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Batch Normalization (BatchNorm) is a technique used to improve the training of deep neural networks by normalizing the input of each layer. It helps in mitigating issues related to internal covariate shift and accelerates the training process. In this tutorial, we will explore how to use Batch Normalization in PyTorch with a practical code example.
Make sure you have PyTorch installed. You can install it using:
Batch Normalization normalizes the input of a layer by scaling and shifting it. It operates on a mini-batch of data during training and calculates the mean and standard deviation for normalization. The normalization is applied using the following formula:
BN(x)=γ(
σ
x−μ
)+β
where:
Let's create a simple neural network with Batch Normalization using PyTorch. We'll use the MNIST dataset for demonstration.
In this example, we added Batch Normalization (nn.BatchNorm1d) after the first fully connected layer (fc1). Feel free to experiment with the network architecture and the placement of Batch Normalization layers to observe different effects on training.
Batch Normalization is a powerful technique to stabilize and accelerate the training of deep neural networks. In this tutorial, we implemented a simple neural network with Batch Normalization using PyTorch. Experiment with Batch Normalization in your own models to observe its impact on training performance and convergence.
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