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主体内容就是题目所示的那样,利用mnist数据集进行的数字识别和数字图片存储操作
下面附上代码:
1.mnist,py文件(书里面用到,但没有找到的文件)
# 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 = 'http://yann.lecun.com/exdb/mnist/'
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'
}
dataset_dir = os.path.dirname(os.path.abspath(__file__))
save_file = dataset_dir + "/mnist.pkl"
train_num = 60000
test_num = 10000
img_dim = (1, 28, 28)
img_size = 784
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_name
print("Converting " + file_name + " to NumPy Array ...")
with gzip.open(file_path, 'rb') as f:
labels = np.frombuffer(f.read(), np.uint8, offset=24)
print("Done")
return labels
def _load_img(file_name):
file_path = dataset_dir + "/" + file_name
print("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 data
def _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 dataset
def init_mnist():
download_mnist()
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]] = 1
return T
def 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0.0~1.0
one_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] /= 255.0
if 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()
2.mnist_show.py文件(同样是书上代码,但自己进行修改,目前可以输出十个识别出来的数字,同时将数字图片保存在test文件夹中)
# coding: utf-8
import sys, os
import cv2
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 dataest.mnist import load_mnist
from PIL import Image
import struct
import shutil
def img_show(img):
pil_img = Image.fromarray(np.uint8(img))
pil_img.show()
def setDir():
if not os.path.exists('./test'):
os.makedirs('./test')
else:
shutil.rmtree('./test')
os.mkdir('./test')
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False)
setDir()
for i in range(0,10):
img = x_train[i]
label = t_train[i]
img = img.reshape(28, 28)
#Image.save('mnistpicture/'+img+'.png')
fileName="./test/"+str(i)+"-"+str(t_train[i])+".bmp"
cv2.imwrite(fileName, img)
print(label) # 5
#img.show()
print(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)
这个里面需要配置相应的环境,就请读者朋友们自己去配吧。毕竟我不熟悉你们的环境。
我的代码是MAC版本的,windows的需要修改一下文件格式。