def main(args):
# process config
c = Configs(args.config)
ROOT = os.environ['TENSOROFLOW']
model_directory = '%s/examples/model/multi_layer_nmt' % ROOT
model_path = '%s/model' % model_directory
dictionary_path = {
'source': '%s/source_dictionary.pickle' % model_directory,
'source_reverse':
'%s/source_reverse_dictionary.pickle' % model_directory,
'target': '%s/target_dictionary.pickle' % model_directory,
'target_reverse':
'%s/target_reverse_dictionary.pickle' % model_directory
}
PAD = c.const['PAD']
EOS = c.const['EOS']
train_step = c.option['train_step']
max_time = c.option['max_time']
batch_size = c.option['batch_size']
vocabulary_size = c.option['vocabulary_size']
input_embedding_size = c.option['embedding_size']
hidden_units = c.option['hidden_units']
layers = c.option['layers']
source_train_data_path = c.data['source_train_data']
target_train_data_path = c.data['target_train_data']
source_valid_data_path = c.data['source_valid_data']
target_valid_data_path = c.data['target_valid_data']
source_test_data_path = c.data['source_test_data']
target_test_data_path = c.data['target_test_data']
# read data
if args.mode == 'train':
source_dictionary, source_reverse_dictionary = build_dictionary(
read_words(source_train_data_path), vocabulary_size)
source_train_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_train_data_path)
]
target_dictionary, target_reverse_dictionary = build_dictionary(
read_words(target_train_data_path), vocabulary_size)
target_train_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_train_data_path)
]
source_valid_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_valid_data_path)
]
target_valid_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_valid_data_path)
]
if args.debug:
source_train_datas = source_train_datas[:1000]
target_train_datas = source_train_datas[:1000]
else:
with open(dictionary_path['source'], 'rb') as f1, \
open(dictionary_path['source_reverse'], 'rb') as f2, \
open(dictionary_path['target'], 'rb') as f3, \
open(dictionary_path['target_reverse'], 'rb') as f4:
source_dictionary = pickle.load(f1)
source_reverse_dictionary = pickle.load(f2)
target_dictionary = pickle.load(f3)
target_reverse_dictionary = pickle.load(f4)
source_test_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_test_data_path)
]
target_test_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_test_data_path)
]
# placeholder
encoder_inputs = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='encoder_inputs')
decoder_inputs = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='decoder_inputs')
decoder_labels = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='decoder_labels')
# embed
embeddings = tf.Variable(tf.random_uniform(
[vocabulary_size, input_embedding_size], -1.0, 1.0),
dtype=tf.float32,
name='embeddings')
encoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
encoder_inputs)
decoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
decoder_inputs)
# encoder
encoder_units = hidden_units
encoder_layers = [
tf.contrib.rnn.LSTMCell(size) for size in [encoder_units] * layers
]
encoder_cell = tf.contrib.rnn.MultiRNNCell(encoder_layers)
encoder_output, encoder_final_state = tf.nn.dynamic_rnn(
encoder_cell,
encoder_inputs_embedded,
dtype=tf.float32,
time_major=True)
del encoder_output
# decoder
decoder_units = encoder_units
decoder_layers = [
tf.contrib.rnn.LSTMCell(size) for size in [decoder_units] * layers
]
decoder_cell = tf.contrib.rnn.MultiRNNCell(decoder_layers)
decoder_output, decoder_final_state = tf.nn.dynamic_rnn(
decoder_cell,
decoder_inputs_embedded,
initial_state=encoder_final_state,
scope="plain_decoder",
dtype=tf.float32,
time_major=True)
decoder_logits = tf.contrib.layers.linear(decoder_output, vocabulary_size)
decoder_prediction = tf.argmax(
decoder_logits, 2) # max_time: axis=0, batch: axis=1, vocab: axis=2
# optimizer
stepwise_cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(decoder_labels,
depth=vocabulary_size,
dtype=tf.float32),
logits=decoder_logits,
)
loss = tf.reduce_mean(stepwise_cross_entropy)
train_op = tf.train.AdamOptimizer().minimize(loss)
saver = tf.train.Saver()
minibatch_idx = {'train': 0, 'valid': 0, 'test': 0}
with tf.Session() as sess:
if args.mode == 'train':
# train
global_max_step = train_step * (
len(source_train_datas) // batch_size + 1)
loss_freq = global_max_step // 100 if global_max_step > 100 else 1
loss_log = []
batch_loss_log = []
loss_suffix = ''
es = EarlyStopper(max_size=5, edge_threshold=0.1)
m = Monitor(global_max_step)
sess.run(tf.global_variables_initializer())
global_step = 0
stop_flag = False
for batch in range(train_step):
if stop_flag:
break
current_batch_loss_log = []
while True: # minibatch process
m.monitor(global_step, loss_suffix)
source_train_batch, _ = batchnize(source_train_datas,
batch_size,
minibatch_idx['train'])
target_train_batch, minibatch_idx['train'] = batchnize(
target_train_datas, batch_size, minibatch_idx['train'])
batch_data = seq2seq(source_train_batch,
target_train_batch, max_time,
vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
sess.run(fetches=[train_op, loss], feed_dict=feed_dict)
if global_step % loss_freq == 0:
source_valid_batch, _ = batchnize(
source_valid_datas, batch_size,
minibatch_idx['valid'])
target_valid_batch, minibatch_idx['valid'] = batchnize(
target_valid_datas, batch_size,
minibatch_idx['valid'])
batch_data = seq2seq(source_valid_batch,
target_valid_batch, max_time,
vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
loss_val = sess.run(fetches=loss, feed_dict=feed_dict)
loss_log.append(loss_val)
current_batch_loss_log.append(loss_val)
loss_suffix = 'loss: %f' % loss_val
es_status = es(loss_val)
if batch > train_step // 2 and es_status:
print('early stopping at step: %d' % global_step)
stop_flag = True
break
global_step += 1
if minibatch_idx['train'] == 0:
batch_loss = np.mean(current_batch_loss_log)
batch_loss_log.append(batch_loss)
print('Batch: {}/{}, batch loss: {}'.format(
batch + 1, train_step, batch_loss))
break
# save tf.graph and variables
saver.save(sess, model_path)
print('save at %s' % model_path)
# save plot of loss
plt.plot(np.arange(len(loss_log)) * loss_freq, loss_log)
plt.savefig('%s_global_loss.png' % model_path)
plt.figure()
plt.plot(np.arange(len(batch_loss_log)), batch_loss_log)
plt.savefig('%s_batch_loss.png' % model_path)
# save dictionary
with open(dictionary_path['source'], 'wb') as f1, \
open(dictionary_path['source_reverse'], 'wb') as f2, \
open(dictionary_path['target'], 'wb') as f3, \
open(dictionary_path['target_reverse'], 'wb') as f4:
pickle.dump(source_dictionary, f1)
pickle.dump(source_reverse_dictionary, f2)
pickle.dump(target_dictionary, f3)
pickle.dump(target_reverse_dictionary, f4)
elif args.mode == 'eval':
saver.restore(sess, model_path)
print('load from %s' % model_path)
else:
raise # args.mode should be train or eval
# evaluate
loss_val = []
input_vectors = None
predict_vectors = None
for i in range(len(source_test_datas) // batch_size + 1):
source_test_batch, _ = batchnize(source_test_datas, batch_size,
minibatch_idx['test'])
target_test_batch, minibatch_idx['test'] = batchnize(
target_test_datas, batch_size, minibatch_idx['test'])
batch_data = seq2seq(source_test_batch, target_test_batch,
max_time, vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
pred = sess.run(fetches=decoder_prediction, feed_dict=feed_dict)
if predict_vectors is None:
predict_vectors = pred.T
else:
predict_vectors = np.vstack((predict_vectors, pred.T))
input_ = batch_data['encoder_inputs']
if input_vectors is None:
input_vectors = input_.T
else:
input_vectors = np.vstack((input_vectors, input_.T))
loss_val.append(sess.run(fetches=loss, feed_dict=feed_dict))
input_sentences = ''
predict_sentences = ''
for i, (input_vector, predict_vector) in enumerate(
zip(input_vectors[:len(source_test_datas)],
predict_vectors[:len(target_test_datas)])):
input_sentences += ' '.join([
source_reverse_dictionary[vector] for vector in input_vector
if not vector == PAD
])
predict_sentences += ' '.join([
target_reverse_dictionary[vector] for vector in predict_vector
if not vector == PAD
])
if i < len(source_test_datas) - 1:
input_sentences += '\n'
predict_sentences += '\n'
evaluate_input_path = '%s.evaluate_input' % model_path
evaluate_predict_path = '%s.evaluate_predict' % model_path
with open(evaluate_input_path, 'w') as f1, \
open(evaluate_predict_path, 'w') as f2:
f1.write(input_sentences)
f2.write(predict_sentences)
print('input sequences at {}'.format(evaluate_input_path))
print('predict sequences at {}'.format(evaluate_predict_path))
print('mean of loss: %f' % np.mean(loss_val))
print('finish.')
def main(args):
tf.reset_default_graph()
# process config
c = Configs(args.config)
ROOT = os.environ['TENSOROFLOW']
model_path = '%s/examples/model/multi_layer_seq2seq/model' % ROOT
PAD = c.const['PAD']
EOS = c.const['EOS']
train_step = c.option['train_step']
max_time = c.option['max_time']
batch_size = c.option['batch_size']
vocabulary_size = c.option['vocabulary_size']
input_embedding_size = c.option['embedding_size']
hidden_units = c.option['hidden_units']
layers = c.option['layers']
datas = []
# placeholder
encoder_inputs = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='encoder_inputs')
decoder_inputs = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='decoder_inputs')
decoder_labels = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='decoder_labels')
# embed
embeddings = tf.Variable(tf.random_uniform(
[vocabulary_size, input_embedding_size], -1.0, 1.0),
dtype=tf.float32,
name='embeddings')
encoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
encoder_inputs)
decoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
decoder_inputs)
# encoder
encoder_units = hidden_units
encoder_layers = [
tf.contrib.rnn.LSTMCell(size) for size in [encoder_units] * layers
]
encoder_cell = tf.contrib.rnn.MultiRNNCell(encoder_layers)
encoder_output, encoder_final_state = tf.nn.dynamic_rnn(
encoder_cell,
encoder_inputs_embedded,
dtype=tf.float32,
time_major=True)
del encoder_output
# decoder
decoder_units = encoder_units
decoder_layers = [
tf.contrib.rnn.LSTMCell(size) for size in [decoder_units] * layers
]
decoder_cell = tf.contrib.rnn.MultiRNNCell(decoder_layers)
decoder_output, decoder_final_state = tf.nn.dynamic_rnn(
decoder_cell,
decoder_inputs_embedded,
initial_state=encoder_final_state,
scope="plain_decoder",
dtype=tf.float32,
time_major=True)
decoder_logits = tf.contrib.layers.linear(decoder_output, vocabulary_size)
decoder_prediction = tf.argmax(
decoder_logits, 2) # max_time: axis=0, batch: axis=1, vocab: axis=2
# optimizer
stepwise_cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(decoder_labels,
depth=vocabulary_size,
dtype=tf.float32),
logits=decoder_logits,
)
loss = tf.reduce_mean(stepwise_cross_entropy)
train_op = tf.train.AdamOptimizer().minimize(loss)
saver = tf.train.Saver()
with tf.Session() as sess:
if args.mode == 'train':
# train
loss_freq = train_step // 100
loss_log = []
loss_suffix = ''
es = EarlyStopper(max_size=5, edge_threshold=0.1)
m = Monitor(train_step)
sess.run(tf.global_variables_initializer())
for i in range(train_step):
m.monitor(i, loss_suffix)
batch_data = through(datas, max_time, batch_size,
vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
sess.run(fetches=[train_op, loss], feed_dict=feed_dict)
if i % loss_freq == 0:
batch_data = through(datas, max_time, batch_size,
vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
loss_val = sess.run(fetches=loss, feed_dict=feed_dict)
loss_log.append(loss_val)
loss_suffix = 'loss: %f' % loss_val
es_status = es(loss_val)
if i > train_step // 2 and es_status:
print('early stopping at step: %d' % i)
break
saver.save(sess, model_path)
print('save at %s' % model_path)
plt.plot(np.arange(len(loss_log)) * loss_freq, loss_log)
plt.savefig('%s_loss.png' % model_path)
elif args.mode == 'eval':
saver.restore(sess, model_path)
print('load from %s' % model_path)
else:
raise
# evaluate
batch_data = through(datas, max_time, batch_size, vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
pred = sess.run(fetches=decoder_prediction, feed_dict=feed_dict)
input_ = batch_data['encoder_inputs']
loss_val = sess.run(fetches=loss, feed_dict=feed_dict)
print('input sequences...\n{}'.format(input_))
print('predict sequences...\n{}'.format(pred))
print('loss: %f' % loss_val)
print('finish.')
def main(args):
# process config
c = Configs(args.config)
ROOT = os.environ['TENSOROFLOW']
model_path = '%s/examples/model/basic_nmt/model' % ROOT
PAD = c.const['PAD']
EOS = c.const['EOS']
train_step = c.option['train_step']
max_time = c.option['max_time']
batch_size = c.option['batch_size']
vocabulary_size = c.option['vocabulary_size']
input_embedding_size = c.option['embedding_size']
hidden_units = c.option['hidden_units']
source_train_data_path = c.data['source_train_data']
target_train_data_path = c.data['target_train_data']
source_valid_data_path = c.data['source_valid_data']
target_valid_data_path = c.data['target_valid_data']
source_test_data_path = c.data['source_test_data']
target_test_data_path = c.data['target_test_data']
# read data
source_dictionary, source_reverse_dictionary = build_dictionary(
read_words(source_train_data_path), vocabulary_size)
source_train_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_train_data_path)
]
target_dictionary, target_reverse_dictionary = build_dictionary(
read_words(target_train_data_path), vocabulary_size)
target_train_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_train_data_path)
]
source_valid_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_valid_data_path)
]
target_valid_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_valid_data_path)
]
source_test_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_test_data_path)
]
target_test_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_test_data_path)
]
# placeholder
encoder_inputs = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='encoder_inputs')
decoder_inputs = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='decoder_inputs')
decoder_labels = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='decoder_labels')
# embed
embeddings = tf.Variable(tf.random_uniform(
[vocabulary_size, input_embedding_size], -1.0, 1.0),
dtype=tf.float32,
name='embeddings')
encoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
encoder_inputs)
decoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
decoder_inputs)
# encoder
encoder_units = hidden_units
encoder_cell = tf.contrib.rnn.LSTMCell(encoder_units)
_, encoder_final_state = tf.nn.dynamic_rnn(encoder_cell,
encoder_inputs_embedded,
dtype=tf.float32,
time_major=True)
# decoder
decoder_units = encoder_units
decoder_cell = tf.contrib.rnn.LSTMCell(decoder_units)
decoder_output, decoder_final_state = tf.nn.dynamic_rnn(
decoder_cell,
decoder_inputs_embedded,
initial_state=encoder_final_state,
scope="plain_decoder",
dtype=tf.float32,
time_major=True)
decoder_logits = tf.contrib.layers.linear(decoder_output, vocabulary_size)
decoder_prediction = tf.argmax(
decoder_logits, 2) # max_time: axis=0, batch: axis=1, vocab: axis=2
# optimizer
stepwise_cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(decoder_labels,
depth=vocabulary_size,
dtype=tf.float32),
logits=decoder_logits,
)
loss = tf.reduce_mean(stepwise_cross_entropy)
train_op = tf.train.AdamOptimizer().minimize(loss)
saver = tf.train.Saver()
batch_idx = {'train': 0, 'valid': 0, 'test': 0}
with tf.Session() as sess:
if args.mode == 'train':
# train
loss_freq = train_step // 100
loss_log = []
loss_suffix = ''
es = EarlyStopper(max_size=5, edge_threshold=0.1)
m = Monitor(train_step)
sess.run(tf.global_variables_initializer())
for i in range(train_step):
m.monitor(i, loss_suffix)
source_train_batch, _ = batchnize(source_train_datas,
batch_size,
batch_idx['train'])
target_train_batch, batch_idx['train'] = batchnize(
target_train_datas, batch_size, batch_idx['train'])
batch_data = seq2seq(source_train_batch, target_train_batch,
max_time, vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
sess.run(fetches=[train_op, loss], feed_dict=feed_dict)
if i % loss_freq == 0:
source_valid_batch, _ = batchnize(source_valid_datas,
batch_size,
batch_idx['valid'])
target_valid_batch, batch_idx['valid'] = batchnize(
target_valid_datas, batch_size, batch_idx['valid'])
batch_data = seq2seq(source_valid_batch,
target_valid_batch, max_time,
vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
loss_val = sess.run(fetches=loss, feed_dict=feed_dict)
loss_log.append(loss_val)
loss_suffix = 'loss: %f' % loss_val
es_status = es(loss_val)
if i > train_step // 2 and es_status:
print('early stopping at step: %d' % i)
break
saver.save(sess, model_path)
print('save at %s' % model_path)
plt.plot(np.arange(len(loss_log)) * loss_freq, loss_log)
plt.savefig('%s_loss.png' % model_path)
elif args.mode == 'eval':
saver.restore(sess, model_path)
print('load from %s' % model_path)
else:
raise
# evaluate
loss_val = []
input_vectors = None
predict_vectors = None
for i in range(len(source_test_datas) // batch_size + 1):
source_test_batch, _ = batchnize(source_test_datas, batch_size,
batch_idx['test'])
target_test_batch, batch_idx['test'] = batchnize(
target_test_datas, batch_size, batch_idx['test'])
batch_data = seq2seq(source_test_batch, target_test_batch,
max_time, vocabulary_size)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
pred = sess.run(fetches=decoder_prediction, feed_dict=feed_dict)
if predict_vectors is None:
predict_vectors = pred.T
else:
predict_vectors = np.vstack((predict_vectors, pred.T))
input_ = batch_data['encoder_inputs']
if input_vectors is None:
input_vectors = input_.T
else:
input_vectors = np.vstack((input_vectors, input_.T))
loss_val.append(sess.run(fetches=loss, feed_dict=feed_dict))
input_sentences = ''
predict_sentences = ''
for i, (input_vector, predict_vector) in enumerate(
zip(input_vectors[:len(source_test_datas)],
predict_vectors[:len(target_test_datas)])):
input_sentences += ' '.join([
source_reverse_dictionary[vector] for vector in input_vector
if not vector == PAD
])
predict_sentences += ' '.join([
target_reverse_dictionary[vector] for vector in predict_vector
if not vector == PAD
])
if i < len(source_test_datas) - 1:
input_sentences += '\n'
predict_sentences += '\n'
evaluate_input_path = '%s.evaluate_input' % model_path
evaluate_predict_path = '%s.evaluate_predict' % model_path
with open(evaluate_input_path, 'w') as f1, \
open(evaluate_predict_path, 'w') as f2:
f1.write(input_sentences)
f2.write(predict_sentences)
print('input sequences at {}'.format(evaluate_input_path))
print('predict sequences at {}'.format(evaluate_predict_path))
print('mean of loss: %f' % np.mean(loss_val))
print('finish.')
def main(args):
# process config
c = Configs(args.config)
ROOT = os.environ['TENSOROFLOW']
output = c.option.get('output', 'examples/model/buf')
model_directory = '%s/%s' % (ROOT, output)
model_path = '%s/model' % model_directory
dictionary_path = {
'source': '%s/source_dictionary.pickle' % model_directory,
'source_reverse':
'%s/source_reverse_dictionary.pickle' % model_directory,
'target': '%s/target_dictionary.pickle' % model_directory,
'target_reverse':
'%s/target_reverse_dictionary.pickle' % model_directory
}
PAD = c.const['PAD']
BOS = c.const['BOS']
EOS = c.const['EOS']
train_step = c.option['train_step']
max_time = c.option['max_time']
batch_size = c.option['batch_size']
vocabulary_size = c.option['vocabulary_size']
input_embedding_size = c.option['embedding_size']
hidden_units = c.option['hidden_units']
layers = c.option['layers']
source_train_data_path = c.data['source_train_data']
target_train_data_path = c.data['target_train_data']
source_valid_data_path = c.data['source_valid_data']
target_valid_data_path = c.data['target_valid_data']
source_test_data_path = c.data['source_test_data']
target_test_data_path = c.data['target_test_data']
# initialize output directory
if pathlib.Path(model_directory).exists():
print('Warning: model %s is exists.')
print('Old model will be overwritten.')
while True:
print('Do you wanna continue? [yes|no]')
command = input('> ')
if command == 'yes':
shutil.rmtree(model_directory)
break
elif command == 'no':
sys.exit()
else:
print('You can only input "yes" or "no".')
print('Make new model: %s' % model_directory)
pathlib.Path(model_directory).mkdir()
# read data
if args.mode == 'train':
source_dictionary, source_reverse_dictionary = build_dictionary(
read_words(source_train_data_path), vocabulary_size)
source_train_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_train_data_path)
]
target_dictionary, target_reverse_dictionary = build_dictionary(
read_words(target_train_data_path), vocabulary_size)
target_train_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_train_data_path)
]
source_valid_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_valid_data_path)
]
target_valid_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_valid_data_path)
]
if args.debug:
source_train_datas = source_train_datas[:1000]
target_train_datas = source_train_datas[:1000]
else:
with open(dictionary_path['source'], 'rb') as f1, \
open(dictionary_path['source_reverse'], 'rb') as f2, \
open(dictionary_path['target'], 'rb') as f3, \
open(dictionary_path['target_reverse'], 'rb') as f4:
source_dictionary = pickle.load(f1)
source_reverse_dictionary = pickle.load(f2)
target_dictionary = pickle.load(f3)
target_reverse_dictionary = pickle.load(f4)
source_test_datas = [
sentence_to_onehot(lines, source_dictionary)
for lines in read_data(source_test_data_path)
]
target_test_datas = [
sentence_to_onehot(lines, target_dictionary)
for lines in read_data(target_test_data_path)
]
# placeholder
encoder_inputs = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='encoder_inputs')
decoder_inputs = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='decoder_inputs')
decoder_labels = tf.placeholder(shape=(None, None),
dtype=tf.int32,
name='decoder_labels')
# embed
embeddings = tf.Variable(tf.random_uniform(
[vocabulary_size, input_embedding_size], -1.0, 1.0),
dtype=tf.float32,
name='embeddings')
encoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
encoder_inputs)
decoder_inputs_embedded = tf.nn.embedding_lookup(embeddings,
decoder_inputs)
# encoder with bidirection
encoder_units = hidden_units
encoder_layers_fw = [
tf.contrib.rnn.LSTMCell(size) for size in [encoder_units] * layers
]
encoder_cell_fw = tf.contrib.rnn.MultiRNNCell(encoder_layers_fw)
encoder_layers_bw = [
tf.contrib.rnn.LSTMCell(size) for size in [encoder_units] * layers
]
encoder_cell_bw = tf.contrib.rnn.MultiRNNCell(encoder_layers_bw)
(encoder_output_fw,
encoder_output_bw), encoder_state = tf.nn.bidirectional_dynamic_rnn(
encoder_cell_fw,
encoder_cell_bw,
encoder_inputs_embedded,
dtype=tf.float32,
time_major=True)
encoder_outputs = tf.concat((encoder_output_fw, encoder_output_bw), 2)
encoder_state = tuple(
tf.contrib.rnn.LSTMStateTuple(
tf.concat((encoder_state[0][layer].c,
encoder_state[1][layer].c), 1),
tf.concat((encoder_state[0][layer].h,
encoder_state[1][layer].h), 1))
for layer in range(layers))
# decoder with attention
decoder_units = encoder_units * 2
attention_units = decoder_units
decoder_layers = [
tf.contrib.rnn.LSTMCell(size) for size in [decoder_units] * layers
]
cell = tf.contrib.rnn.MultiRNNCell(decoder_layers)
sequence_length = tf.cast([max_time] * batch_size, dtype=tf.int32)
beam_width = 1
tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
encoder_outputs, multiplier=beam_width)
tiled_encoder_final_state = tf.contrib.seq2seq.tile_batch(
encoder_state, multiplier=beam_width)
tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
sequence_length, multiplier=beam_width)
attention_mechanism = tf.contrib.seq2seq.LuongAttention(
num_units=attention_units,
memory=tiled_encoder_outputs,
memory_sequence_length=tiled_sequence_length)
attention_cell = tf.contrib.seq2seq.AttentionWrapper(
cell, attention_mechanism, attention_layer_size=256)
decoder_initial_state = attention_cell.zero_state(dtype=tf.float32,
batch_size=batch_size *
beam_width)
decoder_initial_state = decoder_initial_state.clone(
cell_state=tiled_encoder_final_state)
if args.mode == 'train':
helper = tf.contrib.seq2seq.TrainingHelper(
inputs=decoder_inputs_embedded,
sequence_length=tf.cast([max_time] * batch_size, dtype=tf.int32),
time_major=True)
elif args.mode == 'eval':
"""
helper = tf.contrib.seq2seq.TrainingHelper(
inputs=decoder_inputs_embedded,
sequence_length=tf.cast([max_time] * batch_size, dtype=tf.int32),
time_major=True)
"""
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding=embeddings,
start_tokens=tf.tile([BOS], [batch_size]),
end_token=EOS)
decoder = tf.contrib.seq2seq.BasicDecoder(
cell=attention_cell,
helper=helper,
initial_state=decoder_initial_state)
decoder_outputs = tf.contrib.seq2seq.dynamic_decode(
decoder=decoder,
output_time_major=True,
impute_finished=False,
maximum_iterations=max_time)
decoder_logits = tf.contrib.layers.linear(decoder_outputs[0][0],
vocabulary_size)
decoder_prediction = tf.argmax(
decoder_logits, 2) # max_time: axis=0, batch: axis=1, vocab: axis=2
# optimizer
stepwise_cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(decoder_labels,
depth=vocabulary_size,
dtype=tf.float32),
logits=decoder_logits,
)
loss = tf.reduce_mean(stepwise_cross_entropy)
regularizer = 0.0 * tf.nn.l2_loss(decoder_outputs[0][0])
train_op = tf.train.AdamOptimizer().minimize(loss + regularizer)
saver = tf.train.Saver()
minibatch_idx = {'train': 0, 'valid': 0, 'test': 0}
with tf.Session() as sess:
if args.mode == 'train':
# train
global_max_step = train_step * (
len(source_train_datas) // batch_size + 1)
loss_freq = global_max_step // 100 if global_max_step > 100 else 1
loss_log = []
batch_loss_log = []
loss_suffix = ''
es = EarlyStopper(max_size=5, edge_threshold=0.1)
m = Monitor(global_max_step)
log = Logger('%s/log' % model_directory)
sess.run(tf.global_variables_initializer())
global_step = 0
stop_flag = False
for batch in range(train_step):
if stop_flag:
break
current_batch_loss_log = []
while True: # minibatch process
m.monitor(global_step, loss_suffix)
source_train_batch, _ = batchnize(source_train_datas,
batch_size,
minibatch_idx['train'])
target_train_batch, minibatch_idx['train'] = batchnize(
target_train_datas, batch_size, minibatch_idx['train'])
batch_data = seq2seq(source_train_batch,
target_train_batch,
max_time,
vocabulary_size,
reverse=True)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
sess.run(fetches=[train_op, loss], feed_dict=feed_dict)
if global_step % loss_freq == 0:
source_valid_batch, _ = batchnize(
source_valid_datas, batch_size,
minibatch_idx['valid'])
target_valid_batch, minibatch_idx['valid'] = batchnize(
target_valid_datas, batch_size,
minibatch_idx['valid'])
batch_data = seq2seq(source_valid_batch,
target_valid_batch,
max_time,
vocabulary_size,
reverse=True)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
loss_val = sess.run(fetches=loss, feed_dict=feed_dict)
loss_log.append(loss_val)
current_batch_loss_log.append(loss_val)
loss_suffix = 'loss: %f' % loss_val
global_step += 1
if minibatch_idx['train'] == 0:
batch_loss = np.mean(current_batch_loss_log)
batch_loss_log.append(batch_loss)
loss_msg = 'Batch: {}/{}, batch loss: {}'.format(
batch + 1, train_step, batch_loss)
print(loss_msg)
log(loss_msg)
es_status = es(batch_loss)
if batch > train_step // 2 and es_status:
print('early stopping at step: %d' % global_step)
stop_flag = True
break
# save tf.graph and variables
saver.save(sess, model_path)
print('save at %s' % model_path)
# save plot of loss
plt.plot(np.arange(len(loss_log)) * loss_freq, loss_log)
plt.savefig('%s_global_loss.png' % model_path)
plt.figure()
plt.plot(np.arange(len(batch_loss_log)), batch_loss_log)
plt.savefig('%s_batch_loss.png' % model_path)
# save dictionary
with open(dictionary_path['source'], 'wb') as f1, \
open(dictionary_path['source_reverse'], 'wb') as f2, \
open(dictionary_path['target'], 'wb') as f3, \
open(dictionary_path['target_reverse'], 'wb') as f4:
pickle.dump(source_dictionary, f1)
pickle.dump(source_reverse_dictionary, f2)
pickle.dump(target_dictionary, f3)
pickle.dump(target_reverse_dictionary, f4)
elif args.mode == 'eval':
saver.restore(sess, model_path)
print('load from %s' % model_path)
else:
raise # args.mode should be train or eval
# evaluate
loss_val = []
input_vectors = None
predict_vectors = None
for i in range(len(source_test_datas) // batch_size + 1):
source_test_batch, _ = batchnize(source_test_datas, batch_size,
minibatch_idx['test'])
target_test_batch, minibatch_idx['test'] = batchnize(
target_test_datas, batch_size, minibatch_idx['test'])
batch_data = seq2seq(source_test_batch,
target_test_batch,
max_time,
vocabulary_size,
reverse=True)
feed_dict = {
encoder_inputs: batch_data['encoder_inputs'],
decoder_inputs: batch_data['decoder_inputs'],
decoder_labels: batch_data['decoder_labels']
}
pred = sess.run(fetches=decoder_prediction, feed_dict=feed_dict)
if predict_vectors is None:
predict_vectors = pred.T
else:
predict_vectors = np.vstack((predict_vectors, pred.T))
input_ = batch_data['encoder_inputs']
if input_vectors is None:
input_vectors = input_.T
else:
input_vectors = np.vstack((input_vectors, input_.T))
loss_val.append(sess.run(fetches=loss, feed_dict=feed_dict))
input_sentences = ''
predict_sentences = ''
ignore_token = EOS
for i, (input_vector, predict_vector) in enumerate(
zip(input_vectors[:len(source_test_datas)],
predict_vectors[:len(target_test_datas)])):
input_sentences += ' '.join([
source_reverse_dictionary[vector] for vector in input_vector
if not vector == ignore_token
])
predict_sentences += ' '.join([
target_reverse_dictionary[vector] for vector in predict_vector
if not vector == ignore_token
])
if i < len(source_test_datas) - 1:
input_sentences += '\n'
predict_sentences += '\n'
evaluate_input_path = '%s.evaluate_input' % model_path
evaluate_predict_path = '%s.evaluate_predict' % model_path
with open(evaluate_input_path, 'w') as f1, \
open(evaluate_predict_path, 'w') as f2:
f1.write(input_sentences)
f2.write(predict_sentences)
print('input sequences at {}'.format(evaluate_input_path))
print('predict sequences at {}'.format(evaluate_predict_path))
print('mean of loss: %f' % np.mean(loss_val))
print('finish.')
