Class ProgbarLogger
Inherits From: Callback
Defined in tensorflow/python/keras/callbacks.py
.
Callback that prints metrics to stdout.
Arguments:
count_mode
: One of "steps" or "samples". Whether the progress bar should count samples seen or steps (batches) seen.stateful_metrics
: Iterable of string names of metrics that should not be averaged over an epoch. Metrics in this list will be logged as-is. All others will be averaged over time (e.g. loss, etc).
Raises:
ValueError
: In case of invalidcount_mode
.
__init__
__init__(
count_mode='samples',
stateful_metrics=None
)
Initialize self. See help(type(self)) for accurate signature.
Methods
tf.keras.callbacks.ProgbarLogger.on_batch_begin
on_batch_begin(
batch,
logs=None
)
tf.keras.callbacks.ProgbarLogger.on_batch_end
on_batch_end(
batch,
logs=None
)
tf.keras.callbacks.ProgbarLogger.on_epoch_begin
on_epoch_begin(
epoch,
logs=None
)
tf.keras.callbacks.ProgbarLogger.on_epoch_end
on_epoch_end(
epoch,
logs=None
)
tf.keras.callbacks.ProgbarLogger.on_train_batch_begin
on_train_batch_begin(
batch,
logs=None
)
tf.keras.callbacks.ProgbarLogger.on_train_batch_end
on_train_batch_end(
batch,
logs=None
)
tf.keras.callbacks.ProgbarLogger.on_train_begin
on_train_begin(logs=None)
tf.keras.callbacks.ProgbarLogger.on_train_end
on_train_end(logs=None)
tf.keras.callbacks.ProgbarLogger.set_model
set_model(model)
tf.keras.callbacks.ProgbarLogger.set_params
set_params(params)