Creates a DataHandler, providing standardized access to a Dataset.
tfdf.keras.core.get_data_handler(
*args, **kwargs
)
See DataHandler for the list and definition of the arguments. See the
implementation of Model.fit(), evaluate(), or predict() methods
for complete usage examples. As a rule of tumb, get_data_handler() accepts
the same inputs as the x argument of Model.fit().
Example:
def step(iterator):
data = next(iterator)
# result <= Do something with data
return result
tf_step = tf.function(step, reduce_retracing=True)
# Assume x is a tf.data Dataset.
data_handler = data_adapter.get_data_handler(x=x)
# Epoch iteration
for epo_idx, iterator in data_handler.enumerate_epochs():
# Stop on dataset exhaustion.
with data_handler.catch_stop_iteration():
for step in data_handler.steps(): # Step iteration
step_result = step(iterator)
Args | |
|---|---|
*args
|
Arguments passed to the DataHandler constructor.
|
**kwargs
|
Arguments passed to the DataHandler constructor.
|
Returns | |
|---|---|
A DataHandler object. If the model's cluster coordinate is set (e.g. the
model was defined under a parameter-server strategy), returns a
_ClusterCoordinatorDataHandler.
|