tensorboard可视化（先写一点点）

在tensorboard上显示运行图：

import tensorflow as tf
a = tf.constant(10,name="a")
b = tf.constant(90,name="b")
y = tf.Variable(a+b*2,name='y')
init=tf.global_variables_initializer()
with tf.Session() as sess:
    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter('C:/Users/1/Desktop/1',sess.graph)     #自定义tensor到给定路径中，奇葩的tensorboard路径只能设置在文件夹下，不能直接设置在桌面，否则报错
    sess.run(init)
    print(sess.run(y))

通过在终端输入如下：

cd 路径

tensorboard --logdir=路径

浏览器输入：http://localhost:6006/     得到tensorboard展示

另一个例子：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt #python的结果可视化模块
def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
    layer_name = 'layer%s' % n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope("wights"):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W') #定义权重矩阵
            #tf.summary.histogram用于保存变量的变化
            tf.summary.histogram(layer_name+'/weights', Weights)
        with tf.name_scope("biases"):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')#定义偏置
            tf.summary.histogram(layer_name + '/biases', biases)
        with tf.name_scope("Wx_plus_b"):
            Wx_plus_b = tf.matmul(inputs, Weights) + biases #预测出的值
        if activation_function is None:
            outputs = Wx_plus_b #线性激活
        else:
            outputs = activation_function(Wx_plus_b) #非线性激活
        tf.summary.histogram(layer_name + '/outputs', outputs)
        return outputs
#定义输入，linspace产生等差数列，加上数据的维度,定义输入数据为300个例子
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
# print(x_data.shape)
noise = np.random.normal(0, 0.05, x_data.shape) #定义噪声点
y_data = np.square(x_data) - 0.5 + noise # y=x_data*x_data - 0.5

#定义命名空间，使用tensorboard进行可视化
with tf.name_scope("inputs"):
    xs = tf.placeholder(tf.float32, [None, 1], name="x_input") #模型的输入x值
    ys = tf.placeholder(tf.float32, [None, 1], name="y_input") #模型的输入y值
#隐藏层
l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
#输出层
prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)

#损失函数
with tf.name_scope("loss"):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),
                        reduction_indices=[1]))
    tf.summary.scalar('loss', loss) #用于观察常量的变化
#模型训练
with tf.name_scope("train"):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer() #初始化所有变量
with tf.Session() as sess:
    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter("C:/Users/1/Desktop/1", sess.graph) #保存神经网络的所有的信息，方便浏览器访问
    sess.run(init)

    for i in range(1001):
        sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
        if i % 50 == 0:#每训练50次，合并一下结果
            result = sess.run(merged, feed_dict={xs: x_data, ys: y_data})
            writer.add_summary(result, i)

展示tensorboard（损失函数随epoch增加的变化情况）：