Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/pooling/average_pooling1d.py: 90%

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"""Average pooling 1D layer.""" 

16 

17 

18import functools 

19 

20from keras.src import backend 

21from keras.src.layers.pooling.base_pooling1d import Pooling1D 

22 

23# isort: off 

24from tensorflow.python.util.tf_export import keras_export 

25 

26 

27@keras_export("keras.layers.AveragePooling1D", "keras.layers.AvgPool1D") 

28class AveragePooling1D(Pooling1D): 

29 """Average pooling for temporal data. 

30 

31 Downsamples the input representation by taking the average value over the 

32 window defined by `pool_size`. The window is shifted by `strides`. The 

33 resulting output when using "valid" padding option has a shape of: 

34 `output_shape = (input_shape - pool_size + 1) / strides)` 

35 

36 The resulting output shape when using the "same" padding option is: 

37 `output_shape = input_shape / strides` 

38 

39 For example, for strides=1 and padding="valid": 

40 

41 >>> x = tf.constant([1., 2., 3., 4., 5.]) 

42 >>> x = tf.reshape(x, [1, 5, 1]) 

43 >>> x 

44 <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy= 

45 array([[[1.], 

46 [2.], 

47 [3.], 

48 [4.], 

49 [5.]], dtype=float32)> 

50 >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2, 

51 ... strides=1, padding='valid') 

52 >>> avg_pool_1d(x) 

53 <tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy= 

54 array([[[1.5], 

55 [2.5], 

56 [3.5], 

57 [4.5]]], dtype=float32)> 

58 

59 For example, for strides=2 and padding="valid": 

60 

61 >>> x = tf.constant([1., 2., 3., 4., 5.]) 

62 >>> x = tf.reshape(x, [1, 5, 1]) 

63 >>> x 

64 <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy= 

65 array([[[1.], 

66 [2.], 

67 [3.], 

68 [4.], 

69 [5.]], dtype=float32)> 

70 >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2, 

71 ... strides=2, padding='valid') 

72 >>> avg_pool_1d(x) 

73 <tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy= 

74 array([[[1.5], 

75 [3.5]]], dtype=float32)> 

76 

77 For example, for strides=1 and padding="same": 

78 

79 >>> x = tf.constant([1., 2., 3., 4., 5.]) 

80 >>> x = tf.reshape(x, [1, 5, 1]) 

81 >>> x 

82 <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy= 

83 array([[[1.], 

84 [2.], 

85 [3.], 

86 [4.], 

87 [5.]], dtype=float32)> 

88 >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2, 

89 ... strides=1, padding='same') 

90 >>> avg_pool_1d(x) 

91 <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy= 

92 array([[[1.5], 

93 [2.5], 

94 [3.5], 

95 [4.5], 

96 [5.]]], dtype=float32)> 

97 

98 Args: 

99 pool_size: Integer, size of the average pooling windows. 

100 strides: Integer, or None. Factor by which to downscale. 

101 E.g. 2 will halve the input. 

102 If None, it will default to `pool_size`. 

103 padding: One of `"valid"` or `"same"` (case-insensitive). 

104 `"valid"` means no padding. `"same"` results in padding evenly to 

105 the left/right or up/down of the input such that output has the same 

106 height/width dimension as the input. 

107 data_format: A string, 

108 one of `channels_last` (default) or `channels_first`. 

109 The ordering of the dimensions in the inputs. 

110 `channels_last` corresponds to inputs with shape 

111 `(batch, steps, features)` while `channels_first` 

112 corresponds to inputs with shape 

113 `(batch, features, steps)`. 

114 

115 Input shape: 

116 - If `data_format='channels_last'`: 

117 3D tensor with shape `(batch_size, steps, features)`. 

118 - If `data_format='channels_first'`: 

119 3D tensor with shape `(batch_size, features, steps)`. 

120 

121 Output shape: 

122 - If `data_format='channels_last'`: 

123 3D tensor with shape `(batch_size, downsampled_steps, features)`. 

124 - If `data_format='channels_first'`: 

125 3D tensor with shape `(batch_size, features, downsampled_steps)`. 

126 """ 

127 

128 def __init__( 

129 self, 

130 pool_size=2, 

131 strides=None, 

132 padding="valid", 

133 data_format="channels_last", 

134 **kwargs 

135 ): 

136 super().__init__( 

137 functools.partial(backend.pool2d, pool_mode="avg"), 

138 pool_size=pool_size, 

139 strides=strides, 

140 padding=padding, 

141 data_format=data_format, 

142 **kwargs 

143 ) 

144 

145 

146# Alias 

147 

148AvgPool1D = AveragePooling1D 

149