sgugger

from torch.optim.optimizer import Optimizer, required

class LARS(Optimizer):

    def __init__(self, params, lr=required, momentum=0, dampening=0,
                 weight_decay=0, nesterov=False, eta=0.001):
        if lr is not required and lr < 0.0:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if momentum < 0.0:
            raise ValueError("Invalid momentum value: {}".format(momentum))

tokestermw

import torch, torch.nn as nn
from torch.autograd import Variable

text = ['BOS', 'How', 'are', 'you', 'EOS']
seq_len = len(text)
batch_size = 1
embedding_size = 1
hidden_size = 1
output_size = 1

dshulchevskiy

"""
Sublime shortcuts for jupyter notebook
"""

from jupyter_core.paths import jupyter_config_dir
import os
custom_js_path = os.path.join(jupyter_config_dir(), 'custom', 'custom.js')
custom_path = os.path.join(jupyter_config_dir(), 'custom')

dhpollack

import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.optim import lr_scheduler
import torch.utils.data as data
from torch.nn.utils.rnn import pack_padded_sequence as pack, pad_packed_sequence as unpack
import torchaudio
import torchaudio.transforms as tat

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

cbaziotis

# This is inspired by the fantastic guide https://github.com/saiprashanths/dl-setup
# I have just updated the python-related commands so that everything works in Python 3.
# Tested on Xubuntu 16.04.

# First of all let's update the repos:
sudo apt-get update 

# Only if you have a CUDA-compatible Nvidia card, install CUDA.
# Check on the Nvidia website what is the latest driver version which supports your card. 
# At the time of this writing it was 367.

spro

import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, n_layers=1):
        super(RNN, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

fchollet

"""Downsized version of Xception, without residual connections.
"""
from __future__ import print_function
from __future__ import absolute_import

from keras.models import Model
from keras.layers import Dense
from keras.layers import Input
from keras.layers import BatchNormalization
from keras.layers import Activation

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 

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda