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BatchNormalization(axis=chanDim)

时间: 2024-04-25 16:25:10 浏览: 103

Batch Normalization

Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.

BatchNormalization(axis=chanDim) 是一个用于深度学习模型中的批归一化操作。它的作用是对输入数据进行归一化处理，以提高模型的训练效果和泛化能力。在这个函数中，axis=chanDim 表示对于多维输入数据，将会在指定的轴上进行归一化操作。chanDim 是指通道维度的索引，通常在图像数据中表示为通道数。通过批归一化操作，可以使得每个输入样本的特征分布接近于均值为0，方差为1的标准正态分布。这有助于加速模型的收敛速度、增强模型的鲁棒性，并且可以一定程度上缓解梯度消失和梯度爆炸的问题。总的来说，BatchNormalization(axis=chanDim) 是一个用于深度学习模型中对输入数据进行归一化处理的操作，可以提高模型的训练效果和泛化能力。

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