original_docstring: 
          Return the model that describes the ResNet architecture as described
          from Deepsig Inc.
          
docstring: Return the model that describes the ResNet architecture as described
  from Deepsig Inc.
docstring_tokens: Return the model that describes the ResNet architecture as described from Deepsig Inc .
code: def __get_deepsig_resnet(self, residual_stacks=6, dense_layers=2, dr=0.5):
          """
          Return the model that describes the ResNet architecture as described
          from Deepsig Inc.
          """
  
          inp = Input(shape=self.input_shape)
  
          x = Reshape(self.input_shape + [1])(inp)
  
          x = ZeroPadding2D(padding=(0, 1))(x)
          x = Conv2D(32,
                     kernel_size=(2, 3),
                     padding='valid',
                     kernel_initializer="glorot_uniform",
                     name="conv_0")(x)
          # channels last
          x = BatchNormalization(axis=3)(x)
          x = Activation('relu')(x)
  
          for i in range(residual_stacks):
              x = self._residual_stack(x, nb_channels=[32, 32, 32],
                                       kernel_sizes=[
                                          [(1, 3), (1, 3)], [(1, 3), (1, 3)]],
                                       name="resstack_" + str(i))
  
          x = Flatten()(x)
  
          for i in range(dense_layers):
              x = Dense(128,
                        activation='selu',
                        kernel_initializer='he_normal',
                        name="dense" + str(i))(x)
              x = AlphaDropout(dr)(x)
  
          x = Dense(self.target_num, name="output")(x)
          x = Activation('softmax')(x)
          x = Reshape([self.target_num])(x)
          model = models.Model(inp, x, name='deepsig_resnet')
          model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
  
          return model
code_tokens: def __get_deepsig_resnet ( self , residual_stacks = 6 , dense_layers = 2 , dr = 0.5 ) : inp = Input ( shape = self . input_shape ) x = Reshape ( self . input_shape + [ 1 ] ) ( inp ) x = ZeroPadding2D ( padding = ( 0 , 1 ) ) ( x ) x = Conv2D ( 32 , kernel_size = ( 2 , 3 ) , padding = 'valid' , kernel_initializer = "glorot_uniform" , name = "conv_0" ) ( x ) x = BatchNormalization ( axis = 3 ) ( x ) x = Activation ( 'relu' ) ( x ) for i in range ( residual_stacks ) : x = self . _residual_stack ( x , nb_channels = [ 32 , 32 , 32 ] , kernel_sizes = [ [ ( 1 , 3 ) , ( 1 , 3 ) ] , [ ( 1 , 3 ) , ( 1 , 3 ) ] ] , name = "resstack_" + str ( i ) ) x = Flatten ( ) ( x ) for i in range ( dense_layers ) : x = Dense ( 128 , activation = 'selu' , kernel_initializer = 'he_normal' , name = "dense" + str ( i ) ) ( x ) x = AlphaDropout ( dr ) ( x ) x = Dense ( self . target_num , name = "output" ) ( x ) x = Activation ( 'softmax' ) ( x ) x = Reshape ( [ self . target_num ] ) ( x ) model = models . Model ( inp , x , name = 'deepsig_resnet' ) model . compile ( loss = 'sparse_categorical_crossentropy' , optimizer = 'adam' ) return model
short_docstring: Return the model that describes the ResNet architecture as described
  from Deepsig Inc.
short_docstring_tokens: Return the model that describes the ResNet architecture as described from Deepsig Inc .
comment: """
          Return the model that describes the ResNet architecture as described
          from Deepsig Inc.
          """$SEP$# channels last