parameters: (self,
                   x_shape,
                   number_of_classes,
                   metrics=['accuracy'],
                   resnet_min_network_depth=2,
                   resnet_max_network_depth=5,
                   resnet_min_filters_number=32,
                   resnet_max_filters_number=128,
                   resnet_min_max_kernel_size=8,
                   resnet_max_max_kernel_size=32,
                   **_other)
docstring: Parameters
          ----------
          x_shape : tuple
              Shape of the input dataset: (num_samples, num_timesteps, num_channels)
          number_of_classes : int
              Number of classes for classification task
          metrics : list
              Metrics to calculate on the validation set.
              See https://keras.io/metrics/ for possible values.
          resnet_min_network_dept : int
              minimum number of Inception modules in ResNet model
          resnet_max_network_dept : int
              maximum number of Inception modules in ResNet model
          resnet_min_filters_number : int
              minimum number of filters per Conv layer in ResNet model
          resnet_max_filters_number : int
              maximum number of filters per Conv layer in ResNet model
          resnet_min_max_kernel_size : int
              minimum size of CNN kernels in ResNet model
          resnet_max_max_kernel_size : int
              maximum size of CNN kernels in ResNet model
docstring_summary: Parameters
          ----------
          x_shape : tuple
              Shape of the input dataset: (num_samples, num_timesteps, num_channels)
          number_of_classes : int
              Number of classes for classification task
          metrics : list
              Metrics to calculate on the validation set.
              See https://keras.io/metrics/ for possible values.
          resnet_min_network_dept : int
              minimum number of Inception modules in ResNet model
          resnet_max_network_dept : int
              maximum number of Inception modules in ResNet model
          resnet_min_filters_number : int
              minimum number of filters per Conv layer in ResNet model
          resnet_max_filters_number : int
              maximum number of filters per Conv layer in ResNet model
          resnet_min_max_kernel_size : int
              minimum size of CNN kernels in ResNet model
          resnet_max_max_kernel_size : int
              maximum size of CNN kernels in ResNet model
docstring_tokens: Parameters ---------- x_shape : tuple Shape of the input dataset : ( num_samples num_timesteps num_channels ) number_of_classes : int Number of classes for classification task metrics : list Metrics to calculate on the validation set . See https : // keras . io / metrics / for possible values . resnet_min_network_dept : int minimum number of Inception modules in ResNet model resnet_max_network_dept : int maximum number of Inception modules in ResNet model resnet_min_filters_number : int minimum number of filters per Conv layer in ResNet model resnet_max_filters_number : int maximum number of filters per Conv layer in ResNet model resnet_min_max_kernel_size : int minimum size of CNN kernels in ResNet model resnet_max_max_kernel_size : int maximum size of CNN kernels in ResNet model
function: def __init__(self,
                   x_shape,
                   number_of_classes,
                   metrics=['accuracy'],
                   resnet_min_network_depth=2,
                   resnet_max_network_depth=5,
                   resnet_min_filters_number=32,
                   resnet_max_filters_number=128,
                   resnet_min_max_kernel_size=8,
                   resnet_max_max_kernel_size=32,
                   **_other):
          """
          Parameters
          ----------
          x_shape : tuple
              Shape of the input dataset: (num_samples, num_timesteps, num_channels)
          number_of_classes : int
              Number of classes for classification task
          metrics : list
              Metrics to calculate on the validation set.
              See https://keras.io/metrics/ for possible values.
          resnet_min_network_dept : int
              minimum number of Inception modules in ResNet model
          resnet_max_network_dept : int
              maximum number of Inception modules in ResNet model
          resnet_min_filters_number : int
              minimum number of filters per Conv layer in ResNet model
          resnet_max_filters_number : int
              maximum number of filters per Conv layer in ResNet model
          resnet_min_max_kernel_size : int
              minimum size of CNN kernels in ResNet model
          resnet_max_max_kernel_size : int
              maximum size of CNN kernels in ResNet model
          """
          self.x_shape = x_shape
          self.number_of_classes = number_of_classes
          self.metrics = metrics
  
          # Set default parameters
          self.settings = {
              'resnet_min_network_depth': resnet_min_network_depth,
              'resnet_max_network_depth': resnet_max_network_depth,
              'resnet_min_filters_number': resnet_min_filters_number,
              'resnet_max_filters_number': resnet_max_filters_number,
              'resnet_min_max_kernel_size': resnet_min_max_kernel_size,
              'resnet_max_max_kernel_size': resnet_max_max_kernel_size,
              }
  
          # Add missing parameters from default
          for key, value in _other.items():
              if key not in self.settings:
                  self.settings[key] = value
function_tokens: def __init__ ( self , x_shape , number_of_classes , metrics = [ 'accuracy' ] , resnet_min_network_depth = 2 , resnet_max_network_depth = 5 , resnet_min_filters_number = 32 , resnet_max_filters_number = 128 , resnet_min_max_kernel_size = 8 , resnet_max_max_kernel_size = 32 , * * _other ) : self . x_shape = x_shape self . number_of_classes = number_of_classes self . metrics = metrics # Set default parameters self . settings = { 'resnet_min_network_depth' : resnet_min_network_depth , 'resnet_max_network_depth' : resnet_max_network_depth , 'resnet_min_filters_number' : resnet_min_filters_number , 'resnet_max_filters_number' : resnet_max_filters_number , 'resnet_min_max_kernel_size' : resnet_min_max_kernel_size , 'resnet_max_max_kernel_size' : resnet_max_max_kernel_size , } # Add missing parameters from default for key , value in _other . items ( ) : if key not in self . settings : self . settings [ key ] = value