Data Science Stack Exchange
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Questions tagged [dropout]

Dropout is a technique to reduce overfitting during the training phase of a neural network.

Dropout is a regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data. The term "dropout" refers to dropping out units (both hidden and visible) in a neural network.

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When should one use L1, L2 regularization instead of dropout layer, given that both serve same purpose of reducing overfitting?

In Keras, there are 2 methods to reduce over-fitting. L1,L2 regularization or dropout layer. What are some situations to use L1,L2 regularization instead of dropout layer? What are some situations when dropout layer is better?
user781486
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Convolutional neural network overfitting. Dropout not helping

I am playing a little with convnets. Specifically, I am using the kaggle cats-vs-dogs dataset which consists on 25000 images labeled as either cat or dog (12500 each). I've managed to achieve around 85% classification accuracy on my test set,…
Juan Antonio Gomez Moriano
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What is Monte Carlo dropout?

I understand how to use MC dropout from this answer, but I don't understand how MC dropout works, what its purpose is, and how it differs from normal dropout.
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Why should we use (or not) dropout on the input layer?

People generally avoid using dropout at the input layer itself. But wouldn't it be better to use it? Adding dropout (given that it's randomized it will probably end up acting like another regularizer) should make the model more robust. It will make…
Aditya
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Dropout on which layers of LSTM?

Using a multi-layer LSTM with dropout, is it advisable to put dropout on all hidden layers as well as the output Dense layers? In Hinton's paper (which proposed Dropout) he only put Dropout on the Dense layers, but that was because the hidden inner…
BigBadMe
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Why does adding a dropout layer improve deep/machine learning performance, given that dropout suppresses some neurons from the model?

If removing some neurons results in a better performing model, why not use a simpler neural network with fewer layers and fewer neurons in the first place? Why build a bigger, more complicated model in the beginning and suppress parts of it later?
user781486
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How exactly does DropOut work with convolutional layers?

Dropout (paper, explanation) sets the output of some neurons to zero. So for a MLP, you could have the following architecture for the Iris flower dataset: 4 : 50 (tanh) : dropout (0.5) : 20 (tanh) : 3 (softmax) It would work like…
Martin Thoma
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Are there studies which examine dropout vs other regularizations?

Are there any papers published which show differences of the regularization methods for neural networks, preferably on different domains (or at least different datasets)? I am asking because I currently have the feeling that most people seem to use…
Martin Thoma
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How does dropout work during testing in neural network?

The below paragraph is picked from the textbook Hands-On Machine Learning with sci-kit learn & Tensorflow. I couldn't understand what the author is trying to convey. It would be really grateful if someone can provide an explanation for below…
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Dropout vs weight decay

Dropout and weight decay are both regularization techniques. From my experience, dropout has been more widely used in the last few years. Are there scenarios where weight decay shines more than dropout?
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What does SpatialDropout1D() do to output of Embedding() in Keras?

Keras model looks like this inp = Input(shape=(maxlen, )) x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp) x = SpatialDropout1D(dropout)(x) x = Bidirectional(LSTM(num_filters,…
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In neural networks, is applying dropout the same as zeroing random neurons?

Is applying dropout equivalent to zeroing output of random neurons in each mini-batch iteration and leaving rest of forward and backward steps in back-propagation unchanged? I'm implementing network from scratch in numpy.
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Bayesian regularization vs dropout for basic ann

Does it make sense conceptually to apply dropout to an artificial neutral network while also applying bayesian regularization? On one hand I would think that technically this should work just fine, but on the other hand if bayesian regularization is…
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Dropout in a CNN vs Dropout in a FCNN

In the PyTorch nn module there are 2 types of dropouts: A normal Dropout - During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution. Each channel will be zeroed out…
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how to apply MC dropout to an LSTM network keras

I have a simple LSTM network developped using keras: model = Sequential() model.add(LSTM(rnn_size,input_shape=(2,w),dropout = 0.25 , recurrent_dropout=0.25)) model.add(Dense(2)) I would like to apply the MC dropout method. How can I enable dropout…
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