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try1995 avatar try1995 commented on August 24, 2024

self.state.h = self.state.o * np.tanh(self.state.s)

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cs-heibao avatar cs-heibao commented on August 24, 2024

@mikechen66
and also exists problem when do backpropagation, ignoring the derivation of tanh function

    def top_diff_is(self, top_diff_h, top_diff_s):
        # notice that top_diff_s is carried along the constant error carousel
        ds = self.state.o * top_diff_h + top_diff_s
        do = self.state.s * top_diff_h
        di = self.state.g * ds
        dg = self.state.i * ds
        df = self.s_prev * ds
ds = self.state.o *(1-self.state.s^2)* top_diff_h + top_diff_s;
do = np.tanh(self.state.s) * top_diff_h

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nicodjimenez avatar nicodjimenez commented on August 24, 2024

I think you're right some / most implementations use the tanh but that's not how I defined the forward pass in the blog article:

https://nicodjimenez.github.io/2014/08/08/lstm.html
image

If you want to make a PR to add that as an option, that's fine with me.

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bot66 avatar bot66 commented on August 24, 2024

yes

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