windows上打开tensorboard

╰半橙微兮° 2022-05-22 22:51 142阅读 0赞

例子：（例子代码参考：[https://blog.csdn.net/u014665013/article/details/79177232][https_blog.csdn.net_u014665013_article_details_79177232]）

# View more python learning tutorial on my Youtube and Youku channel!!!
    
    # Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
    # Youku video tutorial: http://i.youku.com/pythontutorial
    
    """
    Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
    """
    from __future__ import print_function
    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
        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()
    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})
            writer.add_summary(result, i)
    
    # direct to the local dir and run this in terminal:
    # $ tensorboard --logdir logs

运行上述代码后显示的信息如下：

![70][]

wdir是logs目录，在控制台tensorflow环境中输入命令：tensorboard --logdir=logs(目录路径)

![70 1][]

可以看到有一个地址，将改地址复制用浏览器打开即可。

![70 2][]

[https_blog.csdn.net_u014665013_article_details_79177232]: https://blog.csdn.net/u014665013/article/details/79177232
[70]: /images/20220523/a8d27f8c00be461ca0b857b3e13e77c7.png
[70 1]: /images/20220523/6f8a0c3dbdee48c98dffe4adaa077c1a.png
[70 2]: /images/20220523/03f0b0a64f1f4434b372b0ee51ed6f3d.png