mlp神经网络画sinx

MLP神经网络可以用来拟合函数，包括正弦函数。下面是Python代码示例，使用PyTorch实现MLP神经网络拟合sinx函数：

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# Generate sin(x) data
x = np.linspace(-np.pi, np.pi, 2000)
y = np.sin(x)

# Convert data to tensors
x_tensor = torch.tensor(x, dtype=torch.float).unsqueeze(1)
y_tensor = torch.tensor(y, dtype=torch.float).unsqueeze(1)

# Define neural network model
class MLP(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.hidden = nn.Linear(input_size, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        x = torch.relu(self.hidden(x))
        x = self.out(x)
        return x

# Instantiate model
model = MLP(1, 10, 1)

# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Train model
for epoch in range(10000):
    # Forward pass
    y_pred = model(x_tensor)
    loss = criterion(y_pred, y_tensor)
    
    # Backward pass
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if epoch % 1000 == 0:
        print(f'Epoch {epoch}, loss: {loss.item()}')

# Plot results
y_pred = model(x_tensor).detach().numpy()
plt.plot(x, y, label='sin(x)')
plt.plot(x, y_pred, label='predicted sin(x)')
plt.legend()
plt.show()

运行以上代码后，将生成一个图表，其中包含原始sinx函数及MLP神经网络的预测结果。可以看到，MLP神经网络可以很好地拟合sinx函数。