使用TensorFlow的卷积神经网络识别手写数字（3）-识别篇

 from PIL import Image
 import numpy as np
 import tensorflow as tf
 import time 

 bShowAccuracy = True

 # 加载手写图片
 def loadHandWritingImage(strFilePath):
     im = Image.open(strFilePath, 'r')
     ndarrayImg = np.array(im.convert("L"), dtype='float')

     return ndarrayImg

 # 最大最小值归一化
 def normalizeImage(ndarrayImg, maxVal = 255, minVal = 0):
     ndarrayImg = (ndarrayImg - minVal) / (maxVal - minVal)
     return ndarrayImg

 # 1)构造自己的手写图片集合，用加载的已训练好的模型识别
 print('构造待识别数据...')

 # 待识别的手写图片，文件名是0...39
 fileList = range(0, 39+1) 

 ndarrayImgs = np.zeros((len(fileList), 784)) # x行784列

 for index in range(len(fileList)):

     # 加载图片
     ndarrayImg = loadHandWritingImage('28-pixel-numbers/' + str(index) + '.png')

     # 归一化
     normalizeImage(ndarrayImg)

     # 转为1x784的数组
     ndarrayImg = ndarrayImg.reshape((1, 784))

     # 加入到测试集中
     ndarrayImgs[index] = ndarrayImg

 ##import sys
 ##sys.exit()

 # 构建测试样本的实际值集合，用于计算正确率

 # 真实结果，用于测试准确度。40行10列
 ndarrayLabels = np.eye(10, k=0, dtype='float')
 ndarrayLabels = np.vstack((ndarrayLabels, ndarrayLabels))
 ndarrayLabels = np.vstack((ndarrayLabels, ndarrayLabels))

 # 2)下面开始CNN相关

 print('定义Tensor...')

 #定义变量和计算公式

 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')

 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)

 x = tf.placeholder(tf.float32, shape=[None, 784])
 y_ = tf.placeholder(tf.float32, shape=[None, 10])

 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(tf.float32)
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

 W_fc2 = weight_variable([1024, 10])
 b_fc2 = bias_variable([10])

 y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())

     # 3)创建saver对象并加载模型
     print('加载已训练好的CNN模型...')
     saver = tf.train.Saver()
     saver.restore(sess, "saved_model/cnn_handwrite_number.ckpt")

     # 测试耗时
     print('进行预测:')

     start = time.time()

     # 4）执行预测
     output = sess.run(y_conv, feed_dict={x: ndarrayImgs, keep_prob:1.0})  

     end = time.time()

     print('预测数字为：\n', output.argmax(axis=1)) # axis：0表示按列，1表示按行
     print('实际数字为：\n', ndarrayLabels.argmax(axis=1))

     if(bShowAccuracy):
         accu = accuracy.eval(feed_dict={x: ndarrayImgs, y_: ndarrayLabels, keep_prob: 1.0})
         print('识别HateMath苍劲有力的手写数据%d个, 准确率为 %.2f%%, 每个耗时%.5f秒' %
               (len(ndarrayImgs), accu*100, (end-start)/len(ndarrayImgs)))

 # todo
 # 图像分割的准确度