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Java error with Image Handling and Deep Learning extensions

Hello everyone!

I reach to you because I get an error with java when trying to to a DeepLearning process

1. Overview of the problem

I wanted to breach the topic of image handling, so I went onto the marketplace and got the three extensions Image Handling 0.2.001, Deep Learning 1.2.001, and ND4J Back End 1.2.000

However, after reading the image correctly, I get an error when reaching the Deep Learning (tensor) module. When running for the first time I get:

Exception: java.lang.UnsatisfiedLinkError
Message: no jniopencv_core in java.library.path

When re-running any time after the first time, I get :

Exception: java.lang.NoClassDefFoundError
Message: Could not initialize class org.bytedeco.javacv.OpenCVFrameConverter$ToMat

I am working with the image set MNIST (sorry can't post the link on my message, but the standard image dataset)
I am on Windows 10, using RapidMiner9.10.

2. More details on my process and the error

Here is the XML for my very simple process

<?xml version="1.0" encoding="UTF-8"?><process version="9.10.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.10.001" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="image_handling:read_image_meta_data" compatibility="0.2.001" expanded="true" height="68" name="Read Image Meta Data" width="90" x="45" y="85">
        <parameter key="directory" value="C:/Users/yho67/Desktop/data_science/samy_lesson/MNIST/trainingSet"/>
        <parameter key="use_label" value="true"/>
      </operator>
      <operator activated="true" class="image_handling:image_pre_processor" compatibility="0.2.001" expanded="true" height="103" name="Pre-Process Images" width="90" x="179" y="85">
        <parameter key="path" value="Path"/>
        <parameter key="use_label" value="false"/>
        <process expanded="true">
          <connect from_port="transform" to_port="transform"/>
          <portSpacing port="source_transform" spacing="0"/>
          <portSpacing port="sink_transform" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="deeplearning:dl4j_tensor_sequential_neural_network" compatibility="1.2.001" expanded="true" height="145" name="Deep Learning (Tensor)" width="90" x="313" y="85">
        <parameter key="epochs" value="10"/>
        <parameter key="use_miniBatch" value="false"/>
        <parameter key="batch_size" value="32"/>
        <parameter key="log_each_epoch" value="true"/>
        <parameter key="epochs_per_log" value="10"/>
        <parameter key="optimization_method" value="Stochastic Gradient Descent"/>
        <parameter key="backpropagation" value="Standard"/>
        <parameter key="backpropagation_length" value="50"/>
        <parameter key="use_early_stopping" value="false"/>
        <parameter key="condition_strategy" value="score improvement"/>
        <parameter key="patience" value="5"/>
        <parameter key="minimal_score_improvement" value="0.0"/>
        <parameter key="best_epoch_score" value="0.01"/>
        <parameter key="max_iteration_score" value="3.0"/>
        <parameter key="max_iteration_time" value="10"/>
        <parameter key="updater" value="Adam"/>
        <parameter key="learning_rate" value="0.01"/>
        <parameter key="momentum" value="0.9"/>
        <parameter key="rho" value="0.95"/>
        <parameter key="epsilon" value="1.0E-6"/>
        <parameter key="beta1" value="0.9"/>
        <parameter key="beta2" value="0.999"/>
        <parameter key="RMSdecay" value="0.95"/>
        <parameter key="weight_initialization" value="Normal"/>
        <parameter key="bias_initialization" value="0.0"/>
        <parameter key="use_regularization" value="false"/>
        <parameter key="l1_strength" value="0.1"/>
        <parameter key="l2_strength" value="0.1"/>
        <parameter key="cudnn_algo_mode" value="Prefer fastest"/>
        <parameter key="infer_input_shape" value="true"/>
        <parameter key="network_type" value="Simple Neural Network"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <process expanded="true">
          <operator activated="true" class="deeplearning:dl4j_dense_layer" compatibility="1.2.001" expanded="true" height="68" name="Add Fully-Connected Layer" width="90" x="112" y="34">
            <parameter key="neurons" value="100"/>
            <parameter key="activation_function" value="ReLU (Rectified Linear Unit)"/>
            <parameter key="use_dropout" value="false"/>
            <parameter key="dropout_rate" value="0.25"/>
            <parameter key="overwrite_networks_weight_initialization" value="false"/>
            <parameter key="weight_initialization" value="Normal"/>
            <parameter key="overwrite_networks_bias_initialization" value="false"/>
            <parameter key="bias_initialization" value="0.0"/>
          </operator>
          <operator activated="true" class="deeplearning:dl4j_dense_layer" compatibility="1.2.001" expanded="true" height="68" name="Add Fully-Connected Layer (2)" width="90" x="313" y="34">
            <parameter key="neurons" value="100"/>
            <parameter key="activation_function" value="ReLU (Rectified Linear Unit)"/>
            <parameter key="use_dropout" value="false"/>
            <parameter key="dropout_rate" value="0.25"/>
            <parameter key="overwrite_networks_weight_initialization" value="false"/>
            <parameter key="weight_initialization" value="Normal"/>
            <parameter key="overwrite_networks_bias_initialization" value="false"/>
            <parameter key="bias_initialization" value="0.0"/>
          </operator>
          <operator activated="true" class="deeplearning:dl4j_output_layer" compatibility="1.2.001" expanded="true" height="68" name="Add Output Layer" width="90" x="514" y="34">
            <parameter key="output_type" value="FullyConnected"/>
            <parameter key="loss_function" value="Multiclass Cross Entropy (Classification)"/>
            <parameter key="neurons" value="1"/>
            <parameter key="activation_function" value="Softmax"/>
            <parameter key="use_dropout" value="false"/>
            <parameter key="dropout_rate" value="0.25"/>
            <parameter key="overwrite_networks_weight_initialization" value="false"/>
            <parameter key="weight_initialization" value="Normal"/>
            <parameter key="overwrite_networks_bias_initialization" value="false"/>
            <parameter key="bias_initialization" value="0.0"/>
          </operator>
          <connect from_port="in layerArchitecture" to_op="Add Fully-Connected Layer" to_port="layerArchitecture"/>
          <connect from_op="Add Fully-Connected Layer" from_port="layerArchitecture" to_op="Add Fully-Connected Layer (2)" to_port="layerArchitecture"/>
          <connect from_op="Add Fully-Connected Layer (2)" from_port="layerArchitecture" to_op="Add Output Layer" to_port="layerArchitecture"/>
          <connect from_op="Add Output Layer" from_port="layerArchitecture" to_port="out layerArchitecture"/>
          <portSpacing port="source_in layerArchitecture" spacing="0"/>
          <portSpacing port="sink_out layerArchitecture" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="image_handling:read_image_meta_data" compatibility="0.2.001" expanded="true" height="68" name="Read Image Meta Data (2)" width="90" x="45" y="289">
        <parameter key="directory" value="C:/Users/yho67/Desktop/data_science/samy_lesson/MNIST/testSet"/>
        <parameter key="use_label" value="true"/>
      </operator>
      <operator activated="true" class="image_handling:image_pre_processor" compatibility="0.2.001" expanded="true" height="103" name="Pre-Process Images (2)" width="90" x="179" y="289">
        <parameter key="path" value="Path"/>
        <parameter key="use_label" value="false"/>
        <process expanded="true">
          <connect from_port="transform" to_port="transform"/>
          <portSpacing port="source_transform" spacing="0"/>
          <portSpacing port="sink_transform" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="nd4j:apply_model_generic" compatibility="1.2.000" expanded="true" height="82" name="Apply Model (Generic)" width="90" x="380" y="340"/>
      <connect from_op="Read Image Meta Data" from_port="output" to_op="Pre-Process Images" to_port="example set"/>
      <connect from_op="Pre-Process Images" from_port="tensor" to_op="Deep Learning (Tensor)" to_port="training set"/>
      <connect from_op="Deep Learning (Tensor)" from_port="model" to_op="Apply Model (Generic)" to_port="model"/>
      <connect from_op="Read Image Meta Data (2)" from_port="output" to_op="Pre-Process Images (2)" to_port="example set"/>
      <connect from_op="Pre-Process Images (2)" from_port="tensor" to_op="Apply Model (Generic)" to_port="unlabelled data"/>
      <connect from_op="Apply Model (Generic)" from_port="labelled data" to_port="result 2"/>
      <connect from_op="Apply Model (Generic)" from_port="model" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
    </process>
  </operator>
</process>

As for the error code, the first time I run the process I get :

Exception: java.lang.UnsatisfiedLinkError

Message: no jniopencv_core in java.library.path

Stack trace:

java.lang.ClassLoader.loadLibrary(ClassLoader.java:1860)

java.lang.Runtime.loadLibrary0(Runtime.java:871)

java.lang.System.loadLibrary(System.java:1124)

org.bytedeco.javacpp.Loader.loadLibrary(Loader.java:1718)

org.bytedeco.javacpp.Loader.load(Loader.java:1328)

org.bytedeco.javacpp.Loader.load(Loader.java:1132)

org.bytedeco.opencv.global.opencv_core.<clinit>(opencv_core.java:16)

java.lang.Class.forName0(Native Method)

java.lang.Class.forName(Class.java:348)

org.bytedeco.javacpp.Loader.load(Loader.java:1200)

org.bytedeco.javacpp.Loader.load(Loader.java:1148)

org.bytedeco.javacv.OpenCVFrameConverter.<clinit>(OpenCVFrameConverter.java:43)

org.datavec.image.loader.NativeImageLoader.<init>(NativeImageLoader.java:60)

org.datavec.image.loader.NativeImageLoader.<init>(NativeImageLoader.java:117)

org.datavec.image.recordreader.BaseImageRecordReader.initialize(BaseImageRecordReader.java:131)

org.datavec.image.recordreader.BaseImageRecordReader.initialize(BaseImageRecordReader.java:206)

com.rapidminer.extension.image_handling.tool.SerializableImageRecordReaderProxy.initialize(SerializableImageRecordReaderProxy.java:213)

com.rapidminer.extension.image_handling.tool.SerializableImageRecordReaderProxy.<init>(SerializableImageRecordReaderProxy.java:83)

com.rapidminer.extension.image_handling.ioobject.ImagePreProcessingModel.doApply(ImagePreProcessingModel.java:147)

com.rapidminer.extension.image_handling.ioobject.ImagePreProcessingModel.apply(ImagePreProcessingModel.java:111)

com.rapidminer.extension.image_handling.operator.ImagePreProcessor.doWork(ImagePreProcessor.java:134)

com.rapidminer.operator.Operator.execute(Operator.java:1023)

com.rapidminer.operator.execution.SimpleUnitExecutor.execute(SimpleUnitExecutor.java:77)

com.rapidminer.operator.ExecutionUnit$2.run(ExecutionUnit.java:805)

com.rapidminer.operator.ExecutionUnit$2.run(ExecutionUnit.java:800)

java.security.AccessController.doPrivileged(Native Method)

com.rapidminer.operator.ExecutionUnit.execute(ExecutionUnit.java:800)

com.rapidminer.operator.OperatorChain.doWork(OperatorChain.java:423)

com.rapidminer.operator.Operator.execute(Operator.java:1023)

com.rapidminer.Process.executeRoot(Process.java:1464)

com.rapidminer.Process.lambda$executeRootInPool$5(Process.java:1443)

com.rapidminer.studio.concurrency.internal.AbstractConcurrencyContext$AdaptedCallable.exec(AbstractConcurrencyContext.java:362)

java.util.concurrent.ForkJoinTask.doExec(ForkJoinTask.java:289)

java.util.concurrent.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1056)

java.util.concurrent.ForkJoinPool.runWorker(ForkJoinPool.java:1692)

java.util.concurrent.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:175)

Cause

Exception: java.lang.UnsatisfiedLinkError

Message: C:\Users\yho67\.javacpp\cache\dl-windows-libs-1.2.1-all.jar\org\bytedeco\opencv\windows-x86_64\jniopencv_core.dll: Can't find dependent libraries

Stack trace:

java.lang.ClassLoader$NativeLibrary.load(Native Method)

java.lang.ClassLoader.loadLibrary0(ClassLoader.java:1934)

java.lang.ClassLoader.loadLibrary(ClassLoader.java:1817)

java.lang.Runtime.load0(Runtime.java:810)

java.lang.System.load(System.java:1088)

org.bytedeco.javacpp.Loader.loadLibrary(Loader.java:1668)