机器学习： Softmax Classifier (三个隐含层)

来源：互联网发布：一切用数据说话的感悟编辑：程序博客网时间：2024/06/15 00:31

程序实现 softmax classifier, 含有三个隐含层的情况。activation function 是 ReLU : f(x)=max(0,x)

f1=w1x+b1

h1=max(0,f1)

f2=w2h1+b2

h2=max(0,f2)

f3=w3h2+b3

h3=max(0,f3)

f4=w4h3+b4

y=ef4i∑jef4j

function Out=Softmax_Classifier_3(train_x,  train_y, opts)% activation function RELU. y=max(0, x);% setting learning parametersstep_size=opts.step_size;reg=opts.reg;batchsize = opts.batchsize;numepochs = opts.numepochs;K=opts.class;h1=opts.hidden_1;h2=opts.hidden_2;h3=opts.hidden_3;D=size(train_x, 2);W1=0.01*randn(D, h1);b1=zeros(1, h1);W2=0.01*randn(h1,  h2);b2=zeros(1, h2);W3=0.01*randn(h2,  h3);b3=zeros(1, h3);W4=0.01*randn(h3, K);b4=zeros(1, K);loss(1 : numepochs)=0;num_examples=size(train_x, 1);numbatches = num_examples / batchsize;for epoch=1:numepochs     kk = randperm(num_examples);     loss(epoch)=0;     tic;      sprintf('epoch %d:  \n' , epoch)     for bat=1:numbatches         batch_x = train_x(kk((bat - 1) * batchsize + 1 : bat * batchsize), :);         batch_y = train_y(kk((bat - 1) * batchsize + 1 : bat * batchsize), :);         %% forward         f1=batch_x*W1+repmat(b1, batchsize, 1);         hiddenval_1=max(0, f1);         f2=hiddenval_1*W2+repmat(b2, batchsize, 1);         hiddenval_2=max(0, f2);         f3=hiddenval_2*W3+repmat(b3, batchsize, 1);         hiddenval_3=max(0, f3);         scores=hiddenval_3*W4+repmat(b4, batchsize, 1);         %% the loss         exp_scores=exp(scores);         dd=repmat(sum(exp_scores, 2), 1, K);         probs=exp_scores./dd;         correct_logprobs=-log(sum(probs.*batch_y, 2));         data_loss=sum(correct_logprobs)/batchsize;         reg_loss=0.5*reg*sum(sum(W1.*W1))+0.5*reg*sum(sum(W2.*W2))+0.5*reg*sum(sum(W3.*W3))+0.5*reg*sum(sum(W4.*W4));         loss(epoch) =loss(epoch)+ data_loss + reg_loss;         %% back propagation         % output layer         dscores = probs-batch_y;         dscores=dscores/batchsize;         dW4=hiddenval_3'*dscores;         db4=sum(dscores);         % hidden layer 3         dhiddenval_3=dscores*W4';         mask=max(sign(hiddenval_3), 0);         df_3=dhiddenval_3.*mask;         dW3=hiddenval_2'*df_3;         db3=sum(df_3);         % hidden layer 2         dhiddenval_2=df_3*W3';         mask=max(sign(hiddenval_2), 0);         df_2=dhiddenval_2.*mask;         dW2=hiddenval_1'*df_2;         db2=sum(df_2);         % hidden layer 1         dhiddenval_1=df_2*W2';         mask=max(sign(hiddenval_1), 0);         df_1=dhiddenval_1.*mask;         dW1=batch_x'*df_1;         db1=sum(df_1);         %% update         dW4=dW4+reg*W4;         dW3=dW3+reg*W3;         dW2=dW2+reg*W2;         dW1=dW1+reg*W1;         W4=W4-step_size*dW4;         b4=b4-step_size*db4;         W3=W3-step_size*dW3;         b3=b3-step_size*db3;         W2=W2-step_size*dW2;         b2=b2-step_size*db2;         W1=W1-step_size*dW1;         b1=b1-step_size*db1;     end     loss(epoch)=loss(epoch)/numbatches;     sprintf('training loss is  %f:  \n', loss(epoch))    toc;endOut.W1=W1;Out.W2=W2;Out.W3=W3;Out.W4=W4;Out.b1=b1;Out.b2=b2;Out.b3=b3;Out.b4=b4;Out.loss=loss;

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