def test_bert_trainer(self, dict_outputs):
"""Validate that the Keras object can be created."""
# Build a transformer network to use within the BERT trainer.
vocab_size = 100
sequence_length = 512
test_network = networks.BertEncoder(vocab_size=vocab_size,
num_layers=2,
dict_outputs=dict_outputs)
# Create a BERT trainer with the created network.
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Create a set of 2-dimensional inputs (the first dimension is implicit).
word_ids = tf.keras.Input(shape=(sequence_length, ), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length, ), dtype=tf.int32)
type_ids = tf.keras.Input(shape=(sequence_length, ), dtype=tf.int32)
# Invoke the trainer model on the inputs. This causes the layer to be built.
cls_outs = bert_trainer_model([word_ids, mask, type_ids])
# Validate that there are 2 outputs are of the expected shape.
self.assertEqual(2, len(cls_outs))
expected_shape = [None, sequence_length]
for out in cls_outs:
self.assertAllEqual(expected_shape, out.shape.as_list())
def test_bert_trainer_tensor_call(self):
"""Validate that the Keras object can be invoked."""
# Build a transformer network to use within the BERT trainer. (Here, we use
# a short sequence_length for convenience.)
test_network = networks.BertEncoder(vocab_size=100, num_layers=2)
# Create a BERT trainer with the created network.
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Create a set of 2-dimensional data tensors to feed into the model.
word_ids = tf.constant([[1, 1], [2, 2]], dtype=tf.int32)
mask = tf.constant([[1, 1], [1, 0]], dtype=tf.int32)
type_ids = tf.constant([[1, 1], [2, 2]], dtype=tf.int32)
# Invoke the trainer model on the tensors. In Eager mode, this does the
# actual calculation. (We can't validate the outputs, since the network is
# too complex: this simply ensures we're not hitting runtime errors.)
_ = bert_trainer_model([word_ids, mask, type_ids])
def test_bert_trainer_named_compilation(self):
"""Validate compilation using explicit output names."""
# Build a transformer network to use within the BERT trainer.
vocab_size = 100
test_network = networks.BertEncoder(vocab_size=vocab_size, num_layers=2)
# Create a BERT trainer with the created network.
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Attempt to compile the model using a string-keyed dict of output names to
# loss functions. This will validate that the outputs are named as we
# expect.
bert_trainer_model.compile(
optimizer='sgd',
loss={
'start_positions': 'mse',
'end_positions': 'mse'
})
def test_serialize_deserialize(self):
"""Validate that the BERT trainer can be serialized and deserialized."""
# Build a transformer network to use within the BERT trainer.
test_network = networks.BertEncoder(vocab_size=100, num_layers=2)
# Create a BERT trainer with the created network. (Note that all the args
# are different, so we can catch any serialization mismatches.)
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Create another BERT trainer via serialization and deserialization.
config = bert_trainer_model.get_config()
new_bert_trainer_model = bert_span_labeler.BertSpanLabeler.from_config(
config)
# Validate that the config can be forced to JSON.
_ = new_bert_trainer_model.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(bert_trainer_model.get_config(),
new_bert_trainer_model.get_config())
class BertSpanLabelerTest(keras_parameterized.TestCase):
def test_bert_trainer(self):
"""Validate that the Keras object can be created."""
# Build a transformer network to use within the BERT trainer.
vocab_size = 100
sequence_length = 512
test_network = networks.TransformerEncoder(
<<<<<<< HEAD
vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)
=======
vocab_size=vocab_size, num_layers=2)
>>>>>>> a811a3b7e640722318ad868c99feddf3f3063e36
# Create a BERT trainer with the created network.
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Create a set of 2-dimensional inputs (the first dimension is implicit).
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
# Invoke the trainer model on the inputs. This causes the layer to be built.
cls_outs = bert_trainer_model([word_ids, mask, type_ids])
# Validate that there are 2 outputs are of the expected shape.
self.assertEqual(2, len(cls_outs))
expected_shape = [None, sequence_length]
for out in cls_outs:
self.assertAllEqual(expected_shape, out.shape.as_list())
