tf.nn.lrn() 方法有什么作用？

Question

这是从 cifar10 教程中截取的代码。它来自 cifar10.py.

# conv1
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64],
                                     stddev=1e-4, wd=0.0)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope.name)
_activation_summary(conv1)

# pool1
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                     padding='SAME', name='pool1')
# norm1
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
                name='norm1')

tf.nn.lrn-方法有什么作用？我在 API 关于 https://www.tensorflow.org/versions/r0.8/api_docs/python/index.html

的文档中找不到定义

Answer 1

tf.nn.lrn 是 tf.nn.local_response_normalization 的缩写。因此，您可能想要查看的文档是：https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization

Answer 2

如 mentioned, tf.nn.lrn is short for tf.nn.local_response_normalization (documentation)

此外，this question 为 响应规范化层 .

提供了更多信息的良好资源

发件人： http://caffe.berkeleyvision.org/tutorial/layers.html#data-layers

"The local response normalization layer performs a kind of “lateral inhibition” by normalizing over local input regions. In ACROSS_CHANNELS mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape local_size x 1 x 1). In WITHIN_CHANNEL mode, the local regions extend spatially, but are in separate channels (i.e., they have shape 1 x local_size x local_size). Each input value is divided by (1+(α/n)∑ix2i)β, where n is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary)."

这些层已经失宠，因为它们对结果的影响很小，而其他技术被证明更有用。

tf.nn.lrn() 方法有什么作用？

what does the tf.nn.lrn() method do?

tensorflow