def _getModelFnOpsForMode(self, mode):
"""Helper for testGetRnnModelFn{Train,Eval,Infer}()."""
num_units = [4]
seq_columns = [
feature_column.real_valued_column(
'inputs', dimension=1)
]
features = {
'inputs': constant_op.constant([1., 2., 3.]),
}
labels = constant_op.constant([1., 0., 1.])
model_fn = ssre._get_rnn_model_fn(
cell_type='basic_rnn',
target_column=target_column_lib.multi_class_target(n_classes=2),
optimizer='SGD',
num_unroll=2,
num_units=num_units,
num_threads=1,
queue_capacity=10,
batch_size=1,
# Only CLASSIFICATION yields eval metrics to test for.
problem_type=constants.ProblemType.CLASSIFICATION,
sequence_feature_columns=seq_columns,
context_feature_columns=None,
learning_rate=0.1)
model_fn_ops = model_fn(features=features, labels=labels, mode=mode)
return model_fn_ops
def _getModelFnOpsForMode(self, mode):
"""Helper for testGetRnnModelFn{Train,Eval,Infer}()."""
num_units = [4]
seq_columns = [
feature_column.real_valued_column(
'inputs', dimension=1)
]
features = {
'inputs': constant_op.constant([1., 2., 3.]),
}
labels = constant_op.constant([1., 0., 1.])
model_fn = ssre._get_rnn_model_fn(
cell_type='basic_rnn',
target_column=target_column_lib.multi_class_target(n_classes=2),
optimizer='SGD',
num_unroll=2,
num_units=num_units,
num_threads=1,
queue_capacity=10,
batch_size=1,
# Only CLASSIFICATION yields eval metrics to test for.
problem_type=constants.ProblemType.CLASSIFICATION,
sequence_feature_columns=seq_columns,
context_feature_columns=None,
learning_rate=0.1)
model_fn_ops = model_fn(features=features, labels=labels, mode=mode)
return model_fn_ops
