深度学习--八股_深度学习八股

#coding:utf-8
#0 导入模块，生成模拟数据集
import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
BATCH_SIZE = 8
seed = 23455
 
#基于seed产生随机数
rng=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 = rng.rand(32,2)
 
#从X这个32行2列的矩阵中 取出1行 判断如果和小于1 给Y赋值1 否则给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))
y_= tf.placeholder(tf.float32, shape=(None, 1))
with tf.device('/cpu:0'):
	w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
	w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
with tf.device('/gpu:0'):
	a=tf.matmul(x,w1)
	y=tf.matmul(a,w2)
 
#定义损失函数和反向传播方法
loss = tf.reduce_mean(tf.square(y-y_))
#train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
#train_step = tf.train.MomentumOptimizer(0.001,0.9).minimize(loss)
 
#3 生成会话，训练STEPS轮
with tf.Session() as sess:
	init_op = tf.global_variables_initializer()
	sess.run(init_op)
	#输出目前（未经）训练的参数值
	print "w1:\n", sess.run(w1)
	print "w2:\n", sess.run(w2)
 
	#训练模型
	STEPS = 5000
	for i in range(STEPS):
		start = (i*BATCH_SIZE) % 32
		end = start + BATCH_SIZE
		sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})
		if i % 500 == 0:
			total_loss = sess.run(loss,feed_dict={x:X,y_:Y})
			print("After %d traning steps,loss on all data is %g" %(i,total_loss))
 
	#输出训练后的参数值
	print "\n"
	print "w1:\n", sess.run(w1)
	print "w2:\n", sess.run(w2)