def __init__(self,
is_training,
learning_rate=1.0,
optimizer="sgd",
max_grad_norm=5,
num_layers=2,
use_lstm=True,
num_steps=35,
num_steps_valid=120,
proj_size=650,
hidden_size=650,
hidden_proj=650,
num_samples=512,
init_scale=0.1,
dropout_rate=0.0,
lr_decay=0.8,
batch_size=20,
attentive=False,
projection_attention_f=None,
output_form=lm_ops.OUTPUT_CONCAT,
vocab_size=10000):
with tf.device("/gpu:0"):
if attentive:
assert projection_attention_f is not None
self.batch_size = batch_size = batch_size
self.num_steps = num_steps
self.num_steps_valid = num_steps_valid
vocab_size = vocab_size
self._input_data_train = []
self._targets_train = []
self.mask_train = []
for i in xrange(num_steps): # Last bucket is the biggest one.
self.input_data_train.append(
tf.placeholder(tf.int32,
shape=[None],
name="input_train{0}".format(i)))
self.targets_train.append(
tf.placeholder(tf.int32,
shape=[None],
name="target_train{0}".format(i)))
self.mask_train.append(
tf.placeholder(tf.float32,
shape=[None],
name="mask_train{0}".format(i)))
self._input_data_valid = []
self._targets_valid = []
self.mask_valid = []
for i in xrange(
num_steps_valid): # Last bucket is the biggest one.
self.input_data_valid.append(
tf.placeholder(tf.int32,
shape=[None],
name="input_valid{0}".format(i)))
self.targets_valid.append(
tf.placeholder(tf.int32,
shape=[None],
name="target_valid{0}".format(i)))
self.mask_valid.append(
tf.placeholder(tf.float32,
shape=[None],
name="mask_valid{0}".format(i)))
hidden_projection = None
if hidden_proj > 0:
hidden_projection = hidden_proj
self.cell = cells.build_lm_multicell_rnn(
num_layers,
hidden_size,
proj_size,
use_lstm=use_lstm,
hidden_projection=hidden_projection,
dropout=dropout_rate)
self.dropout_feed = tf.placeholder(tf.float32, name="dropout_rate")
self._initial_state_train = self.cell.zero_state(
batch_size, tf.float32)
self._initial_state_valid = self.cell.zero_state(1, tf.float32)
# learning rate ops
self.learning_rate = tf.Variable(float(learning_rate),
trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * lr_decay)
# epoch ops
self.epoch = tf.Variable(0, trainable=False)
self.epoch_update_op = self.epoch.assign(self.epoch + 1)
# samples seen ops
self.samples_seen = tf.Variable(0, trainable=False)
self.samples_seen_update_op = self.samples_seen.assign(
self.samples_seen + batch_size)
self.samples_seen_reset_op = self.samples_seen.assign(0)
# global step variable - controled by the model
self.global_step = tf.Variable(0.0, trainable=False)
# average loss ops
self.current_ppx = tf.Variable(1.0, trainable=False)
self.current_loss = tf.Variable(0.0, trainable=False)
# self.current_loss_update_op = None
self.best_eval_ppx = tf.Variable(numpy.inf, trainable=False)
self.estop_counter = tf.Variable(0, trainable=False)
self.estop_counter_update_op = self.estop_counter.assign(
self.estop_counter + 1)
self.estop_counter_reset_op = self.estop_counter.assign(0)
initializer = tf.random_uniform_initializer(minval=init_scale,
maxval=init_scale,
seed=_SEED)
out_proj = hidden_size
if hidden_proj > 0:
out_proj = hidden_proj
with tf.device("/cpu:0"):
w = tf.get_variable("proj_w", [out_proj, vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [vocab_size])
self.output_projection = (w, b)
sampled_softmax = False
# Sampled softmax only makes sense if we sample less than vocabulary size.
if 0 < num_samples < vocab_size:
sampled_softmax = True
def sampled_loss(logits, labels):
with tf.device("/cpu:0"):
labels = tf.reshape(labels, [-1, 1])
losses = tf.nn.sampled_softmax_loss(
w_t, b, logits, labels, num_samples, vocab_size)
return losses
loss_function = sampled_loss
with tf.device("/cpu:0"):
# input come as one big tensor so we have to split it into a list of tensors to run the rnn cell
embedding = tf.Variable(tf.random_uniform(
[vocab_size, proj_size],
minval=-init_scale,
maxval=init_scale),
name="embedding")
# embedding = tf.get_variable("embedding", [vocab_size, proj_size])
inputs_train = [
tf.nn.embedding_lookup(embedding, i)
for i in self.input_data_train
]
inputs_valid = [
tf.nn.embedding_lookup(embedding, i)
for i in self.input_data_valid
]
with tf.variable_scope("RNN", initializer=initializer):
if attentive:
outputs_train, state_train, _ = lm_ops.apply_attentive_lm(
self.cell,
inputs_train,
sequence_length=array_ops.squeeze(
math_ops.add_n(self.mask_train)),
projection_attention_f=projection_attention_f,
output_form=output_form,
dropout=self.dropout_feed,
initializer=initializer,
dtype=tf.float32)
outputs_valid, state_valid, _ = lm_ops.apply_attentive_lm(
self.cell,
inputs_valid,
sequence_length=array_ops.squeeze(
math_ops.add_n(self.mask_valid)),
projection_attention_f=projection_attention_f,
output_form=output_form,
dropout=self.dropout_feed,
initializer=initializer,
dtype=tf.float32)
else:
outputs_train, state_train = lm_ops.apply_lm(
self.cell,
inputs_train,
sequence_length=math_ops.add_n(self.mask_train),
dropout=self.dropout_feed,
dtype=tf.float32)
outputs_valid, state_valid = lm_ops.apply_lm(
self.cell,
inputs_valid,
sequence_length=math_ops.add_n(self.mask_valid),
dropout=self.dropout_feed,
dtype=tf.float32)
if sampled_softmax is False:
logits_train = [
tf.nn.xw_plus_b(o, self.output_projection[0],
self.output_projection[1])
for o in outputs_train
]
logits_valid = [
tf.nn.xw_plus_b(o, self.output_projection[0],
self.output_projection[1])
for o in outputs_valid
]
else:
logits_train = outputs_train
logits_valid = outputs_valid
loss_train = seq2seq.sequence_loss_by_example(
logits_train,
self.targets_train,
self.mask_train,
average_across_timesteps=True)
loss_valid = seq2seq.sequence_loss_by_example(
logits_valid,
self.targets_valid,
self.mask_valid,
average_across_timesteps=True)
self._cost_train = cost = tf.reduce_sum(loss_train) / float(
batch_size)
self._final_state_train = state_train
self._cost_valid = tf.reduce_sum(loss_valid) / float(batch_size)
self._final_state_valid = state_valid
if not is_training:
return
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),
max_grad_norm)
opt = optimization_ops.get_optimizer(optimizer, learning_rate)
self._train_op = opt.apply_gradients(zip(grads, tvars),
global_step=self.global_step)
self._valid_op = tf.no_op()
self.saver = tf.train.Saver(tf.all_variables())
self.saver_best = tf.train.Saver(tf.all_variables())
def __init__(self,
source_vocab_size,
target_vocab_size,
buckets,
source_proj_size,
target_proj_size,
encoder_size,
decoder_size,
max_gradient_norm,
batch_size,
learning_rate,
learning_rate_decay_factor,
optimizer='sgd',
input_feeding=False,
combine_inp_attn=False,
dropout=0.0,
attention_f=None,
window_size=10,
content_function=None,
decoder_attention_f="None",
num_samples=512,
forward_only=False,
max_len=100,
cpu_only=False,
early_stop_patience=0,
save_best_model=True,
dtype=tf.float32):
super(NMTModel, self).__init__()
if cpu_only:
device = "/cpu:0"
else:
device = "/gpu:0"
with tf.device(device):
self.source_vocab_size = source_vocab_size
self.target_vocab_size = target_vocab_size
self.buckets = buckets
self.batch_size = batch_size
self.attention_f = attention_f
self.content_function = content_function
self.window_size = window_size
self.combine_inp_attn = combine_inp_attn
if decoder_attention_f == "None":
self.decoder_attention_f = None
else:
self.decoder_attention_f = decoder_attention_f
# learning rate ops
self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(self.learning_rate * learning_rate_decay_factor)
# epoch ops
self.epoch = tf.Variable(0, trainable=False)
self.epoch_update_op = self.epoch.assign(self.epoch + 1)
# samples seen ops
self.samples_seen = tf.Variable(0, trainable=False)
self.samples_seen_update_op = self.samples_seen.assign(self.samples_seen + batch_size)
self.samples_seen_reset_op = self.samples_seen.assign(0)
# global step variable - controled by the model
self.global_step = tf.Variable(0.0, trainable=False)
# average loss ops
self.current_loss = tf.Variable(0.0, trainable=False)
self.current_loss_update_op = None
self.avg_loss = tf.Variable(0.0, trainable=False)
self.avg_loss_update_op = self.avg_loss.assign(tf.div(self.current_loss, self.global_step))
if early_stop_patience > 0 or save_best_model:
self.best_eval_loss = tf.Variable(numpy.inf, trainable=False)
self.estop_counter = tf.Variable(0, trainable=False)
self.estop_counter_update_op = self.estop_counter.assign(self.estop_counter + 1)
self.estop_counter_reset_op = self.estop_counter.assign(0)
else:
self.best_eval_loss = None
self.estop_counter = None
self.estop_counter_update_op = None
self.estop_counter_reset_op = None
self.source_proj_size = source_proj_size
self.target_proj_size = target_proj_size
self.encoder_size = encoder_size
self.decoder_size = decoder_size
self.input_feeding = input_feeding
self.max_len = max_len
self.dropout = dropout
self.dropout_feed = tf.placeholder(tf.float32, name="dropout_rate")
self.step_num = tf.Variable(0, trainable=False)
self.dtype = dtype
# If we use sampled softmax, we need an output projection.
loss_function = None
with tf.device("/cpu:0"):
w = tf.get_variable("proj_w", [decoder_size, self.target_vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [self.target_vocab_size])
self.output_projection = (w, b)
self.sampled_softmax = False
# Sampled softmax only makes sense if we sample less than vocabulary size.
if 0 < num_samples < self.target_vocab_size:
self.sampled_softmax = True
def sampled_loss(inputs, labels):
with tf.device("/cpu:0"):
labels = tf.reshape(labels, [-1, 1])
return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
self.target_vocab_size)
loss_function = sampled_loss
# create the embedding matrix - this must be done in the CPU for now
with tf.device("/cpu:0"):
self.src_embedding = tf.Variable(
tf.truncated_normal(
[source_vocab_size, source_proj_size], stddev=0.01
),
name='embedding_src'
)
# decoder with attention
with tf.name_scope('decoder_with_attention') as scope:
# create this variable to be used inside the embedding_attention_decoder
self.tgt_embedding = tf.Variable(
tf.truncated_normal(
[target_vocab_size, target_proj_size], stddev=0.01
),
name='embedding'
)
# Create the internal multi-layer cell for our RNN.
self.encoder_cell_fw, self.encoder_cell_bw, self.decoder_cell = cells.build_nmt_bidirectional_cell(
encoder_size, decoder_size, source_proj_size, target_proj_size, dropout=dropout)
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, decoder_inputs):
return self.inference(encoder_inputs, decoder_inputs)
# Feeds for inputs.
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.
self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="encoder{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None, ], name="decoder{0}".format(i)))
self.target_weights.append(tf.placeholder(tf.float32, shape=[None], name="weight{0}".format(i)))
# Our targets are decoder inputs shifted by one.
targets = [self.decoder_inputs[i + 1]
for i in xrange(len(self.decoder_inputs) - 1)]
self.decoder_states_holders = None
# Training outputs and losses.
if forward_only:
# self.batch_size = beam_size
for i in xrange(len(self.encoder_inputs), self.max_len):
self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="encoder{0}".format(i)))
b_size = array_ops.shape(self.encoder_inputs[0])[0]
# context, decoder_initial_state, attention_states, input_length
self.ret0, self.ret1, self.ret2 = self.encode(self.encoder_inputs, b_size)
self.decoder_init_plcholder = tf.placeholder(tf.float32,
shape=[None, (target_proj_size) * 2],
name="decoder_init")
# shape of this placeholder: the first None indicate the batch size and the second the input length
self.attn_plcholder = tf.placeholder(tf.float32,
shape=[None, self.ret2.get_shape()[1], target_proj_size],
name="attention_states")
# decoder_states = None
if self.decoder_attention_f is not None:
self.decoder_states_holders = tf.placeholder(tf.float32, shape=[None, None, 1, decoder_size],
name="decoder_state")
decoder_states = self.decoder_states_holders
self.logits, self.states = attention_decoder_nmt(
decoder_inputs=[self.decoder_inputs[0]], initial_state=self.decoder_init_plcholder,
attention_states=self.attn_plcholder, cell=self.decoder_cell,
num_symbols=target_vocab_size, attention_f=attention_f,
window_size=window_size, content_function=content_function,
decoder_attention_f=decoder_attention_f, combine_inp_attn=combine_inp_attn,
input_feeding=input_feeding, dropout=self.dropout_feed, initializer=None,
dtype=dtype
)
# If we use output projection, we need to project outputs for decoding.
self.logits = tf.nn.xw_plus_b(self.logits[-1], self.output_projection[0], self.output_projection[1])
self.logits = nn_ops.softmax(self.logits)
else:
tf_version = pkg_resources.get_distribution("tensorflow").version
if tf_version == "0.6.0" or tf_version == "0.5.0":
self.outputs, self.losses = seq2seq.model_with_buckets(
encoder_inputs=self.encoder_inputs, decoder_inputs=self.decoder_inputs,
targets=targets, weights=self.target_weights, num_decoder_symbols=self.target_vocab_size,
buckets=buckets, seq2seq=lambda x, y: seq2seq_f(x, y), softmax_loss_function=loss_function)
else:
self.outputs, self.losses = model_with_buckets(
encoder_inputs=self.encoder_inputs, decoder_inputs=self.decoder_inputs,
targets=targets, weights=self.target_weights, buckets=buckets,
seq2seq_f=lambda x, y: seq2seq_f(x, y), softmax_loss_function=loss_function)
# Gradients and SGD update operation for training the model.
params = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.updates = []
# opt = tf.train.GradientDescentOptimizer(self.learning_rate)
opt = optimization_ops.get_optimizer(optimizer, learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(self.losses[b], params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients,
max_gradient_norm)
self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(clipped_gradients, params), global_step=self.global_step))
self.saver = tf.train.Saver(tf.all_variables())
self.saver_best = tf.train.Saver(tf.all_variables())
def __init__(self,
is_training,
learning_rate=1.0,
optimizer="sgd",
max_grad_norm=5,
num_layers=2,
use_lstm=True,
num_steps=35,
num_steps_valid=120,
proj_size=650,
hidden_size=650,
hidden_proj=650,
num_samples=512,
init_scale=0.1,
dropout_rate=0.0,
lr_decay=0.8,
batch_size=20,
attentive=False,
projection_attention_f=None,
output_form=lm_ops.OUTPUT_CONCAT,
vocab_size=10000):
with tf.device("/gpu:0"):
if attentive:
assert projection_attention_f is not None
self.batch_size = batch_size = batch_size
self.num_steps = num_steps
self.num_steps_valid = num_steps_valid
vocab_size = vocab_size
self._input_data_train = []
self._targets_train = []
self.mask_train = []
for i in xrange(num_steps): # Last bucket is the biggest one.
self.input_data_train.append(tf.placeholder(tf.int32, shape=[None], name="input_train{0}".format(i)))
self.targets_train.append(tf.placeholder(tf.int32, shape=[None], name="target_train{0}".format(i)))
self.mask_train.append(tf.placeholder(tf.float32, shape=[None], name="mask_train{0}".format(i)))
self._input_data_valid = []
self._targets_valid = []
self.mask_valid = []
for i in xrange(num_steps_valid): # Last bucket is the biggest one.
self.input_data_valid.append(tf.placeholder(tf.int32, shape=[None], name="input_valid{0}".format(i)))
self.targets_valid.append(tf.placeholder(tf.int32, shape=[None], name="target_valid{0}".format(i)))
self.mask_valid.append(tf.placeholder(tf.float32, shape=[None], name="mask_valid{0}".format(i)))
hidden_projection = None
if hidden_proj > 0:
hidden_projection = hidden_proj
self.cell = cells.build_lm_multicell_rnn(num_layers, hidden_size, proj_size, use_lstm=use_lstm,
hidden_projection=hidden_projection, dropout=dropout_rate)
self.dropout_feed = tf.placeholder(tf.float32, name="dropout_rate")
self._initial_state_train = self.cell.zero_state(batch_size, tf.float32)
self._initial_state_valid = self.cell.zero_state(1, tf.float32)
# learning rate ops
self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(self.learning_rate * lr_decay)
# epoch ops
self.epoch = tf.Variable(0, trainable=False)
self.epoch_update_op = self.epoch.assign(self.epoch + 1)
# samples seen ops
self.samples_seen = tf.Variable(0, trainable=False)
self.samples_seen_update_op = self.samples_seen.assign(self.samples_seen + batch_size)
self.samples_seen_reset_op = self.samples_seen.assign(0)
# global step variable - controled by the model
self.global_step = tf.Variable(0.0, trainable=False)
# average loss ops
self.current_ppx = tf.Variable(1.0, trainable=False)
self.current_loss = tf.Variable(0.0, trainable=False)
# self.current_loss_update_op = None
self.best_eval_ppx = tf.Variable(numpy.inf, trainable=False)
self.estop_counter = tf.Variable(0, trainable=False)
self.estop_counter_update_op = self.estop_counter.assign(self.estop_counter + 1)
self.estop_counter_reset_op = self.estop_counter.assign(0)
initializer = tf.random_uniform_initializer(minval=init_scale, maxval=init_scale, seed=_SEED)
out_proj = hidden_size
if hidden_proj > 0:
out_proj = hidden_proj
with tf.device("/cpu:0"):
w = tf.get_variable("proj_w", [out_proj, vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [vocab_size])
self.output_projection = (w, b)
sampled_softmax = False
# Sampled softmax only makes sense if we sample less than vocabulary size.
if 0 < num_samples < vocab_size:
sampled_softmax = True
def sampled_loss(logits, labels):
with tf.device("/cpu:0"):
labels = tf.reshape(labels, [-1, 1])
losses = tf.nn.sampled_softmax_loss(w_t, b, logits, labels, num_samples, vocab_size)
return losses
loss_function = sampled_loss
with tf.device("/cpu:0"):
# input come as one big tensor so we have to split it into a list of tensors to run the rnn cell
embedding = tf.Variable(
tf.random_uniform(
[vocab_size, proj_size],
minval=-init_scale, maxval=init_scale
),
name="embedding"
)
# embedding = tf.get_variable("embedding", [vocab_size, proj_size])
inputs_train = [tf.nn.embedding_lookup(embedding, i) for i in self.input_data_train]
inputs_valid = [tf.nn.embedding_lookup(embedding, i) for i in self.input_data_valid]
with tf.variable_scope("RNN", initializer=initializer):
if attentive:
outputs_train, state_train, _ = lm_ops.apply_attentive_lm(
self.cell, inputs_train, sequence_length=array_ops.squeeze(math_ops.add_n(self.mask_train)),
projection_attention_f=projection_attention_f, output_form=output_form,
dropout=self.dropout_feed, initializer=initializer, dtype=tf.float32
)
outputs_valid, state_valid, _ = lm_ops.apply_attentive_lm(
self.cell, inputs_valid, sequence_length=array_ops.squeeze(math_ops.add_n(self.mask_valid)),
projection_attention_f=projection_attention_f, output_form=output_form,
dropout=self.dropout_feed, initializer=initializer, dtype=tf.float32
)
else:
outputs_train, state_train = lm_ops.apply_lm(
self.cell, inputs_train, sequence_length=math_ops.add_n(self.mask_train),
dropout=self.dropout_feed, dtype=tf.float32
)
outputs_valid, state_valid = lm_ops.apply_lm(
self.cell, inputs_valid, sequence_length=math_ops.add_n(self.mask_valid),
dropout=self.dropout_feed, dtype=tf.float32
)
if sampled_softmax is False:
logits_train = [tf.nn.xw_plus_b(o, self.output_projection[0], self.output_projection[1])
for o in outputs_train]
logits_valid = [tf.nn.xw_plus_b(o, self.output_projection[0], self.output_projection[1])
for o in outputs_valid]
else:
logits_train = outputs_train
logits_valid = outputs_valid
loss_train = seq2seq.sequence_loss_by_example(
logits_train, self.targets_train, self.mask_train, average_across_timesteps=True
)
loss_valid = seq2seq.sequence_loss_by_example(
logits_valid, self.targets_valid, self.mask_valid, average_across_timesteps=True
)
self._cost_train = cost = tf.reduce_sum(loss_train) / float(batch_size)
self._final_state_train = state_train
self._cost_valid = tf.reduce_sum(loss_valid) / float(batch_size)
self._final_state_valid = state_valid
if not is_training:
return
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),
max_grad_norm)
opt = optimization_ops.get_optimizer(optimizer, learning_rate)
self._train_op = opt.apply_gradients(zip(grads, tvars), global_step=self.global_step)
self._valid_op = tf.no_op()
self.saver = tf.train.Saver(tf.all_variables())
self.saver_best = tf.train.Saver(tf.all_variables())
