1. 重要类

• nn.Module
• nn.flatten
• nn.linear
• nn.relu
• to.device
• torch.cuda.is_available
• nn.softmax
• nn.argmax
• nn.sequential
• nn.conv2d
• add_module
• buffer
• load_state_dict
• named_parameters
• requires_grad
• save_check_points

2. 代码测试

import torch
from torch import nn
from torch.nn import Module

torch.set_printoptions(precision=3)


class MyModelTest(Module):
    def __init__(self):
        super(MyModelTest, self).__init__()
        self.linear_1 = nn.Linear(3, 4)
        self.relu = nn.ReLU()
        self.linear_2 = nn.Linear(4, 5)

    def forward(self, x):
        x = self.linear_1(x)
        x = self.relu(x)
        y = self.linear_2(x)
        return y


if __name__ == "__main__":
    matrix = torch.arange(3,dtype=torch.float)
    my_softmax = nn.Softmax(dim=0)
    output = my_softmax(matrix)
    print(f"matrix=\n{matrix}")
    print(f"output=\n{output}")
    my_model = MyModelTest()
    for name, param in my_model.named_parameters():
        print(f"layer:{name}\n|size:{param.size()}\n|values:{param[:2]}\n")

• 结果：

matrix=
tensor([0., 1., 2.])
output=
tensor([0.090, 0.245, 0.665])
layer:linear_1.weight
|size:torch.Size([4, 3])
|values:tensor([[-0.544, -0.492,  0.190],
        [-0.424, -0.068,  0.134]], grad_fn=<SliceBackward0>)

layer:linear_1.bias
|size:torch.Size([4])
|values:tensor([0.295, 0.306], grad_fn=<SliceBackward0>)

layer:linear_2.weight
|size:torch.Size([5, 4])
|values:tensor([[ 0.489,  0.018,  0.314,  0.497],
        [ 0.364, -0.455,  0.047, -0.215]], grad_fn=<SliceBackward0>)

layer:linear_2.bias
|size:torch.Size([5])
|values:tensor([-0.027,  0.190], grad_fn=<SliceBackward0>)