'''
Author: 365JHWZGo
Description: 7.结构默写
Date: 2021-10-14 16:27:43
FilePath: tensorflowtensorflowday03-2.py
LastEditTime: 2021-10-14 16:59:01
LastEditors: 365JHWZGo
'''
#导入包
from numpy import random
import tensorflow as tf
import numpy as np
from tensorflow.python.ops.gen_math_ops import add
#定义数据
x_data = np.linspace(-1,1,300,dtype=np.float32)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)
y_data = np.square(x_data)-0.5+noise
#定义层函数
def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):
Weights = tf.Variable(tf.random_normal([in_size,out_size]),name = 'W')
biases = tf.Variable(tf.zeros([1,out_size])+0.1,name='biases')
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
#定义变量
xs = tf.placeholder(tf.float32,[None,1],name='x_input')
ys = tf.placeholder(tf.float32,[None,1],name='y_input')
#赋值层结构
l1 = add_layer(xs,1,10,n_layer=1,activation_function=None)
prediction = add_layer(l1,10,1,n_layer=2,activation_function=tf.nn.relu)
#优化
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices = [1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for i in range(1000):
sess.run(train_step,feed_dict={
xs:x_data,
ys:y_data
})
sess.close()
