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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Batch Normalization or just z-normalization as a Nonlinearity

It is already common to do something "like"**(see asterisks below) z-standardization of the outputs of one neural network layer before passing it to the next. z-standardization would transform the ...
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Does the BatchNorm scale the distribution of whole layer (all neurons) or each neuron?

I am confused at a very trivial point about the BatchNorm. For illustrations it is a widely used graphics that BatchNorm corrects the distribution of incoming values. To calculate mean, does it ...
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How to derive Mean of Truncated Normal Distribution

How to derive Mean of Truncated Normal Distribution which is equation 17 below ? Note: Image screenshot is taken from L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
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Distorted validation loss when using batch normalization in convolutional autoencoder

I have implemented an variational autoencoder with convolutional layers in Keras. I have around 40'000 training images and 4000 validation images. The images are heat maps. The encoder and decoder are ...
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How does Batch Normalization in Machine Learning address covariate shift and speed up training?

In this video and this answer, it's mentioned that batch normalization doesn't allow the mean and variance of the parameters of any particular hidden layer to vary too much with change in previous ...
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One-sample t test for the non-significant Shapiro-Wilk variables, else Wilcoxon

One of the reviewers for my paper did not understand why I compared the analyses to zero. Can you please help me understand if what I do does not make sense or if it does and I just need to convey the ...
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39 views

Dimensions of Scale (Gamma) and Offset (Beta) in Batch Norm

While implementing Batch Normalization for a particular layer 'L' with 'n' hidden neurons/units in a Neural Network, we first normalize the Activation values of that layer using their respective Mean ...
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29 views

Batch effect correction methodology

could anyone direct me to methodologies to perform the following: We measured proteomic profiles of a set of samples at one point, and after looking at the results, we decided to run more samples on ...
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35 views

Batch-Norm makes the Decision Boundary more non-linear?

Consider a Neural Network and let $L$ be it's last layer or the output layer. Also suppose we're doing Binary Classification, hence the activation function for the last layer is $\sigma$, ie. $g^{[L]}(...
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27 views

Why is batch normalization preserving the capacity of a network?

I have a question regarding the Batch normalization paper of Sergey Ioffe In the paper the author states on page 3 after discussion of derivation: ..., BN transform is a differentiable ...
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17 views

Training with default BN parameters

Training with default BN parameters in tensorflow I obtain strange loss curve. For experiment train and val are the same dataset. Blue is val loss, orange is train loss. BN with momentum 0.99 (...
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Usage of dropout in convolutional GANs with batch norm?

In DCGAN, dropout is not used in either generator or discriminator. When using batch norm, are the benefits of dropout generally so marginal that is is not used? If it is used, in what circumstances?...
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Solving an equation that involves convolution and batch normalization

Basically the equation is $$ x = \mathrm{BN}(\mathrm{Conv}(z)) $$ where the convolution operation uses SAME padding with stride $1$ ensuring that the input and output have the same size. The ...
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Do γ and β “undo” the effects of batch normalization?

Let H be a minibatch of activations for a layer to be normalized, where activations of each example are in a row of the matrix, and each column represents the activation of a given unit in the layer. ...
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Are there “typical” mean and variance patterns that are learned in batch normalization in CNNs?

When we apply batch normalization to the output of a convolutional neural network layer we learn $\gamma$ and $\beta$ and scale the layers output based on a running average and those learned values. ...
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47 views

Does Batch Normalization Introduce non linearity into the Neural Network?

Does Batch Normalization help the network in any way other than keeping the weights alive?
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How to remove pair-wise comparisons that are affected by a batch covariate in pairwise Wilcoxon test

I have a dependent variable that is grouped by an independent.variable (with 3 factor levels) and I want to calculate pairwise comparisons between the groups of the independent.variable. However, I ...
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Should batch size be the same while training and prediction if batch normalization layer is used?

Should batch size be the same while training and prediction if batch normalization layer is used? Apparently, since layer takes mean and stdev across batch, it should be the same. Shouldn't it?
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Tensorflow batch normalization for images - padding issue

I'm trying to train anomaly/defect detection network on custom images. Let say I have to detect scratches on special steel boxes and I have two views: side view with dimension 2300 x 550 (width x ...
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Batch Normalization and increasing batch size reducing the performance

I'm training a 3D U-Net on an EM dataset of a brain. The objective is to segment neurons in it. During the experiments, I've noticed, increasing batch size, adding batch normalization layers (Conv ->...
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22 views

Batch Normalization expectation operator

On page 2 the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift they pull a couple of fast moves with the expectation operator and I'm not sure why. ...
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1k views

Is “batch normalization” applied for output layer as well?

batch normalization in a sense that in a given layer, you standardize the neurons' values, then multiply each with some trainable scaling constant, and shift them with some another trainable shifting ...
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What is best(or proper) normalization method for A3C?

A3C[1] is an asynchronous online learning algorithm in deep reinforcement learning and it uses multiple workers to collect the independent samples asynchronously. In my best knowledge, the popular ...
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153 views

May too much batch normalization hurt learning?

I was experimenting with some CNN models and reading research material when I realized that it could happen that using only a single batch normalization layer at the early stages of the network could ...
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372 views

How does Batch Normalization not lead to the model blowing up? [duplicate]

I was reading the Batch Norm paper and in this paragraph, We could consider whitening activations at every training step or at some interval, either by modifying the network directly or by ...
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153 views

Gradient in batch-size

When we set a batch-size, after each sample of batch passed we take the gradient but wait until last sample of batch to passed and then propagate the sum of gradient of them through the network? Am I ...
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1answer
746 views

Lack of Batch Normalization Before Last Fully Connected Layer

In most neural networks that I've seen, especially CNNs, a commonality has been the lack of batch normalization just before the last fully connected layer. So usually there's a final pooling layer, ...
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118 views

why the parameter 'scale' of tf.layers.batch_normalization is disabled when next layer is relu?

In the tensorflow documentation of tf.layers.batch_normalization,it is said" When the next layer is linear (also e.g. nn.relu), this(the parameter of 'scale' ) can be disabled since the scaling can be ...
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63 views

Half precision in batch normalization

Why is it a common practice to have single precision for batch normalization Even when the rest of the network is of half precision?
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478 views

Why are batch-normalization techniques less popular in natural language applications than in computer vision?

I read in {1} section "5.2.5 SATURATION AND DEAD NEURONS" page 61: The batch-normalization techniques became a key component for effective training of deep networks in computer vision. As of ...
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81 views

Notation to represent the batch-normalised value of x

What notation is used to represent the normalised or z-score value of a vector $x$? Or, given: $? = \dfrac {x - \mu } {\sqrt{\sigma^2}}$ Where $\mu$ and $\sigma$ are calculated across the whole ...
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959 views

Model Training accuracy decreases and stops learning after applying Tensorflow's batch normalization [closed]

So I am working on making a model to classify the German traffic signs dataset and everything is fun and games. I build my model of 3 convolutional layers, 2 fully-connected(dense) layers, and 1 last ...
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1answer
379 views

how to derive the gradient of batch normalization

I'm trying to figure out the gradient of batch norm wrt x for backprop, but I get stuck in what I will call 'the triangle of (gradient) death'. I present to you the triangle of death (in red), in the ...
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Explaining batch normalization in terms of Gaussian processes

In this video on Deep Gaussian Processes, Neil Lawrence (at 37:30) mentions that thinking of the layers of a neural network in terms of basis functions from Gaussian processes can be viewed as a ...
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372 views

Hinton claims SGD with batch norm can help: How?

In Hinton's paper "Layer Normalization", on the first page he says Feedforward neural networks trained using batch normalization converge faster even with simple SGD. By this I think he means ...
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2k views

Batch normalization on MNIST tutorial

I tried to apply batch normalization to my network (several 1D-convolution layers and then a couple of fully-connected layers). Results were bad or no significant improvement so I tried on one of ...
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Matrix form of backpropagation with batch normalization

Batch normalization has been credited with substantial performance improvements in deep neural nets. Plenty of material on the internet shows how to implement it on an activation-by-activation basis. ...
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793 views

Why does training a GAN discriminator on separate updates for real and generated data work better than a single update?

For each batch I update the discriminator first, and then the combined generator/discriminator together. I noticed that if I make two updates to the discriminator, one for the generated data and one ...
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1answer
283 views

Batch normalization: possible pros and cons in one task

I got a problem where batch normalization before the first non-linear activation is a bad idea. Imagine that a neural network has to know the original value of some inputs to get a job done. Inclusion ...
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1answer
297 views

Normalization of a metabolomics dataset: is it too much?

I am working with a dataset of quantitative targeted LC-MS/MS-measured metabolites. I am advised to perform following normalization steps: Removal of metabolites where >20% of samples are below LOD (...
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1answer
4k views

Batch Normalization decreasing model accuracy

I’m a little confused. I have seen that Batch normalization leads to faster convergence and increased accuracy. But the opposite is happening in my case. By normalizing, my accuracy actually decreased....
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Question about batch normalization implementation, does each batch belong to the output of each neuron over the whole training set?

I'm trying to follow this paper https://www.arxiv-vanity.com/papers/1502.03167/ http://proceedings.mlr.press/v37/ioffe15.pdf Since the full whitening of each layer’s inputs is costly and not ...
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P-LSTM conceptual questions

Phased-LSTM was published here. I use it with my models and in my case it shows a satisfying improvement over batch normalized LSTM models. I'm looking for explanation on the following questions: Is ...
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3k views

Batch normalization: How to update gamma and beta during backpropagation training step?

The backpropagation step of batch normalization computes the derivative of gamma (let's call it dgamma) and the derivative of <...
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1answer
272 views

Batch normalization variance calculation

In batch normalization the variance calculation during the training phase is done by ($x_i$ are the individual elements in the training batch of size $m$) $\sigma_B^2 = \frac 1m \sum_{i=1}^{m} (x_i -...
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880 views

Understanding batch normalization

In the paper Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift (here) Before explaining the process of batch normalization the paper tries to explain the ...
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Why does batch norm have learnable scale and shift?

As far as I understand it, batch norm normalises all the input features to a layer to a unit normal distribution, $\mathcal{N}(\mu=0,\sigma=1)$. The mean and variance $\mu, \sigma^2$ are estimated by ...
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134 views

Does batch normalization require standardized input features?

Is it standard operating procedure to preprocess features using feature standardization --- $x' = (x-\mu)/\sigma$ --- before training a neural net that implements batch normalization? My intuition ...
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101 views

Batch normalization : fixed samples or different samples by dimension?

Some questions came to me as I read a paper 'Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift'. In the paper, it says: Since m examples from training ...
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Pros and cons of weight normalization vs batch normalization

What are the pros and cons of weight normalization vs batch normalization in general and for convolutional neural networks specifically?