#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer.layers.core import LayersConfig
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = [
'DropoutLayer',
]
[docs]class DropoutLayer(Layer):
"""
The :class:`DropoutLayer` class is a noise layer which randomly set some
activations to zero according to a keeping probability.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer.
keep : float
The keeping probability.
The lower the probability it is, the more activations are set to zero.
is_fix : boolean
Fixing probability or nor. Default is False.
If True, the keeping probability is fixed and cannot be changed via `feed_dict`.
is_train : boolean
Trainable or not. If False, skip this layer. Default is True.
seed : int or None
The seed for random dropout.
name : str
A unique layer name.
Examples
--------
Method 1: Using ``all_drop`` see `tutorial_mlp_dropout1.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py>`__
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> net = tl.layers.InputLayer(x, name='input_layer')
>>> net = tl.layers.DropoutLayer(net, keep=0.8, name='drop1')
>>> net = tl.layers.DenseLayer(net, n_units=800, act=tf.nn.relu, name='relu1')
>>> ...
>>> # For training, enable dropout as follow.
>>> feed_dict = {x: X_train_a, y_: y_train_a}
>>> feed_dict.update( net.all_drop ) # enable noise layers
>>> sess.run(train_op, feed_dict=feed_dict)
>>> ...
>>> # For testing, disable dropout as follow.
>>> dp_dict = tl.utils.dict_to_one( net.all_drop ) # disable noise layers
>>> feed_dict = {x: X_val_a, y_: y_val_a}
>>> feed_dict.update(dp_dict)
>>> err, ac = sess.run([cost, acc], feed_dict=feed_dict)
>>> ...
Method 2: Without using ``all_drop`` see `tutorial_mlp_dropout2.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mlp_dropout2.py>`__
>>> def mlp(x, is_train=True, reuse=False):
>>> with tf.variable_scope("MLP", reuse=reuse):
>>> tl.layers.set_name_reuse(reuse)
>>> net = tl.layers.InputLayer(x, name='input')
>>> net = tl.layers.DropoutLayer(net, keep=0.8, is_fix=True,
>>> is_train=is_train, name='drop1')
>>> ...
>>> return net
>>> net_train = mlp(x, is_train=True, reuse=False)
>>> net_test = mlp(x, is_train=False, reuse=True)
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
keep=0.5,
is_fix=False,
is_train=True,
seed=None,
name='dropout_layer',
):
super(DropoutLayer, self).__init__(prev_layer=prev_layer, name=name)
logging.info("DropoutLayer %s: keep: %f is_fix: %s" % (self.name, keep, is_fix))
if is_train is False:
logging.info(" skip DropoutLayer")
self.outputs = prev_layer.outputs
else:
# The name of placeholder for keep_prob is the same with the name of the Layer.
if is_fix:
self.outputs = tf.nn.dropout(self.inputs, keep, seed=seed, name=name)
else:
LayersConfig.set_keep[name] = tf.placeholder(LayersConfig.tf_dtype)
self.outputs = tf.nn.dropout(self.inputs, LayersConfig.set_keep[name], seed=seed, name=name) # 1.2
if is_fix is False:
self.all_drop.update({LayersConfig.set_keep[name]: keep})
self._add_layers(self.outputs)