调用数据集mnist（下载+调用全攻略）

1、下载mnist数据集请自取：

通过百度网盘分享的文件：mnist
链接：https://pan.baidu.com/s/1ia3vFA73hEtWK9qU-O-4iQ?pwd=mnis 
提取码：mnis

下载后把数据集放在没有中文的路径下。

# 本文将下载好的数据集放在C:\DeepLearning\dataset路径下

代码中 dataset_dir = 'C:\DeepLearning\dataset'

2、加载mnist数据集

以下是mnist.py文件，用于加载数据集

# coding: utf-8
try:import urllib.request
except ImportError:raise ImportError('You should use Python 3.x')
import os.path
import gzip
import pickle
import os
import numpy as np# url_base = 'https://ossci-datasets.s3.amazonaws.com/mnist/'  # mirror site
key_file = {'train_img':'train-images-idx3-ubyte.gz','train_label':'train-labels-idx1-ubyte.gz','test_img':'t10k-images-idx3-ubyte.gz','test_label':'t10k-labels-idx1-ubyte.gz'
}# 将下载好的数据集放在C:\DeepLearning\dataset路径下
dataset_dir = 'C:\DeepLearning\dataset'
save_file = dataset_dir + "/mnist.pkl"train_num = 60000
test_num = 10000
img_dim = (1, 28, 28)
img_size = 784# 注释掉下载
# def _download(file_name):
#     file_path = dataset_dir + "/" + file_name#     if os.path.exists(file_path):
#         return#     print("Downloading " + file_name + " ... ")
#     urllib.request.urlretrieve(url_base + file_name, file_path)
#     print("Done")# def download_mnist():
#     for v in key_file.values():
#        _download(v)def _load_label(file_name):file_path = dataset_dir + "/" + file_nameprint("Converting " + file_name + " to NumPy Array ...")with gzip.open(file_path, 'rb') as f:labels = np.frombuffer(f.read(), np.uint8, offset=8)print("Done")return labelsdef _load_img(file_name):file_path = dataset_dir + "/" + file_nameprint("Converting " + file_name + " to NumPy Array ...")    with gzip.open(file_path, 'rb') as f:data = np.frombuffer(f.read(), np.uint8, offset=16)data = data.reshape(-1, img_size)print("Done")return datadef _convert_numpy():dataset = {}dataset['train_img'] =  _load_img(key_file['train_img'])dataset['train_label'] = _load_label(key_file['train_label'])    dataset['test_img'] = _load_img(key_file['test_img'])dataset['test_label'] = _load_label(key_file['test_label'])return datasetdef init_mnist():# download_mnist()                                取消下载dataset = _convert_numpy()print("Creating pickle file ...")with open(save_file, 'wb') as f:pickle.dump(dataset, f, -1)print("Done!")def _change_one_hot_label(X):T = np.zeros((X.size, 10))for idx, row in enumerate(T):row[X[idx]] = 1return Tdef load_mnist(normalize=True, flatten=True, one_hot_label=False):"""读入MNIST数据集Parameters----------normalize : 将图像的像素值正规化为0.0~1.0one_hot_label : one_hot_label为True的情况下，标签作为one-hot数组返回one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组flatten : 是否将图像展开为一维数组Returns-------(训练图像, 训练标签), (测试图像, 测试标签)"""if not os.path.exists(save_file):init_mnist()with open(save_file, 'rb') as f:dataset = pickle.load(f)if normalize:for key in ('train_img', 'test_img'):dataset[key] = dataset[key].astype(np.float32)dataset[key] /= 255.0if one_hot_label:dataset['train_label'] = _change_one_hot_label(dataset['train_label'])dataset['test_label'] = _change_one_hot_label(dataset['test_label'])if not flatten:for key in ('train_img', 'test_img'):dataset[key] = dataset[key].reshape(-1, 1, 28, 28)return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label']) if __name__ == '__main__':init_mnist()

3、调用数据集

mnist_show.py文件用于调用数据集。

注意，第三行导入父目录，父目录必须有dataset文件夹，文件夹中有mnist.py文件，此代码才可以调用mnist.py文件。

# coding: utf-8
import sys, os
sys.path.append(os.pardir)  # 为了导入父目录的文件而进行的设定
import numpy as np
from dataset.mnist import load_mnist     #此处要求在当前文件路径下有dataset文件夹，文件夹中有mnist.py文件
from PIL import Imagedef img_show(img):pil_img = Image.fromarray(np.uint8(img))pil_img.show()(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False)img = x_train[0]
label = t_train[0]
print(label)  # 5print(img.shape)  # (784,)
img = img.reshape(28, 28)  # 把图像的形状变为原来的尺寸
print(img.shape)  # (28, 28)img_show(img)

4、批处理

neuralne_mnist_batch.py

# coding: utf-8
import sys, os
sys.path.append(os.pardir)  # 为了导入父目录的文件而进行的设定
import numpy as np
import pickle
from dataset.mnist import load_mnist
from common.functions import sigmoid, softmaxdef get_data():(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False)return x_test, t_testdef init_network():with open("sample_weight.pkl", 'rb') as f:network = pickle.load(f)return networkdef predict(network, x):w1, w2, w3 = network['W1'], network['W2'], network['W3']b1, b2, b3 = network['b1'], network['b2'], network['b3']a1 = np.dot(x, w1) + b1z1 = sigmoid(a1)a2 = np.dot(z1, w2) + b2z2 = sigmoid(a2)a3 = np.dot(z2, w3) + b3y = softmax(a3)return yx, t = get_data()
network = init_network()batch_size = 100 # 批数量
accuracy_cnt = 0for i in range(0, len(x), batch_size):x_batch = x[i:i+batch_size]y_batch = predict(network, x_batch)p = np.argmax(y_batch, axis=1)accuracy_cnt += np.sum(p == t[i:i+batch_size])print("Accuracy:" + str(float(accuracy_cnt) / len(x)))