An exclusive raffle opportunity for active members like you! Complete your profile, answer questions and get your first accepted badge to enter the raffle.
<?xml version="1.0" encoding="UTF-8"?><process version="9.2.000"> <context> <input/> <output/> <macros/> </context> <operator activated="true" class="process" compatibility="9.2.000" expanded="true" name="Process" origin="GENERATED_SAMPLE"> <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="9.2.000" expanded="true" height="68" name="Retrieve Iris" origin="GENERATED_SAMPLE" width="90" x="45" y="238"> <parameter key="repository_entry" value="//Samples/data/Iris"/> <description align="center" color="transparent" colored="false" width="126">Loading Data</description> </operator> <operator activated="true" class="split_data" compatibility="9.2.000" expanded="true" height="103" name="Split Data" origin="GENERATED_SAMPLE" width="90" x="179" y="238"> <enumeration key="partitions"> <parameter key="ratio" value="0.8"/> <parameter key="ratio" value="0.2"/> </enumeration> <parameter key="sampling_type" value="automatic"/> <parameter key="use_local_random_seed" value="false"/> <parameter key="local_random_seed" value="1992"/> <description align="center" color="transparent" colored="false" width="126">Split data into train and test set</description> </operator> <operator activated="true" class="deeplearning:dl4j_sequential_neural_network" compatibility="0.9.000" expanded="true" height="103" name="Deep Learning" origin="GENERATED_SAMPLE" width="90" x="313" y="136"> <parameter key="loss_function" value="Multiclass Cross Entropy (Classification)"/> <parameter key="epochs" value="100"/> <parameter key="use_miniBatch" value="false"/> <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="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="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="85"> <parameter key="number_of_activation_maps" value="64"/> <parameter key="kernel_size" value="2.2"/> <parameter key="stride_size" value="1.1"/> <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_pooling_layer" compatibility="0.9.000" expanded="true" height="68" name="Add Pooling Layer" width="90" x="179" y="187"> <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" origin="GENERATED_SAMPLE" width="90" x="313" y="238"> <parameter key="number_of_neurons" value="64"/> <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"/> <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)" origin="GENERATED_SAMPLE" width="90" x="313" y="85"> <parameter key="number_of_neurons" value="3"/> <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="left" color="gray" colored="true" height="169" resized="true" width="277" x="462" y="130">Since we have a multi-class problem, we want probabilities for all possible class values. Therefore choose &quot;softmax&quot; as an activation function.<br><br>The number of neurons of the last layer needs to reflect the number of possible class values. For iris, this is three.</description> <description align="center" color="yellow" colored="true" height="254" resized="true" width="189" x="60" y="45">First Hidden Layer</description> <description align="center" color="yellow" colored="false" height="254" resized="true" width="189" x="264" y="45">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> <operator activated="true" class="apply_model" compatibility="9.2.000" expanded="true" height="82" name="Apply Model" origin="GENERATED_SAMPLE" width="90" x="447" y="238"> <list key="application_parameters"/> <parameter key="create_view" value="false"/> </operator> <operator activated="true" class="performance_classification" compatibility="9.2.000" expanded="true" height="82" name="Performance" origin="GENERATED_SAMPLE" width="90" x="581" y="238"> <parameter key="main_criterion" value="first"/> <parameter key="accuracy" value="true"/> <parameter key="classification_error" value="false"/> <parameter key="kappa" value="false"/> <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="false"/> <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_op="Retrieve Iris" from_port="output" to_op="Split Data" to_port="example set"/> <connect from_op="Split Data" from_port="partition 1" to_op="Deep Learning" to_port="training set"/> <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model" to_port="unlabelled data"/> <connect from_op="Deep Learning" from_port="model" to_op="Apply Model" to_port="model"/> <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/> <connect from_op="Performance" from_port="performance" to_port="result 1"/> <connect from_op="Performance" from_port="example set" 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 &quot;multiclass cross entropy&quot;.</description> </process> </operator> </process>