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  1. Deep Learning
  2. Basic CNN

Extra: Multiple Input and Multiple Output Channels

PreviousExtra:From Fully Connected Layers to ConvolutionsNextAdvance CNN

Last updated 7 months ago

Channel

  • 当我们添加通道时，我们的输入和隐藏的表示都变成了三维张量。例如，每个RGB输入图像具有3×h×w3\times h \times w3×h×w的形状。我们将这个大小为333的轴称为通道（channel）维度。本节将更深入地研究具有多输入和多输出通道的卷积核。

  • 用cic_ici​和c0c_0c0​分别表示输入和输出通道的数目，并让khk_hkh​和kwk_wkw​为卷积核的高度和宽度。为了获得多个通道的输出，我们可以为每个输出通道创建一个形状为ci×kh×kwc_i \times k_h \times k_wci​×kh​×kw​的卷积核张量，这样卷积核的形状是co×ci×kh×kwc_o \times c_i \times k_h \times k_wco​×ci​×kh​×kw​。在互相关运算中，每个输出通道先获取所有输入通道，再以对应该输出通道的卷积核计算出结果。