DhavalThkkar

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 18 18:08:03 2021

@author: thakkar
"""

# Tekore imports
import tekore as tk

DhavalThkkar

# ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples

# If not running interactively, don't do anything
[ -z "$PS1" ] && return

# don't put duplicate lines in the history. See bash(1) for more options
# ... or force ignoredups and ignorespace
HISTCONTROL=ignoredups:ignorespace

DhavalThkkar

import pandas as pd
import numpy as np
import sys
from datetime import datetime

# Slearn for easy label encoding, textblob for senitment reference
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import StratifiedKFold
from sklearn.cross_validation import train_test_split

DhavalThkkar

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May  7 11:52:37 2017
@author: thakkar_
"""
# Importing Libraries
import numpy as np
import pandas as pd
import seaborn as sns

DhavalThkkar

kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=1)    
for train, test in kfold.split(X, y):
    # Initial tests appear to indicate no overfitting, dropout layer unneccesary
    ann = Sequential()    
    ann.add(Dense(500, activation='tanh', kernel_initializer='random_normal', input_shape=(X[train].shape[1],)))
    ann.add(Dropout(rate=0.2))
    ann.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal'))
    ann.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
    ann.fit(X[train], y[train], batch_size=250, epochs=150, verbose=0)

DhavalThkkar

# coding: utf-8

# In[ ]:




# In[ ]:

DhavalThkkar

from tkinter import *
import tkinter
from tkinter import ttk
import socket


def calculate(*args):
    try:
        str = (domain_name.get())
        ip_add = socket.gethostbyname(str)