pytorch中的Parameter函数可以对某个张量进行参数化。它可以将不可训练的张量转化为可训练的参数类型，同时将转化后的张量绑定到模型可训练参数的列表中，当更新模型的参数时一并将其更新。
torch.nn.parameter.Parameter
pytorch中的Parameter函数具体的代码示例如下所示
import torch import torch.nn as nn class NeuralNetwork(nn.Module): def __init__(self, input_dim, output_dim): super(NeuralNetwork, self).__init__() self.linear = nn.Linear(input_dim, output_dim) self.linear.weight = torch.nn.Parameter(torch.zeros(input_dim, output_dim)) self.linear.bias = torch.nn.Parameter(torch.ones(output_dim)) def forward(self, input_array): output = self.linear(input_array) return output if __name__ == '__main__': net = NeuralNetwork(4, 6) for param in net.parameters(): print(param)
代码的结果如下所示：
当神经网络的参数不是用Parameter函数参数化直接赋值给权重参数时，则会报错，具体的程序
import torch import torch.nn as nn class NeuralNetwork(nn.Module): def __init__(self, input_dim, output_dim): super(NeuralNetwork, self).__init__() self.linear = nn.Linear(input_dim, output_dim) self.linear.weight = torch.zeros(input_dim, output_dim) self.linear.bias = torch.ones(output_dim) def forward(self, input_array): output = self.linear(input_array) return output if __name__ == '__main__': net = NeuralNetwork(4, 6) for param in net.parameters(): print(param)
代码运行报错结果如下所示:
