chmodsss

import subprocess, datetime, time
welcome = "Hello Siva, welcome"
s = '-s 140'
g = '-g 0.8'
f = '-vf3'
a = '-a 150'

day = datetime.date.today().strftime('%A')
month = datetime.date.today().strftime('%B')
date = datetime.date.today().strftime('%d')

chmodsss

'''
Grabbing current tweets using python's tweepy module
'''
from tweepy import Stream
from tweepy import OAuthHandler
from tweepy.streaming import StreamListener
import logging

# get the twitter keys from https://dev.twitter.com/
ckey    = ***

chmodsss

#!/bin/sh
SAVEIFS=$IFS
IFS=$(echo -en "\n\b")
SRC_PATH=path/to/source/folder
DST_PATH=path/to/destination/folder
for src_dir in $(ls $SRC_PATH)
	do 
		for src_file in $(ls $SRC_PATH/$src_dir)
			do 
				src_fpath=$SRC_PATH/$src_dir/$src_file

chmodsss

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

%matplotlib inline

dig = load_digits()
plt.gray()
plt.matshow(dig.images[25])

chmodsss

import pandas as pd
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import RMSprop, Adadelta, Adam
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

dig = load_digits()
onehot_target = pd.get_dummies(dig.target)
x_train, x_val, y_train, y_val = train_test_split(dig.data, onehot_target, test_size=0.1, random_state=20)

chmodsss

import pandas as pd
import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

dig = load_digits()
onehot_target = pd.get_dummies(dig.target)
x_train, x_val, y_train, y_val = train_test_split(dig.data, onehot_target, test_size=0.1, random_state=20)

def sigmoid(s):

chmodsss

chmodsss

import string

puncts = string.punctuation.replace('.','')
punct = str.maketrans('','', puncts)

data = open('story.txt','r').read()

# data pre-processing only for alphabet strings
def clean(xx):
    return ' '.join(x for x in xx.split() if not x.isnumeric())

chmodsss

import collections
import pandas as pd
import numpy as np

sents = [s for s in cdata.split()]

vocab = sorted(collections.Counter(sents))
vocab2idx = {v:idx for idx,v in enumerate(vocab)}
idx2vocab = {idx:v for idx,v in enumerate(vocab)}

chmodsss

class Discriminator(nn.Module):
    def __init__(self):
        super().__init__()
        
        ip_emb = 784
        emb1 = 256
        emb2 = 128
        out_emb = 1
        
        self.layer1 = nn.Sequential(