rouseguy

%%time

# -*- coding: utf-8 -*-
'''An implementation of sequence to sequence learning for performing addition
Input: "535+61"
Output: "596"
Padding is handled by using a repeated sentinel character (space)
Input may optionally be inverted, shown to increase performance in many tasks in:
"Learning to Execute"
http://arxiv.org/abs/1410.4615

rouseguy

baseurl = "the github.io link"
title = "title of the blog"
theme = "hugo-theme-nix"
languageCode = "en-us"
#disqusShortname = "your_disqus_shortname"

[menu]
  [[menu.header]]
    name = "blog"
    weight = 0

rouseguy

execute pathogen#infect()
syntax on
filetype plugin indent on
syntax enable
set background=light
let g:solarized_contrast="high"
colorscheme solarized
set guifont=Hack:h20

set showmatch

rouseguy

# If you come from bash you might have to change your $PATH.
# export PATH=$HOME/bin:/usr/local/bin:$PATH

# Path to your oh-my-zsh installation.
export ZSH=/Users/bsubrama/.oh-my-zsh

# Set name of the theme to load. Optionally, if you set this to "random"
# it'll load a random theme each time that oh-my-zsh is loaded.
# See https://github.com/robbyrussell/oh-my-zsh/wiki/Themes
ZSH_THEME="robbyrussell"

rouseguy

import numpy as np

def fizzbuzz(number):
    if number % 15 == 0:
        return np.array(["fizzbuzz"], dtype="object")
    elif number % 5 == 0:
        return np.array(["buzz"], dtype="object")
    elif number % 3 == 0:
        return np.array(["fizz"], dtype="object")
    else:

rouseguy

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
from tensorflow.examples.tutorials.mnist import input_data
import os

# Helper to get the labels for each class of Fashion Mnist
def fashion_mnist_label(): 
    labels = {

rouseguy

import numpy as np

def input_generate_data(data, SEQ_LENGTH=SEQ_LENGTH, VOCAB_SIZE=VOCAB_SIZE, char_to_ix = char_to_ix):
    X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))

    y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
    
    for i in range(0, int(len(data)/SEQ_LENGTH)):
        X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
        X_sequence_ix = [char_to_ix[value] for value in X_sequence]

rouseguy

import numpy as np

def input_generate_data(data, SEQ_LENGTH=20, VOCAB_SIZE=30, char_to_ix = {}):
    X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))

    y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
    
    for i in range(0, int(len(data)/SEQ_LENGTH)):
        X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
        X_sequence_ix = [char_to_ix[value] for value in X_sequence]

rouseguy

X = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))

y = np.zeros((int(len(data)/SEQ_LENGTH), SEQ_LENGTH, VOCAB_SIZE))
for i in range(0, int(len(data)/SEQ_LENGTH)):
    X_sequence = data[i*SEQ_LENGTH:(i+1)*SEQ_LENGTH]
    X_sequence_ix = [char_to_ix[value] for value in X_sequence]
    input_sequence = np.zeros((SEQ_LENGTH, VOCAB_SIZE))
    for j in range(SEQ_LENGTH):
        input_sequence[j][X_sequence_ix[j]] = 1.
    X[i] = input_sequence

rouseguy

import numpy as np
import pandas as pd

import altair as alt
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Helper to get the labels for each class of Fashion Mnist
def fashion_mnist_label(): 
    labels = {