def load_mnist_dataset(shape=(-1, 784), path='raw_data'):
"""Load the original mnist.
Automatically download MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 digit images respectively.
Parameters
----------
shape : tuple
The shape of digit images (the default is (-1, 784), alternatively (-1, 28, 28, 1)).
path : str
The path that the data is downloaded to.
Returns
-------
X_train, y_train, X_val, y_val, X_test, y_test: tuple
Return splitted training/validation/test set respectively.
Examples
--------
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1,784), path='datasets')
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
"""
return _load_mnist_dataset(shape,
path,
name='mnist',
url='http://yann.lecun.com/exdb/mnist/')
def load_fashion_mnist_dataset(shape=(-1, 784), path='data'):
"""Load the fashion mnist.
Automatically download fashion-MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 fashion images respectively, `examples <http://marubon-ds.blogspot.co.uk/2017/09/fashion-mnist-exploring.html>`__.
Parameters
----------
shape : tuple
The shape of digit images (the default is (-1, 784), alternatively (-1, 28, 28, 1)).
path : str
The path that the data is downloaded to.
Returns
-------
X_train, y_train, X_val, y_val, X_test, y_test: tuple
Return splitted training/validation/test set respectively.
Examples
--------
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_fashion_mnist_dataset(shape=(-1,784), path='datasets')
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_fashion_mnist_dataset(shape=(-1, 28, 28, 1))
"""
return _load_mnist_dataset(
shape, path, name='fashion_mnist', url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'
)
def load_mnist_dataset(shape=(-1, 784), path='data'):
"""Load the original mnist.
Automatically download MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 digit images respectively.
Parameters
----------
shape : tuple
The shape of digit images (the default is (-1, 784), alternatively (-1, 28, 28, 1)).
path : str
The path that the data is downloaded to.
Returns
-------
X_train, y_train, X_val, y_val, X_test, y_test: tuple
Return splitted training/validation/test set respectively.
Examples
--------
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1,784), path='datasets')
>>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
"""
return _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/')