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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow as tf
flags = tf.flags
FLAGS = tf.app.flags.FLAGS
LSTMTuple = collections.namedtuple('LSTMTuple', ['c', 'h'])
def cell_depth(num):
num /= 2
val = np.log2(1 + num)
assert abs(val - int(val)) == 0
return int(val)
class GenericMultiRNNCell(tf.contrib.rnn.RNNCell):
"""More generic version of MultiRNNCell that allows you to pass in a dropout mask"""
def __init__(self, cells):
"""Create a RNN cell composed sequentially of a number of RNNCells.
Args:
cells: list of RNNCells that will be composed in this order.
state_is_tuple: If True, accepted and returned states are n-tuples, where
`n = len(cells)`. If False, the states are all
concatenated along the column axis. This latter behavior will soon be
deprecated.
Raises:
ValueError: if cells is empty (not allowed), or at least one of the cells
returns a state tuple but the flag `state_is_tuple` is `False`.
"""
self._cells = cells
@property
def state_size(self):
return tuple(cell.state_size for cell in self._cells)
@property
def output_size(self):
return self._cells[-1].output_size
def __call__(self, inputs, state, input_masks=None, scope=None):
"""Run this multi-layer cell on inputs, starting from state."""
with tf.variable_scope(scope or type(self).__name__):
cur_inp = inputs
new_states = []
for i, cell in enumerate(self._cells):
with tf.variable_scope('Cell%d' % i):
cur_state = state[i]
if input_masks is not None:
cur_inp *= input_masks[i]
cur_inp, new_state = cell(cur_inp, cur_state)
new_states.append(new_state)
new_states = tuple(new_states)
return cur_inp, new_states
class AlienRNNBuilder(tf.contrib.rnn.RNNCell):
def __init__(self, num_units, params, additional_params, base_size):
self.num_units = num_units
self.cell_create_index = additional_params[0]
self.cell_inject_index = additional_params[1]
self.base_size = base_size
self.cell_params = params[
-2:] # Cell injection parameters are always the last two
params = params[:-2]
self.depth = cell_depth(len(params))
self.params = params
self.units_per_layer = [2**i for i in range(self.depth)
][::-1] # start with the biggest layer
def __call__(self, inputs, state, scope=None):
with tf.variable_scope(scope or type(self).__name__):
definition1 = ['add', 'elem_mult', 'max']
definition2 = [tf.identity, tf.tanh, tf.sigmoid, tf.nn.relu, tf.sin]
layer_outputs = [[] for _ in range(self.depth)]
with tf.variable_scope('rnn_builder'):
curr_index = 0
c, h = state
# Run all dense matrix multiplications at once
big_h_mat = tf.get_variable(
'big_h_mat', [self.num_units,
self.base_size * self.num_units], tf.float32)
big_inputs_mat = tf.get_variable(
'big_inputs_mat', [self.num_units,
self.base_size * self.num_units], tf.float32)
big_h_output = tf.matmul(h, big_h_mat)
big_inputs_output = tf.matmul(inputs, big_inputs_mat)
h_splits = tf.split(big_h_output, self.base_size, axis=1)
inputs_splits = tf.split(big_inputs_output, self.base_size, axis=1)
for layer_num, units in enumerate(self.units_per_layer):
for unit_num in range(units):
with tf.variable_scope(
'layer_{}_unit_{}'.format(layer_num, unit_num)):
if layer_num == 0:
prev1_mat = h_splits[unit_num]
prev2_mat = inputs_splits[unit_num]
else:
prev1_mat = layer_outputs[layer_num - 1][2 * unit_num]
prev2_mat = layer_outputs[layer_num - 1][2 * unit_num + 1]
if definition1[self.params[curr_index]] == 'add':
output = prev1_mat + prev2_mat
elif definition1[self.params[curr_index]] == 'elem_mult':
output = prev1_mat * prev2_mat
elif definition1[self.params[curr_index]] == 'max':
output = tf.maximum(prev1_mat, prev2_mat)
if curr_index / 2 == self.cell_create_index: # Take the new cell before the activation
new_c = tf.identity(output)
output = definition2[self.params[curr_index + 1]](output)
if curr_index / 2 == self.cell_inject_index:
if definition1[self.cell_params[0]] == 'add':
output += c
elif definition1[self.cell_params[0]] == 'elem_mult':
output *= c
elif definition1[self.cell_params[0]] == 'max':
output = tf.maximum(output, c)
output = definition2[self.cell_params[1]](output)
layer_outputs[layer_num].append(output)
curr_index += 2
new_h = layer_outputs[-1][-1]
return new_h, LSTMTuple(new_c, new_h)
@property
def state_size(self):
return LSTMTuple(self.num_units, self.num_units)
@property
def output_size(self):
return self.num_units
class Alien(AlienRNNBuilder):
"""Base 8 Cell."""
def __init__(self, num_units):
params = [
0, 2, 0, 3, 0, 2, 1, 3, 0, 1, 0, 2, 0, 1, 0, 2, 1, 1, 0, 1, 1, 1, 0, 2,
1, 0, 0, 1, 1, 1, 0, 1
]
additional_params = [12, 8]
base_size = 8
super(Alien, self).__init__(num_units, params, additional_params, base_size)
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