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Deep Learning suggestions/guidance

green_duckgreen_duck Member Posts: 4 Learner I
in Help
Hello,

So I have an assignment where I'm constructing a deep learning model to achieve, hopefully, a classification accuracy of at least 63%.  I've started with a few fully-connected layers, then some CNN/RNN combinations and to no avail am able to achieve a rate of about 25%.  Can someone provide any suggestions or feedback on the model?  Also, for this assignment I am unable to use the auto-model(which I have already and it achieved a rate of 63% but am unable to replicate). 

XML below(and data attached):

<?xml version="1.0" encoding="UTF-8"?><process version="9.6.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.6.000" 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="9.6.000" expanded="true" height="68" name="Retrieve Tweets_sequence" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Local Repository/data/Tweets_sequence"/>
      </operator>
      <operator activated="true" class="subprocess" compatibility="9.6.000" expanded="true" height="103" name="Subprocess" width="90" x="179" y="34">
        <process expanded="true">
          <operator activated="true" class="select_attributes" compatibility="9.6.000" expanded="true" height="82" name="Select Attributes" width="90" x="45" y="34">
            <parameter key="attribute_filter_type" value="all"/>
            <parameter key="attribute" value="sentiment"/>
            <parameter key="attributes" value=""/>
            <parameter key="use_except_expression" value="false"/>
            <parameter key="value_type" value="numeric"/>
            <parameter key="use_value_type_exception" value="false"/>
            <parameter key="except_value_type" value="time"/>
            <parameter key="block_type" value="attribute_block"/>
            <parameter key="use_block_type_exception" value="false"/>
            <parameter key="except_block_type" value="value_matrix_row_start"/>
            <parameter key="invert_selection" value="false"/>
            <parameter key="include_special_attributes" value="true"/>
          </operator>
          <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role" width="90" x="246" y="85">
            <parameter key="attribute_name" value="sentiment"/>
            <parameter key="target_role" value="label"/>
            <list key="set_additional_roles"/>
          </operator>
          <operator activated="true" class="split_data" compatibility="9.6.000" expanded="true" height="103" name="Split Data" width="90" x="447" y="85">
            <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"/>
          </operator>
          <connect from_port="in 1" to_op="Select Attributes" to_port="example set input"/>
          <connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>
          <connect from_op="Set Role" from_port="example set output" to_op="Split Data" to_port="example set"/>
          <connect from_op="Split Data" from_port="partition 1" to_port="out 1"/>
          <connect from_op="Split Data" from_port="partition 2" to_port="out 2"/>
          <portSpacing port="source_in 1" spacing="0"/>
          <portSpacing port="source_in 2" spacing="0"/>
          <portSpacing port="sink_out 1" spacing="0"/>
          <portSpacing port="sink_out 2" spacing="0"/>
          <portSpacing port="sink_out 3" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="deeplearning:dl4j_sequential_neural_network" compatibility="0.9.003" expanded="true" height="103" name="Deep Learning" width="90" x="313" y="340">
        <parameter key="loss_function" value="Mean Absolute Error (Regression)"/>
        <parameter key="epochs" value="10"/>
        <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_dense_layer" compatibility="0.9.003" expanded="true" height="68" name="Add Fully-Connected Layer (3)" width="90" x="112" y="85">
            <parameter key="number_of_neurons" value="9"/>
            <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_convolutional_layer" compatibility="0.9.003" expanded="true" height="68" name="Add Convolutional Layer" width="90" x="313" y="85">
            <parameter key="number_of_activation_maps" value="128"/>
            <parameter key="kernel_size" value="2.2"/>
            <parameter key="stride_size" value="2.2"/>
            <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_lstm_layer" compatibility="0.9.003" expanded="true" height="68" name="Add LSTM Layer" width="90" x="447" y="85">
            <parameter key="neurons" value="32"/>
            <parameter key="gate_activation" value="ReLU (Rectified Linear Unit)"/>
            <parameter key="forget_gate_bias_initialization" value="1.0"/>
          </operator>
          <operator activated="true" class="deeplearning:dl4j_pooling_layer" compatibility="0.9.003" expanded="true" height="68" name="Add Pooling Layer" width="90" x="648" y="85">
            <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="0.9.003" expanded="true" height="68" name="Add Fully-Connected Layer" width="90" x="782" y="187">
            <parameter key="number_of_neurons" value="9"/>
            <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="layerArchitecture" to_op="Add Fully-Connected Layer (3)" to_port="layerArchitecture"/>
          <connect from_op="Add Fully-Connected Layer (3)" from_port="layerArchitecture" to_op="Add Convolutional Layer" to_port="layerArchitecture"/>
          <connect from_op="Add Convolutional Layer" from_port="layerArchitecture" to_op="Add LSTM Layer" to_port="layerArchitecture"/>
          <connect from_op="Add LSTM 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_port="layerArchitecture"/>
          <portSpacing port="source_layerArchitecture" spacing="0"/>
          <portSpacing port="sink_layerArchitecture" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="apply_model" compatibility="9.6.000" expanded="true" height="82" name="Apply Model" width="90" x="514" y="187">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="performance_regression" compatibility="9.6.000" expanded="true" height="82" name="Performance (2)" width="90" x="782" y="187">
        <parameter key="main_criterion" value="first"/>
        <parameter key="root_mean_squared_error" value="true"/>
        <parameter key="absolute_error" value="true"/>
        <parameter key="relative_error" value="true"/>
        <parameter key="relative_error_lenient" value="false"/>
        <parameter key="relative_error_strict" value="false"/>
        <parameter key="normalized_absolute_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="prediction_average" value="false"/>
        <parameter key="spearman_rho" value="false"/>
        <parameter key="kendall_tau" value="false"/>
        <parameter key="skip_undefined_labels" value="true"/>
        <parameter key="use_example_weights" value="true"/>
      </operator>
      <connect from_op="Retrieve Tweets_sequence" from_port="output" to_op="Subprocess" to_port="in 1"/>
      <connect from_op="Subprocess" from_port="out 1" to_op="Deep Learning" to_port="training set"/>
      <connect from_op="Subprocess" from_port="out 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 (2)" to_port="labelled data"/>
      <connect from_op="Performance (2)" from_port="performance" to_port="result 1"/>
      <connect from_op="Performance (2)" 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"/>
    </process>
  </operator>
</process>


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