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,
