/
fast_weight_cell.py
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/
fast_weight_cell.py
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import collections
import tensorflow as tf
from tensorflow.contrib.layers.python.layers import layers
from tensorflow.python.framework import ops
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.ops.rnn_cell_impl import _concat
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import variable_scope as vs
#from tensorflow.contrib.rnn.python.ops.core_rnn_cell import _linear
from tensorflow.python.util import nest
_BIAS_VARIABLE_NAME = "bias"
_WEIGHTS_VARIABLE_NAME = "kernel"
def _linear(args,
output_size,
bias,
bias_initializer=None,
kernel_initializer=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_initializer: starting value to initialize the bias
(default is all zeros).
kernel_initializer: starting value to initialize the weight.
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() for a in args]
for shape in shapes:
if shape.ndims != 2:
raise ValueError("linear is expecting 2D arguments: %s" % shapes)
if shape[1].value is None:
raise ValueError("linear expects shape[1] to be provided for shape %s, "
"but saw %s" % (shape, shape[1]))
else:
total_arg_size += shape[1].value
dtype = [a.dtype for a in args][0]
# Now the computation.
scope = vs.get_variable_scope()
with vs.variable_scope(scope) as outer_scope:
weights = vs.get_variable(
_WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size],
dtype=dtype,
initializer=kernel_initializer)
if len(args) == 1:
res = math_ops.matmul(args[0], weights)
else:
res = math_ops.matmul(array_ops.concat(args, 1), weights)
if not bias:
return res
with vs.variable_scope(outer_scope) as inner_scope:
inner_scope.set_partitioner(None)
if bias_initializer is None:
bias_initializer = init_ops.constant_initializer(0.0, dtype=dtype)
biases = vs.get_variable(
_BIAS_VARIABLE_NAME, [output_size],
dtype=dtype,
initializer=bias_initializer)
return nn_ops.bias_add(res, biases)
# following the desing of LSTM state tuples
_DynStateTuple = collections.namedtuple("DynStateTyple", ("A", "h"))
class DynStateTuple(_DynStateTuple):
"""Tuple used by RNN Models with dynamic weight matricies.
Stores two elements: `(A, h)` in that order
where A is the dynamic weight matrix
and h is the state of the RNN
adapted from LSTMStateTuple in tensorflow/python/obs/rnn_cell_impl.py
"""
__slots__ = ()
@property
def dtype(self):
(A, h) = self
if A.dtype != h.dtype:
raise TypeError("Matrix and internal state should agree on type: %s vs %s" %
(str(A.dtype), str(h.dtype)))
return A.dtype
def _zero_state_tuple(state_size, batch_size, dtype):
"""Create a zero state DynamicStateTuple: A zero `3-D` tensor with shape
`[batch_size x net_size x net_size]` and a zero `2-D` tensor with shape
`[batch_size x net_size]`
"""
def get_state_shape(s):
c = _concat(batch_size, s)
c_static = _concat(batch_size, s, static=True)
size = array_ops.zeros(c, dtype=dtype)
size.set_shape(c_static)
return size
# Differs from rnn_cell_impl function here, and is specific to this code.
return DynStateTuple(*[get_state_shape(s) for s in state_size])
class FastWeightCell(rnn_cell_impl.RNNCell):
""" A FastWeight Cell following Ba et al (2016)
TODO: This should overwrite the zero_state function of RNNCell to be applicable
to the fast-weight matrix as well. //problem_solved.
"""
def __init__(self, num_units, lam, eta,
layer_norm=False,
norm_gain=1,
norm_shift=1,
weights_initializer=None,
activation=tf.nn.relu,
reuse=None):
""" Initialize parameters for a FastWeightCell
Args:
num_units: int, Number of units in the recurrent network
lam: float value, decay rate of dynamic fast weight matrix
eta: float value, update rate of dynamic fast weight matrix
layer_norm: bool, switches layer_norm operation, Default: `False`
norm_gain: (Required if layer_norm=True) float value, gain/var of layer norm
norm_shift: (Required if layer_norm=True) float value, shift/mean of layer norm
activation: (optional) specify the activation function, Default: `ReLU`
reuse: (optional) [cp from rnn_cell_impl] bool, describes whether to reuse
variables in existing scope. If not `True`, and the existing scope already
has the given variables, error is raised.
"""
super(FastWeightCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._lam = lam
self._eta = eta
self._layer_norm = layer_norm
# if self._layer_norm:
# if not (norm_gain or norm_shift):
# raise NameError("If Layer-norm is used, initial norm_gain and \
# norm_shift have to be defined")
self._g = norm_gain
self._b = norm_shift
# I need the batch size for A matrix in the model!
# self._batch_s = batch_size
# self._weights_initializer = weights_initializer or init_ops.RandomUniform(-1, 1) #NOTE: NOT USED
self._activation = activation
self._state_size = DynStateTuple([num_units, num_units], num_units)
@property
def state_size(self):
""" TODO
"""
return self._state_size
@property
def output_size(self):
""" TODO
"""
return self._num_units
def _norm(self, inp, scope="layer_norm"):
""" TODO
"""
shape = inp.get_shape()[-1:]
gamma_init = init_ops.constant_initializer(self._g)
beta_init = init_ops.constant_initializer(self._b)
with vs.variable_scope(scope):
vs.get_variable("gamma", shape=shape, initializer=gamma_init)
vs.get_variable("beta", shape=shape, initializer=beta_init)
normalized = layers.layer_norm(inp, reuse=True, scope=scope)
return normalized
def call(self, inputs, state):
""" Run one step of a __BLANK__Cell
Args:
inputs: `2-D` tensor with shape `[batch_size x input_size]`
state: A DynStateTuple
"""
A, h = state
# update network
#initializer = tf.random_normal_initializer(stddev=2/input_shape)
linear = _linear([inputs, h], self._num_units, True) #rnn_cell_impl._linear # shape [?,50]
# since A is [BATCH x N x N], i.e. for every batch a different A is used,
# we need to reshape h to work with that
h_0 = self._activation(linear)
h_A = tf.reshape(tf.matmul(tf.reshape(h_0, [-1,1,self._num_units]), A), [-1, self._num_units])
# h_pre = (linear + math_ops.matmul(h_0, A))
h_pre = linear + h_A
if(self._layer_norm):
h_pre = self._norm(h_pre)
h = self._activation(h_pre)
# update matrix
A = self._matrix_update(A, h)
return h, DynStateTuple(A, h)
def _matrix_update(self, A, h):
""" Updates a second weight matrix according to the
fast weight update rule described by Ba et. al. (2016)
Args:
A: `3-D` tensor with shape `[batch_size x state_size x state_size]`
-> the fast weight matrix
h: `2-D` tensor with shape `[batch_size x state_size]`
-> the last network state
Returns:
A `3-D` tensor with shape `[batch_size x state_size x state_size]`, i.e.
the new fast weight matrix A
"""
#NOTE: Might be a case where name_scope is more appropriate! (ops.name_scope)
with ops.name_scope("fast_weight_update"):
h_reshape = tf.reshape(h, [-1,1,self._num_units])
A = math_ops.scalar_mul(self._lam, A) + \
self._eta * math_ops.matmul(array_ops.transpose(h_reshape, [0,2,1]), h_reshape)
return A
def zero_state(self, batch_size, dtype):
"""Return zero-filled state tensors, including fast-weight matrix
Overloads parent method zero_state inherited from rnn_cell_impl.RNNCell
and forgoes much of the generality included there
Args:
batch_szie: int, float or unit Tesnor representing batch size.
dtype: the data type to use for the state.
Returns:
A DynStateTuple with 2 Tensors of type dtype. The first `N-D-D`
shaped tensor is the fast weight matrix `[batch_size x ]`
"""
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
state_size = self.state_size
return _zero_state_tuple(state_size, batch_size, dtype)