图文并茂的 CNN 介绍 PPT.ppt

Layer 3?4: Convolution Neural Network CNN ver. 4.11a * Layer 3?4: 12 conv. Maps (C) InputMaps=6 OutputMaps=12 Fan_in= 6x52=150 Fan_out= 12x52=300 Kernel =5x5 Layer 4(c): 12x8x8 Layer3 L3(s): 6x12x12 Conv. layer 3 with kernels to produce layer 4 Inputs : 6 maps of layer3(L3{i=1:6}), each is 12x12 Kernel set: totally 6x12 kernels, each is 5x5,i.e. K{i=1:6}{j=1:12}, each K{i}{j} is 5x5 12 bias{j=1:12} in this layer, each is a scalar Output : 12 maps of layer4(L4{j=1:12}), each is 8x8 Algorithm for(j=1:12) {for (i=1:6) {clear z, i.e. z=0; z=z+covn (L3{i}, k{i}{j},’valid’)] %z is 8x8 } L4{j}=sigm(z+bais{j}) %L4{j} is 8x8 } function X = sigm(P) X = 1./(1+exp(-P)); End Discussion Normalization? Index=i=1:6 Index=j=1:12 : net.layers{l}.a{j} Layer 4?5 Convolution Neural Network CNN ver. 4.11a * Layer 4?5: 12 sub-sample Map (S) InputMaps=12 OutputMaps=12 Layer 5: 12x4x4 10 Subs Layer 4: 12x8x8 2x2 Subsample layer 4 to layer 5 Inputs : 12 maps of layer4(L4{i=1:12}), each is 12x8x8 Output : 12 maps of layer5(L5{j=1:12}), each is 4x4 Algorithm Sub sample each 2x2 pixel window in L4 to a pixel in L5 Discussion Normalization? Layer 5?output Convolution Neural Network CNN ver. 4.11a * Each output neuron corresponds to a character (0,1,2,..,9 etc.) net.o{m=1:10} Layer 4?5: 12 sub-sample Map (S) InputMaps=12 OutputMaps=12 Layer 5 (L5{j=1:12}: 12x4x4=192 Totally 192 pixels 10 Subsample layer 4 to layer 5 Inputs : 12 maps of layer5(L5{i=1:12}), each is 4x4, so L5 has 192 pixels in total Output layer weights: Net.ffW{m=1:10}{p=1:192}, total number of weights is 192 Output : 10 output neurons (net.o{m=1:10}) Algorithm For m=1:10%each output neuron {clear net.fv net.fv=Net.ffW{m}{all 192 weight}.*L5(all corresponding 192 pixels) net.o{m}=sign(net.fv + bias) } Discussion : : Totally 192 weights for each output neuron Same for each output neuron Part 2B Back propagation part cnnbp( ) cnnapplyweight( ) Convolution Neural Network CNN ver. 4.11a