3.4.5节 完整神经网络样例程序

参考Tensorflow%20实战Google深度学习框架.pdf 

import os
import tab
import tensorflow as tf

print "hello tensorflow 111"
os.system("clear")

from numpy.random import RandomState
batch_size = 8
w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))

x = tf.placeholder(tf.float32,shape=(None,2),name='x-input')
y_ = tf.placeholder(tf.float32,shape=(None,1),name='y-input')

a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

cross_entropy = -tf.reduce_mean(
    y_ * tf.log(tf.clip_by_value(y,1e-10,1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size,2)

Y = [[int(x1+x2<1)] for (x1,x2) in X ]

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print sess.run(w1)
    print sess.run(w2)

    STEPS = 5000
    for i in range(STEPS):
        start = (i * batch_size) % dataset_size
        end = min(start+batch_size,dataset_size)

        sess.run(train_step, feed_dict = {x: X[start:end], y_: Y[start:end]} )

        if i % 1000 == 0:
            total_cross_entropy = sess.run( cross_entropy, feed_dict={x: X, y_: Y})
            print "After %d training step(s),cross entropy on all data is %g" % (i, total_cross_entropy)

    print sess.run(w1)
    print sess.run(w2)

print "end "