VGG19卷积神经网络模型图

以下是VGG19卷积神经网络模型图：

Input (224 x 224 x 3)
├── [Conv2D (64 filters, 3x3 kernel, same padding)] 
├── [Conv2D (64 filters, 3x3 kernel, same padding)] 
├── [MaxPool2D (2x2 pool size, 2x2 strides)]
├── [Conv2D (128 filters, 3x3 kernel, same padding)] 
├── [Conv2D (128 filters, 3x3 kernel, same padding)] 
├── [MaxPool2D (2x2 pool size, 2x2 strides)]
├── [Conv2D (256 filters, 3x3 kernel, same padding)] 
├── [Conv2D (256 filters, 3x3 kernel, same padding)] 
├── [Conv2D (256 filters, 3x3 kernel, same padding)] 
├── [Conv2D (256 filters, 3x3 kernel, same padding)] 
├── [MaxPool2D (2x2 pool size, 2x2 strides)]
├── [Conv2D (512 filters, 3x3 kernel, same padding)] 
├── [Conv2D (512 filters, 3x3 kernel, same padding)] 
├── [Conv2D (512 filters, 3x3 kernel, same padding)] 
├── [Conv2D (512 filters, 3x3 kernel, same padding)] 
├── [MaxPool2D (2x2 pool size, 2x2 strides)]
├── [Conv2D (512 filters, 3x3 kernel, same padding)] 
├── [Conv2D (512 filters, 3x3 kernel, same padding)] 
├── [Conv2D (512 filters, 3x3 kernel, same padding)] 
├── [Conv2D (512 filters, 3x3 kernel, same padding)] 
├── [MaxPool2D (2x2 pool size, 2x2 strides)]
├── [Flatten]
├── [Dense (4096 units)]
├── [Dropout (0.5)]
├── [Dense (4096 units)]
├── [Dropout (0.5)]
├── [Dense (1000 units)]
├── [Softmax]
Output (1000 classes)

注意，这里的“64 filters”等都是指卷积核数量，而“3x3 kernel”指卷积核大小。此外，所有卷积层都使用“same”填充以保持输出与输入大小相同。