y = [y1(:), y2(:)]; matlab

This code concatenates the columns of two matrices y1 and y2 into a single matrix y. The colon operator in Matlab is used to reshape the matrix into a column vector. The resulting matrix y will have the same number of rows as y1 and y2, but twice the number of columns.

相关问题

matlab注释以下代码load('MnistConv.mat') k = 2; x = X(:, :, k); y1 = Conv(x, W1); % Convolution, 20x20x20 y2 = ReLU(y1); % y3 = Pool(y2); % Pool, 10x10x20 y4 = reshape(y3, [], 1); % 2000 v5 = W5*y4; % ReLU, 360 y5 = ReLU(v5); % v = Wo*y5; % Softmax, 10 y = Softmax(v); % figure; display_network(x(:)); title('Input Image') convFilters = zeros(9*9, 20); for i = 1:20 filter = W1(:, :, i); convFilters(:, i) = filter(:); end figure display_network(convFilters); title('Convolution Filters') fList = zeros(20*20, 20); for i = 1:20 feature = y1(:, :, i); fList(:, i) = feature(:); end figure display_network(fList); title('Features [Convolution]') fList = zeros(20*20, 20); for i = 1:20 feature = y2(:, :, i); fList(:, i) = feature(:); end figure display_network(fList); title('Features [Convolution + ReLU]') fList = zeros(10*10, 20); for i = 1:20 feature = y3(:, :, i); fList(:, i) = feature(:); end figure display_network(fList); title('Features [Convolution + ReLU + MeanPool]')

这段代码加载了一个名为MnistConv.mat的文件，然后使用其中的变量进行卷积神经网络的前向传播计算。具体来说，它首先选择了一个样本k，然后对其进行卷积操作并使用ReLU激活函数进行激活，接着对其进行平均池化操作，并将结果进行形状变换。然后，它将该结果与一个权重矩阵相乘并再次使用ReLU激活函数进行激活，最后使用Softmax函数得到最终的输出。此外，它还使用一些可视化技术，如显示输入图像、卷积滤波器、卷积特征和ReLU特征等。

这两个网络的流量矩阵怎么计算，用MATLAB实现：m0=2 m=2 N=20 x1=100rand(1,m0); y1=100rand(1,m0); x2=100rand(1,m0); y2=100rand(1,m0); for i=1:N z11(i)=10 end z1=z11' for i=1:N z22(i)=90 end z2=z22' %for i=1:N %z1(i)=10 %end %for i=1:N %z2(i)=90 %end for i=1:m0 for j=i+1:m0 p1=rand(1,1); p2=rand(1,1); if p1>0.5 a1(i,j)=1; a1(j,i)=0; end if p2>0.5 a2(i,j)=1; a2(j,i)=0; end end end for k=m0+1:N M=size(a1,1);p=zeros(1,M); M1=size(a2,1);p1=zeros(1,M1); x0=100rand(1,1);y0=100rand(1,1); x1(k)=x0;y1(k)=y0; x2(k)=x0;y2(k)=y0; if length(find(a1==1))==0 p(:)=1/M; else for i=1:M p(i)=length(a1(i,:)==1)/length(find(a1==1)); end if length(find(a2==1))==0 p1(:)=1/M1; else for i=1:M1 p1(i)=length(a2(i,:)==1)/length(find(a2==1)); end end end pp=cumsum(p); pp1=cumsum(p1); for i=1:m random_data=rand(1,1); random_data1=rand(1,1); aa=find(pp>=random_data);jj=aa(1); aa1=find(pp1>=random_data1);jj1=aa1(1); a1(k,jj)=1; a1(jj,k)=1; a2(k,jj1)=1; a2(jj1,k)=1; end end

这段代码的作用是生成两个网络的流量矩阵a1和a2，其中m0是已经存在的节点数，N是总的节点数，x1、y1、x2、y2是节点的坐标，z1和z2是节点的流量需求。下面是计算流量矩阵的代码：

m0 = 2;
m = 2;
N = 20;
x1 = 100*rand(1,m0);
y1 = 100*rand(1,m0);
x2 = 100*rand(1,m0);
y2 = 100*rand(1,m0);

% 初始化节点流量需求
for i = 1:N
    z11(i) = 10;
end
z1 = z11';

for i = 1:N
    z22(i) = 90;
end
z2 = z22';

% 初始化连接矩阵
a1 = zeros(N,N);
a2 = zeros(N,N);

% 随机连接已有节点
for i = 1:m0
    for j = i+1:m0
        p1 = rand(1,1);
        p2 = rand(1,1);
        if p1 > 0.5
            a1(i,j) = 1;
            a1(j,i) = 0;
        end
        if p2 > 0.5
            a2(i,j) = 1;
            a2(j,i) = 0;
        end
    end
end

% 生成新节点并进行连接
for k = m0+1:N
    % 计算节点k与已有节点的连接概率
    M = size(a1,1);
    p = zeros(1,M);
    M1 = size(a2,1);
    p1 = zeros(1,M1);
    x0 = 100*rand(1,1);
    y0 = 100*rand(1,1);
    x1(k) = x0;
    y1(k) = y0;
    x2(k) = x0;
    y2(k) = y0;
    if length(find(a1==1)) == 0
        p(:) = 1/M;
    else
        for i = 1:M
            p(i) = length(a1(i,:)==1)/length(find(a1==1));
        end
    end
    if length(find(a2==1)) == 0
        p1(:) = 1/M1;
    else
        for i = 1:M1
            p1(i) = length(a2(i,:)==1)/length(find(a2==1));
        end
    end
    pp = cumsum(p);
    pp1 = cumsum(p1);
    for i = 1:m
        random_data = rand(1,1);
        random_data1 = rand(1,1);
        aa = find(pp>=random_data);
        jj = aa(1);
        aa1 = find(pp1>=random_data1);
        jj1 = aa1(1);
        a1(k,jj) = 1;
        a1(jj,k) = 1;
        a2(k,jj1) = 1;
        a2(jj1,k) = 1;
    end
end

其中，`a1`和`a2`即为两个网络的流量矩阵。