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/*******************************************************************************
* Copyright (c) 2015-2019 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.layers.recurrent;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMBlockCellConfiguration;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
/**
* LSTM Block cell - represents forward pass for a single time step of an LSTM RNN.
* Same operation used internally in op/layer {@link LSTMLayer}.
* Implementation of operation for LSTM layer with optional peep hole connections.
* S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation and https://research.google.com/pubs/archive/43905.pdf
* Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.
* See also: https://arxiv.org/pdf/1503.04069.pdf
*
* See also {@link LSTMLayer} for 'full time series" op - this lstmBlockCell op is used internally at C++ level in LSTMLayer.
* Input arrays:
* 0: input [bS, inSize] at time t
* 1: previous cell state [bS, numUnits], time t-1
* 2: previous output [bS, numUnits], time t-1
* 3: Weights - concatenated (input-to-hidden, hidden-to-hidden weights) weights, [(inSize+numUnits), 4*numUnits]
* 4: weights - cell peephole (t-1) connections to input modulation gate, [numUnits]
* 5: weights - cell peephole (t-1) connections to forget gate, [numUnits]
* 6: weights - cell peephole (t) connections to output gate, [numUnits]
* 7: biases, shape [4*numUnits]
*
* Input integer arguments: set via {@link LSTMBlockCellConfiguration}
* 0: if not zero, provide peephole connections
*
* Input float arguments: set via {@link LSTMBlockCellConfiguration}
* 0: the bias added to forget gates in order to reduce the scale of forgetting in the beginning of the training
* 1: clipping value for cell state, if it is not equal to zero, then cell state is clipped
*
* Output arrays:
* 0: i - Input modulation gate activations [bS, numUnits]
* 1: c (cs) - Cell state (pre tanh) [bs, numUnits] (cs)
* 2: f - Output - forget gate activations [bs, numUnits]
* 3: o - Output - output gate activations [bs, numUnits]
* 4: z (ci) - Output - block input [bs, numUnits]
* 5: h (co) - Cell state, post tanh [bs, numUnits]
* 6: y (h) - Current cell output [bS, numUnits], time t
*
* @author Alex Black
*/
public class LSTMBlockCell extends DynamicCustomOp {
private LSTMBlockCellConfiguration configuration;
public LSTMBlockCell() {
}
public LSTMBlockCell(SameDiff sameDiff, LSTMBlockCellConfiguration configuration) {
super(null, sameDiff, configuration.args());
this.configuration = configuration;
addIArgument(configuration.iArgs());
addTArgument(configuration.tArgs());
}
@Override
public List calculateOutputDataTypes(List inputDataTypes) {
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 8, "Expected exactly 8 inputs to LSTMBlockCell, got %s", inputDataTypes);
//7 outputs, all of same type as input
DataType dt = inputDataTypes.get(0);
Preconditions.checkState(dt.isFPType(), "Input type 0 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 = LSTMBlockCellConfiguration.builder()
.forgetBias(attributesForNode.get("forget_bias").getF())
.clippingCellValue(attributesForNode.get("cell_clip").getF())
.peepHole(attributesForNode.get("use_peephole").getB())
.build();
addIArgument(configuration.iArgs());
addTArgument(configuration.tArgs());
}
@Override
public String opName() {
return "lstmBlockCell";
}
@Override
public Map propertiesForFunction() {
return configuration.toProperties();
}
@Override
public String tensorflowName() {
return "LSTMBlockCell";
}
}
