def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name', type=str, required=True)
parser.add_argument('--vocabulary_file', type=str, required=True)
parser.add_argument('--output_file', type=str, required=True)
parser.add_argument('--seed', type=str, default="Once upon a time, ")
parser.add_argument('--sample_length', type=int, default=1500)
parser.add_argument('--log_frequency', type=int, default=100)
args = parser.parse_args()
model_name = args.model_name
vocabulary_file = args.vocabulary_file
output_file = args.output_file
seed = args.seed.decode('utf-8')
sample_length = args.sample_length
log_frequency = args.log_frequency
model = Model(model_name)
model.restore()
classifier = model.get_classifier()
vocabulary = Vocabulary()
vocabulary.retrieve(vocabulary_file)
sample_file = codecs.open(output_file, 'w', 'utf_8')
stack = deque([])
for i in range(0, model.sequence_length - len(seed)):
stack.append(u' ')
for char in seed:
if char not in vocabulary.vocabulary:
print char,"is not in vocabulary file"
char = u' '
stack.append(char)
sample_file.write(char)
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(model_name)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
for i in range(0, sample_length):
vector = []
for char in stack:
vector.append(vocabulary.binary_vocabulary[char])
vector = np.array([vector])
prediction = sess.run(classifier, feed_dict={model.x: vector})
predicted_char = vocabulary.char_lookup[np.argmax(prediction)]
stack.popleft()
stack.append(predicted_char)
sample_file.write(predicted_char)
if i % log_frequency == 0:
print "Progress: {}%".format((i * 100) / sample_length)
sample_file.close()
print "Sample saved in {}".format(output_file)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--training_file', type=str, required=True)
parser.add_argument('--vocabulary_file', type=str, required=True)
parser.add_argument('--model_name', type=str, required=True)
parser.add_argument('--epoch', type=int, default=200)
parser.add_argument('--batch_size', type=int, default=50)
parser.add_argument('--sequence_length', type=int, default=50)
parser.add_argument('--log_frequency', type=int, default=100)
parser.add_argument('--learning_rate', type=int, default=0.002)
parser.add_argument('--units_number', type=int, default=128)
parser.add_argument('--layers_number', type=int, default=2)
args = parser.parse_args()
training_file = args.training_file
vocabulary_file = args.vocabulary_file
model_name = args.model_name
epoch = args.epoch
batch_size = args.batch_size
sequence_length = args.sequence_length
log_frequency = args.log_frequency
learning_rate = args.learning_rate
batch = Batch(training_file, vocabulary_file, batch_size, sequence_length)
input_number = batch.vocabulary.size
classes_number = batch.vocabulary.size
units_number = args.units_number
layers_number = args.layers_number
print "Start training with epoch: {}, batch_size: {}, log_frequency: {}," \
"learning_rate: {}".format(epoch, batch_size, log_frequency, learning_rate)
if not os.path.exists(model_name):
os.makedirs(model_name)
model = Model(model_name)
model.build(input_number, sequence_length, layers_number, units_number, classes_number)
classifier = model.get_classifier()
cost = tf.reduce_mean(tf.square(classifier - model.y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
expected_prediction = tf.equal(tf.argmax(classifier, 1), tf.argmax(model.y, 1))
accuracy = tf.reduce_mean(tf.cast(expected_prediction, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
iteration = 0
while batch.dataset_full_passes < epoch:
iteration += 1
batch_x, batch_y = batch.get_next_batch()
batch_x = batch_x.reshape((batch_size, sequence_length, input_number))
sess.run(optimizer, feed_dict={model.x: batch_x, model.y: batch_y})
if iteration % log_frequency == 0:
acc = sess.run(accuracy, feed_dict={model.x: batch_x, model.y: batch_y})
loss = sess.run(cost, feed_dict={model.x: batch_x, model.y: batch_y})
print("Iteration {}, batch loss: {:.6f}, training accuracy: {:.5f}".format(iteration * batch_size,
loss, acc))
batch.clean()
print("Optimization done")
saver = tf.train.Saver(tf.global_variables())
checkpoint_path = "{}/{}.ckpt".format(model_name, model_name)
saver.save(sess, checkpoint_path, global_step=iteration * batch_size)
print("Model saved in {}".format(model_name))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--training_file', type=str, required=True)
parser.add_argument('--vocabulary_file', type=str, required=True)
parser.add_argument('--model_name', type=str, required=True)
parser.add_argument('--epoch', type=int, default=200)
parser.add_argument('--batch_size', type=int, default=50)
parser.add_argument('--sequence_length', type=int, default=50)
parser.add_argument('--log_frequency', type=int, default=100)
parser.add_argument('--learning_rate', type=int, default=0.002)
parser.add_argument('--units_number', type=int, default=128)
parser.add_argument('--layers_number', type=int, default=2)
args = parser.parse_args()
training_file = args.training_file
vocabulary_file = args.vocabulary_file
model_name = args.model_name
epoch = args.epoch
batch_size = args.batch_size
sequence_length = args.sequence_length
log_frequency = args.log_frequency
learning_rate = args.learning_rate
batch = Batch(training_file, vocabulary_file, batch_size, sequence_length)
input_number = batch.vocabulary.size
classes_number = batch.vocabulary.size
units_number = args.units_number
layers_number = args.layers_number
print "Start training with epoch: {}, batch_size: {}, log_frequency: {}," \
"learning_rate: {}".format(epoch, batch_size, log_frequency, learning_rate)
if not os.path.exists(model_name):
os.makedirs(model_name)
model = Model(model_name)
model.build(input_number, sequence_length, layers_number, units_number, classes_number)
classifier = model.get_classifier()
cost = tf.reduce_mean(tf.square(classifier - model.y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
expected_prediction = tf.equal(tf.argmax(classifier, 1), tf.argmax(model.y, 1))
accuracy = tf.reduce_mean(tf.cast(expected_prediction, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
iteration = 0
while batch.dataset_full_passes < epoch:
iteration += 1
batch_x, batch_y = batch.get_next_batch()
batch_x = batch_x.reshape((batch_size, sequence_length, input_number))
sess.run(optimizer, feed_dict={model.x: batch_x, model.y: batch_y})
if iteration % log_frequency == 0:
acc = sess.run(accuracy, feed_dict={model.x: batch_x, model.y: batch_y})
loss = sess.run(cost, feed_dict={model.x: batch_x, model.y: batch_y})
print("Iteration {}, batch loss: {:.6f}, training accuracy: {:.5f}".format(iteration * batch_size,
loss, acc))
batch.clean()
print("Optimization done")
saver = tf.train.Saver(tf.global_variables())
checkpoint_path = "{}/{}.ckpt".format(model_name, model_name)
saver.save(sess, checkpoint_path, global_step=iteration * batch_size)
print("Model saved in {}".format(model_name))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name', type=str, required=True)
parser.add_argument('--vocabulary_file', type=str, required=True)
parser.add_argument('--output_file', type=str, required=True)
parser.add_argument('--seed', type=str, default="Once upon a time, ")
parser.add_argument('--sample_length', type=int, default=1500)
parser.add_argument('--log_frequency', type=int, default=100)
args = parser.parse_args()
model_name = args.model_name
vocabulary_file = args.vocabulary_file
output_file = args.output_file
seed = args.seed.decode('utf-8')
sample_length = args.sample_length
log_frequency = args.log_frequency
model = Model(model_name)
model.restore()
classifier = model.get_classifier()
vocabulary = Vocabulary()
vocabulary.retrieve(vocabulary_file)
sample_file = codecs.open(output_file, 'w', 'utf_8')
stack = deque([])
for i in range(0, model.sequence_length - len(seed)):
stack.append(u' ')
for char in seed:
if char not in vocabulary.vocabulary:
print char,"is not in vocabulary file"
char = u' '
stack.append(char)
sample_file.write(char)
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(model_name)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
for i in range(0, sample_length):
vector = []
for char in stack:
vector.append(vocabulary.binary_vocabulary[char])
vector = np.array([vector])
prediction = sess.run(classifier, feed_dict={model.x: vector})
predicted_char = vocabulary.char_lookup[np.argmax(prediction)]
stack.popleft()
stack.append(predicted_char)
sample_file.write(predicted_char)
if i % log_frequency == 0:
print "Progress: {}%".format((i * 100) / sample_length)
sample_file.close()
print "Sample saved in {}".format(output_file)