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  • TensorFlow 学习可视化 TensorBoard 简易教程

    为了更方便 TensorFlow 程序的理解、调试与优化,我们发布了一套叫做 TensorBoard 的可视化工具。你可以用 TensorBoard 来展现你的 TensorFlow 图像,绘制图像生成的定量指标图以及附加数据。

    我们将之前demo2的http://www.cnblogs.com/guolaomao/p/7901020.html

    稍作修改:

    import tensorflow as tf
    import numpy as np
    
    def add_layer(inputs,in_size,out_size,activation_function=None):
        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
    
    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
    
    #defin placeholder  fort 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')
    
    l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
    prediction = add_layer(l1,10,1,activation_function=None)
    
    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("demo2-log/",sess.graph)
    init = tf.global_variables_initializer()
    sess.run(init)

    然后运行这段代码

    python demo2.py

    就会在demo2-log/文件夹下创建一个文件,并在文件夹内生成相应的TensorBoard文件

    然后执行代码

    tensorboard --logdir demo3-log

    就能开启了服务。在终端里能看到服务的连接。在浏览器里打开相应的连接就能看到一个最简单基础的TensorBoard页面了。

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  • 原文地址:https://www.cnblogs.com/guolaomao/p/7920774.html
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