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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.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.CustomOp;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp;
import java.nio.Buffer;
import java.util.Collections;
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();
}
private int dimension = 1;
public SoftMax(SameDiff sameDiff, SDVariable[] args) {
super(sameDiff, args, false);
}
public SoftMax(SameDiff sameDiff, SDVariable[] args, boolean inPlace) {
super(sameDiff, args, inPlace);
}
public SoftMax(SameDiff sameDiff, SDVariable[] args, int dimension) {
super(sameDiff, args, false);
this.dimension = dimension;
addIArgument(dimension);
}
public SoftMax(SameDiff sameDiff, SDVariable[] args, int dimension, boolean inPlace) {
super(sameDiff, args, inPlace);
this.dimension = dimension;
addIArgument(dimension);
}
public SoftMax(@NonNull INDArray input, int dimension){
this(input, null, dimension);
}
public SoftMax(INDArray input, INDArray result, int dimension){
super(new INDArray[]{input}, wrapOrNull(result));
this.dimension = dimension;
addIArgument(dimension);
}
public SoftMax(INDArray input){
this(input, input);
}
public SoftMax(INDArray input, INDArray result){
this(input, result, -1);
}
@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), this.dimension);
return Collections.singletonList(ret);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0).isFPType(), "Input must be a floating point type, got %s", dataTypes.get(0));
return Collections.singletonList(dataTypes.get(0));
}
}