nlgranger

FROM nvidia/cuda:8.0-cudnn5-devel

RUN apt-get update
RUN apt-get upgrade -y
RUN apt-get install -y --no-install-recommends openssl ca-certificates \
	git-core cmake curl wget unzip gfortran libreadline-dev ncurses-dev \
	libgoogle-glog-dev libboost-dev libboost-thread-dev libopenblas-dev

# Core Torch packages
RUN git clone https://github.com/torch/luajit-rocks.git

nlgranger

FROM nvidia/cuda:8.0-cudnn5-devel

WORKDIR /root

RUN apt-get update
RUN apt-get upgrade -y
RUN apt-get install -y --no-install-recommends openssl ca-certificates \
	git-core cmake curl wget unzip gfortran libreadline-dev ncurses-dev \
	libgoogle-glog-dev libboost-dev libboost-thread-dev libopenblas-dev

nlgranger

$ nosetests .
Using cuDNN version 7005 on context None                                                                                                                       
Preallocating 9494/11170 Mb (0.850000) on cuda1                                                                                                                
Mapped name None to device cuda1: GeForce GTX 1080 Ti (0000:03:00.0)                                                                                           
EEE                                                                                                                                                            
======================================================================                                                                                         
ERROR: test_forward_backward (test_ctc.TestCTC)                                                                                                                
--------------------------------------------------

nlgranger

from lasagne.layers import BatchNormLayer, NonlinearityLayer, Conv2DLayer, \
    DropoutLayer, ElemwiseSumLayer, GlobalPoolLayer
from lasagne.nonlinearities import rectify
from lasagne.init import HeNormal


def wide_resnet(l_in, d, k, dropout=0.):
    """Build a Wide-Resnet WRN-d-k [Zagoruyko2016]_

    Parameters

nlgranger

# the sphinx extension 'sphinx.ext.viewcode' links documentation to an online 
# code repository but requires to bind the code to the url through a user 
# specific `linkcode_resolve` function. This implementation should be fairly 
# generic and easily adaptable.
#
# License: Public Domain, CC0 1.0 Universal (CC0 1.0) 

import sys
import os
import subprocess

nlgranger

import numpy as np
import tensorflow as tf

from procnet.utils import sensible_dir
from experiments.SpatialTransformerNetwork.model import spatial_transformer
from experiments.SpatialTransformerNetwork import dataset


def build_model_layers(inputs, nclasses, is_training):
    initializer = tf.contrib.layers.xavier_initializer()

nlgranger

# Implementation of Matching Networks for Tensorflow.
# O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, and others,
# “Matching networks for one shot learning,” in Advances in Neural Information
# Processing Systems, 2016, pp. 3630–3638.

# Copyright 2018 Nicolas Granger <[email protected]>
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

nlgranger

# like https://gmarti.gitlab.io/ml/2017/09/07/how-to-sort-distance-matrix.html
# but using just 4 lines of code thanks to 'optimal_ordering' argument 
# added to scipy.cluster.hierarchy.linkage

import matplotlib.pyplot as plt
import numpy as np
from scipy.cluster.hierarchy import linkage

# distance matrix
m = np.zeros((500, 500))

nlgranger

#include <stdlib.h>
#include <stdio.h>
#include <errno.h>
#include <libavutil/avutil.h>
#include <libavcodec/avcodec.h>
#include <libavformat/avformat.h>

char err_str[1024];

nlgranger

# Maintainer: Nicolas Granger <[email protected]>
pkgname=libnvidia-container
pkgver=1.0.0
pkgrel=1
pkgdesc="NVIDIA container runtime library"
arch=('x86_64')
url="https://nvidia.github.io/libnvidia-container/"
license=('BSD')
depends=()
makedepends=('bmake' 'rpcsvc-proto')