[DL] Build simple model based on Tensorflow

本篇文章是從莫烦的學習影片學習的,主要是教學我們如何建置一個簡單的學習網絡。
以下的程式,主要是運用此方程式   y=Wx+b,
已知變數x,想利用兩個變數 W(weights),b(biases) 去算出我的們期望值y,
並另用SGD去降低loss值,
算出最能接近期望值y時的 W(weights)與b(biases)


以下程式：

# coding=utf-8
import tensorflow as tf
import numpy as np

# creat data, random value between 0 and 1
x_data = np.random.rand(100).astype(np.float32)

# define the learn value
# to learn value: Weight:0.1  biases:0.3
# y_data= 真實值
y_data = x_data*0.1+0.3

### creat tensorflow structure start ###
# define the range and initial weights
Weights = tf.Variable(tf.random_uniform([1],-1.0,1.0))
biases = tf.Variable(tf.zeros([1]))

# 給定 tensorflow 學習的函數
# y = 預值測
y = Weights*x_data + biases

# build loss  function
loss = tf.reduce_mean(tf.square(y-y_data))

# select optimal methods to reduce loss and learning rate is always less than 1
# Define learning rate=0.5
optimizer = tf.train.GradientDescentOptimizer(0.5)

#Optimal the loss value to small ,the best value is 0
train = optimizer.minimize(loss)

#Initial all variables
init = tf.initialize_all_variables()

### creat tensorflow structure end ###
#-------------start to train ---------------#
# build sess
sess = tf.Session()
# To inital see is very important
sess.run(init)
for step in range(201):
    sess.run(train)
    if step % 20 ==0:
        print(step,sess.run(Weights),sess.run(biases))

執行結果：

參考影片：