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- /**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
- /*!
- * \file nn_ops.h
- * \brief
- */
- #ifndef OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_
- #define OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_
- #include "graph/operator_reg.h"
- #include "nn_pooling_ops.h"
-
- namespace ge {
- /**
- * @brief Says whether the targets are in the top "k" predictions . \n
-
- * @par Inputs:
- * Three inputs, including:
- * @li predictions: A 2D Tensor of type float32. A "batch_size * classes" tensor.
- * @li targets: A 1D Tensor of type IndexNumberType. A batch_size tensor of class ids.
- * @li k: A 1D Tensor of the same type as "targets".
- * Specifies the number of top elements to look at for computing precision . \n
-
- * @par Outputs:
- * precision: A Tensor of type bool . \n
-
- * @attention Constraints:
- * @li targets must be non-negative tensor.
-
- * @par Third-party framework compatibility
- * @li Compatible with the TensorFlow operator InTopKV2.
- */
- REG_OP(InTopKV2)
- .INPUT(predictions, TensorType({DT_FLOAT}))
- .INPUT(targets, TensorType(IndexNumberType))
- .INPUT(k, TensorType({IndexNumberType}))
- .OUTPUT(precision, TensorType({DT_BOOL}))
- .OP_END_FACTORY_REG(InTopKV2)
- }// namespace ge
- #endif // OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_
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