Dataset shape

[RhianTaylor6]

Hello,

Trying to use this to figure out the CSG of MNIST Fashion loaded from keras. No matter what pre-processing I try I keep getting a shape error. I figure it should be similar to whatever pre-processing was used on normal MNIST and was hoping you could help?

Here is the error I am getting:
boolean index did not match indexed array along dimension 1; dimension is 28 but corresponding boolean dimension is 10

[Dref360]

Hello,

Do you have some code I could look at?

We expect an array with shape [N, num_features] so you need to flatten the images.

[RhianTaylor6]

Hi this was my latest attempt, but I don't think I am flattening the images here...not entirely sure how haha

#from spectral_metric.estimator import CumulativeGradientEstimator
#from spectral_metric.visualize import make_graph
from keras.datasets import fashion_mnist,mnist
from keras.utils import to_categorical
import numpy as np

#(X_train, y_train), test_data = fashion_mnist.load_data()
(X_train, y_train), test_data = fashion_mnist.load_data()

Normalize the images.

X_train = (X_train / 255) - 0.5

reshape dataset to have a single channel

X_train = X_train.reshape((X.shape[0],28,28,1))
y_train = to_categorical(y_train)

estimator = CumulativeGradientEstimator()
estimator.fit(data=X_train, target=y_train)
csg = estimator.csg # The actual complexity values.
estimator.evals, estimator.evecs # The eigenvalues and vectors.

[Dref360]

You can flatten the images with:

X_train = X_train.reshape((X.shape[0],-1))

[RhianTaylor6]

then I get a similar error: boolean index did not match indexed array along dimension 1; dimension is 784 but corresponding boolean dimension is 10

[Dref360]

Ah I see the issue, you must not call to_categorical here.
And we expect an array with a single dimension.

y_train = y_train.reshape([-1])

[RhianTaylor6]

Thank you so much! That worked and MNIST Fashion has a CSG of 0.61860741 in case anyone else needs to know!

[Dref360]

Awesome! So a bit easier than notMNIST

[RhianTaylor6]

Yep! And that kinda makes sense doesn't it! :)

[RhianTaylor6]

Hello again, just had a quick question about the metrics in your paper, did you take an average for CSGs at all over a certain number of calculations?

[Dref360]

This is the average over 20 runs I think (it's been a while)? But the standard deviation was very small as you can see in Figure 2.

[RhianTaylor6]

That's amazing thank you so much! As you may have guessed I am using the CSG from your paper in my own work and I just want to make sure the MNIST Fashion CSG value is inline with the others1 I really appreciate your help!

[RhianTaylor6]

Sorry last question, do you have a reference for the github project that you would prefer I use or just reference your paper again?

[Dref360]

Referencing the CVPR paper is perfect thank you.

[Dref360]

WHen it is available, send me a link and I'll add it to the README :)