The original question was in regard to TensorFlow implementations specifically. However, the answers are for implementations in general. This general answer is also the correct answer for TensorFlow.

When using batch normalization and dropout in TensorFlow (specifically using the contrib.layers) do I need to be worried about the ordering?

It seems possible that if I use dropout followed immediately by batch normalization there might be trouble. For example, if the shift in the batch normalization trains to the larger scale numbers of the training outputs, but then that same shift is applied to the smaller (due to the compensation for having more outputs) scale numbers without dropout during testing, then that shift may be off. Does the TensorFlow batch normalization layer automatically compensate for this? Or does this not happen for some reason I’m missing?

Also, are there other pitfalls to look out for in when using these two together? For example, assuming I’m using them in the correct order in regards to the above (assuming there is a correct order), could there be trouble with using both batch normalization and dropout on multiple successive layers? I don’t immediately see a problem with that, but I might be missing something.

Thank you much!

UPDATE:

An experimental test seems to suggest that ordering does matter. I ran the same network twice with only the batch norm and dropout reverse. When the dropout is before the batch norm, validation loss seems to be going up as training loss is going down. They’re both going down in the other case. But in my case the movements are slow, so things may change after more training and it’s just a single test. A more definitive and informed answer would still be appreciated.

In the Ioffe and Szegedy 2015, the authors state that “we would like to ensure that for any parameter values, the network always produces activations with the desired distribution”. So the Batch Normalization Layer is actually inserted right after a Conv Layer/Fully Connected Layer, but before feeding into ReLu (or any other kinds of) activation. See this video at around time 53 min for more details.

As far as dropout goes, I believe dropout is applied after activation layer. In the dropout paper figure 3b, the dropout factor/probability matrix r(l) for hidden layer l is applied to it on y(l), where y(l) is the result after applying activation function f.

So in summary, the order of using batch normalization and dropout is:

-> CONV/FC -> BatchNorm -> ReLu(or other activation) -> Dropout -> CONV/FC ->

As noted in the comments, an amazing resource to read up on the order of layers is here. I have gone through the comments and it is the best resource on topic i have found on internet

My 2 cents:

Dropout is meant to block information from certain neurons completely to make sure the neurons do not co-adapt.
So, the batch normalization has to be after dropout otherwise you are passing information through normalization statistics.

If you think about it, in typical ML problems, this is the reason we don’t compute mean and standard deviation over entire data and then split it into train, test and validation sets. We split and then compute the statistics over the train set and use them to normalize and center the validation and test datasets

so i suggest Scheme 1 (This takes pseudomarvin’s comment on accepted answer into consideration)

-> CONV/FC -> ReLu(or other activation) -> Dropout -> BatchNorm -> CONV/FC

as opposed to Scheme 2

-> CONV/FC -> BatchNorm -> ReLu(or other activation) -> Dropout -> CONV/FC -> in the accepted answer

Please note that this means that the network under Scheme 2 should show over-fitting as compared to network under Scheme 1 but OP ran some tests as mentioned in question and they support Scheme 2

Usually, Just drop the Dropout(when you have BN):

  • “BN eliminates the need for Dropout in some cases cause BN provides similar regularization benefits as Dropout intuitively”
  • “Architectures like ResNet, DenseNet, etc. not using Dropout

For more details, refer to this paper [Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift] as already mentioned by @Haramoz in the comments.

Conv – Activation – DropOut – BatchNorm – Pool –> Test_loss: 0.04261355847120285

Conv – Activation – DropOut – Pool – BatchNorm –> Test_loss: 0.050065308809280396

Conv – Activation – BatchNorm – Pool – DropOut –> Test_loss: 0.04911309853196144

Conv – Activation – BatchNorm – DropOut – Pool –> Test_loss: 0.06809622049331665

Conv – BatchNorm – Activation – DropOut – Pool –> Test_loss: 0.038886815309524536

Conv – BatchNorm – Activation – Pool – DropOut –> Test_loss: 0.04126095026731491

Conv – BatchNorm – DropOut – Activation – Pool –> Test_loss: 0.05142546817660332

Conv – DropOut – Activation – BatchNorm – Pool –> Test_loss: 0.04827788099646568

Conv – DropOut – Activation – Pool – BatchNorm –> Test_loss: 0.04722036048769951

Conv – DropOut – BatchNorm – Activation – Pool –> Test_loss: 0.03238215297460556


Trained on the MNIST dataset (20 epochs) with 2 convolutional modules (see below), followed each time with

model.add(Flatten())
model.add(layers.Dense(512, activation="elu"))
model.add(layers.Dense(10, activation="softmax"))

The Convolutional layers have a kernel size of (3,3), default padding, the activation is elu. The Pooling is a MaxPooling of the poolside (2,2). Loss is categorical_crossentropy and the optimizer is adam.

The corresponding Dropout probability is 0.2 or 0.3, respectively. The amount of feature maps is 32 or 64, respectively.

Edit:
When I dropped the Dropout, as recommended in some answers, it converged faster but had a worse generalization ability than when I use BatchNorm and Dropout.

I found a paper that explains the disharmony between Dropout and Batch Norm(BN). The key idea is what they call the “variance shift”. This is due to the fact that dropout has a different behavior between training and testing phases, which shifts the input statistics that BN learns.
The main idea can be found in this figure which is taken from this paper.
enter image description here

A small demo for this effect can be found in this notebook.

I read the recommended papers in the answer and comments from
https://stackoverflow.com/a/40295999/8625228

From Ioffe and Szegedy (2015)’s point of view, only use BN in the
network structure. Li et al. (2018) give the statistical and
experimental analyses, that there is a variance shift when the
practitioners use Dropout before BN. Thus, Li et al. (2018) recommend
applying Dropout after all BN layers.

From Ioffe and Szegedy (2015)’s point of view, BN is located
inside/before the activation function. However, Chen et al. (2019)
use an IC layer which combines dropout and BN, and Chen et al. (2019)
recommends use BN after ReLU.

On the safety background, I use Dropout or BN only in the network.

Chen, Guangyong, Pengfei Chen, Yujun Shi, Chang-Yu Hsieh, Benben Liao,
and Shengyu Zhang. 2019. “Rethinking the Usage of Batch Normalization
and Dropout in the Training of Deep Neural Networks.” CoRR
abs/1905.05928. http://arxiv.org/abs/1905.05928.

Ioffe, Sergey, and Christian Szegedy. 2015. “Batch Normalization:
Accelerating Deep Network Training by Reducing Internal Covariate
Shift.” CoRR abs/1502.03167. http://arxiv.org/abs/1502.03167.

Li, Xiang, Shuo Chen, Xiaolin Hu, and Jian Yang. 2018. “Understanding
the Disharmony Between Dropout and Batch Normalization by Variance
Shift.” CoRR abs/1801.05134. http://arxiv.org/abs/1801.05134.

Based on the research paper for better performance we should use BN before applying Dropouts

The correct order is: Conv > Normalization > Activation > Dropout > Pooling

ConV/FC – BN – Sigmoid/tanh – dropout.
If activiation func is Relu or otherwise, the order of normalization and dropout depends on your task