vaibhavkumar049

" plugins
let need_to_install_plugins = 0
if empty(glob('~/.vim/autoload/plug.vim'))
    silent !curl -fLo ~/.vim/autoload/plug.vim --create-dirs
        \ https://raw.githubusercontent.com/junegunn/vim-plug/master/plug.vim
    let need_to_install_plugins = 1
endif

call plug#begin()
Plug 'tpope/vim-sensible'

vaibhavkumar049

import time
from selenium import webdriver

# Do not use this without their permission
# change website url
# chnage saving location in line 32
# change email/password
# change range in line 20 according to your book length
# start my code
from selenium.common.exceptions import NoSuchElementException

vaibhavkumar049

#!/usr/bin/env python

import os
import requests
import uuid
from random import randint
from uuid import uuid4

def read_in_chunks(file_object, chunk_size=65536):
    while True:

vaibhavkumar049

Copying data from Firebase Realtime Database to a Google Sheet in real time via Cloud Functions

If you're trying to do this, you came to the right place!

Watch this code work in real time: https://twitter.com/CodingDoug/status/940022568089554944

See also this gist for copying in the other direction: https://gist.github.com/CodingDoug/44ad12f4836e79ca9fa11ba5af6955f7

Setup

vaibhavkumar049

class FirstNetwork_v3(nn.Module):
  
  def __init__(self):
    super().__init__()
    torch.manual_seed(0)
    self.net = nn.Sequential(
        nn.Linear(2, 1024*4), 
        nn.Sigmoid(), 
        nn.Linear(1024*4, 4), 
        nn.Softmax()

vaibhavkumar049

device = torch.device("cuda")

X_train=X_train.to(device)
Y_train=Y_train.to(device)
fn = FirstNetwork_v2()
fn.to(device)
tic = time.time()
print('Final loss', fit_v2(X_train, Y_train, fn, opt, loss_fn))
toc = time.time()
print('Time taken', toc - tic)

vaibhavkumar049

fn = FirstNetwork_v2()
loss_fn = F.cross_entropy
opt = optim.SGD(fn.parameters(), lr=1)
fit_v2(X_train, Y_train, fn, opt, loss_fn)

vaibhavkumar049

def fit_v2(x, y, model, opt, loss_fn, epochs = 1000):
  
  for epoch in range(epochs):
    loss = loss_fn(model(x), y)

    loss.backward()
    opt.step()
    opt.zero_grad()
    
  return loss.item()

vaibhavkumar049

fn = FirstNetwork_v2()
fit_v1()

vaibhavkumar049

class FirstNetwork_v2(nn.Module):
  
  def __init__(self):
    super().__init__()
    torch.manual_seed(0)
    self.net = nn.Sequential(
        nn.Linear(2, 2), 
        nn.Sigmoid(), 
        nn.Linear(2, 4), 
        nn.Softmax()