mrgloom

## 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

## Convolutional Arithmetic.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                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.
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## 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")) {

## mnist_svm_sklearn.py
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]

## gist:6620665
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

mnist = fetch_mldata("MNIST original")
X_train, y_train = mnist.data[:60000] / 255., mnist.target[:60000]

## autoencoder.py
import numpy as np
from matplotlib import pyplot as plt
from scipy.optimize import fmin_l_bfgs_b as bfgs
from scipy.io import loadmat

class params:

    '''
    A wrapper around weights and biases
    for an autoencoder

## linregr.py
from numpy import loadtxt, zeros, ones, array, linspace, logspace
from pylab import scatter, show, title, xlabel, ylabel, plot, contour


#Evaluate the linear regression
def compute_cost(X, y, theta):
    '''
    Comput cost for linear regression
    '''
    #Number of training samples

## 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
	"""
	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
	# 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")) {
	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]
	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

	mnist = fetch_mldata("MNIST original")
	X_train, y_train = mnist.data[:60000] / 255., mnist.target[:60000]
	import numpy as np
	from matplotlib import pyplot as plt
	from scipy.optimize import fmin_l_bfgs_b as bfgs
	from scipy.io import loadmat

	class params:

	'''
	A wrapper around weights and biases
	for an autoencoder
	from numpy import loadtxt, zeros, ones, array, linspace, logspace
	from pylab import scatter, show, title, xlabel, ylabel, plot, contour


	#Evaluate the linear regression
	def compute_cost(X, y, theta):
	'''
	Comput cost for linear regression
	'''
	#Number of training samples
	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