def get_simple_dnn_classifier_and_metadata(n_classes=2, label_vocabulary=None):
"""Returns metadata for creating simple DNN classifier."""
if label_vocabulary:
feature_spec = tf.feature_column.make_parse_example_spec(
feature_columns=util.dnn_columns(False, n_classes=n_classes))
feature_spec['label'] = tf.io.FixedLenFeature(shape=[1],
dtype=tf.string)
else:
feature_spec = tf.feature_column.make_parse_example_spec(
feature_columns=util.dnn_columns(True, n_classes=n_classes))
classifier = tf_estimator.DNNClassifier(
hidden_units=[4],
feature_columns=util.dnn_columns(False),
n_classes=n_classes,
label_vocabulary=label_vocabulary,
loss_reduction=tf.losses.Reduction.SUM)
classifier = tf_estimator.add_metrics(classifier,
util.classifier_extra_metrics)
return {
'estimator':
classifier,
'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':
build_parsing_eval_input_receiver_fn(feature_spec, label_key='label'),
'train_input_fn':
util.make_classifier_input_fn(feature_spec,
n_classes,
label_vocabulary=label_vocabulary),
}
def simple_linear_classifier(export_path, eval_export_path):
"""Trains and exports a simple linear classifier."""
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)
])
classifier = tf.estimator.LinearClassifier(
feature_columns=util.linear_columns(),
loss_reduction=tf.compat.v1.losses.Reduction.SUM)
classifier = tf.estimator.add_metrics(classifier,
util.classifier_extra_metrics)
classifier.train(input_fn=util.make_classifier_input_fn(
tf.feature_column.make_parse_example_spec(util.linear_columns(True))),
steps=1000)
return util.export_model_and_eval_model(
estimator=classifier,
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)
def get_simple_dnn_classifier_and_metadata(n_classes=2):
"""Returns metadata for creating simple DNN classifier."""
feature_spec = tf.feature_column.make_parse_example_spec(
feature_columns=util.dnn_columns(True, n_classes=n_classes))
classifier = tf.estimator.DNNClassifier(
hidden_units=[4],
feature_columns=util.dnn_columns(False),
n_classes=n_classes,
loss_reduction=tf.compat.v1.losses.Reduction.SUM)
classifier = tf.estimator.add_metrics(classifier,
util.classifier_extra_metrics)
return {
'estimator':
classifier,
'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':
build_parsing_eval_input_receiver_fn(
tf.feature_column.make_parse_example_spec(
util.dnn_columns(True, n_classes=n_classes)),
label_key='label'),
'train_input_fn':
util.make_classifier_input_fn(feature_spec, n_classes),
}
def simple_dnn_classifier(export_path, eval_export_path, n_classes=2):
"""Trains and exports a simple DNN classifier."""
feature_spec = tf.feature_column.make_parse_example_spec(
feature_columns=util.dnn_columns(True, n_classes=n_classes))
classifier = tf.estimator.DNNClassifier(
hidden_units=[4],
feature_columns=util.dnn_columns(False),
n_classes=n_classes)
classifier = tf.contrib.estimator.add_metrics(
classifier, util.classifier_extra_metrics)
classifier.train(input_fn=util.make_classifier_input_fn(
feature_spec, n_classes),
steps=1000)
return util.export_model_and_eval_model(
estimator=classifier,
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=build_parsing_eval_input_receiver_fn(
tf.feature_column.make_parse_example_spec(
util.dnn_columns(True, n_classes=n_classes)),
label_key='label'),
export_path=export_path,
eval_export_path=eval_export_path)
def get_simple_dnn_classifier_and_metadata(n_classes=2):
feature_spec = tf.feature_column.make_parse_example_spec(
feature_columns=util.dnn_columns(True, n_classes=n_classes))
classifier = tf.estimator.DNNClassifier(
hidden_units=[4],
feature_columns=util.dnn_columns(False),
n_classes=n_classes)
classifier = tf.contrib.estimator.add_metrics(
classifier, util.classifier_extra_metrics)
return {
'estimator':
classifier,
'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':
build_parsing_eval_input_receiver_fn(
tf.feature_column.make_parse_example_spec(
util.dnn_columns(True, n_classes=n_classes)),
label_key='label'),
'train_input_fn':
util.make_classifier_input_fn(feature_spec, n_classes),
}