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'];
时间: 2023-10-16 13:10:37 浏览: 41
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'];
相关问题
self.dropout = 0.1 self.rnn_hidden = 128 self.num_layers = 2
这段代码很可能是在一个神经网络模型的构造函数中,用于初始化模型的一些参数。
- `self.dropout = 0.1` 表示设置模型中的 dropout 概率为 0.1,用于模型的正则化,以减轻过拟合的现象。
- `self.rnn_hidden = 128` 表示设置模型中的 RNN 层的隐藏层神经元个数为 128,用于学习输入序列中的特征表示。
- `self.num_layers = 2` 表示设置模型中的 RNN 层数为 2,用于提取更高层次的特征表示。
这些参数的设置可以根据具体的任务和数据集进行调整,以获得更好的模型性能。
这段代码哪里错了 class my_BiGRU(torch.nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def _setup_BiGRU_layers(self): self.BiGRU_layers = my_BiGRU(input_size=self.args.capsule_out_dim, output_size=self.args.apsule_out_features, hidden_size=self.args.BiGRU_hiddensize, num_layers=self.args.BiGRU_layers, gru_dropout=self.args.BiGRU_dropout, )
这段代码存在两个问题:
1.函数 `_setup_BiGRU_layers` 中的代码应该是在类的构造函数 `__init__` 中进行初始化,而不是在另外一个函数中进行初始化。
2.在调用 `my_BiGRU` 类时,参数名称有误,应该将 `apsule_out_features` 改为 `capsule_out_features`。
改正后的代码如下所示:
```
class my_BiGRU(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout, capsule_out_features):
super(my_BiGRU, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.gru_dropout = gru_dropout
self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True)
self.fc = torch.nn.Linear(hidden_size * 2, output_size)
self.BiGRU_layers = my_BiGRU(input_size=self.input_size,
output_size=capsule_out_features,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
gru_dropout=self.gru_dropout,
)
```
注意:这里假设 `capsule_out_dim` 和 `args` 都已经在代码中被定义好了。