Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/regularization/spatial_dropout3d.py: 45%
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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 SpatialDropout3D layer."""
18import tensorflow.compat.v2 as tf
20from keras.src import backend
21from keras.src.engine.input_spec import InputSpec
22from keras.src.layers.regularization.dropout import Dropout
24# isort: off
25from tensorflow.python.util.tf_export import keras_export
28@keras_export("keras.layers.SpatialDropout3D")
29class SpatialDropout3D(Dropout):
30 """Spatial 3D version of Dropout.
32 This version performs the same function as Dropout, however, it drops
33 entire 3D feature maps instead of individual elements. If adjacent voxels
34 within feature maps are strongly correlated (as is normally the case in
35 early convolution layers) then regular dropout will not regularize the
36 activations and will otherwise just result in an effective learning rate
37 decrease. In this case, SpatialDropout3D will help promote independence
38 between feature maps and should be used instead.
40 Args:
41 rate: Float between 0 and 1. Fraction of the input units to drop.
42 data_format: 'channels_first' or 'channels_last'. In 'channels_first'
43 mode, the channels dimension (the depth) is at index 1, in
44 'channels_last' mode is it at index 4. It defaults to the
45 `image_data_format` value found in your Keras config file at
46 `~/.keras/keras.json`. If you never set it, then it will be
47 "channels_last".
48 Call arguments:
49 inputs: A 5D tensor.
50 training: Python boolean indicating whether the layer should behave in
51 training mode (adding dropout) or in inference mode (doing nothing).
52 Input shape:
53 5D tensor with shape: `(samples, channels, dim1, dim2, dim3)` if
54 data_format='channels_first'
55 or 5D tensor with shape: `(samples, dim1, dim2, dim3, channels)` if
56 data_format='channels_last'.
57 Output shape: Same as input.
58 References: - [Efficient Object Localization Using Convolutional
59 Networks](https://arxiv.org/abs/1411.4280)
60 """
62 def __init__(self, rate, data_format=None, **kwargs):
63 super().__init__(rate, **kwargs)
64 if data_format is None:
65 data_format = backend.image_data_format()
66 if data_format not in {"channels_last", "channels_first"}:
67 raise ValueError(
68 '`data_format` must be "channels_last" or "channels_first". '
69 f"Received: data_format={data_format}."
70 )
71 self.data_format = data_format
72 self.input_spec = InputSpec(ndim=5)
74 def _get_noise_shape(self, inputs):
75 input_shape = tf.shape(inputs)
76 if self.data_format == "channels_first":
77 return (input_shape[0], input_shape[1], 1, 1, 1)
78 elif self.data_format == "channels_last":
79 return (input_shape[0], 1, 1, 1, input_shape[4])