def train(args):
print("[%s] Preparing dialog data in %s" %
(args.model_name, args.data_dir))
setup_workpath(workspace=args.workspace)
train_data, dev_data, _ = data_utils.prepare_dialog_data(
args.data_dir, args.vocab_size)
#### GET DATA ###### inti beer
def get_gold(workspace=args.workspace):
data_dir = "%s/data" % (workspace)
full_path = str(sys.path[-1]) + "/" + data_dir + "/train/chat.txt.gz"
print(full_path)
with gzip.open(full_path, 'rb') as zi:
test_sentences = zi.read()
test_sentences = test_sentences.decode().split("\n")
zi.close()
return test_sentences
######get data
data_ = get_gold()
if args.reinforce_learn:
args.batch_size = 1 # We decode one sentence at a time.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_usage)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Create model.
print("Creating %d layers of %d units." % (args.num_layers, args.size))
model = seq2seq_model_utils.create_model(sess,
args,
forward_only=False)
# Read data into buckets and compute their sizes.
print("Reading development and training data (limit: %d)." %
args.max_train_data_size)
dev_set = data_utils.read_data(dev_data,
args.buckets,
reversed=args.rev_model)
train_set = data_utils.read_data(train_data,
args.buckets,
args.max_train_data_size,
reversed=args.rev_model)
train_bucket_sizes = [
len(train_set[b]) for b in xrange(len(args.buckets))
]
train_total_size = float(sum(train_bucket_sizes))
# A bucket scale is a list of increasing numbers from 0 to 1 that we'll use
# to select a bucket. Length of [scale[i], scale[i+1]] is proportional to
# the size if i-th training bucket, as used later.
train_buckets_scale = [
sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))
]
# This is the training loop.
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
# Load vocabularies.
vocab_path = os.path.join(args.data_dir,
"vocab%d.in" % args.vocab_size)
vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
while True:
# Choose a bucket according to data distribution. We pick a random number
# in [0, 1] and use the corresponding interval in train_buckets_scale.
random_number_01 = np.random.random_sample()
bucket_id = min([
i for i in xrange(len(train_buckets_scale))
if train_buckets_scale[i] > random_number_01
])
# Get a batch and make a step.
start_time = time.time()
encoder_inputs, decoder_inputs, target_weights, encoder_input, decoder_input = model.get_batch(
train_set, bucket_id)
print("[shape]", np.shape(encoder_inputs),
np.shape(decoder_inputs), np.shape(target_weights))
if args.reinforce_learn:
_, step_loss, _ = model.step_rf(args,
sess,
encoder_inputs,
decoder_inputs,
target_weights,
bucket_id,
data_,
encoder_input,
decoder_input,
rev_vocab=rev_vocab,
forward_only=False)
else:
_, step_loss, _ = model.step(sess,
encoder_inputs,
decoder_inputs,
target_weights,
bucket_id,
forward_only=False,
force_dec_input=True)
step_time += (time.time() - start_time) / args.steps_per_checkpoint
loss += step_loss / args.steps_per_checkpoint
current_step += 1
print("Current step: " + str(current_step))
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % args.steps_per_checkpoint == 0: #and (not args.reinforce_learn):
# Print statistics for the previous epoch.
perplexity = math.exp(loss) if loss < 300 else float('inf')
print(
"global step %d learning rate %.4f step-time %.2f perplexity %.2f @ %s"
% (model.global_step.eval(), model.learning_rate.eval(),
step_time, perplexity, datetime.now()))
# Decrease learning rate if no improvement was seen over last 3 times.
if len(previous_losses) > 2 and loss > max(
previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
# # Save checkpoint and zero timer and loss.
checkpoint_path = os.path.join(args.model_dir, "model.ckpt")
model.saver.save(sess,
checkpoint_path,
global_step=model.global_step)
step_time, loss = 0.0, 0.0
# Run evals on development set and print their perplexity.
for bucket_id in xrange(len(args.buckets)):
encoder_inputs, decoder_inputs, target_weights, _, _ = model.get_batch(
dev_set, bucket_id)
_, eval_loss, _ = model.step(sess,
encoder_inputs,
decoder_inputs,
target_weights,
bucket_id,
forward_only=True,
force_dec_input=False)
eval_ppx = math.exp(
eval_loss) if eval_loss < 300 else float('inf')
print(" eval: bucket %d perplexity %.2f" %
(bucket_id, eval_ppx))
sys.stdout.flush()
def train():
print("Preparing dialog data in %s" % FLAGS.data_dir)
train_data, dev_data, _ = data_utils.prepare_dialog_data(
FLAGS.data_dir, FLAGS.vocab_size)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
record_file_name = "seq2seq_loss"
record_file = open(pjoin(FLAGS.results_dir, record_file_name),
mode="ab",
buffering=0)
# Create model.
print("Creating %d layers of %d units." %
(FLAGS.num_layers, FLAGS.hidden_size))
model = create_model(sess, forward_only=False)
# Read data into buckets and compute their sizes.
print("Reading development and training data (limit: %d)." %
FLAGS.max_train_data_size)
dev_set = read_data(dev_data)
train_set = read_data(train_data, FLAGS.max_train_data_size)
train_bucket_sizes = [len(train_set[b]) for b in xrange(len(BUCKETS))]
train_total_size = float(sum(train_bucket_sizes))
# A bucket scale is a list of increasing numbers from 0 to 1 that we'll use
# to select a bucket. Length of [scale[i], scale[i+1]] is proportional to
# the size if i-th training bucket, as used later.
train_buckets_scale = [
sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))
]
# This is the training loop.
step_time, total_time, loss = 0.0, 0.0, 0.0
previous_losses = []
print("start train...")
while (True):
# Choose a bucket according to data distribution. We pick a random number
# in [0, 1] and use the corresponding interval in train_buckets_scale.
random_number_01 = np.random.random_sample()
bucket_id = min([
i for i in xrange(len(train_buckets_scale))
if train_buckets_scale[i] > random_number_01
])
# Get a batch and make a step.
start_time = time.time()
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
train_set, bucket_id, "train")
_, step_loss, _ = model.step(sess,
encoder_inputs,
decoder_inputs,
target_weights,
bucket_id,
forward_only=False)
step_time += (time.time() -
start_time) / FLAGS.steps_per_checkpoint
loss += step_loss / FLAGS.steps_per_checkpoint
# Once in a while, we save checkpoint, print statistics, and run evals.
if model.global_step.eval() % FLAGS.steps_per_checkpoint == 0:
print("\nTraining...")
if loss > 6:
print("inf !!!")
sys.exit(0)
total_time = step_time * FLAGS.steps_per_checkpoint
print(
"global step %d learning rate %.4f step-time %.2f total_time %.4f, loss %0.7f"
% (model.global_step.eval(), model.learning_rate.eval(),
step_time, total_time, loss))
# Decrease learning rate if no improvement was seen over last 3 times.
if len(previous_losses) > 2 and loss > max(
previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
# Run evals on development set and print their perplexity.
for bucket_id in xrange(len(BUCKETS)):
print("Testing...")
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
dev_set, bucket_id, "train")
_, eval_loss, _ = model.step(sess, encoder_inputs,
decoder_inputs,
target_weights, bucket_id,
True)
if eval_loss > 6:
print("inf !!!")
sys.exit(0)
print("eval: bucket %d, loss %0.5f" %
(bucket_id, eval_loss))
sys.stdout.flush()
record_file.write("%d\t%.5f\t%.5f\n" %
(model.global_step.eval(), loss, eval_loss))
# Save checkpoint and zero timer and loss.
if model.global_step.eval(
) % FLAGS.steps_per_predictpoint == 0:
checkpoint_path = os.path.join(FLAGS.model_dir,
"model.ckpt")
model.saver.save(sess,
checkpoint_path,
global_step=model.global_step)
step_time, loss = 0.0, 0.0
def train():
print("Preparing dialog data in %s" % FLAGS.data_dir)
train_data, dev_data, _ = data_utils.prepare_dialog_data(
FLAGS.data_dir, FLAGS.vocab_size)
with tf.Session() as sess:
# Create model.
print("Creating %d layers of %d units." %
(FLAGS.num_layers, FLAGS.size))
model = create_model(sess, forward_only=False)
print("Reading development and training data (limit: %d)." %
FLAGS.max_train_data_size)
dev_set = read_data(dev_data)
train_set = read_data(train_data, FLAGS.max_train_data_size)
train_bucket_sizes = [len(train_set[b]) for b in xrange(len(BUCKETS))]
train_total_size = float(sum(train_bucket_sizes))
train_buckets_scale = [
sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))
]
# This is the training loop.
print("Start training ...")
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
while True:
random_number_01 = np.random.random_sample()
bucket_id = min([
i for i in xrange(len(train_buckets_scale))
if train_buckets_scale[i] > random_number_01
])
# Get a batch and make a step.
start_time = time.time()
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
train_set, bucket_id)
_, step_loss, _ = model.step(sess,
encoder_inputs,
decoder_inputs,
target_weights,
bucket_id,
forward_only=False)
step_time += (time.time() -
start_time) / FLAGS.steps_per_checkpoint
loss += step_loss / FLAGS.steps_per_checkpoint
current_step += 1
if current_step % FLAGS.steps_per_checkpoint == 0:
perplexity = math.exp(loss) if loss < 300 else float('inf')
print(
"global step %d learning rate %.4f step-time %.2f perplexity %.2f"
% (model.global_step.eval(), model.learning_rate.eval(),
step_time, perplexity))
if len(previous_losses) > 2 and loss > max(
previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
checkpoint_path = os.path.join(FLAGS.model_dir, "model.ckpt")
model.saver.save(sess,
checkpoint_path,
global_step=model.global_step)
step_time, loss = 0.0, 0.0
for bucket_id in xrange(len(BUCKETS)):
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
dev_set, bucket_id)
_, eval_loss, _ = model.step(sess, encoder_inputs,
decoder_inputs,
target_weights, bucket_id,
True)
eval_ppx = math.exp(
eval_loss) if eval_loss < 300 else float('inf')
print(" eval: bucket %d perplexity %.2f" %
(bucket_id, eval_ppx))
sys.stdout.flush()
def train(args):
print("[%s] Preparing dialog data in %s" % (args.model_name, args.data_dir))
setup_workpath(workspace=args.workspace)
train_data, dev_data, _ = data_utils.prepare_dialog_data(args.data_dir, args.vocab_size)
if args.reinforce_learn:
args.batch_size = 1 # We decode one sentence at a time.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_usage)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Create model.
print("Creating %d layers of %d units." % (args.num_layers, args.size))
model = seq2seq_model_utils.create_model(sess, args, forward_only=False)
# Read data into buckets and compute their sizes.
print("Reading development and training data (limit: %d)." % args.max_train_data_size)
dev_set = data_utils.read_data(dev_data, args.buckets, reversed=args.rev_model)
train_set = data_utils.read_data(train_data, args.buckets, args.max_train_data_size, reversed=args.rev_model)
train_bucket_sizes = [len(train_set[b]) for b in xrange(len(args.buckets))]
train_total_size = float(sum(train_bucket_sizes))
# A bucket scale is a list of increasing numbers from 0 to 1 that we'll use
# to select a bucket. Length of [scale[i], scale[i+1]] is proportional to
# the size if i-th training bucket, as used later.
train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))]
# This is the training loop.
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
# Load vocabularies.
vocab_path = os.path.join(args.data_dir, "vocab%d.in" % args.vocab_size)
vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
while True:
# Choose a bucket according to data distribution. We pick a random number
# in [0, 1] and use the corresponding interval in train_buckets_scale.
random_number_01 = np.random.random_sample()
bucket_id = min([i for i in xrange(len(train_buckets_scale))
if train_buckets_scale[i] > random_number_01])
# Get a batch and make a step.
start_time = time.time()
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
train_set, bucket_id)
# print("[shape]", np.shape(encoder_inputs), np.shape(decoder_inputs), np.shape(target_weights))
if args.reinforce_learn:
_, step_loss, _ = model.step_rf(args, sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, rev_vocab=rev_vocab)
else:
_, step_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, forward_only=False, force_dec_input=True)
step_time += (time.time() - start_time) / args.steps_per_checkpoint
loss += step_loss / args.steps_per_checkpoint
current_step += 1
# Once in a while, we save checkpoint, print statistics, and run evals.
if (current_step % args.steps_per_checkpoint == 0) and (not args.reinforce_learn):
# Print statistics for the previous epoch.
perplexity = math.exp(loss) if loss < 300 else float('inf')
print ("global step %d learning rate %.4f step-time %.2f perplexity %.2f @ %s" %
(model.global_step.eval(), model.learning_rate.eval(), step_time, perplexity, datetime.now()))
# Decrease learning rate if no improvement was seen over last 3 times.
if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
# # Save checkpoint and zero timer and loss.
checkpoint_path = os.path.join(args.model_dir, "model.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
step_time, loss = 0.0, 0.0
# Run evals on development set and print their perplexity.
for bucket_id in xrange(len(args.buckets)):
encoder_inputs, decoder_inputs, target_weights = model.get_batch(dev_set, bucket_id)
_, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, forward_only=True, force_dec_input=False)
eval_ppx = math.exp(eval_loss) if eval_loss < 300 else float('inf')
print(" eval: bucket %d perplexity %.2f" % (bucket_id, eval_ppx))
sys.stdout.flush()