TensorFlow Keras Input

####
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
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fashion_mnist = keras.datasets.fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
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imdb = keras.datasets.imdb
(train_data, train_labels),
(test_data , test_labels ) = imdb.load_data(num_words=10000)
####
dataset_path = keras.utils.get_file(
                 "auto-mpg.data",
                 "https://archive.ics.uci.edu/ml/" +
                   "machine-learning-databases/auto-mpg/auto-mpg.data")
column_names = ['MPG'   , 'Cylinders'   , 'Displacement', 'Horsepower',
                'Weight', 'Acceleration', 'Model Year'  , 'Origin'    ]

raw_dataset = pd.read_csv(dataset_path      , names            = column_names,
                          na_values    = "?", comment          = '\t'        ,
                          sep          = " ", skipinitialspace = True        )
####
from keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
####
from keras.datasets import cifar100
(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine')
####
from keras.datasets import reuters
(x_train, y_train), (x_test, y_test) = reuters.load_data(
                                                 path       = "reuters.npz",
                                                 num_words  = None         ,
                                                 skip_top   = 0            ,
                                                 maxlen     = None         ,
                                                 test_split = 0.2          ,
                                                 seed       = 113          ,
                                                 start_char = 1            ,
                                                 oov_char   = 2            ,
                                                 index_from = 3            )
####
from keras.datasets import boston_housing
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()

Reference: keras.io