<filename>DeepLearning UK Lottery -Euro-Hotpick-Latest.py
#!/usr/bin/env python
# coding: utf-8

# In[3]:


# -*- coding: utf-8 -*-
"""
Created on 10th Feb 2021

@author: <NAME>
"""

#importing libraries
import tensorflow as tf #tensorflow lib for defining the architecture of neural network
import pandas as pd #pandas lib we will use to load the dataset from excel (.csv) file
import numpy as np # numpy array to deal with arrays
import random # random lib to generate random numbers

#%%
'''
    Create input data set according to output numbers
'''
numbers = [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           ]

#%%
Data = pd.read_csv('hotpic.csv') #read Data from the file
'''
separate the five output value into five different variable
so that we can pass it while training out machine learning model

'''
num1Output = Data['Ball 1'] 
num2Output = Data['Ball 2']
num3Output = Data['Ball 3']
num4Output = Data['Ball 4']
num5Output = Data['Ball 5']

numbers = np.array(numbers)
numbers.shape # checking the shape of out training data set

#%%
'''
define the NN architecture for number 1
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001 

'''
num1 = tf.keras.Sequential();

num1.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num1.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num1.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num1.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num1.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num1.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num1.fit(numbers,num1Output, epochs=10000)
#%%
'''
define the NN architecture for number 2
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001 

'''

num2 = tf.keras.Sequential();

num2.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num2.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num2.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num2.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num2.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num2.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num2.fit(numbers,num2Output, epochs=10000)

#%%
'''
define the NN architecture for number 3
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001

'''

num3 = tf.keras.Sequential();

num3.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num3.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num3.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num3.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num3.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num3.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num3.fit(numbers,num3Output, epochs=10000)

#%%
'''
define the NN architecture for number 4
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001 

'''

num4 = tf.keras.Sequential();

num4.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num4.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num4.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num4.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num4.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num4.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num4.fit(numbers,num4Output, epochs=10000)
#%%
'''
define the NN architecture for number 5
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001

'''

num5 = tf.keras.Sequential();

num5.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num5.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num5.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num5.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num5.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num5.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num5.fit(numbers,num5Output, epochs=10000)
#%%

#%%
y = []
while(True):
    num = random.randint(1,50)
    if num in y:
        pass
    else:
        y.append(num)
        if len(y)==50:
            break
test = [y]
test = np.array(test)
p1 = num1.predict(test)
p2 = num2.predict(test)
p3 = num3.predict(test)
p4 = num4.predict(test)
p5 = num5.predict(test)


#%%
print('Num1 Maybe: ' ,np.argmax(p1)+1)
print('Num2 Maybe: ' , np.argmax(p2)+1)
print('Num3 Maybe: ' , np.argmax(p3)+1)
print('Num4 Maybe: ' , np.argmax(p4)+1)
print('Num5 Maybe: ' , np.argmax(p5)+1)


# In[ ]:


#Prediction with LR 0.001
#Num1 Maybe:  2
#Num2 Maybe:  16
#Num3 Maybe:  17
#Num4 Maybe:  42
#Num5 Maybe:  51

#Prediction with LR 0.0005

#Num1 Maybe:  2
#Num2 Maybe:  20
#Num3 Maybe:  30
#Num4 Maybe:  40
#Num5 Maybe:  51


<filename>DeepLearning UK Lottery -Euro-Hotpick-Latest.py
#!/usr/bin/env python
# coding: utf-8

# In[3]:


# -*- coding: utf-8 -*-
"""
Created on 10th Feb 2021

@author: <NAME>
"""

#importing libraries
import tensorflow as tf #tensorflow lib for defining the architecture of neural network
import pandas as pd #pandas lib we will use to load the dataset from excel (.csv) file
import numpy as np # numpy array to deal with arrays
import random # random lib to generate random numbers

#%%
'''
    Create input data set according to output numbers
'''
numbers = [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
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           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],
           ]

#%%
Data = pd.read_csv('hotpic.csv') #read Data from the file
'''
separate the five output value into five different variable
so that we can pass it while training out machine learning model

'''
num1Output = Data['Ball 1'] 
num2Output = Data['Ball 2']
num3Output = Data['Ball 3']
num4Output = Data['Ball 4']
num5Output = Data['Ball 5']

numbers = np.array(numbers)
numbers.shape # checking the shape of out training data set

#%%
'''
define the NN architecture for number 1
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001 

'''
num1 = tf.keras.Sequential();

num1.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num1.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num1.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num1.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num1.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num1.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num1.fit(numbers,num1Output, epochs=10000)
#%%
'''
define the NN architecture for number 2
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001 

'''

num2 = tf.keras.Sequential();

num2.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num2.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num2.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num2.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num2.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num2.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num2.fit(numbers,num2Output, epochs=10000)

#%%
'''
define the NN architecture for number 3
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001

'''

num3 = tf.keras.Sequential();

num3.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num3.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num3.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num3.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num3.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num3.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num3.fit(numbers,num3Output, epochs=10000)

#%%
'''
define the NN architecture for number 4
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001 

'''

num4 = tf.keras.Sequential();

num4.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num4.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num4.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num4.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num4.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num4.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num4.fit(numbers,num4Output, epochs=10000)
#%%
'''
define the NN architecture for number 5
we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60
neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer.
in last layer we use softmax activation function for classification
we use adam as an optimizer with learning rate of 0.001

'''

num5 = tf.keras.Sequential();

num5.add(tf.keras.layers.Dense(units=64, activation=tf.nn.relu, input_dim=50))
num5.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
num5.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
num5.add(tf.keras.layers.Dense(units=512, activation=tf.nn.relu))
num5.add(tf.keras.layers.Dense(units=60, activation=tf.nn.softmax))
num5.compile(loss=tf.keras.metrics.sparse_categorical_crossentropy, optimizer=tf.keras.optimizers.Adam(0.0005),metrics=['accuracy'])
num5.fit(numbers,num5Output, epochs=10000)
#%%

#%%
y = []
while(True):
    num = random.randint(1,50)
    if num in y:
        pass
    else:
        y.append(num)
        if len(y)==50:
            break
test = [y]
test = np.array(test)
p1 = num1.predict(test)
p2 = num2.predict(test)
p3 = num3.predict(test)
p4 = num4.predict(test)
p5 = num5.predict(test)


#%%
print('Num1 Maybe: ' ,np.argmax(p1)+1)
print('Num2 Maybe: ' , np.argmax(p2)+1)
print('Num3 Maybe: ' , np.argmax(p3)+1)
print('Num4 Maybe: ' , np.argmax(p4)+1)
print('Num5 Maybe: ' , np.argmax(p5)+1)


# In[ ]:


#Prediction with LR 0.001
#Num1 Maybe:  2
#Num2 Maybe:  16
#Num3 Maybe:  17
#Num4 Maybe:  42
#Num5 Maybe:  51

#Prediction with LR 0.0005

#Num1 Maybe:  2
#Num2 Maybe:  20
#Num3 Maybe:  30
#Num4 Maybe:  40
#Num5 Maybe:  51


#!/usr/bin/env python # coding: utf-8 # In[3]: # -*- coding: utf-8 -*- Created on 10th Feb 2021  @author: <NAME> #importing libraries #tensorflow lib for defining the architecture of neural network #pandas lib we will use to load the dataset from excel (.csv) file # numpy array to deal with arrays # random lib to generate random numbers #%% Create input data set according to output numbers #%% #read Data from the file separate the five output value into five different variable so that we can pass it while training out machine learning model # checking the shape of out training data set #%% define the NN architecture for number 1 we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60 neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer. in last layer we use softmax activation function for classification we use adam as an optimizer with learning rate of 0.001 #%% define the NN architecture for number 2 we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60 neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer. in last layer we use softmax activation function for classification we use adam as an optimizer with learning rate of 0.001 #%% define the NN architecture for number 3 we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60 neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer. in last layer we use softmax activation function for classification we use adam as an optimizer with learning rate of 0.001 #%% define the NN architecture for number 4 we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60 neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer. in last layer we use softmax activation function for classification we use adam as an optimizer with learning rate of 0.001 #%% define the NN architecture for number 5 we have 5 layers in this architecture - we use 64, 128, 256, 512 and 60 neurons in our layers respectively, we use relu as aa activation finction in our all layers except last layer. in last layer we use softmax activation function for classification we use adam as an optimizer with learning rate of 0.001 #%% #%% #%% # In[ ]: #Prediction with LR 0.001 #Num1 Maybe:  2 #Num2 Maybe:  16 #Num3 Maybe:  17 #Num4 Maybe:  42 #Num5 Maybe:  51 #Prediction with LR 0.0005 #Num1 Maybe:  2 #Num2 Maybe:  20 #Num3 Maybe:  30 #Num4 Maybe:  40 #Num5 Maybe:  51