def simple_dnn_regressor(export_path, eval_export_path):
"""Trains and exports a simple DNN regressor."""
feature_spec = tf.feature_column.make_parse_example_spec(
feature_columns=util.dnn_columns(True))
regressor = tf.estimator.DNNRegressor(
hidden_units=[4],
feature_columns=util.dnn_columns(False),
loss_reduction=tf.losses.Reduction.SUM)
regressor = tf.estimator.add_metrics(regressor,
util.regressor_extra_metrics)
regressor.train(input_fn=util.make_regressor_input_fn(feature_spec),
steps=3000)
return util.export_model_and_eval_model(
estimator=regressor,
serving_input_receiver_fn=(
tf.estimator.export.build_parsing_serving_input_receiver_fn(
tf.feature_column.make_parse_example_spec(
util.dnn_columns(False)))),
eval_input_receiver_fn=export.build_parsing_eval_input_receiver_fn(
tf.feature_column.make_parse_example_spec(util.dnn_columns(True)),
label_key='label'),
export_path=export_path,
eval_export_path=eval_export_path)
def simple_linear_regressor(export_path, eval_export_path):
"""Trains and exports a simple linear regressor."""
feature_spec = tf.feature_column.make_parse_example_spec(
util.linear_columns(False))
eval_feature_spec = tf.feature_column.make_parse_example_spec(
util.linear_columns(True) + [
tf.feature_column.categorical_column_with_hash_bucket(
'slice_key', 100)
])
regressor = tf.estimator.LinearRegressor(
feature_columns=util.linear_columns(),
loss_reduction=tf.compat.v1.losses.Reduction.SUM)
regressor = tf.estimator.add_metrics(regressor,
util.regressor_extra_metrics)
regressor.train(input_fn=util.make_regressor_input_fn(
tf.feature_column.make_parse_example_spec(util.linear_columns(True))),
steps=3000)
return util.export_model_and_eval_model(
estimator=regressor,
serving_input_receiver_fn=(
tf.estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec)),
eval_input_receiver_fn=export.build_parsing_eval_input_receiver_fn(
eval_feature_spec, label_key='label'),
export_path=export_path,
eval_export_path=eval_export_path)