hi i am learningpytroch. I’m trying to use tensorboard but I don’t know where I went wrong. Frankly, I do not know the accuracy of my codes, I learn by trying. sorry for my english. thank you
TypeError: forward() takes 2 positional arguments but 17 were given
Can you send the part of your code where this error shows up?
I’m new to pytorch and I’m learning to use tensorboards. I’m not sure if my code is correct
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1=nn.Conv2d(in_channels=1,out_channels=16,kernel_size=(3,3),stride=1,padding=0)
self.activation1=nn.ReLU()
self.max1=nn.MaxPool2d(kernel_size=2)
self.conv2=nn.Conv2d(in_channels=16,out_channels=32,kernel_size=(2,2),stride=1,padding=0)
self.activation2=nn.ReLU()
self.max2=nn.MaxPool2d(kernel_size=2)
self.conv3=nn.Conv2d(in_channels=32,out_channels=64,kernel_size=(3,3),stride=1,padding=0)
self.activation3=nn.ReLU()
self.fc1=nn.Linear(in_features=64*4*4, out_features=50, bias=True)
self.fc_ac1=nn.ReLU()
self.fc2=nn.Linear(in_features=50, out_features=50, bias=True)
self.fc_ac2=nn.ReLU()
self.fc3=nn.Linear(in_features=50, out_features=10, bias=True)
def forward(self, x):
x=self.conv1(x)
x=self.activation1(x)
x=self.max1(x)
x=self.conv2(x)
x=self.activation2(x)
x=self.max2(x)
x=self.conv3(x)
x=self.activation3(x)
x = x.view(-1, 64 * 4 * 4)
x=self.fc1(x)
x=self.fc_ac1(x)
x=self.fc2(x)
x=self.fc_ac2(x)
x=self.fc3(x)
return x
parameters=dict(lr=[0.1,0.01,0.001],
batch_size=[16,32,64,128,256],
shuffle=[True,False])
params=[v for v in parameters.values()]
for lr,batch_size,shuffle in product(*params):
comment = f' batch_size={batch_size} lr={lr} shuffle={shuffle}'
train_loader=torch.utils.data.DataLoader(train,batch_size=batch_size,shuffle=shuffle,drop_last=False)
test_loader=torch.utils.data.DataLoader(test,batch_size=batch_size,shuffle=shuffle,drop_last=False)
net=Net().to(device)
images,labels=next(iter(train_loader))
grid=torchvision.utils.make_grid(images)
tensorboard=SummaryWriter(comment=comment)
tensorboard.add_image("images",grid)
tensorboard.add_graph(net, images)
optimizer=torch.optim.Adam(net.parameters(),lr=lr)
error=nn.CrossEntropyLoss()
for epoch in range(5):
total_loss=0
total_correct=0
total_index=0
for i,(data,target) in enumerate(train_loader):
data=Variable(data.view(-1, 64 * 4 * 4)).to(device)
target=Variable(target).to(device)
out=net(data.float())
loss=error(out,target)
optimizer.zero_grad()
loss.backwars()
optimizer.step()
total_loss+=loss.item()*batch_size
y_pred=torch.max(out.data,1)[1]
total_correct+=(y_pred==target).sum()
total_index+=len(target)
accuracy = 100 * total_correct / float(total_index)
tensorboard.add_scalar("Loss",total_loss,epoch)
tensorboard.add_scalar("Loss",total_correct,epoch)
tensorboard.add_histogram("conv1.bias",net.conv1.bias,epoch)
tensorboard.add_histogram("conv2.bias",net.conv2.bias,epoch)
tensorboard.add_histogram("conv3.bias",net.conv3.bias,epoch)
tensorboard.add_histogram("conv1.weight",net.conv1.weight,epoch)
tensorboard.add_histogram("conv2.weight",net.conv2.weight,epoch)
tensorboard.add_histogram("conv3.weight",net.conv3.weight,epoch)
tensorboard.add_histogram("conv1.weight.grad",net.conv1.weight.grad,epoch)
tensorboard.add_histogram("conv2.weight.grad",net.conv2.weight.grad,epoch)
tensorboard.add_histogram("conv3.weight.grad",net.conv3.weight.grad,epoch)
print(
"epoch", epoch
,"total_correct:", total_correct
,"loss:", total_loss
)
tensorboard.close()
Error occurs, No graph saved
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\utils\tensorboard\_pytorch_graph.py in graph(model, args, verbose, operator_export_type, omit_useless_nodes)
275 try:
--> 276 trace, _ = torch.jit.get_trace_graph(model, args)
277 except RuntimeError:
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\jit\__init__.py in get_trace_graph(f, args, kwargs, _force_outplace, return_inputs)
230 args = (args,)
--> 231 return LegacyTracedModule(f, _force_outplace, return_inputs)(*args, **kwargs)
232
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\jit\__init__.py in forward(self, *args)
293 trace_inputs = _unflatten(all_trace_inputs[:len(in_vars)], in_desc)
--> 294 out = self.inner(*trace_inputs)
295 out_vars, _ = _flatten(out)
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
490 if torch._C._get_tracing_state():
--> 491 result = self._slow_forward(*input, **kwargs)
492 else:
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\nn\modules\module.py in _slow_forward(self, *input, **kwargs)
480 try:
--> 481 result = self.forward(*input, **kwargs)
482 finally:
<ipython-input-8-b62cec869e0e> in forward(self, x)
24 def forward(self, x):
---> 25 x=self.conv1(x)
26 x=self.activation1(x)
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
490 if torch._C._get_tracing_state():
--> 491 result = self._slow_forward(*input, **kwargs)
492 else:
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\nn\modules\module.py in _slow_forward(self, *input, **kwargs)
480 try:
--> 481 result = self.forward(*input, **kwargs)
482 finally:
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
337 return F.conv2d(input, self.weight, self.bias, self.stride,
--> 338 self.padding, self.dilation, self.groups)
339
RuntimeError: Expected 4-dimensional input for 4-dimensional weight 16 1, but got 2-dimensional input of size [16, 784] instead
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-9-307b6a1597c9> in <module>
18 tensorboard=SummaryWriter(comment=comment)
19 tensorboard.add_image("images",grid)
---> 20 tensorboard.add_graph(net, images)
21
22 optimizer=torch.optim.Adam(net.parameters(),lr=lr)
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\utils\tensorboard\writer.py in add_graph(self, model, input_to_model, verbose, **kwargs)
532 print('add_graph() only supports PyTorch v0.2.')
533 return
--> 534 self._get_file_writer().add_graph(graph(model, input_to_model, verbose, **kwargs))
535 else:
536 # Caffe2 models do not have the 'forward' method
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\utils\tensorboard\_pytorch_graph.py in graph(model, args, verbose, operator_export_type, omit_useless_nodes)
277 except RuntimeError:
278 print('Error occurs, No graph saved')
--> 279 _ = model(*args) # don't catch, just print the error message
280 print("Checking if it's onnx problem...")
281 try:
C:\Anaconda3\envs\Genel_ortam\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
TypeError: forward() takes 2 positional arguments but 17 were given
Can you print out the shape of images that you pass into the tensorboard add graph function. Ex:
images,labels=next(iter(train_loader))
print(images.shape)
grid=torchvision.utils.make_grid(images)
tensorboard=SummaryWriter(comment=comment)
tensorboard.add_image("images",grid)
tensorboard.add_graph(net, images)
what is the shape?
Thanks for be interested
images.shape
–>torch.Size([16, 784])
labels.shape
→ torch.Size([16])
I think you are handing data in the wrong shape to your first layer. Conv2D expects an input of dimension N x C x X x Y = 4 (with batch dimension N, C probably equal to 1 and then X and Y dimensions). If I am not mistaken, you use data.view to make your data 2-dimensional (instead of 4). This causes the original RuntimeError in Conv2d.
Assuming you work with the MNIST dataset (I am guessing because of the 784), your view command should probably be (with X, Y = 28):
data=Variable(data.view(-1, 1, X, Y)).to(device)
The TypeError in forward() during the execution of add_graph is probably caused by the images input. You get images, labels from your train_loader, which returns a tensor of shape N x [whatever your data shape is]. tensorboard.add_graph expects either a Tensor or a List of Tensors, according to the documentation.
You can find example code from the devs on how to give add_graph multiple inputs here.
P.S.: The Variable(something) command is deprecated. It’s not causing your error, but I think we’re not meant to use it anymore.
hi FreddyJ . Thank you for your support. I did what they said and it worked. you saved me from a big problem. I appreciate you