def __init__(
self,
vocab_size,
word_dim,
hidden_dim,
label_dim,
parser_leaf_transformation=BinaryTreeBasedModule.no_transformation,
parser_trans_hidden_dim=None,
tree_leaf_transformation=BinaryTreeBasedModule.no_transformation,
tree_trans_hidden_dim=None,
baseline_type=no_baseline,
var_normalization=False):
super().__init__()
self.embd_parser = nn.Embedding(vocab_size, word_dim)
self.parser = BottomUpTreeLstmParser(word_dim, hidden_dim,
parser_leaf_transformation,
parser_trans_hidden_dim)
self.embd_tree = nn.Embedding(vocab_size, word_dim)
self.tree_lstm_rnn = BinaryTreeLstmRnn(word_dim, hidden_dim,
tree_leaf_transformation,
tree_trans_hidden_dim)
self.linear = nn.Linear(in_features=hidden_dim, out_features=label_dim)
self.baseline_params = ReinforceModel.get_baseline_dict(baseline_type)
self.var_norm_params = {
"var_normalization": var_normalization,
"var": 1.0,
"alpha": 0.9
}
self.criterion = nn.CrossEntropyLoss(reduction='none')
self.reset_parameters()
def __init__(self, vocab_size, word_dim, hidden_dim, label_dim,
leaf_transformation=BinaryTreeBasedModule.no_transformation, trans_hidden_dim=None):
super().__init__()
self.embd_tree = nn.Embedding(vocab_size, word_dim)
self.tree_lstm_rnn = BinaryTreeLstmRnn(word_dim, hidden_dim, leaf_transformation, trans_hidden_dim)
self.linear = nn.Linear(in_features=hidden_dim, out_features=label_dim)
self.criterion = nn.CrossEntropyLoss()
self.reset_parameters()