tdeboissiere

import torch
from torch.autograd import Variable
import torch.nn as nn
import numpy as np
import torch.optim as optim
import torch.nn.functional as F
import time
from torchvision.models import vgg

# Initialize network

whophil

import os
import fnmatch

def recursive_glob(rootdir='.', pattern='*'):
	"""Search recursively for files matching a specified pattern.
	
	Adapted from http://stackoverflow.com/questions/2186525/use-a-glob-to-find-files-recursively-in-python
	"""

	matches = []

nikitakit

This gist has been superceded by Meta Graph functionality that has since been added to tensorflow core.

See: https://www.tensorflow.org/versions/master/api_docs/python/state_ops.html#exporting-and-importing-meta-graphs

The code remains posted for archival purposes only.

GuillaumeFavelier

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This example is a prototype demonstrating normal mapping
by comparison between a reference mesh and its flatten
version.
"""

import numpy as np
from vispy import app, gloo

markedphillips

version: "2.2"
services:
  sharelatex:
    restart: always
    image: dennis1f/sharelatex-texlive2018 # sharelatex/sharelatex:latest
    container_name: sharelatex
    depends_on:
      mongo:
        condition: service_healthy
      redis:

ruotianluo

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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

import numpy as np

Dref360

import keras.backend as K
import tensorflow as tf
from tensorflow.keras.layers import Layer

"""Not tested, I'll play around with GANs soon with it."""
from tensorflow.keras.layers import Conv2D
import numpy as np


class CoordConv2D(Layer):

mjdietzx

from keras import layers
from keras import models
import tensorflow as tf


#
# generator input params
#

rand_dim = (1, 1, 2048)  # dimension of the generator's input tensor (gaussian noise)

chris-jamieson

• Download Franz for your distribution from MeetFranz.com
• change into the same directory as the downloaded file, then sudo tar -xf Franz-linux-x64-0.9.10.tgz -C /opt/franz
• (optional) wget "https://cdn-images-1.medium.com/max/360/1*v86tTomtFZIdqzMNpvwIZw.png" -O franz-icon.png then sudo cp franz-icon.png /opt/franz
• (optional) sudo touch /usr/share/applications/franz.desktop then sudo vim /usr/share/applications/franz.desktop

paste the following lines into the file, then save the file:

[Desktop Entry]
Name=Franz
Comment=

bartvm

A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.

Overview

Differentiation

Symbolic: Theano, CGT; Automatic: Torch, MXNet

Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.