# 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 regularizers."""

import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np

import keras
from keras import keras_parameterized
from keras import regularizers
from keras import testing_utils
from keras.utils import np_utils


DATA_DIM = 5
NUM_CLASSES = 2


class KerasRegularizersTest(keras_parameterized.TestCase,
                            parameterized.TestCase):

  def create_model(self, kernel_regularizer=None, activity_regularizer=None):
    model = keras.models.Sequential()
    model.add(keras.layers.Dense(NUM_CLASSES,
                                 kernel_regularizer=kernel_regularizer,
                                 activity_regularizer=activity_regularizer,
                                 input_shape=(DATA_DIM,)))
    return model

  def get_data(self):
    (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
        train_samples=10,
        test_samples=10,
        input_shape=(DATA_DIM,),
        num_classes=NUM_CLASSES)
    y_train = np_utils.to_categorical(y_train, NUM_CLASSES)
    y_test = np_utils.to_categorical(y_test, NUM_CLASSES)
    return (x_train, y_train), (x_test, y_test)

  def create_multi_input_model_from(self, layer1, layer2):
    input_1 = keras.layers.Input(shape=(DATA_DIM,))
    input_2 = keras.layers.Input(shape=(DATA_DIM,))
    out1 = layer1(input_1)
    out2 = layer2(input_2)
    out = keras.layers.Average()([out1, out2])
    model = keras.models.Model([input_1, input_2], out)
    model.add_loss(keras.backend.mean(out2))
    model.add_loss(tf.reduce_sum(input_1))
    return model

  @keras_parameterized.run_all_keras_modes
  @parameterized.named_parameters([
      ('l1', regularizers.l1()),
      ('l2', regularizers.l2()),
      ('l1_l2', regularizers.l1_l2()),
  ])
  def test_kernel_regularization(self, regularizer):
    (x_train, y_train), _ = self.get_data()
    model = self.create_model(kernel_regularizer=regularizer)
    model.compile(
        loss='categorical_crossentropy',
        optimizer='sgd',
        run_eagerly=testing_utils.should_run_eagerly())
    self.assertEqual(len(model.losses), 1)
    model.fit(x_train, y_train, batch_size=10, epochs=1, verbose=0)

  @keras_parameterized.run_all_keras_modes
  @parameterized.named_parameters([
      ('l1', regularizers.l1()),
      ('l2', regularizers.l2()),
      ('l1_l2', regularizers.l1_l2()),
      ('l2_zero', keras.regularizers.l2(0.)),
  ])
  def test_activity_regularization(self, regularizer):
    (x_train, y_train), _ = self.get_data()
    model = self.create_model(activity_regularizer=regularizer)
    model.compile(
        loss='categorical_crossentropy',
        optimizer='sgd',
        run_eagerly=testing_utils.should_run_eagerly())
    self.assertEqual(len(model.losses), 1 if tf.executing_eagerly() else 1)
    model.fit(x_train, y_train, batch_size=10, epochs=1, verbose=0)

  @keras_parameterized.run_all_keras_modes
  @keras_parameterized.run_with_all_model_types
  def test_zero_regularization(self):
    # Verifies that training with zero regularization works.
    x, y = np.ones((10, 10)), np.ones((10, 3))
    model = testing_utils.get_model_from_layers(
        [keras.layers.Dense(3, kernel_regularizer=keras.regularizers.l2(0))],
        input_shape=(10,))
    model.compile(
        'sgd',
        'mse',
        run_eagerly=testing_utils.should_run_eagerly())
    model.fit(x, y, batch_size=5, epochs=1)

  def test_custom_regularizer_saving(self):

    def my_regularizer(weights):
      return tf.reduce_sum(tf.abs(weights))

    inputs = keras.Input((10,))
    outputs = keras.layers.Dense(1, kernel_regularizer=my_regularizer)(inputs)
    model = keras.Model(inputs, outputs)
    model2 = model.from_config(
        model.get_config(), custom_objects={'my_regularizer': my_regularizer})
    self.assertEqual(model2.layers[1].kernel_regularizer, my_regularizer)

  @keras_parameterized.run_all_keras_modes
  @parameterized.named_parameters([
      ('l1', regularizers.l1()),
      ('l2', regularizers.l2()),
      ('l1_l2', regularizers.l1_l2()),
  ])
  def test_regularization_shared_layer(self, regularizer):
    dense_layer = keras.layers.Dense(
        NUM_CLASSES,
        kernel_regularizer=regularizer,
        activity_regularizer=regularizer)
    model = self.create_multi_input_model_from(dense_layer, dense_layer)
    model.compile(
        loss='categorical_crossentropy',
        optimizer='sgd',
        run_eagerly=testing_utils.should_run_eagerly())
    self.assertLen(model.losses, 5)

  @keras_parameterized.run_all_keras_modes
  @parameterized.named_parameters([
      ('l1', regularizers.l1()),
      ('l2', regularizers.l2()),
      ('l1_l2', regularizers.l1_l2()),
  ])
  def test_regularization_shared_model(self, regularizer):
    dense_layer = keras.layers.Dense(
        NUM_CLASSES,
        kernel_regularizer=regularizer,
        activity_regularizer=regularizer)

    input_tensor = keras.layers.Input(shape=(DATA_DIM,))
    dummy_model = keras.models.Model(input_tensor, dense_layer(input_tensor))

    model = self.create_multi_input_model_from(dummy_model, dummy_model)
    model.compile(
        loss='categorical_crossentropy',
        optimizer='sgd',
        run_eagerly=testing_utils.should_run_eagerly())
    self.assertLen(model.losses, 6)

  @keras_parameterized.run_all_keras_modes
  @parameterized.named_parameters([
      ('l1', regularizers.l1()),
      ('l2', regularizers.l2()),
      ('l1_l2', regularizers.l1_l2()),
  ])
  def test_regularization_shared_layer_in_different_models(self, regularizer):
    shared_dense = keras.layers.Dense(
        NUM_CLASSES,
        kernel_regularizer=regularizer,
        activity_regularizer=regularizer)
    models = []
    for _ in range(2):
      input_tensor = keras.layers.Input(shape=(DATA_DIM,))
      unshared_dense = keras.layers.Dense(
          NUM_CLASSES, kernel_regularizer=regularizer)
      out = unshared_dense(shared_dense(input_tensor))
      models.append(keras.models.Model(input_tensor, out))

    model = self.create_multi_input_model_from(
        layer1=models[0], layer2=models[1])
    model.compile(
        loss='categorical_crossentropy',
        optimizer='sgd',
        run_eagerly=testing_utils.should_run_eagerly())

    # We expect to see 9 losses on the model:
    # - 2 from the 2 add_loss calls on the outer model.
    # - 3 from the weight regularizers on the shared_dense layer, unshared_dense
    # in inner model 1, unshared_dense in inner model 2.
    # - 4 from activity regularizers on the shared_dense layer.
    self.assertLen(model.losses, 9)

  def test_deserialization_error(self):
    with self.assertRaisesRegex(ValueError, 'Could not interpret regularizer'):
      keras.regularizers.get(0)

  @parameterized.named_parameters([
      ('l1', regularizers.l1(l1=None), 0.01),
      ('l2', regularizers.l2(l2=None), 0.01),
      ('l1_l2', regularizers.l1_l2(l1=None, l2=None), 0.),
  ])
  def test_default_value_when_init_with_none(self, regularizer, expected_value):
    expected_value = np.asarray(expected_value)
    if hasattr(regularizer, 'l1'):
      self.assertAllClose(regularizer.l1, expected_value)
    if hasattr(regularizer, 'l2'):
      self.assertAllClose(regularizer.l2, expected_value)


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