def __init__(self, nlp, shape, **settings):
Chain.__init__(self,
embed=_Embed(shape['nr_vector'], shape['nr_dim'], shape['nr_hidden'],
initialW=lambda arr: set_vectors(arr, nlp.vocab)),
encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
predict=_Predict(shape['nr_hidden'], shape['nr_class']))
self.to_gpu(0)
def __init__(self, owner, kind_name, link, input=None):
if input == owner:
input = None
if input is not None and not isinstance(input, Node):
raise TypeError("Invalid argument. 'input' must be Node.")
if not isinstance(link, Link):
raise TypeError('Cannot register a non-link object as a child')
Chain.__init__(self)
Node.__init__(self, owner, kind_name)
self.allow_multi_inputs = False
self.output_same_value = True
if input is not None:
input.add_ouput(self)
self.add_input(input)
with self.init_scope():
self.link = link
def __init__(self, model, owner=None, kind_name='root', input=None):
if input == owner:
input = None
if input is not None and not isinstance(input, Node):
raise TypeError("Invalid argument. 'input' must be Node.")
Chain.__init__(self)
Node.__init__(self, owner, kind_name)
self.allow_multi_inputs = False
self.output_same_value = True
if input is not None:
input.add_ouput(self)
self.add_input(input)
self.model = model # この Module が属する Model
self.nodes = [] # 子ノード列
self.kindwise_count = {} # 種類毎の子ノード数
self.firsts = None # 最初のノード列
self.lasts = None # 最後のノード列
self.assembly_depth = 0 # これが 0 以外ならノード生成時に子ノードとして登録される、0 なら self.owner の子ノードとして登録される
def __init__(self):
# super(MyChain, self).__init__() # 多继承的时候，这种比较方便，一次性调用所有父类的构造器
Chain.__init__(self) # 在子类中调用父类的方法，需要加上基类名作为前缀，且还需传入self
with self.init_scope():
self.l1 = L.Linear(4, 3)
self.l2 = L.Linear(3, 2)
def __init__(self, nr_vector, nr_dim, nr_out):
Chain.__init__(self,
embed=L.EmbedID(nr_vector, nr_dim),
project=L.Linear(None, nr_out, nobias=True))
def __init__(self, nr_in, nr_out):
Chain.__init__(self,
l1=L.Linear(nr_in, nr_in),
l2=L.Linear(nr_in, nr_out))
def __init__(self, nr_in, nr_out):
Chain.__init__(self)
def __init__(self, nr_in, nr_out):
Chain.__init__(self,
fwd=L.LSTM(nr_in, nr_out),
bwd=L.LSTM(nr_in, nr_out),
mix=L.Bilinear(nr_out, nr_out, nr_out))
def __init__(self, nr_vector, nr_dim, nr_out, set_vectors=None):
Chain.__init__(self,
embed=L.EmbedID(nr_vector, nr_dim, initialW=set_vectors),
project=L.Linear(None, nr_out, nobias=True))
self.embed.W.volatile = False