Скачать или смотреть pytorch nn sequential example

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PyTorch is a popular deep learning library that provides a flexible and dynamic computational graph. The torch.nn.Sequential module is a convenient way to construct neural networks by sequentially stacking layers. In this tutorial, we'll explore how to use nn.Sequential with a simple example.
Before you begin, make sure you have PyTorch installed. You can install it using pip:
Let's create a simple neural network using nn.Sequential. For this example, we'll create a network with three layers: an input layer with 5 neurons, a hidden layer with 10 neurons, and an output layer with 1 neuron.
In this example:
You can print the summary of the model to see the architecture and learnable parameters:
To perform a forward pass with the model, simply pass input data through it:
You can customize your model by adding more layers or modifying existing ones. For example, let's add a dropout layer after the first linear layer:
To train the model, you need to define a loss function and an optimizer. Additionally, you'll need a dataset and a training loop. This goes beyond the scope of this tutorial, but be sure to explore PyTorch's documentation for a complete understanding.
This tutorial provides a basic introduction to creating a neural network using nn.Sequential in PyTorch. Feel free to experiment with different architectures and datasets to further enhance your understanding of deep learning with PyTorch.
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