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AlexDBlackAlexDBlack
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Updates for snapshots
Signed-off-by: Alex Black <[email protected]>
1 parent a4594ba commit 758e0ab

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10 files changed

+41
-29
lines changed

10 files changed

+41
-29
lines changed

‎dl4j-cuda-specific-examples/pom.xml

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<name>DeepLearning4j CUDA special examples</name>
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<properties>
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<nd4j.backend>nd4j-cuda-10.2-platform</nd4j.backend>
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<nd4j.backend>nd4j-cuda-10.2</nd4j.backend>
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</properties>
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<repositories>
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<dependencies>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-9.2-platform</artifactId>
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<artifactId>nd4j-cuda-9.2</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.0-platform</artifactId>
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<artifactId>nd4j-cuda-10.0</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.1-platform</artifactId>
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<artifactId>nd4j-cuda-10.1</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.2-platform</artifactId>
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<artifactId>nd4j-cuda-10.2</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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</dependencies>

‎dl4j-examples/pom.xml

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<dependencies>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native-platform</artifactId>
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<artifactId>nd4j-native</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-9.2-platform</artifactId>
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<artifactId>nd4j-cuda-9.2</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.0-platform</artifactId>
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<artifactId>nd4j-cuda-10.0</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.1-platform</artifactId>
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<artifactId>nd4j-cuda-10.1</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.2-platform</artifactId>
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<artifactId>nd4j-cuda-10.2</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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</dependencies>

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/modelimport/keras/DeepMojiAttentionLayer.java

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@@ -83,7 +83,7 @@ public void setNIn(InputType inputType, boolean override) {
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public SDVariable defineLayer(SameDiff sd, SDVariable layerInput, Map<String, SDVariable> paramTable, SDVariable mask) {
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SDVariable weights = paramTable.get(DefaultParamInitializer.WEIGHT_KEY);
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SDVariable logits = sd.tensorMmul(layerInput, weights, new int[][] { {2}, {0}});
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SDVariable logits = sd.tensorMmul(layerInput, weights, new int[]{2}, new int[]{0});
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SDVariable reshapedLogits = sd.reshape(logits, layerInput.getShape()[0], layerInput.getShape()[1]);
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SDVariable ai = sd.math().exp(reshapedLogits);
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SDVariable aiSum = sd.sum(ai, 1);

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/samediff/dl4j/Ex3LambdaVertex.java

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.seed(12345)
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.activation(Activation.TANH)
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.convolutionMode(ConvolutionMode.Same)
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.dataType(DataType.DOUBLE)
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.graphBuilder()
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.addInputs("in")
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//Add some standard DL4J layers:
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double max_relative_error = 1e-5; //Maximum relative error allowable for each parameter
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double min_absolute_error = 1e-8; //Minimum absolute error, to avoid failures on 0 vs 1e-30, for example.
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boolean gradOk = GradientCheckUtil.checkGradients(net, gradient_check_epsilon, max_relative_error, min_absolute_error, print,
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return_on_first_failure, new INDArray[]{testFeatures}, new INDArray[]{testLabels});
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// boolean gradOk = GradientCheckUtil.checkGradients(net, gradient_check_epsilon, max_relative_error, min_absolute_error, print,
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// return_on_first_failure, new INDArray[]{testFeatures}, new INDArray[]{testLabels});
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boolean gradOk = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig()
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.net(net)
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.epsilon(gradient_check_epsilon)
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.maxRelError(max_relative_error)
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.minAbsoluteError(min_absolute_error)
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.print(print ? GradientCheckUtil.PrintMode.ALL : GradientCheckUtil.PrintMode.FAILURES_ONLY)
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.exitOnFirstError(return_on_first_failure)
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.inputs(new INDArray[]{testFeatures})
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.labels(new INDArray[]{testLabels}));
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if(!gradOk){
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throw new IllegalStateException("Gradient check failed");
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}

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/samediff/tfimport/SameDiffTransferLearningExample.java

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SDVariable outputs = sd1.nn().softmax("Output", input);
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// we need a loss to train on, the tensorflow model doesn't come with one
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SDVariable loss = sd1.loss().softmaxCrossEntropy("Loss", labels, input);
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SDVariable loss = sd1.loss().softmaxCrossEntropy("Loss", labels, input, null);
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logits.close();
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‎dl4j-examples/src/main/java/org/deeplearning4j/examples/samediff/training/SameDiffMNISTTrainingExample.java

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SDVariable z = sd.nn().linear("z", flat, wOut, bOut);
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// softmax crossentropy loss function
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SDVariable loss = sd.loss().softmaxCrossEntropy("loss", label, z);
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SDVariable loss = sd.loss().softmaxCrossEntropy("loss", label, z, null);
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//noinspection unused
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SDVariable out = sd.nn().softmax("out", z, 1);

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/samediff/training/SameDiffTrainingExample.java

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SDVariable softmax = sd.nn().softmax("softmax", z1);
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//Define loss function:
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SDVariable diff = sd.f().squaredDifference(softmax, label);
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SDVariable diff = sd.math.squaredDifference(softmax, label);
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SDVariable lossMse = diff.mean();
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sd.setLossVariables(lossMse);

‎lstm-hdfs/pom.xml

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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native-platform</artifactId>
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<artifactId>nd4j-native</artifactId>
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<version>${nd4j.version}</version>
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<scope>test</scope>
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</dependency>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-9.2-platform</artifactId>
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<artifactId>nd4j-cuda-9.2</artifactId>
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<version>${nd4j.version}</version>
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<scope>test</scope>
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</dependency>
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<!-- ND4J -->
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native-platform</artifactId>
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<artifactId>nd4j-native</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>

‎pom.xml

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<java.version>1.8</java.version>
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<nd4j.version>1.0.0-beta6</nd4j.version>
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<dl4j.version>1.0.0-beta6</dl4j.version>
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<datavec.version>1.0.0-beta6</datavec.version>
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<arbiter.version>1.0.0-beta6</arbiter.version>
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<rl4j.version>1.0.0-beta6</rl4j.version>
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<nd4j.version>1.0.0-SNAPSHOT</nd4j.version>
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<dl4j.version>1.0.0-SNAPSHOT</dl4j.version>
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<datavec.version>1.0.0-SNAPSHOT</datavec.version>
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<arbiter.version>1.0.0-SNAPSHOT</arbiter.version>
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<rl4j.version>1.0.0-SNAPSHOT</rl4j.version>
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<!-- Scala binary version: DL4J's Spark and UI functionality are released with both Scala 2.10 and 2.11 support -->
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<scala.binary.version>2.11</scala.binary.version>

‎rl4j-examples/pom.xml

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<dependencies>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native-platform</artifactId>
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<artifactId>nd4j-native</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-9.2-platform</artifactId>
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<artifactId>nd4j-cuda-9.2</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.0-platform</artifactId>
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<artifactId>nd4j-cuda-10.0</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.1-platform</artifactId>
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<artifactId>nd4j-cuda-10.1</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-cuda-10.2-platform</artifactId>
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<artifactId>nd4j-cuda-10.2</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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</dependencies>

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