Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/reshaping/zero_padding1d.py: 54%

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"""Keras zero-padding layer for 1D input.""" 

16 

17 

18import tensorflow.compat.v2 as tf 

19 

20from keras.src import backend 

21from keras.src.engine.base_layer import Layer 

22from keras.src.engine.input_spec import InputSpec 

23from keras.src.utils import conv_utils 

24 

25# isort: off 

26from tensorflow.python.util.tf_export import keras_export 

27 

28 

29@keras_export("keras.layers.ZeroPadding1D") 

30class ZeroPadding1D(Layer): 

31 """Zero-padding layer for 1D input (e.g. temporal sequence). 

32 

33 Examples: 

34 

35 >>> input_shape = (2, 2, 3) 

36 >>> x = np.arange(np.prod(input_shape)).reshape(input_shape) 

37 >>> print(x) 

38 [[[ 0 1 2] 

39 [ 3 4 5]] 

40 [[ 6 7 8] 

41 [ 9 10 11]]] 

42 >>> y = tf.keras.layers.ZeroPadding1D(padding=2)(x) 

43 >>> print(y) 

44 tf.Tensor( 

45 [[[ 0 0 0] 

46 [ 0 0 0] 

47 [ 0 1 2] 

48 [ 3 4 5] 

49 [ 0 0 0] 

50 [ 0 0 0]] 

51 [[ 0 0 0] 

52 [ 0 0 0] 

53 [ 6 7 8] 

54 [ 9 10 11] 

55 [ 0 0 0] 

56 [ 0 0 0]]], shape=(2, 6, 3), dtype=int64) 

57 

58 Args: 

59 padding: Int, or tuple of int (length 2), or dictionary. 

60 - If int: 

61 How many zeros to add at the beginning and end of 

62 the padding dimension (axis 1). 

63 - If tuple of int (length 2): 

64 How many zeros to add at the beginning and the end of 

65 the padding dimension (`(left_pad, right_pad)`). 

66 

67 Input shape: 

68 3D tensor with shape `(batch_size, axis_to_pad, features)` 

69 

70 Output shape: 

71 3D tensor with shape `(batch_size, padded_axis, features)` 

72 """ 

73 

74 def __init__(self, padding=1, **kwargs): 

75 super().__init__(**kwargs) 

76 self.padding = conv_utils.normalize_tuple( 

77 padding, 2, "padding", allow_zero=True 

78 ) 

79 self.input_spec = InputSpec(ndim=3) 

80 

81 def compute_output_shape(self, input_shape): 

82 if input_shape[1] is not None: 

83 length = input_shape[1] + self.padding[0] + self.padding[1] 

84 else: 

85 length = None 

86 return tf.TensorShape([input_shape[0], length, input_shape[2]]) 

87 

88 def call(self, inputs): 

89 return backend.temporal_padding(inputs, padding=self.padding) 

90 

91 def get_config(self): 

92 config = {"padding": self.padding} 

93 base_config = super().get_config() 

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

95