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(
# 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(
# 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)),
)
