问题
My code was running perfectly in colab. But today it's not running. It says Can't set the attribute "trainable_weights", likely because it conflicts with an existing read-only @property of the object. Please choose a different name.
I am using LSTM with the attention layer.
class Attention(Layer):
def __init__(self, **kwargs):
self.init = initializers.get('normal')
#self.input_spec = [InputSpec(ndim=3)]
super(Attention, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape)==3
#self.W = self.init((input_shape[-1],1))
self.W = self.init((input_shape[-1],))
#self.input_spec = [InputSpec(shape=input_shape)]
self.trainable_weights = [self.W]
super(Attention, self).build(input_shape) # be sure you call this somewhere!
def call(self, x, mask=None):
eij = K.tanh(K.dot(x, self.W))
ai = K.exp(eij)
weights = ai/K.sum(ai, axis=1).dimshuffle(0,'x')
weighted_input = x*weights.dimshuffle(0,1,'x')
return weighted_input.sum(axis=1)
def get_output_shape_for(self, input_shape):
return (input_shape[0], input_shape[-1])
I am not sure what happened suddenly. Anyone encounter similar problem?
回答1:
change
self.trainable_weights = [self.W]
to
self._trainable_weights = [self.W]
回答2:
This is ongoing issue with tf in colab. I could get a link with this here
Looks the issue got closed , maybe time to reopen.
回答3:
Please remove the build function and use this instead, It worked for me.
def build(self,input_shape):
self.W=self.add_weight(name="att_weight",shape=(input_shape[-1],1),initializer="normal", trainable = True)
self.b=self.add_weight(name="att_bias",shape=(self.attention_dim,),initializer="normal", trainable = True)
self.u=self.add_weight(name="u_bias",shape=(self.attention_dim,1),initializer="normal", trainable = True)
super(Attention, self).build(input_shape)
来源:https://stackoverflow.com/questions/63343563/cant-set-the-attribute-trainable-weights-likely-because-it-conflicts-with-an