| 78 | self.xc = None |
| 79 | |
| 80 | def bottom_data_is(self, x, s_prev = None, h_prev = None): |
| 81 | # if this is the first lstm node in the network |
| 82 | if s_prev == None: s_prev = np.zeros_like(self.state.s) |
| 83 | if h_prev == None: h_prev = np.zeros_like(self.state.h) |
| 84 | # save data for use in backprop |
| 85 | self.s_prev = s_prev |
| 86 | self.h_prev = h_prev |
| 87 | |
| 88 | # concatenate x(t) and h(t-1) |
| 89 | xc = np.hstack((x, h_prev)) |
| 90 | self.state.g = np.tanh(np.dot(self.param.wg, xc) + self.param.bg) |
| 91 | self.state.i = sigmoid(np.dot(self.param.wi, xc) + self.param.bi) |
| 92 | self.state.f = sigmoid(np.dot(self.param.wf, xc) + self.param.bf) |
| 93 | self.state.o = sigmoid(np.dot(self.param.wo, xc) + self.param.bo) |
| 94 | self.state.s = self.state.g * self.state.i + s_prev * self.state.f |
| 95 | self.state.h = self.state.s * self.state.o |
| 96 | self.x = x |
| 97 | self.xc = xc |
| 98 | |
| 99 | def top_diff_is(self, top_diff_h, top_diff_s): |
| 100 | # notice that top_diff_s is carried along the constant error carousel |