def SampleGRUSeq2Seq(enc_inp, dec_inp, weights):
"""Example sequence-to-sequence model that uses GRU cells."""
def GRUSeq2Seq(enc_inp, dec_inp):
cell = core_rnn_cell_impl.MultiRNNCell(
[core_rnn_cell_impl.GRUCell(24)] * 2, state_is_tuple=True)
return seq2seq_lib.embedding_attention_seq2seq(
enc_inp,
dec_inp,
cell,
num_encoder_symbols=classes,
num_decoder_symbols=classes,
embedding_size=24,
output_projection=(w, b))
targets = [dec_inp[i + 1] for i in range(len(dec_inp) - 1)] + [0]
def SampledLoss(labels, inputs):
labels = array_ops.reshape(labels, [-1, 1])
return nn_impl.sampled_softmax_loss(
weights=w_t,
biases=b,
labels=labels,
inputs=inputs,
num_sampled=8,
num_classes=classes)
return seq2seq_lib.model_with_buckets(
enc_inp,
dec_inp,
targets,
weights,
buckets,
GRUSeq2Seq,
softmax_loss_function=SampledLoss)
def SampleGRUSeq2Seq(enc_inp, dec_inp, weights):
"""Example sequence-to-sequence model that uses GRU cells."""
def GRUSeq2Seq(enc_inp, dec_inp):
cell = rnn_cell.MultiRNNCell(
[rnn_cell.GRUCell(24) for _ in range(2)], state_is_tuple=True)
return seq2seq_lib.embedding_attention_seq2seq(
enc_inp,
dec_inp,
cell,
num_encoder_symbols=classes,
num_decoder_symbols=classes,
embedding_size=24,
output_projection=(w, b))
targets = [dec_inp[i + 1] for i in range(len(dec_inp) - 1)] + [0]
def SampledLoss(labels, logits):
labels = array_ops.reshape(labels, [-1, 1])
return nn_impl.sampled_softmax_loss(
weights=w_t,
biases=b,
labels=labels,
inputs=logits,
num_sampled=8,
num_classes=classes)
return seq2seq_lib.model_with_buckets(
enc_inp,
dec_inp,
targets,
weights,
buckets,
GRUSeq2Seq,
softmax_loss_function=SampledLoss)
def SampleGRUSeq2Seq(enc_inp, dec_inp, weights, per_example_loss):
"""Example sequence-to-sequence model that uses GRU cells."""
def GRUSeq2Seq(enc_inp, dec_inp):
cell = core_rnn_cell_impl.MultiRNNCell(
[core_rnn_cell_impl.GRUCell(24)] * 2, state_is_tuple=True)
return seq2seq_lib.embedding_attention_seq2seq(
enc_inp,
dec_inp,
cell,
num_encoder_symbols=classes,
num_decoder_symbols=classes,
embedding_size=24)
targets = [dec_inp[i + 1] for i in range(len(dec_inp) - 1)] + [0]
return seq2seq_lib.model_with_buckets(
enc_inp,
dec_inp,
targets,
weights,
buckets,
GRUSeq2Seq,
per_example_loss=per_example_loss)
def __init__(self,
source_vocab_size,
target_vocab_size,
buckets,
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):
"""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
self.learning_rate = tf.Variable(
float(learning_rate), trainable=False, dtype=dtype)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
# 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, size], dtype=dtype)
w = tf.transpose(w_t)
b = tf.get_variable("proj_b", [self.target_vocab_size], dtype=dtype)
output_projection = (w, b)
def sampled_loss(labels, logits):
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(logits, tf.float32)
return tf.cast(
tf.nn.sampled_softmax_loss(
weights=local_w_t,
biases=local_b,
labels=labels,
inputs=local_inputs,
num_sampled=num_samples,
num_classes=self.target_vocab_size),
dtype)
softmax_loss_function = sampled_loss
# Create the internal multi-layer cell for our RNN.
def single_cell():
return tf.contrib.rnn.GRUCell(size)
if use_lstm:
def single_cell():
return tf.contrib.rnn.BasicLSTMCell(size)
cell = single_cell()
if num_layers > 1:
cell = tf.contrib.rnn.MultiRNNCell([single_cell() for _ in range(num_layers)])
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return seq2seq_patch.embedding_attention_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
cell,
num_encoder_symbols=source_vocab_size,
num_decoder_symbols=target_vocab_size,
embedding_size=size,
output_projection=output_projection,
feed_previous=do_decode,
dtype=dtype)
# 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(dtype, 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.
if forward_only:
self.outputs, self.losses = tf.contrib.legacy_seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs, targets,
self.target_weights, buckets, 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(buckets)):
self.outputs[b] = [
tf.matmul(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, buckets,
lambda x, y: seq2seq_f(x, y, False),
softmax_loss_function=softmax_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)
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.global_variables())
