WebDec 17, 2024 · torch.nn.moduel class implement __call__ function, it will call _call_impl(), … WebModule): def forward (self, X): return torch. matrix_exp (X) layer_orthogonal = nn. Linear ... Module): def forward (self, X): A = X. triu (1) return A-A. transpose (-1,-2) def right_inverse (self, A): # We assume that A is skew-symmetric # We take the upper-triangular elements, as these are those used in the forward return A. triu (1)

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WebDec 17, 2024 · torch.nn.moduel class implement __call__ function, it will call _call_impl(), if we do not create a forward hook, self.forward() function will be called. __call__ can make a torch.nn.module instance be callable, you can find this answer in here. Python Make a Class Instance Callable Like a Function – Python Tutorial. As to this code: WebSep 6, 2024 · PyTorch module__call__ () vs forward () In Python, there is this built-in function __call__ () for a class you can override, this make your object instance callable. In PyTorch, the nn.module is implemented so that one can treat the module as callable like above, e.g. So what’s the different? WebNov 1, 2024 · This line is the cause of your error: images = self.data.iloc [idx, 1:-1].values.astype (np.uint8).reshape ( (1, 16, 16)) images are uint8 ( byte) while the neural network needs inputs as floating point in order to calculate gradients (you can't calculate gradients for backprop using integers as those are not continuous and non-differentiable ... bothy code