def train():
data_loader = InputHelper()
data_loader.create_dictionary(FLAGS.data_dir + '/' + FLAGS.train_file,
FLAGS.data_dir + '/')
data_loader.create_batches(FLAGS.data_dir + '/' + FLAGS.train_file,
FLAGS.batch_size, FLAGS.sequence_length)
FLAGS.vocab_size = data_loader.vocab_size
FLAGS.n_classes = data_loader.n_classes
test_data_loader = InputHelper()
test_data_loader.load_dictionary(FLAGS.data_dir + '/dictionary')
test_data_loader.create_batches(FLAGS.data_dir + '/' + FLAGS.test_file,
1000, FLAGS.sequence_length)
model = BiRNN(FLAGS.rnn_size, FLAGS.layer_size, FLAGS.vocab_size,
FLAGS.batch_size, FLAGS.sequence_length, FLAGS.n_classes,
FLAGS.grad_clip)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
for e in xrange(FLAGS.num_epochs):
data_loader.reset_batch()
sess.run(
tf.assign(model.lr,
FLAGS.learning_rate * (FLAGS.decay_rate**e)))
for b in xrange(data_loader.num_batches):
start = time.time()
x, y = data_loader.next_batch()
feed = {
model.input_data: x,
model.targets: y,
model.output_keep_prob: FLAGS.dropout_keep_prob
}
train_loss, _ = sess.run([model.cost, model.train_op],
feed_dict=feed)
end = time.time()
print("{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \
.format(e * data_loader.num_batches + b,
FLAGS.num_epochs * data_loader.num_batches,
e, train_loss, end - start))
test_data_loader.reset_batch()
for i in xrange(test_data_loader.num_batches):
test_x, test_y = test_data_loader.next_batch()
feed = {
model.input_data: test_x,
model.targets: test_y,
model.output_keep_prob: 1.0
}
accuracy = sess.run(model.accuracy, feed_dict=feed)
print 'accuracy:{0}'.format(accuracy)
checkpoint_path = os.path.join(FLAGS.save_dir, 'model.ckpt')
saver.save(sess,
checkpoint_path,
global_step=e * data_loader.num_batches)
print 'model saved to {}'.format(checkpoint_path)
def train():
data_loader = InputHelper()
# 创建词典
data_loader.load_file()
data_loader.create_dictionary_v2(FLAGS.save_dir + '/')
x_train = data_loader.data_token(data_loader.x_train)
data_loader.create_batches(x_train, data_loader.y_train, FLAGS.batch_size,
FLAGS.sequence_length)
FLAGS.vocab_size = data_loader.vocab_size
FLAGS.n_classes = data_loader.n_classes
FLAGS.num_batches = data_loader.num_batches
test_data_loader = InputHelper()
test_data_loader.load_dictionary(FLAGS.save_dir + '/dictionary',
data_loader.y_train)
x_test = data_loader.data_token(data_loader.x_test)
test_data_loader.create_batches(x_test, data_loader.y_test, 100,
FLAGS.sequence_length)
embeddings_reshape = None
if FLAGS.pre_trained_vec_path:
print('将原始的embedding矩阵重置')
embeddings = np.load(FLAGS.pre_trained_vec_path +
'/word2vec.model.wv.vectors.npy',
allow_pickle=True)
model = Word2Vec.load(FLAGS.pre_trained_vec_path + '/word2vec.model')
embeddings_reshape = np.zeros(embeddings.shape)
print('embeddings_shape:', embeddings_reshape.shape)
dic = data_loader.token_dictionary
print(len(dic))
i = 20
for word in model.wv.index2word:
tmp = dic[word]
if tmp < i:
i = tmp
print(i)
embeddings_reshape[tmp] = model.wv[word]
#print(embeddings_reshape[0])
#print(embeddings_reshape[dic['e850']])
"""
embeddings_reshape = tf.get_variable(name="W", shape=embeddings_reshape.shape,
initializer=tf.constant_initializer(embeddings_reshape),
trainable=False)
"""
print(embeddings_reshape.shape)
FLAGS.vocab_size = embeddings_reshape.shape[0]
FLAGS.embedding_size = embeddings_reshape.shape[1]
'''
if FLAGS.init_from is not None:# fine tune condition
#断言，传参数前捕获参数异常
assert os.path.isdir(FLAGS.init_from), '{} must be a directory'.format(FLAGS.init_from)
ckpt = tf.train.get_checkpoint_state(FLAGS.init_from)
assert ckpt,'No checkpoint found'
assert ckpt.model_checkpoint_path,'No model path found in checkpoint'
'''
print('create model...')
# Define specified Model
model = BiLSTM(embedding_size=FLAGS.embedding_size,
rnn_size=FLAGS.rnn_size,
vocab_size=FLAGS.vocab_size,
sequence_length=FLAGS.sequence_length,
n_classes=FLAGS.n_classes,
learning_rate=FLAGS.learning_rate,
embedding_w=embeddings_reshape)
# define value for tensorboard
tf.summary.scalar('train_loss', model.loss)
tf.summary.scalar('accuracy', model.accuracy)
merged = tf.summary.merge_all()
# 调整GPU内存分配方案
#tf_config = tf.ConfigProto()
#tf_config.gpu_options.allow_growth = True
init = tf.global_variables_initializer()
print('start training...')
with tf.Session() as sess: #tf.Session(config=tf_config) as sess:
train_writer = tf.summary.FileWriter(FLAGS.logs_dir, sess.graph)
saver = tf.train.Saver(tf.global_variables())
# using pre trained embeddings
# if FLAGS.pre_trained_vec_path:
# sess.run(model.embedding.assign(embeddings_reshape))#替换为embeddings
# del embeddings
# del embeddings_reshape
# restore model
sess.run(init)
sess.run(tf.local_variables_initializer())
total_steps = FLAGS.num_epochs * FLAGS.num_batches
for e in range(FLAGS.num_epochs):
data_loader.reset_batch() # 重新洗牌
for b in range(FLAGS.num_batches):
start = time.time()
x, y = data_loader.next_batch()
#print(x.shape,y.shape)
#print(x[0],y[0])
feed = {
model.input_data: x,
model.targets: y,
model.output_keep_prob: FLAGS.dropout_keep_prob
}
train_loss, summary, _, accuracy = sess.run(
[model.loss, merged, model.train_op, model.accuracy],
feed_dict=feed)
end = time.time()
global_step = e * FLAGS.num_batches + b
print(
'{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f},acc = {:.3f}'
.format(global_step, total_steps, e, train_loss,
end - start, accuracy))
if global_step % 20 == 0:
train_writer.add_summary(summary,
e * FLAGS.num_batches + b)
if global_step % FLAGS.save_steps == 0:
checkpoint_path = os.path.join(FLAGS.save_dir,
'model.ckpt')
saver.save(sess, checkpoint_path, global_step=global_step)
print('model saved to {}'.format(checkpoint_path))
test_data_loader.reset_batch()
test_accuracy = []
for i in range(test_data_loader.num_batches):
test_x, test_y = test_data_loader.next_batch()
feed = {
model.input_data: test_x,
model.targets: test_y,
model.output_keep_prob: 1.0
}
accuracy = sess.run(model.accuracy, feed_dict=feed)
test_accuracy.append(accuracy)
print(np.average(test_accuracy))
def train():
data_loader = InputHelper()
data_loader.create_dictionary(FLAGS.data_dir + '/' + FLAGS.train_file,
FLAGS.data_dir + '/')
data_loader.create_batches(FLAGS.data_dir + '/' + FLAGS.train_file,
FLAGS.batch_size, FLAGS.sequence_length)
FLAGS.vocab_size = data_loader.vocab_size
FLAGS.n_classes = data_loader.n_classes
FLAGS.num_batches = data_loader.num_batches
test_data_loader = InputHelper()
test_data_loader.load_dictionary(FLAGS.data_dir + '/dictionary')
test_data_loader.create_batches(FLAGS.data_dir + '/' + FLAGS.test_file,
100, FLAGS.sequence_length)
if FLAGS.pre_trained_vec:
embeddings = np.load(FLAGS.pre_trained_vec)
print(embeddings.shape)
FLAGS.vocab_size = embeddings.shape[0]
FLAGS.embedding_size = embeddings.shape[1]
if FLAGS.init_from is not None:
assert os.path.isdir(FLAGS.init_from), '{} must be a directory'.format(
FLAGS.init_from)
ckpt = tf.train.get_checkpoint_state(FLAGS.init_from)
assert ckpt, 'No checkpoint found'
assert ckpt.model_checkpoint_path, 'No model path found in checkpoint'
# Define specified Model
model = BiRNN(embedding_size=FLAGS.embedding_size,
rnn_size=FLAGS.rnn_size,
layer_size=FLAGS.layer_size,
vocab_size=FLAGS.vocab_size,
attn_size=FLAGS.attn_size,
sequence_length=FLAGS.sequence_length,
n_classes=FLAGS.n_classes,
grad_clip=FLAGS.grad_clip,
learning_rate=FLAGS.learning_rate)
# define value for tensorboard
tf.summary.scalar('train_loss', model.cost)
tf.summary.scalar('accuracy', model.accuracy)
merged = tf.summary.merge_all()
# 调整GPU内存分配方案
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
train_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
# using pre trained embeddings
if FLAGS.pre_trained_vec:
sess.run(model.embedding.assign(embeddings))
del embeddings
# restore model
if FLAGS.init_from is not None:
saver.restore(sess, ckpt.model_checkpoint_path)
total_steps = FLAGS.num_epochs * FLAGS.num_batches
for e in range(FLAGS.num_epochs):
data_loader.reset_batch()
for b in range(FLAGS.num_batches):
start = time.time()
x, y = data_loader.next_batch()
feed = {
model.input_data: x,
model.targets: y,
model.output_keep_prob: FLAGS.dropout_keep_prob
}
train_loss, summary, _ = sess.run(
[model.cost, merged, model.train_op], feed_dict=feed)
end = time.time()
global_step = e * FLAGS.num_batches + b
print(
'{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}'
.format(global_step, total_steps, e, train_loss,
end - start))
if global_step % 20 == 0:
train_writer.add_summary(summary,
e * FLAGS.num_batches + b)
if global_step % FLAGS.save_steps == 0:
checkpoint_path = os.path.join(FLAGS.save_dir,
'model.ckpt')
saver.save(sess, checkpoint_path, global_step=global_step)
print('model saved to {}'.format(checkpoint_path))
test_data_loader.reset_batch()
test_accuracy = []
for i in range(test_data_loader.num_batches):
test_x, test_y = test_data_loader.next_batch()
feed = {
model.input_data: test_x,
model.targets: test_y,
model.output_keep_prob: 1.0
}
accuracy = sess.run(model.accuracy, feed_dict=feed)
test_accuracy.append(accuracy)
print('test accuracy:{0}'.format(np.average(test_accuracy)))
def train():
data_loader = InputHelper('data/stop_words.pkl')
data_loader.create_dictionary(FLAGS.data_dir+'/'+FLAGS.train_file, FLAGS.data_dir+'/')
data_loader.create_batches(FLAGS.data_dir + '/' + FLAGS.train_file, FLAGS.batch_size, FLAGS.sequence_length)
FLAGS.vocab_size = data_loader.vocab_size
FLAGS.n_classes = data_loader.n_classes
FLAGS.num_batches = data_loader.num_batches
test_data_loader = InputHelper('data/stop_words.pkl')
test_data_loader.load_dictionary(FLAGS.data_dir + '/dictionary')
test_data_loader.create_batches(FLAGS.data_dir + '/' + FLAGS.test_file, 100, FLAGS.sequence_length)
if FLAGS.pre_trained_vec:
embeddings = np.load(FLAGS.pre_trained_vec)
print(embeddings.shape)
FLAGS.vocab_size = embeddings.shape[0]
FLAGS.embedding_size = embeddings.shape[1]
if FLAGS.init_from is not None:
assert os.path.isdir(FLAGS.init_from), '{} must be a directory'.format(FLAGS.init_from)
ckpt = tf.train.get_checkpoint_state(FLAGS.init_from)
assert ckpt, 'No checkpoint found'
assert ckpt.model_checkpoint_path, 'No model path found in checkpoint'
# Define specified Model
model = AttentionBiRNN(embedding_size=FLAGS.embedding_size, rnn_size=FLAGS.rnn_size, layer_size=FLAGS.layer_size,
vocab_size=FLAGS.vocab_size, attn_size=FLAGS.attn_size,
sequence_length=FLAGS.sequence_length,
n_classes=FLAGS.n_classes, grad_clip=FLAGS.grad_clip, learning_rate=FLAGS.learning_rate)
# define value for tensor_board
tf.summary.scalar('train_loss', model.cost)
tf.summary.scalar('accuracy', model.accuracy)
merged = tf.summary.merge_all()
# 调整GPU内存分配方案
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
train_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
# using pre trained embeddings
if FLAGS.pre_trained_vec:
sess.run(model.embedding.assign(embeddings))
del embeddings
# restore model
if FLAGS.init_from is not None:
saver.restore(sess, ckpt.model_checkpoint_path)
total_steps = FLAGS.num_epochs * FLAGS.num_batches
for e in range(FLAGS.num_epochs):
data_loader.reset_batch()
for b in range(FLAGS.num_batches):
start = time.time()
x, y = data_loader.next_batch()
feed = {model.input_data: x, model.targets: y, model.output_keep_prob: FLAGS.dropout_keep_prob}
train_loss, summary, acc, _ = sess.run([model.cost, merged, model.accuracy, model.train_op],
feed_dict=feed)
end = time.time()
global_step = e * FLAGS.num_batches + b
print('{}/{}(epoch {}), train_loss = {:.3f}, time/batch = {:.3f}, accuracy = {:.3f}'
.format(global_step, total_steps, e, train_loss, end - start, acc))
if global_step % 20 == 0:
train_writer.add_summary(summary, e * FLAGS.num_batches + b)
if global_step % FLAGS.save_steps == 0:
checkpoint_path = os.path.join(FLAGS.save_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=global_step)
print('model saved to {}'.format(checkpoint_path))
test_data_loader.reset_batch()
test_accuracy, test_loss = [], []
for i in range(test_data_loader.num_batches):
test_x, test_y = test_data_loader.next_batch()
feed = {model.input_data: test_x, model.targets: test_y, model.output_keep_prob: 1.0}
loss, accuracy = sess.run([model.cost, model.accuracy], feed_dict=feed)
test_accuracy.append(accuracy)
test_loss.append(loss)
print('test_loss:{:.5f}, test accuracy:{:.5f}'.format(np.average(test_loss), np.average(test_accuracy)))