| #!/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"] |
| 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 |
| '''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/ |
| 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 |
##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