title: Cifar-100 Dataset Error - Received a label value of 97 which is outside the valid range of [0, 10)
body: <p>getting this error when using the Cifar-100 dataset. I'm pretty sure I have the correct input shape and I have an output layer of 97, which are all the possible outputs in the dataset. What might I be doing wrong? Note: I am new to ML and Tensorflow, teach me some things please. :)</p>
  
  <pre><code>tensorflow.python.framework.errors_impl.InvalidArgumentError:  Received a label value of 97 which is outside the valid range of [0, 10).  Label values: 7 97 79 82 97 33 19 73 28 93 32 6 51 68 67 38 55 1 56 60 97 27 79 36 87 34 20 22 7 42 34 62
  </code></pre>
  
  <p>Thank you. Here is my code below. New to Tensorflow and I have no idea what I am doing wrong.</p>
  
  <pre><code>import tensorflow as tf
  from tensorflow import keras
  import numpy as np
  import matplotlib.pyplot as plt
  
  #print(train_images[0])
  #print("Network Accuracy: " + str(test_acc))
  # plt.imshow(train_images[0], cmap=plt.cm.binary) #greyscale
  # plt.imshow(train_images[0]) #neon
  # plt.show()
  
  cifar100_mnist = keras.datasets.cifar100
  
  (train_images, train_labels), (test_images, test_labels) = cifar100_mnist.load_data()
  
  train_images = train_images/255
  test_images = test_images/255
  classes = [
      'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle',
      'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel',
      'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock',
      'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur',
      'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster',
      'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',
      'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse',
      'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear',
      'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine',
      'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose',
      'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake',
      'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table',
      'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout',
      'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman',
      'worm'
  ]
  model = keras.Sequential([
      keras.layers.Flatten(input_shape=(32, 32, 3)),
      keras.layers.Dense(150, activation="relu"),
      keras.layers.Dense(97, activation="softmax")
  ])
  
  model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
  model.fit(train_images, train_labels, epochs=5)
  
  test_loss, test_acc = model.evaluate(test_images, test_labels)
  print(test_acc)
  # print(test_images)
  prediction = model.predict(test_images)
  answer = np.argmax(prediction[1])
  print(classes[answer])
  # print(train_images[0])
  plt.imshow(train_images[1])
  plt.show()
  </code></pre>