nn.activation_function = 'tanh_opt';

As an AI language model, I do not have knowledge of the specific code or programming language you are referring to. However, based on my understanding of neural networks and activation functions, "tanh_opt" may refer to a variant of the hyperbolic tangent activation function that has been optimized for computational efficiency. The hyperbolic tangent function is a commonly used activation function in neural networks, as it produces values between -1 and 1, which can help normalize the output of a neuron. The "opt" in "tanh_opt" may indicate that the function has been optimized for faster calculations, potentially through approximations or simplifications of the original function.

相关问题

解释nn.activation_function = 'tanh_opt';

这行代码是在设置神经网络的激活函数为tanh_opt。tanh_opt是tanh函数的一种优化版本，它具有更高的计算速度和更好的数值稳定性。激活函数在神经网络中扮演着非常重要的角色，它决定了神经元的输出值，进而影响整个网络的输出结果。常见的激活函数还包括sigmoid、ReLU、Leaky ReLU等。

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'];