??????? 对于深度学习来说，手写数字识别和编程语言第一程式打印hello word应该属于一个级别的了，下面先看下手写数字识别的数据是什么，然后以此学习几个概念。
1 手写数字识别数据集MNIST
??????? MNIST 数据集由 0-9 单个数字的图片构成，训练图像6万个和1万个测试样本。对于数据的组成形式，代码来得更直观一些。简单的来说就是28*28像素的灰度图，shape成一维的就是784个数据，剩下的就是批处理多少个的问题了。这里建议大家熟悉一下数据在张量里的常见表示NCHW，对图像来说，就是多少张，多少通道，高，宽。
??????? pickle呢，这里只是将数据进行了统一的序列化放置在了一个文件下面。序列化后文件中数据会以（（训练图像，训练标签），（测试图像，测试标签））的形式存入，load后亦是如此。独热码是否截图可以看到，一个就是正确解标签，另一个是只有一个1的数组。
# 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 sys, os
import numpy as np
from PIL import Image
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=8)
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 : 将图像的像素值规一化为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'])
init_mnist()
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False)
# 训练的第一张图 数字5
img = x_train[0]
label = t_train[0]
print(label)
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)
pil_img = Image.fromarray(np.uint8(img*255)).convert("1")
pil_img.show()
#(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=True)
# 训练的第一张图 数字[0,0,0,0,0,1,0,0,0,0,]
#label = t_train[0]
#print(label) 
???????? 上面的代码涉及到两个重要的概念：预处理和规一化（正规化），神经网络的输入数据在正式进入处理之前都是需要做一些规格上的统一的，这就是预处理。手段其实很多，除了这里的归一化外，还有数据白化、数据均匀化等。在图像显示的时候为什么*255？