tensorboad 可视化

1.案例　

#!/usr/bin/env python3
# encoding: utf-8

'''
@author: bigcome
@desc:
@time: 2018/11/15 10:15
'''

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


#载入数据
mnist = input_data.read_data_sets("MNIST_DATE/",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))
        tf.summary.histogram('histogram',var) #z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)

#初始化偏置值
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):
    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]
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

#命名空间
with tf.name_scope('input'):
    #none表示第一个维度可以是任意长度
    x = tf.placeholder(tf.float32,[None,784],name='x-input')
    #正确的标签
    y = tf.placeholder(tf.float32,[None,10],name='y-input')
    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')

#定义第一层卷积
with tf.name_scope('Conv1'):
    #初始化第一层卷积的权值和偏置
    with tf.name_scope('W_conv1'):
        # 5*5的采样窗口，32个卷积核从1个平面抽取特征
        W_conv1 = weight_variable([5,5,1,32],name='W_conv1')
    with tf.name_scope('b_conv1'):
        # 每一个卷积核一个偏置值
        b_conv1 = bias_variable([32],name='b_conv1')
    # 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)

#d定义第二层卷积
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'):
        # 5*5的采样窗口，64个卷积核从32个平面抽取特征
        W_conv2 = weight_variable([5,5,32,64],name='W_conv2')
    with tf.name_scope('b_conv2'):
        # 每一个卷积核一个偏置值
        b_conv2 = bias_variable([64],name='b_conv2')
    # 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)

#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

#定义第一个全连接层
with tf.name_scope('fc1'):
    with tf.name_scope('W_fc1'):
        # 初始化第一个全连接层的权值
        W_fc1 = weight_variable([7*7*64,1024],name='W_fc1')
        #上一层有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')
    #把池化层2的输出平化为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('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')

#定义第二个全连接
with tf.name_scope('fc2'):
    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,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
        # argmax返回一维张量中最大的值所在的位置
        correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(cross_entropy,tf.float32))
        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('log/train',sess.graph)
    test_writer = tf.summary.FileWriter('log/test',sess.graph)
    for i in range(1001):
        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.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))