Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167 代码语言:javascript 代码运行次数:0 运行 AI代码解释 tf.contrib.layers.batch_norm( inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, param_regularizers=None, updates_col...
from tensorflow.examples.tutorials.mnist import input_data # define our typical fully-connected + batch normalization + nonlinearity set-up def dense(x, size, scope): return tf.contrib.layers.fully_connected(x, size, activation_fn=None, scope=scope) def dense_batch_relu(x, phase, scope): wi...
tf.contrib.layers.batch_norm( inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, param_regularizers=None, updates_collections=tf.GraphKeys.UPDATE_OPS, is_training=True, reuse=None, variables_collections=None, outputs_collections=None, ...
is_training: Whether or not the layer is in training mode. In training mode it would accumulate the statistics of the moments intomoving_meanandmoving_varianceusing an exponential moving average with the givendecay. When it is not in training mode then it would use the values of themoving_me...
该函数是一种最底层的实现方法,在使用时mean、variance、scale、offset等参数需要自己传递并更新,因此实际使用时还需自己对该函数进行封装,一般不建议使用,但是对了解batch_norm的原理很有帮助。 封装使用的实例如下: importtensorflow as tfdefbatch_norm(x, name_scope, training, epsilon=1e-3, decay=0.99):""...
Tensorflow批量标准化: tf. contrib.layers.batch_norm 我最近开始使用 Tensorflow,并一直在尽力适应环境。这真是太棒了!然而,使用 tf.contrib.layers.batch_norm 进行批量归一化有点棘手。 现在,这是我正在使用的函数: def batch_norm(x, phase): return tf.contrib.layers.batch_norm(x,center = True, scale...
TensorFlow提供了一个batch_normalization()的功能,它可以简化标准正态化输入的过程,但是你必须自行计算上述最后一步的γ、ε和β,并将它们作为参数传给这个函数。 或者你也可以使用batch_norm()函数,它可以为你完成上述所有工作。 import tensorflow as tf from tensorflow.contrib.layers import batch_norm from tenso...
def batch_norm_layer(x, train_phase, scope_bn): bn_train = tf.contrib.layers.batch_norm(x, decay=FLAGS.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=True, reuse=None, scope=scope_bn) bn_infer = tf.contrib.layers.batch_norm(x, decay=FLAGS.batch_nor...
问Tensorflow分配moving_variance和moving_average of tf.contrib.layers.batch_normEN法一： 循环打印 模板...
is to specify the norm_scope name like thistf.contrib.layers.batch_norm(x, scope="name"). When you reuse this norm layer, just dotf.contrib.layers.batch_norm(x, scope="name", reuse=True)or usetf.contrib.layers.batch_norm(x, scope="name")under a reusable scope. Hope this is ...