AtrributeError MomentSGD optimizer has no attribute prepare
0
Recently, I run the code released by other authors. They utilized chainer v1.3, but I installed v4. When I run the code, it errors that Attribute Errors: MomentSGD optimizer has no attribute prepare. Here I post the codes of this part:
class BaseModel(chainer.Chain):
loss = None
accuracy = None
gpu_mode = False
_train = False
def __call__(self, *arg_list, **arg_dict):
raise NotImplementedError()
def clear(self):
self.loss = None
self.accuracy = None
def train(self, data, optimizer):
self._train = True
optimizer.update(self, data)
if self.accuracy is None:
return float(self.loss.data)
else:
return float(self.loss.data), float(self.accuracy.data)
def validate(self, data):
self._train = False
self(data)
if self.accuracy is None:
return float(self.loss.data)
else:
return float(self.loss.data), float(self.accuracy.data)
def test(self, data):
self._train = False
raise NotImplementedError()
def save(self, fname):
serializers.save_hdf5(fname, self)
def load(self, fname):
serializers.load_hdf5(fname, self)
def cache(self):
self.to_cpu()
cached_model = self.copy()
self.to_gpu()
return cached_model
# this part is the error part
def setup(self, optimizer):
self.to_gpu()
optimizer.target = self
optimizer.prepare()
def to_cpu(self):
if not self.gpu_mode:
return
super(BaseModel, self).to_cpu()
self.gpu_mode = False
def to_gpu(self):
if self.gpu_mode:
return
super(BaseModel, self).to_gpu()
self.gpu_mode = True
chainer
asked Nov 21 '18 at 3:06
Meng LiuMeng Liu
1
add a comment |
0
Recently, I run the code released by other authors. They utilized chainer v1.3, but I installed v4. When I run the code, it errors that Attribute Errors: MomentSGD optimizer has no attribute prepare. Here I post the codes of this part:
class BaseModel(chainer.Chain):
loss = None
accuracy = None
gpu_mode = False
_train = False
def __call__(self, *arg_list, **arg_dict):
raise NotImplementedError()
def clear(self):
self.loss = None
self.accuracy = None
def train(self, data, optimizer):
self._train = True
optimizer.update(self, data)
if self.accuracy is None:
return float(self.loss.data)
else:
return float(self.loss.data), float(self.accuracy.data)
def validate(self, data):
self._train = False
self(data)
if self.accuracy is None:
return float(self.loss.data)
else:
return float(self.loss.data), float(self.accuracy.data)
def test(self, data):
self._train = False
raise NotImplementedError()
def save(self, fname):
serializers.save_hdf5(fname, self)
def load(self, fname):
serializers.load_hdf5(fname, self)
def cache(self):
self.to_cpu()
cached_model = self.copy()
self.to_gpu()
return cached_model
# this part is the error part
def setup(self, optimizer):
self.to_gpu()
optimizer.target = self
optimizer.prepare()
def to_cpu(self):
if not self.gpu_mode:
return
super(BaseModel, self).to_cpu()
self.gpu_mode = False
def to_gpu(self):
if self.gpu_mode:
return
super(BaseModel, self).to_gpu()
self.gpu_mode = True
chainer
asked Nov 21 '18 at 3:06
Meng LiuMeng Liu
1
add a comment |
0
0
0
Recently, I run the code released by other authors. They utilized chainer v1.3, but I installed v4. When I run the code, it errors that Attribute Errors: MomentSGD optimizer has no attribute prepare. Here I post the codes of this part:
class BaseModel(chainer.Chain):
loss = None
accuracy = None
gpu_mode = False
_train = False
def __call__(self, *arg_list, **arg_dict):
raise NotImplementedError()
def clear(self):
self.loss = None
self.accuracy = None
def train(self, data, optimizer):
self._train = True
optimizer.update(self, data)
if self.accuracy is None:
return float(self.loss.data)
else:
return float(self.loss.data), float(self.accuracy.data)
def validate(self, data):
self._train = False
self(data)
if self.accuracy is None:
return float(self.loss.data)
else:
return float(self.loss.data), float(self.accuracy.data)
def test(self, data):
self._train = False
raise NotImplementedError()
def save(self, fname):
serializers.save_hdf5(fname, self)
def load(self, fname):
serializers.load_hdf5(fname, self)
def cache(self):
self.to_cpu()
cached_model = self.copy()
self.to_gpu()
return cached_model
# this part is the error part
def setup(self, optimizer):
self.to_gpu()
optimizer.target = self
optimizer.prepare()
def to_cpu(self):
if not self.gpu_mode:
return
super(BaseModel, self).to_cpu()
self.gpu_mode = False
def to_gpu(self):
if self.gpu_mode:
return
super(BaseModel, self).to_gpu()
self.gpu_mode = True
chainer
asked Nov 21 '18 at 3:06
Meng LiuMeng Liu
1
Recently, I run the code released by other authors. They utilized chainer v1.3, but I installed v4. When I run the code, it errors that Attribute Errors: MomentSGD optimizer has no attribute prepare. Here I post the codes of this part:
class BaseModel(chainer.Chain):
loss = None
accuracy = None
gpu_mode = False
_train = False
def __call__(self, *arg_list, **arg_dict):
raise NotImplementedError()
def clear(self):
self.loss = None
self.accuracy = None
def train(self, data, optimizer):
self._train = True
optimizer.update(self, data)
if self.accuracy is None:
return float(self.loss.data)
else:
return float(self.loss.data), float(self.accuracy.data)
def validate(self, data):
self._train = False
self(data)
if self.accuracy is None:
return float(self.loss.data)
else:
return float(self.loss.data), float(self.accuracy.data)
def test(self, data):
self._train = False
raise NotImplementedError()
def save(self, fname):
serializers.save_hdf5(fname, self)
def load(self, fname):
serializers.load_hdf5(fname, self)
def cache(self):
self.to_cpu()
cached_model = self.copy()
self.to_gpu()
return cached_model
# this part is the error part
def setup(self, optimizer):
self.to_gpu()
optimizer.target = self
optimizer.prepare()
def to_cpu(self):
if not self.gpu_mode:
return
super(BaseModel, self).to_cpu()
self.gpu_mode = False
def to_gpu(self):
if self.gpu_mode:
return
super(BaseModel, self).to_gpu()
self.gpu_mode = True
chainer
chainer
asked Nov 21 '18 at 3:06
Meng LiuMeng Liu
1
asked Nov 21 '18 at 3:06
Meng LiuMeng Liu
1
asked Nov 21 '18 at 3:06
Meng LiuMeng Liu
1
asked Nov 21 '18 at 3:06
Meng LiuMeng Liu
1
asked Nov 21 '18 at 3:06
Meng LiuMeng Liu
1
1
add a comment |
add a comment |
1 Answer
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Newer version of chainer uses setup method to initialize optimizer.
Can you try modifing your code as follows?
def setup(self, optimizer):
self.to_gpu()
optimizer.setup(self)
answered Nov 21 '18 at 4:09
corochanncorochann
1,1951618
add a comment |
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1 Answer
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1 Answer
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active
oldest
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oldest
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active
oldest
votes
0
Newer version of chainer uses setup method to initialize optimizer.
Can you try modifing your code as follows?
def setup(self, optimizer):
self.to_gpu()
optimizer.setup(self)
answered Nov 21 '18 at 4:09
corochanncorochann
1,1951618
add a comment |
0
Newer version of chainer uses setup method to initialize optimizer.
Can you try modifing your code as follows?
def setup(self, optimizer):
self.to_gpu()
optimizer.setup(self)
answered Nov 21 '18 at 4:09
corochanncorochann
1,1951618
add a comment |
0
0
0
Newer version of chainer uses setup method to initialize optimizer.
Can you try modifing your code as follows?
def setup(self, optimizer):
self.to_gpu()
optimizer.setup(self)
answered Nov 21 '18 at 4:09
corochanncorochann
1,1951618
Newer version of chainer uses setup method to initialize optimizer.
Can you try modifing your code as follows?
def setup(self, optimizer):
self.to_gpu()
optimizer.setup(self)
answered Nov 21 '18 at 4:09
corochanncorochann
1,1951618
answered Nov 21 '18 at 4:09
corochanncorochann
1,1951618
answered Nov 21 '18 at 4:09
corochanncorochann
1,1951618
answered Nov 21 '18 at 4:09
corochanncorochann
1,1951618
1,1951618
add a comment |
add a comment |
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