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36

The function of dropout is to increase the robustness of the model and also to remove any simple dependencies between the neurons. Neurons are only removed for a single pass forward and backward through the network - meaning their weights are synthetically set to zero for that pass, and so their errors are as well, meaning that the weights are not updated. ...


30

Let's start with normal dropout, i.e. dropout only at training time. Here dropout serves as a regularization to avoid overfitting. During test time, dropout is not applied; instead, all nodes/connections are present, but the weights are adjusted accordingly (e.g. multiplied by the keep ratio, which is 1 - dropout_ratio). Such a model during test time can be ...


22

I am unsure there will be a formal way to show which is best in which situations - simply trying out different combinations is likely best! It is worth noting that Dropout actually does a little bit more than just provide a form of regularisation, in that it is really adding robustness to the network, allowing it to try out many many different networks. ...


21

Ok, so after a lot of experimentation I have managed to get some results/insights. In the first place, everything being equal, smaller batches in the training set help a lot in order to increase the general performance of the network, as a negative side, the training process is muuuuuch slower. Second point, data is important, nothing new here but as I ...


14

I prefer not to add drop out in LSTM cells for one specific and clear reason. LSTMs are good for long terms but an important thing about them is that they are not very well at memorising multiple things simultaneously. The logic of drop out is for adding noise to the neurons in order not to be dependent on any specific neuron. By adding drop out for LSTM ...


11

During training, p neuron activations (usually, p=0.5, so 50%) are dropped. Doing this at the testing stage is not our goal (the goal is to achieve a better generalization). From the other hand, keeping all activations will lead to an input that is unexpected to the network, more precisely, too high (50% higher) input activations for the following layer. ...


8

Why not, because the risks outweigh the benefits. It might work in images, where loss of pixels / voxels could be somewhat "reconstructed" by other layers, also pixel/voxel loss is somewhat common in image processing. But if you use it on other problems like NLP or tabular data, dropping columns of data randomly won't improve performance and you will risk ...


7

These techniques are not mutually exclusive; combining dropout with weight decay has become pretty standard for deep learning. However, where weight decay applies a linear penalty, dropout can cause the penalty to grow exponentially. This property of dropout can lead to hypothetical failures as proposed and proven in section 4.2 of this paper. In general, ...


7

As your network is working without dropout, I think your problem is about how many epoches you run. In your code, it seems that only one epoch will be run. With dropout enabled, each neuron has 50% percent (for example) chance to be activated. Maybe there are some un-trained neurons in your network, which ruin your accuracy. I think it is worth trying more ...


7

There is not a consensus that can be proved across all model types. Thinking of dropout as a form of regularisation, how much of it to apply (and where), will inherently depend on the type and size of the dataset, as well as on the complexity of your built model (how big it is).


6

It is not uncommon to use dropout on the inputs. In the original paper the authors usually use dropout with a retention rate of 50% for hidden units and 80% for (real-valued) inputs. For inputs that represent categorical values (e.g. one-hot encoded) a simple dropout procedure might not be appropriate. They also argue that dropout applied to the inputs of ...


4

Dropout is applied over one network. Sometimes (like with non-dropout networks) you will run your data through it multiple times before it converged and this number will be a bit higher on average with dropout but it is one network. Per layer you have a dropout probability during training and during testing/prediction you use the full network without dropout ...


4

Let's clarify few things about dropout. And Neil Slater is to credit for this answer since his comments helped formulate a more clear explanation. First of all, dropout is a regularization method, it is usually only applied during training (although it can be used in prediction as an approximation to a Bayesian Neural Network as is explained by Yarin Gal's ...


4

Another way of looking at what dropout does is that it is like a slab-and-spike prior for the coefficient for a covariate (that is some complex interaction term of the original covariates with some complicated functional transformations) in a Bayesian model. This is the interpretation proposed by Yarin Gal in his thesis (see his list of publications). Here ...


3

Avoid early stopping and stick with dropout. Andrew Ng does not recommend early stopping in one of his courses on orgothonalization [1] and the reason is as follows. For a typical machine learning project, we have the following chain of assumptions for our model: Fit the training set well on the cost function ↓ Fit the dev set well on the cost function ↓ ...


3

Two points: Dropout is also usually compared with neural networks ensembles. It seems it has some of the performance benefits of training and averaging several neural networks. Dropout is easier to calibrate than regularization. There is only one hyperparameter which is the dropout rate and people widely use 0.5 while training (and then 1.0 on evaluation of ...


3

Dropout does not actually removes neurons, its just that those particular neurons don't play any role (don't get activated) for the given batch of data. Example - Suppose there is a road of 8 lanes - When Trucks come, they pass through lanes 1,2,4,6,7, when Cars come, they pass through lanes 2,3,4,7,8 and when Bikes come, they pass through lanes 1,2,5,8. So ...


3

Co-adaptions in simple English term would mean co-operation. If you think nodes of a NN as workers it would mean missing even a few workers would result in failure of the NN to do something substantial. This happens mainly due to few nodes of NN outputting values which get cancelled by other nodes (so we can remove those nodes altogether - essentially ...


3

It depends on the type of input pattern but to make a decision, I suggest not to. There are different reasons for that. First of all, you are damaging your input signal. I don't know whether you are familiar with the information theory or not but the signal to noise ratio will be too small and if you do so, you will be left with a signal which is far from ...


3

I suggest you analyze the learning plots of your validation accuracy as Neil Slater suggested. Then, if the validation accuracy drops try to reduce the size of your network (seems too deep), add dropout to the CONV layers and BatchNormalization after each layer. It can help get rid of overfitting and increase the test accuracy.


3

There are several possible solutions for your Problem. Use Dropout in the earlier layers (convolutional layers) too. Your network seems somehow quite big for such an "easy" task; try to reduce it. The big architectures are also trained on much bigger datasets. If you want to keep your "big" architecture try: Image augmentation in order to virtually ...


3

The underlying true performance is likely convex or at least likely only has one minimum, but you don't know the true underlying performance, you only get a stochastic sample with a tiny sample size because you apply cross validation. The performance is a random variable where you are interested in the expected value, but you only get k samples where k is ...


3

As given in the links, the answer is yes! note that you divide the mask by p so that you won't need to multiply by p in the test time and since this is a coefficient for the new activation, it will come out of the derivative in chain rule in backprop.


3

The usual processing for your suggested layers: model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) would be (reading left to right) dense output -> relu -> apply dropout mask -> apply "inverse dropout" divide by p The precise combination may vary depending upon optimisations, and can in theory be changed a little without ...


3

You are looking at the Keras code implementing dropout for training step. In the Keras implementation, the output values are corrected during training (by dividing, in addition to randomly dropping out the values) instead of during testing (by multiplying). This is called "inverted dropout". Inverted dropout is functionally equivalent to original dropout (...


3

I guess you have not figured out the concept of dropout very well. First, the reason we apply it is that we add some noise to the architecture in order not be dependant on any special node. The reason is that it was observed that while training a network, after overfitting, the weights for some of neurons increases and cause the network to be dependant on ...


3

Monte Carlo Dropouts (MCDO) is used during the prediction / inference phase to provide an estimate of uncertainty for the model's predictions. Regular dropout during the training phase is a regularization technique.


2

I ended up finding the paper you were referencing, maybe next time, add it to your post. From what I can understand in this paper, label-dropout means that you are dropping the real labels and replace them with others. The section of the paper you're referring to is explaining everything in great detail, so I'd try to read it carefully. In short, instead ...


2

Random forests could be thought of as using a kind of dropout-esque technique as each split node only considers a random subset of the features, effectively 'dropping out' the other ones. Also, sometimes in large tree ensembles, each tree is only given a random subset of features to begin with, akin to dropout on the input layer of a neural network.


2

It depends a bit on your definition, usually the stochastic part of Stochastic Gradient Descent refers to the fact that you sample mini batches and estimate the true gradient with this sample. Dropout adds stochastic behaviour by sampling masks that comes down to sampling new network architectures every time. You could certainly see this as a form of ...


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