tensorboard的可视化（一）

先上代码：

#这个是tensorboard可视化的一个例子，没有训练数据那部分，不过对于讲解如何进行tensorboard的可视化已经够了

from __future__ import print_function
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()

# tf.train.SummaryWriter soon be deprecated, use following
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:  # tensorflow version < 0.12
    writer = tf.train.SummaryWriter('logs/', sess.graph)    #把session写进文件中，然后就可以在浏览器查看了
else: # tensorflow version >= 0.12
    writer = tf.summary.FileWriter("logs/", sess.graph)

# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
    init = tf.initialize_all_variables()
else:
    init = tf.global_variables_initializer()
sess.run(init)

print('done')
# direct to the local dir and run this in terminal:
# $ tensorboard --logdir=logs

我的文件生成在D:\machineLearning\my_tensorflow\tensorboard\logs文件夹下。在浏览器查看步骤如下：

1 打开cmd，定位到D:\machineLearning\my_tensorflow\tensorboard文件夹

2 敲入命令：

tensorboard --logdir=logs

3 到链接 http://DESKTOP-2C6NL8L:6006 查看，这个链接可在第2步的命令运行后得到，根据输出结果得到。

4 用谷歌浏览器（tensorflow是谷歌的亲儿子，其它浏览器可能不兼容，不一定能查看）查看，结果如下：

tensorboard的可视化（一）

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