I want to clip my tensor (not gradient) values to some range. Is there any function in pytorch like there is a function theano.tensor.clip() in theano?
The function you are searching for is called torch.clamp. You can find the documentation here
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I have a bit lengthier ODE function which was simulated by using Scipy solve_ivp function. During this simulation I calculated many parameters but as the output, I am taking out put only some other parameters in return function.
But now I need to get some values of some other variables I used during the intermediate steps. So is there a way I can do that?.
Note-: When I Tried to return those parameters with the main ode outputs, it says "The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part."
Highly appreciate any valuable suggestions. Better if you can show the way by using a sample code.
Is there a built-in PyTorch method that takes a 1D tensor, and returns the element in the tensor with the highest count?
For example, if we input torch.tensor([2,2,2,3,4,5]), the method should return 2 as it occurs the most. In case of a tie in frequency, the element with the lower value should be returned; inputting torch.tensor([1,1,2,2,4,5]) should return 1.
Just to be clear, I only wish to know if there's an existing built-in PyTorch method that does exactly this. If there's no such method, please refrain from posting the solution, as I'd like to try solving it on my own.
yes torch.mode() is builtin function(read here) which handles both of your conditions.
torch.mode(alpha,0) #alpha being the name of tensor
My python function is returning tensor([102],cuda::0) like structure. How do I obtain the first value from this?
Found this on pytorch discussion forums:
a.data.cpu().numpy()[0]
where a is the tensor.
I am minimizing a function with scipy.optimize.fmin_cg. In "args" one can supply additional fixed parameters that are needed to completely specify the functions and it's derivative. I want to use a sparse representation, scipy.sparse.csr_matrix, of some arguments. I have the same function coded both with the sparse matrix as well as a dense ndarray. I have noticed differences in the solution.
Does anyone know whether scipy.optimize.fmin_cg accepts sparse matrices in "args"?
Thanks!
I am building neural networks in Pytorch, I see view and view_as used interchangeably in various implementation what is the difference between them?
view and view_as are very similar with a slight difference. In view() the shape of the desired output tensor is to be passed in as the parameter, whereas in view_as() a tensor whose shape is to be mimicked is passed.
tensor.view_as(other) is equivalent to tensor.view(other.size())