def test_task_graph(self):
with tf.Graph().as_default():
with tf.compat.v1.Session() as sess:
query = tf.constant([[1, 2, 3], [2, 3, 4]], dtype=tf.float32)
candidate = tf.constant([[1, 1, 1], [1, 1, 0]],
dtype=tf.float32)
candidate_dataset = tf.data.Dataset.from_tensor_slices(
np.array([[0, 0, 0]] * 20, dtype=np.float32))
task = retrieval.Retrieval(metrics=metrics.FactorizedTopK(
candidates=candidate_dataset.batch(16),
metrics=[
tf.keras.metrics.TopKCategoricalAccuracy(
k=5, name="factorized_categorical_accuracy_at_5")
]))
expected_metrics = {
"factorized_categorical_accuracy_at_5": 1.0,
}
loss = task(query_embeddings=query,
candidate_embeddings=candidate)
sess.run([var.initializer for var in task.variables])
for metric in task.metrics:
sess.run([var.initializer for var in metric.variables])
sess.run(loss)
metrics_ = {
metric.name: sess.run(metric.result())
for metric in task.metrics
}
self.assertAllClose(expected_metrics, metrics_)
def test_task(self):
query = tf.constant([[1, 2, 3], [2, 3, 4]], dtype=tf.float32)
candidate = tf.constant([[1, 1, 1], [1, 1, 0]], dtype=tf.float32)
candidate_dataset = tf.data.Dataset.from_tensor_slices(
np.array([[0, 0, 0]] * 20, dtype=np.float32))
task = retrieval.Retrieval(metrics=metrics.FactorizedTopK(
candidates=candidate_dataset.batch(16),
metrics=[
tf.keras.metrics.TopKCategoricalAccuracy(
k=5, name="factorized_categorical_accuracy_at_5")
]))
# All_pair_scores: [[6, 3], [9, 5]].
# Normalized logits: [[3, 0], [4, 0]].
expected_loss = -np.log(_sigmoid(3.0)) - np.log(1 - _sigmoid(4.0))
expected_metrics = {
"factorized_categorical_accuracy_at_5": 1.0,
}
loss = task(query_embeddings=query, candidate_embeddings=candidate)
metrics_ = {
metric.name: metric.result().numpy()
for metric in task.metrics
}
self.assertIsNotNone(loss)
self.assertAllClose(expected_loss, loss)
self.assertAllClose(expected_metrics, metrics_)