python导入MNIST数据集代码

以下是Python中导入MNIST数据集的代码：

import gzip
import pickle
import numpy as np

def load_data():
    with gzip.open('mnist.pkl.gz', 'rb') as f:
        training_data, validation_data, test_data = pickle.load(f, encoding='bytes')
    return (training_data, validation_data, test_data)

def load_data_wrapper():
    tr_d, va_d, te_d = load_data()
    training_inputs = [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 = [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)

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

这段代码会读取MNIST数据集并将其分成三个部分：训练数据、验证数据和测试数据。训练数据包括输入和期望输出，验证和测试数据只包括输入。该代码还包括将期望输出向量化的函数。