Tensorflow代码学习-6-2CNN的tensorboard实现

卷积神经网络CNN的tensorboard实现

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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data   #手写数字相关的数据包

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)    #载入数据，{数据集包路径，把标签转化为只有0和1的形式}

#定义变量，即每个批次的大小
batch_size = 100    #一次放100章图片进去
n_batch = mnist.train.num_examples // batch_size   #计算一共有多少个批次；训练集数量（整除）一个批次大小

#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.scalar('histogram',var) #直方图

#初始化权值
def weight_variable(shape):
    initial = tf.truncated_normal(shape,stddev=0.1)   #生成一个截断的正态分布
    return tf.Variable(initial)

#初始化偏置
def bias_variable(shape):
    initial = tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

#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' frome: '"SAME"（补0）, "VALID"（不补0）'
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')

#池化层
def max_pool_2x2(x):
    #ksize [1,x,y,1]（窗口大小）
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

with tf.name_scope('input'):
    #定义两个placeholder
    x = tf.placeholder(tf.float32,[None,784])    #[行不确定，列为784]：28*28
    y = tf.placeholder(tf.float32,[None,10])    #数字为0-9，则为10
    with tf.name_scope('x_image'):
    #改变x的格式转为4D的向量[batch, in_height, in_width, in_channels]
        x_image = tf.reshape(x,[-1,28,28,1],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([5,5,1,32],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')  #每个卷积核一个偏置
    with tf.name_scope('conv2d_1'):
    #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('relu'):
            h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
        with tf.name_scope('h_pool1')
            h_pool1 = max_pool_2x2(h_conv1)   #进行max-pooling

with 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([5,5,32,64],name='W_conv2')  #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_conv2'):
        b_conv2 = bias_variable([64],name='b_conv2')  #每个卷积核一个偏置
    with tf.name_scope('conv2d_2'):
    #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('relu'):
            h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2)
        with tf.name_scope('h_pool2'):
            h_pool2 = max_pool_2x2(h_conv2,name='h_pool2')   #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7*7
#经过上面的操作后得到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')  #上一层有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,[-1,7*7*64],name='h_pool2_flat')
    #求第一个全连接层的输出
    with tf.name_scope('relu'):
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1)

    #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([1024,10],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,Wfc2) + b_fc2
    with tf.name_scope('softmax'):
    #计算输出
        prediction = tf.nn.softmax(wx_plus_b2)

with tf.name_scope('cross_entropy'):
    #定义交叉熵代价函数
    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)

with tf.name_scope('accuracy'):
    #准确数，结果存放在一个布尔型列表中
    with tf.name_scope('correct_prediction'):
        correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))   #比较两个参数大小是否相同，同则返回为true，不同则返回为false；argmax()：返回张量中最大的值所在的位置

    #求准确率
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))   #cast()：将布尔型转换为32位的浮点型；（比方说9个T和1个F，则为9个1，1个0，即准确率为90%）
        tf.summary.scalar('accuracy',accuracy)

#合并所有的summary
merged = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    train_writer = tf.summary.FileWriter('logs/train',sess.graph)
    teat_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,feed_dict={x:batch_xs,y:batch_ys,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,feed_dict={x:batch_xs,y:batch_ys,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.test.images[:10000],y:mnist.test.labels[:10000],keep_prob:1.0})
            print("Iter" + str(i) + ",Testing Accuracy" + str(test_acc) + ",Training Accuracy" + str(train_acc))

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