class_docstr: This class defines an application for image-level classification
  problems mapping from images to scalar labels.
  
  This is the application class to be instantiated by the driver
  and referred to in configuration files.
  
  Although structurally similar to segmentation, this application
  supports different samplers/aggregators (because patch-based
  processing is not appropriate), and monitoring metrics.
class_docstr_tok: ["This","class","defines","an","application","for","image-level","classification","problems","mapping","from","images","to","scalar","labels",".","This","is","the","application","class","to","be","instantiated","by","the","driver","and","referred","to","in","configuration","files",".","Although","structurally","similar","to","segmentation",",","this","application","supports","different","samplers\/aggregators","(","because","patch-based","processing","is","not","appropriate",")",",","and","monitoring","metrics","."]
docstr: Initialise the network and specifies the ordering of elements
  :return:
code: def initialise_network(self):
      """
      Initialise the network and specifies the ordering of elements
      :return:
      """
      w_regularizer = None
      b_regularizer = None
      reg_type = self.net_param.reg_type.lower()
      decay = self.net_param.decay
      if reg_type == "l2" and decay > 0:
          from tensorflow.contrib.layers.python.layers import (
              regularizers,
          )
  
          w_regularizer = regularizers.l2_regularizer(decay)
          b_regularizer = regularizers.l2_regularizer(decay)
      elif reg_type == "l1" and decay > 0:
          from tensorflow.contrib.layers.python.layers import (
              regularizers,
          )
  
          w_regularizer = regularizers.l1_regularizer(decay)
          b_regularizer = regularizers.l1_regularizer(decay)
  
      self.net = ApplicationNetFactory.create(
          "niftynet.contrib.csv_reader.toynet_features.ToyNetFeat"
      )(
          num_classes=self.segmentation_param.num_classes,
          w_initializer=InitializerFactory.get_initializer(
              name=self.net_param.weight_initializer
          ),
          b_initializer=InitializerFactory.get_initializer(
              name=self.net_param.bias_initializer
          ),
          w_regularizer=w_regularizer,
          b_regularizer=b_regularizer,
          acti_func=self.net_param.activation_function,
      )
      self.net_multi = ApplicationNetFactory.create(
          "niftynet.contrib.csv_reader.class_seg_finnet.ClassSegFinnet"
      )(
          num_classes=self.segmentation_param.num_classes,
          w_initializer=InitializerFactory.get_initializer(
              name=self.net_param.weight_initializer
          ),
          b_initializer=InitializerFactory.get_initializer(
              name=self.net_param.bias_initializer
          ),
          w_regularizer=w_regularizer,
          b_regularizer=b_regularizer,
          acti_func=self.net_param.activation_function,
      )
  
imports: import tensorflow
  from niftynet.application.base_application import BaseApplication
  from niftynet.engine.application_factory import ApplicationNetFactory, InitializerFactory, OptimiserFactory
  from niftynet.engine.application_variables import CONSOLE, NETWORK_OUTPUT, TF_SUMMARIES
  from niftynet.contrib.csv_reader.sampler_resize_v2_csv import ResizeSamplerCSV
  from niftynet.contrib.csv_reader.sampler_uniform_v2_csv import UniformSamplerCSV
  from niftynet.contrib.csv_reader.sampler_weighted_v2_csv import WeightedSamplerCSV
  from niftynet.contrib.csv_reader.sampler_balanced_v2_csv import BalancedSamplerCSV
  from niftynet.contrib.csv_reader.sampler_grid_v2_csv import GridSamplerCSV
  from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator
  from niftynet.engine.windows_aggregator_resize import ResizeSamplesAggregator
  from niftynet.io.image_reader import ImageReader
  from niftynet.contrib.csv_reader.csv_reader import CSVReader
  from niftynet.layer.discrete_label_normalisation import DiscreteLabelNormalisationLayer
  from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer
  from niftynet.layer.binary_masking import BinaryMaskingLayer
  from niftynet.layer.post_processing import PostProcessingLayer
  from niftynet.layer.loss_classification import LossFunction
  from niftynet.layer.loss_segmentation import LossFunction
  from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer
  from niftynet.layer.rand_flip import RandomFlipLayer
  from niftynet.layer.rand_rotation import RandomRotationLayer
  from niftynet.layer.rand_spatial_scaling import RandomSpatialScalingLayer
  from niftynet.evaluation.classification_evaluator import ClassificationEvaluator
label_desc: Use image node_id 12 for calling the MultiClassifSegApplication obj's underlying member method code with example usage: obj.initialise_network() without return types