Added convolutional neural network (CNN) for MNIST digit classification. #7
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This first version of a CNN digit classifier compiles and runs. However, it doesn't learn. Either I've got a bug in my code, or the structure I've chosen just isn't very good for digit classification.
Code is now wired to output what I believe is one of the 5x5 convolution kernels in the first processing layer, every 5 iterations. And it doesn't appear to be changing. Might this be caused by "representing" maxPool as a differentiable function? Might that be breaking the back-propagation?
reached accuracy of 0.9018 after 550 generations
Rom4enya
approved these changes
Jan 27, 2023
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This pull request adds a convolutional neural network (CNN) for MNIST digit classification.
There are actually two CNN models included in the new examples/MNIST/MNIST_cnn.hs file:
A complex model, composed of two convolutional layers, both using a maxPool operation to condense their outputs.
This model doesn't work (It doesn't learn.). And it is, currently, commented out in the code. It is my hope to get it working soon.
A simple model, using a single convolutional layer and no maxPooling.
This model achieves 90% accuracy after 550 generations.
The new executable is called MNIST_cnn.
I run it, like this: