# Copyright 2016 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.
# ==============================================================================
"""Tests for Keras core layers."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from tensorflow.contrib.keras.python import keras
from tensorflow.contrib.keras.python.keras import testing_utils
from tensorflow.python.platform import test


class CoreLayersTest(test.TestCase):

  def test_masking(self):
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Masking, kwargs={}, input_shape=(3, 2, 3))

  def test_dropout(self):
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Dropout, kwargs={'rate': 0.5}, input_shape=(3, 2))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Dropout,
          kwargs={'rate': 0.5,
                  'noise_shape': [3, 1]},
          input_shape=(3, 2))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.SpatialDropout1D,
          kwargs={'rate': 0.5},
          input_shape=(2, 3, 4))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.SpatialDropout2D,
          kwargs={'rate': 0.5},
          input_shape=(2, 3, 4, 5))

  def test_activation(self):
    # with string argument
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Activation,
          kwargs={'activation': 'relu'},
          input_shape=(3, 2))

    # with function argument
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Activation,
          kwargs={'activation': keras.backend.relu},
          input_shape=(3, 2))

  def test_reshape(self):
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Reshape,
          kwargs={'target_shape': (8, 1)},
          input_shape=(3, 2, 4))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Reshape,
          kwargs={'target_shape': (-1, 1)},
          input_shape=(3, 2, 4))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Reshape,
          kwargs={'target_shape': (1, -1)},
          input_shape=(3, 2, 4))

  def test_permute(self):
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4))

  def test_flatten(self):
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4))

  def test_repeat_vector(self):
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.RepeatVector, kwargs={'n': 3}, input_shape=(3, 2))

  def test_lambda(self):
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Lambda,
          kwargs={'function': lambda x: x + 1},
          input_shape=(3, 2))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Lambda,
          kwargs={
              'function': lambda x, a, b: x * a + b,
              'arguments': {
                  'a': 0.6,
                  'b': 0.4
              }
          },
          input_shape=(3, 2))

    with self.test_session():
      # test serialization with function
      def f(x):
        return x + 1

      ld = keras.layers.Lambda(f)
      config = ld.get_config()
      ld = keras.layers.deserialize({
          'class_name': 'Lambda',
          'config': config
      })

      # test with lambda
      ld = keras.layers.Lambda(
          lambda x: keras.backend.concatenate([keras.backend.square(x), x]))
      config = ld.get_config()
      ld = keras.layers.Lambda.from_config(config)

  def test_dense(self):
    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 2))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 2))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Dense, kwargs={'units': 3}, input_shape=(None, None, 2))

    with self.test_session():
      testing_utils.layer_test(
          keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 5, 2))

    # Test regularization
    with self.test_session():
      layer = keras.layers.Dense(
          3,
          kernel_regularizer=keras.regularizers.l1(0.01),
          bias_regularizer='l1',
          activity_regularizer='l2',
          name='dense_reg')
      layer(keras.backend.variable(np.ones((2, 4))))
      self.assertEqual(3, len(layer.losses))

    # Test constraints
    with self.test_session():
      layer = keras.layers.Dense(
          3, kernel_constraint='max_norm', bias_constraint='max_norm')
      layer(keras.backend.variable(np.ones((2, 4))))
      self.assertEqual(2, len(layer.constraints))

  def test_activity_regularization(self):
    with self.test_session():
      layer = keras.layers.ActivityRegularization(l1=0.1)
      layer(keras.backend.variable(np.ones((2, 4))))
      self.assertEqual(1, len(layer.losses))


if __name__ == '__main__':
  test.main()
