def build_sentence_encoder(vocabulary_size):
"""
build the computational graph for the lstm sentence encoder. Return only the palceholders and tensors
that are called from other methods
"""
sentence_oh_placeholder = tf.placeholder(shape=[None, vocabulary_size], dtype=tf.float32,
name="sentence_placeholder")
word_embeddings_matrix = tf.get_variable("W_we", # shape=[vocabulary_size, WORD_EMB_SIZE]
initializer=tf.constant(embeddings_matrix, dtype=tf.float32))
sentence_embedded = tf.expand_dims(tf.matmul(sentence_oh_placeholder, word_embeddings_matrix), 0)
# placeholders for sentence and it's length
sent_lengths = tf.placeholder(dtype=tf.int32, name="sent_length_placeholder")
# Forward cell
lstm_fw_cell = BasicLSTMCell(LSTM_HIDDEN_SIZE, forget_bias=1.0)
# Backward cell
lstm_bw_cell = BasicLSTMCell(LSTM_HIDDEN_SIZE, forget_bias=1.0)
# stack cells together in RNN
outputs, _ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell, sentence_embedded, sent_lengths,
dtype=tf.float32)
# outputs: A tuple (output_fw, output_bw) containing the forward and the backward rnn output `Tensor`.
# both output_fw, output_bw will be a `Tensor` shaped: [batch_size, max_time, cell_fw.output_size]`
# outputs is a (output_forward,output_backwards) tuple. concat them together to receive h vector
lstm_outputs = tf.concat(outputs, 2)[0] # shape: [max_time, 2 * hidden_layer_size ]
final_fw = outputs[0][:, -1, :]
final_bw = outputs[1][:, 0, :]
e_m = tf.concat((final_fw, final_bw), axis=1)
sentence_words_bow = tf.placeholder(tf.float32, [None, len(words_vocabulary)], name="sentence_words_bow")
e_m_with_bow = tf.concat([e_m, sentence_words_bow], axis=1)
return sentence_oh_placeholder, sent_lengths, sentence_words_bow, lstm_outputs, e_m_with_bow
# TODO return sentence_oh_placeholder, sent_lengths, sentence_words_bow, lstm_outputs, e_m
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