sbrl-github/research-rainfallradar
1
0
Fork 0
mirror of https://github.com/sbrl/research-rainfallradar synced 2024-11-22 09:13:01 +00:00
Code Issues Projects Releases Packages Wiki Activity

dlr: add learning_rate env var

Browse source
This commit is contained in:
Starbeamrainbowlabs 2023-01-13 18:29:39 +00:00
parent 2f0ce0aa13
commit 7b10f5c5fe
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 4 additions and 2 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
aimodel/slurm-TEST-deeplabv3p-rainfall.job
View file

@ -34,6 +34,7 @@ show_help() {
echo -e " NO_REMOVE_ISOLATED_PIXELS Set to any value to avoid the engine from removing isolated pixels - that is, water pixels with no other surrounding pixels, either side to side to diagonally." >&2; echo -e " NO_REMOVE_ISOLATED_PIXELS Set to any value to avoid the engine from removing isolated pixels - that is, water pixels with no other surrounding pixels, either side to side to diagonally." >&2;
echo -e " EPOCHS The number of epochs to train for." >&2; echo -e " EPOCHS The number of epochs to train for." >&2;
echo -e " LOSS The loss function to use. Default: cross-entropy (possible values: cross-entropy, cross-entropy-dice)." >&2; echo -e " LOSS The loss function to use. Default: cross-entropy (possible values: cross-entropy, cross-entropy-dice)." >&2;
echo -e " LEARNING_RATE The learning rate to use. Default: 0.001." >&2;
echo -e " PATH_CHECKPOINT The path to a checkcpoint to load. If specified, a model will be loaded instead of being trained." >&2; echo -e " PATH_CHECKPOINT The path to a checkcpoint to load. If specified, a model will be loaded instead of being trained." >&2;
echo -e " PREDICT_COUNT The number of items from the (SCRAMBLED) dataset to make a prediction for." >&2; echo -e " PREDICT_COUNT The number of items from the (SCRAMBLED) dataset to make a prediction for." >&2;
echo -e " POSTFIX Postfix to append to the output dir (auto calculated)." >&2; echo -e " POSTFIX Postfix to append to the output dir (auto calculated)." >&2;
@ -61,7 +62,7 @@ DIR_OUTPUT="output/$(date -u --rfc-3339=date)_${CODE}";
echo -e ">>> Additional args: ${ARGS}"; echo -e ">>> Additional args: ${ARGS}";
export PATH=$HOME/software/bin:$PATH; export PATH=$HOME/software/bin:$PATH;
export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS PREDICT_COUNT NO_REMOVE_ISOLATED_PIXELS LOSS; export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS PREDICT_COUNT NO_REMOVE_ISOLATED_PIXELS LOSS LEARNING_RATE;
echo ">>> Installing requirements"; echo ">>> Installing requirements";
conda run -n py38 pip install -q -r requirements.txt; conda run -n py38 pip install -q -r requirements.txt;

3
aimodel/src/deeplabv3_plus_test_rainfall.py
View file

@ -29,6 +29,7 @@ STEPS_PER_EPOCH = int(os.environ["STEPS_PER_EPOCH"]) if "STEPS_PER_EPOCH" in os.
REMOVE_ISOLATED_PIXELS = FALSE if "NO_REMOVE_ISOLATED_PIXELS" in os.environ else True REMOVE_ISOLATED_PIXELS = FALSE if "NO_REMOVE_ISOLATED_PIXELS" in os.environ else True
EPOCHS = int(os.environ["EPOCHS"]) if "EPOCHS" in os.environ else 25 EPOCHS = int(os.environ["EPOCHS"]) if "EPOCHS" in os.environ else 25
LOSS = os.environ["LOSS"] if "LOSS" in os.environ else "cross-entropy" LOSS = os.environ["LOSS"] if "LOSS" in os.environ else "cross-entropy"
LEARNING_RATE = float(os.environ["LEARNING_RATE"]) if "LEARNING_RATE" in os.environ else 0.001
DIR_OUTPUT=os.environ["DIR_OUTPUT"] if "DIR_OUTPUT" in os.environ else f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_rainfall_TEST" DIR_OUTPUT=os.environ["DIR_OUTPUT"] if "DIR_OUTPUT" in os.environ else f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_rainfall_TEST"
@ -165,7 +166,7 @@ if PATH_CHECKPOINT is None:
raise Exception(f"Error: Unknown loss function '{LOSS}' (possible values: cross-entropy, cross-entropy-dice).") raise Exception(f"Error: Unknown loss function '{LOSS}' (possible values: cross-entropy, cross-entropy-dice).")
model.compile( model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE),
loss=loss_fn, loss=loss_fn,
metrics=["accuracy"], metrics=["accuracy"],
) )