This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.
ResNet Paper:
Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # This is a simplified implementation of the LSTM language model (by Graham Neubig) | |
| # | |
| # LSTM Neural Networks for Language Modeling | |
| # Martin Sundermeyer, Ralf Schlüter, Hermann Ney | |
| # InterSpeech 2012 | |
| # | |
| # The structure of the model is extremely simple. At every time step we |
| import numpy as np | |
| from matplotlib import pylab as plt | |
| #from mpltools import style # uncomment for prettier plots | |
| #style.use(['ggplot']) | |
| ''' | |
| function definitions | |
| ''' | |
| # generate all bernoulli rewards ahead of time | |
| def generate_bernoulli_bandit_data(num_samples,K): |
| #!/usr/bin/python | |
| # crf.py (by Graham Neubig) | |
| # This script trains conditional random fields (CRFs) | |
| # stdin: A corpus of WORD_POS WORD_POS WORD_POS sentences | |
| # stdout: Feature vectors for emission and transition properties | |
| from collections import defaultdict | |
| from math import log, exp | |
| import sys |
| """ | |
| From this paper: http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Mihalcea.pdf | |
| I used python with nltk, and pygraph to do an implmentation of of textrank. | |
| for questions: http://twitter.com/voidfiles | |
| """ | |
| import nltk | |
| import itertools |