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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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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8 votes
1 answer
144 views

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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7 votes
4 answers
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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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7 votes
3 answers
4k views

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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  • 123
6 votes
2 answers
413 views

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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5 votes
1 answer
1k views

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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4 votes
2 answers
2k views

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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  • 113
4 votes
1 answer
169 views

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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4 votes
2 answers
189 views

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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4 votes
1 answer
173 views

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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  • 2,551
4 votes
1 answer
371 views

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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3 votes
3 answers
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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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3 votes
1 answer
233 views

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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2 votes
1 answer
241 views

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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2 votes
1 answer
81 views

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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  • 121
2 votes
1 answer
107 views

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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  • 361
2 votes
1 answer
847 views

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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2 votes
1 answer
59 views

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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  • 23
2 votes
0 answers
188 views

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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  • 21
1 vote
2 answers
704 views

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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  • 125
1 vote
1 answer
31 views

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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  • 3,099
1 vote
0 answers
38 views

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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  • 216
1 vote
0 answers
37 views

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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  • 111
1 vote
1 answer
720 views

Why is my validation/test accuracy higher than my training accuracy

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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  • 125
1 vote
1 answer
178 views

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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  • 1,344
0 votes
1 answer
57 views

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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