/
DifferentialFunctionFactory.java
2204 lines (1647 loc) · 86.4 KB
/
DifferentialFunctionFactory.java
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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.autodiff.functions;
import lombok.Data;
import lombok.NonNull;
import lombok.val;
import org.apache.commons.lang3.ArrayUtils;
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.blas.params.MMulTranspose;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.NoOp;
import org.nd4j.linalg.api.ops.impl.controlflow.compat.Merge;
import org.nd4j.linalg.api.ops.impl.controlflow.compat.Switch;
import org.nd4j.linalg.api.ops.impl.image.ExtractImagePatches;
import org.nd4j.linalg.api.ops.impl.loss.SigmoidCrossEntropyLoss;
import org.nd4j.linalg.api.ops.impl.loss.SoftmaxCrossEntropyLoss;
import org.nd4j.linalg.api.ops.impl.loss.bp.*;
import org.nd4j.linalg.api.ops.impl.reduce.*;
import org.nd4j.linalg.api.ops.impl.reduce.custom.*;
import org.nd4j.linalg.api.ops.impl.reduce.floating.*;
import org.nd4j.linalg.api.ops.impl.reduce.same.*;
import org.nd4j.linalg.api.ops.impl.reduce.bool.All;
import org.nd4j.linalg.api.ops.impl.reduce.bool.Any;
import org.nd4j.linalg.api.ops.impl.reduce.bp.*;
import org.nd4j.linalg.api.ops.impl.reduce.longer.CountNonZero;
import org.nd4j.linalg.api.ops.impl.reduce.longer.CountZero;
import org.nd4j.linalg.api.ops.impl.reduce.longer.MatchCondition;
import org.nd4j.linalg.api.ops.impl.reduce.same.AMax;
import org.nd4j.linalg.api.ops.impl.reduce.same.AMin;
import org.nd4j.linalg.api.ops.impl.reduce.same.Max;
import org.nd4j.linalg.api.ops.impl.reduce.same.Min;
import org.nd4j.linalg.api.ops.impl.broadcast.BiasAdd;
import org.nd4j.linalg.api.ops.impl.broadcast.BiasAddGrad;
import org.nd4j.linalg.api.ops.impl.indexaccum.*;
import org.nd4j.linalg.api.ops.impl.layers.convolution.*;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.*;
import org.nd4j.linalg.api.ops.impl.reduce3.*;
import org.nd4j.linalg.api.ops.impl.loss.*;
import org.nd4j.linalg.api.ops.impl.scalar.*;
import org.nd4j.linalg.api.ops.impl.scalar.Pow;
import org.nd4j.linalg.api.ops.impl.scalar.comparison.*;
import org.nd4j.linalg.api.ops.impl.scatter.*;
import org.nd4j.linalg.api.ops.impl.shape.*;
import org.nd4j.linalg.api.ops.impl.shape.Stack;
import org.nd4j.linalg.api.ops.impl.shape.bp.SliceBp;
import org.nd4j.linalg.api.ops.impl.shape.bp.StridedSliceBp;
import org.nd4j.linalg.api.ops.impl.shape.bp.TileBp;
import org.nd4j.linalg.api.ops.impl.summarystats.StandardDeviation;
import org.nd4j.linalg.api.ops.impl.summarystats.Variance;
import org.nd4j.linalg.api.ops.impl.transforms.*;
import org.nd4j.linalg.api.ops.impl.transforms.any.IsMax;
import org.nd4j.linalg.api.ops.impl.transforms.custom.*;
import org.nd4j.linalg.api.ops.impl.transforms.dtype.Cast;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.And;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.Or;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.Xor;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.*;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.bp.*;
import org.nd4j.linalg.api.ops.impl.transforms.bool.*;
import org.nd4j.linalg.api.ops.impl.transforms.clip.ClipByNorm;
import org.nd4j.linalg.api.ops.impl.transforms.clip.ClipByValue;
import org.nd4j.linalg.api.ops.impl.transforms.comparison.*;
import org.nd4j.linalg.api.ops.impl.transforms.floating.*;
import org.nd4j.linalg.api.ops.impl.transforms.gradient.*;
import org.nd4j.linalg.api.ops.impl.transforms.gradient.SigmoidDerivative;
import org.nd4j.linalg.api.ops.impl.transforms.same.*;
import org.nd4j.linalg.api.ops.impl.transforms.custom.segment.*;
import org.nd4j.linalg.api.ops.impl.transforms.strict.*;
import org.nd4j.linalg.api.ops.impl.transforms.custom.SoftMax;
import org.nd4j.linalg.api.ops.impl.transforms.segment.*;
import org.nd4j.linalg.api.ops.impl.transforms.segment.bp.*;
import org.nd4j.linalg.api.ops.impl.layers.ExternalErrorsFunction;
import org.nd4j.linalg.api.ops.random.custom.DistributionUniform;
import org.nd4j.linalg.api.ops.random.custom.RandomBernoulli;
import org.nd4j.linalg.api.ops.random.custom.RandomExponential;
import org.nd4j.linalg.api.ops.random.custom.RandomNormal;
import org.nd4j.linalg.api.ops.random.impl.*;
import org.nd4j.linalg.api.ops.random.impl.Linspace;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.indexing.conditions.Condition;
import org.nd4j.linalg.util.ArrayUtil;
import java.lang.reflect.Method;
import java.util.*;
/**
*
*/
@Data
public class DifferentialFunctionFactory {
protected SameDiff sameDiff;
private static Map<String, Method> methodNames;
/**
* @param sameDiff
*/
public DifferentialFunctionFactory(SameDiff sameDiff) {
if (sameDiff != null) {
this.sameDiff = sameDiff;
if (methodNames == null) {
methodNames = new HashMap<>();
Method[] methods = getClass().getDeclaredMethods();
for (Method method : methods)
methodNames.put(method.getName().toLowerCase(), method);
}
} else {
throw new IllegalArgumentException("Input not null value.");
}
}
public SameDiff sameDiff() {
return sameDiff;
}
public SDVariable invoke(String name, Object[] args) {
try {
return (SDVariable) methodNames.get(name).invoke(this, args);
} catch (Exception e) {
throw new RuntimeException(e);
}
}
public Constant val(SDVariable iX) {
return new Constant(sameDiff(), iX,
iX.getShape());
}
public ExternalErrorsFunction externalErrors(SDVariable... inputs) {
return externalErrors(null, inputs);
}
public ExternalErrorsFunction externalErrors(Map<String, INDArray> externalGradients, SDVariable... inputs) {
Preconditions.checkArgument(inputs != null && inputs.length > 0, "Require at least one SDVariable to" +
" be specified when using external errors: got %s", inputs);
ExternalErrorsFunction fn = new ExternalErrorsFunction(sameDiff(), Arrays.asList(inputs), externalGradients);
fn.outputVariable();
return fn;
}
public SDVariable zerosLike(SDVariable input) {
return zerosLike(null, input);
}
public SDVariable zerosLike(String name, SDVariable input) {
validateDifferentialFunctionsameDiff(input);
return new ZerosLike(name, sameDiff(), input).outputVariable();
}
public SDVariable onesLike(String name, SDVariable input, DataType dataType) {
validateDifferentialFunctionsameDiff(input);
return new OnesLike(name, sameDiff(), input, dataType).outputVariable();
}
public SDVariable constant(SDVariable input, long... shape) {
return new Constant(sameDiff(), input, (shape != null && shape.length > 0 ? shape : null)).outputVariable();
}
public SDVariable linspace(double lower, double upper, long count) {
return new Linspace(sameDiff(), lower, upper, count).outputVariable();
}
public SDVariable linspace(SDVariable lower, SDVariable upper, SDVariable count, DataType dt) {
return new org.nd4j.linalg.api.ops.impl.shape.Linspace(sameDiff(), lower, upper, count, dt).outputVariable();
}
public SDVariable range(double from, double to, double step, DataType dataType) {
return new Range(sameDiff(), from, to, step, dataType).outputVariable();
}
public SDVariable cast(SDVariable toCast, DataType toType){
return new Cast(sameDiff(), toCast, toType).outputVariable();
}
public SDVariable[] meshgrid(boolean cartesian, SDVariable... inputs) {
return new MeshGrid(sameDiff(), cartesian, inputs).outputVariables();
}
public SDVariable randomUniform(double min, double max, SDVariable shape) {
return new DistributionUniform(sameDiff(), shape, min, max).outputVariable();
}
public SDVariable randomUniform(double min, double max, long... shape) {
return new UniformDistribution(sameDiff(), min, max, shape).outputVariable();
}
public SDVariable randomNormal(double mean, double std, SDVariable shape) {
return new RandomNormal(sameDiff(), shape, mean, std).outputVariable();
}
public SDVariable randomNormal(double mean, double std, long... shape) {
return new GaussianDistribution(sameDiff(), mean, std, shape).outputVariable();
}
public SDVariable randomBernoulli(double p, SDVariable shape) {
return new RandomBernoulli(sameDiff(), shape, p).outputVariable();
}
public SDVariable randomBernoulli(double p, long... shape) {
return new BernoulliDistribution(sameDiff(), p, shape).outputVariable();
}
public SDVariable randomBinomial(int nTrials, double p, long... shape) {
return new BinomialDistribution(sameDiff(), nTrials, p, shape).outputVariable();
}
public SDVariable randomLogNormal(double mean, double stdev, long... shape) {
return new LogNormalDistribution(sameDiff(), mean, stdev, shape).outputVariable();
}
public SDVariable randomNormalTruncated(double mean, double stdev, long... shape) {
return new TruncatedNormalDistribution(sameDiff(), mean, stdev, shape).outputVariable();
}
/**
* Exponential distribution: P(x) = lambda * exp(-lambda * x)
*
* @param lambda Must be > 0
* @param shape Shape of the output
*/
public SDVariable randomExponential(double lambda, SDVariable shape) {
return new RandomExponential(sameDiff(), shape, lambda).outputVariable();
}
/**
* Local response normalization operation.
*
* @param input the inputs to lrn
* @param lrnConfig the configuration
* @return
*/
public SDVariable localResponseNormalization(SDVariable input, LocalResponseNormalizationConfig lrnConfig) {
LocalResponseNormalization lrn = LocalResponseNormalization.builder()
.inputFunctions(new SDVariable[]{input})
.sameDiff(sameDiff())
.config(lrnConfig)
.build();
return lrn.outputVariable();
}
/**
* Conv1d operation.
*
* @param input the inputs to conv1d
* @param weights conv1d weights
* @param conv1DConfig the configuration
* @return
*/
public SDVariable conv1d(SDVariable input, SDVariable weights, Conv1DConfig conv1DConfig) {
Conv1D conv1D = Conv1D.builder()
.inputFunctions(new SDVariable[]{input, weights})
.sameDiff(sameDiff())
.config(conv1DConfig)
.build();
return conv1D.outputVariable();
}
/**
* Conv2d operation.
*
* @param inputs the inputs to conv2d
* @param conv2DConfig the configuration
* @return
*/
public SDVariable conv2d(SDVariable[] inputs, Conv2DConfig conv2DConfig) {
Conv2D conv2D = Conv2D.builder()
.inputFunctions(inputs)
.sameDiff(sameDiff())
.config(conv2DConfig)
.build();
return conv2D.outputVariable();
}
public SDVariable upsampling2d(SDVariable input, boolean nchw, int scaleH, int scaleW) {
return new Upsampling2d(sameDiff(), input, nchw, scaleH, scaleW).outputVariable();
}
public SDVariable upsampling2dBp(SDVariable input, SDVariable gradient, boolean nchw, int scaleH, int scaleW) {
return new Upsampling2dDerivative(sameDiff(), input, gradient, nchw, scaleH, scaleW).outputVariable();
}
/**
* Average pooling 2d operation.
*
* @param input the inputs to pooling
* @param pooling2DConfig the configuration
* @return
*/
public SDVariable avgPooling2d(SDVariable input, Pooling2DConfig pooling2DConfig) {
AvgPooling2D avgPooling2D = AvgPooling2D.builder()
.input(input)
.sameDiff(sameDiff())
.config(pooling2DConfig)
.build();
return avgPooling2D.outputVariable();
}
/**
* Max pooling 2d operation.
*
* @param input the inputs to pooling
* @param pooling2DConfig the configuration
* @return
*/
public SDVariable maxPooling2d(SDVariable input, Pooling2DConfig pooling2DConfig) {
MaxPooling2D maxPooling2D = MaxPooling2D.builder()
.input(input)
.sameDiff(sameDiff())
.config(pooling2DConfig)
.build();
return maxPooling2D.outputVariable();
}
/**
* Avg pooling 3d operation.
*
* @param input the inputs to pooling
* @param pooling3DConfig the configuration
* @return
*/
public SDVariable avgPooling3d(SDVariable input, Pooling3DConfig pooling3DConfig) {
pooling3DConfig.setType(Pooling3D.Pooling3DType.AVG);
return pooling3d(input, pooling3DConfig);
}
/**
* Max pooling 3d operation.
*
* @param input the inputs to pooling
* @param pooling3DConfig the configuration
* @return
*/
public SDVariable maxPooling3d(SDVariable input, Pooling3DConfig pooling3DConfig) {
pooling3DConfig.setType(Pooling3D.Pooling3DType.MAX);
return pooling3d(input, pooling3DConfig);
}
public SDVariable pooling3d(SDVariable input, Pooling3DConfig pooling3DConfig) {
Pooling3D pool3d = Pooling3D.builder()
.inputs(new SDVariable[]{input})
.sameDiff(sameDiff())
.pooling3DConfig(pooling3DConfig)
.type(pooling3DConfig.getType())
.build();
return pool3d.outputVariable();
}
/**
* Separable Conv2d operation.
*
* @param inputs the inputs to conv2d
* @param conv2DConfig the configuration
* @return
*/
public SDVariable sconv2d(SDVariable[] inputs, Conv2DConfig conv2DConfig) {
SConv2D sconv2D = SConv2D.sBuilder()
.inputFunctions(inputs)
.sameDiff(sameDiff())
.conv2DConfig(conv2DConfig)
.build();
return sconv2D.outputVariable();
}
/**
* Depth-wise Conv2d operation. This is just separable convolution with
* only the depth-wise weights specified.
*
* @param inputs the inputs to conv2d
* @param depthConv2DConfig the configuration
* @return
*/
public SDVariable depthWiseConv2d(SDVariable[] inputs, Conv2DConfig depthConv2DConfig) {
SConv2D depthWiseConv2D = SConv2D.sBuilder()
.inputFunctions(inputs)
.sameDiff(sameDiff())
.conv2DConfig(depthConv2DConfig)
.build();
return depthWiseConv2D.outputVariable();
}
/**
* Deconv2d operation.
*
* @param inputs the inputs to conv2d
* @param deconv2DConfig the configuration
* @return
*/
public SDVariable deconv2d(SDVariable[] inputs, DeConv2DConfig deconv2DConfig) {
DeConv2D deconv2D = DeConv2D.builder()
.inputs(inputs)
.sameDiff(sameDiff())
.config(deconv2DConfig)
.build();
return deconv2D.outputVariable();
}
public SDVariable deconv3d(SDVariable input, SDVariable weights, SDVariable bias, DeConv3DConfig config) {
DeConv3D d = new DeConv3D(sameDiff(), input, weights, bias, config);
return d.outputVariable();
}
public SDVariable[] deconv3dDerivative(SDVariable input, SDVariable weights, SDVariable bias, SDVariable grad, DeConv3DConfig config) {
DeConv3DDerivative d = new DeConv3DDerivative(sameDiff(), input, weights, bias, grad, config);
return d.outputVariables();
}
/**
* Conv3d operation.
*
* @param inputs the inputs to conv3d
* @param conv3DConfig the configuration
* @return
*/
public SDVariable conv3d(SDVariable[] inputs, Conv3DConfig conv3DConfig) {
Conv3D conv3D = Conv3D.builder()
.inputFunctions(inputs)
.conv3DConfig(conv3DConfig)
.sameDiff(sameDiff())
.build();
val outputVars = conv3D.outputVariables();
return outputVars[0];
}
/**
* Batch norm operation.
*/
public SDVariable batchNorm(SDVariable input, SDVariable mean,
SDVariable variance, SDVariable gamma,
SDVariable beta,
boolean applyGamma, boolean applyBeta,
double epsilon, int... axis) {
BatchNorm batchNorm = BatchNorm.builder()
.inputFunctions(new SDVariable[]{input, mean, variance, gamma, beta})
.applyGamma(applyGamma)
.applyBeta(applyBeta)
.epsilon(epsilon)
.sameDiff(sameDiff())
.axis(axis)
.build();
val outputVars = batchNorm.outputVariables();
return outputVars[0];
}
public SDVariable im2Col(SDVariable input, Conv2DConfig config) {
return new Im2col(sameDiff(), input, config).outputVariable();
}
public SDVariable im2ColBp(SDVariable im2colInput, SDVariable gradientAtOutput, Conv2DConfig config) {
return new Im2colBp(sameDiff(), im2colInput, gradientAtOutput, config).outputVariable();
}
public SDVariable col2Im(SDVariable input, Conv2DConfig config) {
return new Col2Im(sameDiff(), input, config).outputVariable();
}
public SDVariable extractImagePatches(SDVariable input, int kH, int kW, int sH, int sW, int rH, int rW, boolean sameMode){
return new ExtractImagePatches(sameDiff(), input, new int[]{kH, kW}, new int[]{sH, sW}, new int[]{rH, rW}, sameMode).outputVariable();
}
public SDVariable[] moments(SDVariable input, int... axes) {
return new Moments(sameDiff(), input, axes).outputVariables();
}
public SDVariable[] normalizeMoments(SDVariable counts, SDVariable means, SDVariable variances, double shift) {
return new NormalizeMoments(sameDiff(), counts, means, variances, shift).outputVariables();
}
public SDVariable tile(@NonNull SDVariable iX, @NonNull int[] repeat) {
return new Tile(sameDiff(), iX, repeat).outputVariable();
}
public SDVariable tileBp(@NonNull SDVariable in, @NonNull SDVariable grad, @NonNull int[] repeat){
return new TileBp(sameDiff, in, grad, repeat).outputVariable();
}
public SDVariable dropout(SDVariable input, double p) {
return new DropOutInverted(sameDiff(), input, p).outputVariable();
}
public SDVariable sum(SDVariable i_x, boolean keepDims, int... dimensions) {
return new Sum(sameDiff(), i_x, keepDims, dimensions).outputVariable();
}
public SDVariable sumBp(SDVariable i_x, SDVariable grad, boolean keepDims, int... dimensions) {
return new SumBp(sameDiff(), i_x, grad, keepDims, dimensions).outputVariable();
}
public SDVariable prod(SDVariable i_x, boolean keepDims, int... dimensions) {
return new Prod(sameDiff(), i_x, keepDims, dimensions).outputVariable();
}
public SDVariable prodBp(SDVariable preReduceInput, SDVariable grad, boolean keepDims, int... dimensions) {
return new ProdBp(sameDiff(), preReduceInput, grad, keepDims, dimensions).outputVariable();
}
public SDVariable mean(SDVariable in, boolean keepDims, int... dimensions) {
return new Mean(sameDiff(), in, keepDims, dimensions).outputVariable();
}
public SDVariable meanBp(SDVariable in, SDVariable grad, boolean keepDims, int... dimensions) {
return new MeanBp(sameDiff(), in, grad, keepDims, dimensions).outputVariable();
}
public SDVariable std(SDVariable i_x, boolean biasCorrected, boolean keepDims, int... dimensions) {
return new StandardDeviation(sameDiff(), i_x, biasCorrected, keepDims, dimensions).outputVariable();
}
public SDVariable stdBp(SDVariable stdInput, SDVariable gradient, boolean biasCorrected, boolean keepDims, int... dimensions) {
return new StandardDeviationBp(sameDiff(), stdInput, gradient, biasCorrected, keepDims, dimensions).outputVariable();
}
public SDVariable variance(SDVariable i_x, boolean biasCorrected, boolean keepDims, int... dimensions) {
return new Variance(sameDiff(), i_x, biasCorrected, keepDims, dimensions).outputVariable();
}
public SDVariable varianceBp(SDVariable stdInput, SDVariable gradient, boolean biasCorrected, boolean keepDims, int... dimensions) {
return new VarianceBp(sameDiff(), stdInput, gradient, biasCorrected, keepDims, dimensions).outputVariable();
}
public SDVariable squaredNorm(SDVariable input, boolean keepDims, int... dimensions) {
return new SquaredNorm(sameDiff(), input, keepDims, dimensions).outputVariable();
}
public SDVariable squaredNormBp(SDVariable preReduceInput, SDVariable gradient, boolean keepDims, int... dimensions) {
return new SquaredNormBp(sameDiff(), preReduceInput, gradient, keepDims, dimensions).outputVariable();
}
public SDVariable entropy(SDVariable in, int... dimensions) {
return new Entropy(sameDiff(), in, dimensions).outputVariable();
}
public SDVariable logEntropy(SDVariable in, int... dimensions) {
return new LogEntropy(sameDiff(), in, dimensions).outputVariable();
}
public SDVariable shannonEntropy(SDVariable in, int... dimensions){
return new ShannonEntropy(sameDiff(), in, dimensions).outputVariable();
}
public SDVariable countNonZero(SDVariable input, int... dimensions) {
return new CountNonZero(sameDiff(), input, dimensions).outputVariable();
}
public SDVariable countZero(SDVariable input, int... dimensions) {
return new CountZero(sameDiff(), input, dimensions).outputVariable();
}
public SDVariable zeroFraction(SDVariable input) {
return new ZeroFraction(sameDiff(), input).outputVariable();
}
public SDVariable scalarMax(SDVariable in, Number num) {
return new ScalarMax(sameDiff(), in, num).outputVariable();
}
public SDVariable scalarMin(SDVariable in, Number num) {
return new ScalarMin(sameDiff(), in, num).outputVariable();
}
public SDVariable scalarSet(SDVariable in, Number num) {
return new ScalarSet(sameDiff(), in, num).outputVariable();
}
public SDVariable scalarFloorMod(SDVariable in, Number num) {
return new ScalarFMod(sameDiff(), in, num).outputVariable();
}
public SDVariable max(SDVariable i_x, boolean keepDims, int... dimensions) {
return new Max(sameDiff(), i_x, keepDims, dimensions).outputVariable();
}
public SDVariable max(SDVariable first, SDVariable second) {
return new org.nd4j.linalg.api.ops.impl.transforms.custom.Max(sameDiff(), first, second)
.outputVariable();
}
public SDVariable maxBp(SDVariable i_x, SDVariable grad, boolean keepDims, int... dimensions) {
return new MaxBp(sameDiff(), i_x, grad, keepDims, dimensions).outputVariable();
}
public SDVariable min(SDVariable i_x, boolean keepDims, int... dimensions) {
return new Min(sameDiff(), i_x, keepDims, dimensions).outputVariable();
}
public SDVariable minBp(SDVariable i_x, SDVariable grad, boolean keepDims, int... dimensions) {
return new MinBp(sameDiff(), i_x, grad, keepDims, dimensions).outputVariable();
}
public SDVariable min(SDVariable first, SDVariable second) {
return new org.nd4j.linalg.api.ops.impl.transforms.custom.Min(sameDiff(), first, second)
.outputVariable();
}
public SDVariable amax(SDVariable in, int... dimensions) {
return new AMax(sameDiff(), in, dimensions).outputVariable();
}
public SDVariable amin(SDVariable in, int... dimensions) {
return new AMin(sameDiff(), in, dimensions).outputVariable();
}
public SDVariable amean(SDVariable in, int... dimensions) {
return new AMean(sameDiff(), in, dimensions).outputVariable();
}
public SDVariable asum(SDVariable in, int... dimensions) {
return new ASum(sameDiff(), in, dimensions).outputVariable();
}
public SDVariable argmax(SDVariable in, boolean keepDims, int... dimensions) {
return new IMax(sameDiff(), in, keepDims, dimensions).outputVariable();
}
public SDVariable argmin(SDVariable in, boolean keepDims, int... dimensions) {
return new IMin(sameDiff(), in, keepDims, dimensions).outputVariable();
}
public SDVariable iamax(SDVariable in, boolean keepDims, int... dimensions) {
return new IAMax(sameDiff(), in, keepDims, dimensions).outputVariable();
}
public SDVariable iamin(SDVariable in, boolean keepDims, int... dimensions) {
return new IAMin(sameDiff(), in, keepDims, dimensions).outputVariable();
}
public SDVariable firstIndex(SDVariable in, Condition condition, boolean keepDims, int... dimensions) {
return new FirstIndex(sameDiff(), in, condition, keepDims, dimensions).outputVariable();
}
public SDVariable lastIndex(SDVariable in, Condition condition, boolean keepDims, int... dimensions) {
return new LastIndex(sameDiff(), in, condition, keepDims, dimensions).outputVariable();
}
/**
* Returns a count of the number of elements that satisfy the condition
*
* @param in Input
* @param condition Condition
* @return Number of elements that the condition is satisfied for
*/
public SDVariable matchConditionCount(SDVariable in, Condition condition, boolean keepDims, int... dimensions) {
return new MatchCondition(sameDiff(), in, condition, keepDims, dimensions).outputVariable();
}
/**
* Returns a boolean mask of equal shape to the input, where the condition is satisfied
*
* @param in Input
* @param condition Condition
* @return Boolean mask
*/
public SDVariable matchCondition(SDVariable in, Condition condition) {
return new MatchConditionTransform(sameDiff(), in, condition).outputVariable();
}
public SDVariable cumsum(SDVariable in, boolean exclusive, boolean reverse, int... axis) {
return new CumSum(sameDiff(), in, exclusive, reverse, axis).outputVariable();
}
public SDVariable cumsumBp(SDVariable in, SDVariable grad, boolean exclusive, boolean reverse, int... axis) {
return new CumSumBp(sameDiff(), in, grad, exclusive, reverse, axis).outputVariable();
}
public SDVariable cumprod(SDVariable in, boolean exclusive, boolean reverse, int... axis) {
return new CumProd(sameDiff(), in, exclusive, reverse, axis).outputVariable();
}
public SDVariable cumprodBp(SDVariable in, SDVariable grad, boolean exclusive, boolean reverse, int... axis) {
return new CumProdBp(sameDiff(), in, grad, exclusive, reverse, axis).outputVariable();
}
public SDVariable biasAdd(SDVariable input, SDVariable bias) {
return new BiasAdd(sameDiff(), input, bias).outputVariable();
}
public SDVariable[] biasAddBp(SDVariable input, SDVariable bias, SDVariable grad) {
return new BiasAddGrad(sameDiff(), input, bias, grad).outputVariables();
}
public SDVariable norm1(SDVariable i_x, boolean keepDims, int... dimensions) {
return new Norm1(sameDiff(), i_x, keepDims, dimensions).outputVariable();
}
public SDVariable norm1Bp(SDVariable preReduceIn, SDVariable grad, boolean keepDims, int... dimensions) {
return new Norm1Bp(sameDiff(), preReduceIn, grad, keepDims, dimensions).outputVariable();
}
public SDVariable norm2(SDVariable i_x, boolean keepDims, int... dimensions) {
return new Norm2(sameDiff(), i_x, keepDims, dimensions).outputVariable();
}
public SDVariable norm2Bp(SDVariable preReduceIn, SDVariable grad, boolean keepDims, int... dimensions) {
return new Norm2Bp(sameDiff(), preReduceIn, grad, keepDims, dimensions).outputVariable();
}
public SDVariable normmax(SDVariable i_x, boolean keepDims, int... dimensions) {
return new NormMax(sameDiff(), i_x, keepDims, dimensions).outputVariable();
}
public SDVariable normmaxBp(SDVariable preReduceIn, SDVariable grad, boolean keepDims, int... dimensions) {
return new NormMaxBp(sameDiff(), preReduceIn, grad, keepDims, dimensions).outputVariable();
}
public SDVariable reductionShape(SDVariable shape, SDVariable axis, boolean keepDim){
return new ReductionShape(sameDiff(), shape, axis, keepDim).outputVariable();
}
/**
* Add 1s as required to the array make an array possible to be broadcast with the original (pre-reduce) array.
* <p>
* Example: if doing [a,b,c].sum(1), result is [a,c]. To 'undo' this in a way that can be auto-broadcast,
* we want to expand as required - i.e., [a,c] -> [a,1,c] which can be auto-broadcast with the original [a,b,c].
* This is typically only used with reduction operations backprop.
*
* @param origRank Rank of the original array, before the reduction was executed
* @param reduceDims Dimensions that the original array was reduced from
* @param toExpand Array to add 1s to the shape to (such that it can be
* @return Reshaped array.
*/
public SDVariable reductionBroadcastableWithOrigShape(int origRank, int[] reduceDims, SDVariable toExpand) {
if (Shape.isWholeArray(origRank, reduceDims)) {
//Output is [1,1] which is already broadcastable
return toExpand;
} else if (origRank == 2 && reduceDims.length == 1) {
//In this case: [a,b] -> [1,b] or [a,b] -> [a,1]
//both are already broadcastable
return toExpand;
} else {
//Example: [a,b,c].sum(1) -> [a,c]... want [a,1,c]
for (int d : reduceDims) {
toExpand = sameDiff().expandDims(toExpand, d);
}
return toExpand;
}
}
public SDVariable reductionBroadcastableWithOrigShape(SDVariable origInput, SDVariable axis, SDVariable toExpand) {
SDVariable shape = origInput.shape();
SDVariable reduceShape = reductionShape(shape, axis, true);
SDVariable reshaped = toExpand.reshape(reduceShape);
return reshaped;
}
public SDVariable gradientBackwardsMarker(SDVariable iX) {
return new GradientBackwardsMarker(sameDiff(), iX, sameDiff.scalar(iX.getVarName() + "-pairgrad", 1.0)).outputVariable();
}
public SDVariable abs(SDVariable iX) {
return new Abs(sameDiff(), iX, false).outputVariable();
}
public SDVariable neg(SDVariable iX) {
return new Negative(sameDiff(), iX, false).outputVariable();
}
public SDVariable cos(SDVariable iX) {
return new Cos(sameDiff(), iX, false).outputVariable();
}
public SDVariable sin(SDVariable iX) {
return new Sin(sameDiff(), iX, false).outputVariable();
}
public SDVariable tan(SDVariable iX) {
return new Tan(sameDiff(), iX, false).outputVariable();
}
public SDVariable permute(SDVariable iX, int... dimensions) {
return new Permute(sameDiff(), iX, dimensions).outputVariable();
}
public SDVariable noop(SDVariable input) {
return new NoOp(sameDiff(), input).outputVariable();
}
public SDVariable identity(SDVariable input) {
return new Identity(sameDiff(), input).outputVariable();
}
public SDVariable all(SDVariable input, int... dimensions) {
return new All(sameDiff(), input, dimensions).outputVariable();
}
public SDVariable any(SDVariable input, int... dimensions) {
return new Any(sameDiff(), input, dimensions).outputVariable();
}
public SDVariable invertPermutation(SDVariable input, boolean inPlace) {
return new InvertPermutation(sameDiff(), input, inPlace).outputVariable();
}
public SDVariable transpose(SDVariable iX) {
return new Transpose(sameDiff(), iX).outputVariable();
}
public SDVariable acos(SDVariable iX) {
return new ACos(sameDiff(), iX, false).outputVariable();
}
public SDVariable asin(SDVariable iX) {
return new ASin(sameDiff(), iX, false).outputVariable();
}
public SDVariable atan(SDVariable iX) {
return new ATan(sameDiff(), iX, false).outputVariable();
}
public SDVariable atan2(SDVariable y, SDVariable x) {
return new ATan2(sameDiff(), y, x).outputVariable();
}
public SDVariable cosh(SDVariable iX) {
return new Cosh(sameDiff(), iX, false).outputVariable();
}
public SDVariable sinh(SDVariable iX) {
return new Sinh(sameDiff(), iX, false).outputVariable();
}
public SDVariable tanh(SDVariable iX) {
return new Tanh(sameDiff(), iX, false).outputVariable();
}
public SDVariable tanhRational(SDVariable in) {
return new RationalTanh(sameDiff(), in, false).outputVariable();
}
public SDVariable tanhRectified(SDVariable in) {
return new RectifiedTanh(sameDiff(), in, false).outputVariable();
}
public SDVariable tanhDerivative(SDVariable iX, SDVariable wrt) {
return new org.nd4j.linalg.api.ops.impl.transforms.gradient.TanhDerivative(sameDiff(), iX, wrt).outputVariable();
}
public SDVariable tanhRationalDerivative(SDVariable in) {
return new RationalTanhDerivative(sameDiff(), in, false).outputVariable();
}
public SDVariable tanhRectifiedDerivative(SDVariable in) {
return new RectifiedTanhDerivative(sameDiff(), in, false).outputVariable();
}
public SDVariable step(SDVariable in, double cutoff) {
return new Step(sameDiff(), in, false, cutoff).outputVariable();
}
public SDVariable acosh(SDVariable iX) {
return new ACosh(sameDiff(), iX).outputVariable();
}
public SDVariable asinh(SDVariable iX) {
return new ASinh(sameDiff(), iX).outputVariable();
}
public SDVariable atanh(SDVariable iX) {
return new ATanh(sameDiff(), iX).outputVariable();
}
public SDVariable exp(SDVariable iX) {
return new Exp(sameDiff(), iX, false).outputVariable();
}
public SDVariable expm1(SDVariable iX) {
return new Expm1(sameDiff(), iX, false).outputVariable();
}
public SDVariable rsqrt(SDVariable iX) {
return new RSqrt(sameDiff(), iX, false).outputVariable();
}
public SDVariable log(SDVariable iX) {
return new Log(sameDiff(), iX, false).outputVariable();
}
public SDVariable log(SDVariable in, double base) {
return new LogX(sameDiff(), in, base).outputVariable();
}
public SDVariable log1p(SDVariable iX) {
return new Log1p(sameDiff(), iX, false).outputVariable();
}
public SDVariable isFinite(SDVariable ix) {
return new IsFinite(sameDiff(), ix, false).outputVariable();
}
public SDVariable isInfinite(SDVariable ix) {
return new IsInf(sameDiff(), ix, false).outputVariable();
}
public SDVariable isNaN(SDVariable ix) {
return new IsNaN(sameDiff(), ix, false).outputVariable();
}
public SDVariable isMax(SDVariable ix) {
return new IsMax(sameDiff(), ix, false).outputVariable();
}
public SDVariable replaceWhere(SDVariable to, SDVariable from, Condition condition) {
return new CompareAndReplace(sameDiff(), to, from, condition).outputVariable();
}
public SDVariable replaceWhere(SDVariable to, Number set, Condition condition) {
return new CompareAndSet(sameDiff(), to, set, condition).outputVariable();
}
public SDVariable round(SDVariable ix) {
return new Round(sameDiff(), ix, false).outputVariable();
}
public SDVariable or(SDVariable iX, SDVariable i_y) {
return new Or(sameDiff(), iX, i_y).outputVariable();
}
public SDVariable and(SDVariable ix, SDVariable iy) {
return new And(sameDiff(), ix, iy).outputVariable();
}
public SDVariable xor(SDVariable ix, SDVariable iy) {
return new Xor(sameDiff(), ix, iy).outputVariable();
}
public SDVariable eq(SDVariable iX, SDVariable i_y) {
return new EqualTo(sameDiff(), new SDVariable[]{iX, i_y}, false).outputVariable();
}
public SDVariable neq(SDVariable iX, double i_y) {
return new ScalarNotEquals(sameDiff(), iX, i_y).outputVariable();
}