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CNN is a type of deep artificial neural networks based on feed forward architecture that proves efficient when applied in visual imagery. It requires minimal preprocessing due to its multi-layer perceptron design and always assumes that the input it receives is an image which indeed helps to pass certain parameters into the architecture. However, because of this assumption we are able to implement forward function more efficiently and also this will help to reduce the parameters in the network.
For the first fully connected layer we have used 500 channels and for the next three layers 1000 channels are used. The fifth fully connected layer will have 5 channels which will perform classification for our five classes. The dropout layers are used to reduce the over fitting of the network. Here is the layer information of the custom model.
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