# 创建神经网络， 使用tensorboard 展示graph/histogram/scalar

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt  # 若没有 pip install matplotlib

# 定义一个神经层

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.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]  # x_data值为-1到1之间，有300个单位（例子），再加一个维度newaxis，即300行*newaxis列

noise = np.random.normal(0, 0.05, x_data.shape)  # 均值为0.方差为0.05，格式和x_data一样

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_input1')  # none表示无论给多少个例子都行

    ys = tf.placeholder(tf.float32, [None, 1],name='y_input1')

# 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 prediction and real data

with tf.name_scope('loss'):

    loss = tf.reduce_mean(

        tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))  # 对每个例子进行求和并取平均值 reduction_indices=[1]指按行求和

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

with tf.name_scope('train'):

    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  # 以0.1的学习效率对误差进行更正和提升

#两种初始化的方式

#init = tf.initialize_all_variables()

init = tf.global_variables_initializer()

sess = tf.Session()

sess.run(init)

#把所有的summary合并在一起

merged = tf.summary.merge_all()

#把整个框架加载到一个文件中去，再从文件中加载出来放到浏览器中查看

#writer=tf.train.SummaryWriter("logs/",sess.graph)

#首先找到tensorboard.exe的路径并进入c:Anaconda\Scripts,执行tensorboard.exe --logdir=代码生成的图像的路径（不能带中文）

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

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1)

ax.scatter(x_data, y_data)

plt.ion()

plt.show()   #show()是一次性的展示，为了使连续的展示，加入plt.ion()

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)