bentrevett

kevinzakka

"""
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

anatoly-kussul

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])):

bartolsthoorn

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 = []

guillaumevincent

TestService

Windows Service with Python 3.5 and pyinstaller

Requirements

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

karpathy

"""
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)

PurpleBooth

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

obstschale

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

Basics