Autoencoder in Chainer issue
1
I am trying to train Autoencoder by Chainer in python and wrote below code. But it does not work. Why??
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def __call__(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
class Dataset(dataset.DatasetMixin):
def __init__(self,number_of_data, show_initial = False):
noise_level = 1
self.data = np.zeros((number_of_data,3),dtype = np.float32)
OA_vector = np.array([3,2,1])
OB_vector = np.array([2,-1,1])
t = np.random.uniform(-0.5,0.5,number_of_data)
s = np.random.uniform(-0.5,0.5,number_of_data)
for i in range(0,number_of_data):
noise = np.random.uniform(-noise_level, noise_level,3)
self.data[i] = t[i]*OA_vector + s[i]*OB_vector + noise
def __len__(self):
return self.data.shape[0]
def get_example(self,idx):
return self.data[idx]
if __name__ == "__main__":
n_epoch = 5
batch_size = 100
number_of_data = 1000 #データ数
train_data = Dataset(number_of_data,False)
model = Autoencoder()
optimizer = optimizers.SGD(lr=0.05).setup(model)
train_iter = iterators.SerialIterator(train_data,batch_size)
updater = training.StandardUpdater(train_iter,optimizer,device=0)
trainer = training.Trainer(updater,(n_epoch,"epoch"),out="result")
trainer.run()
I am using Chainer. And the Dataset makes 3 dimensitonal vectors. The number of the vectors is "number_of_data".
Should I do that without using trainer?
I don't understand where problem is.
EDIT
When we run above code with device=0, we have error like below.
Exception in main training loop: Unsupported type <class 'NoneType'>
Traceback (most recent call last):
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
File "cupy/core/core.pyx", line 1310, in cupy.core.core.ndarray.__mul__
File "cupy/core/elementwise.pxi", line 753, in cupy.core.core.ufunc.__call__
File "cupy/core/elementwise.pxi", line 68, in cupy.core.core._preprocess_args
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "AC.py", line 70, in <module>
trainer.run()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 322, in run
six.reraise(*sys.exc_info())
File "/home/****/.local/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
File "cupy/core/core.pyx", line 1310, in cupy.core.core.ndarray.__mul__
File "cupy/core/elementwise.pxi", line 753, in cupy.core.core.ufunc.__call__
File "cupy/core/elementwise.pxi", line 68, in cupy.core.core._preprocess_args
TypeError: Unsupported type <class 'NoneType'>
When we run above code with device=-1, we have error like below.
Exception in main training loop: unsupported operand type(s) for *: 'bool' and 'NoneType'
Traceback (most recent call last):
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "AC.py", line 70, in <module>
trainer.run()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 322, in run
six.reraise(*sys.exc_info())
File "/home/****/.local/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'
python machine-learning deep-learning autoencoder chainer
asked Nov 20 '18 at 10:52
Sakurai.JJSakurai.JJ
84
|
show 1 more comment
1
I am trying to train Autoencoder by Chainer in python and wrote below code. But it does not work. Why??
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def __call__(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
class Dataset(dataset.DatasetMixin):
def __init__(self,number_of_data, show_initial = False):
noise_level = 1
self.data = np.zeros((number_of_data,3),dtype = np.float32)
OA_vector = np.array([3,2,1])
OB_vector = np.array([2,-1,1])
t = np.random.uniform(-0.5,0.5,number_of_data)
s = np.random.uniform(-0.5,0.5,number_of_data)
for i in range(0,number_of_data):
noise = np.random.uniform(-noise_level, noise_level,3)
self.data[i] = t[i]*OA_vector + s[i]*OB_vector + noise
def __len__(self):
return self.data.shape[0]
def get_example(self,idx):
return self.data[idx]
if __name__ == "__main__":
n_epoch = 5
batch_size = 100
number_of_data = 1000 #データ数
train_data = Dataset(number_of_data,False)
model = Autoencoder()
optimizer = optimizers.SGD(lr=0.05).setup(model)
train_iter = iterators.SerialIterator(train_data,batch_size)
updater = training.StandardUpdater(train_iter,optimizer,device=0)
trainer = training.Trainer(updater,(n_epoch,"epoch"),out="result")
trainer.run()
I am using Chainer. And the Dataset makes 3 dimensitonal vectors. The number of the vectors is "number_of_data".
Should I do that without using trainer?
I don't understand where problem is.
EDIT
When we run above code with device=0, we have error like below.
Exception in main training loop: Unsupported type <class 'NoneType'>
Traceback (most recent call last):
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
File "cupy/core/core.pyx", line 1310, in cupy.core.core.ndarray.__mul__
File "cupy/core/elementwise.pxi", line 753, in cupy.core.core.ufunc.__call__
File "cupy/core/elementwise.pxi", line 68, in cupy.core.core._preprocess_args
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "AC.py", line 70, in <module>
trainer.run()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 322, in run
six.reraise(*sys.exc_info())
File "/home/****/.local/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
File "cupy/core/core.pyx", line 1310, in cupy.core.core.ndarray.__mul__
File "cupy/core/elementwise.pxi", line 753, in cupy.core.core.ufunc.__call__
File "cupy/core/elementwise.pxi", line 68, in cupy.core.core._preprocess_args
TypeError: Unsupported type <class 'NoneType'>
When we run above code with device=-1, we have error like below.
Exception in main training loop: unsupported operand type(s) for *: 'bool' and 'NoneType'
Traceback (most recent call last):
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "AC.py", line 70, in <module>
trainer.run()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 322, in run
six.reraise(*sys.exc_info())
File "/home/****/.local/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'
python machine-learning deep-learning autoencoder chainer
asked Nov 20 '18 at 10:52
Sakurai.JJSakurai.JJ
84
1
"does not work" is hardly informative; please add the exact problem you have (see How to create a Minimal, Complete, and Verifiable example), and fix your code indentation, too
– desertnaut
Nov 20 '18 at 11:04
1
Thank you. I added the error message.
– Sakurai.JJ
Nov 20 '18 at 11:12
Thank you. I fixed my code indentation.
– Sakurai.JJ
Nov 20 '18 at 11:15
1
I have written all error messages. And when I change "device = 0" to "device = -1", other error messages are shown. "TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'"
– Sakurai.JJ
Nov 21 '18 at 0:26
1
[FYI]This question is also raised in Japanese StackOverflow(ja.stackoverflow.com/questions/50482/…).
– Yuki Hashimoto
Nov 21 '18 at 3:06
|
show 1 more comment
1
1
1
I am trying to train Autoencoder by Chainer in python and wrote below code. But it does not work. Why??
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def __call__(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
class Dataset(dataset.DatasetMixin):
def __init__(self,number_of_data, show_initial = False):
noise_level = 1
self.data = np.zeros((number_of_data,3),dtype = np.float32)
OA_vector = np.array([3,2,1])
OB_vector = np.array([2,-1,1])
t = np.random.uniform(-0.5,0.5,number_of_data)
s = np.random.uniform(-0.5,0.5,number_of_data)
for i in range(0,number_of_data):
noise = np.random.uniform(-noise_level, noise_level,3)
self.data[i] = t[i]*OA_vector + s[i]*OB_vector + noise
def __len__(self):
return self.data.shape[0]
def get_example(self,idx):
return self.data[idx]
if __name__ == "__main__":
n_epoch = 5
batch_size = 100
number_of_data = 1000 #データ数
train_data = Dataset(number_of_data,False)
model = Autoencoder()
optimizer = optimizers.SGD(lr=0.05).setup(model)
train_iter = iterators.SerialIterator(train_data,batch_size)
updater = training.StandardUpdater(train_iter,optimizer,device=0)
trainer = training.Trainer(updater,(n_epoch,"epoch"),out="result")
trainer.run()
I am using Chainer. And the Dataset makes 3 dimensitonal vectors. The number of the vectors is "number_of_data".
Should I do that without using trainer?
I don't understand where problem is.
EDIT
When we run above code with device=0, we have error like below.
Exception in main training loop: Unsupported type <class 'NoneType'>
Traceback (most recent call last):
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
File "cupy/core/core.pyx", line 1310, in cupy.core.core.ndarray.__mul__
File "cupy/core/elementwise.pxi", line 753, in cupy.core.core.ufunc.__call__
File "cupy/core/elementwise.pxi", line 68, in cupy.core.core._preprocess_args
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "AC.py", line 70, in <module>
trainer.run()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 322, in run
six.reraise(*sys.exc_info())
File "/home/****/.local/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
File "cupy/core/core.pyx", line 1310, in cupy.core.core.ndarray.__mul__
File "cupy/core/elementwise.pxi", line 753, in cupy.core.core.ufunc.__call__
File "cupy/core/elementwise.pxi", line 68, in cupy.core.core._preprocess_args
TypeError: Unsupported type <class 'NoneType'>
When we run above code with device=-1, we have error like below.
Exception in main training loop: unsupported operand type(s) for *: 'bool' and 'NoneType'
Traceback (most recent call last):
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "AC.py", line 70, in <module>
trainer.run()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 322, in run
six.reraise(*sys.exc_info())
File "/home/****/.local/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'
python machine-learning deep-learning autoencoder chainer
asked Nov 20 '18 at 10:52
Sakurai.JJSakurai.JJ
84
I am trying to train Autoencoder by Chainer in python and wrote below code. But it does not work. Why??
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def __call__(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
class Dataset(dataset.DatasetMixin):
def __init__(self,number_of_data, show_initial = False):
noise_level = 1
self.data = np.zeros((number_of_data,3),dtype = np.float32)
OA_vector = np.array([3,2,1])
OB_vector = np.array([2,-1,1])
t = np.random.uniform(-0.5,0.5,number_of_data)
s = np.random.uniform(-0.5,0.5,number_of_data)
for i in range(0,number_of_data):
noise = np.random.uniform(-noise_level, noise_level,3)
self.data[i] = t[i]*OA_vector + s[i]*OB_vector + noise
def __len__(self):
return self.data.shape[0]
def get_example(self,idx):
return self.data[idx]
if __name__ == "__main__":
n_epoch = 5
batch_size = 100
number_of_data = 1000 #データ数
train_data = Dataset(number_of_data,False)
model = Autoencoder()
optimizer = optimizers.SGD(lr=0.05).setup(model)
train_iter = iterators.SerialIterator(train_data,batch_size)
updater = training.StandardUpdater(train_iter,optimizer,device=0)
trainer = training.Trainer(updater,(n_epoch,"epoch"),out="result")
trainer.run()
I am using Chainer. And the Dataset makes 3 dimensitonal vectors. The number of the vectors is "number_of_data".
Should I do that without using trainer?
I don't understand where problem is.
EDIT
When we run above code with device=0, we have error like below.
Exception in main training loop: Unsupported type <class 'NoneType'>
Traceback (most recent call last):
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
File "cupy/core/core.pyx", line 1310, in cupy.core.core.ndarray.__mul__
File "cupy/core/elementwise.pxi", line 753, in cupy.core.core.ufunc.__call__
File "cupy/core/elementwise.pxi", line 68, in cupy.core.core._preprocess_args
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "AC.py", line 70, in <module>
trainer.run()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 322, in run
six.reraise(*sys.exc_info())
File "/home/****/.local/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
File "cupy/core/core.pyx", line 1310, in cupy.core.core.ndarray.__mul__
File "cupy/core/elementwise.pxi", line 753, in cupy.core.core.ufunc.__call__
File "cupy/core/elementwise.pxi", line 68, in cupy.core.core._preprocess_args
TypeError: Unsupported type <class 'NoneType'>
When we run above code with device=-1, we have error like below.
Exception in main training loop: unsupported operand type(s) for *: 'bool' and 'NoneType'
Traceback (most recent call last):
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "AC.py", line 70, in <module>
trainer.run()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 322, in run
six.reraise(*sys.exc_info())
File "/home/****/.local/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/trainer.py", line 308, in run
update()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 149, in update
self.update_core()
File "/home/****/.local/lib/python3.5/site-packages/chainer/training/updaters/standard_updater.py", line 164, in update_core
optimizer.update(loss_func, in_arrays)
File "/home/****/.local/lib/python3.5/site-packages/chainer/optimizer.py", line 655, in update
loss.backward(loss_scale=self._loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 966, in backward
self._backward_main(retain_grad, loss_scale)
File "/home/****/.local/lib/python3.5/site-packages/chainer/variable.py", line 1095, in _backward_main
target_input_indexes, out_grad, in_grad)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 548, in backward_accumulate
gxs = self.backward(target_input_indexes, grad_outputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 73, in backward
return ReLUGrad2(y).apply((gy,))
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 258, in apply
outputs = self.forward(in_data)
File "/home/****/.local/lib/python3.5/site-packages/chainer/function_node.py", line 368, in forward
return self.forward_cpu(inputs)
File "/home/****/.local/lib/python3.5/site-packages/chainer/functions/activation/relu.py", line 97, in forward_cpu
y = (self.b > 0) * inputs[0]
TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'
python machine-learning deep-learning autoencoder chainer
python machine-learning deep-learning autoencoder chainer
asked Nov 20 '18 at 10:52
Sakurai.JJSakurai.JJ
84
asked Nov 20 '18 at 10:52
Sakurai.JJSakurai.JJ
84
edited Nov 21 '18 at 0:30
Sakurai.JJ
asked Nov 20 '18 at 10:52
Sakurai.JJSakurai.JJ
84
asked Nov 20 '18 at 10:52
Sakurai.JJSakurai.JJ
84
asked Nov 20 '18 at 10:52
Sakurai.JJSakurai.JJ
84
84
1
"does not work" is hardly informative; please add the exact problem you have (see How to create a Minimal, Complete, and Verifiable example), and fix your code indentation, too
– desertnaut
Nov 20 '18 at 11:04
1
Thank you. I added the error message.
– Sakurai.JJ
Nov 20 '18 at 11:12
Thank you. I fixed my code indentation.
– Sakurai.JJ
Nov 20 '18 at 11:15
1
I have written all error messages. And when I change "device = 0" to "device = -1", other error messages are shown. "TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'"
– Sakurai.JJ
Nov 21 '18 at 0:26
1
[FYI]This question is also raised in Japanese StackOverflow(ja.stackoverflow.com/questions/50482/…).
– Yuki Hashimoto
Nov 21 '18 at 3:06
|
show 1 more comment
1
"does not work" is hardly informative; please add the exact problem you have (see How to create a Minimal, Complete, and Verifiable example), and fix your code indentation, too
– desertnaut
Nov 20 '18 at 11:04
1
Thank you. I added the error message.
– Sakurai.JJ
Nov 20 '18 at 11:12
Thank you. I fixed my code indentation.
– Sakurai.JJ
Nov 20 '18 at 11:15
1
I have written all error messages. And when I change "device = 0" to "device = -1", other error messages are shown. "TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'"
– Sakurai.JJ
Nov 21 '18 at 0:26
1
[FYI]This question is also raised in Japanese StackOverflow(ja.stackoverflow.com/questions/50482/…).
– Yuki Hashimoto
Nov 21 '18 at 3:06
1
1
"does not work" is hardly informative; please add the exact problem you have (see How to create a Minimal, Complete, and Verifiable example), and fix your code indentation, too
– desertnaut
Nov 20 '18 at 11:04
"does not work" is hardly informative; please add the exact problem you have (see How to create a Minimal, Complete, and Verifiable example), and fix your code indentation, too
– desertnaut
Nov 20 '18 at 11:04
1
1
Thank you. I added the error message.
– Sakurai.JJ
Nov 20 '18 at 11:12
Thank you. I added the error message.
– Sakurai.JJ
Nov 20 '18 at 11:12
Thank you. I fixed my code indentation.
– Sakurai.JJ
Nov 20 '18 at 11:15
Thank you. I fixed my code indentation.
– Sakurai.JJ
Nov 20 '18 at 11:15
1
1
I have written all error messages. And when I change "device = 0" to "device = -1", other error messages are shown. "TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'"
– Sakurai.JJ
Nov 21 '18 at 0:26
I have written all error messages. And when I change "device = 0" to "device = -1", other error messages are shown. "TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'"
– Sakurai.JJ
Nov 21 '18 at 0:26
1
1
[FYI]This question is also raised in Japanese StackOverflow(ja.stackoverflow.com/questions/50482/…).
– Yuki Hashimoto
Nov 21 '18 at 3:06
[FYI]This question is also raised in Japanese StackOverflow(ja.stackoverflow.com/questions/50482/…).
– Yuki Hashimoto
Nov 21 '18 at 3:06
|
show 1 more comment
1 Answer
1
active
oldest
votes
0
I think the model need to return loss in __call__ method.
Sample modification is as follows:
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def forward(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
def __call__(self,x):
h = self.forward(x)
# Instead of h, __call__ should return loss.
loss = F.mean_squared_error(h, x)
return loss
answered Nov 21 '18 at 4:07
corochanncorochann
1,1951618
Good! If issue is solved, could you mark as solved and upvote?
– corochann
Nov 21 '18 at 9:36
add a comment |
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I think the model need to return loss in __call__ method.
Sample modification is as follows:
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def forward(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
def __call__(self,x):
h = self.forward(x)
# Instead of h, __call__ should return loss.
loss = F.mean_squared_error(h, x)
return loss
answered Nov 21 '18 at 4:07
corochanncorochann
1,1951618
Good! If issue is solved, could you mark as solved and upvote?
– corochann
Nov 21 '18 at 9:36
add a comment |
0
I think the model need to return loss in __call__ method.
Sample modification is as follows:
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def forward(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
def __call__(self,x):
h = self.forward(x)
# Instead of h, __call__ should return loss.
loss = F.mean_squared_error(h, x)
return loss
answered Nov 21 '18 at 4:07
corochanncorochann
1,1951618
Good! If issue is solved, could you mark as solved and upvote?
– corochann
Nov 21 '18 at 9:36
add a comment |
0
0
0
I think the model need to return loss in __call__ method.
Sample modification is as follows:
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def forward(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
def __call__(self,x):
h = self.forward(x)
# Instead of h, __call__ should return loss.
loss = F.mean_squared_error(h, x)
return loss
answered Nov 21 '18 at 4:07
corochanncorochann
1,1951618
I think the model need to return loss in __call__ method.
Sample modification is as follows:
class Autoencoder(Chain):
def __init__(self):
super().__init__()
with self.init_scope():
self.l1 = L.Linear(3,2)
self.l2 = L.Linear(2,3)
def forward(self,x):
h1 = self.l1(x)
h2 = self.l2(h1)
return h2
def __call__(self,x):
h = self.forward(x)
# Instead of h, __call__ should return loss.
loss = F.mean_squared_error(h, x)
return loss
answered Nov 21 '18 at 4:07
corochanncorochann
1,1951618
answered Nov 21 '18 at 4:07
corochanncorochann
1,1951618
answered Nov 21 '18 at 4:07
corochanncorochann
1,1951618
answered Nov 21 '18 at 4:07
corochanncorochann
1,1951618
1,1951618
Good! If issue is solved, could you mark as solved and upvote?
– corochann
Nov 21 '18 at 9:36
add a comment |
Good! If issue is solved, could you mark as solved and upvote?
– corochann
Nov 21 '18 at 9:36
Good! If issue is solved, could you mark as solved and upvote?
– corochann
Nov 21 '18 at 9:36
Good! If issue is solved, could you mark as solved and upvote?
– corochann
Nov 21 '18 at 9:36
add a comment |
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1
"does not work" is hardly informative; please add the exact problem you have (see How to create a Minimal, Complete, and Verifiable example), and fix your code indentation, too
– desertnaut
Nov 20 '18 at 11:04
1
Thank you. I added the error message.
– Sakurai.JJ
Nov 20 '18 at 11:12
Thank you. I fixed my code indentation.
– Sakurai.JJ
Nov 20 '18 at 11:15
1
I have written all error messages. And when I change "device = 0" to "device = -1", other error messages are shown. "TypeError: unsupported operand type(s) for *: 'bool' and 'NoneType'"
– Sakurai.JJ
Nov 21 '18 at 0:26
1
[FYI]This question is also raised in Japanese StackOverflow(ja.stackoverflow.com/questions/50482/…).
– Yuki Hashimoto
Nov 21 '18 at 3:06