用python写一个GRU模型pytorch基于minist数据集完成手写字符识别

好的，我明白了。以下是一个基于 PyTorch 和 MNIST 数据集的手写数字识别 GRU 模型代码示例：

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

class GRUNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(GRUNet, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        h0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size))
        out, _ = self.gru(x, h0)
        out = self.fc(out[:, -1, :])
        return out

input_size = 28
sequence_length = 28
hidden_size = 128
num_layers = 2
num_classes = 10

train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=100, shuffle=True)

model = GRUNet(input_size, hidden_size, num_layers, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(5):
    for i, (images, labels) in enumerate(train_loader):
        images = Variable(images.view(-1, sequence_length, input_size))
        labels = Variable(labels)

        optimizer.zero_grad()
        outputs = model(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, 5, i+1, len(train_dataset)//100, loss.data[0]))

model.eval()
correct = 0
total = 0
for images, labels in test_loader:
    images = Variable(images.view(-1, sequence_length, input_size))
    outputs = model(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum()

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

这个 GRU 模型基于输入的 28x28 的图片，以一维序列的形式输入到 GRU 中进行处理。在 GRU 的输出序列中只使用了最后一个时间步的输出来进行分类。在我们的示例中，我们定义了包含两个 GRU 层和一个全连接层的模型。我们使用交叉熵损失函数和 Adam 优化器来训练模型。在训练过程中，我们将模型应用于 MNIST 训练集上的 5 个 epochs，使用批量大小为 100，输出训练损失。在训练完成后，我们将模型应用于 MNIST 测试集上，计算其在测试集上的准确率。