W09PP83F4z+4T9K4Fe/+efI+ly65M3nsXXdsmTz25o3zk8c+OXVgfUuidpC0vmgEbKHW0qgP1bKzAcZRwYPpxnsnPacZhlV9WXyIpI9LmiHpUxFx8rj7d5X0WUlbF485NiLOraUYAJjOalwtW8LrJZ0aER+x/QeSPl+sqJ2wKFYGAKhGfSeh2lnSZ4vjpEbUOg7qnLp2BgBZqyGL6zhOFQAGVmIOl1gtu1pS+6fWS4pt7Y5WawWtIuLS4hLd20u6a6InpBkAoBo1fRoVEddI2r+WJweAQVNPFld+nCoADKz6zhlwhaS9be+hVhPgCElHjnvMrZJeKOlU24+XNFfS3ZM9Ic0AANXgpFUA0LyELC6xNLXy41QBYGDV9wHZBtvHSPq2WodjfSYirrP9AUkrIuJsSe+U9Enbf6NWk/ZNEZMvVaAZAKAanLQKAJqXkMW9nMi1g56OUwWAgVVj7BXnYjl33LYT2r6+XtKzyj4fzQAAlYhRTlEHAE2rKYsrP04VAAZVTnPirs0A2wdIioi4ojghzCGSfsoZYgFshsMEakMOAyitniyu/DjVHJHFAErJaE7csRlg+/2SXiJppu3z1To+7AJJx9rePyI+OAU1AsgBK0FrQQ4D6EkNWVzHcaq5IYsBlJbRnLjbyoBXS9pP0hxJd0haEhEP2P5nSZdLmjD42k9E88xt99djt9yzsoIBTFMZLYnKTFIOS5tnsWcs1MjI/PqrBdCsmrK46uNUM9T3nJgcBoZERnPikS73b4iIjRGxVtLNEfGAJEXEQ5ImbXlExPKIWBoRS2kEAEBfknK4eMymLGYCCgB96XtOTA4DmG66rQxYZ3teEXxPHdtoe6G6TEIBDJmMjo/KDDkMoDyyuC5kMYByMsrhbs2A50bEI5I07tIwsyS9sbaqAOQno+DLDDkMoDyyuC5kMYByMsrhjs2AsdCbYPuvJf26looA5GlwzhM1rZDDAHpCFteCLAZQWkY53PXSggBQSkZdUAAYWGQxADQroxymGQCgGhmdORUABhZZDADNyiiHaQYAqEZG11QFgIFFFgNAszLKYZoBAKqRURcUAAYWWQwAzcooh2tvBuys2cljH/vKh5PHzthnv+Sxl155a9K4WbPTrx/7/Pn3J4/dYu3c5LFnbVidNO5vDkvf5/qfpb/W69YuTh776P0OTBr3pOddlrzPb1ycXu96Jw/V83e+I31wosjo+KhhtHjL7ZouYcrcuea+pkuYMhtHNzZdwpTZfeFOTZeQBbIYAJqVUw6zMgBANTLqggLAwCKLAaBZGeUwzQAA1cjo+CgAGFhkMQA0K6McphkAoBoZdUEBYGCRxQDQrIxymGYAgGpkdHwUAAwsshgAmpVRDtMMAFCNjLqgADCwyGIAaFZGOTzS6wDbn6ujEACZi9G0G3pGDgOYFDk8ZchiABPKaE7ccWWA7bPHb5L0fNtbS1JEHFZTXQByU1MX1PYukj4naZGkkLQ8Ij5ey86mIXIYQE8y+kQqJ2QxgNIyyuFuhwkskXS9pE+pNQm3pKWSPtJpkO1lkpZJ0ku2fZr233Kv/isFMK3VeE3VDZLeGRFX2t5S0o9snx8R19e1w2kmKYelzbN423mLtWDutjWWCWA6yOn61pnpe07sGQs1MjK/5jIBNC2nHO52mMBSST+SdLyk+yPiQkkPRcT3IuJ7kw2KiOURsTQiltIIANCPiLg9Iq4svv6tpBskLW62qimVlMPS5llMIwAA+tL3nJhGAIDppuPKgIgYlfRR2/9T/P+d3cYAGFJTsCTK9u6S9pd0ee07mybIYQA9yWh5ak7IYgClZZTDpUIsIlZJeo3tl0l6oN6SAGQpMfjal1AWlkfE8gket0DSVyW9PSKGLofIYQClZDQJzRFZDKCrjHK4p45mRHxD0jdqqgVAzhLPglr84f97f/y3sz1LrUbAaRHxtaQdDQhyGEBHXB1gSpDFACaVUQ6zvAlANeq7moAlfVrSDRHxL7XsBAAGRUafSAHAQMooh2kGAKhE1Bd8z5L0R5J+YvuqYtt7I+LcunYIALmqMYsBACXklMM0AwBUo6bgi4jvq3UJJwBANxlNQgFgIGWUwzQDAFQjo2uqAsDAIosBoFkZ5XDtzYCXPrwueew1p89NHjtjZGXy2Lclfgi5bvTh5H3+5v55yWOf7rXJY58yun3SuJ98Pb3jNcPpr3VXP5Q89rqX/UfSuHUbt0ne5659/E7MGkkPktW3L0weu1vqwIy6oMNo9W9/03QJqMEwLZn5xf13NF1CHshiAGhWRjnMygAA1cgo+ABgYJHFANCsjHKYZgCASkTkE3wAMKjIYgBoVk45TDMAQDUy6oICwMAiiwGgWRnlMM0AANXIKPgAYGCRxQDQrIxymGYAgErkdE1VABhUZDEANCunHO6pGWD72ZIOkHRtRJxXT0kAspRR8OWOLAYwKbJ4SpDDACaVUQ6PdLrT9g/bvn6LpH+XtKWk99s+tubaAORkNPGGrshiAKWRw7UghwGUltGcuGMzQNKstq+XSXpRRJwk6cWS3jDZINvLbK+wveLra2+poEwA012MRtINpfSdxaOja+quEcA0QA7XhhwGUEpOc+JuhwmM2N5GraaBI+JuSYqINbY3TDYoIpZLWi5JP9jp1bzLAMOACWWd+s7imbMX8w8EDAOyuC7kMIByMsrhbs2AhZJ+JMmSwvbOEXG77QXFNgBA/chiAGgWOQxg4HRsBkTE7pPcNSrpDyuvBkC+OO60NmQxgNLI4lqQwwBKyyiHky4tGBFrJf2i4loAZIzjTqceWQxgPLJ4apHDAMbLKYeTmgEA8Hsy6oICwMAiiwGgWRnlMM0AAJXIqQsKAIOKLAaAZuWUwzQDAFQjoy4oAAwsshgAmpVRDtMMAFCJyCj4AGBQkcUA0Kyccrj2ZsDdmp089qAjH0zf8Uj6VV7+6YwFSeNOe/CG5H2eNHuf5LGP9ZrksV+cOyNp3D9f+K7kfa4/7V+Tx773k+uSxx7+UNp/mfs/+67kff7Fiq2Tx9664YHksef95ZLksckyCr5hNHMk7b/1HC2YPbfpEqbMA4+sbbqEKbPjvIVNl5AHshgAmlVjDts+RNLHJc2Q9KmIOHmCx7xW0omSQtLVEXHkZM/HygAAlcipCwoAg4osBoBm1ZXDtmdI+oSkF0laJekK22dHxPVtj9lb0nGSnhUR99resdNz0gwAUA0moADQPLIYAJpVXw4fIOmmiLhFkmx/WdLhkq5ve8xbJH0iIu6VpIjouMR5pKZCAQyZGE27AQCqU1cO2z7E9o22b7J97CSPea3t621fZ/uLVb4uAMhFjXPixZJWtn2/qtjW7jGSHmP7B7YvKw4rmBQrAwBUosYlUZ+RdKikuyLiifXsBQAGQx1ZXMfSVAAYVKk5bHuZpGVtm5ZHxPIen2ampL0lHShpiaSLbD8pIu6b7MEA0LcaP+U/VdK/S/pcbXsAgAFRUxZXvjQVAAZVag4Xf/h3+uN/taRd2r5fUmxrt0rS5RGxXtIvbP9MrebAFRM9YcfDBGw/3fZWxddb2D7J9v/a/pBtTusL4HfCabduTxtxkaR76n8B0xM5DKAnNeSwaliamhuyGEBpNc2J1fqDfm/be9ieLekISWePe8xZaq0KkO3t1crmWyZ7wm7nDPiMpLHrFn1c0kJJHyq2nVKmYgDDIfX4KNvLbK9ouy3rvrehQg4DKK3BHG5fmvp6SZ+0vXWFL61pZDGAUuo6Z0BEbJB0jKRvS7pB0ukRcZ3tD9g+rHjYtyX9xvb1ki6Q9K6I+M1kz9ntMIGRYqeStDQinlJ8/X3bV002qP14hz/f8ml68by9uuwGQO5itFRH8/fHdV8SNeyScljaPItnztxGM2YsqK9KANNCShY3sTQ1Q33PiT1joUZG5tdbJYDGpc6JSz13xLmSzh237YS2r0PSO4pbV91WBlxr+83F11fbXipJth8jaX2HIpdHxNKIWEojABgOXE2gNkk5LG2exTQCgOFQUw5XvjQ1Q33PiWkEAMMhpzlxt2bAn0p6nu2bJe0j6VLbt0j6ZHEfAKBe5DCARtWxNDVDZDGAgdPxMIGIuF/Sm4oTpuxRPH5VRNw5FcUByEeUO/FJz2x/Sa1Pm7a3vUrS+yPi07XsbBoihwH0oq4srnppam7IYgBl1ZXDdSh1acGIeEDS1TXXAiBjdS1viojX1/PMeSGHAZTB4Vf1IosBdJNTDpdqBgBAN3WeLAUAUA5ZDADNyimHaQYAqERE0xUAAMhiAGhWTjlMMwBAJXLqggLAoCKLAaBZOeUwzQAAlcgp+ABgUJHFANCsnHK49mbAp2bfnzx2r7PSf5B7/vmOyWNXKq3m983ZJ3mfz9/1tuSxn7vtUcljFyWuY/nZC05M3ueex+ycPPZ5D29IHvvsFccljfv1696evM//fGb67/8l312UPPbtn3woeewnj08bl9OSqGG0YXRj0yVMmfseXtN0CajBnWvua7qELJDFANCsnHKYlQEAKpFTFxQABhVZDADNyimHaQYAqERO11QFgEFFFgNAs3LKYZoBACqR0zVVAWBQkcUA0KyccphmAIBKjGbUBQWAQUUWA0CzcsphmgEAKpHTkigAGFRkMQA0K6ccHul0p+2/tr3LVBUDIF8x6qQbuiOLAZRFDteDHAZQVk5z4o7NAEl/L+ly2xfb/gvbO0xFUQDyE5F2QylkMYBSyOHakMMASslpTtytGXCLpCVqBeBTJV1v+1u232h7y8kG2V5me4XtFbc+eGuF5QKYrnLqgmao7yweHV0zVbUCaBA5XBtyGEApOc2JuzUDIiJGI+K8iDha0qMk/YekQ9QKxckGLY+IpRGxdNcFu1ZYLoDpajScdEMpfWfxyMj8qaoVQIPI4dqQwwBKyWlO3O0EgptVFRHrJZ0t6Wzb82qrCgDQjiwGgGaRwwAGTrdmwOsmuyMi1lZcC4CM5XTm1AyRxQBKIYtrQw4DKCWnHO7YDIiIn01VIQDyxkmo6kMWAyiLLK4HOQygrJxyuNvKAAAoheNOAaB5ZDEANCunHKYZAKASOS2JAoBBRRYDQLNyymGaAQAqkdOSKAAYVGQxADQrpxymGQCgEjktiQKAQUUWA0Czcsrh2psBpz7hweSx3//x4uSxN33k4eSx/370SNK4M09J3qW+tPpRyWOXPWll8tjLVqTtd+bC0eR93vixu5LHvvDg9N+n6551QtK4uXO2SN7np1dvkzz20Dnpr/UDSx5IHpuqziVRtg+R9HFJMyR9KiJOrm1nA2rR/K2bLmHK3LnmvqZLmDIfXfT8pkuYMu+6+6KmS8hCTstTAWAQ5ZTDrAwAUIm6uqC2Z0j6hKQXSVol6QrbZ0fE9bXsEAAyltMnUgAwiHLKYZoBACpR4+FRB0i6KSJukSTbX5Z0uCSaAQAwTkaHqgLAQMoph2kGAKhEjV3QxZLaj4VZJenpde0MAHKW0ydSADCIcsphmgEAKpF6fJTtZZKWtW1aHhHLKykKAIZMTseqAsAgyimHaQYAqETqKSWLP/w7/fG/WtIubd8vKbYBAMZJP70vAKAKOeVwx2aA7dmSjpB0W0R8x/aRkp4p6Qa1Pr1bPwU1AshAqLYu6BWS9ra9h1pNgCMkHVnXzqYbchhAL2rM4qFGFgMoK6cc7rYy4JTiMfNsv1HSAklfk/RCtU7q9cZ6ywOQi9GazpYSERtsHyPp22pdWvAzEXFdPXublshhAKXVlcUgiwGUk1MOd2sGPCkinmx7plqfyD0qIjba/oKkqycb1H4M8EeesLf+eJedKysYwPQ0WmMXNCLOlXRubTuY3pJyWNo8ixdusbPmz9mm/moBNKrOLB5yfc+JPWOhRkbmT021ABqTUw6PdLu/WBa1paR5khYW2+dImjXZoIhYHhFLI2IpjQBgOIScdENXSTksbZ7FNAKA4UAO16bvOTGNAGA45DQn7rYy4NOSfqrW0tzjJf2P7VskPUPSl2uuDQBADgPAdEAWAxg4HZsBEfFR218pvr7N9uckHSTpkxHxw6koEEAecjpzak7IYQC9IIvrQRYDKCunHO56acGIuK3t6/sknVFnQQDyxFLT+pDDAMoii+tDFgMoI6cc7toMAIAycuqCAsCgIosBoFk55TDNAACVyCn4AGBQkcUA0KyccphmAIBK5LQkCgAGFVkMAM3KKYdpBgCoxGg+uQcAA4ssBoBm5ZTDtTcDbr56u+Sx22ld8tiR5JHSzz6/PmncbqORvM9d16X/1txy1bbJY7dy2mu978G5yfvsx83fXZA8duNo2m/FvQ9ukbzP54ym/w6vmzEjeeytt6RfU37nxHGjGXVBh9F9j6xpugTU4KtxR9MlTJmNoxubLiELZDEANCunHGZlAIBKpLfCAABVIYsBoFk55TDNAACVyOlkKQAwqMhiAGhWTjlMMwBAJUadz5IoABhUZDEANCunHKYZAKASOS2JAoBBRRYDQLNyyuF+zrMHAJuMJt4AANUhhwGgWXXOiW0fYvtG2zfZPrbD415lO2wv7fR8XVcG2N5T0isl7SJpo6SfSfpiRDxQsmYAQyCny6jkhhwGUFZdWWz7EEkflzRD0qci4uRJHvcqSWdIelpErKinmmaQxQDKqDGHZ0j6hKQXSVol6QrbZ0fE9eMet6Wkt0m6vNtzdlwZYPuvJf2XpLmSniZpjloBeJntA3t/CQAG1aicdENn5DCAXtSRw20T0JdI2kfS623vM8HjSk9Ac0MWAyirxjnxAZJuiohbImKdpC9LOnyCx/29pA9JerjbE3Y7TOAtkl4SEf8g6SBJT4iI4yUdIumjkw2yvcz2Ctsrzlr7i241ABgAkXhDV0k5LG2exRs2/HYKSgXQtJpyuPIJaIb6nhOPjq6ZolIBNCl1TtyeF8Vt2binXixpZdv3q4ptm9h+iqRdIuIbZWotcwLBmWothZojaYEkRcSttmdNNiAilktaLkmXP+qVzPeBIcBhArXqOYeLx2zK4vnzdieLgSFQUxZPNAF9evsD2iegtt9VSxXN62tOPHP2YnIYGAKpOdyeFylsj0j6F0lvKjumWzPgU2odi3C5pOeo1e2V7R0k3ZNWJgCgB+QwgFoVnz61fwK1vJiUlh3f8wQ0Q2QxgKatVuvwpDFLim1jtpT0REkXunV5w50knW37sMnO4dKxGRARH7f9HUmPl/SRiPhpsf1uSc9NfRUABg9npK4HOQygFylZXOLTqMonoLkhiwGUVeOc+ApJe9veQ60MPkLSkWN3RsT9krYf+972hZL+tlMOdz1MICKuk3Rdes0AhgFrH+tDDgMoq6YsrnwCmiOyGEAZdc2JI2KD7WMkfVutK7t8JiKus/0BSSsi4uxen7PMOQMAoKsmzhlg+zWSTlTrk5oDBm3iCQC9qiOL65iAAsCgqnNOHBHnSjp33LYTJnnsgd2ej2YAgEo0dJjAtWpd8/m/m9k9AEwvdWVx1RNQABhUOR06SzMAQCWaCL6IuEGSimNUAWDo5TQJBYBBlFMO0wwAUIng73EAaBxZDADNyimHa28GPDI6I3ns7JGNyWNnOP3UDQ+tT/uxhNL/5d3HqSbWbUz/Ga+LtLGz++h5zZqR/u+6cXQkeeycWRuSxq1/ZHbyPvv5PdzYR5L0s99Uqb8R3S5pVZy9eacJhh4fEV9P3O3Q2XL2Fk2XMGXWb0z7bz1Hl/76xqZLmDIzZ/D5RRk5fSIFAIMopxzmnRVAJVKDr9slrSLioMSnBoChk9MkFAAGUU45TDMAQCW4tCAANI8sBoBm5ZTDNAMAVKKhSwv+oaR/k7SDpG/YvioiDp76SgBgemgiiwEAv5NTDtMMAFCJhq4mcKakMxvYNQBMSzktTwWAQZRTDtMMAFCJnIIPAAYVWQwAzcoph2kGAKhETsdHAcCgIosBoFk55TDNAACVyOn4KAAYVGQxADQrpxzueNF22wttn2z7p7bvsf0b2zcU27buMG6Z7RW2V5y99pbKiwYw/Ywm3tBdFVm8dt19U1cwgMaQw/WoIodHR9dMYcUAmpLTnLhjM0DS6ZLulXRgRGwbEdtJen6x7fTJBkXE8ohYGhFLD5u3Z3XVApi2IvGGUvrO4nmzt56aSgE0ihyuTd85PDIyf4pKBdCknObE3ZoBu0fEhyLijrENEXFHRHxI0m71lgYgJ6OKpBtKIYsBlEIO14YcBlBKTnPibs2AX9l+t+1FYxtsL7L9Hkkr6y0NAFAgiwGgWeQwgIHTrRnwOknbSfpecXzUPZIulLStpNfUXBuAjOR0fFSGyGIApZDDtSGHAZSS05y449UEIuJeSe8pbpux/WZJp9RUF4DMsNC0PmQxgLLI4nqQwwDKyimHu60M6OSkyqoAkL2cuqADhiwGsAk53AhyGMAmOc2JO64MsH3NZHdJWjTJfQCGUE7XVM0NWQygLLK4HuQwgLJyyuGOzQC1wu1gtS6b0s6SLqmlIgBZ4ozUtSKLAZRCFteGHAZQSk453K0ZcI6kBRFx1fg7bF9YZgf3aFbvVRX23eG+5LHbHZDekrns7G2Txr3h4R8n7/P9Wz0teexBW9yTPPasR9Je69u/8srkfcaN6T+nT7xvVfLY561fkzRu37fOTt7n6z7zQPLYLZ3+387ylz2SPDZVPrGXpb6z+Ddr038XczN7Zvp/O7lZt2F90yVMmVkzu01ZIJHFNeo7h2eOzKi4JADTUU453O0Egkd3uO/I6ssBkCuOO60PWQygLLK4HuQwgLJyymHa7AAqkdOSKAAYVGQxADQrpxymGQCgEvnEHgAMLrIYAJqVUw7TDABQiZyWRAHAoCKLAaBZOeUwzQAAlchpSRQADCqyGACalVMO0wwAUIl8Yg8ABhdZDADNyimHaQYAqEROS6IAYFCRxQDQrJxyeCR1oO1vdrhvme0Vtlect/am1F0AyEgk/g/9KZvFo6NrprIsAA0hh6de2RzeuPHBqSwLQENymhN3XBlg+ymT3SVpv8nGRcRyScsl6aydjuRdBhgCOXVBc1NFFs+avZgsBoYAWVyPKnJ47txdyWFgCOSUw90OE7hC0vfUCrrxtq68GgDZauJkKbY/LOnlktZJulnSmyPivikvpH5kMYBScjpxVWbIYQCl5JTD3ZoBN0h6a0T8fPwdtlfWUxIAlHa+pOMiYoPtD0k6TtJ7Gq6pDmQxADSLHAYwcLqdM+DEDo/5q2pLAZCzSLz1tc+I8yJiQ/HtZZKW9PmU09WJIosBlDDVOTxEThQ5DKCEJubEqTquDIiIMzrcvU3FtQDIWOqSKNvLJC1r27S8OMayV38i6StJRUxzZDGAsnJanpoTchhAWTnlcD+XFjxJ0ilVFQIgb6knS2k/udJEbH9H0k4T3HV8RHy9eMzxkjZIOi2xjJyRxQA2yenEVQOEHAawSU453O1qAtdMdpekRdWXAyBXdV0SJSIO6nS/7TdJOlTSCyMin1ZsD8hiAGVxqcB6kMMAysoph7utDFgk6WBJ947bbkmX1FIRgCw10QW1fYikd0t6XkSsbaCEqUIWAyglp0+kMkMOAyglpxzu1gw4R9KCiLhq/B22Lyyzg51GHu69qsLtd22VPvac5KHadlZazV/Rvsn7HFn3SPLY347OSR77rI1pr/XaV38peZ/9eM5ot3NeTm50wqsBdXft8vR/m7/ZuCB57FxvTB5749nJQ7X0P9LGNdQF/XdJcySdb1uSLouIP2uikJr1ncXzZs+tuKTpa+269Ped3CyYvUXTJUyZhzasa7qELOT0iVRm+s7hDaPp7+sA8pFTDnc7geDRHe47svpyAOSqiS5oROzVwG6nHFkMoKycPpHKCTkMoKyccrifEwgCwCajg3m4PgBkhSwGgGbllMM0AwBUIp/YA4DBRRYDQLNyymGaAQAqkdM1VQFgUJHFANCsnHKYZgCASuR0shQAGFRkMQA0K6ccphkAoBI5nSwFAAYVWQwAzcoph2kGAKhETkuiAGBQkcUA0KyccrjjRdttb2X7/9n+vO0jx9036dXIbS+zvcL2irPW/qKqWgFMY5H4P3RXRRavW/9A/YUCaBw5XI8qcnh0dE39hQJoXE5z4o7NAEmnSLKkr0o6wvZXbc8p7nvGZIMiYnlELI2Ipa+Yt0dFpQKYzkYTbyil7yyePWurqagTQMPI4dr0ncMjI/Onok4ADatzTmz7ENs32r7J9rET3P8O29fbvsb2/9nerdPzdWsGPDoijo2IsyLiMElXSvqu7e1K1gtgSERE0g2lkMUASqkrh6uegGaIHAZQSl1zYtszJH1C0ksk7SPp9bb3GfewH0taGhFPlnSGpH/q9Jzdzhkwx/ZIRIwWL+yDtldLukjSgq4VAwCqQBYDaEzbBPRFklZJusL22RFxfdvDxiaga23/uVoT0NdNfbW1IYcBNO0ASTdFxC2SZPvLkg6XtCmLI+KCtsdfJumoTk/YbWXA/0p6QfuGiDhV0jslrStbNYDBN6pIuqEUshhAKTXl8KYJaESskzQ2Ad0kIi6IiLXFt5dJWlLpC2seOQyglBrnxIslrWz7flWxbTJHS/pmpyfsuDIgIt49yfZv2f7HTmMBDBeOO60PWQygrJQstr1M0rK2TcsjYnnb9xNNQJ/e4Sm7TkBzQw4DKCt1Tlwii3t5rqMkLZX0vE6P6+fSgiepdTIVAOCM1M0hiwFskpLFxWQzacI5XtkJ6IAhhwFskjonLpHFqyXt0vb9kmLbZmwfJOl4Sc+LiEc67bNjM8D2NZPdJWlRp7EAhgtL/utDFgMoq6YsrnwCmhtyGEBZNc6Jr5C0t+091MrgIySNv9Tp/pL+W9IhEXFXtyfstjJgkaSDJd07brslXVKyaABDgCsD1IosBlBKTVlc+QQ0Q+QwgFLqmhNHxAbbx0j6tqQZkj4TEdfZ/oCkFRFxtqQPq3VS0/+xLUm3FldAmVC3ZsA5khZExFXj77B9YZmiN452O0dhPez0f4TUmmc5/ajpfn5KG8N9jE6zbuOM5LGzRvr4OfXx75r6O9HPa53tjcljZ/bxc9rQwH93nDOgVn1n8dp1D1dc0vQ1TG2phzYMz3nLNoym5+kwqSOL65iAZqjvHAYwHOqcE0fEuZLOHbfthLavD+rl+bqdQPDoDvcdOdl9AIYP5wyoD1kMoKy6srjqCWhuyGEAZeU0J+7nBIIAsAnnDACA5pHFANCsnHKYZgCASnDOAABoHlkMAM3KKYdpBgCoRE5dUAAYVGQxADQrpxymGQCgEjkdHwUAg4osBoBm5ZTDNAMAVGK0gSVRtv9e0uFqnbj1LklviojbprwQAJgmmshiAMDv5JTDzVz3D8DAicRbnz4cEU+OiP3UuuzTCV0eDwADrYEcBgC0aWhOnKRjM8D2Trb/0/YnbG9n+0TbP7F9uu2dO4xbZnuF7RVfX3tL9VUDmHZGFUm3fkTEA23fzteAzmuryOLR0TVTWTKAhkx1Dg8LchhAWU3MiVN1WxlwqqTrJa2UdIGkhyS9VNLFkv5rskERsTwilkbE0sPn7VlRqQCms6aCz/YHba+U9AYN7sqAU9VnFo+MzJ+KOgE0LJcJaIZOFTkMoIRBagYsioh/i4iTJW0dER+KiJUR8W+SdpuC+gBkIiKSbu2fmhS3Ze3Pa/s7tq+d4HZ4sd/jI2IXSadJOqaJ1z4FyGIApaTkMEohhwGUkjonbkK3Ewi2Nws+N+6+GRXXAmAIRcRyScs73H9Qyac6TdK5kt5fRV3TDFkMAM0ihwEMnG7NgK/bXhARD0bE341ttL2XpBvrLQ1ATppY3mR774j4efHt4ZJ+OuVFTA2yGEApLPuvDTkMoJSccrhjMyAiJjz+NiJusv2NekoCkKOGrql6su3HqnVpwV9J+rMmiqgbWQygrJyub50TchhAWTnlcLeVAZ2cJOmUqgoBkLcmjnWKiFdN+U6nH7IYwCacA6AR5DCATXLK4Y7NANvXTHaXpEXVlwMgVzkticoNWQygLLK4HuQwgLJyyuFuKwMWSTpY0r3jtlvSJbVUBCBLOXVBM0QWAyiFLK4NOQyglJxyuFsz4BxJCyLiqvF32L6wzA6unTm396oKr953ZfLYuc9Mv8rL2z65Nmnck0bTX+tVIw8lj332hnnJY49dc0XSuFvesGfyPjfe+3Dy2Osu2C557JNedn/SuIduXp+8zzNvXpI8dkPySOmoZ67uY3SanLqgGeo7i1c/Z6+KS5q+tnzfG5suYcpsc8iJTZcwZe572wFNl5AFsrg2fefwQ7/6TsUlAZiOcsrhbicQPLrDfUdWXw6AXOV0spTckMUAyiKL60EOAygrpxzu5wSCALDJaEZLogBgUJHFANCsnHKYZgCASuTUBQWAQUUWA0CzcsphmgEAKpFTFxQABhVZDADNyimHaQYAqEROXVAAGFRkMQA0K6ccphkAoBI5dUEBYFCRxQDQrJxyuOdmgO0dI+KuOooBkK+cuqCDgCwGMBGyeOqQwwAmklMOj3S60/a2427bSfqh7W1sb9th3DLbK2yvuHjNzysvGsD0MxqRdEN3VWTx51bfPoUVA2gKOVyPKnL4U184fQorBtCUnObE3VYG/FrSr8ZtWyzpSkkhac+JBkXEcknLJem/lxzFuwwwBHLqgmao7yy+64XP4x8IGAJkcW36zuH1t9/APw4wBHLK4W7NgHdJepGkd0XETyTJ9i8iYo/aKwOQlYjRpksYZGQxgFLI4tqQwwBKySmHOx4mEBEfkfSnkk6w/S+2t5QyanUAwAAgiwGgWeQwgEHU9QSCEbFK0mtsHybpfEnzaq8KQHZGmRPViiwGUAZZXB9yGEAZOeVwx5UB7SLibEnPl3SQJNl+c11FAchPRCTd0BuyGEAn5HD9yGEAneQ0Jy7dDJCkiHgoIq4tvj2phnoAZGpUkXRD78hiAJMhh6cGOQxgMjnNiTseJmD7msnukrSo+nIA5IpPl+pDFgMoiyyuBzkMoKyccrjbOQMWSTpY0r3jtlvSJbVUBCBLXKu6VmQxgFLI4tqQwwBKySmHuzUDzpG0ICKuGn+H7QvL7GC/0bW9V1X46Y92SB7rK9ckj/3LWRuSxt3/iJP3uW9vR2xsZv7M9Nf6tS2elDTu+rPSL5kx4rnJY7eYtT557A3f3DJ5bKq9N6bXu8VI2u+hJP38+1snj31a4ricrqmaob6z+Mk/uqvikqavX7/4fU2XMGUet80uTZcwZbb7txVNlzBlHvlw+liyuDZ95/AWux1UcUkA6rRh3eqkcTnlcMdmQEQc3eG+I6svB0CucloSlRuyGEBZZHE9yGEAZeWUw+kfRwNAmyZPlmL7nbbD9vaVPCEAZCqXk1YBwKAamBMIAkBZTXVBbe8i6cWSbm2kAACYRnL6RAoABlFOOUwzAEAlGjxZykclvVvS15sqAACmi5xOXAUAgyinHKYZAKASTXRBbR8uaXVEXG2nn8ATAAZFTp9IAcAgyimHaQYAqETqsU62l0la1rZpeUQsb7v/O5J2mmDo8ZLeq9YhAgAApWcxAKAaOeUwzQAAlUjtghZ/+C/vcP+E12Ky/SRJe0gaWxWwRNKVtg+IiDuSigGAzOX0iRQADKKccrjj1QRsH9L29ULbn7Z9je0v2l7UYdwy2ytsrzhr7S+qrBfANDUakXRLFRE/iYgdI2L3iNhd0ipJTxnERkAVWbx23X1TUiuAZk1lDg+TKnJ4dHTN1BQLoFFTPSfuR7dLC/5j29cfkXS7pJdLukLSf082KCKWR8TSiFj6inl79F8lgGkvEv+HUvrO4nmzt663QgDTAjlcm75zeGRkfs0lApgOcpoT93KYwNKI2K/4+qO231hDPQAy1fSnS8XqgGFAFgOYVNNZPCTIYQCTyimHuzUDdrT9DkmWtJVtx+8Ogui2qgDAEMnp+KgMkcUASiGLa0MOAyglpxzuFl6flLSlpAWSPitpe0myvZOkq2qtDAAwhiwGgGaRwwAGTseVARFx0iTb77B9QT0lAcgRx53WhywGUBZZXA9yGEBZOeVwP8uaJgxFAMMpIpJu6BtZDGATcrgR5DCATXKaE3dcGWD7msnukjTpZVQADB8mlPUhiwGURRbXgxwGUFZOOdztBIKLJB0s6d5x2y3pkloqApClfGIvS2QxgFLI4tqQwwBKySmHuzUDzpG0ICKuGn+H7QvL7ODpt33Nne63vSwilpd5rirG5Tg2t3qbGptbvf2MbareTjasW93xv3X0pe8svuO+G6b836eu37XpiNc6mHJ8rWRxbfrOYf5tBlOOOYF65fTfuptexmB7RUQsnapxOY7Nrd6mxuZWbz9jm6oX6MUw/a7xWgfTML1WAGnICeSM66ICAAAAADBkaAYAAAAAADBkpkMzIPUYm36OzcltbG71NjU2t3r7GdtUvUAvhul3jdc6mIbptQJIQ04gW42fMwAAAAAAAEyt6bAyAAAAAAAATKHGmgG2D7F9o+2bbB/bw7jP2L7L9rUJ+9zF9gW2r7d9ne239TB2ru0f2r66GHtSj/ueYfvHts/pcdwvbf/E9lW2V/Q4dmvbZ9j+qe0bbP9ByXGPLfY3dnvA9ttLjv2b4udzre0v2Z7bQ71vK8Zd121/E/0e2N7W9vm2f178/zY9jH1Nsd9R25OeEXaSsR8ufsbX2D7T9tYlx/19MeYq2+fZflTZfbbd907bYXv7Huo90fbqtn/fl072eoFUqRmfm37ek3LTz3tobvp9zwcwHIblvQ6Dq5FmgO0Zkj4h6SWS9pH0etv7lBx+qqRDEne9QdI7I2IfSc+Q9Jc97PcRSS+IiH0l7SfpENvP6GHfb5N0Qy/Ftnl+ROyXcNmSj0v6VkQ8TtK+ZfcfETcW+9tP0lMlrZV0ZrdxthdL+mtJSyPiiZJmSDqizD5tP1HSWyQdUNR6qO29Ogw5Vb//e3CspP+LiL0l/V/xfdmx10p6paSLupQ60djzJT0xIp4s6WeSjis57sMR8eTi53yOpBN62Kds7yLpxZJu7bFeSfro2L9xRJzbYTzQsz4zPjenKv09KTf9vIfmpt/3fAADbsje6zCgmloZcICkmyLilohYJ+nLkg4vMzAiLpJ0T8pOI+L2iLiy+Pq3av1xvLjk2IiIB4tvZxW3UidcsL1E0sskfarnohPZXijpuZI+LUkRsS4i7kt4qhdKujkiflXy8TMlbWF7pqR5km4rOe7xki6PiLURsUHS99T643xCk/weHC7ps8XXn5X0irJjI+KGiLixW5GTjD2vqFmSLpO0pOS4B9q+na9Jfp86/M5/VNK7JxvXZSxQp+SMz80w/TfWz3tobvp5zwcwNIbmvQ6Dq6lmwGJJK9u+X6UpnlDY3l3S/pIu72HMDNtXSbpL0vkRUXbsx9T6o220tyoltSYf59n+ke1lPYzbQ9Ldkk4pDk/4lO35Cfs/QtKXShUasVrSP6v1SfXtku6PiPNK7udaSc+xvZ3teZJeKmmXHmtdFBG3F1/fIWlRj+Or8CeSvln2wbY/aHulpDdo8pUBE407XNLqiLi69xIlSccUhyh8ZrLDKYA+NJ7xqFfKe2hu+njPBzAceK9D9obyBIK2F0j6qqS3j/t0tqOI2Fgs6V4i6YBiaXu3fR0q6a6I+FFiuc+OiKeotQTpL20/t+S4mZKeIuk/I2J/SWs0+bL5CdmeLekwSf9T8vHbqNUR3UPSoyTNt31UmbERcYOkD0k6T9K3JF0laWMv9Y57vtAUf4pj+3i1ltGeVnZMRBwfEbsUY44puZ95kt6rHpoH4/ynpEertfT1dkkfSXweAEMo9T00Nynv+QAA5KSpZsBqbf6p75JiW+1sz1JrEnNaRHwt5TmK5fYXqNxxos+SdJjtX6q1fOgFtr/Qw75WF/9/l1rH7R9QcugqSavaPsk4Q63mQC9eIunKiLiz5OMPkvSLiLg7ItZL+pqkZ5bdWUR8OiKeGhHPlXSvWsff9+JO2ztLUvH/d/U4PpntN0k6VNIbIu16nadJelXJxz5arYbL1cXv1RJJV9reqczgiLizmOSOSvqkyv9OAWU1lvGoVxXvobnp8T0fwPDgvQ7Za6oZcIWkvW3vUXz6fISks+veqW2rdQz9DRHxLz2O3WHsLPG2t5D0Ikk/7TYuIo6LiCURsbtar/O7EVHq03Lb821vOfa1WieLK3XG6oi4Q9JK248tNr1Q0vVlxrZ5vUoeIlC4VdIzbM8rftYvVA8nTbS9Y/H/u6p1voAv9rBvqfU79Mbi6zdK+nqP45PYPkStw0AOi4i1PYzbu+3bw1Xi90mSIuInEbFjROxe/F6tkvSU4t+8zH53bvv2D1XydwroQSMZj3r18x6am9T3fABDhfc6ZG9mEzuNiA22j5H0bbXOOP+ZiLiuzFjbX5J0oKTtba+S9P6I+HTJXT9L0h9J+klxHKAkvbfk2dR3lvTZ4syhI5JOj4ieLhOYYJGkM1vzL82U9MWI+FYP4/9K0mlFQN0i6c1lBxbNhxdJemvZMRFxue0zJF2p1nL5H0ta3kO9X7W9naT1kv6y0wkPJ/o9kHSypNNtHy3pV5Je28PYeyT9m6QdJH3D9lURcXDJscdJmiPp/OLf6rKI+LMS415aNGtGi3o3G9NpbNnf+Un2e6Dt/dQ6jOKX6uHfGCijn4zPTZ/vSbnp5z00N0285wPIyDC912FwOW1FMwAAAAAAyNVQnkAQAAAAAIBhRjMAAAAAAIAhQzMAAAAAAIAhQzMAAAAAAIAhQzMAAAAAAIAhQzMAAAAAAIAhQzMAAAAAAIAhQzMAAAAAAIAh8/8BMqlW3zluDrEAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 12\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 13\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 14\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 15\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 16\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 17\n" + ] + }, + { + "data": { + "image/png": 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UGaSfa08y+kQKAPpSRjnMzAAA1UisgtpeKmlp26ZlEbGs2He4pLsj4he2D+y1iwDQ9zL6RAoA+lJGOUwxAEAlUm+zXPzhv2yC3QdIOsL2iyRtJmlL21+MiNek9RIA+hu3vAeAZuWUwxQDAFSjhilREfFuSe+WpGJmwN9TCACADjKangoAfSmjHKYYAKAaGU2JAoC+RRYDQLMyymGKAQCqUXMVNCIukHRBrScBgNxl9IkUAPSljHKYYgCAaozkc30UAPQtshgAmpVRDiffp832MVV2BEDmYiTtgZ6QxQA2QQ5POXIYwCYyGhOn37RdOmmiHbaX2l5ue/m319zcwykAZGNkJO2BXpXK4pGRh6ayTwCaQg43gRwG8EcZjYk7XiZg++qJdklaOFG79luFXbzjyyK5dwCASrJ4xqxFZDEAJCKHAfSjbmsGLJR0qKR7x2y3pItr6RGAPDHVtE5kMYByyOK6kMMAyskoh7sVA86WNC8irhy7w/YFdXQIQKaYalonshhAOWRxXchhAOVklMMdiwERcWyHfUdX3x0A2coo+HJDFgMojSyuBTkMoLSMcphbCwKoREQ+t1EBgH5FFgNAs3LKYYoBAKqRURUUAPoWWQwAzcoohykGAKhGRoulAEDfIosBoFkZ5TDFAADVyKgKCgB9iywGgGZllMO1FwPmzFqX3Pa3j2xeYU/Ke9yi3ye1u2/1NsnnDDm57RabP5rc9ncPzUlq9/D69F+dzYbTr6NZMO+R5LYPPzIzqd3a9cPJ5/yD09sunPNQctuRkfTfp2QZVUHR3xr47W/Mk7batekuTJkV997adBfyQBZPW8NDQ013AcBUyCiHmRkAoBoZVUEBoG+RxQDQrIxymGIAgGpkVAUFgL5FFgNAszLKYYoBAKqRURUUAPoWWQwAzcoohykGAKhGRsEHAH2LLAaAZmWUwxQDAFQjoylRANC3yGIAaFZGOdx1WVPbj7d9kO15Y7YfVl+3AGRnZCTtga7IYQClkcO1IYsBlJLRmLhjMcD2myV9W9LfSVph+8i23R+ps2MAMhMjaQ90RA4DmBRyuBZkMYDSMhoTd7tM4A2Snh4RD9reTdKZtneLiFPV4VbOtpdKWipJ7936KXrZvMG5DzIwsPh0qS5JOSxtmsUenq+hobm1dxZAw8jiuvQ8Jh6esUDDw/MmeiqAfpFRDncrBgxFxIOSFBG32D5QrfDbVR2CLyKWSVomSVfuekRU01UAGEhJOVw8f2MWz5i1iCwGgHQ9j4lnb7YzOQxgWum2ZsBdtvcZ/aYIwcMlbSvpyTX2C0BuMpoSlRlyGEB55HBdyGIA5WQ0Ju42M+C1kta3b4iI9ZJea/u/ausVgPzUNCXK9maSLpQ0W63MOjMi3l/LyaYnchhAeRlNT80MWQygnIxyuGMxICJWddj30+q7AyBb9QXfo5KeX1ynOVPST2x/LyIuqeuE0wk5DGBSMhqE5oQsBlBaRjnc9daCAFBKRNqj62EjRq/TlDSzeHDdJQCMp4YcBgBMQk1jYql1K1PbN9i+0fYJ4+zfxfb5tq+wfbXtF3U6XrfLBACgnBqroLaHJf1C0h6SPhkRl9Z2MgDIWUafSAFAX6rv0tlhSZ+UdIikVZIus31WRFzb9rT3SvpqRPyn7b0lnSNpt4mOSTEAQDUSg6/9tkuFZcXqyxtFxAZJ+9heIOmbtp8UEStSuwoAfYtiAAA0q74c3k/SjRFxkyTZ/oqkIyW1FwNC0pbF1/Ml3d7pgBQDAFQjcRXU9tsulXjufbbPl3SYJIoBADAWdwcAgGbVl8OLJK1s+36VpGeOec4HJJ1r++8kzZV0cKcD1l4MONUzk9v+7cxHk9vuccAfktue9rNFaef0huRzzlF622+v3yq57YObpf2yvmzDQ8nn3Ou1s5Lb3vSl9d2fNIHH/9NTktqteGf635zP2Pmu5LZ33r5l9ydN4MaYk9w27V9JdU6J2k7SuqIQsLlaU6NOqeVkfWyvrRY33YUpc8eae5ruwpRZuea3TXdhyjxuQdp788CpL4sPk3SqpGFJn4mIk8fs30XS5yUtKJ5zQkScU0tnMhWszwAMhhpny5bwKkmnR8THbP+ZpP8uZtSO2ylmBgCoRn2DnB0lfb64TmpIreugzq7rZACQtRqyuI7rVAGgbyXmcInZsqsl7dz2/eJiW7tj1ZpBq4j4WXGL7m0l3T3eASkGAKhGTZ9GRcTVkvat5eAA0G/qyeLKr1MFgL5V35oBl0na0/buahUBjpJ09Jjn3CbpIEmn236CpM0kTTiNkGIAgGqwaBUANC8hi0tMTa38OlUA6Fv1fUC23vZxkn6g1uVYn4uIa2z/o6TlEXGWpOMlfdr229Qq0r4+OlyjRDEAQDVYtAoAmpeQxZNZyLWDSV2nCgB9q8bYK9ZiOWfMtve1fX2tpAPKHo9iAIBKxAgLIwFA02rK4sqvUwWAfpXTmLhrMcD2fpIiIi4rFoQ5TNL1rBALYBNcJlAbchhAafVkceXXqeaILAZQSkZj4o7FANvvl/RCSTNsn6fW9WHnSzrB9r4R8eEp6COAHDATtBbkMIBJqSGL67hONTdkMYDSMhoTd5sZ8H8l7SNptqQ7JS2OiPtt/7OkSyWNG3ztC9H82db7aq8tdq+swwCmqYymRGUmKYelTbN4xy1201abb19/bwE0q6Ysrvo61Qz1PCYeHl6goeG5U9NbAM3JaEw81GX/+ojYEBFrJP0mIu6XpIh4WNKEJY+IWBYRSyJiCYUAAOhJUg4Xz9mYxRQCAKAnPY+JKQQAmG66zQxYa3tOEXxPH91oe766DEIBDJiMro/KDDkMoDyyuC5kMYByMsrhbsWA50TEo5I05tYwMyW9rrZeAchPRsGXGXIYQHlkcV3IYgDlZJTDHYsBo6E3zvbfSfpdLT0CkKf+WSdqWiGHAUwKWVwLshhAaRnlcNdbCwJAKRlVQQGgb5HFANCsjHKYYgCAamS0cioA9C2yGACalVEOUwwAUI2M7qkKAH2LLAaAZmWUwxQDAFQjoyooAPQtshgAmpVRDtdeDLht/QPJbWfNmJ3cdvYRz0pu+6tLb0hq9+jsWcnnfOWC9LVn7v9D+nl/seGepHbPXzs/+ZxDez0mue3v19yY3Pbxhx6T1O4JVx6ffM5Hr12f3Pb+29J/rhdtvi657asS20VG10cNohdsvnvTXZgyd222U9NdmDLfvPvyprswZd4zf0nTXcgCWTx9jWS0qBiAdDnlMDMDAFQjoyooAPQtshgAmpVRDlMMAFCNjK6PAoC+RRYDQLMyymGKAQCqkVEVFAD6FlkMAM3KKIcpBgCoRkbXRwFA3yKLAaBZGeUwxQAA1cioCgoAfYssBoBmZZTDQ5NtYPsLdXQEQOZiJO2BSSOHAUyIHJ4yZDGAcWU0Ju44M8D2WWM3SXqe7QWSFBFH1NQvALmpqQpqe2dJX5C0UFJIWhYRp9ZysmmIHAYwKRl9IpUTshhAaRnlcLfLBBZLulbSZ9QahFvSEkkf69TI9lJJSyVprwVP0KK5i3vvKYBprcZ7qq6XdHxEXG57C0m/sH1eRFxb1wmnmaQcljbN4oO2XqInb/HYGrsJYDrI6f7Wmel5TOzh+RoamltzNwE0Lacc7naZwBJJv5B0oqQ/RMQFkh6OiB9HxI8nahQRyyJiSUQsoRAAoBcRcUdEXF58/YCk6yQtarZXUyoph6VNs5hCAAD0pOcxMYUAANNNx5kBETEi6eO2v1b8/13d2gAYUFMwJcr2bpL2lXRp7SebJshhAJOS0fTUnJDFAErLKIdLhVhErJL0ctt/Lun+ersEIEuJwdc+hbKwLCKWjfO8eZK+LumtETFwOUQOAyglo0FojshiAF1llMOTqmhGxHclfbemvgDIWeIqqMUf/n/yx3872zPVKgScERHfSDpRnyCHAXTE3QGmBFkMYEIZ5TDTmwBUo767CVjSZyVdFxH/UstJAKBfZPSJFAD0pYxymGIAgEpEfcF3gKS/lPRL21cW294TEefUdUIAyFWNWQwAKCGnHKYYAKAaNQVfRPxErVs4AQC6yWgQCgB9KaMcphgAoBoZ3VMVAPoWWQwAzcooh2svBjxxxlbJbWdvlr5Iazz0YHLbB2N9Urt7PJx8zpvu3Dq57fzNhpLb/m592r/T5j3cK3fDL69PbnvLjPTzLvno36c13LAh+ZwrV8xPbnvbjJnJbV/y8LrktskyqoIOoi/de2XTXZgyCzdPf9/JzfoNae9XOXrn3RPeyr3vvKWXxmQxADQroxxmZgCAamQUfADQt8hiAGhWRjlMMQBAJSLyCT4A6FdkMQA0K6ccphgAoBoZVUEBoG+RxQDQrIxymGIAgGpkFHwA0LfIYgBoVkY5TDEAQCVyuqcqAPQrshgAmpVTDk+qGGD7WZL2k7QiIs6tp0sAspRR8OWOLAYwIbJ4SpDDACaUUQ53vCed7Z+3ff0GSf8uaQtJ77d9Qs19A5CTkcQHuiKLAZRGDteCHAZQWkZj4m43qG+/0flSSYdExEmSXiDp1RM1sr3U9nLby1c88JsKuglguouRSHqglJ6z+OG199XcRQDTATlcm55zeGTkobr7CGAayGlM3K0YMGR7K9vbSHJE/FaSIuIhSesnahQRyyJiSUQsedIWj62wuwCmrZFIe6CMnrN481kLpqirABpFDtel5xweGpo7VX0F0KSMxsTd1gyYL+kXkiwpbO8YEXfYnldsAwDUjywGgGaRwwD6TsdiQETsNsGuEUkvqbw3APLFdae1IYsBlEYW14IcBlBaRjmcdGvBiFgj6eaK+wIgY1x3OvXIYgBjkcVTixwGMFZOOZxUDACAP5FRFRQA+hZZDADNyiiHKQYAqEROVVAA6FdkMQA0K6ccphgAoBoZVUEBoG+RxQDQrIxymGIAgEpERsEHAP2KLAaAZuWUw7UXA57/yHBy20UvnZncduRXNyW33d3zktqd8eB1yed8yqy9k9setP6h5La3z16U1G7JBe9IPue6M/41ue0VM9cmt93l9LRf932fdU/yOU/RguS2t+n25LbnvnlxcttkGQXfILr3kQeb7sKU+cOja5ruwpTJZyJi7zaMEDKl8M8EAM2qMYdtHybpVEnDkj4TESeP85xXSPqAWsOEqyLi6ImOx8wAAJXIqQoKAP2KLAaAZtWVw7aHJX1S0iGSVkm6zPZZEXFt23P2lPRuSQdExL22t+90TIoBAKrBABQAmkcWA0Cz6svh/STdGBE3SZLtr0g6UtK1bc95g6RPRsS9khQRd3c64FBNHQUwYGIk7QEAqE5dOWz7MNs32L7R9gkTPOcVtq+1fY3tL1X5ugAgFzWOiRdJWtn2/apiW7vHSXqc7Z/avqS4rGBCzAwAUIkap0R9TtLhku6OiCfVcxYA6A91ZHEdU1MBoF+l5rDtpZKWtm1aFhHLJnmYGZL2lHSgpMWSLrT95Ii4b6InA0DPavyU/3RJ/y7pC7WdAQD6RE1ZXPnUVADoV6k5XPzh3+mP/9WSdm77fnGxrd0qSZdGxDpJN9v+lVrFgcvGO2DHywRsP9P2lsXXm9s+yfZ3bJ9ie37nlwNgoITTHt0OG3GhpPRbOmSOHAYwKTXksGqYmpobshhAaTWNidX6g35P27vbniXpKElnjXnOt9SaFSDb26qVzRPeZq/bmgGfkzR6j6ZTJc2XdEqx7bQyPQYwGFKvj7K91PbytsfS7mcbKOQwgNIazOH2qamvkvRp2wsqfGlNI4sBlFLXmgERsV7ScZJ+IOk6SV+NiGts/6PtI4qn/UDS721fK+l8Se+IiN9PdMxulwkMFSeVpCUR8bTi65/YvnKiRu3XO/zNFs/QC+bs0eU0AHIXI6Uqmn/arvuUqEGXlMPSplk8PLxAQ8Nz6+slgGkhJYubmJqaoZ7HxB6er6Ehchjod6lj4lLHjjhH0jljtr2v7euQ9Pbi0VW3mQErbB9TfH2V7SWSZPtxktZ16OSyiFgSEUsoBACDgbsJ1CYph6VNs5hCADAYasrhyqemZqjnMTGFAGAw5DQm7lYM+GtJz7X9G0l7S/qZ7ZskfbrYBwCoFzkMoFF1TE3NEFkMoO90vEwgIv4g6fXFgim7F89fFRF3TUXnAOQjyi18Mmm2v6zWp03b2l4l6f0R8dlaTjYNkcMAJqOuLK56ampuyGIAZdWVw3UodWvBiLhf0lU19wVAxuqa3hQRr6rnyHkhhwGUweVX9SKLAXSTUw6XKgYAQDd1LpYCACiHLAaAZuWUwxQDAFQioukeAADIYgBoVk45TDEAQCVyqoICQL8iiwGgWTnlMMUAAJXIKfgAoF+RxQDQrJxyuPZiwMlDtye33es7c5Lb7vKK9Hu53qf1Se3ePGfv5HPuHQ8kt/3P4VnJbR+Ojrcpn9DFzzwl+ZzP/OgeyW0Pffjm5LbPvubkpHaPfuStyef81Jz0n+t5P9wxue0/fTrtd1iSPnRiWrucpkQNonfs+JymuzBl/ndt+vtObq645zdNd2HKvGOHZzfdhSyQxQDQrJxymJkBACqRUxUUAPoVWQwAzcophykGAKhETvdUBYB+RRYDQLNyymGKAQAqkdM9VQGgX5HFANCsnHKYYgCASoxkVAUFgH5FFgNAs3LKYYoBACqR05QoAOhXZDEANCunHB7qtNP2m23vPFWdAZCvGHHSA92RxQDKIofrQQ4DKCunMXHHYoCkD0q61PZFtv8/29tNRacA5Cci7YFSyGIApZDDtSGHAZSS05i4WzHgJkmL1QrAp0u61vb3bb/O9hYTNbK91PZy28vvXjM493sGBllOVdAM9ZzFVzxw41T1FUCDyOHa9JzDIyMPTVVfATQopzFxt2JARMRIRJwbEcdK2knSf0g6TK1QnKjRsohYEhFLtp+zU4XdBTBdjYSTHiil5yzed4s9pqqvABpEDtem5xweGpo7VX0F0KCcxsTdFhDcpFcRsU7SWZLOsj2ntl4BANqRxQDQLHIYQN/pVgx45UQ7ImJNxX0BkLGcVk7NEFkMoBSyuDbkMIBScsrhjsWAiPjVVHUEQN5YhKo+ZDGAssjiepDDAMrKKYe7zQwAgFK47hQAmkcWA0CzcsphigEAKpHTlCgA6FdkMQA0K6ccphgAoBI5TYkCgH5FFgNAs3LKYYoBACqR05QoAOhXZDEANCunHK69GHD2U9NLIz+5YkFy2xs+m9xU/3TsuqR23zwt/Zw/mrFFctuTn7gyue0ly3dKarfD/D8kn/P6916b3PaAwx5MbrviaW9LarfZ7PXJ5zxz7Q7JbQ+fmf5aD1h8f3LbVHVOibJ9mKRTJQ1L+kxEnFzbyfrUKbf/uOkuoAZzZs5uugtT5uQB+h3+UA9tc5qeCgD9KKccZmYAgErUVQW1PSzpk5IOkbRK0mW2z4qI9KoSAPSpnD6RAoB+lFMOUwwAUIkaL4/aT9KNEXGTJNn+iqQjJVEMAIAxMrpUFQD6Uk45TDEAQCVqrIIuktR+LcwqSc+s62QAkLOcPpECgH6UUw5TDABQidTro2wvlbS0bdOyiFhWSacAYMDkdK0qAPSjnHKYYgCASowktiv+8O/0x/9qSTu3fb+42AYAGCM1iwEA1cgphzsWA2zPknSUpNsj4oe2j5b0fyRdp9and2nL7gPoO6HaqqCXSdrT9u5qFQGOknR0XSebbshhAJNRYxYPNLIYQFk55XC3mQGnFc+ZY/t1kuZJ+oakg9Ra1Ot19XYPQC5GalotJSLW2z5O0g/UurXg5yLimnrONi2RwwBKqyuLQRYDKCenHO5WDHhyRDzF9gy1PpHbKSI22P6ipKsmatR+DfDHnrinXrvzjpV1GMD0NFJjFTQizpF0Tm0nmN6ScljaNIs9PF9DQ3Pr7y2ARtWZxQOu5zExOQwMhpxyeKjb/mJa1BaS5kiaX2yfLWnmRI0iYllELImIJRQCgMEQctIDXSXlsLRpFjMABQYDOVybnsfE5DAwGHIaE3ebGfBZSderNTX3RElfs32TpP0lfaXmvgEAyGEAmA7IYgB9p2MxICI+bvt/iq9vt/0FSQdL+nRE/HwqOgggDzmtnJoTchjAZJDF9SCLAZSVUw53vbVgRNze9vV9ks6ss0MA8sRU0/qQwwDKIovrQxYDKCOnHO5aDACAMnKqggJAvyKLAaBZOeUwxQAAlcgp+ACgX5HFANCsnHKYYgCASuQ0JQoA+hVZDADNyimHKQYAqMRIPrkHAH2LLAaAZuWUw7UXA268apvktttobXLbYUdy2xu+sD6p3S49zAnZJf2l6sYr0v+N5yrttd734GbJ5+zlZ/ObH81LbjsSaf9l3v9Q+mt97kj6D/bRoeHktrfdtFVy2x0T241kVAUF+sUx2+3XdBemzKfu/GnTXcgCWQwAzcoph5kZAKAS6SUeAEBVyGIAaFZOOUwxAEAlclosBQD6FVkMAM3KKYcpBgCoxIjzmRIFAP2KLAaAZuWUwxQDAFQipylRANCvyGIAaFZOOTzUdAcA9IeRxAcAoDrkMAA0q84xse3DbN9g+0bbJ3R43stsh+0lnY7XdWaA7cdIeqmknSVtkPQrSV+KiPtL9hnAAMjpNiq5IYcBlFVXFts+TNKpkoYlfSYiTp7geS+TdKakZ0TE8np60wyyGEAZNebwsKRPSjpE0ipJl9k+KyKuHfO8LSS9RdKl3Y7ZcWaA7TdL+pSkzSQ9Q9JstQLwEtsHTv4lAOhXI3LSA52RwwAmo44cbhuAvlDS3pJeZXvvcZ5XegCaG7IYQFk1jon3k3RjRNwUEWslfUXSkeM874OSTpH0SLcDdrtM4A2SXhgRH5J0sKQnRsSJkg6T9PGJGtleanu57eXfWnNztz4A6AOR+EBXSTksbZrFIyMPTUFXATStphyufACaoZ7HxOQwMBhSx8TteVE8lo459CJJK9u+X1Vs28j20yTtHBHfLdPXMgsIzlBrKtRsSfMkKSJusz1zogYRsUzSMkm6ZKeXMt4HBgCXCdRq0jlcPGdjFs+YtYgsBgZATVk83gD0me1PaB+A2n5HLb1oXk9jYnIYGAypOdyeFylsD0n6F0mvL9umWzHgM2pdi3CppGerVe2V7e0k3ZPWTQDAJJDDAGpVfPrU/gnUsmJQWrb9pAegGSKLATRttVqXJ41aXGwbtYWkJ0m6wK3bG+4g6SzbR0y0hkvHYkBEnGr7h5KeIOljEXF9sf23kp6T+ioA9B9WpK4HOQxgMlKyuMSnUZUPQHNDFgMoq8Yx8WWS9rS9u1oZfJSko0d3RsQfJG07+r3tCyT9facc7nqZQERcI+ma9D4DGATMfawPOQygrJqyuPIBaI7IYgBl1DUmjoj1to+T9AO17uzyuYi4xvY/SloeEWdN9phl1gwAgK6aWDPA9sslfUCtT2r267eBJwBMVh1ZXMcAFAD6VZ1j4og4R9I5Y7a9b4LnHtjteBQDAFSiocsEVqh1z+f/aub0ADC91JXFVQ9AAaBf5XTpLMUAAJVoIvgi4jpJKq5RBYCBl9MgFAD6UU45TDEAQCWCv8cBoHFkMQA0K6ccrr0YsHZkOLntrKENyW2Hnb50wyPrp75G4h6Wmlg/MpTcdoPS2lo9/GyG0utlG3p4rbNnrk9q9+Cj6b/DvfxcR3pIkl5+/1Ol/lS73dKqWL15h3GanhgR30487cAZGqDZExGDs5zlhgFaunPI6fk/SHL6RAoA+lFOOczMAACVSA2+bre0ioiDEw8NAAMnp0EoAPSjnHKYYgCASgzO55MAMH2RxQDQrJxymGIAgEo0dGvBl0j6N0nbSfqu7Ssj4tCp7wkATA9NZDEA4I9yymGKAQAq0dDdBL4p6ZsNnBoApqWcpqcCQD/KKYcpBgCoRE7BBwD9iiwGgGbllMMUAwBUIqfrowCgX5HFANCsnHKYYgCASuR0fRQA9CuyGACalVMOd7xpr+35tk+2fb3te2z/3vZ1xbYFHdottb3c9vKz1txUeacBTD8jiQ90V0UWj2x4aAp7DKAp5HA9KsnhEXIYGAQ5jYk7FgMkfVXSvZIOjIitI2IbSc8rtn11okYRsSwilkTEkiPmPKa63gKYtiLxgVJ6zuKh4blT1FUATSKHa9N7Dg+Rw8AgyGlM3K0YsFtEnBIRd45uiIg7I+IUSbvW2zUAORlRJD1QClkMoBRyuDbkMIBSchoTdysG3Gr7nbYXjm6wvdD2uyStrLdrAIACWQwAzSKHAfSdbsWAV0raRtKPi+uj7pF0gaStJb285r4ByEhO10dliCwGUAo5XBtyGEApOY2JO95NICLulfSu4rEJ28dIOq2mfgHIDBNN60MWAyiLLK4HOQygrJxyuNvMgE5OqqwXALKXUxW0z5DFADYihxtBDgPYKKcxcceZAbavnmiXpIUT7AMwgHK6p2puyGIAZZHF9SCHAZSVUw53LAaoFW6HqnXblHaWdHEtPQKQJVakrhVZDKAUsrg25DCAUnLK4W7FgLMlzYuIK8fusH1BmRP8TjMn36vCvtvdl9x2m/3Tr4D4ybe2Smp3zCNXJp/zH7Zcktz2oM3uSW77rbVbJ7V725eOSD5n3PjL5Lb/8Q+rkts+b8MDSe2e+sZZyec8+nNp55SkuU7/b+e/XvhwcttU+cRelnrO4pEYnJ9QRgX5nn3q9p803QVMM4PzX/qU6zmHAQyGnHK42wKCx3bYd3T13QGQK647rQ9ZDKAssrge5DCAsnLK4W4zAwCglJymRAFAvyKLAaBZOeUwxQAAlcgn9gCgf5HFANCsnHKYYgCASuQ0JQoA+hVZDADNyimHKQYAqEROU6IAoF+RxQDQrJxymGIAgErkE3sA0L/IYgBoVk45TDEAQCVymhIFAP2KLAaAZuWUw0OpDW1/r8O+pbaX215+7pobU08BICOR+D/0pmwWj4w8NJXdAtAQcnjqkcMA2uU0Ju44M8D20ybaJWmfidpFxDJJyyTpGzsczbsMMAByqoLmpoosnjFrEVkMDACyuB7kMICycsrhbpcJXCbpx2oF3VgLKu8NgGw1sViK7Y9KerGktZJ+I+mYiLhvyjtSP7IYQCk5LVyVGXIYQCk55XC3YsB1kt4YEb8eu8P2ynq6BAClnSfp3RGx3vYpkt4t6V0N96kOZDEANIscBtB3uq0Z8IEOz/m7arsCIGeR+OjpnBHnRsT64ttLJC3u8ZDT1QdEFgMoYapzeIB8QOQwgBKaGBOn6jgzICLO7LB7q4r7AiBjqVOibC+VtLRt07LiGsvJ+itJ/5PUiWmOLAZQVk7TU3NCDgMoK6cc7uXWgidJOq2qjgDIW+piKe2LK43H9g8l7TDOrhMj4tvFc06UtF7SGYndyBlZDGCjnBau6iPkMICNcsrhbncTuHqiXZIWVt8dALmq65YoEXFwp/22Xy/pcEkHRUQ+pdhJIIsBlMWtAutBDgMoK6cc7jYzYKGkQyXdO2a7JV1cS48AZKmJKqjtwyS9U9JzI2JNA12YKmQxgFJy+kQqM+QwgFJyyuFuxYCzJc2LiCvH7rB9QZkT7DT0yOR7Vbjj7i3T256V3FTbz0zr85f1lORzDq19NLntgyOzk9s+a0Paa13xyq8ln7MXzxrptublxNZpOKndimXpP5s3b5ib3HYzb0hue8PZmyW3XZLYrqEq6L9Lmi3pPNuSdElEvKmJjtSs5yweJPnU43s3c7iXq/3ysm7D+u5PQlafSGWGHAZQSk453G0BwWM77Du6+u4AyFUTVdCI2KOB0045shhAWTl9IpUTchhAWTnl8OB8pACgViP9ebk+AGSFLAaAZuWUwxQDAFQin9gDgP5FFgNAs3LKYYoBACqR0z1VAaBfkcUA0KyccphiAIBK5LRYCgD0K7IYAJqVUw5TDABQiZwWSwGAfkUWA0CzcsphigEAKpHTlCgA6FdkMQA0K6cc7njTdttb2v4n2/9t++gx+/6jQ7ultpfbXv6tNTdX1VcA01gk/g/dVZHFIyMP1d9RAI0jh+tBDgMoK6cxccdigKTTJFnS1yUdZfvrtmcX+/afqFFELIuIJRGx5C/m7F5RVwFMZyOJD5TScxYPDc2din4CaBg5XBtyGEApdY6JbR9m+wbbN9o+YZz9b7d9re2rbf+v7V07Ha9bMeCxEXFCRHwrIo6QdLmkH9nepmR/AQyIiEh6oBSyGEApdeVw1QPQDJHDAEqpa0xse1jSJyW9UNLekl5le+8xT7tC0pKIeIqkMyX9v07H7LZmwGzbQxExUrywD9teLelCSfO69hgAUAWyGEBj2gagh0haJeky22dFxLVtTxsdgK6x/TdqDUBfOfW9rQ05DKBp+0m6MSJukiTbX5F0pKSNWRwR57c9/xJJr+l0wG4zA74j6fntGyLidEnHS1pbttcA+t+IIumBUshiAKXUlMMbB6ARsVbS6AB0o4g4PyLWFN9eImlxpS+seeQwgFJqHBMvkrSy7ftVxbaJHCvpe50O2HFmQES8c4Lt37f9kU5tAQwWrjutD1kMoKyULLa9VNLStk3LImJZ2/fjDUCf2eGQXQeguSGHAZSVOiYukcWTOdZrJC2R9NxOz+vl1oInqbWYCgCwInVzyGIAG6VkcTHYTBpwjlV2ANpnyGEAG6WOiUtk8WpJO7d9v7jYtgnbB0s6UdJzI+LRTufsWAywffVEuyQt7NQWwGBhyn99yGIAZdWUxZUPQHNDDgMoq8Yx8WWS9rS9u1oZfJSksbc63VfSf0k6LCLu7nbAbjMDFko6VNK9Y7Zb0sUlOw1gAHBngFqRxQBKqSmLKx+AZogcBlBKXWPiiFhv+zhJP5A0LOlzEXGN7X+UtDwizpL0UbUWNf2abUm6rbgDyri6FQPOljQvIq4cu8P2BWU6ferM9CuJT5rzSHLbnf/+ScltP/TB25PaDc9w8jmXLrwjue1H79o+ue1FI6uS2r19ZLfkc7707Zsltz3nYw8nt33R8ZsntfPOO3d/0gSufttVyW0f3DAzue1d62clt12S2I41A2rVcxbvvfUuFXdp+tp11uDc6ev7d17RdBemzJaz5zTdhSzUkcV1DEAz1HMOP3zrDyvuEoDpqM4xcUScI+mcMdve1/b1wZM5XrcFBI/tsO/oifYBGDysGVAfshhAWXVlcdUD0NyQwwDKymlM3MsCggCwEWsGAEDzyGIAaFZOOUwxAEAlWDMAAJpHFgNAs3LKYYoBACqRUxUUAPoVWQwAzcophykGAKhETtdHAUC/IosBoFk55TDFAACVGGlgSpTtD0o6Uq2FW++W9PqISLsdCAD0gSayGADwRznl8FDTHQDQHyLx0aOPRsRTImIftW779L4uzweAvtZADgMA2jQ0Jk7SsRhgewfb/2n7k7a3sf0B27+0/VXbO3Zot9T2ctvLb3zwlso7DWD6GVEkPXoREfe3fTtXfTqurSKL71lz11R2GUBDpjqHB0UVOfyZL351KrsMoCFNjIlTdZsZcLqkayWtlHS+pIclvUjSRZI+NVGjiFgWEUsiYske83arpqcAprWmgs/2h22vlPRq9e/MgNPVYxZvPWfhVPQTQMNyGYBm6HT1mMN//ZpXTEU/ATSsn4oBCyPi3yLiZEkLIuKUiFgZEf8madcp6B+ATERE0qP9U5PisbT9uLZ/aHvFOI8ji/OeGBE7SzpD0nFNvPYpQBYDKCUlh1EKOQyglNQxcRO6LSDYXiz4wph9wxX3BcAAiohlkpZ12H9wyUOdIekcSe+vol/TDFkMAM0ihwH0nW7FgG/bnhcRD0bEe0c32t5D0g31dg1ATpqY3mR7z4j4dfHtkZKun/JOTA2yGEApTPuvDTkMoJSccrhjMSAixr3+NiJutP3deroEIEcN3VP1ZNt7qXVrwVslvamJTtSNLAZQVk73t84JOQygrJxyuNvMgE5OknRaVR0BkLcmrnWKiJdN+UmnH7IYwEasAdAIchjARjnlcMdigO2rJ9oliaWpAWyU05So3JDFAMoii+tBDgMoK6cc7jYzYKGkQyXdO2a7JV1cS48AZCmnKmiGyGIApZDFtSGHAZSSUw53KwacLWleRFw5doftC8qcYBdvPvleFWbOeii5rXbdI7nprXFTUru9lP5ab7116+S2i2anX+1x9e9vTmp3yAHpC+eO3Lkgue1QzE1ve8BBSe02fO/byedcP9Lt7p0T28wbkts2UZDMqQqaoZ6z+PeP3l9xl6avbWbMa7oLU2bGcC9X++VlyG66C1kgi2vTcw5vvmvZm+MAmA7Wr12d1C6nHO62gOCxHfYdXX13AOQqp8VSckMWAyiLLK4HOQygrJxyeHA+UgBQq5GMpkQBQL8iiwGgWTnlMMUAAJXIqQoKAP2KLAaAZuWUwxQDAFQipyooAPQrshgAmpVTDlMMAFCJnKqgANCvyGIAaFZOOUwxAEAlcqqCAkC/IosBoFk55fCkiwG2t4+Iu+voDIB85VQF7QdkMYDxkMVThxwGMJ6ccrhjMcD21mM3Sfq57X0lOSLumaDdUklLJekFWy/RPlvsUUVfAUxjOVVBc1NFFs/ffEfNnb1VvR0F0DiyuB5V5LCH52toaG69HQXQuJxyuNvMgN9JunXMtkWSLpcUkh4zXqOIWCZpmSS9a7dX5fOvASBZTlXQDPWcxYu2eiI/IGAAkMW16TmHZ8xaxA8HGAA55XC3YsA7JB0i6R0R8UtJsn1zROxee88AZCVipOku9DOyGEApZHFtyGEApeSUw0OddkbExyT9taT32f4X21tIGZU6AKAPkMUA0CxyGEA/6rqAYESskvRy20dIOk/SnNp7BSA7I4yJakUWAyiDLK4POQygjJxyuOPMgHYRcZak50k6WJJsH1NXpwDkJyKSHpgcshhAJ+Rw/chhAJ3kNCYuXQyQpIh4OCJWFN+eVEN/AGRqRJH0wOSRxQAmQg5PDXIYwERyGhN3u7Xg1RPtkrSw+u4AyBWfLtWHLAZQFllcD3IYQFk55XC3NQMWSjpU0r1jtlvSxbX0CECWcrqnaobIYgClkMW1IYcBlJJTDncrBpwtaV5EXDl2h+0LypzgJWsfnXyvCnfcvWVy27tfe25y27cPb0hqd//atcnnXCcnt33e+oeS25671QFJ7W65YX3yOYd/nX67jd1mpr/WFS//n6R2Eek/m0diVnLbzYfS/423HE7/XUyV0z1VM9RzFt/10H3V9mgaG6TXuvv8HZruwpS55Q93Nt2FLJDFtek5hwEMhpxyuGMxICKO7bDv6Oq7AyBXOU2Jyg1ZDKAssrge5DCAsnLK4UktIAgAE2lysRTbx9sO29tWckAAyFQui1YBQL/qmwUEAaCspqqgtneW9AJJtzXSAQCYRnL6RAoA+lFOOUwxAEAlGlws5eOS3inp2011AACmi5wWrgKAfpRTDlMMAFCJJqqgto+UtDoirrLTF3oEgH6R0ydSANCPcsphigEAKpF6rZPtpZKWtm1aFhHL2vb/UNJ4S6afKOk9al0iAABQehYDAKqRUw5TDABQidQqaPGH/7IO+w8eb7vtJ0vaXdLorIDFki63vV9EcA8yAAMpp0+kAKAf5ZTDHe8mYPuwtq/n2/6s7attf8n2wg7tltpebnv5t9bcXGV/AUxTIxFJj1QR8cuI2D4idouI3SStkvS0fiwEVJHFIyMPTU1nATRqKnN4kJDDAMqa6jFxL7rdWvAjbV9/TNIdkl4s6TJJ/zVRo4hYFhFLImLJX8zZvfdeApj2IvF/KKXnLB4amltzFwFMB+RwbchhAKXkNCaezGUCSyJin+Lrj9t+XQ39AZCppj9dKmYHDAKyGMCEms7iAUEOA5hQTjncrRiwve23S7KkLW07/ngRRLdZBQAGSE7XR2WILAZQCllcG3IYQCk55XC38Pq0pC0kzZP0eUnbSpLtHSRdWWvPAACjyGIAaBY5DKDvdJwZEBEnTbD9Ttvn19MlADniutP6kMUAyiKL60EOAygrpxzuZVrTuKEIYDBFRNIDPSOLAWxEDjeCHAawUU5j4o4zA2xfPdEuSRPeRgXA4GFAWR+yGEBZZHE9yGEAZeWUw90WEFwo6VBJ947ZbkkX19IjAFnKJ/ayRBYDKIUsrg05DKCUnHK4WzHgbEnzIuLKsTtsX1DmBPvf/g132m97aUQsK3OsKtrl2Da3/jbVNrf+9tK2qf52sn7t6o7/raMnPWdxEz+fun7XpiNea3/K8bWSxbXJModRvxxzAvXK6b91Nz2NwfbyiFgyVe1ybJtbf5tqm1t/e2nbVH+ByRik3zVea38apNcKIA05gZxxX1QAAAAAAAYMxQAAAAAAAAbMdCgGpF5j08u1Obm1za2/TbXNrb+9tG2qv8BkDNLvGq+1Pw3SawWQhpxAthpfMwAAAAAAAEyt6TAzAAAAAAAATKHGigG2D7N9g+0bbZ8wiXafs3237RUJ59zZ9vm2r7V9je23TKLtZrZ/bvuqou1Jkzz3sO0rbJ89yXa32P6l7SttL59k2wW2z7R9ve3rbP9ZyXZ7Fecbfdxv+60l276t+PdZYfvLtjebRH/fUrS7ptv5xvs9sL217fNs/7r4/60m0fblxXlHbE+4IuwEbT9a/BtfbfubtheUbPfBos2Vts+1vVPZc7btO9522N52Ev39gO3VbT/fF030eoFUqRmfm17ek3LTy3tobnp9zwcwGAblvQ79q5FigO1hSZ+U9EJJe0t6le29SzY/XdJhiadeL+n4iNhb0v6S/nYS531U0vMj4qmS9pF0mO39J3Hut0i6bjKdbfO8iNgn4bYlp0r6fkQ8XtJTy54/Im4ozrePpKdLWiPpm93a2V4k6c2SlkTEkyQNSzqqzDltP0nSGyTtV/T1cNt7dGhyuv709+AESf8bEXtK+t/i+7JtV0h6qaQLu3R1vLbnSXpSRDxF0q8kvbtku49GxFOKf+ezJb1vEueU7Z0lvUDSbZPsryR9fPRnHBHndGgPTFqPGZ+b05X+npSbXt5Dc9Prez6APjdg73XoU03NDNhP0o0RcVNErJX0FUlHlmkYERdKuiflpBFxR0RcXnz9gFp/HC8q2TYi4sHi25nFo9SCC7YXS/pzSZ+ZdKcT2Z4v6TmSPitJEbE2Iu5LONRBkn4TEbeWfP4MSZvbniFpjqTbS7Z7gqRLI2JNRKyX9GO1/jgf1wS/B0dK+nzx9ecl/UXZthFxXUTc0K2TE7Q9t+izJF0iaXHJdve3fTtXE/w+dfid/7ikd07UrktboE7JGZ+bQfpvrJf30Nz08p4PYGAMzHsd+ldTxYBFkla2fb9KUzygsL2bpH0lXTqJNsO2r5R0t6TzIqJs20+o9UfbyOR6Kak1+DjX9i9sL51Eu90l/VbSacXlCZ+xPTfh/EdJ+nKpjkaslvTPan1SfYekP0TEuSXPs0LSs21vY3uOpBdJ2nmSfV0YEXcUX98paeEk21fhryR9r+yTbX/Y9kpJr9bEMwPGa3ekpNURcdXkuyhJOq64ROFzE11OAfSg8YxHvVLeQ3PTw3s+gMHAex2yN5ALCNqeJ+nrkt465tPZjiJiQzGle7Gk/Yqp7d3OdbikuyPiF4ndfVZEPE2tKUh/a/s5JdvNkPQ0Sf8ZEftKekgTT5sfl+1Zko6Q9LWSz99KrYro7pJ2kjTX9mvKtI2I6ySdIulcSd+XdKWkDZPp75jjhab4UxzbJ6o1jfaMsm0i4sSI2Lloc1zJ88yR9B5Nongwxn9KeqxaU1/vkPSxxOMAGECp76G5SXnPBwAgJ00VA1Zr0099Fxfbamd7plqDmDMi4hspxyim25+vcteJHiDpCNu3qDV96Pm2vziJc60u/v9uta7b369k01WSVrV9knGmWsWByXihpMsj4q6Szz9Y0s0R8duIWCfpG5L+T9mTRcRnI+LpEfEcSfeqdf39ZNxle0dJKv7/7km2T2b79ZIOl/TqSLtf5xmSXlbyuY9Vq+ByVfF7tVjS5bZ3KNM4Iu4qBrkjkj6t8r9TQFmNZTzqVcV7aG4m+Z4PYHDwXofsNVUMuEzSnrZ3Lz59PkrSWXWf1LbVuob+uoj4l0m23W50lXjbm0s6RNL13dpFxLsjYnFE7KbW6/xRRJT6tNz2XNtbjH6t1mJxpVasjog7Ja20vVex6SBJ15Zp2+ZVKnmJQOE2SfvbnlP8Wx+kSSyaaHv74v93UWu9gC9N4txS63fodcXXr5P07Um2T2L7MLUuAzkiItZMot2ebd8eqRK/T5IUEb+MiO0jYrfi92qVpKcVP/My592x7duXqOTvFDAJjWQ86tXLe2huUt/zAQwU3uuQvRlNnDQi1ts+TtIP1Fpx/nMRcU2Ztra/LOlASdvaXiXp/RHx2ZKnPkDSX0r6ZXEdoCS9p+Rq6jtK+nyxcuiQpK9GxKRuE5hgoaRvtsZfmiHpSxHx/Um0/ztJZxQBdZOkY8o2LIoPh0h6Y9k2EXGp7TMlXa7WdPkrJC2bRH+/bnsbSesk/W2nBQ/H+z2QdLKkr9o+VtKtkl4xibb3SPo3SdtJ+q7tKyPi0JJt3y1ptqTzip/VJRHxphLtXlQUa0aK/m7SplPbsr/zE5z3QNv7qHUZxS2axM8YKKOXjM9Nj+9JuenlPTQ3TbznA8jIIL3XoX85bUYzAAAAAADI1UAuIAgAAAAAwCCjGAAAAAAAwIChGAAAAAAAwIChGAAAAAAAwIChGAAAAAAAwIChGAAAAAAAwIChGAAAAAAAwIChGAAAAAAAwID5/wGFS0KYXvRkhAAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 18\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timestep 22\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "a = debug_model([[0, 0,0,0], [1,1,1,1], [0,1,0,1], [1,0,1,0], [1,1,1,0]], 3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b73f6272", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/access_test.py b/tests/access_test.py index b65c3f2..5343f4a 100644 --- a/tests/access_test.py +++ b/tests/access_test.py @@ -43,21 +43,21 @@ class MemoryAccessTest(tf.test.TestCase): def setUp(self): self.cell = access.MemoryAccess( MEMORY_SIZE, WORD_SIZE, NUM_READS, NUM_WRITES) - + #self.initial_state = self.cell.get_initial_state(BATCH_SIZE) self.module = tf.keras.layers.RNN( cell=self.cell, - time_major=True) + time_major=True, + return_sequences=True, + ) def testBuildAndTrain(self): inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) targets = np.random.rand(TIME_STEPS, BATCH_SIZE, NUM_READS, WORD_SIZE) loss = lambda outputs, targets: tf.reduce_mean(input_tensor=tf.square(outputs - targets)) - print(self.module.get_initial_state(inputs)) - import ipdb; ipdb.set_trace() with tf.GradientTape() as tape: - outputs, _ = self.module( + outputs = self.module( inputs=inputs, - #initial_state=self.initial_state, + initial_state=self.module.get_initial_state(inputs),#self.initial_state, ) loss_value = loss(outputs, targets) gradients = tape.gradient(loss_value, self.module.trainable_variables) @@ -147,21 +147,21 @@ def testReadWeights(self): read_weights[0, 0, :], util.one_hot(MEMORY_SIZE, 3), atol=1e-3) def testGradients(self): - inputs = tf.constant(np.random.randn(BATCH_SIZE, INPUT_SIZE), dtype=DTYPE) - initial_state = self.module.get_initial_state(inputs) - + inputs = tf.constant(np.random.randn(1, BATCH_SIZE, INPUT_SIZE), dtype=DTYPE) + test_initial_state = self.module.get_initial_state(inputs=inputs) + initial_state = test_initial_state #self.initial_state def evaluate_module(inputs, memory, read_weights, precedence_weights, link): - initial_state = access.AccessState( + init_state = list(access.AccessState( memory=memory, read_weights=read_weights, write_weights=initial_state[access.WRITE_WEIGHTS], - linkage=addressing.TemporalLinkageState( + linkage=list(addressing.TemporalLinkageState( precedence_weights=precedence_weights, link=link - ), + )), usage=initial_state[access.USAGE], - ) - output, _ = self.module(inputs, initial_state) + )) + output = self.module(inputs, init_state) loss = tf.reduce_sum(input_tensor=output) return loss diff --git a/tests/dnc_test.py b/tests/dnc_test.py index 3956b31..3c02b95 100644 --- a/tests/dnc_test.py +++ b/tests/dnc_test.py @@ -75,7 +75,7 @@ def setUp(self): name='dnc_test', dtype=DTYPE, ) - self.initial_state = self.module.get_initial_state(BATCH_SIZE) + self.initial_state = self.module.get_initial_state(batch_size=BATCH_SIZE) def testBuildAndTrain(self): inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) @@ -85,11 +85,15 @@ def testBuildAndTrain(self): LEARNING_RATE, epsilon=OPTIMIZER_EPSILON) with tf.GradientTape() as tape: - outputs, _ = tf.compat.v1.nn.dynamic_rnn( + #outputs, _ = tf.compat.v1.nn.dynamic_rnn( + outputs = tf.keras.layers.RNN( cell=self.module, + time_major=True, + return_sequences=True, + )( inputs=inputs, - #initial_state=self.initial_state, - time_major=True) + initial_state=self.initial_state, + ) loss_value = loss(outputs, targets) gradients = tape.gradient(loss_value, self.module.trainable_variables) diff --git a/train.py b/train.py index c70fed3..f9bb200 100644 --- a/train.py +++ b/train.py @@ -54,13 +54,13 @@ "Batch size for training.") parser.add_argument("--num_bits", default=4, type=int, help= "Dimensionality of each vector to copy") -parser.add_argument("--min_length", default=1, type=int, help= +parser.add_argument("--min_length", default=2, type=int, help= "Lower limit on number of vectors in the observation pattern to copy") parser.add_argument("--max_length", default=3, type=int, help= "Upper limit on number of vectors in the observation pattern to copy") parser.add_argument("--min_repeats", default=1, type=int, help= "Lower limit on number of copy repeats.") -parser.add_argument("--max_repeats", default=7, type=int, help= +parser.add_argument("--max_repeats", default=3, type=int, help= "Upper limit on number of copy repeats.") # Training options. @@ -98,7 +98,7 @@ def train_step_graphed( loss_fn, ): """Runs model on input sequence.""" - initial_state = rnn_model.get_initial_state() + initial_state = rnn_model.get_initial_state(x) with tf.GradientTape() as tape: """output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( cell=rnn_model, @@ -107,9 +107,7 @@ def train_step_graphed( initial_state=initial_state) # Unable to migrate to tf.keras.layers.RNN due to contraints on RNN state structure """ - output_sequence = tf.keras.layers.RNN( - cell=rnn_model, - time_major=True, + output_sequence = rnn_model( inputs=x, initial_state=initial_state, ) @@ -136,12 +134,11 @@ def test_step_graphed( rnn_model, loss_fn, ): - initial_state = rnn_model.get_initial_state() - output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( - cell=rnn_model, + initial_state = rnn_model.get_initial_state(x) + output_sequence = rnn_model( inputs=x, - time_major=True, - initial_state=initial_state) + initial_state=initial_state, + ) loss_value = loss_fn(output_sequence, y, mask) # Used for visualization. output = tf.round( @@ -170,8 +167,13 @@ def train(num_training_iterations, report_interval): } clip_value = FLAGS.clip_value - dnc_core = dnc.DNC( + dnc_cell = dnc.DNC( access_config, controller_config, dataset.target_size, FLAGS.batch_size, clip_value) + dnc_core = tf.keras.layers.RNN( + cell=dnc_cell, + time_major=True, + return_sequences=True, + ) optimizer = tf.compat.v1.train.RMSPropOptimizer( FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon) loss_fn = dataset.cost From 4d99c9749c49f6b8f921fd8842dfd7c97e0f6374 Mon Sep 17 00:00:00 2001 From: kwliu Date: Fri, 18 Jun 2021 11:57:14 -0700 Subject: [PATCH 10/20] migrate off nametuple rnn states and apply black formatting --- Makefile | 3 + dnc/access.py | 602 ++++++++++++++++---------------- dnc/addressing.py | 702 +++++++++++++++++++------------------ dnc/dnc.py | 256 +++++++------- dnc/repeat_copy.py | 724 ++++++++++++++++++++------------------- dnc/util.py | 93 +++-- requirements.txt | 1 + setup.py | 25 +- tests/access_test.py | 288 ++++++++-------- tests/addressing_test.py | 686 +++++++++++++++++++------------------ tests/dnc_test.py | 87 ++--- tests/util_test.py | 71 ++-- train.py | 413 ++++++++++++---------- 13 files changed, 2042 insertions(+), 1909 deletions(-) diff --git a/Makefile b/Makefile index e834d28..8bba7b1 100644 --- a/Makefile +++ b/Makefile @@ -14,6 +14,9 @@ venv: test: venv python -m pytest + black . + black dnc/ + black tests/ run: : # Run your app here, e.g diff --git a/dnc/access.py b/dnc/access.py index 362f814..4c3f9c9 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -18,320 +18,348 @@ from __future__ import division from __future__ import print_function -import collections import numpy as np import sonnet as snt import tensorflow as tf from dnc import addressing, util -AccessState = collections.namedtuple('AccessState', ( - 'memory', 'read_weights', 'write_weights', 'linkage', 'usage')) - +# For indexing directly into MemoryAccess state MEMORY = 0 READ_WEIGHTS = 1 WRITE_WEIGHTS = 2 LINKAGE = 3 USAGE = 4 -def _erase_and_write(memory, address, reset_weights, values): - """Module to erase and write in the external memory. - - Erase operation: - M_t'(i) = M_{t-1}(i) * (1 - w_t(i) * e_t) - - Add operation: - M_t(i) = M_t'(i) + w_t(i) * a_t - - where e are the reset_weights, w the write weights and a the values. - - Args: - memory: 3-D tensor of shape `[batch_size, memory_size, word_size]`. - address: 3-D tensor `[batch_size, num_writes, memory_size]`. - reset_weights: 3-D tensor `[batch_size, num_writes, word_size]`. - values: 3-D tensor `[batch_size, num_writes, word_size]`. - - Returns: - 3-D tensor of shape `[batch_size, num_writes, word_size]`. - """ - expand_address = tf.expand_dims(address, 3) - reset_weights = tf.expand_dims(reset_weights, 2) - weighted_resets = expand_address * reset_weights - reset_gate = util.reduce_prod(1 - weighted_resets, 1) - memory *= reset_gate - - add_matrix = tf.matmul(address, values, adjoint_a=True) - memory += add_matrix - - return memory - - -class MemoryAccess(snt.RNNCore): - """Access module of the Differentiable Neural Computer. - - This memory module supports multiple read and write heads. It makes use of: - - * `addressing.TemporalLinkage` to track the temporal ordering of writes in - memory for each write head. - * `addressing.FreenessAllocator` for keeping track of memory usage, where - usage increase when a memory location is written to, and decreases when - memory is read from that the controller says can be freed. - - Write-address selection is done by an interpolation between content-based - lookup and using unused memory. - - Read-address selection is done by an interpolation of content-based lookup - and following the link graph in the forward or backwards read direction. - """ - def __init__(self, - memory_size=128, - word_size=20, - num_reads=1, - num_writes=1, - name='memory_access', - dtype=tf.float32): - """Creates a MemoryAccess module. - - Args: - memory_size: The number of memory slots (N in the DNC paper). - word_size: The width of each memory slot (W in the DNC paper) - num_reads: The number of read heads (R in the DNC paper). - num_writes: The number of write heads (fixed at 1 in the paper). - name: The name of the module. - """ - super(MemoryAccess, self).__init__(name=name) - self._memory_size = memory_size - self._word_size = word_size - self._num_reads = num_reads - self._num_writes = num_writes - - self._dtype = dtype - - self._write_content_weights_mod = addressing.CosineWeights( - num_writes, word_size, name='write_content_weights') - self._read_content_weights_mod = addressing.CosineWeights( - num_reads, word_size, name='read_content_weights') - - self._linkage = addressing.TemporalLinkage(memory_size, num_writes, dtype=dtype) - self._freeness = addressing.Freeness(memory_size, dtype=dtype) +def _erase_and_write(memory, address, reset_weights, values): + """Module to erase and write in the external memory. - self._linear_layers = {} + Erase operation: + M_t'(i) = M_{t-1}(i) * (1 - w_t(i) * e_t) - def call(self, inputs, prev_state): - return self.__call__(inputs, prev_state) + Add operation: + M_t(i) = M_t'(i) + w_t(i) * a_t - def __call__(self, inputs, prev_state): - """Connects the MemoryAccess module into the graph. + where e are the reset_weights, w the write weights and a the values. Args: - inputs: tensor of shape `[batch_size, input_size]`. This is used to - control this access module. - prev_state: Instance of `AccessState` containing the previous state. + memory: 3-D tensor of shape `[batch_size, memory_size, word_size]`. + address: 3-D tensor `[batch_size, num_writes, memory_size]`. + reset_weights: 3-D tensor `[batch_size, num_writes, word_size]`. + values: 3-D tensor `[batch_size, num_writes, word_size]`. Returns: - A tuple `(output, next_state)`, where `output` is a tensor of shape - `[batch_size, num_reads, word_size]`, and `next_state` is the new - `AccessState` named tuple at the current time t. + 3-D tensor of shape `[batch_size, num_writes, word_size]`. """ - prev_state = AccessState(*prev_state) - #import ipdb; ipdb.set_trace() - inputs = self._read_inputs(inputs) - - # Update usage using inputs['free_gate'] and previous read & write weights. - usage = self._freeness( - write_weights=prev_state.write_weights, - free_gate=inputs['free_gate'], - read_weights=prev_state.read_weights, - prev_usage=prev_state.usage) - - # Write to memory. - write_weights = self._write_weights(inputs, prev_state.memory, usage) - memory = _erase_and_write( - prev_state.memory, - address=write_weights, - reset_weights=inputs['erase_vectors'], - values=inputs['write_vectors']) - - linkage_state = addressing.TemporalLinkageState(*self._linkage(write_weights, prev_state.linkage)) - - # Read from memory. - read_weights = self._read_weights( - inputs, - memory=memory, - prev_read_weights=prev_state.read_weights, - link=linkage_state.link) - read_words = tf.matmul(read_weights, memory) - - return (read_words, list(AccessState( - memory=memory, - read_weights=read_weights, - write_weights=write_weights, - linkage=list(linkage_state), - usage=usage))) - - def _read_inputs(self, inputs): - """Applies transformations to `inputs` to get control for this module.""" - - def _linear(dims, name, activation=None): - """Returns a linear transformation of `inputs`, followed by a reshape.""" - linear = self._linear_layers.get(name) - if not linear: - linear = snt.Linear(np.prod(dims), name=name) - self._linear_layers[name] = linear - - linear = linear(inputs) - if activation is not None: - linear = activation(linear, name=name + '_activation') - return tf.reshape(linear, [-1, *dims]) - - # v_t^i - The vectors to write to memory, for each write head `i`. - write_vectors = _linear([self._num_writes, self._word_size], 'write_vectors') - - # e_t^i - Amount to erase the memory by before writing, for each write head. - erase_vectors = _linear([self._num_writes, self._word_size], 'erase_vectors', - tf.sigmoid) - - # f_t^j - Amount that the memory at the locations read from at the previous - # time step can be declared unused, for each read head `j`. - free_gate = _linear([self._num_reads], 'free_gate', tf.sigmoid) - - # g_t^{a, i} - Interpolation between writing to unallocated memory and - # content-based lookup, for each write head `i`. Note: `a` is simply used to - # identify this gate with allocation vs writing (as defined below). - allocation_gate = _linear([self._num_writes], 'allocation_gate', tf.sigmoid) - - # g_t^{w, i} - Overall gating of write amount for each write head. - write_gate = _linear([self._num_writes], 'write_gate', tf.sigmoid) - - # \pi_t^j - Mixing between "backwards" and "forwards" positions (for - # each write head), and content-based lookup, for each read head. - num_read_modes = 1 + 2 * self._num_writes - read_mode = snt.BatchApply(tf.nn.softmax)( - _linear([self._num_reads, num_read_modes], name='read_mode')) - - # Parameters for the (read / write) "weights by content matching" modules. - write_keys = _linear([self._num_writes, self._word_size], 'write_keys') - write_strengths = _linear([self._num_writes], name='write_strengths') - - read_keys = _linear([self._num_reads, self._word_size], 'read_keys') - read_strengths = _linear([self._num_reads], name='read_strengths') - - result = { - 'read_content_keys': read_keys, - 'read_content_strengths': read_strengths, - 'write_content_keys': write_keys, - 'write_content_strengths': write_strengths, - 'write_vectors': write_vectors, - 'erase_vectors': erase_vectors, - 'free_gate': free_gate, - 'allocation_gate': allocation_gate, - 'write_gate': write_gate, - 'read_mode': read_mode, - } - return result - - def _write_weights(self, inputs, memory, usage): - """Calculates the memory locations to write to. - - This uses a combination of content-based lookup and finding an unused - location in memory, for each write head. + expand_address = tf.expand_dims(address, 3) + reset_weights = tf.expand_dims(reset_weights, 2) + weighted_resets = expand_address * reset_weights + reset_gate = util.reduce_prod(1 - weighted_resets, 1) + memory *= reset_gate - Args: - inputs: Collection of inputs to the access module, including controls for - how to chose memory writing, such as the content to look-up and the - weighting between content-based and allocation-based addressing. - memory: A tensor of shape `[batch_size, memory_size, word_size]` - containing the current memory contents. - usage: Current memory usage, which is a tensor of shape `[batch_size, - memory_size]`, used for allocation-based addressing. + add_matrix = tf.matmul(address, values, adjoint_a=True) + memory += add_matrix - Returns: - tensor of shape `[batch_size, num_writes, memory_size]` indicating where - to write to (if anywhere) for each write head. - """ - # c_t^{w, i} - The content-based weights for each write head. - write_content_weights = self._write_content_weights_mod( - memory, inputs['write_content_keys'], - inputs['write_content_strengths']) + return memory - # a_t^i - The allocation weights for each write head. - write_allocation_weights = self._freeness.write_allocation_weights( - usage=usage, - write_gates=(inputs['allocation_gate'] * inputs['write_gate']), - num_writes=self._num_writes) - # Expands gates over memory locations. - allocation_gate = tf.expand_dims(inputs['allocation_gate'], -1) - write_gate = tf.expand_dims(inputs['write_gate'], -1) +class MemoryAccess(snt.RNNCore): + """Access module of the Differentiable Neural Computer. - # w_t^{w, i} - The write weightings for each write head. - return write_gate * (allocation_gate * write_allocation_weights + - (1 - allocation_gate) * write_content_weights) + This memory module supports multiple read and write heads. It makes use of: - def _read_weights(self, inputs, memory, prev_read_weights, link): - """Calculates read weights for each read head. + * `addressing.TemporalLinkage` to track the temporal ordering of writes in + memory for each write head. + * `addressing.Freeness` for keeping track of memory usage, where + usage increase when a memory location is written to, and decreases when + memory is read from that the controller says can be freed. - The read weights are a combination of following the link graphs in the - forward or backward directions from the previous read position, and doing - content-based lookup. The interpolation between these different modes is - done by `inputs['read_mode']`. + Write-address selection is done by an interpolation between content-based + lookup and using unused memory. - Args: - inputs: Controls for this access module. This contains the content-based - keys to lookup, and the weightings for the different read modes. - memory: A tensor of shape `[batch_size, memory_size, word_size]` - containing the current memory contents to do content-based lookup. - prev_read_weights: A tensor of shape `[batch_size, num_reads, - memory_size]` containing the previous read locations. - link: A tensor of shape `[batch_size, num_writes, memory_size, - memory_size]` containing the temporal write transition graphs. - - Returns: - A tensor of shape `[batch_size, num_reads, memory_size]` containing the - read weights for each read head. + Read-address selection is done by an interpolation of content-based lookup + and following the link graph in the forward or backwards read direction. """ - # c_t^{r, i} - The content weightings for each read head. - content_weights = self._read_content_weights_mod( - memory, inputs['read_content_keys'], inputs['read_content_strengths']) - - # Calculates f_t^i and b_t^i. - forward_weights = self._linkage.directional_read_weights( - link, prev_read_weights, forward=True) - backward_weights = self._linkage.directional_read_weights( - link, prev_read_weights, forward=False) - - backward_mode = inputs['read_mode'][:, :, :self._num_writes] - forward_mode = ( - inputs['read_mode'][:, :, self._num_writes:2 * self._num_writes]) - content_mode = inputs['read_mode'][:, :, 2 * self._num_writes] - - read_weights = ( - tf.expand_dims(content_mode, 2) * content_weights + tf.reduce_sum( - input_tensor=tf.expand_dims(forward_mode, 3) * forward_weights, axis=2) + - tf.reduce_sum(input_tensor=tf.expand_dims(backward_mode, 3) * backward_weights, axis=2)) - - return read_weights - - # keras uses get_initial_state - def get_initial_state(self, batch_size=None, inputs=None, dtype=None): - return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) - - # snt.RNNCore uses initial_state - def initial_state(self, batch_size): - return self.get_initial_state(batch_size) - - @property - def state_size(self): - """Returns a tuple of the shape of the state tensors.""" - return list(AccessState( - memory=tf.TensorShape([self._memory_size, self._word_size]), - read_weights=tf.TensorShape([self._num_reads, self._memory_size]), - write_weights=tf.TensorShape([self._num_writes, self._memory_size]), - linkage=self._linkage.state_size, - usage=self._freeness.state_size)) - - @property - def output_size(self): - """Returns the output shape.""" - return tf.TensorShape([self._num_reads, self._word_size]) + + def __init__( + self, + memory_size=128, + word_size=20, + num_reads=1, + num_writes=1, + name="memory_access", + dtype=tf.float32, + ): + """Creates a MemoryAccess module. + + Args: + memory_size: The number of memory slots (N in the DNC paper). + word_size: The width of each memory slot (W in the DNC paper) + num_reads: The number of read heads (R in the DNC paper). + num_writes: The number of write heads (fixed at 1 in the paper). + name: The name of the module. + """ + super(MemoryAccess, self).__init__(name=name) + self._memory_size = memory_size + self._word_size = word_size + self._num_reads = num_reads + self._num_writes = num_writes + + self._dtype = dtype + + self._write_content_weights_mod = addressing.CosineWeights( + num_writes, word_size, name="write_content_weights" + ) + self._read_content_weights_mod = addressing.CosineWeights( + num_reads, word_size, name="read_content_weights" + ) + + self._linkage = addressing.TemporalLinkage(memory_size, num_writes, dtype=dtype) + self._freeness = addressing.Freeness(memory_size, dtype=dtype) + + self._linear_layers = {} + + def call(self, inputs, prev_state): + return self.__call__(inputs, prev_state) + + def __call__(self, inputs, prev_state): + """Connects the MemoryAccess module into the graph. + + Args: + inputs: tensor of shape `[batch_size, input_size]`. This is used to + control this access module. + prev_state: nested list of tensors containing the previous state. + + Returns: + A tuple `(output, next_state)`, where `output` is a tensor of shape + `[batch_size, num_reads, word_size]`, and `next_state` is the new + nested list of tensors at the current time t. + """ + ( + prev_memory, + prev_read_weights, + prev_write_weights, + prev_linkage, + prev_usage, + ) = prev_state + + inputs = self._read_inputs(inputs) + + # Update usage using inputs['free_gate'] and previous read & write weights. + usage = self._freeness( + write_weights=prev_write_weights, + free_gate=inputs["free_gate"], + read_weights=prev_read_weights, + prev_usage=prev_usage, + ) + + # Write to memory. + write_weights = self._write_weights(inputs, prev_memory, usage) + memory = _erase_and_write( + prev_memory, + address=write_weights, + reset_weights=inputs["erase_vectors"], + values=inputs["write_vectors"], + ) + + [link, precedence_weights] = self._linkage(write_weights, prev_linkage) + + # Read from memory. + read_weights = self._read_weights( + inputs, memory=memory, prev_read_weights=prev_read_weights, link=link + ) + read_words = tf.matmul(read_weights, memory) + + return ( + read_words, + [memory, read_weights, write_weights, [link, precedence_weights], usage], + ) + + def _read_inputs(self, inputs): + """Applies transformations to `inputs` to get control for this module.""" + + def _linear(dims, name, activation=None): + """Returns a linear transformation of `inputs`, followed by a reshape.""" + linear = self._linear_layers.get(name) + if not linear: + linear = snt.Linear(np.prod(dims), name=name) + self._linear_layers[name] = linear + + linear = linear(inputs) + if activation is not None: + linear = activation(linear, name=name + "_activation") + return tf.reshape(linear, [-1, *dims]) + + # v_t^i - The vectors to write to memory, for each write head `i`. + write_vectors = _linear([self._num_writes, self._word_size], "write_vectors") + + # e_t^i - Amount to erase the memory by before writing, for each write head. + erase_vectors = _linear( + [self._num_writes, self._word_size], "erase_vectors", tf.sigmoid + ) + + # f_t^j - Amount that the memory at the locations read from at the previous + # time step can be declared unused, for each read head `j`. + free_gate = _linear([self._num_reads], "free_gate", tf.sigmoid) + + # g_t^{a, i} - Interpolation between writing to unallocated memory and + # content-based lookup, for each write head `i`. Note: `a` is simply used to + # identify this gate with allocation vs writing (as defined below). + allocation_gate = _linear([self._num_writes], "allocation_gate", tf.sigmoid) + + # g_t^{w, i} - Overall gating of write amount for each write head. + write_gate = _linear([self._num_writes], "write_gate", tf.sigmoid) + + # \pi_t^j - Mixing between "backwards" and "forwards" positions (for + # each write head), and content-based lookup, for each read head. + num_read_modes = 1 + 2 * self._num_writes + read_mode = snt.BatchApply(tf.nn.softmax)( + _linear([self._num_reads, num_read_modes], name="read_mode") + ) + + # Parameters for the (read / write) "weights by content matching" modules. + write_keys = _linear([self._num_writes, self._word_size], "write_keys") + write_strengths = _linear([self._num_writes], name="write_strengths") + + read_keys = _linear([self._num_reads, self._word_size], "read_keys") + read_strengths = _linear([self._num_reads], name="read_strengths") + + result = { + "read_content_keys": read_keys, + "read_content_strengths": read_strengths, + "write_content_keys": write_keys, + "write_content_strengths": write_strengths, + "write_vectors": write_vectors, + "erase_vectors": erase_vectors, + "free_gate": free_gate, + "allocation_gate": allocation_gate, + "write_gate": write_gate, + "read_mode": read_mode, + } + return result + + def _write_weights(self, inputs, memory, usage): + """Calculates the memory locations to write to. + + This uses a combination of content-based lookup and finding an unused + location in memory, for each write head. + + Args: + inputs: Collection of inputs to the access module, including controls for + how to chose memory writing, such as the content to look-up and the + weighting between content-based and allocation-based addressing. + memory: A tensor of shape `[batch_size, memory_size, word_size]` + containing the current memory contents. + usage: Current memory usage, which is a tensor of shape `[batch_size, + memory_size]`, used for allocation-based addressing. + + Returns: + tensor of shape `[batch_size, num_writes, memory_size]` indicating where + to write to (if anywhere) for each write head. + """ + # c_t^{w, i} - The content-based weights for each write head. + write_content_weights = self._write_content_weights_mod( + memory, inputs["write_content_keys"], inputs["write_content_strengths"] + ) + + # a_t^i - The allocation weights for each write head. + write_allocation_weights = self._freeness.write_allocation_weights( + usage=usage, + write_gates=(inputs["allocation_gate"] * inputs["write_gate"]), + num_writes=self._num_writes, + ) + + # Expands gates over memory locations. + allocation_gate = tf.expand_dims(inputs["allocation_gate"], -1) + write_gate = tf.expand_dims(inputs["write_gate"], -1) + + # w_t^{w, i} - The write weightings for each write head. + return write_gate * ( + allocation_gate * write_allocation_weights + + (1 - allocation_gate) * write_content_weights + ) + + def _read_weights(self, inputs, memory, prev_read_weights, link): + """Calculates read weights for each read head. + + The read weights are a combination of following the link graphs in the + forward or backward directions from the previous read position, and doing + content-based lookup. The interpolation between these different modes is + done by `inputs['read_mode']`. + + Args: + inputs: Controls for this access module. This contains the content-based + keys to lookup, and the weightings for the different read modes. + memory: A tensor of shape `[batch_size, memory_size, word_size]` + containing the current memory contents to do content-based lookup. + prev_read_weights: A tensor of shape `[batch_size, num_reads, + memory_size]` containing the previous read locations. + link: A tensor of shape `[batch_size, num_writes, memory_size, + memory_size]` containing the temporal write transition graphs. + + Returns: + A tensor of shape `[batch_size, num_reads, memory_size]` containing the + read weights for each read head. + """ + # c_t^{r, i} - The content weightings for each read head. + content_weights = self._read_content_weights_mod( + memory, inputs["read_content_keys"], inputs["read_content_strengths"] + ) + + # Calculates f_t^i and b_t^i. + forward_weights = self._linkage.directional_read_weights( + link, prev_read_weights, forward=True + ) + backward_weights = self._linkage.directional_read_weights( + link, prev_read_weights, forward=False + ) + + backward_mode = inputs["read_mode"][:, :, : self._num_writes] + forward_mode = inputs["read_mode"][ + :, :, self._num_writes : 2 * self._num_writes + ] + content_mode = inputs["read_mode"][:, :, 2 * self._num_writes] + + read_weights = ( + tf.expand_dims(content_mode, 2) * content_weights + + tf.reduce_sum( + input_tensor=tf.expand_dims(forward_mode, 3) * forward_weights, axis=2 + ) + + tf.reduce_sum( + input_tensor=tf.expand_dims(backward_mode, 3) * backward_weights, axis=2 + ) + ) + + return read_weights + + # keras uses get_initial_state + def get_initial_state(self, batch_size=None, inputs=None, dtype=None): + return util.initial_state_from_state_size( + self.state_size, batch_size, self._dtype + ) + + # snt.RNNCore uses initial_state + def initial_state(self, batch_size): + return self.get_initial_state(batch_size=batch_size) + + @property + def state_size(self): + """Returns a list of the shape of the state tensors.""" + return [ + # memory + tf.TensorShape([self._memory_size, self._word_size]), + # read_weights + tf.TensorShape([self._num_reads, self._memory_size]), + # write_weights + tf.TensorShape([self._num_writes, self._memory_size]), + # linkage + self._linkage.state_size, + # usage + self._freeness.state_size, + ] + + @property + def output_size(self): + """Returns the output shape.""" + return tf.TensorShape([self._num_reads, self._word_size]) diff --git a/dnc/addressing.py b/dnc/addressing.py index 2b2ee3d..6c4832c 100644 --- a/dnc/addressing.py +++ b/dnc/addressing.py @@ -18,7 +18,6 @@ from __future__ import division from __future__ import print_function -import collections import sonnet as snt import tensorflow as tf @@ -27,392 +26,391 @@ # Ensure values are greater than epsilon to avoid numerical instability. _EPSILON = 1e-6 -TemporalLinkageState = collections.namedtuple('TemporalLinkageState', - ('link', 'precedence_weights')) - +# For indexing directly into TemporalLinkage state LINK = 0 PRECEDENCE_WEIGHTS = 1 + def _vector_norms(m): - squared_norms = tf.compat.v1.reduce_sum(input_tensor=m * m, axis=2, keepdims=True) - return tf.sqrt(squared_norms + _EPSILON) + squared_norms = tf.compat.v1.reduce_sum(input_tensor=m * m, axis=2, keepdims=True) + return tf.sqrt(squared_norms + _EPSILON) def weighted_softmax(activations, strengths, strengths_op): - """Returns softmax over activations multiplied by positive strengths. - - Args: - activations: A tensor of shape `[batch_size, num_heads, memory_size]`, of - activations to be transformed. Softmax is taken over the last dimension. - strengths: A tensor of shape `[batch_size, num_heads]` containing strengths to - multiply by the activations prior to the softmax. - strengths_op: An operation to transform strengths before softmax. - - Returns: - A tensor of same shape as `activations` with weighted softmax applied. - """ - transformed_strengths = tf.expand_dims(strengths_op(strengths), -1) - sharp_activations = activations * transformed_strengths - softmax = snt.BatchApply(module=tf.nn.softmax) - return softmax(sharp_activations) - - -class CosineWeights(snt.Module): - """Cosine-weighted attention. - - Calculates the cosine similarity between a query and each word in memory, then - applies a weighted softmax to return a sharp distribution. - """ - - def __init__(self, - num_heads, - word_size, - strength_op=tf.nn.softplus, - name='cosine_weights'): - """Initializes the CosineWeights module. + """Returns softmax over activations multiplied by positive strengths. Args: - num_heads: number of memory heads. - word_size: memory word size. - strength_op: operation to apply to strengths (default is tf.nn.softplus). - name: module name (default 'cosine_weights') - """ - super(CosineWeights, self).__init__(name=name) - self._num_heads = num_heads - self._word_size = word_size - self._strength_op = strength_op - - def __call__(self, memory, keys, strengths): - """Connects the CosineWeights module into the graph. - - Args: - memory: A 3-D tensor of shape `[batch_size, memory_size, word_size]`. - keys: A 3-D tensor of shape `[batch_size, num_heads, word_size]`. - strengths: A 2-D tensor of shape `[batch_size, num_heads]`. + activations: A tensor of shape `[batch_size, num_heads, memory_size]`, of + activations to be transformed. Softmax is taken over the last dimension. + strengths: A tensor of shape `[batch_size, num_heads]` containing strengths to + multiply by the activations prior to the softmax. + strengths_op: An operation to transform strengths before softmax. Returns: - Weights tensor of shape `[batch_size, num_heads, memory_size]`. + A tensor of same shape as `activations` with weighted softmax applied. """ - # Calculates the inner product between the query vector and words in memory. - dot = tf.matmul(keys, memory, adjoint_b=True) - - # Outer product to compute denominator (euclidean norm of query and memory). - memory_norms = _vector_norms(memory) - key_norms = _vector_norms(keys) - norm = tf.matmul(key_norms, memory_norms, adjoint_b=True) - - # Calculates cosine similarity between the query vector and words in memory. - similarity = dot / (norm + _EPSILON) - - return weighted_softmax(similarity, strengths, self._strength_op) - - -class TemporalLinkage(snt.RNNCore): - """Keeps track of write order for forward and backward addressing. + transformed_strengths = tf.expand_dims(strengths_op(strengths), -1) + sharp_activations = activations * transformed_strengths + softmax = snt.BatchApply(module=tf.nn.softmax) + return softmax(sharp_activations) - This is a pseudo-RNNCore module, whose state is a pair `(link, - precedence_weights)`, where `link` is a (collection of) graphs for (possibly - multiple) write heads (represented by a tensor with values in the range - [0, 1]), and `precedence_weights` records the "previous write locations" used - to build the link graphs. - - The function `directional_read_weights` computes addresses following the - forward and backward directions in the link graphs. - """ - - def __init__(self, memory_size, num_writes, name='temporal_linkage', dtype=tf.float32): - """Construct a TemporalLinkage module. - - Args: - memory_size: The number of memory slots. - num_writes: The number of write heads. - name: Name of the module. - """ - super(TemporalLinkage, self).__init__(name=name) - self._memory_size = memory_size - self._num_writes = num_writes - self._dtype = dtype - - def __call__(self, write_weights, prev_state): - """Calculate the updated linkage state given the write weights. - - Args: - write_weights: A tensor of shape `[batch_size, num_writes, memory_size]` - containing the memory addresses of the different write heads. - prev_state: `TemporalLinkageState` tuple containg a tensor `link` of - shape `[batch_size, num_writes, memory_size, memory_size]`, and a - tensor `precedence_weights` of shape `[batch_size, num_writes, - memory_size]` containing the aggregated history of recent writes. - - Returns: - A `TemporalLinkageState` tuple `next_state`, which contains the updated - link and precedence weights. - """ - link = self._link(prev_state[LINK], prev_state[PRECEDENCE_WEIGHTS], - write_weights) - precedence_weights = self._precedence_weights(prev_state[PRECEDENCE_WEIGHTS], - write_weights) - return list(TemporalLinkageState( - link=link, precedence_weights=precedence_weights)) - - def directional_read_weights(self, link, prev_read_weights, forward): - """Calculates the forward or the backward read weights. - - For each read head (at a given address), there are `num_writes` link graphs - to follow. Thus this function computes a read address for each of the - `num_reads * num_writes` pairs of read and write heads. - - Args: - link: tensor of shape `[batch_size, num_writes, memory_size, - memory_size]` representing the link graphs L_t. - prev_read_weights: tensor of shape `[batch_size, num_reads, - memory_size]` containing the previous read weights w_{t-1}^r. - forward: Boolean indicating whether to follow the "future" direction in - the link graph (True) or the "past" direction (False). - - Returns: - tensor of shape `[batch_size, num_reads, num_writes, memory_size]` - """ - # We calculate the forward and backward directions for each pair of - # read and write heads; hence we need to tile the read weights and do a - # sort of "outer product" to get this. - expanded_read_weights = tf.stack([prev_read_weights] * self._num_writes, - 1) - result = tf.matmul(expanded_read_weights, link, adjoint_b=forward) - # Swap dimensions 1, 2 so order is [batch, reads, writes, memory]: - return tf.transpose(a=result, perm=[0, 2, 1, 3]) - - def _link(self, prev_link, prev_precedence_weights, write_weights): - """Calculates the new link graphs. - - For each write head, the link is a directed graph (represented by a matrix - with entries in range [0, 1]) whose vertices are the memory locations, and - an edge indicates temporal ordering of writes. - Args: - prev_link: A tensor of shape `[batch_size, num_writes, memory_size, - memory_size]` representing the previous link graphs for each write - head. - prev_precedence_weights: A tensor of shape `[batch_size, num_writes, - memory_size]` which is the previous "aggregated" write weights for - each write head. - write_weights: A tensor of shape `[batch_size, num_writes, memory_size]` - containing the new locations in memory written to. - - Returns: - A tensor of shape `[batch_size, num_writes, memory_size, memory_size]` - containing the new link graphs for each write head. - """ - batch_size = tf.shape(input=prev_link)[0] - write_weights_i = tf.expand_dims(write_weights, 3) - write_weights_j = tf.expand_dims(write_weights, 2) - prev_precedence_weights_j = tf.expand_dims(prev_precedence_weights, 2) - prev_link_scale = 1 - write_weights_i - write_weights_j - new_link = write_weights_i * prev_precedence_weights_j - link = prev_link_scale * prev_link + new_link - # Return the link with the diagonal set to zero, to remove self-looping - # edges. - return tf.linalg.set_diag( - link, - tf.zeros( - [batch_size, self._num_writes, self._memory_size], - dtype=link.dtype)) - - def _precedence_weights(self, prev_precedence_weights, write_weights): - """Calculates the new precedence weights given the current write weights. - - The precedence weights are the "aggregated write weights" for each write - head, where write weights with sum close to zero will leave the precedence - weights unchanged, but with sum close to one will replace the precedence - weights. - - Args: - prev_precedence_weights: A tensor of shape `[batch_size, num_writes, - memory_size]` containing the previous precedence weights. - write_weights: A tensor of shape `[batch_size, num_writes, memory_size]` - containing the new write weights. +class CosineWeights(snt.Module): + """Cosine-weighted attention. - Returns: - A tensor of shape `[batch_size, num_writes, memory_size]` containing the - new precedence weights. + Calculates the cosine similarity between a query and each word in memory, then + applies a weighted softmax to return a sharp distribution. """ - write_sum = tf.reduce_sum(input_tensor=write_weights, axis=2, keepdims=True) - return (1 - write_sum) * prev_precedence_weights + write_weights - - def initial_state(self, batch_size): - return util.initial_state_from_state_size(self.state_size, batch_size, self._dtype) + def __init__( + self, num_heads, word_size, strength_op=tf.nn.softplus, name="cosine_weights" + ): + """Initializes the CosineWeights module. - @property - def state_size(self): - """Returns a `TemporalLinkageState` tuple of the state tensors' shapes.""" - return list(TemporalLinkageState( - link=tf.TensorShape( - [self._num_writes, self._memory_size, self._memory_size]), - precedence_weights=tf.TensorShape( - [self._num_writes, self._memory_size]) - )) + Args: + num_heads: number of memory heads. + word_size: memory word size. + strength_op: operation to apply to strengths (default is tf.nn.softplus). + name: module name (default 'cosine_weights') + """ + super(CosineWeights, self).__init__(name=name) + self._num_heads = num_heads + self._word_size = word_size + self._strength_op = strength_op -class Freeness(snt.RNNCore): - """Memory usage that is increased by writing and decreased by reading. - - This module is a pseudo-RNNCore whose state is a tensor with values in - the range [0, 1] indicating the usage of each of `memory_size` memory slots. - - The usage is: + def __call__(self, memory, keys, strengths): + """Connects the CosineWeights module into the graph. - * Increased by writing, where usage is increased towards 1 at the write - addresses. - * Decreased by reading, where usage is decreased after reading from a - location when free_gate is close to 1. + Args: + memory: A 3-D tensor of shape `[batch_size, memory_size, word_size]`. + keys: A 3-D tensor of shape `[batch_size, num_heads, word_size]`. + strengths: A 2-D tensor of shape `[batch_size, num_heads]`. - The function `write_allocation_weights` can be invoked to get free locations - to write to for a number of write heads. - """ + Returns: + Weights tensor of shape `[batch_size, num_heads, memory_size]`. + """ + # Calculates the inner product between the query vector and words in memory. + dot = tf.matmul(keys, memory, adjoint_b=True) - def __init__(self, memory_size, name='freeness', dtype=tf.float32): - """Creates a Freeness module. + # Outer product to compute denominator (euclidean norm of query and memory). + memory_norms = _vector_norms(memory) + key_norms = _vector_norms(keys) + norm = tf.matmul(key_norms, memory_norms, adjoint_b=True) - Args: - memory_size: Number of memory slots. - name: Name of the module. - """ - super(Freeness, self).__init__(name=name) - self._memory_size = memory_size - self._dtype = dtype - - def __call__(self, write_weights, free_gate, read_weights, prev_usage): - """Calculates the new memory usage u_t. - - Memory that was written to in the previous time step will have its usage - increased; memory that was read from and the controller says can be "freed" - will have its usage decreased. - - Args: - write_weights: tensor of shape `[batch_size, num_writes, - memory_size]` giving write weights at previous time step. - free_gate: tensor of shape `[batch_size, num_reads]` which indicates - which read heads read memory that can now be freed. - read_weights: tensor of shape `[batch_size, num_reads, - memory_size]` giving read weights at previous time step. - prev_usage: tensor of shape `[batch_size, memory_size]` giving - usage u_{t - 1} at the previous time step, with entries in range - [0, 1]. + # Calculates cosine similarity between the query vector and words in memory. + similarity = dot / (norm + _EPSILON) - Returns: - tensor of shape `[batch_size, memory_size]` representing updated memory - usage. - """ - # Calculation of usage is not differentiable with respect to write weights. - write_weights = tf.stop_gradient(write_weights) - usage = self._usage_after_write(prev_usage, write_weights) - usage = self._usage_after_read(usage, free_gate, read_weights) - return usage + return weighted_softmax(similarity, strengths, self._strength_op) - def write_allocation_weights(self, usage, write_gates, num_writes): - """Calculates freeness-based locations for writing to. - This finds unused memory by ranking the memory locations by usage, for each - write head. (For more than one write head, we use a "simulated new usage" - which takes into account the fact that the previous write head will increase - the usage in that area of the memory.) +class TemporalLinkage(snt.RNNCore): + """Keeps track of write order for forward and backward addressing. - Args: - usage: A tensor of shape `[batch_size, memory_size]` representing - current memory usage. - write_gates: A tensor of shape `[batch_size, num_writes]` with values in - the range [0, 1] indicating how much each write head does writing - based on the address returned here (and hence how much usage - increases). - num_writes: The number of write heads to calculate write weights for. + This is a pseudo-RNNCore module, whose state is a pair `(link, + precedence_weights)`, where `link` is a (collection of) graphs for (possibly + multiple) write heads (represented by a tensor with values in the range + [0, 1]), and `precedence_weights` records the "previous write locations" used + to build the link graphs. - Returns: - tensor of shape `[batch_size, num_writes, memory_size]` containing the - freeness-based write locations. Note that this isn't scaled by - `write_gate`; this scaling must be applied externally. + The function `directional_read_weights` computes addresses following the + forward and backward directions in the link graphs. """ - # expand gatings over memory locations - write_gates = tf.expand_dims(write_gates, -1) - allocation_weights = [] - for i in range(num_writes): - allocation_weights.append(self._allocation(usage)) - # update usage to take into account writing to this new allocation - usage += ((1 - usage) * write_gates[:, i, :] * allocation_weights[i]) + def __init__( + self, memory_size, num_writes, name="temporal_linkage", dtype=tf.float32 + ): + """Construct a TemporalLinkage module. + + Args: + memory_size: The number of memory slots. + num_writes: The number of write heads. + name: Name of the module. + """ + super(TemporalLinkage, self).__init__(name=name) + self._memory_size = memory_size + self._num_writes = num_writes + self._dtype = dtype + + def __call__(self, write_weights, prev_state): + """Calculate the updated linkage state given the write weights. + + Args: + write_weights: A tensor of shape `[batch_size, num_writes, memory_size]` + containing the memory addresses of the different write heads. + prev_state: list of tensors containg a tensor `link` of + shape `[batch_size, num_writes, memory_size, memory_size]`, and a + tensor `precedence_weights` of shape `[batch_size, num_writes, + memory_size]` containing the aggregated history of recent writes. + + Returns: + A list of tensors `next_state`, which contains the updated + link and precedence weights. + """ + prev_link, prev_precedence_weights = prev_state + + return [ + self._link(prev_link, prev_precedence_weights, write_weights), + self._precedence_weights(prev_precedence_weights, write_weights), + ] + + def directional_read_weights(self, link, prev_read_weights, forward): + """Calculates the forward or the backward read weights. + + For each read head (at a given address), there are `num_writes` link graphs + to follow. Thus this function computes a read address for each of the + `num_reads * num_writes` pairs of read and write heads. + + Args: + link: tensor of shape `[batch_size, num_writes, memory_size, + memory_size]` representing the link graphs L_t. + prev_read_weights: tensor of shape `[batch_size, num_reads, + memory_size]` containing the previous read weights w_{t-1}^r. + forward: Boolean indicating whether to follow the "future" direction in + the link graph (True) or the "past" direction (False). + + Returns: + tensor of shape `[batch_size, num_reads, num_writes, memory_size]` + """ + # We calculate the forward and backward directions for each pair of + # read and write heads; hence we need to tile the read weights and do a + # sort of "outer product" to get this. + expanded_read_weights = tf.stack([prev_read_weights] * self._num_writes, 1) + result = tf.matmul(expanded_read_weights, link, adjoint_b=forward) + # Swap dimensions 1, 2 so order is [batch, reads, writes, memory]: + return tf.transpose(a=result, perm=[0, 2, 1, 3]) + + def _link(self, prev_link, prev_precedence_weights, write_weights): + """Calculates the new link graphs. + + For each write head, the link is a directed graph (represented by a matrix + with entries in range [0, 1]) whose vertices are the memory locations, and + an edge indicates temporal ordering of writes. + + Args: + prev_link: A tensor of shape `[batch_size, num_writes, memory_size, + memory_size]` representing the previous link graphs for each write + head. + prev_precedence_weights: A tensor of shape `[batch_size, num_writes, + memory_size]` which is the previous "aggregated" write weights for + each write head. + write_weights: A tensor of shape `[batch_size, num_writes, memory_size]` + containing the new locations in memory written to. + + Returns: + A tensor of shape `[batch_size, num_writes, memory_size, memory_size]` + containing the new link graphs for each write head. + """ + batch_size = tf.shape(input=prev_link)[0] + write_weights_i = tf.expand_dims(write_weights, 3) + write_weights_j = tf.expand_dims(write_weights, 2) + prev_precedence_weights_j = tf.expand_dims(prev_precedence_weights, 2) + prev_link_scale = 1 - write_weights_i - write_weights_j + new_link = write_weights_i * prev_precedence_weights_j + link = prev_link_scale * prev_link + new_link + # Return the link with the diagonal set to zero, to remove self-looping + # edges. + return tf.linalg.set_diag( + link, + tf.zeros( + [batch_size, self._num_writes, self._memory_size], dtype=link.dtype + ), + ) + + def _precedence_weights(self, prev_precedence_weights, write_weights): + """Calculates the new precedence weights given the current write weights. + + The precedence weights are the "aggregated write weights" for each write + head, where write weights with sum close to zero will leave the precedence + weights unchanged, but with sum close to one will replace the precedence + weights. + + Args: + prev_precedence_weights: A tensor of shape `[batch_size, num_writes, + memory_size]` containing the previous precedence weights. + write_weights: A tensor of shape `[batch_size, num_writes, memory_size]` + containing the new write weights. + + Returns: + A tensor of shape `[batch_size, num_writes, memory_size]` containing the + new precedence weights. + """ + write_sum = tf.reduce_sum(input_tensor=write_weights, axis=2, keepdims=True) + return (1 - write_sum) * prev_precedence_weights + write_weights + + def initial_state(self, batch_size): + return util.initial_state_from_state_size( + self.state_size, batch_size, self._dtype + ) + + @property + def state_size(self): + """Returns a list of the state tensors' shapes.""" + return [ + # link + tf.TensorShape([self._num_writes, self._memory_size, self._memory_size]), + # precedence_weights + tf.TensorShape([self._num_writes, self._memory_size]), + ] - # Pack the allocation weights for the write heads into one tensor. - return tf.stack(allocation_weights, axis=1) - def _usage_after_write(self, prev_usage, write_weights): - """Calculates the new usage after writing to memory. - - Args: - prev_usage: tensor of shape `[batch_size, memory_size]`. - write_weights: tensor of shape `[batch_size, num_writes, memory_size]`. +class Freeness(snt.RNNCore): + """Memory usage that is increased by writing and decreased by reading. - Returns: - New usage, a tensor of shape `[batch_size, memory_size]`. - """ - # Calculate the aggregated effect of all write heads - write_weights = 1 - util.reduce_prod(1 - write_weights, 1) - return prev_usage + (1 - prev_usage) * write_weights + This module is a pseudo-RNNCore whose state is a tensor with values in + the range [0, 1] indicating the usage of each of `memory_size` memory slots. - def _usage_after_read(self, prev_usage, free_gate, read_weights): - """Calcualtes the new usage after reading and freeing from memory. + The usage is: - Args: - prev_usage: tensor of shape `[batch_size, memory_size]`. - free_gate: tensor of shape `[batch_size, num_reads]` with entries in the - range [0, 1] indicating the amount that locations read from can be - freed. - read_weights: tensor of shape `[batch_size, num_reads, memory_size]`. + * Increased by writing, where usage is increased towards 1 at the write + addresses. + * Decreased by reading, where usage is decreased after reading from a + location when free_gate is close to 1. - Returns: - New usage, a tensor of shape `[batch_size, memory_size]`. + The function `write_allocation_weights` can be invoked to get free locations + to write to for a number of write heads. """ - free_gate = tf.expand_dims(free_gate, -1) - free_read_weights = free_gate * read_weights - phi = util.reduce_prod(1 - free_read_weights, 1, name='phi') - return prev_usage * phi - - def _allocation(self, usage): - r"""Computes allocation by sorting `usage`. - - This corresponds to the value a = a_t[\phi_t[j]] in the paper. - Args: - usage: tensor of shape `[batch_size, memory_size]` indicating current - memory usage. This is equal to u_t in the paper when we only have one - write head, but for multiple write heads, one should update the usage - while iterating through the write heads to take into account the - allocation returned by this function. - - Returns: - Tensor of shape `[batch_size, memory_size]` corresponding to allocation. - """ - # Ensure values are not too small prior to cumprod. - usage = _EPSILON + (1 - _EPSILON) * usage - - nonusage = 1 - usage - sorted_nonusage, indices = tf.nn.top_k( - nonusage, k=self._memory_size, name='sort') - sorted_usage = 1 - sorted_nonusage - prod_sorted_usage = tf.math.cumprod(sorted_usage, axis=1, exclusive=True) - sorted_allocation = sorted_nonusage * prod_sorted_usage - inverse_indices = tf.cast( - util.batch_invert_permutation(indices), - tf.int32 - ) - - # This final line "unsorts" sorted_allocation, so that the indexing - # corresponds to the original indexing of `usage`. - return util.batch_gather(sorted_allocation, inverse_indices) - - # freeness size is independent of batch size - def initial_state(self, batch_size): - return tf.zeros([self._memory_size], dtype=self._dtype) - - @property - def state_size(self): - """Returns the shape of the state tensor.""" - return tf.TensorShape([self._memory_size]) + def __init__(self, memory_size, name="freeness", dtype=tf.float32): + """Creates a Freeness module. + + Args: + memory_size: Number of memory slots. + name: Name of the module. + """ + super(Freeness, self).__init__(name=name) + self._memory_size = memory_size + self._dtype = dtype + + def __call__(self, write_weights, free_gate, read_weights, prev_usage): + """Calculates the new memory usage u_t. + + Memory that was written to in the previous time step will have its usage + increased; memory that was read from and the controller says can be "freed" + will have its usage decreased. + + Args: + write_weights: tensor of shape `[batch_size, num_writes, + memory_size]` giving write weights at previous time step. + free_gate: tensor of shape `[batch_size, num_reads]` which indicates + which read heads read memory that can now be freed. + read_weights: tensor of shape `[batch_size, num_reads, + memory_size]` giving read weights at previous time step. + prev_usage: tensor of shape `[batch_size, memory_size]` giving + usage u_{t - 1} at the previous time step, with entries in range + [0, 1]. + + Returns: + tensor of shape `[batch_size, memory_size]` representing updated memory + usage. + """ + # Calculation of usage is not differentiable with respect to write weights. + write_weights = tf.stop_gradient(write_weights) + usage = self._usage_after_write(prev_usage, write_weights) + usage = self._usage_after_read(usage, free_gate, read_weights) + return usage + + def write_allocation_weights(self, usage, write_gates, num_writes): + """Calculates freeness-based locations for writing to. + + This finds unused memory by ranking the memory locations by usage, for each + write head. (For more than one write head, we use a "simulated new usage" + which takes into account the fact that the previous write head will increase + the usage in that area of the memory.) + + Args: + usage: A tensor of shape `[batch_size, memory_size]` representing + current memory usage. + write_gates: A tensor of shape `[batch_size, num_writes]` with values in + the range [0, 1] indicating how much each write head does writing + based on the address returned here (and hence how much usage + increases). + num_writes: The number of write heads to calculate write weights for. + + Returns: + tensor of shape `[batch_size, num_writes, memory_size]` containing the + freeness-based write locations. Note that this isn't scaled by + `write_gate`; this scaling must be applied externally. + """ + # expand gatings over memory locations + write_gates = tf.expand_dims(write_gates, -1) + + allocation_weights = [] + for i in range(num_writes): + allocation_weights.append(self._allocation(usage)) + # update usage to take into account writing to this new allocation + usage += (1 - usage) * write_gates[:, i, :] * allocation_weights[i] + + # Pack the allocation weights for the write heads into one tensor. + return tf.stack(allocation_weights, axis=1) + + def _usage_after_write(self, prev_usage, write_weights): + """Calculates the new usage after writing to memory. + + Args: + prev_usage: tensor of shape `[batch_size, memory_size]`. + write_weights: tensor of shape `[batch_size, num_writes, memory_size]`. + + Returns: + New usage, a tensor of shape `[batch_size, memory_size]`. + """ + # Calculate the aggregated effect of all write heads + write_weights = 1 - util.reduce_prod(1 - write_weights, 1) + return prev_usage + (1 - prev_usage) * write_weights + + def _usage_after_read(self, prev_usage, free_gate, read_weights): + """Calculates the new usage after reading and freeing from memory. + + Args: + prev_usage: tensor of shape `[batch_size, memory_size]`. + free_gate: tensor of shape `[batch_size, num_reads]` with entries in the + range [0, 1] indicating the amount that locations read from can be + freed. + read_weights: tensor of shape `[batch_size, num_reads, memory_size]`. + + Returns: + New usage, a tensor of shape `[batch_size, memory_size]`. + """ + free_gate = tf.expand_dims(free_gate, -1) + free_read_weights = free_gate * read_weights + phi = util.reduce_prod(1 - free_read_weights, 1, name="phi") + return prev_usage * phi + + def _allocation(self, usage): + r"""Computes allocation by sorting `usage`. + + This corresponds to the value a = a_t[\phi_t[j]] in the paper. + + Args: + usage: tensor of shape `[batch_size, memory_size]` indicating current + memory usage. This is equal to u_t in the paper when we only have one + write head, but for multiple write heads, one should update the usage + while iterating through the write heads to take into account the + allocation returned by this function. + + Returns: + Tensor of shape `[batch_size, memory_size]` corresponding to allocation. + """ + # Ensure values are not too small prior to cumprod. + usage = _EPSILON + (1 - _EPSILON) * usage + + nonusage = 1 - usage + sorted_nonusage, indices = tf.nn.top_k( + nonusage, k=self._memory_size, name="sort" + ) + sorted_usage = 1 - sorted_nonusage + prod_sorted_usage = tf.math.cumprod(sorted_usage, axis=1, exclusive=True) + sorted_allocation = sorted_nonusage * prod_sorted_usage + inverse_indices = tf.cast(util.batch_invert_permutation(indices), tf.int32) + + # This final line "unsorts" sorted_allocation, so that the indexing + # corresponds to the original indexing of `usage`. + return util.batch_gather(sorted_allocation, inverse_indices) + + # freeness size is independent of batch size + def initial_state(self, batch_size): + return tf.zeros([self._memory_size], dtype=self._dtype) + + @property + def state_size(self): + """Returns the shape of the state tensor.""" + return tf.TensorShape([self._memory_size]) diff --git a/dnc/dnc.py b/dnc/dnc.py index a7094b1..213d3d6 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -22,145 +22,139 @@ from __future__ import division from __future__ import print_function -import collections -import numpy as np import sonnet as snt import tensorflow as tf from dnc import access, util -DNCState = collections.namedtuple('DNCState', - ('access_output', 'access_state', 'controller_state')) - +# For directly indexing into DNC state ACCESS_OUTPUT = 0 ACCESS_STATE = 1 CONTROLLER_STATE = 2 + class DNC(snt.RNNCore): - """DNC core module. - - Contains controller and memory access module. - """ - def __init__(self, - access_config, - controller_config, - output_size, - batch_size, - clip_value=None, - name='dnc', - dtype=tf.float32): - """Initializes the DNC core. - - Args: - access_config: dictionary of access module configurations. - controller_config: dictionary of controller (LSTM) module configurations. - output_size: output dimension size of core. - clip_value: clips controller and core output values to between - `[-clip_value, clip_value]` if specified. - name: module name (default 'dnc'). - - Raises: - TypeError: if direct_input_size is not None for any access module other - than KeyValueMemory. - """ - super(DNC, self).__init__(name=name) - - self._dtype = dtype - # dm-sonnet=2.0.0 LSTM is not integrated with TF2 tracing. - # Use keras to allow for Tensorboard visualization - #self._controller = snt.LSTM(**controller_config, dtype=tf.float64) - self._controller = tf.keras.layers.LSTMCell(**controller_config, dtype=dtype) - self._access = access.MemoryAccess(**access_config, dtype=dtype) - - self._output_size = output_size - self._batch_size = batch_size - self._clip_value = clip_value or 0 - - self._output_size = tf.TensorShape([output_size]) - self._state_size = list(DNCState( - access_output=self._access.output_size, - access_state=self._access.state_size, - controller_state=[tf.TensorShape([i]) for i in self._controller.state_size], - )) - self._output_linear = snt.Linear( - output_size=output_size, - name='output_linear') - - def _clip_if_enabled(self, x): - if self._clip_value > 0: - return tf.clip_by_value(x, -self._clip_value, self._clip_value) - else: - return x - - def call(self, inputs, prev_state): - return self.__call__(inputs, prev_state) - - def __call__(self, inputs, prev_state): - """Connects the DNC core into the graph. - - Args: - inputs: Tensor input. - prev_state: A `DNCState` tuple containing the fields `access_output`, - `access_state` and `controller_state`. `access_state` is a 3-D Tensor - of shape `[batch_size, num_reads, word_size]` containing read words. - `access_state` is a tuple of the access module's state, and - `controller_state` is a tuple of controller module's state. - - Returns: - A tuple `(output, next_state)` where `output` is a tensor and `next_state` - is a `DNCState` tuple containing the fields `access_output`, - `access_state`, and `controller_state`. + """DNC core module. + + Contains controller and memory access module. """ - prev_state = DNCState(*prev_state) - - batch_flatten = tf.keras.layers.Flatten() - controller_input = tf.concat( - [batch_flatten(inputs), batch_flatten(prev_state.access_output)], 1) - - controller_output, controller_state = self._controller( - controller_input, prev_state.controller_state) - - controller_output = self._clip_if_enabled(controller_output) - controller_state = tf.nest.map_structure(self._clip_if_enabled, controller_state) - - access_output, access_state = self._access(controller_output, - prev_state.access_state) - - output = tf.concat([controller_output, batch_flatten(access_output)], 1) - output = self._output_linear(output) - output = self._clip_if_enabled(output) - - return output, list(DNCState( - access_output, - access_state, - controller_state, - )) - - def initial_state(self, batch_size=None): - return self.get_initial_state(batch_size) - - def get_initial_state(self, batch_size=None, inputs=None, dtype=None): - return list(DNCState( - controller_state=self._controller.get_initial_state(batch_size=batch_size, dtype=self._dtype), - access_state=self._access.get_initial_state(batch_size=batch_size), - access_output=tf.zeros( - [batch_size] + self._access.output_size.as_list(), dtype=self._dtype))) - - """def initial_state(self, batch_size): - return [ - #controller_state - self._controller.get_initial_state(batch_size=batch_size, dtype=self._dtype), - #access_state - self._access.initial_state(batch_size), - #access_output - tf.zeros( - [batch_size] + self._access.output_size.as_list(), dtype=self._dtype) - ]""" - - @property - def state_size(self): - return self._state_size - - @property - def output_size(self): - return self._output_size + + def __init__( + self, + access_config, + controller_config, + output_size, + batch_size, + clip_value=None, + name="dnc", + dtype=tf.float32, + ): + """Initializes the DNC core. + + Args: + access_config: dictionary of access module configurations. + controller_config: dictionary of controller (LSTM) module configurations. + output_size: output dimension size of core. + clip_value: clips controller and core output values to between + `[-clip_value, clip_value]` if specified. + name: module name (default 'dnc'). + + Raises: + TypeError: if direct_input_size is not None for any access module other + than KeyValueMemory. + """ + super(DNC, self).__init__(name=name) + + self._dtype = dtype + # dm-sonnet=2.0.0 LSTM is not integrated with TF2 tracing. + # Use keras to allow for Tensorboard visualization + # self._controller = snt.LSTM(**controller_config, dtype=tf.float64) + self._controller = tf.keras.layers.LSTMCell(**controller_config, dtype=dtype) + self._access = access.MemoryAccess(**access_config, dtype=dtype) + + self._output_size = output_size + self._batch_size = batch_size + self._clip_value = clip_value or 0 + + self._output_linear = snt.Linear(output_size=output_size, name="output_linear") + + def _clip_if_enabled(self, x): + if self._clip_value > 0: + return tf.clip_by_value(x, -self._clip_value, self._clip_value) + else: + return x + + def call(self, inputs, prev_state): + return self.__call__(inputs, prev_state) + + def __call__(self, inputs, prev_state): + """Connects the DNC core into the graph. + + Args: + inputs: Tensor input. + prev_state: A `DNCState` tuple containing the fields `access_output`, + `access_state` and `controller_state`. `access_state` is a 3-D Tensor + of shape `[batch_size, num_reads, word_size]` containing read words. + `access_state` is a tuple of the access module's state, and + `controller_state` is a tuple of controller module's state. + + Returns: + A tuple `(output, next_state)` where `output` is a tensor and `next_state` + is a `DNCState` tuple containing the fields `access_output`, + `access_state`, and `controller_state`. + """ + [prev_access_output, prev_access_state, prev_controller_state] = prev_state + + batch_flatten = tf.keras.layers.Flatten() + controller_input = tf.concat( + [batch_flatten(inputs), batch_flatten(prev_access_output)], 1 + ) + + controller_output, controller_state = self._controller( + controller_input, prev_controller_state + ) + + controller_output = self._clip_if_enabled(controller_output) + controller_state = tf.nest.map_structure( + self._clip_if_enabled, controller_state + ) + + access_output, access_state = self._access(controller_output, prev_access_state) + + output = tf.concat([controller_output, batch_flatten(access_output)], 1) + output = self._output_linear(output) + output = self._clip_if_enabled(output) + + return ( + output, + [ + access_output, + access_state, + controller_state, + ], + ) + + # keras uses get_initial_state + def get_initial_state(self, batch_size=None, inputs=None, dtype=None): + return util.initial_state_from_state_size( + self.state_size, batch_size, self._dtype + ) + + # snt.RNNCore uses initial_state + def initial_state(self, batch_size=None): + return self.get_initial_state(batch_size=batch_size) + + @property + def state_size(self): + return [ + # access_output + self._access.output_size, + # access_state + self._access.state_size, + # controller_state + self._controller.state_size, + ] + + @property + def output_size(self): + return tf.TensorShape([self._output_size]) diff --git a/dnc/repeat_copy.py b/dnc/repeat_copy.py index 393fb11..05e6ebb 100644 --- a/dnc/repeat_copy.py +++ b/dnc/repeat_copy.py @@ -22,382 +22,398 @@ import sonnet as snt import tensorflow as tf -DatasetTensors = collections.namedtuple('DatasetTensors', ('observations', - 'target', 'mask')) +DatasetTensors = collections.namedtuple( + "DatasetTensors", ("observations", "target", "mask") +) -def masked_sigmoid_cross_entropy(logits, - target, - mask, - time_average=False, - log_prob_in_bits=False): - """Adds ops to graph which compute the (scalar) NLL of the target sequence. +def masked_sigmoid_cross_entropy( + logits, target, mask, time_average=False, log_prob_in_bits=False +): + """Adds ops to graph which compute the (scalar) NLL of the target sequence. - The logits parametrize independent bernoulli distributions per time-step and - per batch element, and irrelevant time/batch elements are masked out by the - mask tensor. + The logits parametrize independent bernoulli distributions per time-step and + per batch element, and irrelevant time/batch elements are masked out by the + mask tensor. - Args: - logits: `Tensor` of activations for which sigmoid(`logits`) gives the - bernoulli parameter. - target: time-major `Tensor` of target. - mask: time-major `Tensor` to be multiplied elementwise with cost T x B cost - masking out irrelevant time-steps. - time_average: optionally average over the time dimension (sum by default). - log_prob_in_bits: iff True express log-probabilities in bits (default nats). - - Returns: - A `Tensor` representing the log-probability of the target. - """ - xent = tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=logits) - loss_time_batch = tf.reduce_sum(input_tensor=xent, axis=2) - loss_batch = tf.reduce_sum(input_tensor=loss_time_batch * mask, axis=0) + Args: + logits: `Tensor` of activations for which sigmoid(`logits`) gives the + bernoulli parameter. + target: time-major `Tensor` of target. + mask: time-major `Tensor` to be multiplied elementwise with cost T x B cost + masking out irrelevant time-steps. + time_average: optionally average over the time dimension (sum by default). + log_prob_in_bits: iff True express log-probabilities in bits (default nats). + + Returns: + A `Tensor` representing the log-probability of the target. + """ + xent = tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=logits) + loss_time_batch = tf.reduce_sum(input_tensor=xent, axis=2) + loss_batch = tf.reduce_sum(input_tensor=loss_time_batch * mask, axis=0) - batch_size = tf.cast(tf.shape(input=logits)[1], dtype=loss_time_batch.dtype) + batch_size = tf.cast(tf.shape(input=logits)[1], dtype=loss_time_batch.dtype) - if time_average: - mask_count = tf.reduce_sum(input_tensor=mask, axis=0) - loss_batch /= (mask_count + np.finfo(np.float32).eps) + if time_average: + mask_count = tf.reduce_sum(input_tensor=mask, axis=0) + loss_batch /= mask_count + np.finfo(np.float32).eps - loss = tf.reduce_sum(input_tensor=loss_batch) / batch_size - if log_prob_in_bits: - loss /= tf.math.log(2.) + loss = tf.reduce_sum(input_tensor=loss_batch) / batch_size + if log_prob_in_bits: + loss /= tf.math.log(2.0) - return loss + return loss def bitstring_readable(data, batch_size, model_output=None, whole_batch=False): - """Produce a human readable representation of the sequences in data. + """Produce a human readable representation of the sequences in data. - Args: - data: data to be visualised - batch_size: size of batch - model_output: optional model output tensor to visualize alongside data. - whole_batch: whether to visualise the whole batch. Only the first sample - will be visualized if False - - Returns: - A string used to visualise the data batch - """ + Args: + data: data to be visualised + batch_size: size of batch + model_output: optional model output tensor to visualize alongside data. + whole_batch: whether to visualise the whole batch. Only the first sample + will be visualized if False + + Returns: + A string used to visualise the data batch + """ - def _readable(datum): - return '+' + ' '.join(['-' if x == 0 else '%d' % x for x in datum]) + '+' + def _readable(datum): + return "+" + " ".join(["-" if x == 0 else "%d" % x for x in datum]) + "+" - obs_batch = data.observations - targ_batch = data.target + obs_batch = data.observations + targ_batch = data.target - iterate_over = range(batch_size) if whole_batch else range(1) + iterate_over = range(batch_size) if whole_batch else range(1) - batch_strings = [] - for batch_index in iterate_over: - obs = obs_batch[:, batch_index, :] - targ = targ_batch[:, batch_index, :] + batch_strings = [] + for batch_index in iterate_over: + obs = obs_batch[:, batch_index, :] + targ = targ_batch[:, batch_index, :] - obs_channels = range(obs.shape[1]) - targ_channels = range(targ.shape[1]) - obs_channel_strings = [_readable(obs[:, i]) for i in obs_channels] - targ_channel_strings = [_readable(targ[:, i]) for i in targ_channels] + obs_channels = range(obs.shape[1]) + targ_channels = range(targ.shape[1]) + obs_channel_strings = [_readable(obs[:, i]) for i in obs_channels] + targ_channel_strings = [_readable(targ[:, i]) for i in targ_channels] - readable_obs = 'Observations:\n' + '\n'.join(obs_channel_strings) - readable_targ = 'Targets:\n' + '\n'.join(targ_channel_strings) - strings = [readable_obs, readable_targ] + readable_obs = "Observations:\n" + "\n".join(obs_channel_strings) + readable_targ = "Targets:\n" + "\n".join(targ_channel_strings) + strings = [readable_obs, readable_targ] - if model_output is not None: - output = model_output[:, batch_index, :] - output_strings = [_readable(output[:, i]) for i in targ_channels] - strings.append('Model Output:\n' + '\n'.join(output_strings)) + if model_output is not None: + output = model_output[:, batch_index, :] + output_strings = [_readable(output[:, i]) for i in targ_channels] + strings.append("Model Output:\n" + "\n".join(output_strings)) - batch_strings.append('\n\n'.join(strings)) + batch_strings.append("\n\n".join(strings)) - return '\n' + '\n\n\n\n'.join(batch_strings) + return "\n" + "\n\n\n\n".join(batch_strings) class RepeatCopy(snt.Module): - """Sequence data generator for the task of repeating a random binary pattern. - - When called, an instance of this class will return a tuple of tensorflow ops - (obs, targ, mask), representing an input sequence, target sequence, and - binary mask. Each of these ops produces tensors whose first two dimensions - represent sequence position and batch index respectively. The value in - mask[t, b] is equal to 1 iff a prediction about targ[t, b, :] should be - penalized and 0 otherwise. - - For each realisation from this generator, the observation sequence is - comprised of I.I.D. uniform-random binary vectors (and some flags). - - The target sequence is comprised of this binary pattern repeated - some number of times (and some flags). Before explaining in more detail, - let's examine the setup pictorially for a single batch element: - - ```none - Note: blank space represents 0. - - time ------------------------------------------> - - +-------------------------------+ - mask: |0000000001111111111111111111111| - +-------------------------------+ - - +-------------------------------+ - target: | 1| 'end-marker' channel. - | 101100110110011011001 | - | 010101001010100101010 | - +-------------------------------+ - - +-------------------------------+ - observation: | 1011001 | - | 0101010 | - |1 | 'start-marker' channel - | 3 | 'num-repeats' channel. - +-------------------------------+ - ``` - - The length of the random pattern and the number of times it is repeated - in the target are both discrete random variables distributed according to - uniform distributions whose parameters are configured at construction time. - - The obs sequence has two extra channels (components in the trailing dimension) - which are used for flags. One channel is marked with a 1 at the first time - step and is otherwise equal to 0. The other extra channel is zero until the - binary pattern to be repeated ends. At this point, it contains an encoding of - the number of times the observation pattern should be repeated. Rather than - simply providing this integer number directly, it is normalised so that - a neural network may have an easier time representing the number of - repetitions internally. To allow a network to be readily evaluated on - instances of this task with greater numbers of repetitions, the range with - respect to which this encoding is normalised is also configurable by the user. - - As in the diagram, the target sequence is offset to begin directly after the - observation sequence; both sequences are padded with zeros to accomplish this, - resulting in their lengths being equal. Additional padding is done at the end - so that all sequences in a minibatch represent tensors with the same shape. - """ - - def __init__( - self, - num_bits=6, - batch_size=1, - min_length=1, - max_length=1, - min_repeats=1, - max_repeats=2, - norm_max=10, - log_prob_in_bits=False, - time_average_cost=False, - name='repeat_copy', - dtype=tf.float32): - """Creates an instance of RepeatCopy task. - - Args: - name: A name for the generator instance (for name scope purposes). - num_bits: The dimensionality of each random binary vector. - batch_size: Minibatch size per realization. - min_length: Lower limit on number of random binary vectors in the - observation pattern. - max_length: Upper limit on number of random binary vectors in the - observation pattern. - min_repeats: Lower limit on number of times the obervation pattern - is repeated in targ. - max_repeats: Upper limit on number of times the observation pattern - is repeated in targ. - norm_max: Upper limit on uniform distribution w.r.t which the encoding - of the number of repetitions presented in the observation sequence - is normalised. - log_prob_in_bits: By default, log probabilities are expressed in units of - nats. If true, express log probabilities in bits. - time_average_cost: If true, the cost at each time step will be - divided by the `true`, sequence length, the number of non-masked time - steps, in each sequence before any subsequent reduction over the time - and batch dimensions. + """Sequence data generator for the task of repeating a random binary pattern. + + When called, an instance of this class will return a tuple of tensorflow ops + (obs, targ, mask), representing an input sequence, target sequence, and + binary mask. Each of these ops produces tensors whose first two dimensions + represent sequence position and batch index respectively. The value in + mask[t, b] is equal to 1 iff a prediction about targ[t, b, :] should be + penalized and 0 otherwise. + + For each realisation from this generator, the observation sequence is + comprised of I.I.D. uniform-random binary vectors (and some flags). + + The target sequence is comprised of this binary pattern repeated + some number of times (and some flags). Before explaining in more detail, + let's examine the setup pictorially for a single batch element: + + ```none + Note: blank space represents 0. + + time ------------------------------------------> + + +-------------------------------+ + mask: |0000000001111111111111111111111| + +-------------------------------+ + + +-------------------------------+ + target: | 1| 'end-marker' channel. + | 101100110110011011001 | + | 010101001010100101010 | + +-------------------------------+ + + +-------------------------------+ + observation: | 1011001 | + | 0101010 | + |1 | 'start-marker' channel + | 3 | 'num-repeats' channel. + +-------------------------------+ + ``` + + The length of the random pattern and the number of times it is repeated + in the target are both discrete random variables distributed according to + uniform distributions whose parameters are configured at construction time. + + The obs sequence has two extra channels (components in the trailing dimension) + which are used for flags. One channel is marked with a 1 at the first time + step and is otherwise equal to 0. The other extra channel is zero until the + binary pattern to be repeated ends. At this point, it contains an encoding of + the number of times the observation pattern should be repeated. Rather than + simply providing this integer number directly, it is normalised so that + a neural network may have an easier time representing the number of + repetitions internally. To allow a network to be readily evaluated on + instances of this task with greater numbers of repetitions, the range with + respect to which this encoding is normalised is also configurable by the user. + + As in the diagram, the target sequence is offset to begin directly after the + observation sequence; both sequences are padded with zeros to accomplish this, + resulting in their lengths being equal. Additional padding is done at the end + so that all sequences in a minibatch represent tensors with the same shape. """ - super(RepeatCopy, self).__init__(name=name) - - self._batch_size = batch_size - self._num_bits = num_bits - self._min_length = min_length - self._max_length = max_length - self._min_repeats = min_repeats - self._max_repeats = max_repeats - self._norm_max = norm_max - self._log_prob_in_bits = log_prob_in_bits - self._time_average_cost = time_average_cost - self._dtype=dtype - - def _normalise(self, val): - return val / self._norm_max - - def _unnormalise(self, val): - return val * self._norm_max - - @property - def time_average_cost(self): - return self._time_average_cost - - @property - def log_prob_in_bits(self): - return self._log_prob_in_bits - - @property - def num_bits(self): - """The dimensionality of each random binary vector in a pattern.""" - return self._num_bits - - @property - def target_size(self): - """The dimensionality of the target tensor.""" - return self._num_bits + 1 - - @property - def batch_size(self): - return self._batch_size - - def __call__(self): - return self._build() - #return self.datasettensor - - def _build(self): - """Implements build method which adds ops to graph.""" - - # short-hand for private fields. - min_length, max_length = self._min_length, self._max_length - min_reps, max_reps = self._min_repeats, self._max_repeats - num_bits = self.num_bits - batch_size = self.batch_size - - # We reserve one dimension for the num-repeats and one for the start-marker. - full_obs_size = num_bits + 2 - # We reserve one target dimension for the end-marker. - full_targ_size = num_bits + 1 - start_end_flag_idx = full_obs_size - 2 - num_repeats_channel_idx = full_obs_size - 1 - - # Samples each batch index's sequence length and the number of repeats. - sub_seq_length_batch = tf.random.uniform( - [batch_size], minval=min_length, maxval=max_length + 1, dtype=tf.int32) - num_repeats_batch = tf.random.uniform( - [batch_size], minval=min_reps, maxval=max_reps + 1, dtype=tf.int32) - - # Pads all the batches to have the same total sequence length. - total_length_batch = sub_seq_length_batch * (num_repeats_batch + 1) + 3 - max_length_batch = tf.reduce_max(input_tensor=total_length_batch) - residual_length_batch = max_length_batch - total_length_batch - - obs_batch_shape = [max_length_batch, batch_size, full_obs_size] - targ_batch_shape = [max_length_batch, batch_size, full_targ_size] - mask_batch_trans_shape = [batch_size, max_length_batch] - - obs_tensors = [] - targ_tensors = [] - mask_tensors = [] - - # Generates patterns for each batch element independently. - for batch_index in range(batch_size): - sub_seq_len = sub_seq_length_batch[batch_index] - num_reps = num_repeats_batch[batch_index] - - # The observation pattern is a sequence of random binary vectors. - obs_pattern_shape = [sub_seq_len, num_bits] - obs_pattern = tf.cast( - tf.random.uniform( - obs_pattern_shape, minval=0, maxval=2, dtype=tf.int32), - tf.float32) - - # The target pattern is the observation pattern repeated n times. - # Some reshaping is required to accomplish the tiling. - targ_pattern_shape = [sub_seq_len * num_reps, num_bits] - flat_obs_pattern = tf.reshape(obs_pattern, [-1]) - flat_targ_pattern = tf.tile(flat_obs_pattern, tf.stack([num_reps])) - targ_pattern = tf.reshape(flat_targ_pattern, targ_pattern_shape) - - # Expand the obs_pattern to have two extra channels for flags. - # Concatenate start flag and num_reps flag to the sequence. - obs_flag_channel_pad = tf.zeros([sub_seq_len, 2]) - obs_start_flag = tf.one_hot( - [start_end_flag_idx], full_obs_size, on_value=1., off_value=0.) - num_reps_flag = tf.one_hot( - [num_repeats_channel_idx], - full_obs_size, - on_value=self._normalise(tf.cast(num_reps, tf.float32)), - off_value=0.) - - # note the concatenation dimensions. - obs = tf.concat([obs_pattern, obs_flag_channel_pad], 1) - obs = tf.concat([obs_start_flag, obs], 0) - obs = tf.concat([obs, num_reps_flag], 0) - - # Now do the same for the targ_pattern (it only has one extra channel). - targ_flag_channel_pad = tf.zeros([sub_seq_len * num_reps, 1]) - targ_end_flag = tf.one_hot( - [start_end_flag_idx], full_targ_size, on_value=1., off_value=0.) - targ = tf.concat([targ_pattern, targ_flag_channel_pad], 1) - targ = tf.concat([targ, targ_end_flag], 0) - - # Concatenate zeros at end of obs and begining of targ. - # This aligns them s.t. the target begins as soon as the obs ends. - obs_end_pad = tf.zeros([sub_seq_len * num_reps + 1, full_obs_size]) - targ_start_pad = tf.zeros([sub_seq_len + 2, full_targ_size]) - - # The mask is zero during the obs and one during the targ. - mask_off = tf.zeros([sub_seq_len + 2]) - mask_on = tf.ones([sub_seq_len * num_reps + 1]) - - obs = tf.concat([obs, obs_end_pad], 0) - targ = tf.concat([targ_start_pad, targ], 0) - mask = tf.concat([mask_off, mask_on], 0) - - obs_tensors.append(obs) - targ_tensors.append(targ) - mask_tensors.append(mask) - - # End the loop over batch index. - # Compute how much zero padding is needed to make tensors sequences - # the same length for all batch elements. - residual_obs_pad = [ - tf.zeros([residual_length_batch[i], full_obs_size]) - for i in range(batch_size) - ] - residual_targ_pad = [ - tf.zeros([residual_length_batch[i], full_targ_size]) - for i in range(batch_size) - ] - residual_mask_pad = [ - tf.zeros([residual_length_batch[i]]) for i in range(batch_size) - ] - - # Concatenate the pad to each batch element. - obs_tensors = [ - tf.concat([o, p], 0) for o, p in zip(obs_tensors, residual_obs_pad) - ] - targ_tensors = [ - tf.concat([t, p], 0) for t, p in zip(targ_tensors, residual_targ_pad) - ] - mask_tensors = [ - tf.concat([m, p], 0) for m, p in zip(mask_tensors, residual_mask_pad) - ] - - # Concatenate each batch element into a single tensor. - obs = tf.cast(tf.reshape(tf.concat(obs_tensors, 1), obs_batch_shape), dtype=self._dtype) - targ = tf.cast(tf.reshape(tf.concat(targ_tensors, 1), targ_batch_shape), dtype=self._dtype) - mask = tf.cast(tf.transpose( - a=tf.reshape(tf.concat(mask_tensors, 0), mask_batch_trans_shape)), dtype=self._dtype) - return DatasetTensors(obs, targ, mask) - - def cost(self, logits, targ, mask): - return masked_sigmoid_cross_entropy( - logits, - targ, - mask, - time_average=self.time_average_cost, - log_prob_in_bits=self.log_prob_in_bits) - - def to_human_readable(self, data, model_output=None, whole_batch=False): - data = DatasetTensors( - observations=data.observations.numpy(), - target=data.target.numpy(), - mask=data.mask.numpy() - ) - obs = data.observations - unnormalised_num_reps_flag = self._unnormalise(obs[:,:,-1:]).round() - obs = np.concatenate([obs[:,:,:-1], unnormalised_num_reps_flag], axis=2) - data = data._replace(observations=obs) - return bitstring_readable(data, self.batch_size, model_output, whole_batch) + + def __init__( + self, + num_bits=6, + batch_size=1, + min_length=1, + max_length=1, + min_repeats=1, + max_repeats=2, + norm_max=10, + log_prob_in_bits=False, + time_average_cost=False, + name="repeat_copy", + dtype=tf.float32, + ): + """Creates an instance of RepeatCopy task. + + Args: + name: A name for the generator instance (for name scope purposes). + num_bits: The dimensionality of each random binary vector. + batch_size: Minibatch size per realization. + min_length: Lower limit on number of random binary vectors in the + observation pattern. + max_length: Upper limit on number of random binary vectors in the + observation pattern. + min_repeats: Lower limit on number of times the obervation pattern + is repeated in targ. + max_repeats: Upper limit on number of times the observation pattern + is repeated in targ. + norm_max: Upper limit on uniform distribution w.r.t which the encoding + of the number of repetitions presented in the observation sequence + is normalised. + log_prob_in_bits: By default, log probabilities are expressed in units of + nats. If true, express log probabilities in bits. + time_average_cost: If true, the cost at each time step will be + divided by the `true`, sequence length, the number of non-masked time + steps, in each sequence before any subsequent reduction over the time + and batch dimensions. + """ + super(RepeatCopy, self).__init__(name=name) + + self._batch_size = batch_size + self._num_bits = num_bits + self._min_length = min_length + self._max_length = max_length + self._min_repeats = min_repeats + self._max_repeats = max_repeats + self._norm_max = norm_max + self._log_prob_in_bits = log_prob_in_bits + self._time_average_cost = time_average_cost + self._dtype = dtype + + def _normalise(self, val): + return val / self._norm_max + + def _unnormalise(self, val): + return val * self._norm_max + + @property + def time_average_cost(self): + return self._time_average_cost + + @property + def log_prob_in_bits(self): + return self._log_prob_in_bits + + @property + def num_bits(self): + """The dimensionality of each random binary vector in a pattern.""" + return self._num_bits + + @property + def target_size(self): + """The dimensionality of the target tensor.""" + return self._num_bits + 1 + + @property + def batch_size(self): + return self._batch_size + + def __call__(self): + return self._build() + # return self.datasettensor + + def _build(self): + """Implements build method which adds ops to graph.""" + + # short-hand for private fields. + min_length, max_length = self._min_length, self._max_length + min_reps, max_reps = self._min_repeats, self._max_repeats + num_bits = self.num_bits + batch_size = self.batch_size + + # We reserve one dimension for the num-repeats and one for the start-marker. + full_obs_size = num_bits + 2 + # We reserve one target dimension for the end-marker. + full_targ_size = num_bits + 1 + start_end_flag_idx = full_obs_size - 2 + num_repeats_channel_idx = full_obs_size - 1 + + # Samples each batch index's sequence length and the number of repeats. + sub_seq_length_batch = tf.random.uniform( + [batch_size], minval=min_length, maxval=max_length + 1, dtype=tf.int32 + ) + num_repeats_batch = tf.random.uniform( + [batch_size], minval=min_reps, maxval=max_reps + 1, dtype=tf.int32 + ) + + # Pads all the batches to have the same total sequence length. + total_length_batch = sub_seq_length_batch * (num_repeats_batch + 1) + 3 + max_length_batch = tf.reduce_max(input_tensor=total_length_batch) + residual_length_batch = max_length_batch - total_length_batch + + obs_batch_shape = [max_length_batch, batch_size, full_obs_size] + targ_batch_shape = [max_length_batch, batch_size, full_targ_size] + mask_batch_trans_shape = [batch_size, max_length_batch] + + obs_tensors = [] + targ_tensors = [] + mask_tensors = [] + + # Generates patterns for each batch element independently. + for batch_index in range(batch_size): + sub_seq_len = sub_seq_length_batch[batch_index] + num_reps = num_repeats_batch[batch_index] + + # The observation pattern is a sequence of random binary vectors. + obs_pattern_shape = [sub_seq_len, num_bits] + obs_pattern = tf.cast( + tf.random.uniform( + obs_pattern_shape, minval=0, maxval=2, dtype=tf.int32 + ), + tf.float32, + ) + + # The target pattern is the observation pattern repeated n times. + # Some reshaping is required to accomplish the tiling. + targ_pattern_shape = [sub_seq_len * num_reps, num_bits] + flat_obs_pattern = tf.reshape(obs_pattern, [-1]) + flat_targ_pattern = tf.tile(flat_obs_pattern, tf.stack([num_reps])) + targ_pattern = tf.reshape(flat_targ_pattern, targ_pattern_shape) + + # Expand the obs_pattern to have two extra channels for flags. + # Concatenate start flag and num_reps flag to the sequence. + obs_flag_channel_pad = tf.zeros([sub_seq_len, 2]) + obs_start_flag = tf.one_hot( + [start_end_flag_idx], full_obs_size, on_value=1.0, off_value=0.0 + ) + num_reps_flag = tf.one_hot( + [num_repeats_channel_idx], + full_obs_size, + on_value=self._normalise(tf.cast(num_reps, tf.float32)), + off_value=0.0, + ) + + # note the concatenation dimensions. + obs = tf.concat([obs_pattern, obs_flag_channel_pad], 1) + obs = tf.concat([obs_start_flag, obs], 0) + obs = tf.concat([obs, num_reps_flag], 0) + + # Now do the same for the targ_pattern (it only has one extra channel). + targ_flag_channel_pad = tf.zeros([sub_seq_len * num_reps, 1]) + targ_end_flag = tf.one_hot( + [start_end_flag_idx], full_targ_size, on_value=1.0, off_value=0.0 + ) + targ = tf.concat([targ_pattern, targ_flag_channel_pad], 1) + targ = tf.concat([targ, targ_end_flag], 0) + + # Concatenate zeros at end of obs and begining of targ. + # This aligns them s.t. the target begins as soon as the obs ends. + obs_end_pad = tf.zeros([sub_seq_len * num_reps + 1, full_obs_size]) + targ_start_pad = tf.zeros([sub_seq_len + 2, full_targ_size]) + + # The mask is zero during the obs and one during the targ. + mask_off = tf.zeros([sub_seq_len + 2]) + mask_on = tf.ones([sub_seq_len * num_reps + 1]) + + obs = tf.concat([obs, obs_end_pad], 0) + targ = tf.concat([targ_start_pad, targ], 0) + mask = tf.concat([mask_off, mask_on], 0) + + obs_tensors.append(obs) + targ_tensors.append(targ) + mask_tensors.append(mask) + + # End the loop over batch index. + # Compute how much zero padding is needed to make tensors sequences + # the same length for all batch elements. + residual_obs_pad = [ + tf.zeros([residual_length_batch[i], full_obs_size]) + for i in range(batch_size) + ] + residual_targ_pad = [ + tf.zeros([residual_length_batch[i], full_targ_size]) + for i in range(batch_size) + ] + residual_mask_pad = [ + tf.zeros([residual_length_batch[i]]) for i in range(batch_size) + ] + + # Concatenate the pad to each batch element. + obs_tensors = [ + tf.concat([o, p], 0) for o, p in zip(obs_tensors, residual_obs_pad) + ] + targ_tensors = [ + tf.concat([t, p], 0) for t, p in zip(targ_tensors, residual_targ_pad) + ] + mask_tensors = [ + tf.concat([m, p], 0) for m, p in zip(mask_tensors, residual_mask_pad) + ] + + # Concatenate each batch element into a single tensor. + obs = tf.cast( + tf.reshape(tf.concat(obs_tensors, 1), obs_batch_shape), dtype=self._dtype + ) + targ = tf.cast( + tf.reshape(tf.concat(targ_tensors, 1), targ_batch_shape), dtype=self._dtype + ) + mask = tf.cast( + tf.transpose( + a=tf.reshape(tf.concat(mask_tensors, 0), mask_batch_trans_shape) + ), + dtype=self._dtype, + ) + return DatasetTensors(obs, targ, mask) + + def cost(self, logits, targ, mask): + return masked_sigmoid_cross_entropy( + logits, + targ, + mask, + time_average=self.time_average_cost, + log_prob_in_bits=self.log_prob_in_bits, + ) + + def to_human_readable(self, data, model_output=None, whole_batch=False): + data = DatasetTensors( + observations=data.observations.numpy(), + target=data.target.numpy(), + mask=data.mask.numpy(), + ) + obs = data.observations + unnormalised_num_reps_flag = self._unnormalise(obs[:, :, -1:]).round() + obs = np.concatenate([obs[:, :, :-1], unnormalised_num_reps_flag], axis=2) + data = data._replace(observations=obs) + return bitstring_readable(data, self.batch_size, model_output, whole_batch) diff --git a/dnc/util.py b/dnc/util.py index 76ebbaf..2ba467b 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -23,77 +23,64 @@ def batch_invert_permutation(permutations): - """Returns batched `tf.invert_permutation` for every row in `permutations`.""" - perm = tf.cast(permutations, tf.float32) - dim = int(perm.get_shape()[-1]) - size = tf.cast(tf.shape(input=perm)[0], tf.float32) - delta = tf.cast(tf.shape(input=perm)[-1], tf.float32) - rg = tf.range(0, size * delta, delta, dtype=tf.float32) - rg = tf.expand_dims(rg, 1) - rg = tf.tile(rg, [1, dim]) - perm = tf.add(perm, rg) - flat = tf.reshape(perm, [-1]) - perm = tf.math.invert_permutation(tf.cast(flat, tf.int32)) - perm = tf.reshape(perm, [-1, dim]) - return tf.subtract(perm, tf.cast(rg, tf.int32)) + """Returns batched `tf.invert_permutation` for every row in `permutations`.""" + perm = tf.cast(permutations, tf.float32) + dim = int(perm.get_shape()[-1]) + size = tf.cast(tf.shape(input=perm)[0], tf.float32) + delta = tf.cast(tf.shape(input=perm)[-1], tf.float32) + rg = tf.range(0, size * delta, delta, dtype=tf.float32) + rg = tf.expand_dims(rg, 1) + rg = tf.tile(rg, [1, dim]) + perm = tf.add(perm, rg) + flat = tf.reshape(perm, [-1]) + perm = tf.math.invert_permutation(tf.cast(flat, tf.int32)) + perm = tf.reshape(perm, [-1, dim]) + return tf.subtract(perm, tf.cast(rg, tf.int32)) def batch_gather(values, indices): - """Returns batched `tf.gather` for every row in the input.""" - idx = tf.expand_dims(tf.cast(indices, tf.int32), -1) - size = tf.shape(input=indices)[0] - rg = tf.range(tf.cast(size, tf.int32), dtype=tf.int32) - rg = tf.expand_dims(rg, -1) - rg = tf.tile(rg, [1, int(indices.get_shape()[-1])]) - rg = tf.expand_dims(rg, -1) - gidx = tf.concat([rg, idx], -1) - return tf.gather_nd(values, gidx) + """Returns batched `tf.gather` for every row in the input.""" + idx = tf.expand_dims(tf.cast(indices, tf.int32), -1) + size = tf.shape(input=indices)[0] + rg = tf.range(tf.cast(size, tf.int32), dtype=tf.int32) + rg = tf.expand_dims(rg, -1) + rg = tf.tile(rg, [1, int(indices.get_shape()[-1])]) + rg = tf.expand_dims(rg, -1) + gidx = tf.concat([rg, idx], -1) + return tf.gather_nd(values, gidx) def one_hot(length, index): - """Return an nd array of given `length` filled with 0s and a 1 at `index`.""" - result = np.zeros(length) - result[index] = 1 - return result + """Return an nd array of given `length` filled with 0s and a 1 at `index`.""" + result = np.zeros(length) + result[index] = 1 + return result + def reduce_prod(x, axis, name=None): - """Efficient reduce product over axis. + """Efficient reduce product over axis. - Uses tf.cumprod and tf.gather_nd as a workaround to the poor performance of calculating tf.reduce_prod's gradient on CPU. - """ - """with tf.compat.v1.name_scope(name, 'util_reduce_prod', values=[x]): + Uses tf.cumprod and tf.gather_nd as a workaround to the poor performance of calculating tf.reduce_prod's gradient on CPU. + """ + """with tf.compat.v1.name_scope(name, 'util_reduce_prod', values=[x]): cp = tf.math.cumprod(x, axis, reverse=True) size = tf.shape(input=cp)[0] idx1 = tf.range(tf.cast(size, tf.float32), dtype=tf.float32) idx2 = tf.zeros([size], tf.float32) indices = tf.stack([idx1, idx2], 1) return tf.gather_nd(cp, tf.cast(indices, tf.int32))""" - return tf.math.reduce_prod(x, axis=axis, name=name) - -# tf2 and sonnet2 compatibility -def state_size_from_initial_state(initial_state): - if isinstance(initial_state, tf.Tensor): - return initial_state.shape[1:] # remove batch size + return tf.math.reduce_prod(x, axis=axis, name=name) - state_size_dict = {} - #import ipdb; ipdb.set_trace() - for field, value in initial_state._asdict().items(): - state_size_dict[field] = state_size_from_initial_state(value) - return type(initial_state)(**state_size_dict) +# Utility function to convert nested state_size to compatible zero initial_state. def initial_state_from_state_size(state_size, batch_size, dtype): + if isinstance(state_size, int): + return tf.zeros([batch_size, state_size], dtype=dtype) if isinstance(state_size, tf.TensorShape): return tf.zeros([batch_size] + state_size.as_list(), dtype=dtype) elif isinstance(state_size, list): - return [ - initial_state_from_state_size(s, batch_size, dtype) - for s in state_size - ] - # Not used anymore since migration off of namedtuple state representation - elif isinstance(state_size, namedtuple): - initial_state_dict = {} - for field, value in state_size._asdict().items(): - initial_state_dict[field] = initial_state_from_state_size(value, batch_size, dtype) - return type(state_size)(**initial_state_dict) - - raise NotImplemented(f"Cannot parse initial_state from state_size of type {type(state)}: {state}") + return [initial_state_from_state_size(s, batch_size, dtype) for s in state_size] + + raise NotImplemented( + f"Cannot parse initial_state from state_size of type {type(state)}: {state}" + ) diff --git a/requirements.txt b/requirements.txt index 5417974..5adb76a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,7 @@ absl-py==0.12.0 astunparse==1.6.3 attrs==21.2.0 +black==21.6b0 cachetools==4.2.2 certifi==2020.12.5 chardet==4.0.0 diff --git a/setup.py b/setup.py index a5c3ee4..2a6bb1f 100644 --- a/setup.py +++ b/setup.py @@ -1,12 +1,21 @@ from setuptools import setup setup( - name='dnc', - version='0.0.2', - description='This package provides an implementation of the Differentiable Neural Computer, as published in Nature.', - license='Apache Software License 2.0', - packages=['dnc'], - author='DeepMind', - keywords=['tensorflow', 'differentiable neural computer', 'dnc', 'deepmind', 'deep mind', 'sonnet', 'dm-sonnet', 'machine learning'], - url='https://github.com/deepmind/dnc' + name="dnc", + version="0.0.2", + description="This package provides an implementation of the Differentiable Neural Computer, as published in Nature.", + license="Apache Software License 2.0", + packages=["dnc"], + author="DeepMind", + keywords=[ + "tensorflow", + "differentiable neural computer", + "dnc", + "deepmind", + "deep mind", + "sonnet", + "dm-sonnet", + "machine learning", + ], + url="https://github.com/deepmind/dnc", ) diff --git a/tests/access_test.py b/tests/access_test.py index 5343f4a..b28040d 100644 --- a/tests/access_test.py +++ b/tests/access_test.py @@ -31,155 +31,157 @@ TIME_STEPS = 4 INPUT_SIZE = 10 -DTYPE=tf.float32 +DTYPE = tf.float32 # set seeds for determinism np.random.seed(42) from tensorflow.python.framework import random_seed + random_seed.set_seed(42) + class MemoryAccessTest(tf.test.TestCase): + def setUp(self): + self.cell = access.MemoryAccess(MEMORY_SIZE, WORD_SIZE, NUM_READS, NUM_WRITES) + self.module = tf.keras.layers.RNN( + cell=self.cell, + time_major=True, + return_sequences=True, + ) + + def testBuildAndTrain(self): + inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) + targets = np.random.rand(TIME_STEPS, BATCH_SIZE, NUM_READS, WORD_SIZE) + loss = lambda outputs, targets: tf.reduce_mean( + input_tensor=tf.square(outputs - targets) + ) + with tf.GradientTape() as tape: + outputs = self.module( + inputs=inputs, + initial_state=self.module.get_initial_state(inputs), + ) + loss_value = loss(outputs, targets) + gradients = tape.gradient(loss_value, self.module.trainable_variables) + + optimizer = tf.keras.optimizers.SGD(learning_rate=0.1) + optimizer.apply_gradients(zip(gradients, self.module.trainable_variables)) + + def testValidReadMode(self): + inputs = self.cell._read_inputs( + tf.random.normal([BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) + ) + + # Check that the read modes for each read head constitute a probability + # distribution. + self.assertAllClose( + inputs["read_mode"].numpy().sum(2), np.ones([BATCH_SIZE, NUM_READS]) + ) + self.assertGreaterEqual(inputs["read_mode"].numpy().min(), 0) + + def testWriteWeights(self): + memory = 10 * (np.random.rand(BATCH_SIZE, MEMORY_SIZE, WORD_SIZE) - 0.5) + usage = np.random.rand(BATCH_SIZE, MEMORY_SIZE) + + allocation_gate = np.random.rand(BATCH_SIZE, NUM_WRITES) + write_gate = np.random.rand(BATCH_SIZE, NUM_WRITES) + write_content_keys = np.random.rand(BATCH_SIZE, NUM_WRITES, WORD_SIZE) + write_content_strengths = np.random.rand(BATCH_SIZE, NUM_WRITES) + + # Check that turning on allocation gate fully brings the write gate to + # the allocation weighting (which we will control by controlling the usage). + usage[:, 3] = 0 + allocation_gate[:, 0] = 1 + write_gate[:, 0] = 1 + + inputs = { + "allocation_gate": tf.constant(allocation_gate, dtype=DTYPE), + "write_gate": tf.constant(write_gate, dtype=DTYPE), + "write_content_keys": tf.constant(write_content_keys, dtype=DTYPE), + "write_content_strengths": tf.constant( + write_content_strengths, dtype=DTYPE + ), + } + + weights = self.cell._write_weights( + inputs, tf.constant(memory, dtype=DTYPE), tf.constant(usage, dtype=DTYPE) + ) + + weights = weights.numpy() - def setUp(self): - self.cell = access.MemoryAccess( - MEMORY_SIZE, WORD_SIZE, NUM_READS, NUM_WRITES) - #self.initial_state = self.cell.get_initial_state(BATCH_SIZE) - self.module = tf.keras.layers.RNN( - cell=self.cell, - time_major=True, - return_sequences=True, - ) - - def testBuildAndTrain(self): - inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) - targets = np.random.rand(TIME_STEPS, BATCH_SIZE, NUM_READS, WORD_SIZE) - loss = lambda outputs, targets: tf.reduce_mean(input_tensor=tf.square(outputs - targets)) - with tf.GradientTape() as tape: - outputs = self.module( - inputs=inputs, - initial_state=self.module.get_initial_state(inputs),#self.initial_state, + # Check the weights sum to their target gating. + self.assertAllClose(np.sum(weights, axis=2), write_gate, atol=5e-2) + + # Check that we fully allocated to the third row. + weights_0_0_target = util.one_hot(MEMORY_SIZE, 3) + self.assertAllClose(weights[0, 0], weights_0_0_target, atol=1e-3) + + def testReadWeights(self): + memory = 10 * (np.random.rand(BATCH_SIZE, MEMORY_SIZE, WORD_SIZE) - 0.5) + prev_read_weights = np.random.rand(BATCH_SIZE, NUM_READS, MEMORY_SIZE) + prev_read_weights /= prev_read_weights.sum(2, keepdims=True) + 1 + + link = np.random.rand(BATCH_SIZE, NUM_WRITES, MEMORY_SIZE, MEMORY_SIZE) + # Row and column sums should be at most 1: + link /= np.maximum(link.sum(2, keepdims=True), 1) + link /= np.maximum(link.sum(3, keepdims=True), 1) + + # We query the memory on the third location in memory, and select a large + # strength on the query. Then we select a content-based read-mode. + read_content_keys = np.random.rand(BATCH_SIZE, NUM_READS, WORD_SIZE) + read_content_keys[0, 0] = memory[0, 3] + read_content_strengths = tf.constant( + 100.0, shape=[BATCH_SIZE, NUM_READS], dtype=DTYPE + ) + read_mode = np.random.rand(BATCH_SIZE, NUM_READS, 1 + 2 * NUM_WRITES) + read_mode[0, 0, :] = util.one_hot(1 + 2 * NUM_WRITES, 2 * NUM_WRITES) + inputs = { + "read_content_keys": tf.constant(read_content_keys, dtype=DTYPE), + "read_content_strengths": read_content_strengths, + "read_mode": tf.constant(read_mode, dtype=DTYPE), + } + read_weights = self.cell._read_weights( + inputs, + tf.cast(memory, dtype=DTYPE), + tf.cast(prev_read_weights, dtype=DTYPE), + tf.cast(link, dtype=DTYPE), + ) + read_weights = read_weights.numpy() + + # read_weights for batch 0, read head 0 should be memory location 3 + self.assertAllClose( + read_weights[0, 0, :], util.one_hot(MEMORY_SIZE, 3), atol=1e-3 + ) + + def testGradients(self): + inputs = tf.constant(np.random.randn(1, BATCH_SIZE, INPUT_SIZE), dtype=DTYPE) + initial_state = self.module.get_initial_state(inputs=inputs) + + def evaluate_module(inputs, memory, read_weights, precedence_weights, link): + # construct initial state with tensors to check + init_state = [ + memory, + read_weights, + initial_state[access.WRITE_WEIGHTS], + [link, precedence_weights], + initial_state[access.USAGE], + ] + output = self.module(inputs, init_state) + loss = tf.reduce_sum(input_tensor=output) + return loss + + tensors_to_check = [ + inputs, + initial_state[access.MEMORY], + initial_state[access.READ_WEIGHTS], + initial_state[access.LINKAGE][addressing.PRECEDENCE_WEIGHTS], + initial_state[access.LINKAGE][addressing.LINK], + ] + + theoretical, numerical = tf.test.compute_gradient( + evaluate_module, tensors_to_check, delta=1e-5 + ) + self.assertLess( + sum([tf.norm(numerical[i] - theoretical[i]) for i in range(2)]), + 0.02, + tensors_to_check, ) - loss_value = loss(outputs, targets) - gradients = tape.gradient(loss_value, self.module.trainable_variables) - - optimizer = tf.keras.optimizers.SGD(learning_rate=0.1) - optimizer.apply_gradients(zip(gradients, self.module.trainable_variables)) - - def testValidReadMode(self): - inputs = self.cell._read_inputs( - tf.random.normal([BATCH_SIZE, INPUT_SIZE], dtype=DTYPE)) - - # Check that the read modes for each read head constitute a probability - # distribution. - self.assertAllClose(inputs['read_mode'].numpy().sum(2), - np.ones([BATCH_SIZE, NUM_READS])) - self.assertGreaterEqual(inputs['read_mode'].numpy().min(), 0) - - def testWriteWeights(self): - memory = 10 * (np.random.rand(BATCH_SIZE, MEMORY_SIZE, WORD_SIZE) - 0.5) - usage = np.random.rand(BATCH_SIZE, MEMORY_SIZE) - - allocation_gate = np.random.rand(BATCH_SIZE, NUM_WRITES) - write_gate = np.random.rand(BATCH_SIZE, NUM_WRITES) - write_content_keys = np.random.rand(BATCH_SIZE, NUM_WRITES, WORD_SIZE) - write_content_strengths = np.random.rand(BATCH_SIZE, NUM_WRITES) - - # Check that turning on allocation gate fully brings the write gate to - # the allocation weighting (which we will control by controlling the usage). - usage[:, 3] = 0 - allocation_gate[:, 0] = 1 - write_gate[:, 0] = 1 - - inputs = { - 'allocation_gate': tf.constant(allocation_gate, dtype=DTYPE), - 'write_gate': tf.constant(write_gate, dtype=DTYPE), - 'write_content_keys': tf.constant(write_content_keys, dtype=DTYPE), - 'write_content_strengths': tf.constant(write_content_strengths, dtype=DTYPE) - } - - weights = self.cell._write_weights(inputs, - tf.constant(memory, dtype=DTYPE), - tf.constant(usage, dtype=DTYPE)) - - weights = weights.numpy() - - # Check the weights sum to their target gating. - self.assertAllClose(np.sum(weights, axis=2), write_gate, atol=5e-2) - - # Check that we fully allocated to the third row. - weights_0_0_target = util.one_hot(MEMORY_SIZE, 3) - self.assertAllClose(weights[0, 0], weights_0_0_target, atol=1e-3) - - def testReadWeights(self): - memory = 10 * (np.random.rand(BATCH_SIZE, MEMORY_SIZE, WORD_SIZE) - 0.5) - prev_read_weights = np.random.rand(BATCH_SIZE, NUM_READS, MEMORY_SIZE) - prev_read_weights /= prev_read_weights.sum(2, keepdims=True) + 1 - - link = np.random.rand(BATCH_SIZE, NUM_WRITES, MEMORY_SIZE, MEMORY_SIZE) - # Row and column sums should be at most 1: - link /= np.maximum(link.sum(2, keepdims=True), 1) - link /= np.maximum(link.sum(3, keepdims=True), 1) - - # We query the memory on the third location in memory, and select a large - # strength on the query. Then we select a content-based read-mode. - read_content_keys = np.random.rand(BATCH_SIZE, NUM_READS, WORD_SIZE) - read_content_keys[0, 0] = memory[0, 3] - read_content_strengths = tf.constant( - 100., shape=[BATCH_SIZE, NUM_READS], dtype=DTYPE) - read_mode = np.random.rand(BATCH_SIZE, NUM_READS, 1 + 2 * NUM_WRITES) - read_mode[0, 0, :] = util.one_hot(1 + 2 * NUM_WRITES, 2 * NUM_WRITES) - inputs = { - 'read_content_keys': tf.constant(read_content_keys, dtype=DTYPE), - 'read_content_strengths': read_content_strengths, - 'read_mode': tf.constant(read_mode, dtype=DTYPE), - } - read_weights = self.cell._read_weights( - inputs, - tf.cast(memory, dtype=DTYPE), - tf.cast(prev_read_weights, dtype=DTYPE), - tf.cast(link, dtype=DTYPE), - ) - read_weights = read_weights.numpy() - - - # read_weights for batch 0, read head 0 should be memory location 3 - self.assertAllClose( - read_weights[0, 0, :], util.one_hot(MEMORY_SIZE, 3), atol=1e-3) - - def testGradients(self): - inputs = tf.constant(np.random.randn(1, BATCH_SIZE, INPUT_SIZE), dtype=DTYPE) - test_initial_state = self.module.get_initial_state(inputs=inputs) - initial_state = test_initial_state #self.initial_state - def evaluate_module(inputs, memory, read_weights, precedence_weights, link): - init_state = list(access.AccessState( - memory=memory, - read_weights=read_weights, - write_weights=initial_state[access.WRITE_WEIGHTS], - linkage=list(addressing.TemporalLinkageState( - precedence_weights=precedence_weights, - link=link - )), - usage=initial_state[access.USAGE], - )) - output = self.module(inputs, init_state) - loss = tf.reduce_sum(input_tensor=output) - return loss - - tensors_to_check = [ - inputs, - initial_state[access.MEMORY], - initial_state[access.READ_WEIGHTS], - initial_state[access.LINKAGE][addressing.PRECEDENCE_WEIGHTS], - initial_state[access.LINKAGE][addressing.LINK], - ] - - theoretical, numerical = tf.test.compute_gradient( - evaluate_module, - tensors_to_check, - delta=1e-5 - ) - self.assertLess( - sum([tf.norm(numerical[i] - theoretical[i]) for i in range(2)]), - 0.02, - tensors_to_check - ) diff --git a/tests/addressing_test.py b/tests/addressing_test.py index eba29e0..f997487 100644 --- a/tests/addressing_test.py +++ b/tests/addressing_test.py @@ -27,360 +27,380 @@ # set seeds for determinism np.random.seed(42) from tensorflow.python.framework import random_seed + random_seed.set_seed(42) -class WeightedSoftmaxTest(tf.test.TestCase): - def testValues(self): - batch_size = 5 - num_heads = 3 - memory_size = 7 +class WeightedSoftmaxTest(tf.test.TestCase): + def testValues(self): + batch_size = 5 + num_heads = 3 + memory_size = 7 - activations = np.random.randn(batch_size, num_heads, memory_size) - weights = np.ones((batch_size, num_heads)) + activations = np.random.randn(batch_size, num_heads, memory_size) + weights = np.ones((batch_size, num_heads)) - # Run weighted softmax with identity placed on weights. Output should be - # equal to a standalone softmax. - observed = addressing.weighted_softmax(activations, weights, tf.identity) - expected = snt.BatchApply(tf.nn.softmax, num_dims=1)((activations)) - self.assertAllClose(observed, expected) + # Run weighted softmax with identity placed on weights. Output should be + # equal to a standalone softmax. + observed = addressing.weighted_softmax(activations, weights, tf.identity) + expected = snt.BatchApply(tf.nn.softmax, num_dims=1)((activations)) + self.assertAllClose(observed, expected) class CosineWeightsTest(tf.test.TestCase): + def testShape(self): + batch_size = 5 + num_heads = 3 + memory_size = 7 + word_size = 2 + + module = addressing.CosineWeights(num_heads, word_size) + mem = np.random.randn(batch_size, memory_size, word_size) + keys = np.random.randn(batch_size, num_heads, word_size) + strengths = np.random.randn(batch_size, num_heads) + weights = module(mem, keys, strengths) + self.assertTrue( + weights.get_shape().is_compatible_with([batch_size, num_heads, memory_size]) + ) - def testShape(self): - batch_size = 5 - num_heads = 3 - memory_size = 7 - word_size = 2 - - module = addressing.CosineWeights(num_heads, word_size) - mem = np.random.randn(batch_size, memory_size, word_size) - keys = np.random.randn(batch_size, num_heads, word_size) - strengths = np.random.randn(batch_size, num_heads) - weights = module(mem, keys, strengths) - self.assertTrue(weights.get_shape().is_compatible_with( - [batch_size, num_heads, memory_size])) - - def testValues(self): - batch_size = 5 - num_heads = 4 - memory_size = 10 - word_size = 2 - - mem = np.random.randn(batch_size, memory_size, word_size) - np.copyto(mem[0, 0], [1, 2]) - np.copyto(mem[0, 1], [3, 4]) - np.copyto(mem[0, 2], [5, 6]) - - keys = np.random.randn(batch_size, num_heads, word_size) - np.copyto(keys[0, 0], [5, 6]) - np.copyto(keys[0, 1], [1, 2]) - np.copyto(keys[0, 2], [5, 6]) - np.copyto(keys[0, 3], [3, 4]) - strengths = np.random.randn(batch_size, num_heads) - - module = addressing.CosineWeights(num_heads, word_size) - weights = module(mem, keys, strengths) - - - # Manually checks results. - strengths_softplus = np.log(1 + np.exp(strengths)) - similarity = np.zeros((memory_size)) - - for b in range(batch_size): - for h in range(num_heads): - key = keys[b, h] - key_norm = np.linalg.norm(key) - - for m in range(memory_size): - row = mem[b, m] - similarity[m] = np.dot(key, row) / (key_norm * np.linalg.norm(row)) - - similarity = np.exp(similarity * strengths_softplus[b, h]) - similarity /= similarity.sum() - self.assertAllClose(weights[b, h], similarity, atol=1e-4, rtol=1e-4) - - def testDivideByZero(self): - batch_size = 5 - num_heads = 4 - memory_size = 10 - word_size = 2 - - module = addressing.CosineWeights(num_heads, word_size) - keys = tf.Variable(tf.random.normal([batch_size, num_heads, word_size], dtype=tf.float64)) - strengths = tf.Variable(tf.random.normal([batch_size, num_heads], dtype=tf.float64)) - - # First row of memory is non-zero to concentrate attention on this location. - # Remaining rows are all zero. - first_row_ones = tf.ones([batch_size, 1, word_size], dtype=tf.float64) - remaining_zeros = tf.zeros( - [batch_size, memory_size - 1, word_size], dtype=tf.float64) - mem = tf.Variable(tf.concat((first_row_ones, remaining_zeros), 1)) - - with tf.GradientTape() as gtape: - output = module(mem, keys, strengths) - gradients = gtape.gradient(target=output, sources=[mem, keys, strengths]) - - self.assertFalse(np.any(np.isnan(output))) - self.assertFalse(np.any(np.isnan(gradients[0]))) - self.assertFalse(np.any(np.isnan(gradients[1]))) - self.assertFalse(np.any(np.isnan(gradients[2]))) + def testValues(self): + batch_size = 5 + num_heads = 4 + memory_size = 10 + word_size = 2 + + mem = np.random.randn(batch_size, memory_size, word_size) + np.copyto(mem[0, 0], [1, 2]) + np.copyto(mem[0, 1], [3, 4]) + np.copyto(mem[0, 2], [5, 6]) + + keys = np.random.randn(batch_size, num_heads, word_size) + np.copyto(keys[0, 0], [5, 6]) + np.copyto(keys[0, 1], [1, 2]) + np.copyto(keys[0, 2], [5, 6]) + np.copyto(keys[0, 3], [3, 4]) + strengths = np.random.randn(batch_size, num_heads) + + module = addressing.CosineWeights(num_heads, word_size) + weights = module(mem, keys, strengths) + + # Manually checks results. + strengths_softplus = np.log(1 + np.exp(strengths)) + similarity = np.zeros((memory_size)) + + for b in range(batch_size): + for h in range(num_heads): + key = keys[b, h] + key_norm = np.linalg.norm(key) + + for m in range(memory_size): + row = mem[b, m] + similarity[m] = np.dot(key, row) / (key_norm * np.linalg.norm(row)) + + similarity = np.exp(similarity * strengths_softplus[b, h]) + similarity /= similarity.sum() + self.assertAllClose(weights[b, h], similarity, atol=1e-4, rtol=1e-4) + + def testDivideByZero(self): + batch_size = 5 + num_heads = 4 + memory_size = 10 + word_size = 2 + + module = addressing.CosineWeights(num_heads, word_size) + keys = tf.Variable( + tf.random.normal([batch_size, num_heads, word_size], dtype=tf.float64) + ) + strengths = tf.Variable( + tf.random.normal([batch_size, num_heads], dtype=tf.float64) + ) + + # First row of memory is non-zero to concentrate attention on this location. + # Remaining rows are all zero. + first_row_ones = tf.ones([batch_size, 1, word_size], dtype=tf.float64) + remaining_zeros = tf.zeros( + [batch_size, memory_size - 1, word_size], dtype=tf.float64 + ) + mem = tf.Variable(tf.concat((first_row_ones, remaining_zeros), 1)) + + with tf.GradientTape() as gtape: + output = module(mem, keys, strengths) + gradients = gtape.gradient(target=output, sources=[mem, keys, strengths]) + + self.assertFalse(np.any(np.isnan(output))) + self.assertFalse(np.any(np.isnan(gradients[0]))) + self.assertFalse(np.any(np.isnan(gradients[1]))) + self.assertFalse(np.any(np.isnan(gradients[2]))) class TemporalLinkageTest(tf.test.TestCase): + def testModule(self): + batch_size = 7 + memory_size = 4 + num_reads = 11 + num_writes = 5 + module = addressing.TemporalLinkage( + memory_size=memory_size, num_writes=num_writes + ) + + state = [ + # link + np.zeros([batch_size, num_writes, memory_size, memory_size]), + # precedence_weights + np.zeros([batch_size, num_writes, memory_size]), + ] + + num_steps = 5 + for i in range(num_steps): + write_weights = np.random.rand(batch_size, num_writes, memory_size) + write_weights /= write_weights.sum(2, keepdims=True) + 1 + + # Simulate (in final steps) link 0-->1 in head 0 and 3-->2 in head 1 + if i == num_steps - 2: + write_weights[0, 0, :] = util.one_hot(memory_size, 0) + write_weights[0, 1, :] = util.one_hot(memory_size, 3) + elif i == num_steps - 1: + write_weights[0, 0, :] = util.one_hot(memory_size, 1) + write_weights[0, 1, :] = util.one_hot(memory_size, 2) + + prev_link_in = state[addressing.LINK] + prev_precedence_weights_in = state[addressing.PRECEDENCE_WEIGHTS] + write_weights_in = write_weights + + state = module( + write_weights_in, + [ + # link + prev_link_in, + # precedence_weights + prev_precedence_weights_in, + ], + ) + + result_link = state[addressing.LINK] + + # link should be bounded in range [0, 1] + self.assertGreaterEqual(tf.math.reduce_min(result_link), 0) + self.assertLessEqual(tf.math.reduce_max(result_link), 1) + + # link diagonal should be zero + self.assertAllEqual( + tf.linalg.diag_part(result_link), + np.zeros([batch_size, num_writes, memory_size]), + ) + + # link rows and columns should sum to at most 1 + self.assertLessEqual( + tf.math.reduce_max(tf.math.reduce_sum(result_link, axis=2)), 1 + ) + self.assertLessEqual( + tf.math.reduce_max(tf.math.reduce_sum(result_link, axis=3)), 1 + ) + + # records our transitions in batch 0: head 0: 0->1, and head 1: 3->2 + self.assertAllEqual(result_link[0, 0, :, 0], util.one_hot(memory_size, 1)) + self.assertAllEqual(result_link[0, 1, :, 3], util.one_hot(memory_size, 2)) + + # Now test calculation of forward and backward read weights + prev_read_weights = np.random.rand(batch_size, num_reads, memory_size) + prev_read_weights[0, 5, :] = util.one_hot(memory_size, 0) # read 5, posn 0 + prev_read_weights[0, 6, :] = util.one_hot(memory_size, 2) # read 6, posn 2 + forward_read_weights = module.directional_read_weights( + tf.constant(result_link), + tf.constant(prev_read_weights, dtype=tf.float64), + forward=True, + ) + backward_read_weights = module.directional_read_weights( + tf.constant(result_link), + tf.constant(prev_read_weights, dtype=tf.float64), + forward=False, + ) - def testModule(self): - batch_size = 7 - memory_size = 4 - num_reads = 11 - num_writes = 5 - module = addressing.TemporalLinkage( - memory_size=memory_size, num_writes=num_writes) + # Check directional weights calculated correctly. + self.assertAllEqual( + forward_read_weights[0, 5, 0, :], # read=5, write=0 + util.one_hot(memory_size, 1), + ) + self.assertAllEqual( + backward_read_weights[0, 6, 1, :], # read=6, write=1 + util.one_hot(memory_size, 3), + ) - state = addressing.TemporalLinkageState( - link=np.zeros([batch_size, num_writes, memory_size, memory_size]), - precedence_weights=np.zeros([batch_size, num_writes, memory_size])) + def testPrecedenceWeights(self): + batch_size = 7 + memory_size = 3 + num_writes = 5 + module = addressing.TemporalLinkage( + memory_size=memory_size, num_writes=num_writes + ) - num_steps = 5 - for i in range(num_steps): + prev_precedence_weights = np.random.rand(batch_size, num_writes, memory_size) write_weights = np.random.rand(batch_size, num_writes, memory_size) + + # These should sum to at most 1 for each write head in each batch. write_weights /= write_weights.sum(2, keepdims=True) + 1 + prev_precedence_weights /= prev_precedence_weights.sum(2, keepdims=True) + 1 - # Simulate (in final steps) link 0-->1 in head 0 and 3-->2 in head 1 - if i == num_steps - 2: - write_weights[0, 0, :] = util.one_hot(memory_size, 0) - write_weights[0, 1, :] = util.one_hot(memory_size, 3) - elif i == num_steps - 1: - write_weights[0, 0, :] = util.one_hot(memory_size, 1) - write_weights[0, 1, :] = util.one_hot(memory_size, 2) - - prev_link_in = state[addressing.LINK] - prev_precedence_weights_in = state[addressing.PRECEDENCE_WEIGHTS] - write_weights_in = write_weights - - state = module( - write_weights_in, - addressing.TemporalLinkageState( - link=prev_link_in, - precedence_weights=prev_precedence_weights_in - ) + write_weights[0, 1, :] = 0 # batch 0 head 1: no writing + write_weights[1, 2, :] /= write_weights[1, 2, :].sum() # b1 h2: all writing + + precedence_weights = module._precedence_weights( + prev_precedence_weights=tf.constant(prev_precedence_weights), + write_weights=tf.constant(write_weights), ) - result_link = state[addressing.LINK] - - # link should be bounded in range [0, 1] - self.assertGreaterEqual(tf.math.reduce_min(result_link), 0) - self.assertLessEqual(tf.math.reduce_max(result_link), 1) - - # link diagonal should be zero - self.assertAllEqual( - tf.linalg.diag_part(result_link), - np.zeros([batch_size, num_writes, memory_size])) - - # link rows and columns should sum to at most 1 - self.assertLessEqual( - tf.math.reduce_max(tf.math.reduce_sum(result_link, axis=2)), 1) - self.assertLessEqual( - tf.math.reduce_max(tf.math.reduce_sum(result_link, axis=3)), 1) - - # records our transitions in batch 0: head 0: 0->1, and head 1: 3->2 - self.assertAllEqual(result_link[0, 0, :, 0], util.one_hot(memory_size, 1)) - self.assertAllEqual(result_link[0, 1, :, 3], util.one_hot(memory_size, 2)) - - # Now test calculation of forward and backward read weights - prev_read_weights = np.random.rand(batch_size, num_reads, memory_size) - prev_read_weights[0, 5, :] = util.one_hot(memory_size, 0) # read 5, posn 0 - prev_read_weights[0, 6, :] = util.one_hot(memory_size, 2) # read 6, posn 2 - forward_read_weights = module.directional_read_weights( - tf.constant(result_link), - tf.constant(prev_read_weights, dtype=tf.float64), - forward=True) - backward_read_weights = module.directional_read_weights( - tf.constant(result_link), - tf.constant(prev_read_weights, dtype=tf.float64), - forward=False) - - # Check directional weights calculated correctly. - self.assertAllEqual( - forward_read_weights[0, 5, 0, :], # read=5, write=0 - util.one_hot(memory_size, 1)) - self.assertAllEqual( - backward_read_weights[0, 6, 1, :], # read=6, write=1 - util.one_hot(memory_size, 3)) - - def testPrecedenceWeights(self): - batch_size = 7 - memory_size = 3 - num_writes = 5 - module = addressing.TemporalLinkage( - memory_size=memory_size, num_writes=num_writes) - - prev_precedence_weights = np.random.rand(batch_size, num_writes, - memory_size) - write_weights = np.random.rand(batch_size, num_writes, memory_size) - - # These should sum to at most 1 for each write head in each batch. - write_weights /= write_weights.sum(2, keepdims=True) + 1 - prev_precedence_weights /= prev_precedence_weights.sum(2, keepdims=True) + 1 - - write_weights[0, 1, :] = 0 # batch 0 head 1: no writing - write_weights[1, 2, :] /= write_weights[1, 2, :].sum() # b1 h2: all writing - - precedence_weights = module._precedence_weights( - prev_precedence_weights=tf.constant(prev_precedence_weights), - write_weights=tf.constant(write_weights)) - - # precedence weights should be bounded in range [0, 1] - self.assertGreaterEqual(tf.math.reduce_min(precedence_weights), 0) - self.assertLessEqual(tf.math.reduce_max(precedence_weights), 1) - - # no writing in batch 0, head 1 - self.assertAllClose(precedence_weights[0, 1, :], - prev_precedence_weights[0, 1, :]) - - # all writing in batch 1, head 2 - self.assertAllClose(precedence_weights[1, 2, :], write_weights[1, 2, :]) + # precedence weights should be bounded in range [0, 1] + self.assertGreaterEqual(tf.math.reduce_min(precedence_weights), 0) + self.assertLessEqual(tf.math.reduce_max(precedence_weights), 1) + + # no writing in batch 0, head 1 + self.assertAllClose( + precedence_weights[0, 1, :], prev_precedence_weights[0, 1, :] + ) + + # all writing in batch 1, head 2 + self.assertAllClose(precedence_weights[1, 2, :], write_weights[1, 2, :]) class FreenessTest(tf.test.TestCase): + def testModule(self): + batch_size = 5 + memory_size = 11 + num_reads = 3 + num_writes = 7 + module = addressing.Freeness(memory_size) + + free_gate = np.random.rand(batch_size, num_reads) + + # Produce read weights that sum to 1 for each batch and head. + prev_read_weights = np.random.rand(batch_size, num_reads, memory_size) + prev_read_weights[1, :, 3] = 0 # no read at batch 1, position 3; see below + prev_read_weights /= prev_read_weights.sum(2, keepdims=True) + prev_write_weights = np.random.rand(batch_size, num_writes, memory_size) + prev_write_weights /= prev_write_weights.sum(2, keepdims=True) + prev_usage = np.random.rand(batch_size, memory_size) + + # Add some special values that allows us to test the behaviour: + prev_write_weights[1, 2, 3] = 1 # full write in batch 1, head 2, position 3 + prev_read_weights[2, 0, 4] = 1 # full read at batch 2, head 0, position 4 + free_gate[2, 0] = 1 # can free up all locations for batch 2, read head 0 + + usage = module( + tf.constant(prev_write_weights), + tf.constant(free_gate), + tf.constant(prev_read_weights), + tf.constant(prev_usage), + ) - def testModule(self): - batch_size = 5 - memory_size = 11 - num_reads = 3 - num_writes = 7 - module = addressing.Freeness(memory_size) - - free_gate = np.random.rand(batch_size, num_reads) - - # Produce read weights that sum to 1 for each batch and head. - prev_read_weights = np.random.rand(batch_size, num_reads, memory_size) - prev_read_weights[1, :, 3] = 0 # no read at batch 1, position 3; see below - prev_read_weights /= prev_read_weights.sum(2, keepdims=True) - prev_write_weights = np.random.rand(batch_size, num_writes, memory_size) - prev_write_weights /= prev_write_weights.sum(2, keepdims=True) - prev_usage = np.random.rand(batch_size, memory_size) - - # Add some special values that allows us to test the behaviour: - prev_write_weights[1, 2, 3] = 1 # full write in batch 1, head 2, position 3 - prev_read_weights[2, 0, 4] = 1 # full read at batch 2, head 0, position 4 - free_gate[2, 0] = 1 # can free up all locations for batch 2, read head 0 - - usage = module( - tf.constant(prev_write_weights), - tf.constant(free_gate), - tf.constant(prev_read_weights), tf.constant(prev_usage)) - - usage = usage.numpy() - - # Check all usages are between 0 and 1. - self.assertGreaterEqual(usage.min(), 0) - self.assertLessEqual(usage.max(), 1) - - # Check that the full write at batch 1, position 3 makes it fully used. - self.assertEqual(usage[1][3], 1) - - # Check that the full free at batch 2, position 4 makes it fully free. - self.assertEqual(usage[2][4], 0) - - def testWriteAllocationWeights(self): - batch_size = 7 - memory_size = 23 - num_writes = 5 - module = addressing.Freeness(memory_size) - - usage = np.random.rand(batch_size, memory_size) - write_gates = np.random.rand(batch_size, num_writes) - - # Turn off gates for heads 1 and 3 in batch 0. This doesn't scaling down the - # weighting, but it means that the usage doesn't change, so we should get - # the same allocation weightings for: (1, 2) and (3, 4) (but all others - # being different). - write_gates[0, 1] = 0 - write_gates[0, 3] = 0 - # and turn heads 0 and 2 on for full effect. - write_gates[0, 0] = 1 - write_gates[0, 2] = 1 - - # In batch 1, make one of the usages 0 and another almost 0, so that these - # entries get most of the allocation weights for the first and second heads. - usage[1] = usage[1] * 0.9 + 0.1 # make sure all entries are in [0.1, 1] - usage[1][4] = 0 # write head 0 should get allocated to position 4 - usage[1][3] = 1e-4 # write head 1 should get allocated to position 3 - write_gates[1, 0] = 1 # write head 0 fully on - write_gates[1, 1] = 1 # write head 1 fully on - - weights = module.write_allocation_weights( - usage=tf.constant(usage), - write_gates=tf.constant(write_gates), - num_writes=num_writes) - - weights = weights.numpy() - - # Check that all weights are between 0 and 1 - self.assertGreaterEqual(weights.min(), 0) - self.assertLessEqual(weights.max(), 1) - - # Check that weights sum to close to 1 - self.assertAllClose( - np.sum(weights, axis=2), np.ones([batch_size, num_writes]), atol=1e-3) - - # Check the same / different allocation weight pairs as described above. - self.assertGreater(np.abs(weights[0, 0, :] - weights[0, 1, :]).max(), 0.1) - self.assertAllEqual(weights[0, 1, :], weights[0, 2, :]) - self.assertGreater(np.abs(weights[0, 2, :] - weights[0, 3, :]).max(), 0.1) - self.assertAllEqual(weights[0, 3, :], weights[0, 4, :]) - - self.assertAllClose(weights[1][0], util.one_hot(memory_size, 4), atol=1e-3) - self.assertAllClose(weights[1][1], util.one_hot(memory_size, 3), atol=1e-3) - - def testWriteAllocationWeightsGradient(self): - batch_size = 7 - memory_size = 5 - num_writes = 3 - module = addressing.Freeness(memory_size) - - usage = tf.constant(np.random.rand(batch_size, memory_size)) - write_gates = tf.constant(np.random.rand(batch_size, num_writes)) - #weights = module.write_allocation_weights(usage, write_gates, num_writes) - - theoretical, numerical = tf.test.compute_gradient( - lambda usage, write_gates: module.write_allocation_weights(usage, write_gates, num_writes), - [usage, write_gates], - delta=1e-5 - ) - self.assertLess( - sum([tf.norm(numerical[i] - theoretical[i]) for i in range(2)]), - 0.01 - ) - - def testAllocation(self): - batch_size = 7 - memory_size = 13 - usage = np.random.rand(batch_size, memory_size) - module = addressing.Freeness(memory_size) - allocation = module._allocation(tf.constant(usage)) - - # 1. Test that max allocation goes to min usage, and vice versa. - self.assertAllEqual(np.argmin(usage, axis=1), np.argmax(allocation, axis=1)) - self.assertAllEqual(np.argmax(usage, axis=1), np.argmin(allocation, axis=1)) - - # 2. Test that allocations sum to almost 1. - self.assertAllClose(np.sum(allocation, axis=1), np.ones(batch_size), 0.01) - - def testAllocationGradient(self): - batch_size = 1 - memory_size = 5 - usage = tf.constant(np.random.rand(batch_size, memory_size)) - module = addressing.Freeness(memory_size) - allocation = module._allocation(usage) - theoretical, numerical = tf.test.compute_gradient( - module._allocation, - [usage], - delta=1e-5 - ) - self.assertLess( - sum([tf.norm(numerical[i] - theoretical[i]) for i in range(1)]), - 0.01 - ) + usage = usage.numpy() + + # Check all usages are between 0 and 1. + self.assertGreaterEqual(usage.min(), 0) + self.assertLessEqual(usage.max(), 1) + + # Check that the full write at batch 1, position 3 makes it fully used. + self.assertEqual(usage[1][3], 1) + + # Check that the full free at batch 2, position 4 makes it fully free. + self.assertEqual(usage[2][4], 0) + + def testWriteAllocationWeights(self): + batch_size = 7 + memory_size = 23 + num_writes = 5 + module = addressing.Freeness(memory_size) + + usage = np.random.rand(batch_size, memory_size) + write_gates = np.random.rand(batch_size, num_writes) + + # Turn off gates for heads 1 and 3 in batch 0. This doesn't scaling down the + # weighting, but it means that the usage doesn't change, so we should get + # the same allocation weightings for: (1, 2) and (3, 4) (but all others + # being different). + write_gates[0, 1] = 0 + write_gates[0, 3] = 0 + # and turn heads 0 and 2 on for full effect. + write_gates[0, 0] = 1 + write_gates[0, 2] = 1 + + # In batch 1, make one of the usages 0 and another almost 0, so that these + # entries get most of the allocation weights for the first and second heads. + usage[1] = usage[1] * 0.9 + 0.1 # make sure all entries are in [0.1, 1] + usage[1][4] = 0 # write head 0 should get allocated to position 4 + usage[1][3] = 1e-4 # write head 1 should get allocated to position 3 + write_gates[1, 0] = 1 # write head 0 fully on + write_gates[1, 1] = 1 # write head 1 fully on + + weights = module.write_allocation_weights( + usage=tf.constant(usage), + write_gates=tf.constant(write_gates), + num_writes=num_writes, + ) + + weights = weights.numpy() + + # Check that all weights are between 0 and 1 + self.assertGreaterEqual(weights.min(), 0) + self.assertLessEqual(weights.max(), 1) + + # Check that weights sum to close to 1 + self.assertAllClose( + np.sum(weights, axis=2), np.ones([batch_size, num_writes]), atol=1e-3 + ) + + # Check the same / different allocation weight pairs as described above. + self.assertGreater(np.abs(weights[0, 0, :] - weights[0, 1, :]).max(), 0.1) + self.assertAllEqual(weights[0, 1, :], weights[0, 2, :]) + self.assertGreater(np.abs(weights[0, 2, :] - weights[0, 3, :]).max(), 0.1) + self.assertAllEqual(weights[0, 3, :], weights[0, 4, :]) + + self.assertAllClose(weights[1][0], util.one_hot(memory_size, 4), atol=1e-3) + self.assertAllClose(weights[1][1], util.one_hot(memory_size, 3), atol=1e-3) + + def testWriteAllocationWeightsGradient(self): + batch_size = 7 + memory_size = 5 + num_writes = 3 + module = addressing.Freeness(memory_size) + + usage = tf.constant(np.random.rand(batch_size, memory_size)) + write_gates = tf.constant(np.random.rand(batch_size, num_writes)) + # weights = module.write_allocation_weights(usage, write_gates, num_writes) + + theoretical, numerical = tf.test.compute_gradient( + lambda usage, write_gates: module.write_allocation_weights( + usage, write_gates, num_writes + ), + [usage, write_gates], + delta=1e-5, + ) + self.assertLess( + sum([tf.norm(numerical[i] - theoretical[i]) for i in range(2)]), 0.01 + ) + + def testAllocation(self): + batch_size = 7 + memory_size = 13 + usage = np.random.rand(batch_size, memory_size) + module = addressing.Freeness(memory_size) + allocation = module._allocation(tf.constant(usage)) + + # 1. Test that max allocation goes to min usage, and vice versa. + self.assertAllEqual(np.argmin(usage, axis=1), np.argmax(allocation, axis=1)) + self.assertAllEqual(np.argmax(usage, axis=1), np.argmin(allocation, axis=1)) + + # 2. Test that allocations sum to almost 1. + self.assertAllClose(np.sum(allocation, axis=1), np.ones(batch_size), 0.01) + + def testAllocationGradient(self): + batch_size = 1 + memory_size = 5 + usage = tf.constant(np.random.rand(batch_size, memory_size)) + module = addressing.Freeness(memory_size) + allocation = module._allocation(usage) + theoretical, numerical = tf.test.compute_gradient( + module._allocation, [usage], delta=1e-5 + ) + self.assertLess( + sum([tf.norm(numerical[i] - theoretical[i]) for i in range(1)]), 0.01 + ) diff --git a/tests/dnc_test.py b/tests/dnc_test.py index 3c02b95..75c12cf 100644 --- a/tests/dnc_test.py +++ b/tests/dnc_test.py @@ -28,6 +28,7 @@ # set seeds for determinism np.random.seed(42) from tensorflow.python.framework import random_seed + random_seed.set_seed(42) DTYPE = tf.float32 @@ -53,49 +54,51 @@ class DNCCoreTest(tf.test.TestCase): + def setUp(self): + access_config = { + "memory_size": MEMORY_SIZE, + "word_size": WORD_SIZE, + "num_reads": NUM_READ_HEADS, + "num_writes": NUM_WRITE_HEADS, + } + controller_config = { + # "hidden_size": FLAGS.hidden_size, + "units": HIDDEN_SIZE, + } - def setUp(self): - access_config = { - "memory_size": MEMORY_SIZE, - "word_size": WORD_SIZE, - "num_reads": NUM_READ_HEADS, - "num_writes": NUM_WRITE_HEADS, - } - controller_config = { - #"hidden_size": FLAGS.hidden_size, - "units": HIDDEN_SIZE, - } - - self.module = dnc.DNC( - access_config, - controller_config, - OUTPUT_SIZE, - BATCH_SIZE, - CLIP_VALUE, - name='dnc_test', - dtype=DTYPE, - ) - self.initial_state = self.module.get_initial_state(batch_size=BATCH_SIZE) + self.module = dnc.DNC( + access_config, + controller_config, + OUTPUT_SIZE, + BATCH_SIZE, + CLIP_VALUE, + name="dnc_test", + dtype=DTYPE, + ) + self.initial_state = self.module.get_initial_state(batch_size=BATCH_SIZE) - def testBuildAndTrain(self): - inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) - targets = np.random.rand(TIME_STEPS, BATCH_SIZE, OUTPUT_SIZE) - loss = lambda outputs, targets: tf.reduce_mean(input_tensor=tf.square(outputs - targets)) - optimizer = tf.compat.v1.train.RMSPropOptimizer( - LEARNING_RATE, epsilon=OPTIMIZER_EPSILON) + def testBuildAndTrain(self): + inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) + targets = np.random.rand(TIME_STEPS, BATCH_SIZE, OUTPUT_SIZE) + loss = lambda outputs, targets: tf.reduce_mean( + input_tensor=tf.square(outputs - targets) + ) + optimizer = tf.compat.v1.train.RMSPropOptimizer( + LEARNING_RATE, epsilon=OPTIMIZER_EPSILON + ) - with tf.GradientTape() as tape: - #outputs, _ = tf.compat.v1.nn.dynamic_rnn( - outputs = tf.keras.layers.RNN( - cell=self.module, - time_major=True, - return_sequences=True, - )( - inputs=inputs, - initial_state=self.initial_state, - ) - loss_value = loss(outputs, targets) - gradients = tape.gradient(loss_value, self.module.trainable_variables) + with tf.GradientTape() as tape: + # outputs, _ = tf.compat.v1.nn.dynamic_rnn( + outputs = tf.keras.layers.RNN( + cell=self.module, + time_major=True, + return_sequences=True, + )( + inputs=inputs, + initial_state=self.initial_state, + ) + loss_value = loss(outputs, targets) + gradients = tape.gradient(loss_value, self.module.trainable_variables) - grads, _ = tf.clip_by_global_norm(gradients, MAX_GRAD_NORM) - optimizer.apply_gradients(zip(gradients, self.module.trainable_variables)) + grads, _ = tf.clip_by_global_norm(gradients, MAX_GRAD_NORM) + optimizer.apply_gradients(zip(gradients, self.module.trainable_variables)) diff --git a/tests/util_test.py b/tests/util_test.py index f5d139e..d281722 100644 --- a/tests/util_test.py +++ b/tests/util_test.py @@ -20,38 +20,67 @@ import numpy as np import tensorflow as tf +import pytest from dnc import util # set seeds for determinism np.random.seed(42) -class BatchInvertPermutation(tf.test.TestCase): - def test(self): - # Tests that the _batch_invert_permutation function correctly inverts a - # batch of permutations. - batch_size = 5 - length = 7 +class BatchInvertPermutation(tf.test.TestCase): + def test(self): + # Tests that the _batch_invert_permutation function correctly inverts a + # batch of permutations. + batch_size = 5 + length = 7 - permutations = np.empty([batch_size, length], dtype=int) - for i in range(batch_size): - permutations[i] = np.random.permutation(length) + permutations = np.empty([batch_size, length], dtype=int) + for i in range(batch_size): + permutations[i] = np.random.permutation(length) - inverse = util.batch_invert_permutation(tf.constant(permutations, tf.int32)) - inverse = inverse.numpy() + inverse = util.batch_invert_permutation(tf.constant(permutations, tf.int32)) + inverse = inverse.numpy() - for i in range(batch_size): - for j in range(length): - self.assertEqual(permutations[i][inverse[i][j]], j) + for i in range(batch_size): + for j in range(length): + self.assertEqual(permutations[i][inverse[i][j]], j) class BatchGather(tf.test.TestCase): + def test(self): + values = np.array([[3, 1, 4, 1], [5, 9, 2, 6], [5, 3, 5, 7]]) + indexs = np.array([[1, 2, 0, 3], [3, 0, 1, 2], [0, 2, 1, 3]]) + target = np.array([[1, 4, 3, 1], [6, 5, 9, 2], [5, 5, 3, 7]]) + result = util.batch_gather(tf.constant(values), tf.constant(indexs)) + result = result.numpy() + self.assertAllEqual(target, result) + - def test(self): - values = np.array([[3, 1, 4, 1], [5, 9, 2, 6], [5, 3, 5, 7]]) - indexs = np.array([[1, 2, 0, 3], [3, 0, 1, 2], [0, 2, 1, 3]]) - target = np.array([[1, 4, 3, 1], [6, 5, 9, 2], [5, 5, 3, 7]]) - result = util.batch_gather(tf.constant(values), tf.constant(indexs)) - result = result.numpy() - self.assertAllEqual(target, result) +@pytest.mark.parametrize( + "batch_size, state_size, initial_state", + [ + (2, [], []), + (2, 2, tf.zeros([2, 2], dtype=tf.float32)), + ( + 2, + [tf.TensorShape([1, 3]), 2], + [tf.zeros([2, 1, 3], dtype=tf.float32), tf.zeros([2, 2], dtype=tf.float32)], + ), + ( + 2, + [2, [2, [tf.TensorShape([1, 3])]]], + [ + tf.zeros([2, 2], dtype=tf.float32), + [ + tf.zeros([2, 2], dtype=tf.float32), + [tf.zeros([2, 1, 3], dtype=tf.float32)], + ], + ], + ), + ], +) +def test_initial_state_from_state_size(batch_size, state_size, initial_state): + assert str(initial_state) == str( + util.initial_state_from_state_size(state_size, batch_size, tf.float32) + ) diff --git a/train.py b/train.py index f9bb200..0468cd2 100644 --- a/train.py +++ b/train.py @@ -25,68 +25,107 @@ from dnc import dnc from dnc import repeat_copy -parser = argparse.ArgumentParser(description='Train DNC for repeat copy task.') +parser = argparse.ArgumentParser(description="Train DNC for repeat copy task.") # Model parameters -parser.add_argument("--hidden_size", default=64, type=int, help= - "Size of LSTM hidden layer.") -parser.add_argument("--memory_size", default=16, type=int, help= - "The number of memory slots.") -parser.add_argument("--word_size", default=16, type=int, help= - "The width of each memory slot.") -parser.add_argument("--num_write_heads", default=1, type=int, help= - "Number of memory write heads.") -parser.add_argument("--num_read_heads", default=4, type=int, help= - "Number of memory read heads.") -parser.add_argument("--clip_value", default=20, type=int, help= - "Maximum absolute value of controller and dnc outputs.") +parser.add_argument( + "--hidden_size", default=64, type=int, help="Size of LSTM hidden layer." +) +parser.add_argument( + "--memory_size", default=16, type=int, help="The number of memory slots." +) +parser.add_argument( + "--word_size", default=16, type=int, help="The width of each memory slot." +) +parser.add_argument( + "--num_write_heads", default=1, type=int, help="Number of memory write heads." +) +parser.add_argument( + "--num_read_heads", default=4, type=int, help="Number of memory read heads." +) +parser.add_argument( + "--clip_value", + default=20, + type=int, + help="Maximum absolute value of controller and dnc outputs.", +) # Optimizer parameters. -parser.add_argument("--max_grad_norm", default=50, type=float, help= - "Gradient clipping norm limit.") -parser.add_argument("--learning_rate", default=1e-4, type=float, help= - "Optimizer learning rate.") -parser.add_argument("--optimizer_epsilon", default=1e-10, type=float, help= - "Epsilon used for RMSProp optimizer.") +parser.add_argument( + "--max_grad_norm", default=50, type=float, help="Gradient clipping norm limit." +) +parser.add_argument( + "--learning_rate", default=1e-4, type=float, help="Optimizer learning rate." +) +parser.add_argument( + "--optimizer_epsilon", + default=1e-10, + type=float, + help="Epsilon used for RMSProp optimizer.", +) # Task parameters -parser.add_argument("--batch_size", default=16, type=int, help= - "Batch size for training.") -parser.add_argument("--num_bits", default=4, type=int, help= - "Dimensionality of each vector to copy") -parser.add_argument("--min_length", default=2, type=int, help= - "Lower limit on number of vectors in the observation pattern to copy") -parser.add_argument("--max_length", default=3, type=int, help= - "Upper limit on number of vectors in the observation pattern to copy") -parser.add_argument("--min_repeats", default=1, type=int, help= - "Lower limit on number of copy repeats.") -parser.add_argument("--max_repeats", default=3, type=int, help= - "Upper limit on number of copy repeats.") +parser.add_argument( + "--batch_size", default=16, type=int, help="Batch size for training." +) +parser.add_argument( + "--num_bits", default=4, type=int, help="Dimensionality of each vector to copy" +) +parser.add_argument( + "--min_length", + default=2, + type=int, + help="Lower limit on number of vectors in the observation pattern to copy", +) +parser.add_argument( + "--max_length", + default=3, + type=int, + help="Upper limit on number of vectors in the observation pattern to copy", +) +parser.add_argument( + "--min_repeats", default=1, type=int, help="Lower limit on number of copy repeats." +) +parser.add_argument( + "--max_repeats", default=3, type=int, help="Upper limit on number of copy repeats." +) # Training options. -parser.add_argument("--epochs", default=10000, type=int, help= - "Number of epochs to train for.") -parser.add_argument("--log_dir", default="./logs/dnc/", type=str, help= - "Logging directory.") -parser.add_argument("--report_interval", default=100, type=int, help= - "Epochs between reports (samples, valid loss).") -parser.add_argument("--checkpoint_dir", default="./checkpoints/repeat_copy", type=str, help= - "Checkpointing directory.") -parser.add_argument("--checkpoint_interval", default=2000, type=int, help= - "Checkpointing step interval.") +parser.add_argument( + "--epochs", default=10000, type=int, help="Number of epochs to train for." +) +parser.add_argument( + "--log_dir", default="./logs/dnc/", type=str, help="Logging directory." +) +parser.add_argument( + "--report_interval", + default=100, + type=int, + help="Epochs between reports (samples, valid loss).", +) +parser.add_argument( + "--checkpoint_dir", + default="./checkpoints/repeat_copy", + type=str, + help="Checkpointing directory.", +) +parser.add_argument( + "--checkpoint_interval", default=2000, type=int, help="Checkpointing step interval." +) FLAGS = parser.parse_args() def train_step(dataset_tensors, rnn_model, optimizer, loss_fn): - return train_step_graphed( - dataset_tensors.observations, - dataset_tensors.target, - dataset_tensors.mask, - rnn_model, - optimizer, - loss_fn, - ) + return train_step_graphed( + dataset_tensors.observations, + dataset_tensors.target, + dataset_tensors.mask, + rnn_model, + optimizer, + loss_fn, + ) + @tf.function def train_step_graphed( @@ -97,34 +136,29 @@ def train_step_graphed( optimizer, loss_fn, ): - """Runs model on input sequence.""" - initial_state = rnn_model.get_initial_state(x) - with tf.GradientTape() as tape: - """output_sequence, _ = tf.compat.v1.nn.dynamic_rnn( - cell=rnn_model, - inputs=x, - time_major=True, - initial_state=initial_state) - # Unable to migrate to tf.keras.layers.RNN due to contraints on RNN state structure - """ - output_sequence = rnn_model( - inputs=x, - initial_state=initial_state, - ) - loss_value = loss_fn(output_sequence, y, mask) - grads = tape.gradient(loss_value, rnn_model.trainable_variables) - grads, _ = tf.clip_by_global_norm(grads, FLAGS.max_grad_norm) - optimizer.apply_gradients(zip(grads, rnn_model.trainable_variables)) - return loss_value + """Runs model on input sequence.""" + initial_state = rnn_model.get_initial_state(x) + with tf.GradientTape() as tape: + output_sequence = rnn_model( + inputs=x, + initial_state=initial_state, + ) + loss_value = loss_fn(output_sequence, y, mask) + grads = tape.gradient(loss_value, rnn_model.trainable_variables) + grads, _ = tf.clip_by_global_norm(grads, FLAGS.max_grad_norm) + optimizer.apply_gradients(zip(grads, rnn_model.trainable_variables)) + return loss_value + def test_step(dataset_tensors, rnn_model, optimizer, loss_fn): - return test_step_graphed( - dataset_tensors.observations, - dataset_tensors.target, - dataset_tensors.mask, - rnn_model, - loss_fn, - ) + return test_step_graphed( + dataset_tensors.observations, + dataset_tensors.target, + dataset_tensors.mask, + rnn_model, + loss_fn, + ) + @tf.function def test_step_graphed( @@ -134,126 +168,135 @@ def test_step_graphed( rnn_model, loss_fn, ): - initial_state = rnn_model.get_initial_state(x) - output_sequence = rnn_model( - inputs=x, - initial_state=initial_state, - ) - loss_value = loss_fn(output_sequence, y, mask) - # Used for visualization. - output = tf.round( - tf.expand_dims(mask, -1) * tf.sigmoid(output_sequence)) - return loss_value, output + initial_state = rnn_model.get_initial_state(x) + output_sequence = rnn_model( + inputs=x, + initial_state=initial_state, + ) + loss_value = loss_fn(output_sequence, y, mask) + # Used for visualization. + output = tf.round(tf.expand_dims(mask, -1) * tf.sigmoid(output_sequence)) + return loss_value, output def train(num_training_iterations, report_interval): - """Trains the DNC and periodically reports the loss.""" - - dataset = repeat_copy.RepeatCopy(FLAGS.num_bits, FLAGS.batch_size, - FLAGS.min_length, FLAGS.max_length, - FLAGS.min_repeats, FLAGS.max_repeats, - dtype=tf.float32) - dataset_tensor = dataset() - - access_config = { - "memory_size": FLAGS.memory_size, - "word_size": FLAGS.word_size, - "num_reads": FLAGS.num_read_heads, - "num_writes": FLAGS.num_write_heads, - } - controller_config = { - #"hidden_size": FLAGS.hidden_size, - "units": FLAGS.hidden_size, - } - clip_value = FLAGS.clip_value - - dnc_cell = dnc.DNC( - access_config, controller_config, dataset.target_size, FLAGS.batch_size, clip_value) - dnc_core = tf.keras.layers.RNN( - cell=dnc_cell, - time_major=True, - return_sequences=True, - ) - optimizer = tf.compat.v1.train.RMSPropOptimizer( - FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon) - loss_fn = dataset.cost - - #saver = tf.train.Checkpoint() - - # Set up logging and metrics - train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32) - test_loss = tf.keras.metrics.Mean('test_loss', dtype=tf.float32) - - current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") - train_log_dir = FLAGS.log_dir + current_time + '/train' - test_log_dir = FLAGS.log_dir + current_time + '/test' - train_summary_writer = tf.summary.create_file_writer(train_log_dir) - test_summary_writer = tf.summary.create_file_writer(test_log_dir) - - # Test once to initialize - graph_log_dir = FLAGS.log_dir + current_time + '/graph' - graph_writer = tf.summary.create_file_writer(graph_log_dir) - with graph_writer.as_default(): - tf.summary.trace_on(graph=True, profiler=True) - test_step(dataset_tensor, dnc_core, optimizer, loss_fn) - tf.summary.trace_export( - name="dnc_trace", - step=0, - profiler_outdir=graph_log_dir) - - # Set up model checkpointing - checkpoint = tf.train.Checkpoint(model=dnc_core, optimizer=optimizer) - manager = tf.train.CheckpointManager(checkpoint, FLAGS.checkpoint_dir, max_to_keep=10) - - checkpoint.restore(manager.latest_checkpoint) - if manager.latest_checkpoint: - print("Restored from {}".format(manager.latest_checkpoint)) - else: - print("Initializing from scratch.") - - # Train. - for epoch in range(num_training_iterations): + """Trains the DNC and periodically reports the loss.""" + + dataset = repeat_copy.RepeatCopy( + FLAGS.num_bits, + FLAGS.batch_size, + FLAGS.min_length, + FLAGS.max_length, + FLAGS.min_repeats, + FLAGS.max_repeats, + dtype=tf.float32, + ) dataset_tensor = dataset() - train_loss_value = train_step( - dataset_tensor, dnc_core, optimizer, loss_fn + + access_config = { + "memory_size": FLAGS.memory_size, + "word_size": FLAGS.word_size, + "num_reads": FLAGS.num_read_heads, + "num_writes": FLAGS.num_write_heads, + } + controller_config = { + # "hidden_size": FLAGS.hidden_size, + "units": FLAGS.hidden_size, + } + clip_value = FLAGS.clip_value + + dnc_cell = dnc.DNC( + access_config, + controller_config, + dataset.target_size, + FLAGS.batch_size, + clip_value, + ) + dnc_core = tf.keras.layers.RNN( + cell=dnc_cell, + time_major=True, + return_sequences=True, ) - train_loss(train_loss_value) - - if (epoch) % report_interval == 0: - dataset_tensor = dataset() - test_loss_value, output = test_step( - dataset_tensor, dnc_core, optimizer, loss_fn - ) - test_loss(test_loss_value) - with test_summary_writer.as_default(): - tf.summary.scalar('loss', test_loss.result(), step=epoch) - with train_summary_writer.as_default(): - tf.summary.scalar('loss', train_loss.result(), step=epoch) - - template = 'Epoch {}, Loss: {}, Test Loss: {}' - print(template.format( - epoch, - train_loss.result(), - test_loss.result(), - )) - - dataset_string = dataset.to_human_readable(dataset_tensor,output.numpy()) - print(dataset_string) - - # reset metrics every epoch - train_loss.reset_states() - test_loss.reset_states() - - if (1 + epoch) % FLAGS.checkpoint_interval == 0: - manager.save() + optimizer = tf.compat.v1.train.RMSPropOptimizer( + FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon + ) + loss_fn = dataset.cost + + # Set up logging and metrics + train_loss = tf.keras.metrics.Mean("train_loss", dtype=tf.float32) + test_loss = tf.keras.metrics.Mean("test_loss", dtype=tf.float32) + + current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + train_log_dir = FLAGS.log_dir + current_time + "/train" + test_log_dir = FLAGS.log_dir + current_time + "/test" + train_summary_writer = tf.summary.create_file_writer(train_log_dir) + test_summary_writer = tf.summary.create_file_writer(test_log_dir) + + # Test once to initialize + graph_log_dir = FLAGS.log_dir + current_time + "/graph" + graph_writer = tf.summary.create_file_writer(graph_log_dir) + with graph_writer.as_default(): + tf.summary.trace_on(graph=True, profiler=True) + test_step(dataset_tensor, dnc_core, optimizer, loss_fn) + tf.summary.trace_export(name="dnc_trace", step=0, profiler_outdir=graph_log_dir) + + # Set up model checkpointing + checkpoint = tf.train.Checkpoint(model=dnc_core, optimizer=optimizer) + manager = tf.train.CheckpointManager( + checkpoint, FLAGS.checkpoint_dir, max_to_keep=10 + ) + + checkpoint.restore(manager.latest_checkpoint) + if manager.latest_checkpoint: + print("Restored from {}".format(manager.latest_checkpoint)) + else: + print("Initializing from scratch.") + + # Train. + for epoch in range(num_training_iterations): + dataset_tensor = dataset() + train_loss_value = train_step(dataset_tensor, dnc_core, optimizer, loss_fn) + train_loss(train_loss_value) + + # report metrics + if (epoch) % report_interval == 0: + dataset_tensor = dataset() + test_loss_value, output = test_step( + dataset_tensor, dnc_core, optimizer, loss_fn + ) + test_loss(test_loss_value) + with test_summary_writer.as_default(): + tf.summary.scalar("loss", test_loss.result(), step=epoch) + with train_summary_writer.as_default(): + tf.summary.scalar("loss", train_loss.result(), step=epoch) + + template = "Epoch {}, Loss: {}, Test Loss: {}" + print( + template.format( + epoch, + train_loss.result(), + test_loss.result(), + ) + ) + + dataset_string = dataset.to_human_readable(dataset_tensor, output.numpy()) + print(dataset_string) + + # reset metrics every epoch + train_loss.reset_states() + test_loss.reset_states() + + # save model at defined intervals + if (1 + epoch) % FLAGS.checkpoint_interval == 0: + manager.save() # At the end, checkpoint as well manager.save() def main(unused_argv): - tf.compat.v1.logging.set_verbosity(3) # Print INFO log messages. - train(FLAGS.epochs, FLAGS.report_interval) + tf.compat.v1.logging.set_verbosity(3) # Print INFO log messages. + train(FLAGS.epochs, FLAGS.report_interval) if __name__ == "__main__": - tf.compat.v1.app.run() + tf.compat.v1.app.run() From 3fff8b1e10c1ca0dd6e000faf2e7659189bec9c0 Mon Sep 17 00:00:00 2001 From: kwliu Date: Sat, 19 Jun 2021 10:16:08 -0700 Subject: [PATCH 11/20] update inspection notebook --- Makefile | 4 +- dnc/repeat_copy.py | 118 ++++--- interactive.ipynb | 827 +++++++-------------------------------------- 3 files changed, 197 insertions(+), 752 deletions(-) diff --git a/Makefile b/Makefile index 8bba7b1..66c8642 100644 --- a/Makefile +++ b/Makefile @@ -14,9 +14,7 @@ venv: test: venv python -m pytest - black . - black dnc/ - black tests/ + black . dnc/ tests/ run: : # Run your app here, e.g diff --git a/dnc/repeat_copy.py b/dnc/repeat_copy.py index 05e6ebb..67e770a 100644 --- a/dnc/repeat_copy.py +++ b/dnc/repeat_copy.py @@ -223,11 +223,13 @@ def __init__( self._time_average_cost = time_average_cost self._dtype = dtype - def _normalise(self, val): - return val / self._norm_max + @classmethod + def _normalise(cls, val, normalise_factor): + return val / normalise_factor - def _unnormalise(self, val): - return val * self._norm_max + @classmethod + def _unnormalise(cls, val, normalise_factor): + return val * normalise_factor @property def time_average_cost(self): @@ -268,8 +270,6 @@ def _build(self): full_obs_size = num_bits + 2 # We reserve one target dimension for the end-marker. full_targ_size = num_bits + 1 - start_end_flag_idx = full_obs_size - 2 - num_repeats_channel_idx = full_obs_size - 1 # Samples each batch index's sequence length and the number of repeats. sub_seq_length_batch = tf.random.uniform( @@ -306,51 +306,9 @@ def _build(self): tf.float32, ) - # The target pattern is the observation pattern repeated n times. - # Some reshaping is required to accomplish the tiling. - targ_pattern_shape = [sub_seq_len * num_reps, num_bits] - flat_obs_pattern = tf.reshape(obs_pattern, [-1]) - flat_targ_pattern = tf.tile(flat_obs_pattern, tf.stack([num_reps])) - targ_pattern = tf.reshape(flat_targ_pattern, targ_pattern_shape) - - # Expand the obs_pattern to have two extra channels for flags. - # Concatenate start flag and num_reps flag to the sequence. - obs_flag_channel_pad = tf.zeros([sub_seq_len, 2]) - obs_start_flag = tf.one_hot( - [start_end_flag_idx], full_obs_size, on_value=1.0, off_value=0.0 + (obs, targ, mask) = self.derive_data_from_inputs( + obs_pattern, num_reps, self._norm_max ) - num_reps_flag = tf.one_hot( - [num_repeats_channel_idx], - full_obs_size, - on_value=self._normalise(tf.cast(num_reps, tf.float32)), - off_value=0.0, - ) - - # note the concatenation dimensions. - obs = tf.concat([obs_pattern, obs_flag_channel_pad], 1) - obs = tf.concat([obs_start_flag, obs], 0) - obs = tf.concat([obs, num_reps_flag], 0) - - # Now do the same for the targ_pattern (it only has one extra channel). - targ_flag_channel_pad = tf.zeros([sub_seq_len * num_reps, 1]) - targ_end_flag = tf.one_hot( - [start_end_flag_idx], full_targ_size, on_value=1.0, off_value=0.0 - ) - targ = tf.concat([targ_pattern, targ_flag_channel_pad], 1) - targ = tf.concat([targ, targ_end_flag], 0) - - # Concatenate zeros at end of obs and begining of targ. - # This aligns them s.t. the target begins as soon as the obs ends. - obs_end_pad = tf.zeros([sub_seq_len * num_reps + 1, full_obs_size]) - targ_start_pad = tf.zeros([sub_seq_len + 2, full_targ_size]) - - # The mask is zero during the obs and one during the targ. - mask_off = tf.zeros([sub_seq_len + 2]) - mask_on = tf.ones([sub_seq_len * num_reps + 1]) - - obs = tf.concat([obs, obs_end_pad], 0) - targ = tf.concat([targ_start_pad, targ], 0) - mask = tf.concat([mask_off, mask_on], 0) obs_tensors.append(obs) targ_tensors.append(targ) @@ -397,6 +355,66 @@ def _build(self): ) return DatasetTensors(obs, targ, mask) + @classmethod + def derive_data_from_inputs(cls, obs_pattern, num_reps, num_rep_normalise_factor): + sub_seq_len, num_bits = obs_pattern.shape + + full_obs_size = num_bits + 2 + # We reserve one target dimension for the end-marker. + full_targ_size = num_bits + 1 + start_end_flag_idx = full_obs_size - 2 + num_repeats_channel_idx = full_obs_size - 1 + + # The target pattern is the observation pattern repeated n times. + # Some reshaping is required to accomplish the tiling. + targ_pattern_shape = [sub_seq_len * num_reps, num_bits] + flat_obs_pattern = tf.reshape(obs_pattern, [-1]) + flat_targ_pattern = tf.tile(flat_obs_pattern, tf.stack([num_reps])) + targ_pattern = tf.reshape(flat_targ_pattern, targ_pattern_shape) + + # Expand the obs_pattern to have two extra channels for flags. + # Concatenate start flag and num_reps flag to the sequence. + obs_flag_channel_pad = tf.zeros([sub_seq_len, 2]) + obs_start_flag = tf.one_hot( + [start_end_flag_idx], full_obs_size, on_value=1.0, off_value=0.0 + ) + num_reps_flag = tf.one_hot( + [num_repeats_channel_idx], + full_obs_size, + on_value=cls._normalise( + tf.cast(num_reps, tf.float32), num_rep_normalise_factor + ), + off_value=0.0, + ) + + # note the concatenation dimensions. + obs = tf.concat([obs_pattern, obs_flag_channel_pad], 1) + obs = tf.concat([obs_start_flag, obs], 0) + obs = tf.concat([obs, num_reps_flag], 0) + + # Now do the same for the targ_pattern (it only has one extra channel). + targ_flag_channel_pad = tf.zeros([sub_seq_len * num_reps, 1]) + targ_end_flag = tf.one_hot( + [start_end_flag_idx], full_targ_size, on_value=1.0, off_value=0.0 + ) + targ = tf.concat([targ_pattern, targ_flag_channel_pad], 1) + targ = tf.concat([targ, targ_end_flag], 0) + + # Concatenate zeros at end of obs and begining of targ. + # This aligns them s.t. the target begins as soon as the obs ends. + obs_end_pad = tf.zeros([sub_seq_len * num_reps + 1, full_obs_size]) + targ_start_pad = tf.zeros([sub_seq_len + 2, full_targ_size]) + + # The mask is zero during the obs and one during the targ. + mask_off = tf.zeros([sub_seq_len + 2]) + mask_on = tf.ones([sub_seq_len * num_reps + 1]) + + obs = tf.concat([obs, obs_end_pad], 0) + targ = tf.concat([targ_start_pad, targ], 0) + mask = tf.concat([mask_off, mask_on], 0) + + return (obs, targ, mask) + def cost(self, logits, targ, mask): return masked_sigmoid_cross_entropy( logits, diff --git a/interactive.ipynb b/interactive.ipynb index 758b98a..8ac22e3 100644 --- a/interactive.ipynb +++ b/interactive.ipynb @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "3112d2e0", "metadata": {}, "outputs": [ @@ -87,117 +87,54 @@ "name": "stdout", "output_type": "stream", "text": [ - "Restored from ./checkpoints/repeat_copy/ckpt-90045\n" + "Restored from ./checkpoints/repeat_copy/ckpt-97\n" ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ "def load_model():\n", - " \"\"\"Trains the DNC and periodically reports the loss.\"\"\"\n", - " access_config = {\n", - " \"memory_size\": FLAGS.memory_size,\n", - " \"word_size\": FLAGS.word_size,\n", - " \"num_reads\": FLAGS.num_read_heads,\n", - " \"num_writes\": FLAGS.num_write_heads,\n", - " }\n", - " controller_config = {\n", - " #\"hidden_size\": FLAGS.hidden_size,\n", - " \"units\": FLAGS.hidden_size,\n", - " }\n", - " clip_value = FLAGS.clip_value\n", - "\n", - " dnc_cell = dnc.DNC(\n", - " access_config, controller_config, dataset.target_size, FLAGS.batch_size, clip_value)\n", - " dnc_core = tf.keras.layers.RNN(\n", - " cell=dnc_cell,\n", - " time_major=True,\n", - " return_sequences=True,\n", - " return_state=True,\n", - " )\n", - " optimizer = tf.compat.v1.train.RMSPropOptimizer(\n", - " FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon)\n", - "\n", - " # Set up model checkpointing\n", - " checkpoint = tf.train.Checkpoint(model=dnc_core, optimizer=optimizer)\n", - " manager = tf.train.CheckpointManager(checkpoint, FLAGS.checkpoint_dir, max_to_keep=10)\n", - "\n", - " checkpoint.restore(manager.latest_checkpoint)\n", - " if manager.latest_checkpoint:\n", - " print(\"Restored from {}\".format(manager.latest_checkpoint))\n", - " else:\n", - " print(\"Initializing from scratch.\")\n", - " return dnc_core\n", + " \"\"\"Trains the DNC and periodically reports the loss.\"\"\"\n", + " access_config = {\n", + " \"memory_size\": FLAGS.memory_size,\n", + " \"word_size\": FLAGS.word_size,\n", + " \"num_reads\": FLAGS.num_read_heads,\n", + " \"num_writes\": FLAGS.num_write_heads,\n", + " }\n", + " controller_config = {\n", + " #\"hidden_size\": FLAGS.hidden_size,\n", + " \"units\": FLAGS.hidden_size,\n", + " }\n", + " clip_value = FLAGS.clip_value\n", "\n", + " dnc_cell = dnc.DNC(\n", + " access_config, controller_config, FLAGS.num_bits + 1, FLAGS.batch_size, clip_value)\n", + " dnc_core = tf.keras.layers.RNN(\n", + " cell=dnc_cell,\n", + " time_major=True,\n", + " return_sequences=True,\n", + " return_state=True,\n", + " )\n", + " optimizer = tf.compat.v1.train.RMSPropOptimizer(\n", + " FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon)\n", "\n", - "def get_inputs(x, num_reps):\n", - " if len(x[0]) > FLAGS.num_bits:\n", - " print(f\"Max input sequence length is {FLAGS.num_bits}\")\n", - " return\n", - " sub_seq_len = len(x)\n", - " num_bits = FLAGS.num_bits\n", - " \n", - " # We reserve one dimension for the num-repeats and one for the start-marker.\n", - " full_obs_size = num_bits + 2\n", - " start_end_flag_idx = full_obs_size - 2\n", - " num_repeats_channel_idx = full_obs_size - 1\n", - " \n", - " obs_pattern = tf.cast(x, tf.float32)\n", - " obs_flag_channel_pad = tf.zeros([sub_seq_len, 2])\n", - " obs_start_flag = tf.one_hot(\n", - " [start_end_flag_idx], full_obs_size, on_value=1., off_value=0.)\n", - " num_reps_flag = tf.one_hot(\n", - " [num_repeats_channel_idx],\n", - " full_obs_size,\n", - " on_value=tf.cast(num_reps / 10.0, tf.float32),\n", - " off_value=0.)\n", - " # note the concatenation dimensions.\n", - " obs = tf.concat([obs_pattern, obs_flag_channel_pad], 1)\n", - " obs = tf.concat([obs_start_flag, obs], 0)\n", - " obs = tf.concat([obs, num_reps_flag], 0)\n", - " # add padding\n", - " obs = tf.concat([\n", - " obs,\n", - " tf.zeros((sub_seq_len * num_reps + 1, full_obs_size))\n", - " ], 0)\n", - " obs = tf.reshape(obs, [sub_seq_len * (num_reps + 1) + 3, 1, full_obs_size])\n", - " return obs\n", + " # Set up model checkpointing\n", + " checkpoint = tf.train.Checkpoint(model=dnc_core, optimizer=optimizer)\n", + " manager = tf.train.CheckpointManager(checkpoint, FLAGS.checkpoint_dir, max_to_keep=10)\n", "\n", - "dataset = repeat_copy.RepeatCopy(FLAGS.num_bits, FLAGS.batch_size,\n", - " FLAGS.min_length, FLAGS.max_length,\n", - " FLAGS.min_repeats, FLAGS.max_repeats,\n", - " dtype=tf.float32)\n", - "dataset_tensor = dataset()\n", + " checkpoint.restore(manager.latest_checkpoint)\n", + " if manager.latest_checkpoint:\n", + " print(\"Restored from {}\".format(manager.latest_checkpoint))\n", + " else:\n", + " print(\"Initializing from scratch.\")\n", + " return dnc_core\n", "\n", - "dnc_core = load_model()\n", "\n", - "x = get_inputs([[1,1,1,1]], 2)\n", - "x" + "dnc_core = load_model()" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "id": "8daa62a5", "metadata": {}, "outputs": [], @@ -212,31 +149,7 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "5e67e26a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor([[0. 0. 0. 0. 1. 0.]], shape=(1, 6), dtype=float32)\n", - "tf.Tensor([[1. 1. 1. 1. 0. 0.]], shape=(1, 6), dtype=float32)\n", - "tf.Tensor([[0. 0. 0. 0. 0. 0.2]], shape=(1, 6), dtype=float32)\n", - "tf.Tensor([[0. 0. 0. 0. 0. 0.]], shape=(1, 6), dtype=float32)\n", - "tf.Tensor([[0. 0. 0. 0. 0. 0.]], shape=(1, 6), dtype=float32)\n", - "tf.Tensor([[0. 0. 0. 0. 0. 0.]], shape=(1, 6), dtype=float32)\n" - ] - } - ], - "source": [ - "for i in x:\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "6500f979", "metadata": {}, "outputs": [], @@ -266,43 +179,7 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "d960721d", - "metadata": {}, - "outputs": [], - "source": [ - "y = get_outputs(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cf911c67", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 121, "id": "b29aad3a", "metadata": {}, "outputs": [], @@ -311,15 +188,46 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", + "def visualize_results(obs, targ, pred, mask):\n", + " obs = tf.transpose(obs)\n", + " targ = tf.transpose(targ)\n", + " pred = tf.transpose(tf.squeeze(pred))\n", + " \n", + " seaborn.set(rc = {'figure.figsize':(\n", + " 15.0 / 64 * obs.shape[1], # time, x-axis\n", + " 15.0 / 64 * obs.shape[0], # biz position, y-axis\n", + " )})\n", + " \n", + " seaborn.heatmap(obs)\n", + " plt.title('RepeatCopy Task Inputs')\n", + " plt.xlabel('time step')\n", + " plt.ylabel('bit position')\n", + " plt.show()\n", + " \n", + " seaborn.heatmap(targ)\n", + " plt.title('RepeatCopy Task Target')\n", + " plt.xlabel('time step')\n", + " plt.ylabel('bit position')\n", + " plt.show()\n", + " \n", + " seaborn.heatmap(pred)\n", + " plt.title('RepeatCopy Task Model Outputs')\n", + " plt.xlabel('time step')\n", + " plt.ylabel('bit position')\n", + " plt.show()\n", + "\n", "def visualize_states(states):\n", - " memory = [memory_from_dnc_state(state)[0] for state in states]\n", - " read_weights = [tf.transpose(read_weights_from_dnc_state(state)[0]) for state in states]\n", - " write_weights = [tf.transpose(write_weights_from_dnc_state(state)[0]) for state in states]\n", + " #memory = [memory_from_dnc_state(state)[0] for state in states]\n", + " read_weights = [read_weights_from_dnc_state(state)[0] for state in states]\n", + " read_weights = tf.transpose(tf.stack(read_weights), [1,2,0])\n", " \n", - " memory_color_range = {\n", + " write_weights = [write_weights_from_dnc_state(state)[0] for state in states]\n", + " write_weights = tf.transpose(tf.stack(write_weights), [1,2,0])\n", + " \n", + " \"\"\"memory_color_range = {\n", " 'vmin': np.min(memory),\n", " 'vmax': np.max(memory)\n", - " }\n", + " }\"\"\"\n", " read_weights_color_range = {\n", " 'vmin': np.min(read_weights),\n", " 'vmax': np.max(read_weights),\n", @@ -328,616 +236,137 @@ " 'vmin': np.min(write_weights),\n", " 'vmax': np.max(write_weights),\n", " }\n", - "\n", - " for i in range(len(states)):\n", - " print(f'Timestep {i}')\n", - " fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(18,5))\n", - " ax1.set_title('Memory')\n", - " ax2.set_title('Read Weights')\n", - " ax3.set_title('Write Weights')\n", - "\n", - " seaborn.heatmap(memory[i], ax=ax1, **memory_color_range)\n", - " seaborn.heatmap(read_weights[i], ax=ax2, **read_weights_color_range)\n", - " seaborn.heatmap(write_weights[i], ax=ax3, **write_weights_color_range)\n", - " plt.show()\n" + " \n", + " \n", + " seaborn.set(rc = {'figure.figsize':(\n", + " 15.0 / 64 * write_weights.shape[2], # time, x-axis\n", + " 15.0 / 64 * write_weights.shape[1], # memory, y-axis\n", + " )})\n", + " \n", + " # Visualize write weights over time\n", + " for i, write_head in enumerate(write_weights):\n", + " seaborn.heatmap(write_head, **write_weights_color_range)\n", + " plt.title(f'Write Weights for Write Head {i}')\n", + " plt.xlabel('time step')\n", + " plt.ylabel('memory slot')\n", + " plt.show()\n", + " \n", + " # Visualize read weights over time\n", + " for i, read_head in enumerate(read_weights):\n", + " seaborn.heatmap(read_head, **read_weights_color_range)\n", + " plt.title(f'Read Weights for Read Head {i}')\n", + " plt.xlabel('time step')\n", + " plt.ylabel('memory slot')\n", + " plt.show()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 122, "id": "89115c0e", "metadata": {}, "outputs": [], "source": [ "def debug_model(x, num_repeats):\n", - " inputs = get_inputs(x, num_repeats)\n", - " print(f\"Input Sequence:\\n {inputs}\")\n", - " output_sequence, states = evaluate_model(inputs, None, dnc_core)\n", - " print(\"Output Sequence:\")\n", - " print(\"Reading input phase:\")\n", - " for i, output in enumerate(output_sequence):\n", - " if i == len(x) + 2:\n", - " print(\"Ouput printing phase:\")\n", - " print(output)\n", + " x = tf.convert_to_tensor(x, dtype=tf.float32)\n", + " obs, targ, mask = repeat_copy.RepeatCopy.derive_data_from_inputs(x, num_repeats, 10)\n", + " \n", + " output_sequence, states = evaluate_model(tf.expand_dims(obs, [1]), None, dnc_core)\n", + " \n", + " visualize_results(obs, targ, tf.stack(output_sequence), mask)\n", " visualize_states(states)\n", " return output_sequence, states" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 123, "id": "eeb76634", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input Sequence:\n", - " [[[0. 0. 0. 0. 1. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[1. 1. 1. 1. 0. 0. ]]\n", - "\n", - " [[0. 1. 0. 1. 0. 0. ]]\n", - "\n", - " [[1. 0. 1. 0. 0. 0. ]]\n", - "\n", - " [[1. 1. 1. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0.3]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]\n", - "\n", - " [[0. 0. 0. 0. 0. 0. ]]]\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._controller.kernel\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._controller.recurrent_kernel\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._controller.bias\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._output_linear.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._output_linear.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_vectors.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_vectors.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.erase_vectors.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.erase_vectors.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.free_gate.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.free_gate.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.allocation_gate.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.allocation_gate.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_gate.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_gate.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_mode.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_mode.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_keys.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_keys.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_strengths.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.write_strengths.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_keys.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_keys.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_strengths.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'momentum' for (root).model.cell._access._linear_layers.read_strengths.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._controller.kernel\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._controller.recurrent_kernel\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._controller.bias\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._output_linear.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._output_linear.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_vectors.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_vectors.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.erase_vectors.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.erase_vectors.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.free_gate.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.free_gate.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.allocation_gate.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.allocation_gate.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_gate.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_gate.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_mode.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_mode.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_keys.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_keys.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_strengths.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.write_strengths.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_keys.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_keys.b\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_strengths.w\n", - "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).model.cell._access._linear_layers.read_strengths.b\n", - "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Output Sequence:\n", - "Reading input phase:\n", - "tf.Tensor([[[1. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 0. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 0. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "Ouput printing phase:\n", - "tf.Tensor([[[1. 1. 1. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 1. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 1. 1. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 1. 1. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 1. 0. 1. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 1. 1. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 1. 1. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[1. 1. 1. 0. 0.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", - "tf.Tensor([[[0. 0. 0. 0. 1.]]], shape=(1, 1, 5), dtype=float32)\n", - "Timestep 0\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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t79i0IiJWFPe9SNIdEfED2wcM2kUAGHoUTQCgWRnlMMUAAJVIvcxy8Yf/iinufqakw2y/UNJWkraz/dmIOCqtlwAw3LjkPQA0K6ccphgAoBo1TImKiLdLerskFTMD/o5CAAB0kdH0VAAYShnlMMUAANXIaEoUAAwtshgAmpVRDlMMAFCNmqugEXGRpItqPQgA5C6jT6QAYChllMMUAwBUo5XP+VEAMLTIYgBoVkY5nHzdM9tHV9kRAJmLVtoNAyGLAWyBHJ525DCALWQ0Jk6/CLp00lR32F5ue6XtlWevvXmAQwDIRquVdsOgSmVxq/XAdPYJQFPI4SaQwwB+K6MxcdfTBGxfPdVdkhZP1a7zUmGX7fqySO4dAKCSLJ49dwlZDACJyGEAw6jXmgGLJR0s6e4J2y3pe7X0CECemGpaJ7IYQDlkcV3IYQDlZJTDvYoB50paEBFXTrzD9kV1dAhApphqWieyGEA5ZHFdyGEA5WSUw12LARFxTJf7jqy+OwCylVHw5YYsBlAaWVwLchhAaRnlMJcWBFCJiHwuowIAw4osBoBm5ZTDFAMAVCOjKigADC2yGACalVEOUwwAUI2MFksBgKFFFgNAszLKYYoBAKqRURUUAIYWWQwAzcooh2svBsyftyG57e1rt0luu0lObvu4pXcmtbv3FzsmHzMG6O+28x9Kbrv+3rSf8bqN6S+dOWPp/0C2325dctv77p+X1G79plnJx7x7LP3ntM829ye33dQaS26bLKMqKDAs5s/ZqukuTJv716fn/0ghiwGgWRnlMDMDAFQjoyooAAwtshgAmpVRDlMMAFCNjKqgADC0yGIAaFZGOUwxAEA1MqqCAsDQIosBoFkZ5TDFAADVyCj4AGBokcUA0KyMcphiAIBqZDQlCgCGFlkMAM3KKId7Ljlu+7G2n297wYTth9TXLQDZabXSbuiJHAZQGjlcG7IYQCkZjYm7FgNs/42kr0r6a0nX2D684+731dkxAJmJVtoNXZHDAPpCDteCLAZQWkZj4l6nCbxe0lMi4n7be0g60/YeEfFRSZ6qke3lkpZL0ok7PEF/tO0jquovgJmKT5fqkpTD0pZZ7FkLNTY2v/bOAmgYWVyXgcfE5DAwIjLK4V7FgLGIuF+SIuIW2weoHX6PUJfgi4gVklZI0o/3fHFU01UAGElJOVw8fnMWz567hCwGgHQDj4nJYQAzTa81A35pe9/xb4oQfJGkHSU9ocZ+AchNRlOiMkMOAyiPHK4LWQygnIzGxL1mBvyppI2dGyJio6Q/tf0ftfUKQH5qmhJleytJF0uap3ZmnRkR76zlYDMTOQygvIymp2aGLAZQTkY53LUYEBGru9z3v9V3B0C26gu+hyQ9rzhPc46k79r+RkRcVtcBZxJyGEBfMhqE5oQsBlBaRjnc89KCAFBKRNqt524jxs/TlDSnuHHeJQBMpoYcBgD0oaYxsdS+lKntG2zfaPv4Se7f3faFtn9k+2rbL+y2v16nCQBAOTVWQW3PkvQDSXtJOjUiLq/tYACQs4w+kQKAoVTfqbOzJJ0q6SBJqyVdYfuciLiu42F/L+mLEfHvtveRdJ6kPabaJ8UAANVIDL7Oyy4VVhSrL28WEZsk7Wt7kaSzbD8+Iq5J7SoADC2KAQDQrPpyeH9JN0bETZJk+wuSDpfUWQwISdsVXy+UdGu3HVIMAFCNxFVQOy+7VOKx99i+UNIhkigGAMBEXB0AAJpVXw4vkbSq4/vVkp424THvknS+7b+WNF/Sgd12WHsxYO1Dc5LbbuVNFfakvDtvX5DUrqn+3vfAvOS2dtq5grOSjyi1ouul0bu6976tkttuGuC4qR4e65Pb3rcu/ffaiPqmRO0kaUNRCNha7alRp9RyMCAzc2eNTk3fnv4Mz1J9WXyIpI+qPQQ4LSJOnnD/7pI+LWlR8ZjjI+K8WjoDADNZjbNlS3iVpNMj4oO2nyHpP4sZtZN2anRGEQDqVd8iVLtI+nRxntSY2udBnVvXwQAgazVkcR3nqQLA0ErM4RKzZddI2q3j+6XFtk7HqD2DVhFxaXGJ7h0l3THZDikGAKhGTZ9GRcTVkvarZecAMGzqyeLKz1MFgKFV35oBV0ja2/aeahcBjpB05ITH/ELS8yWdbvv3JG0l6c6pdkgxAEA1WLQKAJqXkMUlpqZWfp4qAAyt+j4g22j7WEnfUvt0rE9GxLW23y1pZUScI+k4SR+3/bdqF2lfGzH1VAWKAQCqwaJVANC8hCzuZyHXLvo6TxUAhlaNsVesxXLehG0ndnx9naRnlt0fxQAAlYhWbWsGAABKqimLKz9PFQCGVU5j4p7FANv7S4qIuKJYEOYQST9hhVgAW+A0gdqQwwBKqyeLKz9PNUdkMYBSMhoTdy0G2H6npEMlzbZ9gdrnh10o6Xjb+0XEe6ehjwBywEzQWpDDAPpSQxbXcZ5qbshiAKVlNCbuNTPgjyTtK2mepNslLY2Ie21/QNLlkiYNvs6FaI5fuK9ess2elXUYwAyV0ZSozCTlsLRlFnvWQo2Nza+/twCaVVMWV32eaoYGHhOTw8CIyGhMPNbj/o0RsSki1kr6WUTcK0kRsU7SlCWPiFgREcsiYhmFAAAYSFIOF4/ZnMUMQAFgIAOPiclhADNNr5kB621vUwTfU8Y32l6oHoNQACMmo/OjMkMOAyiPLK4LWQygnIxyuFcx4DkR8ZAkTbg0zBxJr6mtVwDyk1HwZYYcBlAeWVwXshhAORnlcNdiwHjoTbL9V5J+VUuPAORpeNaJmlHIYQB9IYtrQRYDKC2jHO55aUEAKCWjKigADC2yGACalVEOUwwAUI2MVk4FgKFFFgNAszLKYYoBAKqR0TVVAWBokcUA0KyMcphiAIBqZFQFBYChRRYDQLMyyuHaiwG/3jQ3ue1z3+DkthtuuD257d9e+rCkdvt6q+RjXjM26bo0pTx3ffp1az83566kdmcet3vyMVf9+8+T215/z/bJbf/g0DuS2t1+6ZzkY35xXdprSZJWe31y2w8cti65barI6PyoUbT2pm823YVps+G09zXdhWnziA/9oOkuTJtfve4JTXchC2TxzLX2lvOb7gKAaZBTDjMzAEA1MqqCAsDQIosBoFkZ5TDFAADVyOj8KAAYWmQxADQroxymGACgGhlVQQFgaJHFANCsjHKYYgCAamR0fhQADC2yGACalVEOUwwAUI2MqqAAMLTIYgBoVkY5PNZvA9ufqaMjADIXrbQb+kYOA5gSOTxtyGIAk8poTNx1ZoDtcyZukvQHthdJUkQcVlO/AOSmpiqo7d0kfUbSYkkhaUVEfLSWg81A5DCAvmT0iVROyGIApWWUw71OE1gq6TpJp6k9CLekZZI+2K2R7eWSlkvSsdsu06FbP2rwngKY0Wq8pupGScdFxA9tbyvpB7YviIjr6jrgDJOUw9KWWXzqySfqda/+oxq7CWAmyOn61pkZeEx86inv1OuOekXN3QTQtJxyuFcxYJmkN0o6QdJbIuJK2+si4jvdGkXECkkrJOm8xUfkUxoBMONExG2Sbiu+vs/29ZKWqD0oGwVJOSxtmcXrV/+YLAaAdAOPidffei05DGBG6VoMiIiWpA/b/lLx/1/2agNgRE3DlCjbe0jaT9LltR9shiCHAfQlo+mpOSGLAZSWUQ6XCrGIWC3pFbb/UNK99XYJQJYSg69zCmVhRfFJysTHLZD0ZUlvioiRyyFyGEApGQ1Cc0QWA+gpoxzuq6IZEV+X9PWa+gIgZ4mroHZOoZyK7TlqFwLOiIivJB1oSJDDALri6gDTgiwGMKWMcpjpTQCqUd/VBCzpE5Kuj4gP1XIQABgWGX0iBQBDKaMcphgAoBJRX/A9U9KfSPqx7SuLbe+IiPPqOiAA5KrGLAYAlJBTDlMMAFCNmoIvIr6r9iWcAAC9ZDQIBYChlFEOUwwAUI2MrqkKAEOLLAaAZmWUw46ot3Jx+a4vSz7AxtZY8nEH+RVsPXtjUruHNjZTW5k9lv5sN0VeH7jOaeC5tgb4GW2M9NfwvLFNyW0H8bRbv5L0hO/7y0OT/q1v+2/fyOtFmKnZc5fkU6Ye0Ci9oB629bZNd2Ha3PPQA013Ydo89OCq5JdxShaTw9NjlHIYGAYb168Z+jExMwMAVCOjKVEAMLTIYgBoVkY5TDEAQCXqnmUEAOiNLAaAZuWUwxQDAFQjoyooAAwtshgAmpVRDlMMAFCNjIIPAIYWWQwAzcoohykGAKhETtdUBYBhRRYDQLNyyuG+igG2nyVpf0nXRMT59XQJQJYyCr7ckcUApkQWTwtyGMCUMsrhrtc9s/39jq9fL+lfJW0r6Z22j6+5bwBy0kq8oSeyGEBp5HAtyGEApWU0Ju51EfQ5HV8vl3RQRJwk6QWSXj1VI9vLba+0vfLstTdX0E0AM120IumGUgbO4lZrdK7RDowycrg25DCAUnIaE/c6TWDM9vZqFw0cEXdKUkQ8YHvjVI0iYoWkFZJ0+a4v410GGAUMKOs0cBbPnruEXxAwCsjiupDDAMrJKId7FQMWSvqBJEsK27tExG22FxTbAAD1I4sBoFnkMICh07UYEBF7THFXS9JLK+8NgHxx3mltyGIApZHFtSCHAZSWUQ4nXVowItZKYjEAAJtx3un0I4sBTEQWTy9yGMBEOeVwUjEAAH5HRlVQABhaZDEANCujHKYYAKASOVVBAWBYkcUA0KyccphiAIBqZFQFBYChRRYDQLMyymGKAQAqERkFHwAMK7IYAJqVUw7XXgy4rbVVctsDj7w//cBj6Vd5+aczFyS1O+PB65OPedLcfZLbPsYPJLc9Y+6cpHYfuOgtycfccMY/J7d9x8fXJ7c9bF3alJ0nP+uXycf8y5WLktv+YuO9yW3P/8ulyW2TZRR8o2jW2FjTXZg2W82e23QXps1d6+5rugvTZmyEXsMDIYsBoFk15rDtQyR9VNIsSadFxMmTPOaVkt4lKSRdFRFHTrU/ZgYAqEROVVAAGFZkMQA0q64ctj1L0qmSDpK0WtIVts+JiOs6HrO3pLdLemZE3G374d32STEAQDUYgAJA88hiAGhWfTm8v6QbI+ImSbL9BUmHS7qu4zGvl3RqRNwtSRFxR7cdMucOQCWilXYDAFSnrhy2fYjtG2zfaPv4KR7zStvX2b7W9ueqfF4AkIsax8RLJK3q+H51sa3ToyU92vb/2r6sOK1gSswMAFCJGqdEfVLSiyTdERGPr+coADAc6sjiOqamAsCwSs1h28slLe/YtCIiVvS5m9mS9pZ0gKSlki62/YSIuGeqBwPAwGr8lP90Sf8q6TO1HQEAhkRNWVz51FQAGFapOVz84d/tj/81knbr+H5psa3TakmXR8QGSTfb/j+1iwNXTLbDrqcJ2H6a7e2Kr7e2fZLtr9k+xfbC7k8HwEgJp9167TbiYkl31f8EZiZyGEBfashh1TA1NTdkMYDSahoTq/0H/d6297Q9V9IRks6Z8Jiz1Z4VINs7qp3NN021w15rBnxS0tri649KWijplGLbp8r0GMBoSD0/yvZy2ys7bst7H22kkMMASmswhzunpr5K0sdtL6rwqTWNLAZQSl1rBkTERknHSvqWpOslfTEirrX9btuHFQ/7lqRf275O0oWS3hIRv55qn71OExgrDipJyyLiycXX37V95VSNOs93eMO2T9ULttmrx2EA5C5apSqav9uu95SoUZeUw9KWWTxr9iLNmrWgvl4CmBFSsriJqakZGnhM7FkLNTY2v95eAmhc6pi41L4jzpN03oRtJ3Z8HZLeXNx66jUz4BrbRxdfX2V7mSTZfrSkDV06uSIilkXEMgoBwGjgagK1ScphacssphAAjIaacrjyqakZGnhMTCEAGA05jYl7FQNeJ+m5tn8maR9Jl9q+SdLHi/sAAPUihwE0qo6pqRkiiwEMna6nCUTEbyS9tlgwZc/i8asj4pfT0TkA+YhyC5/0zfbn1f60aUfbqyW9MyI+UcvBZiByGEA/6sriqqem5oYsBlBWXTlch1KXFoyIeyVdVXNfAGSsrulNEfGqevacF3IYQBmcflUvshhALznlcKliAAD0UudiKQCAcshiAGhWTjlMMQBAJSKa7gEAgCwGgGbllMMUAwBUIqcqKAAMK7IYAJqVUw5TDABQiZyCDwCGFVkMAM3KKYdrLwbsMvZgcturPjc3ue0g6zYcNvuBpHYHz35k+kFb6T+n1lj6C+6IhzYmtVv5jH9MPuYgjhpL/81umpX2c7r+ezskH/MN0evqnVObN7Youe01p96f3PZpJ6S1y2lK1Cja1MpoNZsBrdvwUNNdmDYPn7+o6S5Mm1+tu7fpLmSBLAaAZuWUw8wMAFCJnKqgADCsyGIAaFZOOUwxAEAlcrqmKgAMK7IYAJqVUw5TDABQiZyuqQoAw4osBoBm5ZTDFAMAVKKVURUUAIYVWQwAzcophykGAKhETlOiAGBYkcUA0KyccrjrUue2/8b2btPVGQD5ipaTbuiNLAZQFjlcD3IYQFk5jYl7XffsPZIut32J7b+0vdN0dApAfiLSbiiFLAZQCjlcG3IYQCk5jYl7FQNukrRU7QB8iqTrbH/T9mtsbztVI9vLba+0vfLstTdX2F0AM1VOVdAMDZzFrdYD09VXAA0ih2tDDgMoJacxca9iQEREKyLOj4hjJO0q6d8kHaJ2KE7VaEVELIuIZS/ZZs8KuwtgpmqFk24oZeAsHhubP119BdAgcrg25DCAUnIaE/daQHCLXkXEBknnSDrH9ja19QoA0IksBoBmkcMAhk6vYsAfT3VHRKytuC8AMpbTyqkZIosBlEIW14YcBlBKTjnctRgQEf83XR0BkDcWoaoPWQygLLK4HuQwgLJyyuFeMwMAoBTOOwWA5pHFANCsnHKYYgCASuQ0JQoAhhVZDADNyimHKQYAqEROU6IAYFiRxQDQrJxymGIAgErkNCUKAIYVWQwAzcoph2svBuy136+T217ygyXJbVvJLaWD/yztx3LWaek/ztsH+E0c/aRVyW0v+/6uSe0etfA3ycdcu25OcttHPe/+5LY//e9tk9rN33p98jG/tH775LYvjvTnusse6b+fVHVOibJ9iKSPSpol6bSIOLm2gyF7dj5vwoPadesdmu7CtLnjgXua7kIWcpqeCgDDKKccZmYAgErUVQW1PUvSqZIOkrRa0hW2z4mI62o5IABkLKdPpABgGOWUwxQDAFSixtOj9pd0Y0TcJEm2vyDpcEkUAwBggoxOVQWAoZRTDlMMAFCJGqugSyR1nguzWtLT6joYAOQsp0+kAGAY5ZTDFAMAVCL1/CjbyyUt79i0IiJWVNIpABgxOZ2rCgDDKKccphgAoBKpi3YWf/h3++N/jaTdOr5fWmwDAEwwyALKAIDB5ZTDXYsBtudKOkLSrRHxbdtHSvp9Sder/endhmnoI4AMhGqrgl4haW/be6pdBDhC0pF1HWymIYcB9KPGLB5pZDGAsnLK4V4zAz5VPGYb26+RtEDSVyQ9X+1FvV5Tb/cA5KJV02opEbHR9rGSvqX2pQU/GRHX1nO0GYkcBlBaXVkMshhAOTnlcK9iwBMi4om2Z6v9idyuEbHJ9mclXTVVo85zgD/4+L31mt13qazDAGamVo1V0Ig4T9J5tR1gZkvKYWnLLPashRobm19/bwE0qs4sHnEDj4nJYWA05JTDY73uL6ZFbStpG0kLi+3zJM2ZqlFErIiIZRGxjEIAMBpCTrqhp6QclrbMYgagwGggh2sz8JiYHAZGQ05j4l4zAz4h6SdqT809QdKXbN8k6emSvlBz3wAA5DAAzARkMYCh07UYEBEftv1fxde32v6MpAMlfTwivj8dHQSQh5xWTs0JOQygH2RxPchiAGXllMM9Ly0YEbd2fH2PpDPr7BCAPDHVtD7kMICyyOL6kMUAysgph3sWAwCgjJyqoAAwrMhiAGhWTjlMMQBAJXIKPgAYVmQxADQrpxymGACgEjlNiQKAYUUWA0CzcsphigEAKtHKJ/cAYGiRxQDQrJxyuPZiwM1XbZ/cdgetr7An5f3002nHfURrbvIxd1+f/qq5+UfpP+NF3pDU7p77t0o+ZivSn+vP/mdBctuNrbGkdnffv3XyMZ/VSvv5StL6WbOS2676WfprYufEdq2MqqAYbhHRdBemzT0bHmi6C9PGJmPKIIsBoFk55TAzAwBUYnT+/AKAmYssBoBm5ZTDFAMAVCKnxVIAYFiRxQDQrJxymGIAgEq0mMILAI0jiwGgWTnlMMUAAJXIaUoUAAwrshgAmpVTDqetqAYAE7QSbwCA6pDDANCsOsfEtg+xfYPtG20f3+VxL7cdtpd121/PmQG2HynpZZJ2k7RJ0v9J+lxE3FuyzwBGQE6XUckNOQygrLqy2PYhkj4qaZak0yLi5Cke93JJZ0p6akSsrKc3zSCLAZRRYw7PknSqpIMkrZZ0he1zIuK6CY/bVtIbJV3ea59dZwbY/htJH5O0laSnSpqndgBeZvuA/p8CgGHVkpNu6I4cBtCPOnK4YwB6qKR9JL3K9j6TPK70ADQ3ZDGAsmocE+8v6caIuCki1kv6gqTDJ3nceySdIunBXjvsdZrA6yUdGhH/IOlASY+LiBMkHSLpw1M1sr3c9krbK7/ywC29+gBgCETiDT0l5bC0ZRa3WqNzPXpglNWUw5UPQDM08JiYHAZGQ+qYuDMvitvyCbteImlVx/eri22b2X6ypN0i4utl+lpmAcHZak+FmidpgSRFxC9sz5mqQUSskLRCklYufQnjfWAEcJpArfrO4eIxm7N49twlZDEwAmrK4skGoE/rfEDnANT2W2rpRfMGGhOTw8BoSM3hzrxIYXtM0ockvbZsm17FgNPUPhfhcknPVrvaK9s7SborrZsAgD6QwwBqVXz61PkJ1IpiUFq2fd8D0AyRxQCatkbt05PGLS22jdtW0uMlXeT25Q13lnSO7cOmWsOlazEgIj5q+9uSfk/SByPiJ8X2OyU9J/VZABg+rEhdD3IYQD9SsrjEp1GVD0BzQxYDKKvGMfEVkva2vafaGXyEpCPH74yI30jacfx72xdJ+rtuOdzzNIGIuFbStel9BjAKmPtYH3IYQFk1ZXHlA9AckcUAyqhrTBwRG20fK+lbal/Z5ZMRca3td0taGRHn9LvPMmsGAEBPTawZYPsVkt6l9ic1+w/bwBMA+lVHFtcxAAWAYVXnmDgizpN03oRtJ07x2AN67Y9iAIBKNHSawDVqX/P5P5o5PADMLHVlcdUDUAAYVjmdOksxAEAlmgi+iLhekopzVAFg5OU0CAWAYZRTDlMMAFCJ4O9xAGgcWQwAzcoph2svBmxqjSW3nTOWXldpDfBbeGhDXjWSQX7Gm5T2c2rqJ7Rh06zktk38uxwbYAmRTQO8hsc8/cv5pf5r7XVJq2L15p0naXpCRHw18bAjJ6P3pYFtN2+bprswbZbM277pLkyb3XfaoekuZCGnT6RGzVaz5zbdBQDTIKcczuuvXgAzVmrw9bqkVUQcmLhrABg5OQ1CAWAY5ZTDFAMAVIJLCwJA88hiAGhWTjlMMQBAJRq6tOBLJf2LpJ0kfd32lRFx8PT3BABmhiayGADwWznlMMUAAJVo6GoCZ0k6q4FDA8CMlNP0VAAYRjnlMMUAAJXIKfgAYFiRxQDQrJxymGIAgErkdH4UAAwrshgAmpVTDlMMAFCJnM6PAoBhRRYDQLNyyuGuF6i3vdD2ybZ/Yvsu27+2fX2xbVGXdsttr7S98uy1N1feaQAzTyvxht6qyOJW64Fp7DGAppDD9agihzdsvG8aewygKTmNibsWAyR9UdLdkg6IiIdFxA6S/qDY9sWpGkXEiohYFhHLXrLNntX1FsCMFYk3lDJwFo+NzZ+mrgJoEjlcm4FzeM7sbaepqwCalNOYuFcxYI+IOCUibh/fEBG3R8Qpkh5Rb9cA5KSlSLqhFLIYQCnkcG3IYQCl5DQm7lUM+Lntt9pePL7B9mLbb5O0qt6uAQAKZDEANIscBjB0ehUD/ljSDpK+U5wfdZekiyQ9TNIrau4bgIzkdH5UhshiAKWQw7UhhwGUktOYuOvVBCLibklvK25bsH20pE/V1C8AmWGiaX3IYgBlkcX1IIcBlJVTDveaGdDNSZX1AkD2cqqCDhmyGMBm5HAjyGEAm+U0Ju46M8D21VPdJWnxFPcBGEE5XVM1N2QxgLLI4nqQwwDKyimHuxYD1A63g9W+bEonS/peLT0CkCVWpK4VWQygFLK4NuQwgFJyyuFexYBzJS2IiCsn3mH7ojIHuK21Vf+9Kjxp8Z3JbXfYP70kc/k52ye1++O1P0w+5rsXPS257UHzf53c9mtrd0hqd+yZhycfM36S/nM69e/TF+x99sa1Se2e9Ib01/ArTps4Zihvoecmt13xhw8lt02VT+xlaeAsHqXfz/pNG5vuwrT57h3XN92FaZPRBy2NGqV/69Ns4Bx+cOP6irsEYCbKKYd7LSB4TJf7jqy+OwByxXmn9SGLAZRFFteDHAZQVk453GtmAACUktOUKAAYVmQxADQrpxymGACgEvnEHgAML7IYAJqVUw5TDABQiZymRAHAsCKLAaBZOeUwxQAAlchpShQADCuyGACalVMOUwwAUIl8Yg8AhhdZDADNyimHKQYAqEROU6IAYFiRxQDQrJxyeCy1oe1vdLlvue2Vtleev/bG1EMAyEgk/ofBlM3iVuuB6ewWgIaQw9OPHAbQKacxcdeZAbafPNVdkvadql1ErJC0QpLO3vlI3mWAEZBTFTQ3VWTx7LlLyGJgBJDF9SCHAZSVUw73Ok3gCknfUTvoJlpUeW8AZKuJxVJsv1/SiyWtl/QzSUdHxD3T3pH6kcUASslp4arMkMMASskph3sVA66X9OcR8dOJd9heVU+XAKC0CyS9PSI22j5F0tslva3hPtWBLAaAZpHDAIZOrzUD3tXlMX9dbVcA5CwSbwMdM+L8iNhYfHuZpKUD7nKmepfIYgAlTHcOj5B3iRwGUEITY+JUXWcGRMSZXe7evuK+AMhY6pQo28slLe/YtKI4x7Jffybpv5I6McORxQDKyml6ak7IYQBl5ZTDg1xa8CRJn6qqIwDylrpYSufiSpOx/W1JO09y1wkR8dXiMSdI2ijpjMRu5IwsBrBZTgtXDRFyGMBmOeVwr6sJXD3VXZIWV98dALmq65IoEXFgt/ttv1bSiyQ9PyLyKcX2gSwGUBaXCqwHOQygrJxyuNfMgMWSDpZ094TtlvS9WnoEIEtNVEFtHyLprZKeGxFrG+jCdCGLAZSS0ydSmSGHAZSSUw73KgacK2lBRFw58Q7bF5U5wC5jD/bfq8Jtd2yX3PbWr0125Zdydpib1ucztV/yMcfWP5Tc9r6Yl9z2GRvTnus1L0ufjR2R/rt59gBtU2t013xsXfIxj9u0bXLbrbwpue0N5yQ31bJ/S2vXUBX0XyXNk3SBbUm6LCL+oomO1GzgLB4lD25c33QXps1Ws+c23YVpM0q/10Hk9IlUZshhAKXklMO9FhA8pst9R1bfHQC5aqIKGhF7NXDYaUcWAygrp0+kckIOAygrpxweZAFBANisNZyn6wNAVshiAGhWTjlMMQBAJfKJPQAYXmQxADQrpxymGACgEjldUxUAhhVZDADNyimHKQYAqEROi6UAwLAiiwGgWTnlMMUAAJXIabEUABhWZDEANCunHKYYAKASOU2JAoBhRRYDQLNyyuGxbnfa3s72P9r+T9tHTrhvyquR215ue6XtlWevvbmqvgKYwSLxP/RWRRa3Wg/U31EAjSOH60EOAygrpzFx12KApE9JsqQvSzrC9pdtzyvue/pUjSJiRUQsi4hlL9lmz4q6CmAmayXeUMrAWTw2Nn86+gmgYeRwbchhAKXUOSa2fYjtG2zfaPv4Se5/s+3rbF9t+79tP6Lb/noVAx4VEcdHxNkRcZikH0r6H9s7lOwvgBEREUk3lEIWAyilrhyuegCaIXIYQCl1jYltz5J0qqRDJe0j6VW295nwsB9JWhYRT5R0pqR/6rbPXmsGzLM9FhGt4om91/YaSRdLWtCzxwCAKpDFABrTMQA9SNJqSVfYPicirut42PgAdK3tN6g9AP3j6e9tbchhAE3bX9KNEXGTJNn+gqTDJW3O4oi4sOPxl0k6qtsOe80M+Jqk53VuiIjTJR0naX3ZXgMYfi1F0g2lkMUASqkphzcPQCNivaTxAehmEXFhRKwtvr1M0tJKn1jzyGEApdQ4Jl4iaVXH96uLbVM5RtI3uu2w68yAiHjrFNu/aft93doCGC2cd1ofshhAWSlZbHu5pOUdm1ZExIqO7ycbgD6tyy57DkBzQw4DKCt1TFwii/vZ11GSlkl6brfHDXJpwZPUXkwFAFiRujlkMYDNUrK4GGwmDTgnKjsAHTLkMIDNUsfEJbJ4jaTdOr5fWmzbgu0DJZ0g6bkR8VC3Y3YtBti+eqq7JC3u1hbAaGHKf33IYgBl1ZTFlQ9Ac0MOAyirxjHxFZL2tr2n2hl8hKSJlzrdT9J/SDokIu7otcNeMwMWSzpY0t0TtlvS90p2GsAI4MoAtSKLAZRSUxZXPgDNEDkMoJS6xsQRsdH2sZK+JWmWpE9GxLW23y1pZUScI+n9ai9q+iXbkvSL4gook+pVDDhX0oKIuHLiHbYvSnoWfYhwcttBzl9uJR53nps5a3pTq9c6kFOz016s6zfNSj7mIOaMpf+MNyX+XlPbSdKcAV4Tswd5rgO8JlKxZkCtGs1izFwL5m7VdBemzYbWxqa7kIU6sriOAWiGyGEApdQ5Jo6I8ySdN2HbiR1fH9jP/notIHhMl/uOnOo+AKOHNQPqQxYDKKuuLK56AJobchhAWTmNiQdZQBAANmPNAABoHlkMAM3KKYcpBgCoBGsGAEDzyGIAaFZOOUwxAEAlcqqCAsCwIosBoFk55TDFAACVyOn8KAAYVmQxADQrpxymGACgEq0GpkTZfo+kw9VeuPUOSa+NiFunvSMAMEM0kcUAgN/KKYen//pjAIZSJN4G9P6IeGJE7Kv2ZZ9O7PF4ABhqDeQwAKBDQ2PiJF2LAbZ3tv3vtk+1vYPtd9n+se0v2t6lS7vltlfaXnn22pur7zWAGaelSLoNIiLu7fh2voZ0XFtFFrdaD0xnlwE0ZLpzeFSQwwDKamJMnKrXzIDTJV0naZWkCyWtk/RCSZdI+thUjSJiRUQsi4hlL9lmz4q6CmAmayr4bL/X9ipJr9bwzgw4XQNm8djY/OnoJ4CG5TIAzdDpIocBlDBMxYDFEfEvEXGypEURcUpErIqIf5H0iGnoH4BMRETSrfNTk+K2vHO/tr9t+5pJbocXxz0hInaTdIakY5t47tOALAZQSkoOoxRyGEApqWPiJvRaQLCzWPCZCffNqrgvAEZQRKyQtKLL/QeW3NUZks6T9M4q+jXDkMUA0CxyGMDQ6VUM+KrtBRFxf0T8/fhG23tJuqHergHISRPTm2zvHRE/Lb49XNJPpr0T04MsBlAK0/5rQw4DKCWnHO5aDIiISc+/jYgbbX+9ni4ByFFD11Q92fZj1L604M8l/UUTnagbWQygrJyub50TchhAWTnlcK+ZAd2cJOlTVXUEQN6aONcpIl4+7QedechiAJuxBkAjyGEAm+WUw12LAbavnuouSYur7w6AXOU0JSo3ZDGAssjiepDDAMrKKYd7zQxYLOlgSXdP2G5J36ulRwCylFMVNENkMYBSyOLakMMASskph3sVA86VtCAirpx4h+2Lyhxgl11/03+vCvfetXVy21bLyW0f+Ue9rrg4ufecOSf5mD+NB5LbfmLZvcltv37p0qR2hzxpVfIxZy1I/92MLUg/s+XqbyxKave4Z9yZfMw3/HD75LZPjwXJbY9+5prktqlyqoJmaOAsHiXz52zVdBemzeyx0VnEfFOr1XQXskAW14YcBlBKTjncawHBY7rcd2T13QGQq5wWS8kNWQygLLK4HuQwgLJyyuFBFhAEgM1aGU2JAoBhRRYDQLNyymGKAQAqkVMVFACGFVkMAM3KKYcpBgCoRE5VUAAYVmQxADQrpxymGACgEjlVQQFgWJHFANCsnHKYYgCASuRUBQWAYUUWA0CzcsrhvosBth8eEXfU0RkA+cqpCjoMyGIAkyGLpw85DGAyOeVw12KA7YdN3CTp+7b3k+SIuGuKdsslLZekf9z9MTpypyVV9BXADJZTFTQ3VWSxZy3U2Nj8ejsKoHFkcT3IYQBl5ZTDvWYG/ErSzydsWyLph5JC0iMnaxQRKyStkKRfLHt+Pj8NAMlyqoJmaOAsnj13Cb8gYASQxbUhhwGUklMO9yoGvEXSQZLeEhE/liTbN0fEnrX3DEBWIlpNd2GYkcUASiGLa0MOAyglpxwe63ZnRHxQ0usknWj7Q7a3lTIqdQDAECCLAaBZ5DCAYdRzAcGIWC3pFbYPk3SBpG1q7xWA7LQYE9WKLAZQBllcH3IYQBk55XDXmQGdIuIcSX8g6UBJsn10XZ0CkJ+ISLqhP2QxgG7I4fqRwwC6yWlMXLoYIEkRsS4irim+PamG/gDIVEuRdEP/yGIAUyGHpwc5DGAqOY2Je11a8Oqp7pK0uPruAMgVny7VhywGUBZZXA9yGEBZOeVwrzUDFks6WNLdE7Zb0vdq6RGALOV0TdUMkcUASiGLa0MOAyglpxzuVQw4V9KCiLhy4h22LypzgF/etl3/vSqs3zQrue0gbvzS+qR2f/jQxuRjtmJectufXb4oue2erQfTjnnVDsnHHMScWZvS246lXebjp5enP9flm+Ykt916bF1y2xsvWZjcdlliu5yuqZqhgbN4lKzbmJbhOVo0b37TXZg2broDmSCLa0MOAyglpxzuWgyIiGO63Hdk9d0BkKucpkTlhiwGUBZZXA9yGEBZOeVwXwsIAsBUmlwsxfZxtsP2jpXsEAAylcuiVQAwrIZmAUEAKKupKqjt3SS9QNIvGukAAMwgOX0iBQDDKKccphgAoBINLpbyYUlvlfTVpjoAADNFTgtXAcAwyimHKQYAqEQTVVDbh0taExFX2SwvBgA5fSIFAMMopxymGACgEqnnOtleLml5x6YVEbGi4/5vS9p5kqYnSHqH2qcIAACUnsUAgGrklMMUAwBUIrUKWvzhv6LL/QdOtt32EyTtKWl8VsBSST+0vX9E3J7UGQDIXE6fSAHAMMoph7teTcD2IR1fL7T9CdtX2/6c7cVd2i23vdL2yrMeuKXC7gKYqVoRSbdUEfHjiHh4ROwREXtIWi3pycNYCKgii1utB6answAaNZ05PErIYQBlTfeYeBC9Li34vo6vPyjpNkkvlnSFpP+YqlFErIiIZRGx7KXz9xi4kwBmvkj8D6UMnMVjY/Nr7iKAmYAcrg05DKCUnMbE/ZwmsCwi9i2+/rDt19TQHwCZavrTpWJ2wCggiwFMqeksHhHkMIAp5ZTDvYoBD7f9ZkmWtJ1tx29Pgug1qwDACMnp/KgMkcUASiGLa0MOAyglpxzuFV4fl7StpAWSPi1pR0myvbOkK2vtGQBgHFkMAM0ihwEMna4zAyLipCm23277wnq6BCBHnHdaH7IYQFlkcT3IYQBl5ZTDg0xrmjQUAYymiEi6YWBkMYDNyOFGkMMANstpTNx1ZoDtq6e6S9KUl1EBMHoYUNaHLAZQFllcD3IYQFk55XCvBQQXSzpY0t0TtlvS92rpEYAs5RN7WSKLAZRCFteGHAZQSk453KsYcK6kBRFx5cQ7bF9U5gBPXXOWu91ve3lErCizryra5dg2t/421Ta3/g7Stqn+drNx/Zqu/9YxkIGzuInfT12vtZmI5zqccnyuZHFtssxh1C/HnEC9cvq37qanMdheGRHLpqtdjm1z629TbXPr7yBtm+ov0I9Req3xXIfTKD1XAGnICeSM66ICAAAAADBiKAYAAAAAADBiZkIxIPUcm0HOzcmtbW79baptbv0dpG1T/QX6MUqvNZ7rcBql5wogDTmBbDW+ZgAAAAAAAJheM2FmAAAAAAAAmEaNFQNsH2L7Bts32j6+j3aftH2H7WsSjrmb7QttX2f7Wttv7KPtVra/b/uqou1JfR57lu0f2T63z3a32P6x7Sttr+yz7SLbZ9r+ie3rbT+jZLvHFMcbv91r+00l2/5t8fO5xvbnbW/VR3/fWLS7ttfxJnsd2H6Y7Qts/7T4//Z9tH1FcdyW7SlXhJ2i7fuLn/HVts+yvahku/cUba60fb7tXcses+O+42yH7R376O+7bK/p+P2+cKrnC6RKzfjcDPKelJtB3kNzM+h7PoDRMCrvdRhejRQDbM+SdKqkQyXtI+lVtvcp2fx0SYckHnqjpOMiYh9JT5f0V30c9yFJz4uIJ0naV9Ihtp/ex7HfKOn6fjrb4Q8iYt+Ey5Z8VNI3I+Kxkp5U9vgRcUNxvH0lPUXSWkln9Wpne4mkv5G0LCIeL2mWpCPKHNP24yW9XtL+RV9fZHuvLk1O1+++Do6X9N8Rsbek/y6+L9v2Gkkvk3Rxj65O1vYCSY+PiCdK+j9Jby/Z7v0R8cTi53yupBP7OKZs7ybpBZJ+0Wd/JenD47/jiDivS3ugbwNmfG5OV/p7Um4GeQ/NzaDv+QCG3Ii912FINTUzYH9JN0bETRGxXtIXJB1epmFEXCzprpSDRsRtEfHD4uv71P7jeEnJthER9xffzilupRZcsL1U0h9KOq3vTieyvVDScyR9QpIiYn1E3JOwq+dL+llE/Lzk42dL2tr2bEnbSLq1ZLvfk3R5RKyNiI2SvqP2H+eTmuJ1cLikTxdff1rSS8q2jYjrI+KGXp2cou35RZ8l6TJJS0u2u7fj2/ma4vXU5TX/YUlvnapdj7ZAnZIzPjej9G9skPfQ3Azyng9gZIzMex2GV1PFgCWSVnV8v1rTPKCwvYek/SRd3kebWbavlHSHpAsiomzbj6j9R1urv15Kag8+zrf9A9vL+2i3p6Q7JX2qOD3hNNvzE45/hKTPl+poxBpJH1D7k+rbJP0mIs4veZxrJD3b9g62t5H0Qkm79dnXxRFxW/H17ZIW99m+Cn8m6RtlH2z7vbZXSXq1pp4ZMFm7wyWtiYir+u+iJOnY4hSFT051OgUwgMYzHvVKeQ/NzQDv+QBGA+91yN5ILiBoe4GkL0t604RPZ7uKiE3FlO6lkvYvprb3OtaLJN0RET9I7O6zIuLJak9B+ivbzynZbrakJ0v694jYT9IDmnra/KRsz5V0mKQvlXz89mpXRPeUtKuk+baPKtM2Iq6XdIqk8yV9U9KVkjb1098J+wtN86c4tk9QexrtGWXbRMQJEbFb0ebYksfZRtI71EfxYIJ/l/Qotae+3ibpg4n7ATCCUt9Dc5Pyng8AQE6aKgas0Zaf+i4tttXO9hy1BzFnRMRXUvZRTLe/UOXOE32mpMNs36L29KHn2f5sH8daU/z/DrXP29+/ZNPVklZ3fJJxptrFgX4cKumHEfHLko8/UNLNEXFnRGyQ9BVJv1/2YBHxiYh4SkQ8R9Ldap9/349f2t5Fkor/39Fn+2S2XyvpRZJeHWnX6zxD0stLPvZRahdcripeV0sl/dD2zmUaR8Qvi0FuS9LHVf41BZTVWMajXlW8h+amz/d8AKOD9zpkr6liwBWS9ra9Z/Hp8xGSzqn7oLat9jn010fEh/psu9P4KvG2t5Z0kKSf9GoXEW+PiKURsYfaz/N/IqLUp+W259vedvxrtReLK7VidUTcLmmV7ccUm54v6boybTu8SiVPESj8QtLTbW9T/Kyfrz4WTbT98OL/u6u9XsDn+ji21H4Nvab4+jWSvtpn+yS2D1H7NJDDImJtH+327vj2cJV4PUlSRPw4Ih4eEXsUr6vVkp5c/M7LHHeXjm9fqpKvKaAPjWQ86jXIe2huUt/zAYwU3uuQvdlNHDQiNto+VtK31F5x/pMRcW2ZtrY/L+kASTvaXi3pnRHxiZKHfqakP5H04+I8QEl6R8nV1HeR9Oli5dAxSV+MiL4uE5hgsaSz2uMvzZb0uYj4Zh/t/1rSGUVA3STp6LINi+LDQZL+vGybiLjc9pmSfqj2dPkfSVrRR3+/bHsHSRsk/VW3BQ8nex1IOlnSF20fI+nnkl7ZR9u7JP2LpJ0kfd32lRFxcMm2b5c0T9IFxe/qsoj4ixLtXlgUa1pFf7do061t2df8FMc9wPa+ap9GcYv6+B0DZQyS8bkZ8D0pN4O8h+amifd8ABkZpfc6DC+nzWgGAAAAAAC5GskFBAEAAAAAGGUUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDEUAwAAAAAAGDH/H7OmumawDDTIAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Timestep 15\n" - ] - }, { "data": { - "image/png": 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\n", 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yJzQ0VElJSbJYLPLz85PFYlFycrJCQ0PzLJeQkKCGDRtKynvFjz18hg8AAAAAAIAXVKlSRQ0aNNDGjRslSRs3blSDBg1sbueSpM6dO2vt2rXKzs7W5cuXtX37dnXq1Mlpto9hGEahVQ4AAAAAAACHTp48qUmTJunKlSsKDAxUbGys7rvvPg0ZMkRjxozRQw89JIvFolmzZunbb7+VJA0ZMkRPPfWU01wmfAAAAAAAAEoYbukCAAAAAAAoYZjwAQAAAAAAKGGY8AEAAAAAAChhmPABAAAAAAAoYfy9XYAZp0+f1qRJk5SamqqgoCDFxsaqTp06buelpKToxRdf1Llz5xQQEKB77rlHs2bNyvN1aO5auHChFixYoA0bNqh+/fqmstLT0/XKK69o7969Kl26tBo1aqSXX37ZVObXX3+t+fPnyzAMGYahUaNGqWPHjgXKiI2N1bZt2xQfH2+znWb6yl6m2b5yVGcOd/rKUaa7feUoz0w/OdtvP/74o6ZNm6b09HTVqFFDc+bMUZUqVdzO/OOPPzRt2jT99ttv8vf310MPPaTp06erTJkypurMMXnyZH322Wc6dOiQypcvbyozNTVVs2bN0s8//yx/f3916dJFo0aNMpX573//W6tXr5avr6/8/Pw0ZcoURURE5JspSSNGjNCFCxfk6+urcuXK6aWXXlKDBg1MjSN7mdWrVzc1jhzVmaOg48hRnpnjnaNMTxzvbt8+d8eQo8xSpUq5PYac1ZmjoGPIWaa7Y8hZppkxFBkZqYCAAJUuXVqSNH78eLVp08ZUH9nLrFmzpqk+clRnDnf6yFGmmT5ylOluHzka02aOcfYyx40bZ+oYl9+xx51zBUeZZo5zjtZ19zh34cIFjRw50vr86tWrunbtmvbv3+92HznK3LZtm9t95KzOHAXtI2eZ7vaRs0wzv4scrWtmHNnLbNq0qalxlN82FrSPHOWZGUOOMs30z86dOzV//nxlZWWpYsWKevXVV1WrVi1T/WMvs0KFCqb6x1GdOTz59yu8xCjGBgwYYKxbt84wDMNYt26dMWDAAFN5KSkpxvfff299/tprrxmTJ082lZnj6NGjxjPPPGP813/9l/Gf//zHdN7LL79s/POf/zSys7MNwzCM3377zVRedna2ERERYa3t2LFjRqNGjQyLxVKgnAMHDhgJCQl5ttNMX9nLNNtXjuo0DPf7ylGmu31lL89sPznabxaLxejQoYNx4MABwzAMY9GiRcakSZNMZZ4/f974+eefDcMwDIvFYowdO9ZYuHChqcwcO3bsMCZPnmzUr1/fuHbtmunMoUOHGitXrrS+l5ycbCrz8uXLRnh4uLWvt2/fbnTp0sWlTMMwjCtXrlh//vLLL42ePXsahmFuHNnLNDuOHNVpGO6NI0d5Zo539jI9cby7ffvMjCFHmWbGkKPMHO6MIWeZ7o4hR5lmx5C9f3dm+8heptk+cjY+3O0jR5lm+sheppk+cjSmzRzj7GWaPcY5O/a4e67gKNPMcc7eup46rzMMw5g9e7Yxc+ZMwzA8d+6dk+nJc+/cdRqGZ869c2d66tw7J9NMHzlb190+cpRppo/y28aC9pGzPHf7x1FmVlaW2/2TmppqNGvWzDh16pRhGLf6YfDgwYZhuD+GHGWa6R9ndRqG5/9+hXcU21u6fv/9d8XFxSkqKkqSFBUVpbi4OF2+fNntzKCgIDVv3tz6vFGjRkpISDBda0ZGhmbNmqUZM2aYzpKk69eva926dRo7dqx8fHwkSVWrVjWd6+vrq6tXr0q69b8PwcHB8vUt2D+RiIgIhYaG2rxmtq/sZZrtK3uZkrm+spdppq8c1Wimnxztt6NHj6p06dLW/5nt27evtm7daiqzZs2aeuCBB6w1N2zY0OU+cta/KSkpWrhwoSZPnuxSVn6ZZ86c0fHjxzVw4EDre9WqVTOVafz//wm6fv26pFv9VL16dZdr/dOf/mT9+dq1a/Lx8TE9juxlmh1H9jIl98eRvTyzxztHNZoZR/a2z8wYcpRpZgw5ypTcH0OOMs2MIUeZZseQPWb7yB6zfeSImT6yx2wf2eNuHzka02aOcY4yzRzjnB173D3GOco0c5xztq4nzusyMjK0YcMG9e7d22Pn3rkzPXXunTsz57nZc+/cmZ469769TjN9ZG/dlJQUU31kL9NsHznaRnf7yF7ezZs3TfWPoxrd7Z+zZ8+qatWquvfeeyVJ7dq10zfffGNqDDnKzM7Odrt/HGVevnzZ43+/wnuK7S1diYmJCgkJkZ+fnyTJz89PwcHBSkxM9MgtWNnZ2froo48UGRlpOmv+/Pnq0aOHatasaTpLks6fP6+goCAtXLhQ+/btU/ny5TV27FiXL3W3x8fHR/PmzdOIESNUrlw5Xb9+XcuXL/dIvfSV5/rKk/2Ue78lJiYqLCzM+l7lypWVnZ1tvdzUnczc0tLS9Omnn2rcuHGm6pSkWbNmacyYMTZ/yJvJPHHihEJCQhQTE6Njx46patWqevHFF/XnP//Z7czKlStr1qxZ6tWrlwIDA5Wdna0PPvigQHkxMTH69ttvZRiG3nnnHY+Mo9szHdVvpk7J3Di6Pc8TY+j2TLPjyN72mR1D+e0zd8aQo0wzY8heptkxZC/TE2No/PjxMgxDTZo00bhx4zxynLs9MzAw0Pqeu8c5e5lmj3O3Z3riOHd7prt95GhMlylTxu1jnCvHiYIe45xlunuMc5RZoUIFt49zzur0xPnCV199pZCQEP3lL3/R0aNHPXI+lzszNzPnc7dneuJ8LnfmL7/84pHzudvrdLePHP0eM3Ou4MrvxoL2kbNMd/rIUZ6ZcwVHmWbOFe69915dunRJP/30kxo2bKgNGzZIMvc3kbPMnHUL2j/OMjdv3uzRv4ngRXf+oiLPOHLkiNG1a1eb17p06WIcPXrUI/kzZswwhg8f7talr7kdOnTIePrpp62XF3rikrijR48a9evXN9avX28YhmH8+OOPxiOPPGJcvXrV7czMzExj4MCBxsGDBw3DMIyDBw8a7dq1K/Cl/jlyb6en+srRvjPTV7kzPdVXudfzRF/lzvNkP+Xeb1u3bjWGDBli837Dhg2NlJQUtzNzZGZmGsOGDTNmzZpV4Bpvz9y0aZMxYcIE63vu3I5ye+a2bduMBg0aWG/z2LZtm9G+fXtTmVevXjWeeuop4+TJk4ZhGMamTZuMqKgo67+tgvif//kf49lnn/XoMS8n01H97sjJ9NQ4ysnz5PEuJ9PMOHK0fWbGUH77zJ0x5CjTzBhylGlmDDnKNDuGEhISDMMwjPT0dGPatGnGCy+8YPo4Zy8zh7vHOXuZZo9z9jLNHufsZbrbR47G9L59+9w+xrlynCjoMc5Zne4e4xxl7t+/3+3jnLNt98T5wrPPPmusXr3aMAzPnc/lzszNzO+h3Jme+j2UO9NTv4tyZ5r5XeRoXTPjyJV6CtpHjjIPHDjgVh85yjMzhpxtt5kx9O233xp9+/Y1evXqZcydO9eIiIgw1T+OMo8dO2Z9350x5KhOT//9Cu8ptrd0hYaGKikpSRaLRZJksViUnJxs9xaYgoqNjdXZs2c1b968Al/6ersDBw7o5MmTat++vSIjI3Xx4kU988wz+uabb9zODA0Nlb+/v/VywIcffliVKlXS6dOn3c48duyYkpOT1aRJE0lSkyZNVLZsWZ08edLtzNz10lee6StP9dPt+y00NNTm8s/Lly/L19e3QFf32OsLi8Wi8ePHq2LFipo6dWqBarSXuX//fn3//feKjIy0/u9FVFSUTpw44XZmaGioQkNDrf8L1LFjR/32228FukT99sxvvvlGf/rTn3TfffdJkrp27apz584pJSWlAFt/S8+ePbVv3z5Vr17dY+MoJzOnHk+Mo5zM77//3iPjKCcvJCTEY2MoJ/Pnn392exw5Ok6cPXvW7THk7Njj7hhylLlw4UK3x5CzbXd3DDnK3L17t6kxlDMuAgIC1K9fPx06dMj0cc5epmTuOGcv0+xxztG2mznO2ct09zjn6PdimTJl3D7G5fe71p1jnKPMgwcPun2Mc7bt7h7nnG272fOFpKQkHThwQN27d7e2Zfb30O2ZOcz8Hro90xPnc/a23ezvotszzZzTOVq3dOnSbvdRfvW400eOMvft2+dWHznbbnf7x9l2mxlDLVu21EcffaTPPvtM/fv3V1pammrUqGFqDNnLrF27tiT3x5C9zB9++MHjfxPBi7w942RG//79bT70qn///qYz33zzTaN///7GjRs3TGfZ46kZ0kGDBhl79uwxDMMwTp06ZTRr1sz4448/3M5LTk42wsPDrf9Td+LECaNp06YFvsIjx+3b6Ym+uj3TE33lrD88cYWPYZjvq9x5nugne/vNYrEY7du3d/vDTB1ljh8/3hg3bpyRlZXlcpazzNsV9H++7WVmZ2cbUVFRxvHjxw3DMIz9+/cbbdq0cflKAnuZR44cMVq2bGlcunTJMAzD2Lt3r9GyZUuXMq9du2b933TDuPXBra1btzays7PdHkfOMt0dR84yc3N1HDnLc3cMOcpMSkry2PEu94c2mxlDjjLNjCF7mbdz9yq53Jlmx5C9TDNj6Pr169YP687OzjbeeustY8SIEab6yFmmu33kKPN2BekjR5lm+shRppk+cjSmzZwrOMo0c67gyrGnoOcKjjLNnCvYW9cT5wtLliwxxowZY/Oa2fM5e5lmz+fsZebmzvmcvUyz53O3Z5rpI2fruttHzjLd7SNXt9HVPnKW527/OMq8ePGiqTGU88H4FovFmDx5sjF79mzDMMyNIUeZZsaQo8zcuMKnePMxDMPw9qSTu06ePKlJkybpypUrCgwMVGxsrPV/mtzx66+/KioqSnXq1LF+pWrNmjW1aNEiT5WsyMhILV261PTX2p0/f15TpkxRamqq/P399fzzz6tdu3amMtevX68VK1ZYP+xszJgx6tChQ4EyZs+erS+++EKXLl1SpUqVFBQUpE2bNpnqK3uZ8+bNM9VXjurMraB95SjT3b5ylGemn5z9Gz906JCmT59u83XFrnzgnaPMPn36aOjQoapfv771fxoaN26s6dOnm6ozt/vvv9/lryt2lnnkyBHNnDlTGRkZKlu2rGJiYtSwYUNTmStXrtQnn3yiUqVKKSAgQJMmTXLpXvJLly5pxIgRunnzpnx9fVWxYkVNnDhRf/nLX9weR44yAwIC3B5HzurMzdVx5CzP3THkLNMTx7vbt8/dMeQoMyEhwe0x5KzO3AoyhpxlujuGnGW6O4bOnz+v0aNHy2KxKDs7W3Xr1tXUqVMVHBzsdh85yoyLi3O7j5zVmVtB+shZprt95CzTTB/ZG9NmzhXsZYaFhZk6V3Dl2FPQcwVHmWbO6xyta/Y416lTJ8XExKht27bW18yee9+e6Ylzb3t15ubOube9TLPn3vYyzfSRo3XN9JG9zHvuucdUH7myjQXpI0d5ZvrHUaaZ/omJidGhQ4eUmZmpVq1aacqUKSpdurSp/rGXee7cOVP946jO3Dz19yu8o1hP+AAAAAAAACCvYvsZPgAAAAAAALCPCR8AAAAAAIAShgkfAAAAAACAEoYJHwAAAAAAgBKGCR8AAAAAAIAShgkfAADgUEJCgsLDw2WxWLxdCgAAAAqACR8AAGAVGRmp7777zvo8LCxMhw8flp+fn9dq+uyzz/TXv/7Va+0DAAAUR0z4AAAAAAAAlDBM+AAAAEnShAkTlJCQoGHDhik8PFwrVqzQhQsXdP/99ysrK0uSNGDAAM2dO1d9+/ZVeHi4hg0bppSUFL3wwgtq3LixevfurQsXLlgzT548qUGDBqlZs2bq1KmTNm/e7LD9zz77TO3bt1d4eLgiIyO1fv16nTx5UtOnT9ePP/6o8PBwRURESJIyMjIUGxurRx99VC1bttS0adOUlpYmSdq3b5/atm2rpUuXqnnz5tYsAACAuwkTPgAAQJI0Z84chYWFaenSpTp8+LCGDBlid7nNmzfr9ddf1+7du3Xu3Dn17dtXvXv31v79+1W3bl0tWrRIknTjxg0NHjxYUVFR+u677zR37lzNnDlTJ06cyJN548YNzZ49WytWrNDhw4f18ccfq0GDBqpbt65mzpypRo0a6fDhwzp48KAk6Y033tDp06e1bt06ffHFF0pOTra2K0mXLl1SSkqK9uzZo9dee03Tpk3TqVOnCmGvAQAAFE1M+AAAgAJ5/PHHVbt2bf3pT39S27ZtVatWLbVs2VL+/v7q3Lmz4uLiJEk7d+5UjRo11Lt3b/n7++uBBx5Qp06dtHXrVru5vr6++vXXX5WWlqbg4GD9+c9/trucYRj65JNPNGXKFAUFBalChQoaOnSoNm3aZLPc2LFjFRAQoGbNmqldu3basmWLZ3cEAABAEebv7QIAAEDxUrVqVevPpUuXtnlepkwZ3bhxQ5IUHx+vn376yXobliRZLBb16NEjT2a5cuU0d+5cvffee4qJiVHjxo01ceJE1a1bN8+yly9f1s2bN/X4449bXzMMQ9nZ2dbngYGBKleunPV5WFiYkpOT3dxiAACA4ocJHwAAUChCQ0PVtGlTrVy50qXl27RpozZt2igtLU3z5s3TSy+9pDVr1sjHx8dmuUqVKqlMmTLatGmTQkJC7GZduXJFN27csE76JCYmOrxiCAAAoCTili4AAGBVtWpVnT9/3iNZjz76qM6cOaN169YpMzNTmZmZ+umnn3Ty5Mk8y166dEnbt2/XjRs3FBAQoHLlysnX99ZpSpUqVZSUlKSMjAxJt2796tOnj1555RX9/vvvkqSkpCTt2bPHJnPBggXKyMjQwYMHtXPnTnXu3Nkj2wUAAFAcMOEDAACsnnvuOS1ZskQRERF69913TWVVqFBB7777rjZv3qw2bdqodevWeuONN6wTN7llZ2dr1apVatOmjZo1a6YDBw5oxowZkqRHHnlE9erVU+vWrdW8eXNJt75R7J577tGTTz6pxo0b6+9//7tOnz5tzatataoCAwPVpk0bjR8/XjNmzLB7exgAAEBJ5WMYhuHtIgAAADxl3759mjBhgnbv3u3tUgAAALyGK3wAAAAAAABKGCZ8AAAAAAAAShhu6QIAAAAAAChhuMIHAAAAAACghGHCBwAAAAAAoIRhwgcAAAAAAKCEYcIHAAAAAACghGHCBwAAAAAAoIRhwgcAAAAAAKCE+X+k72kCqsMrSQAAAABJRU5ErkJggg==\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Timestep 16\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Timestep 17\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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rrrpKe/bsUW5urh97/4trr71WGzduVJMmTWw7jk7EPJGPHcfRiZifffaZLcfRiXjx8fG2HUMnYn799dcBH0e+zhO7d+8O+Biq6NwT6DHkK+asWbMCPoYq2vdAjyFfMT/66KOgjqETx0VUVJQGDx6sLVu2BH2eKy+mFNx5rryYwZ7nfO17MOe58mIGep7z9b1Yq1atgM9xZ/quDeQc5yvm559/HvA5rqJ9D/Q8V9G+B3u9kJGRoc2bN+uaa64prSvY76FTY54QzPfQqTHtuJ4rb9+D/S46NWYw13S+XluzZs2A2+hM+QTSRr5ibty4MaA2qmi/A22fivY7mGPokksu0T/+8Q+98847GjJkiI4dO6ZmzZoFdQyVF7Nly5aSAj+Gyov5n//8x/bfiRCmnO6dstOQIUPKLOg1ZMiQoGP++c9/toYMGWIVFBQEHas8dvW8Dhs2zFq/fr1lWZa1c+dOq2vXrtahQ4cCjpeZmWl17Nix9C+AP/74o9WlSxe/R46ccOp+2tFWp8a0o60qag87Rg5ZVvBtdXI8O9qpvPfN4/FYffr0CXihVl8xx44daz3wwANWSUmJcayKYp7K37+olxfT6/Va/fv3t77//nvLsixr06ZNVo8ePYxHKJQX86uvvrIuueQSKysry7Isy/r000+tSy65xCjm0aNHS/9Kb1m/LErbvXt3y+v1BnwcVRQz0OOoopgnMz2OKooX6DHkK2ZGRoZt57uTF6QO5hjyFTOYY6i8mKcKdPTdyTGDPYbKixnMMZSfn1+6ELnX67WeffZZa8SIEUG1UUUxA20jXzFP5U8b+YoZTBv5ihlMG/k6poO5VvAVM5hrBZNzj7/XCr5iBnOtUN5r7bhe+Mtf/mKNHj26zLZgr+fKixns9Vx5MU8WyPVceTGDvZ47NWYwbVTRawNto4piBtpGpvto2kYVxQu0fXzFPHjwYFDH0IlF/z0ej/Xoo49a06ZNsywruGPIV8xgjiFfMU/GyKGzl8uyLMvpDiq77NixQ6mpqTp8+LBiYmKUlpZW+hesQPzwww/q37+/WrduXXob2ubNm2v27Nl2pazk5GTNnTs36FsB7t27V+PGjVNeXp4iIyN13333qVevXkHFfPfdd/Xyyy+XLuQ2evRoXX755X7FmDZtmtasWaOsrCw1aNBAsbGxWrFiRVBtVV7MmTNnBtVWvvI8mb9t5StmoG3lK14w7VTRZ3zLli2aNGlSmVs8myzm5yvmjTfeqLvvvltt27Yt/QtGp06dNGnSpKDyPFm7du2Mb/FcUcyvvvpKjz/+uIqKilS7dm2NHz9e7du3DyrmvHnz9MYbb6hGjRqKiopSamqq0dz3rKwsjRgxQoWFhYqIiFD9+vX1yCOP6De/+U3Ax5GvmFFRUQEfRxXleTLT46iieIEeQxXFtON8d+r+BXoM+Yp54MCBgI+hivI8mT/HUEUxAz2GKooZ6DG0d+9ejRo1Sh6PR16vV23atNGECRMUFxcXcBv5irl9+/aA26iiPE/mTxtVFDPQNqooZjBtVN4xHcy1QnkxmzZtGtS1gsm5x99rBV8xg7mu8/XaYM9zffv21fjx49WzZ8/SbcFee58a045r7/LyPFkg197lxQz22ru8mMG0ka/XBtNG5cVs1apVUG1kso/+tJGveMG0j6+YwbTP+PHjtWXLFhUXF+vSSy/VuHHjVLNmzaDap7yYe/bsCap9fOV5Mrt+f0X4Oas6hwAAAAAAAOCfs2bNIQAAAAAAAPiPziEAAAAAAIBqjM4hAAAAAACAaozOIQAAAAAAgGqMziEAAAAAAIBqjM4hAABg7MCBA+rYsaM8Ho/TqQAAAMAmdA4BAACfkpOT9cknn5Q+b9q0qbZu3Sq32+1YTu+8847+8Ic/OFY/AADA2YbOIQAAAAAAgGqMziEAAFCuhx56SAcOHNA999yjjh076uWXX9a+ffvUrl07lZSUSJJuueUWzZgxQ4MGDVLHjh11zz33KDc3Vw8++KA6deqk66+/Xvv27SuNuWPHDg0bNkxdu3ZV3759tXLlSp/1v/POO+rTp486duyo5ORkvfvuu9qxY4cmTZqkL774Qh07dlRSUpIkqaioSGlpaerdu7cuueQSTZw4UceOHZMkbdy4UT179tTcuXPVrVu30lgAAAD4BZ1DAACgXE8//bSaNm2quXPnauvWrRo+fHi55VauXKmnnnpKH330kfbs2aNBgwbp+uuv16ZNm9SmTRvNnj1bklRQUKDbb79d/fv31yeffKIZM2bo8ccf148//nhazIKCAk2bNk0vv/yytm7dqkWLFikxMVFt2rTR448/rg4dOmjr1q36/PPPJUnPPPOMdu3apSVLlmjNmjXKzMwsrVeSsrKylJubq/Xr1+tPf/qTJk6cqJ07d4bgXQMAAKh66BwCAABBue6669SyZUvVq1dPPXv2VIsWLXTJJZcoMjJS/fr10/bt2yVJH3zwgZo1a6brr79ekZGROv/889W3b1+tWrWq3LgRERH64YcfdOzYMcXFxem8884rt5xlWXrjjTc0btw4xcbGqm7durr77ru1YsWKMuXGjBmjqKgode3aVb169dK//vUve98IAACAKirS6QQAAEDV1rhx49L/16xZs8zzWrVqqaCgQJK0f/9+ffnll6VTwSTJ4/FowIABp8WMjo7WjBkz9Oqrr2r8+PHq1KmTHnnkEbVp0+a0sjk5OSosLNR1111Xus2yLHm93tLnMTExio6OLn3etGlTZWZmBrjHAAAAZxc6hwAAQKVISEhQly5dNG/ePKPyPXr0UI8ePXTs2DHNnDlTjz32mP7+97/L5XKVKdegQQPVqlVLK1asUHx8fLmxDh8+rIKCgtIOovT0dJ8jkQAAAKobppUBAACfGjdurL1799oSq3fv3vrpp5+0ZMkSFRcXq7i4WF9++aV27NhxWtmsrCytXbtWBQUFioqKUnR0tCIifrlsadSokTIyMlRUVCTpl+lnN954o6ZPn67s7GxJUkZGhtavX18m5gsvvKCioiJ9/vnn+uCDD9SvXz9b9gsAAKCqo3MIAAD4dNddd+kvf/mLkpKS9MorrwQVq27dunrllVe0cuVK9ejRQ927d9czzzxT2slzMq/Xq7/97W/q0aOHunbtqs2bN2vy5MmSpIsuuki//vWv1b17d3Xr1k3SL3dWa9WqlX7/+9+rU6dOuu2227Rr167SeI0bN1ZMTIx69OihsWPHavLkyeVOUQMAAKiOXJZlWU4nAQAAECobN27UQw89pI8++sjpVAAAAMISI4cAAAAAAACqMTqHAAAAAAAAqjGmlQEAAAAAAFRjjBwCAAAAAACoxugcAgAAAAAAqMboHAIAAAAAAKjG6BwCAAAAAACoxugcAgAAAAAAqMb+HxTEx02H3EReAAAAAElFTkSuQmCC\n", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "a = debug_model([[0, 0,0,0], [1,1,1,1], [0,1,0,1], [1,0,1,0], [1,1,1,0]], 3)" + "a = debug_model([[1,0,1,0], [0,1,0,1], [0,0,1,1]], 30)" ] }, { From 4e749808258939d204ddf9718da63cc73802606d Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 20 Jun 2021 18:26:35 -0700 Subject: [PATCH 12/20] pre commit checks --- .pre-commit-config.yaml | 11 +++++++++ Makefile | 1 - dnc/repeat_copy.py | 4 +++- requirements.txt | 1 + train.py | 49 +++++++++++++++++++++++------------------ 5 files changed, 43 insertions(+), 23 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..fcf4c34 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,11 @@ +repos: +- repo: https://github.com/ambv/black + rev: stable + hooks: + - id: black + language_version: python3.9 +- repo: https://gitlab.com/pycqa/flake8 + rev: stable + hooks: + - id: flake8 + diff --git a/Makefile b/Makefile index 66c8642..e834d28 100644 --- a/Makefile +++ b/Makefile @@ -14,7 +14,6 @@ venv: test: venv python -m pytest - black . dnc/ tests/ run: : # Run your app here, e.g diff --git a/dnc/repeat_copy.py b/dnc/repeat_copy.py index 67e770a..29b5e25 100644 --- a/dnc/repeat_copy.py +++ b/dnc/repeat_copy.py @@ -431,7 +431,9 @@ def to_human_readable(self, data, model_output=None, whole_batch=False): mask=data.mask.numpy(), ) obs = data.observations - unnormalised_num_reps_flag = self._unnormalise(obs[:, :, -1:]).round() + unnormalised_num_reps_flag = self._unnormalise( + obs[:, :, -1:], self._norm_max + ).round() obs = np.concatenate([obs[:, :, :-1], unnormalised_num_reps_flag], axis=2) data = data._replace(observations=obs) return bitstring_readable(data, self.batch_size, model_output, whole_batch) diff --git a/requirements.txt b/requirements.txt index 5adb76a..bfbad53 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,6 +24,7 @@ oauthlib==3.1.0 opt-einsum==3.3.0 packaging==20.9 pluggy==0.13.1 +pre-commit==2.13.0 protobuf==3.17.0 py==1.10.0 pyasn1==0.4.8 diff --git a/train.py b/train.py index 0468cd2..29b339a 100644 --- a/train.py +++ b/train.py @@ -95,20 +95,14 @@ "--epochs", default=10000, type=int, help="Number of epochs to train for." ) parser.add_argument( - "--log_dir", default="./logs/dnc/", type=str, help="Logging directory." + "--log_dir", default="./logs/repeat_copy", type=str, help="Logging directory." ) parser.add_argument( "--report_interval", - default=100, + default=500, type=int, help="Epochs between reports (samples, valid loss).", ) -parser.add_argument( - "--checkpoint_dir", - default="./checkpoints/repeat_copy", - type=str, - help="Checkpointing directory.", -) parser.add_argument( "--checkpoint_interval", default=2000, type=int, help="Checkpointing step interval." ) @@ -182,7 +176,7 @@ def test_step_graphed( def train(num_training_iterations, report_interval): """Trains the DNC and periodically reports the loss.""" - dataset = repeat_copy.RepeatCopy( + train_dataset = repeat_copy.RepeatCopy( FLAGS.num_bits, FLAGS.batch_size, FLAGS.min_length, @@ -191,7 +185,19 @@ def train(num_training_iterations, report_interval): FLAGS.max_repeats, dtype=tf.float32, ) - dataset_tensor = dataset() + # Generate test data with double maximum repeat length + test_dataset = repeat_copy.RepeatCopy( + FLAGS.num_bits, + 100, # FLAGS.batch_size, + FLAGS.min_length, + FLAGS.max_length, + FLAGS.max_repeats * 2, + FLAGS.max_repeats * 2, + dtype=tf.float32, + ) + + dataset_tensor = train_dataset() + test_dataset_tensor = test_dataset() access_config = { "memory_size": FLAGS.memory_size, @@ -208,7 +214,7 @@ def train(num_training_iterations, report_interval): dnc_cell = dnc.DNC( access_config, controller_config, - dataset.target_size, + train_dataset.target_size, FLAGS.batch_size, clip_value, ) @@ -220,20 +226,20 @@ def train(num_training_iterations, report_interval): optimizer = tf.compat.v1.train.RMSPropOptimizer( FLAGS.learning_rate, epsilon=FLAGS.optimizer_epsilon ) - loss_fn = dataset.cost + loss_fn = train_dataset.cost # Set up logging and metrics train_loss = tf.keras.metrics.Mean("train_loss", dtype=tf.float32) test_loss = tf.keras.metrics.Mean("test_loss", dtype=tf.float32) - current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") - train_log_dir = FLAGS.log_dir + current_time + "/train" - test_log_dir = FLAGS.log_dir + current_time + "/test" + # current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + train_log_dir = FLAGS.log_dir + "/train" + test_log_dir = FLAGS.log_dir + "/test" train_summary_writer = tf.summary.create_file_writer(train_log_dir) test_summary_writer = tf.summary.create_file_writer(test_log_dir) # Test once to initialize - graph_log_dir = FLAGS.log_dir + current_time + "/graph" + graph_log_dir = FLAGS.log_dir + "/graph" graph_writer = tf.summary.create_file_writer(graph_log_dir) with graph_writer.as_default(): tf.summary.trace_on(graph=True, profiler=True) @@ -243,7 +249,7 @@ def train(num_training_iterations, report_interval): # Set up model checkpointing checkpoint = tf.train.Checkpoint(model=dnc_core, optimizer=optimizer) manager = tf.train.CheckpointManager( - checkpoint, FLAGS.checkpoint_dir, max_to_keep=10 + checkpoint, FLAGS.log_dir + "/checkpoint", max_to_keep=10 ) checkpoint.restore(manager.latest_checkpoint) @@ -254,15 +260,14 @@ def train(num_training_iterations, report_interval): # Train. for epoch in range(num_training_iterations): - dataset_tensor = dataset() + dataset_tensor = train_dataset() train_loss_value = train_step(dataset_tensor, dnc_core, optimizer, loss_fn) train_loss(train_loss_value) # report metrics if (epoch) % report_interval == 0: - dataset_tensor = dataset() test_loss_value, output = test_step( - dataset_tensor, dnc_core, optimizer, loss_fn + test_dataset_tensor, dnc_core, optimizer, test_dataset.cost ) test_loss(test_loss_value) with test_summary_writer.as_default(): @@ -279,7 +284,9 @@ def train(num_training_iterations, report_interval): ) ) - dataset_string = dataset.to_human_readable(dataset_tensor, output.numpy()) + dataset_string = test_dataset.to_human_readable( + test_dataset_tensor, output.numpy() + ) print(dataset_string) # reset metrics every epoch From f204039858d1809fa7d6a1955ac2332ca5599054 Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 20 Jun 2021 18:33:58 -0700 Subject: [PATCH 13/20] pre commit --- .pre-commit-config.yaml | 4 ++-- Makefile | 1 + requirements.txt | 16 ++++++++++++++++ 3 files changed, 19 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index fcf4c34..1028092 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,11 +1,11 @@ repos: - repo: https://github.com/ambv/black - rev: stable + rev: 21.6b0 hooks: - id: black language_version: python3.9 - repo: https://gitlab.com/pycqa/flake8 - rev: stable + rev: 3.9.2 hooks: - id: flake8 diff --git a/Makefile b/Makefile index e834d28..306099a 100644 --- a/Makefile +++ b/Makefile @@ -6,6 +6,7 @@ install: venv mkdir tmp . venv/bin/activate && TMPDIR=tmp pip install -r requirements.txt rm -r tmp/ + pre-commit install venv: : # Create venv if it doesn't exist diff --git a/requirements.txt b/requirements.txt index bfbad53..75560f7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,18 @@ absl-py==0.12.0 +appdirs==1.4.4 astunparse==1.6.3 attrs==21.2.0 black==21.6b0 cachetools==4.2.2 certifi==2020.12.5 +cfgv==3.3.0 chardet==4.0.0 +click==8.0.1 +distlib==0.3.2 dm-sonnet==2.0.0 dm-tree==0.1.6 +filelock==3.0.12 +flake8==3.9.2 flatbuffers==1.12 gast==0.4.0 google-auth==1.30.0 @@ -14,23 +20,32 @@ google-auth-oauthlib==0.4.4 google-pasta==0.2.0 grpcio==1.34.1 h5py==3.1.0 +identify==2.2.10 idna==2.10 iniconfig==1.1.1 keras-nightly==2.5.0.dev2021032900 Keras-Preprocessing==1.1.2 Markdown==3.3.4 +mccabe==0.6.1 +mypy-extensions==0.4.3 +nodeenv==1.6.0 numpy==1.19.5 oauthlib==3.1.0 opt-einsum==3.3.0 packaging==20.9 +pathspec==0.8.1 pluggy==0.13.1 pre-commit==2.13.0 protobuf==3.17.0 py==1.10.0 pyasn1==0.4.8 pyasn1-modules==0.2.8 +pycodestyle==2.7.0 +pyflakes==2.3.1 pyparsing==2.4.7 pytest==6.2.4 +PyYAML==5.4.1 +regex==2021.4.4 requests==2.25.1 requests-oauthlib==1.3.0 rsa==4.7.2 @@ -45,5 +60,6 @@ termcolor==1.1.0 toml==0.10.2 typing-extensions==3.7.4.3 urllib3==1.26.4 +virtualenv==20.4.7 Werkzeug==2.0.0 wrapt==1.12.1 From e476bdb9d02f83ccb298483bb60b4ba1eacaf15e Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 20 Jun 2021 18:46:32 -0700 Subject: [PATCH 14/20] fix flake8 style --- dnc/util.py | 4 ++-- tests/access_test.py | 10 +++++----- tests/addressing_test.py | 4 +--- tests/dnc_test.py | 10 +++++----- 4 files changed, 13 insertions(+), 15 deletions(-) diff --git a/dnc/util.py b/dnc/util.py index 2ba467b..dbf7c2a 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -81,6 +81,6 @@ def initial_state_from_state_size(state_size, batch_size, dtype): elif isinstance(state_size, list): return [initial_state_from_state_size(s, batch_size, dtype) for s in state_size] - raise NotImplemented( - f"Cannot parse initial_state from state_size of type {type(state)}: {state}" + raise NotImplementedError( + f"Cannot parse initial_state from state_size of type {type(state_size)}: {state_size}" ) diff --git a/tests/access_test.py b/tests/access_test.py index b28040d..678accc 100644 --- a/tests/access_test.py +++ b/tests/access_test.py @@ -20,6 +20,7 @@ import numpy as np import tensorflow as tf +from tensorflow.python.framework import random_seed from dnc import access, addressing, util @@ -35,8 +36,6 @@ # set seeds for determinism np.random.seed(42) -from tensorflow.python.framework import random_seed - random_seed.set_seed(42) @@ -52,9 +51,10 @@ def setUp(self): def testBuildAndTrain(self): inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) targets = np.random.rand(TIME_STEPS, BATCH_SIZE, NUM_READS, WORD_SIZE) - loss = lambda outputs, targets: tf.reduce_mean( - input_tensor=tf.square(outputs - targets) - ) + + def loss(outputs, targets): + return tf.reduce_mean(input_tensor=tf.square(outputs - targets)) + with tf.GradientTape() as tape: outputs = self.module( inputs=inputs, diff --git a/tests/addressing_test.py b/tests/addressing_test.py index f997487..9907ee7 100644 --- a/tests/addressing_test.py +++ b/tests/addressing_test.py @@ -21,13 +21,12 @@ import numpy as np import sonnet as snt import tensorflow as tf +from tensorflow.python.framework import random_seed from dnc import addressing, util # set seeds for determinism np.random.seed(42) -from tensorflow.python.framework import random_seed - random_seed.set_seed(42) @@ -397,7 +396,6 @@ def testAllocationGradient(self): memory_size = 5 usage = tf.constant(np.random.rand(batch_size, memory_size)) module = addressing.Freeness(memory_size) - allocation = module._allocation(usage) theoretical, numerical = tf.test.compute_gradient( module._allocation, [usage], delta=1e-5 ) diff --git a/tests/dnc_test.py b/tests/dnc_test.py index 75c12cf..8a5765a 100644 --- a/tests/dnc_test.py +++ b/tests/dnc_test.py @@ -21,14 +21,13 @@ import datetime import numpy as np import tensorflow as tf +from tensorflow.python.framework import random_seed from dnc import dnc, access, addressing from dnc import repeat_copy # set seeds for determinism np.random.seed(42) -from tensorflow.python.framework import random_seed - random_seed.set_seed(42) DTYPE = tf.float32 @@ -80,9 +79,10 @@ def setUp(self): def testBuildAndTrain(self): inputs = tf.random.normal([TIME_STEPS, BATCH_SIZE, INPUT_SIZE], dtype=DTYPE) targets = np.random.rand(TIME_STEPS, BATCH_SIZE, OUTPUT_SIZE) - loss = lambda outputs, targets: tf.reduce_mean( - input_tensor=tf.square(outputs - targets) - ) + + def loss(outputs, targets): + return tf.reduce_mean(input_tensor=tf.square(outputs - targets)) + optimizer = tf.compat.v1.train.RMSPropOptimizer( LEARNING_RATE, epsilon=OPTIMIZER_EPSILON ) From 68c1eb826563ce755f18cbe6033d62ea7e2b6bd0 Mon Sep 17 00:00:00 2001 From: kwliu Date: Sun, 20 Jun 2021 18:47:02 -0700 Subject: [PATCH 15/20] add flake8 configuration --- .flake8 | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 .flake8 diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..cfa3a61 --- /dev/null +++ b/.flake8 @@ -0,0 +1,3 @@ +[flake8] +ignore = E203, E266, E501, W503, F403, F401 +max-line-length = 88 From 1e8e6d4dd833d23fddc614ab6ee0eb004147bea1 Mon Sep 17 00:00:00 2001 From: kwliu Date: Mon, 21 Jun 2021 19:44:04 -0700 Subject: [PATCH 16/20] streamline inspection notebook --- Makefile | 13 ++--- dnc/repeat_copy.py | 24 +++++++++- interactive.ipynb | 116 ++++++++++++++++++++++++--------------------- train.py | 6 +-- 4 files changed, 92 insertions(+), 67 deletions(-) diff --git a/Makefile b/Makefile index 306099a..d79820b 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,4 @@ - -all: install run +all: install install: venv : # Activate venv and install smthing inside @@ -16,16 +15,10 @@ venv: test: venv python -m pytest -run: - : # Run your app here, e.g - : # determine if we are in venv, - : # see https://stackoverflow.com/q/1871549 - bash -c ". venv/bin/activate && pip -V" - clean: - rm -rf venv + rm -rf venv/ find -iname "*.pyc" -delete - rm -rf logs + rm -rf logs/ rm -rf .pytest_cache rm -rf tmp/ diff --git a/dnc/repeat_copy.py b/dnc/repeat_copy.py index 29b5e25..fde4e1f 100644 --- a/dnc/repeat_copy.py +++ b/dnc/repeat_copy.py @@ -356,7 +356,29 @@ def _build(self): return DatasetTensors(obs, targ, mask) @classmethod - def derive_data_from_inputs(cls, obs_pattern, num_reps, num_rep_normalise_factor): + def derive_data_from_inputs( + cls, obs_pattern, num_reps, num_rep_normalise_factor=10 + ): + """Derive observation, target, and mask patterns from input observation tensor. + + Extracted from _build so it can be used for manual inspection of user defined sequences. + + Args: + cls: The RepeatCopy class + obs_pattern: Tensor representing the bit sequences to copy. + Of shape (sub_seq_len, num_bits). + num_reps: Int, number of times to repeat obs_pattern. + num_rep_normalise_factor: Double, normalisation factor for repeat parameter. + + Returns: + obs: Input tensor, obs_pattern with appropriate start sequence flag, num_reps flag + and zero padding after pattern. + targ: Target tensor, obs_pattern repeated num_reps times with appropriate stop flag + and zero padding before pattern. + mask: Mask tensor, 0s for input phase and 1s for model output phase to be used for + determining what timesteps should be considered for calculating loss + """ + sub_seq_len, num_bits = obs_pattern.shape full_obs_size = num_bits + 2 diff --git a/interactive.ipynb b/interactive.ipynb index 8ac22e3..31ada88 100644 --- a/interactive.ipynb +++ b/interactive.ipynb @@ -2,22 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "474c9cfa", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Enabling eager execution\n", - "INFO:tensorflow:Enabling v2 tensorshape\n", - "INFO:tensorflow:Enabling resource variables\n", - "INFO:tensorflow:Enabling tensor equality\n", - "INFO:tensorflow:Enabling control flow v2\n" - ] - } - ], + "outputs": [], "source": [ "# Copyright 2017 Google Inc.\n", "#\n", @@ -48,6 +36,7 @@ "\n", "from collections import namedtuple\n", "\n", + "# Update hyper parameters based on trained model\n", "flags_dict = {\n", " # Model parameters\n", " \"hidden_size\": 64, # Size of LSTM hidden layer.\n", @@ -55,7 +44,7 @@ " \"word_size\": 16, #\"The width of each memory slot.\"\n", " \"num_write_heads\": 1, #\"Number of memory write heads.\"\n", " \"num_read_heads\": 4, #\"Number of memory read heads.\"\n", - " \"clip_value\": 20,#\"Maximum absolute value of controller and dnc outputs.\"\n", + " \"clip_value\": 20, #\"Maximum absolute value of controller and dnc outputs.\"\n", "\n", " # Optimizer parameters.\n", " \"max_grad_norm\": 50, #\"Gradient clipping norm limit.\"\n", @@ -63,14 +52,14 @@ " \"optimizer_epsilon\": 1e-10, #\"Epsilon used for RMSProp optimizer.\"\n", "\n", " # Task parameters\n", - " \"batch_size\": 16, #\"Batch size for training.\"\n", - " \"num_bits\": 4, #\"Dimensionality of each vector to copy\"\n", + " \"batch_size\": 1, #\"Batch size for training.\"\n", + " \"num_bits\": 8, #\"Dimensionality of each vector to copy\"\n", " \"min_length\": 1,#\"Lower limit on number of vectors in the observation pattern to copy\"\n", - " \"max_length\": 2,#\"Upper limit on number of vectors in the observation pattern to copy\"\n", + " \"max_length\": 3,#\"Upper limit on number of vectors in the observation pattern to copy\"\n", " \"min_repeats\": 1,#\"Lower limit on number of copy repeats.\"\n", - " \"max_repeats\": 2, #\"Upper limit on number of copy repeats.\"\n", + " \"max_repeats\": 3, #\"Upper limit on number of copy repeats.\"\n", "\n", - " \"checkpoint_dir\": \"./checkpoints/repeat_copy\", #\"Checkpointing directory.\"\n", + " \"checkpoint_dir\": \"./logs/repeat_copy/checkpoint\", #\"Checkpointing directory.\"\n", "}\n", "\n", "flags_schema = namedtuple('flags_schema', list(flags_dict.keys()))\n", @@ -79,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "3112d2e0", "metadata": {}, "outputs": [ @@ -87,13 +76,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Restored from ./checkpoints/repeat_copy/ckpt-97\n" + "Restored from ./logs/repeat_copy/checkpoint/ckpt-161\n" ] } ], "source": [ "def load_model():\n", - " \"\"\"Trains the DNC and periodically reports the loss.\"\"\"\n", + " \"\"\"Load dnc core model from checkpoint directory\"\"\"\n", " access_config = {\n", " \"memory_size\": FLAGS.memory_size,\n", " \"word_size\": FLAGS.word_size,\n", @@ -134,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "8daa62a5", "metadata": {}, "outputs": [], @@ -149,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "id": "6500f979", "metadata": {}, "outputs": [], @@ -159,27 +148,44 @@ " mask,\n", " rnn_model,\n", "):\n", + " \"\"\"Obtain output sequence and intermediate states when evaluating x.\n", + " \n", + " Args:\n", + " x: input tensor\n", + " mask: Mask tensor, currently unused\n", + " rnn_model: keras.layers.RNN instance\n", + " \n", + " Returns:\n", + " output_sequence: List of tensors representing the model output\n", + " sequence for each time step\n", + " output_states: List of rnn states (may be nested list of tensors)\n", + " output for each time step\n", + " \"\"\"\n", " output_sequence = []\n", " output_states = []\n", " input_state = rnn_model.get_initial_state(inputs=x)\n", + " \n", " for input_seq in x:\n", - " #print(tf.expand_dims(input_seq, axis=0))\n", - " #print(input_state)\n", " output = rnn_model(\n", " inputs=tf.expand_dims(input_seq, axis=0),\n", " initial_state=input_state,\n", " )\n", - " output_sequence.append(tf.round(tf.sigmoid(output[0])))\n", - " input_state = output[1:]\n", - " output_states.append(input_state)\n", - " return output_sequence, output_states\n", + " \n", + " output_seq = output[0]\n", + " output_state = output[1:]\n", + " \n", + " output_sequence.append(tf.round(tf.sigmoid(output_seq)))\n", + " #output_sequence.append(output_seq)\n", + " output_states.append(output_state)\n", "\n", - "get_outputs = lambda x: evaluate_model(x, None, dnc_core)\n" + " input_state = output_state\n", + " \n", + " return output_sequence, output_states" ] }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 19, "id": "b29aad3a", "metadata": {}, "outputs": [], @@ -262,14 +268,18 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 20, "id": "89115c0e", "metadata": {}, "outputs": [], "source": [ "def debug_model(x, num_repeats):\n", " x = tf.convert_to_tensor(x, dtype=tf.float32)\n", - " obs, targ, mask = repeat_copy.RepeatCopy.derive_data_from_inputs(x, num_repeats, 10)\n", + " obs, targ, mask = repeat_copy.RepeatCopy.derive_data_from_inputs(\n", + " x, \n", + " num_repeats, \n", + " 10 # repeat_copy._norm_max, default value of 10, modify if using different norm\n", + " )\n", " \n", " output_sequence, states = evaluate_model(tf.expand_dims(obs, [1]), None, dnc_core)\n", " \n", @@ -280,15 +290,15 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 33, "id": "eeb76634", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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yJzQ0VElJSbJYLPLz85PFYlFycrJCQ0PzLJeQkKCGDRtKynvFjz18hg8AAAAAAIAXVKlSRQ0aNNDGjRslSRs3blSDBg1sbueSpM6dO2vt2rXKzs7W5cuXtX37dnXq1Mlpto9hGEahVQ4AAAAAAACHTp48qUmTJunKlSsKDAxUbGys7rvvPg0ZMkRjxozRQw89JIvFolmzZunbb7+VJA0ZMkRPPfWU01wmfAAAAAAAAEoYbukCAAAAAAAoYZjwAQAAAAAAKGGY8AEAAAAAAChhmPABAAAAAAAoYfy9XYAZp0+f1qRJk5SamqqgoCDFxsaqTp06buelpKToxRdf1Llz5xQQEKB77rlHs2bNyvN1aO5auHChFixYoA0bNqh+/fqmstLT0/XKK69o7969Kl26tBo1aqSXX37ZVObXX3+t+fPnyzAMGYahUaNGqWPHjgXKiI2N1bZt2xQfH2+znWb6yl6m2b5yVGcOd/rKUaa7feUoz0w/OdtvP/74o6ZNm6b09HTVqFFDc+bMUZUqVdzO/OOPPzRt2jT99ttv8vf310MPPaTp06erTJkypurMMXnyZH322Wc6dOiQypcvbyozNTVVs2bN0s8//yx/f3916dJFo0aNMpX573//W6tXr5avr6/8/Pw0ZcoURURE5JspSSNGjNCFCxfk6+urcuXK6aWXXlKDBg1MjSN7mdWrVzc1jhzVmaOg48hRnpnjnaNMTxzvbt8+d8eQo8xSpUq5PYac1ZmjoGPIWaa7Y8hZppkxFBkZqYCAAJUuXVqSNH78eLVp08ZUH9nLrFmzpqk+clRnDnf6yFGmmT5ylOluHzka02aOcfYyx40bZ+oYl9+xx51zBUeZZo5zjtZ19zh34cIFjRw50vr86tWrunbtmvbv3+92HznK3LZtm9t95KzOHAXtI2eZ7vaRs0wzv4scrWtmHNnLbNq0qalxlN82FrSPHOWZGUOOMs30z86dOzV//nxlZWWpYsWKevXVV1WrVi1T/WMvs0KFCqb6x1GdOTz59yu8xCjGBgwYYKxbt84wDMNYt26dMWDAAFN5KSkpxvfff299/tprrxmTJ082lZnj6NGjxjPPPGP813/9l/Gf//zHdN7LL79s/POf/zSys7MNwzCM3377zVRedna2ERERYa3t2LFjRqNGjQyLxVKgnAMHDhgJCQl5ttNMX9nLNNtXjuo0DPf7ylGmu31lL89sPznabxaLxejQoYNx4MABwzAMY9GiRcakSZNMZZ4/f974+eefDcMwDIvFYowdO9ZYuHChqcwcO3bsMCZPnmzUr1/fuHbtmunMoUOHGitXrrS+l5ycbCrz8uXLRnh4uLWvt2/fbnTp0sWlTMMwjCtXrlh//vLLL42ePXsahmFuHNnLNDuOHNVpGO6NI0d5Zo539jI9cby7ffvMjCFHmWbGkKPMHO6MIWeZ7o4hR5lmx5C9f3dm+8heptk+cjY+3O0jR5lm+sheppk+cjSmzRzj7GWaPcY5O/a4e67gKNPMcc7eup46rzMMw5g9e7Yxc+ZMwzA8d+6dk+nJc+/cdRqGZ869c2d66tw7J9NMHzlb190+cpRppo/y28aC9pGzPHf7x1FmVlaW2/2TmppqNGvWzDh16pRhGLf6YfDgwYZhuD+GHGWa6R9ndRqG5/9+hXcU21u6fv/9d8XFxSkqKkqSFBUVpbi4OF2+fNntzKCgIDVv3tz6vFGjRkpISDBda0ZGhmbNmqUZM2aYzpKk69eva926dRo7dqx8fHwkSVWrVjWd6+vrq6tXr0q69b8PwcHB8vUt2D+RiIgIhYaG2rxmtq/sZZrtK3uZkrm+spdppq8c1Wimnxztt6NHj6p06dLW/5nt27evtm7daiqzZs2aeuCBB6w1N2zY0OU+cta/KSkpWrhwoSZPnuxSVn6ZZ86c0fHjxzVw4EDre9WqVTOVafz//wm6fv26pFv9VL16dZdr/dOf/mT9+dq1a/Lx8TE9juxlmh1H9jIl98eRvTyzxztHNZoZR/a2z8wYcpRpZgw5ypTcH0OOMs2MIUeZZseQPWb7yB6zfeSImT6yx2wf2eNuHzka02aOcY4yzRzjnB173D3GOco0c5xztq4nzusyMjK0YcMG9e7d22Pn3rkzPXXunTsz57nZc+/cmZ469769TjN9ZG/dlJQUU31kL9NsHznaRnf7yF7ezZs3TfWPoxrd7Z+zZ8+qatWquvfeeyVJ7dq10zfffGNqDDnKzM7Odrt/HGVevnzZ43+/wnuK7S1diYmJCgkJkZ+fnyTJz89PwcHBSkxM9MgtWNnZ2froo48UGRlpOmv+/Pnq0aOHatasaTpLks6fP6+goCAtXLhQ+/btU/ny5TV27FiXL3W3x8fHR/PmzdOIESNUrlw5Xb9+XcuXL/dIvfSV5/rKk/2Ue78lJiYqLCzM+l7lypWVnZ1tvdzUnczc0tLS9Omnn2rcuHGm6pSkWbNmacyYMTZ/yJvJPHHihEJCQhQTE6Njx46patWqevHFF/XnP//Z7czKlStr1qxZ6tWrlwIDA5Wdna0PPvigQHkxMTH69ttvZRiG3nnnHY+Mo9szHdVvpk7J3Di6Pc8TY+j2TLPjyN72mR1D+e0zd8aQo0wzY8heptkxZC/TE2No/PjxMgxDTZo00bhx4zxynLs9MzAw0Pqeu8c5e5lmj3O3Z3riOHd7prt95GhMlylTxu1jnCvHiYIe45xlunuMc5RZoUIFt49zzur0xPnCV199pZCQEP3lL3/R0aNHPXI+lzszNzPnc7dneuJ8LnfmL7/84pHzudvrdLePHP0eM3Ou4MrvxoL2kbNMd/rIUZ6ZcwVHmWbOFe69915dunRJP/30kxo2bKgNGzZIMvc3kbPMnHUL2j/OMjdv3uzRv4ngRXf+oiLPOHLkiNG1a1eb17p06WIcPXrUI/kzZswwhg8f7talr7kdOnTIePrpp62XF3rikrijR48a9evXN9avX28YhmH8+OOPxiOPPGJcvXrV7czMzExj4MCBxsGDBw3DMIyDBw8a7dq1K/Cl/jlyb6en+srRvjPTV7kzPdVXudfzRF/lzvNkP+Xeb1u3bjWGDBli837Dhg2NlJQUtzNzZGZmGsOGDTNmzZpV4Bpvz9y0aZMxYcIE63vu3I5ye+a2bduMBg0aWG/z2LZtm9G+fXtTmVevXjWeeuop4+TJk4ZhGMamTZuMqKgo67+tgvif//kf49lnn/XoMS8n01H97sjJ9NQ4ysnz5PEuJ9PMOHK0fWbGUH77zJ0x5CjTzBhylGlmDDnKNDuGEhISDMMwjPT0dGPatGnGCy+8YPo4Zy8zh7vHOXuZZo9z9jLNHufsZbrbR47G9L59+9w+xrlynCjoMc5Zne4e4xxl7t+/3+3jnLNt98T5wrPPPmusXr3aMAzPnc/lzszNzO+h3Jme+j2UO9NTv4tyZ5r5XeRoXTPjyJV6CtpHjjIPHDjgVh85yjMzhpxtt5kx9O233xp9+/Y1evXqZcydO9eIiIgw1T+OMo8dO2Z9350x5KhOT//9Cu8ptrd0hYaGKikpSRaLRZJksViUnJxs9xaYgoqNjdXZs2c1b968Al/6ersDBw7o5MmTat++vSIjI3Xx4kU988wz+uabb9zODA0Nlb+/v/VywIcffliVKlXS6dOn3c48duyYkpOT1aRJE0lSkyZNVLZsWZ08edLtzNz10lee6StP9dPt+y00NNTm8s/Lly/L19e3QFf32OsLi8Wi8ePHq2LFipo6dWqBarSXuX//fn3//feKjIy0/u9FVFSUTpw44XZmaGioQkNDrf8L1LFjR/32228FukT99sxvvvlGf/rTn3TfffdJkrp27apz584pJSWlAFt/S8+ePbVv3z5Vr17dY+MoJzOnHk+Mo5zM77//3iPjKCcvJCTEY2MoJ/Pnn392exw5Ok6cPXvW7THk7Njj7hhylLlw4UK3x5CzbXd3DDnK3L17t6kxlDMuAgIC1K9fPx06dMj0cc5epmTuOGcv0+xxztG2mznO2ct09zjn6PdimTJl3D7G5fe71p1jnKPMgwcPun2Mc7bt7h7nnG272fOFpKQkHThwQN27d7e2Zfb30O2ZOcz8Hro90xPnc/a23ezvotszzZzTOVq3dOnSbvdRfvW400eOMvft2+dWHznbbnf7x9l2mxlDLVu21EcffaTPPvtM/fv3V1pammrUqGFqDNnLrF27tiT3x5C9zB9++MHjfxPBi7w942RG//79bT70qn///qYz33zzTaN///7GjRs3TGfZ46kZ0kGDBhl79uwxDMMwTp06ZTRr1sz4448/3M5LTk42wsPDrf9Td+LECaNp06YFvsIjx+3b6Ym+uj3TE33lrD88cYWPYZjvq9x5nugne/vNYrEY7du3d/vDTB1ljh8/3hg3bpyRlZXlcpazzNsV9H++7WVmZ2cbUVFRxvHjxw3DMIz9+/cbbdq0cflKAnuZR44cMVq2bGlcunTJMAzD2Lt3r9GyZUuXMq9du2b933TDuPXBra1btzays7PdHkfOMt0dR84yc3N1HDnLc3cMOcpMSkry2PEu94c2mxlDjjLNjCF7mbdz9yq53Jlmx5C9TDNj6Pr169YP687OzjbeeustY8SIEab6yFmmu33kKPN2BekjR5lm+shRppk+cjSmzZwrOMo0c67gyrGnoOcKjjLNnCvYW9cT5wtLliwxxowZY/Oa2fM5e5lmz+fsZebmzvmcvUyz53O3Z5rpI2fruttHzjLd7SNXt9HVPnKW527/OMq8ePGiqTGU88H4FovFmDx5sjF79mzDMMyNIUeZZsaQo8zcuMKnePMxDMPw9qSTu06ePKlJkybpypUrCgwMVGxsrPV/mtzx66+/KioqSnXq1LF+pWrNmjW1aNEiT5WsyMhILV261PTX2p0/f15TpkxRamqq/P399fzzz6tdu3amMtevX68VK1ZYP+xszJgx6tChQ4EyZs+erS+++EKXLl1SpUqVFBQUpE2bNpnqK3uZ8+bNM9VXjurMraB95SjT3b5ylGemn5z9Gz906JCmT59u83XFrnzgnaPMPn36aOjQoapfv771fxoaN26s6dOnm6ozt/vvv9/lryt2lnnkyBHNnDlTGRkZKlu2rGJiYtSwYUNTmStXrtQnn3yiUqVKKSAgQJMmTXLpXvJLly5pxIgRunnzpnx9fVWxYkVNnDhRf/nLX9weR44yAwIC3B5HzurMzdVx5CzP3THkLNMTx7vbt8/dMeQoMyEhwe0x5KzO3AoyhpxlujuGnGW6O4bOnz+v0aNHy2KxKDs7W3Xr1tXUqVMVHBzsdh85yoyLi3O7j5zVmVtB+shZprt95CzTTB/ZG9NmzhXsZYaFhZk6V3Dl2FPQcwVHmWbO6xyta/Y416lTJ8XExKht27bW18yee9+e6Ylzb3t15ubOube9TLPn3vYyzfSRo3XN9JG9zHvuucdUH7myjQXpI0d5ZvrHUaaZ/omJidGhQ4eUmZmpVq1aacqUKSpdurSp/rGXee7cOVP946jO3Dz19yu8o1hP+AAAAAAAACCvYvsZPgAAAAAAALCPCR8AAAAAAIAShgkfAAAAAACAEoYJHwAAAAAAgBKGCR8AAAAAAIAShgkfAADgUEJCgsLDw2WxWLxdCgAAAAqACR8AAGAVGRmp7777zvo8LCxMhw8flp+fn9dq+uyzz/TXv/7Va+0DAAAUR0z4AAAAAAAAlDBM+AAAAEnShAkTlJCQoGHDhik8PFwrVqzQhQsXdP/99ysrK0uSNGDAAM2dO1d9+/ZVeHi4hg0bppSUFL3wwgtq3LixevfurQsXLlgzT548qUGDBqlZs2bq1KmTNm/e7LD9zz77TO3bt1d4eLgiIyO1fv16nTx5UtOnT9ePP/6o8PBwRURESJIyMjIUGxurRx99VC1bttS0adOUlpYmSdq3b5/atm2rpUuXqnnz5tYsAACAuwkTPgAAQJI0Z84chYWFaenSpTp8+LCGDBlid7nNmzfr9ddf1+7du3Xu3Dn17dtXvXv31v79+1W3bl0tWrRIknTjxg0NHjxYUVFR+u677zR37lzNnDlTJ06cyJN548YNzZ49WytWrNDhw4f18ccfq0GDBqpbt65mzpypRo0a6fDhwzp48KAk6Y033tDp06e1bt06ffHFF0pOTra2K0mXLl1SSkqK9uzZo9dee03Tpk3TqVOnCmGvAQAAFE1M+AAAgAJ5/PHHVbt2bf3pT39S27ZtVatWLbVs2VL+/v7q3Lmz4uLiJEk7d+5UjRo11Lt3b/n7++uBBx5Qp06dtHXrVru5vr6++vXXX5WWlqbg4GD9+c9/trucYRj65JNPNGXKFAUFBalChQoaOnSoNm3aZLPc2LFjFRAQoGbNmqldu3basmWLZ3cEAABAEebv7QIAAEDxUrVqVevPpUuXtnlepkwZ3bhxQ5IUHx+vn376yXobliRZLBb16NEjT2a5cuU0d+5cvffee4qJiVHjxo01ceJE1a1bN8+yly9f1s2bN/X4449bXzMMQ9nZ2dbngYGBKleunPV5WFiYkpOT3dxiAACA4ocJHwAAUChCQ0PVtGlTrVy50qXl27RpozZt2igtLU3z5s3TSy+9pDVr1sjHx8dmuUqVKqlMmTLatGmTQkJC7GZduXJFN27csE76JCYmOrxiCAAAoCTili4AAGBVtWpVnT9/3iNZjz76qM6cOaN169YpMzNTmZmZ+umnn3Ty5Mk8y166dEnbt2/XjRs3FBAQoHLlysnX99ZpSpUqVZSUlKSMjAxJt2796tOnj1555RX9/vvvkqSkpCTt2bPHJnPBggXKyMjQwYMHtXPnTnXu3Nkj2wUAAFAcMOEDAACsnnvuOS1ZskQRERF69913TWVVqFBB7777rjZv3qw2bdqodevWeuONN6wTN7llZ2dr1apVatOmjZo1a6YDBw5oxowZkqRHHnlE9erVU+vWrdW8eXNJt75R7J577tGTTz6pxo0b6+9//7tOnz5tzatataoCAwPVpk0bjR8/XjNmzLB7exgAAEBJ5WMYhuHtIgAAADxl3759mjBhgnbv3u3tUgAAALyGK3wAAAAAAABKGCZ8AAAAAAAAShhu6QIAAAAAAChhuMIHAAAAAACghGHCBwAAAAAAoIRhwgcAAAAAAKCEYcIHAAAAAACghGHCBwAAAAAAoIRhwgcAAAAAAKCE+X+k72kCqsMrSQAAAABJRU5ErkJggg==\n", 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\n", 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\n", + "image/png": 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\n", 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Qr5yB7EPF5Qx0HyouZzD2oeHDh8uyLLVt21aPP/54UI5zZ+eMiYnx/szf41xxOQM9zp2dMxjHubNz+ttHvvbpChUq+H2MMzlO2D3GlZTT32Ocr5xVqlTx+zhXUp3BOF/48ssvFR8fryuvvFLr168Pyvnc6TlPF8j53Nk5g3E+d3rOTZs2BeV87uw6/e0jX99jgZwrmHw32u2jknL600e+8gVyruArZyDnCpdeeqkOHDigdevWqUWLFlqwYIGkwP5NVFLOU79rt39Kyrl48eKg/psIYarsJyuVjp9//tm65ZZbzlh28803W+vXrw9K/nHjxll/+tOf/Jp+e7o1a9ZYDz74oHeKYzCm5a1fv95q2rSpNX/+fMuyLOunn36yrr76aisnJ8fvnIWFhdZDDz1krV692rIsy1q9erXVpUsX25cbnHL6egarr3xtu0D66vScweqr038vGH11er5g9tPp2+2zzz6z+vfvf8bPW7RoYR06dMjvnKcUFhZaAwcOtMaPH2+7xrNzLlq0yHryySe9P/Pnkpizcy5ZssRKTEz0XmqyZMkSq2vXrgHlzMnJsXr16mVt27bNsizLWrRokdWjRw/vZ8uOTz/91OrXr19Qj3mncvqq3x+ncgZrPzqVL5jHu1M5A9mPfK1fIPvQ+baZP/uQr5yB7EO+cgayD/nKGeg+tHfvXsuyLOvEiRPWmDFjrCeeeCLg41xxOU/x9zhXXM5Aj3PF5Qz0OFdcTn/7yNc+vWLFCr+PcSbHCbvHuJLq9PcY5yvnypUr/T7OlbTuwThf6NevnzVjxgzLsoJ3Pnd6ztMF8j10es5gfQ+dnjNY30Wn5wzku8jX7wayH5nUY7ePfOVctWqVX33kK18g+1BJ6x3IPvTtt99avXv3tu644w5r0qRJVlJSUkD94yvnxo0bvT/3Zx/yVWew//2K8HTBXFaWkJCgzMxMud1uSZLb7VZWVlaxl+HYlZaWpvT0dL3yyiu2p9+ebdWqVdq2bZu6du2q5ORk7du3T4888oi++eYbv3MmJCQoIiLCOyWxZcuWql69unbs2OF3zo0bNyorK0tt27aVJLVt21YVK1bUtm3b/M55er30VXD6Klj9dPZ2S0hIOGMK6sGDB+V0Om3NGiquL9xut4YPH65q1aopNTXVVo3F5Vy5cqV++OEHJScne/8q0qNHD23dutXvnAkJCUpISPD+demmm27S/v37bU2TPzvnN998o6pVq+qyyy6TJN1yyy3atWuXDh06ZGPtT7r99tu1YsUK1alTJ2j70amcp+oJxn50KucPP/wQlP3oVL74+Pig7UOncv7yyy9+70e+jhPp6el+70MlHXv83Yd85Zw8ebLf+1BJ6+7vPuQr59dffx3QPnRqv4iKilKfPn20Zs2agI9zxeWUAjvOFZcz0OOcr3UP5DhXXE5/j3O+vhcrVKjg9zHufN+1/hzjfOVcvXq138e4ktbd3+NcSese6PlCZmamVq1apdtuu83bVqDfQ2fnPCWQ76GzcwbjfK64dQ/0u+jsnIGc0/n63ejoaL/76Hz1+NNHvnKuWLHCrz4qab397Z+S1juQfejaa6/VBx98oDlz5uj+++/X8ePHVa9evYD2oeJyNmzYUJL/+1BxOX/88ceg/5sIYSrUo1PBdP/9959xQ6/7778/4Jx///vfrfvvv9/Ky8sLOFdxgjXy2rdvX2v58uWWZVnW9u3brfbt21tHjhzxO19WVpbVunVr718At27darVr1872zJFTzl7PYPTV2TmD0Vcl9UcwZg5ZVuB9dXq+YPRTcdvN7XZbXbt29ftGrb5yDh8+3Hr88cetoqIi41wl5Tyb3b+oF5fT4/FYPXr0sDZv3mxZlmWtXLnS6tSpk/EMheJy/vzzz9a1115rHThwwLIsy/r++++ta6+91ijnsWPHvH+lt6yTN6Xt2LGj5fF4/N6PSsrp735UUs7Tme5HJeXzdx/ylTMzMzNox7vTb0gdyD7kK2cg+1BxOc/m7+y703MGug8VlzOQfSg3N9d7I3KPx2O9/PLL1qBBgwLqo5Jy+ttHvnKezU4f+coZSB/5yhlIH/napwM5V/CVM5BzBZNjj91zBV85AzlXKO53g3G+8MYbb1hDhw49Y1mg53PF5Qz0fK64nKfz53yuuJyBns+dnTOQPirpd/3to5Jy+ttHputo2kcl5fO3f3zl3LdvX0D70Kmb/rvdbmvkyJHWxIkTLcsKbB/ylTOQfchXztMxc+jC5bAsywr1AFWwbNu2TSNGjNDRo0cVExOjtLQ071+w/LFlyxb16NFDjRo18j6Gtn79+poyZUqwSlZycrKmTZsW8KMAd+/erVGjRunw4cOKiIjQn//8Z3Xp0iWgnPPnz9fbb7/tvZHb0KFDdcMNN9jKMXHiRC1dulQHDhxQ9erVFRsbq0WLFgXUV8XlfOWVVwLqK191ns5uX/nK6W9f+coXSD+V9Blfs2aNxo4de8Yjnk1u5ucr5z333KNHH31UTZs29f4Fo02bNho7dmxAdZ6uWbNmxo94Linnzz//rGeeeUYFBQWqWLGiRo8erRYtWgSUc/r06frwww8VGRmpqKgojRgxwuja9wMHDmjQoEHKz8+X0+lUtWrV9NRTT+nKK6/0ez/ylTMqKsrv/aikOk9nuh+VlM/ffaiknME43p29fv7uQ75y7t271+99qKQ6T2dnHyopp7/7UEk5/d2Hdu/erSFDhsjtdsvj8ahx48ZKTU1VXFyc333kK+eGDRv87qOS6jydnT4qKae/fVRSzkD6qLh9OpBzheJy1q1bN6BzBZNjj91zBV85Azmv8/W7gR7nunXrptGjR6tz587eZYGee5+dMxjn3sXVeTp/zr2LyxnouXdxOQPpI1+/G0gfFZfzkksuCaiPTNbRTh/5yhdI//jKGUj/jB49WmvWrFFhYaGuu+46jRo1StHR0QH1T3E5d+3aFVD/+KrzdMH69yvCzwU1OAQAAAAAAAB7Lph7DgEAAAAAAMA+BocAAAAAAAAuYgwOAQAAAAAAXMQYHAIAAAAAALiIMTgEAAAAAABwEWNwCAAAGNu7d69at24tt9sd6lIAAAAQJAwOAQAAn5KTk/Xdd99539etW1dr166Vy+UKWU1z5szRvffeG7L2AQAALjQMDgEAAAAAAFzEGBwCAADFevLJJ7V3714NHDhQrVu31ttvv63ffvtNzZo1U1FRkSTpgQce0KRJk9S7d2+1bt1aAwcO1KFDh/TEE0+oTZs2uuuuu/Tbb795c27btk19+/ZV+/bt1a1bNy1evNhn+3PmzFHXrl3VunVrJScna/78+dq2bZvGjh2rn376Sa1bt1ZSUpIkqaCgQGlpabr++ut17bXXasyYMTp+/LgkacWKFercubOmTZumDh06eHMBAADgJAaHAABAsV588UXVrVtX06ZN09q1a9W/f/9i4xYvXqwXXnhBX3/9tXbt2qXevXvrrrvu0sqVK9W4cWNNmTJFkpSXl6eHH35YPXr00HfffadJkybpmWee0datW8/JmZeXp4kTJ+rtt9/W2rVrNXv2bCUmJqpx48Z65pln1KpVK61du1arV6+WJL300kvasWOH5s6dq6VLlyorK8vbriQdOHBAhw4d0vLly/W3v/1NY8aM0fbt20thqwEAAJQ/DA4BAICA3HnnnWrYsKGqVq2qzp07q0GDBrr22msVERGh7t27a8OGDZKkr776SvXq1dNdd92liIgIXXHFFerWrZs+++yzYvM6nU5t2bJFx48fV1xcnJo0aVJsnGVZ+vDDDzVq1CjFxsaqSpUqevTRR7Vo0aIz4oYNG6aoqCi1b99eXbp00b///e/gbggAAIByKiLUBQAAgPKtVq1a3v+Pjo4+432FChWUl5cnSdqzZ4/WrVvnvRRMktxut3r27HlOzkqVKmnSpEn65z//qdGjR6tNmzZ66qmn1Lhx43NiDx48qPz8fN15553eZZZlyePxeN/HxMSoUqVK3vd169ZVVlaWn2sMAABwYWFwCAAAlImEhAS1a9dO06dPN4rv1KmTOnXqpOPHj+uVV17R008/rX/9619yOBxnxFWvXl0VKlTQokWLFB8fX2yuo0ePKi8vzztAlJGR4XMmEgAAwMWGy8oAAIBPtWrV0u7du4OS6/rrr9fOnTs1d+5cFRYWqrCwUOvWrdO2bdvOiT1w4IC++OIL5eXlKSoqSpUqVZLTefK0pWbNmsrMzFRBQYGkk5ef3XPPPXruueeUnZ0tScrMzNTy5cvPyPn666+roKBAq1ev1ldffaXu3bsHZb0AAADKOwaHAACATwMGDNAbb7yhpKQkvfPOOwHlqlKlit555x0tXrxYnTp1UseOHfXSSy95B3lO5/F49O6776pTp05q3769Vq1apXHjxkmSrr76al1++eXq2LGjOnToIOnkk9UuueQS/eEPf1CbNm30xz/+UTt27PDmq1WrlmJiYtSpUycNHz5c48aNK/YSNQAAgIuRw7IsK9RFAAAAlJYVK1boySef1Ndffx3qUgAAAMISM4cAAAAAAAAuYgwOAQAAAAAAXMS4rAwAAAAAAOAixswhAAAAAACAixiDQwAAAAAAABcxBocAAAAAAAAuYgwOAQAAAAAAXMQYHAIAAAAAALiI/X8rHHA1DI+m1AAAAABJRU5ErkJggg==\n", 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\n", 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PFi1a4PLly3KnQ0RERHJQF4k9zITsBVRAQADWr1+Phg0byp0KERERycXCljGQ/Raej4+P3CkQERGR3MxonzsRshdQREREROY0uiSCBRQRERHJz4zmN4lgAUVERETy4zIGRERERDqysAJK9k/hzZs3D926dcOdO3cwcuRI9O3bV+6UiIiIyMQkSS30MBeyj0DNmDEDM2bMkDsNIiIikhPnQBERERHpyMJu4SkkSdJlb1az4+roKdROYYQNgtPy7wm3tegXmSqkho1SuO39IpURM6laRDdWBcQ3Z9aFlULsvUZjhLde0b6N0b+cfQOAY41aQu1EN2bWxTOODYTbXs1KEmqny1+ssAZim9AvTRLfhL5IdVuHDAyjYF+MULuaPccaORMxHIEiIiIi+VnYCBQLKCIiIpIf50DpJjMzEx9++CH++ecfKJVKNG7cGHPmzIGTk5PcqREREZGpWNgIlOzLGCgUCrz11lvYs2cPtm/fDg8PDyxcuFDutIiIiMiULGwzYdkLKEdHR3Ts2FH7vG3btkhKEptkR0RERJWERiP2MBOy38J7nEajwffffw9/f3+5UyEiIiJT4hyoips7dy5sbW0xbNgwuVMhIiIiUzKj23MizKaAioqKwo0bNxATEwMrHdZxISIiokrAjG7PiTCLAurzzz/HuXPnsHz5ciiV4osPEhERUSWhNp997kTIXkBduXIFX3/9NZo0aYIhQ4YAANzd3bF06VKZMyMiIiKT4QiUbp599llcunRJ7jSIiIhITiygiIiIiHRkhEnkiYmJiIiIQFZWFhwdHREVFYUmTZoUa5Oeno6pU6ciOTkZRUVF6NixI2bMmAEbm6eXSFWmgJJ02M5XdH9lhQ6bZ1r4ns1UAdwgWB7G2CBYF8bYKJd9l88YmwSLunYv2eAxdXk1v0s7afD+ZWGEOVCRkZEIDQ1FSEgItm7dilmzZmHNmjXF2sTExKBZs2ZYvnw5CgsLERoair179+Kll156auwqU0ARERGRGRP8x092djays7NLHLe3t4e9vb32eXp6OuLj47Fq1SoAQFBQEObOnYuMjIxi28UpFArk5eVBo9FApVKhsLAQLi4u5ebBAoqIiIjkJ1hArV69GtHR0SWOjxs3DuPHj9c+T05OhouLC6ytrQEA1tbWcHZ2RnJycrECKiwsDOPHj0fXrl1RUFCAoUOHwtvbu9w8zKKACgsLw61bt2BlZQVbW1vMnDkTnp6ecqdFREREpiI4B2rEiBEYMGBAieOPjz7pYvfu3WjRogVWr16NvLw8jB49Grt370bv3r2f+n1mUUBFRUXBzs4OALBv3z5MmzYNmzdvljkrIiIiMhWpSGwO1JO36sri5uaGlJQUqNVqWFtbQ61WIzU1FW5ubsXarVu3DgsWLICVlRXs7Ozg7++P2NjYcgsos1jy+1HxBAC5ubk6Tc4mIiKiSkDSiD0E1a1bF56entixYwcAYMeOHfD09Cx2+w54uPbkoUOHAAAqlQrHjh3Ds88+W258sxiBAoDp06fj6NGjkCQJ3377rdzpEBERkSlpDP9JztmzZyMiIgLLli2Dvb09oqKiAACjR49GeHg4WrVqhWnTpiEyMhL9+vWDWq1Gx44d8corr5QbWyGZ2efrt2zZgp07d+Kbb74Rau/qaPi5UqIvSeb9XOGYcn/El4iIjMNKh7smxvhbUEtZQ6hdnuq+cMwi1e2KplNh+UvChNrZjl9m5EzEmMUtvMf1798fsbGxyMzMlDsVIiIiMhW1WuxhJmQvoPLy8pCc/L9FyPbv3w8HBwc4OjrKlxQRERGZlkYj9jATss+BKigowIQJE1BQUAArKys4ODggJiaGE8mJiIiqEiPMgTIm2QuoevXq4ccff5Q7DSIiIpKTEfbCMybZCygiIrlUt6km1O5BUaFwTBsra6F241y7CMdcnHRIqJ0uk5kHuPoItduUbPh91p6r4yHU7mLmTYP3baesKdw2R1Ug1E6XieGivx9FGvG5PqKTw62tZJ+181Si60CZCxZQREREJD/ewiMiIiLSEW/hEREREenIwkagzOqGaHR0NFq0aIHLly/LnQoRERGZUpFa7GEmzGYE6vz58zh9+jQaNmwodypERERkahZ2C88sRqBUKhXmzJmD2bNny50KERERyUEjiT3MhFmMQH3xxRcIDg6Gu7u73KkQERGRDCQzWmVchOwjUHFxcTh37hxCQ0PlToWIiIjkUqQRe5gJ2QuokydPIiEhAQEBAfD398edO3cwatQoHDlyRO7UiIiIyFQkjdjDTMh+C+/tt9/G22+/rX3u7++PmJgYNG/eXMasiIiIyKTMaH6TCNkLKCIiIiKJBZR+9u/fL3cKREREZGpmtMaTCLMroCyJQoeNO6HDZpNEZBq6bBIsSnQTWNENgnWhy6a2xtgkWJQxNgkWJbpBsLHoskmwoanN/VNuHIEiIiIi0pGFFVBCn8KbN29eqcfnz59v0GSIiIioapIkSehhLoQKqJ9//rnU49u2bTNIEv7+/ujduzdCQkIQEhKCw4cPGyQuERERWQgLWwfqqbfwNm7cCABQq9Xa/3/k5s2bcHR0NFgiX375JZcuICIiqqIq1afwtm7dCgAoLCzU/j/wcPJ0vXr1EBUVZdzsiIiIqGqoTAXU2rVrAQCLFi3Ce++9Z9REJk+eDEmS4O3tjUmTJsHe3t6o/REREZEZMZ+7c0IUkuCMrHv37uHAgQNISUmBi4sLunfvbrBbeMnJyXBzc4NKpcL8+fORl5eHhQsXCn2vq6OnQXJ4nOgktawHecIxzf7jo0RERP+vSHXb5H1mvdZDqJ3j9weMnIkYoUnkcXFx6NWrFzZs2IBLly5hw4YNePHFFxEXF2eQJNzc3AAASqUSoaGhOHXqlEHiEhERkYXQCD7MhNA6UAsWLEBkZCT69u2rPbZr1y7MmzcPmzZt0iuB/Px8qNVq2NnZQZIk7Nq1C56ehh9VIiIiIvNVqSaRP3L9+nX06dOn2LHAwEBERkbqnUB6ejrGjx8PtVoNjUaDZs2aGSQuERERWRAzGl0SIVRANW7cGDt37kS/fv20x3bv3g0PDw+9E/Dw8MCWLVv0jkNERESWSyqqhCNQ06ZNw9ixY7F27Vo0aNAAt2/fxo0bNxATE2Ps/IiIiKgKkCrjCFS7du3w66+/4uDBg0hNTUWPHj3g5+dn0IU0iYiILJGVDhvL67Lhs6hq1mLb2haqiwzet0FVxgIKABwcHBASEmLMXIiIiKiKksy8vntSmQVUaGgoFAJV9fr16w2aEBEREVU9xriFl5iYiIiICGRlZcHR0RFRUVFo0qRJiXa7du3CV199BUmSoFAosGrVKtSrV++pscssoAYPHqx34qIePHiABQsW4NixY6hevTratm2LuXPnmqx/IiIikpcxCqjIyEiEhoYiJCQEW7duxaxZs7BmzZpibc6ePYvo6GisXr0a9evXR05ODpRKZbmxyyygBgwYoH/mgj799FNUr14de/bsgUKhQFpamsn6JiIiIvmJFlDZ2dnIzs4ucdze3r7YNnDp6emIj4/HqlWrAABBQUGYO3cuMjIy4OTkpG333Xff4c0330T9+vUBAHZ2dkJ5CM2B2rFjBzw9PdGsWTNcu3YNs2bNgkKhwOzZs9GsWTOhjsqSl5eHLVu24Pfff9feMixv2IyIiIgqF0ktNhl/9erViI6OLnF83LhxGD9+vPZ5cnIyXFxcYG1tDQCwtraGs7MzkpOTixVQCQkJcHd3x9ChQ5Gfn49evXrhnXfeKXcak1ABtXjxYmzYsAEA8Mknn6BVq1awtbXFRx99VGIoTFc3b96Eo6MjoqOjERsbi1q1amHChAnw8fHRKy4RERFZDkkjVkCNGDGi1Ltkj48+6UKtVuPSpUtYtWoVVCoV3nrrLTRo0AD9+/d/6vcJFVAZGRmoV68eHjx4gL/++gtffvklbGxs0KlTpwol+2TiN2/eRMuWLTFlyhT8/fffGDt2LH799VfUrl1b7/hERERk/kRv4T15q64sbm5uSElJgVqthrW1NdRqNVJTU7X77z7SoEED9O7dG0qlEkqlEgEBAThz5ky5BZTQZsJOTk64ceMGDh06hFatWkGpVOLBgweQDLCehZubG2xsbBAUFAQAaNOmDerUqYPExES9YxMREZFlkCSF0ENU3bp14enpiR07dgD433Skx2/fAQ/nRh05cgSSJKGwsBDHjx/Hc889V258oRGosLAwDBw4ENbW1li0aBEA4I8//hDqoDxOTk7o2LEjjh49iq5duyIxMRHp6elo3Lix3rGJiIjIMmiKxIsjUbNnz0ZERASWLVsGe3t7REVFAQBGjx6N8PBwtGrVCn379sW5c+fw0ksvwcrKCl27dsWgQYPKja2QBIeRCgoKAAA1a9YE8HB2u0aj0c5a18fNmzcxbdo0ZGVlwcbGBhMnToSfn5/Q97o6eurd/5NER9ayHuQJx1RrLGyJVSIiElIZVyIvUt2uaDoV9o9PgFC7Rn/+ZuRMxAivRP6ocHqkbt26BkvCw8MDa9euNVg8IiIisiyik8jNhXABRURERGQsLKAqgYa2YutQ3VcXCsdMu7JDqF3Nxj2F2hUkHRbuu2YDX4PHrO0udos199bvwjHrNekl1C7t+q/CMesKvp7pN/YJxxSlSbsp3LZW61ChdqLnKMxninDfq5OPCbXLu31IOOYI7/fF+v7rM+GYogqmjBFu67Q2XiymDtfGXJ+ZQu0WJB0Ujinav+jrDgA/Jp8waN/BXu8K9/1ryhmD9g0AzZqL7dWalJshHFO0f1vB91hdFNw6KNy2pnt3g/cvB43gOlDmggUUERERyU6XT9iZA6FlDN59913s27cPhYXiIy5EREREoiSN2MNcCI1A+fj4YOnSpZg+fTp69+6NkJAQtGvXziAJ3Lp1C++++7+h35ycHOTm5uLECbHhZSIiIrJ8GgsbgRIqoEaOHImRI0fiypUr2LZtG95//31Uq1YNwcHBCA4ORqNGjSqcgLu7O7Zu3ap9Pn/+fKjV6grHIyIiIsujUQvdFDMbOmX77LPP4v3338enn36KGjVqYOnSpRgwYADeeOMNXLx4Ue9kVCoVtm/fjpdfflnvWERERGQ5JEnsYS6EJ5Ffu3YN27Ztw44dO1CtWjWEhIQgJCQETk5O+O9//4uwsDDs379fr2T2798PFxcX/Pvf/9YrDhEREVmWSrmMwcCBA3H79m289NJL+Oyzz9CmTZtiXx85cqRBFsLctGkTR5+IiIiqoEo3B0qSJPTt2xfDhw+HUqkss52+o08pKSk4efIkPvnkE73iEBERkeXRWNgIVLlzoBQKBZYsWQIbG+MuGbV582b4+fmhTp06Ru2HiIiIzI9GUgg9zIXQJHJPT08kJiYaNZHNmzfz9h0REVEVJUkKoYe5EBpW6tChA0aPHo0BAwbA1dUVisd2nh40aJBBEtmzZ49B4hAREZHlMadP2IlQSFL5KQ8fPrz0b1YosGbNGoMnpQtXR0+Dx7S1qSHU7mZ2qsH7trDfHzJTYQ26Cred5Ci2N9gz8WL7xgGAn7PYJ2l/Tz0vHNMYurs8L9TuYMo54ZjWVmKrw/y7TmPhmGfSxe4AiPYNAF5OzYTa/Zl2RaidLuMCzeu4C7W7lHlLh6hi6tnaC7dNy882eP+21aoLtcsvfGDwvqvbVBNum5d/3eD9l+dkwwFC7drf3mzkTMQIjUAZ4hN2RERERGUxp/lNIoRnht+7dw8HDhxASkoKXFxc0KNHDzg4OBgzNyIiIqoiLO0OjNB4b1xcHHr16oUNGzbg0qVL2LBhA3r16oW4uDiDJHHgwAH0798fISEhCA4Oxt69ew0Sl4iIiCyDpX0KT2gEasGCBYiMjETfvn21x3bt2oV58+Zh06ZNeiUgSRI+/PBDrF+/Hs2bN8fFixfx2muvoWfPnrDS4X4+ERERWS61GRVHIoQqlOvXr6NPnz7FjgUGBuKff/4xTBJWVsjJyQEA5OTkwNnZmcUTERFRFSJBIfQwF0IjUI0bN8bOnTvRr18/7bHdu3fDw8ND7wQUCgUWL16MsLAw2NraIi8vD8uXL9c7LhEREVkOjYVNghIqoKZNm4axY8di7dq1aNCgAW7fvo0bN24gJiZG7wSKiorw9ddfY9myZfD29sZff/2FiRMnYufOnahVq5be8YmIiMj8acxodEmEUAHVrl07/Prrrzh48CBSU1PRo0cP+Pn5wdHRUe8ELly4gNTUVHh7ewMAvL29UbNmTSQkJKB169Z6xyciIiLzp66MBRQAODg4ICQkxOAJuLq64s6dO7h27RqaNm2KhIQEpKeno1GjRgbvi4iIiMyTOc1vEiFUQCUlJSE6OhoXLlxAfn5+sa/puwVL/fr1MXv2bEyYMEG7RcyCBQsMMrpFRERElkEjdwI6EiqgJkyYgKZNmyI8PBw1aohtc6KL4OBgBAcHGzwuERERWYZKWUBdu3YNP/zwA5cWICIiIqNQKyzrFp7QZsKTJ0/GoEGD0KlTJ1PkpBNjbCYs8JIAADLv5wrH1FjaNtNERFRlFalum7zPra6hQu1C7vzXyJmIERqBmjFjBoYMGYJGjRqhbt26xb728ccfGyUxIiIiqjosbZhBqICaOnUqrK2t0axZM1SvXt3YOREREVEVUynnQB0/fhyHDx9G7dq1jZLEwYMH8cUXX6CoqAgODg74+OOPDbLKOREREVkGS5sDJTQrvEWLFsjKyjJKAvfu3cOUKVPw+eefY/v27Rg8eDBmz55tlL6IiIjIPGkEH+ZCaASqU6dOGDVqFAYOHFhiDtSgQYP0SuDGjRuoV68e/vWvfwEA/Pz88OGHHyIjIwNOTk56xSYiIiLLoDHCAFRiYiIiIiKQlZUFR0dHREVFoUmTJqW2vXbtGgYMGIDQ0FBMmTKl3NhCBdRff/0FZ2dnHDlypNhxhUKhdwH1r3/9C2lpaThz5gxat26N7du3AwCSk5NZQBEREVURxtjKJTIyEqGhoQgJCcHWrVsxa9YsrFmzpmTfajUiIyPRs2dP4dhCBdTatWvFs9WRnZ0dFi1ahI8//hgPHjxAt27dYG9vD2tra6P1SUREROZFdAQqOzsb2dnZJY7b29vD3t5e+zw9PR3x8fFYtWoVACAoKAhz584t9Q7X8uXL0b17d+Tn55fYcaUswnvhZWZm4vfff0daWhreeustpKSkQJIkuLq6ioYoU+fOndG5c2cAQFpaGlasWMG98IiIiKoQ0flNq1evRnR0dInj48aNw/jx47XPk5OT4eLioh2Qsba2hrOzc4k7XBcvXsSRI0ewZs0aLFu2TDhfoQLqxIkTGD9+PJ5//nmcOnUKb731Fm7cuIGVK1ciJiZGuLOy3L17F/Xr14dGo8Hnn3+OIUOGwNbWVu+4REREZBlE14EaMWIEBgwYUOL446NPogoLCzFz5kx8/PHHOt/5EiqgFixYgMWLF+OFF15A+/btAQBt2rTBmTNndE62NIsXL8apU6dQWFiILl26YPLkyQaJS0RERJahSPAW3pO36sri5uaGlJQUqNVqWFtbQ61WIzU1FW5ubto2d+/exT///IO3334bwMPbg5IkITc3F3Pnzn1qfKEC6vbt23jhhRcAPJw4DgDVqlWDWq0W+fZyzZ8/3yBxiIiIyDIZeomCunXrwtPTEzt27EBISAh27NgBT0/PYrfvGjRogNjYWO3zJUuWID8/X+hTeELrQDVr1gyHDx8uduyPP/5A8+bNRX8OIiIiojJJCrGHLmbPno1169YhMDAQ69atw0cffQQAGD16NM6ePatXvkKbCZ8+fRpjxoxB9+7d8csvv6B///7Yv38/li1bhtatW+uVgL7k3Ew460GecEy1Rr7lv0R/33TZh8jaSqj21unntpQ8jcHQP7su7zHG2H/KSnBFYbk32Zbz91ihw6rLoq+TLuddtH9j9G1tJTbXpEhjmLscj7NT1hRum6MqMHj/1azFPrtVqC4yeN+6nKNCGTYTXuYxTKhd2M11Rs5EjNC7R9u2bbFt2zY888wzePnll+Hu7o6NGzfKXjwRERFR5aAWfJgL4WUMXFxcMHr0aGPmQkRERFWUMVYiNyahAionJwdr1qzBhQsXSiwwtXLlynK/PyoqCnv27MHt27exfft27dwpXZZYJyIiosrLnPa5EyFUQE2YMAFqtRq9evVC9erVde4kICAAr7/+OoYOHVrsuOgS60RERFS5VcoC6vTp0zh+/DiUSmWFOvHx8SlxTJcl1omIiKhyU1vYLTyhSeTe3t64du2aQTt+2hLrREREVLVoBB/mQmgE6j//+Q9Gjx6NNm3aoG7dusW+Nm7cOKMkRkRERFWHvIua6E6ogFq0aBHu3LkDd3d35Obmao/rso7Jk0SWWCciIqKqQWNhJZRQAbVz507s2bMHzs7OButYZIl1IiIiqhrMaY0nEUIFlIeHB2xshJeMKmHevHnYu3cv0tLSMHLkSDg6OmLnzp2YPXs2IiIisGzZMtjb2yMqKqrCfRAREZHlMqf5TSKEtnJZsWIFfv31VwwbNqzEHKhHmwzLhVu5lM9StkixlDyNgVu5yINbucjTN7dyKV9V3MplVpOh5TcCMOf6eiNnIkboTK5f/zDZzz//vNhxhUKB3377zfBZERERUZVSKedA7d+/39h5kBEZ41eympXYv6LUGpVwzP5uJdcLK83m5D+FYw537SjU7rukY8IxjWF8A1+hdl8mHRZq51iztnDfnrXdhdr9cfeicMymDmIfBrmalSQcU5Qu/8ru7+It1G5T8knhmEqbakLt2tZpKhwz9u4loXbVbcTX6vNxaibU7kjqBaF2NoIjKwDQse6zBu0bED/vzezEP6h0Ot2wy/cAQBM7F6F2V7IMPwLUyF6sb7lUyjlQRERERMZUKUegiIiIiIzJssonwZXI9RUVFQV/f3+0aNECly9fLvc4ERERVS2WthK5SQqogIAArF+/Hg0bNhQ6TkRERFWLGpLQw1yY5BZeaZsJP+04ERERVS3mNLokgnOgiIiISHaSGY0uiWABRURERLLjCBQRERGRjsxpfpMIFlBEREQkO0tbB8okn8KbN28eunXrhjt37mDkyJHo27fvU48TERFR1WJpyxiYZARqxowZmDFjhvBxIiIiqlo4ibwKSX+vg3Bbx8+OGzET0zPGL7oue9yJ+jn9b4PHFGVtJT7AK7rHnajnBPe3A3Tb406USmP4neRF9XBpJdxWlz3uRAXVbytb373qPS/cdvudUwbt+xUX8WVp1icZ/v1wTb3uQu2Gpx00eN8Jz3sKt212Tnx/P1Hf1O8h1G703QMG79uQOAeKiIiISEfmdHtOBAsoIiIikp1GsqwRKJNMIgdK3/cuMzMTo0ePRmBgIPr164dx48YhIyPDVCkRERGRmZAEH+bCZAVUafveKRQKvPXWW9izZw+2b98ODw8PLFy40FQpERERkZlQQyP0MBcmK6B8fHzg5uZW7JijoyM6duyofd62bVskJSWZKiUiIiIyE1zGoII0Gg2+//57+Pv7y50KERERmZilLaRpNgXU3LlzYWtri2HDhsmdChEREZmYMZbHSUxMREREBLKysuDo6IioqCg0adKkWJulS5di165dsLKyQrVq1fDee+/B19e33NhmUUBFRUXhxo0biImJgZUOa+cQERFR5aA2wqfwIiMjERoaipCQEGzduhWzZs3CmjVrirVp3bo13nzzTdSsWRMXL17EsGHDcOTIEdSoUeOpsWWvVj7//HOcO3cOS5cuhVKplDsdIiIikoEGktBDVHp6OuLj4xEUFAQACAoKQnx8fIlP+/v6+qJmzZoAgBYtWkCSJGRlZZUb32QjUPPmzcPevXuRlpaGkSNHwtHREYsXL8bXX3+NJk2aYMiQIQAAd3d3LF261FRpERERkRkQnSCenZ2N7OzsEsft7e1hb2+vfZ6cnAwXFxdYW1sDAKytreHs7Izk5GQ4OTmVGnvLli1o1KgRXF1dy83DZAVUWfveXbp0yVQpEBERkZkSXaJg9erViI6OLnF83LhxGD9+fIX7P3HiBL744gusXLlSqL1ZzIEiIiKiqk0SnAM1YsQIDBgwoMTxx0efAMDNzQ0pKSlQq9WwtraGWq1GampqiSWVACAuLg4ffPABli1bhqZNmwrloZBEMzZTro7imziKEn1J6lS3E455NYvrW5HpKHRoa9FvAGZI9LU3xusu53m3Uoj3bowtO0Q371ZrDL+SUA0b8fm794tUBu+/vq2DULu7+feEYxapblc0nQoL9Ogj1G7PzV+EYw4fPhyDBg3STiLfuHEj1q5dW6zNmTNnEB4eji+++AJt2rQRji37JHIiIiIiSfA/XcyePRvr1q1DYGAg1q1bh48++ggAMHr0aJw9exYA8NFHH+H+/fuYNWsWQkJCEBISIjS9iCNQpeAIFFk6jkDJhyNQ5eMIlGFVlhGoAPcXhdr9dmuvkTMRY5I5UFFRUdizZw9u376N7du3o3nz5gCAsLAw3Lp1C1ZWVrC1tcXMmTPh6Wn4goiIiIjMG1ciL0VAQABef/11DB06tNjxqKgo2Nk9HMXZt28fpk2bhs2bN5siJSIiIjIjxliJ3JhMUkD5+PiUevxR8QQAubm5UOgwBExERESVhzFu7RqT7MsYTJ8+HUePHoUkSfj222/lToeIiIhkoOYIlG7mz58P4OHqn5988gm++eYbmTMiIiIiU7O0OVBms4xB//79ERsbi8zMTLlTISIiIhOTJEnoYS5kK6Dy8vKQnJysfb5//344ODjA0dFRrpSIiIhIJobeTNjYTHILr7SNhFevXo0JEyagoKAAVlZWcHBwQExMDCeSExERVUEayfBrdBkTF9IsBRfSJEvHhTTlw4U0y8eFNA2rsiyk2c6tq1C7U8lHjJyJGNknkRMRERFZ2ngOCyg9JGbfkTsF2eyt00Wo3YuZR4VjxjdrJdSuZcJZ4ZhDG3QSarc+6bhwTGP4yrmHULt3Ug8Itasn+C9SAHimVsmdyUtz7O5F4ZitnJoItTubcV04pihdRmHGNfAVarck6bBwTFtlDaF2neo0F475W8oZoXa1lTWFY77gJNb/3jt/C7VTWlcT7rtXveeF2m2/c0o4pkZwZOlFV/HNYkV/dl1GlTrXf06o3R86XG+iI0s+9Z4VjikHc5rfJIIFFBEREclObWFzoFhAERERkewsbSsXky1jEBUVBX9/f7Ro0QKXL18u8fXo6Ogyv0ZERESVm0aShB7mwmQFVEBAANavX4+GDRuW+Nr58+dx+vTpUr9GRERElZ8k+J+5MFkB5ePjAze3kpNVVSoV5syZg9mzZ5sqFSIiIjIzakkj9DAXss+B+uKLLxAcHAx3d3e5UyEiIiKZmNPtORGy7oUXFxeHc+fOITQ0VM40iIiISGa8haeDkydPIiEhAQEBAfD398edO3cwatQoHDliHquMEhERkWlIkkboYS5kvYX39ttv4+2339Y+9/f3R0xMDJo3F19gjoiIiCyfOc1vEmGyEah58+ahW7duuHPnDkaOHIm+ffuaqmsiIiIycxpIQg9zYbIRqBkzZmDGjBlPbbN//34TZUNERETmhHvhEREREenI0j6FxwLKzJxv2lqo3b+viW0uCgA5q94Uamc3cqVwzB9qCv6iZwqHxIRcXbaBFaO2kAvymo1h7/1bW4nfnf/n/l2D9g0A1gr5Pp9ipcPPfg9FBu/fsXotoXZqGH6+R72a4ptIqyS1Qft2qlFbuG0dq+oG7RsAqtsohdq9JDkJx9wr2E6X622S2kWo3R8Q30zYSiH23vm5VFc4phw0FjYHigUUERERyc6c5jeJYAFFREREsuMcqDJERUVhz549uH37NrZv365dqsDf3x9KpRLVqz8c0p08eTJ8fX1NlRYRERGZAc6BKkNAQABef/11DB06tMTXvvzyS679REREVIVZ2jpQJiugfHx8TNUVERERWRjewquAyZMnQ5IkeHt7Y9KkSbC3t5c7JSIiIjIhS7uFJ+teeACwfv16bNu2DZs2bYIkSZgzZ47cKREREZGJcTNhHbm5uQEAlEolQkNDcerUKZkzIiIiIlNTazRCD3Mh6y28/Px8qNVq2NnZQZIk7Nq1C56ennKmRERERDIwp9ElESYroObNm4e9e/ciLS0NI0eOhKOjI2JiYjB+/Hio1WpoNBo0a9YMkZGRpkqJiIiIzAQnkZehrM2Et2zZYqoUiIiIyExZWgGlkCwtYyIiIiKZyT6JnIiIiMjSsIAiIiIi0hELKCIiIiIdsYAiIiIi0hELKCIiIiIdsYAiIiIi0hELKCIiIiIdsYAiIiIi0hELKCIiIiIdsYAiIiIi0lGlKqASExPx6quvIjAwEK+++iquX7+uV7zMzEyMHj0agYGB6NevH8aNG4eMjAzDJAsgOjoaLVq0wOXLl/WO9eDBA0RGRuLFF19Ev379MHPmTL1jHjhwAP3790dISAiCg4Oxd+9enWNERUXB39+/xM+pz7kqLaa+56qsPB+pyLkqK2ZFz1VZ8fQ5T0973U6fPo3g4GAEBgbizTffRHp6ul4xExMTMXz4cPTu3RtBQUGYOnUq7t+/r3eej0ydOhUtWrRAXl6e3jGzsrIwadIkBAYGom/fvoiOjtY75saNG9GvXz+EhIRg4MCB+PPPP4ViAkBYWBiCg4PRv39/hIaG4sKFCwD0u45Ki6nvdVRWno/oeh2VFU+f97uyYhri/e7Jn6+i11BZMfW5hp6W5yO6XkNVnlSJDB8+XNqyZYskSZK0ZcsWafjw4XrFy8zMlI4fP659/p///EeaOnWqXjEfOXfunDRq1CipR48e0qVLl/SON3fuXGn+/PmSRqORJEmS7t69q1c8jUYj+fj4aHO7cOGC1LZtW0mtVusU5+TJk1JSUlKJn1Ofc1VaTH3PVVl5SlLFz1VZMSt6rkqLp+95Kut1U6vVUs+ePaWTJ09KkiRJS5culSIiIvSKefPmTen8+fOSJEmSWq2WJkyYIEVHR+sV85HffvtNmjp1qtS8eXMpNzdX75hjxoyRVq1apf1aamqqXjEzMjIkLy8v7bnet2+f1KdPH6GYkiRJ2dnZ2v//9ddfpf79+0uSpN91VFpMfa+jsvKUpIpdR2XF0+f9rrSYhni/e/Ln0+caKiumPtdQWTEfqcg1VNVVmhGo9PR0xMfHIygoCAAQFBSE+Ph4vUaMHB0d0bFjR+3ztm3bIikpSe9cVSoV5syZg9mzZ+sdCwDy8vKwZcsWTJgwAQqFAgBQr149veNaWVkhJycHAJCTkwNnZ2dYWen2K+Pj4wM3N7dix/Q9V6XF1PdclRYT0O9clRZTn3NVVo76nKeyXrdz586hevXq8PHxAQAMGTIEu3fv1iumu7s7WrZsqc25devWwufoaec3MzMT0dHRmDp1qlCs8mJev34dly9fxogRI7Rfq1+/vl4xJUmCJEnaf9nn5OTA1dVVOFc7Ozvt/+fm5kKhUOh9HZUWU9/rqLSYQMWvo9Li6ft+V1aO+lxHpf18+lxDZcXU5xoqKyZQ8WuoqrOROwFDSU5OhouLC6ytrQEA1tbWcHZ2RnJyMpycnPSOr9Fo8P3338Pf31/vWF988QWCg4Ph7u6udywAuHnzJhwdHREdHY3Y2FjUqlULEyZM0F64FaFQKLB48WKEhYXB1tYWeXl5WL58uUHy5bky3Lky5Hl6/HVLTk5GgwYNtF9zcnKCRqNBVlYWHB0dKxTzcffv38emTZswadIkvfIEgDlz5iA8PLzYH0Z9Yl69ehUuLi6YPn06Lly4gHr16uHDDz/Es88+W+GYTk5OmDNnDgYMGAB7e3toNBqsXbtWp3jTp0/H0aNHIUkSvv32W4NcR0/GLCt/ffIE9LuOnoxniGvoyZj6Xkel/Xz6XkPlvWYVuYbKimmIa6gqqjQjUMY2d+5c2NraYtiwYXrFiYuLw7lz5xAaGmqgzAC1Wo2bN2+iZcuW+PnnnzF58mSMHz8eubm5FY5ZVFSEr7/+GsuWLcOBAwfw1VdfYeLEiRZxb7wqnStDnidDvW7lxSwqKsJ7772HTp06ISAgQK+Yu3btQrVq1dC9e3eD5anRaPD3339j4MCB2Lx5MwYPHox33nlHr5i5ublYv349Nm7ciIMHDyIiIgLjxo2DJEnC8ebPn4+DBw/ivffewyeffKJzPrrGrOjvw5Mx9b2OnoxniGvoyZj6XEfGeJ8oL2ZFrqGyYhrqGqqKKk0B5ebmhpSUFKjVagAP/1ClpqaWestDV1FRUbhx4wYWL16s8y2sJ508eRIJCQkICAiAv78/7ty5g1GjRuHIkSMVjunm5gYbGxvtUH6bNm1Qp04dJCYmVjjmhQsXkJqaCm9vbwCAt7c3atasiYSEhArHfDxfnivDnCtDnacnXzc3N7ditwYyMjJgZWWl0+hTaedCrVZj8uTJcHBwwIwZM3TKsbSYJ06cwPHjx+Hv768dKQkKCsLVq1crHNPNzQ1ubm7aEY0XX3wRd+/e1flDCY/HPHLkCOzs7NC0aVMAwEsvvYR//vkHmZmZOvz0D/Xv3x+xsbFwdXU12HX0KOajfAxxHT2Kefz4cYNcR4/iubi4GOwaehTz/PnzFb6OynqfuHHjRoWvoae991T0GiorZnR0tN7XUJUl3/Qrwxs2bFixCZXDhg3TO+Znn30mDRs2TMrPz9c7VmkMNYl85MiR0uHDhyVJkqRr165JHTp0kO7du1fheKmpqZKXl5eUkJAgSZIkXb16VWrfvr2UmZlZoXhP/pyGOFdPxjTEuXra+ajouXry+/Q9V4/HM8R5Ku11U6vVUkBAQIUnwJYVc/LkydKkSZOkoqIi4VhPi/kkXSfAlhZTo9FIQUFB0uXLlyVJkqQTJ05Ivr6+2gnLFYl59uxZqXPnzlJaWpokSZJ07NgxqXPnzkIxc3NzpaSkJO3z3377Teratauk0WgqfB09LWZFr6OnxXyc6HX0tHgVvYbKipmSkmKw97vHJ5Hrcw2VFVOfa6i0mE/iJHJxCknSYQzZzCUkJCAiIgLZ2dmwt7dHVFSU9l98FXHlyhUEBQWhSZMmqFGjBoCHk/iWLl1qqJTh7++PmJgYNG/eXK84N2/exLRp05CVlQUbGxtMnDgRfn5+esXctm0bvvnmG+0ky/DwcPTs2VOnGPPmzcPevXuRlpaGOnXqwNHRETt37tTrXJUWc/HixXqdq7LyfJyu56qsmBU9V2XF0+c8Pe13/NSpU4iMjMSDBw/QsGFDfPrpp0KTdcuKOXjwYIwZMwbNmzfXjmq0a9cOkZGReuX5uBYtWuDUqVOoVauWXjHPnj2Ljz76CCqVCjVr1sT06dPRunVrvWKuWrUKP/74I6pVqwalUomIiAiheTtpaWkICwtDQUEBrKys4ODggClTpuDf//53ha+jsmIqlcoKX0dPy/NxotfR0+JV9Bp6WkxDvN89+fNV9BoqK2ZSUlKFr6Gn5fk4Xa6hqq5SFVBEREREplBp5kARERERmQoLKCIiIiIdsYAiIiIi0hELKCIiIiIdsYAiIiIi0hELKCICACQlJcHLy0u7MCMREZWNBRRRFeXv748//vhD+7xBgwaIi4vT7q0mh59//hmvvfaabP0TEYliAUVERESkIxZQRFXQBx98gKSkJIwdOxZeXl745ptvcOvWLbRo0QJFRUUAgOHDh2PRokUYMmQIvLy8MHbsWGRmZuL9999Hu3bt8PLLL+PWrVvamAkJCRg5ciQ6dOiAwMBA7Nq1q8z+f/75ZwQEBMDLywv+/v7Ytm0bEhISEBkZidOnT8PLy0u7SrdKpUJUVBS6d++Ozp07Y9asWbh//z4AIDY2Ft26dUNMTAw6duyojUVEZHTy7iRDRHLp0aOHdPToUe3zmzdvSs2bN5cKCwslSXq4X2HPnj2lGzduSNnZ2VKfPn2kF198UTp69KhUWFgoffDBB9q9vfLy8qRu3bpJGzdulAoLC6Xz589LHTp0kK5cuVKi37y8vGL7jqWkpGj3ndu0aZM0ZMiQYu3nz58vjRkzRsrMzJRycnKkMWPGSAsXLpQkSZKOHz8ueXp6SgsWLJAePHggxcbGSm3atNHGJiIyFo5AEVGZBg4ciEaNGsHOzg7dunWDh4cHOnfuDBsbG/Tu3Rvx8fEAgIMHD6Jhw4Z4+eWXYWNjg5YtWyIwMBC7d+8uNa6VlRWuXLmC+/fvw9nZGc8++2yp7SRJwo8//ohp06bB0dERtWvXxpgxY0rsUzhhwgQolUp06NABfn5++OWXXwz7QhARPcFG7gSIyHw9vvFp9erViz2vUaMG8vPzAQC3b9/GmTNnim2Oq1arERwcXCKmra0tFi1ahJUrV2L69Olo164dpkyZgmbNmpVom5GRgYKCAgwcOFB7TJIkaDQa7XN7e3vY2tpqnzdo0ACpqakV/ImJiMSwgCIivbm5uaF9+/ZYtWqVUHtfX1/4+vri/v37WLx4MWbOnIn//ve/UCgUxdrVqVMHNWrUwM6dO+Hi4lJqrOzsbOTn52uLqOTk5DJHtIiIDIW38IiqqHr16uHmzZsGidW9e3dcv34dW7ZsQWFhIQoLC3HmzBkkJCSUaJuWloZ9+/YhPz8fSqUStra2sLJ6+FZUt25dpKSkQKVSAXh4q2/w4MFYsGAB0tPTAQApKSk4fPhwsZhLliyBSqXCn3/+iYMHD6J3794G+bmIiMrCAoqoinr77bfx1VdfwcfHBytWrNArVu3atbFixQrs2rULvr6+6Nq1KxYuXKgthB6n0Wjw3XffwdfXFx06dMDJkycxe/ZsAECnTp3wzDPPoGvXrujYsSOAh58YbNy4MV555RW0a9cOb7zxBhITE7Xx6tWrB3t7e/j6+mLy5MmYPXt2qbcDiYgMSSFJkiR3EkREFREbG4sPPvgAhw4dkjsVIqpiOAJFREREpCMWUEREREQ64i08IiIiIh1xBIqIiIhIRyygiIiIiHTEAoqIiIhIRyygiIiIiHTEAoqIiIhIR/8HKX6/JYSF1s4AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -366,7 +376,7 @@ } ], "source": [ - "a = debug_model([[1,0,1,0], [0,1,0,1], [0,0,1,1]], 30)" + "a = debug_model([[1]*8, [0]*8], 20)" ] }, { diff --git a/train.py b/train.py index 29b339a..aadbfd0 100644 --- a/train.py +++ b/train.py @@ -69,11 +69,11 @@ "--batch_size", default=16, type=int, help="Batch size for training." ) parser.add_argument( - "--num_bits", default=4, type=int, help="Dimensionality of each vector to copy" + "--num_bits", default=8, type=int, help="Dimensionality of each vector to copy" ) parser.add_argument( "--min_length", - default=2, + default=1, type=int, help="Lower limit on number of vectors in the observation pattern to copy", ) @@ -92,7 +92,7 @@ # Training options. parser.add_argument( - "--epochs", default=10000, type=int, help="Number of epochs to train for." + "--epochs", default=100000, type=int, help="Number of epochs to train for." ) parser.add_argument( "--log_dir", default="./logs/repeat_copy", type=str, help="Logging directory." From 84aec581944d85e8aaab3964cb63192c9f8db1a2 Mon Sep 17 00:00:00 2001 From: kwliu Date: Mon, 21 Jun 2021 20:20:25 -0700 Subject: [PATCH 17/20] add comments --- dnc/access.py | 2 ++ dnc/dnc.py | 6 ++++-- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/dnc/access.py b/dnc/access.py index 4c3f9c9..87fcd89 100644 --- a/dnc/access.py +++ b/dnc/access.py @@ -120,9 +120,11 @@ def __init__( self._linear_layers = {} + # keras.layers.RNN abstract method def call(self, inputs, prev_state): return self.__call__(inputs, prev_state) + # sonnet.RNNCore abstract method def __call__(self, inputs, prev_state): """Connects the MemoryAccess module into the graph. diff --git a/dnc/dnc.py b/dnc/dnc.py index 213d3d6..9bac3f2 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -84,9 +84,11 @@ def _clip_if_enabled(self, x): else: return x + # keras.layers.RNN abstract method def call(self, inputs, prev_state): return self.__call__(inputs, prev_state) + # sonnet.RNNCore abstract method def __call__(self, inputs, prev_state): """Connects the DNC core into the graph. @@ -134,13 +136,13 @@ def __call__(self, inputs, prev_state): ], ) - # keras uses get_initial_state + # keras.layers.RNN uses get_initial_state def get_initial_state(self, batch_size=None, inputs=None, dtype=None): return util.initial_state_from_state_size( self.state_size, batch_size, self._dtype ) - # snt.RNNCore uses initial_state + # sonnet.RNNCore uses initial_state def initial_state(self, batch_size=None): return self.get_initial_state(batch_size=batch_size) From fc0948af7fdc817967fb4e2d5bb4d0db1758955d Mon Sep 17 00:00:00 2001 From: kwliu Date: Wed, 23 Jun 2021 08:08:52 -0700 Subject: [PATCH 18/20] fix comments --- Makefile | 6 +++--- README.md | 38 ++++++++++++++++++++++++++++++++++---- dnc/dnc.py | 2 +- dnc/repeat_copy.py | 2 +- dnc/util.py | 20 +++++++++++++------- interactive.ipynb | 2 +- train.py | 28 +++++++++++++++++++--------- 7 files changed, 72 insertions(+), 26 deletions(-) diff --git a/Makefile b/Makefile index d79820b..35deb69 100644 --- a/Makefile +++ b/Makefile @@ -1,9 +1,9 @@ all: install install: venv - : # Activate venv and install smthing inside + : # Activate venv and install requirements mkdir tmp - . venv/bin/activate && TMPDIR=tmp pip install -r requirements.txt + source venv/bin/activate && TMPDIR=tmp pip install -r requirements.txt rm -r tmp/ pre-commit install @@ -13,7 +13,7 @@ venv: test -d venv || python -m venv venv test: venv - python -m pytest + source venv/bin/activate && python -m pytest clean: rm -rf venv/ diff --git a/README.md b/README.md index f604c3c..d3e0a55 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,23 @@ architecture. ![DNC architecture](images/dnc_model.png) +## Installation +```shell +make install +``` + +The above command will create a virtual environment and install the dependencies and pre-commit hooks. + +Run `source venv/bin/activate` in the root directory of this repository to activate the installed virtual env. + +## Testing +```shell +make test +``` + +Run unit tests in `tests/` using pytest. + + ## Train The `DNC` requires an installation of [TensorFlow](https://www.tensorflow.org/) and [Sonnet](https://github.com/deepmind/sonnet). An example training script is @@ -59,13 +76,26 @@ $ ipython train.py -- --memory_size=64 --num_bits=8 --max_length=3 Periodically saving, or 'checkpointing', the model is disabled by default. To enable, use the `checkpoint_interval` flag. E.g. `--checkpoint_interval=10000` will ensure a checkpoint is created every `10,000` steps. The model will be -checkpointed to `/tmp/tf/dnc/` by default. From there training can be resumed. -To specify an alternate checkpoint directory, use the `checkpoint_dir` flag. -Note: ensure that `/tmp/tf/dnc/` is deleted before training is resumed with +checkpointed to `./logs/repeat_copy/checkpoint` by default. From there training can be resumed. +To specify an alternate checkpoint directory, use the `log_dir` flag. +Note: ensure that existing checkpoints are deleted or moved before training is resumed with different model parameters, to avoid shape inconsistency errors. More generally, the `DNC` class found within `dnc.py` can be used as a standard TensorFlow rnn core and unrolled with TensorFlow rnn ops, such as -`tf.nn.dynamic_rnn` on any sequential task. +`keras.layers.RNN` on any sequential task. + +## Model Inspection +```shell +jupyter notebook interactive.ipynb +``` + +Jupyter notebook that loads a trained model from checkpoints. It provides helper functions for evaluating arbitrary input bit sequences and visualizing output and intermediate read/write states. + +```shell +tensorboard --logdir logs/repeat_copy/ +``` + +Tensorboard visualization of test/train loss and TensorFlow Graph. Test/Train loss is emitted based on `report_interval`. Disclaimer: This is not an official Google product diff --git a/dnc/dnc.py b/dnc/dnc.py index 9bac3f2..4a35675 100644 --- a/dnc/dnc.py +++ b/dnc/dnc.py @@ -102,7 +102,7 @@ def __call__(self, inputs, prev_state): Returns: A tuple `(output, next_state)` where `output` is a tensor and `next_state` - is a `DNCState` tuple containing the fields `access_output`, + is a nested list of tensors representing the dnc state: `access_output`, `access_state`, and `controller_state`. """ [prev_access_output, prev_access_state, prev_controller_state] = prev_state diff --git a/dnc/repeat_copy.py b/dnc/repeat_copy.py index fde4e1f..ea2c8ac 100644 --- a/dnc/repeat_copy.py +++ b/dnc/repeat_copy.py @@ -258,7 +258,7 @@ def __call__(self): # return self.datasettensor def _build(self): - """Implements build method which adds ops to graph.""" + """Implements build method which returns a new labelled data set every invocation.""" # short-hand for private fields. min_length, max_length = self._min_length, self._max_length diff --git a/dnc/util.py b/dnc/util.py index dbf7c2a..2b6d29d 100644 --- a/dnc/util.py +++ b/dnc/util.py @@ -61,14 +61,20 @@ def reduce_prod(x, axis, name=None): """Efficient reduce product over axis. Uses tf.cumprod and tf.gather_nd as a workaround to the poor performance of calculating tf.reduce_prod's gradient on CPU. + + As of TF2, reduce_prod seems not to be the a culprit of increased timings: + https://github.com/tensorflow/tensorflow/issues/40748 + + Workaround code for future reference: + + with tf.compat.v1.name_scope(name, 'util_reduce_prod', values=[x]): + cp = tf.math.cumprod(x, axis, reverse=True) + size = tf.shape(input=cp)[0] + idx1 = tf.range(tf.cast(size, tf.float32), dtype=tf.float32) + idx2 = tf.zeros([size], tf.float32) + indices = tf.stack([idx1, idx2], 1) + return tf.gather_nd(cp, tf.cast(indices, tf.int32)) """ - """with tf.compat.v1.name_scope(name, 'util_reduce_prod', values=[x]): - cp = tf.math.cumprod(x, axis, reverse=True) - size = tf.shape(input=cp)[0] - idx1 = tf.range(tf.cast(size, tf.float32), dtype=tf.float32) - idx2 = tf.zeros([size], tf.float32) - indices = tf.stack([idx1, idx2], 1) - return tf.gather_nd(cp, tf.cast(indices, tf.int32))""" return tf.math.reduce_prod(x, axis=axis, name=name) diff --git a/interactive.ipynb b/interactive.ipynb index 31ada88..211f565 100644 --- a/interactive.ipynb +++ b/interactive.ipynb @@ -21,7 +21,7 @@ "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "# ==============================================================================\n", - "\"\"\"Example script to train the DNC on a repeated copy task.\"\"\"\n", + "\"\"\"Example notebook for inspecting the DNC model trained on the repeat copy task.\"\"\"\n", "\n", "from __future__ import absolute_import\n", "from __future__ import division\n", diff --git a/train.py b/train.py index aadbfd0..e98a5c4 100644 --- a/train.py +++ b/train.py @@ -104,7 +104,13 @@ help="Epochs between reports (samples, valid loss).", ) parser.add_argument( - "--checkpoint_interval", default=2000, type=int, help="Checkpointing step interval." + "--checkpoint_interval", default=-1, type=int, help="Checkpointing step interval." +) +parser.add_argument( + "--test_set_size", + default=100, + type=int, + help="Number of datapoints in the test/validation data set.", ) FLAGS = parser.parse_args() @@ -188,7 +194,7 @@ def train(num_training_iterations, report_interval): # Generate test data with double maximum repeat length test_dataset = repeat_copy.RepeatCopy( FLAGS.num_bits, - 100, # FLAGS.batch_size, + FLAGS.test_set_size, # FLAGS.batch_size, FLAGS.min_length, FLAGS.max_length, FLAGS.max_repeats * 2, @@ -206,7 +212,9 @@ def train(num_training_iterations, report_interval): "num_writes": FLAGS.num_write_heads, } controller_config = { + # snt.LSTM takes hidden_size as parameter # "hidden_size": FLAGS.hidden_size, + # keras.layers.LSTM takes units as parameter "units": FLAGS.hidden_size, } clip_value = FLAGS.clip_value @@ -289,15 +297,17 @@ def train(num_training_iterations, report_interval): ) print(dataset_string) - # reset metrics every epoch - train_loss.reset_states() - test_loss.reset_states() + # reset metrics every report_interval + train_loss.reset_states() + test_loss.reset_states() - # save model at defined intervals - if (1 + epoch) % FLAGS.checkpoint_interval == 0: + # save model at defined intervals after training begins if enabled + if ( + FLAGS.checkpoint_interval > 0 + and epoch + and epoch % FLAGS.checkpoint_interval == 0 + ): manager.save() - # At the end, checkpoint as well - manager.save() def main(unused_argv): From 86e536e4a0adff9d5b4788a56ec83045e44417b1 Mon Sep 17 00:00:00 2001 From: solar464 Date: Wed, 23 Jun 2021 08:27:02 -0700 Subject: [PATCH 19/20] Delete report.txt --- report.txt | 238 ----------------------------------------------------- 1 file changed, 238 deletions(-) delete mode 100644 report.txt diff --git a/report.txt b/report.txt deleted file mode 100644 index 50ceedb..0000000 --- a/report.txt +++ /dev/null @@ -1,238 +0,0 @@ -TensorFlow 2.0 Upgrade Script ------------------------------ -Converted 10 files -Detected 21 issues that require attention --------------------------------------------------------------------------------- --------------------------------------------------------------------------------- -File: dnc/train.py --------------------------------------------------------------------------------- -dnc/train.py:27:8: ERROR: Using member tf.flags.FLAGS in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:30:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:31:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:32:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:33:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:34:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:35:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:39:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:40:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:41:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:45:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:46:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:47:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:50:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:53:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:55:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:59:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:61:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:63:0: ERROR: Using member tf.flags.DEFINE_string in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:65:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -dnc/train.py:115:16: WARNING: tf.get_variable requires manual check. tf.get_variable returns ResourceVariables by default in 2.0, which have well-defined semantics and are stricter about shapes. You can disable this behavior by passing use_resource=False, or by calling tf.compat.v1.disable_resource_variables(). -================================================================================ -Detailed log follows: - -================================================================================ -================================================================================ -Input tree: 'dnc/' -================================================================================ --------------------------------------------------------------------------------- -Processing file 'dnc/addressing_test.py' - outputting to 'dncV2/dnc/addressing_test.py' --------------------------------------------------------------------------------- - -39:18: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -41:14: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -65:10: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -66:11: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -67:16: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -91:10: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -92:11: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -93:16: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -127:11: INFO: Renamed 'tf.random_normal' to 'tf.random.normal' -128:16: INFO: Renamed 'tf.random_normal' to 'tf.random.normal' -138:16: INFO: Added keywords to args of function 'tf.gradients' -158:19: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -160:33: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -162:23: INFO: Renamed 'tf.placeholder' to 'tf.compat.v1.placeholder' -379:12: INFO: Renamed 'tf.test.compute_gradient_error' to 'tf.compat.v1.test.compute_gradient_error' -410:12: INFO: Renamed 'tf.test.compute_gradient_error' to 'tf.compat.v1.test.compute_gradient_error' --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/access.py' - outputting to 'dncV2/dnc/access.py' --------------------------------------------------------------------------------- - -52:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -52:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -59:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -59:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -239:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -239:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -281:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -281:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -299:62: INFO: Added keywords to args of function 'tf.reduce_sum' -301:10: INFO: Added keywords to args of function 'tf.reduce_sum' --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/dnc.py' - outputting to 'dncV2/dnc/dnc.py' --------------------------------------------------------------------------------- - -113:23: INFO: Renamed 'tf.contrib.framework.nest.map_structure' to 'tf.nest.map_structure' --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/train.py' - outputting to 'dncV2/dnc/train.py' --------------------------------------------------------------------------------- - -27:8: ERROR: Using member tf.flags.FLAGS in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -30:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -31:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -32:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -33:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -34:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -35:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -39:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -40:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -41:0: ERROR: Using member tf.flags.DEFINE_float in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -45:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -46:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -47:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -50:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -53:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -55:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -59:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -61:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -63:0: ERROR: Using member tf.flags.DEFINE_string in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -65:0: ERROR: Using member tf.flags.DEFINE_integer in deprecated module tf.flags. tf.flags and tf.app.flags have been removed, please use the argparse or absl modules if you need command line parsing. -85:23: INFO: Renamed 'tf.nn.dynamic_rnn' to 'tf.compat.v1.nn.dynamic_rnn' -111:24: INFO: Renamed 'tf.trainable_variables' to 'tf.compat.v1.trainable_variables' -113:6: INFO: Added keywords to args of function 'tf.gradients' -115:16: WARNING: tf.get_variable requires manual check. tf.get_variable returns ResourceVariables by default in 2.0, which have well-defined semantics and are stricter about shapes. You can disable this behavior by passing use_resource=False, or by calling tf.compat.v1.disable_resource_variables(). -115:16: INFO: Renamed 'tf.get_variable' to 'tf.compat.v1.get_variable' -119:18: INFO: tf.zeros_initializer requires manual check. Initializers no longer have the dtype argument in the constructor or partition_info argument in the __call__ method. -The calls have been converted to compat.v1 for safety (even though they may already have been correct). -119:18: INFO: Renamed 'tf.zeros_initializer' to 'tf.compat.v1.zeros_initializer' -121:19: INFO: Renamed 'tf.GraphKeys' to 'tf.compat.v1.GraphKeys' -121:50: INFO: Renamed 'tf.GraphKeys' to 'tf.compat.v1.GraphKeys' -123:14: INFO: Renamed 'tf.train.RMSPropOptimizer' to 'tf.compat.v1.train.RMSPropOptimizer' -128:10: INFO: Renamed 'tf.train.Saver' to 'tf.compat.v1.train.Saver' -132:8: INFO: Renamed 'tf.train.CheckpointSaverHook' to 'tf.estimator.CheckpointSaverHook' -141:7: INFO: Renamed 'tf.train.SingularMonitoredSession' to 'tf.compat.v1.train.SingularMonitoredSession' -155:8: INFO: Renamed 'tf.logging.info' to 'tf.compat.v1.logging.info' -162:2: INFO: Renamed 'tf.logging.set_verbosity' to 'tf.compat.v1.logging.set_verbosity' -167:2: INFO: Renamed 'tf.app.run' to 'tf.compat.v1.app.run' --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/repeat_copy.py' - outputting to 'dncV2/dnc/repeat_copy.py' --------------------------------------------------------------------------------- - -53:20: INFO: Added keywords to args of function 'tf.reduce_sum' -54:15: INFO: Added keywords to args of function 'tf.reduce_sum' -56:23: INFO: Added keywords to args of function 'tf.shape' -59:17: INFO: Added keywords to args of function 'tf.reduce_sum' -62:9: INFO: Added keywords to args of function 'tf.reduce_sum' -64:12: INFO: Renamed 'tf.log' to 'tf.math.log' -269:27: INFO: Renamed 'tf.random_uniform' to 'tf.random.uniform' -271:24: INFO: Renamed 'tf.random_uniform' to 'tf.random.uniform' -276:23: INFO: Added keywords to args of function 'tf.reduce_max' -295:10: INFO: Renamed 'tf.random_uniform' to 'tf.random.uniform' -375:11: INFO: Added keywords to args of function 'tf.transpose' --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/util_test.py' - outputting to 'dncV2/dnc/util_test.py' --------------------------------------------------------------------------------- - - --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/addressing.py' - outputting to 'dncV2/dnc/addressing.py' --------------------------------------------------------------------------------- - -35:18: INFO: Added keywords to args of function 'tf.reduce_sum' -173:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -173:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -181:13: INFO: Added keywords to args of function 'tf.transpose' -204:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -204:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -205:19: INFO: Added keywords to args of function 'tf.shape' -214:13: INFO: Renamed 'tf.matrix_set_diag' to 'tf.linalg.set_diag' -238:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -238:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -239:18: INFO: Added keywords to args of function 'tf.reduce_sum' -329:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -329:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -352:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -352:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -370:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -370:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -391:9: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -391:9: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -399:26: INFO: Renamed 'tf.cumprod' to 'tf.math.cumprod' --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/__init__.py' - outputting to 'dncV2/dnc/__init__.py' --------------------------------------------------------------------------------- - - --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/util.py' - outputting to 'dncV2/dnc/util.py' --------------------------------------------------------------------------------- - -27:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -27:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -30:19: INFO: Added keywords to args of function 'tf.shape' -31:20: INFO: Added keywords to args of function 'tf.shape' -37:11: INFO: Renamed 'tf.invert_permutation' to 'tf.math.invert_permutation' -44:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -44:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -46:11: INFO: Added keywords to args of function 'tf.shape' -66:7: INFO: `name` passed to `name_scope`. Because you may be re-entering an existing scope, it is not safe to convert automatically, the v2 name_scope does not support re-entering scopes by name. - -66:7: INFO: Renamed 'tf.name_scope' to 'tf.compat.v1.name_scope' -67:9: INFO: Renamed 'tf.cumprod' to 'tf.math.cumprod' -68:11: INFO: Added keywords to args of function 'tf.shape' --------------------------------------------------------------------------------- - --------------------------------------------------------------------------------- -Processing file 'dnc/access_test.py' - outputting to 'dncV2/dnc/access_test.py' --------------------------------------------------------------------------------- - -45:13: INFO: Renamed 'tf.random_normal' to 'tf.random.normal' -54:11: INFO: Added keywords to args of function 'tf.reduce_mean' -55:15: INFO: Renamed 'tf.train.GradientDescentOptimizer' to 'tf.compat.v1.train.GradientDescentOptimizer' -56:11: INFO: Renamed 'tf.global_variables_initializer' to 'tf.compat.v1.global_variables_initializer' -64:8: INFO: Renamed 'tf.random_normal' to 'tf.random.normal' -65:11: INFO: Renamed 'tf.global_variables_initializer' to 'tf.compat.v1.global_variables_initializer' -148:11: INFO: Added keywords to args of function 'tf.reduce_sum' -157:15: INFO: Renamed 'tf.global_variables_initializer' to 'tf.compat.v1.global_variables_initializer' -158:12: INFO: Renamed 'tf.test.compute_gradient_error' to 'tf.compat.v1.test.compute_gradient_error' --------------------------------------------------------------------------------- - From 6a5bc7c943d9a51c9c8d1d8b298f956ccab3cedc Mon Sep 17 00:00:00 2001 From: kwliu Date: Wed, 23 Jun 2021 08:48:25 -0700 Subject: [PATCH 20/20] remove interactive.ipynb output for smaller file size --- interactive.ipynb | 107 ++++------------------------------------------ 1 file changed, 9 insertions(+), 98 deletions(-) diff --git a/interactive.ipynb b/interactive.ipynb index 211f565..13dc6fb 100644 --- a/interactive.ipynb +++ b/interactive.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "474c9cfa", "metadata": {}, "outputs": [], @@ -68,18 +68,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "3112d2e0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restored from ./logs/repeat_copy/checkpoint/ckpt-161\n" - ] - } - ], + "outputs": [], "source": [ "def load_model():\n", " \"\"\"Load dnc core model from checkpoint directory\"\"\"\n", @@ -123,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "8daa62a5", "metadata": {}, "outputs": [], @@ -138,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "6500f979", "metadata": {}, "outputs": [], @@ -185,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "b29aad3a", "metadata": {}, "outputs": [], @@ -268,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "89115c0e", "metadata": {}, "outputs": [], @@ -290,91 +282,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "eeb76634", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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4cEHudIiIiEgOOq3Yw0rIXkAFBwdj3bp1aN68udypEBERkVw4jYFxAgIC5E6BiIiI5MZpDIiIiIiMZEW9SyJYQBEREZH8rGh8kwgWUERERCQ/TmNAREREZCQbK6Bkvwtv7ty56NmzJ27duoVRo0ahf//+cqdERERE1UySdEIPayF7D9SMGTMwY8YMudMgIiIiOXEMFBEREZGRbOwSnkKSJEnuJEzRxMVHqJ3o4qYAoIBCqF2BVmzxSkB8oUu9jRwOsXcIsMSrEd03ACiVYsddZ8SJaycY0xii+7dX2pm1HSC++G1de5VwTK3gfC46I+Z9ca3XQKhdTnGBcEzR993RQXyxaYVC7BOarykSjim6f2O+P0SPe31VXaF2xpxDovt2UokvRq7Ri/VeGLPgsnMdR6F2BYLf74D4udG0QUPhmBmFuULtSozo4dFqbgq3NZfCvWILXNd7WmzheUtjDxQRERHJz8Z6oFhAERERkfw4Bso4WVlZePfdd/H3339DpVKhZcuWmD17Ntzc3OROjYiIiKqLjfVAyT6NgUKhwOuvv47du3dj27Zt8Pb2xoIFC+ROi4iIiKqTjS0mLHsB5erqii5duhied+jQAcnJyTJmRERERNVOrxd7WAnZL+HdT6/X4+uvv0ZQUJDcqRAREVF14hioqpszZw4cHR0xfPhwuVMhIiKi6mRFl+dEWE0BpVarce3aNcTHxwvP3UNEREQ1hBVdnhNhFQXUwoULcerUKSxbtgwqlfhkfURERFRD6KxnnTsRshdQFy9exNKlS9GqVSsMGzYMAODl5YUlS5bInBkRERFVG/ZAGeexxx7D+fPn5U6DiIiI5MQCioiIiMhIFhhEnpSUhKioKGRnZ8PV1RVqtRqtWrUq1SYjIwNTp05FSkoKtFotunTpghkzZsDe/uElUq0poIxaM1lwtVpjYtrKIsGi5Hw1xuzbmAVO5Ywpvm+xMQKiC5Yao1hwAVjAMp+PO0X5Qu0sca6JLn4r9/7l3LclzotCI953S3zmNYK31Vti30ojlk3X2tjt/xWywBio6OhoREREIDw8HFu2bMGsWbOwevXqUm3i4+PRunVrLFu2DCUlJYiIiMCePXvw7LPPPjR2rSmgiIiIyIoJFuE5OTnIyckps93Z2RnOzs6G5xkZGThz5gxWrlwJAAgNDcWcOXOQmZlZark4hUKB/Px86PV6aDQalJSUwMPDo9I8WEARERGR/AQLqFWrViE2NrbM9vHjx2PChAmG5ykpKfDw8ICdnR0AwM7ODu7u7khJSSlVQI0bNw4TJkxA9+7dUVhYiJdeegn+/v6V5mEVBdS4ceNw48YNKJVKODo6YubMmfD19ZU7LSIiIqougmOgRo4ciUGDBpXZfn/vkzF27doFHx8frFq1Cvn5+Rg9ejR27dqFvn37PvT3rKKAUqvVcHJyAgDs3bsX06ZNw6ZNm2TOioiIiKqLpBUbA/XgpbqKeHp6IjU1FTqdDnZ2dtDpdEhLS4Onp2epdmvXrsX8+fOhVCrh5OSEoKAgJCQkVFpAWcWU3/eKJwDIy8uDQiE+eI6IiIhqAEkv9hDUqFEj+Pr6Yvv27QCA7du3w9fXt9TlO+Du3JO//PILAECj0eC3337DY489Vml8q+iBAoDp06fj8OHDkCQJX375pdzpEBERUXXSm/8u0piYGERFRSEuLg7Ozs5Qq9UAgNGjRyMyMhJt27bFtGnTEB0djQEDBkCn06FLly54/vnnK42tkIy6v9/yNm/ejB07duCLL74Qat/ExUeonTG3hIr2gBWUFAvHNKYtUUVEP8WWOKmN6Re2xP6VguelJW7lF9233Pu3xL7tBNcmtcQ0BvZKO+G2lphKoK692NJixkxzIapZA7fKG/1PSl6mUDtjPh1azU0jWptHwWfjhNo5ToizcCZirOIS3v0GDhyIhIQEZGVlyZ0KERERVRedTuxhJWQvoPLz85GSkmJ4vm/fPri4uMDV1VW+pIiIiKh66fViDysh+xiowsJCTJw4EYWFhVAqlXBxcUF8fDwHkhMREdUmFhgDZUmyF1CNGzfGt99+K3caREREJCcLrIVnSbIXUNbIysbVExnYyhqEliDnepJyr2Up5/7lXPvREgPDjWGJweGiRAeGA/Kfm+YiOg+UtWABRURERPLjJTwiIiIiI/ESHhEREZGRbKwHSvZpDO4XGxsLHx8fXLhwQe5UiIiIqDppdWIPK2E1PVCnT5/GiRMn0Lx5c7lTISIioupmY5fwrKIHSqPRYPbs2YiJiZE7FSIiIpKDXhJ7WAmr6IH65JNPEBYWBi8vL7lTISIiIhlIVjTLuAjZe6ASExNx6tQpREREyJ0KERERyUWrF3tYCdkLqGPHjuHy5csIDg5GUFAQbt26hddeew2HDh2SOzUiIiKqLpJe7GElFJKVTbsdFBSE+Ph4tGnTRqh9ExcfoXZKmH9tvUIjZqktKCk2+/6JiKhmMuYvliX+iGs1Ny0Q9eHyJocJtWuwcKuFMxFjFWOgiIiIqHaTrGiAuAirK6D27dsndwpERERU3axojicRVldAERERWQPRy2iW6DexU9oJt5V70WWzYQ8UERERkZFsrIASugtv7ty55W6fN2+eWZMhIiKi2kmSJKGHtRAqoL7//vtyt2/dap6R8EFBQejbty/Cw8MRHh6OgwcPmiUuERER2QgbmwfqoZfwNmzYAADQ6XSGf99z/fp1uLq6mi2RTz/9VHjqAiIiIqpZatRdeFu2bAEAlJSUGP4NAAqFAo0bN4ZarbZsdkRERFQ71KQCas2aNQCARYsW4a233rJoIlOmTIEkSfD398fkyZPh7Oxs0f0RERGRFbGeq3NChGciv3PnDvbv34/U1FR4eHigV69eZruEl5KSAk9PT2g0GsybNw/5+flYsGCB0O9yJnIiIrIEOacxsJd5GgM5ZiLPfrG3UDvXr/dbOBMxQoPIExMT0adPH6xfvx7nz5/H+vXr8cwzzyAxMdEsSXh6egIAVCoVIiIicPz4cbPEJSIiIhuhF3xYCaF5oObPn4/o6Gj079/fsG3nzp2YO3cuNm7caFICBQUF0Ol0cHJygiRJ2LlzJ3x9fU2KSURERLalRg0iv+fq1avo169fqW0hISGIjo42OYGMjAxMmDABOp0Oer0erVu3NktcIiIisiFW1LskQqiAatmyJXbs2IEBAwYYtu3atQve3t4mJ+Dt7Y3NmzebHIeIiIhsl6StgT1Q06ZNw9ixY7FmzRo0a9YMN2/exLVr1xAfH2/p/IiIiKgWkGpiD1THjh3x448/4sCBA0hLS0Pv3r0RGBho1ok0ybaY/55Gy9zJYok8FQqxqHoLLDkg511BxryXovs3JqZSKXTPC3R6838LW+K1W2L/lti3UsbPu53gMQcsc9xFF/S1xF1wKjvxpWprzmLCcidgHOEj5OLigvDwcEvmQkRERLWUpJU7A+NUWEBFREQI/Z/2unXrzJoQERER1T6WuISXlJSEqKgoZGdnw9XVFWq1Gq1atSrTbufOnfj8888hSRIUCgVWrlyJxo0bPzR2hQXU0KFDTU5cVHFxMebPn4/ffvsNderUQYcOHTBnzpxq2z8RERHJyxIFVHR0NCIiIhAeHo4tW7Zg1qxZWL16dak2f/31F2JjY7Fq1So0adIEubm5UKlUlcausIAaNGiQ6ZkL+uijj1CnTh3s3r0bCoUC6enp1bZvIiIikp9oAZWTk4OcnJwy252dnUstA5eRkYEzZ85g5cqVAIDQ0FDMmTMHmZmZcHNzM7T76quv8Oqrr6JJkyYAACcnJ6E8hMZAbd++Hb6+vmjdujWuXLmCWbNmQaFQICYmBq1btxbaUUXy8/OxefNm/Pzzz4ZLhpV1mxEREVHNIunEblhYtWoVYmNjy2wfP348JkyYYHiekpICDw8P2NndvRnAzs4O7u7uSElJKVVAXb58GV5eXnjppZdQUFCAPn364N///nelw5iECqjFixdj/fr1AIAPP/wQbdu2haOjI95///0yXWHGun79OlxdXREbG4uEhATUr18fEydOREBAgElxiYiIyHZIerECauTIkeVeJbu/98kYOp0O58+fx8qVK6HRaPD666+jWbNmGDhw4EN/T6iAyszMROPGjVFcXIw//vgDn376Kezt7dG1a9cqJftg4tevX8fjjz+O9957D3/++SfGjh2LH3/8EQ0aNDA5PhEREVk/0Ut4D16qq4inpydSU1Oh0+lgZ2cHnU6HtLQ0w/q79zRr1gx9+/aFSqWCSqVCcHAwTp48WWkBJTTJhpubG65du4ZffvkFbdu2hUqlQnFxMSQzzPvh6ekJe3t7hIaGAgDat2+Phg0bIikpyeTYREREZBskSSH0ENWoUSP4+vpi+/btAP5/ONL9l++Au2OjDh06BEmSUFJSgiNHjuCf//xnpfGFeqDGjRuHwYMHw87ODosWLQIA/Prrr0I7qIybmxu6dOmCw4cPo3v37khKSkJGRgZatmxpcmwiIiKyDXqt+ac+jomJQVRUFOLi4uDs7Ay1Wg0AGD16NCIjI9G2bVv0798fp06dwrPPPgulUonu3btjyJAhlcZWSILdSIWFhQCAevXqAbg7ul2v1xtGrZvi+vXrmDZtGrKzs2Fvb49JkyYhMDBQ6HebuPgItVNaYE7qQq1GuG1BSbHZ9y8nzkReOc5Ebt6YnIlcnn3X5pnI7WWcidzRoY5wW0v8fdFqbpo9ZmX+DggWatfi958snIkY4ZnI7xVO9zRq1MhsSXh7e2PNmjVmi0dERES2RXQQubUQX2yHiIiIyEJYQNUADRwchdr5uzwiHHPj4u5C7ZyGLRFqZ4mubVu5nCJ6SQEAHAQX5NRoS4Rjil7Cq2PEYqDFgvtv7OgiFk8n/npyiguE2nk5ic/Pdruw7CR35Sk24jK4UiH2mdMbsSKp6EWnf7h4Vt7of24XZgu1y9UUCsds5iTW459VlCccU/SyT/MGYvvOKMo1+76bCH7eAfHXLnquAYBTnXqVNwKQWyx+LEUv99kJft4BeS+zmpNecB4oa8ECioiIiGRnzB121kCoxH3zzTexd+9elJSIV+5EREREoiS92MNaCPVABQQEYMmSJZg+fTr69u2L8PBwdOzY0SwJ3LhxA2+++abheW5uLvLy8nD06FGzxCciIiLrp7exHiihAmrUqFEYNWoULl68iK1bt+Ltt9+Gg4MDwsLCEBYWhhYtWlQ5AS8vL2zZssXwfN68edDpzH9LKBEREVkvvU583Jc1MCrbxx57DG+//TY++ugj1K1bF0uWLMGgQYPwyiuv4Ny5cyYno9FosG3bNjz33HMmxyIiIiLbIUliD2shPIj8ypUr2Lp1K7Zv3w4HBweEh4cjPDwcbm5u+O9//4tx48Zh3759JiWzb98+eHh44F//+pdJcYiIiMi21MhpDAYPHoybN2/i2Wefxccff4z27duX+vmoUaPMMhHmxo0b2ftERERUC9W4MVCSJKF///4YMWIEVCpVhe1M7X1KTU3FsWPH8OGHH5oUh4iIiGyP3sZ6oCodA6VQKPDZZ5/B3t6yU0Zt2rQJgYGBaNiwoUX3Q0RERNZHLymEHtZCaBC5r68vkpKSLJrIpk2bePmOiIiolpIkhdDDWgh1K3Xu3BmjR4/GoEGD0LRp01JLWQwZMsQsiezevdsscYiIiMj2WNMddiIUklR5yiNGjCj/lxUKrF692uxJGaOJi49QO6URK73ZK+2E2mn0WuGYoms/GbNOExER1UzG9LNYou7Qam5aIOrDHWs+SKhdp5ubLJyJGKEeKHPcYUdERERUEWsa3yRCeGT4nTt3sH//fqSmpsLDwwO9e/eGi4v4StlEREREFbGxK3hig8gTExPRp08frF+/HufPn8f69evRp08fJCYmmiWJ/fv3Y+DAgQgPD0dYWBj27NljlrhERERkG2ztLjyhMVBDhw7FK6+8gv79+xu27dy5E8uXL8fGjRtNSkCSJHTu3Bnr1q1DmzZtcO7cObz44ov4448/oFRWXt9xDBQREdU0tXEM1MGmYjel9bi1wcKZiBHqgbp69Sr69etXaltISAj+/vtv8yShVCI3NxcAkJubC3d3d6HiiYiIiGoGCQqhh7UQGgPVsmVL7NixAwMGDDBs27VrF7y9vU1OQKFQYPHixRg3bhwcHR2Rn5+PZcuWmRyXiIiIbIfexgZBCRVQ06ZNw9ixY7FmzRo0a9YMN2/exLVr1xAfH29yAlqtFkuXLkVcXBz8/f3xxx9/YNKkSdixYwfq169vcnwiIiKyfnor6l0SIVRAdezYET/++CMOHDiAtLQ09O7dG4GBgXB1dTU5gbNnzyItLQ3+/v4AAH9/f9SrVw+XL19Gu3btTI5PRERE1k9XEwsoAHBxcUF4eLjZE2jatClu3bqFK1eu4JFHHsHly5eRkZGBFi1amH1fREREZJ2saXyTCKECKjk5GbGxsTh79iwKCgpK/czUJViaNGmCmJgYTJw40bBEzPz5883Su0VERES2QS93AkYSKqAmTpyIRx55BJGRkahbt67ZkwgLC0NYWJjZ4xIREZFtqJEF1JUrV/DNN99wagEiIiKyCJ2iBl7C6927N44ePYquXbtaOh+roNXrhNoZM+klJ8gkIiJRxnRY6PS21ndTvhp5F96MGTMwbNgwtGjRAo0aNSr1sw8++MAiiREREVHtYWPTQIkVUFOnToWdnR1at26NOnXqWDonIiIiqmVsrR9NqIA6cuQIDh48iAYNGlgkiQMHDuCTTz6BVquFi4sLPvjgA7PMck5ERES2wdbGQAldZPXx8UF2drZFErhz5w7ee+89LFy4ENu2bcPQoUMRExNjkX0RERGRddILPqyFUA9U165d8dprr2Hw4MFlxkANGSK2enJFrl27hsaNG+Mf//gHACAwMBDvvvsuMjMz4ebmZlJsIiIisg16C3RAJSUlISoqCtnZ2XB1dYVarUarVq3KbXvlyhUMGjQIEREReO+99yqNLVRA/fHHH3B3d8ehQ4dKbVcoFCYXUP/4xz+Qnp6OkydPol27dti2bRsAICUlhQUUERFRLWGJpVyio6MRERGB8PBwbNmyBbNmzcLq1avL7lunQ3R0NJ5++mnh2EIF1Jo1a8SzNZKTkxMWLVqEDz74AMXFxejZsyecnZ1hZ2dnsX0SERGRdRHtgcrJyUFOTk6Z7c7OznB2djY8z8jIwJkzZ7By5UoAQGhoKObMmVPuFa5ly5ahV69eKCgoKLPiSkWE18LLysrCzz//jPT0dLz++utITU2FJElo2rSpaIgKdevWDd26dQMApKenY/ny5VwLj4iIqBYRHd+0atUqxMbGltk+fvx4TJgwwfA8JSUFHh4ehg4ZOzs7uLu7l7nCde7cORw6dAirV69GXFyccL5CBdTRo0cxYcIEPPHEEzh+/Dhef/11XLt2DStWrEB8fLzwzipy+/ZtNGnSBHq9HgsXLsSwYcPg6OhoclwiIiKyDaLzQI0cORKDBg0qs/3+3idRJSUlmDlzJj744AOjr3wJFVDz58/H4sWL8eSTT6JTp04AgPbt2+PkyZNGJ1uexYsX4/jx4ygpKcFTTz2FKVOmmCUuERER2Qat4CW8By/VVcTT0xOpqanQ6XSws7ODTqdDWloaPD09DW1u376Nv//+G2+88QaAu5cHJUlCXl4e5syZ89D4QgXUzZs38eSTTwK4O3AcABwcHKDTiS15Upl58+aZJQ4RERHZJnNPUdCoUSP4+vpi+/btCA8Px/bt2+Hr61vq8l2zZs2QkJBgeP7ZZ5+hoKBA6C48oXmgWrdujYMHD5ba9uuvv6JNmzair4OIiIioQpJC7GGMmJgYrF27FiEhIVi7di3ef/99AMDo0aPx119/mZSvQpKkSi87njhxAmPGjEGvXr3www8/YODAgdi3bx/i4uLQrl07kxIwVRMXH6F2SgvcHlmo1Qi3LSgpNvv+iWoT0TPY1tbTIiqP0ohZufWV/xk3mlZz0+wxKxPnPVyo3bjray2ciRihHqgOHTpg69atePTRR/Hcc8/By8sLGzZskL14IiIioppBJ/iwFsLTGHh4eGD06NGWzIWIiIhqKUvMRG5JQgVUbm4uVq9ejbNnz5aZYGrFihWV/r5arcbu3btx8+ZNbNu2zTB2ypgp1omIiKjmsqZ17kQIFVATJ06ETqdDnz59UKdOHaN3EhwcjJdffhkvvfRSqe2iU6wTERFRzVYjC6gTJ07gyJEjUKlUVdpJQEBAmW3GTLFORERENZvOxi7hCQ0i9/f3x5UrV8y644dNsU5ERES1i17wYS2EeqD+85//YPTo0Wjfvj0aNWpU6mfjx4+3SGJERERUe9jaFCRCBdSiRYtw69YteHl5IS8vz7BdYcQ8FQ8SmWKdiIiIage9jZVQQgXUjh07sHv3bri7u5ttxyJTrBMREVHtYE1zPIkQKqC8vb1hby88ZVQZc+fOxZ49e5Ceno5Ro0bB1dUVO3bsQExMDKKiohAXFwdnZ2eo1eoq74OIiIhslzWNbxIhtJTL8uXL8eOPP2L48OFlxkDdW2RYLlzKhah24FIuVJvUxqVcZrV6qfJGAGZfXWfhTMQIdSutW3c32YULF5barlAo8NNPP5k/KyIiIqpVauQYqH379lk6D6Jajb0rlbPEa+f7Lg8533djrkXI+pmzQK+SvdLO7DHNqUaOgSIiIiKypBrZA0VERERkSbZVPgnORG4qtVqNoKAg+Pj44MKFC5VuJyIiotrF1mYir5YCKjg4GOvWrUPz5s2FthMREVHtooMk9LAW1XIJr7zFhB+2nYiIiGoXa+pdEsExUERERCQ7yYp6l0SwgCIiIiLZsQeKiIiIyEjWNL5JBAsoIiIikp2tzQNVLXfhzZ07Fz179sStW7cwatQo9O/f/6HbiYiIqHaxtWkMhBYTtmZcTJhqAi4pIg++7/LgUi7mJZqnMUu5FBX9XbVkTPB6qyFC7b68usHCmYjhJTwTvNykk3DbpcmHhNqJngj5J/8rvO8G7SKE2uUZEXNvYLxQu+7P3haO6fG12GSq2b/GCsfUJ/wo1G7D3CzhmF2d04XatVg9Wjhmg6cihdrlrnpdLKCTi/C+3YZ+ItQuM3aocEy0flyoWfEXa8Vj6sXOjnqzZgmHrN9+uFC7O9HBwjGVbdsJtXMZulg4ZvbcZ4TaKdp1FI7pGq4WapcZ1V2onbLLk8L7dhbcd9Y48ddj/2w/oXZOofOEY6b1e1SoXb0JLwrHdHp2jlC7xR69hWO+9rnY++Q0+GPhmHLgGCgiIiIiI1nT5TkRLKCIiIhIdnobG1FULYPIgfLXvcvKysLo0aMREhKCAQMGYPz48cjMzKyulIiIiMhKSIIPa1FtBVR5694pFAq8/vrr2L17N7Zt2wZvb28sWLCgulIiIiIiK6GDXuhhLaqtgAoICICnp2epba6urujSpYvheYcOHZCcnFxdKREREZGVsLVpDKxmDJRer8fXX3+NoKAguVMhIiKiamZrE2laTQE1Z84cODo6YvhwsduKiYiIqOawxGLCSUlJiIqKQnZ2NlxdXaFWq9GqVatSbZYsWYKdO3dCqVTCwcEBb731Fnr06FFpbKsooNRqNa5du4b4+HgoldV2VZGIiIishM4Cd+FFR0cjIiIC4eHh2LJlC2bNmoXVq1eXatOuXTu8+uqrqFevHs6dO4fhw4fj0KFDqFu37kNjy16tLFy4EKdOncKSJUugUqnkToeIiIhkoIck9BCVkZGBM2fOIDQ0FAAQGhqKM2fOlLnbv0ePHqhXrx4AwMfHB5IkITs7u9L41dYDNXfuXOzZswfp6ekYNWoUXF1dsXjxYixduhStWrXCsGHDAABeXl5YsmRJdaVFREREVkB0gHhOTg5ycnLKbHd2doazs7PheUpKCjw8PGBnd3cJGzs7O7i7uyMlJQVubm7lxt68eTNatGiBpk2bVppHtRVQM2bMwIwZM8psP3/+fHWlQERERFZKdIqCVatWITa27JJe48ePx4QJE6q8/6NHj+KTTz7BihUrhNpbxRgoIiIiqt0kwTFQI0eOxKBBg8psv7/3CQA8PT2RmpoKnU4HOzs76HQ6pKWllZlSCQASExPxzjvvIC4uDo888ohQHgpJNGMr1cTFR6id0gJrYDva1xFuez1XbAFaUXZGDLbX6cWqekcH8ddTpNUItVMoxN93S+Sp0WmF2ukl8dlFlArzDx3U6nVC7eqrHj6o8R7R1w0AJYJtRfcNAMXaEqF2xtx1U8fOQahdieB7CYi/duc6jsIxi3Vir130PQKAhvUaCLUrKCkWjim6/8aOzpU3ApCnKRLet+j3R3OnRsIxs4vzhdrlG5HnPxt6C7VLLRJfjDyrME+oXaD7v4RjXiq8JdTuZm6GcEyt5qZwW3MJ8RZbEHr39R+EY44YMQJDhgwxDCLfsGED1qxZU6rNyZMnERkZiU8++QTt27cXji37IHIiIiIiSfA/Y8TExGDt2rUICQnB2rVr8f777wMARo8ejb/++gsA8P7776OoqAizZs1CeHg4wsPDhYYX8RIeERERyU5nxJUAUa1bt8Z3331XZvsXX3xh+PfGjRurFLtaCii1Wo3du3fj5s2b2LZtG9q0aQMAGDduHG7cuAGlUglHR0fMnDkTvr6+1ZESERERWRHORF6O4OBgvPzyy3jppZdKbVer1XBycgIA7N27F9OmTcOmTZuqIyUiIiKyIpaYidySqqWACggIKHf7veIJAPLy8owacExEREQ1h97G7mmTfQzU9OnTcfjwYUiShC+//FLudIiIiEgGOvZAGWfevHkA7s7++eGHH5Ya2EVERES1g62NgbKaaQwGDhyIhIQEZGWJz6dBRERENYMkSUIPayFbAZWfn4+UlBTD83379sHFxQWurq5ypUREREQyMfdiwpZWLZfwyltIeNWqVZg4cSIKCwuhVCrh4uKC+Ph4DiQnIiKqhYxZEcIaVEsBVdFCwt9++2117J6IiIisnDX1LomQfRA5ERERkTWNbxLBxYRNUCi4ICZg3CKftZXoEbLpD2wFlIKXrm1tnhRrJ+dnzphvJDn3X9P2LXquAZY530T3b8yfZtGWde1VwjHzCpKE25pL+6bdhNr9eetXC2cihj1QREREJDtLrIVnSSygiIiISHa2tpRLtU1joFarERQUBB8fH1y4cKHMz2NjYyv8GREREdVsekkSeliLaiuggoODsW7dOjRv3rzMz06fPo0TJ06U+zMiIiKq+STB/6xFtRVQAQEB8PT0LLNdo9Fg9uzZiImJqa5UiIiIyMroJL3Qw1rIPgbqk08+QVhYGLy8vOROhYiIiGRiTZfnRMi6Fl5iYiJOnTqFiIgIOdMgIiIimfESnhGOHTuGy5cvIzg4GEFBQbh16xZee+01HDp0SM60iIiIqJpJkl7oYS1kvYT3xhtv4I033jA8DwoKQnx8PNq0aSNjVkRERFTdrGl8k4hq64GaO3cuevbsiVu3bmHUqFHo379/de2aiIiIrJwektDDWnApFxNwKRfz4lIulbO1QZbWzhaWM5F7/zVt31zKRYwcS7k0b/gvoXY3s05bOBMxst+FR0RERGRr/4PIAsrKdGz8qFC74+mXhGPWsXcQalesLRGOaYn/g7S3E/s4lui0RkQVY0xvgMICvUUNVPWE2uUUFwjHFOVQi99313oNhNplFeYJxxRVz6GOcFtL9GA71XEUameJz5xbPSehdhmFuWbft3t9V+G2qXlZQu2M+Z7zdnIXancj77ZwTJ1ebOzQU43+KRxTDnobGwPFAoqIiIhkZ03jm0SwgCIiIiLZ2dqQ7GoroNRqNXbv3o2bN29i27ZthqkKgoKCoFKpUKfO3e7sKVOmoEePHtWVFhEREVkBjoGqQHBwMF5++WW89NJLZX726aefcu4nIiKiWszW5oGqtgIqICCgunZFRERENoaX8KpgypQpkCQJ/v7+mDx5MpydneVOiYiIiKqRrV3Ck3UtPABYt24dtm7dio0bN0KSJMyePVvulIiIiKiacTFhI3l6egIAVCoVIiIicPz4cZkzIiIiouqm0+uFHtZC1kt4BQUF0Ol0cHJygiRJ2LlzJ3x9feVMiYiIiGRgTb1LIqqtgJo7dy727NmD9PR0jBo1Cq6uroiPj8eECROg0+mg1+vRunVrREdHV1dKREREZCU4iLwCM2bMwIwZM8ps37x5c3WlQERERFbK1goohWRrGRMRERHJTPZB5ERERES2hgUUERERkZFYQBEREREZiQUUERERkZFYQBEREREZiQUUERERkZFYQBEREREZiQUUERERkZFYQBEREREZiQUUERERkZFqVAGVlJSEF154ASEhIXjhhRdw9epVk+JlZWVh9OjRCAkJwYABAzB+/HhkZmaaJ1kAsbGx8PHxwYULF0yOVVxcjOjoaDzzzDMYMGAAZs6caXLM/fv3Y+DAgQgPD0dYWBj27NljdAy1Wo2goKAyr9OUY1VeTFOPVUV53lOVY1VRzKoeq4rimXKcHva+nThxAmFhYQgJCcGrr76KjIwMk2ImJSVhxIgR6Nu3L0JDQzF16lQUFRWZnOc9U6dOhY+PD/Lz802OmZ2djcmTJyMkJAT9+/dHbGysyTE3bNiAAQMGIDw8HIMHD8bvv/8uFBMAxo0bh7CwMAwcOBARERE4e/YsANPOo/JimnoeVZTnPcaeRxXFM+X7rqKY5vi+e/D1VfUcqiimKefQw/K8x9hzqNaTapARI0ZImzdvliRJkjZv3iyNGDHCpHhZWVnSkSNHDM//85//SFOnTjUp5j2nTp2SXnvtNal3797S+fPnTY43Z84cad68eZJer5ckSZJu375tUjy9Xi8FBAQYcjt79qzUoUMHSafTGRXn2LFjUnJycpnXacqxKi+mqceqojwlqerHqqKYVT1W5cUz9ThV9L7pdDrp6aeflo4dOyZJkiQtWbJEioqKMinm9evXpdOnT0uSJEk6nU6aOHGiFBsba1LMe3766Sdp6tSpUps2baS8vDyTY44ZM0ZauXKl4WdpaWkmxczMzJT8/PwMx3rv3r1Sv379hGJKkiTl5OQY/v3jjz9KAwcOlCTJtPOovJimnkcV5SlJVTuPKopnyvddeTHN8X334Osz5RyqKKYp51BFMe+pyjlU29WYHqiMjAycOXMGoaGhAIDQ0FCcOXPGpB4jV1dXdOnSxfC8Q4cOSE5ONjlXjUaD2bNnIyYmxuRYAJCfn4/Nmzdj4sSJUCgUAIDGjRubHFepVCI3NxcAkJubC3d3dyiVxn1kAgIC4OnpWWqbqceqvJimHqvyYgKmHavyYppyrCrK0ZTjVNH7durUKdSpUwcBAQEAgGHDhmHXrl0mxfTy8sLjjz9uyLldu3bCx+hhxzcrKwuxsbGYOnWqUKzKYl69ehUXLlzAyJEjDT9r0qSJSTElSYIkSYb/s8/NzUXTpk2Fc3VycjL8Oy8vDwqFwuTzqLyYpp5H5cUEqn4elRfP1O+7inI05Twq7/WZcg5VFNOUc6iimEDVz6Hazl7uBMwlJSUFHh4esLOzAwDY2dnB3d0dKSkpcHNzMzm+Xq/H119/jaCgIJNjffLJJwgLC4OXl5fJsQDg+vXrcHV1RWxsLBISElC/fn1MnDjRcOJWhUKhwOLFizFu3Dg4OjoiPz8fy5YtM0u+PFbmO1bmPE73v28pKSlo1qyZ4Wdubm7Q6/XIzs6Gq6trlWLer6ioCBs3bsTkyZNNyhMAZs+ejcjIyFJ/GE2JeenSJXh4eGD69Ok4e/YsGjdujHfffRePPfZYlWO6ublh9uzZGDRoEJydnaHX67FmzRqj4k2fPh2HDx+GJEn48ssvzXIePRizovxNyRMw7Tx6MJ45zqEHY5p6HpX3+kw9hyp7z6pyDlUU0xznUG1UY3qgLG3OnDlwdHTE8OHDTYqTmJiIU6dOISIiwkyZATqdDtevX8fjjz+O77//HlOmTMGECROQl5dX5ZharRZLly5FXFwc9u/fj88//xyTJk2yiWvjtelYmfM4met9qyymVqvFW2+9ha5duyI4ONikmDt37oSDgwN69epltjz1ej3+/PNPDB48GJs2bcLQoUPx73//26SYeXl5WLduHTZs2IADBw4gKioK48ePhyRJwvHmzZuHAwcO4K233sKHH35odD7Gxqzq5+HBmKaeRw/GM8c59GBMU84jS3xPVBazKudQRTHNdQ7VRjWmgPL09ERqaip0Oh2Au3+o0tLSyr3kYSy1Wo1r165h8eLFRl/CetCxY8dw+fJlBAcHIygoCLdu3cJrr72GQ4cOVTmmp6cn7O3tDV357du3R8OGDZGUlFTlmGfPnkVaWhr8/f0BAP7+/qhXrx4uX75c5Zj358tjZZ5jZa7j9OD75unpWerSQGZmJpRKpVG9T+UdC51OhylTpsDFxQUzZswwKsfyYh49ehRHjhxBUFCQoackNDQUly5dqnJMT09PeHp6Gno0nnnmGdy+fdvomxLuj3no0CE4OTnhkUceAQA8++yz+Pvvv5GVlWXEq79r4MCBSEhIQNOmTc12Ht2LeS8fc5xH92IeOXLELOfRvXgeHh5mO4fuxTx9+nSVz6OKvieuXbtW5XPoYd89VT2HKooZGxtr8jlUa8k3/Mr8hg8fXmpA5fDhw02O+fHHH0vDhw+XCgoKTI5VHnMNIh81apR08OBBSZIk6cqVK1Lnzp2lO3fuVDleWlqa5OfnJ12+fFmSJEm6dOmS1KlTJykrK6tK8R58neY4Vg/GNMexetjxqOqxevD3TD1W98czx3Eq733T6XRScHBwlQfAVhRzypQp0uTJkyWtVisc62ExH2TsANjyYur1eik0NFS6cOGCJEmSdPToUalHjx6GActVifnXX39J3bp1k9LT0yVJkqTffvtN6tatm1DMvLw8KTk52fD8p59+krp37y7p9foqn0cPi1nV8+hhMe8neh49LF5Vz6GKYqampprt++7+QeSmnEMVxTTlHCov5oM4iFycQpKM6EO2cpcvX0ZUVBRycnLg7OwMtVpt+D++qrh48SJCQ0PRqlUr1K1bF8DdQXxLliwxV8oICgpCfHw82rRpY1Kc69evY9q0acjOzoa9vT0mTZqEwMBAk2Ju3boVX3zxhWGQZWRkJJ5++mmjYsydOxd79uxBeno6GjZsCFdXV+zYscOkY1VezMWLF5t0rCrK837GHquKYlb1WFUUz5Tj9LDP+PHjxxEdHY3i4mI0b94cH330kdBg3YpiDh06FGPGjEGbNm0MvRodO3ZEdHS0SXnez8fHB8ePH0f9+vVNivnXX3/h/fffh0ajQb169TB9+nS0a9fOpJgrV67Et99+CwcHB6hUKkRFRQmN20lPT8e4ceNQWFgIpVIJFxcXvPfee/jXv/5V5fOoopgqlarK59HD8ryf6Hn0sHhVPYceFtMc33cPvr6qnkMVxUxOTq7yOfSwPO9nzDlU29WoAoqIiIioOtSYMVBERERE1YUFFBEREZGRWEARERERGYkFFBEREZGRWEARERERGYkFFBEBAJKTk+Hn52eYmJGIiCrGAoqolgoKCsKvv/5qeN6sWTMkJiYa1laTw/fff48XX3xRtv0TEYliAUVERERkJBZQRLXQO++8g+TkZIwdOxZ+fn744osvcOPGDfj4+ECr1QIARowYgUWLFmHYsGHw8/PD2LFjkZWVhbfffhsdO3bEc889hxs3bhhiXr58GaNGjULnzp0REhKCnTt3Vrj/77//HsHBwfDz80NQUBC2bt2Ky5cvIzo6GidOnICfn59hlm6NRgO1Wo1evXqhW7dumDVrFoqKigAACQkJ6NmzJ+Lj49GlSxdDLCIii5N3JRkikkvv3r2lw4cPG55fv35datOmjVRSUiJJ0t31Cp9++mnp2rVrUk5OjtSvXz/pmWeekQ4fPiyVlJRI77zzjmFtr/z8fKlnz57Shg0bpJKSEun06dNS586dpYsXL5bZb35+fql1x1JTUw3rzm3cuFEaNmxYqfbz5s2TxowZI2VlZUm5ubnSmDFjpAULFkiSJElHjhyRfH19pfnz50vFxcVSQkKC1L59e0NsIiJLYQ8UEVVo8ODBaNGiBZycnNCzZ094e3ujW7dusLe3R9++fXHmzBkAwIEDB9C8eXM899xzsLe3x+OPP46QkBDs2rWr3LhKpRIXL15EUVER3N3d8dhjj5XbTpIkfPvtt5g2bRpcXV3RoEEDjBkzpsw6hRMnToRKpULnzp0RGBiIH374wbxvBBHRA+zlToCIrNf9C5/WqVOn1PO6deuioKAAAHDz5k2cPHmy1OK4Op0OYWFhZWI6Ojpi0aJFWLFiBaZPn46OHTvivffeQ+vWrcu0zczMRGFhIQYPHmzYJkkS9Hq94bmzszMcHR0Nz5s1a4a0tLQqvmIiIjEsoIjIZJ6enujUqRNWrlwp1L5Hjx7o0aMHioqKsHjxYsycORP//e9/oVAoSrVr2LAh6tatix07dsDDw6PcWDk5OSgoKDAUUSkpKRX2aBERmQsv4RHVUo0bN8b169fNEqtXr164evUqNm/ejJKSEpSUlODkyZO4fPlymbbp6enYu3cvCgoKoFKp4OjoCKXy7ldRo0aNkJqaCo1GA+Dupb6hQ4di/vz5yMjIAACkpqbi4MGDpWJ+9tln0Gg0+P3333HgwAH07dvXLK+LiKgiLKCIaqk33ngDn3/+OQICArB8+XKTYjVo0ADLly/Hzp070aNHD3Tv3h0LFiwwFEL30+v1+Oqrr9CjRw907twZx44dQ0xMDACga9euePTRR9G9e3d06dIFwN07Blu2bInnn38eHTt2xCuvvIKkpCRDvMaNG8PZ2Rk9evTAlClTEBMTU+7lQCIic1JIkiTJnQQRUVUkJCTgnXfewS+//CJ3KkRUy7AHioiIiMhILKCIiIiIjMRLeERERERGYg8UERERkZFYQBEREREZiQUUERERkZFYQBEREREZiQUUERERkZH+D9Nv3qmlKMwYAAAAAElFTkSuQmCC\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "a = debug_model([[1]*8, [0]*8], 20)" ]