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The CNN does not start to recognize images better after adjusting the weights and biases on the last fully connected layers. Is it necessary or is it a mistake?
I am writing a convolutional neural network in C++ from scratch (only for educational purposes). Wrote the implementation of direct propagation of all layers used. I wrote backpropagation and weight adjustment with offsets of the last fully connected layers, but even after several full passes of the entire MNIST dataset , the grid does not start to recognize images better (as it was 9-10% , 9-10 % remains ).
Question. Did I make a mistake in the implementation of something, or is it not enough to train only the last layers in order for the CNN to get at least a little better at recognizing images?
PS Architecture of the CNN used :
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