# Copyright 2024 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
import jax.numpy as jnp
from jax import lax, random
from flax.linen.module import Module, compact, merge_param
from flax.typing import PRNGKey
[docs]class Dropout(Module):
"""Create a dropout layer.
.. note::
When using :meth:`Module.apply() <flax.linen.Module.apply>`, make sure
to include an RNG seed named ``'dropout'``. Dropout isn't necessary for
variable initialization.
Example usage::
>>> import flax.linen as nn
>>> import jax, jax.numpy as jnp
>>> class MLP(nn.Module):
... @nn.compact
... def __call__(self, x, train):
... x = nn.Dense(4)(x)
... x = nn.Dropout(0.5, deterministic=not train)(x)
... return x
>>> model = MLP()
>>> x = jnp.ones((1, 3))
>>> variables = model.init(jax.random.key(0), x, train=False) # don't use dropout
>>> model.apply(variables, x, train=False) # don't use dropout
Array([[-0.88686204, -0.5928178 , -0.5184689 , -0.4345976 ]], dtype=float32)
>>> model.apply(variables, x, train=True, rngs={'dropout': jax.random.key(1)}) # use dropout
Array([[ 0. , -1.1856356, -1.0369378, 0. ]], dtype=float32)
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.
rng_collection: the rng collection name to use when requesting an rng key.
"""
rate: float
broadcast_dims: Sequence[int] = ()
deterministic: Optional[bool] = None
rng_collection: str = 'dropout'
[docs] @compact
def __call__(
self,
inputs,
deterministic: Optional[bool] = None,
rng: Optional[PRNGKey] = 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.
rng: an optional PRNGKey used as the random key, if not specified, one
will be generated using ``make_rng`` with the ``rng_collection`` name.
Returns:
The masked inputs reweighted to preserve mean.
"""
deterministic = merge_param(
'deterministic', self.deterministic, deterministic
)
if (self.rate == 0.0) or deterministic:
return inputs
# Prevent gradient NaNs in 1.0 edge-case.
if self.rate == 1.0:
return jnp.zeros_like(inputs)
keep_prob = 1.0 - self.rate
if rng is None:
rng = self.make_rng(self.rng_collection)
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))
