Java Code Examples for org.nd4j.autodiff.samediff.SDVariable#add()
The following examples show how to use
org.nd4j.autodiff.samediff.SDVariable#add() .
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Example 1
| Source File: GraphExecutionerTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
/**
* VarSpace should dump everything. 4 variables in our case
* @throws Exception
*/
@Test
public void testEquality1() {
OpValidationSuite.ignoreFailing(); //Failing 2019/01/24
GraphExecutioner executionerA = new BasicGraphExecutioner();
GraphExecutioner executionerB = new NativeGraphExecutioner();
SameDiff sameDiff = SameDiff.create();
INDArray ones = Nd4j.ones(4);
SDVariable sdVariable = sameDiff.var("ones",ones);
SDVariable scalarOne = sameDiff.var("scalar",Nd4j.scalar(1.0));
SDVariable result = sdVariable.add(scalarOne);
SDVariable total = sameDiff.sum(result,Integer.MAX_VALUE);
log.info("TOTAL: {}; Id: {}", total.name(), total);
INDArray[] resB = executionerB.executeGraph(sameDiff, configVarSpace);
//Variables: ones, scalar, result, total
assertEquals(sameDiff.variables().size(), resB.length);
assertEquals(Nd4j.ones(4), resB[0]);
assertEquals(Nd4j.scalar(1), resB[1]);
assertEquals(Nd4j.create(new float[]{2f, 2f, 2f, 2f}), resB[2]);
assertEquals(Nd4j.scalar(8.0), resB[3]);
}
Example 2
| Source File: SameDiffDense.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public SDVariable defineLayer(SameDiff sd, SDVariable layerInput, Map<String, SDVariable> paramTable, SDVariable mask) {
SDVariable weights = paramTable.get(DefaultParamInitializer.WEIGHT_KEY);
SDVariable bias = paramTable.get(DefaultParamInitializer.BIAS_KEY);
SDVariable mmul = sd.mmul("mmul", layerInput, weights);
SDVariable z = mmul.add("z", bias);
return activation.asSameDiff("out", sd, z);
}
Example 3
| Source File: SameDiffDenseVertex.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public SDVariable defineVertex(SameDiff sameDiff, Map<String, SDVariable> layerInput, Map<String, SDVariable> paramTable, Map<String, SDVariable> maskVars) {
SDVariable weights = paramTable.get(DefaultParamInitializer.WEIGHT_KEY);
SDVariable bias = paramTable.get(DefaultParamInitializer.BIAS_KEY);
SDVariable mmul = sameDiff.mmul("mmul", layerInput.get("in"), weights);
SDVariable z = mmul.add("z", bias);
return activation.asSameDiff("out", sameDiff, z);
}
Example 4
| Source File: Gather.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> i_v){
//2 args: input and indices. Plus integer dimension arg
//Gather backprop is just scatter add
SDVariable indicesGrad = sameDiff.zerosLike(arg(1));
SDVariable inputGrad = sameDiff.zerosLike(arg(0));
SDVariable[] inputs = args();
SDVariable axis;
SDVariable rank = inputs[0].rank();
if(inputs.length == 2){
axis = sameDiff.constant(jaxis);
if(jaxis < 0)
axis = axis.add(rank);
} else {
axis = inputs[2];
}
//Use scatter add plus permute
SDVariable dimsExAxis = sameDiff.range(null, sameDiff.constant(0), rank, sameDiff.constant(1), DataType.INT);
SDVariable axisRank1 = axis.reshape(1);
dimsExAxis = sameDiff.math().listDiff(dimsExAxis, axisRank1)[0]; //Don't need indices
SDVariable permuteDims = sameDiff.concat(0, axisRank1, dimsExAxis);
SDVariable invertDims = sameDiff.invertPermutation(permuteDims);
//Permute gradients so original axis is at position 0... then scatter add, and reverse
SDVariable gradAtOut = i_v.get(0);
SDVariable permuteGrad = gradAtOut.permute(permuteDims);
SDVariable inputGradPermute = inputGrad.permute(permuteDims);
inputGrad = sameDiff.scatterAdd(inputGradPermute, arg(1), permuteGrad);
//Now, invert the permutation so axis is back where it was
inputGrad = inputGrad.permute(invertDims);
return Arrays.asList(inputGrad, indicesGrad);
}
Example 5
| Source File: MinimalSameDiffDense.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public SDVariable defineLayer(SameDiff sd, SDVariable layerInput, Map<String, SDVariable> paramTable, SDVariable mask) {
SDVariable weights = paramTable.get(DefaultParamInitializer.WEIGHT_KEY);
SDVariable bias = paramTable.get(DefaultParamInitializer.BIAS_KEY);
SDVariable mmul = sd.mmul("mmul", layerInput, weights);
SDVariable z = mmul.add("z", bias);
return activation.asSameDiff("out", sd, z);
}
Example 6
| Source File: GraphExecutionerTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
@Ignore
public void testSums1() {
SameDiff sameDiff = SameDiff.create();
INDArray ones = Nd4j.ones(4);
SDVariable sdVariable = sameDiff.var("ones",ones);
SDVariable result = sdVariable.add(1.0);
SDVariable total = sameDiff.sum(result,Integer.MAX_VALUE);
val executioner = new NativeGraphExecutioner();
INDArray[] res = executioner.executeGraph(sameDiff);
assertEquals(8.0, res[0].getDouble(0), 1e-5);
}
Example 7
| Source File: SameDiffInferenceExecutionerTests.java From konduit-serving with Apache License 2.0 | 5 votes |
|
@Test(timeout = 60000)
public void testSameDiff() throws Exception {
SameDiffInferenceExecutioner sameDiffInferenceExecutioner = new SameDiffInferenceExecutioner();
SameDiff sameDiff = SameDiff.create();
SDVariable input1 = sameDiff.placeHolder("input1", DataType.FLOAT,2, 2);
SDVariable input2 = sameDiff.placeHolder("input2", DataType.FLOAT,2, 2);
SDVariable result = input1.add("output", input2);
INDArray input1Arr = Nd4j.linspace(1, 4, 4).reshape(2, 2);
INDArray input2Arr = Nd4j.linspace(1, 4, 4).reshape(2, 2);
sameDiff.associateArrayWithVariable(input1Arr, input1.name());
sameDiff.associateArrayWithVariable(input2Arr, input2.name());
Map<String, INDArray> indArrays = new LinkedHashMap<>();
indArrays.put(input1.name(), input1Arr);
indArrays.put(input2.name(), input2Arr);
Map<String, INDArray> outputs = sameDiff.outputAll(indArrays);
assertEquals(3, outputs.size());
ParallelInferenceConfig parallelInferenceConfig = ParallelInferenceConfig.defaultConfig();
File newFile = temporary.newFile();
sameDiff.asFlatFile(newFile);
SameDiffModelLoader sameDiffModelLoader = new SameDiffModelLoader(newFile, Arrays.asList("input1", "input2"), Arrays.asList("output"));
sameDiffInferenceExecutioner.initialize(sameDiffModelLoader, parallelInferenceConfig);
INDArray[] execute = sameDiffInferenceExecutioner.execute(new INDArray[]{input1Arr, input2Arr});
assertEquals(outputs.values().iterator().next(), execute[0]);
}
Example 8
| Source File: GraphExecutionerTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
@Ignore
public void testConversion() throws Exception {
SameDiff sameDiff = SameDiff.create();
INDArray ones = Nd4j.ones(4);
SDVariable sdVariable = sameDiff.var("ones",ones);
SDVariable result = sdVariable.add(1.0);
SDVariable total = sameDiff.sum(result,Integer.MAX_VALUE);
val executioner = new NativeGraphExecutioner();
ByteBuffer buffer = executioner.convertToFlatBuffers(sameDiff, ExecutorConfiguration.builder().profilingMode(OpExecutioner.ProfilingMode.DISABLED).executionMode(ExecutionMode.SEQUENTIAL).outputMode(OutputMode.IMPLICIT).build());
val offset = buffer.position();
val array = buffer.array();
try (val fos = new FileOutputStream("../../libnd4j/tests/resources/adam_sum.fb"); val dos = new DataOutputStream(fos)) {
dos.write(array, offset, array.length - offset);
}
//INDArray[] res = executioner.executeGraph(sameDiff);
//assertEquals(8.0, res[0].getDouble(0), 1e-5);
/*
INDArray output = null;
for(int i = 0; i < 5; i++) {
output = sameDiff.execAndEndResult(ops);
System.out.println("Ones " + ones);
System.out.println(output);
}
assertEquals(Nd4j.valueArrayOf(4,7),ones);
assertEquals(28,output.getDouble(0),1e-1);
*/
}
Example 9
| Source File: TestSessions.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testInferenceSessionBasic2(){
//So far: trivial test to check execution order
SameDiff sd = SameDiff.create();
SDVariable ph1 = sd.placeHolder("x", DataType.FLOAT, 3,3);
SDVariable ph2 = sd.placeHolder("y", DataType.FLOAT, 3,3);
SDVariable a = ph1.add("a", ph2);
SDVariable b = ph1.mmul("b", ph2);
SDVariable c = ph1.sub("c", ph2);
SDVariable d = a.add("d", b);
//To get array d - need to execute: a, b, d - NOT the sub op (c)
//NOTE: normally sessions are internal and completely hidden from users
InferenceSession is = new InferenceSession(sd);
INDArray x = Nd4j.linspace(1, 9, 9).castTo(DataType.FLOAT).reshape(3,3);
INDArray y = Nd4j.linspace(0.0, 0.9, 9, DataType.DOUBLE).castTo(DataType.FLOAT).reshape(3,3);
INDArray aExp = x.add(y);
INDArray bExp = x.mmul(y);
INDArray dExp = aExp.add(bExp);
Map<String,INDArray> m = new HashMap<>();
m.put("x", x);
m.put("y", y);
Map<String,INDArray> outMap = is.output(Collections.singletonList("d"), m, null,
Collections.<String>emptyList(), null, At.defaultAt(Operation.TRAINING));
assertEquals(1, outMap.size());
assertEquals(dExp, outMap.get("d"));
}
Example 10
| Source File: SameDiffVerticleNd4jTest.java From konduit-serving with Apache License 2.0 | 5 votes |
|
@Override
public JsonObject getConfigObject() throws Exception {
SameDiff sameDiff = SameDiff.create();
SDVariable x = sameDiff.placeHolder("x", DataType.FLOAT, 2);
SDVariable y = sameDiff.placeHolder("y", DataType.FLOAT, 2);
SDVariable add = x.add("output", y);
File tmpSameDiffFile = temporary.newFile();
sameDiff.asFlatFile(tmpSameDiffFile);
SameDiff values = SameDiff.fromFlatFile(tmpSameDiffFile);
ServingConfig servingConfig = ServingConfig.builder()
.outputDataFormat(Output.DataFormat.ND4J)
.httpPort(port)
.build();
SameDiffStep modelPipelineConfig = SameDiffStep.builder()
.path(tmpSameDiffFile.getAbsolutePath())
.inputNames(Arrays.asList("x", "y"))
.outputNames(Collections.singletonList("output"))
.build();
InferenceConfiguration inferenceConfiguration = InferenceConfiguration.builder()
.servingConfig(servingConfig)
.step(modelPipelineConfig)
.build();
return new JsonObject(inferenceConfiguration.toJson());
}
Example 11
| Source File: SameDiffVerticleNumpyTest.java From konduit-serving with Apache License 2.0 | 5 votes |
|
@Override
public JsonObject getConfigObject() throws Exception {
SameDiff sameDiff = SameDiff.create();
SDVariable x = sameDiff.placeHolder("x", DataType.FLOAT, 2);
SDVariable y = sameDiff.placeHolder("y", DataType.FLOAT, 2);
SDVariable add = x.add("output", y);
File tmpSameDiffFile = temporary.newFile();
sameDiff.asFlatFile(tmpSameDiffFile);
ServingConfig servingConfig = ServingConfig.builder()
.outputDataFormat(Output.DataFormat.NUMPY)
.httpPort(port)
.build();
SameDiffStep config = SameDiffStep.builder()
.path(tmpSameDiffFile.getAbsolutePath())
.inputNames(Arrays.asList("x", "y"))
.outputNames(Collections.singletonList("output"))
.build();
InferenceConfiguration inferenceConfiguration = InferenceConfiguration.builder()
.servingConfig(servingConfig)
.step(config)
.build();
return new JsonObject(inferenceConfiguration.toJson());
}
Example 12
| Source File: SameDiffVerticleClassificationMetricsTest.java From konduit-serving with Apache License 2.0 | 5 votes |
|
@Override
public JsonObject getConfigObject() throws Exception {
SameDiff sameDiff = SameDiff.create();
SDVariable x = sameDiff.placeHolder("x", DataType.FLOAT, 2);
SDVariable y = sameDiff.placeHolder("y", DataType.FLOAT, 2);
SDVariable add = x.add("output", y);
File tmpSameDiffFile = temporary.newFile();
sameDiff.asFlatFile(tmpSameDiffFile);
SameDiff values = SameDiff.fromFlatFile(tmpSameDiffFile);
ServingConfig servingConfig = ServingConfig.builder()
.outputDataFormat(Output.DataFormat.ND4J)
.metricsConfigurations(Collections.singletonList(ClassificationMetricsConfig.builder()
.classificationLabels(Arrays.asList("0", "1")).build()))
.metricTypes(Collections.singletonList(MetricType.CLASSIFICATION))
.httpPort(port)
.build();
SameDiffStep modelPipelineConfig = SameDiffStep.builder()
.path(tmpSameDiffFile.getAbsolutePath())
.inputNames(Arrays.asList("x", "y"))
.outputNames(Collections.singletonList("output"))
.build();
InferenceConfiguration inferenceConfiguration = InferenceConfiguration.builder()
.servingConfig(servingConfig)
.step(modelPipelineConfig)
.build();
return new JsonObject(inferenceConfiguration.toJson());
}
Example 13
| Source File: GradCheckMisc.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast2() {
Nd4j.getRandom().setSeed(12345);
List<String> allFailed = new ArrayList<>();
for (int[] dim_sz1s : new int[][]{{0, 1}, {0, 2}, {1, 2}, {0,1,2}}) {
int[] otherShape = {3, 4, 5};
otherShape[dim_sz1s[0]] = 1;
otherShape[dim_sz1s[1]] = 1;
if(dim_sz1s.length == 3){
otherShape[dim_sz1s[2]] = 1;
}
for (int i = 0; i < 6; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in3", new int[]{3, 4, 5});
SDVariable in2 = sd.var("inToBc", otherShape);
String name;
SDVariable bcOp;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
bcOp = sd.f().floorDiv(in3, in2);
name = "floordiv";
break;
case 7:
bcOp = sd.f().floorMod(in3, in2);
name = "floormod";
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", dimensions=" + Arrays.toString(dim_sz1s) + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.randn(new int[]{3,4,5}).muli(100);
INDArray in2Arr = Nd4j.randn(otherShape).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
try {
INDArray out = sd.execAndEndResult();
assertNotNull(out);
assertArrayEquals(new long[]{1, 1}, out.shape());
// System.out.println(sd.asFlatPrint());
boolean ok = GradCheckUtil.checkGradients(sd);
if (!ok) {
allFailed.add(msg);
}
} catch (Exception e) {
e.printStackTrace();
allFailed.add(msg + " - EXCEPTION");
}
}
}
assertEquals("Failed: " + allFailed, 0, allFailed.size());
}
Example 14
| Source File: GradCheckMisc.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast1() {
Nd4j.getRandom().setSeed(12345);
List<String> allFailed = new ArrayList<>();
for (int dim_sz1 : new int[]{0, 1, 2}) {
int[] in2Shape = {3, 4, 5};
in2Shape[dim_sz1] = 1;
for (int i = 2; i < 3; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in3", Nd4j.rand(new int[]{3, 4, 5}));
SDVariable in2 = sd.var("in2", in2Shape);
SDVariable bcOp;
String name;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
bcOp = sd.f().floorDiv(in3, in2);
name = "floordiv";
break;
case 7:
bcOp = sd.f().floorMod(in3, in2);
name = "floormod";
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", dimension=" + dim_sz1 + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.randn(new int[]{3, 4, 5}).muli(100);
INDArray in2Arr = Nd4j.randn(in2Shape).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
try {
INDArray out = sd.execAndEndResult();
assertNotNull(out);
assertArrayEquals(new long[]{1, 1}, out.shape());
// System.out.println(sd.asFlatPrint());
boolean ok = GradCheckUtil.checkGradients(sd);
if (!ok) {
allFailed.add(msg);
}
} catch (Exception e) {
e.printStackTrace();
allFailed.add(msg + " - EXCEPTION");
}
}
}
assertEquals("Failed: " + allFailed, 0, allFailed.size());
}
Example 15
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast1() {
Nd4j.getRandom().setSeed(12345);
List<String> failed = new ArrayList<>();
for (int dim_sz1 : new int[]{0, 1, 2}) {
int[] in2Shape = {3, 4, 5};
in2Shape[dim_sz1] = 1;
for (int i = 0; i < 8; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in3", Nd4j.rand(new int[]{3, 4, 5}));
SDVariable in2 = sd.var("in2", in2Shape);
SDVariable bcOp;
String name;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
//bcOp = sd.scalarFloorDiv(in3, in2);
bcOp = new FloorDivOp(sd, in3, in2).outputVariable();
name = "floordiv";
break;
case 7:
//bcOp = sd.scalarFloorMod(in3, in2);
bcOp = new FloorModOp(sd, in3, in2).outputVariable();
name = "floormod";
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/5976
continue;
}
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", dimension=" + dim_sz1 + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.randn(new int[]{3, 4, 5}).muli(100);
INDArray in2Arr = Nd4j.randn(in2Shape).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
TestCase tc = new TestCase(sd);
String error = OpValidation.validate(tc);
if(error != null){
failed.add(name);
}
}
}
assertEquals("Failed: " + failed, 0, failed.size());
}
Example 16
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast2() {
Nd4j.getRandom().setSeed(12345);
List<String> failed = new ArrayList<>();
for (int[] dim_sz1s : new int[][]{{0, 1}, {0, 2}, {1, 2}, {0, 1, 2}}) {
long[] otherShape = {3, 4, 5};
otherShape[dim_sz1s[0]] = 1;
otherShape[dim_sz1s[1]] = 1;
if (dim_sz1s.length == 3) {
otherShape[dim_sz1s[2]] = 1;
}
for (int i = 0; i < 8; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in3", DataType.DOUBLE, 3, 4, 5);
SDVariable in2 = sd.var("inToBc", DataType.DOUBLE, otherShape);
String name;
SDVariable bcOp;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
//bcOp = sd.scalarFloorDiv(in3, in2);
bcOp = new FloorDivOp(sd, in3, in2).outputVariable();
name = "floordiv";
break;
case 7:
//bcOp = sd.scalarFloorMod(in3, in2);
bcOp = new FloorModOp(sd, in3, in2).outputVariable();
name = "floormod";
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/5976
continue;
}
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", dimensions=" + Arrays.toString(dim_sz1s) + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.randn(DataType.DOUBLE, 3, 4, 5).muli(100);
INDArray in2Arr = Nd4j.randn(DataType.DOUBLE, otherShape).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
TestCase tc = new TestCase(sd);
String error = OpValidation.validate(tc);
if(error != null){
failed.add(name);
}
}
}
assertEquals("Failed: " + failed, 0, failed.size());
}
Example 17
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast3() {
//These tests: output size > input sizes
Nd4j.getRandom().setSeed(12345);
List<String> failed = new ArrayList<>();
//Test cases: in1Shape, in2Shape, shapeOf(op(in1,in2))
List<Triple<long[], long[], long[]>> testCases = new ArrayList<>();
testCases.add(new Triple<>(new long[]{3, 1}, new long[]{1, 4}, new long[]{3, 4}));
testCases.add(new Triple<>(new long[]{3, 1}, new long[]{3, 4}, new long[]{3, 4}));
testCases.add(new Triple<>(new long[]{3, 4}, new long[]{1, 4}, new long[]{3, 4}));
testCases.add(new Triple<>(new long[]{3, 4, 1}, new long[]{1, 1, 5}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 4, 1}, new long[]{3, 1, 5}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 1, 5}, new long[]{1, 4, 1}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 1, 5}, new long[]{1, 4, 5}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 1, 5}, new long[]{3, 4, 5}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 1, 1, 1}, new long[]{1, 4, 5, 6}, new long[]{3, 4, 5, 6}));
testCases.add(new Triple<>(new long[]{1, 1, 1, 6}, new long[]{3, 4, 5, 6}, new long[]{3, 4, 5, 6}));
testCases.add(new Triple<>(new long[]{1, 4, 5, 1}, new long[]{3, 1, 1, 6}, new long[]{3, 4, 5, 6}));
if(!OpValidationSuite.IGNORE_FAILING) {
testCases.add(new Triple<>(new long[]{1, 6}, new long[]{3, 4, 5, 1}, new long[]{3, 4, 5, 6}));
}
for (val p : testCases) {
for (int i = 0; i < 8; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in1", DataType.DOUBLE, p.getFirst());
SDVariable in2 = sd.var("in2", DataType.DOUBLE, p.getSecond());
String name;
SDVariable bcOp;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
//bcOp = sd.scalarFloorDiv(in3, in2);
bcOp = new FloorDivOp(sd, in3, in2).outputVariable();
name = "floordiv";
break;
case 7:
//bcOp = sd.scalarFloorMod(in3, in2);
bcOp = new FloorModOp(sd, in3, in2).outputVariable();
name = "floormod";
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/5976
continue;
}
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", array 1 size =" + Arrays.toString(p.getFirst())
+ ", array 2 size = " + Arrays.toString(p.getSecond()) + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.rand(DataType.DOUBLE, p.getFirst()).muli(100);
INDArray in2Arr = Nd4j.rand(DataType.DOUBLE, p.getSecond()).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
TestCase tc = new TestCase(sd);
String error = OpValidation.validate(tc);
if(error != null){
failed.add(name + " " + i + " - " + error);
}
}
}
assertEquals("Failed: " + failed, 0, failed.size());
}
Example 18
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testMeshGrid(){
List<String> failed = new ArrayList<>();
for( int rank=2; rank<=4; rank++ ){
SameDiff sd = SameDiff.create();
SDVariable[] arr = new SDVariable[rank];
String[] names = new String[rank];
for( int i=0; i<rank; i++ ){
INDArray in = Nd4j.linspace(1,3+i, 3+i).reshape(3+i).castTo(DataType.DOUBLE);
arr[i] = sd.var("in"+i, in);
names[i] = "meshgrid-" + i;
}
SDVariable[] meshgrid = sd.math().meshgrid(names, arr, false);
TestCase tc = new TestCase(sd);
long[] shape;
if(rank == 2){
shape = new long[]{3,4};
} else if(rank == 3) {
shape = new long[]{3,4,5};
} else {
shape = new long[]{3,4,5,6};
}
INDArray[] exp = new INDArray[shape.length]; //Nd4j.create(shape);
for( int i=0; i<exp.length; i++ ){
exp[i] = Nd4j.create(DataType.DOUBLE, shape);
long nTensors = exp[i].tensorsAlongDimension(i);
for( long j=0; j<nTensors; j++ ){
INDArray tad = exp[i].tensorAlongDimension((int)j, i);
tad.assign(arr[i].getArr());
}
tc.expected(meshgrid[i], exp[i]);
}
SDVariable loss = null;
for( int i=0; i<rank; i++ ){
if(i == 0)
loss = meshgrid[i].std(true);
else {
loss = loss.add("loss-" + i, meshgrid[i].std(true));
}
}
String err = OpValidation.validate(tc, true);
if(err != null)
failed.add(err);
}
assertEquals(failed.toString(), 0, failed.size());
}
Example 19
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testScalarOps() {
int d0 = 2;
int d1 = 3;
int d2 = 4;
int n = d0 * d1 * d2;
List<String> failed = new ArrayList<>();
for (int i = 0; i < 11; i++) {
for (char inOrder : new char[]{'c', 'f'}) {
SameDiff sd = SameDiff.create();
INDArray inArr = Nd4j.linspace(1, n, n, DataType.DOUBLE).reshape('c', d0, d1, d2).dup(inOrder);
SDVariable in = sd.var("in", inArr);
TestCase tc = new TestCase(sd).gradientCheck(true);
SDVariable out;
String msg;
switch (i) {
case 0:
out = in.mul(2);
tc.expectedOutput(out.name(), inArr.mul(2));
msg = "mul - " + inOrder;
break;
case 1:
out = in.div(2);
tc.expectedOutput(out.name(), inArr.div(2));
msg = "div - " + inOrder;
break;
case 2:
out = in.add(2);
tc.expectedOutput(out.name(), inArr.add(2));
msg = "add - " + inOrder;
break;
case 3:
out = in.sub(2);
tc.expectedOutput(out.name(), inArr.sub(2));
msg = "sub - " + inOrder;
break;
case 4:
out = in.rdiv(2);
tc.expectedOutput(out.name(), inArr.rdiv(2));
msg = "rdiv - " + inOrder;
break;
case 5:
out = in.rsub(2);
tc.expectedOutput(out.name(), inArr.rsub(2));
msg = "rsub - " + inOrder;
break;
case 6:
out = sd.math().pow(in, 2);
tc.expectedOutput(out.name(), Transforms.pow(inArr, 2));
msg = "pow - " + inOrder;
break;
case 7:
inArr.assign(Nd4j.rand(inArr.dataType(), inArr.shape()).muli(5).subi(2.5));
out = sd.math().floorMod(in, 2.0);
tc.expected(out, Nd4j.getExecutioner().exec(new ScalarFMod(inArr.dup(), 2.0)));
msg = "scalarFloorMod - " + inOrder;
break;
case 8:
inArr.assign(Nd4j.rand(inArr.shape()));
out = sd.scalarMax(in, 0.5);
tc.expected(out, Transforms.max(inArr.dup(), 0.5));
msg = "scalarMax - " + inOrder;
break;
case 9:
inArr.assign(Nd4j.rand(inArr.shape()));
out = sd.scalarMin(in, 0.5);
tc.expected(out, Transforms.min(inArr.dup(), 0.5));
msg = "scalarMin - " + inOrder;
break;
case 10:
out = in.assign(0.5);
tc.expected(out, Nd4j.valueArrayOf(inArr.shape(), 0.5));
msg = "scalarSet - " + inOrder;
break;
default:
throw new RuntimeException();
}
tc.testName(msg);
SDVariable loss = sd.standardDeviation(out, true);
log.info("Starting test: " + msg);
String err = OpValidation.validate(tc, true);
if (err != null) {
failed.add(err);
}
}
}
assertEquals(failed.toString(), 0, failed.size());
}
Example 20
| Source File: TestSessions.java From deeplearning4j with Apache License 2.0 | 3 votes |
|
@Test
public void testInferenceSessionBasic(){
//So far: trivial test to check execution order
SameDiff sd = SameDiff.create();
SDVariable ph1 = sd.placeHolder("x", DataType.FLOAT, 3,4);
SDVariable ph2 = sd.placeHolder("y", DataType.FLOAT, 1,4);
SDVariable out = ph1.add("out", ph2);
//NOTE: normally sessions are internal and completely hidden from users
InferenceSession is = new InferenceSession(sd);
INDArray x = Nd4j.linspace(1, 12, 12).castTo(DataType.FLOAT).reshape(3,4);
INDArray y = Nd4j.linspace(0.1, 0.4, 4, DataType.DOUBLE).castTo(DataType.FLOAT).reshape(1,4);
INDArray outExp = x.addRowVector(y);
Map<String,INDArray> m = new HashMap<>();
m.put("x", x);
m.put("y", y);
Map<String,INDArray> outMap = is.output(Collections.singletonList("out"), m, null,
Collections.<String>emptyList(), null, At.defaultAt(Operation.TRAINING));
assertEquals(1, outMap.size());
assertEquals(outExp, outMap.get("out"));
}
