#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorlayerx as tlx
from tensorlayerx import logging
from tensorlayerx.nn.core import Module
__all__ = [
'Dropout',
]
[docs]class Dropout(Module):
"""
The :class:`Dropout` class is a noise layer which randomly set some
activations to zero according to a keeping probability.
Parameters
----------
keep : float
The keeping probability.
The lower the probability it is, the more activations are set to zero.
seed : int or None
The seed for random dropout.
name : None or str
A unique layer name.
Examples
--------
>>> net = tlx.nn.Input([10, 200])
>>> net = tlx.nn.Dropout(keep=0.2)(net)
"""
def __init__(self, keep, seed=0, name=None): #"dropout"):
super(Dropout, self).__init__(name)
self.keep = keep
self.seed = seed
self.build()
self._built = True
logging.info("Dropout %s: keep: %f " % (self.name, self.keep))
def __repr__(self):
s = ('{classname}(keep={keep}')
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
self.dropout = tlx.ops.Dropout(keep=self.keep, seed=self.seed)
# @tf.function
def forward(self, inputs):
if self.is_train:
outputs = self.dropout(inputs)
else:
outputs = inputs
return outputs