将Keras NN转换为Pytorch NN

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from keras.models import load_model

keras_model = load_model('path_to_keras_model.h5')

class PytorchModel(nn.Module):
    def __init__(self):
        super(PytorchModel, self).__init__()
        # 定义Pytorch模型的结构，可以参考Keras模型的结构

    def forward(self, x):
        # 定义Pytorch模型的前向传播过程，可以参考Keras模型的前向传播过程
        return x

pytorch_model = PytorchModel()

state_dict = pytorch_model.state_dict()
for i, layer in enumerate(keras_model.layers):
    if i in state_dict:
        state_dict[i].copy_(torch.from_numpy(layer.get_weights()[0]))
        state_dict[i + 1].copy_(torch.from_numpy(layer.get_weights()[1]))

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(pytorch_model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(num_epochs):
    # 前向传播
    outputs = pytorch_model(inputs)
    loss = criterion(outputs, labels)

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

# 使用模型进行推理
outputs = pytorch_model(inputs)