def test_transformer_encoder_save_and_load(tmp_path):
layer = layer_module.BertEncoder()
inputs = [
tf.keras.Input(shape=(500,), dtype=tf.int64),
tf.keras.Input(shape=(500,), dtype=tf.int64),
tf.keras.Input(shape=(500,), dtype=tf.int64),
]
model = tf.keras.Model(inputs, layer(inputs))
model.save(os.path.join(tmp_path, "model"))
tf.keras.models.load_model(os.path.join(tmp_path, "model"))
def build(self, hp, inputs=None):
input_tensor = nest.flatten(inputs)[0]
tokenizer_layer = keras_layers.BertTokenizer(
max_sequence_length=utils.add_to_hp(self.max_sequence_length, hp))
output_node = tokenizer_layer(input_tensor)
bert_encoder = keras_layers.BertEncoder()
output_node = bert_encoder(output_node)
bert_encoder.load_pretrained_weights()
return output_node
def build(self, hp, inputs=None):
input_tensor = nest.flatten(inputs)[0]
max_sequence_length = self.max_sequence_length or hp.Choice(
"max_seq_len", [128, 256, 512], default=128)
tokenizer_layer = keras_layers.BertTokenizer(
max_sequence_length=max_sequence_length)
output_node = tokenizer_layer(input_tensor)
bert_encoder = keras_layers.BertEncoder()
output_node = bert_encoder(output_node)
bert_encoder.load_pretrained_weights()
return output_node