5
votes
Accepted
What is a "batch" in batch normalization?
The "batch" is same as in mini-batch gradient descent. The mean in batch-norm here would be the average of each feature map in your batch (in your case either 32 or 64 depending on which you use)
...
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5
votes
What are the consequences of layer norm vs batch norm?
This is how I understand it.
Batch normalization is used to remove internal "covariate shift" (wich may be not the case) by normalizing the input for each hidden layer using the statistics ...
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4
votes
Why does Batch Normalization work?
I believe anything in machine learning that works, works because it flattens and smoothens the loss landscape.
Batch and layer normalization would help ensure that the feature vectors (i.e. channels) ...
- 346
4
votes
Accepted
Why do we update all layers simultaneously while training a neural network?
Why do we update all layers simultaneously while training a neural network?
We typically train a neural network with gradient descent and back-propagation. Gradient descent is the iterative algorithm ...
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3
votes
Does it make sense to apply batch normalization to a batch size of 1?
It's not possible to use batch normalization with a batch size of 1. Batch normalization requires you to calculate the variance of activation values in the current batch, and variance is undefined for ...
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3
votes
What can go wrong using batch norm?
I think the problems with batch normalization arise mainly due to the large misunderstanding of what it does, combined with a simple but confusing name.
Despite the simple computation (everybody knows ...
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3
votes
What effect does batch norm have on the gradient?
"Naturally, this will affect the gradient through the network." this statement is only partially true, let's see why by starting explaining the real aim of batch normalisation.
As the title of the ...
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2
votes
Why do current models use multiple normalization layers?
One issue is that a normalized set of initial weights may not stay normalized as learning progresses; so given that we adjust weights proportionately according to their relative values and also when ...
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2
votes
How does a batch normalization layer work?
Look, this is currently a quite contentious issue. J3soons answer already links to the original paper and another one. However, most of the evidence for batch normalization is still empirical, and ...
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2
votes
Accepted
Additional Optimizations for Convolutional Models On Inferencing
You could look into models pruning. There are several techniques out here, and all of them aim to reduce the amount of parameters of a model without affecting its performance metrics. Of course less ...
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2
votes
Accepted
Normalizing float prices with movements up to a factor of 100
Mathematically speaking when dealing with fat tailed distributions or values that varies in a range of several order of magnitude the most common and simplest solution is to move to logarithmic space. ...
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2
votes
Accepted
Is there a correct order of "conv2d", "batchnorm2d", "ReLU/LeakyReLU", "MaxPool2d" for UNet like architectures?
I suggest to follow the official U-NET implementation.
To me, the second option ...
- 3,111
2
votes
Why does Batch-Normalization before ReLU work?
Batch-normalization (BN) does NOT transform all values by restricting them to a value between zero and one.
BN performs two operations: a normalization, and a shifting with scaling. The normalization ...
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2
votes
What does linear regime of nonlinearity mean in normalisation?
The normalization makes the signal small enough to remain in the region of the sigmoid that can be well approximated by a straight line.
The idea is the same as in electronics: https://en.wikipedia....
1
vote
How to handle BatchNorm in the last layers of Neural Networks?
Keep in mind that BN (batch-norm) is suggested with deep networks and large batch sizes (the larger, the better). Let's say that for a 2 or 3 layer neural net, BN may not play a big role so in most ...
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1
vote
computational complexity for batch normalization technique
Batch Normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. For a Batch of size N, computing the mean and variance will have a complexity of ...
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1
vote
During batch normalization is the mini-batch gone through twice, one to calculate the mean and variance and then to normalize them?
I think it'd be helpful to refer to the batchnorm formula given in the PyTorch implementation.
In particular, given an input $x$, you would get the mean and variance ($\mathbf{E}[x]$ and $\text{Var}[x]...
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1
vote
What can be an example other than batch normalization that uses statistics of batches?
Here's some examples:
Group Normalization
Layer Normalization
Switchable Normalization
Attentive Normalization
Spectrl Normalization
Notice how in general different normalization techniques are ...
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1
vote
Where does batch normalization layers present in a neural network?
I think the answer to your question is much more a rule of thumb than an appropriate analytical answer. First of all, I would like to remark that Batch Normalization [1] are applied most commonly to ...
1
vote
Does this tutorial use normalization the right way?
The normalize() function used in the tutorial is the incorrect function to use for normalization in this context. This normalize function is for finding L1 and L2 norms. The correct layer/function ...
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1
vote
Accepted
Why does my model not improve when training with mini-batch gradient descent, while it does with Adam?
Well, some time ago I also faced the same issue in the semantic segmentation task. Batch normalization is expected to improve convergence, because the normalization of activations prevents the ...
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1
vote
Accepted
In Batch Normalisation, are $\hat{\mu}$, $\hat{\sigma}$ the mean and stdev of the original mini-batch or of the input into the current layer?
Your intuition is correct. We will be normalizing the inputs of the layer under consideration (just right before applying the activation function).
So, if this layer receives an input $\mathrm{x}=\...
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1
vote
How does a batch normalization layer work?
Definition and Explaination
For how Batch Normalization works exactly, I'll suggest you to read the following papers:
Batch Normalization: Accelerating Deep Network Training by Reducing Internal ...
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1
vote
Is batch normalization not suitable for non-gaussian input?
So batch-normalization helps descent based learning have an easier time traversing the loss manifold, but in your case you use it along with a relu as a final activation is problematic, it means the ...
- 2,379
1
vote
What is the most common practice to apply batch normalization?
In the literature, it differs. You will see models do it after or before pooling only, and sometimes you see it after every single convolution.
Batch normalization's assistance to neural networks ...
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