From 727dda616799061b42d3f932526ab79b340e8f8a Mon Sep 17 00:00:00 2001
From: Alex Moreo <alejandro.moreo@isti.cnr.it>
Date: Fri, 1 May 2020 22:54:34 +0200
Subject: [PATCH] 10% test 10% val
---
src/data/fetch_imdb62.py | 2 +-
src/model/classifiers.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/src/data/fetch_imdb62.py b/src/data/fetch_imdb62.py
index 7e747c1..4822c96 100644
--- a/src/data/fetch_imdb62.py
+++ b/src/data/fetch_imdb62.py
@@ -7,7 +7,7 @@ from data.AuthorshipDataset import AuthorshipDataset, LabelledCorpus
class Imdb62(AuthorshipDataset):
- TEST_SIZE = 0.30
+ TEST_SIZE = 0.10
NUM_AUTHORS = 62
NUM_DOCS_BY_AUTHOR = int(1000-(1000*TEST_SIZE))
diff --git a/src/model/classifiers.py b/src/model/classifiers.py
index 572ba42..a346494 100644
--- a/src/model/classifiers.py
+++ b/src/model/classifiers.py
@@ -16,7 +16,7 @@ class AuthorshipAttributionClassifier(nn.Module):
self.padder = Padding(pad_index=pad_index, max_length=pad_length, dynamic=True, pad_at_end=False, device=device)
self.device = device
- def fit(self, X, y, batch_size, epochs, lr=0.001, val_prop=0.2, log='../log/tmp.csv'):
+ def fit(self, X, y, batch_size, epochs, lr=0.001, val_prop=0.1, log='../log/tmp.csv'):
batcher = Batch(batch_size=batch_size, n_epochs=epochs)
criterion = torch.nn.CrossEntropyLoss().to(self.device)
optim = torch.optim.Adam(self.parameters(), lr=lr)