lamhoangtung

How to install nvidia-docker offline for Ubuntu 18.04

Prerequisite

Before install nvidia-docker, you will need these first:

• nvidia-driver
• docker

Then download these file:

• libnvidia-container1_1.3.0-1_amd64.deb
• libnvidia-container-tools_1.3.0-1_amd64.deb

peteflorence

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

VikingPenguinYT

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.contrib.distributions import Bernoulli


class VariationalDense:
    """Variational Dense Layer Class"""
    def __init__(self, n_in, n_out, model_prob, model_lam):
        self.model_prob = model_prob

gorayni

from __future__ import division

import numpy as np


class Edge(object):
    def __init__(self, p, q):
        self.xmin = np.min((p[0], q[0]))
        self.xmax = np.max((p[0], q[0]))
        self.ymin = np.min((p[1], q[1]))

swyoon

import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.

ishuca

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Example employing Lasagne for digit generation using the MNIST dataset and
Wasserstein Generative Adversarial Networks
(WGANs, see https://arxiv.org/abs/1701.07875 for the paper and
https://github.com/martinarjovsky/WassersteinGAN for the "official" code).

It is based on a DCGAN example:

dvm-shlee

from __future__ import absolute_import
import json
import sys
import re
import requests
import urllib
import urlparse
from urllib2 import HTTPError
from urllib2 import URLError
from urllib2 import urlopen

anantzoid

Conditional Image Generation with PixelCNN Decoder

Implemenetation:

What

• New image density model based on PixelCNN
• Can generate variety of images from text embeddings or CNN layer weights
• Serves as decoder in image autoencoder
• Gated PixelCNN: Matches PixelRNN accuracy

yaroslavvb

# testing variable order init

import tensorflow as tf

def initialize_all_variables(sess=None):
  """Initializes all uninitialized variables in correct order. Initializers
  are only run for uninitialized variables, so it's safe to run this multiple
  times.

  Args: