#coding:utf-8
#0导入模块，生成模拟数据集。
#tensorflow学习笔记(北京大学) tf3_6.py 完全解析神经网络搭建学习
#QQ群：476842922（欢迎加群讨论学习
import tensorflow as tf
import numpy as np
BATCH_SIZE = 8
SEED = 23455
#基于seed产生随机数
rdm = np.random.RandomState(SEED)
#随机数返回32行2列的矩阵 表示32组 体积和重量 作为输入数据集
X = rdm.rand(32,2)
#从X这个32行2列的矩阵中 取出一行 判断如果和小于1 给Y赋值1 如果和不小于1 给Y赋值0
#作为输入数据集的标签（正确答案）
Y_ = [[int(x0 + x1 < 1)] for (x0, x1) in X]
print "X:\n",X
print "Y_:\n",Y_
#1定义神经网络的输入、参数和输出,定义前向传播过程。
x = tf.placeholder(tf.float32, shape=(None, 2))#用placeholder实现输入定义
y_= tf.placeholder(tf.float32, shape=(None, 1))#用placeholder实现占位
w1= tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))#正态分布随机数
w2= tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))#正态分布随机数
a = tf.matmul(x, w1)#点积
y = tf.matmul(a, w2)#点积
#2定义损失函数及反向传播方法。
loss_mse = tf.reduce_mean(tf.square(y-y_))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss_mse)
#train_step = tf.train.MomentumOptimizer(0.001,0.9).minimize(loss_mse)
#train_step = tf.train.AdamOptimizer(0.001).minimize(loss_mse)
#3生成会话，训练STEPS轮
with tf.Session() as sess:
init_op = tf.global_variables_initializer()#初始化
sess.run(init_op)
# 输出目前（未经训练）的参数取值。
print "w1:\n", sess.run(w1)
print "w2:\n", sess.run(w2)
print "\n"
# 训练模型。
STEPS = 3000
for i in range(STEPS):#3000轮
start = (i*BATCH_SIZE) % 32 #i*8%32
end = start + BATCH_SIZE #i*8%32+8
sess.run(train_step, feed_dict={x: X[start:end], y_: Y_[start:end]})
if i % 500 == 0:
total_loss = sess.run(loss_mse, feed_dict={x: X, y_: Y_})
print("After %d training step(s), loss_mse on all data is %g" % (i, total_loss))
# 输出训练后的参数取值。
print "\n"
print "w1:\n", sess.run(w1)
print "w2:\n", sess.run(w2)
"""
X:
[[ 0.83494319 0.11482951]
[ 0.66899751 0.46594987]
[ 0.60181666 0.58838408]
[ 0.31836656 0.20502072]
[ 0.87043944 0.02679395]
[ 0.41539811 0.43938369]
[ 0.68635684 0.24833404]
[ 0.97315228 0.68541849]
[ 0.03081617 0.89479913]
[ 0.24665715 0.28584862]
[ 0.31375667 0.47718349]
[ 0.56689254 0.77079148]
[ 0.7321604 0.35828963]
[ 0.15724842 0.94294584]
[ 0.34933722 0.84634483]
[ 0.50304053 0.81299619]
[ 0.23869886 0.9895604 ]
[ 0.4636501 0.32531094]
[ 0.36510487 0.97365522]
[ 0.73350238 0.83833013]
[ 0.61810158 0.12580353]
[ 0.59274817 0.18779828]
[ 0.87150299 0.34679501]
[ 0.25883219 0.50002932]
[ 0.75690948 0.83429824]
[ 0.29316649 0.05646578]
[ 0.10409134 0.88235166]
[ 0.06727785 0.57784761]
[ 0.38492705 0.48384792]
[ 0.69234428 0.19687348]
[ 0.42783492 0.73416985]
[ 0.09696069 0.04883936]]
Y_:
[[1], [0], [0], [1], [1], [1], [1], [0], [1], [1], [1], [0], [0], [0], [0], [0], [0], [1], [0], [0], [1], [1], [0], [1], [0], [1], [1], [1], [1], [1], [0], [1]]
w1:
[[-0.81131822 1.48459876 0.06532937]
[-2.4427042 0.0992484 0.59122431]]
w2:
[[-0.81131822]
[ 1.48459876]
[ 0.06532937]]
After 0 training step(s), loss_mse on all data is 5.13118
After 500 training step(s), loss_mse on all data is 0.429111
After 1000 training step(s), loss_mse on all data is 0.409789
After 1500 training step(s), loss_mse on all data is 0.399923
After 2000 training step(s), loss_mse on all data is 0.394146
After 2500 training step(s), loss_mse on all data is 0.390597
w1:
[[-0.70006633 0.9136318 0.08953571]
[-2.3402493 -0.14641267 0.58823055]]
w2:
[[-0.06024267]
[ 0.91956186]
[-0.0682071 ]]
"""
