Comments (2)
I have an implementation of Autoencoder with tied weights. It's basically two-layers-in-one. After pre-training you can extract the weights and initialize two Dense layers.
def get_output(self, train):
X = self.get_input(train)
encode = self.activation1(T.dot(X, self.W) + self.b)
decode = self.activation2(T.dot(encode, self.W.T) + self.b_p)
return decode
The trick for fitting multi-layer autoencoders is how to not backprop through the previously fit weights. The method I use is to create a regularizer function
def zero_grad(g, p):
return 0.
Then I pass that into the pre-trained Dense layer to prevent backprop from changing the weights.
from keras.
Implemented #180
from keras.
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from keras.