Day #118 - Synonym verbs check

	from itertools import chain
	from nltk.corpus import wordnet
	import nltk
	sentence = "I am buying this book"
	tokens = nltk.word_tokenize(sentence)
	tagged = nltk.pos_tag(tokens)
	length = len(tagged)
	verbs = list()
	#https://stackoverflow.com/questions/15388831/what-are-all-possible-pos-tags-of-nltk
	#VB verb, base form take
	#VBD verb, past tense took
	#VBG verb, gerund/present participle taking
	#VBN verb, past participle taken
	#VBP verb, sing. present, non-3d take
	#VBZ verb, 3rd person sing. present takes
	print(tagged)
	for i in range(0, length):
	print(tagged[i][1])
	if(tagged[i][1] == 'VB' or tagged[i][1] == 'VBD' or tagged[i][1] == 'VBG' or tagged[i][1] == 'VBP' or tagged[i][1] == 'VBZ'):
	verbs.append(tagged [i][0])
	print(verbs)
	for word in verbs:
	synonyms = wordnet.synsets(word)
	lemmas = set(chain.from_iterable([word.lemma_names() for word in synonyms]))
	print('Word in Context')
	print(word)
	print('Similar Synomynms')
	for word in lemmas:
	print(word)

view raw Synonymverbs.py hosted with ❤ by GitHub

Happy Learning!!!

Day #285 - Experimenting on Custom Segmentation using Unet

1. Data Annotation - Leveraged tool - eraseimage by Akshay Bhat (Nice Effort)

Pick Green - Foreground, Red - Background to mark regions

Segment and Save the Output Image

Step 2 - Labelling in Sequence (Follow the same structure replace with Image / Label) - Labelling Training from 0 to 28

3. Labelling Test from 0 to 4

Performed a rename as all files were in jpeg, used ren *.* to *.png to port to png format

4. Performed Data Augmentation and took those masked images and labels (Replaced Earlier ones)

Output Results



Happy Learning!!!