将用tensorflow1框架的代码修改为用pytorch框架的代码

由于没有提供具体的tensorflow1代码，下面是一个简单的示例，将其转换为使用pytorch框架的代码：

Tensorflow1代码：

import tensorflow as tf

# 定义输入和输出
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])

# 定义模型
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)

# 定义损失函数和优化器
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    
    # 测试模型
    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

Pytorch代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 10)
    
    def forward(self, x):
        x = self.fc1(x)
        return nn.functional.softmax(x, dim=1)

model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.5)

# 加载数据
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('./data', train=False, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=100, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, shuffle=False)

# 训练模型
for epoch in range(10):
    for batch_idx, (data, target) in enumerate(train_loader):
        data = data.view(-1, 784)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
    
    # 测试模型
    correct = 0
    total = 0
    with torch.no_grad():
        for data, target in test_loader:
            data = data.view(-1, 784)
            output = model(data)
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
    print('Accuracy: %f' % (correct/total))