org.nd4j.linalg.activations.impl.ActivationTanH Maven / Gradle / Ivy
/* *****************************************************************************
* 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.activations.impl;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import org.nd4j.linalg.api.ops.impl.transforms.gradient.TanhDerivative;
import org.nd4j.common.primitives.Pair;
import org.nd4j.linalg.activations.BaseActivationFunction;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.strict.Tanh;
import org.nd4j.linalg.factory.Nd4j;
/**
* f(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
*/
@EqualsAndHashCode(callSuper = false)
@Getter
public class ActivationTanH extends BaseActivationFunction {
@Override
public INDArray getActivation(INDArray in, boolean training) {
Nd4j.getExecutioner().execAndReturn(new Tanh(in));
return in;
}
@Override
public Pair backprop(INDArray in, INDArray epsilon) {
assertShape(in, epsilon);
Nd4j.getExecutioner().execAndReturn(new TanhDerivative(in, epsilon, in));
return new Pair<>(in, null);
}
@Override
public String toString() {
return "tanh";
}
}