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Is the vanishing gradient problem more likely to arise at the input or output layers of a neural network?

Machine Learning Engineer

Dropbox

Mozilla

Klarna

Grubhub

Novartis

Lyft

Is the vanishing gradient problem more likely to arise at the input or output layers of a neural network?
In a neural network's architecture, does the vanishing gradient typically affect the initial layers or the final layers?
Where in a neural network is the vanishing gradient issue most prevalent, near the start or the end?
Does the vanishing gradient problem manifest nearer to the first or last layer of a neural network?
Can you tell if the vanishing gradient issue occurs at the start or closer to the completion of the neural network's layers?
In the structure of a neural network, are the early or later layers more affected by vanishing gradients?
Are the beginning layers or the ending layers of a neural network more susceptible to vanishing gradients?
Regarding neural networks, do vanishing gradients primarily impact the layers closer to the input or to the output?
In the layers of a neural network, where is the vanishing gradient issue more likely to be observed: closer to the beginning or towards the end?
In which part of a neural network does the vanishing gradient tend to be a bigger issue: the initial layers or the ones at the end?
Does the issue of vanishing gradient occur closer to the beginning or end of a neural network?

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Is the vanishing gradient problem more likely to arise at the input or output layers of a neural network?

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