A Convolutional Neural Network (CNN) is a type of neural network that is widely used for image recognition and processing tasks. The architecture of a CNN consists of a series of convolutional and pooling layers that are designed to identify local features in an image.
A CNN is composed of a series of layers, each of which performs a specific function in the image recognition process. These layers typically include:
1. Convolutional layers: A convolutional layer applies a set of filters to the input image in order to extract local features. A filter is a small matrix of values that is moved over the image to compute a dot product at each spatial position. These operations produce a set of feature maps that highlight different aspects of the input image.
2. Pooling layers: A pooling layer downsamples the feature maps produced by the convolutional layer. This is done to reduce the spatial dimensionality of the feature maps and improve computational efficiency. The most common type of pooling is max pooling, which takes the maximum value within a spatially-defined neighborhood.
3. Fully connected layers: The fully connected layers are the same as those in a standard neural network architecture. These layers are used to classify the image, based on the features extracted by the convolutional layers.
The output of the last layer is passed through a softmax activation function, which converts the output of the fully connected layer into a set of probabilities that represent the likelihood of a given image belonging to a certain class.
The training of a CNN involves adjusting the weights of the filters in the convolutional layers to maximize prediction accuracy on a set of labeled training data. This is typically done using gradient descent optimization methods such as backpropagation.
In summary, a CNN is a type of neural network architecture that is specifically designed for image recognition tasks. It consists of a series of convolutional and pooling layers that identify local features in the input image, which are then passed through fully connected layers to generate a prediction.