local_response_normalization
local_response_normalization出现在论文”ImageNet Classification with deep Convolutional Neural Networks”中,论文中说,这种normalization对于泛化是有好处的.
经过了一个conv2d或pooling后,我们获得了[batch_size, height, width, channels]这样一个tensor.现在,将channels称之为层,不考虑batch_size
1 tf.nn.local_response_normalization(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None) 2 ''' 3 Local Response Normalization. 4 The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within depth_radius. In detail, 5 ''' 6 """ 7 input: A Tensor. Must be one of the following types: float32, half. 4-D. 8 depth_radius: An optional int. Defaults to 5. 0-D. Half-width of the 1-D normalization window. 9 bias: An optional float. Defaults to 1. An offset (usually positive to avoid dividing by 0). 10 alpha: An optional float. Defaults to 1. A scale factor, usually positive. 11 beta: An optional float. Defaults to 0.5. An exponent. 12 name: A name for the operation (optional). 13 """
