介紹
在TensorFlow的官方入門課程中，多次用到mnist資料集。mnist資料集是一個數字手寫體圖檔庫，但它的存儲格式并非常見的圖檔格式，所有的圖檔都集中儲存在四個擴充名為idx*-ubyte.gz的二進制檔案。
資料集
可以直接從官網進行下載下傳
http://yann.lecun.com/exdb/mnist/
資料集
如果我們想要知道大名鼎鼎的mnist手寫體數字都長什麼樣子，就需要從mnist資料集中導出手寫體數字圖檔。了解這些手寫體的總體形狀，也有助于加深我們對TensorFlow入門課程的了解。
訓練資料集
當我們下載下傳了資料集後，需要對資料集進行訓練。并儲存訓練的模型
#!/usr/bin/python3.5# -*- coding: utf-8 -*-from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfmnist = input_data.read_data_sets('MNIST_data', one_hot=True)x = tf.placeholder(tf.float32, [None, 784])y_ = tf.placeholder(tf.float32, [None, 10])def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial)def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial)def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')W_conv1 = weight_variable([5, 5, 1, 32])b_conv1 = bias_variable([32])x_image = tf.reshape(x, [-1, 28, 28, 1])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)h_pool1 = max_pool_2x2(h_conv1)W_conv2 = weight_variable([5, 5, 32, 64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)h_pool2 = max_pool_2x2(h_conv2)W_fc1 = weight_variable([7 * 7 * 64, 1024])b_fc1 = bias_variable([1024])h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)keep_prob = tf.placeholder("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))saver = tf.train.Saver()with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(20000): batch = mnist.train.next_batch(50) if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={ x: batch[0], y_: batch[1], keep_prob: 1.0}) print('step %d, training accuracy %g' % (i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) saver.save(sess, 'WModel/model.ckpt') print('test accuracy %g' % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))對應的模型檔案如圖所示
模型
用畫圖手寫數字
通過電腦自帶畫圖工具，手寫一個數字，像素為28，如圖所示
生成手寫體
識别手寫數字
把上面生成的圖檔儲存為bmp或png
然後通過程式調用，在使用之前需要先加載前面儲存的模型
#!/usr/bin/python3.5# -*- coding: utf-8 -*-from PIL import Image, ImageFilterimport tensorflow as tfimport matplotlib.pyplot as pltimport timedef imageprepare(): """ This function returns the pixel values. The imput is a png file location. """ file_name='result/4.bmp'#導入自己的圖檔位址 #in terminal 'mogrify -format png *.jpg' convert jpg to png im = Image.open(file_name) # plt.imshow(im) # plt.show() im = im.convert('L') im.save("sample.png") tv = list(im.getdata()) #get pixel values #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black. tva = [ (255-x)*1.0/255.0 for x in tv] #print(tva) return tva """ This function returns the predicted integer. The imput is the pixel values from the imageprepare() function. """ # Define the model (same as when creating the model file)result=imageprepare()x = tf.placeholder(tf.float32, [None, 784])y_ = tf.placeholder(tf.float32, [None, 10])def weight_variable(shape): initial = tf.truncated_normal(shape,stddev = 0.1) return tf.Variable(initial)def bias_variable(shape): initial = tf.constant(0.1,shape = shape) return tf.Variable(initial)def conv2d(x,W): return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = 'SAME')def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')W_conv1 = weight_variable([5, 5, 1, 32])b_conv1 = bias_variable([32])x_image = tf.reshape(x,[-1,28,28,1])h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)h_pool1 = max_pool_2x2(h_conv1)W_conv2 = weight_variable([5, 5, 32, 64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)h_pool2 = max_pool_2x2(h_conv2)W_fc1 = weight_variable([7 * 7 * 64, 1024])b_fc1 = bias_variable([1024])h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)keep_prob = tf.placeholder("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))saver = tf.train.Saver()with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver.restore(sess, "./WModel/model.ckpt")#這裡使用了之前儲存的模型參數 print ("Model restored.") prediction=tf.argmax(y_conv,1) predint=prediction.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess) print(h_conv2) print('識别結果:') print(predint[0])識别結果如圖所示:
運作結果