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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Understanding Test Output Calculation in DropConnect

I've been studying the DropConnect regularization technique for neural networks and I'm trying to understand how the test output is calculated. I understand that during training, DropConnect randomly ...
amir abbas 's user avatar
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Regression Model overestimates in train-mode

I have a Deep Learning Regression model to predict some values. The results are fine when I use the model in Evaluation Mode, but when I turn Training Mode on the model tends to overestimate the ...
nmb's user avatar
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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 ...
LearnerAL's user avatar
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1 answer
57 views

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 ...
Apples14's user avatar
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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-...
rac.coon's user avatar
1 vote
1 answer
1k views

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 ...
Connor's user avatar
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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 ...
helloworld's user avatar
1 vote
1 answer
42 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 ...
hanugm's user avatar
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2 votes
1 answer
358 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 ...
prat__'s user avatar
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2 votes
1 answer
281 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 ...
bridgemnc's user avatar
1 vote
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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 ...
Marcus's user avatar
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1 answer
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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 ...
nachofest's user avatar
7 votes
3 answers
9k 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 ...
Leo's user avatar
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3 votes
0 answers
427 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,...
lebebop's user avatar
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4 votes
1 answer
265 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 ...
Seal-Trainer's user avatar
1 vote
0 answers
41 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 ...
Matt's user avatar
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2 votes
1 answer
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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 ...
Tobi's user avatar
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2 answers
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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 ...
Tobi's user avatar
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3 votes
1 answer
413 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: ...
GKozinski's user avatar
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6 votes
2 answers
850 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 ...
pascal sautot's user avatar
4 votes
1 answer
246 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 ...
malioboro's user avatar
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1 vote
1 answer
263 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 ...
Recessive's user avatar
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5 votes
2 answers
3k 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-...
nsaura's user avatar
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2 votes
2 answers
151 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 ...
hirschme's user avatar
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4 votes
2 answers
229 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 ...
user1050421's user avatar
2 votes
1 answer
137 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 ...
Astariul's user avatar
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17 votes
1 answer
6k views

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 ...
Matt Hamilton's user avatar
8 votes
2 answers
173 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 ...
user209974's user avatar
4 votes
1 answer
696 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 ...
nicolas's user avatar
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4 votes
3 answers
1k views

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 ...
user12754's user avatar
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7 votes
4 answers
3k views

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 ...
이희준's user avatar
2 votes
1 answer
877 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 ...
abhinavkulkarni's user avatar
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 ...
Blaszard's user avatar
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