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QuaPy
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Merge pull request #6 from pglez82/master 

fixing two problems with parameters: hidden_size and lstm_nlayers
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Alejandro Moreo Fernandez 2021-06-21 10:32:58 +02:00 committed by GitHub
parent 8239947746 cb40b71a38
commit 8cc11c0276
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1 changed files with 5 additions and 5 deletions
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10
quapy/classification/neural.py
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@ -249,7 +249,7 @@ class TextClassifierNet(torch.nn.Module, metaclass=ABCMeta):
class LSTMnet(TextClassifierNet): class LSTMnet(TextClassifierNet):
def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, lstm_nlayers=1, def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, lstm_class_nlayers=1,
drop_p=0.5): drop_p=0.5):
super().__init__() super().__init__()
self.vocabulary_size_ = vocabulary_size self.vocabulary_size_ = vocabulary_size
@ -258,12 +258,12 @@ class LSTMnet(TextClassifierNet):
'embedding_size': embedding_size, 'embedding_size': embedding_size,
'hidden_size': hidden_size, 'hidden_size': hidden_size,
'repr_size': repr_size, 'repr_size': repr_size,
'lstm_nlayers': lstm_nlayers, 'lstm_class_nlayers': lstm_class_nlayers,
'drop_p': drop_p 'drop_p': drop_p
} }
self.word_embedding = torch.nn.Embedding(vocabulary_size, embedding_size) self.word_embedding = torch.nn.Embedding(vocabulary_size, embedding_size)
self.lstm = torch.nn.LSTM(embedding_size, hidden_size, lstm_nlayers, dropout=drop_p, batch_first=True) self.lstm = torch.nn.LSTM(embedding_size, hidden_size, lstm_class_nlayers, dropout=drop_p, batch_first=True)
self.dropout = torch.nn.Dropout(drop_p) self.dropout = torch.nn.Dropout(drop_p)
self.dim = repr_size self.dim = repr_size
@ -272,8 +272,8 @@ class LSTMnet(TextClassifierNet):
def init_hidden(self, set_size): def init_hidden(self, set_size):
opt = self.hyperparams opt = self.hyperparams
var_hidden = torch.zeros(opt['lstm_nlayers'], set_size, opt['lstm_hidden_size']) var_hidden = torch.zeros(opt['lstm_class_nlayers'], set_size, opt['hidden_size'])
var_cell = torch.zeros(opt['lstm_nlayers'], set_size, opt['lstm_hidden_size']) var_cell = torch.zeros(opt['lstm_class_nlayers'], set_size, opt['hidden_size'])
if next(self.lstm.parameters()).is_cuda: if next(self.lstm.parameters()).is_cuda:
var_hidden, var_cell = var_hidden.cuda(), var_cell.cuda() var_hidden, var_cell = var_hidden.cuda(), var_cell.cuda()
return var_hidden, var_cell return var_hidden, var_cell