Tensorflow framework to build neural network (3)

The parameters of the neural network: refers to the weight w on the neuron line, which is represented by variables, and these parameters are generally generated randomly first. The way to generate parameters is to make w equal to tf.Variable, and write the generation method in parentheses. The functions commonly used to generate random numbers/arrays in neural networks are:
tf.random_normal() generates normal distribution random numbers
tf.truncated_normal() generates normally distributed random numbers with excessive deviation points removed
tf.random_uniform() generates uniform distribution random number

tf.zeros	Indicates that an array of all 0s is generated
tf.ones	Indicates that an array of all 1s is generated
tf.fill	Represents generating an array of fully definite values
tf.constant	Represents an array that generates directly given values

# Copyright (c)2018, Student at School of Software, Northeastern University
# All rightsreserved
# File name: test.py
# Author: Kong Yun
#Problem description: Using Tensorflow to implement the forward propagation process
#coding:utf-8
# Two-layer simple neural network (full connection)
import tensorflow as tf
#define input and parameters
x = tf.constant([[0.7, 0.5]])
w1= tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2= tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))
#Define the forward propagation process, multiply the input of each layer by the weight w on the line, so that the output y can be calculated by matrix multiplication
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)
#Calculate result with session
with tf.Session() as sess:
    init_op = tf.global_variables_initializer() #Initialize all variables
    sess.run (init_op)
    print ("the result of y is:\n",sess.run(y))

The results are as follows:

Note: This is an implementation of the neural network forward propagation process, the network can automatically infer the value of the output y.

In addition: ①The random seed will be inconsistent if the random number generated each time is removed.
          ②If there are no special requirements, the standard deviation, mean, and random seed can be omitted.