vinupriyesh

"""
Performing OR, AND, XOR operation using logistic regression which is a very simple neural network without any hidden layer
This model cannot predict XOR just like any other logistic regression as the XOR cannot be seperated by a straight line
@Author : Vinu Priyesh V.A.
"""
import numpy as np

#Compute functions for OR, AND, XOR, this will be used to generate the test set and to validate our results
def compute(x,m,label):
	if(label == "XOR"):

vinupriyesh

"""
A simple neural network with 1 hidden layer and 4 neurons, an enhancement to the previous logistic regression to compute the XOR 
@Author : Vinu Priyesh V.A.
"""
import numpy as np

#Compute functions for OR, AND, XOR, this will be used to generate the test set and to validate our results
def compute(x,m,label):
	if(label == "XOR"):
		return np.logical_xor(x[0,:],x[1,:]).reshape(1,m).astype(int)

vinupriyesh

'''
Dataset is taken from http://www.cs.toronto.edu/~kriz/cifar.html
2 layer neural network to categorize a 32x32 pixel color image in 10 different categories
@Author : Vinu Priyesh V.A.
'''
import matplotlib.pyplot as plt
import numpy as np

def unpickle(file):
	import pickle

vinupriyesh

vinupriyesh

vinupriyesh

• Parameters can be passed by value or reference
• ref or out keyword makes it pass by reference
• const is always static
• constants and method names are PascalCase
• Open braces({) are NOT placed in same line
• foreach (element in iterable-item)