/
train_context.py
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train_context.py
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# -*- coding: utf-8 -*-
from datetime import datetime
import time
import os
import tensorflow as tf
import numpy as np
import cnn_context
import util
FLAGS = tf.app.flags.FLAGS
# train parameters
this_dir = os.path.abspath(os.path.dirname(__file__))
tf.app.flags.DEFINE_string('data_dir', os.path.join(this_dir, 'data', 'mlmi'), 'Directory of the data')
tf.app.flags.DEFINE_string('train_dir', os.path.join(this_dir, 'train'),
'Directory to save training checkpoint files')
tf.app.flags.DEFINE_integer('train_size', 100000, 'Number of training examples')
tf.app.flags.DEFINE_integer('num_epochs', 10, 'Number of epochs to run')
tf.app.flags.DEFINE_boolean('use_pretrain', False, 'Use word2vec pretrained embeddings or not')
tf.app.flags.DEFINE_string('optimizer', 'adam',
'Optimizer to use. Must be one of "sgd", "adagrad", "adadelta" and "adam"')
tf.app.flags.DEFINE_float('init_lr', 1e-3, 'Initial learning rate')
tf.app.flags.DEFINE_float('lr_decay', 0.95, 'LR decay rate')
tf.app.flags.DEFINE_integer('tolerance_step', 500,
'Decay the lr after loss remains unchanged for this number of steps')
tf.app.flags.DEFINE_float('dropout', 0.5, 'Dropout rate. 0 is no dropout.')
# logging
tf.app.flags.DEFINE_integer('log_step', 10, 'Display log to stdout after this step')
tf.app.flags.DEFINE_integer('summary_step', 50,
'Write summary (evaluate model on dev set) after this step')
tf.app.flags.DEFINE_integer('checkpoint_step', 100, 'Save model after this step')
def train(train_data, test_data):
# train_dir
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(FLAGS.train_dir, timestamp))
# save flags
if not os.path.exists(out_dir):
os.mkdir(out_dir)
FLAGS._parse_flags()
config = dict(FLAGS.__flags.items())
# Window_size must not be larger than the sent_len
if config['sent_len'] < config['max_window']:
config['max_window'] = config['sent_len']
# flag to restore the contextwise model
config['contextwise'] = True
# save flags
util.dump_to_file(os.path.join(out_dir, 'flags.cPickle'), config)
print "Parameters:"
for k, v in config.iteritems():
print '%20s %r' % (k, v)
# max number of steps
num_batches_per_epoch = int(np.ceil(float(len(train_data))/FLAGS.batch_size))
max_steps = num_batches_per_epoch * FLAGS.num_epochs
with tf.Graph().as_default():
with tf.variable_scope('cnn', reuse=None):
m = cnn_context.Model(config, is_train=True)
with tf.variable_scope('cnn', reuse=True):
mtest = cnn_context.Model(config, is_train=False)
# checkpoint
saver = tf.train.Saver(tf.global_variables())
save_path = os.path.join(out_dir, 'model.ckpt')
summary_op = tf.summary.merge_all()
# session
with tf.Session().as_default() as sess:
proj_config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_left = proj_config.embeddings.add()
embedding_middle = proj_config.embeddings.add()
embedding_right = proj_config.embeddings.add()
embedding_left.tensor_name = m.W_emb_left.name
embedding_middle.tensor_name = m.W_emb_middle.name
embedding_right.tensor_name = m.W_emb_right.name
embedding_left.metadata_path = os.path.join(FLAGS.data_dir, 'vocab.txt')
embedding_middle.metadata_path = os.path.join(FLAGS.data_dir, 'vocab.txt')
embedding_right.metadata_path = os.path.join(FLAGS.data_dir, 'vocab.txt')
train_summary_writer = tf.summary.FileWriter(os.path.join(out_dir, "train"), graph=sess.graph)
dev_summary_writer = tf.summary.FileWriter(os.path.join(out_dir, "dev"), graph=sess.graph)
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(train_summary_writer, proj_config)
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(dev_summary_writer, proj_config)
sess.run(tf.global_variables_initializer())
# assign pretrained embeddings
if FLAGS.use_pretrain:
print "Initialize model with pretrained embeddings..."
pretrained_embedding = np.load(os.path.join(FLAGS.data_dir, 'emb.npy'))
m.assign_embedding(sess, pretrained_embedding)
# initialize parameters
current_lr = FLAGS.init_lr
lowest_loss_value = float("inf")
decay_step_counter = 0
global_step = 0
# evaluate on dev set
def dev_step(mtest, sess):
dev_loss = []
dev_auc = []
dev_f1_score = []
# create batch
test_batches = util.batch_iter(test_data, batch_size=FLAGS.batch_size, num_epochs=1, shuffle=False)
for batch in test_batches:
left_batch, middle_batch, right_batch, y_batch, _ = zip(*batch)
feed = {mtest.left: np.array(left_batch),
mtest.middle: np.array(middle_batch),
mtest.right: np.array(right_batch),
mtest.labels: np.array(y_batch)}
loss_value, eval_value = sess.run([mtest.total_loss, mtest.eval_op], feed_dict=feed)
dev_loss.append(loss_value)
pre, rec = zip(*eval_value)
dev_auc.append(util.calc_auc_pr(pre, rec))
dev_f1_score.append((2.0 * pre[5] * rec[5]) / (pre[5] + rec[5])) # threshold = 0.5
return np.mean(dev_loss), np.mean(dev_auc), np.mean(dev_f1_score)
# train loop
print "\nStart training (save checkpoints in %s)\n" % out_dir
train_loss = []
train_auc = []
train_f1_score = []
train_batches = util.batch_iter(train_data, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
for batch in train_batches:
batch_size = len(batch)
m.assign_lr(sess, current_lr)
global_step += 1
left_batch, middle_batch, right_batch, y_batch, a_batch = zip(*batch)
feed = {m.left: np.array(left_batch),
m.middle: np.array(middle_batch),
m.right: np.array(right_batch),
m.labels: np.array(y_batch)}
if FLAGS.attention:
feed[m.attention] = np.array(a_batch)
start_time = time.time()
_, loss_value, eval_value = sess.run([m.train_op, m.total_loss, m.eval_op], feed_dict=feed)
proc_duration = time.time() - start_time
train_loss.append(loss_value)
pre, rec = zip(*eval_value)
auc = util.calc_auc_pr(pre, rec)
f1 = (2.0 * pre[5] * rec[5]) / (pre[5] + rec[5]) # threshold = 0.5
train_auc.append(auc)
train_f1_score.append(f1)
assert not np.isnan(loss_value), "Model loss is NaN."
# print log
if global_step % FLAGS.log_step == 0:
examples_per_sec = batch_size / proc_duration
format_str = '%s: step %d/%d, f1 = %.4f, auc = %.4f, loss = %.4f ' + \
'(%.1f examples/sec; %.3f sec/batch), lr: %.6f'
print format_str % (datetime.now(), global_step, max_steps, f1, auc, loss_value,
examples_per_sec, proc_duration, current_lr)
# write summary
if global_step % FLAGS.summary_step == 0:
summary_str = sess.run(summary_op)
train_summary_writer.add_summary(summary_str, global_step)
dev_summary_writer.add_summary(summary_str, global_step)
# summary loss, f1
train_summary_writer.add_summary(
_summary_for_scalar('loss', np.mean(train_loss)), global_step=global_step)
train_summary_writer.add_summary(
_summary_for_scalar('auc', np.mean(train_auc)), global_step=global_step)
train_summary_writer.add_summary(
_summary_for_scalar('f1', np.mean(train_f1_score)), global_step=global_step)
dev_loss, dev_auc, dev_f1 = dev_step(mtest, sess)
dev_summary_writer.add_summary(
_summary_for_scalar('loss', dev_loss), global_step=global_step)
dev_summary_writer.add_summary(
_summary_for_scalar('auc', dev_auc), global_step=global_step)
dev_summary_writer.add_summary(
_summary_for_scalar('f1', dev_f1), global_step=global_step)
print "\n===== write summary ====="
print "%s: step %d/%d: train_loss = %.6f, train_auc = %.4f train_f1 = %.4f" \
% (datetime.now(), global_step, max_steps,
np.mean(train_loss), np.mean(train_auc), np.mean(train_f1_score))
print "%s: step %d/%d: dev_loss = %.6f, dev_auc = %.4f dev_f1 = %.4f\n" \
% (datetime.now(), global_step, max_steps, dev_loss, dev_auc, dev_f1)
# reset container
train_loss = []
train_auc = []
train_f1_score = []
# decay learning rate if necessary
if loss_value < lowest_loss_value:
lowest_loss_value = loss_value
decay_step_counter = 0
else:
decay_step_counter += 1
if decay_step_counter >= FLAGS.tolerance_step:
current_lr *= FLAGS.lr_decay
print '%s: step %d/%d, Learning rate decays to %.5f' % \
(datetime.now(), global_step, max_steps, current_lr)
decay_step_counter = 0
# stop learning if learning rate is too low
if current_lr < 1e-5:
break
# save checkpoint
if global_step % FLAGS.checkpoint_step == 0:
saver.save(sess, save_path, global_step=global_step)
saver.save(sess, save_path, global_step=global_step)
def _summary_for_scalar(name, value):
return tf.Summary(value=[tf.Summary.Value(tag=name, simple_value=float(value))])
def main(argv=None):
if not os.path.exists(FLAGS.train_dir):
os.mkdir(FLAGS.train_dir)
# load contextwise dataset
source_path = os.path.join(FLAGS.data_dir, 'ids')
target_path = os.path.join(FLAGS.data_dir, 'target.txt')
attention_path = None
if FLAGS.attention:
if os.path.exists(os.path.join(FLAGS.data_dir, 'source.att')):
attention_path = os.path.join(FLAGS.data_dir, 'source.att')
else:
raise ValueError("Attention file %s not found.", os.path.join(FLAGS.data_dir, 'source.att'))
train_data, test_data = util.read_data_contextwise(source_path, target_path, FLAGS.sent_len,
attention_path=attention_path, train_size=FLAGS.train_size)
train(train_data, test_data)
if __name__ == '__main__':
tf.app.run()