nn module

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.