| from nltk import pos_tag | |
| from nltk.tokenize import word_tokenize | |
| from nltk.stem import PorterStemmer, WordNetLemmatizer | |
| stemmer = PorterStemmer() | |
| lemmatiser = WordNetLemmatizer() | |
| print("Stem %s: %s" % ("going", stemmer.stem("going"))) | |
| print("Stem %s: %s" % ("gone", stemmer.stem("gone"))) | |
| print("Stem %s: %s" % ("goes", stemmer.stem("goes"))) |
| import string | |
| import nltk | |
| from nltk.tokenize import RegexpTokenizer | |
| from nltk.corpus import stopwords | |
| import re | |
| def preprocess(sentence): | |
| sentence = sentence.lower() | |
| tokenizer = RegexpTokenizer(r'\w+') | |
| tokens = tokenizer.tokenize(sentence) |
##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