Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/activation/leaky_relu.py: 55%

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"""Leaky version of a Rectified Linear Unit activation layer.""" 

16 

17 

18from keras.src import backend 

19from keras.src.engine.base_layer import Layer 

20from keras.src.utils import tf_utils 

21 

22# isort: off 

23from tensorflow.python.util.tf_export import keras_export 

24 

25 

26@keras_export("keras.layers.LeakyReLU") 

27class LeakyReLU(Layer): 

28 """Leaky version of a Rectified Linear Unit. 

29 

30 It allows a small gradient when the unit is not active: 

31 

32 ``` 

33 f(x) = alpha * x if x < 0 

34 f(x) = x if x >= 0 

35 ``` 

36 

37 Usage: 

38 

39 >>> layer = tf.keras.layers.LeakyReLU() 

40 >>> output = layer([-3.0, -1.0, 0.0, 2.0]) 

41 >>> list(output.numpy()) 

42 [-0.9, -0.3, 0.0, 2.0] 

43 >>> layer = tf.keras.layers.LeakyReLU(alpha=0.1) 

44 >>> output = layer([-3.0, -1.0, 0.0, 2.0]) 

45 >>> list(output.numpy()) 

46 [-0.3, -0.1, 0.0, 2.0] 

47 

48 Input shape: 

49 Arbitrary. Use the keyword argument `input_shape` 

50 (tuple of integers, does not include the batch axis) 

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

52 

53 Output shape: 

54 Same shape as the input. 

55 

56 Args: 

57 alpha: Float >= `0.`. Negative slope coefficient. Defaults to `0.3`. 

58 

59 """ 

60 

61 def __init__(self, alpha=0.3, **kwargs): 

62 super().__init__(**kwargs) 

63 if alpha is None: 

64 raise ValueError( 

65 "The alpha value of a Leaky ReLU layer cannot be None, " 

66 f"Expecting a float. Received: {alpha}" 

67 ) 

68 self.supports_masking = True 

69 self.alpha = backend.cast_to_floatx(alpha) 

70 

71 def call(self, inputs): 

72 return backend.relu(inputs, alpha=self.alpha) 

73 

74 def get_config(self): 

75 config = {"alpha": float(self.alpha)} 

76 base_config = super().get_config() 

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

78 

79 @tf_utils.shape_type_conversion 

80 def compute_output_shape(self, input_shape): 

81 return input_shape 

82