Java Code Examples for org.nd4j.autodiff.samediff.SameDiff#output()
The following examples show how to use
org.nd4j.autodiff.samediff.SameDiff#output() .
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Example 1
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testBatchMmulBasic() {
OpValidationSuite.ignoreFailing(); //https://github.com/deeplearning4j/deeplearning4j/issues/6873
int M = 5;
int N = 3;
int K = 4;
INDArray A = Nd4j.create(new float[]{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}).reshape(M, N).castTo(DataType.DOUBLE);
INDArray B = Nd4j.create(new float[]{1,2,3,4,5,6,7,8,9,10,11,12}).reshape(N, K).castTo(DataType.DOUBLE);
SameDiff sd = SameDiff.create();
SDVariable A1 = sd.var("A1", A);
SDVariable A2 = sd.var("A2", A);
SDVariable B1 = sd.var("B1", B);
SDVariable B2 = sd.var("B2", B);
SDVariable[] batchMul = sd.batchMmul(new SDVariable[] {A1, A2}, new SDVariable[] {B1, B2});
Map<String,INDArray> m = sd.output(Collections.emptyMap(), sd.outputs());
INDArray resultingMatrix = m.get(batchMul[0].name());
//System.out.print(resultingMatrix);
}
Example 2
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testMergeRank1(){
SameDiff sd = SameDiff.create();
SDVariable var = sd.var("in", Nd4j.create(new long[]{1}).assign(5));
SDVariable merged = sd.math().mergeAvg("merged", new SDVariable[]{var});
SDVariable sum = sd.sum(merged);
Map<String,INDArray> m = sd.output(Collections.emptyMap(), "merged");
Map<String,INDArray> gm = sd.calculateGradients(null, "in");
INDArray out = m.get("merged");
assertEquals(1, out.rank());
INDArray inGrad = gm.get("in");
assertEquals(1, inGrad.rank());
}
Example 3
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testGather2(){
SameDiff sd = SameDiff.create();
SDVariable input = sd.var("in", Nd4j.arange(6).castTo(DataType.FLOAT).reshape(2,3));
SDVariable indices = sd.constant("indices", Nd4j.createFromArray(0));
SDVariable gathered = sd.gather(input, indices, 1);
SDVariable loss = gathered.std(true);
sd.output((Map<String,INDArray>)null, gathered.name());
sd.setLossVariables(gathered.name());
String err = OpValidation.validate(new TestCase(sd)
.gradCheckEpsilon(1e-3)
.gradCheckMaxRelativeError(1e-4));
assertNull(err);
}
Example 4
| Source File: RnnOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testRnnBlockCell(){
Nd4j.getRandom().setSeed(12345);
int mb = 2;
int nIn = 3;
int nOut = 4;
SameDiff sd = SameDiff.create();
SDVariable x = sd.constant(Nd4j.rand(DataType.FLOAT, mb, nIn));
SDVariable cLast = sd.constant(Nd4j.rand(DataType.FLOAT, mb, nOut));
SDVariable yLast = sd.constant(Nd4j.rand(DataType.FLOAT, mb, nOut));
SDVariable W = sd.constant(Nd4j.rand(DataType.FLOAT, (nIn+nOut), 4*nOut));
SDVariable Wci = sd.constant(Nd4j.rand(DataType.FLOAT, nOut));
SDVariable Wcf = sd.constant(Nd4j.rand(DataType.FLOAT, nOut));
SDVariable Wco = sd.constant(Nd4j.rand(DataType.FLOAT, nOut));
SDVariable b = sd.constant(Nd4j.rand(DataType.FLOAT, 4*nOut));
double fb = 1.0;
LSTMConfiguration conf = LSTMConfiguration.builder()
.peepHole(true)
.forgetBias(fb)
.clippingCellValue(0.0)
.build();
LSTMWeights weights = LSTMWeights.builder().weights(W).bias(b)
.inputPeepholeWeights(Wci).forgetPeepholeWeights(Wcf).outputPeepholeWeights(Wco).build();
LSTMCellOutputs v = new LSTMCellOutputs(sd.rnn().lstmCell(x, cLast, yLast, weights, conf)); //Output order: i, c, f, o, z, h, y
List<String> toExec = new ArrayList<>();
for(SDVariable sdv : v.getAllOutputs()){
toExec.add(sdv.name());
}
//Test forward pass:
Map<String,INDArray> m = sd.output(null, toExec);
//Weights and bias order: [i, f, z, o]
//Block input (z) - post tanh:
INDArray wz_x = W.getArr().get(NDArrayIndex.interval(0,nIn), NDArrayIndex.interval(nOut, 2*nOut)); //Input weights
INDArray wz_r = W.getArr().get(NDArrayIndex.interval(nIn,nIn+nOut), NDArrayIndex.interval(nOut, 2*nOut)); //Recurrent weights
INDArray bz = b.getArr().get(NDArrayIndex.interval(nOut, 2*nOut));
INDArray zExp = x.getArr().mmul(wz_x).addiRowVector(bz); //[mb,nIn]*[nIn, nOut] + [nOut]
zExp.addi(yLast.getArr().mmul(wz_r)); //[mb,nOut]*[nOut,nOut]
Transforms.tanh(zExp, false);
INDArray zAct = m.get(toExec.get(4));
assertEquals(zExp, zAct);
//Input modulation gate (post sigmoid) - i: (note: peephole input - last time step)
INDArray wi_x = W.getArr().get(NDArrayIndex.interval(0,nIn), NDArrayIndex.interval(0, nOut)); //Input weights
INDArray wi_r = W.getArr().get(NDArrayIndex.interval(nIn,nIn+nOut), NDArrayIndex.interval(0, nOut)); //Recurrent weights
INDArray bi = b.getArr().get(NDArrayIndex.interval(0, nOut));
INDArray iExp = x.getArr().mmul(wi_x).addiRowVector(bi); //[mb,nIn]*[nIn, nOut] + [nOut]
iExp.addi(yLast.getArr().mmul(wi_r)); //[mb,nOut]*[nOut,nOut]
iExp.addi(cLast.getArr().mulRowVector(Wci.getArr())); //Peephole
Transforms.sigmoid(iExp, false);
assertEquals(iExp, m.get(toExec.get(0)));
//Forget gate (post sigmoid): (note: peephole input - last time step)
INDArray wf_x = W.getArr().get(NDArrayIndex.interval(0,nIn), NDArrayIndex.interval(2*nOut, 3*nOut)); //Input weights
INDArray wf_r = W.getArr().get(NDArrayIndex.interval(nIn,nIn+nOut), NDArrayIndex.interval(2*nOut, 3*nOut)); //Recurrent weights
INDArray bf = b.getArr().get(NDArrayIndex.interval(2*nOut, 3*nOut));
INDArray fExp = x.getArr().mmul(wf_x).addiRowVector(bf); //[mb,nIn]*[nIn, nOut] + [nOut]
fExp.addi(yLast.getArr().mmul(wf_r)); //[mb,nOut]*[nOut,nOut]
fExp.addi(cLast.getArr().mulRowVector(Wcf.getArr())); //Peephole
fExp.addi(fb);
Transforms.sigmoid(fExp, false);
assertEquals(fExp, m.get(toExec.get(2)));
//Cell state (pre tanh): tanh(z) .* sigmoid(i) + sigmoid(f) .* cLast
INDArray cExp = zExp.mul(iExp).add(fExp.mul(cLast.getArr()));
INDArray cAct = m.get(toExec.get(1));
assertEquals(cExp, cAct);
//Output gate (post sigmoid): (note: peephole input: current time step)
INDArray wo_x = W.getArr().get(NDArrayIndex.interval(0,nIn), NDArrayIndex.interval(3*nOut, 4*nOut)); //Input weights
INDArray wo_r = W.getArr().get(NDArrayIndex.interval(nIn,nIn+nOut), NDArrayIndex.interval(3*nOut, 4*nOut)); //Recurrent weights
INDArray bo = b.getArr().get(NDArrayIndex.interval(3*nOut, 4*nOut));
INDArray oExp = x.getArr().mmul(wo_x).addiRowVector(bo); //[mb,nIn]*[nIn, nOut] + [nOut]
oExp.addi(yLast.getArr().mmul(wo_r)); //[mb,nOut]*[nOut,nOut]
oExp.addi(cExp.mulRowVector(Wco.getArr())); //Peephole
Transforms.sigmoid(oExp, false);
assertEquals(oExp, m.get(toExec.get(3)));
//Cell state, post tanh
INDArray hExp = Transforms.tanh(cExp, true);
assertEquals(hExp, m.get(toExec.get(5)));
//Final output
INDArray yExp = hExp.mul(oExp);
assertEquals(yExp, m.get(toExec.get(6)));
}
Example 5
| Source File: RnnOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testRnnBlockCellManualTFCompare() {
//Test case: "rnn/lstmblockcell/static_batch1_n3-2_tsLength1_noPH_noClip_fBias1_noIS"
SameDiff sd = SameDiff.create();
INDArray zero2d = Nd4j.createFromArray(new float[][]{{0,0}});
INDArray zero1d = Nd4j.createFromArray(new float[]{0,0});
SDVariable x = sd.constant(Nd4j.createFromArray(new float[][]{{0.7787856f,0.80119777f,0.72437465f}}));
SDVariable cLast = sd.constant(zero2d);
SDVariable yLast = sd.constant(zero2d);
//Weights shape: [(nIn+nOut), 4*nOut]
SDVariable W = sd.constant(Nd4j.createFromArray(-0.61977,-0.5708851,-0.38089648,-0.07994056,-0.31706482,0.21500933,-0.35454142,-0.3239095,-0.3177906,
0.39918554,-0.3115911,0.540841,0.38552666,0.34270835,-0.63456273,-0.13917702,-0.2985368,0.343238,
-0.3178353,0.017154932,-0.060259163,0.28841054,-0.6257687,0.65097713,0.24375653,-0.22315514,0.2033832,
0.24894875,-0.2062299,-0.2242794,-0.3809483,-0.023048997,-0.036284804,-0.46398938,-0.33979666,0.67012596,
-0.42168984,0.34208286,-0.0456419,0.39803517).castTo(DataType.FLOAT).reshape(5,8));
SDVariable Wci = sd.constant(zero1d);
SDVariable Wcf = sd.constant(zero1d);
SDVariable Wco = sd.constant(zero1d);
SDVariable b = sd.constant(Nd4j.zeros(DataType.FLOAT, 8));
double fb = 1.0;
LSTMConfiguration conf = LSTMConfiguration.builder()
.peepHole(false)
.forgetBias(fb)
.clippingCellValue(0.0)
.build();
LSTMWeights weights = LSTMWeights.builder().weights(W).bias(b)
.inputPeepholeWeights(Wci).forgetPeepholeWeights(Wcf).outputPeepholeWeights(Wco).build();
LSTMCellOutputs v = new LSTMCellOutputs(sd.rnn().lstmCell(x, cLast, yLast, weights, conf)); //Output order: i, c, f, o, z, h, y
List<String> toExec = new ArrayList<>();
for(SDVariable sdv : v.getAllOutputs()){
toExec.add(sdv.name());
}
//Test forward pass:
Map<String,INDArray> m = sd.output(null, toExec);
INDArray out0 = Nd4j.create(new float[]{0.27817473f, 0.53092605f}, new int[]{1,2}); //Input mod gate
INDArray out1 = Nd4j.create(new float[]{-0.18100877f, 0.19417824f}, new int[]{1,2}); //CS (pre tanh)
INDArray out2 = Nd4j.create(new float[]{0.73464274f, 0.83901811f}, new int[]{1,2}); //Forget gate
INDArray out3 = Nd4j.create(new float[]{0.22481689f, 0.52692068f}, new int[]{1,2}); //Output gate
INDArray out4 = Nd4j.create(new float[]{-0.65070170f, 0.36573499f}, new int[]{1,2}); //block input
INDArray out5 = Nd4j.create(new float[]{-0.17905743f, 0.19177397f}, new int[]{1,2}); //Cell state
INDArray out6 = Nd4j.create(new float[]{-0.04025514f, 0.10104967f}, new int[]{1,2}); //Output
// for(int i=0; i<toExec.size(); i++ ){
// System.out.println(i + "\t" + m.get(toExec.get(i)));
// }
assertEquals(out0, m.get(toExec.get(0))); //Input modulation gate
assertEquals(out1, m.get(toExec.get(1))); //Cell state (pre tanh)
assertEquals(out2, m.get(toExec.get(2))); //Forget gate
assertEquals(out3, m.get(toExec.get(3))); //Output gate
assertEquals(out4, m.get(toExec.get(4))); //block input
assertEquals(out5, m.get(toExec.get(5))); //Cell state
assertEquals(out6, m.get(toExec.get(6))); //Output
}
Example 6
| Source File: RnnOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGRUCell(){
Nd4j.getRandom().setSeed(12345);
int mb = 2;
int nIn = 3;
int nOut = 4;
SameDiff sd = SameDiff.create();
SDVariable x = sd.constant(Nd4j.rand(DataType.FLOAT, mb, nIn));
SDVariable hLast = sd.constant(Nd4j.rand(DataType.FLOAT, mb, nOut));
SDVariable Wru = sd.constant(Nd4j.rand(DataType.FLOAT, (nIn+nOut), 2*nOut));
SDVariable Wc = sd.constant(Nd4j.rand(DataType.FLOAT, (nIn+nOut), nOut));
SDVariable bru = sd.constant(Nd4j.rand(DataType.FLOAT, 2*nOut));
SDVariable bc = sd.constant(Nd4j.rand(DataType.FLOAT, nOut));
double fb = 1.0;
GRUWeights weights = GRUWeights.builder()
.ruWeight(Wru)
.cWeight(Wc)
.ruBias(bru)
.cBias(bc)
.build();
SDVariable[] v = sd.rnn().gruCell(x, hLast, weights);
List<String> toExec = new ArrayList<>();
for(SDVariable sdv : v){
toExec.add(sdv.name());
}
//Test forward pass:
Map<String,INDArray> m = sd.output(null, toExec);
//Weights and bias order: [r, u], [c]
//Reset gate:
INDArray wr_x = Wru.getArr().get(NDArrayIndex.interval(0,nIn), NDArrayIndex.interval(0, nOut)); //Input weights
INDArray wr_r = Wru.getArr().get(NDArrayIndex.interval(nIn,nIn+nOut), NDArrayIndex.interval(0, nOut)); //Recurrent weights
INDArray br = bru.getArr().get(NDArrayIndex.interval(0, nOut));
INDArray rExp = x.getArr().mmul(wr_x).addiRowVector(br); //[mb,nIn]*[nIn, nOut] + [nOut]
rExp.addi(hLast.getArr().mmul(wr_r)); //[mb,nOut]*[nOut,nOut]
Transforms.sigmoid(rExp,false);
INDArray rAct = m.get(toExec.get(0));
assertEquals(rExp, rAct);
//Update gate:
INDArray wu_x = Wru.getArr().get(NDArrayIndex.interval(0,nIn), NDArrayIndex.interval(nOut, 2*nOut)); //Input weights
INDArray wu_r = Wru.getArr().get(NDArrayIndex.interval(nIn,nIn+nOut), NDArrayIndex.interval(nOut, 2*nOut)); //Recurrent weights
INDArray bu = bru.getArr().get(NDArrayIndex.interval(nOut, 2*nOut));
INDArray uExp = x.getArr().mmul(wu_x).addiRowVector(bu); //[mb,nIn]*[nIn, nOut] + [nOut]
uExp.addi(hLast.getArr().mmul(wu_r)); //[mb,nOut]*[nOut,nOut]
Transforms.sigmoid(uExp,false);
INDArray uAct = m.get(toExec.get(1));
assertEquals(uExp, uAct);
//c = tanh(x * Wcx + Wcr * (hLast .* r))
INDArray Wcx = Wc.getArr().get(NDArrayIndex.interval(0,nIn), NDArrayIndex.all());
INDArray Wcr = Wc.getArr().get(NDArrayIndex.interval(nIn, nIn+nOut), NDArrayIndex.all());
INDArray cExp = x.getArr().mmul(Wcx);
cExp.addi(hLast.getArr().mul(rExp).mmul(Wcr));
cExp.addiRowVector(bc.getArr());
Transforms.tanh(cExp, false);
assertEquals(cExp, m.get(toExec.get(2)));
//h = u * hLast + (1-u) * c
INDArray hExp = uExp.mul(hLast.getArr()).add(uExp.rsub(1.0).mul(cExp));
assertEquals(hExp, m.get(toExec.get(3)));
}
Example 7
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testDistancesExec(){
//https://github.com/deeplearning4j/deeplearning4j/issues/7001
for(String s : new String[]{"euclidean", "manhattan", "cosinesim", "cosinedist", "jaccard"}) {
log.info("Starting: {}", s);
INDArray defaultTestCase = Nd4j.create(4, 4);
defaultTestCase.putRow(0, Nd4j.create(new float[]{0, 2, -2, 0}));
defaultTestCase.putRow(1, Nd4j.create(new float[]{0, 1, -1, 0}));
defaultTestCase.putRow(2, Nd4j.create(new float[]{0, -1, 1, 0}));
defaultTestCase.putRow(3, Nd4j.create(new float[]{0, -2, 2, 0}));
long singleEmbeddingSize = defaultTestCase.size(1) / 2L;
// Split vectors
INDArray x = defaultTestCase.get(NDArrayIndex.all(), NDArrayIndex.interval(0, singleEmbeddingSize));
INDArray y = defaultTestCase.get(NDArrayIndex.all(), NDArrayIndex.interval(singleEmbeddingSize, defaultTestCase.size(1)));
log.info(y.shapeInfoToString());
SameDiff sd = SameDiff.create();
sd.enableDebugMode();
SDVariable xSd = sd.var("x", x);
SDVariable ySd = sd.var("y", y);
ySd = ySd.add(ySd);
SDVariable dist;
switch (s){
case "euclidean":
dist = sd.math().euclideanDistance(s, ySd, xSd, 0);
break;
case "manhattan":
dist = sd.math().manhattanDistance(s, ySd, xSd, 0);
break;
case "cosinesim":
dist = sd.math().cosineSimilarity(s, ySd, xSd, 0);
break;
case "cosinedist":
dist = sd.math().cosineDistance(s, ySd, xSd, 0);
break;
case "jaccard":
dist = sd.math().jaccardDistance(s, ySd, xSd, 0);
break;
default:
throw new RuntimeException();
}
SDVariable loss = dist.sum();
// log.info(sd.summary());
sd.output(Collections.emptyMap(), Lists.newArrayList(s));
sd.calculateGradients(Collections.emptyMap(), sd.getVariables().keySet());
}
}
Example 8
| Source File: ValidateZooModelPredictions.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testMobilenetV1() throws Exception {
if(TFGraphTestZooModels.isPPC()){
/*
Ugly hack to temporarily disable tests on PPC only on CI
Issue logged here: https://github.com/deeplearning4j/deeplearning4j/issues/7657
These will be re-enabled for PPC once fixed - in the mean time, remaining tests will be used to detect and prevent regressions
*/
log.warn("TEMPORARILY SKIPPING TEST ON PPC ARCHITECTURE DUE TO KNOWN JVM CRASH ISSUES - SEE https://github.com/deeplearning4j/deeplearning4j/issues/7657");
OpValidationSuite.ignoreFailing();
}
TFGraphTestZooModels.currentTestDir = testDir.newFolder();
//Load model
String path = "tf_graphs/zoo_models/mobilenet_v1_0.5_128/tf_model.txt";
File resource = new ClassPathResource(path).getFile();
SameDiff sd = TFGraphTestZooModels.LOADER.apply(resource, "mobilenet_v1_0.5_128");
//Load data
//Because we don't have DataVec NativeImageLoader in ND4J tests due to circular dependencies, we'll load the image previously saved...
File imgFile = new ClassPathResource("deeplearning4j-zoo/goldenretriever_rgb128_unnormalized_nchw_INDArray.bin").getFile();
INDArray img = Nd4j.readBinary(imgFile).castTo(DataType.FLOAT);
img = img.permute(0,2,3,1).dup(); //to NHWC
//Mobilenet V1 - not sure, but probably using inception preprocessing
//i.e., scale to (-1,1) range
//Image is originally 0 to 255
img.divi(255).subi(0.5).muli(2);
double min = img.minNumber().doubleValue();
double max = img.maxNumber().doubleValue();
assertTrue(min >= -1 && min <= -0.6);
assertTrue(max <= 1 && max >= 0.6);
//Perform inference
List<String> inputs = sd.inputs();
assertEquals(1, inputs.size());
String out = "MobilenetV1/Predictions/Softmax";
Map<String,INDArray> m = sd.output(Collections.singletonMap(inputs.get(0), img), out);
INDArray outArr = m.get(out);
System.out.println("SHAPE: " + Arrays.toString(outArr.shape()));
System.out.println(outArr);
INDArray argmax = outArr.argMax(1);
//Load labels
List<String> labels = labels();
int classIdx = argmax.getInt(0);
String className = labels.get(classIdx);
String expClass = "golden retriever";
double prob = outArr.getDouble(classIdx);
System.out.println("Predicted class: \"" + className + "\" - probability = " + prob);
assertEquals(expClass, className);
}
Example 9
| Source File: ValidateZooModelPredictions.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testResnetV2() throws Exception {
if(TFGraphTestZooModels.isPPC()){
/*
Ugly hack to temporarily disable tests on PPC only on CI
Issue logged here: https://github.com/deeplearning4j/deeplearning4j/issues/7657
These will be re-enabled for PPC once fixed - in the mean time, remaining tests will be used to detect and prevent regressions
*/
log.warn("TEMPORARILY SKIPPING TEST ON PPC ARCHITECTURE DUE TO KNOWN JVM CRASH ISSUES - SEE https://github.com/deeplearning4j/deeplearning4j/issues/7657");
OpValidationSuite.ignoreFailing();
}
TFGraphTestZooModels.currentTestDir = testDir.newFolder();
//Load model
String path = "tf_graphs/zoo_models/resnetv2_imagenet_frozen_graph/tf_model.txt";
File resource = new ClassPathResource(path).getFile();
SameDiff sd = TFGraphTestZooModels.LOADER.apply(resource, "resnetv2_imagenet_frozen_graph");
//Load data
//Because we don't have DataVec NativeImageLoader in ND4J tests due to circular dependencies, we'll load the image previously saved...
File imgFile = new ClassPathResource("deeplearning4j-zoo/goldenretriever_rgb224_unnormalized_nchw_INDArray.bin").getFile();
INDArray img = Nd4j.readBinary(imgFile).castTo(DataType.FLOAT);
img = img.permute(0,2,3,1).dup(); //to NHWC
//Resnet v2 - NO external normalization, just resize and center crop
// https://github.com/tensorflow/models/blob/d32d957a02f5cffb745a4da0d78f8432e2c52fd4/research/tensorrt/tensorrt.py#L70
// https://github.com/tensorflow/models/blob/1af55e018eebce03fb61bba9959a04672536107d/official/resnet/imagenet_preprocessing.py#L253-L256
//Perform inference
List<String> inputs = sd.inputs();
assertEquals(1, inputs.size());
String out = "softmax_tensor";
Map<String,INDArray> m = sd.output(Collections.singletonMap(inputs.get(0), img), out);
INDArray outArr = m.get(out);
System.out.println("SHAPE: " + Arrays.toString(outArr.shape()));
System.out.println(outArr);
INDArray argmax = outArr.argMax(1);
//Load labels
List<String> labels = labels();
int classIdx = argmax.getInt(0);
String className = labels.get(classIdx);
String expClass = "golden retriever";
double prob = outArr.getDouble(classIdx);
System.out.println("Predicted class: " + classIdx + " - \"" + className + "\" - probability = " + prob);
assertEquals(expClass, className);
}
Example 10
| Source File: ImportModelDebugger.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
public static void main(String[] args) {
File modelFile = new File("C:\\Temp\\TF_Graphs\\cifar10_gan_85\\tf_model.pb");
File rootDir = new File("C:\\Temp\\TF_Graphs\\cifar10_gan_85");
SameDiff sd = TFGraphMapper.importGraph(modelFile);
ImportDebugListener l = ImportDebugListener.builder(rootDir)
.checkShapesOnly(true)
.floatingPointEps(1e-5)
.onFailure(ImportDebugListener.OnFailure.EXCEPTION)
.logPass(true)
.build();
sd.setListeners(l);
Map<String,INDArray> ph = loadPlaceholders(rootDir);
List<String> outputs = sd.outputs();
sd.output(ph, outputs);
}
