| import numpy as np | |
| import pymc | |
| import pdb | |
| def unconditionalProbability(Ptrans): | |
| """Compute the unconditional probability for the states of a | |
| Markov chain.""" | |
| m = Ptrans.shape[0] |
| """ Theano CRBM implementation. | |
| For details, see: | |
| http://www.uoguelph.ca/~gwtaylor/publications/nips2006mhmublv | |
| Sample data: | |
| http://www.uoguelph.ca/~gwtaylor/publications/nips2006mhmublv/motion.mat | |
| @author Graham Taylor""" | |
| import numpy |
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
##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
| // Use Gists to store code you would like to remember later on | |
| console.log(window); // log the "window" object to the console |
| - word2vec https://arxiv.org/abs/1310.4546 | |
| - sentenc2vec, paragraph2vec, doc2vec https://cs.stanford.edu/~quocle/paragraph_vector.pdf | |
| - tweet2vec http://arxiv.org/abs/1605.03481 | |
| - tweet2vec http://socialmachines.media.mit.edu/wp-content/uploads/sites/27/2016/05/tweet2vec_vvr.pdf | |
| - author2vec http://dl.acm.org/citation.cfm?id=2889382 | |
| - item2vec http://arxiv.org/abs/1603.04259 | |
| - lda2vec https://arxiv.org/abs/1605.02019 | |
| - illustration2vec http://dl.acm.org/citation.cfm?id=2820907 | |
| - tag2vec http://ktsaurabh.weebly.com/uploads/3/1/7/8/31783965/distributed_representations_for_content-based_and_personalized_tag_recommendation.pdf | |
| - category2vec http://www.anlp.jp/proceedings/annual_meeting/2015/pdf_dir/C4-3.pdf |
| # source : http://code.google.com/p/natvpn/source/browse/trunk/stun_server_list | |
| # A list of available STUN server. | |
| stun.l.google.com:19302 | |
| stun1.l.google.com:19302 | |
| stun2.l.google.com:19302 | |
| stun3.l.google.com:19302 | |
| stun4.l.google.com:19302 | |
| stun01.sipphone.com | |
| stun.ekiga.net |
Want to move computation on machine with much power. We will set up Anaconda 4.0.0 and XGBoost 0.4 (it is tricky installable).
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |