Test basic Python code



In [1]:

    
print "hello, there"









    



hello, there



In [2]:

    
a=2
c=a+2
print c

Test Python package import



In [3]:

    
import numpy as np



In [4]:

    
import tensorflow as tf

Run basic TensorFlow code



In [5]:

    
a1=tf.ones([10])
sess = tf.Session()
print(sess.run(a1))









    



[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]

this is different from print a0 itself



In [6]:

    
print(a1)









    



Tensor("ones:0", shape=(10,), dtype=float32)



In [7]:

    
hello = tf.constant('Hello, Joe!')
print(sess.run(hello))
print(hello)









    



Hello, Joe!
Tensor("Const:0", shape=(), dtype=string)

Note that sess.run() gives us the actual output value, it takes tf variable The following tf.constant is required, otherwise it will generate error



In [8]:

    
a = tf.constant(10)
b = tf.constant(20)
c=a+b
print(sess.run(c))

Put everything in a session, no need to call session, just use .eval()



In [9]:

    
with tf.Session():
    a = tf.constant(10)
    b = tf.constant(20)
    c=a+b
    print c
    print(c.eval())









    



Tensor("add_1:0", shape=(), dtype=int32)
30

the following code will generate error, that because c is not tf variable, thus cannot be run by sess.run



In [21]:









    Out[21]:





<tf.Tensor 'Add:0' shape=(4,) dtype=float32>

See different tensor dimensions



In [10]:

    
a2=tf.ones([10,3])
print(sess.run(a2))









    



[[ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]]



In [11]:

    
a3=tf.ones([10,3,2])
print(sess.run(a3))









    



[[[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]

 [[ 1.  1.]
  [ 1.  1.]
  [ 1.  1.]]]

b1 = tf.ones([4]) b2 = tf.constant([2.0, 2.0, 2.0, 2.0]) c = tf.add(b1, b2) print c



In [12]:

    
print(sess.run(c))



In [13]:

    
with tf.Session():
  b1 = tf.ones([4])
  b2 = tf.constant([2.0, 2.0, 2.0, 2.0])
  c = tf.add(b1, b2)
  print c.eval()









    



[ 3.  3.  3.  3.]

Get random number and data type



In [14]:

    
shape=[2,3]
x=tf.random_normal(shape, stddev=0.7) 
with tf.Session():
    print x.eval()









    



[[-0.40981114  0.88485193 -0.27486104]
 [ 0.83717912 -0.34084049  0.35752657]]



In [15]:

    
shape=[2,2]
x=tf.truncated_normal(shape, stddev=0.1) 
with tf.Session():
    print x.eval()









    



[[-0.12661348 -0.15962534]
 [-0.12176304 -0.0294427 ]]

Exercise: Print out the sigmoid value of a 2x2 marix of 1's, using TensorFlow



In [25]:

    
with tf.Session():
    ones = tf.ones([2,2])
    print ones.eval()
    sigmoid = tf.sigmoid(ones, name="sigmoid")
    print sigmoid.eval()









    



[[ 1.  1.]
 [ 1.  1.]]
[[ 0.7310586  0.7310586]
 [ 0.7310586  0.7310586]]



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