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July 6, 2020 at 8:32 pm #898
NPTEL Week 0 Unit 2 Assignment Basic of Numpy
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https://github.com/AbhishekTyagi404/DeepLearningwithComputerVision/tree/master/Assignment#!/usr/bin/env python # coding: utf8 # #### **Welcome to Assignment 0 on Deep Learning for Computer Vision.** # In this assignment you will get a chance to understand and work on some of the commonly used functionalities of several Machine Learnign and Data Analysis libraries # # #### **Instructions** # 1. Use Python 3.x to run this notebook # 2. Write your code only in between the lines 'YOUR CODE STARTS HERE' and 'YOUR CODE ENDS HERE'. # 3. Look up the documentation for each of the Python and Numpy function used. # 4. This Assignment is just to make you understand several commonly used library functions. # 5. This assignment is **NOT** evaluated # In[1]: # DO NOT CHANGE THIS CODE import numpy as np np.random.seed(0) # #### Python Basics # # In[4]: ### Implement recursive fibonacci in the function below def recursive_fibonacci(n): if n<0: print("Incorrect Input") elif n==0: return 0 elif n==1: return 1 else: return recursive_fibonacci(n1) + recursive_fibonacci(n2) # In[5]: ### Test your code using the function call below. recursive_fibonacci(3) # #### Lambda function # In[33]: def sigmoid(x): sigmoid = 1 / (1 + np.exp(x)) return sigmoid ## Implement the above sigmoid function using python lambda functions ## YOUR CODE STARTS HERE sigmoid_lambda = lambda x : sigmoid(x) ## YOUR CODE ENDS HERE # In[34]: ## Check you implementation by running this cell (should return True) sigmoid(1) == sigmoid_lambda(1) # #### List Comprehension # In[1]: div_by_four = [] for number in range(200): if number%4 == 0: div_by_four.append(number) ## Implement the above function using list comprehensions ## YOUR CODE STARTS HERE div_by_four_lc = div_by_four ## YOUR CODE ENDS HERE # In[2]: ## Check you implementation by running this cell (should return True) div_by_four == div_by_four_lc # #### Python map keyword # Using the lambda functions and python's map keyword apply sigmoid on a list of numbers from 0 to 99. Store the output in a list and print it. # # **Observe what happens to the value of sigmoid as the value of x increases. Do you see any trend?** # In[46]: ## YOUR CODE STARTS HERE out = map(sigmoid_lambda, div_by_four) ## YOUR CODE ENDS HERE print(list(out)) # # Numpy Basics: # # #### Creating a numpy array with particular value # # In[41]: # Create a numpy array with size 2*2 with all elements as 8. # YOUR CODE STARTS HERE arr = np.array([[2, 4, 6], [6, 8, 10]], np.int32) # YOUR CODE ENDS HERE # #### Basic Numpy operations: # In[48]: #Create a numpy array of size 3*3 with random values rand_arr = np.random.rand(3,3) print(rand_arr) #YOUR CODE STARTS HERE #YOUR CODE ENDS HERE # In[61]: #Find the sum of all the elements, mean, maximum and minimum value of the numpy array defined above #YOUR CODE STARTS HERE arr_sum = np.sum([rand_arr]) arr_mean = np.mean([rand_arr]) arr_max = np.max([rand_arr]) arr_min = np.min([rand_arr]) print("Sum =", arr_sum, "\n" , "Mean=", arr_mean, "\n" ,"Max=", arr_max, "\n" ,"Min=", arr_min) #YOUR CODE ENDS HERE # #### Reshaping and Indexing of Numpy Array: # In[92]: #create a 1D numpy array of shape 35 with values 1,2,3..,35 #YOUR CODE STARTS HERE y = np.arange(35) print(y) #YOUR CODE ENDS HERE # In[98]: # Reshape it as 2D array of shape (5,7) #YOUR CODE STARTS HERE y = y.reshape(5,7) #YOUR CODE ENDS HERE print(y.shape) print(y) # In[96]: # Extract all the elements from 2nd and 3rd row #YOUR CODE STARTS HERE y_row = y[2:4,:] #YOUR CODE ENDS HERE y_row # In[108]: # Extract all the elements from 3rd and 5th and 7th column #YOUR CODE STARTS HERE y_column = y[:,2:7:2] #YOUR CODE ENDS HERE print(y_column) # #### Horizontal and vertical stacking of numpy array # In[111]: ## horizontal and vertical stacking of 1D arrays a = np.array([4.,2.]) b = np.array([3.,8.]) #YOUR CODE STARTS HERE # Horizontal stacking h_stack = np.hstack((a,b)) # Vertical Stacking v_stack = np.vstack((a,b)) #YOUR CODE ENDS HERE print(h_stack) print(v_stack) # #### <code>argmin</code> and <code>argmax</code> in numpy array # In[112]: # Define an array arr = np.array([[5,12,51,25] ,[25,29,2,27]]) # YOUR CODE STARTS HERE #Find the position of maximum and minimum value of above array max_idx = np.argmax(arr) min_idx = np.argmax(arr) print (max_idx,min_idx) #Find the indices of maximum and minimum value along each of its columns. max_col = np.argmax(arr,axis = 0) min_col = np.argmin(arr,axis = 0) print (max_col,min_col) #Find the indices of maximum and minimum value along each of the its rows. max_row = np.argmax(arr,axis = 1) min_row = np.argmin(arr,axis = 1) print (max_row,min_row) #YOUR CODE ENDS HERE
 This topic was modified 2 years, 8 months ago by Abhishek Tyagi.
 This topic was modified 2 years, 8 months ago by Abhishek Tyagi.


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