What is CNN?
This is the simple application of a filter to an input that results in inactivation.
Repeated application of the same filter to input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in input, such as an image.
Convolutional layers are the primary building blocks used in convolutional neural networks.
A convert is the sequence of layers, and every layer transforms one volume to another through differentiable functions.
Different types of layers in CNN:
Let’s take an example by running a comment on of image of dimensions 32 x 32 x 3.
Input Layer:
- It holds the raw input of the image with width 32, height 32, and depth 3.
Convolution Layer:
- It computes the output volume by adding dot products between all filters and image patches. Suppose we use a total of 12 filters for this layer we’ll get an output volume of dimension 32 x 32 x 12.
Activation Function Layer:
- This layer will apply the element-wise activation function to the output of the convolution layer. Some activation functions are RELU: max(0, x), Sigmoid: 1/(1+e^-x), Tanh, Leaky RELU, etc. So the volume remains unchanged. Hence output volume will have dimensions 32 x 32 x 12.
Pool Layer:
This layer is periodically inserted within the covnets, and its main function is to reduce the size of volume which makes the computation fast reduces memory, and also prevents overfitting.
Two common types of pooling layers are max pooling and average pooling. If we use a max pool with 2 x 2 filters and stride 2, the resultant volume will be of dimension 16x16x12.
Fully-Connected Layer:
- This layer is a regular neural network layer that takes input from the previous layer and computes the class scores and outputs the 1-D array of size equal to the number of classes.
