From 9c912abe91b87844093de026bf0a1b1c70dbb22b Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Tue, 21 Dec 2021 14:56:12 +0100
Subject: [PATCH] Set learning and weight decay rates for supersampling task.
---
climax/main/config.py | 37 ++++++++++++++++++++-----------------
1 file changed, 20 insertions(+), 17 deletions(-)
diff --git a/climax/main/config.py b/climax/main/config.py
index 6e7816e..4c796a3 100644
--- a/climax/main/config.py
+++ b/climax/main/config.py
@@ -22,14 +22,14 @@ from climax.core.loss import (BernoulliGammaLoss, MSELoss, L1Loss,
# -----------------------------------------------------------------------------
# ERA5 predictor variables on pressure levels
-ERA5_P_PREDICTORS = ['geopotential', 'temperature', 'u_component_of_wind',
- 'v_component_of_wind', 'specific_humidity']
-# ERA5_P_PREDICTORS = []
+# ERA5_P_PREDICTORS = ['geopotential', 'temperature', 'u_component_of_wind',
+# 'v_component_of_wind', 'specific_humidity']
+ERA5_P_PREDICTORS = []
assert all([var in ERA5_P_VARIABLES for var in ERA5_P_PREDICTORS])
# ERA5 predictor variables on single levels
-ERA5_S_PREDICTORS = ['surface_pressure']
-# ERA5_S_PREDICTORS = ['total_precipitation']
+# ERA5_S_PREDICTORS = ['surface_pressure']
+ERA5_S_PREDICTORS = ['total_precipitation']
assert all([var in ERA5_S_VARIABLES for var in ERA5_S_PREDICTORS])
# ERA5 predictor variables
@@ -50,10 +50,10 @@ CHUNKS = {'time': 365}
# -----------------------------------------------------------------------------
# include day of year as predictor
-DOY = True
+DOY = False
# use digital elevation model instead of model orography
-DEM = True
+DEM = False
if DEM:
# remove model orography when using DEM
if 'orography' in ERA5_S_PREDICTORS:
@@ -91,14 +91,14 @@ VALID_SIZE = 0.2
CV = 5
# number of bootstrapped model trainings
-BOOTSTRAP = 10
+BOOTSTRAP=10
# -----------------------------------------------------------------------------
# Observations ----------------------------------------------------------------
# -----------------------------------------------------------------------------
# target variable: check if target variable is valid
-PREDICTAND = 'pr'
+PREDICTAND='pr'
assert PREDICTAND in PREDICTANDS
# threshold defining the minimum amount of precipitation (mm) for a wet day
@@ -122,10 +122,10 @@ FILTERS = [32, 64, 128, 256]
# for precipitation:
# BernoulliGammaLoss (NLL of Bernoulli-Gamma distribution)
# BernoulliWeibullLoss (NLL of Bernoulli-Weibull distribution)
-# LOSS = L1Loss()
-LOSS = MSELoss()
-# LOSS = BernoulliGammaLoss(min_amount=1)
-# LOSS = BernoulliWeibullLoss(min_amount=1)
+# LOSS=MSELoss()
+LOSS=MSELoss()
+# LOSS=MSELoss()
+# LOSS=MSELoss()
# batch size: number of time steps processed by the net in each iteration
BATCH_SIZE = 16
@@ -164,17 +164,20 @@ if PREDICTAND is 'pr':
WEIGHT_DECAY = 1e-5
if isinstance(LOSS, MSELoss):
MAX_LR = 0.0004
- WEIGHT_DECAY = 1e-3
+ # weight decay rates for supersampling task
+ if not any(ERA5_P_PREDICTORS) and ERA5_S_PREDICTORS == 'pr':
+ WEIGHT_DECAY = 1e-5
+ else:
+ WEIGHT_DECAY = 1e-3
if isinstance(LOSS, BernoulliGammaLoss):
# learning rates for supersampling task
if not any(ERA5_P_PREDICTORS) and ERA5_S_PREDICTORS == 'pr':
MAX_LR = 0.001
+ WEIGHT_DECAY = 1e-4
else:
MAX_LR = 0.0005 if OPTIM is torch.optim.Adam else 0.001
-
- # weight decay
- WEIGHT_DECAY = 1e-2
+ WEIGHT_DECAY = 1e-2
# base learning rate: MAX_LR / 4 (Smith L. (2017))
BASE_LR = MAX_LR / 4
--
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