pytorch-天气识别

train_transforms = transforms.Compose([
    transforms.Resize([224,224]),# resize输入图片
    transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换成tensor
    transforms.Normalize(
        mean = [0.485, 0.456, 0.406],
        std = [0.229,0.224,0.225]) # 从数据集中随机抽样计算得到
])
total_data = datasets.ImageFolder(data_dir,transform=train_transforms)
total_data

train_size = int(0.8*len(total_data))
test_size = len(total_data) - train_size
train_size,test_size

train_dataset, test_dataset = torch.utils.data.random_split(total_data,[train_size,test_size])
train_dataset,test_dataset

import torch.nn.functional as F
num_classes = 4  # 图片的类别数
class Network_bn(nn.Module):
     def __init__(self):
        super().__init__()
         # 特征提取网络
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=0) 
        self.bn1 = nn.BatchNorm2d(12)                
        self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=0) 
        self.bn2 = nn.BatchNorm2d(12)
        self.pool = nn.MaxPool2d(2,2)
        self.conv3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=0)
        self.bn3 = nn.BatchNorm2d(24) 
        self.conv4 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=0)
        self.bn4 = nn.BatchNorm2d(24)  
        # 分类网络
        self.fc1 = nn.Linear(24*50*50,num_classes)
     # 前向传播
     def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = self.pool(x)
        x = F.relu(self.bn3(self.conv3(x)))
        x = F.relu(self.bn4(self.conv4(x)))
        x = self.pool(x)
        x = x.view(-1,24*50*50)
        x = self.fc1(x)
        return x
model = Network_bn().to(device)
model

# 训练循环
def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)  # 训练集的大小，一共900张图片
    num_batches = len(dataloader)   # 批次数目，29（900/32）
    train_loss, train_acc = 0, 0  # 初始化训练损失和正确率
    for X, y in dataloader:  # 获取图片及其标签
        X, y = X.to(device), y.to(device)
        # 计算预测误差
        pred = model(X)          # 网络输出
        loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
        # 反向传播
        optimizer.zero_grad()  # grad属性归零
        loss.backward()        # 反向传播
        optimizer.step()       # 每一步自动更新
        # 记录acc与loss
        train_acc  += (pred.argmax(1) == y).type(torch.float).sum().item()
        train_loss += loss.item()
    train_acc  /= size
    train_loss /= num_batches
    return train_acc, train_loss

epochs     = 20
train_loss = []
train_acc  = []
test_loss  = []
test_acc   = []
for epoch in range(epochs):
    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
    model.eval()
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)
    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')
    print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息
plt.rcParams['font.sans-serif']    = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号
plt.rcParams['figure.dpi']         = 100        #分辨率
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()