mrgloom

## MNIST PCA projection
import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from sklearn.decomposition import RandomizedPCA
from sklearn.datasets import fetch_mldata
from sklearn.utils import shuffle

#use all digits
mnist = fetch_mldata("MNIST original")
X_train, y_train = mnist.data[:70000] / 255., mnist.target[:70000]

## gist:2101cef88005987e9870
+/*
+ * Author: Samyak Datta (datta[dot]samyak[at]gmail.com)
+ *
+ * A program to demonstrate contour detection using the Active Contour
+ * Model (Snake). The cvSnakeImage() method is applied to detect lip
+ * contour in a lip ROI image.
+ *
+ */
+
+#include "opencv2/highgui/highgui.hpp"

## 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

## 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

## 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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## 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>

## 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"
  }

## 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
	import numpy as np
	import matplotlib.pyplot as plt
	from itertools import product
	from sklearn.decomposition import RandomizedPCA
	from sklearn.datasets import fetch_mldata
	from sklearn.utils import shuffle

	#use all digits
	mnist = fetch_mldata("MNIST original")
	X_train, y_train = mnist.data[:70000] / 255., mnist.target[:70000]
	+/*
	+ * Author: Samyak Datta (datta[dot]samyak[at]gmail.com)
	+ *
	+ * A program to demonstrate contour detection using the Active Contour
	+ * Model (Snake). The cvSnakeImage() method is applied to detect lip
	+ * contour in a lip ROI image.
	+ *
	+ */
	+
	+#include "opencv2/highgui/highgui.hpp"
	"""
	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
	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
	# 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