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train.py
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train.py
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import os
import sys
import json
import time
import shutil
import pickle
import logging
import data_helper
import numpy as np
import pandas as pd
import tensorflow as tf
from text_cnn_rnn import TextCNNRNN
from sklearn.model_selection import train_test_split
logging.getLogger().setLevel(logging.INFO)
def train_cnn_rnn():
input_file = sys.argv[1]
x_, y_, vocabulary, vocabulary_inv, df, labels = data_helper.load_data(input_file)
training_config = 'config.json'
params = json.loads(open(training_config).read())
# Assign a 300 dimension vector to each word
word_embeddings = data_helper.load_embeddings(vocabulary)
embedding_mat = [word_embeddings[word] for index, word in enumerate(vocabulary_inv)]
embedding_mat = np.array(embedding_mat, dtype = np.float32)
# Split the original dataset into train set and test set
x, x_test, y, y_test = train_test_split(x_, y_, test_size=0.1)
# Split the train set into train set and dev set
x_train, x_dev, y_train, y_dev = train_test_split(x, y, test_size=0.1)
logging.info('x_train: {}, x_dev: {}, x_test: {}'.format(len(x_train), len(x_dev), len(x_test)))
logging.info('y_train: {}, y_dev: {}, y_test: {}'.format(len(y_train), len(y_dev), len(y_test)))
# Create a directory, everything related to the training will be saved in this directory
timestamp = str(int(time.time()))
trained_dir = './trained_results/'
if os.path.exists(trained_dir):
shutil.rmtree(trained_dir)
os.makedirs(trained_dir)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn_rnn = TextCNNRNN(
embedding_mat=embedding_mat,
sequence_length=x_train.shape[1],
num_classes = y_train.shape[1],
non_static=params['non_static'],
hidden_unit=params['hidden_unit'],
max_pool_size=params['max_pool_size'],
filter_sizes=map(int, params['filter_sizes'].split(",")),
num_filters = params['num_filters'],
embedding_size = params['embedding_dim'],
l2_reg_lambda = params['l2_reg_lambda'])
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.RMSPropOptimizer(1e-3, decay=0.9)
grads_and_vars = optimizer.compute_gradients(cnn_rnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
# Checkpoint files will be saved in this directory during training
checkpoint_dir = './checkpoints/'
if os.path.exists(checkpoint_dir):
shutil.rmtree(checkpoint_dir)
os.makedirs(checkpoint_dir)
checkpoint_prefix = os.path.join(checkpoint_dir, 'model')
def real_len(batches):
return [np.ceil(np.argmin(batch + [0]) * 1.0 / params['max_pool_size']) for batch in batches]
def train_step(x_batch, y_batch):
feed_dict = {
cnn_rnn.input_x: x_batch,
cnn_rnn.input_y: y_batch,
cnn_rnn.dropout_keep_prob: params['dropout_keep_prob'],
cnn_rnn.batch_size: len(x_batch),
cnn_rnn.pad: np.zeros([len(x_batch), 1, params['embedding_dim'], 1]),
cnn_rnn.real_len: real_len(x_batch),
}
_, step, loss, accuracy = sess.run([train_op, global_step, cnn_rnn.loss, cnn_rnn.accuracy], feed_dict)
def dev_step(x_batch, y_batch):
feed_dict = {
cnn_rnn.input_x: x_batch,
cnn_rnn.input_y: y_batch,
cnn_rnn.dropout_keep_prob: 1.0,
cnn_rnn.batch_size: len(x_batch),
cnn_rnn.pad: np.zeros([len(x_batch), 1, params['embedding_dim'], 1]),
cnn_rnn.real_len: real_len(x_batch),
}
step, loss, accuracy, num_correct, predictions = sess.run(
[global_step, cnn_rnn.loss, cnn_rnn.accuracy, cnn_rnn.num_correct, cnn_rnn.predictions], feed_dict)
return accuracy, loss, num_correct, predictions
saver = tf.train.Saver(tf.all_variables())
sess.run(tf.initialize_all_variables())
# Training starts here
train_batches = data_helper.batch_iter(list(zip(x_train, y_train)), params['batch_size'], params['num_epochs'])
best_accuracy, best_at_step = 0, 0
# Train the model with x_train and y_train
# print('=========training batches: ', len(train_batches))
for train_batch in train_batches:
if len(train_batch) == 0:
continue
x_train_batch, y_train_batch = zip(*train_batch)
train_step(x_train_batch, y_train_batch)
current_step = tf.train.global_step(sess, global_step)
# Evaluate the model with x_dev and y_dev
if current_step % params['evaluate_every'] == 0:
dev_batches = data_helper.batch_iter(list(zip(x_dev, y_dev)), params['batch_size'], 1)
total_dev_correct = 0
for dev_batch in dev_batches:
if len(dev_batch) == 0:
continue
x_dev_batch, y_dev_batch = zip(*dev_batch)
acc, loss, num_dev_correct, predictions = dev_step(x_dev_batch, y_dev_batch)
total_dev_correct += num_dev_correct
accuracy = float(total_dev_correct) / len(y_dev)
logging.info('Accuracy on dev set: {}'.format(accuracy))
if accuracy >= best_accuracy:
best_accuracy, best_at_step = accuracy, current_step
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
logging.critical('Saved model {} at step {}'.format(path, best_at_step))
logging.critical('Best accuracy {} at step {}'.format(best_accuracy, best_at_step))
logging.critical('Training is complete, testing the best model on x_test and y_test')
# Evaluate x_test and y_test
# best_at_step = 400
# checkpoint_prefix = 'checkpoints_1502365645/model'
saver.restore(sess, checkpoint_prefix + '-' + str(best_at_step))
test_batches = data_helper.batch_iter(list(zip(x_test, y_test)), params['batch_size'], 1, shuffle=False)
total_test_correct = 0
cnt = 0
for test_batch in test_batches:
if len(test_batch) == 0:
continue
cnt += 1
x_test_batch, y_test_batch = zip(*test_batch)
acc, loss, num_test_correct, predictions = dev_step(x_test_batch, y_test_batch)
total_test_correct += int(num_test_correct)
logging.critical('Accuracy on test set: {} test instance {}'.format(float(total_test_correct) / len(y_test), cnt))
path = checkpoint_prefix + '-' + str(best_at_step)
# print (path)
os.rename(checkpoint_dir, trained_dir)
logging.critical('{} has been removed'.format(checkpoint_dir))
# Save trained parameters and files since predict.py needs them
with open(trained_dir + 'words_index.json', 'w') as outfile:
json.dump(vocabulary, outfile, indent=4, ensure_ascii=False)
with open(trained_dir + 'embeddings.pickle', 'wb') as outfile:
pickle.dump(embedding_mat, outfile, pickle.HIGHEST_PROTOCOL)
with open(trained_dir + 'labels.json', 'w') as outfile:
json.dump(labels, outfile, indent=4, ensure_ascii=False)
params['sequence_length'] = x_train.shape[1]
with open(trained_dir + 'trained_parameters.json', 'w') as outfile:
json.dump(params, outfile, indent=4, sort_keys=True, ensure_ascii=False)
if __name__ == '__main__':
train_cnn_rnn()