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train.py
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train.py
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from __future__ import print_function
import os
import pprint
import time
import tensorflow as tf
import numpy as np
from model import Model
from data_helpers import get_vocab
pp = pprint.PrettyPrinter()
flags = tf.app.flags
flags.DEFINE_integer('batch_size', 64, 'Batch size')
flags.DEFINE_integer('epochs', 100, 'epochs')
flags.DEFINE_integer('rnn_size', 300, 'RNN unit size')
flags.DEFINE_integer('word_attention_size', 300, 'Word level attention unit size')
flags.DEFINE_integer('sent_attention_size', 300, 'Sentence level attention unit size')
flags.DEFINE_integer('char_embedding_size', 300, 'Embedding dimension')
flags.DEFINE_string('checkpoint_dir', 'checkpoint', 'Directory name to save the checkpoints [checkpoint]')
flags.DEFINE_integer('vocab_size', 6790, 'vocabulary size')
flags.DEFINE_float('keep_prob', 0.5, 'Dropout keep prob')
flags.DEFINE_integer('document_size', 30, 'document size')
flags.DEFINE_integer('sentence_size', 50, 'sentence size')
flags.DEFINE_float('learning_rate', 1e-4, 'learning rate')
flags.DEFINE_float('grad_clip', 5.0, 'grad clip')
FLAGS = flags.FLAGS
def read_records(index=0):
train_queue = tf.train.string_input_producer(['./data/train.tfrecords'], num_epochs=FLAGS.epochs)
valid_queue = tf.train.string_input_producer(['./data/valid.tfrecords'], num_epochs=FLAGS.epochs)
queue = tf.QueueBase.from_list(index, [train_queue, valid_queue])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(queue)
features = tf.parse_single_example(
serialized_example,
features={
'sentence_lengths': tf.FixedLenFeature([FLAGS.document_size], tf.int64),
'document_lengths': tf.FixedLenFeature([], tf.int64),
'label': tf.FixedLenFeature([], tf.int64),
'text': tf.FixedLenFeature([FLAGS.document_size * FLAGS.sentence_size], tf.int64),
})
sentence_lengths = features['sentence_lengths']
document_lengths = features['document_lengths']
label = features['label']
text = features['text']
sentence_lengths_batch, document_lengths_batch, label_batch, text_batch = tf.train.shuffle_batch(
[sentence_lengths, document_lengths, label, text],
batch_size=FLAGS.batch_size,
capacity=5000,
min_after_dequeue=1000)
return sentence_lengths_batch, document_lengths_batch, label_batch, text_batch
def main(_):
pp.pprint(FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
print(' [*] Creating checkpoint directory...')
os.makedirs(FLAGS.checkpoint_dir)
checkpoint_path = os.path.join(FLAGS.checkpoint_dir, 'model.ckpt')
# load pre-trained char embedding
char_emb = np.load('./data/emb.npy')
sentence_lengths_batch, document_lengths_batch, label_batch, text_batch = read_records()
valid_sentence_lengths_batch, valid_document_lengths_batch, valid_label_batch, valid_text_batch = read_records(1)
text_batch = tf.reshape(text_batch, (-1, FLAGS.document_size, FLAGS.sentence_size))
valid_text_batch = tf.reshape(valid_text_batch, (-1, FLAGS.document_size, FLAGS.sentence_size))
with tf.variable_scope('model'):
train_model = Model(FLAGS)
with tf.variable_scope('model', reuse=True):
valid_model = Model(FLAGS)
# training operator
global_step = tf.Variable(0, name='global_step', trainable=False)
lr = tf.train.exponential_decay(FLAGS.learning_rate, global_step, 10000, 0.9)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(train_model.cost, tvars), FLAGS.grad_clip)
optimizer = tf.train.AdamOptimizer(lr)
train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step)
tf.summary.scalar('train_loss', train_model.cost)
tf.summary.scalar('valid_loss', valid_model.cost)
saver = tf.train.Saver()
with tf.Session() as sess:
tf.local_variables_initializer().run()
tf.global_variables_initializer().run()
# assign char embedding
sess.run([], feed_dict={train_model.embedding: char_emb})
sess.run([], feed_dict={valid_model.embedding: char_emb})
# saver.restore(sess, checkpoint_path)
# stock_emb = train_model.label_embedding.eval()
#
# np.save('./data/stock_emb.npy', stock_emb)
# print('done')
# summary_op = tf.summary.merge_all()
# train_writer = tf.summary.FileWriter('./log/train', sess.graph)
# valid_writer = tf.summary.FileWriter('./log/test')
current_step = 0
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
valid_cost = 0
valid_accuracy = 0
train_cost = 0
VALID_SIZE = 54
_, chars = get_vocab()
try:
while not coord.should_stop():
start = time.time()
if current_step % 500 == 0:
valid_cost = 0
for _ in range(VALID_SIZE):
valid_text, valid_label, valid_sentence_lengths, valid_document_lengths =\
sess.run([valid_text_batch, valid_label_batch, valid_sentence_lengths_batch, valid_document_lengths_batch])
valid_outputs = sess.run([valid_model.cost, valid_model.accuracy], feed_dict={
valid_model.inputs: valid_text,
valid_model.labels: valid_label,
valid_model.sentence_lengths: valid_sentence_lengths,
valid_model.document_lengths: valid_document_lengths,
valid_model.is_training: False
})
valid_cost += valid_outputs[0]
valid_accuracy += valid_outputs[1]
valid_cost /= VALID_SIZE
valid_accuracy /= VALID_SIZE
inputs, labels, sentence_lengths, document_lengths =\
sess.run([text_batch, label_batch, sentence_lengths_batch, document_lengths_batch])
# valid_writer.add_summary(summary, current_step)
train_cost, train_accuracy, _ = sess.run([train_model.cost, train_model.accuracy, train_op], feed_dict={
train_model.inputs: inputs,
train_model.labels: labels,
train_model.sentence_lengths: sentence_lengths,
train_model.document_lengths: document_lengths,
train_model.is_training: True
})
# train_writer.add_summary(summary, current_step)
end = time.time()
print('Cost at step %s: %s(%s), test cost: %s(%s), time: %s' %
(current_step, train_cost, train_accuracy, valid_cost, valid_accuracy, end - start))
current_step = tf.train.global_step(sess, global_step)
if current_step != 0 and current_step % 1000 == 0:
save_path = saver.save(sess, checkpoint_path)
print('Model saved in file:', save_path)
except tf.errors.OutOfRangeError:
print('Done training!')
finally:
coord.request_stop()
save_path = saver.save(sess, checkpoint_path)
print('Model saved in file:', save_path)
coord.join(threads)
if __name__ == '__main__':
tf.app.run()