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/
    """

## VGG-16
name: "VGG_ILSVRC_16_layers"

layer {
  name: "train-data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    stage: "train"
  }

## Find header and libs cheatsheet
#To find function in headers
grep -n -r <function_name> <path_to_opencv_include_folder>

#To find function in libs
nm -C -A <path_to_opencv_lib_folder>/*.so | grep <function_name> | grep -v U

#Find package installed via apt-get
apt list --installed | grep <package_name>
#Find files related to package
dpkg -L <package_name>

## 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")) {

## segnet_simple_train.prototxt
name: "segnet"

layer {
  name: "data"
  type: "DenseImageData"
  top: "data"
  top: "label"
  dense_image_data_param {
    source: "/SegNet/CamVid/train.txt"	# Change this to the absolute path to your data file
    batch_size: 4   			# Change this number to a batch size that will fit on your GPU

## segnet_train_downsampled.prototxt
name: "VGG_ILSVRC_16_layer"
layer {
  name: "data"
  type: "DenseImageData"
  top: "data"
  top: "label"
  dense_image_data_param {
    source: "/home/myuser/Downloads/SegNet/SegNet-Tutorial/CamVid/train.txt"	# Change this to the absolute path to your data file
    batch_size: 1   			# Change this number to a batch size that will fit on your GPU
    shuffle: true

## 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
	# 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/
	"""
	name: "VGG_ILSVRC_16_layers"

	layer {
	name: "train-data"
	type: "Data"
	top: "data"
	top: "label"
	include {
	stage: "train"
	}
	#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")) {
	name: "segnet"

	layer {
	name: "data"
	type: "DenseImageData"
	top: "data"
	top: "label"
	dense_image_data_param {
	source: "/SegNet/CamVid/train.txt" # Change this to the absolute path to your data file
	batch_size: 4 # Change this number to a batch size that will fit on your GPU
	name: "VGG_ILSVRC_16_layer"
	layer {
	name: "data"
	type: "DenseImageData"
	top: "data"
	top: "label"
	dense_image_data_param {
	source: "/home/myuser/Downloads/SegNet/SegNet-Tutorial/CamVid/train.txt" # Change this to the absolute path to your data file
	batch_size: 1 # Change this number to a batch size that will fit on your GPU
	shuffle: true
	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