def _linear_only_estimator_fn(feature_columns,
model_dir=None,
label_dimension=1,
weight_column=None,
optimizer='Ftrl',
config=None,
partitioner=None):
return dnn_linear_combined.DNNLinearCombinedEstimator(
head=head_lib.regression_head(weight_column=weight_column,
label_dimension=label_dimension),
model_dir=model_dir,
linear_feature_columns=feature_columns,
linear_optimizer=optimizer,
input_layer_partitioner=partitioner,
config=config)
def _linear_only_estimator_fn(
feature_columns,
model_dir=None,
label_dimension=1,
weight_column=None,
optimizer='Ftrl',
config=None,
partitioner=None):
return dnn_linear_combined.DNNLinearCombinedEstimator(
head=head_lib.regression_head(
weight_column=weight_column, label_dimension=label_dimension,
# Tests in core (from which this test inherits) test the sum loss.
loss_reduction=losses.Reduction.SUM),
model_dir=model_dir,
linear_feature_columns=feature_columns,
linear_optimizer=optimizer,
input_layer_partitioner=partitioner,
config=config)
def _dnn_only_estimator_fn(hidden_units,
feature_columns,
model_dir=None,
label_dimension=1,
weight_column=None,
optimizer='Adagrad',
activation_fn=nn.relu,
dropout=None,
input_layer_partitioner=None,
config=None):
return dnn_linear_combined.DNNLinearCombinedEstimator(
head=head_lib.regression_head(weight_column=weight_column,
label_dimension=label_dimension),
model_dir=model_dir,
dnn_feature_columns=feature_columns,
dnn_optimizer=optimizer,
dnn_hidden_units=hidden_units,
dnn_activation_fn=activation_fn,
dnn_dropout=dropout,
input_layer_partitioner=input_layer_partitioner,
config=config)
def _test_complete_flow(self, train_input_fn, eval_input_fn,
predict_input_fn, input_dimension, label_dimension,
batch_size):
linear_feature_columns = [
feature_column.numeric_column('x', shape=(input_dimension, ))
]
dnn_feature_columns = [
feature_column.numeric_column('x', shape=(input_dimension, ))
]
feature_columns = linear_feature_columns + dnn_feature_columns
est = dnn_linear_combined.DNNLinearCombinedEstimator(
head=head_lib.regression_head(label_dimension=label_dimension),
linear_feature_columns=linear_feature_columns,
dnn_feature_columns=dnn_feature_columns,
dnn_hidden_units=(2, 2),
model_dir=self._model_dir)
# TRAIN
num_steps = 10
est.train(train_input_fn, steps=num_steps)
# EVALUTE
scores = est.evaluate(eval_input_fn)
self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
self.assertIn('loss', six.iterkeys(scores))
# PREDICT
predictions = np.array([
x[prediction_keys.PredictionKeys.PREDICTIONS]
for x in est.predict(predict_input_fn)
])
self.assertAllEqual((batch_size, label_dimension), predictions.shape)
# EXPORT
feature_spec = feature_column.make_parse_example_spec(feature_columns)
serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
feature_spec)
export_dir = est.export_savedmodel(tempfile.mkdtemp(),
serving_input_receiver_fn)
self.assertTrue(gfile.Exists(export_dir))
def _dnn_only_estimator_fn(
hidden_units,
feature_columns,
model_dir=None,
label_dimension=1,
weight_column=None,
optimizer='Adagrad',
activation_fn=nn.relu,
dropout=None,
input_layer_partitioner=None,
config=None):
return dnn_linear_combined.DNNLinearCombinedEstimator(
head=head_lib.regression_head(
weight_column=weight_column, label_dimension=label_dimension,
# Tests in core (from which this test inherits) test the sum loss.
loss_reduction=losses.Reduction.SUM),
model_dir=model_dir,
dnn_feature_columns=feature_columns,
dnn_optimizer=optimizer,
dnn_hidden_units=hidden_units,
dnn_activation_fn=activation_fn,
dnn_dropout=dropout,
input_layer_partitioner=input_layer_partitioner,
config=config)
