#coding=utf-8
# 随机产生 32 组生产出的零件的体积和重量,训练 3000 轮,每 500 轮输出一次损 失函数。下面我们通过源代码进一步理解神经网络的实现过程:
import tensorflow as tf
import numpy as np
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,作为输入的数据集
X = rng.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,就给Y赋值1,如果和不小于1则给Y赋值为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
#定义神经网络的输入,参数、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))
w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
a = tf.matmul(x,w1)
y = tf.matmul(a,w2)
#定义损失函数及反向传播方法
loss = tf.reduce_mean(tf.square(y-y_))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
# train_step = tf.train.MomentumOptimizer(0.001,0.9).minimize(loss)
# train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
# 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):
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 training steps(s), loss 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)
以上代码输出的结果是:
结果太多了,请自行运行