Chainer CNN- TypeError: forward() missing 1 required positional argument: 'x'
Chainer CNN- TypeError: forward() missing 1 required positional argument: 'x'
我正在尝试 运行 Chainer 上的分类器，但由于以下错误而失败。
我不知道这个错误，因为我确认迭代器实际上向训练器发送了一个批次。
是不是神经网络模型有问题？或者，数据输入模型的方式有误？
Input.py
from chainer.datasets import split_dataset_random
from chainer.iterators import SerialIterator
from chainer.optimizers import Adam
from chainer.training import Trainer
from chainer.training.updaters import StandardUpdater
from chainer import functions as F, links as L
from chainer import Sequential
import numpy as np
batch_size = 3
X_train = np.ones((9957, 60, 80, 3), dtype=np.float32)
X_train, _ = split_dataset_random(X_train, 8000, seed=0)
train_iter = SerialIterator(X_train, batch_size)
model = Sequential(
L.Convolution2D(None, 64, 3, 2),
F.relu,
L.Convolution2D(64, 32, 3, 2),
F.relu,
L.Linear(None, 16),
F.dropout,
L.Linear(16, 4)
)
model_loss = L.Classifier(model)
optimizer = Adam()
optimizer.setup(model_loss)
updater = StandardUpdater(train_iter, optimizer)
trainer = Trainer(updater, (25, 'epoch'))
trainer.run()
Stacktrace.py
Exception in main training loop: forward() missing 1 required positional argument: 'x'
Traceback (most recent call last):
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 315, in run
update()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 165, in update
self.update_core()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 181, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/optimizer.py", line 680, in update
loss = lossfun(*args, **kwds)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/links/model/classifier.py", line 143, in forward
self.y = self.predictor(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/sequential.py", line 210, in forward
x = layer(*x)
File "/home/user/miniconda3/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.
Traceback (most recent call last):
File "/home/user/deploy/aaa.py", line 33, in <module>
trainer.run()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 348, in run
six.reraise(*exc_info)
File "/home/user/miniconda3/lib/python3.7/site-packages/six.py", line 693, in reraise
raise value
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 315, in run
update()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 165, in update
self.update_core()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 181, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/optimizer.py", line 680, in update
loss = lossfun(*args, **kwds)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/links/model/classifier.py", line 143, in forward
self.y = self.predictor(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/sequential.py", line 210, in forward
x = layer(*x)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
TypeError: forward() missing 1 required positional argument: 'x'
神经网络模型或数据输入模型的方式是否有问题？如果您需要查看整个代码，请告诉我
你所要做的就是给模型一个 ndarray 和 int 的元组，因为这是 L.Classifier.
的规范
Is there a problem with the neural network model? Or, the way the data has been fed into the model is wrong?
因此，绝对答案是"the way the data has been fed in to the model is wrong"。
在下面的代码中，我定义了一个 class 继承 DatasetMixin 来提供 ndarray 和 int 的元组。 （这是Chainer的常规方式）
需要注意的是L.Convolution2D的输入参数必须是一个ndarray，shape是(batch, channel, width, height)。所以我转置数据集中的数组。
Solution.py
from chainer.datasets import split_dataset_random
from chainer.iterators import SerialIterator
from chainer.optimizers import Adam
from chainer.training import Trainer
from chainer.training.updaters import StandardUpdater
from chainer import functions as F, links as L
from chainer import Sequential
from chainer.dataset import DatasetMixin
import numpy as np
class MyDataset(DatasetMixin):
def __init__(self, X, labels):
super(MyDataset, self).__init__()
self.X_ = X
self.labels_ = labels
self.size_ = X.shape[0]
def __len__(self):
return self.size_
def get_example(self, i):
return np.transpose(self.X_[i, ...], (2, 0, 1)), self.labels_[i]
batch_size = 3
X_train = np.ones((9957, 60, 80, 3), dtype=np.float32)
label_train = np.random.randint(0, 4, (9957,), dtype=np.int32)
dataset = MyDataset(X_train, label_train)
dataset_train, _ = split_dataset_random(dataset, 8000, seed=0)
train_iter = SerialIterator(dataset_train, batch_size)
model = Sequential(
L.Convolution2D(None, 64, 3, 2),
F.relu,
L.Convolution2D(64, 32, 3, 2),
F.relu,
L.Linear(None, 16),
F.dropout,
L.Linear(16, 4)
)
model_loss = L.Classifier(model)
optimizer = Adam()
optimizer.setup(model_loss)
updater = StandardUpdater(train_iter, optimizer)
trainer = Trainer(updater, (25, 'epoch'))
trainer.run()
我正在尝试 运行 Chainer 上的分类器，但由于以下错误而失败。
我不知道这个错误，因为我确认迭代器实际上向训练器发送了一个批次。
是不是神经网络模型有问题？或者，数据输入模型的方式有误？
Input.py
from chainer.datasets import split_dataset_random
from chainer.iterators import SerialIterator
from chainer.optimizers import Adam
from chainer.training import Trainer
from chainer.training.updaters import StandardUpdater
from chainer import functions as F, links as L
from chainer import Sequential
import numpy as np
batch_size = 3
X_train = np.ones((9957, 60, 80, 3), dtype=np.float32)
X_train, _ = split_dataset_random(X_train, 8000, seed=0)
train_iter = SerialIterator(X_train, batch_size)
model = Sequential(
L.Convolution2D(None, 64, 3, 2),
F.relu,
L.Convolution2D(64, 32, 3, 2),
F.relu,
L.Linear(None, 16),
F.dropout,
L.Linear(16, 4)
)
model_loss = L.Classifier(model)
optimizer = Adam()
optimizer.setup(model_loss)
updater = StandardUpdater(train_iter, optimizer)
trainer = Trainer(updater, (25, 'epoch'))
trainer.run()
Stacktrace.py
Exception in main training loop: forward() missing 1 required positional argument: 'x'
Traceback (most recent call last):
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 315, in run
update()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 165, in update
self.update_core()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 181, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/optimizer.py", line 680, in update
loss = lossfun(*args, **kwds)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/links/model/classifier.py", line 143, in forward
self.y = self.predictor(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/sequential.py", line 210, in forward
x = layer(*x)
File "/home/user/miniconda3/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.
Traceback (most recent call last):
File "/home/user/deploy/aaa.py", line 33, in <module>
trainer.run()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 348, in run
six.reraise(*exc_info)
File "/home/user/miniconda3/lib/python3.7/site-packages/six.py", line 693, in reraise
raise value
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/trainer.py", line 315, in run
update()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 165, in update
self.update_core()
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 181, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/optimizer.py", line 680, in update
loss = lossfun(*args, **kwds)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/links/model/classifier.py", line 143, in forward
self.y = self.predictor(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/sequential.py", line 210, in forward
x = layer(*x)
File "/home/user/miniconda3/lib/python3.7/site-packages/chainer/link.py", line 242, in __call__
out = forward(*args, **kwargs)
TypeError: forward() missing 1 required positional argument: 'x'
神经网络模型或数据输入模型的方式是否有问题？如果您需要查看整个代码，请告诉我
你所要做的就是给模型一个 ndarray 和 int 的元组，因为这是 L.Classifier.
Is there a problem with the neural network model? Or, the way the data has been fed into the model is wrong?
因此，绝对答案是"the way the data has been fed in to the model is wrong"。
在下面的代码中，我定义了一个 class 继承 DatasetMixin 来提供 ndarray 和 int 的元组。 （这是Chainer的常规方式）
需要注意的是L.Convolution2D的输入参数必须是一个ndarray，shape是(batch, channel, width, height)。所以我转置数据集中的数组。
Solution.py
from chainer.datasets import split_dataset_random
from chainer.iterators import SerialIterator
from chainer.optimizers import Adam
from chainer.training import Trainer
from chainer.training.updaters import StandardUpdater
from chainer import functions as F, links as L
from chainer import Sequential
from chainer.dataset import DatasetMixin
import numpy as np
class MyDataset(DatasetMixin):
def __init__(self, X, labels):
super(MyDataset, self).__init__()
self.X_ = X
self.labels_ = labels
self.size_ = X.shape[0]
def __len__(self):
return self.size_
def get_example(self, i):
return np.transpose(self.X_[i, ...], (2, 0, 1)), self.labels_[i]
batch_size = 3
X_train = np.ones((9957, 60, 80, 3), dtype=np.float32)
label_train = np.random.randint(0, 4, (9957,), dtype=np.int32)
dataset = MyDataset(X_train, label_train)
dataset_train, _ = split_dataset_random(dataset, 8000, seed=0)
train_iter = SerialIterator(dataset_train, batch_size)
model = Sequential(
L.Convolution2D(None, 64, 3, 2),
F.relu,
L.Convolution2D(64, 32, 3, 2),
F.relu,
L.Linear(None, 16),
F.dropout,
L.Linear(16, 4)
)
model_loss = L.Classifier(model)
optimizer = Adam()
optimizer.setup(model_loss)
updater = StandardUpdater(train_iter, optimizer)
trainer = Trainer(updater, (25, 'epoch'))
trainer.run()