mnist.py

import gzip
import pickle

import numpy as np


class Import(object):
    def __init__(self):
        pass

    @staticmethod
    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

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

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