PyTorch

利用PyTorch进行深度学习编程实践 #生活技巧# #学习技巧# #深度学习技巧#

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The torch.nn.relu module in PyTorch implements the Rectified Linear Unit activation function. It is a commonly used activation function in neural networks and is defined as: f(x) = max(0, x) Where x is the input to the function and f(x) is the output. The relu function applies a simple threshold to the input, setting all negative values to zero and leaving positive values unchanged. This leads to a sparse representation of the inputs, which can help to prevent overfitting and improve the generalization of the model. In PyTorch, the relu function is implemented as a module, which can be easily added to a neural network using the following code: ``` import torch.nn as nn model = nn.Sequential( nn.Linear(10, 20), nn.ReLU(), nn.Linear(20, 1) ) ``` In this example, a two-layer neural network is defined with a 10-dimensional input, a hidden layer with 20 units, and a single output unit. The ReLU activation function is applied after the first linear layer to introduce non-linearity into the model.

网址：PyTorch https://www.yuejiaxmz.com/news/view/179666

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