From fcc5bc7f04addb41f8524e28c6013b2060f0a36f Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Tue, 19 Oct 2021 16:34:11 +0200
Subject: [PATCH] Default configuration.
---
climax/main/config.py | 20 ++++++++++----------
1 file changed, 10 insertions(+), 10 deletions(-)
diff --git a/climax/main/config.py b/climax/main/config.py
index 2ab3734..a86c8d9 100644
--- a/climax/main/config.py
+++ b/climax/main/config.py
@@ -50,7 +50,7 @@ CHUNKS = {'time': 365}
# -----------------------------------------------------------------------------
# include day of year as predictor
-DOY = False
+DOY = True
# use digital elevation model instead of model orography
DEM = True
@@ -85,7 +85,7 @@ STRATIFY = False
# size of the validation set w.r.t. the training set
# e.g., VALID_SIZE = 0.1 means: 90% of CALIB_PERIOD for training
# 10% of CALIB_PERIOD for validation
-VALID_SIZE = 0.1
+VALID_SIZE = 0.2
# number of folds for training with KFold cross-validation
CV = 5
@@ -117,18 +117,18 @@ FILTERS = [32, 64, 128, 256]
# BernoulliGammaLoss (NLL of Bernoulli-Gamma distribution)
# BernoulliWeibullLoss (NLL of Bernoulli-Weibull distribution)
# LOSS = L1Loss()
-LOSS = MSELoss()
-# LOSS = BernoulliGammaLoss(min_amount=1)
+# LOSS = MSELoss()
+LOSS = BernoulliGammaLoss(min_amount=1)
# LOSS = BernoulliWeibullLoss(min_amount=1)
# stochastic optimization algorithm
OPTIM = torch.optim.SGD
# OPTIM = torch.optim.Adam
-OPTIM_PARAMS = {'lr': 1e-1, # learning rate
- 'weight_decay': 1e-6 # regularization rate
+OPTIM_PARAMS = {'lr': 1e-3, # learning rate
+ 'weight_decay': 0 # regularization rate
}
if OPTIM == torch.optim.SGD:
- OPTIM_PARAMS['momentum'] = 0.9
+ OPTIM_PARAMS['momentum'] = 0.99
# learning rate scheduler
# LR_SCHEDULER = torch.optim.lr_scheduler.MultiStepLR
@@ -148,14 +148,14 @@ BATCH_SIZE = 16
# network training configuration
TRAIN_CONFIG = {
'checkpoint_state': {},
- 'epochs': 250,
+ 'epochs': 50,
'save': True,
'save_loaders': False,
'early_stop': True,
- 'patience': 25,
+ 'patience': 10,
'multi_gpu': True,
'classification': False,
- 'clip_gradients': True
+ 'clip_gradients': False
}
# whether to overwrite existing models
--
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