我想把下面的代码(神经网络)从keras转换成pytorch。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.fc1 = nn.Linear(10, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, 1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.sigmoid(self.fc3(x))
        return x

model = NeuralNetwork()

criterion = nn.BCELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

X = torch.from_numpy(X).float()
y = torch.from_numpy(y).float()

num_epochs = 100
for epoch in range(num_epochs):
    # 前向传播
    outputs = model(X)
    loss = criterion(outputs, y)

    # 反向传播和优化
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # 打印损失
    if (epoch+1) % 10 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

predicted = model(X).round()