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import numpy as np import json

import numpy as np import json

Computer Networking: A Top-Down Approach (7th Edition)
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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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#"]
 
def compute_loss(network, image_vectors, captions_ix):
"""
:param image_vectors: torch tensor containing inception vectors. shape: [batch, cnn_feature_size]
:param captions_ix: torch tensor containing captions as matrix. shape: [batch, word_i].
padded with pad_ix
:returns: crossentropy (neg llh) loss for next captions_ix given previous ones. Scalar float tensor
"""
 
# captions for input - all except last because we don't know next token for last one.
captions_ix_inp = captions_ix[:, :-1].contiguous()
captions_ix_next = captions_ix[:, 1:].contiguous()
 
# apply the network, get predictions for captions_ix_next
logits_for_next = network.forward(image_vectors, captions_ix_inp)
 
# compute the loss function between logits_for_next and captions_ix_next
# Use the mask!
# Make sure that predicting next tokens after EOS do not contribute to loss
# You can do that either by multiplying elementwise loss by (captions_ix_next != pad_ix)
# or by using ignore_index in some losses.
 
# YOUR CODE HERE
 
#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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