The Linear() class defines a fully connected network layer. Because of this, some neural networks will name the layers as "fc1," "fc2," and so on. You can loosely think of each of the three layers as ...

In principle it's possible to create a neural network classifier for MNIST data using just a single linear layer that accepts 784 input values and emits 10 logits or pseudo-probabilities. But this ...

Common Layers: Use predefined layers in torch.nn, such as nn.Linear for fully connected layers, nn.Conv2d for convolutional layers, and nn.ReLU for activation functions. Training Neural Networks ...

A new study proposes NdLinear, a multi-dimensional linear layer that preserves data structure and slashes parameter counts across neural networks — from CNNs to Transformers. It outperforms ...

As neural networks scale to dozens of layers and billions of parameters, Facebook offers greater parallelism for models with PyTorch 1.1. Facebook’s PyTorch 1.1 does the heavy lifting for ...