Source code for tensorlayer.layers.noise

#! /usr/bin/python
# -*- coding: utf-8 -*-

import tensorflow as tf

import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer

__all__ = [
    'GaussianNoise',
]


[docs]class GaussianNoise(Layer): """ The :class:`GaussianNoise` class is noise layer that adding noise with gaussian distribution to the activation. Parameters ------------ mean : float The mean. Default is 0.0. stddev : float The standard deviation. Default is 1.0. is_always : boolean Is True, add noise for train and eval mode. If False, skip this layer in eval mode. seed : int or None The seed for random noise. name : str A unique layer name. Examples -------- With TensorLayer >>> net = tl.layers.Input([64, 200], name='input') >>> net = tl.layers.Dense(n_units=100, act=tf.nn.relu, name='dense')(net) >>> gaussianlayer = tl.layers.GaussianNoise(name='gaussian')(net) >>> print(gaussianlayer) >>> output shape : (64, 100) """ def __init__( self, mean=0.0, stddev=1.0, is_always=True, seed=None, name=None, # 'gaussian_noise', ): super().__init__(name) self.mean = mean self.stddev = stddev self.seed = seed self.is_always = is_always self.build() self._built = True logging.info("GaussianNoise %s: mean: %f stddev: %f" % (self.name, self.mean, self.stddev)) def __repr__(self): s = '{classname}(mean={mean}, stddev={stddev}' if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs=None): pass def forward(self, inputs): if (self.is_train or self.is_always) is False: return inputs else: # noise = np.random.normal(0.0 , sigma , tf.to_int64(self.inputs).get_shape()) noise = tf.random.normal(shape=inputs.get_shape(), mean=self.mean, stddev=self.stddev, seed=self.seed) outputs = inputs + noise return outputs