experiments/STT-tensorflow
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add some more comments for the dir param that used by ModelCheckpoint, BackupAndRestore, TensorBoard callbacks that ask end user to use separate directories for different callbacks.

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This is to guide user with correct usage.

PiperOrigin-RevId: 345737485
Change-Id: Ic4029f4aa24156861cbdc062abf2a17d602d925a
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A. Unique TensorFlower 2020-12-04 12:49:54 -08:00 committed by TensorFlower Gardener
parent 2e9507af08
commit 023a9b14f8
1 changed files with 14 additions and 9 deletions
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23
tensorflow/python/keras/callbacks.py
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@ -1172,12 +1172,14 @@ class ModelCheckpoint(Callback):
``` ```
Arguments: Arguments:
filepath: string or `PathLike`, path to save the model file. `filepath` filepath: string or `PathLike`, path to save the model file. e.g.
filepath = os.path.join(working_dir, 'ckpt', file_name). `filepath`
can contain named formatting options, which will be filled the value of can contain named formatting options, which will be filled the value of
`epoch` and keys in `logs` (passed in `on_epoch_end`). For example: if `epoch` and keys in `logs` (passed in `on_epoch_end`). For example: if
`filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`, then the model `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`, then the model
checkpoints will be saved with the epoch number and the validation loss checkpoints will be saved with the epoch number and the validation loss
in the filename. in the filename. The directory of the filepath should not be reused by
any other callbacks to avoid conflicts.
monitor: The metric name to monitor. Typically the metrics are set by the monitor: The metric name to monitor. Typically the metrics are set by the
`Model.compile` method. Note: `Model.compile` method. Note:
@ -1573,7 +1575,7 @@ class BackupAndRestore(Callback):
... if epoch == 4: ... if epoch == 4:
... raise RuntimeError('Interrupting!') ... raise RuntimeError('Interrupting!')
>>> callback = tf.keras.callbacks.experimental.BackupAndRestore( >>> callback = tf.keras.callbacks.experimental.BackupAndRestore(
... backup_dir="/tmp") ... backup_dir="/tmp/backup")
>>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
>>> model.compile(tf.keras.optimizers.SGD(), loss='mse') >>> model.compile(tf.keras.optimizers.SGD(), loss='mse')
>>> try: >>> try:
@ -1590,11 +1592,13 @@ class BackupAndRestore(Callback):
6 6
Arguments: Arguments:
backup_dir: String, path to save the model file. This is the directory in backup_dir: String, path to store the checkpoint.
which the system stores temporary files to recover the model from jobs e.g. backup_dir = os.path.join(working_dir, 'backup')
terminated unexpectedly. The directory cannot be reused elsewhere to This is the directory in which the system stores temporary files to
store other checkpoints, e.g. by BackupAndRestore callback of another recover the model from jobs terminated unexpectedly. The directory
training, or by another callback (ModelCheckpoint) of the same training. cannot be reused elsewhere to store other files, e.g. by
BackupAndRestore callback of another training, or by another callback
(ModelCheckpoint) of the same training.
""" """
def __init__(self, backup_dir): def __init__(self, backup_dir):
@ -1991,7 +1995,8 @@ class TensorBoard(Callback, version_utils.TensorBoardVersionSelector):
Arguments: Arguments:
log_dir: the path of the directory where to save the log files to be log_dir: the path of the directory where to save the log files to be
parsed by TensorBoard. parsed by TensorBoard. e.g. log_dir = os.path.join(working_dir, 'logs')
This directory should not be reused by any other callbacks.
histogram_freq: frequency (in epochs) at which to compute activation and histogram_freq: frequency (in epochs) at which to compute activation and
weight histograms for the layers of the model. If set to 0, histograms weight histograms for the layers of the model. If set to 0, histograms
won't be computed. Validation data (or split) must be specified for won't be computed. Validation data (or split) must be specified for