davecg

#!/usr/bin/env python3

'''
This will convert Keras 1 model weights and configurations
to their Keras 2 equivalent.  Please note that all optimizer
configuration is ignored with this script and models will 
need to be recompiled.
'''

import h5py

spro

import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
from torch import optim
import numpy as np
import math, random

# Generating a noisy multi-sin wave 

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

khardix

#!/usr/bin/env python3.6

import asyncio
from contextlib import closing

import aiohttp
import tqdm


async def download(session, url, progress_queue):

f0k

MIT License

Copyright (c) 2017 Jan Schlüter

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

bmcfee

carlthome

markemus

"""Easily save tf.data.Datasets as tfrecord files, and restore tfrecords as Datasets.

The goal of this module is to create a SIMPLE api to tfrecords that can be used without
learning all of the underlying mechanics.

Users only need to deal with 2 functions:
save(dataset)
dataset = load(tfrecord, header)

To make this work, we create a .header file for each tfrecord which encodes metadata