Questions tagged [dropout]
For questions related to the concept of dropout, which refers to the dropping out units in a neural network (NN), during the training of the NN, so that to avoid overfitting. The dropout method is a regularisation technique, which was introduced in "Dropout: A Simple Way to Prevent Neural Networks from Overfitting" (2014) by Nitish Srivastava et al.
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How does "noises" in computing convolution affect the model precision and the training speed?
Consider the discrete convolution written in a matrix form, if a small amount $s$ of the zero entries (represented as white blocks) are deviated from zero, can the model precision or the training ...
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In the Dropout paper, why would increasing the dropout increase the error rate if the capacity is constant?
In the original paper on dropout, in section 7.3.2, we see that while keeping $pn$ constant, we get a (test) error increase by decreasing retainment below 0.6. Why would that happen? If $pn$ is ...
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Is there merit in sampling dropout from a more complex distribution?
In practice, Dropout is typically applied uniformly over hidden neurons in a network. Is there merit in sampling dropout from a more complex distribution? For example, would learning a data-...
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How does dropout work during backpropagation?
I've searched for an answer to this, and read several scientific articles on the subject, but I can't find a practical explanation of how Dropout actually drops nodes in an algorithm.
I've read that ...
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Why does L1/L2 regularization not prevent co-adaptation?
I thought L2 regularization decrease a length of weight vector by almost removing many weights.
So, it would result that output neurons' activations are sparse and then less effected of co-adaptation ...
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Higher validation loss after using Dropout
I’m working on a classification problem (500 classes). My NN has 3 fully connected layers, followed by an LSTM layer. I use nn.CrossEntropyLoss() as my loss ...
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Is it mandatory to multiply every activation of a layer by droupout factor during testing?
Dropout is a regularization technique used in neural networks. It is useful in preventing overfitting by making a neural network as good as an ensemble system.
In dropout, we switch off $p$ percent of ...
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Why doesn't dropout mislead results during evaluation?
I have seen that, usually, the dropout layer is used differently in training and evaluation modes, i.e. it is recommended to use during training but not in evaluation/testing.
Dropout does remove a ...
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Is the dropout technique specific only to neural networks?
In one Udemy course was mentioned that "dropout is unique to neural networks". However, I remember an example of decision trees where nodes that are not participating in the overall result ...
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How to prove that a regularisation method simplified a neural network?
There are a few ways to regularise a neural network, for example dropout or the L1. Now, both these methods, and possibly most other regularisation methods, tend to remove from, or simplify the neural ...
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Is it possible that the model is overfitting when the training and validation accuracy increase?
I am aware of similar questions that have been asked, and I have gone through many. I want to bring my case to SE to understand better what my results are.
I am working with a large dataset (around ...
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How should we regularize an LSTM model?
There are five parameters from an LSTM layer for regularization if I am correct.
To deal with overfitting, I would start with
reducing the layers
reducing the hidden units
Applying dropout or ...
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How can I use Monte Carlo Dropout in a pre-trained CNN model?
In Monte Carlo Dropout (MCD), I know that I should enable dropout during training and testing, then get multiple predictions for the same input $x$ by performing multiple forward passes with $x$, then,...
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Is some kind of dropout used in the human brain?
I've read that ANNs are based on how the human brain works. Now, I am reading about dropout.
Is some kind of dropout used in the human brain? Can we say that the ability to forget is some kind of ...
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How to make binary neural networks resilient to flipped activation values?
Assume I am given a binary neural network where the activation values are constrained to be 0 or 1 (by clipping the ReLU function). Additionally, assume the neural network is supposed to work in a ...
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Why is my validation/test accuracy higher than my training accuracy [duplicate]
Is this due to my dropout layers being disabled during evaluation?
I'm classifying the CIFAR-10 dataset with a CNN using the Keras library.
There are 50000 samples in the training set; I'm using a ...
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Does the performance of a model increase if dropout is disabled at evaluation time?
I know dropout layers are used in neural networks during training to provide a form of regularisation in an attempt to mitigate over-fitting.
Would you not get an increased fitness if you disabled ...
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Can dropout layers not influence LSTM training?
I am working on a project that requires time-series prediction (regression) and I use LSTM network with first 1D conv layer in Keras/TF-gpu as follows:
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Why is dropout favoured compared to reducing the number of units in hidden layers?
Why is dropout favored compared to reducing the number of units in hidden layers for the convolutional networks?
If a large set of units leads to overfitting and dropping out "averages" the response ...
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Can Google's patented ML algorithms be used commercially?
I just find that Google patents some of the widely used machine learning algorithms. For example:
System and method for addressing overfitting in a neural network (Dropout?)
Processing images using ...
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Dropout causes too much noise for network to train
I am using dropout of different values to train my network. The problem is, dropout is contributing almost nothing to training, either causing so much noise the error never changes, or seemingly ...
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Is pooling a kind of dropout?
If I got well the idea of dropout, it allows improving the sparsity of the information that comes from one layer to another by setting some weights to zero.
On the other hand, pooling, let's say max-...
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Should I remove the units of a neural network or increase dropout?
When adding dropout to a neural network, we are randomly removing a fraction of the connections (setting those weights to zero for that specific weight update iteration). If the dropout probability is ...
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Price Movement Forecasting Issue
I am working on a project for price movement forecasting and I am stuck with poor quality predictions.
At every time-step I am using an LSTM to predict the next 10 time-steps. The input is the ...
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Can the addition of dropout in a non-overfitting neural network increase accuracy?
According to Wikipedia
Dropout is a regularization technique for reducing overfitting in neural networks
My neural network is simple enough and does not overfit.
Can the addition of dropout, in a ...
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Why do you not see dropout layers on reinforcement learning examples?
I've been looking at reinforcement learning, and specifically playing around with creating my own environments to use with the OpenAI Gym AI. I am using agents from the stable_baselines project to ...
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5 years later, are maxout networks dead, and why?
Maxout networks were a simple yet brilliant idea of Goodfellow et al. from 2013 to max feature maps to get a universal approximator of convex activations. The design was tailored for use in ...
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How to compute the action probabilities with Thompson sampling in deep Q-learning?
In some implementations of off-policy Q-learning, we need to know the action probabilities given by the behavior policy $\mu(a)$ (e.g., if we want to use importance sampling).
In my case, I am using ...
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What to do if CNN cannot overfit a training set on adding dropout?
I have been trying to use CNN for a regression problem. I followed the standard recommendation of disabling dropout and overfitting a small training set prior to trying for generalization. With a 10 ...
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Why is my test error lower than the training error?
I am trying to train a CNN regression model using the ADAM optimizer, dropout and weight decay.
My test accuracy is better than training accuracy. But, as far as I know, usually, the training accuracy ...
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What are the counterparts of non-linearities and dropout in fully convolutional networks?
I am trying to replicate the fully convolutional networks (FCN) concept described here for semantic segmentation. It seems people have successfully trained such models by removing fully connected ...
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Does a bias also have a chance to be dropped out in Dropout layer?
Suppose that you have 80 neurons in a layer, where one neuron is bias. Then you add a dropout layer after the activation function of this layer.
In this case, does it have a chance to drop out the ...
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