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/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

package org.nd4j.linalg.api.ops.impl.transforms;

import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseTransformOp;

import java.util.Arrays;
import java.util.List;

/**
 * ELU: Exponential Linear Unit (alpha=1.0)

 * Introduced in paper:

 * Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

 * Djork-Arn?? Clevert, Thomas Unterthiner, Sepp Hochreiter (2015)

 * http://arxiv.org/abs/1511.07289
 *
 * @author Alex Black
 */
public class ELU extends BaseTransformOp {
    public ELU(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
        super(sameDiff, i_v, inPlace);
    }

    public ELU(SameDiff sameDiff, SDVariable i_v, long[] shape, boolean inPlace, Object[] extraArgs) {
        super(sameDiff, i_v, shape, inPlace, extraArgs);
    }

    public ELU(SameDiff sameDiff, SDVariable i_v, Object[] extraArgs) {
        super(sameDiff, i_v, extraArgs);
    }

    public ELU() {
    }

    public ELU(INDArray x, INDArray z) {
        super(x, z);
    }

    public ELU(INDArray x, INDArray z, long n) {
        super(x, z, n);
    }

    public ELU(INDArray x, INDArray y, INDArray z, long n) {
        super(x, y, z, n);
    }

    public ELU(INDArray x, INDArray y, INDArray z) {
        super(x, y, z, x.lengthLong());
    }

    public ELU(INDArray x) {
        super(x);
    }

    @Override
    public int opNum() {
        return 21;
    }

    @Override
    public String opName() {
        return "elu";
    }

    @Override
    public String onnxName() {
        throw new NoOpNameFoundException("No onnx op opName found for " + opName());
    }

    @Override
    public String tensorflowName() {
        return "Elu";
    }

    @Override
    public List doDiff(List i_v) {
        //ELU: e^x-1 if x<0, x otherwise
        //dL/dIn = dL/Out * dOut/dIn
        SDVariable ret = f().eluDerivative(arg()).mul(i_v.get(0));
        return Arrays.asList(ret);
    }

}