KhyatiMahendru

# importing requirements
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import adam

# alpha = 0.001 as given in the lr parameter in adam() optimizer

# build the model
model_alpha1 = Sequential()
model_alpha1.add(Dense(50, input_dim=2, activation='relu'))

KhyatiMahendru

def update_weights_Hinge(m1, m2, b, X1, X2, Y, learning_rate):
    m1_deriv = 0
    m2_deriv = 0
    b_deriv = 0
    N = len(X1)
    for i in range(N):
        # Calculate partial derivatives
        if Y[i]*(m1*X1[i] + m2*X2[i] + b) <= 1:
          m1_deriv += -X1[i] * Y[i]
          m2_deriv += -X2[i] * Y[i]

KhyatiMahendru

def update_weights_BCE(m1, m2, b, X1, X2, Y, learning_rate):
    m1_deriv = 0
    m2_deriv = 0
    b_deriv = 0
    N = len(X1)
    for i in range(N):
        s = 1 / (1 / (1 + math.exp(-m1*X1[i] - m2*X2[i] - b)))
        
        # Calculate partial derivatives
        m1_deriv += -X1[i] * (s - Y[i])

KhyatiMahendru

def update_weights_Huber(m, b, X, Y, delta, learning_rate):
    m_deriv = 0
    b_deriv = 0
    N = len(X)
    for i in range(N):
        # derivative of quadratic for small values and of linear for large values
        if abs(Y[i] - m*X[i] - b) <= delta:
          m_deriv += -X[i] * (Y[i] - (m*X[i] + b))
          b_deriv += - (Y[i] - (m*X[i] + b))
        else:

KhyatiMahendru

def update_weights_MAE(m, b, X, Y, learning_rate):
    m_deriv = 0
    b_deriv = 0
    N = len(X)
    for i in range(N):
        # Calculate partial derivatives
        # -x(y - (mx + b)) / |mx + b|
        m_deriv += - X[i] * (Y[i] - (m*X[i] + b)) / abs(Y[i] - (m*X[i] + b))

        # -(y - (mx + b)) / |mx + b|

KhyatiMahendru

def update_weights_MSE(m, b, X, Y, learning_rate):
    m_deriv = 0
    b_deriv = 0
    N = len(X)
    for i in range(N):
        # Calculate partial derivatives
        # -2x(y - (mx + b))
        m_deriv += -2*X[i] * (Y[i] - (m*X[i] + b))

        # -2(y - (mx + b))

KhyatiMahendru

import requests
from bs4 import BeautifulSoup
import csv

file = open('Art-galleries-in-Malta.csv', 'w')
f = csv.writer(file)
f.writerow(['Name', 'Phone_no', 'Website', 'Address', 'State/Province'])


country = 'Malta'