【图像识别】MNIST的分类问题（CNN）

 import tensorflow as tf

    
 import numpy as np
    
 from tensorflow.examples.tutorials.mnist import input_data
    
  
    
  
    
 # ---1. 加载数据 ---
    
  
    
 # 加载数据
    
 mnist=input_data.read_data_sets("MNIST_data/", one_hot=True)
    
 trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
    
  
    
 trX = trX.reshape(-1, 28, 28, 1)
    
 teX = teX.reshape(-1, 28, 28, 1)
    
 X = tf.placeholder("float", [None, 28, 28, 1])
    
 Y = tf.placeholder("float", [None, 10])
    
  
    
 # ---2. 构建模型 ---
    
  
    
 # 初始化权重与定义网络结构，构建3个卷积层和3个池化层，1个全连接层和1个输出层
    
  
    
 def init_weights(shape):
    
     return tf.Variable(tf.random_normal(shape, stddev=0.01))
    
  
    
 # 三层卷积层
    
 w = init_weights([3, 3, 1, 32]) # 卷积核大小为3*3，输入维度为1，输出维度为32（下同）
    
 w2 = init_weights([3, 3, 32, 64])
    
 w3 = init_weights([3, 3, 64, 128])
    
 # 全连接层
    
 w4 = init_weights([128 * 4 * 4, 625]) # 输入维度为128*4*4，输出维度为625
    
 # 输出层
    
 w_o = init_weights([625, 10]) # 输入维度为625，输出维度为10
    
  
    
  
    
 # 神经网络模型的构建函数
    
 # X: 输入数据  w: 每一层的权重  p_keep_conv, p_keep_hidden: dropout要保留的神经元比例
    
  
    
 def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
    
     # 第一层卷积层及池化层，最后dropout一些神经元
    
     # relu(): 这个函数的作用是计算激活函数 relu，即 max(features, 0)
    
     # conv2d(X: 输入数据, w: 每一层的权重, strides: 步长<一般是[1, stride,stride, 1]>, padding: 填充类型)
    
     l1a = tf.nn.relu(tf.nn.conv2d(X, w, strides=[1, 1, 1, 1], padding='SAME'))
    
     # max_pool 采用最大池化，ksize：池化窗口的大小<一般是[1, height, width, 1]>
    
     l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')
    
     # dropout：按照传入的p_keep_conv(保留比例) 对神经元做处理
    
     l1 = tf.nn.dropout(l1, p_keep_conv)
    
  
    
     # 第二组卷积层及池化层，最后dropout 一些神经元
    
     l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, strides=[1, 1, 1, 1], padding='SAME'))
    
     l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    
     l2 = tf.nn.dropout(l2, p_keep_conv)
    
  
    
     # 第三组卷积层及池化层，最后dropout 一些神经元
    
     l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, strides=[1, 1, 1, 1], padding='SAME'))
    
     l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    
     l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]]) # reshape to (?, 2048)
    
     l3 = tf.nn.dropout(l3, p_keep_conv)
    
  
    
     # 全连接层
    
     l4 = tf.nn.relu(tf.matmul(l3,w4))
    
     l4 = tf.nn.dropout(l4, p_keep_hidden)
    
  
    
     # 输出层
    
     pyx = tf.matmul(l4, w_o)
    
     # 返回预测值
    
     return pyx 
    
     
    
 p_keep_conv = tf.placeholder("float")
    
 p_keep_hidden = tf.placeholder("float")
    
 py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden) #得到预测值
    
  
    
 # 定义损失函数，比较预测值和实际值，并做均值处理
    
 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
    
 # 采用RMSProp算法的优化器，学习率为0.001，衰减率为0.9
    
 train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
    
 predict_op = tf.argmax(py_x, 1)
    
  
    
 # ---3. 训练模型 ---
    
  
    
 # 定义训练时和评估时的批次大小
    
 batch_size = 128
    
 test_size = 256
    
  
    
 # 开始训练
    
 with tf.Session() as sess:
    
     tf.global_variables_initializer().run()
    
  
    
     for i in range(100):
    
     training_batch = zip(range(0, len(trX), batch_size),
    
         range(batch_size, len(trX)+1, batch_size))
    
     for start, end in training_batch:
    
         sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],
    
                                         p_keep_conv: 0.8, p_keep_hidden: 0.5})
    
     test_indices = np.arange(len(teX)) # Get A Test Batch
    
     np.random.shuffle(test_indices)
    
     test_indices = test_indices[0:test_size]
    
     print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==
    
                     sess.run(predict_op, feed_dict={X: teX[test_indices],
    
                                                     p_keep_conv: 1.0,
    
                                                     p_keep_hidden: 1.0})))