卷积+池化+卷积+池化+全连接

#!/usr/bin/env pythonimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# In[2]:mnist = input_data.read_data_sets(‘MNIST_data‘,one_hot=True)# 每个批次的大小batch_size = 100# 计算一共有多少个批次n_batch = mnist.train.num_examples // batch_size# 参数概要def variable_summaries(var):    with tf.name_scope(‘summaries‘):        mean = tf.reduce_mean(var)        tf.summary.scalar(‘mean‘,mean)  # 平均值        with tf.name_scope(‘stddev‘):            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))        tf.summary.scalar(‘stddev‘,stddev)  # 标准差        tf.summary.scalar(‘max‘,tf.reduce_max(var))  # 最大值        tf.summary.scalar(‘min‘,tf.reduce_min(var))  # 最小值        tf.summary.histogram(‘histogram‘,var)  # 直方图# 初始化权值def weight_variable(shape,name):    initial = tf.truncated_normal(shape,stddev=0.1)  # 生成一个截断的正态分布    return tf.Variable(initial,name=name)# 初始化偏置def bias_variable(shape,name):    initial = tf.constant(0.1,shape=shape)    return tf.Variable(initial,name=name)# 卷积层def conv2d(x,W):    # x input tensor of shape `[batch,in_height,in_width,in_channels]`    # W filter / kernel tensor of shape [filter_height,filter_width,in_channels,out_channels]    # `strides[0] = strides[3] = 1`. strides[1]代表x方向的步长，strides[2]代表y方向的步长    # padding: A `string` from: `"SAME","VALID"`    return tf.nn.conv2d(x,W,strides=[1,1,1],padding=‘SAME‘)# 池化层def max_pool_2x2(x):    # ksize [1,x,y,1]    return tf.nn.max_pool(x,ksize=[1,2,padding=‘SAME‘)# 命名空间with tf.name_scope(‘input‘):    # 定义两个placeholder    x = tf.placeholder(tf.float32,[None,784],name=‘x-input‘)    y = tf.placeholder(tf.float32,10],name=‘y-input‘)    with tf.name_scope(‘x_image‘):        # 改变x的格式转为4D的向量[batch,in_channels]`        x_image = tf.reshape(x,[-1,28,name=‘x_image‘)with tf.name_scope(‘Conv1‘):    # 初始化第一个卷积层的权值和偏置    with tf.name_scope(‘W_conv1‘):        W_conv1 = weight_variable([5,5,32],name=‘W_conv1‘)  # 5*5的采样窗口，32个卷积核从1个平面抽取特征    with tf.name_scope(‘b_conv1‘):        b_conv1 = bias_variable([32],name=‘b_conv1‘)  # 每一个卷积核一个偏置值    # 把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数    with tf.name_scope(‘conv2d_1‘):        conv2d_1 = conv2d(x_image,W_conv1) + b_conv1    with tf.name_scope(‘relu‘):        h_conv1 = tf.nn.relu(conv2d_1)    with tf.name_scope(‘h_pool1‘):        h_pool1 = max_pool_2x2(h_conv1)  # 进行max-poolingwith tf.name_scope(‘Conv2‘):    # 初始化第二个卷积层的权值和偏置    with tf.name_scope(‘W_conv2‘):        W_conv2 = weight_variable([5,32,64],name=‘W_conv2‘)  # 5*5的采样窗口，64个卷积核从32个平面抽取特征    with tf.name_scope(‘b_conv2‘):        b_conv2 = bias_variable([64],name=‘b_conv2‘)  # 每一个卷积核一个偏置值    # 把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数    with tf.name_scope(‘conv2d_2‘):        conv2d_2 = conv2d(h_pool1,W_conv2) + b_conv2    with tf.name_scope(‘relu‘):        h_conv2 = tf.nn.relu(conv2d_2)    with tf.name_scope(‘h_pool2‘):        h_pool2 = max_pool_2x2(h_conv2)  # 进行max-pooling# 28*28的图片第一次卷积后还是28*28，第一次池化后变为14*14# 第二次卷积后为14*14，第二次池化后变为了7*7# 进过上面操作后得到64张7*7的平面with tf.name_scope(‘fc1‘):    # 初始化第一个全连接层的权值    with tf.name_scope(‘W_fc1‘):        W_fc1 = weight_variable([7 * 7 * 64,1024],name=‘W_fc1‘)  # 上一场有7*7*64个神经元，全连接层有1024个神经元    with tf.name_scope(‘b_fc1‘):        b_fc1 = bias_variable([1024],name=‘b_fc1‘)  # 1024个节点    # 把池化层2的输出扁平化为1维    with tf.name_scope(‘h_pool2_flat‘):        h_pool2_flat = tf.reshape(h_pool2,7 * 7 * 64],name=‘h_pool2_flat‘)    # 求第一个全连接层的输出    with tf.name_scope(‘wx_plus_b1‘):        wx_plus_b1 = tf.matmul(h_pool2_flat,W_fc1) + b_fc1    with tf.name_scope(‘relu‘):        h_fc1 = tf.nn.relu(wx_plus_b1)    # keep_prob用来表示神经元的输出概率    with tf.name_scope(‘keep_prob‘):        keep_prob = tf.placeholder(tf.float32,name=‘keep_prob‘)    with tf.name_scope(‘h_fc1_drop‘):        h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob,name=‘h_fc1_drop‘)with tf.name_scope(‘fc2‘):    # 初始化第二个全连接层    with tf.name_scope(‘W_fc2‘):        W_fc2 = weight_variable([1024,name=‘W_fc2‘)    with tf.name_scope(‘b_fc2‘):        b_fc2 = bias_variable([10],name=‘b_fc2‘)    with tf.name_scope(‘wx_plus_b2‘):        wx_plus_b2 = tf.matmul(h_fc1_drop,W_fc2) + b_fc2    with tf.name_scope(‘softmax‘):        # 计算输出        prediction = tf.nn.softmax(wx_plus_b2)# 交叉熵代价函数with tf.name_scope(‘cross_entropy‘):    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction),name=‘cross_entropy‘)    tf.summary.scalar(‘cross_entropy‘,cross_entropy)# 使用AdamOptimizer进行优化with tf.name_scope(‘train‘):    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)# 求准确率with tf.name_scope(‘accuracy‘):    with tf.name_scope(‘correct_prediction‘):        # 结果存放在一个布尔列表中        correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))  # argmax返回一维张量中最大的值所在的位置    with tf.name_scope(‘accuracy‘):        # 求准确率        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))        tf.summary.scalar(‘accuracy‘,accuracy)# 合并所有的summarymerged = tf.summary.merge_all()with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    train_writer = tf.summary.FileWriter(‘logs/train‘,sess.graph)    test_writer = tf.summary.FileWriter(‘logs/test‘,sess.graph)    for i in range(1001):        # 训练模型        batch_xs,batch_ys = mnist.train.next_batch(batch_size)        sess.run(train_step,feed_dict={x: batch_xs,y: batch_ys,keep_prob: 0.5})        # 记录训练集计算的参数        summary = sess.run(merged,keep_prob: 1.0})        train_writer.add_summary(summary,i)        # 记录测试集计算的参数        batch_xs,batch_ys = mnist.test.next_batch(batch_size)        summary = sess.run(merged,keep_prob: 1.0})        test_writer.add_summary(summary,i)        if i % 100 == 0:            test_acc = sess.run(accuracy,feed_dict={x: mnist.test.images,y: mnist.test.labels,keep_prob: 1.0})            train_acc = sess.run(accuracy,feed_dict={x: mnist.train.images[:10000],y: mnist.train.labels[:10000],keep_prob: 1.0})            print("Iter " + str(i) + ",Testing Accuracy= " + str(test_acc) + ",Training Accuracy= " + str(train_acc))