Comments (4)
Hi,
thank you for pointing it out, this is clearly a bug. I think that the actual correct changes should be:
self.layer.kernel = K.in_train_phase(K.dropout(self.layer.kernel, self.prob) * (1-self.prob), self.layer.kernel)
self.layer.bias = K.in_train_phase(K.dropout(self.layer.bias, self.prob) * (1-self.prob) , self.layer.bias)
because we have to scale back weights and biases only if dropout is applied. Correct me if I'm wrong
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My bad. You are absolutely right.
from kerasdropconnect.
Thank you very much for your contribution! Fixed with the last commit.
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"self.layer.kernel = K.in_train_phase(K.dropout(self.layer.kernel, self.prob) * (1-self.prob), self.layer.kernel)"
hello,something seems wrong with it.
as K.dropout already scaled up with (1-self.prob) and does not have training option. so in the training, we do not need to return the condition back.
so it should be like this
self.W = K.in_train_phase(K.dropout(self.layer.kernel, self.prob) , self.layer.kernel)
from kerasdropconnect.
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