I’m starting the Lesson 3 lecture video, and the review of the key concepts, and I’m walking through the convolution-intro.ipynb notebook. I don’t have Tensorflow installed currently, so I followed @jeremy’s advice (which now I cannot find) and used the Keras MNIST dataset instead. Now, I’m getting strange results:
- The number of images in the Keras dataset is different. TF has 55000, and Keras has 60000.
- The ordering of images is different. The 0-th image in TF is number ‘7’, but the 0-th in Keras is ‘5’.
- Most concerning is that the images demonstrating the
corrtopdetails are very different.
Getting the dataset from Keras:
from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() # saves to /root/.keras/datasets/mnist.pkl.gz
Then I assigned the
labels variables as follows:
images=x_train labels=y_train n=len(images) images.shape
(60000, 28, 28) (NOTE: TF was (55000, 28, 28)).
I computed the
corrtop value using the existing code:
corrtop = correlate(images[inspect_idx], top)
Here are the plots of the resulting
corrtop data. The original TF versions on the left, mine on the right. Note that the numeral ‘7’ is a different index and a different sample, so the shape is different. That’s not my concern. My concern is the overall appearance. It looks like something is wrong with the filters or something.
Can anyone shed light on why such a difference here? The only change I’ve made really is to use the Keras dataset instead of the Tensorflow dataset.