net is the instance of your model. In your case you should call model.apply(weight_init) 
thanks, Sorry to ask silly questions:)
Haha, please don’t be sorry for asking questions and feel free to ask again, in case you get stuck somewhere.
Excuse me 
I am trying my trained networks on the test set which is patch of (11117)=847 , the model is defined as: model = ConvNetRedo1(32,64,(7,7,5),(5,5,3),500,100)
the input is the test patch with size of 847, why it is expected to have 32 1 7 7 5?!!
*** RuntimeError: Expected 5-dimensional input for 5-dimensional weight 32 1 7 7 5, but got 2-dimensional input of size [847, 1] instead
(Pdb) model
ConvNetRedo1(
(layer1): Sequential(
(0): Conv3d(1, 32, kernel_size=(7, 7, 5), stride=(1, 1, 1), padding=(2, 2, 2))
(1): ReLU()
(2): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(layer2): Sequential(
(0): Conv3d(32, 64, kernel_size=(5, 5, 3), stride=(1, 1, 1), padding=(2, 2, 2))
(1): ReLU()
)
(fc1): Linear(in_features=5120, out_features=500, bias=True)
(drop_out1): Dropout(p=0.5, inplace=False)
(relu1): ReLU()
(fc2): Linear(in_features=500, out_features=100, bias=True)
(drop_out2): Dropout(p=0.5, inplace=False)
(relu2): ReLU()
(fc3): Linear(in_features=100, out_features=1, bias=True)
)
The first nn.Conv3d layer expects a 5-dimensional input with the shapes [batch_size, channels, depth, height, width], while you are apparently passing 1-dimensional input.
Many thanks for your answer.Yes because I want to test . I load my saved networks were CNN and now want to test on my patch which is 11117= 847 . ,the input should be 847 for testing . Am I wrong?
nn.Conv3d will not work with an input tensor of [11, 11, 7], as it’s expecting a 5-dimensional input.
I’m not sure which dimension represents what in your tensor, but you might want to add a channel and batch dimension to the tensor.
Once the dimensions are correct, you would have to make sure the input of the linear layer match the number of flattened activations.
I did data2=data1.reshape(1,11,11,7) which give me [1,1,11,11,7] for each patch to test. And it is working
sorry, how the pytorch initialize the weight basically, if it is not normal?
sorry, it is outcome
of my training for different iterations for 60 epoch in one fold. I think overfitting happen ,Do you agree?
Hi Ptrblck
Sorry to take your time. I have a question if I want to find points location in 3 dimension from a MRI images , what is the command?
In Matlab I used (ind2sub) which used for 3 dimension.
np.unravel_index might be the numpy equivalent of the MATLAB function.
Side note: I’m not sure, if you are working with MATLAB arrays (and I’m not sure if you would run into this issue at all), but note that MATLAB uses column-major format, if I’m not mistaken. So you should test your indexing with some dummy data.
Sorry, I want to add label to my tensor board scalars in x and y axis (the x axis is epoch and y axis is Trainingloss). I start use tensor board with:
tb = SummaryWriter(root_dir3+’/Fold_’+str(FoldNum)+‘NumDarw=’+str(NumDraw)+str(Iteration))
and then I save the scalars by tb.add_scalar(‘Trainingloss’,LossScalar/Iterationnum,epoch+1)
Would you please tell me where I can add the label for x axis and y axis?
I’m not sure how to add labels to axes, but since your question is unrelated to the original topic, I would suggest to create a new thread so that more experienced users might see it.