torch.randn(1, num_classes - num_known)的作用
时间: 2024-05-26 18:12:29 浏览: 32
这段代码的作用是生成一个1行,列数为(num_classes-num_known)的随机正态分布张量。其中,num_classes代表总的分类数,num_known代表已知的分类数,所以num_classes - num_known就是需要进行分类的未知分类数。这段代码通常用于初始化未知类别的分类参数,给模型一个初始权重。
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解释W1 = nn.Parameter(torch.randn( num_inputs, num_hiddens, requires_grad=True) * 0.01)
W1 = nn.Parameter(torch.randn(num_inputs, num_hiddens, requires_grad=True) * 0.01) 是用来定义神经网络中第一层的参数矩阵的代码。其中,num_inputs 是输入层的神经元数量,num_hiddens 是第一层隐含层的神经元数量,nn.Parameter 则是在神经网络中定义可训练的变量的方法。而 torch.randn(num_inputs, num_hiddens) 会返回一个均值为0,标准差为1的正态分布的数组,并且 * 0.01 是为了将初始值的范围缩小到较小的范围内,从而更容易训练模型。所以,这行代码定义了一个大小为 num_inputs * num_hiddens 的参数矩阵 W1,且其中的数值已经被初始化为一个均值为0,标准差为0.01的正态分布。
y = torch.eye(num_classes)
This line of code creates an identity matrix with size `num_classes` using PyTorch's `eye()` function. The resulting matrix `y` is a `num_classes` by `num_classes` tensor where the diagonal elements are all 1's and all other elements are 0's.
This type of matrix is commonly used in machine learning for one-hot encoding of categorical variables. For example, if `num_classes` is 5 and we want to one-hot encode a label variable that takes values from 0 to 4, we can use the identity matrix to create a one-hot encoding tensor where each row corresponds to a label value and each column corresponds to a class. The value in a particular row and column is 1 if the label corresponds to that class, and 0 otherwise.