walterreade

Walter Reade Publications

Peer Reviewed

• Patton, P. T., et al., (2023). A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species. Methods in Ecology and Evolution, 00, 1– 15. [Open Access]
• Wayment-Steele, H.K., Kladwang, W., Watkins, A.M. et al. "Deep learning models for predicting RNA degradation via dual crowdsourcing" Nat Mach Intell 4, 1174–1184 (2022). [Abstract] [pre-print]
• Cheeseman, T., Southerland, K., Park, J. et al., "Advanced image recognition: a fully automated, high-accuracy photo-identification matching system for humpback whales", Mammalian Biology (2021) [Abstract]
• Bratholm, L.A, et al., "A community-powered search of machine learning strategy space to find NMR property prediction models," PLOS ONE, July 20, 2021,

walterreade

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import warnings
import numpy as np
import pandas as pd
import sys

__author__ = "Mohsen Mesgarpour"
__copyright__ = "Copyright 2016, https://github.com/mesgarpour"
__credits__ = ["Mohsen Mesgarpour"]

walterreade

import numpy
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

walterreade

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/

walterreade

walterreade

# -*- coding: utf-8 -*-
"""
Regularized Tree Ensemble

@author: gert.jacobusse@rogatio.nl
@license: FreeBSD

Originally posted:
https://www.kaggle.com/c/bnp-paribas-cardif-claims-management/forums/t/20207/why-every-good-script-is-using-extratreeclassifier-one-way-or-the-other/115621

walterreade

def removeLink(content):
    urls = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', content)
    for i in range(0, len(urls)):
        content = content.replace(urls[i], '')
    return content

walterreade

tag = 'h1'
text 'This is a headline'
sentence = '<{0}>{1}</{0}>'.format(tag, text)
 
person = {'name': 'Jenn', 'age': 23}
sentence = 'My name is {0[name]} and I am {0[age]} years old.'.format(person)
# can do the same for attributes, e.g., {0.name}, or list {0[0]}

# You can just unpack a dictionary!
sentence = 'My name is {name} and I am {age} years old.'.format(**person)

walterreade

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

walterreade

from scipy import exp, log
from scipy.special import gammaln

def prob_unique(N, r):
  """ If you have a set of N things to choose from, and take r samples, 
  the probability that all r samples are unique.
  http://www.johndcook.com/blog/2016/01/30/general-birthday-problem 
  """
    return exp( gammaln(N+1) - gammaln(N-r+1) - r*log(N) )