"Bug Report"

micheljanos

Trying to run  KerasSamples/iris_classification, I got this error


Michel

varunm1

Hi @micheljanos

Just from my experience, I recommend you use Deep Learning extension rather than Keras as this is unstable in RM. 

You can download from the market place and samples are provided for the same. This extension has both CNN and LSTM and you can run it on CPU or GPU. This extension runs on DL4j backend rather than tensorflow.

Which python did you set as default for keras extension? If you are using new anaconda which runs on 3.7 version, I think the issue is related to the incompatibility of Keras with new python 3.7 version as the official keras GitHub says they still didn't test for 3.7.

If you want to use keras, you can try 3.5 or 3.6 python with keras installed and the preferences for both keras and python in RM set to 3.6 version.

Here are a couple of members who work on this @hughesfleming68 @pschlunder

Thanks,
Varun

micheljanos

Hi Varun,
Thanks for your answer.
I'm using RM 9.1.000 and python 3.6.7


Regards,
Michel

varunm1

Hi @micheljanos

I see that we need to use 3.5.2 as mentioned in below thread. Please take a look at below thread where @jpuente mentioned all the required modules to use keras in RapidMiner. Just try to install all the version recommended in below link and check if that works out. This is the major issue with this extension as it throws errors if any installation requirement is not fulfilled (mainly the required version).

https://community.rapidminer.com/discussion/comment/54662#Comment_54662

Thanks,
Varun

micheljanos

Hi Varun,

Yes, I see I should use python 3.5.2 (I have all required python packages  already installed). I hope RM will fix this error because I already use the "Execute Python" operator with no problems.

Regards,

Michel

sgenzer

hi yes I will say that the Keras extension will likely be deprecated sometime in the future, being replaced with the new DL extension as @varunm1 said. One thought is to release the extension to GitHub for those that want to continue to work on it. Make sense?

Scott

micheljanos

Hi Scott,
I wanted to use Deep Learning in RM, so I went to the video "An Introduction to Deep Laerning with RapidMiner," which is quite nice but at the end shows an example using Keras operator (as I'm using Keras in Python, this is a nice choice for me). 
Than I learned that I we need to use Python 3.5.2. The problem with that is that if I include this version in Anaconda/Python, it requires to update/downgrade many packages as Pandas, Numpy, etc.. But if I, for example downgrade a pakage, It may not be possible to use it in my original Python (3.6.7). I'm not an expert but it seems complicated to work with diferent versions of Python in the same ecosystem.
But the major problem I found in trying to use DL in RM is that it does not finish processing. I did an experience with the file bellow (included in Keras package) using the same settings in Python Keras and in RM H2O. The Python model was finished in few seconds but RM was processing for half hour thant I  stopped the process.






 



Maybe I'm doing something wrong, so If somebody can reproduce this process in RM it will be good to know.

Regards,

Michel 

varunm1

Hi @micheljanos

Did you try to create a new environment in Anaconda with python 3.5.2 and install all packages and point this to RM in preferences? This works out generally. I don't think it downgrades packages that are used by 3.6.7 as the environment itself is separate.

If you are looking for a deep learning extension. I have a sample code with network setup (CNN) here not in keras but the java based DL extension. In case this might help  

<?xml version="1.0" encoding="UTF-8"?><process version="9.1.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.1.000" 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="retrieve" compatibility="9.1.000" expanded="true" height="68" name="Retrieve Subject_Assistment_Bored_Clean_100" width="90" x="112" y="289">
        <parameter key="repository_entry" value="../../data/AIED_2019_100/Subject_Assistment_Bored_Clean_100"/>
      </operator>
      <operator activated="true" class="concurrency:cross_validation" compatibility="9.1.000" expanded="true" height="166" name="Cross Validation" width="90" x="447" y="340">
        <parameter key="split_on_batch_attribute" value="false"/>
        <parameter key="leave_one_out" value="false"/>
        <parameter key="number_of_folds" value="5"/>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <parameter key="enable_parallel_execution" value="true"/>
        <process expanded="true">
          <operator activated="true" class="deeplearning:dl4j_sequential_neural_network" compatibility="0.9.000" expanded="true" height="103" name="Deep Learning" width="90" x="179" y="34">
            <parameter key="loss_function" value="Cross Entropy (Binary Classification)"/>
            <parameter key="epochs" value="20"/>
            <parameter key="use_miniBatch" value="true"/>
            <parameter key="batch_size" value="32"/>
            <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="ReLU"/>
            <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="optimization_method" value="Stochastic Gradient Descent"/>
            <parameter key="backpropagation" value="Standard"/>
            <parameter key="backpropagation_length" value="50"/>
            <parameter key="infer_input_shape" value="true"/>
            <parameter key="network_type" value="Simple Neural Network"/>
            <parameter key="log_each_epoch" value="true"/>
            <parameter key="epochs_per_log" value="10"/>
            <parameter key="use_local_random_seed" value="false"/>
            <parameter key="local_random_seed" value="1992"/>
            <process expanded="true">
              <operator activated="true" class="deeplearning:dl4j_convolutional_layer" compatibility="0.9.000" expanded="true" height="68" name="Add Convolutional Layer" width="90" x="45" y="340">
                <parameter key="number_of_activation_maps" value="32"/>
                <parameter key="kernel_size" value="102.5"/>
                <parameter key="stride_size" value="1.1"/>
                <parameter key="activation_function" value="ReLU (Rectified Linear Unit)"/>
                <parameter key="use_dropout" value="true"/>
                <parameter key="dropout_rate" value="0.5"/>
                <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_pooling_layer" compatibility="0.9.000" expanded="true" height="68" name="Add Pooling Layer" width="90" x="179" y="340">
                <parameter key="Pooling Method" value="max"/>
                <parameter key="PNorm Value" value="1.0"/>
                <parameter key="Kernel Size" value="2.2"/>
                <parameter key="Stride Size" value="1.1"/>
              </operator>
              <operator activated="true" class="deeplearning:dl4j_dense_layer" compatibility="0.9.000" expanded="true" height="68" name="Add Fully-Connected Layer" width="90" x="112" y="85">
                <parameter key="number_of_neurons" value="256"/>
                <parameter key="activation_function" value="ReLU (Rectified Linear Unit)"/>
                <parameter key="use_dropout" value="true"/>
                <parameter key="dropout_rate" value="0.5"/>
                <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"/>
                <description align="center" color="transparent" colored="false" width="126">You can choose a number of neurons to decide how many internal attributes are created.</description>
              </operator>
              <operator activated="true" class="deeplearning:dl4j_dense_layer" compatibility="0.9.000" expanded="true" height="68" name="Add Fully-Connected Layer (2)" width="90" x="514" y="85">
                <parameter key="number_of_neurons" value="2"/>
                <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"/>
                <description align="center" color="transparent" colored="false" width="126">The last layer needs to be setup with an activation function, that fits the problem type.</description>
              </operator>
              <connect from_port="layerArchitecture" to_op="Add Convolutional Layer" to_port="layerArchitecture"/>
              <connect from_op="Add Convolutional Layer" from_port="layerArchitecture" to_op="Add Pooling Layer" to_port="layerArchitecture"/>
              <connect from_op="Add Pooling Layer" from_port="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_port="layerArchitecture"/>
              <portSpacing port="source_layerArchitecture" spacing="0"/>
              <portSpacing port="sink_layerArchitecture" spacing="0"/>
              <description align="center" color="yellow" colored="true" height="254" resized="false" width="189" x="60" y="45">First Hidden Layer</description>
              <description align="center" color="yellow" colored="false" height="254" resized="false" width="189" x="470" y="49">Output Layer</description>
            </process>
            <description align="center" color="transparent" colored="true" width="126">Open the Deep Learning operator by double-clicking on it, to discovere the layer setup.</description>
          </operator>
          <connect from_port="training set" to_op="Deep Learning" to_port="training set"/>
          <connect from_op="Deep Learning" from_port="model" to_port="model"/>
          <portSpacing port="source_training set" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
        </process>
        <process expanded="true">
          <operator activated="true" class="apply_model" compatibility="9.1.000" expanded="true" height="82" name="Apply Model" width="90" x="112" y="187">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="multiply" compatibility="9.1.000" expanded="true" height="103" name="Multiply" width="90" x="112" y="289"/>
          <operator activated="true" class="performance" compatibility="9.1.000" expanded="true" height="82" name="Performance (2)" width="90" x="246" y="340">
            <parameter key="use_example_weights" value="true"/>
          </operator>
          <operator activated="true" class="performance_classification" compatibility="9.1.000" expanded="true" height="82" name="Performance" width="90" x="246" y="34">
            <parameter key="main_criterion" value="first"/>
            <parameter key="accuracy" value="true"/>
            <parameter key="classification_error" value="false"/>
            <parameter key="kappa" value="true"/>
            <parameter key="weighted_mean_recall" value="false"/>
            <parameter key="weighted_mean_precision" value="false"/>
            <parameter key="spearman_rho" value="false"/>
            <parameter key="kendall_tau" value="false"/>
            <parameter key="absolute_error" value="false"/>
            <parameter key="relative_error" value="false"/>
            <parameter key="relative_error_lenient" value="false"/>
            <parameter key="relative_error_strict" value="false"/>
            <parameter key="normalized_absolute_error" value="false"/>
            <parameter key="root_mean_squared_error" value="true"/>
            <parameter key="root_relative_squared_error" value="false"/>
            <parameter key="squared_error" value="false"/>
            <parameter key="correlation" value="false"/>
            <parameter key="squared_correlation" value="false"/>
            <parameter key="cross-entropy" value="false"/>
            <parameter key="margin" value="false"/>
            <parameter key="soft_margin_loss" value="false"/>
            <parameter key="logistic_loss" value="false"/>
            <parameter key="skip_undefined_labels" value="true"/>
            <parameter key="use_example_weights" value="true"/>
            <list key="class_weights"/>
            <description align="center" color="transparent" colored="false" width="126">Calculate model performance</description>
          </operator>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Multiply" to_port="input"/>
          <connect from_op="Multiply" from_port="output 1" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Multiply" from_port="output 2" to_op="Performance (2)" to_port="labelled data"/>
          <connect from_op="Performance (2)" from_port="performance" to_port="performance 2"/>
          <connect from_op="Performance" from_port="performance" to_port="performance 1"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_test set results" spacing="0"/>
          <portSpacing port="sink_performance 1" spacing="0"/>
          <portSpacing port="sink_performance 2" spacing="0"/>
          <portSpacing port="sink_performance 3" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve Subject_Assistment_Bored_Clean_100" from_port="output" to_op="Cross Validation" to_port="example set"/>
      <connect from_op="Cross Validation" from_port="performance 1" to_port="result 1"/>
      <connect from_op="Cross Validation" from_port="performance 2" to_port="result 2"/>
      <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"/>
      <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="45" y="40">Creating a simple neural network with one hidden layer and an output layer.</description>
      <description align="center" color="green" colored="true" height="331" resized="true" width="275" x="285" y="79">Iris is a multi-class classification problem, therefore the network loss is set to &amp;quot;multiclass cross entropy&amp;quot;.</description>
    </process>
  </operator>
</process>

Thanks,
Varun

sgenzer

hi @micheljanos so I would recommend going to this tutorial instead: https://community.rapidminer.com/discussion/52670

Scott