batchnorm2d

BatchNorm2d is a type of normalization layer used in deep learning models. It is used to normalize the input to a neural network layer, which helps in preventing overfitting and improving the accuracy of the model.

In BatchNorm2d, the inputs are normalized across the batch dimension and the channel dimension. The mean and standard deviation of the input are calculated across the batch dimension for each channel. The input is then normalized using these mean and standard deviation values.

BatchNorm2d is typically used after a convolutional layer in a neural network. It helps in stabilizing the training process by reducing the internal covariate shift, which is the change in the distribution of the input that occurs during training. By reducing this shift, BatchNorm2d helps in improving the convergence of the model during training.

BatchNorm2d is a widely used layer in deep learning models and is available in most deep learning frameworks, including PyTorch and TensorFlow.