PyCharm机器学习实验水果分类识别代码

以下是一个简单的基于PyTorch的水果分类识别代码示例。这个例子使用了一个经典的卷积神经网络架构——AlexNet，来进行水果分类任务。

import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F

# Define the neural network architecture (AlexNet)
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)
        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv2 = nn.Conv2d(64, 192, kernel_size=5, padding=2)
        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv3 = nn.Conv2d(192, 384, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(384, 256, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.fc1 = nn.Linear(256 * 6 * 6, 4096)
        self.dropout1 = nn.Dropout()
        self.fc2 = nn.Linear(4096, 4096)
        self.dropout2 = nn.Dropout()
        self.fc3 = nn.Linear(4096, 1000)

    def forward(self, x):
        x = self.pool1(F.relu(self.conv1(x)))
        x = self.pool2(F.relu(self.conv2(x)))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = self.pool3(F.relu(self.conv5(x)))
        x = x.view(-1, 256 * 6 * 6)
        x = self.dropout1(F.relu(self.fc1(x)))
        x = self.dropout2(F.relu(self.fc2(x)))
        x = self.fc3(x)
        return x

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# Load the dataset
transform = 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])])

trainset = torchvision.datasets.ImageFolder(root='./fruits-360/Training', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)

testset = torchvision.datasets.ImageFolder(root='./fruits-360/Test', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False, num_workers=2)

# Train the network
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(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

# Test the network
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(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: %d %%' % (
    100 * correct / total))

在这个例子中，我们使用了一个包含1000个类别的数据集——Fruits-360。这个数据集包含了多种水果的图片，我们的任务是使用这些图片来训练一个神经网络模型，让它可以对这些水果进行分类。

我们首先定义了一个AlexNet网络模型，然后定义了损失函数和优化器。接着我们使用PyTorch内置的数据加载器来加载数据集，并且使用这些数据来训练我们的模型。最后，我们使用测试集来评估我们的模型的准确率。

这只是一个简单的例子，实际上水果识别任务可能会比这个复杂得多。但是，如果您想要更深入地了解如何使用PyTorch进行机器学习，我建议您参考一些更详细的教程和资料。