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, self.__n_labels),
trans=functions.EmbedID(
self.__n_labels * self.__n_labels,
1), #各ラベル(0,1)間の遷移のweight #確率としておく softmaxかます
)
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
weight_xi = functions.EmbedID(len(self.src_vocab), self.n_embed),
weight_ip = functions.Linear(self.n_embed, 4 * self.n_hidden),
weight_pp = functions.Linear(self.n_hidden, 4 * self.n_hidden),
# decoder
weight_pq = functions.Linear(self.n_hidden, 4 * self.n_hidden),
weight_qj = functions.Linear(self.n_hidden, self.n_embed),
weight_jy = functions.Linear(self.n_embed, len(self.trg_vocab)),
weight_yq = functions.EmbedID(len(self.trg_vocab), 4 * self.n_hidden),
weight_qq = functions.Linear(self.n_hidden, 4 * self.n_hidden),
)
def make_model(self):
self.model = wrapper.make_model(
# encoder
weight_xi=functions.EmbedID(len(self.src_vocab), self.n_embed),
weight_ip=functions.Linear(self.n_embed, 4 * self.n_hidden),
weight_pp=functions.Linear(self.n_hidden, 4 * self.n_hidden),
# decoder
weight_pq=functions.Linear(self.n_hidden, 4 * self.n_hidden),
weight_qj=functions.Linear(self.n_hidden, self.n_embed),
weight_jy=functions.Linear(self.n_embed, len(self.trg_vocab)),
weight_yq=functions.EmbedID(len(self.trg_vocab),
4 * self.n_hidden),
weight_qq=functions.Linear(self.n_hidden, 4 * self.n_hidden),
)
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, self.__n_labels),
trans = functions.EmbedID(self.__n_labels * self.__n_labels, 1), #各ラベル(0,1)間の遷移のweight #確率としておく softmaxかます
)
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),
)
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),
)
