import numpy as np import json img_codes = np.load("data/image_codes.npy") captions = json.load(open('data/captions_tokenized.json')) for img_i in range(len(captions)): for caption_i inrange(len(captions[img_i])): sentence = captions[img_i][caption_i] captions[img_i][caption_i] = ["#START#"] + sentence.split(' ') + ["#END#"] # Build a Vocabulary from collections import Counter word_counts = Counter() # Compute word frequencies for each word in captions. See code above for data structure # YOUR CODE HERE #Testing condition:- vocab = ['#UNK#', '#START#', '#END#', '#PAD#'] vocab += [k for k, v in word_counts.items() if v >= 5 if k not in vocab] n_tokens = len(vocab) assert 10000 <= n_tokens <= 10500 word_to_index = {w: i for i, w in enumerate(vocab)} #for reference and more detail go to ---> https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week06/week06_final_project_image_captioning.ipynb#scrollTo=x_wH1bYu-QK8
import numpy as np import json img_codes = np.load("data/image_codes.npy") captions = json.load(open('data/captions_tokenized.json')) for img_i in range(len(captions)): for caption_i inrange(len(captions[img_i])): sentence = captions[img_i][caption_i] captions[img_i][caption_i] = ["#START#"] + sentence.split(' ') + ["#END#"] # Build a Vocabulary from collections import Counter word_counts = Counter() # Compute word frequencies for each word in captions. See code above for data structure # YOUR CODE HERE #Testing condition:- vocab = ['#UNK#', '#START#', '#END#', '#PAD#'] vocab += [k for k, v in word_counts.items() if v >= 5 if k not in vocab] n_tokens = len(vocab) assert 10000 <= n_tokens <= 10500 word_to_index = {w: i for i, w in enumerate(vocab)} #for reference and more detail go to ---> https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week06/week06_final_project_image_captioning.ipynb#scrollTo=x_wH1bYu-QK8
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Question
import numpy as np
import json
img_codes = np.load("data/image_codes.npy")
captions = json.load(open('data/captions_tokenized.json'))
for img_i in range(len(captions)):
for caption_i inrange(len(captions[img_i])):
sentence = captions[img_i][caption_i]
captions[img_i][caption_i] = ["#START#"] + sentence.split(' ') + ["#END#"]
# Build a Vocabulary
from collections import Counter
word_counts = Counter()
# Compute word frequencies for each word in captions. See code above for data structure
# YOUR CODE HERE
#Testing condition:-
vocab = ['#UNK#', '#START#', '#END#', '#PAD#']
vocab += [k for k, v in word_counts.items() if v >= 5 if k not in vocab]
n_tokens = len(vocab)
assert 10000 <= n_tokens <= 10500
word_to_index = {w: i for i, w in enumerate(vocab)}
#for reference and more detail go to ---> https://colab.research.google.com/github/hse-aml/intro-to-dl-pytorch/blob/main/week06/week06_final_project_image_captioning.ipynb#scrollTo=x_wH1bYu-QK8
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