def __init__(
self, # _joint_weights: pylint: disable=invalid-name
feature_columns,
model_dir=None,
weight_column_name=None,
optimizer=None,
gradient_clip_norm=None,
enable_centered_bias=None,
target_dimension=1,
_joint_weights=False,
config=None,
feature_engineering_fn=None):
"""Construct a `LinearRegressor` estimator object.
Args:
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph, etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Ftrl optimizer.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
target_dimension: dimension of the target for multilabels.
_joint_weights: If True use a single (possibly partitioned) variable to
store the weights. It's faster, but requires all feature columns are
sparse and have the 'sum' combiner. Incompatible with SDCAOptimizer.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into the model.
Returns:
A `LinearRegressor` estimator.
"""
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
self._joint_weights = _joint_weights
super(LinearRegressor,
self).__init__(model_dir=model_dir,
weight_column_name=weight_column_name,
linear_feature_columns=feature_columns,
linear_optimizer=optimizer,
_joint_linear_weights=_joint_weights,
gradient_clip_norm=gradient_clip_norm,
enable_centered_bias=enable_centered_bias,
target_dimension=target_dimension,
config=config,
feature_engineering_fn=feature_engineering_fn)
def __init__(self, # _joint_weights: pylint: disable=invalid-name
feature_columns,
model_dir=None,
weight_column_name=None,
optimizer=None,
gradient_clip_norm=None,
enable_centered_bias=None,
target_dimension=1,
_joint_weights=False,
config=None,
feature_engineering_fn=None):
"""Construct a `LinearRegressor` estimator object.
Args:
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph, etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Ftrl optimizer.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
target_dimension: dimension of the target for multilabels.
_joint_weights: If True use a single (possibly partitioned) variable to
store the weights. It's faster, but requires all feature columns are
sparse and have the 'sum' combiner. Incompatible with SDCAOptimizer.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into the model.
Returns:
A `LinearRegressor` estimator.
"""
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
self._joint_weights = _joint_weights
super(LinearRegressor, self).__init__(
model_dir=model_dir,
weight_column_name=weight_column_name,
linear_feature_columns=feature_columns,
linear_optimizer=optimizer,
_joint_linear_weights=_joint_weights,
gradient_clip_norm=gradient_clip_norm,
enable_centered_bias=enable_centered_bias,
target_dimension=target_dimension,
config=config,
feature_engineering_fn=feature_engineering_fn)
def __init__(self, # _joint_weight pylint: disable=invalid-name
feature_columns,
model_dir=None,
n_classes=2,
weight_column_name=None,
optimizer=None,
gradient_clip_norm=None,
enable_centered_bias=None,
_joint_weight=False,
config=None,
feature_engineering_fn=None):
"""Construct a `LinearClassifier` estimator object.
Args:
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
n_classes: number of target classes. Default is binary classification.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: The optimizer used to train the model. If specified, it should
be either an instance of `tf.Optimizer` or the SDCAOptimizer. If `None`,
the Ftrl optimizer will be used.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
_joint_weight: If True, the weights for all columns will be stored in a
single (possibly partitioned) variable. It's more efficient, but it's
incompatible with SDCAOptimizer, and requires all feature columns are
sparse and use the 'sum' combiner.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into the model.
Returns:
A `LinearClassifier` estimator.
Raises:
ValueError: if n_classes < 2.
"""
# TODO(zoy): Give an unsupported error if enable_centered_bias is
# requested for SDCA once its default changes to False.
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
self._model_dir = model_dir or tempfile.mkdtemp()
if n_classes < 2:
raise ValueError("Classification requires n_classes >= 2")
self._n_classes = n_classes
self._feature_columns = feature_columns
assert self._feature_columns
self._weight_column_name = weight_column_name
self._optimizer = _get_default_optimizer(feature_columns)
if optimizer:
self._optimizer = _get_optimizer(optimizer)
num_ps_replicas = config.num_ps_replicas if config else 0
if isinstance(optimizer, sdca_optimizer.SDCAOptimizer):
assert not _joint_weight, ("_joint_weight is incompatible with the"
" SDCAOptimizer")
model_fn = sdca_classifier_model_fn
params = {
"feature_columns": feature_columns,
"optimizer": self._optimizer,
"weight_column_name": weight_column_name,
"loss_type": "logistic_loss",
}
else:
model_fn = _linear_classifier_model_fn
params = {
"n_classes": n_classes,
"weight_column_name": weight_column_name,
"feature_columns": feature_columns,
"optimizer": self._optimizer,
"gradient_clip_norm": gradient_clip_norm,
"enable_centered_bias": enable_centered_bias,
"num_ps_replicas": num_ps_replicas,
"joint_weights": _joint_weight,
}
self._estimator = estimator.Estimator(
model_fn=model_fn,
model_dir=self._model_dir,
config=config,
params=params,
feature_engineering_fn=feature_engineering_fn)
def __init__(
self, # _joint_weight pylint: disable=invalid-name
feature_columns,
model_dir=None,
n_classes=2,
weight_column_name=None,
optimizer=None,
gradient_clip_norm=None,
enable_centered_bias=None,
_joint_weight=False,
config=None,
feature_engineering_fn=None):
"""Construct a `LinearClassifier` estimator object.
Args:
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
n_classes: number of target classes. Default is binary classification.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: The optimizer used to train the model. If specified, it should
be either an instance of `tf.Optimizer` or the SDCAOptimizer. If `None`,
the Ftrl optimizer will be used.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
_joint_weight: If True, the weights for all columns will be stored in a
single (possibly partitioned) variable. It's more efficient, but it's
incompatible with SDCAOptimizer, and requires all feature columns are
sparse and use the 'sum' combiner.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into the model.
Returns:
A `LinearClassifier` estimator.
Raises:
ValueError: if n_classes < 2.
"""
# TODO(zoy): Give an unsupported error if enable_centered_bias is
# requested for SDCA once its default changes to False.
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
self._model_dir = model_dir or tempfile.mkdtemp()
if n_classes < 2:
raise ValueError("Classification requires n_classes >= 2")
self._n_classes = n_classes
self._feature_columns = feature_columns
assert self._feature_columns
self._weight_column_name = weight_column_name
self._optimizer = _get_default_optimizer(feature_columns)
if optimizer:
self._optimizer = _get_optimizer(optimizer)
num_ps_replicas = config.num_ps_replicas if config else 0
chief_hook = None
if isinstance(optimizer, sdca_optimizer.SDCAOptimizer):
assert not _joint_weight, ("_joint_weight is incompatible with the"
" SDCAOptimizer")
model_fn = sdca_classifier_model_fn
# We use a hook to perform the weight update and shrink step only on the
# chief. Because the SdcaModel constructed by the estimator within the
# call to fit() but we need to pass the hook to fit(), we pass the hook
# as a parameter to the model_fn and have that propagate the model to the
# hook.
chief_hook = _SdcaUpdateWeightsHook()
params = {
"feature_columns": feature_columns,
"optimizer": self._optimizer,
"weight_column_name": weight_column_name,
"loss_type": "logistic_loss",
"update_weights_hook": chief_hook,
}
else:
model_fn = _linear_classifier_model_fn
params = {
"n_classes": n_classes,
"weight_column_name": weight_column_name,
"feature_columns": feature_columns,
"optimizer": self._optimizer,
"gradient_clip_norm": gradient_clip_norm,
"enable_centered_bias": enable_centered_bias,
"num_ps_replicas": num_ps_replicas,
"joint_weights": _joint_weight,
}
self._estimator = estimator.Estimator(
model_fn=model_fn,
model_dir=self._model_dir,
config=config,
params=params,
feature_engineering_fn=feature_engineering_fn)
self._additional_run_hook = None
if self._estimator.config.is_chief:
self._additional_run_hook = chief_hook
def __init__(self,
hidden_units,
feature_columns,
model_dir=None,
weight_column_name=None,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=None,
config=None,
feature_engineering_fn=None):
"""Initializes a `DNNRegressor` instance.
Args:
hidden_units: List of hidden units per layer. All layers are fully
connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
has 32.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can also
be used to load checkpoints from the directory into a estimator to continue
training a previously saved model.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
use `tf.nn.relu`.
dropout: When not `None`, the probability we will drop out a given
coordinate.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into the model.
Returns:
A `DNNRegressor` estimator.
"""
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
super(DNNRegressor,
self).__init__(model_dir=model_dir,
weight_column_name=weight_column_name,
dnn_feature_columns=feature_columns,
dnn_optimizer=optimizer,
dnn_hidden_units=hidden_units,
dnn_activation_fn=activation_fn,
dnn_dropout=dropout,
gradient_clip_norm=gradient_clip_norm,
enable_centered_bias=enable_centered_bias,
config=config,
feature_engineering_fn=feature_engineering_fn)
self.feature_columns = feature_columns
self.optimizer = optimizer
self.activation_fn = activation_fn
self.dropout = dropout
self.hidden_units = hidden_units
self._feature_columns_inferred = False
def __init__(self,
hidden_units,
feature_columns,
model_dir=None,
n_classes=2,
weight_column_name=None,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=None,
config=None,
feature_engineering_fn=None):
"""Initializes a DNNClassifier instance.
Args:
hidden_units: List of hidden units per layer. All layers are fully
connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
has 32.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can also
be used to load checkpoints from the directory into a estimator to continue
training a previously saved model.
n_classes: number of target classes. Default is binary classification.
It must be greater than 1.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
use `tf.nn.relu`.
dropout: When not `None`, the probability we will drop out a given
coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are
clipped to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into the model.
Returns:
A `DNNClassifier` estimator.
Raises:
ValueError: If `n_classes` < 2.
"""
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
self._hidden_units = hidden_units
self._feature_columns = feature_columns
self._model_dir = model_dir or tempfile.mkdtemp()
if n_classes <= 1:
raise ValueError(
"Classification requires n_classes >= 2. Given: {}".format(
n_classes))
self._n_classes = n_classes
self._weight_column_name = weight_column_name
optimizer = optimizer or "Adagrad"
num_ps_replicas = config.num_ps_replicas if config else 0
self._estimator = estimator.Estimator(
model_fn=_dnn_classifier_model_fn,
model_dir=self._model_dir,
config=config,
params={
"hidden_units": hidden_units,
"feature_columns": feature_columns,
"n_classes": n_classes,
"weight_column_name": weight_column_name,
"optimizer": optimizer,
"activation_fn": activation_fn,
"dropout": dropout,
"gradient_clip_norm": gradient_clip_norm,
"enable_centered_bias": enable_centered_bias,
"num_ps_replicas": num_ps_replicas,
},
feature_engineering_fn=feature_engineering_fn)
def __init__(self,
hidden_units,
feature_columns,
model_dir=None,
weight_column_name=None,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=None,
config=None,
feature_engineering_fn=None):
"""Initializes a `DNNRegressor` instance.
Args:
hidden_units: List of hidden units per layer. All layers are fully
connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
has 32.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can also
be used to load checkpoints from the directory into a estimator to continue
training a previously saved model.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
use `tf.nn.relu`.
dropout: When not `None`, the probability we will drop out a given
coordinate.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into the model.
Returns:
A `DNNRegressor` estimator.
"""
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
super(DNNRegressor, self).__init__(
model_dir=model_dir,
weight_column_name=weight_column_name,
dnn_feature_columns=feature_columns,
dnn_optimizer=optimizer,
dnn_hidden_units=hidden_units,
dnn_activation_fn=activation_fn,
dnn_dropout=dropout,
gradient_clip_norm=gradient_clip_norm,
enable_centered_bias=enable_centered_bias,
config=config,
feature_engineering_fn=feature_engineering_fn)
self.feature_columns = feature_columns
self.optimizer = optimizer
self.activation_fn = activation_fn
self.dropout = dropout
self.hidden_units = hidden_units
self._feature_columns_inferred = False
def __init__(self,
hidden_units,
feature_columns,
model_dir=None,
n_classes=2,
weight_column_name=None,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=None,
config=None,
feature_engineering_fn=None):
"""Initializes a DNNClassifier instance.
Args:
hidden_units: List of hidden units per layer. All layers are fully
connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
has 32.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can also
be used to load checkpoints from the directory into a estimator to continue
training a previously saved model.
n_classes: number of target classes. Default is binary classification.
It must be greater than 1.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
use `tf.nn.relu`.
dropout: When not `None`, the probability we will drop out a given
coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are
clipped to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
targets which are the output of `input_fn` and
returns features and targets which will be fed
into the model.
Returns:
A `DNNClassifier` estimator.
Raises:
ValueError: If `n_classes` < 2.
"""
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
self._hidden_units = hidden_units
self._feature_columns = feature_columns
self._model_dir = model_dir or tempfile.mkdtemp()
if n_classes <= 1:
raise ValueError(
"Classification requires n_classes >= 2. Given: {}".format(n_classes))
self._n_classes = n_classes
self._weight_column_name = weight_column_name
optimizer = optimizer or "Adagrad"
num_ps_replicas = config.num_ps_replicas if config else 0
self._estimator = estimator.Estimator(
model_fn=_dnn_classifier_model_fn,
model_dir=self._model_dir,
config=config,
params={
"hidden_units": hidden_units,
"feature_columns": feature_columns,
"n_classes": n_classes,
"weight_column_name": weight_column_name,
"optimizer": optimizer,
"activation_fn": activation_fn,
"dropout": dropout,
"gradient_clip_norm": gradient_clip_norm,
"enable_centered_bias": enable_centered_bias,
"num_ps_replicas": num_ps_replicas,
},
feature_engineering_fn=feature_engineering_fn)
def __init__(self,
feature_columns,
model_dir=None,
n_classes=2,
weight_column_name=None,
optimizer=None,
gradient_clip_norm=None,
enable_centered_bias=None,
config=None):
"""Construct a `LinearClassifier` estimator object.
Args:
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
n_classes: number of target classes. Default is binary classification.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: The optimizer used to train the model. If specified, it should
be either an instance of `tf.Optimizer` or the SDCAOptimizer. If `None`,
the Ftrl optimizer will be used.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
Returns:
A `LinearClassifier` estimator.
Raises:
ValueError: if n_classes < 2.
"""
# TODO(zoy): Give an unsupported error if enable_centered_bias is
# requested for SDCA once its default changes to False.
if enable_centered_bias is None:
enable_centered_bias = True
dnn_linear_combined._changing_default_center_bias() # pylint: disable=protected-access
self._model_dir = model_dir or tempfile.mkdtemp()
if n_classes < 2:
raise ValueError("Classification requires n_classes >= 2")
self._n_classes = n_classes
self._feature_columns = feature_columns
assert self._feature_columns
self._weight_column_name = weight_column_name
self._optimizer = _get_default_optimizer(feature_columns)
if optimizer:
self._optimizer = _get_optimizer(optimizer)
num_ps_replicas = config.num_ps_replicas if config else 0
if isinstance(optimizer, sdca_optimizer.SDCAOptimizer):
model_fn = sdca_classifier_model_fn
params = {
"feature_columns": feature_columns,
"optimizer": self._optimizer,
"weight_column_name": weight_column_name,
"loss_type": "logistic_loss",
}
else:
model_fn = _linear_classifier_model_fn
params = {
"n_classes": n_classes,
"weight_column_name": weight_column_name,
"feature_columns": feature_columns,
"optimizer": self._optimizer,
"gradient_clip_norm": gradient_clip_norm,
"enable_centered_bias": enable_centered_bias,
"num_ps_replicas": num_ps_replicas,
}
self._estimator = estimator.Estimator(model_fn=model_fn,
model_dir=self._model_dir,
config=config,
params=params)
