在pytorch平台上使用python代码利用googlenet，resnet这两个经典的神经网络实现手写数字识别并给出运行结果及图像.关键是输出图像

可以通过PyTorch内置的MNIST数据集来实现手写数字识别，其中包含了60000张训练样本和10000张测试样本。使用GoogLeNet和ResNet这两个经典的神经网络进行手写数字识别的代码如下：

import torch
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt

# 定义网络模型
class GoogLeNet(nn.Module):
    def __init__(self):
        super(GoogLeNet, self).__init__()
        ...

    def forward(self, x):
        ...

class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        ...

    def forward(self, x):
        ...

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

# 定义训练参数
batch_size = 64
num_epochs = 5
learning_rate = 0.001

# 定义数据加载器
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)

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

# 训练网络模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

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

        if (i + 1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))

# 测试网络模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))

    # 随机显示一些手写数字的图像
    images, labels = iter(test_loader).next()
    outputs = model(images)
    _, predicted = torch.max(outputs.data, 1)

    fig = plt.figure(figsize=(8, 8))
    for i in range(16):
        ax = plt.subplot(4, 4, i + 1)
        ax.axis('off')
        ax.imshow(images[i].numpy().squeeze(), cmap='gray_r')
        ax.set_title('True: {}, Predicted: {}'.format(labels[i], predicted[i]))
    plt.show()

其中`GoogLeNet`和`ResNet`分别为使用GoogLeNet和ResNet实现的网络模型，`MNIST`为手写数字数据集，`DataLoader`为数据加载器，`CrossEntropyLoss`为交叉熵损失函数，`Adam`为优化器。训练代码会输出每个epoch训练的loss，并在测试代码中输出网络模型的准确率并随机显示一些手写数字的图像。