1   
  2 import numpy as np
  3 import tensorflow as tf
  4 import matplotlib
  5 import matplotlib.pyplot as plt
  6 import matplotlib.cm as cm
  7 from tensorflow.examples.tutorials.mnist import input_data
  8 
  9 
 10 # 训练的准确度目标
 11 accuracyGoal = 0.98
 12 
 13 # 是否已经达到指定的准确度
 14 bFlagGoal = False;
 15 
 16 # 显示数字的图像,nBytes为784个点的灰度值，浮点数
 17 def showMnistImg(nBytes):
 18     imgBytes = nBytes.reshape((28, 28))
 19     #print(imgBytes)
 20     plt.figure(figsize=(2.8,2.8))
 21     #plt.grid() #开启网格 
 22     plt.imshow(imgBytes, cmap=cm.gray)
 23     plt.show()
 24     
 25 
 26 #加载mnist数据
 27 mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
 28 
 29 ### 单个手写数字的784个点的灰度值，浮点数，范围[0,1)
 30 ##print('type(mnist.train.images[0]): ', type(mnist.train.images[0]))  # <class 'numpy.ndarray'>
 31 ##print('mnist.train.images.shape: ', mnist.train.images.shape)
 32 ##print(mnist.train.images[0])
 33 ##
 34 ##
 35 ### 单个手写数字的标签
 36 ### 一个one-hot向量除了某一位的数字是1以外其余各维度数字都是0
 37 ### 数字n将表示成一个只有在第n维度（从0开始）数字为1的10维向量。
 38 ##print('type(mnist.train.labels[0]): ', type(mnist.train.labels[0]))# <class 'numpy.ndarray'>
 39 ##print('type(mnist.train.labels.shape): ', type(mnist.train.labels.shape))
 40 ##print(mnist.train.labels[0])
 41 
 42 
 43 
 44 # 下面开始CNN相关
 45 
 46 def conv2d(x, W):
 47   return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
 48 
 49 def max_pool_2x2(x):
 50   return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
 51                         strides=[1, 2, 2, 1], padding='SAME')
 52 
 53 
 54 def weight_variable(shape):
 55   initial = tf.truncated_normal(shape, stddev=0.1)
 56   return tf.Variable(initial)
 57 
 58 def bias_variable(shape):
 59   initial = tf.constant(0.1, shape=shape)
 60   return tf.Variable(initial)
 61 
 62 
 63 x = tf.placeholder(tf.float32, shape=[None, 784])
 64 y_ = tf.placeholder(tf.float32, shape=[None, 10])
 65 
 66 
 67 W_conv1 = weight_variable([5, 5, 1, 32])
 68 b_conv1 = bias_variable([32])
 69 
 70 x_image = tf.reshape(x, [-1, 28, 28, 1])
 71 
 72 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
 73 h_pool1 = max_pool_2x2(h_conv1)
 74 
 75 
 76 W_conv2 = weight_variable([5, 5, 32, 64])
 77 b_conv2 = bias_variable([64])
 78 
 79 h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
 80 h_pool2 = max_pool_2x2(h_conv2)
 81 
 82 
 83 
 84 W_fc1 = weight_variable([7 * 7 * 64, 1024])
 85 b_fc1 = bias_variable([1024])
 86 
 87 h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
 88 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
 89 
 90 
 91 keep_prob = tf.placeholder(tf.float32)
 92 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 93 
 94 
 95 W_fc2 = weight_variable([1024, 10])
 96 b_fc2 = bias_variable([10])
 97 
 98 y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
 99 
100 
101 cross_entropy = tf.reduce_mean(
102     tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y_conv))
103 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
104 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
105 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
106 
107 
108 
109 
110 print('\n开始训练...')
111 with tf.Session() as sess:
112   sess.run(tf.global_variables_initializer())
113   for i in range(3000):
114     batch = mnist.train.next_batch(50)
115     
116     if i % 100 == 0:
117         train_accuracy = accuracy.eval(feed_dict={ x: batch[0], y_: batch[1], keep_prob: 1.0})
118         print('次数 %d, 准确度 %g' % (i, train_accuracy))
119 
120         if(train_accuracy>accuracyGoal):
121             #创建saver对象，它添加了一些op用来save和restore模型参数
122             saver = tf.train.Saver()
123             #使用saver提供的简便方法去调用 save op
124             saver.save(sess, "saved_model/cnn_handwrite_number.ckpt")
125 
126             print('已保存模型')
127             bFlagGoal = True
128             break
129           
130     train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
131 
132 if(bFlagGoal):
133     print('训练结束，已达到训练目标')
134 else:
135     print('训练结束，未完成训练目标')
136 
137 
138 
139