deep learning architecture can explain how connected layers and filters

some one can explain how this connections fit each to previous stage

layers = [  imageInputLayer([28 28 1])
            convolution2dLayer(5,20)
            reluLayer
            maxPooling2dLayer(2, 'Stride', 2)
            fullyConnectedLayer(10)
            softmaxLayer
            classificationLayer()   ]

Answer 1

[Krishna]

Hi,

The layers you've listed are part of a Convolutional Neural Network (CNN) architecture commonly used for image classification tasks/ Object detection. Here's a brief explanation of how each layer connects to the previous stage:

1. imageInputLayer([28 28 1]): This is the input layer that accepts images of size 28x28 with a single channel (grayscale images). It acts as the entry point for your network, where each image is fed into the CNN.
2. convolution2dLayer(5,20): This layer performs convolution operations on the input image with 20 filters of size 5x5. It extracts features from the input image by sliding the filters across it and producing 20 feature maps.
3. reluLayer: This layer applies the Rectified Linear Unit (ReLU) activation function to introduce non-linearity. It replaces negative values in the feature maps with zero, helping the network learn complex patterns.
4. maxPooling2dLayer(2, 'Stride', 2): This layer performs down-sampling on the feature maps using a 2x2 window, moving with a stride of 2. It reduces the spatial dimensions (width and height) of the feature maps, keeping only the most prominent features.
5. fullyConnectedLayer(10):This layer is a dense layer with 10 neurons, where each neuron is connected to all the activations from the previous layer. It transforms the pooled feature maps into a 10-dimensional vector, usually corresponding to the number of classes in a classification task.
6. softmaxLayer: This layer applies the softmax function to the output of the fully connected layer. It converts the 10-dimensional vector into a probability distribution over the 10 classes.
7. classificationLayer():This is the final layer used during training to compute the loss and evaluate the model's performance. It compares the predicted class probabilities with the true labels to update the network's weights.

Together, these layers form a pipeline that processes input images, extracts features, and makes predictions about the class of each image.

For more detailed example look at the following dcoumentation,

https://in.mathworks.com/help/deeplearning/ug/train-a-convolutional-neural-network-for-regression.html

Hope this helps.