softmax.hpp

//*****************************************************************************
// Copyright 2017-2021 Intel Corporation
//
// 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.
//*****************************************************************************
 
#pragma once
 
#include "ngraph/op/op.hpp"
 
namespace ngraph
{
    namespace op
    {
        namespace v1
        {
            class NGRAPH_API Softmax : public Op
            {
            public:
                static constexpr NodeTypeInfo type_info{"Softmax", 1};
                const NodeTypeInfo& get_type_info() const override { return type_info; }
                Softmax()
                    : m_axis(0)
                {
                }
                /// \brief Constructs a softmax operation.
                ///
                /// \param arg Node that produces the first input tensor.<br>
                /// `[d0, ...]`
                /// \param axis The axis position (0-based) on which to calculate the softmax.
                ///
                /// Output `[d0, ...]`
                ///
                Softmax(const Output<Node>& arg, const size_t axis = 1);
 
                bool visit_attributes(AttributeVisitor& visitor) override;
                void validate_and_infer_types() override;
 
                size_t get_version() const override { return 1; }
                virtual std::shared_ptr<Node>
                    clone_with_new_inputs(const OutputVector& new_args) const override;
 
                size_t get_axis() const { return m_axis; }
                void set_axis(const size_t axis) { m_axis = axis; }
                bool evaluate(const HostTensorVector& outputs,
                              const HostTensorVector& inputs) const override;
 
            private:
                size_t m_axis;
            };
        }
    }
}