def _kernel_model_fn(features, labels, mode, params, config=None):
"""model_fn for the Estimator using kernel methods.
Args:
features: `Tensor` or dict of `Tensor` (depends on data passed to `fit`).
labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
dtype `int32` or `int64` in the range `[0, n_classes)`.
mode: Defines whether this is training, evaluation or prediction. See
`ModeKeys`.
params: A dict of hyperparameters.
The following hyperparameters are expected:
* head: A `Head` instance.
* feature_columns: An iterable containing all the feature columns used by
the model.
* optimizer: string, `Optimizer` object, or callable that defines the
optimizer to use for training. If `None`, will use a FTRL optimizer.
* kernel_mappers: Dictionary of kernel mappers to be applied to the input
features before training.
config: `RunConfig` object to configure the runtime settings.
Returns:
A `ModelFnOps` instance.
Raises:
ValueError: If mode is not any of the `ModeKeys`.
"""
feature_columns = params[_FEATURE_COLUMNS]
kernel_mappers = params[_KERNEL_MAPPERS]
updated_features, updated_columns = _update_features_and_columns(
features, feature_columns, kernel_mappers)
params[_FEATURE_COLUMNS] = updated_columns
return linear._linear_model_fn( # pylint: disable=protected-access
updated_features, labels, mode, params, config)
def _kernel_model_fn(features, labels, mode, params, config=None):
"""model_fn for the Estimator using kernel methods.
Args:
features: `Tensor` or dict of `Tensor` (depends on data passed to `fit`).
labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of
dtype `int32` or `int64` in the range `[0, n_classes)`.
mode: Defines whether this is training, evaluation or prediction. See
`ModeKeys`.
params: A dict of hyperparameters.
The following hyperparameters are expected:
* head: A `Head` instance.
* feature_columns: An iterable containing all the feature columns used by
the model.
* optimizer: string, `Optimizer` object, or callable that defines the
optimizer to use for training. If `None`, will use a FTRL optimizer.
* kernel_mappers: Dictionary of kernel mappers to be applied to the input
features before training.
config: `RunConfig` object to configure the runtime settings.
Returns:
A `ModelFnOps` instance.
Raises:
ValueError: If mode is not any of the `ModeKeys`.
"""
feature_columns = params[_FEATURE_COLUMNS]
kernel_mappers = params[_KERNEL_MAPPERS]
updated_features, updated_columns = _update_features_and_columns(
features, feature_columns, kernel_mappers)
params[_FEATURE_COLUMNS] = updated_columns
return linear._linear_model_fn( # pylint: disable=protected-access
updated_features, labels, mode, params, config)