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increasing patience

This commit is contained in:
Alejandro Moreo Fernandez 2021-02-04 18:40:45 +01:00
parent 829d2150ec
commit 1385d2ac95
1 changed files with 6 additions and 6 deletions
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12
src/model/classifiers.py
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@ -29,7 +29,7 @@ class AuthorshipAttributionClassifier(nn.Module):
if p.dim() > 1 and p.requires_grad:
nn.init.xavier_uniform_(p)
def fit(self, X, y, batch_size, epochs, patience=10, lr=0.001, val_prop=0.1, alpha=1., log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
def fit(self, X, y, batch_size, epochs, patience=50, lr=0.001, val_prop=0.1, alpha=1., log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
assert 0 <= alpha <= 1, 'wrong range, alpha must be in [0,1]'
early_stop = EarlyStop(patience)
@ -132,11 +132,11 @@ class AuthorshipAttributionClassifier(nn.Module):
self.load_state_dict(torch.load(checkpointpath))
return early_stop.best_score
def supervised_contrastive_learning(self, X, y, Xval, yval, batch_size, epochs, patience=10, lr=0.001, log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
def supervised_contrastive_learning(self, X, y, Xval, yval, batch_size, epochs, patience=50, lr=0.001, log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
early_stop = EarlyStop(patience)
criterion = SupConLoss1View().to(self.device)
optim = torch.optim.Adam(self.projector.parameters(), lr=lr)
optim = torch.optim.Adam(self.parameters(), lr=lr)
tr_data = IndexedDataset(X, y, self.pad_length, self.pad_index, self.device)
val_data = IndexedDataset(Xval, yval, self.pad_length, self.pad_index, self.device)
@ -191,11 +191,11 @@ class AuthorshipAttributionClassifier(nn.Module):
self.load_state_dict(torch.load(checkpointpath))
return early_stop.best_score
def train_linear_classifier(self, X, y, Xval, yval, batch_size, epochs, patience=10, lr=0.001, log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
def train_linear_classifier(self, X, y, Xval, yval, batch_size, epochs, patience=50, lr=0.001, log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
early_stop = EarlyStop(patience)
criterion = torch.nn.CrossEntropyLoss().to(self.device)
optim = torch.optim.Adam(self.ff.parameters(), lr=lr)
optim = torch.optim.Adam(self.parameters(), lr=lr)
tr_data = IndexedDataset(X, y, self.pad_length, self.pad_index, self.device)
val_data = IndexedDataset(Xval, yval, self.pad_length, self.pad_index, self.device)
@ -221,7 +221,7 @@ class AuthorshipAttributionClassifier(nn.Module):
tr_loss = np.mean(losses)
pbar.set_description(f'training epoch={epoch} '
f'loss={tr_loss:.5f} '
f'val_loss={val_loss:.5f} val_acc={acc:.4f} macrof1={macrof1:.4f} microf1={microf1:.4f}'
f'val_loss={val_loss:.5f} val_acc={acc:.4f} macrof1={macrof1:.4f} microf1={microf1:.4f} '
f'patience={early_stop.patience}/{early_stop.patience_limit}')
# validation