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rhn.py
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rhn.py
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from __future__ import absolute_import, division, print_function
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import math_ops, array_ops
from tensorflow.python.util import nest
from tensorflow.python.ops.nn import rnn_cell
RNNCell = rnn_cell.RNNCell
class Model(object):
"""A Variational RHN model."""
def __init__(self, is_training, config):
self.batch_size = batch_size = config.batch_size
self.num_steps = num_steps = config.num_steps
self.depth = depth = config.depth
self.size = size = config.hidden_size
self.num_layers = num_layers = config.num_layers
vocab_size = config.vocab_size
if vocab_size < self.size and not config.tied:
in_size = vocab_size
else:
in_size = self.size
self.in_size = in_size
self._input_data = tf.placeholder(tf.int32, [batch_size, num_steps])
self._targets = tf.placeholder(tf.int32, [batch_size, num_steps])
self._noise_x = tf.placeholder(tf.float32, [batch_size, num_steps, 1])
self._noise_i = tf.placeholder(tf.float32, [batch_size, in_size, num_layers])
self._noise_h = tf.placeholder(tf.float32, [batch_size, size, num_layers])
self._noise_o = tf.placeholder(tf.float32, [batch_size, 1, size])
with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding", [vocab_size, in_size])
inputs = tf.nn.embedding_lookup(embedding, self._input_data) * self._noise_x
outputs = []
self._initial_state = [0] * self.num_layers
state = [0] * self.num_layers
self._final_state = [0] * self.num_layers
for l in range(config.num_layers):
with tf.variable_scope('RHN' + str(l)):
cell = RHNCell(size, in_size, is_training, depth=depth, forget_bias=config.init_bias)
self._initial_state[l] = cell.zero_state(batch_size, tf.float32)
state[l] = [self._initial_state[l], self._noise_i[:, :, l], self._noise_h[:, :, l]]
for time_step in range(num_steps):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
(cell_output, state[l]) = cell(inputs[:, time_step, :], state[l])
outputs.append(cell_output)
inputs = tf.pack(outputs, axis=1)
outputs = []
output = tf.reshape(inputs * self._noise_o, [-1, size])
softmax_w = tf.transpose(embedding) if config.tied else tf.get_variable("softmax_w", [size, vocab_size])
softmax_b = tf.get_variable("softmax_b", [vocab_size])
logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.nn.seq2seq.sequence_loss_by_example(
[logits],
[tf.reshape(self._targets, [-1])],
[tf.ones([batch_size * num_steps])])
self._final_state = [s[0] for s in state]
pred_loss = tf.reduce_sum(loss) / batch_size
self._cost = cost = pred_loss
if not is_training:
return
tvars = tf.trainable_variables()
l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in tvars])
self._cost = cost = pred_loss + config.weight_decay * l2_loss
self._lr = tf.Variable(0.0, trainable=False)
self._nvars = np.prod(tvars[0].get_shape().as_list())
print(tvars[0].name, tvars[0].get_shape().as_list())
for var in tvars[1:]:
sh = var.get_shape().as_list()
print(var.name, sh)
self._nvars += np.prod(sh)
print(self._nvars, 'total variables')
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))
def assign_lr(self, session, lr_value):
session.run(tf.assign(self.lr, lr_value))
@property
def input_data(self):
return self._input_data
@property
def targets(self):
return self._targets
@property
def noise_x(self):
return self._noise_x
@property
def noise_i(self):
return self._noise_i
@property
def noise_h(self):
return self._noise_h
@property
def noise_o(self):
return self._noise_o
@property
def initial_state(self):
return self._initial_state
@property
def cost(self):
return self._cost
@property
def final_state(self):
return self._final_state
@property
def lr(self):
return self._lr
@property
def train_op(self):
return self._train_op
@property
def nvars(self):
return self._nvars
class RHNCell(RNNCell):
"""Variational Recurrent Highway Layer
Reference: https://arxiv.org/abs/1607.03474
"""
def __init__(self, num_units, in_size, is_training, depth=3, forget_bias=None):
self._num_units = num_units
self._in_size = in_size
self.is_training = is_training
self.depth = depth
self.forget_bias = forget_bias
@property
def input_size(self):
return self._in_size
@property
def output_size(self):
return self._num_units
@property
def state_size(self):
return self._num_units
def __call__(self, inputs, state, scope=None):
current_state = state[0]
noise_i = state[1]
noise_h = state[2]
for i in range(self.depth):
with tf.variable_scope('h_'+str(i)):
if i == 0:
h = tf.tanh(linear([inputs * noise_i, current_state * noise_h], self._num_units, True))
else:
h = tf.tanh(linear([current_state * noise_h], self._num_units, True))
with tf.variable_scope('t_'+str(i)):
if i == 0:
t = tf.sigmoid(linear([inputs * noise_i, current_state * noise_h], self._num_units, True, self.forget_bias))
else:
t = tf.sigmoid(linear([current_state * noise_h], self._num_units, True, self.forget_bias))
current_state = (h - current_state)* t + current_state
return current_state, [current_state, noise_i, noise_h]
def linear(args, output_size, bias, bias_start=None, scope=None):
"""
This is a slightly modified version of _linear used by Tensorflow rnn.
The only change is that we have allowed bias_start=None.
Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
Args:
args: a 2D Tensor or a list of 2D, batch x n, Tensors.
output_size: int, second dimension of W[i].
bias: boolean, whether to add a bias term or not.
bias_start: starting value to initialize the bias; 0 by default.
scope: VariableScope for the created subgraph; defaults to "Linear".
Returns:
A 2D Tensor with shape [batch x output_size] equal to
sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
Raises:
ValueError: if some of the arguments has unspecified or wrong shape.
"""
if args is None or (nest.is_sequence(args) and not args):
raise ValueError("`args` must be specified")
if not nest.is_sequence(args):
args = [args]
# Calculate the total size of arguments on dimension 1.
total_arg_size = 0
shapes = [a.get_shape().as_list() for a in args]
for shape in shapes:
if len(shape) != 2:
raise ValueError("Linear is expecting 2D arguments: %s" % str(shapes))
if not shape[1]:
raise ValueError("Linear expects shape[1] of arguments: %s" % str(shapes))
else:
total_arg_size += shape[1]
dtype = [a.dtype for a in args][0]
# Now the computation.
with vs.variable_scope(scope or "Linear"):
matrix = vs.get_variable(
"Matrix", [total_arg_size, output_size], dtype=dtype)
if len(args) == 1:
res = math_ops.matmul(args[0], matrix)
else:
res = math_ops.matmul(array_ops.concat(1, args), matrix)
if not bias:
return res
elif bias_start is None:
bias_term = vs.get_variable("Bias", [output_size], dtype=dtype)
else:
bias_term = vs.get_variable("Bias", [output_size], dtype=dtype,
initializer=tf.constant_initializer(bias_start, dtype=dtype))
return res + bias_term