parameters: (self)
return_statement: return hyperparameters
docstring: Generate a hyperparameter set that define a CNN model.
  
          Returns
          ----------
          hyperparameters : dict
              parameters for a CNN model
docstring_summary: Generate a hyperparameter set that define a CNN model.
docstring_tokens: Generate a hyperparameter set that define a CNN model .
function: def generate_hyperparameters(self):
          """Generate a hyperparameter set that define a CNN model.
  
          Returns
          ----------
          hyperparameters : dict
              parameters for a CNN model
          """
          params = Namespace(**self.settings)
          hyperparameters = generate_base_hyperparameter_set(params.low_lr,
                                                             params.high_lr,
                                                             params.low_reg,
                                                             params.high_reg)
          number_of_layers = np.random.randint(params.cnn_min_layers,
                                               params.cnn_max_layers + 1)
          hyperparameters['filters'] = np.random.randint(params.cnn_min_filters,
                                                         params.cnn_max_filters + 1,
                                                         number_of_layers)
          hyperparameters['fc_hidden_nodes'] = np.random.randint(params.cnn_min_fc_nodes,
                                                                 params.cnn_max_fc_nodes + 1)
          return hyperparameters
function_tokens: def generate_hyperparameters ( self ) : params = Namespace ( * * self . settings ) hyperparameters = generate_base_hyperparameter_set ( params . low_lr , params . high_lr , params . low_reg , params . high_reg ) number_of_layers = np . random . randint ( params . cnn_min_layers , params . cnn_max_layers + 1 ) hyperparameters [ 'filters' ] = np . random . randint ( params . cnn_min_filters , params . cnn_max_filters + 1 , number_of_layers ) hyperparameters [ 'fc_hidden_nodes' ] = np . random . randint ( params . cnn_min_fc_nodes , params . cnn_max_fc_nodes + 1 ) return hyperparameters