def test_model_with_lookup(self):
inputs = tf.keras.Input(shape=(1, ), dtype=tf.string)
lookup_out = IndexLookup(vocabulary=["A", "B", "C"])(inputs)
model = tf.keras.Model(inputs=inputs, outputs=lookup_out)
out = model.call(tf.constant([["A"], ["C"], ["B"], ["D"], ["E"]]))
self.assertTrue(
np.array_equal(np.array([[0], [2], [1], [3], [3]], dtype=int),
out.numpy()))
def test_lookup_with_file(self):
with tempfile.TemporaryDirectory() as temp_dir:
vocab_file = os.path.join(temp_dir, "vocab_test.txt")
with open(vocab_file, "w") as f:
f.write("A\n")
f.write("B\n")
f.write("C\n")
lookup_layer = IndexLookup(vocabulary=vocab_file)
self._check_lookup(lookup_layer)
def transform_from_code_gen(source_inputs):
inputs = source_inputs.copy()
education_hash_out = Hashing(education_hash.hash_bucket_size)(
ToSparse()(inputs["education"])
)
occupation_hash_out = Hashing(occupation_hash.hash_bucket_size)(
ToSparse()(inputs["occupation"])
)
native_country_hash_out = Hashing(native_country_hash.hash_bucket_size)(
ToSparse()(inputs["native_country"])
)
workclass_lookup_out = IndexLookup(workclass_lookup.vocabulary_list)(
ToSparse()(inputs["workclass"])
)
marital_status_lookup_out = IndexLookup(
marital_status_lookup.vocabulary_list
)(ToSparse()(inputs["marital_status"]))
relationship_lookup_out = IndexLookup(relationship_lookup.vocabulary_list)(
ToSparse()(inputs["relationship"])
)
race_lookup_out = IndexLookup(race_lookup.vocabulary_list)(
ToSparse()(inputs["race"])
)
sex_lookup_out = IndexLookup(sex_lookup.vocabulary_list)(
ToSparse()(inputs["sex"])
)
age_bucketize_out = Discretization(age_bucketize.boundaries)(
ToSparse()(inputs["age"])
)
capital_gain_bucketize_out = Discretization(
capital_gain_bucketize.boundaries
)(ToSparse()(inputs["capital_gain"]))
capital_loss_bucketize_out = Discretization(
capital_loss_bucketize.boundaries
)(ToSparse()(inputs["capital_loss"]))
hours_per_week_bucketize_out = Discretization(
hours_per_week_bucketize.boundaries
)(ToSparse()(inputs["hours_per_week"]))
group1_out = ConcatenateWithOffset(group1.id_offsets)(
[
workclass_lookup_out,
hours_per_week_bucketize_out,
capital_gain_bucketize_out,
capital_loss_bucketize_out,
]
)
group2_out = ConcatenateWithOffset(group2.id_offsets)(
[
education_hash_out,
marital_status_lookup_out,
relationship_lookup_out,
occupation_hash_out,
]
)
group3_out = ConcatenateWithOffset(group3.id_offsets)(
[
age_bucketize_out,
sex_lookup_out,
race_lookup_out,
native_country_hash_out,
]
)
group1_embedding_wide_out = SparseEmbedding(
input_dim=group1_embedding_wide.input_dim,
output_dim=group1_embedding_wide.output_dim,
)(group1_out)
group2_embedding_wide_out = SparseEmbedding(
input_dim=group2_embedding_wide.input_dim,
output_dim=group2_embedding_wide.output_dim,
)(group2_out)
group1_embedding_deep_out = SparseEmbedding(
input_dim=group1_embedding_deep.input_dim,
output_dim=group1_embedding_deep.output_dim,
)(group1_out)
group2_embedding_deep_out = SparseEmbedding(
input_dim=group2_embedding_deep.input_dim,
output_dim=group2_embedding_deep.output_dim,
)(group2_out)
group3_embedding_deep_out = SparseEmbedding(
input_dim=group3_embedding_deep.input_dim,
output_dim=group3_embedding_deep.output_dim,
)(group3_out)
wide_embeddings_out = [
group1_embedding_wide_out,
group2_embedding_wide_out,
]
deep_embeddings_out = [
group1_embedding_deep_out,
group2_embedding_deep_out,
group3_embedding_deep_out,
]
return wide_embeddings_out, deep_embeddings_out
def test_lookup_with_list(self):
lookup_layer = IndexLookup(vocabulary=["A", "B", "C"])
self._check_lookup(lookup_layer)
self.assertEqual(lookup_layer.vocab_size(), 4)
def test_lookup_with_list(self):
lookup_layer = IndexLookup(vocabulary=["A", "B", "C"])
self._check_lookup(lookup_layer)