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.
Questions tagged [dropout]
34 questions
16
votes
1 answer
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 test with it.
One thing I've noticed in virtually…
Matt Hamilton
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8
votes
2 answers
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 conjunction with dropout (then recently introduced) and…
user209974
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7
votes
4 answers
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 is better than test accuracy.
So I wonder how…
이희준
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7
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3 answers
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 regularizers.
There are kernel_regularizer,…
Leo
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6
votes
2 answers
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 units, why not just suppress units?
I have read…
pascal sautot
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5
votes
2 answers
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-pooling, takes the maximum value in a…
nsaura
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5
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1 answer
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 bias neuron, or does the dropout only affect the…
Blaszard
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4
votes
2 answers
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 sequence of the last 45-60 observations. I tested…
user1050421
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4
votes
1 answer
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 Deep Q-Learning and selecting actions using…
nicolas
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4
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3 answers
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 layer deep architecture, I could overfit a…
user12754
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4
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1 answer
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 dropout?
Seal-Trainer
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4
votes
1 answer
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 deep neural networks
Methods and apparatus for…
malioboro
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3
votes
0 answers
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, for example, average these predictions.
Let's…
lebebop
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3
votes
1 answer
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:
model = Sequential()
model.add(Conv1D(filters=60, activation='relu',…
GKozinski
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2
votes
2 answers
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 $p$, then we are effectively training with a…
hirschme
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