Layers in Convolutional Neural Networks

Layers in Convolutional Neural Networks#

Overview#

In Convolutional Neural Networks (CNNs), multiple filters are learned simultaneously during training to capture various features of the input data. Each layer in a CNN transforms the input data through convolution operations, followed by optional addition of bias, non-linear activation, and pooling operations. The process involves both forward and backward passes.

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Learning Multiple Filters#

Filters (Kernels): In each convolutional layer, multiple filters (or kernels) are learned simultaneously. These filters are used to extract different features from the input data, such as edges, textures, and patterns.

Forward Pass#

During the forward pass, data moves from the input layer through several convolutional layers, each applying a set of filters and optional biases, and then through activation functions.

First Layer#

Input: Raw input data (e.g., an image).
Operation: Each filter convolves with the input data to produce feature maps.

Second Layer#

Input: Feature maps from the first layer.
Operation: Each filter convolves with the input feature maps to produce new feature maps.

Adding Bias#

Bias Term: To add bias to the convolution operation, replace ( x * h_i ) with ( x * h_i + b ), where ( x ) is the input, ( h_i ) is the filter, and ( b ) is the bias term.

Backward Pass#

The backward pass, or backpropagation, involves calculating the gradients of the loss with respect to each parameter (filters and biases) and updating the parameters to minimize the loss.