解释nn.output = 'sigm';
时间: 2024-06-01 11:09:43 浏览: 10
这段代码中,nn表示神经网络模型,output是其属性之一,它指定了神经网络的输出激活函数。'sigm'表示使用sigmoid函数作为输出激活函数。sigmoid函数是一种常用的激活函数,它可以将实数映射到[0,1]的区间内,常用于二分类问题。在训练过程中,神经网络会根据数据调整权重和偏置,使得输出结果更加接近真实值。
相关问题
改写以下代码,使其具有dropout功能: dbnParams.numLayers = 5; dbnParams.hidden_sizes = 3; num_class = 4; dbn.sizes = 100,100,100; opts.numepochs = 10;opts.batchsize = 27;opts.momentum = 0.1; opts.alpha = 0.01; opts.plot = 1; dbn = dbnsetup(dbn, trainXn, opts);%初始化RBM的参数 dbn = dbntrain(dbn, trainXn, opts); % 将DBN展开到神经网络,建立包含输出层的神经网络 nn = dbnunfoldtonn(dbn,size(trainY,2));%输出类别数 nn.activation_function = 'tanh_opt'; %激活函数'sigm' (sigmoid) or 'tanh_opt' (optimal tanh). nn.learningRate = 0.1; nn.dropoutFraction = 0.; opts.numepochs = 200; . opts.batchsize = 1; nn = nntrain(nn, [trainXn;testXn], [trainY;testY], opts); nn.output = ['softmax'];
dbnParams.numLayers = 5;
dbnParams.hidden_sizes = 3;
num_class = 4;
dbn.sizes = 100,100,100;
opts.numepochs = 10;
opts.batchsize = 27;
opts.momentum = 0.1;
opts.alpha = 0.01;
opts.plot = 1;
dbn = dbnsetup(dbn, trainXn, opts);%初始化RBM的参数
dbn = dbntrain(dbn, trainXn, opts); % 将DBN展开到神经网络,建立包含输出层的神经网络
nn = dbnunfoldtonn(dbn,size(trainY,2));%输出类别数
nn.activation_function = 'tanh_opt'; %激活函数'sigm' (sigmoid) or 'tanh_opt' (optimal tanh).
nn.learningRate = 0.1;
nn.dropoutFraction = 0.5; % 设置dropout的比例为0.5
opts.numepochs = 200;
opts.batchsize = 1;
nn = nntrain(nn, [trainXn;testXn], [trainY;testY], opts);
nn.output = ['softmax'];
改写以下代码,使数据中增加随机噪声: dbnParams.numLayers = 5; dbnParams.hidden_sizes = 3; num_class = 4; dbn.sizes = 100,100,100; opts.numepochs = 10;opts.batchsize = 27;opts.momentum = 0.1; opts.alpha = 0.01; opts.plot = 1; dbn = dbnsetup(dbn, trainXn, opts);%初始化RBM的参数 dbn = dbntrain(dbn, trainXn, opts); % 将DBN展开到神经网络,建立包含输出层的神经网络 nn = dbnunfoldtonn(dbn,size(trainY,2));%输出类别数 nn.activation_function = 'tanh_opt'; %激活函数'sigm' (sigmoid) or 'tanh_opt' (optimal tanh). nn.learningRate = 0.1; nn.dropoutFraction = 0.; opts.numepochs = 200; . opts.batchsize = 1; nn = nntrain(nn, [trainXn;testXn], [trainY;testY], opts); nn.output = ['softmax'];
以下是修改后的代码,增加了高斯噪声:
```
dbnParams.numLayers = 5;
dbnParams.hidden_sizes = 3;
num_class = 4;
dbn.sizes = 100,100,100;
opts.numepochs = 10;
opts.batchsize = 27;
opts.momentum = 0.1;
opts.alpha = 0.01;
opts.plot = 1;
% 在训练数据中增加高斯噪声
noise = randn(size(trainXn)) * 0.1;
trainXn_noisy = trainXn + noise;
dbn = dbnsetup(dbn, trainXn_noisy, opts); % 初始化RBM的参数
dbn = dbntrain(dbn, trainXn_noisy, opts); % 将DBN展开到神经网络,建立包含输出层的神经网络
nn = dbnunfoldtonn(dbn,size(trainY,2)); % 输出类别数
nn.activation_function = 'tanh_opt'; % 激活函数'sigm' (sigmoid) or 'tanh_opt' (optimal tanh).
nn.learningRate = 0.1;
nn.dropoutFraction = 0.;
opts.numepochs = 200;
opts.batchsize = 1;
nn = nntrain(nn, [trainXn_noisy;testXn], [trainY;testY], opts);
nn.output = ['softmax'];
```
在这个例子中,我使用 randn 函数生成均值为 0,标准差为 0.1 的高斯噪声,并将其加到训练数据 trainXn 中,得到 trainXn_noisy。然后,我使用 trainXn_noisy 来训练 DBN 和神经网络。这样,训练出来的模型将更加鲁棒,能够更好地处理噪声数据。
相关推荐
![docx](https://img-home.csdnimg.cn/images/20210720083331.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)