Comments (2)
Thanks for pointing this out. I'll be working on this.
from attention_keras.
Model saving should be fine. There's no reason to implement get_config
as that will be inherited from the base Layer
object. And there are no special parameters to save either that are not in the base Layer
object. But you need to pass the custom_objects
argument as
full_model = load_model(os.path.join('..', 'h5.models', 'nmt.h5'), custom_objects={'AttentionLayer': AttentionLayer})
from attention_keras.
Related Issues (20)
- None Input Shape HOT 3
- Installation failed HOT 1
- TypeError: The added layer must be an instance of class Layer. HOT 2
- AttributeError: 'tuple' object has no attribute '_keras_mask' HOT 1
- The vocab size definition for English and French Tokenizers is incorrect HOT 1
- AttributeError: 'tuple' object has no attribute 'layer' HOT 3
- state HOT 1
- Failed to convert object of type HOT 3
- Minor issues - but nothing's broken HOT 12
- TypeError: object of type 'Concatenate' has no len() HOT 3
- Bahdanau attention HOT 2
- Help Required
- Very important!!!! HOT 2
- Support for using None in sequence length HOT 3
- Masking Support
- I got this error: 'tuple' object has no attribute '_keras_shape' any help please?
- ValueError: The first argument to `Layer.call` must always be passed. HOT 1
- Getting "TypeError: Exception encountered when calling layer "tf.keras.backend.rnn" (type TFOpLambda)" when I employ the Attention Layer HOT 11
- "tf.keras.backend.rnn"
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from attention_keras.