recurrent_convolutional_keras.py

# Michael A. Alcorn (malcorn@redhat.com)
# A (slightly modified) implementation of the Recurrent Convolutional Neural Network (RCNN) found in [1].
# [1] Siwei, L., Xu, L., Kang, L., and Zhao, J. 2015. Recurrent convolutional
#         neural networks for text classification. In AAAI, pp. 2267-2273.
#         http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9745

import gensim
import numpy as np
import string

from keras import backend
from keras.layers import Conv1D, Dense, Input, Lambda, LSTM
from keras.layers.merge import concatenate
from keras.layers.embeddings import Embedding
from keras.models import Model

word2vec = gensim.models.Word2Vec.load("word2vec.gensim")
# We add an additional row of zeros to the embeddings matrix to represent unseen words and the NULL token.
embeddings = np.zeros((word2vec.syn0.shape[0] + 1, word2vec.syn0.shape[1]), dtype = "float32")
embeddings[:word2vec.syn0.shape[0]] = word2vec.syn0

MAX_TOKENS = word2vec.syn0.shape[0]
embedding_dim = word2vec.syn0.shape[1]
hidden_dim_1 = 200
hidden_dim_2 = 100
NUM_CLASSES = 10

document = Input(shape = (None, ), dtype = "int32")
left_context = Input(shape = (None, ), dtype = "int32")
right_context = Input(shape = (None, ), dtype = "int32")

embedder = Embedding(MAX_TOKENS + 1, embedding_dim, weights = [embeddings], trainable = False)
doc_embedding = embedder(document)
l_embedding = embedder(left_context)
r_embedding = embedder(right_context)

# I use LSTM RNNs instead of vanilla RNNs as described in the paper.
forward = LSTM(hidden_dim_1, return_sequences = True)(l_embedding) # See equation (1).
backward = LSTM(hidden_dim_1, return_sequences = True, go_backwards = True)(r_embedding) # See equation (2).
# Keras returns the output sequences in reverse order.
backward = Lambda(lambda x: backend.reverse(x, axes = 1))(backward)
together = concatenate([forward, doc_embedding, backward], axis = 2) # See equation (3).

semantic = Conv1D(hidden_dim_2, kernel_size = 1, activation = "tanh")(together) # See equation (4).

# Keras provides its own max-pooling layers, but they cannot handle variable length input
# (as far as I can tell). As a result, I define my own max-pooling layer here.
pool_rnn = Lambda(lambda x: backend.max(x, axis = 1), output_shape = (hidden_dim_2, ))(semantic) # See equation (5).

output = Dense(NUM_CLASSES, input_dim = hidden_dim_2, activation = "softmax")(pool_rnn) # See equations (6) and (7).

model = Model(inputs = [document, left_context, right_context], outputs = output)
model.compile(optimizer = "adadelta", loss = "categorical_crossentropy", metrics = ["accuracy"])

text = "This is some example text."
text = text.strip().lower().translate(str.maketrans({key: " {0} ".format(key) for key in string.punctuation}))
tokens = text.split()
tokens = [word2vec.vocab[token].index if token in word2vec.vocab else MAX_TOKENS for token in tokens]

doc_as_array = np.array([tokens])
# We shift the document to the right to obtain the left-side contexts.
left_context_as_array = np.array([[MAX_TOKENS] + tokens[:-1]])
# We shift the document to the left to obtain the right-side contexts.
right_context_as_array = np.array([tokens[1:] + [MAX_TOKENS]])

target = np.array([NUM_CLASSES * [0]])
target[0][3] = 1

history = model.fit([doc_as_array, left_context_as_array, right_context_as_array], target, epochs = 1, verbose = 0)
loss = history.history["loss"][0]