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sentiment_skim.py
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sentiment_skim.py
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from __future__ import print_function
from __future__ import division
import sys
import time
import numpy as np
import tensorflow as tf
from tensorflow.contrib.rnn import BasicLSTMCell, GRUCell
import properties as p
class ModelSentiment():
def __init__(self, word_embedding=None, max_input_len=None, using_compression=False, book=None, words=None, we_trainable=False, \
learning_rate = 0.001, lr_decayable=True, using_bidirection=False, fw_cell='basic', bw_cell='gru'):
self.word_embedding = word_embedding
self.we_trainable = we_trainable
self.max_input_len = max_input_len
self.using_compression = using_compression
self.book = book
self.words = words
self.learning_rate = learning_rate
self.lr_decayable = lr_decayable
self.using_bidirection = using_bidirection
self.fw_cell = fw_cell
self.bw_cell = bw_cell
def set_data(self, train, valid):
self.train = train
self.valid = valid
def set_embedding(self):
self.word_embedding = word_embedding
def init_ops(self):
with tf.device('/%s' % p.device):
# init memory
self.add_placeholders()
# init model
self.output = self.inference()
# init prediction step
self.pred = self.get_predictions(self.output)
# init cost function
self.calculate_loss = self.add_loss_op(self.output)
# init gradient
self.train_step = self.add_training_op(self.calculate_loss)
self.merged = tf.summary.merge_all()
def add_placeholders(self):
"""add data placeholder to graph """
self.input_placeholder = tf.placeholder(tf.int32, shape=(
p.batch_size, self.max_input_len))
self.input_len_placeholder = tf.placeholder(
tf.int32, shape=(p.batch_size,))
self.pred_placeholder = tf.placeholder(
tf.int32, shape=(p.batch_size,))
# place holder for start vs end position
self.dropout_placeholder = tf.placeholder(tf.float32)
self.iteration = tf.placeholder(tf.int32)
def inference(self):
"""Performs inference on the DMN model"""
# set up embedding
embeddings = None
if not self.using_compression:
embeddings = tf.Variable(
self.word_embedding.astype(np.float32), name="Embedding", trainable=self.we_trainable)
with tf.variable_scope("input", initializer=tf.contrib.layers.xavier_initializer()):
print('==> get input representation')
word_reps = self.get_input_representation(embeddings)
word_reps = tf.reduce_mean(word_reps, axis=1)
# print(word_reps)
with tf.variable_scope("hidden", initializer=tf.contrib.layers.xavier_initializer()):
# output = tf.layers.dense(word_reps,
# p.embed_size,
# activation=tf.nn.tanh,
# name="h1")
output = tf.layers.dense(word_reps,
p.hidden_size,
activation=tf.nn.tanh,
name="h2")
output = tf.nn.dropout(output, self.dropout_placeholder)
output = tf.layers.dense(output,
p.sentiment_classes,
name="fn")
return output
def build_book(self):
b = tf.Variable(self.book, name="book", trainable=False)
w = tf.Variable(self.words, name="words", trainable=False)
return b, w
def get_input_representation(self, embeddings):
"""Get fact (sentence) vectors via embedding, positional encoding and bi-directional GRU"""
inputs = None
if self.using_compression:
b_embedding, w_embedding = self.build_book()
# from code words => build one hot
# B x L x M: batchsize x length_sentence
d = tf.nn.embedding_lookup(w_embedding, self.input_placeholder)
# d_ is flatten to make one hot vector then reshape to cube later
d_ = tf.reshape(d, [-1])
# => B x L x M x K
d_ = tf.one_hot(d_, depth=p.code_size, axis=-1)
d_ = tf.reshape(d_, [p.batch_size * self.max_input_len, p.book_size, p.code_size]);
# => M x B * L x K => B * L x K
inputs = tf.reduce_sum(tf.matmul(tf.transpose(d_, perm=[1, 0, 2]), b_embedding), axis=0);
inputs = tf.reshape(tf.reshape(inputs, [-1]), [p.batch_size, self.max_input_len, p.embed_size])
else:
# get word vectors from embedding
inputs = tf.nn.embedding_lookup(embeddings, self.input_placeholder)
# chunking_len = int(self.max_input_len / p.fixation)
# inputs = tf.reshape(tf.reshape(inputs, [-1]), [p.batch_size, chunking_len, p.fixation * p.embed_size])
# use encoding to get sentence representation plus position encoding
# (like fb represent)
if self.fw_cell == 'basic':
fw_cell = BasicLSTMCell(p.embed_size)
else:
fw_cell = GRUCell(p.embed_size)
if not self.using_bidirection:
# outputs with [batch_size, max_time, cell_bw.output_size]
outputs, _ = tf.nn.dynamic_rnn(
fw_cell,
inputs,
dtype=np.float32,
sequence_length=self.input_len_placeholder,
)
else:
if self.bw_cell == 'basic':
back_cell = BasicLSTMCell(p.embed_size)
else:
back_cell = GRUCell(p.embed_size)
outputs, _ = tf.nn.bidirectional_dynamic_rnn(
fw_cell,
back_cell,
inputs,
dtype=np.float32,
sequence_length=self.input_len_placeholder,
)
outputs = tf.concat(outputs, 2)
return outputs
def add_loss_op(self, output):
"""Calculate loss"""
loss = tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=output, labels=self.pred_placeholder))
# add l2 regularization for all variables except biases
for v in tf.trainable_variables():
if not 'bias' in v.name.lower():
loss += p.l2 * tf.nn.l2_loss(v)
tf.summary.scalar('loss', loss)
return loss
def add_training_op(self, loss):
"""Calculate and apply gradients"""
if self.lr_decayable:
lr = tf.train.exponential_decay(learning_rate=p.lr, global_step=self.iteration, decay_steps=p.lr_depr, decay_rate=p.decay_rate)
else:
lr = self.learning_rate
opt = tf.train.AdamOptimizer(learning_rate=lr)
gvs = opt.compute_gradients(loss)
train_op = opt.apply_gradients(gvs)
return train_op
def get_predictions(self, output):
pred = tf.nn.softmax(output)
pred = tf.argmax(pred, 1)
return pred
def run_epoch(self, session, data, num_epoch=0, train_writer=None, train_op=None, verbose=2, train=False):
dp = p.dropout
if train_op is None:
train_op = tf.no_op()
dp = 1
total_steps = len(data[0]) // p.batch_size
total_loss = []
accuracy = 0
# shuffle data
r = np.random.permutation(len(data[0]))
ct, ct_l, pr = data
ct, ct_l, pr = np.asarray(ct, dtype=np.float32), np.asarray(ct_l, dtype=np.float32), np.asarray(pr, dtype=np.float32)
ct, ct_l, pr = ct[r], ct_l[r], pr[r]
for step in range(total_steps):
index = range(step * p.batch_size,
(step + 1) * p.batch_size)
feed = {self.input_placeholder: ct[index],
self.input_len_placeholder: ct_l[index],
self.pred_placeholder: pr[index],
self.dropout_placeholder: dp,
self.iteration: num_epoch}
pred_labels = pr[step * p.batch_size:(step + 1) * p.batch_size]
loss, pred, summary, _ = session.run(
[self.calculate_loss, self.pred, self.merged, train_op], feed_dict=feed)
if train_writer is not None:
train_writer.add_summary(
summary, num_epoch * total_steps + step)
accuracy += (np.sum(pred == pred_labels)) / float(len(pred_labels))
total_loss.append(loss)
if verbose and step % verbose == 0:
sys.stdout.write('\r{} / {} : loss = {}'.format(
step, total_steps, np.mean(total_loss)))
sys.stdout.flush()
if verbose:
sys.stdout.write('\r')
avg_acc = 0.
if total_steps:
avg_acc = accuracy / float(total_steps)
return np.sum(total_loss), avg_acc