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.Getter;
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 org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.RnnDataFormat;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.LSTMWeights;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
public class LSTMBlock extends DynamicCustomOp {
private LSTMConfiguration configuration;
@Getter
private LSTMWeights weights;
public LSTMBlock() {
}
public LSTMBlock(@NonNull SameDiff sameDiff, SDVariable maxTSLength, SDVariable x, SDVariable cLast, SDVariable yLast, LSTMWeights weights, LSTMConfiguration configuration) {
super(null, sameDiff, weights.argsWithInputs(x, maxTSLength, cLast, yLast));
this.configuration = configuration;
this.weights = weights;
addIArgument(configuration.iArgs(true));
addTArgument(configuration.tArgs());
}
public LSTMBlock(INDArray x, INDArray cLast, INDArray yLast, INDArray maxTSLength, LSTMWeights lstmWeights, LSTMConfiguration lstmConfiguration) {
super(null, null, lstmWeights.argsWithInputs(maxTSLength, x, cLast, yLast));
this.configuration = lstmConfiguration;
this.weights = lstmWeights;
addIArgument(configuration.iArgs(true));
addTArgument(configuration.tArgs());
}
@Override
public List calculateOutputDataTypes(List inputDataTypes) {
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 9, "Expected exactly 9 inputs to LSTMBlock, got %s", inputDataTypes);
//7 outputs, all of same type as input. Note that input 0 is max sequence length (int64), input 1 is actual input
DataType dt = inputDataTypes.get(1);
Preconditions.checkState(dt.isFPType(), "Input type 1 must be a floating point type, got %s", dt);
return Arrays.asList(dt, dt, dt, dt, dt, dt, dt);
}
@Override
public List doDiff(List grads) {
throw new UnsupportedOperationException("Not yet implemented");
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
configuration = LSTMConfiguration.builder()
.forgetBias(attributesForNode.get("forget_bias").getF())
.clippingCellValue(attributesForNode.get("cell_clip").getF())
.peepHole(attributesForNode.get("use_peephole").getB())
.dataFormat(RnnDataFormat.TNS) //Always time major for TF BlockLSTM
.build();
addIArgument(configuration.iArgs(true));
addTArgument(configuration.tArgs());
}
@Override
public String opName() {
return "lstmBlock";
}
@Override
public Map propertiesForFunction() {
if(configuration != null)
return configuration.toProperties(true);
else
return Collections.emptyMap();
}
@Override
public String tensorflowName() {
return "BlockLSTM";
}
}