def get_cell(n_hidden):
logging.info("Constructing cell of size={}".format(n_hidden))
if use_lstm:
logging.info("Using LSTM cells")
if initializer:
cell = rnn_cell.LSTMCell(n_hidden, initializer=initializer)
else:
# to use peephole connections, cell clipping or a projection layer, use LSTMCell
cell = rnn_cell.BasicLSTMCell(n_hidden)
else:
logging.info("Using GRU cells")
cell = rnn_cell.GRUCell(n_hidden)
if not forward_only and use_lstm and keep_prob < 1:
logging.info("Adding dropout wrapper around lstm cells")
cell = rnn_cell.DropoutWrapper(cell,
output_keep_prob=keep_prob)
if encoder == "bidirectional":
logging.info("Bidirectional model")
if init_backward:
logging.info(
"Use backward encoder state to initialize decoder state"
)
cell = BidirectionalRNNCell([cell] * 2)
elif encoder == "bow":
logging.info("BOW model")
if num_layers > 1:
logging.info("Model with %d layers for the decoder" %
num_layers)
cell = BOWCell(rnn_cell.MultiRNNCell([cell] * num_layers))
else:
cell = BOWCell(single_cell)
elif num_layers > 1:
logging.info("Model with %d layers" % num_layers)
cell = rnn_cell.MultiRNNCell([cell] * num_layers)
return cell
def __init__(self,
vocab_size,
buckets_or_sentence_length,
size,
num_layers,
max_gradient_norm,
batch_size,
learning_rate,
learning_rate_decay_factor,
model_type,
use_lstm=True,
num_samples=512,
forward_only=False):
"""Create the model. This constructor can be used to created an embedded or embedded-attention, bucketed or non-bucketed model made of single or multi-layer RNN cells.
Args:
vocab_size: size of the vocabulary.
target_vocab_size: size of the target vocabulary.
buckets_or_sentence_length:
if using buckets:
a list of pairs (I, O), where I specifies maximum input length
that will be processed in that bucket, and O specifies maximum output
length. Training instances that have inputs longer than I or outputs
longer than O will be pushed to the next bucket and padded accordingly.
We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
else:
number of the maximum number of words per sentence.
size: number of units in each layer of the model.
num_layers: number of layers in the model.
max_gradient_norm: gradients will be clipped to maximally this norm.
batch_size: the size of the batches used during training;
the model construction is independent of batch_size, so it can be
changed after initialization if this is convenient, e.g., for decoding.
learning_rate: learning rate to start with.
learning_rate_decay_factor: decay learning rate by this much when needed.
num_samples: number of samples for sampled softmax.
forward_only: if set, we do not construct the backward pass in the model.
"""
# Need to determine if we're using buckets or not:
if type(buckets_or_sentence_length) == list:
self.buckets = buckets_or_sentence_length
else:
self.max_sentence_length = buckets_or_sentence_length
self.vocab_size = vocab_size
self.batch_size = batch_size
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)
self.global_step = tf.Variable(0, trainable=False)
# Summary variables. NOTE: added these.
# self.summary_op_learning_rate = tf.scalar_summary('learning rate', self.learning_rate)
# self.summary_op_global_step = tf.scalar_summary('global step', self.global_step)
# If we use sampled softmax, we need an output projection.
output_projection = None
softmax_loss_function = None
# Sampled softmax only makes sense if we sample less than vocabulary size.
if num_samples > 0 and num_samples < self.vocab_size:
with tf.device("/cpu:0"):
w = tf.get_variable("proj_w", [size, self.vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [self.vocab_size])
output_projection = (w, b)
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.vocab_size)
softmax_loss_function = sampled_loss
# Create the internal multi-layer cell for our RNN.
single_cell = rnn_cell.GRUCell(size)
if use_lstm:
single_cell = rnn_cell.BasicLSTMCell(size)
cell = single_cell #i, j, f, o = array_ops.split(1, 4, concat)
if num_layers > 1:
cell = rnn_cell.MultiRNNCell(
[single_cell] *
num_layers) #cur_inp, array_ops.concat(1, new_states)
# The seq2seq function: we use embedding for the input and attention (if applicable).
if model_type is 'embedding_attention':
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return seq2seq.embedding_attention_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
vocab_size,
vocab_size,
output_projection=output_projection,
feed_previous=do_decode)
else: # just build embedding model, I should probably change this to throw an error
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return seq2seq.embedding_rnn_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
vocab_size,
vocab_size,
output_projection=output_projection,
feed_previous=do_decode)
# Feeds for inputs.
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
# NOTE: If the model is not bucketed, these try blocks will throw an AttributeError and execute code to build a non-bucketed model.
try:
encoder_range = self.buckets[-1][0]
decoder_range = self.buckets[-1][1]
except AttributeError:
encoder_range, decoder_range = self.max_sentence_length, self.max_sentence_length
for i in xrange(encoder_range): # 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(decoder_range + 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)
]
# Training outputs and losses.
try:
if forward_only:
self.outputs, self.losses = seq2seq.model_with_buckets(
self.encoder_inputs,
self.decoder_inputs,
targets,
self.target_weights,
self.buckets,
self.vocab_size,
lambda x, y: seq2seq_f(x, y, True),
softmax_loss_function=softmax_loss_function)
# If we use output projection, we need to project outputs for decoding.
if output_projection is not None:
for b in xrange(len(self.buckets)):
self.outputs[b] = [
tf.nn.xw_plus_b(output, output_projection[0],
output_projection[1])
for output in self.outputs[b]
]
else:
self.outputs, self.losses = seq2seq.model_with_buckets(
self.encoder_inputs,
self.decoder_inputs,
targets,
self.target_weights,
self.buckets,
self.vocab_size,
lambda x, y: seq2seq_f(x, y, False),
softmax_loss_function=softmax_loss_function)
except AttributeError:
if forward_only:
self.outputs, self.states = seq2seq_f(self.encoder_inputs,
self.decoder_inputs[:-1],
True)
self.losses = seq2seq.sequence_loss(
self.outputs,
targets,
self.target_weights[:-1],
self.vocab_size,
softmax_loss_function=softmax_loss_function)
# Project outputs for decoding
if output_projection is not None:
self.outputs = [
tf.nn.xw_plus_b(output, output_projection[0],
output_projection[1])
for output in self.outputs
]
else:
self.outputs, self.states = seq2seq_f(self.encoder_inputs,
self.decoder_inputs[:-1],
False)
self.losses = (seq2seq.sequence_loss(
self.outputs,
targets,
self.target_weights[:-1],
self.vocab_size,
softmax_loss_function=softmax_loss_function))
# Gradients and SGD update operation for training the model.
params = tf.trainable_variables()
self.params = params # Hold onto this for Woz
if not forward_only:
self.gradient_norms = []
self.updates = []
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
try:
for b in xrange(len(self.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))
except AttributeError:
gradients = tf.gradients(self.losses, params)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.gradient_norms = norm
self.updates = opt.apply_gradients(
zip(clipped_gradients, params),
global_step=self.global_step)
self.saver = tf.train.Saver(tf.all_variables())
def __init__(self, enc_out, target_vocab_size, buckets, embedding_size, hidden_size,
num_layers, batch_size, use_lstm=False, num_samples=512,
encoder="reverse", use_src_mask=False, maxout_layer=False, init_backward=False,
variable_prefix=None, init_const=False, use_bow_mask=False, initializer=None):
super(TFSeq2SeqSingleStepDecodingGraph, self).__init__(buckets, batch_size)
self.target_vocab_size = target_vocab_size
self.num_heads = 1
# If we use sampled softmax, we need an output projection.
output_projection = None
softmax_loss_function = None
# Sampled softmax only makes sense if we sample less than vocabulary size.
if num_samples > 0 and num_samples < self.target_vocab_size:
with variable_scope.variable_scope(variable_scope.get_variable_scope(),
reuse=True), tf.device("/cpu:0"):
w = tf.get_variable("proj_w", [hidden_size, self.target_vocab_size])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [self.target_vocab_size])
output_projection = (w, b)
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)
softmax_loss_function = sampled_loss
else:
logging.info("Using maxout_layer=%d and full softmax loss" % maxout_layer)
# Create the internal multi-layer cell for our RNN.
if use_lstm:
logging.info("Using LSTM cells of size={}".format(hidden_size))
if initializer:
single_cell = rnn_cell.LSTMCell(hidden_size, initializer=initializer)
else:
# NOTE: to use peephole connections, cell clipping or a projection layer, use LSTMCell instead
single_cell = rnn_cell.BasicLSTMCell(hidden_size)
else:
logging.info("Using GRU cells of size={}".format(hidden_size))
single_cell = rnn_cell.GRUCell(hidden_size)
cell = single_cell
if encoder == "bidirectional":
logging.info("Bidirectional model")
if init_backward:
logging.info("Use backward encoder state to initialize decoder state")
cell = BidirectionalRNNCell([single_cell] * 2)
elif encoder == "bow":
logging.info("BOW model")
if num_layers > 1:
logging.info("Model with %d layers for the decoder" % num_layers)
cell = BOWCell(rnn_cell.MultiRNNCell([single_cell] * num_layers))
else:
cell = BOWCell(single_cell)
elif num_layers > 1:
logging.info("Model with %d layers" % num_layers)
cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
# List of placeholders deeper within the decoder (i.e. bucket dependent)
self.enc_hidden = []
self.enc_hidden_features = []
self.enc_v = []
self.dec_attns = []
# Placeholder for last state
if encoder == "bidirectional":
if cell._cells[0]._state_is_tuple:
dec_state_c = tf.placeholder(dtypes.float32, shape=[None, cell.fw_state_size/2], name="dec_state_c")
dec_state_h = tf.placeholder(dtypes.float32, shape=[None, cell.fw_state_size/2], name="dec_state_h")
self.dec_state = rnn_cell.LSTMStateTuple(dec_state_c, dec_state_h)
else:
self.dec_state = tf.placeholder(dtypes.float32, shape=[None, cell.fw_state_size], name="dec_state")
elif encoder == "reverse" or encoder == "bow":
if cell._state_is_tuple:
dec_state_c = tf.placeholder(dtypes.float32, shape=[None, cell.state_size/2], name="dec_state_c")
dec_state_h = tf.placeholder(dtypes.float32, shape=[None, cell.state_size/2], name="dec_state_h")
self.dec_state = rnn_cell.LSTMStateTuple(dec_state_c, dec_state_h)
else:
self.dec_state = tf.placeholder(dtypes.float32, shape=[None, cell.state_size], name="dec_state")
if use_src_mask:
logging.info("Using source mask for decoder")
self.src_mask = tf.placeholder(dtypes.float32, shape=[None, None],
name="src_mask")
else:
self.src_mask = None
if use_bow_mask:
logging.info("Using bow mask for output layer")
self.bow_mask = tf.placeholder(dtypes.float32, shape=[None, None],
name="bow_mask")
else:
self.bow_mask = None
# placeholder to indicate whether we're at the start of the target sentence
self.start = tf.placeholder(tf.bool, name="start")
# The seq2seq function: we use embedding for the input and attention.
scope = None
if variable_prefix is not None:
scope = variable_prefix+"/embedding_attention_seq2seq"
logging.info("Using variable scope {}".format(scope))
def seq2seq_f(bucket_enc_out, decoder_input):
return self._tf_dec_embedding_attention_seq2seq(bucket_enc_out,
decoder_input, self.dec_state, cell, target_vocab_size, embedding_size,
output_projection=output_projection, encoder=encoder,
src_mask=self.src_mask, maxout_layer=maxout_layer, init_backward=init_backward,
start=self.start, scope=scope, init_const=init_const, bow_mask=self.bow_mask)
self.dec_decoder_input = tf.placeholder(tf.int32, shape=[None],
name="dec_decoder_input")
self.outputs = self._tf_dec_model_with_buckets(enc_out,
self.dec_decoder_input, buckets, seq2seq_f)
# If we use output projection, we need to project outputs for decoding.
if output_projection is not None:
# self.outputs contains outputs, new_attns, new_state in flattened list
for b in xrange(len(buckets)):
output = self.outputs[b][0]
''' Standard implementation uses following code here to get the previous output (_extract_argmax_and_embed):
output = tf.nn.xw_plus_b(output, output_projection[0],
output_projection[1])
However, we have to normalize during decoding using a softmax
(and then taking a log to produce logprobs),
as described in nn.py, def sampled_softmax_loss:
"This operation is for training only. It is generally an underestimate of the full softmax loss.
At inference time, you can compute full softmax probabilities with the expression
`tf.nn.softmax(tf.matmul(inputs, weights) + biases)`."
Note: tf.matmul(i, w) + b does the same as tf.nn.xw_plus_b(i, w, b)
'''
output = tf.log(tf.nn.softmax(tf.nn.xw_plus_b(output, output_projection[0],
output_projection[1])))
self.outputs[b][0] = output
else:
logging.info("Apply full softmax")
for b in xrange(len(buckets)):
self.outputs[b][0] = tf.log(tf.nn.softmax(self.outputs[b][0]))
# for update_buckets
self.enc_out = enc_out
self.seq2seq_f = seq2seq_f
self.output_projection = output_projection
def __init__(self, source_vocab_size, buckets, embedding_size, hidden_size,
num_layers, batch_size, use_lstm=False, num_samples=512,
encoder="reverse", use_sequence_length=False, init_backward=False,
variable_prefix=None, initializer=None):
super(TFSeq2SeqEncodingGraph, self).__init__(buckets, batch_size)
self.source_vocab_size = source_vocab_size
self.num_heads = 1
# Create the internal multi-layer cell for our RNN.
if use_lstm:
logging.info("Using LSTM cells of size={}".format(hidden_size))
if initializer:
single_cell = rnn_cell.LSTMCell(hidden_size, initializer=initializer)
else:
# NOTE: to use peephole connections, cell clipping or a projection layer, use LSTMCell instead
single_cell = rnn_cell.BasicLSTMCell(hidden_size)
else:
logging.info("Using GRU cells of size={}".format(hidden_size))
single_cell = rnn_cell.GRUCell(hidden_size)
cell = single_cell
if encoder == "bidirectional":
logging.info("Bidirectional model")
if init_backward:
logging.info("Use backward encoder state to initialize decoder state")
cell = BidirectionalRNNCell([single_cell] * 2)
elif encoder == "bow":
logging.info("BOW model")
if num_layers > 1:
logging.info("Model with %d layers for the decoder" % num_layers)
cell = BOWCell(rnn_cell.MultiRNNCell([single_cell] * num_layers))
else:
cell = BOWCell(single_cell)
elif num_layers > 1:
logging.info("Model with %d layers" % num_layers)
cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
# The seq2seq function: we use embedding for the input and attention.
scope = None
if variable_prefix is not None:
scope = variable_prefix+"/embedding_attention_seq2seq"
logging.info("Using variable scope {}".format(scope))
def seq2seq_f(encoder_inputs, bucket_length):
return self._tf_enc_embedding_attention_seq2seq(encoder_inputs, cell, source_vocab_size, embedding_size,
encoder=encoder,
sequence_length=self.sequence_length,
bucket_length=bucket_length,
init_backward=init_backward,
bow_emb_size=hidden_size,
scope=scope)
# Feeds for inputs.
self.encoder_inputs = []
self.sequence_lengths = []
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)))
if use_sequence_length:
logging.info("Using sequence length for encoder")
self.sequence_length = tf.placeholder(tf.int32, shape=[None], name="seq_len")
else:
self.sequence_length = None
self.outputs = self._tf_enc_model_with_buckets(self.encoder_inputs, buckets, seq2seq_f)
# for update_buckets
self.seq2seq_f = seq2seq_f
def __init__(self,
source_vocab_size,
target_vocab_size,
buckets,
embedding_size,
hidden_size,
num_layers,
max_gradient_norm,
batch_size,
learning_rate,
learning_rate_decay_factor,
use_lstm=False,
num_samples=512,
forward_only=False,
dtype=tf.float32,
opt_algorithm="sgd",
encoder="reverse",
use_sequence_length=False,
use_src_mask=False,
maxout_layer=False,
init_backward=False,
no_pad_symbol=False,
variable_prefix=None,
rename_variable_prefix=None,
init_const=False,
use_bow_mask=False,
max_to_keep=0,
keep_prob=1.0,
initializer=None,
legacy=False,
train_align=None):
"""Create the model.
Args:
source_vocab_size: size of the source vocabulary.
target_vocab_size: size of the target vocabulary.
buckets: a list of pairs (I, O), where I specifies maximum input length
that will be processed in that bucket, and O specifies maximum output
length. Training instances that have inputs longer than I or outputs
longer than O will be pushed to the next bucket and padded accordingly.
We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
size: number of units in each layer of the model.
num_layers: number of layers in the model.
max_gradient_norm: gradients will be clipped to maximally this norm.
batch_size: the size of the batches used during training;
the model construction is independent of batch_size, so it can be
changed after initialization if this is convenient, e.g., for decoding.
learning_rate: learning rate to start with.
learning_rate_decay_factor: decay learning rate by this much when needed.
use_lstm: if true, we use LSTM cells instead of GRU cells.
num_samples: number of samples for sampled softmax.
forward_only: if set, we do not construct the backward pass in the model.
dtype: the data type to use to store internal variables.
"""
self.source_vocab_size = source_vocab_size
self.target_vocab_size = target_vocab_size
self.buckets = buckets
self.batch_size = batch_size
with tf.variable_scope(variable_prefix or ""):
self.learning_rate = tf.Variable(float(learning_rate),
trainable=False)
self.global_step = tf.Variable(0, trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.no_pad_symbol = no_pad_symbol
# If we use sampled softmax, we need an output projection.
output_projection = None
softmax_loss_function = None
# Sampled softmax only makes sense if we sample less than vocabulary size.
if num_samples > 0 and num_samples < self.target_vocab_size:
w_t = tf.get_variable("proj_w",
[self.target_vocab_size, hidden_size],
dtype=dtype)
w = tf.transpose(w_t)
b = tf.get_variable("proj_b", [self.target_vocab_size],
dtype=dtype)
logging.info("Using output projection of shape (%d, %d)" %
(hidden_size, self.target_vocab_size))
output_projection = (w, b)
def sampled_loss(inputs, labels):
labels = tf.reshape(labels, [-1, 1])
# We need to compute the sampled_softmax_loss using 32bit floats to
# avoid numerical instabilities.
local_w_t = tf.cast(w_t, tf.float32)
local_b = tf.cast(b, tf.float32)
local_inputs = tf.cast(inputs, tf.float32)
return tf.cast(
tf.nn.sampled_softmax_loss(local_w_t, local_b,
local_inputs, labels,
num_samples,
self.target_vocab_size), dtype)
softmax_loss_function = sampled_loss
else:
logging.info("Using maxout_layer=%r and full softmax loss" %
maxout_layer)
# Create the internal multi-layer cell for our RNN.
if use_lstm:
logging.info("Using LSTM cells of size={}".format(hidden_size))
if initializer:
single_cell = rnn_cell.LSTMCell(hidden_size,
initializer=initializer)
else:
# NOTE: to use peephole connections, cell clipping or a projection layer, use LSTMCell instead
single_cell = rnn_cell.BasicLSTMCell(hidden_size)
else:
logging.info("Using GRU cells of size={}".format(hidden_size))
single_cell = rnn_cell.GRUCell(hidden_size)
cell = single_cell
if encoder == "bidirectional":
logging.info("Bidirectional model")
if init_backward:
logging.info(
"Use backward encoder state to initialize decoder state")
cell = BidirectionalRNNCell([single_cell] * 2)
elif encoder == "bow":
logging.info("BOW model")
if not forward_only and use_lstm and keep_prob < 1:
logging.info("Adding dropout wrapper around lstm cells")
single_cell = rnn_cell.DropoutWrapper(
single_cell, output_keep_prob=keep_prob)
if num_layers > 1:
logging.info("Model with %d layers for the decoder" %
num_layers)
cell = BOWCell(
rnn_cell.MultiRNNCell([single_cell] * num_layers))
else:
cell = BOWCell(single_cell)
elif num_layers > 1:
logging.info("Model with %d layers" % num_layers)
cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
# The seq2seq function: we use embedding for the input and attention.
logging.info("Embedding size={}".format(embedding_size))
scope = None
if variable_prefix is not None:
scope = variable_prefix + "/embedding_attention_seq2seq"
logging.info("Using variable scope {}".format(scope))
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode,
bucket_length):
return embedding_attention_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
num_encoder_symbols=source_vocab_size,
num_decoder_symbols=target_vocab_size,
embedding_size=embedding_size,
output_projection=output_projection,
feed_previous=do_decode,
dtype=dtype,
encoder=encoder,
sequence_length=self.sequence_length,
bucket_length=bucket_length,
src_mask=self.src_mask,
maxout_layer=maxout_layer,
init_backward=init_backward,
bow_emb_size=hidden_size,
scope=scope,
init_const=init_const,
bow_mask=self.bow_mask,
keep_prob=keep_prob,
legacy=legacy)
# Feeds for inputs.
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
self.alignments = []
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(dtype, shape=[None],
name="weight{0}".format(i)))
if train_align is not None and not forward_only:
for i in xrange(self.batch_size):
self.alignments.append(
tf.placeholder(tf.float32,
shape=[None],
name="align{0}".format(i)))
if use_sequence_length is True:
logging.info("Using sequence length for encoder")
self.sequence_length = tf.placeholder(tf.int32,
shape=[None],
name="seq_len")
else:
self.sequence_length = None
if use_src_mask:
logging.info("Using source mask for decoder")
self.src_mask = tf.placeholder(tf.float32,
shape=[None, None],
name="src_mask")
else:
self.src_mask = None
if use_bow_mask:
logging.info("Using bow mask for output layer")
self.bow_mask = tf.placeholder(tf.float32,
shape=[None, None],
name="bow_mask")
else:
self.bow_mask = None
# Our targets are decoder inputs shifted by one.
targets = [
self.decoder_inputs[i + 1]
for i in xrange(len(self.decoder_inputs) - 1)
]
# Training outputs and losses.
if forward_only:
self.outputs, self.losses = model_with_buckets(
self.encoder_inputs,
self.decoder_inputs,
targets,
self.target_weights,
buckets,
lambda x, y, z: seq2seq_f(x, y, True, z),
softmax_loss_function=softmax_loss_function)
# If we use output projection, we need to project outputs for decoding.
if output_projection is not None:
for b in xrange(len(buckets)):
# This is similar to what is done in the loop function (where xw_plus_b is used instead of matmul).
# The loop function also takes the argmax, but the result is not saved, we pass the logits
# and take the argmax again in the vanilla decoder.
self.outputs[b] = [
tf.matmul(output, output_projection[0]) +
output_projection[1] for output in self.outputs[b]
]
else:
self.outputs, self.losses = model_with_buckets(
self.encoder_inputs,
self.decoder_inputs,
targets,
self.target_weights,
buckets,
lambda x, y, z: seq2seq_f(x, y, False, z),
softmax_loss_function=softmax_loss_function,
alignments=self.alignments)
# Gradients and SGD update operation for training the model.
params = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.updates = []
if opt_algorithm == "sgd":
logging.info("Using optimizer GradientDescentOptimizer")
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
elif opt_algorithm == "adagrad":
print("Using optimizer AdagradOptimizer")
lr = 3.0
init_acc = 0.1
opt = tf.train.AdagradOptimizer(lr, init_acc)
elif opt_algorithm == "adadelta":
print("Using optimizer AdadeltaOptimizer")
rho = 0.95
epsilon = 1e-6
opt = tf.train.AdadeltaOptimizer(rho=rho, epsilon=epsilon)
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))
if variable_prefix:
# save only the variables that belong to the prefix
logging.info("Using variable prefix={}".format(variable_prefix))
self.saver = tf.train.Saver(
{
v.op.name: v
for v in tf.global_variables()
if v.op.name.startswith(variable_prefix)
},
max_to_keep=max_to_keep,
write_version=saver_pb2.SaverDef.V1)
else:
self.saver = tf.train.Saver(tf.global_variables(),
max_to_keep=max_to_keep,
write_version=saver_pb2.SaverDef.V1)
if rename_variable_prefix:
# create a saver that explicitly stores model variables with a prefix
logging.info("Saving model with new prefix={}".format(
rename_variable_prefix))
self.saver_prefix = tf.train.Saver(
{
v.op.name.replace(variable_prefix, rename_variable_prefix):
v
for v in tf.global_variables()
},
write_version=saver_pb2.SaverDef.V1)