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keras_dnn_tfrecord_test.py
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keras_dnn_tfrecord_test.py
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# Copyright 2024 The TensorFlow Ranking Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for keras_dnn_tfrecord.py."""
import os
from absl.testing import flagsaver
from absl.testing import parameterized
import tensorflow as tf
from google.protobuf import text_format
from tensorflow_ranking.examples.keras import keras_dnn_tfrecord
from tensorflow_serving.apis import input_pb2
ELWC = text_format.Parse(
"""
context {
}
examples {
features {
feature {
key: "custom_features_1"
value { float_list { value: 1.0 } }
}
feature {
key: "custom_features_2"
value { float_list { value: 1.0 } }
}
feature {
key: "utility"
value { float_list { value: 0.0 } }
}
}
}
examples {
features {
feature {
key: "custom_features_1"
value { float_list { value: 1.0 } }
}
feature {
key: "custom_features_3"
value { float_list { value: 1.0 } }
}
feature {
key: "utility"
value { float_list { value: 1.0 } }
}
}
}
""", input_pb2.ExampleListWithContext())
EXAMPLE_PROTO_1 = text_format.Parse(
"""
features {
feature {
key: "custom_features_1"
value { float_list { value: 1.0 } }
}
feature {
key: "custom_features_2"
value { float_list { value: 1.0 } }
}
feature {
key: "utility"
value { float_list { value: 0.0 } }
}
}
""", tf.train.Example())
EXAMPLE_PROTO_2 = text_format.Parse(
"""
features {
feature {
key: "custom_features_1"
value { float_list { value: 1.0 } }
}
feature {
key: "custom_features_3"
value { float_list { value: 1.0 } }
}
feature {
key: "utility"
value { float_list { value: 1.0 } }
}
}
""", tf.train.Example())
class KerasDNNUnitTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
("Mirrored", "MirroredStrategy"),
("MultiWorker", "MultiWorkerMirroredStrategy"))
def test_train_and_eval(self, strategy):
data_dir = self.create_tempdir()
data_file = os.path.join(data_dir, "elwc.tfrecord")
with tf.io.TFRecordWriter(data_file) as writer:
for _ in range(256):
writer.write(ELWC.SerializeToString())
model_dir = os.path.join(data_dir, "model")
with flagsaver.flagsaver(
strategy=strategy,
train_input_pattern=data_file,
valid_input_pattern=data_file,
model_dir=model_dir,
num_features=3,
num_train_steps=10,
num_epochs=2,
list_size=2,
train_batch_size=128,
valid_batch_size=128,
hidden_layer_dims="16,8",
loss="softmax_loss",
export_best_model=True):
keras_dnn_tfrecord.train_and_eval()
latest_model_path = os.path.join(model_dir, "export/latest_model")
self.assertTrue(tf.saved_model.contains_saved_model(latest_model_path))
self.assertIsInstance(
tf.keras.models.load_model(latest_model_path), tf.keras.Model)
latest_model = tf.compat.v2.saved_model.load(export_dir=latest_model_path)
listwise_predictor = latest_model.signatures[
tf.saved_model.PREDICT_METHOD_NAME]
listwise_logits = listwise_predictor(
tf.convert_to_tensor([ELWC.SerializeToString()] *
2))[tf.saved_model.PREDICT_OUTPUTS]
self.assertAllEqual([2, 2], listwise_logits.get_shape().as_list())
pointwise_predictor = latest_model.signatures[
tf.saved_model.REGRESS_METHOD_NAME]
pointwise_logits = pointwise_predictor(
tf.convert_to_tensor([
EXAMPLE_PROTO_1.SerializeToString(),
EXAMPLE_PROTO_2.SerializeToString()
]))[tf.saved_model.REGRESS_OUTPUTS]
self.assertAllEqual([2], pointwise_logits.get_shape().as_list())
self.assertAllClose(pointwise_logits, listwise_logits[0])
best_model_path = os.path.join(model_dir, "export/best_model_by_metric")
self.assertTrue(tf.saved_model.contains_saved_model(best_model_path))
best_model = tf.compat.v2.saved_model.load(export_dir=best_model_path)
listwise_predictor = best_model.signatures[
tf.saved_model.PREDICT_METHOD_NAME]
best_listwise_logits = listwise_predictor(
tf.convert_to_tensor([ELWC.SerializeToString()] *
2))[tf.saved_model.PREDICT_OUTPUTS]
self.assertAllEqual([2, 2], best_listwise_logits.get_shape().as_list())
pointwise_predictor = best_model.signatures[
tf.saved_model.REGRESS_METHOD_NAME]
pointwise_logits = pointwise_predictor(
tf.convert_to_tensor([
EXAMPLE_PROTO_1.SerializeToString(),
EXAMPLE_PROTO_2.SerializeToString()
]))[tf.saved_model.REGRESS_OUTPUTS]
self.assertAllEqual([2], pointwise_logits.get_shape().as_list())
self.assertAllClose(pointwise_logits, best_listwise_logits[0])
if __name__ == "__main__":
tf.test.main()