Questions tagged [vanishing-gradient-problem]

For questions related to the vanishing gradient problem, which is a numerical problem that occurs while training a (deep) neural network with a gradient-based optimization technique. There's also the related exploding gradient problem.

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How does vanish gradient restrict RNN to not work for long range dependencies?

I am really trying to understand deep learning models like RNN, LSTMs etc. I have gone through many tutorials of RNN and have learned that RNN cannot work for long Range dependencies, like: Consider ...
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0answers
20 views

How to decide if gradients are vanishing?

I am trying to debug a neural network. I am seeing gradients close to zero. How can I decide whether these gradients are vanishing or not. Is there some threshold to decide on vanishing gradient by ...
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In LSTMs, how does the additive property enables better balancing of gradient values during backpropagation?

There are two sources that I'm using to to try and understand why LSTMs reduce the likelihood of the vanishing gradient problem associated with RNNs. Both of these sources mention the reason LSTMs are ...
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37 views

Would a different learning rate for every neuron and layer mitigate or solve the vanishing gradient problem?

I'm interested in using the sigmoid (or tanh) activation function instead of RELU. I'm aware of RELU advantages on faster computation and no vanishing gradient problem. But about vanishing gradient, ...
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2answers
67 views

What are the common pitfalls that we could face when training neural networks?

Apart from the vanishing or exploding gradient problems, what are other problems or pitfalls that we could face when training neural networks?
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1answer
51 views

Does the paper “On the difficulty of training Recurrent Neural Networks” (2013) assume, falsely, that spectral radii are $\ge$ square matrix norms?

(link to paper in arxiv) In section 2.1 the authors define $\gamma$ as the maximum possible value of the derivative of the activation function (e.g. 1 for tanh.) Then they have this to say: We ...
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0answers
21 views

How do LSTM and GRU avoid to overcome the vanishing gradient problem?

I'm watching the video Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorflow Tutorial | Edureka where the author says that the LSTM and GRU architecture help to reduce the ...
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1answer
69 views

What effect does batch norm have on the gradient?

Batch norm is a technique where they essentially standardize the activations at each layer, before passing it on to the next layer. Naturally, this will affect the gradient through the network. I have ...
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1answer
77 views

If vanishing gradients are NOT the problem that ResNets solve, then what is the explanation behind ResNet success?

I often see blog posts or questions on here starting with the premise that ResNets solve the vanishing gradient problem. The original 2015 paper contains the following passage in section 4.1: We ...
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0answers
43 views

Which activation functions can lead to the vanishing gradient problem?

From this video tutorial Vanishing Gradient Tutorial, the sigmoid function and the hyperbolic tangent can produce the vanishing gradient problem. What other activation functions can lead to the ...
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1answer
964 views

Why do ResNets avoid the vanishing gradient problem?

I read that, if we use the sigmoid or hyperbolic tangent activation functions in deep neural networks, we can have some problems with the vanishing of the gradient, and this is visible by the shapes ...