Question

我正在尝试训练和测试一个简单的多层感知器，就像在第一个Chainer教程中一样，但是使用我自己的数据集而不是MNIST。这是我使用的代码（主要来自教程）：

class MLP(Chain):
    def __init__(self, n_units, n_out):
        super(MLP, self).__init__()
        with self.init_scope():
            self.l1 = L.Linear(None, n_units)
            self.l2 = L.Linear(None, n_units)
            self.l3 = L.Linear(None, n_out)
    def __call__(self, x):
        h1 = F.relu(self.l1(x))
        h2 = F.relu(self.l2(h1))
        y = self.l3(h2)
        return y

X, X_test, y, y_test, xHeaders, yHeaders = load_train_test_data('xHeuristicData.csv', 'yHeuristicData.csv')

print 'dataset shape   X:', X.shape, '  y:', y.shape

model = MLP(100, 1)
optimizer = optimizers.SGD()
optimizer.setup(model)

train = tuple_dataset.TupleDataset(X, y)
test = tuple_dataset.TupleDataset(X_test, y_test)

train_iter = iterators.SerialIterator(train, batch_size=100, shuffle=True)
test_iter = iterators.SerialIterator(test, batch_size=100, repeat=False, shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer)
trainer = training.Trainer(updater, (10, 'epoch'), out='result')

trainer.extend(extensions.Evaluator(test_iter, model))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(['epoch', 'main/accuracy', 'validation/main/accuracy']))
trainer.extend(extensions.ProgressBar())

trainer.run()

print 'Predicted value for a test example'
print model(X_test[0])

而不是训练和打印预测值，我在＆＃34; trainer.run（）＆＃34;

中得到以下错误：

dataset shape   X: (1003, 116)   y: (1003,)
Exception in main training loop: __call__() takes exactly 2 arguments (3 given)
Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/chainer/training/trainer.py", line 299, in run
    update()
  File "/usr/local/lib/python2.7/dist-packages/chainer/training/updater.py", line 223, in update
    self.update_core()
  File "/usr/local/lib/python2.7/dist-packages/chainer/training/updater.py", line 234, in update_core
    optimizer.update(loss_func, *in_arrays)
  File "/usr/local/lib/python2.7/dist-packages/chainer/optimizer.py", line 534, in update
    loss = lossfun(*args, **kwds)
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
  File "trainHeuristicChainer.py", line 76, in <module>
    trainer.run()
  File "/usr/local/lib/python2.7/dist-packages/chainer/training/trainer.py", line 313, in run
    six.reraise(*sys.exc_info())
  File "/usr/local/lib/python2.7/dist-packages/chainer/training/trainer.py", line 299, in run
    update()
  File "/usr/local/lib/python2.7/dist-packages/chainer/training/updater.py", line 223, in update
    self.update_core()
  File "/usr/local/lib/python2.7/dist-packages/chainer/training/updater.py", line 234, in update_core
    optimizer.update(loss_func, *in_arrays)
  File "/usr/local/lib/python2.7/dist-packages/chainer/optimizer.py", line 534, in update
    loss = lossfun(*args, **kwds)
TypeError: __call__() takes exactly 2 arguments (3 given)

我不清楚如何处理错误。我已经使用其他框架成功训练了类似的网络，但我对Chainer感兴趣，因为它与PyPy兼容。

这里提供了包含文件的tgz：https://mega.nz/#!wwsBiSwY!g72pC5ZgekeMiVr-UODJOqQfQZZU3lCqm9Er2jH4UD8

Answer 1

您正在向MLP发送(X, y)元组，而实施的__call__仅接受x。

您可以将实施修改为

class MLP(Chain):
    def __init__(self, n_units, n_out):
        super(MLP, self).__init__()
        with self.init_scope():
            self.l1 = L.Linear(None, n_units)
            self.l2 = L.Linear(None, n_units)
            self.l3 = L.Linear(None, n_out)
    def __call__(self, x, y):
        h1 = F.relu(self.l1(x))
        h2 = F.relu(self.l2(h1))
        predict = self.l3(h2)
        loss = F.squared_error(predict, y)
        // or you can write it on your own as follows
        // loss = F.sum(F.square(predict - y))
        return loss

在chainer中可能与其他框架不同，默认情况下，标准更新程序假定__call__为损失函数。因此，呼叫model(X, y)将返回当前小批量的丢失。这就是为什么chainer教程引入另一个Classifier类来计算损失函数并保持MLP简单的原因。分类器在MNIST中很有意义，但不适合您的任务，因此您可以自行实现丢失功能。

完成培训后，您可以保存模型实例（可以通过向培训师添加snapshot_object的扩展名）。

要使用已保存的模型，就像在测试中一样，您必须在类中编写另一个方法，可能名为test，其代码与当前__call__相同，只有X手边输入，因此不需要其他y。

此外，如果您不想在MLP类中添加任何额外的方法，使其变为纯粹，那么您需要自己编写更新程序并更自然地计算损失函数。要继承标准版本更容易，您可以按如下方式编写，

class MyUpdater(chainer.training.StandardUpdater):
    def __init__(self, data_iter, model, opt, device=-1):
        super(MyUpdater, self).__init__(data_iter, opt, device=device)
        self.mlp = model

    def update_core(self):
        batch = self.get_iterator('main').next()
        x, y = self.converter(batch, self.device)
        predict = self.mlp(x)
        loss = F.squared_error(predict, y)
        self.mlp.cleargrads()
        loss.backward()
        self.get_iterator('main').update()

updater = MyUpdater(train_iter, model, optimizer)

使用Chainer训练MLP时出错

1 个答案: