Aditya K 97k

TestService

Windows Service with Python 3.5 and pyinstaller

Requirements

Python 3.5.x
Visual C++ Build Tools 2015
PyInstaller 3.2

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

Octave CheatSheet

GNU Octave is a high-level interpreted language, primarily intended for numerical computations.
(via GNU Octave)

Hint: I also mad an octave docset for Dash: https://github.com/obstschale/octave-docset

	"""
	Create train, valid, test iterators for CIFAR-10 [1].
	Easily extended to MNIST, CIFAR-100 and Imagenet.

	[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
	"""

	import torch
	import numpy as np

	class SyncAsyncDecoratorFactory:
	"""
	Factory creates decorator which can wrap either a coroutine or function.
	To return something from wrapper use self._return
	If you need to modify args or kwargs, you can yield them from wrapper
	"""
	def __new__(cls, args, *kwargs):
	instance = super().__new__(cls)
	# This is for using decorator without parameters
	if len(args) == 1 and not kwargs and (inspect.iscoroutinefunction(args[0]) or inspect.isfunction(args[0])):

	import torch
	import torch.nn as nn
	import numpy as np
	import torch.optim as optim
	from torch.autograd import Variable

	# (1, 0) => target labels 0+2
	# (0, 1) => target labels 1
	# (1, 1) => target labels 3
	train = []

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)