136 lines
4.9 KiB
Python
136 lines
4.9 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 Dropout layer."""
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import numbers
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras.engine import base_layer
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from keras.utils import control_flow_util
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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.Dropout")
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class Dropout(base_layer.BaseRandomLayer):
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"""Applies Dropout to the input.
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The Dropout layer randomly sets input units to 0 with a frequency of `rate`
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at each step during training time, which helps prevent overfitting.
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Inputs not set to 0 are scaled up by 1/(1 - rate) such that the sum over
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all inputs is unchanged.
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Note that the Dropout layer only applies when `training` is set to True
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such that no values are dropped during inference. When using `model.fit`,
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`training` will be appropriately set to True automatically, and in other
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contexts, you can set the kwarg explicitly to True when calling the layer.
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(This is in contrast to setting `trainable=False` for a Dropout layer.
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`trainable` does not affect the layer's behavior, as Dropout does
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not have any variables/weights that can be frozen during training.)
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>>> tf.random.set_seed(0)
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>>> layer = tf.keras.layers.Dropout(.2, input_shape=(2,))
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>>> data = np.arange(10).reshape(5, 2).astype(np.float32)
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>>> print(data)
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[[0. 1.]
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[2. 3.]
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[4. 5.]
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[6. 7.]
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[8. 9.]]
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>>> outputs = layer(data, training=True)
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>>> print(outputs)
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tf.Tensor(
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[[ 0. 1.25]
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[ 2.5 3.75]
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[ 5. 6.25]
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[ 7.5 8.75]
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[10. 0. ]], shape=(5, 2), dtype=float32)
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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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noise_shape: 1D integer tensor representing the shape of the
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binary dropout mask that will be multiplied with the input.
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For instance, if your inputs have shape
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`(batch_size, timesteps, features)` and
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you want the dropout mask to be the same for all timesteps,
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you can use `noise_shape=(batch_size, 1, features)`.
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seed: A Python integer to use as random seed.
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Call arguments:
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inputs: Input tensor (of any rank).
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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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"""
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def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
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super().__init__(seed=seed, **kwargs)
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if isinstance(rate, (int, float)) and not 0 <= rate <= 1:
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raise ValueError(
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f"Invalid value {rate} received for "
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"`rate`, expected a value between 0 and 1."
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)
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self.rate = rate
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self.noise_shape = noise_shape
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self.seed = seed
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self.supports_masking = True
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def _get_noise_shape(self, inputs):
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# Subclasses of `Dropout` may implement `_get_noise_shape(self,
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# inputs)`, which will override `self.noise_shape`, and allows for
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# custom noise shapes with dynamically sized inputs.
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if self.noise_shape is None:
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return None
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concrete_inputs_shape = tf.shape(inputs)
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noise_shape = []
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for i, value in enumerate(self.noise_shape):
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noise_shape.append(
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concrete_inputs_shape[i] if value is None else value
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)
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return tf.convert_to_tensor(noise_shape)
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def call(self, inputs, training=None):
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if isinstance(self.rate, numbers.Real) and self.rate == 0:
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return tf.identity(inputs)
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if training is None:
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training = backend.learning_phase()
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def dropped_inputs():
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return self._random_generator.dropout(
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inputs, self.rate, noise_shape=self._get_noise_shape(inputs)
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)
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output = control_flow_util.smart_cond(
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training, dropped_inputs, lambda: tf.identity(inputs)
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)
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return output
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def compute_output_shape(self, input_shape):
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return input_shape
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def get_config(self):
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config = {
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"rate": self.rate,
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"noise_shape": self.noise_shape,
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"seed": self.seed,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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