在PaddlePaddle中实现图像分类任务通常使用卷积神经网络（CNN）。以下是一个简单的图像分类示例：

导入必要的库和模块：

import paddle
import paddle.nn.functional as F
from paddle.vision import transforms

定义一个简单的卷积神经网络模型：

class Net(paddle.nn.Layer):
    def __init__(self, num_classes=10):
        super(Net, self).__init__()
        self.conv1 = paddle.nn.Conv2D(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        self.fc1 = paddle.nn.Linear(in_features=64*8*8, out_features=128)
        self.fc2 = paddle.nn.Linear(in_features=128, out_features=num_classes)
    def forward(self, x):
        x = self.pool1(F.relu(self.conv1(x)))
        x = self.pool2(F.relu(self.conv2(x)))
        x = paddle.flatten(x, start_axis=1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

准备数据和数据增强：

transform = transforms.Compose([
    transforms.Resize(size=32),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor()
])
train_dataset = paddle.vision.datasets.CIFAR10(mode='train', transform=transform)
train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataset = paddle.vision.datasets.CIFAR10(mode='test', transform=transform)
test_loader = paddle.io.DataLoader(test_dataset, batch_size=32, shuffle=False)

训练模型：

model = Net()
optimizer = paddle.optimizer.Adam(parameters=model.parameters())
criterion = paddle.nn.CrossEntropyLoss()
model.train()
for epoch in range(10):
    for data in train_loader:
        images, labels = data
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        optimizer.clear_grad()
        loss.backward()
        optimizer.step()

在测试集上评估模型：

model.eval()
accs = []
for data in test_loader:
    images, labels = data
    outputs = model(images)
    acc = paddle.metric.accuracy(outputs, labels)
    accs.append(acc.numpy())
    
print("Test Accuracy: ", sum(accs) / len(accs))

这是一个简单的图像分类示例，实际应用中可以根据需求调整网络结构、数据增强方式、优化器等参数进行优化。