def _setup_metrics(self): self.metric_functions[LOSS] = self.eval_loss_function self.metric_functions[ERROR] = ErrorScore(name='metric_error') self.metric_functions[MEAN_SQUARED_ERROR] = MeanSquaredErrorMetric( name='metric_mse') self.metric_functions[MEAN_ABSOLUTE_ERROR] = MeanAbsoluteErrorMetric( name='metric_mae') self.metric_functions[R2] = R2Score(name='metric_r2')
def _setup_metrics(self): self.metric_functions = {} # needed to shadow class variable self.metric_functions[LOSS] = self.eval_loss_function self.metric_functions[MEAN_SQUARED_ERROR] = MeanSquaredErrorMetric( name='metric_mse' ) self.metric_functions[MEAN_ABSOLUTE_ERROR] = MeanAbsoluteErrorMetric( name='metric_mae' ) self.metric_functions[R2] = R2Score(name='metric_r2')
def _setup_metrics(self): self.metric_functions = {} # needed to shadow class variable if self.loss[TYPE] == 'mean_squared_error': self.metric_functions[LOSS] = MSEMetric(name='eval_loss') else: self.metric_functions[LOSS] = MAEMetric(name='eval_loss') self.metric_functions[ERROR] = ErrorScore(name='metric_error') self.metric_functions[MEAN_SQUARED_ERROR] = MeanSquaredErrorMetric( name='metric_mse') self.metric_functions[MEAN_ABSOLUTE_ERROR] = MeanAbsoluteErrorMetric( name='metric_mae') self.metric_functions[R2] = R2Score(name='metric_r2')
def _setup_metrics(self): self.metric_functions = {} # needed to shadow class variable if self.loss[TYPE] == "mean_squared_error": self.metric_functions[LOSS] = MSEMetric(name="eval_loss") elif self.loss[TYPE] == "mean_absolute_error": self.metric_functions[LOSS] = MAEMetric(name="eval_loss") elif self.loss[TYPE] == "root_mean_squared_error": self.metric_functions[LOSS] = RMSEMetric(name="eval_loss") elif self.loss[TYPE] == "root_mean_squared_percentage_error": self.metric_functions[LOSS] = RMSPEMetric(name="eval_loss") self.metric_functions[MEAN_SQUARED_ERROR] = MeanSquaredErrorMetric( name="metric_mse" ) self.metric_functions[MEAN_ABSOLUTE_ERROR] = MeanAbsoluteErrorMetric( name="metric_mae" ) self.metric_functions[ ROOT_MEAN_SQUARED_ERROR ] = RootMeanSquaredErrorMetric(name="metric_rmse") self.metric_functions[ ROOT_MEAN_SQUARED_PERCENTAGE_ERROR ] = RMSPEMetric(name="metric_rmspe") self.metric_functions[R2] = R2Score(name="metric_r2")
X_train, X_test, y_train, y_test = train_test_split(diabetes_data, diabetes_target, test_size=.1) model = Sequential([ Dense(units=128, activation=relu, input_shape=(X_train.shape[1], )), Dense(units=64, activation=relu), Dense(units=64, activation=relu), Dense(units=64, activation=relu), Dense(units=1), ]) model.compile(loss=MeanSquaredError(), optimizer=Adam(), metrics=[MeanAbsoluteErrorMetric(), MeanSquaredErrorMetric()]) # Learning rate scheduler def lr_schedule(epoch, lr): if epoch % 2 == 0: return lr else: return lr + epoch / 1000 # history = model.fit(X_train, # y_train, # epochs=100,