mnist_loader_pytorch.py

import pickle
import gzip
import numpy as np
import torch


def load_data():
    f = gzip.open('mnist.pkl.gz', 'rb')
    training_data, validation_data, test_data = pickle.load(f, encoding="latin1")
    f.close()
    return training_data, validation_data, test_data


def load_data_wrapper():
    tr_d, va_d, te_d = load_data()
    training_inputs = [torch.from_numpy(np.reshape(x, (784, 1))) for x in tr_d[0]]
    training_results = [vectorized_result(y) for y in tr_d[1]]
    training_data = zip(training_inputs, training_results)
    validation_inputs = [torch.from_numpy(np.reshape(x, (784, 1))) for x in va_d[0]]
    validation_data = zip(validation_inputs, va_d[1])
    test_inputs = [torch.from_numpy(np.reshape(x, (784, 1))) for x in te_d[0]]
    test_data = zip(test_inputs, te_d[1])
    return training_data, validation_data, test_data


def vectorized_result(j):
    e = torch.zeros(10, 1)
    e[j] = 1.0
    return e