Tensor Transformations



In [1]:

    
from __future__ import print_function
import tensorflow as tf
import numpy as np



In [2]:

    
from datetime import date
date.today()









    Out[2]:





datetime.date(2017, 2, 22)



In [3]:

    
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"



In [4]:

    
tf.__version__









    Out[4]:





'1.0.0'



In [5]:

    
np.__version__









    Out[5]:





'1.12.0'



In [6]:

    
sess = tf.InteractiveSession()

NOTE on notation

_x, _y, _z, ...: NumPy 0-d or 1-d arrays
_X, _Y, _Z, ...: NumPy 2-d or higer dimensional arrays
x, y, z, ...: 0-d or 1-d tensors
X, Y, Z, ...: 2-d or higher dimensional tensors

Casting

Q1. Let X be a tensor of [["1.1", "2.2"], ["3.3", "4.4"]]. Convert the datatype of X to float32.



In [7]:









    



[[ 1.10000002  2.20000005]
 [ 3.29999995  4.4000001 ]]

Q2. Let X be a tensor [[1, 2], [3, 4]] of int32. Convert the data type of X to float64.



In [8]:









    



[[ 1.  2.]
 [ 3.  4.]]

Q3. Let X be a tensor [[1, 2], [3, 4]] of int32. Convert the data type of X to float32.



In [9]:









    



[[ 1.  2.]
 [ 3.  4.]]

Q4. Let X be a tensor [[1, 2], [3, 4]] of float32. Convert the data type of X to int32.



In [10]:









    



[[1 2]
 [3 4]]

Q5. Let X be a tensor [[1, 2], [3, 4]] of float32. Convert the data type of X to int64.



In [11]:









    



[[1 2]
 [3 4]]

Shapes and Shaping

Q6. Let X be a tensor of [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]. Create a tensor representing the shape of X.



In [12]:

Q7. Let X be a tensor of [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]) and y be a tensor [10, 20]. Create a list of tensors representing the shape of X and y.



In [13]:









    



[3 2 2] [2]

Q8. Let X be a tensor of [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]. Create a tensor representing the size (=total number of elements) of X.



In [14]:

Q9. Let X be a tensor of [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]. Create a tensor representing the rank (=number of dimensions) of X.



In [15]:

Q10. Let X be tf.ones([10, 10, 3]). Reshape X so that the size of the second dimension equals 150.



In [16]:









    



[[ 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.  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.  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.  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.
   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.]]

Q11. Let X be tf.ones([10, 10, 1, 1]). Remove all the dimensions of size 1 in X.



In [17]:

Q12. Let X be tf.ones([10, 10, 1, 1]). Remove only the third dimension in X.



In [18]:









    



(10, 10, 1)

Q13. Let X be tf.ones([10, 10]). Add a dimension of 1 at the end of X.



In [19]:









    



(10, 10, 1)

Slicing and Joining

Q14. Let X be a tensor
[[[1, 1, 1], [2, 2, 2]],
[[3, 3, 3], [4, 4, 4]],
[[5, 5, 5], [6, 6, 6]]].
Extract the [[[3, 3, 3], [5, 5, 5]] from X.



In [20]:









    



[[[3 3 3]]

 [[5 5 5]]]

Q15. Let X be a tensor of
[[ 1 2]
[ 3 4]
[ 5 6]
[ 7 8]
[ 9 10]].
Extract the [[1, 2], [5, 6], [9, 10]]] from X.



In [21]:









    



[[ 1  2]
 [ 5  6]
 [ 9 10]]

Q16. Let X be a tensor of
[[ 1 2 3 4 5]
[ 6 7 8 9 10]].
Split X into 5 same-sized tensors along the second dimension.



In [22]:









    



[array([[1],
       [6]]), array([[2],
       [7]]), array([[3],
       [8]]), array([[4],
       [9]]), array([[ 5],
       [10]])]

Q17. Lex X be a tensor
[[ 1 2 3]
[ 4 5 6].
Create a tensor looking like
[[ 1 2 3 1 2 3 1 2 3 ]
[ 4 5 6 4 5 6 4 5 6 ]].



In [23]:









    



[[1 2 3 1 2 3 1 2 3]
 [4 5 6 4 5 6 4 5 6]]

Q18. Lex X be a tensor
[[ 1 2 3]
[ 4 5 6].
Pad 2 0's before the first dimension, 3 0's after the second dimension.



In [24]:









    



[[0 0 0 0 0 0]
 [0 0 0 0 0 0]
 [1 2 3 0 0 0]
 [4 5 6 0 0 0]]

Q19. Lex X be a tensor
[[ 1 2 3]
[ 4 5 6].
and Y be a tensor
[[ 7 8 9]
[10 11 12]].
Concatenate X and Y so that the new tensor looks like [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]].



In [25]:









    



[[ 1  2  3  7  8  9]
 [ 4  5  6 10 11 12]]

Q20. Let x, y, and z be tensors [1, 4], [2, 5], and [3, 6], respectively.
Create a single tensor from these such that it looks [[1, 2, 3], [4, 5, 6]].



In [26]:









    



[[1 2 3]
 [4 5 6]]

Q21. Let X be a tensor [[1, 2, 3], [4, 5, 6]]. Convert X into Y such that Y looks like [[1, 4], [2, 5], [3, 6]].



In [27]:









    



[array([1, 4]), array([2, 5]), array([3, 6])]

Q22. Given X below, reverse the sequence along the second axis except the zero-paddings.



In [28]:

    
X = tf.constant(
[[[0, 0, 1],
  [0, 1, 0],
  [0, 0, 0]],
 
 [[0, 0, 1],
  [0, 1, 0],
  [1, 0, 0]]])









    Out[28]:





array([[[0, 1, 0],
        [0, 0, 1],
        [0, 0, 0]],

       [[1, 0, 0],
        [0, 1, 0],
        [0, 0, 1]]])

Q23. Given X below, reverse the last dimension.



In [29]:

    
_X = np.arange(1, 1*2*3*4 + 1).reshape((1, 2, 3, 4))
X = tf.convert_to_tensor(_X)









    



[[[[ 4  3  2  1]
   [ 8  7  6  5]
   [12 11 10  9]]

  [[16 15 14 13]
   [20 19 18 17]
   [24 23 22 21]]]]

Q24. Given X below, permute its dimensions such that the new tensor has shape (3, 1, 2).



In [30]:

    
_X = np.ones((1, 2, 3))
X = tf.convert_to_tensor(_X)









    



(3, 1, 2)

Q25. Given X, below, get the first, and third rows.



In [31]:

    
_X = np.arange(1, 10).reshape((3, 3))
X = tf.convert_to_tensor(_X)









    



[[1 2 3]
 [7 8 9]]

Q26. Given X below, get the elements 5 and 7.



In [32]:

    
_X = np.arange(1, 10).reshape((3, 3))
X = tf.convert_to_tensor(_X)

Q27. Let x be a tensor [2, 2, 1, 5, 4, 5, 1, 2, 3]. Get the tensors of unique elements and their counts.



In [33]:









    



[2 1 5 4 3] [3 2 2 1 1]

Q28. Let x be a tensor [1, 2, 3, 4, 5]. Divide the elements of x into a list of tensors that looks like [[3, 5], [1], [2, 4]].



In [34]:









    



[array([3, 5]), array([1]), array([2, 4])]

Q29. Let X be a tensor [[7, 8], [5, 6]] and Y be a tensor [[1, 2], [3, 4]]. Create a single tensor looking like [[1, 2], [3, 4], [5, 6], [7, 8]].



In [35]:









    



[[1 2]
 [3 4]
 [5 6]
 [7 8]]

Q30. Let x be a tensor [0, 1, 2, 3] and y be a tensor [True, False, False, True].
Apply mask y to x.



In [36]:

Q31. Let x be a tensor [[0, 5, 3], [4, 2, 1]]. Convert X into one-hot.



In [37]:









    



[[ 1.  0.  0.  0.  0.  0.]
 [ 0.  1.  0.  0.  0.  0.]
 [ 0.  0.  1.  0.  0.  0.]
 [ 0.  0.  0.  1.  0.  0.]]



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