快速开始

这里有一个简单的例子，展示如何使用 TensorPlay 定义神经网络并进行训练。

python

import tensorplay as tp
import tensorplay.nn as nn
import tensorplay.optim as optim

# 定义一个简单的网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10, 20)
        self.fc2 = nn.Linear(20, 1)

    def forward(self, x):
        x = tp.relu(self.fc1(x))
        return self.fc2(x)

# 初始化模型、损失函数和优化器
model = Net()
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 模拟数据
input = tp.randn(32, 10)
target = tp.randn(32, 1)

# 训练循环
for epoch in range(100):
    optimizer.zero_grad()
    output = model(input)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()
    
    if epoch % 10 == 0:
        print(f"Epoch {epoch}, Loss: {loss.item()}")

快速开始 ​

快速开始