TensorFlow实战第四课(tensorboard数据可视化)

2022-11-20 11:34:50

tensorboard可视化工具

tensorboard是tensorflow的可视化工具，通过这个工具我们可以很清楚的看到整个神经网络的结构及框架。

通过之前展示的代码，我们进行修改从而展示其神经网络结构。

一、搭建图纸

首先对input进行修改，将xs，ys进行新的名称指定x_in y_in

这里指定的名称，之后会在可视化图层中inputs中显示出来

xs= tf.placeholder(tf.float32, [None, 1],name='x_in')

ys= tf.placeholder(tf.loat32, [None, 1],name='y_in')

使用with.tf.name_scope('inputs')可以将xs ys包含进来，形成一个大的图层，图层的名字就是

with.tf.name_scope()方法中的参数

with tf.name_scope('inputs'):

    # define placeholder for inputs to network

    xs = tf.placeholder(tf.float32, [None, 1])

    ys = tf.placeholder(tf.float32, [None, 1])

接下来编辑layer

编辑前的代码片段：

def add_layer(inputs, in_size, out_size, activation_function=None):

    # add one more layer and return the output of this layer

    Weights = tf.Variable(tf.random_normal([in_size, out_size]))

    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)

    Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)

    if activation_function is None:

        outputs = Wx_plus_b

    else:

        outputs = activation_function(Wx_plus_b, )

    return outputs

编辑后

def add_layer(inputs, in_size, out_size, activation_function=None):

    # add one more layer and return the output of this layer

    with tf.name_scope('layer'):

        Weights= tf.Variable(tf.random_normal([in_size, out_size]))

        # and so on...

定义完大的框架layer后，通知需要定义里面小的部件weights biases activationfunction

定义方法有两种，一是用tf.name_scope（），二是在Weights中指定名称W

    def add_layer(inputs, in_size, out_size, activation_function=None):

    #define layer name

    with tf.name_scope('layer'):

        #define weights name

        with tf.name_scope('weights'):

            Weights= tf.Variable(tf.random_normal([in_size, out_size]),name='W')

        #and so on......

接着定义biases，方法同上

def add_layer(inputs, in_size, out_size, activation_function=None):

    #define layer name

    with tf.name_scope('layer'):

        #define weights name

        with tf.name_scope('weights')

            Weights= tf.Variable(tf.random_normal([in_size, out_size]),name='W')

        # define biase

        with tf.name_scope('Wx_plus_b'):

            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)

        # and so on....

最后编辑loss 将with.tf.name_scope( )添加在loss上方并起名为loss

这句话就是绘制了loss

最后再对train_step进行编辑

with tf.name_scope('train'):

    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

我们还需要运用tf.summary.FileWriter( )将上面绘画的图保存到一个目录中，方便用浏览器浏览。

这个方法中的第二个参数需要使用sess.graph。因此我们把这句话放在获取session后面。

这里的graph是将前面定义的框架信息收集起来，然后放在logs/目录下面。

sess = tf.Session() # get session

# tf.train.SummaryWriter soon be deprecated, use following

writer = tf.summary.FileWriter("logs/", sess.graph)

最后在终端中使用命令获取网址即可查看

tensorboard --logdir logs

完整代码：

#如何可视化神经网络

#tensorboard

import tensorflow as tf

def add_layer(inputs, in_size, out_size, activation_function=None):

    # add one more layer and return the output of this layer

    with tf.name_scope('layer'):

        with tf.name_scope('weights'):

            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')

        with tf.name_scope('biases'):

            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')

        with tf.name_scope('Wx_plus_b'):

            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)

        if activation_function is None:

            outputs = Wx_plus_b

        else:

            outputs = activation_function(Wx_plus_b)

        return outputs

# define placeholder for inputs to network

with tf.name_scope('inputs'):

    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')

    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')

# add hidden layer

l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

# add output layer

prediction = add_layer(l1, 10, 1, activation_function=None)

# the error between prediciton and real data

with tf.name_scope('loss'):

    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))

with tf.name_scope('train'):

    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()

writer = tf.summary.FileWriter("logs/", sess.graph)

init = tf.global_variables_initializer()

sess.run(init)

-------------------------------------------------------------------

tensorflow可视化训练过程的图标是如何制作的？

首先要添加一些模拟数据。nump可以帮助我们添加一些模拟数据。

利用np.linespace（）产生随机的数字同时为了模拟更加真实我们会添加一些噪声这些噪声是通过np.random.normal（）随机产生的。

 x_data= np.linspace(-1, 1, 300, dtype=np.float32)[:,np.newaxis]

 noise=  np.random.normal(0, 0.05, x_data.shape).astype(np.float32)

 y_data= np.square(x_data) -0.5+ noise

在layer中为weights biases设置变化图表

首先我们在add_layer（）方法中添加一个参数n_layer 来标识层数并且用变量layer_name代表其每层的名称

def add_layer(

    inputs ,

    in_size,

    out_size,

    n_layer,

    activation_function=None):

    ## add one more layer and return the output of this layer

    layer_name='layer%s'%n_layer  ## 定义一个新的变量

    ## and so on ……

接下来我们层中的Weights设置变化图 tensorflow中提供了tf.histogram_summary( )方法，用来绘制图片，第一个参数是图表的名称，第二个参数是图标要记录的变量。

def add_layer(inputs ,

            in_size,

            out_size,n_layer,

            activation_function=None):

    ## add one more layer and return the output of this layer

    layer_name='layer%s'%n_layer

    with tf.name_scope('layer'):

         with tf.name_scope('weights'):

              Weights= tf.Variable(tf.random_normal([in_size, out_size]),name='W')

              tf.summary.histogram(layer_name + '/weights', Weights)

    ##and so no ……

同样的方法我们对biases进行绘制图标：

with tf.name_scope('biases'):

    biases = tf.Variable(tf.zeros([1,out_size])+0.1, name='b')

    tf.summary.histogram(layer_name + '/biases', biases)

至于activation_function( ) 可以不用绘制，我们对output 使用同样的方法

最后通过修改 addlayer（）方法如下所示

def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):

    # add one more layer and return the output of this layer

    #对神经层进行命名

    layer_name = 'layer%s' % n_layer

    with tf.name_scope(layer_name):

        with tf.name_scope('weights'):

            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')

            tf.summary.histogram(layer_name + '/weights', Weights)

        with tf.name_scope('biases'):

            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')

            tf.summary.histogram(layer_name + '/biases', biases)

        with tf.name_scope('Wx_plus_b'):

            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)

        if activation_function is None:

            outputs = Wx_plus_b

        else:

            outputs = activation_function(Wx_plus_b, )

        tf.summary.histogram(layer_name + '/outputs', outputs)

    return outputs

设置loss的变化图

loss是tensorb的event下面的这是由于我们使用的是tf.scalar_summary（）方法。

当你的loss函数图像呈现的是下降的趋势说明学习是有效的

将所有训练图合并

接下来进行合并打包，tf.merge_all_summaries（）方法会对我们所有的summaries合并到一起

sess = tf.Session()

#合并

merged = tf.summary.merge_all()

writer = tf.summary.FileWriter("logs/", sess.graph)

init = tf.global_variables_initializer()

训练数据

忽略不想写

完整代码如下：（运行代码后需要在终端中执行tensorboard --logdir logs）

import tensorflow as tf

import numpy as np

def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):

    # add one more layer and return the output of this layer

    #对神经层进行命名

    layer_name = 'layer%s' % n_layer

    with tf.name_scope(layer_name):

        with tf.name_scope('weights'):

            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')

            tf.summary.histogram(layer_name + '/weights', Weights)

        with tf.name_scope('biases'):

            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')

            tf.summary.histogram(layer_name + '/biases', biases)

        with tf.name_scope('Wx_plus_b'):

            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)

        if activation_function is None:

            outputs = Wx_plus_b

        else:

            outputs = activation_function(Wx_plus_b, )

        tf.summary.histogram(layer_name + '/outputs', outputs)

    return outputs

# Make up some real data

x_data = np.linspace(-1, 1, 300)[:, np.newaxis]

noise = np.random.normal(0, 0.05, x_data.shape)

y_data = np.square(x_data) - 0.5 + noise

# define placeholder for inputs to network

with tf.name_scope('inputs'):

    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')

    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')

# add hidden layer

l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)

# add output layer

prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)

# the error between prediciton and real data

with tf.name_scope('loss'):

    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),

                                        reduction_indices=[1]))

    tf.summary.scalar('loss', loss)

with tf.name_scope('train'):

    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()

#合并

merged = tf.summary.merge_all()

writer = tf.summary.FileWriter("logs/", sess.graph)

init = tf.global_variables_initializer()

sess.run(init)

for i in range(1000):

    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})

    if i % 50 == 0:

        result = sess.run(merged,feed_dict={xs: x_data, ys: y_data})

        #i 就是记录的步数

        writer.add_summary(result, i)

tensorboard查看效果使用命令tensorboard --logdir logs

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