| /* | |
| A Minimal Capture Program | |
| This program opens an audio interface for capture, configures it for | |
| stereo, 16 bit, 44.1kHz, interleaved conventional read/write | |
| access. Then its reads a chunk of random data from it, and exits. It | |
| isn't meant to be a real program. | |
| From on Paul David's tutorial : http://equalarea.com/paul/alsa-audio.html |
| #!/usr/bin/env python | |
| from scipy.stats import t | |
| from numpy import average, std | |
| from math import sqrt | |
| if __name__ == '__main__': | |
| # data we want to evaluate: average height of 30 one year old male and | |
| # female toddlers. Interestingly, at this age height is not bimodal yet | |
| data = [63.5, 81.3, 88.9, 63.5, 76.2, 67.3, 66.0, 64.8, 74.9, 81.3, 76.2, |
(Codecs are extracted from https://web.archive.org/web/20120722124832/http://opencv.willowgarage.com/wiki/QuickTimeCodecs )
| # Mathieu Blondel, September 2010 | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from numpy import linalg | |
| import cvxopt | |
| import cvxopt.solvers | |
| def linear_kernel(x1, x2): | |
| return np.dot(x1, x2) |
| # $Id: Nielsen2012Python_case.py,v 1.2 2012/09/02 16:55:25 fn Exp $ | |
| # Define a url as a Python string (note we are only getting 100 documents) | |
| url = "http://wikilit.referata.com/" + \ | |
| "wiki/Special:Ask/" + \ | |
| "-5B-5BCategory:Publications-5D-5D/" + \ | |
| "-3FHas-20author%3DAuthor(s)/-3FYear/" + \ | |
| "-3FPublished-20in/-3FAbstract/-3FHas-20topic%3DTopic(s)/" + \ | |
| "-3FHas-20domain%3DDomain(s)/" + \ | |
| "format%3D-20csv/limit%3D-20100/offset%3D0" |
| #----------------------------------------------------------------------------------------- | |
| # | |
| # Spectral Matting | |
| # | |
| import time | |
| import logging | |
| import numpy as np | |
| from scipy import ndimage, sparse | |
| import scipy.sparse.linalg as sparse_linalg |
| '''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/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
##VGG19 model for Keras
This is the Keras model of the 19-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
##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
| # Keras==1.0.6 | |
| from keras.models import Sequential | |
| import numpy as np | |
| from keras.layers.recurrent import LSTM | |
| from keras.layers.core import TimeDistributedDense, Activation | |
| from keras.preprocessing.sequence import pad_sequences | |
| from keras.layers.embeddings import Embedding | |
| from sklearn.cross_validation import train_test_split | |
| from keras.layers import Merge | |
| from keras.backend import tf |