labels are oh-encoded when reading in the datawe are using one-hot-encoding to represent the labels (the actual digit drawne " "of the images one-hotencoding uses vector of binary values to represent numeric or categorical values as our labels are for the digits - the vector contains ten valuesone for each possible digit one of these values is set to to represent the digit at that index of the vectorand the rest are set to for examplethe digit is represented using the vector [ as the value at index is stored as the vector therefore represents the digit to represent the actual images themselvesthe pixels are flattened into vector which is pixels in size each of the pixels making up the image is stored as value between and this determines the grayscale of the pixelas our images are presented in black and white only so black pixel is represented by and white pixel by with the various shades of gray somewhere in between we can use the mnist variable to find out the size of the dataset we have just imported looking at the num_examples for each of the three subsetswe can determine that the dataset has been split into , images for training for validationand , for testing add the following lines to your filemain py n_train mnist train num_examples , n_validation mnist validation num_examples