65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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CS224N 2018-19: Homework 5
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model_embeddings.py: Embeddings for the NMT model
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Pencheng Yin <pcyin@cs.cmu.edu>
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Sahil Chopra <schopra8@stanford.edu>
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Anand Dhoot <anandd@stanford.edu>
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Michael Hahn <mhahn2@stanford.edu>
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"""
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import torch.nn as nn
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# Do not change these imports; your module names should be
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# `CNN` in the file `cnn.py`
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# `Highway` in the file `highway.py`
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# Uncomment the following two imports once you're ready to run part 1(j)
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from cnn import CNN
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from highway import Highway
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# End "do not change"
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class ModelEmbeddings(nn.Module):
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"""
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Class that converts input words to their CNN-based embeddings.
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"""
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def __init__(self, embed_size, vocab):
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"""
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Init the Embedding layer for one language
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@param embed_size (int): Embedding size (dimensionality) for the output
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@param vocab (VocabEntry): VocabEntry object. See vocab.py for documentation.
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"""
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super(ModelEmbeddings, self).__init__()
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## A4 code
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# pad_token_idx = vocab.src['<pad>']
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# self.embeddings = nn.Embedding(len(vocab.src), embed_size, padding_idx=pad_token_idx)
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## End A4 code
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### YOUR CODE HERE for part 1j
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### END YOUR CODE
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def forward(self, input):
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"""
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Looks up character-based CNN embeddings for the words in a batch of sentences.
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@param input: Tensor of integers of shape (sentence_length, batch_size, max_word_length) where
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each integer is an index into the character vocabulary
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@param output: Tensor of shape (sentence_length, batch_size, embed_size), containing the
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CNN-based embeddings for each word of the sentences in the batch
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"""
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## A4 code
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# output = self.embeddings(input)
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# return output
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## End A4 code
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### YOUR CODE HERE for part 1j
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return x_word_embed
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### END YOUR CODE
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