def __init__(self, is_training, length):
self.batch_size = batch_size = FLAGS.batch_size
self.num_steps = num_steps = length
hidden_size = FLAGS.hidden_dim
self._input_data = tf.placeholder(tf.float32, [batch_size, None, FLAGS.input_dim])
self._targets = tf.placeholder(tf.float32, [batch_size, None, FLAGS.output_dim])
if FLAGS.model == "rnn":
vanilla_rnn_cell = rnn_cell.BasicRNNCell(num_units=FLAGS.hidden_dim)
if is_training and FLAGS.keep_prob < 1:
vanilla_rnn_cell = rnn_cell.DropoutWrapper(vanilla_rnn_cell,
output_keep_prob=FLAGS.keep_prob)
if FLAGS.layer == 1:
cell = vanilla_rnn_cell
elif FLAGS.layer == 2:
cell = rnn_cell.MultiRNNCell([vanilla_rnn_cell] * 2)
elif FLAGS.model == "lstm":
lstm_cell = rnn_cell.BasicLSTMCell(num_units=FLAGS.hidden_dim,
forget_bias=1.0)
if is_training and FLAGS.keep_prob < 1:
lstm_cell = rnn_cell.DropoutWrapper(lstm_cell,
output_keep_prob=FLAGS.keep_prob)
if FLAGS.layer == 1:
cell = lstm_cell
elif FLAGS.layer == 2:
cell = rnn_cell.MultiRNNCell([lstm_cell] * 2)
elif FLAGS.model == "gru":
gru_cell = rnn_cell.GRUCell(num_units=FLAGS.hidden_dim)
if is_training and FLAGS.keep_prob < 1:
gru_cell = rnn_cell.DropoutWrapper(gru_cell,
output_keep_prob=FLAGS.keep_prob)
cell = gru_cell
else:
raise ValueError("Invalid model: %s", FLAGS.model)
self._initial_state = cell.zero_state(batch_size, tf.float32)
outputs = []
state = self._initial_state
with tf.variable_scope("RNN"):
for time_step in range(num_steps):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(self._input_data[:, time_step, :], state)
outputs.append(cell_output)
self._final_state = state
hidden_output = tf.reshape(tf.concat(1, outputs), [-1, hidden_size])
V_1 = tf.get_variable("v_1", shape=[hidden_size, FLAGS.output_dim],
initializer=tf.random_uniform_initializer(-tf.sqrt(1./hidden_size),tf.sqrt(1./hidden_size)))
b_1 = tf.get_variable("b_1", shape=[FLAGS.output_dim], initializer=tf.constant_initializer(0.1))
logits = tf.add(tf.matmul(hidden_output, V_1), b_1)
target = tf.reshape(self._targets, [-1, FLAGS.output_dim])
training_loss = tf.reduce_sum(tf.pow(logits-target, 2)) / 2
mse = tf.reduce_mean(tf.pow(logits-target, 2))
self._cost = mse
if not is_training:
return
self._lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(training_loss, tvars), FLAGS.max_grad_norm)
optimizer = tf.train.GradientDescentOptimizer(self.lr)
self._train_op = optimizer.apply_gradients(zip(grads, tvars))
def __init__(self, is_training, config, input_):
self._input = input_
batch_size = input_.batch_size
num_steps = input_.num_steps
size = config.hidden_size
vocab_size = config.vocab_size
# Slightly better results can be obtained with forget gate biases
# initialized to 1 but the hyperparameters of the model would need to be
# different than reported in the paper.
if FLAGS.use_gru:
lstm_cell = rnn_cell.GRUCell(size)
else:
lstm_cell = rnn_cell.BasicLSTMCell(size, forget_bias=0.0, state_is_tuple=True)
if is_training and config.keep_prob < 1:
lstm_cell = tf.nn.rnn_cell.DropoutWrapper(
lstm_cell, output_keep_prob=config.keep_prob)
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * config.num_layers, state_is_tuple=True)
self._initial_state = cell.zero_state(batch_size, data_type())
with tf.device("/cpu:0"):
self._embedding = tf.get_variable(
"embedding", [vocab_size, size], dtype=data_type())
inputs = tf.nn.embedding_lookup(self._embedding, input_.input_data)
if is_training and config.keep_prob < 1:
inputs = tf.nn.dropout(inputs, config.keep_prob)
# Simplified version of tensorflow.models.rnn.rnn.py's rnn().
# This builds an unrolled LSTM for tutorial purposes only.
# In general, use the rnn() or state_saving_rnn() from rnn.py.
#
# The alternative version of the code below is:
#
# inputs = [tf.squeeze(input_step, [1])
# for input_step in tf.split(1, num_steps, inputs)]
# outputs, state = tf.nn.rnn(cell, inputs, initial_state=self._initial_state)
outputs = []
state = self._initial_state
with tf.variable_scope("RNN"):
for time_step in range(num_steps):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(inputs[:, time_step, :], state)
outputs.append(cell_output)
output = tf.reshape(tf.concat(1, outputs), [-1, size])
softmax_w = tf.get_variable(
"softmax_w", [size, vocab_size], dtype=data_type())
softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=data_type())
logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.nn.seq2seq.sequence_loss_by_example(
[logits],
[tf.reshape(input_.targets, [-1])],
[tf.ones([batch_size * num_steps], dtype=data_type())])
self._cost = cost = tf.reduce_sum(loss) / batch_size
self._final_state = state
if not is_training:
return
self._lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
print('Trainable variables:')
print([var.name for var in tvars])
grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),
config.max_grad_norm)
optimizer = tf.train.GradientDescentOptimizer(self._lr)
self._train_op = optimizer.apply_gradients(
zip(grads, tvars),
global_step=tf.contrib.framework.get_or_create_global_step())
self._new_lr = tf.placeholder(
tf.float32, shape=[], name="new_learning_rate")
self._lr_update = tf.assign(self._lr, self._new_lr)
def __init__(self, config):
self._config = config
# Input placeholders
self._input_seq = tf.placeholder(tf.int32, [None, config.seq_length],
name='input_seq')
self._target_seq = tf.placeholder(tf.int32, [None, config.seq_length],
name='target_seq')
embedding = tf.get_variable('embedding',
[config.vocab_size, config.hidden_size])
inputs = tf.gather(embedding, self._input_seq)
# Hidden layers: stacked LSTM cells with Dropout.
with tf.variable_scope("RNN"):
if config.cell_type == 'lstm':
cell = rnn_cell.BasicLSTMCell(config.is_training,
config.hidden_size)
elif config.cell_type == 'bnlstm':
cell = rnn_cell.BNLSTMCell(config.is_training,
config.hidden_size)
elif config.cell_type == 'gru':
cell = rnn_cell.GRUCell(config.is_training, config.hidden_size)
elif config.cell_type == 'bngru.full':
cell = rnn_cell.BNGRUCell(config.is_training,
config.hidden_size,
full_bn=True)
elif config.cell_type == 'bngru.simple':
cell = rnn_cell.BNGRUCell(config.is_training,
config.hidden_size,
full_bn=False)
else:
raise ValueError('Unknown cell_type: %s' % config.cell_type)
# Apply dropout if we're training.
if config.is_training and config.keep_prob < 1.0:
self._cell = cell = rnn_cell.DropoutWrapper(
cell,
input_keep_prob=config.keep_prob,
output_keep_prob=config.keep_prob)
# No implementation of MultiRNNCell in our own rnn_cell.py yet
# self._multi_cell = multi_cell = (
# tf.nn.rnn_cell.MultiRNNCell([cell] * config.hidden_depth))
self._cell = cell
# Placeholder for initial hidden state.
self._initial_state = tf.placeholder(tf.float32,
[None, cell.state_size],
name="initial_state")
# Split inputs into individual timesteps for BPTT.
split_input = [
tf.squeeze(_input, squeeze_dims=[1])
for _input in tf.split(1, config.seq_length, inputs)
]
# Create the recurrent network.
with tf.variable_scope("RNN"):
state = self._initial_state
outputs = []
for time_step in range(config.seq_length):
if time_step > config.pop_step:
tf.get_variable_scope().reuse_variables()
cell_output, state = cell(split_input[time_step], state,
config.pop_step)
else:
cell_output, state = cell(split_input[time_step], state,
time_step)
outputs.append(cell_output)
self._final_state = state
# Reshape the output to [(batch_size * seq_length), hidden_size]
outputs = tf.reshape(tf.concat(1, outputs), [-1, config.hidden_size])
# Softmax
softmax_w = tf.get_variable(
'softmax_w',
[config.vocab_size, config.hidden_size],
#initializer=orthogonal_initializer)
initializer=None)
softmax_b = tf.get_variable('softmax_b', [config.vocab_size])
self._logits = tf.matmul(outputs, tf.transpose(softmax_w)) + softmax_b
self._probs = tf.nn.softmax(self._logits)
# Average cross-entropy loss within the batch.
loss_tensor = tf.nn.sparse_softmax_cross_entropy_with_logits(
self._logits, tf.reshape(self._target_seq, [-1]))
self._loss = tf.reduce_sum(loss_tensor) / config.batch_size
self._perplexity = tf.exp(self._loss / config.seq_length)
# Optimizer
if config.is_training: # shouldn't need this if but just in case
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.loss, tvars),
config.max_grad_norm)
if config.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(config.learning_rate)
elif config.optimizer == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(
config.learning_rate)
elif config.optimizer == 'adagrad':
optimizer = tf.train.AdagradOptimizer(config.learning_rate)
else:
raise ValueError('Invalid optimizer: %s' % config.optimizer)
self._train_op = optimizer.apply_gradients(zip(grads, tvars))
if not config.is_training:
self.merged_summaries = tf.merge_all_summaries()
def __init__(self,
source_vocab_size,
target_vocab_size,
buckets,
hidden_edim,
hidden_units,
num_layers,
keep_prob,
max_gradient_norm,
batch_size,
learning_rate,
learning_rate_decay_factor,
beam_size,
use_lstm=False,
forward_only=False):
"""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)].
hidden_edim: number of dimensions for word embedding
hidden_units: number of hidden units for each layer
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.
beam_size: the beam size used in beam search.
use_lstm: if true, we use LSTM cells instead of GRU cells.
forward_only: if set, we do not construct the backward pass in the model.
"""
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)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
def loss_function(logit, target, output_projection):
logit = math_ops.matmul(logit, output_projection, transpose_b=True)
target = array_ops.reshape(target, [-1])
crossent = nn_ops.sparse_softmax_cross_entropy_with_logits(
logit, target)
return crossent
softmax_loss_function = loss_function
# Create the internal multi-layer cell for our RNN.
single_cell = rnn_cell.GRUCell(hidden_units)
if use_lstm:
single_cell = rnn_cell.BasicLSTMCell(
hidden_units) # added by yfeng
cell = single_cell
if num_layers > 1:
cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
if not forward_only:
cell = rnn_cell.DropoutWrapper(cell,
input_keep_prob=keep_prob,
seed=SEED)
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, encoder_mask, encoder_probs, encoder_ids,
encoder_hs, mem_mask, decoder_inputs, do_decode):
return seq2seq_fy.embedding_attention_seq2seq(
encoder_inputs,
encoder_mask,
encoder_probs,
encoder_ids,
encoder_hs,
mem_mask,
decoder_inputs,
cell,
num_encoder_symbols=source_vocab_size,
num_decoder_symbols=target_vocab_size,
embedding_size=hidden_edim,
beam_size=beam_size,
num_layers=num_layers,
feed_previous=do_decode)
# Feeds for inputs.
self.encoder_inputs = []
self.decoder_inputs = []
self.target_weights = []
self.decoder_aligns = []
self.decoder_align_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)))
self.decoder_aligns.append(
tf.placeholder(tf.float32,
shape=[None, None],
name="align{0}".format(i)))
self.decoder_align_weights.append(
tf.placeholder(tf.float32,
shape=[None],
name="align_weight{0}".format(i)))
self.encoder_mask = tf.placeholder(tf.int32,
shape=[None, None],
name="encoder_mask")
self.encoder_probs = tf.placeholder(
tf.float32,
shape=[None, None, self.target_vocab_size],
name="encoder_prob")
self.encoder_ids = tf.placeholder(tf.int32,
shape=[None, None],
name="encoder_id")
self.encoder_hs = tf.placeholder(tf.float32,
shape=[None, None, None],
name="encoder_h")
self.mem_mask = tf.placeholder(tf.float32,
shape=[None, None],
name="mem_mask")
# 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, self.symbols = seq2seq_fy.model_with_buckets(
self.encoder_inputs,
self.encoder_mask,
self.encoder_probs,
self.encoder_ids,
self.encoder_hs,
self.mem_mask,
self.decoder_inputs,
targets,
self.target_weights,
self.decoder_aligns,
self.decoder_align_weights,
buckets,
lambda x, y, z, s, a, b, c: seq2seq_f(x, y, z, s, a, b, c, True
),
softmax_loss_function=softmax_loss_function)
else:
self.outputs, self.losses, self.symbols = seq2seq_fy.model_with_buckets(
self.encoder_inputs,
self.encoder_mask,
self.encoder_probs,
self.encoder_ids,
self.encoder_hs,
self.mem_mask,
self.decoder_inputs,
targets,
self.target_weights,
self.decoder_aligns,
self.decoder_align_weights,
buckets,
lambda x, y, z, s, a, b, c: seq2seq_f(x, y, z, s, a, b, c,
False),
softmax_loss_function=softmax_loss_function)
# only update memory attention parameters
params_to_update = [
p for p in tf.trainable_variables() if p.name in [
u'beta1_power:0', u'beta2_power:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias/Adam_1:0'
]
]
if not forward_only:
self.gradient_norms = []
self.gradient_norms_print = []
self.updates = []
opt = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(
self.losses[b],
params_to_update,
aggregation_method=tf.AggregationMethod.EXPERIMENTAL_TREE)
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_to_update),
global_step=self.global_step))
# load trained NMT parameters
params_to_load = [
p for p in tf.all_variables() if p.name not in [
u'beta1_power:0', u'beta2_power:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias/Adam_1:0'
]
]
# only save memory attention parameters
params_to_save = [
p for p in tf.all_variables() if p.name in [
u'Variable:0',
u'Variable_1:0',
u'beta1_power:0',
u'beta2_power:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnVt_0/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnWt_0/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Matrix/Adam_1:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias/Adam:0',
u'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/attention/AttnU_0/Linear_mem/Bias/Adam_1:0',
]
]
self.saver_old = tf.train.Saver(params_to_load,
max_to_keep=1000,
keep_checkpoint_every_n_hours=6)
self.saver = tf.train.Saver(params_to_save,
max_to_keep=1000,
keep_checkpoint_every_n_hours=6)
def __init__(self):
# Input
self.point = tf.placeholder(tf.float32, [m, 1],
'points') # Used in training only
self.variances = tf.placeholder(tf.float32, [k, 1], 'variances')
self.weights = tf.placeholder(tf.float32, [k, 1], 'weights')
self.hyperplanes = tf.placeholder(
tf.float32, [m, m, k],
'hyperplanes') # Points which define the hyperplanes
if rnn_type == 'lstm':
self.initial_rnn_state = tf.placeholder_with_default(
input=tf.zeros([m, 2 * num_rnn_layers * rnn_size]),
shape=[None, 2 * num_rnn_layers * rnn_size])
else:
# initial_rnn_state is passed during evaluation but not during training
# each dimension has an independent hidden state, required in order to simulate Adam, RMSProp etc.
self.initial_rnn_state = tf.placeholder_with_default(
input=tf.zeros([m, num_rnn_layers * rnn_size]),
shape=[None, num_rnn_layers * rnn_size])
# The scope allows these variables to be excluded from being reinitialized during the comparison phase
with tf.variable_scope("optimizer"):
if rnn_type == 'rnn':
cell = rnn_cell.BasicRNNCell(rnn_size)
elif rnn_type == 'gru':
cell = rnn_cell.GRUCell(rnn_size)
elif rnn_type == 'lstm':
cell = rnn_cell.LSTMCell(rnn_size)
self.cell = rnn_cell.MultiRNNCell([cell] * num_rnn_layers)
updates = []
snf_losses = []
# Arguments passed to the condition and body functions
time = tf.constant(0)
point = self.point
snf_loss = snf.calc_snf_loss_tf(point, self.hyperplanes,
self.variances, self.weights)
snf_losses.append(snf_loss)
snf_grads = snf.calc_grads_tf(snf_loss, point)
snf_grads = tf.squeeze(snf_grads, [0])
snf_loss_ta = tf.TensorArray(dtype=tf.float32, size=seq_length)
update_ta = tf.TensorArray(dtype=tf.float32, size=seq_length)
rnn_state = tf.zeros([m, rnn_size * num_rnn_layers])
loop_vars = [
time, point, snf_grads, rnn_state, snf_loss_ta, update_ta,
self.hyperplanes, self.variances, self.weights
]
def condition(time, point, snf_grads, rnn_state, snf_loss_ta,
update_ta, hyperplanes, variances, weights):
return tf.less(time, seq_length)
def body(time, point, snf_grads, rnn_state, snf_loss_ta, update_ta,
hyperplanes, variances, weights):
h, rnn_state_out = self.cell(snf_grads, rnn_state)
# Final layer of the optimizer
# Cannot use fc_layer due to a 'must be from the same frame' error
d = np.sqrt(1.0) / np.sqrt(
rnn_size + 1) ### should be sqrt(2, 3 or 6?)
initializer = tf.random_uniform_initializer(-d, d)
W = tf.get_variable("W", [rnn_size, 1],
initializer=initializer)
# No bias, linear activation function
update = tf.matmul(h, W)
update = tf.reshape(update, [m, 1])
update = inv_scale_grads(update)
new_point = point + update
snf_loss = snf.calc_snf_loss_tf(new_point, hyperplanes,
variances, weights)
snf_losses.append(snf_loss)
snf_loss_ta = snf_loss_ta.write(time, snf_loss)
update_ta = update_ta.write(time, update)
snf_grads_out = snf.calc_grads_tf(snf_loss, point)
snf_grads_out = tf.reshape(snf_grads_out, [m, 1])
time += 1
return [
time, new_point, snf_grads_out, rnn_state_out, snf_loss_ta,
update_ta, hyperplanes, variances, weights
]
# Do the computation
with tf.variable_scope("o1"):
res = tf.while_loop(condition, body, loop_vars)
self.new_point = res[1]
self.rnn_state_out = res[3]
losses = res[4].pack()
updates = res[5].pack()
# Total change in the SNF loss
# Improvement: 2 - 3 = -1 (small loss)
snf_loss_change = losses[seq_length - 1] - losses[0]
snf_loss_change = tf.maximum(snf_loss_change, loss_asymmetry *
snf_loss_change) # Asymmetric loss
self.loss_change_sign = tf.sign(snf_loss_change)
# Oscillation cost
overall_update = tf.zeros([m, 1])
norm_sum = 0.0
for i in range(seq_length):
overall_update += updates[i, :, :]
norm_sum += tf_norm(updates[i, :, :])
osc_cost = norm_sum / tf_norm(overall_update) # > 1
self.total_loss = snf_loss_change * tf.pow(
osc_cost, tf.sign(snf_loss_change))
#===# Model training #===#
#opt = tf.train.RMSPropOptimizer(0.01,momentum=0.5)
opt = tf.train.AdamOptimizer()
vars = tf.trainable_variables()
gvs = opt.compute_gradients(self.total_loss, vars)
self.gvs = [(tf.clip_by_value(grad, -1.0, 1.0), var)
for (grad, var) in gvs]
self.grads_input = [(tf.placeholder(tf.float32,
shape=v.get_shape()), v)
for (g, v) in gvs]
self.train_step = opt.apply_gradients(self.grads_input)
#===# Comparison code #===#
self.input_grads = tf.placeholder(
tf.float32, [1, None, 1],
'input_grads') ### Remove first dimension?
input_grads = tf.squeeze(self.input_grads, [0])
with tf.variable_scope("o1", reuse=True) as scope:
h, self.rnn_state_out_compare = self.cell(
input_grads, self.initial_rnn_state)
W = tf.get_variable("W")
update = tf.matmul(h, W)
update = tf.reshape(update, [-1, 1])
self.update = inv_scale_grads(update)
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):
"""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.
"""
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)
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:
with tf.device("/cpu:0"):
w = tf.get_variable("proj_w", [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
# 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
if num_layers > 1:
cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
return seq2seq.embedding_attention_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
source_vocab_size,
target_vocab_size,
output_projection=output_projection,
feed_previous=do_decode)
# 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)
]
# Training outputs and losses.
if forward_only:
self.outputs, self.losses = seq2seq.model_with_buckets(
self.encoder_inputs,
self.decoder_inputs,
targets,
self.target_weights,
buckets,
self.target_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(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,
buckets,
self.target_vocab_size,
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.all_variables())
def __init__(self, num_classes, vocab_size, hidden_size=128, \
embedding_dim=100, batch_size=32, bidirectional=False):
tf.set_random_seed(1234)
# Placeholders
# can add assert statements to ensure shared None dimensions are equal (batch_size)
self.seq_lens = tf.placeholder(tf.int32, [
None,
], name="seq_lens")
self.input_x = tf.placeholder(tf.int32, [None, None], name="input_x")
self.input_y = tf.placeholder(tf.int32, [
None,
], name="input_y")
mask_x = tf.cast(tf.sequence_mask(self.seq_lens), tf.int32)
# Document and Query embeddings; One-hot-encoded answers
masked_x = tf.mul(self.input_x, mask_x)
one_hot_y = tf.one_hot(self.input_y, num_classes)
# Buildling Graph (Network Layers)
# ==================================================
with tf.variable_scope("embedding"):
self.W_embeddings = tf.get_variable(shape=[vocab_size, embedding_dim], \
initializer=tf.random_uniform_initializer(-0.01, 0.01),\
name="W_embeddings")
# Dimensions: batch x max_length x embedding_dim
input_embedding = tf.gather(self.W_embeddings, masked_x)
with tf.variable_scope("rnn"):
if bidirectional:
# Bidirectional RNNs
forward_cell = rnn_cell.GRUCell(state_size=hidden_size,
input_size=embedding_dim,
scope="GRU-Forward")
backward_cell = rnn_cell.GRUCell(state_size=hidden_size,
input_size=embedding_dim,
scope="GRU-Backward")
hidden_states, last_state = rnn.bidirectional_rnn(forward_cell, backward_cell, \
input_embedding, self.seq_lens, concatenate=True)
else:
# One directional RNN (start to end)
cell = rnn_cell.GRUCell(state_size=hidden_size,
input_size=embedding_dim,
scope="GRU")
hidden_states, last_state = rnn.rnn(cell, input_embedding,
self.seq_lens)
with tf.variable_scope("prediction"):
if bidirectional:
W_predict = tf.get_variable(name="predict_weight", shape=[hidden_size*2, num_classes], \
initializer=tf.random_normal_initializer(mean=0.0, stddev=0.1))
else:
W_predict = tf.get_variable(name="predict_weight", shape=[hidden_size, num_classes], \
initializer=tf.random_normal_initializer(mean=0.0, stddev=0.1))
b_predict = tf.get_variable(
name="predict_bias",
shape=[num_classes],
initializer=tf.constant_initializer(0.0))
# Dimensions (batch_size x num_classes)
prediction_probs_unnormalized = tf.matmul(last_state,
W_predict) + b_predict
# Softmax
# Dimensions (batch x time)
prediction_probs = tf.nn.softmax(prediction_probs_unnormalized,
name="prediction_probs")
likelihoods = tf.reduce_sum(tf.mul(prediction_probs, one_hot_y), 1)
log_likelihoods = tf.log(likelihoods)
# Negative log-likelihood loss
self.loss = tf.mul(tf.reduce_sum(log_likelihoods), -1)
predictions = tf.argmax(prediction_probs, 1, name="predictions")
correct_vector = tf.cast(tf.equal(tf.argmax(one_hot_y, 1), tf.argmax(prediction_probs, 1)), \
tf.float32, name="correct_vector")
self.accuracy = tf.reduce_mean(correct_vector)
def __init__(
self,
source_vocab_size_1,
source_vocab_size_2,
target_vocab_size,
buckets,
# size, #annotated by yfeng
hidden_edim,
hidden_units, # added by yfeng
num_layers,
max_gradient_norm,
batch_size,
learning_rate,
learning_rate_decay_factor,
beam_size, # added by shiyue
constant_emb_en, # added by al
constant_emb_fr, # added by al
use_lstm=False,
num_samples=10240,
forward_only=False):
"""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.#annotated by yfeng
hidden_edim: number of dimensions for word embedding
hidden_units: number of hidden units for each layer
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.
"""
self.source_vocab_size_1 = source_vocab_size_1
self.source_vocab_size_2 = source_vocab_size_2
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)
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:
if num_samples > 0:
# w = tf.get_variable("proj_w", [size, self.target_vocab_size]) #annotated by feng
w = tf.get_variable("proj_w",
[hidden_units // 2, self.target_vocab_size],
initializer=tf.random_normal_initializer(
0, 0.01, seed=SEED)) # added by yfeng
# w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [self.target_vocab_size],
initializer=tf.constant_initializer(0.0),
trainable=False) # added by yfeng
output_projection = (w, b)
def sampled_loss(logit, target):
# labels = tf.reshape(labels, [-1, 1])
logit = nn_ops.xw_plus_b(logit, output_projection[0],
output_projection[1])
# return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
# self.target_vocab_size)
target = array_ops.reshape(target, [-1])
return nn_ops.sparse_softmax_cross_entropy_with_logits(
logit, target)
softmax_loss_function = sampled_loss
# Create the internal multi-layer cell for our RNN.
# single_cell = tf.nn.rnn_cell.GRUCell(hidden_units) #annotated by yfeng
single_cell = rnn_cell.GRUCell(hidden_units) # added by yfeng
if use_lstm:
# single_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_units) #annotated by yfeng
single_cell = rnn_cell.BasicLSTMCell(
hidden_units) # added by yfeng
cell = single_cell
if num_layers > 1:
# modified by yfeng
# cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers)
cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
# end by yfeng
cell = rnn_cell.DropoutWrapper(cell, input_keep_prob=0.8, seed=SEED)
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs_1, encoder_inputs_2, encoder_mask_1,
encoder_mask_2, decoder_inputs, do_decode):
# return tf.nn.seq2seq.embedding_attention_seq2seq( #annnotated by yfeng
return seq2seq_al.embedding_attention_seq2seq( # added by yfeng
encoder_inputs_1,
encoder_inputs_2,
encoder_mask_1,
encoder_mask_2,
decoder_inputs,
cell,
num_encoder_symbols_1=source_vocab_size_1,
num_encoder_symbols_2=source_vocab_size_2,
num_decoder_symbols=target_vocab_size,
# embedding_size=size, #annotated by yfeng
embedding_size=hidden_edim, # added by yfeng
beam_size=beam_size, # added by shiyue
constant_emb_en=constant_emb_en, # added by al
constant_emb_fr=constant_emb_fr, # added by al
output_projection=output_projection,
feed_previous=do_decode)
# Feeds for inputs.
self.encoder_inputs_1 = []
self.encoder_inputs_2 = []
self.decoder_inputs = []
self.target_weights = []
for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.
self.encoder_inputs_1.append(
tf.placeholder(tf.int32,
shape=[None],
name="encoder{0}_1".format(i)))
for i in xrange(buckets[-1][1]): # Last bucket is the biggest one.
self.encoder_inputs_2.append(
tf.placeholder(tf.int32,
shape=[None],
name="encoder{0}_2".format(i)))
for i in xrange(buckets[-1][2] + 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)))
self.encoder_mask_1 = tf.placeholder(tf.int32,
shape=[None, None],
name="encoder_mask_1")
self.encoder_mask_2 = tf.placeholder(tf.int32,
shape=[None, None],
name="encoder_mask_2")
# 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.nn.seq2seq.model_with_buckets( #annotated by yfeng
self.outputs, self.losses, self.symbols = seq2seq_al.model_with_buckets( # added by yfeng and shiyue
self.encoder_inputs_1,
self.encoder_inputs_2,
self.encoder_mask_1,
self.encoder_mask_2,
self.decoder_inputs,
targets,
self.target_weights,
buckets,
lambda x1, x2, y1, y2, z: seq2seq_f(x1, x2, y1, y2, z, True),
softmax_loss_function=softmax_loss_function)
# If we use output projection, we need to project outputs for decoding.
# annotated by shiyue, when using beam search, no need to do decoding projection
# 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]
# ]
# ended by shiyue
else:
# self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets( #annotated by yfeng
self.outputs, self.losses, self.symbols = seq2seq_al.model_with_buckets( # added by yfeng and shiyue
self.encoder_inputs_1,
self.encoder_inputs_2,
self.encoder_mask_1,
self.encoder_mask_2,
self.decoder_inputs,
targets,
self.target_weights,
buckets,
lambda x1, x2, y1, y2, z: seq2seq_f(x1, x2, y1, y2, z, False),
softmax_loss_function=softmax_loss_function)
# Gradients and SGD update operation for training the model.
params_to_update = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.gradient_norms_print = []
self.updates = []
# opt = tf.train.AdadeltaOptimizer(learning_rate=self.learning_rate, rho=0.95, epsilon=1e-6)
opt = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
# opt = tf.train.GradientDescentOptimizer(self.learning_rate) #added by yfeng
for b in xrange(len(buckets)):
gradients = tf.gradients(
self.losses[b],
params_to_update,
aggregation_method=tf.AggregationMethod.EXPERIMENTAL_TREE)
# gradients_print = tf.gradients(self.losses[b], params_to_print)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
# _, norm_print = tf.clip_by_global_norm(gradients_print,
# max_gradient_norm)
self.gradient_norms.append(norm)
# self.gradient_norms_print.append(norm_print)
self.updates.append(
opt.apply_gradients(zip(clipped_gradients,
params_to_update),
global_step=self.global_step))
# self.saver = tf.train.Saver(tf.all_variables()) #annotated by yfeng
self.saver = tf.train.Saver(
tf.all_variables(),
max_to_keep=1000,
keep_checkpoint_every_n_hours=6) # added by yfeng
def __init__(self, source_vocab_size, target_vocab_size, buckets, hidden_edim, hidden_units,
num_layers, keep_prob, max_gradient_norm, batch_size, learning_rate, learning_rate_decay_factor,
beam_size, forward_only=False):
"""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)].
hidden_edim: number of dimensions for word embedding
hidden_units: number of hidden units for each layer
num_layers: number of layers in the model.
keep_prob: keep probability used for dropout.
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.
beam_size: the beam size for beam search decoding
forward_only: if set, we do not construct the backward pass in the model.
"""
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)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
w = tf.get_variable("proj_w", [hidden_units // 2, self.target_vocab_size],
initializer=tf.random_normal_initializer(0, 0.01, seed=123))
b = tf.get_variable("proj_b", [self.target_vocab_size],
initializer=tf.constant_initializer(0.0), trainable=False)
output_projection = (w, b) # before softmax, there is an output projection
def softmax_loss_function(logit, target): # loss function of seq2seq model
logit = nn_ops.xw_plus_b(logit, output_projection[0], output_projection[1])
target = array_ops.reshape(target, [-1])
return nn_ops.sparse_softmax_cross_entropy_with_logits(
logit, target)
single_cell = rnn_cell.GRUCell(hidden_units)
cell = single_cell
if num_layers > 1:
cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
if not forward_only:
cell = rnn_cell.DropoutWrapper(cell, output_keep_prob=float(keep_prob), seed=123)
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, encoder_mask, decoder_inputs, do_decode):
return seq2seq.embedding_attention_seq2seq(
encoder_inputs, encoder_mask, decoder_inputs, cell,
num_encoder_symbols=source_vocab_size,
num_decoder_symbols=target_vocab_size,
embedding_size=hidden_edim,
beam_size=beam_size,
output_projection=output_projection,
num_layers=num_layers,
feed_previous=do_decode)
# 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)))
self.encoder_mask = tf.placeholder(tf.int32, shape=[None, None],
name="encoder_mask")
# 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, self.symbols = seq2seq.model_with_buckets(
self.encoder_inputs, self.encoder_mask, self.decoder_inputs, targets,
self.target_weights, buckets, lambda x, y, z: seq2seq_f(x, y, z, True),
softmax_loss_function=softmax_loss_function)
else:
self.outputs, self.losses, self.symbols = seq2seq.model_with_buckets(
self.encoder_inputs, self.encoder_mask, self.decoder_inputs, targets,
self.target_weights, buckets,
lambda x, y, z: seq2seq_f(x, y, z, False),
softmax_loss_function=softmax_loss_function)
# Gradients and SGD update operation for training the model.
params_to_update = tf.trainable_variables()
if not forward_only:
self.gradient_norms = []
self.gradient_norms_print = []
self.updates = []
opt = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(self.losses[b], params_to_update,
aggregation_method=tf.AggregationMethod.EXPERIMENTAL_TREE)
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_to_update), global_step=self.global_step))
self.saver = tf.train.Saver(tf.all_variables(), max_to_keep=1000, # keep all checkpoints
keep_checkpoint_every_n_hours=6)
