tensorflow程序-最简单的CNN模型

#!/usr/bin/python#-*- coding=utf-8 -*-import tensorflow as tfimport numpy as npimport sys  from tensorflow.examples.tutorials.mnist import input_data def weight_variable(shape):      return tf.Variable( tf.truncated_normal(shape, stddev=0.1) )  def bias_variable(shape):      return tf.Variable( tf.constant(0.1, shape=shape) )  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')# 随机取batch个训练样本def next_batch(train_data, train_target, batch_size):    idx = [ i for i in range(0,len(train_target)) ]    np.random.shuffle(idx);    batch_data = []; batch_target = [];    for i in range(0,batch_size):        batch_data.append(train_data[idx[i]]);        batch_target.append(train_target[idx[i]])    return batch_data, batch_target"""   初始化参数    """mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) #注意存放数据的路径train_data = mnist.train.images  #55000的数据量train_target = mnist.train.labelstest_data = mnist.test.images    #10000的数据量test_target = mnist.test.labelsx = tf.placeholder("float", shape=[None, 784])  #训练向量y = tf.placeholder("float", shape=[None, 10])   #真实结果keep_prob = tf.placeholder("float") # keep_probability 隐含层节点保持工作的概率epochs_num = 5000 #训练次数batch_size = 100 #分批次大小"""   创建CNN第一卷积层     """# 定义卷积核的大小5*5,传入一个图像，32个卷积核，所以传出32个图像。W_conv1 = weight_variable([5, 5, 1, 32])  b_conv1 = bias_variable([32])  # 定义bias的大小，为卷积核的个数x_image = tf.reshape(x, [-1, 28, 28, 1])  # 图片变成标准网络的输入参数h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  # 使用relu激活函数h_pool1 = max_pool_2x2(h_conv1)   # 输出[14,14,32,64]"""   创建CNN第二卷积层     """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) # 输出[7,7,64,1024]"""   创建CNN第一全连接层     """ 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 )   h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  #某些隐含层节点的权重不工作"""   创建CNN第二全连接层     """ 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 )  """   session     """sess = tf.InteractiveSession()  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"))  sess.run(tf.initialize_all_variables())  for i in range(epochs_num):      batch_data, batch_target = next_batch(train_data,train_target,batch_size)      if i%100 == 0:          train_accuracy = accuracy.eval(feed_dict={ x:batch_data, y: batch_target, keep_prob: 1.0} )          print "step %d, training accuracy %.3f"%(i, train_accuracy)      train_step.run(feed_dict={x: batch_data, y:batch_target, keep_prob: 0.5})  print "Training finished"  print "test accuracy %.3f" % accuracy.eval(feed_dict={ x: test_data, y:test_target , keep_prob: 1.0})