mnist数据集的处理和获取load_data，load_data_wrapper

import pickle
import gzip
import numpy as np
def load_data():
    f = gzip.open('D://dataset/mnist.pkl.gz', 'rb')
    training_data, validation_data, test_data = pickle.load(f, encoding='bytes')
    f.close()
    return training_data, validation_data, test_data
def vectorized_result(j):
    e = np.zeros((10, 1))
    e[j] = 1.0
    return e
def load_data_wrapper():
    tr_data, va_data, te_d = load_data()
    # training data
    training_inputs = [np.reshape(x, (784, 1)) for x in tr_data[0]]
    training_results = [vectorized_result(y) for y in tr_data[1]]
    training_data = zip(training_inputs, training_results)
    # validation data
    validation_inputs = [np.reshape(x, (784, 1)) for x in va_data[0]]
    validation_results = [vectorized_result(y) for y in va_data[1]]
    validation_data = zip(validation_inputs, validation_results)
    # test_data
    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)


def get_Dataset():
    t, v, tt = load_data_wrapper()
    validation_data = list(v)
    # training_data = list(t) + validation_data
    training_data = list(t)
    testing_data = list(tt)

    # len_t = len(training_data)
    # len_tdi = len(training_data[0][0])
    # len_tl = len(training_data[0][1])
    # x_train = np.zeros((len_t, len_tdi))
    # y_train = np.zeros((len_t, len_tl))
    # for i in range(len_t):
    #     x_train[i] = np.array(training_data[i][0]).transpose()
    #     y_train[i] = np.array(training_data[i][1]).transpose()
    #
    # len_tt = len(testing_data)
    # x_test = np.zeros((len_tt, len_tdi))
    # y_test = np.zeros(len_tt)
    # for i in range(len_tt):
    #     x_test[i] = np.array(testing_data[i][0]).transpose()
    #     y_test[i] = testing_data[i][1]
    # return x_train, y_train, x_test, y_test
    return training_data, validation_data, testing_data

来源：CSDN

作者：mercies

链接：https://blog.csdn.net/mercies/article/details/104552108