def _GetModelFnOpsForMode(self, mode):
"""Helper for testGetDynamicRnnModelFn{Train,Eval,Infer}()."""
model_fn = dynamic_rnn_estimator._get_dynamic_rnn_model_fn(
self.rnn_cell,
target_column=target_column_lib.multi_class_target(n_classes=2),
# Only CLASSIFICATION yields eval metrics to test for.
problem_type=dynamic_rnn_estimator.ProblemType.CLASSIFICATION,
prediction_type=dynamic_rnn_estimator.PredictionType.MULTIPLE_VALUE,
optimizer='SGD',
sequence_feature_columns=self.sequence_feature_columns,
context_feature_columns=self.context_feature_columns,
learning_rate=0.1)
labels = self.GetClassificationTargetsOrNone(mode)
model_fn_ops = model_fn(
features=self.GetColumnsToTensors(), labels=labels, mode=mode)
return model_fn_ops
def _GetModelFnOpsForMode(self, mode):
"""Helper for testGetDynamicRnnModelFn{Train,Eval,Infer}()."""
model_fn = dynamic_rnn_estimator._get_dynamic_rnn_model_fn(
self.rnn_cell,
target_column=tf.contrib.layers.multi_class_target(n_classes=2),
# Only CLASSIFICATION yields eval metrics to test for.
problem_type=dynamic_rnn_estimator.ProblemType.CLASSIFICATION,
prediction_type=dynamic_rnn_estimator.PredictionType.MULTIPLE_VALUE,
optimizer='SGD',
sequence_feature_columns=self.sequence_feature_columns,
context_feature_columns=self.context_feature_columns,
learning_rate=0.1)
labels = self.GetClassificationTargetsOrNone(mode)
model_fn_ops = model_fn(features=self.GetColumnsToTensors(),
labels=labels, mode=mode)
return model_fn_ops
