Upload New File

paddle/image_test.py

+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+import numpy as np
+
+# 检查 GPU 并设置设备
+if paddle.device.is_compiled_with_cuda():
+    paddle.device.set_device('gpu')
+    print("使用 GPU 进行训练")
+else:
+    paddle.device.set_device('cpu')
+    print("使用 CPU 进行训练")
+
+# 定义一个简单的 CNN 模型
+class SimpleCNN(nn.Layer):
+    def __init__(self, num_classes=10):
+        super().__init__()
+        self.conv1 = nn.Conv2D(in_channels=1, out_channels=16, kernel_size=3, padding=1)
+        self.pool1 = nn.MaxPool2D(kernel_size=2, stride=2)
+        
+        self.conv2 = nn.Conv2D(in_channels=16, out_channels=32, kernel_size=3, padding=1)
+        self.pool2 = nn.MaxPool2D(kernel_size=2, stride=2)
+        
+        self.flatten = nn.Flatten()
+        self.fc1 = nn.Linear(in_features=32 * 7 * 7, out_features=128)
+        self.dropout = nn.Dropout(p=0.5)
+        self.fc2 = nn.Linear(in_features=128, out_features=num_classes)
+    
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = self.pool1(x)
+        x = F.relu(self.conv2(x))
+        x = self.pool2(x)
+        x = self.flatten(x)
+        x = F.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = self.fc2(x)
+        return x
+
+# 创建模拟数据（MNIST 风格的数据）
+def create_dummy_data():
+    # 模拟 32 个 28x28 的灰度图像
+    batch_size = 32
+    train_data = np.random.randn(batch_size, 1, 28, 28).astype('float32')
+    train_labels = np.random.randint(0, 10, (batch_size, 1)).astype('int64')
+    
+    return paddle.to_tensor(train_data), paddle.to_tensor(train_labels)
+
+# 训练函数
+def train_cnn():
+    # 初始化模型
+    model = SimpleCNN(num_classes=10)
+    
+    # 创建模拟数据
+    train_data, train_labels = create_dummy_data()
+    
+    # 定义损失函数和优化器
+    criterion = nn.CrossEntropyLoss()
+    optimizer = paddle.optimizer.Adam(
+        parameters=model.parameters(),
+        learning_rate=0.001
+    )
+    
+    # 训练循环
+    epochs = 10
+    for epoch in range(epochs):
+        # 前向传播
+        outputs = model(train_data)
+        
+        # 计算损失
+        loss = criterion(outputs, train_labels.squeeze())
+        
+        # 计算准确率
+        predictions = paddle.argmax(outputs, axis=1)
+        accuracy = paddle.mean(paddle.cast(predictions == train_labels.squeeze(), 'float32'))
+        
+        # 反向传播和优化
+        loss.backward()
+        optimizer.step()
+        optimizer.clear_grad()
+        
+        # 使用 .item() 方法获取标量值
+        loss_value = loss.item()
+        accuracy_value = accuracy.item()
+        
+        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss_value:.4f}, Accuracy: {accuracy_value:.4f}')
+    
+    print("\nCNN 训练完成！")
+    return model
+
+# 运行训练
+if __name__ == "__main__":
+    model = train_cnn()
+    
+    # 测试模型
+    test_data = paddle.to_tensor(np.random.randn(5, 1, 28, 28).astype('float32'))
+    with paddle.no_grad():
+        predictions = model(test_data)
+        predicted_classes = paddle.argmax(predictions, axis=1)
+        print(f"\n测试数据预测结果: {predicted_classes.numpy()}")