优化耗时：
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
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")
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)
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
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)
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
model = MLP().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
num_epochs = 20000
losses = []
start_time = time.time()
for epoch in range(num_epochs):
outputs = model(X_train)
loss = criterion(outputs, y_train)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch + 1) % 200 == 0:
losses.append(loss.item())
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
if (epoch + 1) % 100 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
time_all = time.time() - start_time
print(f'Training time: {time_all:.2f} seconds')
plt.plot(range(len(losses)), losses)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training Loss over Epochs')@浙大疏锦行
plt.show()