paddle 实现手势识别代码

以下是一个简单的手势识别代码示例：

import paddle
from paddle.vision.transforms import Compose, Resize, Grayscale
from paddle.vision.models import LeNet

# 加载手势数据集
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=Compose([Resize((32, 32)), Grayscale()]))
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=Compose([Resize((32, 32)), Grayscale()]))

# 初始化 LeNet 模型
model = LeNet(num_classes=10)

# 定义优化器和损失函数
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
criterion = paddle.nn.CrossEntropyLoss()

# 训练模型
epochs = 10
batch_size = 64
train_loader = paddle.io.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = paddle.io.DataLoader(test_dataset, batch_size=batch_size)
for epoch in range(epochs):
    for batch_id, (data, label) in enumerate(train_loader()):
        output = model(data)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()

    # 在测试集上评估模型
    model.eval()
    correct = 0
    total = 0
    for data, label in test_loader():
        output = model(data)
        pred = paddle.argmax(output, axis=1)
        correct += (pred == label).numpy().sum()
        total += label.shape[0]
    accuracy = correct / total
    print('Epoch {}, Test Accuracy: {:.2f}%'.format(epoch+1, accuracy*100))
    model.train()

在这个示例中，我们使用 PaddlePaddle 框架加载 MNIST 手势数据集，使用 LeNet 模型对手势图像进行分类，使用 Adam 优化器和交叉熵损失函数进行训练。在每个 epoch 结束时，在测试集上评估模型的准确率。