12dl batchNormalization

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= Batch Normalization =

Batch normalization is a technique used in deep learning to normalize the inputs to a layer within the neural network. The goal of batch normalization is to reduce the internal covariate shift, which is the change in the distribution of the layer activations during training. This can improve the stability and speed of the training process, as well as prevent overfitting.

In batch normalization, the inputs to a layer are normalized for each mini-batch of data, rather than for the entire training set. The normalization is performed by subtracting the mean of the inputs and dividing by the standard deviation. The normalization is then scaled and shifted using learned parameters to produce the normalized activations.

Batch normalization has several benefits:


 * Improved training stability: By reducing internal covariate shift, batch normalization can stabilize the training process and prevent the network from getting stuck in suboptimal solutions.


 * Faster convergence: Batch normalization can reduce the internal covariate shift, which can lead to faster convergence and better optimization of the network.


 * Regularization: Batch normalization can act as a regularizer by adding noise to the activations, which can prevent overfitting.


 * Improved generalization: By normalizing the inputs to the layers, batch normalization can improve the generalization ability of the network and reduce the risk of overfitting.

Batch normalization is usually applied to hidden layers of the network, rather than to the inputs or outputs. It's important to choose the mini-batch size carefully, as a large mini-batch size can reduce the effectiveness of batch normalization, while a small mini-batch size can increase the computational overhead.

Batch Normalization uses two trainable parameters that allow the network to undo the normalization effect of this layer if needed
Batch normalization uses two trainable parameters, gamma and beta, to allow the network to undo the normalization effect of the batch normalization layer if needed.

Gamma is a scaling parameter that is used to scale the normalized activations, and beta is a shift parameter that is used to shift the mean of the normalized activations. By having these two trainable parameters, the network can control the magnitude and mean of the activations, which can be useful in some cases where the normalization may have a negative impact on the performance of the network.

For example, if the batch normalization layer normalizes the activations too much, the network may struggle to learn the features in the data. In this case, increasing the value of gamma can increase the scale of the activations, allowing the network to better capture the features in the data. Similarly, if the normalization has a negative impact on the mean of the activations, the network can use beta to adjust the mean of the activations.

In summary, batch normalization uses two trainable parameters, gamma and beta, to allow the network to undo the normalization effect of the batch normalization layer if needed. This can be useful in some cases where the normalization may have a negative impact on the performance of the network, and the network needs to be able to control the magnitude and mean of the activations.

Batch Normalization makes the gradients more stable so that we can train deeper networks
Batch normalization makes the gradients more stable, allowing us to train deeper networks because it reduces the internal covariate shift.

Internal covariate shift refers to the change in the distribution of activations within a network during training. This change in distribution can cause the gradients to become unstable, making it difficult for the network to learn the optimal weights. Batch normalization helps to reduce internal covariate shift by normalizing the activations, which results in a more stable distribution of activations during training.

By reducing internal covariate shift, batch normalization makes the gradients more stable, which allows the network to learn the optimal weights more effectively. This, in turn, enables us to train deeper networks without encountering the vanishing gradients or exploding gradients problems.

In summary, batch normalization makes the gradients more stable by reducing internal covariate shift, which allows us to train deeper networks. This is because the network is able to learn the optimal weights more effectively, which results in improved performance on the task at hand.

At test time, Batch Normalization uses a mean and variance computed on training samples to normalize the data.
At test time, batch normalization uses a mean and variance computed on training samples to normalize the data because the mean and variance of the activations for the test data may be different from those of the training data.

The batch normalization layer computes the mean and variance of the activations during the training phase, and these statistics are used to normalize the activations during both the training and test phases. At test time, the batch normalization layer uses the mean and variance computed on the training samples to normalize the activations of the test data.

This is because the mean and variance of the activations for the test data may be different from those of the training data. This difference can occur because the test data may have a different distribution or contain different types of examples compared to the training data. By using the mean and variance computed on the training samples, batch normalization helps to ensure that the normalization is performed in a consistent way across both the training and test phases.

In summary, at test time, batch normalization uses a mean and variance computed on the training samples to normalize the data because the mean and variance of the activations for the test data may be different from those of the training data, and using the mean and variance computed on the training samples helps to ensure a consistent normalization across both the training and test phases.

Batch Normalization has learnable parameters
Batch normalization has learnable parameters because the normalization process needs to be scaled and shifted to produce the normalized activations. The scaling and shifting are performed using two learnable parameters, gamma and beta, respectively.

Gamma is a scaling factor that is used to control the scale of the normalized activations. This allows the network to learn how much the activations should be scaled for a particular layer.

Beta is a shift factor that is used to control the mean of the normalized activations. This allows the network to learn how much the activations should be shifted for a particular layer.

The learnable parameters, gamma and beta, are learned during the training process, along with the weights of the connections between the neurons. This allows the network to learn the optimal scaling and shifting parameters that are required to produce the best normalized activations for a particular layer and a particular dataset.

In summary, batch normalization has learnable parameters because the normalization process needs to be scaled and shifted to produce the best normalized activations for a particular layer and a particular dataset. The scaling and shifting parameters, gamma and beta, are learned during the training process.