64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
import math
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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 import operations as ops
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from keras_core.api_export import keras_core_export
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@keras_core_export("keras_core.layers.GaussianDropout")
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class GaussianDropout(layers.Layer):
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"""Apply multiplicative 1-centered Gaussian noise.
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As it is a regularization layer, it is only active at training time.
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Args:
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rate: Float, drop probability (as with `Dropout`).
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The multiplicative noise will have
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standard deviation `sqrt(rate / (1 - rate))`.
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seed: Integer, optional random seed to enable deterministic behavior.
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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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stddev = math.sqrt(self.rate / (1.0 - self.rate))
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return inputs * backend.random.normal(
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shape=ops.shape(inputs),
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mean=1.0,
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stddev=stddev,
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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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}
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return {**base_config, **config}
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