NegatioN

def _to_one_hot(y, num_classes):
    scatter_dim = len(y.size())
    y_tensor = y.view(*y.size(), -1)
    zeros = torch.zeros(*y.size(), num_classes, dtype=y.dtype)
        
    return zeros.scatter(scatter_dim, y_tensor, 1)


print(_to_one_hot(torch.as_tensor([2, 4, 7]), num_classes=10))
print(_to_one_hot(torch.as_tensor([[1, 5 ,6], [2, 4, 7]]), num_classes=10))

akshaychawla

import numpy as np
from keras.models import Model
from keras.layers import Dense, Activation, Lambda, Input 
import keras.backend as K 
from keras.utils import to_categorical

# Model definition 
def foo(ip):
    a = ip[1]
    x = ip[0]

jacobkimmel

def make_one_hot(labels, C=2):
    '''
    Converts an integer label torch.autograd.Variable to a one-hot Variable.
    
    Parameters
    ----------
    labels : torch.autograd.Variable of torch.cuda.LongTensor
        N x 1 x H x W, where N is batch size. 
        Each value is an integer representing correct classification.
    C : integer. 

DianaEromosele

$ git remote rm origin
$ git remote add origin git@github.com:aplikacjainfo/proj1.git
$ git config master.remote origin
$ git config master.merge refs/heads/master

wngreene

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

# Copyright 2016 Massachusetts Institute of Technology

"""Extract images from a rosbag.
"""

import os
import argparse

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

saliksyed

""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k

zacstewart

import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix, f1_score

NEWLINE = '\n'

djq

#!/bin/bash
#
# Install Postgres 9.1, PostGIS 2.0 and pgRouting on a clean Ubuntu 12.04 install (64 bit)
# updated to PostGIS 2.0.1

# basics
apt-get install python-software-properties
apt-add-repository ppa:sharpie/for-science  # To get GEOS 3.3.3

# install the following pacakages