Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.layers.recurrent;
import lombok.NoArgsConstructor;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
@NoArgsConstructor
public class GRU extends DynamicCustomOp {
public GRU(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable hI, @NonNull SDVariable Wx, @NonNull SDVariable Wh, @NonNull SDVariable biases) {
super(null, sameDiff, new SDVariable[]{x, hI, Wx, Wh, biases});
}
public GRU(@NonNull INDArray x, @NonNull INDArray hI, @NonNull INDArray Wx, @NonNull INDArray Wh, @NonNull INDArray biases) {
super(new INDArray[]{x, hI, Wx, Wh, biases}, null);
}
@Override
public List calculateOutputDataTypes(List inputDataTypes) {
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 5, "Expected 5 inputs to GRU: initial cell output, input-to-hidden weights, hidden-to-hidden weights and biases got %s", inputDataTypes);
DataType dt = inputDataTypes.get(1);
for (int i = 0; i < inputDataTypes.size(); i++) {
Preconditions.checkState(inputDataTypes.get(i).isFPType(), "All input types must be a floating point type, got %s", dt);
}
Preconditions.checkState(dt.isFPType(), "Input type 1 must be a floating point type, got %s", dt);
return Collections.singletonList(dt);
}
@Override
public List doDiff(List grads) {
return Arrays.asList(new GRUBp(sameDiff, arg(0), arg(1), arg(2), arg(3),
arg(4), grads.get(0)).outputVariables());
}
@Override
public String opName() {
return "gru";
}
}