x_train = x_train.astype('float32') / 255. x_test = x_test.astype('float32') / 255. x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:]))) x_test = x ...
I kept wanting to train sparse autoencoders on Keras models without reimplementing the gated SAE from scratch or dragging everything over to PyTorch. So I packaged the version I kept rewriting: the ...
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