mnist_png | MNIST converted to PNG format | Image Editing library

A few points to take note of:

• Most of OpenCV's functions work with white foreground and black background. So comment out this line:

gray=cv2.bitwise_not(gray)

• Make sure you're computing the EXTERNEL contours of the letters. This means that you need to ignore all the child contours. For this use cv2.RETR_EXTERNAL.

contours=cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[1]

• Finally make sure you're assigning the correct angle to find rotation matrix.

With these changes:

So first of all, you may want to understand what a tensor is: http://nd4j.org/tensor

The record reader returns a multi dimensional image, you need to flatten it in order for it to be used with a 2d neural net unless you plan on using CNNs for part of your training.

If you take a look at the exception (again you really should be familiar with ndarrays, they aren't new and are used in every deep learning library): you'll see a shape of: [128, 1, 28, 28]

That is batch size by channels by rows x columns. You need to do a: .setInputType(InputType.convolutional(28,28,1))

This will tell dl4j that it needs to flatten the 4d to a 2d. In this case it indicates that there's a rows,columns,channels of 28 x 28 x 1

If you add this to the bottom of your config it will work.

Of note if you are trying to do anomaly detection is we also have variational autoencoders you may want to look in to as well.