From cb40b71a383c76dc8d9a0c8b66c0f505cd3979bc Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pglez82@gmail.com>
Date: Mon, 7 Jun 2021 12:22:06 +0200
Subject: [PATCH] fixing two problems with parameters: hidden_size and
lstm_nlayers
---
quapy/classification/neural.py | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/quapy/classification/neural.py b/quapy/classification/neural.py
index afeb649..0e003a2 100644
--- a/quapy/classification/neural.py
+++ b/quapy/classification/neural.py
@@ -249,7 +249,7 @@ class TextClassifierNet(torch.nn.Module, metaclass=ABCMeta):
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):
super().__init__()
self.vocabulary_size_ = vocabulary_size
@@ -258,12 +258,12 @@ class LSTMnet(TextClassifierNet):
'embedding_size': embedding_size,
'hidden_size': hidden_size,
'repr_size': repr_size,
- 'lstm_nlayers': lstm_nlayers,
+ 'lstm_class_nlayers': lstm_class_nlayers,
'drop_p': drop_p
}
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.dim = repr_size
@@ -272,8 +272,8 @@ class LSTMnet(TextClassifierNet):
def init_hidden(self, set_size):
opt = self.hyperparams
- var_hidden = torch.zeros(opt['lstm_nlayers'], set_size, opt['lstm_hidden_size'])
- var_cell = 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_class_nlayers'], set_size, opt['hidden_size'])
if next(self.lstm.parameters()).is_cuda:
var_hidden, var_cell = var_hidden.cuda(), var_cell.cuda()
return var_hidden, var_cell