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https://github.com/sbrl/research-rainfallradar
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Starbeamrainbowlabs
5f8d6dc6ea
this is important, because with large batches it can be difficult to tell what's happening inside each epoch.
34 lines
935 B
Python
34 lines
935 B
Python
import os
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import tensorflow as tf
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from ..components.CallbackCustomModelCheckpoint import CallbackCustomModelCheckpoint
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from ..components.CallbackNBatchCsv import CallbackNBatchCsv
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def make_callbacks(dirpath, model_predict):
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dirpath_checkpoints = os.path.join(dirpath, "checkpoints")
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filepath_metrics = os.path.join(dirpath, "metrics.tsv")
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filepath_metrics_batch = os.path.join(dirpath, "metrics_batch64.tsv")
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if not os.path.exists(dirpath_checkpoints):
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os.mkdir(dirpath_checkpoints)
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return [
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CallbackCustomModelCheckpoint(
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model_to_checkpoint=model_predict,
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filepath=os.path.join(
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dirpath_checkpoints,
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"checkpoint_e{epoch:d}_loss{loss:.3f}.hdf5"
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),
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monitor="loss"
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),
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tf.keras.callbacks.CSVLogger(
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filename=filepath_metrics,
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separator="\t"
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),
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CallbackNBatchCsv(
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filepath=filepath_metrics_batch,
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n_batches=64
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),
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tf.keras.callbacks.ProgbarLogger(count_mode="steps") # batches
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] |