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| import torch as tr | |
| class WordModel(tr.nn.Module): | |
| def __init__(self, IN_DIMS:int, HIDDEN_DIMS:int, VOCAB_SIZE:int) -> None: | |
| super(WordModel, self).__init__() | |
| self.l1 = tr.nn.Linear(in_features=IN_DIMS, out_features=HIDDEN_DIMS) | |
| self.l2 = tr.nn.Linear(in_features=HIDDEN_DIMS, out_features=HIDDEN_DIMS) | |
| self.l3 = tr.nn.Linear(in_features=HIDDEN_DIMS, out_features=VOCAB_SIZE) | |
| self.activation = tr.nn.ReLU() | |
| def forward(self, inputs): | |
| x = self.l1(inputs) | |
| x = self.activation(x) | |
| x = self.l2(x) | |
| x = self.activation(x) | |
| logits = self.l3(x) | |
| return logits | |