mrgloom

## Convolutional Arithmetic.ipynb

      
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                mrgloom
                / Convolutional Arithmetic.ipynb
            
            
              Created
              May 12, 2017 16:44
                — forked from akiross/Convolutional Arithmetic.ipynb
            
              
                Few experiments on how convolution and transposed convolution (deconvolution) should work in tensorflow.
              
          
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## online_stats.py
# Author: Kyle Kaster
# License: BSD 3-clause
import numpy as np


def online_stats(X):
    """
    Converted from John D. Cook
    http://www.johndcook.com/blog/standard_deviation/
    """

## detect_multiscale.cpp
#include <opencv2/opencv.hpp>
#include <vector>

using namespace cv;
using namespace std;

int main () {
  Mat img = imread("lena.jpg");
  CascadeClassifier cascade;
  if (cascade.load("haarcascade_frontalface_alt.xml")) {

## pegasos.py
class OnlineLearner(object):
    def __init__(self, **kwargs):
        self.last_misses = 0.
        self.iratio = 0.
        self.it = 1.
        self.l = kwargs["l"]
        self.max_ratio = -np.inf
        self.threshold = 500.

    def hinge_loss(self, vector, cls, weight):

## rbm.py
"""
Code for training RBMs with contrastive divergence. Tries to be as
quick and memory-efficient as possible while utilizing only pure Python
and NumPy.
"""

# Copyright (c) 2009, David Warde-Farley
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without

## kmtransformer.py
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics.pairwise import rbf_kernel


class KMeansTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, centroids):
        self.centroids = centroids

    def fit(self, X, y=None):
        return self

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

import numpy
import theano
import sys
import math
from theano import tensor as T
from theano import shared

## roll_ipython_in_aws.md

      
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                mrgloom
                / roll_ipython_in_aws.md
            
            
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              August 29, 2015 14:25
                — forked from iamatypeofwalrus/roll_ipython_in_aws.md
            
          
      View roll_ipython_in_aws.md
  
      
    What
Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.
What are we using? What do you need?

An active AWS account. First time sign-ups are eligible for the free tier for a year
One Micro Tier EC2 Instance
With AWS we will use the stock Ubuntu Server AMI and customize it.
Anaconda for Python.
Coffee/Beer/Time


## dlib_plus_osm.md

      
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                mrgloom
                / dlib_plus_osm.md
            
            
              Last active
              August 29, 2015 14:23
                — forked from iandees/dlib_plus_osm.md
            
          
      View dlib_plus_osm.md
  
      
I've been interested in computer vision for a long time, but I haven't had any free time to make any progress until this holiday season. Over Christmas and the New Years I experimented with various methodologies in OpenCV to detect road signs and other objects of interest to OpenStreetMap. After some failed experiments with thresholding and feature detection, the excellent /r/computervision suggested using the dlib C++ module because it has more consistently-good documentation and the pre-built tools are faster.
After a day or two figuring out how to compile the examples, I finally made some progress:
Compiling dlib C++ on a Mac with Homebrew

Clone dlib from Github to your local machine:


## CUR4FIC
# clear the workspace
rm(list = ls())

# load the relevant libraries

# install.packages(rCUR)
library(rCUR)  # for CUR decomposition
# install.packages(irlba)
library(irlba)  # for fast svd
	# Author: Kyle Kaster
	# License: BSD 3-clause
	import numpy as np


	def online_stats(X):
	"""
	Converted from John D. Cook
	http://www.johndcook.com/blog/standard_deviation/
	"""
	#include <opencv2/opencv.hpp>
	#include <vector>

	using namespace cv;
	using namespace std;

	int main () {
	Mat img = imread("lena.jpg");
	CascadeClassifier cascade;
	if (cascade.load("haarcascade_frontalface_alt.xml")) {
	class OnlineLearner(object):
	def __init__(self, **kwargs):
	self.last_misses = 0.
	self.iratio = 0.
	self.it = 1.
	self.l = kwargs["l"]
	self.max_ratio = -np.inf
	self.threshold = 500.

	def hinge_loss(self, vector, cls, weight):
	"""
	Code for training RBMs with contrastive divergence. Tries to be as
	quick and memory-efficient as possible while utilizing only pure Python
	and NumPy.
	"""

	# Copyright (c) 2009, David Warde-Farley
	# All rights reserved.
	#
	# Redistribution and use in source and binary forms, with or without
	from sklearn.base import BaseEstimator, TransformerMixin
	from sklearn.metrics.pairwise import rbf_kernel


	class KMeansTransformer(BaseEstimator, TransformerMixin):
	def __init__(self, centroids):
	self.centroids = centroids

	def fit(self, X, y=None):
	return self
	"""
	A deep neural network with or w/o dropout in one file.
	"""

	import numpy
	import theano
	import sys
	import math
	from theano import tensor as T
	from theano import shared
	# clear the workspace
	rm(list = ls())

	# load the relevant libraries

	# install.packages(rCUR)
	library(rCUR) # for CUR decomposition
	# install.packages(irlba)
	library(irlba) # for fast svd