Python3 tensorflow code for hello word

Tensorflow：实战Google深度学习框架

code copy : hello word

import tensorflow as tf

w1=tf.random_normal([2,3],stddev=1,seed=1)
w2=tf.random_normal([3,1],stddev=1,seed=1)
x=tf.constant([[0.7,0.9]])

a=tf.matmul(x,w1)
y=tf.matmul(a,w2)
sess=tf.Session()
#sess.run(w1)
#sess.run(w2)
print(sess.run(y))
sess.close()

output :[[ 3.95757794]]

with placehoder:

import tensorflow as tf

w1=tf.random_normal([2,3],stddev=1,seed=1)
w2=tf.random_normal([3,1],stddev=1,seed=1)

#x=tf.constant([[0.7,0.9]])
x=tf.placeholder(tf.float32,shape=(3,2),name="input")

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



sess=tf.Session()
#sess.run(w1)
#sess.run(w2)
#print(sess.run(y))

print(sess.run(y,feed_dict={x:[[0.7,0.9],[0.1,0.4],[0.5,0.8]]}))
sess.close()

 output :[[ 3.95757794]
 [ 1.15376544]
 [ 3.16749191]]

full code to train :

import tensorflow as tf

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.constant([[0.7,0.9]])
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:
  #sess.run(w1)
  #sess.run(w2)
  #print(sess.run(y))
  
  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 steps,cross entropy on all data is %g"%(i,total_cross_entropy))

  print(sess.run(w1))
  print(sess.run(w2))
  #print(sess.run(y,feed_dict={x:[[0.7,0.9],[0.1,0.4],[0.5,0.8]]}))
#sess.close()

output:[[-0.81131822  1.48459876  0.06532937]
 [-2.44270396  0.0992484   0.59122431]]
[[-0.81131822]
 [ 1.48459876]
 [ 0.06532937]]
After 0 training steps,cross entropy on all data is 0.0674925
After 1000 training steps,cross entropy on all data is 0.0163385
After 2000 training steps,cross entropy on all data is 0.00907547
After 3000 training steps,cross entropy on all data is 0.00714436
After 4000 training steps,cross entropy on all data is 0.00578471
[[-1.9618274   2.58235407  1.68203783]
 [-3.4681716   1.06982327  2.11788988]]
[[-1.8247149 ]
 [ 2.68546653]
 [ 1.41819501]]