forked from dsindex/segm-lstm
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train.py
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train.py
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#!/bin/env python
#-*- coding: utf8 -*-
import sys
import os
import re
import pickle
from optparse import OptionParser
import numpy as np
import tensorflow as tf
import util
import model
# --verbose
VERBOSE = 0
if __name__ == '__main__':
parser = OptionParser()
parser.add_option("--verbose", action="store_const", const=1, dest="verbose", help="verbose mode")
parser.add_option("-t", "--train", dest="train_path", help="train file path", metavar="train_path")
parser.add_option("-v", "--validation", dest="validation_path", help="validation file path", metavar="validation_path")
parser.add_option("-m", "--model", dest="model_dir", help="dir path to save model", metavar="model_dir")
parser.add_option("-i", "--iters", dest="training_iters", help="training iterations", metavar="training_iters")
(options, args) = parser.parse_args()
if options.verbose == 1 : VERBOSE = 1
train_path = options.train_path
if train_path == None :
parser.print_help()
exit(1)
validation_path = options.validation_path
model_dir = options.model_dir
if model_dir == None :
parser.print_help()
exit(1)
if not os.path.isdir(model_dir) :
os.makedirs(model_dir)
training_iters = options.training_iters
if not training_iters : training_iters = 30
training_iters = int(training_iters)
# config
n_steps = 30 # time steps
padd = '\t' # special padding chracter
char_dic = util.build_dictionary(train_path, padd)
n_input = len(char_dic) # input dimension, vocab size
n_hidden = 8 # hidden layer size
n_classes = 2 # output classes, space or not
vocab_size = n_input
'''
util.test_next_batch(train_path, char_dic, vocab_size, n_steps, padd)
'''
x = tf.placeholder(tf.float32, [None, n_steps, n_input])
y_ = tf.placeholder(tf.int32, [None, n_steps])
early_stop = tf.placeholder(tf.int32)
# LSTM layer
# 2 x n_hidden length (state & cell)
istate = tf.placeholder(tf.float32, [None, 2*n_hidden])
weights = {
'hidden' : model.weight_variable([n_input, n_hidden]),
'out' : model.weight_variable([n_hidden, n_classes])
}
biases = {
'hidden' : model.bias_variable([n_hidden]),
'out': model.bias_variable([n_classes])
}
# training
y = model.RNN(x, istate, weights, biases, n_hidden, n_steps, n_input, early_stop)
batch_size = 1
learning_rate = 0.01
logits = tf.reshape(tf.concat(1, y), [-1, n_classes])
targets = y_
seq_weights = tf.ones([n_steps * batch_size])
loss = tf.nn.seq2seq.sequence_loss_by_example([logits], [targets], [seq_weights])
cost = tf.reduce_sum(loss) / batch_size
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(logits,1), tf.cast(y_, tf.int64))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
NUM_THREADS = 1
config = tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS,
inter_op_parallelism_threads=NUM_THREADS,
log_device_placement=False)
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
saver = tf.train.Saver() # save all variables
checkpoint_dir = model_dir
checkpoint_file = 'segm.ckpt'
if validation_path :
validation_data = util.get_validation_data(validation_path, char_dic, vocab_size, n_steps, padd)
seq = 0
while seq < training_iters :
c_istate = np.zeros((batch_size, 2*n_hidden))
i = 0
fid = util.open_file(train_path, 'r')
for line in fid :
line = line.strip()
if line == "" : continue
line = line.decode('utf-8')
sentence = util.snorm(line)
pos = 0
while pos != -1 :
batch_xs, batch_ys, next_pos, count = util.next_batch(sentence, pos, char_dic, vocab_size, n_steps, padd)
'''
print 'window : ' + sentence[pos:pos+n_steps].encode('utf-8')
print 'count : ' + str(count)
print 'next_pos : ' + str(next_pos)
print batch_ys
print batch_xs
'''
feed={x: batch_xs, y_: batch_ys, istate: c_istate, early_stop:count}
sess.run(optimizer, feed_dict=feed)
pos = next_pos
sys.stderr.write('%s th sentence ... done\n' % i)
i += 1
util.close_file(fid)
# validation
if validation_path :
validation_cost = 0
validation_accuracy = 0
for validation_xs, validation_ys, count in validation_data :
feed={x: validation_xs, y_: validation_ys, istate: c_istate, early_stop:count}
validation_cost += sess.run(cost, feed_dict=feed)
validation_accuracy += sess.run(accuracy, feed_dict=feed)
validation_cost /= len(validation_data)
validation_accuracy /= len(validation_data)
sys.stderr.write('seq : %s' % (seq) + ',' + 'validation cost : %s' % validation_cost + ',' + 'validation accuracy : %s\n' % (validation_accuracy))
seq += 1
sys.stderr.write('save dic\n')
dic_path = model_dir + '/' + 'dic.pickle'
with open(dic_path, 'wb') as handle :
pickle.dump(char_dic, handle)
sys.stderr.write('save model(final)\n')
saver.save(sess, checkpoint_dir + '/' + checkpoint_file)
sys.stderr.write('end of training\n')
sess.close()