Training Function#
- fedgraph.train_func.accuracy(output: Tensor, labels: Tensor) Tensor[source]#
This function returns the accuracy of the output with respect to the ground truth given
- Parameters:
output (torch.Tensor) – the output labels predicted by the model
labels (torch.Tensor) – ground truth labels
- Returns:
(tensor) – Accuracy of the output with respect to the ground truth given
- Return type:
- fedgraph.train_func.test(model: Module, features: Tensor, adj: Tensor, test_labels: Tensor, idx_test: Tensor) tuple[source]#
This function tests the model and calculates the loss and accuracy
- Parameters:
model (torch.nn.Module) – Specific model passed
features (torch.Tensor) – Tensor representing the input features
adj (torch.Tensor) – Adjacency matrix
labels (torch.Tensor) – Contains the ground truth labels for the data.
idx_test (torch.Tensor) – Indices specifying the test data points
- Returns:
loss_test.item() (float) – Loss of the model on the test data
acc_test.item() (float) – Accuracy of the model on the test data
- fedgraph.train_func.train(epoch: int, model: Module, optimizer: Optimizer, features: Tensor, adj: Tensor, train_labels: Tensor, idx_train: Tensor) tuple[source]#
Trains the model and calculates the loss and accuracy of the model on the training data, performs backpropagation, and updates the model parameters.
- Parameters:
epoch (int) – Specifies the number of epoch on which the model is trained
model (torch.nn.Module) – Specific model to be trained
optimizer (optimizer) – Type of the optimizer used for updating the model parameters
features (torch.FloatTensor) – Tensor representing the input features
adj (torch_sparse.tensor.SparseTensor) – Adjacency matrix
train_labels (torch.LongTensor) – Contains the ground truth labels for the data.
idx_train (torch.LongTensor) – Indices specifying the test data points
- Returns:
loss_train.item() (float) – Loss of the model on the training data
acc_train.item() (float) – Accuracy of the model on the training data