small refactoring for report

Author: blublinsky

data/pima-indians-diabetes.csv

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+# tutorial from http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
+# Model saving is at https://gist.github.com/ismaeIfm/eeb24fad2623dfb69ca81bb0f254543f
+# A good TF/Keras interoperability post https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html
+
+from keras.models import Sequential
+from keras.layers import Dense
+import numpy
+import tensorflow as tf
+from keras.models import model_from_json
+from tensorflow.python.tools import freeze_graph
+from tensorflow.python.tools import optimize_for_inference_lib
+from keras import backend as K
+
+
+# fix random seed for reproducibility
+numpy.random.seed(7)
+
+# create TF session and set it in Keras
+sess = tf.Session()
+K.set_session(sess)
+K.set_learning_phase(1)
+
+# load pima indians dataset
+dataset = numpy.loadtxt("../data/pima-indians-diabetes.csv", delimiter=",")
+# split into input (X) and output (Y) variables
+X = dataset[:,0:8]
+Y = dataset[:,8]
+
+# create model
+model = Sequential()
+model.add(Dense(12, input_dim=8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+
+# Compile model
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+# Fit the model
+model.fit(X, Y, epochs=150, batch_size=10)
+
+print('Done training!')
+
+modelInput = model.input
+modelOutput = model.output
+print "input", modelInput.name
+print "output", modelOutput.name
+
+print("Model's parameters")
+print(model.get_weights())
+
+
+K.set_learning_phase(0)  # all new operations will be in test mode from now on
+
+# serialize the model and get its weights, for quick re-building
+config = model.to_json()
+weights = model.get_weights()
+
+# re-build a model where the learning phase is now hard-coded to 0
+new_model = model_from_json(config)
+new_model.set_weights(weights)
+
+#create the saver
+# Saver op to save and restore all the variables
+saver = tf.train.Saver()
+
+# Save produced model
+model_path = "/Users/boris/Projects/TensorFlowPython/models/"
+model_name = "KerasModel"
+save_path = saver.save(sess, model_path+model_name+".ckpt")
+print "Saved model at ", save_path
+graph_path = tf.train.write_graph(sess.graph_def, model_path, model_name+".pb", as_text=True)
+print "Saved graph at :", graph_path
+
+# Now freeze the graph (put variables into graph)
+
+input_saver_def_path = ""
+input_binary = False
+output_node_names = "dense_3_1/Sigmoid"           # Model result node
+restore_op_name = "save/restore_all"
+filename_tensor_name = "save/Const:0"
+output_frozen_graph_name = model_path + 'frozen_' + model_name + '.pb'
+clear_devices = True
+
+
+freeze_graph.freeze_graph(graph_path, input_saver_def_path,
+                          input_binary, save_path, output_node_names,
+                          restore_op_name, filename_tensor_name,
+                          output_frozen_graph_name, clear_devices, "")
+print "Model is frozen"
+
+# optimizing graph
+
+input_graph_def = tf.GraphDef()
+with tf.gfile.Open(output_frozen_graph_name, "r") as f:
+    data = f.read()
+    input_graph_def.ParseFromString(data)
+
+
+output_graph_def = optimize_for_inference_lib.optimize_for_inference(
+    input_graph_def,
+    ["dense_1_input_1"],        # an array of the input node(s)
+    ["dense_3_1/Sigmoid"],      # an array of output nodes
+    tf.float32.as_datatype_enum)
+
+# Save the optimized graph
+
+tf.train.write_graph(output_graph_def, model_path, "optimized_" + model_name + ".pb", as_text=False)
+tf.train.write_graph(output_graph_def, model_path, "optimized_text_" + model_name + ".pb", as_text=True)
+
+
+# evaluate the model
+#scores = model.evaluate(X, Y)
+#print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
+
+# calculate predictions
+#predictions = model.predict(X)
+# round predictions
+# rounded = [round(x[0]) for x in predictions]
+# print(rounded)

keras/KerasTutorial.py

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