multiplication-neural-network.py

import numpy as np
import random

X = []
logx=[]
logy=[]

for i in range(10000):
    X.append([random.randint(1,1000), random.randint(1,1000)])
    logx.append([np.log(X[i][0]), np.log(X[i][1])])
    logy.append([logx[i][0] + logx[i][1]])


logx = np.array(logx)
logy = np.array(logy).reshape(-1,1)


#### ml algo #######
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(logx, logy,test_size=0.2)


from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)

lr.score(X_test, y_test)


############### neural network

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()

model.add(Dense(output_dim=1, input_dim=2, activation='relu'))

model.compile('adam', 'mean_squared_error')

model.fit(logx, logy, epochs=20, batch_size=12)


test = np.array([np.log(10), np.log(5)]).reshape(-1,1).T
pred = model.predict(test)
pred = exp(pred)


#### ml algo
pred = lr.predict(test)
pred=exp(pred)