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[question] Training on high dim dataset
Hi, thanks for sharing this.
If you don't mind I'd like to ask regarding training rotational invariance on MNIST.
From my understanding you didn't train on the whole dataset, you trained on a single class/digit and then tested on another, right?
The dim of MNIST is 784, did you train using all 784 dim in data X?
e.g.
net = ReduNetVector(num_classes=10, data_dim=784, num_layers=2000, eta=0.5, eps=0.1)
Z_train = net.init(X, y)
For instance on CIFAR, I've tried reducing the dim from 3072 to 192 by obtaining feature map from a convnet and finally calling
net = ReduNetVector(num_classes=10, data_dim=192, num_layers=2000, eta=0.5, eps=0.1)
Z_train = net.init(feature_map, y)
But as you can see the error is still quite high, any ideas why?
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