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dlr eo: add VAL_STEPS_PER_EPOCH

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Starbeamrainbowlabs 2023-02-03 16:41:30 +00:00
parent 8446a842d1
commit e43274cd91
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 6 additions and 3 deletions
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5
aimodel/slurm-encoderonly-rainfall.job
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@ -34,7 +34,8 @@ show_help() {
echo -e " CHANNELS=8 The number of channels the input data has." >&2;
echo -e " WINDOW_SIZE=33 The window size to use when convolving the input dataset for single pixel prediction." >&2;
echo -e " STEPS_PER_EPOCH The number of steps to consider an epoch. Defaults to None, which means use the entire dataset." >&2;
echo -e " STEPS_PER_EXECUTION The number of steps to do before returning to do callbacks. High numbers boost performance. Defaults to 1." >&2;
echo -e " VAL_STEPS_PER_EPOCH The number of validation steps to consider an epoch. Defaults to None, which means use the entire dataset." >&2;
echo -e " STEPS_PER_EXECUTION The number of steps to do before returning to do callbacks. High numbers boost performance. Defaults to 1. If set then STEPS_PER_EPOCH and VAL_STEPS_PER_EPOCH must also be set." >&2;
echo -e " EPOCHS=25 The number of epochs to train for." >&2;
echo -e " LEARNING_RATE The learning rate to use. Default: 0.001." >&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;
@ -67,7 +68,7 @@ echo -e ">>> DIR_OUTPUT: ${DIR_OUTPUT}";
echo -e ">>> Additional args: ${ARGS}";
export PATH=$HOME/software/bin:$PATH;
export BATCH_SIZE DIRPATH_RAINFALLWATER PATH_HEIGHTMAP STEPS_PER_EPOCH DIRPATH_OUTPUT PATH_CHECKPOINT CHANNELS WINDOW_SIZE EPOCHS LEARNING_RATE STEPS_PER_EXECUTION;
export BATCH_SIZE DIRPATH_RAINFALLWATER PATH_HEIGHTMAP STEPS_PER_EPOCH VAL_STEPS_PER_EPOCH DIRPATH_OUTPUT PATH_CHECKPOINT CHANNELS WINDOW_SIZE EPOCHS LEARNING_RATE STEPS_PER_EXECUTION;
#LOSS ;
echo ">>> Installing requirements";

4
aimodel/src/encoderonly_test_rainfall.py
View file

@ -26,6 +26,7 @@ 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
VAL_STEPS_PER_EPOCH = int(os.environ["VAL_STEPS_PER_EPOCH"]) if "VAL_STEPS_PER_EPOCH" in os.environ else None
STEPS_PER_EXECUTION = int(os.environ["STEPS_PER_EXECUTION"]) if "STEPS_PER_EXECUTION" in os.environ else None
LEARNING_RATE = float(os.environ["LEARNING_RATE"]) if "LEARNING_RATE" in os.environ else 0.001
JIT_COMPILE = True if "JIT_COMPILE" in os.environ else False
@ -126,7 +127,7 @@ summarywriter(model, os.path.join(DIRPATH_OUTPUT, "summary.txt"))
history = model.fit(dataset_train,
validation_data=dataset_validate,
epochs=EPOCHS,
callbacks=[
tf.keras.callbacks.CSVLogger(
filename=os.path.join(DIRPATH_OUTPUT, "metrics.tsv"),
@ -143,6 +144,7 @@ history = model.fit(dataset_train,
),
],
steps_per_epoch=STEPS_PER_EPOCH,
validation_steps=VAL_STEPS_PER_EPOCH
)
logger.info(">>> Training complete")