# 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

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

from tensorflow.python.layers import maxout

from tensorflow.python.layers import convolutional as conv_layers

from tensorflow.python.layers import core as core_layers

from tensorflow.python.ops import random_ops

from tensorflow.python.platform import test

import numpy as np

"""

Contains the maxout layer tests

"""

class MaxOutTest(test.TestCase):

def test_simple(self):

inputs = random_ops.random_uniform((64, 10, 36), seed=1)

graph = maxout.maxout(inputs, num_units=3)

self.assertEqual(graph.get_shape().as_list(), [64, 10, 3])

def test_fully_connected(self):

inputs = random_ops.random_uniform((64, 50), seed=1)

graph = core_layers.dense(inputs, 50)

graph = maxout.maxout(graph, num_units=10)

self.assertEqual(graph.get_shape().as_list(), [64, 10])

def test_nchw(self):

inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1)

graph = conv_layers.conv2d(inputs, 10, 3, padding="SAME")

graph = maxout.maxout(graph, num_units=1)

self.assertEqual(graph.get_shape().as_list(), [10, 100, 100, 1])

def test_invalid_shape(self):

inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1)

graph = conv_layers.conv2d(inputs, 3, 10, strides=(1, 1))

with self.assertRaisesRegexp(ValueError, 'number of features'):

graph = maxout.maxout(graph, num_units=2)

if __name__ == '__main__':

test.main()