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This commit is contained in:
Francois Chollet 2023-09-05 13:47:23 -07:00
parent 01c9f38515
commit 5fca251cf0
1 changed files with 11 additions and 14 deletions
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25
keras_core/trainers/trainer.py
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@ -88,17 +88,18 @@ class Trainer:
`keras_core.metrics.BinaryAccuracy`,
`keras_core.metrics.CategoricalAccuracy`,
`keras_core.metrics.SparseCategoricalAccuracy` based on the
shapes of the targets and of the model output. We do a similar
conversion for the strings 'crossentropy' and 'ce' as well.
shapes of the targets and of the model output. A similar
conversion is done for the strings `"crossentropy"`
and `"ce"` as well.
The metrics passed here are evaluated without sample weighting;
if you would like sample weighting to apply, you can specify
your metrics via the `weighted_metrics` argument instead.
weighted_metrics: List of metrics to be evaluated and weighted by
`sample_weight` or `class_weight` during training and testing.
run_eagerly: Bool. If `True`, this `Model`'s logic will never be
compiled (e.g. with `tf.function` or `jax.jit`). Recommended to
leave this as `False` when training for best performance, and
`True` when debugging.
run_eagerly: Bool. If `True`, this model's forward pass
will never be compiled. It is recommended to leave this
as `False` when training (for best performance),
and to set it to `True` when debugging.
steps_per_execution: Int. The number of batches to run
during each a single compiled function call. Running multiple
batches inside a single a single compiled function call can
@ -110,15 +111,11 @@ class Trainer:
`Callback.on_batch_begin` and `Callback.on_batch_end` methods
will only be called every `N` batches (i.e. before/after
each compiled function execution).
Not supported with the PyTorch backend.
jit_compile: Bool or `"auto"`. Whether to use XLA compilation when
compiling a model. This value should currently never be `True`
on the torch backed, and should always be `True` or `"auto"` on
the jax backend. On tensorflow, this value can be `True` or
`False`, and will toggle the `jit_compile` option for any
`tf.function` owned by the model. See
https://www.tensorflow.org/xla/tutorials/jit_compile for more
details. If `"auto"`, XLA compilation will be enabled if the
backend supports it, and disabled otherwise.
compiling a model. Not supported with the PyTorch backend.
If `"auto"`, XLA compilation will be enabled if the
the model supports it, and disabled otherwise.
"""
self.optimizer = optimizers.get(optimizer)
if hasattr(self, "output_names"):