original_docstring: 
          Return the model that describes the CNN architecture as described from
          Deepsig Inc.
          
docstring: Return the model that describes the CNN architecture as described from
  Deepsig Inc.
docstring_tokens: Return the model that describes the CNN architecture as described from Deepsig Inc .
code: def __get_deepsig_cnn(self):
          """
          Return the model that describes the CNN architecture as described from
          Deepsig Inc.
          """
          dr = 0.5
          model = models.Sequential()
          model.add(Reshape(self.input_shape + [1],
                            input_shape=self.input_shape))
          model.add(ZeroPadding2D((0, 2)))
          model.add(Conv2D(256, (1, 3), activation="relu", name="conv1",
                           padding="valid", kernel_initializer="glorot_uniform"))
          model.add(Dropout(dr))
          model.add(ZeroPadding2D((0, 2)))
          model.add(Conv2D(80, (1, 3), activation="relu", name="conv12",
                           padding="valid", kernel_initializer="glorot_uniform"))
          model.add(Dropout(dr))
          model.add(Flatten())
          model.add(Dense(256, activation='relu', name="dense1"))
          model.add(Dropout(dr))
          model.add(Dense(self.target_num, name="dense2"))
          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_cnn ( self ) : dr = 0.5 model = models . Sequential ( ) model . add ( Reshape ( self . input_shape + [ 1 ] , input_shape = self . input_shape ) ) model . add ( ZeroPadding2D ( ( 0 , 2 ) ) ) model . add ( Conv2D ( 256 , ( 1 , 3 ) , activation = "relu" , name = "conv1" , padding = "valid" , kernel_initializer = "glorot_uniform" ) ) model . add ( Dropout ( dr ) ) model . add ( ZeroPadding2D ( ( 0 , 2 ) ) ) model . add ( Conv2D ( 80 , ( 1 , 3 ) , activation = "relu" , name = "conv12" , padding = "valid" , kernel_initializer = "glorot_uniform" ) ) model . add ( Dropout ( dr ) ) model . add ( Flatten ( ) ) model . add ( Dense ( 256 , activation = 'relu' , name = "dense1" ) ) model . add ( Dropout ( dr ) ) model . add ( Dense ( self . target_num , name = "dense2" ) ) 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 CNN architecture as described from
  Deepsig Inc.
short_docstring_tokens: Return the model that describes the CNN architecture as described from Deepsig Inc .
comment: """
          Return the model that describes the CNN architecture as described from
          Deepsig Inc.
          """