sbrl-github/research-rainfallradar
1
0
Fork 0
mirror of https://github.com/sbrl/research-rainfallradar synced 2024-06-30 18:44:55 +00:00
Code Issues Packages Projects Releases Wiki Activity

Make dirpath_rainfallwater consistent with other experiments

Browse source
This commit is contained in:
Starbeamrainbowlabs 2023-01-20 20:31:26 +00:00
parent e72d3991b8
commit 818d77c733
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
1 changed files with 5 additions and 4 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

9
aimodel/src/encoderonly_test_rainfall.py
View file

@ -17,17 +17,18 @@ from lib.ai.helpers.summarywriter import summarywriter
# ███████ ██ ████ ████
# TODO: env vars & settings here
DIRPATH_INPUT = os.environ["DIRPATH_INPUT"]
DIRPATH_RAINFALLWATER = os.environ["DIRPATH_RAINFALLWATER"]
DIRPATH_OUTPUT = os.environ["DIRPATH_OUTPUT"]
PATH_HEIGHTMAP = os.environ["PATH_HEIGHTMAP"] if "PATH_HEIGHTMAP" in os.environ else None
CHANNELS = os.environ["CHANNELS"] if "CHANNELS" in os.environ else 8
EPOCHS = int(os.environ["EPOCHS"]) if "EPOCHS" in os.environ else 25
BATCH_SIZE = int(os.environ["BATCH_SIZE"]) if "BATCH_SIZE" in os.environ else 64
WINDOW_SIZE = int(os.environ["WINDOW_SIZE"]) if "WINDOW_SIZE" in os.environ else 33
STEPS_PER_EPOCH = int(os.environ["STEPS_PER_EPOCH"]) if "STEPS_PER_EPOCH" in os.environ else None
logger.info("Encoder-only rainfall radar TEST")
logger.info(f"> DIRPATH_INPUT {DIRPATH_INPUT}")
logger.info(f"> DIRPATH_RAINFALLWATER {DIRPATH_RAINFALLWATER}")
logger.info(f"> DIRPATH_OUTPUT {DIRPATH_OUTPUT}")
logger.info(f"> PATH_HEIGHTMAP {PATH_HEIGHTMAP}")
logger.info(f"> CHANNELS {CHANNELS}")
@ -47,7 +48,7 @@ if not os.path.exists(DIRPATH_OUTPUT):
# ██████ ██ ██ ██ ██ ██ ███████ ███████ ██
dataset_train, dataset_validate = dataset_encoderonly(
dirpath_input=DIRPATH_INPUT,
dirpath_input=DIRPATH_RAINFALLWATER,
filepath_heightmap=PATH_HEIGHTMAP,
batch_size=BATCH_SIZE,
windowsize=WINDOW_SIZE,
@ -112,7 +113,7 @@ summarywriter(model, os.path.join(DIRPATH_OUTPUT, "summary.txt"))
history = model.fit(dataset_train,
validation_data=dataset_validate,
epochs=25,
epochs=EPOCHS,
callbacks=[
tf.keras.callbacks.CSVLogger(