一个完整神经网络过程的实践

#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