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import torch.nn as nn
import torch.nn.functional as F
class DummyModel(nn.Module):
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
dense_input_size: int,
dense_output_size: int,
dense_layers_count: int,
sparse: bool
):
r"""
A dummy model with an EmbeddingBag Layer and Dense Layer.
Args:
num_embeddings (int): size of the dictionary of embeddings
embedding_dim (int): the size of each embedding vector
dense_input_size (int): size of each input sample
dense_output_size (int): size of each output sample
dense_layers_count: (int): number of dense layers in dense Sequential module
sparse (bool): if True, gradient w.r.t. weight matrix will be a sparse tensor
"""
super().__init__()
self.embedding = nn.EmbeddingBag(
num_embeddings, embedding_dim, sparse=sparse
)
self.dense = nn.Sequential(*[nn.Linear(dense_input_size, dense_output_size) for _ in range(dense_layers_count)])
def forward(self, x):
x = self.embedding(x)
return F.softmax(self.dense(x), dim=1)
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