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Exercise 1

Task: The code below is not quite right for prototyping purposes. Fix it so that the indices is more like an actual batched data point and has batch_size=30 and seq_length=10.

indices = torch.from_numpy(np.random.randint(0, 100, size=(10,)))

emb = nn.Embedding(num_embeddings=100, embedding_dim=16)
assert emb(indices).shape == (30, 10, 16)

Exercise 2

Task: Create a MultiEmbedding class which can input two sets of indices, embed them, and concat the results!

class MultiEmbedding(nn.Module):
    def __init__(self, num_embeddings1, num_embeddings2, embedding_dim1, embedding_dim2):

    def forward(self, indices1, indices2):
        # use something like
        # z =[x, y], dim=1)


# testing

# use indices method from above
# the batch dimensions should agree
# indices1 =
# indices2 =
# multiemb = MutliEmbedding(num_emb1, num_emb2, size_emb1, size_emb2)
# output = multiemb(indices1, indices2)
# print(output.shape) # should be (batch, size_emb1 + size_emb2)
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