tf.keras.datasets.cifar100.load_data      View source on GitHub    Loads the CIFAR100 dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.datasets.cifar100.load_data  
tf.keras.datasets.cifar100.load_data(
    label_mode='fine'
)
 This is a dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 100 fine-grained classes that are grouped into 20 coarse-grained classes. See more info at the CIFAR homepage.
 


 Args
  label_mode   one of "fine", "coarse". If it is "fine" the category labels are the fine-grained labels, if it is "coarse" the output labels are the coarse-grained superclasses.   
 


 Returns   Tuple of NumPy arrays: (x_train, y_train), (x_test, y_test).  
 x_train: uint8 NumPy array of grayscale image data with shapes (50000, 32, 32, 3), containing the training data. Pixel values range from 0 to 255. y_train: uint8 NumPy array of labels (integers in range 0-99) with shape (50000, 1) for the training data. x_test: uint8 NumPy array of grayscale image data with shapes (10000, 32, 32, 3), containing the test data. Pixel values range from 0 to 255. y_test: uint8 NumPy array of labels (integers in range 0-99) with shape (10000, 1) for the test data. Example: (x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()
assert x_train.shape == (50000, 32, 32, 3)
assert x_test.shape == (10000, 32, 32, 3)
assert y_train.shape == (50000, 1)
assert y_test.shape == (10000, 1)
  

    
{'path': 'public/docs/tensorflow~2.9/keras/datasets/cifar100/load_data.html', 'source': 'DevDocs.IO'}