pycharm实现手写数字识别

以下是使用Pycharm实现手写数字识别的步骤：

1.安装Python3.X 64bit版本和Pycharm IDE。

2.在Pycharm中创建一个新项目，选择Python解释器。

3.在项目中创建一个新的Python文件，将以下代码复制并粘贴到文件中：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable

# 定义超参数
input_size = 784  # 输入大小为28x28
hidden_size = 500  # 隐藏层大小
num_classes = 10  # 输出类别为10
num_epochs = 5  # 训练次数
batch_size = 100  # 批处理大小
learning_rate = 0.001  # 学习率

# 加载MNIST数据集
train_dataset = dsets.MNIST(root='./data',
                            train=True,
                            transform=transforms.ToTensor(),
                            download=True)

test_dataset = dsets.MNIST(root='./data',
                           train=False,
                           transform=transforms.ToTensor())

# 加载数据集
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)

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

net = Net(input_size, hidden_size, num_classes)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = Variable(images.view(-1, 28 * 28))
        labels = Variable(labels)

        optimizer.zero_grad()
        outputs = net(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        if (i + 1) % 100 == 0:
            print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f'
                  % (epoch + 1, num_epochs, i + 1, len(train_dataset) // batch_size, loss.data[0]))

# 测试模型
correct = 0
total = 0
for images, labels in test_loader:
    images = Variable(images.view(-1, 28 * 28))
    outputs = net(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum()

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

4.运行代码，等待训练和测试完成。

5.最后，你将得到一个准确率的输出结果。