save model error

[kcsmta]

In textClassifierHATT.py, I try to save the model using the following callback: mcp = ModelCheckpoint('HANmodel_weights.h5', monitor="val_acc", save_best_only=True, save_weights_only=False) model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=20, batch_size=50, callbacks = [mcp]) But the following error occurred ... RuntimeError: Unable to create link (Name already exists)

There are suggestions that non-unique layer names cause this problem here but I haven't seen any duplicate names in this model.

[pmm-511]

Have you solved it? I also have this problem. Thanks.

[grommy]

Hey guys, @kcsmta was right!

the issue comes from saving weights of the Attention Layer. I've made the fix AttLayer class, where I gave custom names for variables:

    def build(self, input_shape):
        assert len(input_shape) == 3
        self.W = K.variable(self.init((input_shape[-1], self.attention_dim)), name="W")
        self.b = K.variable(self.init((self.attention_dim, )), name="b")
        self.u = K.variable(self.init((self.attention_dim, 1)), name="u")
        self.trainable_weights = [self.W, self.b, self.u]
        super(AttLayer, self).build(input_shape)

@richliao, could you accept the pull_request

@pmm-511 , ... if you still need a fix

[RishikMani]

Just using a named layer wasn't enough for me to save the model. I tried saving both complete model and model with weights as well. The problem was, if you are initializing variables in the __init__ method, then you should also return them in method get_config.

class AttentionLayer(Layer):
  def __init__(self, attention_dim, supports_masking=True, **kwargs):
      super(AttentionLayer, self).__init__(name="attention_layer")
      self.init = initializers.get("normal")
      self.supports_masking = supports_masking
      self.attention_dim = attention_dim
      super(AttentionLayer, self).__init__(**kwargs)

  def get_config(self):
      config = {
        "supports_masking": self.supports_masking,
        "attention_dim": self.attention_dim,
      }
      base_config = super(AttentionLayer, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))

Please ensure that you are not returning the init variable in get_config method.