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# Copyright 2015 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.
# =============================================================================
# pylint: disable=unused-import,g-bad-import-order
"""Contains the maxout layer
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.layers import base
def maxout(inputs, num_units, axis=-1, name=None):
"""Adds a maxout op from https://arxiv.org/abs/1302.4389
"Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville,
Yoshua Bengio
Usually the operation is performed in the filter/channel dimension. This can also be
used after fully-connected layers to reduce number of features.
Arguments:
inputs: Tensor input
num_units: Specifies how many features will remain after maxout in the `axis` dimension
(usually channel). This must be multiple of number of `axis`.
axis: The dimension where max pooling will be performed. Default is the
last dimension.
name: Optional scope for name_scope.
Returns:
A `Tensor` representing the results of the pooling operation.
Raises:
ValueError: if num_units is not multiple of number of features.
"""
return MaxOut(num_units=num_units, axis=axis, name=name)(inputs)
class MaxOut(base.Layer):
"""Adds a maxout op from https://arxiv.org/abs/1302.4389
"Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua
Bengio
Usually the operation is performed in the filter/channel dimension. This can also be
used after fully-connected layers to reduce number of features.
Arguments:
inputs: Tensor input
num_units: Specifies how many features will remain after maxout in the `axis` dimension
(usually channel).
This must be multiple of number of `axis`.
axis: The dimension where max pooling will be performed. Default is the
last dimension.
name: Optional scope for name_scope.
Returns:
A `Tensor` representing the results of the pooling operation.
Raises:
ValueError: if num_units is not multiple of number of features.
"""
def __init__(self,
num_units,
axis=-1,
name=None,
**kwargs):
super(MaxOut, self).__init__(
name=name, trainable=False, **kwargs)
self.axis = axis
self.num_units = num_units
def call(self, inputs):
inputs = ops.convert_to_tensor(inputs)
shape = inputs.get_shape().as_list()
num_channels = shape[self.axis]
if num_channels % self.num_units:
raise ValueError('number of features({}) is not '
'a multiple of num_units({})'
.format(num_channels, self.num_units))
shape[self.axis] = -1
shape += [num_channels // self.num_units]
# Dealing with batches with arbitrary sizes
for i in range(len(shape)):
if shape[i] is None:
shape[i] = gen_array_ops.shape(inputs)[i]
outputs = math_ops.reduce_max(gen_array_ops.reshape(inputs, shape), -1, keep_dims=False)
return outputs
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