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model_classification.py
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model_classification.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
# Modifications Copyright 2017 Abigail See
#
# 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.
# ==============================================================================
"""This file contains code to build and run the tensorflow graph for the sequence-to-sequence model"""
import os
import time
import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
import data
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import math_ops
FLAGS = tf.app.flags.FLAGS
class Classification(object):
"""A class to represent a sequence-to-sequence model for text summarization. Supports both baseline mode, pointer-generator mode, and coverage"""
def __init__(self, hps, vocab):
self._hps = hps
self._vocab = vocab
def _add_placeholders(self):
"""Add placeholders to the graph. These are entry points for any input data."""
hps = self._hps
# encoder part
self._enc_batch = tf.placeholder(tf.int32, [hps.batch_size, hps.max_dec_steps], name='enc_batch')
self._enc_padding_mask = tf.placeholder(tf.float32, [hps.batch_size,hps.max_dec_steps], name='enc_padding_mask')
self._enc_lens = tf.placeholder(tf.int32, [hps.batch_size], name='enc_lens')
#self._enc_padding_mask = tf.placeholder(tf.float32, [hps.batch_size, None], name='enc_padding_mask')
#self._decay = tf.placeholder(tf.float32, name="decay_learning_rate")
self._target_batch = tf.placeholder(tf.int32,
[hps.batch_size],
name='target_batch')
def _make_feed_dict(self, batch):
feed_dict = {}
feed_dict[self._enc_batch] = batch.enc_batch
feed_dict[self._enc_lens] = batch.enc_lens
#feed_dict[self._enc_padding_mask] = batch.enc_padding_mask
feed_dict[self._enc_padding_mask] = batch.enc_padding_mask
feed_dict[self._target_batch] = batch.labels
return feed_dict
def _add_encoder(self, encoder_inputs, seq_len, hps):
"""Add a single-layer bidirectional LSTM encoder to the graph.
Args:
encoder_inputs: A tensor of shape [batch_size, <=max_sen_number <=max_enc_steps, emb_size].
seq_len: Lengths of encoder_inputs (before padding). A tensor of shape [batch_size*max_sen_num].
seq_num: [batch]
Returns:
encoder_outputs:
A tensor of shape [batch_size, <=max_enc_steps, 2*hidden_dim]. It's 2*hidden_dim because it's the concatenation of the forwards and backwards states.
fw_state, bw_state:
Each are LSTMStateTuples of shape ([batch_size,hidden_dim],[batch_size,hidden_dim])
"""
with tf.variable_scope('encoder'):
with tf.variable_scope('word'):
cell_fw = tf.contrib.rnn.LSTMCell(self._hps.hidden_dim, initializer=self.rand_unif_init,
state_is_tuple=True)
with tf.variable_scope('word-rnn'):
(encoder_state, (fw_st)) = tf.nn.dynamic_rnn(cell_fw, encoder_inputs,
dtype=tf.float32, sequence_length=seq_len,
swap_memory=True)
return fw_st, encoder_state
def attention(self,decoder_state,encoder_states, attention_vec_size,enc_padding_mask,hps):
"""Calculate the context vector and attention distribution from the decoder state.
Args:
decoder_state: state of the decoder
Returns:
context_vector: weighted sum of encoder_states
attn_dist: attention distribution
"""
with tf.variable_scope('attention'):
w_dec = tf.get_variable('w_dec', [attention_vec_size,hps.hidden_dim], dtype=tf.float32,
initializer=self.trunc_norm_init)
v_dec = tf.get_variable('v_dec', [attention_vec_size], dtype=tf.float32, initializer=self.trunc_norm_init)
# Pass the decoder state through a linear layer (this is W_s s_t + b_attn in the paper)
decoder_features = tf.nn.xw_plus_b(decoder_state, w_dec,v_dec) # shape (batch_size, attention_vec_size)
decoder_features = tf.expand_dims(tf.expand_dims(decoder_features, 1),
1) # reshape to (batch_size, 1, 1, attention_vec_size)
def masked_attention(e):
"""Take softmax of e then apply enc_padding_mask and re-normalize"""
attn_dist = nn_ops.softmax(e) # take softmax. shape (batch_size, attn_length)
attn_dist *= enc_padding_mask # apply mask
masked_sums = tf.reduce_sum(attn_dist, axis=1) # shape (batch_size)
return attn_dist / tf.reshape(masked_sums, [-1, 1]) # re-normalize
encoder_states = tf.expand_dims(encoder_states, axis=2) # now is shape (batch_size, attn_len, 1, attn_size)
W_h = tf.get_variable("W_h", [1, 1, hps.hidden_dim, attention_vec_size])
encoder_features = nn_ops.conv2d(encoder_states, W_h, [1, 1, 1, 1],
"SAME") # shape (batch_size,attn_length,1,attention_vec_size)
# Calculate v^T tanh(W_h h_i + W_s s_t + b_attn)
v = tf.get_variable("v_h", [attention_vec_size])
e = math_ops.reduce_sum(v * math_ops.tanh(encoder_features + decoder_features), [2, 3]) # calculate e
# Calculate attention distribution
attn_dist = masked_attention(e)
# Calculate the context vector from attn_dist and encoder_states
context_vector = math_ops.reduce_sum(array_ops.reshape(attn_dist, [hps.batch_size, -1, 1, 1]) * encoder_states,
[1, 2]) # shape (batch_size, attn_size).
context_vector = array_ops.reshape(context_vector, [-1, hps.hidden_dim])
return context_vector, attn_dist
def _build_model(self):
"""Add the whole sequence-to-sequence model to the graph."""
hps = self._hps
vsize = self._vocab.size() # size of the vocabulary
with tf.variable_scope('classification'):
# Some initializers
self.rand_unif_init = tf.random_uniform_initializer(-hps.rand_unif_init_mag, hps.rand_unif_init_mag,
seed=123)
self.trunc_norm_init = tf.truncated_normal_initializer(stddev=hps.trunc_norm_init_std)
# Add embedding matrix (shared by the encoder and decoder inputs)
with tf.variable_scope('embedding'):
embedding = tf.get_variable('embedding', [vsize, hps.emb_dim], dtype=tf.float32,
initializer=self.trunc_norm_init)
emb_enc_inputs = tf.nn.embedding_lookup(embedding,
self._enc_batch) # tensor with shape (batch_size, max_enc_steps, emb_size)
self.emb_enc_inputs = emb_enc_inputs
# Add the encoder.
fw_st, encoder_vector = self._add_encoder(emb_enc_inputs, self._enc_lens, hps)
context, self.atten_weight = self.attention(fw_st.h, encoder_vector, hps.hidden_dim, self._enc_padding_mask, hps)
self.atten_weight = self.atten_weight*(-1)+1
with tf.variable_scope('output_projection'):
w = tf.get_variable('w_output', [hps.hidden_dim, 2], dtype=tf.float32,
initializer=self.trunc_norm_init)
v = tf.get_variable('v_output', [2], dtype=tf.float32, initializer=self.trunc_norm_init)
logits = tf.nn.xw_plus_b(context, w, v)
self.y_pred_auc = tf.nn.softmax(logits)
batch_nums = tf.range(0, hps.batch_size)
indices = tf.stack([batch_nums, self._target_batch], axis=1) # batch 2
self.y_pred_auc = tf.gather_nd(self.y_pred_auc, indices) # batch dim
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self._target_batch, logits = logits)
self.loss = tf.reduce_mean(loss)
self.best_output = tf.argmax(tf.nn.softmax(logits),1)
def _add_train_op(self):
"""Sets self._train_op, the op to run for training."""
# Take gradients of the trainable variables w.r.t. the loss function to minimize
loss_to_minimize = self.loss
tvars = tf.trainable_variables()
gradients = tf.gradients(loss_to_minimize, tvars, aggregation_method=tf.AggregationMethod.EXPERIMENTAL_TREE)
grads, global_norm = tf.clip_by_global_norm(gradients, self._hps.max_grad_norm)
# Add a summary
tf.summary.scalar('global_norm', global_norm)
# Apply adagrad optimizer
optimizer = tf.train.AdagradOptimizer(self._hps.lr, initial_accumulator_value=self._hps.adagrad_init_acc)
self._train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=self.global_step, name='train_step')
def build_graph(self):
"""Add the placeholders, model, global step, train_op and summaries to the graph"""
with tf.device("/gpu:" + str(FLAGS.gpuid)):
tf.logging.info('Building graph...')
t0 = time.time()
self._add_placeholders()
self._build_model()
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self._add_train_op()
t1 = time.time()
tf.logging.info('Time to build graph: %i seconds', t1 - t0)
def run_train_step(self, sess, batch, decay=False):
"""Runs one training iteration. Returns a dictionary containing train op, summaries, loss, global_step and (optionally) coverage loss."""
feed_dict = self._make_feed_dict(batch)
to_return = {
'train_op': self._train_op,
'loss': self.loss,
"predictions":self.best_output,
'global_step': self.global_step,
}
return sess.run(to_return, feed_dict)
def run_pre_train_step(self, sess, batch):
"""Runs one training iteration. Returns a dictionary containing train op, summaries, loss, global_step and (optionally) coverage loss."""
feed_dict = self._make_feed_dict(batch)
to_return = {
'train_op': self._train_op,
'loss': self.loss,
'predictions': self.best_output,
'global_step': self.global_step,
}
return sess.run(to_return, feed_dict)
def run_ypred_auc(self, sess, batch):
"""Runs one training iteration. Returns a dictionary containing train op, summaries, loss, global_step and (optionally) coverage loss."""
feed_dict = self._make_feed_dict(batch)
to_return = {
'y_pred_auc': self.y_pred_auc,
}
return sess.run(to_return, feed_dict)
def run_attention_weight_ypred_auc(self, sess, batch):
"""Runs one training iteration. Returns a dictionary containing train op, summaries, loss, global_step and (optionally) coverage loss."""
feed_dict = self._make_feed_dict(batch)
to_return = {
'y_pred_auc': self.y_pred_auc,
'weight':self.atten_weight
}
return sess.run(to_return, feed_dict)
def run_eval_step(self, sess, batch):
"""Runs one evaluation iteration. Returns a dictionary containing summaries, loss, global_step and (optionally) coverage loss."""
feed_dict = self._make_feed_dict(batch)
error_list =[]
error_label = []
#right_label = []
to_return = {
'predictions': self.best_output
}
results = sess.run(to_return, feed_dict)
right =0
for i in range(len(batch.labels)):
if results['predictions'][i] == batch.labels[i]:
right +=1
error_label.append(results['predictions'][i])
error_list.append(batch.original_reviews[i])
#right_label.append(batch.labels[i])
return right, len(batch.labels),error_list,error_label