title: How to manipulate tfds.load() datasets correctly in tensorflow 2.x?
body: <p>I am learning how to create a MNIST model from scratch in tensorflow 2.0 and Keras from a Udemy course.</p>
  
  <p>So, I got the mnist dataset as follows</p>
  
  <pre><code>mnist_dataset, mnist_info = tfds.load(name = 'mnist', with_info=True, as_supervised=True)
  mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']
  </code></pre>
  
  <p>and everything was fine, even I got 97% accuracy testing my model and I was happy.</p>
  
  <p>The problem started when I tried to do something different from the course. I tried to print some examples from mnist_dataset using matplotlib <code>plt.imshow()</code> and I totally failed. Then I started some research and I got a solution, I needed to get the dataset like this:</p>
  
  <pre><code>mnist_dataset2 = tfds.load(name = 'mnist')
  mnistt = mnist_dataset2['train']
  </code></pre>
  
  <p>where <code>mnistt</code> is the dataset I can manipulate and print using matplotlib.</p>
  
  <p>So my question is as follows: where can I get information about types of tfds.load() you can get and how to correctly manipulate them as you want?  (and being somewhat extendible from a beginner in tensorflow like me).</p>