@SpaceCowboy850 I had similar problem.
I got my initial MNIST dataset using Keras function mnist.load_data
It by default downloads this data set: https://s3.amazonaws.com/img-datasets/mnist.npz
In the set you get 60000 images, so you need to delete the first 5000 to adjust it to the shape/size Jeremy works with.
Another point is that the pixel values in the Keras-downloaded set are coded as integers between 0 and 255. Pixels in Jeremy’s data set are all floats between 0.0 and 1.0. This is what I believe is causing your image to be all spotty rather than seeing smooth transitions.
To adjust to format Jeremy uses you can divide the data by 255 to convert the values from 0 - 255 to 0.0 – 1.0.
Here the code to adjust the downloaded data. It has resolved my spotty image issue:
# Delete the first 5000 images. After that the set should start with number 7
images = np.delete(images, slice(0,5000), axis=0)
labels = np.delete(labels, slice(0,5000))
# Convert the array type from int to float
images = images.astype(np.float32)
# Modify the data from 0 to 255 range to 0.0 to 1.0 range
images = images / 255