original_docstring: 
          Return the model that describes the VGG CNN architecture as described
          from Deepsig Inc.
          
docstring: Return the model that describes the VGG CNN architecture as described
  from Deepsig Inc.
docstring_tokens: Return the model that describes the VGG CNN architecture as described from Deepsig Inc .
code: def __get_deepsig_vgg(self, conv_layers=7, dense_layers=2, dr=0.5):
          """
          Return the model that describes the VGG CNN architecture as described
          from Deepsig Inc.
          """
  
          model = models.Sequential()
          model.add(Reshape(self.input_shape + [1],
                            input_shape=self.input_shape))
          model.add(ZeroPadding2D(padding=(0, 1)))
          model.add(Conv2D(filters=64,
                           kernel_size=(2, 3),
                           activation="relu",
                           name="conv0",
                           padding="valid",
                           kernel_initializer="glorot_uniform"))
          model.add(MaxPooling2D(pool_size=(1, 2)))
          model.add(Dropout(dr))
  
          # add convolutional layers
          for i in range(conv_layers-1):
              model.add(ZeroPadding2D(padding=(0, 1)))
              model.add(Conv2D(filters=64,
                               kernel_size=(1, 3),
                               activation="relu",
                               padding="valid",
                               kernel_initializer="glorot_uniform",
                               name="conv" + str(i+1)))
              model.add(MaxPooling2D(pool_size=(1, 2)))
              model.add(Dropout(dr))
  
          model.add(Flatten())
  
          # add dense layers
          for i in range(dense_layers):
              model.add(Dense(128,
                              activation='selu',
                              kernel_initializer="he_normal",
                              name="dense" + str(i)))
              model.add(AlphaDropout(dr))
  
          model.add(Dense(self.target_num, name="output"))
          model.add(Activation('softmax'))
          model.add(Reshape([self.target_num]))
  
          model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
          return model
code_tokens: def __get_deepsig_vgg ( self , conv_layers = 7 , dense_layers = 2 , dr = 0.5 ) : model = models . Sequential ( ) model . add ( Reshape ( self . input_shape + [ 1 ] , input_shape = self . input_shape ) ) model . add ( ZeroPadding2D ( padding = ( 0 , 1 ) ) ) model . add ( Conv2D ( filters = 64 , kernel_size = ( 2 , 3 ) , activation = "relu" , name = "conv0" , padding = "valid" , kernel_initializer = "glorot_uniform" ) ) model . add ( MaxPooling2D ( pool_size = ( 1 , 2 ) ) ) model . add ( Dropout ( dr ) ) for i in range ( conv_layers - 1 ) : model . add ( ZeroPadding2D ( padding = ( 0 , 1 ) ) ) model . add ( Conv2D ( filters = 64 , kernel_size = ( 1 , 3 ) , activation = "relu" , padding = "valid" , kernel_initializer = "glorot_uniform" , name = "conv" + str ( i + 1 ) ) ) model . add ( MaxPooling2D ( pool_size = ( 1 , 2 ) ) ) model . add ( Dropout ( dr ) ) model . add ( Flatten ( ) ) for i in range ( dense_layers ) : model . add ( Dense ( 128 , activation = 'selu' , kernel_initializer = "he_normal" , name = "dense" + str ( i ) ) ) model . add ( AlphaDropout ( dr ) ) model . add ( Dense ( self . target_num , name = "output" ) ) model . add ( Activation ( 'softmax' ) ) model . add ( Reshape ( [ self . target_num ] ) ) model . compile ( loss = 'sparse_categorical_crossentropy' , optimizer = 'adam' ) return model
short_docstring: Return the model that describes the VGG CNN architecture as described
  from Deepsig Inc.
short_docstring_tokens: Return the model that describes the VGG CNN architecture as described from Deepsig Inc .
comment: """
          Return the model that describes the VGG CNN architecture as described
          from Deepsig Inc.
          """$SEP$# add convolutional layers$SEP$# add dense layers