78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
from keras_core import backend
|
|
from keras_core import layers
|
|
from keras_core.api_export import keras_core_export
|
|
|
|
|
|
@keras_core_export("keras_core.layers.Dropout")
|
|
class Dropout(layers.Layer):
|
|
"""Applies dropout to the input.
|
|
|
|
The `Dropout` layer randomly sets input units to 0 with a frequency of
|
|
`rate` at each step during training time, which helps prevent overfitting.
|
|
Inputs not set to 0 are scaled up by `1 / (1 - rate)` such that the sum over
|
|
all inputs is unchanged.
|
|
|
|
Note that the `Dropout` layer only applies when `training` is set to `True`
|
|
in `call()`, such that no values are dropped during inference.
|
|
When using `model.fit`, `training` will be appropriately set to `True`
|
|
automatically. In other contexts, you can set the argument explicitly
|
|
to `True` when calling the layer.
|
|
|
|
(This is in contrast to setting `trainable=False` for a `Dropout` layer.
|
|
`trainable` does not affect the layer's behavior, as `Dropout` does
|
|
not have any variables/weights that can be frozen during training.)
|
|
|
|
Args:
|
|
rate: Float between 0 and 1. Fraction of the input units to drop.
|
|
noise_shape: 1D integer tensor representing the shape of the
|
|
binary dropout mask that will be multiplied with the input.
|
|
For instance, if your inputs have shape
|
|
`(batch_size, timesteps, features)` and
|
|
you want the dropout mask to be the same for all timesteps,
|
|
you can use `noise_shape=(batch_size, 1, features)`.
|
|
seed: A Python integer to use as random seed.
|
|
|
|
Call arguments:
|
|
inputs: Input tensor (of any rank).
|
|
training: Python boolean indicating whether the layer should behave in
|
|
training mode (adding dropout) or in inference mode (doing nothing).
|
|
"""
|
|
|
|
def __init__(
|
|
self, rate, noise_shape=None, seed=None, name=None, dtype=None
|
|
):
|
|
super().__init__(name=name, dtype=dtype)
|
|
if not 0 <= rate <= 1:
|
|
raise ValueError(
|
|
f"Invalid value received for argument "
|
|
"`rate`. Expected a float value between 0 and 1. "
|
|
f"Received: rate={rate}"
|
|
)
|
|
self.rate = rate
|
|
self.seed = seed
|
|
self.noise_shape = noise_shape
|
|
self.seed_generator = backend.random.SeedGenerator(seed)
|
|
self.supports_masking = True
|
|
|
|
def call(self, inputs, training=False):
|
|
if training and self.rate > 0:
|
|
return backend.random.dropout(
|
|
inputs,
|
|
self.rate,
|
|
noise_shape=self.noise_shape,
|
|
seed=self.seed_generator,
|
|
)
|
|
return inputs
|
|
|
|
def compute_output_shape(self, input_shape):
|
|
return input_shape
|
|
|
|
def get_config(self):
|
|
base_config = super().get_config()
|
|
config = {
|
|
"rate": self.rate,
|
|
"seed": self.seed,
|
|
"noise_shape": self.noise_shape,
|
|
}
|
|
return {**base_config, **config}
|