Questions tagged [batch-normalization]
Batch Normalization is a technique to improve learning in neural networks by normalizing the distribution of each input feature in each layer across each minibatch to N(0, 1).
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Why does batch normalization make lower layers 'useless' in purely linear networks?
I'm reading the Deep Learning book by Goodfellow, Bengio, and Courville (Chapter 8 section 8.7.1 on Batch Normalization, page 315). The authors use a simple example of a deep linear network without ...
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Does Batch Normalization act as a regularizer when we don't shuffle the dataset at each epoch?
Batch Normalization (BN) is a technique to accelerate the convergence when training neural networks. It is also assumed to act as a regularizer, since the the mean and standard deviation are ...
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If the main benefit of BatchNorm is loss landscape smoothing, why do we use z-score normalisation instead of min-max?
According to recent papers, the main reason why BatchNorm works is because it smooths the loss landscape. So if the main benefit is loss landscape smoothing, why do we need mean subtraction at all? ...
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Batch Normalization and the effect of scaled weights on the gradients
I have been reading the following paper: https://arxiv.org/pdf/1706.05350, and I am having a hard time with some claims and derivations made in the paper.
First of all, the main thing I am interested ...
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The meaning of linear transformation in a batch norm revisited
I'm reading BatchNorm Wikipedia page, where they explain that BatchNorm.
I think the actual formulas are easier than words in this case. The norm statistics are calculated as:
$$\large{\displaystyle \...
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Why it is called "BatchNorm" not "Batch Standardize"?
Regarding the differences between "Normalization" and "Standardization," I found that:
Normalization: Is the process of making a dataset having a specified range, probably [0,1] ...
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Should you normalize covariates in a linear mixed model
I am using lmer for a set of mixed models, each comparing a protein quantity of interest with a biomarker. Even after experimental batch correction & ...
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Metabolomics run showing batch effects due to non authentic standards - how to present biological effects across 3 different runs
Within each run, the experiment is set up as below:
Genotypes refer to: WT (Wild type as blue), PKO (Partial Knockout in green), FKO (Full Knockout in red)
Biological triplicates means the same ...
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Why is the layer normalization same with the instance normalization in transformers (or NLP)?
This picture is from Group Normalization paper and the Layer Norm shows averaging in Channel and H/W dimension.
However, this picture is from Power Normalization paper focusing on NLP problems and ...
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How to handle BatchNorm in the last layers of a deep learning model?
I am creating a neural network using batchnorm as a regularization method to enable deep models and prevent overfitting.
I understand that batchnorming supresses the internal covariance shift ...
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How does batch normalization enable larger learning rates (according to the original paper)?
I struggle to understand how batch normalization (BN) enables larger learning rates during gradient descent according to the original paper. I am aware that some of the explanations given in the ...
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Can the limma package be applied to Log2 RUV-normalized data? [closed]
So I have a dataset that consists of the batch correction through RUV-normalization of several microarray datasets containing tumoral and non-tumoral samples. The data is in Log2 RUV-normalized ...
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Batch Normalization derivatives
I'm following the derivative calculation of Batch Norm paper:
Something doesn't seem right. In the 3rd equation shouldn't we lose the 2nd term as the sum is equal to 0 ($\mu_B$ is the mean of the $...
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Why do we use moving averages in evaluation process for Batch Normalization layer?
I have seen many links about MA for batch normalization but nothing answered my question.
In Batch normalization, you get means and variance for each mini-batches in the training process. And the ...
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Best statistical practise to take into account batch effects and biological variation
I've the following dataframe: https://drive.google.com/file/d/1IxwI52nIdolzL9wzbxiDmu5NGR5eoukX/view?usp=sharing
I'm wondering the best statistical analysis to investigate the relationship with the ...
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Comparing standardised values of microbial colony perimeters
I’m having a statistical problem (a rather major one) and I was wondering if you could help. I’m researching microbial chemotaxis and analysing colony perimeters by scanning their fluorescence. ...
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Why in batch norm we don't restrict beta to be positive
From this answer https://stats.stackexchange.com/a/437474/346940 seems that batch norm scales the standardized input by a factor $ \beta $... why don't we restrict this $\beta$ to be greater than zero?...
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Why is the notion of a batch problematic for RNNs?
This paper says that the notion of a batch problematic for RNNs (page 9) (which is why you can't apply batch normalization for RNNs?). Why is it hard to talk about batches for RNNs?
Eg. the Pytorch ...
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Should I correct for batch effect before selecting features using random forest for RNA-Seq data?
This is a mix of bioinformatics and ML problem. Hope someone with both expertise can help. Please forgive me if it's unclear or I used the wrong words as I am very new to ML.
I am trying to pick out ...
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When adding batch norm layer do I need to added to all layers in DNN?
While developing deepfm model network I want to add batch norm layer because model seems to suffer from vanishing gradient. There are embedding layers, 2 layers a in deep model part and one dense ...
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Building a model matrix for batch correction; problem with linear combinations
I recently conducted some MASS SPEC for my samples. Each sample was run thrice through the machine. However, there was a large space of time between the first run and the consequent second and third ...
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Why not perform weight decay on layernorm/embedding?
I am learning the code of minGPT. In the function, the author excluded layernorm and embedding layer from experiencing weight decay and I want to know the reasons. Besides, what about batchnorm?
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Preforming backward pass in network with batch normalization
if we have a network model like this:
input_layer (linear) [0]
hidden_layer (linear) [1]
batchnorm1d() [2]
output_layer(linear) [3]
When preforming a backward pass would you calculate
$$\delta^3$$
...
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Should I be using batchnorm and/or dropout in a VAE or GAN?
I am trying to design some generative NN models on datasets of RGB images and was debating on whether I should be using dropout and/or batch norm.
Here are my thoughts (I may be completely wrong):
...
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Is it okay to not use batchnorm and relu before global average pooling?
I have built and experiment with a small network by batchnorm-relu-conv rather than conv-batchnorm-relu as suggested by DenseNet(2017). In denseNet, Before global average pooling layer, there are ...
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which training mode is more convenient for small datasets?
I have a regression problem to be solved using one of neural networks models, but I have a small dataset which contains 30 samples.
Which training mode is more suitable for such dataset: stochastic or ...
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How to use batch norm to perform input standardization?
I need to train a model with an un-normalized dataset and I can not directly standardize it (subtract the mean and divide by the std), but I do have the mean and std for each feature. Thus I'm ...
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Batch Normalizaton before or after activation?
Can someone kindly explain what are the benefits and disadvantages of applying Batch Normalisation before or after Activation Functions? I know that popular practice is to normalize before activation, ...
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Are Batch Normalization and Kaiming Initialization addressing the same issue (Internal Covariate Shift)?
In the original Batch Norm paper (Ioffe and Szegedy 2015), the autors define Internal Covariate Shift as the "the change in the distributions of internal nodes of a deep network, in the course of ...
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What do they mean by "batchnormalization allows to initialization of weights less carefully?"
In Towards Data Science - Manish Chablani - Batch Normalization, it is stated that:
Makes weights easier to initialize — Weight initialization can be
difficult, and it’s even more difficult when ...
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Why does Group Normalization work?
In their paper Group Normalization the author introduce GroupNorm(GN) as a replacement for BatchNorm. They show that LayerNorm(LN) and InstanceNorm(IN) are extreme cases of GN.
They also show that GN ...
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Why does batch norm uses exponentially weighted average (EWA) instead of simple average at test time?
I was watching a lecture by Andrew Ng on batch normalization. When discussing the inference (prediction) on a test it is said that an exponentially weighted average (EWA) of batch normalization ...
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What happens to the data distribution and results if we calculate z-score of a z-scored data?
The data that I am using is already z-scored and batch normalized. I accidentally calculated the z-score again and then performed further analysis and calculated results. Does it make sense to take z-...
Aadi
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Using batchnorm and dropout simultaneously?
I am a bit confused about the relation between terms "Dropout" and "BatchNorm". As I understand,
Dropout is regularization technique, which is using only during training.
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Should I use batch normalization synchronization across multiple GPUs for classification training
I'm wondering if for regular classification training it's crucial to use batch normalization synchronization when training on multiple GPUs. Many papers report improved model quality when training ...
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In Batch Normalization we normalize the test input every layers or only the first layer
We know that batch normalization will normalize the net activations $z_n^{(l)*}$ for each layer. But I am not sure how to normalize the input of test? Here we ignore the final step of scaling and ...
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What is meant by Expressiveness in neural network?
While studying Batch normalization, I came across the parameter sigma and beta in the output. And all the information said that they are added in order to retain the "expressive power of the ...
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What exactly is Batch Normalization doing?
I have recently read about Batch Normalization for Deep Learning online.
Unfortunately, the notation is really inconsistent and confusing, so perhaps someone can help.
Main Question:
Let's assume we ...
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Overfitting small dataset necessary for deep NNs when training with big dataset works?
In the CS231n course from Standford, they state that a network should be able to overfit a small dataset by getting zero cost, otherwise it is not worth training.
However, what if a network is not ...
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Does Batch Normalized network still need scaled inputs?
I'm a bit new on this topic. Does Batch Normalization replace feature scaling?
As far as my understanding goes, the batch normalization uses an exponential moving average to estimate $\mu$ and $\sigma$...
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Is group normalization with G=1 equivalent to layer normalization?
References:
Batch normalization (BN)
Layer normalization (LN)
Group normalization (GN)
I will use pseudo TensorFlow-like code to be very specific about the tensor axes.
I assume an input tensor <...
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Batch normalization and the need for bias in neural networks
I've read that batch normalization eliminates the need for a bias vector in neural networks, since it introduces a shift parameter that functions similarly as a bias. As far as I'm aware though, a ...
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Why it's necessary to frozen all inner state of a Batch Normalization layer when fine-tuning
The following content comes from Keras tutorial
This behavior has been introduced in TensorFlow 2.0, in order to enable layer.trainable = False to produce the most commonly expected behavior in the ...
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Why the batch normalization is not applied on the last layer of a neural network
As I found in some tutorials, they didn't perform BN on last layer. It seems like a best practice, but I didn't find any detailed explanation of why this helps training.
Can anyone kindly help me ...
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How to set the tolerance in Gradient descent?
I understand that one solution of setting the number of iterations, is to set it to a large number and then interrupt it when the gradient vector becomes tiny, so tiny that it is smaller than a ...
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Where is BatchNorm performed in ResNeXT https://github.com/facebookresearch/ResNeXt neural network?
In the original paper that described ResNeXT (variation of Resnet) at https://arxiv.org/pdf/1611.05431.pdf.
On Page-5 top right column, it says:
ReLU is performed right after eachBN, expect for the ...
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Batch normalization leads to unstable validation loss
I'm working on a regression problem, and I'm trying to solve it using a simple multilayer perceptron with batch normalization. However, there are uncomfortably large fluctuations in the validation ...
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How to implement Batch Norm to Deep learning Neural Networks?
I'm studying at coursea.com Neural Networks with deep learning course.
I have a problem with implementing A Batch Norm to Mini-Batch Gradient descent.
More accurately, in gamma and beta hyper-...
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Why is Testing Error Spiking late in the training process?
I'm training a FCN on 550K datapoints (90/10 train-test split) and tracking training error, testing error, and actual MAE (un-z-scored true error project cares about) over each epoch. Below is plots ...
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What exactly is InstanceNormalization and BatchNormalization?
I know this is a question that has been asked a lot. I know there are many good explanations on this topic and videos. However, I still have a hard time to understand the relationship visually between ...
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