2023-04-19 02:22:13 +00:00
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from keras_core import backend
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from keras_core import layers
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from keras_core.api_export import keras_core_export
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@keras_core_export("keras_core.layers.Dropout")
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class Dropout(layers.Layer):
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"""Applies dropout to the input.
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2023-04-22 06:16:39 +00:00
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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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2023-04-19 02:22:13 +00:00
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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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in `call()`, such that no values are dropped during inference.
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When using `model.fit`, `training` will be appropriately set to `True`
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automatically. In other contexts, you can set the argument explicitly
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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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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__(
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self, rate, noise_shape=None, seed=None, name=None, dtype=None
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):
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super().__init__(name=name, dtype=dtype)
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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 received for argument "
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"`rate`. Expected a float value between 0 and 1. "
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f"Received: rate={rate}"
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)
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self.rate = rate
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self.seed = seed
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self.noise_shape = noise_shape
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self.seed_generator = backend.random.SeedGenerator(seed)
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self.supports_masking = True
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def call(self, inputs, training=False):
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if training and self.rate > 0:
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return backend.random.dropout(
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inputs,
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self.rate,
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noise_shape=self.noise_shape,
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seed=self.seed_generator,
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)
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return inputs
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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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base_config = super().get_config()
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config = {
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"rate": self.rate,
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"seed": self.seed,
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"noise_shape": self.noise_shape,
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}
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return {**base_config, **config}
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