An implementation of Chaos-Free Network (CFN) in TensorFlow

CFNCell.py

# -*- coding: utf-8 -*-

# Copyright (C) 2017 by Akira TAMAMORI

# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# this program.  If not, see <http://www.gnu.org/licenses/>.

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
#
# 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.

import tensorflow as tf

from tensorflow.python.ops.rnn_cell import RNNCell


class CFNCell(RNNCell):
    """ Chaos-Free Network (CFN).

       Thomas Laurent and James von Brecht,
       "A recurrent neural network without chaos,"
       https://arxiv.org/abs/1612.06212
    """

    def __init__(self, num_units, input_size=None):
        self._num_units = num_units
        self._input_size = num_units if input_size is None else input_size

    @property
    def input_size(self):
        return self._input_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):
        """Call CFN."""

        with tf.variable_scope(scope or type(self).__name__):
            with tf.variable_scope("ForgetGate"):
                theta = tf.sigmoid(
                    linear([inputs, state], self._num_units, True))
            with tf.variable_scope("InputGate"):
                eta = tf.sigmoid(
                    linear([inputs, state], self._num_units, True))
            with tf.variable_scope("Input"):
                Wx = linear([inputs], self._num_units, False)
            h = theta * tf.tanh(state) + eta * tf.tanh(Wx)

        # hidden output and state
        return h, h


def linear(args, output_size, bias, bias_start=0.0, scope=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 (isinstance(args, (list, tuple)) and not args):
        raise ValueError("`args` must be specified")
    if not isinstance(args, (list, tuple)):
        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]

    # Now the computation.
    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [total_arg_size, output_size])
        if len(args) == 1:
            res = tf.matmul(args[0], matrix)
        else:
            res = tf.matmul(tf.concat(1, args), matrix)
        if not bias:
            return res
        bias_term = tf.get_variable(
            "Bias", [output_size],
            initializer=tf.constant_initializer(bias_start))
    return res + bias_term