Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/regularization/alpha_dropout.py: 37%

1# Copyright 2015 The TensorFlow Authors. All Rights Reserved. 

2# 

3# Licensed under the Apache License, Version 2.0 (the "License"); 

4# you may not use this file except in compliance with the License. 

5# You may obtain a copy of the License at 

6# 

7# http://www.apache.org/licenses/LICENSE-2.0 

8# 

9# Unless required by applicable law or agreed to in writing, software 

10# distributed under the License is distributed on an "AS IS" BASIS, 

11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

12# See the License for the specific language governing permissions and 

13# limitations under the License. 

14# ============================================================================== 

15"""Contains the AlphaDropout layer.""" 

16 

17 

18import tensorflow.compat.v2 as tf 

19 

20from keras.src import backend 

21from keras.src.engine import base_layer 

22from keras.src.utils import tf_utils 

23 

24# isort: off 

25from tensorflow.python.util.tf_export import keras_export 

26 

27 

28@keras_export("keras.layers.AlphaDropout") 

29class AlphaDropout(base_layer.BaseRandomLayer): 

30 """Applies Alpha Dropout to the input. 

31 

32 Alpha Dropout is a `Dropout` that keeps mean and variance of inputs 

33 to their original values, in order to ensure the self-normalizing property 

34 even after this dropout. 

35 Alpha Dropout fits well to Scaled Exponential Linear Units 

36 by randomly setting activations to the negative saturation value. 

37 

38 Args: 

39 rate: float, drop probability (as with `Dropout`). 

40 The multiplicative noise will have 

41 standard deviation `sqrt(rate / (1 - rate))`. 

42 seed: Integer, optional random seed to enable deterministic behavior. 

43 

44 Call arguments: 

45 inputs: Input tensor (of any rank). 

46 training: Python boolean indicating whether the layer should behave in 

47 training mode (adding dropout) or in inference mode (doing nothing). 

48 

49 Input shape: 

50 Arbitrary. Use the keyword argument `input_shape` 

51 (tuple of integers, does not include the samples axis) 

52 when using this layer as the first layer in a model. 

53 

54 Output shape: 

55 Same shape as input. 

56 """ 

57 

58 def __init__(self, rate, noise_shape=None, seed=None, **kwargs): 

59 super().__init__(seed=seed, **kwargs) 

60 self.rate = rate 

61 self.noise_shape = noise_shape 

62 self.seed = seed 

63 self.supports_masking = True 

64 

65 def _get_noise_shape(self, inputs): 

66 return self.noise_shape if self.noise_shape else tf.shape(inputs) 

67 

68 def call(self, inputs, training=None): 

69 if 0.0 < self.rate < 1.0: 

70 noise_shape = self._get_noise_shape(inputs) 

71 

72 def dropped_inputs(inputs=inputs, rate=self.rate): 

73 alpha = 1.6732632423543772848170429916717 

74 scale = 1.0507009873554804934193349852946 

75 alpha_p = -alpha * scale 

76 

77 kept_idx = tf.greater_equal( 

78 self._random_generator.random_uniform(noise_shape), rate 

79 ) 

80 kept_idx = tf.cast(kept_idx, inputs.dtype) 

81 

82 # Get affine transformation params 

83 a = ((1 - rate) * (1 + rate * alpha_p**2)) ** -0.5 

84 b = -a * alpha_p * rate 

85 

86 # Apply mask 

87 x = inputs * kept_idx + alpha_p * (1 - kept_idx) 

88 

89 # Do affine transformation 

90 return a * x + b 

91 

92 return backend.in_train_phase( 

93 dropped_inputs, inputs, training=training 

94 ) 

95 return inputs 

96 

97 def get_config(self): 

98 config = {"rate": self.rate, "seed": self.seed} 

99 base_config = super().get_config() 

100 return dict(list(base_config.items()) + list(config.items())) 

101 

102 @tf_utils.shape_type_conversion 

103 def compute_output_shape(self, input_shape): 

104 return input_shape 

105