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Assignments/assignment4/MakiNaruto/README.md

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+做了这个作业后，发现要充分了解模型的各个输入输出维度。很关键
+![词级翻译模型](./word_nmt.jpg)

Assignments/assignment4/MakiNaruto/model_embeddings.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+CS224N 2019-20: Homework 4
+model_embeddings.py: Embeddings for the NMT model
+Pencheng Yin <[email protected]>
+Sahil Chopra <[email protected]>
+Anand Dhoot <[email protected]>
+Vera Lin <[email protected]>
+"""
+
+import torch.nn as nn
+
+class ModelEmbeddings(nn.Module): 
+    """
+    Class that converts input words to their embeddings.
+    """
+    def __init__(self, embed_size, vocab):
+        """
+        Init the Embedding layers.
+
+        @param embed_size (int): Embedding size (dimensionality)
+        @param vocab (Vocab): Vocabulary object containing src and tgt languages
+                              See vocab.py for documentation.
+        """
+        super(ModelEmbeddings, self).__init__()
+        self.embed_size = embed_size
+
+        # default values
+        self.source = None
+        self.target = None
+
+        src_pad_token_idx = vocab.src['<pad>']
+        tgt_pad_token_idx = vocab.tgt['<pad>']
+
+        ### YOUR CODE HERE (~2 Lines)
+        ### TODO - Initialize the following variables:
+        ###     self.source (Embedding Layer for source language)
+        ###     self.target (Embedding Layer for target langauge)
+        ###
+        ### Note:
+        ###     1. `vocab` object contains two vocabularies:
+        ###            `vocab.src` for source
+        ###            `vocab.tgt` for target
+        ###     2. You can get the length of a specific vocabulary by running:
+        ###             `len(vocab.<specific_vocabulary>)`
+        ###     3. Remember to include the padding token for the specific vocabulary
+        ###        when creating your Embedding.
+        ###
+        ### Use the following docs to properly initialize these variables:
+        ###     Embedding Layer:
+        ###         https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding
+
+        self.source = nn.Embedding(len(vocab.src), self.embed_size, padding_idx=src_pad_token_idx)
+        self.target = nn.Embedding(len(vocab.tgt), self.embed_size, padding_idx=tgt_pad_token_idx)
+
+        ### END YOUR CODE
+
+