# Why isn't my Neural Network based calculator working?

I am playing around with neural networks in Tensorflow and I figured an interesting test would be whether I can write a calculator using a Tensorflow Neural Network.

I started with simple addition and it kinda worked (so given 2, 4 it would get around 5.9 or 6.1).

Then I wanted to add the ability to calculate using "+", "-", and "*".

Here is the code I came up with in the end:

import numpy as np
import tensorflow as tf
from random import randrange

def generate_input(size):
nn_input = []
for i in range(0,size):
symbol = float(randrange(3))
nn_input.append([
float(randrange(1000)),
float(randrange(1000)),
1 if symbol == 0 else 0,
1 if symbol == 1 else 0,
1 if symbol == 2 else 0,
])
return nn_input

def generate_output(input_data):
return [[generate_single_output(i)] for i in input_data]

def generate_single_output(input_data):
plus = input_data[2]
minus = input_data[3]
multiplication = input_data[4]

if (plus):
return input_data[0] + input_data[1]

if (minus):
return input_data[0] - input_data[1]

if (multiplication):
return input_data[0] * input_data[1]

def user_input_to_nn_input(user_input):
symbol = user_input[1]
return np.array([[
float(user_input[0]),
float(user_input[2]),
1 if symbol == '+' else 0,
1 if symbol == '-' else 0,
1 if symbol == '*' else 0,
]])

if __name__ == '__main__':
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(5,)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1),
])

model.compile(tf.keras.optimizers.RMSprop(0.001), loss=tf.keras.losses.MeanSquaredError())

input_data = np.array(generate_input(10000))
output_data = np.array(generate_output(input_data))

model.fit(input_data, output_data, epochs=20)

while True:
user_calculation = input("Enter expression (e.g. 2 + 3):")
user_input = user_calculation.split()
nn_input = user_input_to_nn_input(user_input)
print(model.predict(nn_input)[0][0])



The idea is built on this tutorial: https://www.tensorflow.org/tutorials/keras/basic_regression

The input is 5 fields: number 1, number 2, plus?, minus?, multiplication?

Where the last 3 are simply 1 or 0 depending on whether that is the calculation I am trying to do.

As an output for say [1,4,1,0,0] I would expect 1 + 4 = 5 for [1,4,0,1,0] I would expect 1 - 4 = -3 etc.

For some reason though the numbers I am getting are completely off and seem random.

Basically I am trying to understand what I went wrong? The data being input to the NN seems correct and the model is based on the model used in the tutorial I quoted (and the problems seem fairly similar so I expect if one would work the other would too).

## 1 Answer

A neural network is not good at selecting a function based on those 3 input parameters, because of the way a neuron is setup.

What you should do is either make a neural network for each operation, or use different input neurons for each operation. E.g. 2 input neurons for the addition operation, 2 for the multiplication, and 2 for the minus. 6 inputs in total of which 4 will always be 0.

This will make it easier for the neural network to calculate the result.

• Thanks :) That seems to work to an extent. Now addition and subtraction work but multiplication doesn't work at all. Why exactly isn't a neural network not good at selecting a function the way I had it? Also any idea why multiplication wouldn't be working here? Sep 13, 2019 at 22:52
• I looked into it and it seems a simple multiplication function is very hard to approximate for a neural network with certain activation funtions (such as relu). And this is only learning multiplication, not trying to do the other operations in the same network. link:sololearn.com/Discuss/1467062/… Sep 16, 2019 at 8:14
• Thanks Lustwelpintje, that's what I've discovered too. I'll continue looking into it and see if I can figure out a way to do it Sep 16, 2019 at 11:54