def _test_multi_bleu(self, hypotheses, references, lowercase,
expected_bleu):
#pylint: disable=R0201
"""Runs a multi-bleu test."""
result = bleu.moses_multi_bleu(hypotheses=hypotheses,
references=references,
lowercase=lowercase)
np.testing.assert_almost_equal(result, expected_bleu, decimal=2)
def metric_fn(self, hypotheses, references):
return bleu.moses_multi_bleu(hypotheses, references, lowercase=False)
def metric_fn(self, hypotheses, references): return bleu.moses_multi_bleu(hypotheses, references, lowercase=False)
def metric_fn(self, hypotheses, references):
score = bleu.moses_multi_bleu(hypotheses, references, lowercase=False)
print ("Done calculating BLEU " + str(score))
return score
def _test_multi_bleu(self, hypotheses, references, lowercase, expected_bleu):
#pylint: disable=R0201
"""Runs a multi-bleu test."""
result = bleu.moses_multi_bleu(
hypotheses=hypotheses, references=references, lowercase=lowercase)
np.testing.assert_almost_equal(result, expected_bleu, decimal=2)
def metric_fn(self, hypotheses, references):
print("hypotheses={}, references={}".format(hypotheses, references))
return bleu.moses_multi_bleu(hypotheses, references, lowercase=False)
def train(self, X, Y, batch_size, no_of_epochs, checkpoint_factor, model_save_path, model_name, mem_fraction=0.5):
'''
The training_function for the model.
'''
# Setup a tensorboard_writer:
tensorboard_writer = self.__get_tensorboard_writer(model_save_path)
# setup the data for training:
# obtain the padded training data:
train_X_field = X[0]; train_X_content = X[1]
train_Y = Y; no_of_total_examples = len(train_X_field)
# print len(train_X_field), len(train_X_content), len(train_Y)
assert len(train_X_field) == len(train_X_content) and len(train_X_field) == len(train_Y), "input data lengths incompatible"
''' Start the actual Training loop: '''
print("\n\nStarting the Training ... ")
# use the given memory usage fraction for this training process
tensor_config = tf.ConfigProto()
tensor_config.gpu_options.allow_growth = False
tensor_config.gpu_options.per_process_gpu_memory_fraction = mem_fraction
print("tensorflow configuration user:"******"gpu memory fraction used:", mem_fraction)
with tf.Session(config=tensor_config, graph=self.graph) as sess:
# create a saver object:
saver = tf.train.Saver(max_to_keep=3)
# create the checkpoint file:
checkpoint_file = os.path.join(model_save_path, "checkpoint")
# If old weights found, restart the training from there:
if(os.path.isfile(checkpoint_file)):
# load the saved weights:
saver.restore(sess, tf.train.latest_checkpoint(model_save_path))
# load the global_step value from the checkpoint file
with open(checkpoint_file, 'r') as checkpoint:
path = checkpoint.readline().strip()
global_step = int((path.split(':')[1]).split('-')[1][:-1])
# otherwise initialize all the weights
else:
global_step = 0
sess.run(self.init)
# print("uninitialized variables:")
# print(sess.run(tf.report_uninitialized_variables()))
# run a loop for no_of_epochs iterations:
for epoch in range(no_of_epochs):
print("------------------------------------------------------------------------------------------------------------")
print("current_epoch: ", (epoch + 1))
# Iterate over the batches of the given train data:
for batch_no in range(int(np.ceil(float(no_of_total_examples) / batch_size))):
# obtain the current batch of data:
start = (batch_no * batch_size); end = start + batch_size
batch_inp_field = train_X_field[start: end]
batch_inp_conte = train_X_content[start: end]
batch_inp_label = train_Y[start: end]
# pad the current batch of data:
inp_field = pad_sequences(batch_inp_field)
inp_conte = pad_sequences(batch_inp_conte)
inp_label = pad_sequences(batch_inp_label)
# extract the sequence lengths of examples in this batch
inp_lengths = get_lengths(batch_inp_field)
lab_lengths = get_lengths(batch_inp_label)
# execute the cost and the train_step
_, cost = sess.run([self.train_step, self.loss], feed_dict = {
self.inp_field_encodings: inp_field,
self.inp_content_encodings: inp_conte,
self.inp_label_encodings: inp_label,
self.inp_sequence_lengths: inp_lengths,
self.lab_sequence_lengths: lab_lengths
})
print("Range: ", "[", start, "-", (start + len(inp_field)), "]", " Cost: ", cost)
if((global_step + 1) % checkpoint_factor == 0 or global_step == 0):
# generate the summary for this batch:
sums, predicts = sess.run([self.all_summaries, self.outputs], feed_dict = {
self.inp_field_encodings: inp_field,
self.inp_content_encodings: inp_conte,
self.inp_label_encodings: inp_label,
self.inp_sequence_lengths: inp_lengths,
self.lab_sequence_lengths: lab_lengths
})
# save this generated summary to the summary file
tensorboard_writer.add_summary(sums, global_step=global_step)
# also save the model
saver.save(sess, os.path.join(model_save_path, model_name), global_step=global_step)
# print a random sample from this batch:
random_index = np.random.randint(len(inp_field))
random_label_sample = inp_label[random_index]
random_predicts_sample = np.argmax(predicts, axis = -1)[random_index]
hypotheses = reduce(lambda x,y: x + " " + y,
[self.content_label_vocabulary[label] for label in random_predicts_sample])
references = reduce(lambda x,y: x + " " + y,
[self.content_label_vocabulary[label] for label in random_label_sample])
# print the extracted sample in meaningful format
print("\nOriginal Summary: ")
print(references)
print("\nPredicted Summary: ")
print(hypotheses)
# calculate the Bleu score
print("\nCalculating the Blue score ...")
print("Bleu_Score =", moses_multi_bleu(np.array([hypotheses]), np.array([references])))
global_step += 1
print("------------------------------------------------------------------------------------------------------------")
print("Training complete ...\n\n")
def train(self,
X,
Y,
batch_size,
no_of_epochs,
checkpoint_factor,
model_save_path,
model_name,
mem_fraction=0.5):
'''
The training_function for the model.
'''
# Setup a tensorboard_writer:
tensorboard_writer = self.__get_tensorboard_writer(model_save_path)
# setup the data for training:
# obtain the padded training data:
train_X_field = X[0]
train_X_content = X[1]
train_Y = Y
no_of_total_examples = len(train_X_field)
# print len(train_X_field), len(train_X_content), len(train_Y)
assert len(train_X_field) == len(train_X_content) and len(
train_X_field) == len(train_Y), "input data lengths incompatible"
''' Start the actual Training loop: '''
print("\n\nStarting the Training ... ")
# use the given memory usage fraction for this training process
tensor_config = tf.ConfigProto()
tensor_config.gpu_options.allow_growth = False
tensor_config.gpu_options.per_process_gpu_memory_fraction = mem_fraction
print("tensorflow configuration user:"******"gpu memory fraction used:", mem_fraction)
with tf.Session(config=tensor_config, graph=self.graph) as sess:
# create a saver object:
saver = tf.train.Saver(max_to_keep=3)
# create the checkpoint file:
checkpoint_file = os.path.join(model_save_path, "checkpoint")
# If old weights found, restart the training from there:
if (os.path.isfile(checkpoint_file)):
# load the saved weights:
saver.restore(sess,
tf.train.latest_checkpoint(model_save_path))
# load the global_step value from the checkpoint file
with open(checkpoint_file, 'r') as checkpoint:
path = checkpoint.readline().strip()
global_step = int((path.split(':')[1]).split('-')[1][:-1])
# otherwise initialize all the weights
else:
global_step = 0
sess.run(self.init)
# print("uninitialized variables:")
# print(sess.run(tf.report_uninitialized_variables()))
# run a loop for no_of_epochs iterations:
for epoch in range(no_of_epochs):
print(
"------------------------------------------------------------------------------------------------------------"
)
print("current_epoch: ", (epoch + 1))
# Iterate over the batches of the given train data:
for batch_no in range(
int(np.ceil(float(no_of_total_examples) /
batch_size))):
# obtain the current batch of data:
start = (batch_no * batch_size)
end = start + batch_size
batch_inp_field = train_X_field[start:end]
batch_inp_conte = train_X_content[start:end]
batch_inp_label = train_Y[start:end]
# pad the current batch of data:
inp_field = pad_sequences(batch_inp_field)
inp_conte = pad_sequences(batch_inp_conte)
inp_label = pad_sequences(batch_inp_label)
# extract the sequence lengths of examples in this batch
inp_lengths = get_lengths(batch_inp_field)
lab_lengths = get_lengths(batch_inp_label)
# execute the cost and the train_step
_, cost = sess.run(
[self.train_step, self.loss],
feed_dict={
self.inp_field_encodings: inp_field,
self.inp_content_encodings: inp_conte,
self.inp_label_encodings: inp_label,
self.inp_sequence_lengths: inp_lengths,
self.lab_sequence_lengths: lab_lengths
})
print("Range: ", "[", start, "-", (start + len(inp_field)),
"]", " Cost: ", cost)
if ((global_step + 1) % checkpoint_factor == 0
or global_step == 0):
# generate the summary for this batch:
sums, predicts = sess.run(
[self.all_summaries, self.outputs],
feed_dict={
self.inp_field_encodings: inp_field,
self.inp_content_encodings: inp_conte,
self.inp_label_encodings: inp_label,
self.inp_sequence_lengths: inp_lengths,
self.lab_sequence_lengths: lab_lengths
})
# save this generated summary to the summary file
tensorboard_writer.add_summary(sums,
global_step=global_step)
# also save the model
saver.save(sess,
os.path.join(model_save_path, model_name),
global_step=global_step)
# print a random sample from this batch:
random_index = np.random.randint(len(inp_field))
random_label_sample = inp_label[random_index]
random_predicts_sample = np.argmax(
predicts, axis=-1)[random_index]
hypotheses = reduce(lambda x, y: x + " " + y, [
self.content_label_vocabulary[label]
for label in random_predicts_sample
])
references = reduce(lambda x, y: x + " " + y, [
self.content_label_vocabulary[label]
for label in random_label_sample
])
# print the extracted sample in meaningful format
print("\nOriginal Summary: ")
print(references)
print("\nPredicted Summary: ")
print(hypotheses)
# calculate the Bleu score
print("\nCalculating the Blue score ...")
print(
"Bleu_Score =",
moses_multi_bleu(np.array([hypotheses]),
np.array([references])))
global_step += 1
print(
"------------------------------------------------------------------------------------------------------------"
)
print("Training complete ...\n\n")