【PyTorch】（三）模型的创建、参数初始化、保存和加载

import torch.nn as nn

model =	nn.Linear(10, 10),
print(model)

输出结果：

Linear(in_features=10, out_features=10, bias=True)

在__init__方法中使用模型组件定义模型各层。在for war d 方法中实现前向传播。

import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Linear(10, 10)
        self.layer2 = nn.Linear(10, 10)
        self.layer3 = nn.Sequential(
            nn.Linear(10, 10),
            nn.ReLU(),
            nn.Linear(10, 10)
        )

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        return x

model = Model()
print(model)

输出结果：

Model(
  (layer1): Linear(in_features=10, out_features=10, bias=True)
  (layer2): Linear(in_features=10, out_features=10, bias=True)
  (layer3): Sequential(
    (0): Linear(in_features=10, out_features=10, bias=True)
    (1): ReLU()
    (2): Linear(in_features=10, out_features=10, bias=True)
  )
)

方法与将数据转移到GPU类似，都有两种方法：

import torch
import torch.nn as nn

# 创建模型实例
model = nn.Sequential(
    nn.Linear(10, 10),
    nn.ReLU(),
    nn.Linear(10, 10)
)

# 将模型移动到GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# 也可以
model = model.cuda()

import torch
import torch.nn as nn

# 创建模型实例
model1 = nn.Sequential(
    nn.Linear(10, 10),
    nn.ReLU(),
    nn.Linear(10, 10)
)

# 保存和加载参数
torch.save(model1.state_dict(), '../model/model_params.pkl')
model1.load_state_dict(torch.load('../model/model_params.pkl'))

import torch
import torch.nn as nn

# 创建模型实例
model1 = nn.Sequential(
    nn.Linear(10, 10),
    nn.ReLU(),
    nn.Linear(10, 10)
)

# 保存和加载模型和参数
torch.save(model1, '../model/model.pt')
model2 = torch.load('../model/model.pt')
print(model2)