I'm creating a convolutional neural network (CNN), where I have a convolutional layer followed by a pooling layer and I want to apply dropout to reduce overfitting. I have this feeling that the dropout layer should be applied after the pooling layer, but I don't really have anything to back that up. Where is the right place to add the dropout layer? Before or after the pooling layer?


3 Answers 3


Edit: As @Toke Faurby correctly pointed out, the default implementation in tensorflow actually uses an element-wise dropout. What I described earlier applies to a specific variant of dropout in CNNs, called spatial dropout:

In a CNN, each neuron produces one feature map. Since dropout spatial dropout works per-neuron, dropping a neuron means that the corresponding feature map is dropped - e.g. each position has the same value (usually 0). So each feature map is either fully dropped or not dropped at all.

Pooling usually operates separately on each feature map, so it should not make any difference if you apply dropout before or after pooling. At least this is the case for pooling operations like maxpooling or averaging.

Edit: However, if you actually use element-wise dropout (which seems to be set as default for tensorflow), it actually makes a difference if you apply dropout before or after pooling. However, there is not necessarily a wrong way of doing it. Consider the average pooling operation: if you apply dropout before pooling, you effectively scale the resulting neuron activations by 1.0 - dropout_probability, but most neurons will be non-zero (in general). If you apply dropout after average pooling, you generally end up with a fraction of (1.0 - dropout_probability) non-zero "unscaled" neuron activations and a fraction of dropout_probability zero neurons. Both seems viable to me, neither is outright wrong.

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    $\begingroup$ I am not sure this is the standard way of performing dropout. E.g. in tf.nn.dropout it states "By default, each element is kept or dropped independently". Do you have a source backing this up? $\endgroup$ Jan 11, 2018 at 10:55
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    $\begingroup$ Oh! What I described is now called spatial dropout: arxiv.org/pdf/1411.4280.pdf . So @TokeFaurby is right in doubting my claim. However, as you also can read in the linked paper, dropping whole feature maps in the spatial dropout manner improves performance. This comes at no surprise, as adjacent activations are highly correlated and dropping out one specific element actually does not drop the information carried by that element at all (as it is very unlikely to drop a continuous "hole" in a feature map when doing it element-wise). I'll edit my answer to reflect this difference. $\endgroup$
    – schreon
    Jan 11, 2018 at 14:59

This tutorial uses pooling before dropout and gets good results.

That doesn't necessarily mean the other order doesn't work of course. My experience is limited, I've only used them on dense layers without pooling.


Example of VGG-like convnet from Keras (dropout used after pooling):

import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD

# Generate dummy data
x_train = np.random.random((100, 100, 100, 3))
y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
x_test = np.random.random((20, 100, 100, 3))
y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)

model = Sequential()
# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.
# this applies 32 convolution filters of size 3x3 each.
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dense(256, activation='relu'))
model.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)

model.fit(x_train, y_train, batch_size=32, epochs=10)
score = model.evaluate(x_test, y_test, batch_size=32)

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