how to save and load custom siamese bert model

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I am following this tutorial on how to train a siamese bert network:

https://keras.io/examples/nlp/semantic_similarity_with_bert/

all good, but I am not sure what is the best way to save the model after train it and save it. any suggestion?

I was trying with

model.save('models/bert_siamese_v1')

which creates a folder with save_model.bp keras_metadata.bp and two subfolders (variables and assets)

then I try to load it with:

model.load_weights('models/bert_siamese_v1/')

and it gives me this error:

2022-03-08 14:11:52.567762: W tensorflow/core/util/tensor_slice_reader.cc:95] Could not open models/bert_siamese_v1/: Failed precondition: models/bert_siamese_v1; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?

what is the best way to proceed?

bert-language-model keras tensorflow deep-learning python
2 Answers

Try using tf.saved_model.save to save your model:

tf.saved_model.save(model, 'models/bert_siamese_v1')
model = tf.saved_model.load('models/bert_siamese_v1')

The warning you get during saving can apparently be ignored. After loading your model, you can use it for inference f(test_data):

f = model.signatures["serving_default"]
x1 = tf.random.uniform((1, 128), maxval=100, dtype=tf.int32)
x2 = tf.random.uniform((1, 128), maxval=100, dtype=tf.int32)
x3 = tf.random.uniform((1, 128), maxval=100, dtype=tf.int32)
print(f)
print(f(attention_masks = x1, input_ids = x2, token_type_ids = x3))

ConcreteFunction signature_wrapper(*, token_type_ids, attention_masks, input_ids)
  Args:
    attention_masks: int32 Tensor, shape=(None, 128)
    input_ids: int32 Tensor, shape=(None, 128)
    token_type_ids: int32 Tensor, shape=(None, 128)
  Returns:
    {'dense': <1>}
      <1>: float32 Tensor, shape=(None, 3)
{'dense': <tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[0.40711606, 0.13456087, 0.45832306]], dtype=float32)>}
user_96578610

It seems you have two options

 model.save_weights('./checkpoints/my_checkpoint')
   
 model = create_model()
   
 model.load_weights('./checkpoints/my_checkpoint')

Call model.save to save a model's architecture, weights, and training configuration in a single file/folder. This allows you to export a model so it can be used without access to the original Python code*. Since the optimizer-state is recovered, you can resume training from exactly where you left off.

Save model

# Create and train a new model instance.
model = create_model()
model.fit(train_images, train_labels, epochs=5)

# Save the entire model as a SavedModel.
!mkdir -p saved_model
model.save('saved_model/my_model')

load model

new_model = tf.keras.models.load_model('saved_model/my_model')

It seems that you are mixing both approaches, saving model and loading weights.

user_11414930
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