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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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Keras: the difference between LSTM dropout and LSTM recurrent dropout

From the Keras documentation: dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the…
Alonzorz
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Using Dropout in Pytorch: nn.Dropout vs. F.dropout

By using pyTorch there is two ways to dropout torch.nn.Dropout and torch.nn.functional.Dropout. I struggle to see the difference between the use of them: When to use what? Does it make a difference? I don't see any performance difference when I…
CutePoison
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How to understand SpatialDropout1D and when to use it?

Occasionally I see some models are using SpatialDropout1D instead of Dropout. For example, in the Part of speech tagging neural network, they use: model = Sequential() model.add(Embedding(s_vocabsize, EMBED_SIZE, …
Raven Cheuk
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Implementing dropout from scratch

This code attempts to utilize a custom implementation of dropout : %reset -f import torch import torch.nn as nn # import torchvision # import torchvision.transforms as transforms import torch import torch.nn as nn import torch.utils.data as…
blue-sky
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PyTorch - How to deactivate dropout in evaluation mode

This is the model I defined it is a simple lstm with 2 fully connect layers. import copy import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim class mylstm(nn.Module): def __init__(self,input_dim,…
Tommy Yu
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Using Dropout with Keras and LSTM/GRU cell

In Keras you can specify a dropout layer like this: model.add(Dropout(0.5)) But with a GRU cell you can specify the dropout as a parameter in the constructor: model.add(GRU(units=512, return_sequences=True, dropout=0.5, …
BigBadMe
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ReLu and Dropout in CNN

I am studying Convolutional Neural Networks. I am confused about some layers in CNN. Regarding ReLu... I just know that it is the sum of an infinite logistic function, but ReLu doesn't connect to any upper layers. Why do we need ReLu, and how does…
user3783676
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Tensorflow LSTM Dropout Implementation

How specifically does tensorflow apply dropout when calling tf.nn.rnn_cell.DropoutWrapper() ? Everything I read about applying dropout to rnn's references this paper by Zaremba et. al which says don't apply dropout between recurrent connections.…
beeCwright
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Dropout layer before or after LSTM. What is the difference?

Suppose that we have an LSTM model for time series forecasting. Also, this is a multivariate case, so we're using more than one feature for training the model. ipt = Input(shape = (shape[0], shape[1]) x = Dropout(0.3)(ipt) ## Dropout before…
Eghbal
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Keras LSTM: dropout vs recurrent_dropout

I realize this post is asking a similar question to this. But I just wanted some clarification, preferably a link to some kind of Keras documentation that says the difference. In my mind, dropout works between neurons. And recurrent_dropout works…
Oliver Crow
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Where to add dropout in neural network?

I have seen description about the dropout in different parts of the neural network: dropout in the weight matrix, dropout in the hidden layer after the matrix multiplication and before relu, dropout in the hidden layer after the relu, and…
DiveIntoML
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How inverting the dropout compensates the effect of dropout and keeps expected values unchanged?

I'm learning regularization in Neural networks from deeplearning.ai course. Here in dropout regularization, the professor says that if dropout is applied, the calculated activation values will be smaller then when the dropout is not applied (while…
Kaushal28
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Correct usage of keras SpatialDropout2D inside TimeDistributed layer - CNN LSTM network

I have a burning issue on applying same dropout mask for all of the timesteps within a time series sample so that LSTM layer sees same inputs in one forward pass. I read multiple articles but did not find a solution to this. Does the following…
PraAnj
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Does MaxPooling reduce overfitting?

I have trained the following CNN model with a smaller data set, therefore it does overfitting: model = Sequential() model.add(Conv2D(32, kernel_size=(3,3), input_shape=(28,28,1),…
Code Now
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correct order for SpatialDropout2D, BatchNormalization and activation function?

For a CNN architecture I want to use SpatialDropout2D layer instead of Dropout layer. Additionaly I want to use BatchNormalization. So far I had always set the BatchNormalization directly after a Convolutional layer but before the activation…
Code Now
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