Day39 PythonStudy
@浙大疏锦行
import torch torch.cuda import torch # 检查CUDA是否可用 if torch.cuda.is_available(): print("CUDA可用！") # 获取可用的CUDA设备数量 device_count = torch.cuda.device_count() print(f"可用的CUDA设备数量: {device_count}") # 获取当前使用的CUDA设备索引 current_device = torch.cuda.current_device() print(f"当前使用的CUDA设备索引: {current_device}") # 获取当前CUDA设备的名称 device_name = torch.cuda.get_device_name(current_device) print(f"当前CUDA设备的名称: {device_name}") # 获取CUDA版本 cuda_version = torch.version.cuda print(f"CUDA版本: {cuda_version}") else: print("CUDA不可用。")from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split import numpy as np iris = load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42) print(X_train.shape) print(y_train.shape) print(X_test.shape) print(y_test.shape)(120, 4)
(120,)
(30, 4)
(30,)
# 归一化数据，神经网络对于输入数据的尺寸敏感，归一化是最常见的处理方式 from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # 将数据转换为 PyTorch 张量，因为 PyTorch 使用张量进行训练 # y_train和y_test是整数，所以需要转化为long类型，如果是float32，会输出1.0 0.0 X_train = torch.FloatTensor(X_train) y_train = torch.LongTensor(y_train) X_test = torch.FloatTensor(X_test) y_test = torch.LongTensor(y_test) import torch import torch.nn as nn import torch.optim as optim class MLP(nn.Module): def __init__(self): super(MLP,self).__init__() #开始自定义部分 self.fc1 = nn.Linear(4,10) self.relu = nn.ReLU() self.fc2 = nn.Linear(10,3) def forward(self,x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) return out #模型实例化 model = MLP() # 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 num_epochs = 10 losses = [] for epoch in range(num_epochs): #前向传播 outputs = model.forward(X_train) loss = criterion(outputs,y_train) #反向传播 optimizer.zero_grad() #梯度清零 loss.backward()#反向传播计算梯度 optimizer.step()#更新参数 #记录损失 losses.append(loss.item()) #打印训练信息 print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')Epoch [1/10], Loss: 1.1156
Epoch [2/10], Loss: 1.1153
Epoch [3/10], Loss: 1.1150
Epoch [4/10], Loss: 1.1147
Epoch [5/10], Loss: 1.1144
Epoch [6/10], Loss: 1.1141
Epoch [7/10], Loss: 1.1138
Epoch [8/10], Loss: 1.1135
Epoch [9/10], Loss: 1.1132
Epoch [10/10], Loss: 1.1130
import matplotlib.pyplot as plt plt.plot(range(num_epochs),losses) plt.xlabel('Epoch') plt.ylabel('Loss') plt.title('Training Loss over Epochs') plt.show()