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error_copy.py
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error_copy.py
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# Copyright 2016 Stanford University
#
# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import random
import sys
import time
import random
import json
import numpy as np
from six.moves import xrange
import tensorflow as tf
import nlc_model_global_pre as nlc_model
import nlc_data
from util import pair_iter
from util import get_tokenizer
import logging
logging.basicConfig(level=logging.INFO)
tf.app.flags.DEFINE_float("learning_rate", 0.0003, "Learning rate.")
tf.app.flags.DEFINE_float("learning_rate_decay_factor", 0.95, "Learning rate decays by this much.")
tf.app.flags.DEFINE_float("max_gradient_norm", 10.0, "Clip gradients to this norm.")
tf.app.flags.DEFINE_float("dropout", 0.15, "Fraction of units randomly dropped on non-recurrent connections.")
tf.app.flags.DEFINE_integer("batch_size", 128, "Batch size to use during training.")
tf.app.flags.DEFINE_integer("epochs", 40, "Number of epochs to train.")
tf.app.flags.DEFINE_integer("size", 400, "Size of each model layer.")
tf.app.flags.DEFINE_integer("num_layers", 3, "Number of layers in the model.")
tf.app.flags.DEFINE_integer("max_vocab_size", 40000, "Vocabulary size limit.")
#tf.app.flags.DEFINE_integer("max_seq_len", 200, "Maximum sequence length.")
tf.app.flags.DEFINE_integer("max_seq_len", 100, "Maximum sequence length.")
tf.app.flags.DEFINE_string("data_dir", "/tmp", "Data directory")
tf.app.flags.DEFINE_string("train_dir", "/tmp", "Training directory.")
tf.app.flags.DEFINE_string("tokenizer", "CHAR", "BPE / CHAR / WORD.")
tf.app.flags.DEFINE_string("optimizer", "adam", "adam / sgd")
tf.app.flags.DEFINE_integer("print_every", 1, "How many iterations to do per print.")
FLAGS = tf.app.flags.FLAGS
def create_model(session, vocab_size, forward_only):
model = nlc_model.NLCModel(
vocab_size, FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
FLAGS.learning_rate, FLAGS.learning_rate_decay_factor, FLAGS.dropout,
forward_only=forward_only, optimizer=FLAGS.optimizer)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
num_epoch = 0
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
logging.info("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
num_epoch = int(ckpt.model_checkpoint_path.split('-')[1])
print (num_epoch)
else:
logging.info("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
logging.info('Num params: %d' % sum(v.get_shape().num_elements() for v in tf.trainable_variables()))
return model, num_epoch
def validate(model, sess, x_dev, y_dev):
valid_costs, valid_lengths = [], []
for source_tokens, source_mask, target_tokens, target_mask in pair_iter(x_dev, y_dev, FLAGS.batch_size, FLAGS.num_layers):
cost = model.test(sess, source_tokens, source_mask, target_tokens, target_mask)
valid_costs.append(cost * target_mask.shape[1])
valid_lengths.append(np.sum(target_mask[1:, :]))
valid_cost = sum(valid_costs) / float(sum(valid_lengths))
return valid_cost
def train():
"""Train a translation model using NLC data."""
# Prepare NLC data.
logging.info("Preparing NLC data in %s" % FLAGS.data_dir)
x_train, y_train, x_dev, y_dev, vocab_path = nlc_data.prepare_nlc_data(
FLAGS.data_dir, FLAGS.max_vocab_size,
tokenizer=get_tokenizer(FLAGS))
vocab, _ = nlc_data.initialize_vocabulary(vocab_path)
vocab_size = len(vocab)
logging.info("Vocabulary size: %d" % vocab_size)
FLAGS.print_every=100
if not os.path.exists(FLAGS.train_dir):
os.makedirs(FLAGS.train_dir)
file_handler = logging.FileHandler("{0}/log.txt".format(FLAGS.train_dir))
logging.getLogger().addHandler(file_handler)
print(vars(FLAGS))
with open(os.path.join(FLAGS.train_dir, "flags.json"), 'w') as fout:
json.dump(FLAGS.__flags, fout)
FLAGS.print_every=1
with tf.Session() as sess:
logging.info("Creating %d layers of %d units." % (FLAGS.num_layers, FLAGS.size))
model, epoch = create_model(sess, vocab_size, False)
#logging.info('Initial validation cost: %f' % validate(model, sess, x_dev, y_dev))
if False:
tic = time.time()
params = tf.trainable_variables()
num_params = sum(map(lambda t: np.prod(tf.shape(t.value()).eval()), params))
toc = time.time()
print ("Number of params: %d (retreival took %f secs)" % (num_params, toc - tic))
#epoch = 0
best_epoch = 0
previous_losses = []
exp_cost = None
exp_length = None
exp_norm = None
total_iters = 0
start_time = time.time()
while (FLAGS.epochs == 0 or epoch < FLAGS.epochs):
epoch += 1
print(epoch)
current_step = 0
## Train
epoch_tic = time.time()
for source_tokens, source_mask, target_tokens, target_mask in pair_iter(x_train, y_train, FLAGS.batch_size, FLAGS.num_layers):
# Get a batch and make a step.
tic = time.time()
grad_norm, cost, param_norm = model.train(sess, source_tokens, source_mask, target_tokens, target_mask)
if np.isnan(grad_norm) or np.isinf(grad_norm):
#np.savetxt(os.path.join(FLAGS.train_dir, 'pre'), pre, fmt='%.5f')
#np.savetxt(os.path.join(FLAGS.train_dir,'prob1'), prob1)
#np.savetxt(os.path.join(FLAGS.train_dir,'prob2'), prob2)
raise 'Nan error'
break
toc = time.time()
iter_time = toc - tic
total_iters += np.sum(target_mask)
tps = total_iters / (time.time() - start_time)
current_step += 1
lengths = np.sum(target_mask, axis=0)
mean_length = np.mean(lengths)
std_length = np.std(lengths)
if not exp_cost:
exp_cost = cost
exp_length = mean_length
exp_norm = grad_norm
else:
exp_cost = 0.99*exp_cost + 0.01*cost
exp_length = 0.99*exp_length + 0.01*mean_length
exp_norm = 0.99*exp_norm + 0.01*grad_norm
cost = cost / mean_length
if current_step % FLAGS.print_every == 0:
logging.info('epoch %d, iter %d, cost %f, exp_cost %f, grad norm %f, param norm %f, tps %f, length mean/std %f/%f' %
(epoch, current_step, cost, exp_cost / exp_length, grad_norm, param_norm, tps, mean_length, std_length))
epoch_toc = time.time()
## Checkpoint
checkpoint_path = os.path.join(FLAGS.train_dir, "best.ckpt")
## Validate
valid_cost = validate(model, sess, x_dev, y_dev)
logging.info("Epoch %d Validation cost: %f time: %f" % (epoch, valid_cost, epoch_toc - epoch_tic))
model.saver.save(sess, checkpoint_path, global_step=epoch)
sys.stdout.flush()
def main(_):
train()
if __name__ == "__main__":
tf.app.run()