教程Python代碼如下：

import torch

import torch.nn as nn

# 定義卷積神經(jīng)網(wǎng)絡(luò)模型

class ConvNet(nn.Module):

??def __init__(self):

????super(ConvNet, self).__init__()

????self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)

????self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)

????self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)

????self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

????self.fc1 = nn.Linear(64 * 28 * 28, 512)

????self.fc2 = nn.Linear(512, 5)

????self.dropout = nn.Dropout(0.5)

??def forward(self, x):

????x = self.pool(nn.functional.relu(self.conv1(x)))

????x = self.pool(nn.functional.relu(self.conv2(x)))

????x = self.pool(nn.functional.relu(self.conv3(x)))

????x = x.view(-1, 64 * 28 * 28)

????x = self.dropout(nn.functional.relu(self.fc1(x)))

????x = self.fc2(x)

????return x

PATH = "./CNN_Model/Model_73.pth"

"""

load_model = ConvNet()

load_model.load_state_dict(torch.load(PATH))

load_model.eval()

for param in load_model.parameters():

??print(param)

"""

#Load on GPU

device = torch.device("cuda")

load_model = ConvNet()

load_model.load_state_dict(torch.load(PATH, map_location=device))

for param in load_model.parameters():

??print(param)