There is a scalar function F with 1000 inputs. I want to train a model to predict F given the inputs. However, in the training dataset, we only know the derivative of F with respect to each input, not the value of F itself. How I can construct a neural network with this limitation in ..
If there is a project structure like: Project ├── folder1 │ └── F1_file1 │ └── . . . ├── folder2 │ └── datasets │ │ └── file1 │ │ └── file2 │ │ └── __init__.py │ └── __init__.py │ └── . . . F1_file1 contains main function and there is an import like this: from ..
I am trying to use pytorch in a case for nlp binary classification and i need help to finish the neural network training and validate. I am newbie and this is my first time using pytorch, see the code below and the error… X_train_tensor = torch.from_numpy(np.asarray(X_train)).type(torch.FloatTensor).to(device) y_train_tensor = torch.from_numpy(np.asarray(y_train)).type(torch.FloatTensor).unsqueeze(1).to(device) X_valid_tensor = torch.from_numpy(np.asarray(X_valid)).type(torch.FloatTensor).to(device) y_valid_tensor = torch.from_numpy(np.asarray(y_valid)).type(torch.FloatTensor).unsqueeze(1).to(device) ..
I’m trying to program a GNN using pytorch. I currently use scatter_add and scatter_mean to summerize the edge input for the node update, and then everything works fine. However if I also want to use scatter_std, things go wrong: when the model updates it’s parameters after the first batch, all weights are set to ‘nan’. ..
enter image description here i i’m confused about how this part works: enter image description here Source: Python..
I was tasked with creating a CI workflow for building a PyTorch CUDA extension for this application. Up until now, the application was deployed by creating the target AWS VM with a CUDA GPU, pushing all the sources there and running setup.py, but instead I want to do the build in our CI system and ..
I’m working on a GNN project associate with molecule classification. The project is to classify if the atom in the molecule will initiate a certain reaction. e.g. A molecule can be represented as [0,1,2,3,4], where the number indicates the index of the atoms, if the reaction happens on index 0 and 3, the y_label will ..
I am trying to train a neural network which takes as input an initial hidden state (call it s_t0) at time 0 and returns an transformation of s_t0 as s_t1. The goal of optimization is to ensure the distance between s_t0 and s_t1 is small, as s_t1 is supposed to be an updated version of ..
I am new to Pytorch, and I obtained a pertained model only (without model definition in python), and I can load it using command: mnet=torch.hub.load(…) which was successful, and I can pass input data to mnet. Now I hope to use the feature from the 3rd last layer as output, so I am doing this: ..
I have been trying to load the PyGad trained instance in another file, in order to make some prediction. But I have been having some problems in the loading process. After the training phase, I saved the instance like this: The saving function: filename = ‘GNN_CPTNet’ #GNN_CPTNet.pkl ga_instance.save(filename=filename) The loading function: loaded_ga_instance = pygad.load(filename=filename) loaded_ga_instance.plot_result() ..