Created
October 3, 2017 09:54
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A pyTorch LSTM Cell with a hard sigmoid recurrent activation
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| def LSTMCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None): | |
| """ | |
| A modified LSTM cell with hard sigmoid activation on the input, forget and output gates. | |
| """ | |
| hx, cx = hidden | |
| gates = F.linear(input, w_ih, b_ih) + F.linear(hx, w_hh, b_hh) | |
| ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1) | |
| ingate = hard_sigmoid(ingate) | |
| forgetgate = hard_sigmoid(forgetgate) | |
| cellgate = F.tanh(cellgate) | |
| outgate = hard_sigmoid(outgate) | |
| cy = (forgetgate * cx) + (ingate * cellgate) | |
| hy = outgate * F.tanh(cy) | |
| return hy, cy | |
| def hard_sigmoid(x): | |
| """ | |
| Computes element-wise hard sigmoid of x. | |
| See e.g. https://github.com/Theano/Theano/blob/master/theano/tensor/nnet/sigm.py#L279 | |
| """ | |
| x = (0.2 * x) + 0.5 | |
| x = F.threshold(-x, -1, -1) | |
| x = F.threshold(-x, 0, 0) | |
| return x |
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