# Copyright 2022 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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"""Stochastic modules."""
from typing import Optional, Sequence
from flax.linen.module import compact
from flax.linen.module import merge_param
from flax.linen.module import Module
from jax import lax
from jax import random
import jax.numpy as jnp
[docs]class Dropout(Module):
"""Create a dropout layer.
Attributes:
rate: the dropout probability. (_not_ the keep rate!)
broadcast_dims: dimensions that will share the same dropout mask
deterministic: if false the inputs are scaled by `1 / (1 - rate)` and
masked, whereas if true, no mask is applied and the inputs are returned
as is.
"""
rate: float
broadcast_dims: Sequence[int] = ()
deterministic: Optional[bool] = None
[docs] @compact
def __call__(self, inputs, deterministic: Optional[bool] = None):
"""Applies a random dropout mask to the input.
Args:
inputs: the inputs that should be randomly masked.
deterministic: if false the inputs are scaled by `1 / (1 - rate)` and
masked, whereas if true, no mask is applied and the inputs are returned
as is.
Returns:
The masked inputs reweighted to preserve mean.
"""
deterministic = merge_param(
'deterministic', self.deterministic, deterministic)
if self.rate == 0.:
return inputs
# Prevent gradient NaNs in 1.0 edge-case.
if self.rate == 1.0:
return jnp.zeros_like(inputs)
keep_prob = 1. - self.rate
if deterministic:
return inputs
else:
rng = self.make_rng('dropout')
broadcast_shape = list(inputs.shape)
for dim in self.broadcast_dims:
broadcast_shape[dim] = 1
mask = random.bernoulli(rng, p=keep_prob, shape=broadcast_shape)
mask = jnp.broadcast_to(mask, inputs.shape)
return lax.select(mask, inputs / keep_prob, jnp.zeros_like(inputs))
