import torch import torch.nn as nn import torch.optim as optim from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler import time import matplotlib.pyplot as plt # 设置GPU设备 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # 加载鸢尾花数据集 iris = load_iris() X = iris.data # 特征数据 y = iris.target # 标签数据 # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 归一化数据 scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # 将数据转换为PyTorch张量并移至GPU X_train = torch.FloatTensor(X_train).to(device) y_train = torch.LongTensor(y_train).to(device) X_test = torch.FloatTensor(X_test).to(device) y_test = torch.LongTensor(y_test).to(device) class MLP(nn.Module): def __init__(self): super(MLP, self).__init__() self.fc1 = nn.Linear(4, 10) # 输入层到隐藏层 self.relu = nn.ReLU() self.fc2 = nn.Linear(10, 3) # 隐藏层到输出层 def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) return out # 实例化模型并移至GPU model = MLP().to(device) # 分类问题使用交叉熵损失函数 criterion = nn.CrossEntropyLoss() # 使用随机梯度下降优化器 optimizer = optim.SGD(model.parameters(), lr=0.01) # 训练模型 num_epochs = 20000 # 训练的轮数 # 用于存储每100个epoch的损失值和对应的epoch数 losses = [] start_time = time.time() # 记录开始时间 for epoch in range(num_epochs): # 前向传播 outputs = model(X_train) # 隐式调用forward函数 loss = criterion(outputs, y_train) # 反向传播和优化 optimizer.zero_grad() #梯度清零，因为PyTorch会累积梯度，所以每次迭代需要清零，梯度累计是那种小的bitchsize模拟大的bitchsize loss.backward() # 反向传播计算梯度 optimizer.step() # 更新参数 # 记录损失值 if (epoch + 1) % 200 == 0: losses.append(loss.item()) # item()方法返回一个Python数值，loss是一个标量张量 print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') # 打印训练信息 if (epoch + 1) % 100 == 0: # range是从0开始，所以epoch+1是从当前epoch开始，每100个epoch打印一次 print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') time_all = time.time() - start_time # 计算训练时间 print(f'Training time: {time_all:.2f} seconds') # 可视化损失曲线 plt.plot(range(len(losses)), losses) plt.xlabel('Epoch') plt.ylabel('Loss') plt.title('Training Loss over Epochs') plt.show()@浙大疏锦行
