anujdutt9

# Step1: Training Data and Preprocessing
#       a) Tokenize the Input text (sentence to words)
#       b) Form the Vocabulary and remove Infrequent words
#       c) Add "Start" and "End" Tokens to the sentences

# Vocabulary Size: 8000 words
vocab_size = 8000

# Token to replace the infrequent words
unknown_token = 'Unknown_Token'

anujdutt9

# Recurrent Neural Network based Language Model
# Aim: To train the NN so that it can generate random text using words from Vocabulary which is
#      grammatically correct and makes some sense.
#      The vocabulary is made using the input raw text data

# Import Dependencies
import os
import sys
import csv
import itertools

anujdutt9

# Basic 3 Layer Feed Forward Neural Network with Back Propagation from Scratch

import numpy as np
from scipy import optimize
import matplotlib.pyplot as plt

# Sample Input Data
x = np.array(([3,5],[5,1],[10,2]), dtype=float)
y = np.array(([75],[82],[93]),dtype=float)