def inference_graph(self, input_data, data_spec, sparse_features=None):
"""Constructs a TF graph for evaluating a random tree.
Args:
input_data: A tensor or placeholder for input data.
data_spec: A TensorForestDataSpec proto specifying the original
input columns.
sparse_features: A tf.SparseTensor for sparse input data.
Returns:
A tuple of (probabilities, tree_paths).
"""
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 = []
return model_ops.tree_predictions_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto)
def inference_graph(self, input_data, data_spec, sparse_features=None):
"""Constructs a TF graph for evaluating a random tree.
Args:
input_data: A tensor or placeholder for input data.
data_spec: A TensorForestDataSpec proto specifying the original
input columns.
sparse_features: A tf.SparseTensor for sparse input data.
Returns:
A tuple of (probabilities, tree_paths).
"""
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 = []
return model_ops.tree_predictions_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto)
def inference_graph(self, input_data, data_spec, sparse_features=None):
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 = []
return model_ops.tree_predictions_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto)
def inference_graph(self, input_data, data_spec, sparse_features=None):
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 = []
return model_ops.tree_predictions_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto)
