Convolutional Neural Network Architecture
common architecture
The architecture of CNN usually includes the following layers:
1. Convolutional layer
This layer is the most important part of the CNN, which uses convolution kernels to extract features from the input image. Each convolution kernel slides over the image, computing a feature response for each location. Multiple convolution kernels can extract different features, such as edges, colors, textures, etc.
2. Pooling layer
This layer is used to downsample the feature map, reducing the size of the feature map while preserving the most important features. Common pooling methods include Max pooling and Average pooling.
3. Activation layer
This layer is used to introduce non-linearity so that CNN can learn more complex features. Common activation functions include ReLU, Sigmoid, and tanh, etc.
4. Fully connected layer
This layer converts the previous feature map into a one-dimensional vector, which is then classified using a fully connected neural network.
5.Softmax layer
This layer is used to calculate the classification probability and output the final classification result.
In general, the input layer is the input to the network, which can be data such as images, audio, or text. The convolutional layer is the core layer of the convolutional neural network, which uses convolution operations to extract image features. A convolution layer usually includes multiple convolution kernels, and each convolution kernel can extract different features. The convolution operation calculates the feature value at each position by sliding the convolution kernel to each position of the image. The pooling layer is used to reduce the resolution of the feature map, reduce the amount of calculation, and can improve the robustness of the model to a certain extent. Usually this is done using max pooling or average pooling. Fully connected layers are used to connect the output of convolutional and pooling layers to the output layer. Finally there is the output layer, which can be a classifier or a regressor, depending on the specific task.