ferryzhou

function mkdir_if_not_exist(dirpath)
    if dirpath(end) ~= '/', dirpath = [dirpath '/']; end
    if (exist(dirpath, 'dir') == 0), mkdir(dirpath); end
end

ryin

#!/bin/bash

# Script for installing tmux on systems where you don't have root access.
# tmux will be installed in $HOME/local/bin.
# It's assumed that wget and a C/C++ compiler are installed.

# exit on error
set -e

TMUX_VERSION=1.8

deepankarsharma

from glfwpy.glfw import (AUTO_POLL_EVENTS, OPENED, OPENGL_CORE_PROFILE,
    OPENGL_FORWARD_COMPAT, OPENGL_PROFILE, OPENGL_VERSION_MAJOR,
    OPENGL_VERSION_MINOR, WINDOW,
    Enable, GetWindowParam, Init, OpenWindow, OpenWindowHint,
    SetKeyCallback, SetWindowTitle, SwapBuffers, Terminate)
import numpy as np
from OpenGL.arrays import ArrayDatatype
from OpenGL.GL import (GL_ARRAY_BUFFER, GL_COLOR_BUFFER_BIT,
    GL_COMPILE_STATUS, GL_FALSE, GL_FLOAT, GL_FRAGMENT_SHADER,
    GL_LINK_STATUS, GL_RENDERER, GL_SHADING_LANGUAGE_VERSION,

syhw

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

adam-p

It's not immediately obvious how to pull down the code for a PR and test it locally. But it's pretty easy. (This assumes you have a remote for the main repo named upstream.)

Getting the PR code

1. Make note of the PR number. For example, Rod's latest is PR #37: Psiphon-Labs/psiphon-tunnel-core#37

2. Fetch the PR's pseudo-branch (or bookmark or rev pointer whatever the word is), and give it a local branch name. Here we'll name it pr37:

$ git fetch upstream pull/37/head:pr37

f0k

# -*- coding: utf-8 -*-

"""
Preliminary implementation of batch normalization for Lasagne.
Does not include a way to properly compute the normalization factors over the
full training set for testing, but can be used as a drop-in for training and
validation.

Author: Jan Schlüter
"""

JBed

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization

#AlexNet with batch normalization in Keras 
#input image is 224x224

model = Sequential()
model.add(Convolution2D(64, 3, 11, 11, border_mode='full'))

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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.

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.