mnist手寫數字體識別，算是計算機視覺、圖像識別領域的入門級demo了。就好像你學習編程首先要打出一個’hello world’。
實現手寫數字體識別有多種方式，可以用迴歸法，可以用CNN，可以用RNN，等等。代碼簡潔程度不同，訓練的精度也不盡相同。
1、mnist數據集介紹
這個數據集是來自美國國家標準與技術研究所, National Institute of Standards and Technology (NIST)。訓練集 (training set) 由來自 250 個不同人手寫的數字構成, 其中 50% 是高中學生, 50% 來自人口普查局 (the Census Bureau) 的工作人員. 測試集(test set) 也是同樣比例的手寫數字數據。
爲什麼要找這麼多人來寫呢？就是要增強學習結果的泛化能力，避免最後只認識某幾個人的手寫體。
import pylab
print('訓練數據：',data.train.images)
print('訓練集尺寸：',data.train.images.shape)
print('訓練集標籤尺寸：',data.train.labels.shape)
print('測試集尺寸：',data.test.images.shape)
print('測試集標籤尺寸：',data.test.labels.shape)
'''
迴歸：
訓練數據： [[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
訓練集尺寸： (55000, 784)
訓練集標籤尺寸： (55000, 10)
測試集尺寸： (10000, 784)
測試集標籤尺寸： (10000, 10)
'''
可以看到，在這個數據集中，訓練集一共有55000個圖片，存放在一個矩陣數組中，每一行是一個圖片。測試機一共有10000個圖片。他們對應的標籤就是one_hot編碼，例如數字0的標籤是1000000000，數字1的標籤是0100000000，以此類推。
打印幾張mnist中的圖片來看一下：
import pylab
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
DATA_DIR = 'MNIST_data'
data = input_data.read_data_sets(DATA_DIR, one_hot=True)
pylab.figure(figsize=(10,10))
for i in range(10):
im = data.train.images[i]
im = im.reshape((28,28))
pylab.imshow(im)
pylab.show()
im
這個數字。。。有的連我自己都不認識。
每一個圖片是這樣保存的，都是一個28*28的數組。
2、softmax迴歸法實現
迴歸就是直接根據圖片每個位置的像素特徵，來訓練圖片每個位置關於數字0-9的權重，這裏把一個28*28的圖片直接拉成一個784的向量，缺點顯然易見：
- 沒有利用到圖片的一個空間信息，也就是我們所說的上下文信息。
- 另外，整個程序就像是一個黑盒子，我們不知道機器是怎麼學習的。
下面是運行結果：（代碼和數據集我會打包進行分享）
Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Accuracy: 91.83%
Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Accuracy: 91.29%
Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Accuracy: 91.82%
Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Accuracy: 91.67%
Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Accuracy: 91.29%
精度不是很高，只有91％多一點。
3、卷積神經網絡實現及試驗
再來看一下強大的神經網絡：
step 0, training accuracy 0.08
step 100, training accuracy 0.82
step 200, training accuracy 0.94
step 300, training accuracy 0.92
step 400, training accuracy 0.92
step 500, training accuracy 0.98
step 600, training accuracy 0.92
step 700, training accuracy 0.94
step 800, training accuracy 1
step 900, training accuracy 0.98
step 1000, training accuracy 1
step 1100, training accuracy 0.98
step 1200, training accuracy 0.98
step 1300, training accuracy 0.98
step 1400, training accuracy 0.98
step 1500, training accuracy 0.94
step 1600, training accuracy 1
step 1700, training accuracy 0.96
step 1800, training accuracy 0.98
step 1900, training accuracy 1
step 2000, training accuracy 1
step 2100, training accuracy 0.98
step 2200, training accuracy 0.98
step 2300, training accuracy 0.98
step 2400, training accuracy 1
step 2500, training accuracy 1
step 2600, training accuracy 0.98
step 2700, training accuracy 0.94
step 2800, training accuracy 0.96
step 2900, training accuracy 1
step 3000, training accuracy 0.92
step 3100, training accuracy 1
step 3200, training accuracy 0.98
step 3300, training accuracy 0.98
step 3400, training accuracy 0.98
step 3500, training accuracy 1
step 3600, training accuracy 1
step 3700, training accuracy 1
step 3800, training accuracy 0.96
step 3900, training accuracy 0.98
step 4000, training accuracy 1
step 4100, training accuracy 0.98
step 4200, training accuracy 0.98
step 4300, training accuracy 0.98
step 4400, training accuracy 1
step 4500, training accuracy 0.98
step 4600, training accuracy 1
step 4700, training accuracy 1
step 4800, training accuracy 0.98
step 4900, training accuracy 0.96
step 5000, training accuracy 1
step 5100, training accuracy 0.96
step 5200, training accuracy 1
step 5300, training accuracy 0.98
step 5400, training accuracy 0.98
step 5500, training accuracy 1
step 5600, training accuracy 0.98
step 5700, training accuracy 1
step 5800, training accuracy 1
step 5900, training accuracy 0.98
step 6000, training accuracy 0.92
step 6100, training accuracy 0.98
step 6200, training accuracy 0.96
step 6300, training accuracy 1
step 6400, training accuracy 1
step 6500, training accuracy 1
step 6600, training accuracy 1
step 6700, training accuracy 1
step 6800, training accuracy 1
step 6900, training accuracy 1
step 7000, training accuracy 1
step 7100, training accuracy 1
step 7200, training accuracy 1
step 7300, training accuracy 1
step 7400, training accuracy 1
step 7500, training accuracy 1
step 7600, training accuracy 1
step 7700, training accuracy 0.98
step 7800, training accuracy 1
step 7900, training accuracy 1
step 8000, training accuracy 1
step 8100, training accuracy 1
step 8200, training accuracy 1
step 8300, training accuracy 1
step 8400, training accuracy 0.98
step 8500, training accuracy 0.98
step 8600, training accuracy 1
step 8700, training accuracy 1
step 8800, training accuracy 1
step 8900, training accuracy 1
step 9000, training accuracy 1
step 9100, training accuracy 1
step 9200, training accuracy 1
step 9300, training accuracy 0.98
step 9400, training accuracy 1
step 9500, training accuracy 1
step 9600, training accuracy 1
step 9700, training accuracy 1
step 9800, training accuracy 0.98
step 9900, training accuracy 1
step 10000, training accuracy 1
step 10100, training accuracy 1
step 10200, training accuracy 1
step 10300, training accuracy 1
step 10400, training accuracy 1
step 10500, training accuracy 1
step 10600, training accuracy 1
step 10700, training accuracy 1
step 10800, training accuracy 1
step 10900, training accuracy 1
step 11000, training accuracy 1
step 11100, training accuracy 1
step 11200, training accuracy 1
step 11300, training accuracy 1
step 11400, training accuracy 1
step 11500, training accuracy 1
step 11600, training accuracy 1
step 11700, training accuracy 1
step 11800, training accuracy 1
step 11900, training accuracy 1
step 12000, training accuracy 1
step 12100, training accuracy 1
step 12200, training accuracy 0.96
step 12300, training accuracy 1
step 12400, training accuracy 1
step 12500, training accuracy 1
step 12600, training accuracy 1
step 12700, training accuracy 1
step 12800, training accuracy 1
step 12900, training accuracy 0.98
step 13000, training accuracy 1
step 13100, training accuracy 1
step 13200, training accuracy 1
step 13300, training accuracy 1
step 13400, training accuracy 1
step 13500, training accuracy 1
step 13600, training accuracy 1
step 13700, training accuracy 1
step 13800, training accuracy 1
step 13900, training accuracy 1
step 14000, training accuracy 0.98
step 14100, training accuracy 1
step 14200, training accuracy 1
step 14300, training accuracy 1
step 14400, training accuracy 1
step 14500, training accuracy 0.98
step 14600, training accuracy 1
step 14700, training accuracy 1
step 14800, training accuracy 1
step 14900, training accuracy 1
step 15000, training accuracy 1
step 15100, training accuracy 1
step 15200, training accuracy 1
step 15300, training accuracy 1
step 15400, training accuracy 1
step 15500, training accuracy 1
step 15600, training accuracy 1
step 15700, training accuracy 0.98
step 15800, training accuracy 1
step 15900, training accuracy 1
step 16000, training accuracy 1
step 16100, training accuracy 1
step 16200, training accuracy 1
step 16300, training accuracy 1
step 16400, training accuracy 1
step 16500, training accuracy 1
step 16600, training accuracy 1
step 16700, training accuracy 1
step 16800, training accuracy 1
step 16900, training accuracy 1
step 17000, training accuracy 1
step 17100, training accuracy 1
step 17200, training accuracy 1
step 17300, training accuracy 1
step 17400, training accuracy 1
step 17500, training accuracy 1
step 17600, training accuracy 1
step 17700, training accuracy 1
step 17800, training accuracy 1
step 17900, training accuracy 1
step 18000, training accuracy 1
step 18100, training accuracy 1
step 18200, training accuracy 1
step 18300, training accuracy 1
step 18400, training accuracy 1
step 18500, training accuracy 1
step 18600, training accuracy 1
step 18700, training accuracy 1
step 18800, training accuracy 1
step 18900, training accuracy 1
step 19000, training accuracy 1
step 19100, training accuracy 1
step 19200, training accuracy 1
step 19300, training accuracy 0.98
step 19400, training accuracy 1
step 19500, training accuracy 1
step 19600, training accuracy 1
step 19700, training accuracy 1
step 19800, training accuracy 1
step 19900, training accuracy 1
test accuracy 0.9921
在訓練到200步的時候，就超過了迴歸法，很快就達到了幾乎百分之百的準確率。
這個準確率有沒有水分呢，我們可以試驗一下：
用畫圖工具手寫數字，注意畫板要改成28*28的像素，因爲這個網絡還比較低級。
訓練時將權重保存在tf中，自己測試時加載上這個權重就可以了，在代碼中都可以看到。
重複測試，可以發現它識別的是非常準的，隨便畫一個都能識別出來。
這個數據集和代碼，有需要的可以在評論區留言，我可以發給你。