def training_graph(self,
input_data,
input_labels,
random_seed,
data_spec,
sparse_features=None,
input_weights=None):
if input_weights is None:
input_weights = []
sparse_indices = []
sparse_values = []
sparse_shape = []
if sparse_features is not None:
sparse_indices = sparse_features.indices
sparse_values = sparse_features.values
sparse_shape = sparse_features.dense_shape
if input_data is None:
input_data = []
leaf_ids = model_ops.traverse_tree_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto)
update_model = model_ops.update_model_v4(
self.variables.tree,
leaf_ids,
input_labels,
input_weights,
params=self.params.serialized_params_proto)
finished_nodes = stats_ops.process_input_v4(
self.variables.tree,
self.variables.stats,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_labels,
input_weights,
leaf_ids,
input_spec=data_spec.SerializeToString(),
random_seed=random_seed,
params=self.params.serialized_params_proto)
with ops.control_dependencies([update_model]):
return stats_ops.grow_tree_v4(
self.variables.tree,
self.variables.stats,
finished_nodes,
params=self.params.serialized_params_proto)
def training_graph(self, input_data,
input_labels,
random_seed,
data_spec,
sparse_features=None,
input_weights=None):
if input_weights is None:
input_weights = []
sparse_indices = []
sparse_values = []
sparse_shape = []
if sparse_features is not None:
sparse_indices = sparse_features.indices
sparse_values = sparse_features.values
sparse_shape = sparse_features.dense_shape
if input_data is None:
input_data = []
leaf_ids = model_ops.traverse_tree_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto)
update_model = model_ops.update_model_v4(
self.variables.tree,
leaf_ids,
input_labels,
input_weights,
params=self.params.serialized_params_proto)
finished_nodes = stats_ops.process_input_v4(
self.variables.tree,
self.variables.stats,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_labels,
input_weights,
leaf_ids,
input_spec=data_spec.SerializeToString(),
random_seed=random_seed,
params=self.params.serialized_params_proto)
with ops.control_dependencies([update_model]):
return stats_ops.grow_tree_v4(
self.variables.tree,
self.variables.stats,
finished_nodes,
params=self.params.serialized_params_proto)
def training_graph(self,
input_data,
input_labels,
random_seed,
data_spec,
sparse_features=None,
input_weights=None):
"""Constructs a TF graph for training a random tree.
Args:
input_data: A tensor or placeholder for input data.
input_labels: A tensor or placeholder for labels associated with
input_data.
random_seed: The random number generator seed to use for this tree. 0
means use the current time as the seed.
data_spec: A data_ops.TensorForestDataSpec object specifying the
original feature/columns of the data.
sparse_features: A tf.SparseTensor for sparse input data.
input_weights: A float tensor or placeholder holding per-input weights,
or None if all inputs are to be weighted equally.
Returns:
The last op in the random tree training graph.
"""
# TODO(gilberth): Use this.
unused_epoch = math_ops.to_int32(get_epoch_variable())
if input_weights is None:
input_weights = []
sparse_indices = []
sparse_values = []
sparse_shape = []
if sparse_features is not None:
sparse_indices = sparse_features.indices
sparse_values = sparse_features.values
sparse_shape = sparse_features.dense_shape
if input_data is None:
input_data = []
leaf_ids = model_ops.traverse_tree_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto)
update_model = model_ops.update_model_v4(
self.variables.tree,
leaf_ids,
input_labels,
input_weights,
params=self.params.serialized_params_proto)
finished_nodes = stats_ops.process_input_v4(
self.variables.tree,
self.variables.stats,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_labels,
input_weights,
leaf_ids,
input_spec=data_spec.SerializeToString(),
random_seed=random_seed,
params=self.params.serialized_params_proto)
with ops.control_dependencies([update_model]):
return stats_ops.grow_tree_v4(
self.variables.tree,
self.variables.stats,
finished_nodes,
params=self.params.serialized_params_proto)
def training_graph(self,
input_data,
input_labels,
random_seed,
data_spec,
sparse_features=None,
input_weights=None):
"""Constructs a TF graph for training a random tree.
Args:
input_data: A tensor or placeholder for input data.
input_labels: A tensor or placeholder for labels associated with
input_data.
random_seed: The random number generator seed to use for this tree. 0
means use the current time as the seed.
data_spec: A data_ops.TensorForestDataSpec object specifying the
original feature/columns of the data.
sparse_features: A tf.SparseTensor for sparse input data.
input_weights: A float tensor or placeholder holding per-input weights,
or None if all inputs are to be weighted equally.
Returns:
The last op in the random tree training graph.
"""
# TODO(gilberth): Use this.
unused_epoch = math_ops.to_int32(get_epoch_variable())
if input_weights is None:
input_weights = []
sparse_indices = []
sparse_values = []
sparse_shape = []
if sparse_features is not None:
sparse_indices = sparse_features.indices
sparse_values = sparse_features.values
sparse_shape = sparse_features.dense_shape
if input_data is None:
input_data = []
leaf_ids = model_ops.traverse_tree_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto)
update_model = model_ops.update_model_v4(
self.variables.tree,
leaf_ids,
input_labels,
input_weights,
params=self.params.serialized_params_proto)
finished_nodes = stats_ops.process_input_v4(
self.variables.tree,
self.variables.stats,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_labels,
input_weights,
leaf_ids,
input_spec=data_spec.SerializeToString(),
random_seed=random_seed,
params=self.params.serialized_params_proto)
with ops.control_dependencies([update_model]):
return stats_ops.grow_tree_v4(
self.variables.tree,
self.variables.stats,
finished_nodes,
params=self.params.serialized_params_proto)
