Explain different components of CNN Architecture with the help of diagram

Sure, I can explain the different components of CNN architecture with the help of a diagram.

1. Input Layer: The input layer receives the input image and passes it to the next layer. Each pixel of the image is represented as a node in the input layer.

2. Convolutional Layer: The convolutional layer applies a set of filters to the input image. Each filter detects specific features in the image, such as edges or textures. The output of this layer is a set of feature maps.

3. Activation Function: The activation function introduces non-linearity to the network. It helps in capturing complex patterns and relationships in the data. Common activation functions used in CNNs are ReLU (Rectified Linear Unit) and sigmoid.

4. Pooling Layer: The pooling layer reduces the spatial dimensions of the feature maps. It helps in reducing the computational complexity and makes the network more robust to variations in the input. Max pooling and average pooling are commonly used pooling techniques.

5. Fully Connected Layer: The fully connected layer connects every neuron in the previous layer to the neurons in the next layer. It performs the classification task by learning the weights and biases associated with each connection.

6. Output Layer: The output layer produces the final output of the network. In classification tasks, it typically uses a softmax activation function to produce the probabilities of each class.

Here is a diagram illustrating the CNN architecture:

Input Layer -> Convolutional Layer -> Activation Function -> Pooling Layer -> Fully Connected Layer -> Output Layer

I hope this explanation helps you understand the different components of CNN architecture.