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train_cnn.py
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train_cnn.py
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import json
import logging
import os
import time
import numpy as np
import tensorflow as tf
from sklearn.cross_validation import train_test_split
from tensorflow.contrib import learn
import data_helper
from text_cnn import TextCNN
logging.getLogger().setLevel(logging.INFO)
def train_cnn():
"""Step 0: load sentences, labels, and training parameters"""
train_file = 'Data/iseardataset.csv'
x_raw, y_raw, df, labels, embedding_mat = data_helper.load_data_and_labels(train_file)
parameter_file = './parameters.json'
params = json.loads(open(parameter_file).read())
"""Step 1: pad each sentence to the same length and map each word to an id"""
max_document_length = max([len(x.split(' ')) for x in x_raw])
logging.info('The maximum length of all sentences: {}'.format(max_document_length))
vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
x = np.array(list(vocab_processor.fit_transform(x_raw)))
y = np.array(y_raw)
# print x.shape
"""Step 2: split the original dataset into train and test sets"""
x_, x_test, y_, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
"""Step 3: shuffle the train set and split the train set into train and dev sets"""
shuffle_indices = np.random.permutation(np.arange(len(y_)))
x_shuffled = x_[shuffle_indices]
y_shuffled = y_[shuffle_indices]
x_train, x_dev, y_train, y_dev = train_test_split(x_shuffled, y_shuffled, test_size=0.2)
"""Step 4: save the labels into labels.json since predict.py needs it"""
with open('./labels.json', 'w') as outfile:
json.dump(labels, outfile, indent=4)
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)))
"""Step 5: build a graph and cnn object"""
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(
sequence_length=x_train.shape[1],
num_classes=y_train.shape[1],
vocab_size=9000,
embedding_size=params['embedding_dim'],
filter_sizes=list(map(int, params['filter_sizes'].split(","))),
num_filters=params['num_filters'], embedding_mat=embedding_mat,
l2_reg_lambda=params['l2_reg_lambda'])
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(os.path.curdir, "trained_model_" + timestamp))
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.all_variables())
# One training step: train the model with one batch
def train_step(x_batch, y_batch):
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: params['dropout_keep_prob']}
_, step, loss, acc = sess.run([train_op, global_step, cnn.loss, cnn.accuracy], feed_dict)
# One evaluation step: evaluate the model with one batch
def dev_step(x_batch, y_batch):
feed_dict = {cnn.input_x: x_batch, cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0}
step, loss, acc, num_correct = sess.run([global_step, cnn.loss, cnn.accuracy, cnn.num_correct],
feed_dict)
return num_correct
# Save the word_to_id map since predict.py needs it
vocab_processor.save(os.path.join(out_dir, "vocab.pickle"))
sess.run(tf.initialize_all_variables())
print "Loading Embeddings !"
initW = data_helper.load_embedding_vectors(vocab_processor.vocabulary_)
sess.run(cnn.W.assign(initW))
print "Loaded Embeddings !"
# 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
"""Step 6: train the cnn model with x_train and y_train (batch by batch)"""
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)
"""Step 6.1: evaluate the model with x_dev and y_dev (batch by batch)"""
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)
num_dev_correct = dev_step(x_dev_batch, y_dev_batch)
total_dev_correct += num_dev_correct
dev_accuracy = float(total_dev_correct) / len(y_dev)
logging.critical('Accuracy on dev set: {}'.format(dev_accuracy))
"""Step 6.2: save the model if it is the best based on accuracy of the dev set"""
if dev_accuracy >= best_accuracy:
best_accuracy, best_at_step = dev_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))
"""Step 7: predict x_test (batch by batch)"""
test_batches = data_helper.batch_iter(list(zip(x_test, y_test)), params['batch_size'], 1)
total_test_correct = 0
for test_batch in test_batches:
if len(test_batch) == 0:
continue
print "Non Zero Length"
x_test_batch, y_test_batch = zip(*test_batch)
num_test_correct = dev_step(x_test_batch, y_test_batch)
total_test_correct += num_test_correct
test_accuracy = float(total_test_correct) / len(y_test)
train_batches = data_helper.batch_iter(list(zip(x_train, y_train)), params['batch_size'], 1)
total_train_correct = 0
for train_batch in train_batches:
if len(train_batch) == 0:
continue
print "Non Zero Length"
x_train_batch, y_train_batch = zip(*train_batch)
num_test_correct = dev_step(x_train_batch, y_train_batch)
total_train_correct += num_test_correct
train_accuracy = float(total_train_correct) / len(y_train)
print 'Accuracy on test set is {} based on the best model'.format(test_accuracy)
print 'Accuracy on train set is {} based on the best model'.format(train_accuracy)
# logging.critical('Accuracy on test set is {} based on the best model {}'.format(test_accuracy, path))
logging.critical('The training is complete')
if __name__ == '__main__':
# python3 train_cnn.py
train_cnn()