

@neural_computation
def compute_new_state(self, step_inputs):
    (xh_t, h_tm1) = map(step_inputs.get, ['xh_t', 'state'])
    if (not xh_t):
        xh_t = 0
    h_t = self.activate(((xh_t + T.dot(self, self.W_h)) + self.b_h))
    return {
        'state': h_t,
    }

dataset/ETHPy150Open zomux/deepy/deepy/layers/recurrent.py RNN.compute_new_state/VarMisuse@61/92 `h_tm1`->`self`