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(' 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