Summary of 1_notmnist

Basically 1_notmnist is to learn how to display data in Jupyter Notebook. Besides, it also let us know on sklearn - a python machine library - so that we can then compare with TensorFlow. This is the exact ipynb file at Tensorflow Github Repo.

Notice

This is as a form of sharing and discuss on better way to solve 1_notmnist problem. Do not copy and paste directly as it does not help on improving yourself + the answer is not optimized.

The entire series of TensorFlow Udacity can be found at tensorflow-udacity tag

Preparation

# start a docker container
docker run -p 8888:8888 -it b.gcr.io/tensorflow-udacity/assignments:latest

Problem 1

Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different >font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.

Solving Problem 1

import os, random
dir_name = "notMNIST_large"
folder_names = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]
for folder in folder_names:
  im_name = random.choice(os.listdir(dir_name + "/" + folder))
  im_file = dir_name + "/" + folder + "/" + im_name
  display(Image(filename=im_file))

Comment

Before I get to Problem 1 I have spent a lot of time download the notMNIST_large due to low RAM I have given to my VM that run the docker container.

For the problem part, it teaches us on using display(Image(filename=im_file)) which is a very useful function of showing image file on Jupyter Notebook.