LeNet:( CNN C3:f.maps 16@10x10 INPUT C1:feature maps S4:f.maps 16@5x5 6@28x28 32x32 S2:f.maps 6@14x14 C5:layer F6:layer 120 OUTPUT 84 10 Full connection Gaussian connections Convolutions Subsampling Convolutions Subsampling Full connection CNNs are basically layers of convolutions followed by su bsampling and fully connected layers. 口三大关键思想 ■ 局部感受野（卷积操作) ■ 权值共享 ■ 池化 2LeNet：CNN 2 CNNs are basically layers of convolutions followed by su bsampling and fully connected layers. o 三大关键思想 n 局部感受野（卷积操作） n 权值共享 n 池化