Exercises on mathematical operations in TensorFlow 1.4

12 grudnia, 2019 admin TENSORFLOW 0

EN121220190807

Practice makes perfect

In [1]:
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt



















Exercise 1: Perform the following equation on the tensor.


image.png












In [2]:








R = tf.constant(12, tf.int16, name="twelve") 
print(R)





















Tensor("twelve:0", shape=(), dtype=int16)

















In [3]:








F = tf.constant(4, tf.int16, name="twelve") 
print(F)





















Tensor("twelve_1:0", shape=(), dtype=int16)

















In [4]:








KOT = tf.div (R, F)
print(KOT)





















Tensor("div:0", shape=(), dtype=int16)

















In [5]:








with tf.Session() as sess:    
    result_2 = KOT.eval()
print(result_2) 





















3

















In [6]:








sess = tf.Session()
print(sess.run(KOT))





















3





















Exercise 2: Perform the following equation on the tensor


image1.png












In [7]:








F = tf.constant(8) 
R = tf.constant(2) 
FFT = tf.multiply (F, R)

















In [8]:








sess = tf.Session()
print(sess.run(FFT))





















16

















In [9]:








with tf.Session() as sess:    
    result_2 = FFT.eval()
print(result_2) 





















16





















Exercise 3: Mathematical operations on tensors


image2.png












In [10]:








A = tf.constant(5) 
B = tf.constant(2) 
PKO = tf.pow(A, B)

















In [11]:








sess = tf.Session()
print(sess.run(PKO))





















25





















Exercise 4: Mathematical operations on tensors


You do not need to specify the data type because tensorflow alone guesses what type of data was used in the constant.


image3.png












In [12]:








A = tf.constant(15.9) 
B = tf.constant(2.1) 
SKO = tf.subtract(A, B)

















In [13]:








sess = tf.Session()
print(sess.run(SKO))





















13.799999





















Exercise 5: Perform the following equation on the tensor


Tensorflow does not like to have different data formats.

We create a mathematical formula in Python:


image4.png












In [14]:








A = tf.constant(1.7, tf.float32) 
B = tf.constant(2.4, tf.float32)
C = tf.constant(15, tf.float32)

SZK = tf.add(tf.subtract(A, B), C)

















In [15]:








sess = tf.Session()
print(sess.run(SZK))





















14.3





















Exercise 5: Perform the following equation on the tensor


Tensorflow does not like to have different data formats.

We create a mathematical formula in Python:

image5.png












In [16]:








A = tf.constant(2.07) 
B = tf.constant(1.3)
C = tf.constant(2.7)

SSF = tf.subtract(A, B)
PKO = tf.pow(SSF,C)

















In [17]:








with tf.Session() as sess:    
    result_2 = PKO.eval()
print(result_2) 





















0.49377024





















Exercise 6: Mathematical operations on tensors


We create a mathematical formula in Python:

image6.png
















We can use the math library resources:

https://docs.python.org/3/library/math.html












In [18]:








import math
a = math.pi
a

















Out[18]:




3.141592653589793
















In [19]:








C = tf.constant(2.1) 
D = tf.constant(math.pi)

GG = tf.multiply(C, D)
PKK = tf.sqrt(GG)

















In [20]:








with tf.Session() as sess:    
    result_2 = PKK.eval()
print("The x value sought is: ",result_2) 





















The x value sought is:  2.5685296





















Exercise 7: actions on Tensorflow tensors


Please calculate in Tensorflow the value of n:

image7.png
















Let’s check how much it will be:












In [21]:








math.pow(15,(1/3))

















Out[21]:




2.46621207433047




















In Tensorflow, be careful about the fixed format.












In [22]:








C = tf.constant(15, tf.float32) 
A = tf.constant(3, tf.float32)
H = tf.constant(1, tf.float32)
G = tf.div(H,A)

PK = tf.pow(C,G)

















In [23]:








with tf.Session() as sess:    
    result_2 = G.eval()
print(result_2) 





















0.33333334

















In [24]:








with tf.Session() as sess:    
    result_2 = PK.eval()
print(result_2) 





















2.466212





















Exercise 8: actions on Tensorflow tensors


Please calculate in Tensorflow the value of n:

image8.png
















We can use the math library resources: https://docs.python.org/3/library/math.html












In [25]:








math.e

















Out[25]:




2.718281828459045
















In [26]:








math.pow(math.e,(1/4))

















Out[26]:




1.2840254166877414
















In [27]:








C = tf.constant(math.e, tf.float32) 
A = tf.constant(4, tf.float32)
H = tf.constant(1, tf.float32)
G = tf.div(H,A)

ZHP = tf.pow(C,G)

















In [28]:








with tf.Session() as sess:    
    result_2 = ZHP.eval()
print(result_2) 





















1.2840254





















Exercise 9. Change of the tensor type from float to int


TensorFlow automatically selects the data type when the argument is not specified when creating the tensor.












In [29]:








PKP = tf.constant(3.123456789, tf.float32)
ZNP = tf.cast(PKP, dtype=tf.int32)

print(PKP.dtype)
print(ZNP.dtype)





















<dtype: 'float32'>
<dtype: 'int32'>