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)