MakeModel

[github-learning-lab[bot]]

Preprocessing the dataset

The greyscale assigned to each pixel within an image has a value range of 0-255. We will want to flatten (smoosh… scale…) this range to 0-1. To achieve this flattening, we will exploit the data structure that our images are stored in, arrays. You see, each image is stored as a 2-dimensional array where each numerical value in the array is the greyscale code of particular pixel. Conveniently, if we divide an entire array by a scalar we generate a new array whose elements are the original elements divided by the scalar.

>>> train_images = train_images / 255.0
>>> test_images = test_images / 255.0
>>>

Two vital notes about the above.

1. Use the value "255.0". This value is a floating point number (float), and will always return a float during algebraic operations. In Python, the division operator always returns a float to avoid rounding; but, that is not true for all programming languages, so it's a good habit to include that decimal because it automatically sets that number to be a float.
2. Do not rescale the train_labels or test_labels arrays, these values are already in the range 0-9, as they should be!

Enter a comment (TRUE or FALSE) about the following statement:

We need to rescale both the images and labels, so they are on the same scale.

[harr-i]

TRUE

[github-learning-lab[bot]]

Sorry! 😭 "TRUE " is not the right answer. The correct answer is: "FALSE" Leave a comment with the right answer to continue.😄

[harr-i]

TRUE

[github-learning-lab[bot]]

Sorry! 😭 "TRUE" is not the right answer. The correct answer is: "FALSE" Leave a comment with the right answer to continue.😄