import tensorflow as tf
import numpy as np
# 创建添加层
def add_layer(inputs,in_size,out_size,activation_fuction = None):
    
    Weight = tf.Variable(tf.random.normal([in_size,out_size]))
    biases  = tf.Variable(tf.zeros([1,out_size])+0.1)    
    wx = tf.matmul(inputs,Weight)+biases
    
    if activation_fuction is None:
        output = wx
    else :
        output = activation_fuction(wx)
    return output 



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

xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
hidden = add_layer(xs,1,10,activation_fuction =tf.nn.relu)
prediction = add_layer(hidden,10,1,activation_fuction = None)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(prediction - ys),reduction_indices=[1]))
train = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(1000):
#   # training train_step 和 loss 都是由 placeholder 定义的运算，所以这里要用 feed 传入参数
        sess.run(train, feed_dict={xs: x_data, ys: y_data})
        if i%50 == 0:
            print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
    

1、输入数据

2、创建模型

3、求loss , 训练loss最小化