def __make_model(self): self.__model = wrapper.make_model( w_xe = functions.EmbedID(len(self.__vocab), self.__n_embed), w_ea = functions.Linear(self.__n_embed, 4 * self.__n_hidden), w_aa = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), w_eb = functions.Linear(self.__n_embed, 4 * self.__n_hidden), w_bb = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), w_ay1 = functions.Linear(self.__n_hidden, 1), w_by1 = functions.Linear(self.__n_hidden, 1), w_ay2 = functions.Linear(self.__n_hidden, 1), w_by2 = functions.Linear(self.__n_hidden, 1), )
def __make_model(self): self.__model = wrapper.make_model( # encoder w_xi = functions.EmbedID(len(self.__src_vocab), self.__n_embed), w_ip = functions.Linear(self.__n_embed, 4 * self.__n_hidden), w_pp = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), # decoder w_pq = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), w_qj = functions.Linear(self.__n_hidden, self.__n_embed), w_jy = functions.Linear(self.__n_embed, len(self.__trg_vocab)), w_yq = functions.EmbedID(len(self.__trg_vocab), 4 * self.__n_hidden), w_qq = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), )
def __make_model(self): self.__model = wrapper.make_model( # input embedding w_xi = functions.EmbedID(len(self.__src_vocab), self.__n_embed), # forward encoder w_ia = functions.Linear(self.__n_embed, 4 * self.__n_hidden), w_aa = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), # backward encoder w_ib = functions.Linear(self.__n_embed, 4 * self.__n_hidden), w_bb = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), # attentional weight estimator w_aw = functions.Linear(self.__n_hidden, self.__n_hidden), w_bw = functions.Linear(self.__n_hidden, self.__n_hidden), w_pw = functions.Linear(self.__n_hidden, self.__n_hidden), w_we = functions.Linear(self.__n_hidden, 1), # decoder w_ap = functions.Linear(self.__n_hidden, self.__n_hidden), w_bp = functions.Linear(self.__n_hidden, self.__n_hidden), w_yp = functions.EmbedID(len(self.__trg_vocab), 4 * self.__n_hidden), w_pp = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), w_cp = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), w_dp = functions.Linear(self.__n_hidden, 4 * self.__n_hidden), w_py = functions.Linear(self.__n_hidden, len(self.__trg_vocab)), )
def __make_model(self): self.__model = wrapper.make_model( w_xh = functions.EmbedID(2 * self.__n_context * len(self.__vocab), self.__n_hidden), w_hy = functions.Linear(self.__n_hidden, 1), )