python-kerasHow can I use the 'datasets' attribute in the 'tensorflow.python.keras' module?

The 'datasets' attribute in the 'tensorflow.python.keras' module can be used to access and manipulate datasets. It provides functions to import, preprocess, and load data into a TensorFlow model.

For example, we can use the 'datasets.load_data' function to load the MNIST dataset into a TensorFlow model.

from tensorflow.python.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

The above code loads the MNIST dataset into variables x_train, y_train, x_test, and y_test.

The 'datasets' attribute also provides functions to preprocess and normalize data. For example, we can use the 'datasets.normalize' function to normalize the MNIST dataset.

from tensorflow.python.keras.datasets import normalize

x_train = normalize(x_train)
x_test = normalize(x_test)

The above code normalizes the MNIST dataset stored in the x_train and x_test variables.

The 'datasets' attribute can also be used to access and manipulate other datasets, such as the CIFAR-10 dataset.

Helpful links

• TensorFlow Datasets
• Keras Datasets

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