def test_basic_invocation_train(self):
batch_size = 2
seq_length = 8
hidden_size = 4
sequence_data = np.random.uniform(size=(batch_size, seq_length,
hidden_size)).astype('float32')
paragraph_mask = np.random.uniform(size=(batch_size,
seq_length)).astype('float32')
class_index = np.random.uniform(size=(batch_size)).astype('uint8')
start_positions = np.zeros(shape=(batch_size)).astype('uint8')
layer = span_labeling.XLNetSpanLabeling(input_width=hidden_size,
start_n_top=2,
end_n_top=2,
activation='tanh',
dropout_rate=0.,
initializer='glorot_uniform')
output = layer(sequence_data=sequence_data,
class_index=class_index,
paragraph_mask=paragraph_mask,
start_positions=start_positions,
training=True)
expected_keys = {
'start_logits',
'end_logits',
'class_logits',
'start_predictions',
'end_predictions',
}
self.assertSetEqual(expected_keys, set(output.keys()))
def test_basic_invocation_beam_search(self, top_n):
batch_size = 2
seq_length = 8
hidden_size = 4
sequence_data = np.random.uniform(size=(batch_size, seq_length,
hidden_size)).astype('float32')
position_mask = np.random.uniform(size=(batch_size,
seq_length)).astype('float32')
class_index = np.random.uniform(size=(batch_size)).astype('uint8')
layer = span_labeling.XLNetSpanLabeling(input_width=hidden_size,
start_n_top=top_n,
end_n_top=top_n,
activation='tanh',
dropout_rate=0.,
initializer='glorot_uniform')
output = layer(sequence_data=sequence_data,
class_index=class_index,
position_mask=position_mask,
training=False)
expected_keys = {
'start_top_log_probs',
'end_top_log_probs',
'class_logits',
'start_top_index',
'end_top_index',
}
self.assertSetEqual(expected_keys, set(output.keys()))
def test_subclass_invocation(self):
"""Tests basic invocation of this layer wrapped in a subclass."""
seq_length = 8
hidden_size = 4
batch_size = 2
sequence_data = tf.keras.Input(shape=(seq_length, hidden_size),
dtype=tf.float32)
class_index = tf.keras.Input(shape=(), dtype=tf.uint8)
position_mask = tf.keras.Input(shape=(seq_length), dtype=tf.float32)
start_positions = tf.keras.Input(shape=(), dtype=tf.int32)
layer = span_labeling.XLNetSpanLabeling(input_width=hidden_size,
start_n_top=5,
end_n_top=5,
activation='tanh',
dropout_rate=0.,
initializer='glorot_uniform')
output = layer(sequence_data=sequence_data,
class_index=class_index,
position_mask=position_mask,
start_positions=start_positions)
model = tf.keras.Model(inputs={
'sequence_data': sequence_data,
'class_index': class_index,
'position_mask': position_mask,
'start_positions': start_positions,
},
outputs=output)
sequence_data = tf.random.uniform(shape=(batch_size, seq_length,
hidden_size),
dtype=tf.float32)
position_mask = tf.random.uniform(shape=(batch_size, seq_length),
dtype=tf.float32)
class_index = tf.ones(shape=(batch_size, ), dtype=tf.uint8)
start_positions = tf.random.uniform(shape=(batch_size, ),
maxval=5,
dtype=tf.int32)
inputs = dict(sequence_data=sequence_data,
position_mask=position_mask,
class_index=class_index,
start_positions=start_positions)
output = model(inputs)
self.assertIsInstance(output, dict)
# Test `call` without training flag.
output = model(inputs, training=False)
self.assertIsInstance(output, dict)
# Test `call` with training flag.
# Note: this fails due to incompatibility with the functional API.
with self.assertRaisesRegexp(AssertionError,
'Could not compute output KerasTensor'):
model(inputs, training=True)
def test_functional_model_invocation(self):
"""Tests basic invocation of this layer wrapped by a Functional model."""
seq_length = 8
hidden_size = 4
batch_size = 2
sequence_data = tf.keras.Input(shape=(seq_length, hidden_size),
dtype=tf.float32)
class_index = tf.keras.Input(shape=(), dtype=tf.uint8)
position_mask = tf.keras.Input(shape=(seq_length), dtype=tf.float32)
start_positions = tf.keras.Input(shape=(), dtype=tf.float32)
layer = span_labeling.XLNetSpanLabeling(
input_width=hidden_size,
start_n_top=5,
end_n_top=5,
activation='tanh',
dropout_rate=0.,
initializer='glorot_uniform')
output = layer(sequence_data=sequence_data,
class_index=class_index,
position_mask=position_mask,
start_positions=start_positions)
model = tf.keras.Model(
inputs={
'sequence_data': sequence_data,
'class_index': class_index,
'position_mask': position_mask,
'start_positions': start_positions,
},
outputs=output)
sequence_data = tf.random.uniform(
shape=(batch_size, seq_length, hidden_size), dtype=tf.float32)
position_mask = tf.random.uniform(
shape=(batch_size, seq_length), dtype=tf.float32)
class_index = tf.ones(shape=(batch_size,), dtype=tf.uint8)
start_positions = tf.random.uniform(shape=(batch_size,), dtype=tf.float32)
inputs = dict(sequence_data=sequence_data,
position_mask=position_mask,
class_index=class_index,
start_positions=start_positions)
output = model(inputs)
self.assertIsInstance(output, dict)
# Test `call` with training flag.
output = model.call(inputs, training=True)
self.assertIsInstance(output, dict)
# Test `call` without training flag.
output = model.call(inputs, training=False)
self.assertIsInstance(output, dict)
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
network = span_labeling.XLNetSpanLabeling(input_width=128,
start_n_top=5,
end_n_top=1,
activation='tanh',
dropout_rate=0.34,
initializer='zeros')
# Create another network object from the first object's config.
new_network = span_labeling.XLNetSpanLabeling.from_config(
network.get_config())
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
