# https://www.leiphone.com/news/201704/PgRxGpwtFpSgJoAZ.html
import tensorflow as tf
import numpy as np

def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): #activation_function=None线性函数
    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")   #Weight中都是随机变量
            tf.summary.histogram(layer_name+"/weights",Weights)                   #可视化观看变量
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1,out_size])+0.1,name="b")     #biases推荐初始值不为0
            tf.summary.histogram(layer_name+"/biases",biases)             #可视化观看变量
        with tf.name_scope('w_b'):
            w_b = tf.matmul(inputs,Weights)+biases                  #inputs*Weight+biases
            tf.summary.histogram(layer_name+"/w_b",w_b)           #可视化观看变量
        if activation_function is None:
            outputs = w_b
        else:
            outputs = activation_function(w_b)    #Relu
            tf.summary.histogram(layer_name+"/outputs",outputs)   #可视化观看变量
        return outputs

#创建数据x_data，y_data
x_data = np.linspace(-1,1,300)[:,np.newaxis]    #[-1,1]区间，300个单位，np.newaxis增加维度
noise = np.random.normal(0,0.05,x_data.shape)    #噪点
y_data = np.square(x_data)-0.5 + noise

with tf.name_scope('inputs'): #结构化
    xs = tf.placeholder(tf.float32,[None,1],name='x_input')
    ys = tf.placeholder(tf.float32,[None,1],name='y_input')

#三层神经，输入层（1个神经元），隐藏层（10神经元），输出层（1个神经元）
l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu) #隐藏层
prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None) #输出层

#predition值与y_data差别
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1])) #square()平方,sum()求和,mean()平均值
    tf.summary.scalar('loss',loss)  #可视化观看常量
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)           #0.1学习效率,minimize(loss)减小loss误差

#初始化变量
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

#合并到Summary中
merged = tf.summary.merge_all()
#选定可视化存储目录
writer = tf.summary.FileWriter("Desktop/",sess.graph)

#训练
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})    #merged也是需要run的
        writer.add_summary(result,i)     #result是summary类型的，需要放入writer中，i步数（x轴）