Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/activation/elu.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"""Exponential 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.ELU") 

27class ELU(Layer): 

28 """Exponential Linear Unit. 

29 

30 It follows: 

31 

32 ``` 

33 f(x) = alpha * (exp(x) - 1.) for x < 0 

34 f(x) = x for x >= 0 

35 ``` 

36 

37 Input shape: 

38 Arbitrary. Use the keyword argument `input_shape` 

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

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

41 

42 Output shape: 

43 Same shape as the input. 

44 

45 Args: 

46 alpha: Scale for the negative factor. 

47 """ 

48 

49 def __init__(self, alpha=1.0, **kwargs): 

50 super().__init__(**kwargs) 

51 if alpha is None: 

52 raise ValueError( 

53 "Alpha of an ELU layer cannot be None, expecting a float. " 

54 f"Received: {alpha}" 

55 ) 

56 self.supports_masking = True 

57 self.alpha = backend.cast_to_floatx(alpha) 

58 

59 def call(self, inputs): 

60 return backend.elu(inputs, self.alpha) 

61 

62 def get_config(self): 

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

64 base_config = super().get_config() 

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

66 

67 @tf_utils.shape_type_conversion 

68 def compute_output_shape(self, input_shape): 

69 return input_shape 

70