def test_forward_backward_explicit_vocab(self):
vocab_data = [42, 1138, 725, 1729]
input_array = np.array([[42, 1138, 725, 1729], [1729, 725, 42, 203]])
expected_output = np.array([[42, 1138, 725, 1729], [1729, 725, 42,
-1]])
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(vocabulary=vocab_data)
inverse_layer = integer_lookup.IntegerLookup(vocabulary=vocab_data,
invert=True)
int_data = layer(input_data)
inverse_data = inverse_layer(int_data)
model = keras.Model(inputs=input_data, outputs=inverse_data)
output_dataset = model.predict(input_array)
self.assertAllEqual(expected_output, output_dataset)
def test_non_unique_vocab_from_file_fails(self):
vocab_list = [42, 1138, 725, 1729, 42]
vocab_path = self._write_to_temp_file("repeat_vocab_file", vocab_list)
with self.assertRaisesRegex(
tf.errors.FailedPreconditionError,
".*HashTable has different value for same key.*42.*"):
_ = integer_lookup.IntegerLookup(vocabulary=vocab_path)
def test_layer_with_list_input(self):
vocab = [12, 36, 1138, 42]
data = [[12, 1138, 42], [42, 1000, 36]] # Note OOV tokens
layer = integer_lookup.IntegerLookup(vocabulary=vocab)
output = layer(data)
expected_output = np.array([[1, 3, 4], [4, 0, 2]])
self.assertEqual(output.numpy().tolist(), expected_output.tolist())
def test_get_vocab_returns_int(self):
vocab_data = [42, 1138, 725, 1729]
expected_vocab = [-1, 42, 1138, 725, 1729]
layer = integer_lookup.IntegerLookup(vocabulary=vocab_data)
layer_vocab = layer.get_vocabulary()
self.assertAllEqual(expected_vocab, layer_vocab)
self.assertIsInstance(layer_vocab[0], np.int64)
def test_too_long_vocab_fails_in_single_setting(self):
vocab_data = [42, 1138, 725, 1729]
layer = integer_lookup.IntegerLookup(max_tokens=4, num_oov_indices=1)
with self.assertRaisesRegex(
ValueError, "vocabulary larger than the maximum vocab.*"):
layer.set_vocabulary(vocab_data)
def test_sparse_int_input_multi_bucket(self):
vocab_data = np.array([10, 11, 12, 13], dtype=np.int64)
input_array = tf.SparseTensor(
indices=[[0, 0], [1, 2]],
values=np.array([13, 133], dtype=np.int64),
dense_shape=[3, 4],
)
expected_indices = [[0, 0], [1, 2]]
expected_values = [6, 2]
expected_dense_shape = [3, 4]
input_data = keras.Input(shape=(None, ), dtype=tf.int64, sparse=True)
layer = integer_lookup.IntegerLookup(
max_tokens=None,
dtype=tf.int64,
num_oov_indices=2,
mask_token=0,
oov_token=-1,
)
layer.set_vocabulary(vocab_data)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_data = model.predict(input_array, steps=1)
self.assertAllEqual(expected_indices, output_data.indices)
self.assertAllEqual(expected_values, output_data.values)
self.assertAllEqual(expected_dense_shape, output_data.dense_shape)
def test_vocabulary_persistence_across_saving(self):
vocab_data = [42, 1138, 725, 1729]
input_array = np.array([[42, 1138, 725, 1729], [1729, 725, 42, 203]])
expected_output = [[1, 2, 3, 4], [4, 3, 1, 0]]
# Build and validate a golden model.
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(max_tokens=None,
num_oov_indices=1)
layer.set_vocabulary(vocab_data)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_dataset = model.predict(input_array)
self.assertAllEqual(output_dataset, expected_output)
# Save the model to disk.
output_path = os.path.join(self.get_temp_dir(), "tf_keras_saved_model")
model.save(output_path, save_format="tf")
# Delete the session and graph to ensure that the loaded model is generated
# from scratch.
# TODO(b/149526183): Can't clear session when TF2 is disabled.
if tf.__internal__.tf2.enabled():
keras.backend.clear_session()
loaded_model = keras.models.load_model(
output_path,
custom_objects={"IntegerLookup": integer_lookup.IntegerLookup})
# Ensure that the loaded model is unique (so that the save/load is real)
self.assertIsNot(model, loaded_model)
# Validate correctness of the new model.
new_output_dataset = loaded_model.predict(input_array)
self.assertAllEqual(new_output_dataset, expected_output)
def test_ragged_adapt(self):
vocab_data = tf.ragged.constant([[203], [1729, 203]])
vocab_dataset = tf.data.Dataset.from_tensors(vocab_data)
layer = integer_lookup.IntegerLookup()
layer.adapt(vocab_dataset)
expected_vocabulary = [-1, 203, 1729]
self.assertAllEqual(expected_vocabulary, layer.get_vocabulary())
def test_sparse_output(self):
vocab_data = [2, 3, 4, 5]
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(vocabulary=vocab_data,
output_mode="multi_hot",
sparse=True)
res = layer(input_data)
self.assertTrue(res.__class__.__name__, "SparseKerasTensor")
def test_sparse_adapt(self):
vocab_data = tf.SparseTensor(indices=[[0, 0], [0, 1], [1, 2]],
values=[203, 1729, 203],
dense_shape=[3, 4])
vocab_dataset = tf.data.Dataset.from_tensors(vocab_data)
layer = integer_lookup.IntegerLookup()
layer.adapt(vocab_dataset)
expected_vocabulary = [-1, 203, 1729]
self.assertAllEqual(expected_vocabulary, layer.get_vocabulary())
def test_tensor_vocab(self):
vocab_data = [-1, 42, 1138, 725, 1729]
vocab_tensor = tf.constant(vocab_data, tf.int64)
layer = integer_lookup.IntegerLookup(vocabulary=vocab_tensor)
returned_vocab = layer.get_vocabulary()
self.assertAllEqual(vocab_data, returned_vocab)
self.assertAllEqual(layer.vocabulary_size(), 5)
fn = tf.function(lambda: layer.set_vocabulary(vocab_tensor))
with self.assertRaisesRegex(RuntimeError,
"Cannot set a tensor vocabulary"):
fn()
def test_int_output_explicit_vocab(self):
vocab_data = [42, 1138, 725, 1729]
input_array = np.array([[42, 1138, 725, 1729], [1729, 725, 42, 203]])
expected_output = [[1, 2, 3, 4], [4, 3, 1, 0]]
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(vocabulary=vocab_data)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_dataset = model.predict(input_array)
self.assertAllEqual(expected_output, output_dataset)
def test_int_output_with_mask(self):
vocab_data = [42, 1138, 725, 1729]
input_array = np.array([[42, 1138, 725, 1729], [1729, 725, 42, 203]])
expected_output = [[2, 3, 4, 5], [5, 4, 2, 1]]
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(max_tokens=None, mask_token=0)
layer.set_vocabulary(vocab_data)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_dataset = model.predict(input_array)
self.assertAllEqual(expected_output, output_dataset)
def test_count_output(self):
vocab_data = [2, 3, 4, 5]
input_array = np.array([[2, 2, 3, 4], [0, 1, 5, 6]])
expected_output = [[0, 2, 1, 1, 0], [3, 0, 0, 0, 1]]
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(vocabulary=vocab_data,
output_mode="count")
res = layer(input_data)
model = keras.Model(inputs=input_data, outputs=res)
output_data = model.predict(input_array)
self.assertAllEqual(expected_output, output_data)
def test_inverse_output(self):
vocab_data = [0, -1, 42, 1138, 725, 1729]
input_array = np.array([[2, 3, 4, 5], [5, 4, 2, 1]])
expected_output = np.array([[42, 1138, 725, 1729], [1729, 725, 42,
-1]])
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(invert=True)
layer.set_vocabulary(vocab_data)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_dataset = model.predict(input_array)
self.assertAllEqual(expected_output, output_dataset)
def test_int_output_explicit_vocab_from_file(self):
vocab_list = [42, 1138, 725, 1729]
vocab_path = self._write_to_temp_file("vocab_file", vocab_list)
input_array = np.array([[42, 1138, 725, 1729], [1729, 725, 42, 203]])
expected_output = [[2, 3, 4, 5], [5, 4, 2, 1]]
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(vocabulary=vocab_path)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_dataset = model.predict(input_array)
self.assertAllEqual(expected_output, output_dataset)
def test_ragged_int_input(self):
vocab_data = np.array([10, 11, 12, 13], dtype=np.int64)
input_array = tf.ragged.constant([[10, 11, 13], [13, 12, 10, 42]],
dtype=np.int64)
expected_output = [[1, 2, 4], [4, 3, 1, 0]]
input_data = keras.Input(shape=(None, ), dtype=tf.int64, ragged=True)
layer = integer_lookup.IntegerLookup(max_tokens=None)
layer.set_vocabulary(vocab_data)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_dataset = model.predict(input_array)
self.assertAllEqual(expected_output, output_dataset)
def test_ragged_int_input_multi_bucket(self):
vocab_data = np.array([10, 11, 12, 13], dtype=np.int64)
input_array = tf.ragged.constant([[10, 11, 13], [13, 12, 10, 133]],
dtype=np.int64)
expected_output = [[3, 4, 6], [6, 5, 3, 2]]
input_data = keras.Input(shape=(None, ), dtype=tf.int64, ragged=True)
layer = integer_lookup.IntegerLookup(max_values=None,
num_oov_indices=2)
layer.set_vocabulary(vocab_data)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_dataset = model.predict(input_array)
self.assertAllEqual(expected_output, output_dataset)
def test_single_int_generator_dataset(self):
def word_gen():
for _ in itertools.count(1):
yield random.randint(0, 100)
ds = tf.data.Dataset.from_generator(word_gen, tf.int64,
tf.TensorShape([]))
batched_ds = ds.take(2)
input_t = keras.Input(shape=(), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(max_tokens=10,
num_oov_indices=0,
mask_token=None,
oov_token=None)
_ = layer(input_t)
layer.adapt(batched_ds)
def test_int_output_no_oov(self):
vocab_data = [42, 1138, 725, 1729]
valid_input = np.array([[42, 1138, 725, 1729], [1729, 725, 42, 0]])
invalid_input = np.array([[42, 1138, 725, 203], [1729, 725, 42, 203]])
expected_output = [[1, 2, 3, 4], [4, 3, 1, 0]]
input_data = keras.Input(shape=(None, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(vocabulary=vocab_data,
mask_token=0,
num_oov_indices=0)
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_data = model.predict(valid_input)
self.assertAllEqual(expected_output, output_data)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
"found OOV values.*203"):
_ = model.predict(invalid_input)
def test_zero_max_tokens_fails(self):
with self.assertRaisesRegex(ValueError, ".*max_tokens.*"):
_ = integer_lookup.IntegerLookup(max_tokens=0, num_oov_indices=1)
def test_no_vocab(self):
with self.assertRaisesRegex(ValueError,
"You must set the layer's vocabulary"):
layer = integer_lookup.IntegerLookup()
layer([[1]])
def test_output_shape(self):
input_data = keras.Input(shape=(4, ), dtype=tf.int64)
layer = integer_lookup.IntegerLookup(max_tokens=2, num_oov_indices=1)
int_data = layer(input_data)
self.assertAllEqual(int_data.shape[1:], input_data.shape[1:])
def test_non_unique_vocab_fails(self):
vocab_data = [42, 1138, 725, 1729, 1729]
with self.assertRaisesRegex(ValueError, ".*repeated term.*1729.*"):
_ = integer_lookup.IntegerLookup(vocabulary=vocab_data)
def test_non_unique_vocab_from_file_fails(self):
vocab_list = [42, 1138, 725, 1729, 42]
vocab_path = self._write_to_temp_file("repeat_vocab_file", vocab_list)
with self.assertRaisesRegex(ValueError, ".*repeated term.*42.*"):
_ = integer_lookup.IntegerLookup(vocabulary=vocab_path)
def test_no_vocab(self):
with self.assertRaisesRegex(RuntimeError,
"you must set the layer's vocabulary"):
layer = integer_lookup.IntegerLookup(output_mode="binary")
layer([[1]])
