numpy.save_compressed version of MNIST handwriiten digits dataset. Usage :
npzfile = np.load('hdigits.npz')
train_images = npzfile['mnist_train_images']
train_labels = npzfile['mnist_train_labels']
test_images = npzfile['mnist_test_images']
test_labels = npzfile['mnist_test_labels']
arrays would be of shape (n,1,28,28) where n denotes number of instances, 1 is number of channels in images, and 28 * 28 is dimensions for train and test images
labels are 1D numpy array
Contains the class defination of dense neural network having sigmoid activation function.
Can be directly used or used to make components of bigger, more complex networks
Trains on MNIST train dataset and saves the network using pickle.
Tests MNIST test dataset on the model made by train.py and prints accuracy.