我有一个关于Chainer迭代器接口的问题，以及它如何与Trainer、Updater和Model接口。
我的数据是图形，因此具有不同的矩阵形状。我已经将特征矩阵连接成一个大的稠密矩阵，将邻接矩阵连接到一个大稀疏COO矩阵，将求和算子连接到一一个大疏COO矩阵。因为这是用分子数据完成的，所以每个样本都有一个原子图和一个键图。因此，输入数据是一个六元组，出于深度学习的目的，我认为这是一个巨大的训练数据点。(在我使用这个巨大的矩阵之前，我还不打算进行训练/测试拆分，以保持我的代码简单。)
xs = (atom_Fs, atom_As, atom_Ss, bond_Fs, bond_As, bond_Ss)
ts = data['target'].values
dataset = [(xs, ts)]
我的模型前向传球如下：
# model boilerplate above this comment
def forward(self, data):
atom_feats, atom_adjs, atom_sums, bond_feats, bond_adjs, bond_sums = data
atom_feats = self.atom_mp1(atom_feats, atom_adjs)
atom_feats = self.atom_mp2(atom_feats, atom_adjs)
atom_feats = self.atom_gather(atom_feats, atom_sums)
bond_feats = self.atom_mp1(bond_feats, bond_adjs)
bond_feats = self.atom_mp2(bond_feats, bond_adjs)
bond_feats = self.atom_gather(bond_feats, bond_sums)
feats = F.hstack([atom_feats, bond_feats])
feats = F.tanh(self.dense1(feats))
feats = F.tanh(self.dense2(feats))
feats = self.dense3(feats)
return feats
然后我把所有东西都交给一个培训师：
from chainer import iterators, training
from chainer.optimizers import SGD, Adam
iterator = iterators.SerialIterator(dataset, batch_size=1)
optimizer = Adam()
optimizer.setup(mpnn)
updater = training.updaters.StandardUpdater(iterator, optimizer)
max_epoch = 50
trainer = training.Trainer(updater, (max_epoch, 'epoch'))
trainer.run()
然而，当我运行训练器时，我会出现以下错误：
Exception in main training loop: forward() takes 2 positional arguments but 3 were given
Traceback (most recent call last):
File "/home/ericmjl/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/training/trainer.py", line 315, in run
update()
File "/home/ericmjl/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 165, in update
self.update_core()
File "/home/ericmjl/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 177, in update_core
optimizer.update(loss_func, *in_arrays)
File "/home/ericmjl/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/optimizer.py", line 680, in update
loss = lossfun(*args, **kwds)
File "/home/ericmjl/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
Will finalize trainer extensions and updater before reraising the exception.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-45-ea26cece43b3> in <module>
9 max_epoch = 50
10 trainer = training.Trainer(updater, (max_epoch, 'epoch'))
---> 11 trainer.run()
~/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/training/trainer.py in run(self, show_loop_exception_msg)
327 f.write('Will finalize trainer extensions and updater before '
328 'reraising the exception.n')
--> 329 six.reraise(*sys.exc_info())
330 finally:
331 for _, entry in extensions:
~/anaconda/envs/mpnn/lib/python3.7/site-packages/six.py in reraise(tp, value, tb)
691 if value.__traceback__ is not tb:
692 raise value.with_traceback(tb)
--> 693 raise value
694 finally:
695 value = None
~/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/training/trainer.py in run(self, show_loop_exception_msg)
313 self.observation = {}
314 with reporter.scope(self.observation):
--> 315 update()
316 for name, entry in extensions:
317 if entry.trigger(self):
~/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py in update(self)
163
164 """
--> 165 self.update_core()
166 self.iteration += 1
167
~/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py in update_core(self)
175
176 if isinstance(in_arrays, tuple):
--> 177 optimizer.update(loss_func, *in_arrays)
178 elif isinstance(in_arrays, dict):
179 optimizer.update(loss_func, **in_arrays)
~/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/optimizer.py in update(self, lossfun, *args, **kwds)
678 if lossfun is not None:
679 use_cleargrads = getattr(self, '_use_cleargrads', True)
--> 680 loss = lossfun(*args, **kwds)
681 if use_cleargrads:
682 self.target.cleargrads()
~/anaconda/envs/mpnn/lib/python3.7/site-packages/chainer/link.py in __call__(self, *args, **kwargs)
240 if forward is None:
241 forward = self.forward
--> 242 out = forward(*args, **kwargs)
243
244 # Call forward_postprocess hook
TypeError: forward() takes 2 positional arguments but 3 were given
这让我很困惑，因为我以与mnist示例相同的方式设置了数据集，其中输入数据与元组中的输出数据成对。由于Chainer中的抽象层，我不太确定如何调试这个问题。有人对此有什么见解吗？
您是否使用只获取xs(或data)并输出feats的mpnn模型？我认为问题出在模型上，而不是迭代器或数据集。
您需要准备一个模型，将xs和ts作为输入参数，并计算loss作为输出。例如，
class GraphNodeClassifier(chainer.Chain):
def __init__(self, mpnn):
with self.init_scope():
self.mpnn = mpnn
def forward(self, xs, ts):
feat = self.mpnn(xs)
loss = "calculate loss between `feat` and `ts` here..."
return loss
并使用该CCD_ 8作为优化器的CCD_。
在上面的MNIST示例中，它使用chainer内置的L.Classifier类，该类封装MLP模型(仅获得x)以获得x和t来计算分类损失。