<gh_stars>0
from tensorflow import keras
import numpy as np

def get_test_data(keras_dataset='mnist', num_samples=None, preprocess=True, preprocessing_function=None, **kwargs):
    """
    Returns (x_test, y_test) of a chosen built-in Keras dataset. 
    Also preprocesses the image datasets (mnist, fashion_mnist, cifar10, cifar100) by default.

    Parameters
    ----------
    keras_dataset: str
        one of the available Keras datasets: 
        ['mnist', 'fashion_mnist', 'cifar10', 'cifar100', 'imdb', 'reuters', 'boston_housing']

    num_samples: int 
        randomly samples n data points from (x_test, y_test). Set to None by default.

    preprocess: boolean
        if True, preprocesses (x_test, y_test) 

    preprocessing_function: function
        a custom user-provided function that processes (x_test, y_test) and outputs 
        a tuple (x_test, y_test)

    Returns
    -------
    (x_test, y_test): tuple of numpy arrays
        testing data

    """
    available_datasets = ['mnist', 'fashion_mnist', 'cifar10', 'cifar100', 'imdb', 'reuters', 'boston_housing']
    dataset_mapping= {
        'mnist': keras.datasets.mnist,
        'fashion_mnist': keras.datasets.fashion_mnist,
        'cifar10': keras.datasets.cifar10,
        'cifar100': keras.datasets.cifar100,
        'imdb': keras.datasets.imdb,
        'reuters': keras.datasets.reuters,
        'boston_housing': keras.datasets.boston_housing
    }
    image_datasets = ['mnist', 'fashion_mnist', 'cifar10', 'cifar100']

    # check if the dataset exists
    if keras_dataset not in available_datasets:
        raise Exception(f"Please select one of the built-in Keras datasets: {available_datasets}")

    else:
        _, (x_test, y_test) = dataset_mapping[keras_dataset].load_data()

        # take a random set of images
        if num_samples:
            idx = np.random.choice(np.arange(len(x_test)), num_samples, replace=True)
            x_test = x_test[idx]
            y_test = y_test[idx]

        if preprocess:
            if preprocessing_function and callable(preprocessing_function):
                (x_test, y_test) = preprocessing_function(x_test, y_test)

            else:
                # do default image processing for built-in Keras images    
                if keras_dataset in image_datasets:
                    # Scale images to the [0, 1] range
                    x_test = x_test.astype("float32") / 255

                    # Make sure images have shape (num_samples, x, y, 1) if working with MNIST images
                    if x_test.shape[-1] not in [1, 3]:
                        x_test = np.expand_dims(x_test, -1)

                    # convert class vectors to binary class matrices
                    if keras_dataset == 'cifar100':
                        num_classes=100
                    else:
                        num_classes=10
                        
                    y_test = keras.utils.to_categorical(y_test, num_classes)

        return (x_test, y_test)
from tensorflow import keras
import numpy as np

def get_test_data(keras_dataset='mnist', num_samples=None, preprocess=True, preprocessing_function=None, **kwargs):
    """
    Returns (x_test, y_test) of a chosen built-in Keras dataset. 
    Also preprocesses the image datasets (mnist, fashion_mnist, cifar10, cifar100) by default.

    Parameters
    ----------
    keras_dataset: str
        one of the available Keras datasets: 
        ['mnist', 'fashion_mnist', 'cifar10', 'cifar100', 'imdb', 'reuters', 'boston_housing']

    num_samples: int 
        randomly samples n data points from (x_test, y_test). Set to None by default.

    preprocess: boolean
        if True, preprocesses (x_test, y_test) 

    preprocessing_function: function
        a custom user-provided function that processes (x_test, y_test) and outputs 
        a tuple (x_test, y_test)

    Returns
    -------
    (x_test, y_test): tuple of numpy arrays
        testing data

    """
    available_datasets = ['mnist', 'fashion_mnist', 'cifar10', 'cifar100', 'imdb', 'reuters', 'boston_housing']
    dataset_mapping= {
        'mnist': keras.datasets.mnist,
        'fashion_mnist': keras.datasets.fashion_mnist,
        'cifar10': keras.datasets.cifar10,
        'cifar100': keras.datasets.cifar100,
        'imdb': keras.datasets.imdb,
        'reuters': keras.datasets.reuters,
        'boston_housing': keras.datasets.boston_housing
    }
    image_datasets = ['mnist', 'fashion_mnist', 'cifar10', 'cifar100']

    # check if the dataset exists
    if keras_dataset not in available_datasets:
        raise Exception(f"Please select one of the built-in Keras datasets: {available_datasets}")

    else:
        _, (x_test, y_test) = dataset_mapping[keras_dataset].load_data()

        # take a random set of images
        if num_samples:
            idx = np.random.choice(np.arange(len(x_test)), num_samples, replace=True)
            x_test = x_test[idx]
            y_test = y_test[idx]

        if preprocess:
            if preprocessing_function and callable(preprocessing_function):
                (x_test, y_test) = preprocessing_function(x_test, y_test)

            else:
                # do default image processing for built-in Keras images    
                if keras_dataset in image_datasets:
                    # Scale images to the [0, 1] range
                    x_test = x_test.astype("float32") / 255

                    # Make sure images have shape (num_samples, x, y, 1) if working with MNIST images
                    if x_test.shape[-1] not in [1, 3]:
                        x_test = np.expand_dims(x_test, -1)

                    # convert class vectors to binary class matrices
                    if keras_dataset == 'cifar100':
                        num_classes=100
                    else:
                        num_classes=10
                        
                    y_test = keras.utils.to_categorical(y_test, num_classes)

        return (x_test, y_test)
Returns (x_test, y_test) of a chosen built-in Keras dataset.      Also preprocesses the image datasets (mnist, fashion_mnist, cifar10, cifar100) by default.      Parameters     ----------     keras_dataset: str         one of the available Keras datasets:          ['mnist', 'fashion_mnist', 'cifar10', 'cifar100', 'imdb', 'reuters', 'boston_housing']      num_samples: int          randomly samples n data points from (x_test, y_test). Set to None by default.      preprocess: boolean         if True, preprocesses (x_test, y_test)       preprocessing_function: function         a custom user-provided function that processes (x_test, y_test) and outputs          a tuple (x_test, y_test)      Returns     -------     (x_test, y_test): tuple of numpy arrays         testing data # check if the dataset exists # take a random set of images # do default image processing for built-in Keras images # Scale images to the [0, 1] range # Make sure images have shape (num_samples, x, y, 1) if working with MNIST images # convert class vectors to binary class matrices