mrgloom

from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import StratifiedKFold

def main():
    mnist = fetch_mldata("MNIST original")
    X_all, y_all = mnist.data/255., mnist.target
    print("scaling")
    X = X_all[:60000, :]
    y = y_all[:60000]

mrgloom

import numpy as np
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.linear_model.stochastic_gradient import SGDClassifier
from sklearn.datasets import fetch_mldata
from sklearn.utils import shuffle
import time

#out-of-core \ online
#http://scikit-learn.org/stable/auto_examples/applications/plot_out_of_core_classification.html

mrgloom

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

mrgloom

#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")) {

mrgloom

name: "VGG_ILSVRC_16_layers"

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

mrgloom

# 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/
    """

mrgloom

#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>

mrgloom

mrgloom

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

mrgloom

"""
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