# Torch view vs reshape

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Syntax — torch.matmul(input, other, out=None) → Tensor.matmul() is a matrix multiplication function for two tensors. The behavior depends on the dimensionality of the tensors: If any one of the values in matrix multiplication are scalar then dot product is calculated.

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torch.nn.Linear(in_features, out_features) - fully connected layer (multiply inputs by learned weights) Writing CNN code in PyTorch can get a little complex, since everything is defined inside of one class. We'll create a SimpleCNN class, which inherits from the master torch.nn.Module class.

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Does it behave the same as numpy reshape -1? Yes, it does behave like -1 in numpy.reshape (), i.e. the actual value for this dimension will be inferred so that the number of elements in the view matches the original number of elements. For instance: import torch x = torch.arange (6) print (x.view (3, -1)) # inferred size will be 2 as 6 / 3 = 2 ...

Before you start the training process, you need to convert the numpy array to Variables that supported by Torch and autograd as shown in the below PyTorch regression example. # convert numpy array to tensor in shape of input size x = torch.from_numpy(x.reshape(-1,1)).float() y = torch.from_numpy(y.reshape(-1,1)).float() print(x, y)[Download notes as jupyter notebook](adversarial_training.tar.gz) ## From adversarial examples to training robust models In the previous chapter, we focused on methods for solving the inner maximization problem over perturbations; that is, to finding the solution to the problem  \DeclareMathOperator*{\maximize}{maximize} \maximize_{\|\delta\| \leq \epsilon} \ell(h_\theta(x + \delta), y ...