神经网络AlexNet搭建奥特曼分类

神经网络AlexNet是一个深度卷积神经网络，在ImageNet数据集上取得了优异的成绩。本文将使用AlexNet来对奥特曼进行分类。

1. 数据准备

首先，我们需要收集奥特曼的图片数据集。可以在网上搜索奥特曼图片，也可以通过爬虫的方式自行收集。

然后，将收集到的图片数据集划分为训练集、验证集和测试集。一般来说，训练集占总数据集的70%，验证集占20%，测试集占10%。

2. 神经网络搭建

接着，我们使用Pytorch搭建AlexNet神经网络，并进行训练和测试。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision

class AlexNet(nn.Module):
    def __init__(self, num_classes=2):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

model = AlexNet(num_classes=2)

在训练神经网络之前，我们需要对数据进行预处理。在这里，我们使用Pytorch提供的transforms对图片进行缩放、裁剪、翻转等操作。

transform_train = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

3. 训练神经网络

接下来，我们使用训练集对神经网络进行训练，并在验证集上进行验证。

train_dataset = torchvision.datasets.ImageFolder(root='./train', transform=transform_train)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

val_dataset = torchvision.datasets.ImageFolder(root='./val', transform=transform_test)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for data in val_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('Accuracy on validation set: %d %%' % (100 * correct / total))

4. 测试神经网络

最后，我们使用测试集对神经网络进行测试，并计算准确率。

test_dataset = torchvision.datasets.ImageFolder(root='./test', transform=transform_test)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)

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

print('Accuracy on test set: %d %%' % (100 * correct / total))

使用以上代码，我们可以搭建一个基于AlexNet的奥特曼分类器，并对其进行训练和测试。