60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains the SpatialDropout1D layer."""
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import tensorflow.compat.v2 as tf
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from keras.engine.input_spec import InputSpec
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from keras.layers.regularization.dropout import Dropout
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.SpatialDropout1D")
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class SpatialDropout1D(Dropout):
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"""Spatial 1D version of Dropout.
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This version performs the same function as Dropout, however, it drops
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entire 1D feature maps instead of individual elements. If adjacent frames
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within feature maps are strongly correlated (as is normally the case in
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early convolution layers) then regular dropout will not regularize the
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activations and will otherwise just result in an effective learning rate
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decrease. In this case, SpatialDropout1D will help promote independence
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between feature maps and should be used instead.
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Args:
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rate: Float between 0 and 1. Fraction of the input units to drop.
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Call arguments:
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inputs: A 3D tensor.
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training: Python boolean indicating whether the layer should behave in
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training mode (adding dropout) or in inference mode (doing nothing).
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Input shape:
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3D tensor with shape: `(samples, timesteps, channels)`
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Output shape: Same as input.
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References: - [Efficient Object Localization Using Convolutional
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Networks](https://arxiv.org/abs/1411.4280)
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"""
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def __init__(self, rate, **kwargs):
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super().__init__(rate, **kwargs)
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self.input_spec = InputSpec(ndim=3)
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def _get_noise_shape(self, inputs):
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input_shape = tf.shape(inputs)
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noise_shape = (input_shape[0], 1, input_shape[2])
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return noise_shape
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