CNN Layers

Convolutional Neural Networks (CNNs) are composed of various layers, each serving a distinct function and purpose. The most common layers found in CNNs include:

• Convolutional Layer
• Pooling Layer
• Fully Connected Layer
• Flatten Layer
• Dropout Layer
• Softmax Layer

Let's explore some of these layers in detail.

Pooling Layer

This layer reduces the spatial dimensions of the input feature map, thereby decreasing the computational load of the network and the risk of overfitting. Pooling operations, such as max pooling or average pooling, summarize the presence of features in patches of the feature map.

Watch the following video to understand the concept of pooling in CNNs:

Dropout Layer

The dropout layer is a regularization technique that randomly sets the outputs of several neurons in the layer to zero during training. This prevents the network from becoming too dependent on any single neuron and helps in reducing overfitting.

Watch the following video to understand the concept of dropout in CNNs:

Softmax Layer

The softmax layer is typically used in the final layer of a neural network-based classifier. It converts the outputs into probability distributions, making it easier to determine the target class.