org.nd4j.linalg.api.ops.impl.transforms.SoftMax Maven / Gradle / Ivy

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/*-
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    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.
 *
 *
 */

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

import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;

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

/**
 * Soft max function
 * row_maxes is a row vector (max for each row)
 * row_maxes = rowmaxes(input)
 * diff = exp(input - max) / diff.rowSums()
 * Outputs a probability distribution.
 * Note that this is a parameterized model and requires
 * the sum and max for the vector being calculated
 *
 * @author Adam Gibson
 */

public class SoftMax extends BaseDynamicTransformOp {
    public SoftMax() {
        super();
    }

    public SoftMax(SameDiff sameDiff, SDVariable[] args) {
        super(sameDiff, args, false);
    }


    public SoftMax(SameDiff sameDiff, SDVariable[] args, boolean inPlace) {
        super(sameDiff, args, inPlace);
    }


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


    @Override
    public String onnxName() {
        return "Softmax";
    }

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


    @Override
    public List doDiff(List i_v) {
        SDVariable ret = f().softmaxDerivative(arg(), i_v.get(0));
        return Arrays.asList(ret);
    }
}