python - 如何使用迭代器接口(interface)将多个矩阵加载到 Chainer 模型中？

Question

我有一个关于 Chainer 迭代器接口(interface)以及它如何与训练器、更新器和模型交互的问题。

我的数据是图表，因此具有不同的矩阵形状。我已将特征矩阵连接成一个大的密集矩阵，将邻接矩阵连接成一个大的稀疏 COO 矩阵，并将求和运算符连接成一个大的稀疏 COO 矩阵。因为这是通过分子数据完成的，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，我认为这是用于训练的一个巨大数据点。 (在我使用这个巨大的矩阵之前，我还不打算进行训练/测试分割，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)]

我的模型前向传播如下:

# 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

然后我将所有内容传递给培训师:

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()

但是，当我运行训练器时，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

这让我感到困惑，因为我已经以与 mnist example 相同的方式设置了数据集。 ，其中输入数据与输出数据在元组中配对。由于 Chainer 中的抽象层，我不太确定如何调试这个问题。有人对此有任何见解吗？

Answer 1

您是否使用仅获取xs(或data)并输出feats的mpnn模型？我认为问题出在模型中，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

并使用此GraphNodeClassifier作为优化器的setup参数。

在 MNIST example above它使用 chainer 的内置 L.Classifier 类来包装 MLP 模型(仅获取 x)来获取 x 和 t 计算分类损失。