nn module · Hello CUDA

nn module provides a high-level interface for building neural networks. It encapsulates layers, loss functions, and optimizers, making it easier to construct and train models.

Neural network components

Layers: Define the computational operations performed on input data. Loss functions: Measure the difference between predicted and actual values, guiding the optimization process. Containers: Organize layers and other components into a cohesive model structure. Initializers: Set initial values for model parameters, crucial for effective training.

nn.Sequential

nn.Sequential is a container that allows you to build a neural network by stacking layers in a sequential manner. It simplifies the model definition process by automatically managing the flow of data through the layers.

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(input_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, output_size),
    nn.Softmax(dim=1)
)

Linear Layer Also known as a fully connected layer, it applies a linear transformation to the input data.
Convolutional Layer Applies convolution operations, commonly used in image processing tasks to extract features.
Pooling Layer Reduces the spatial dimensions of the input, retaining essential features while reducing computational load.
Recurrent Layer Processes sequential data, maintaining a hidden state to capture temporal dependencies.
Dropout Layer Randomly drops out a fraction of neurons during training, preventing overfitting.