transforms.Normalize()

transforms.Normalize() 是 PyTorch 中的一个预处理函数，用于对数据进行标准化处理。它对数据进行按通道的标准化，即将每个通道的数据减去均值，然后除以标准差。这个函数通常用于神经网络训练的数据预处理中，目的是使数据更容易收敛和训练。具体而言，它输入的参数是均值和标准差，这些参数需要在训练数据集上计算得出，然后将这些参数应用到测试数据集上。

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transforms.Normalize

`transforms.Normalize` is a data preprocessing step in PyTorch that normalizes the input data. It takes in a mean and standard deviation value and applies a normalization formula to the input data.

The normalization formula is:

input[channel] = (input[channel] - mean[channel]) / std[channel]

where `channel` represents the color channel (e.g. red, green, blue) of the input data.

The purpose of normalization is to bring the input data to a common scale, making it easier for the model to learn from the data. It reduces the impact of the scale of the input data on the performance of the model.

`transforms.Normalize` is commonly used in computer vision tasks, such as image classification, object detection, and segmentation.

transforms.normalize

`transforms.normalize()` is a method in the PyTorch library's `transforms` module that is used to normalize a tensor image with mean and standard deviation.

It takes two arguments: mean and standard deviation. These values are usually pre-calculated for the dataset that the tensor image belongs to. The method then normalizes each channel of the tensor image by subtracting the mean and dividing by the standard deviation.

Here is an example of how to use `transforms.normalize()`:

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

In this example, we are creating a `transform` object that first converts a PIL image to a tensor using `transforms.ToTensor()`, and then normalizes the tensor image using `transforms.Normalize()` with the mean and standard deviation values specified. This transform can then be applied to a dataset using a data loader to prepare the data for a machine learning model.