使用TensorBoard进行数据可视化

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除了python的matplotlib这个数据图像化的包，我们还可以使用Tensorflow自带的TensorBoard来将数据进行可视化，实现的代码如下

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import tensorflow as tfimport numpy as npdef add_layer(inputs, in_size, out_size, n_layer, activation_function=None):    layer_name = 'layer%s' % n_layer    with tf.name_scope("layer"):         with tf.name_scope("weight"):            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.matmul(inputs, Weights)+biases        if activation_function == None:            outputs = Wx_plus_b        else:            outputs = activation_function(Wx_plus_b)            tf.summary.histogram(layer_name+'/outputs',outputs)        return outputsx_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 + noisewith tf.name_scope("inputs"):    xs = tf.placeholder(tf.float32,[None,1],name="x_input")    ys = tf.placeholder(tf.float32,[None,1],name="y_input")l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None) with tf.name_scope("loss"):    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]),name="loss")    tf.summary.scalar('loss',loss)with tf.name_scope("train"):    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)init = tf.global_variables_initializer()sess = tf.Session()merged = tf.summary.merge_all()writer = tf.summary.FileWriter("logs/",sess.graph)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})        writer.add_summary(result,i)

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代码进行保存以及运行之后，将会在指定文件夹中看到保存的文件，这是我们需要在终端中运行下面的命令，然后将网址复制到浏览器即可打开

$ tensorboard --logdir='logs/' #'logs'是指保存的文件所在的文件夹

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注：

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1.

tf.name_scope("name")

该函数中的name是指GRAPHS中的

以及每个神经网络的节点打开后里面的各种量的名字

这是部分节点点开后的样子

这是所有节点点开后的样子

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2.

tf.summary.histogram(layer_name+'name',name)

该函数是用来绘制histogram中图可视化绘图

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3.

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

该函数主要适用于损失函数在scalar的可视化绘图

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4.

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

该函数是将TensorBoard文件保存到指定路径，例如”logs/”即是指路径，正常运行后将会在logs文件夹下产生一个文件

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5.

tf.summary.merge_all()

该函数是将所有summary文件合并到同一个文件里并在下一步的tf.summary.FileWriter(“logs/”,sess.graph)进行保存

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6.

add_summary(result,i)

该函数是将result这个数据加入到writer = tf.summary.FileWriter(“logs/”,sess.graph)这个函数的write中去，其中i是指步数，例如本函数中就是50布记录一个点

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7.运行应该在ipython中运行，如果在jupyter中运行可能会产生报错