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<>池化层(Pooling Layer):
对信号进行 “收集” 和 “总结”
“收集” 指:多变少; “总结” 指:最大值/平均值
池化操作也可以剔除冗余信息,减少后面的计算量
用一个像素值代表4个像素值
池化层也是三种:一维池化,二维池化,三维池化
nn.MaxPool2d:
#池化窗口为2*2,步长也是2*2 #步长与池化窗口一样大小,为了不重叠 maxpool_layer = nn.MaxPool2d((2, 2),
stride=(2, 2))
其中,return_indices用来记录池化像素最大的位置,为反池化时填入数据
nn.AvgPool2d:
avgpoollayer = nn.AvgPool2d((2, 2), stride=(2, 2))
最大池化会比平均池化亮(像素值大些)
反池化:
注意在forward中要要传入indices
<>线性层(linear layer)
线性层又称为全连接层,其每一个神经元与上一个神经元相连实现对前一层的线性组合,线性变换
<>激活函数层(activation layer)
激活函数对特征继续非线性变换,赋予多层神经网络具有深度的意义。
nn.sigmoid
nn.tanh
nn.ReLU