def get_compiled_multi_io_model_temporal(sample_weight_mode):
model = get_multi_io_temporal_model()
model.compile(optimizer=optimizer_v2.gradient_descent.SGD(0.1),
loss='mae',
metrics=[metrics.MeanAbsoluteError(name='mae')],
weighted_metrics=[metrics.MeanAbsoluteError(name='mae_2')],
sample_weight_mode=sample_weight_mode,
run_eagerly=testing_utils.should_run_eagerly(),
run_distributed=testing_utils.should_run_distributed())
return model
def metrics(self, regularization_losses=None):
"""Creates metrics. See `base_head.Head` for details."""
keys = metric_keys.MetricKeys
with ops.name_scope('metrics', values=(regularization_losses,)):
# Mean metric.
eval_metrics = {}
eval_metrics[self._loss_mean_key] = metrics.Mean(name=keys.LOSS_MEAN)
eval_metrics[self._prediction_mean_key] = metrics.Mean(
name=keys.PREDICTION_MEAN)
eval_metrics[self._label_mean_key] = metrics.Mean(name=keys.LABEL_MEAN)
if regularization_losses is not None:
eval_metrics[self._loss_regularization_key] = metrics.Mean(
name=keys.LOSS_REGULARIZATION)
if self._model_hparams.da_tlen > 0 and not self._model_hparams.event_relation:
eval_metrics[self._auc_roc_24] = metrics.AUC(name=AUC_ROC % '24')
eval_metrics[self._auc_roc_48] = metrics.AUC(name=AUC_ROC % '48')
eval_metrics[self._auc_pr] = metrics.AUC(
curve='PR', name=AUC_PR % 'avg')
eval_metrics[self._auc_roc] = metrics.AUC(name=AUC_ROC % 'avg')
eval_metrics[self._mean_abs_error] = metrics.MeanAbsoluteError(
name=MEAN_ABS_ERROR % 'avg')
for i in range(int(self._model_hparams.da_tlen / SLOT_TO_WINDOW) + 1):
eval_metrics[self._probablity_within_window_list[i]] = metrics.Mean(
name=PROBABILITY_AT_WINDOW % i)
return eval_metrics
def test_config(self):
mae_obj = metrics.MeanAbsoluteError(name='my_mae', dtype=dtypes.int32)
self.assertEqual(mae_obj.name, 'my_mae')
self.assertEqual(mae_obj._dtype, dtypes.int32)
# Check save and restore config
mae_obj2 = metrics.MeanAbsoluteError.from_config(mae_obj.get_config())
self.assertEqual(mae_obj2.name, 'my_mae')
self.assertEqual(mae_obj2._dtype, dtypes.int32)
def test_weighted(self):
mae_obj = metrics.MeanAbsoluteError()
self.evaluate(variables.variables_initializer(mae_obj.variables))
y_true = constant_op.constant(((0, 1, 0, 1, 0), (0, 0, 1, 1, 1),
(1, 1, 1, 1, 0), (0, 0, 0, 0, 1)))
y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
(0, 1, 0, 1, 0), (1, 1, 1, 1, 1)))
sample_weight = constant_op.constant((1., 1.5, 2., 2.5))
result = mae_obj(y_true, y_pred, sample_weight=sample_weight)
self.assertAllClose(0.54285, self.evaluate(result), atol=1e-5)
def test_unweighted(self):
mae_obj = metrics.MeanAbsoluteError()
self.evaluate(variables.variables_initializer(mae_obj.variables))
y_true = constant_op.constant(((0, 1, 0, 1, 0), (0, 0, 1, 1, 1),
(1, 1, 1, 1, 0), (0, 0, 0, 0, 1)))
y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
(0, 1, 0, 1, 0), (1, 1, 1, 1, 1)))
update_op = mae_obj.update_state(y_true, y_pred)
self.evaluate(update_op)
result = mae_obj.result()
self.assertAllClose(0.5, result, atol=1e-5)
def metrics(self, regularization_losses=None):
"""Creates metrics. See `base_head.Head` for details."""
keys = metric_keys.MetricKeys
with ops.name_scope('metrics', values=(regularization_losses, )):
# Mean metric.
eval_metrics = {}
eval_metrics[self._loss_mean_key] = metrics.Mean(
name=keys.LOSS_MEAN)
eval_metrics[self._prediction_mean_key] = metrics.Mean(
name=keys.PREDICTION_MEAN)
eval_metrics[self._label_mean_key] = metrics.Mean(
name=keys.LABEL_MEAN)
if regularization_losses is not None:
eval_metrics[self._loss_regularization_key] = metrics.Mean(
name=keys.LOSS_REGULARIZATION)
eval_metrics[self._mean_abs_error] = metrics.MeanAbsoluteError(
name=MEAN_ABS_ERROR, dtype=tf.float32)
return eval_metrics