import tensorflow as tf from tensorflow.contrib import rnn from utils import get_init_embedding class Model(object): def __init__(self, reversed_dict, article_max_len, summary_max_len, args, forward_only=False): self.vocabulary_size = len(reversed_dict) self.embedding_size = args.embedding_size self.num_hidden = args.num_hidden self.num_layers = args.num_layers self.learning_rate = args.learning_rate self.beam_width = args.beam_width if not forward_only: self.keep_prob = args.keep_prob else: self.keep_prob = 1.0 self.cell = tf.nn.rnn_cell.BasicLSTMCell with tf.variable_scope("decoder/projection"): self.projection_layer = tf.layers.Dense( self.vocabulary_size, use_bias=False) self.batch_size = tf.placeholder(tf.int32, (), name="batch_size") self.X = tf.placeholder(tf.int32, [None, article_max_len]) self.X_len = tf.placeholder(tf.int32, [None]) self.decoder_input = tf.placeholder(tf.int32, [None, summary_max_len]) self.decoder_len = tf.placeholder(tf.int32, [None]) self.decoder_target = tf.placeholder(tf.int32, [None, summary_max_len]) self.global_step = tf.Variable(0, trainable=False) with tf.name_scope("embedding"): if not forward_only and args.glove: init_embeddings = tf.constant(get_init_embedding( reversed_dict, self.embedding_size), dtype=tf.float32) else: init_embeddings = tf.random_uniform( [self.vocabulary_size, self.embedding_size], -1.0, 1.0) self.embeddings = tf.get_variable( "embeddings", initializer=init_embeddings) self.encoder_emb_inp = tf.transpose( tf.nn.embedding_lookup(self.embeddings, self.X), perm=[1, 0, 2]) self.decoder_emb_inp = tf.transpose(tf.nn.embedding_lookup( self.embeddings, self.decoder_input), perm=[1, 0, 2]) with tf.name_scope("encoder"): fw_cells = [self.cell(self.num_hidden) for _ in range(self.num_layers)] bw_cells = [self.cell(self.num_hidden) for _ in range(self.num_layers)] fw_cells = [rnn.DropoutWrapper(cell) for cell in fw_cells] bw_cells = [rnn.DropoutWrapper(cell) for cell in bw_cells] encoder_outputs, encoder_state_fw, encoder_state_bw = tf.contrib.rnn.stack_bidirectional_dynamic_rnn( fw_cells, bw_cells, self.encoder_emb_inp, sequence_length=self.X_len, time_major=True, dtype=tf.float32) self.encoder_output = tf.concat(encoder_outputs, 2) encoder_state_c = tf.concat( (encoder_state_fw[0].c, encoder_state_bw[0].c), 1) encoder_state_h = tf.concat( (encoder_state_fw[0].h, encoder_state_bw[0].h), 1) self.encoder_state = rnn.LSTMStateTuple( c=encoder_state_c, h=encoder_state_h) with tf.name_scope("decoder"), tf.variable_scope("decoder") as decoder_scope: decoder_cell = self.cell(self.num_hidden * 2) if not forward_only: attention_states = tf.transpose(self.encoder_output, [1, 0, 2]) attention_mechanism = tf.contrib.seq2seq.BahdanauAttention( self.num_hidden * 2, attention_states, memory_sequence_length=self.X_len, normalize=True) decoder_cell = tf.contrib.seq2seq.AttentionWrapper(decoder_cell, attention_mechanism, attention_layer_size=self.num_hidden * 2) initial_state = decoder_cell.zero_state( dtype=tf.float32, batch_size=self.batch_size) initial_state = initial_state.clone( cell_state=self.encoder_state) helper = tf.contrib.seq2seq.TrainingHelper( self.decoder_emb_inp, self.decoder_len, time_major=True) decoder = tf.contrib.seq2seq.BasicDecoder( decoder_cell, helper, initial_state) outputs, _, _ = tf.contrib.seq2seq.dynamic_decode( decoder, output_time_major=True, scope=decoder_scope) self.decoder_output = outputs.rnn_output self.logits = tf.transpose( self.projection_layer(self.decoder_output), perm=[1, 0, 2]) self.logits_reshape = tf.concat( [self.logits, tf.zeros([self.batch_size, summary_max_len - tf.shape(self.logits)[1], self.vocabulary_size])], axis=1) else: tiled_encoder_output = tf.contrib.seq2seq.tile_batch( tf.transpose(self.encoder_output, perm=[1, 0, 2]), multiplier=self.beam_width) tiled_encoder_final_state = tf.contrib.seq2seq.tile_batch( self.encoder_state, multiplier=self.beam_width) tiled_seq_len = tf.contrib.seq2seq.tile_batch( self.X_len, multiplier=self.beam_width) attention_mechanism = tf.contrib.seq2seq.BahdanauAttention( self.num_hidden * 2, tiled_encoder_output, memory_sequence_length=tiled_seq_len, normalize=True) decoder_cell = tf.contrib.seq2seq.AttentionWrapper(decoder_cell, attention_mechanism, attention_layer_size=self.num_hidden * 2) initial_state = decoder_cell.zero_state( dtype=tf.float32, batch_size=self.batch_size * self.beam_width) initial_state = initial_state.clone( cell_state=tiled_encoder_final_state) decoder = tf.contrib.seq2seq.BeamSearchDecoder( cell=decoder_cell, embedding=self.embeddings, start_tokens=tf.fill([self.batch_size], tf.constant(2)), end_token=tf.constant(3), initial_state=initial_state, beam_width=self.beam_width, output_layer=self.projection_layer ) outputs, _, _ = tf.contrib.seq2seq.dynamic_decode( decoder, output_time_major=True, maximum_iterations=summary_max_len, scope=decoder_scope) self.prediction = tf.transpose( outputs.predicted_ids, perm=[1, 2, 0]) with tf.name_scope("loss"): if not forward_only: crossent = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=self.logits_reshape, labels=self.decoder_target) weights = tf.sequence_mask( self.decoder_len, summary_max_len, dtype=tf.float32) self.loss = tf.reduce_sum( crossent * weights / tf.to_float(self.batch_size)) params = tf.trainable_variables() gradients = tf.gradients(self.loss, params) clipped_gradients, _ = tf.clip_by_global_norm(gradients, 5.0) optimizer = tf.train.AdamOptimizer(self.learning_rate) self.update = optimizer.apply_gradients( zip(clipped_gradients, params), global_step=self.global_step)