Comments (4)
self.state.h = self.state.o * np.tanh(self.state.s)
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@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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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
If you want to make a PR to add that as an option, that's fine with me.
from lstm.
yes
from lstm.
Related Issues (20)
- Usage of the LSTM
- action recogniton
- A parameter (x_dim) appears to be unused
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- The tanh_derivative should be : 1. + values**2 HOT 4
- self.state.h = self.state.s * self.state.o HOT 1
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from lstm.