averagePooling2dLayer
Average pooling layer
Description
A 2D average pooling layer performs downsampling by dividing the input into rectangular pooling regions, then computing the average of each region.
Creation
Description
sets optional properties using one or more namevalue arguments. layer
= averagePooling2dLayer(poolSize
,Name=Value
)
Input Arguments
poolSize
— Dimensions of pooling regions
vector of two positive integers  positive integer
Dimensions of the pooling regions, specified as a vector of two
positive integers [h w]
, where h
is the height and w
is the width. When creating the
layer, you can specify poolSize
as a scalar to use
the same value for both dimensions.
If the stride dimensions Stride
are less than the
respective pooling dimensions, then the pooling regions overlap.
The padding dimensions PaddingSize
must be less
than the pooling region dimensions poolSize
.
Example:
[2 1]
specifies pooling regions of height 2 and width
1.
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: averagePooling2dLayer(2,Stride=2)
creates an
average pooling layer with pool size [2 2]
and stride
[2 2]
.
Stride
— Step size for traversing input
[1 1]
(default)  vector of two positive integers  positive integer
Step size for traversing the input vertically and horizontally,
specified as a vector of two positive integers [a
b]
, where a
is the vertical step
size and b
is the horizontal step size. When
creating the layer, you can specify Stride
as a
scalar to use the same value for both dimensions.
If the stride dimensions Stride
are less than
the respective pooling dimensions, then the pooling regions
overlap.
The padding dimensions PaddingSize
must be
less than the pooling region dimensions
PoolSize
.
Example:
[2 3]
specifies a vertical step size of 2 and a
horizontal step size of 3.
Padding
— Input edge padding
[0 0 0 0]
(default)  vector of nonnegative integers  "same"
Input edge padding, specified as one of these values:
"same"
— Add padding of size calculated by the software at training or prediction time so that the output has the same size as the input when the stride equals 1. If the stride is larger than 1, then the output size isceil(inputSize/stride)
, whereinputSize
is the height or width of the input andstride
is the stride in the corresponding dimension. The software adds the same amount of padding to the top and bottom, and to the left and right, if possible. If the padding that must be added vertically has an odd value, then the software adds extra padding to the bottom. If the padding that must be added horizontally has an odd value, then the software adds extra padding to the right.Nonnegative integer
p
— Add padding of sizep
to all the edges of the input.Vector
[a b]
of nonnegative integers — Add padding of sizea
to the top and bottom of the input and padding of sizeb
to the left and right.Vector
[t b l r]
of nonnegative integers — Add padding of sizet
to the top,b
to the bottom,l
to the left, andr
to the right of the input.
Example:
Padding=1
adds one row of padding to the top and bottom, and one
column of padding to the left and right of the input.
Example:
Padding="same"
adds padding so that the output has the same size as
the input (if the stride equals 1).
PaddingValue
— Value used to pad input
0
(default)  "mean"
Value used to pad input, specified as 0
or
"mean"
.
When you use the Padding
option to add
padding to the input, the value of the padding applied can be one of
the following:
0
— Input is padded with zeros at the positions specified by thePadding
property. The padded areas are included in the calculation of the average value of the pooling regions along the edges."mean"
— Input is padded with the mean of the pooling region at the positions specified by thePadding
option. The padded areas are effectively excluded from the calculation of the average value of each pooling region.
Name
— Layer name
""
(default)  character vector  string scalar
Properties
Average Pooling
PoolSize
— Dimensions of pooling regions
vector of two positive integers
Dimensions of the pooling regions, specified as a vector of two positive integers
[h w]
, where h
is the height and
w
is the width. When creating the layer, you can specify
PoolSize
as a scalar to use the same value for both
dimensions.
If the stride dimensions Stride
are less than the respective
pooling dimensions, then the pooling regions overlap.
The padding dimensions PaddingSize
must be less than the pooling
region dimensions PoolSize
.
Example:
[2 1]
specifies pooling regions of height 2 and width
1.
Stride
— Step size for traversing input
[1 1]
(default)  vector of two positive integers
Step size for traversing the input vertically and horizontally, specified as a vector
of two positive integers [a b]
, where a
is the
vertical step size and b
is the horizontal step size. When creating
the layer, you can specify Stride
as a scalar to use the same value
for both dimensions.
If the stride dimensions Stride
are less than the respective
pooling dimensions, then the pooling regions overlap.
The padding dimensions PaddingSize
must be less than the pooling
region dimensions PoolSize
.
Example:
[2 3]
specifies a vertical step size of 2 and a horizontal step size
of 3.
PaddingSize
— Size of padding
[0 0 0 0]
(default)  vector of four nonnegative integers
Size of padding to apply to input borders, specified as a vector
[t b l r]
of four nonnegative
integers, where t
is the padding applied to
the top, b
is the padding applied to the
bottom, l
is the padding applied to the left,
and r
is the padding applied to the right.
When you create a layer, use the 'Padding'
namevalue pair argument to specify the padding size.
Example:
[1 1 2 2]
adds one row of padding to the top
and bottom, and two columns of padding to the left and right of
the input.
PaddingMode
— Method to determine padding size
"manual"
(default)  "same"
Method to determine padding size, specified as "manual"
or
"same"
.
The software automatically sets the value of PaddingMode
based on the Padding
value you specify
when creating a layer.
If you set the
Padding
option to a scalar or a vector of nonnegative integers, then the software automatically setsPaddingMode
to"manual"
.If you set the
Padding
option to"same"
, then the software automatically setsPaddingMode
to'same'
and calculates the size of the padding at training time so that the output has the same size as the input when the stride equals 1. If the stride is larger than 1, then the output size isceil(inputSize/stride)
, whereinputSize
is the height or width of the input andstride
is the stride in the corresponding dimension. The software adds the same amount of padding to the top and bottom, and to the left and right, if possible. If the padding that must be added vertically has an odd value, then the software adds extra padding to the bottom. If the padding that must be added horizontally has an odd value, then the software adds extra padding to the right.
PaddingValue
— Value used to pad input
0
(default)  "mean"
Value used to pad input, specified as 0
or
"mean"
.
When you use the Padding
option to add padding to the input, the value of
the padding applied can be one of the following:
0
— Input is padded with zeros at the positions specified by thePadding
property. The padded areas are included in the calculation of the average value of the pooling regions along the edges."mean"
— Input is padded with the mean of the pooling region at the positions specified by thePadding
option. The padded areas are effectively excluded from the calculation of the average value of each pooling region.
Padding
— Size of padding
[0 0]
(default)  vector of two nonnegative integers
Note
Padding
property will be removed in a future release. Use
PaddingSize
instead. When creating a layer, use the
Padding
namevalue argument to specify the padding
size.
Size of padding to apply to input borders vertically and horizontally, specified as a
vector [a b]
of two nonnegative integers, where a
is the padding applied to the top and bottom of the input data and b
is the padding applied to the left and right.
Example:
[1 1]
adds one row of padding to the top and bottom, and one column
of padding to the left and right of the input.
Layer
Name
— Layer name
""
(default)  character vector  string scalar
NumInputs
— Number of inputs
1
(default)
This property is readonly.
Number of inputs to the layer, returned as 1
. This layer accepts a
single input only.
Data Types: double
InputNames
— Input names
{'in'}
(default)
This property is readonly.
Input names, returned as {'in'}
. This layer accepts a single input
only.
Data Types: cell
NumOutputs
— Number of outputs
1
(default)
This property is readonly.
Number of outputs from the layer, returned as 1
. This layer has a
single output only.
Data Types: double
OutputNames
— Output names
{'out'}
(default)
This property is readonly.
Output names, returned as {'out'}
. This layer has a single output
only.
Data Types: cell
Examples
Create Average Pooling Layer
Create an average pooling layer with the name avg1
.
layer = averagePooling2dLayer(2,Name="avg1")
layer = AveragePooling2DLayer with properties: Name: 'avg1' Hyperparameters PoolSize: [2 2] Stride: [1 1] PaddingMode: 'manual' PaddingSize: [0 0 0 0] PaddingValue: 0
Include an average pooling layer in a Layer
array.
layers = [ ...
imageInputLayer([28 28 1])
convolution2dLayer(5,20)
reluLayer
averagePooling2dLayer(2)
fullyConnectedLayer(10)
softmaxLayer]
layers = 6x1 Layer array with layers: 1 '' Image Input 28x28x1 images with 'zerocenter' normalization 2 '' 2D Convolution 20 5x5 convolutions with stride [1 1] and padding [0 0 0 0] 3 '' ReLU ReLU 4 '' 2D Average Pooling 2x2 average pooling with stride [1 1] and padding [0 0 0 0] 5 '' Fully Connected 10 fully connected layer 6 '' Softmax softmax
Create Average Pooling Layer with Nonoverlapping Pooling Regions
Create an average pooling layer with nonoverlapping pooling regions.
layer = averagePooling2dLayer(2,'Stride',2)
layer = AveragePooling2DLayer with properties: Name: '' Hyperparameters PoolSize: [2 2] Stride: [2 2] PaddingMode: 'manual' PaddingSize: [0 0 0 0] PaddingValue: 0
The height and width of the rectangular regions (pool size) are both 2. The pooling regions do not overlap because the step size for traversing the images vertically and horizontally (stride) is also 2.
Include an average pooling layer with nonoverlapping regions in a Layer
array.
layers = [ ... imageInputLayer([28 28 1]) convolution2dLayer(5,20) reluLayer averagePooling2dLayer(2,'Stride',2) fullyConnectedLayer(10) softmaxLayer]
layers = 6x1 Layer array with layers: 1 '' Image Input 28x28x1 images with 'zerocenter' normalization 2 '' 2D Convolution 20 5x5 convolutions with stride [1 1] and padding [0 0 0 0] 3 '' ReLU ReLU 4 '' 2D Average Pooling 2x2 average pooling with stride [2 2] and padding [0 0 0 0] 5 '' Fully Connected 10 fully connected layer 6 '' Softmax softmax
Create Average Pooling Layer with Overlapping Pooling Regions
Create an average pooling layer with overlapping pooling regions.
layer = averagePooling2dLayer([3 2],'Stride',2)
layer = AveragePooling2DLayer with properties: Name: '' Hyperparameters PoolSize: [3 2] Stride: [2 2] PaddingMode: 'manual' PaddingSize: [0 0 0 0] PaddingValue: 0
This layer creates pooling regions of size [3 2] and takes the average of the six elements in each region. The pooling regions overlap because Stride
includes dimensions that are less than the respective pooling dimensions PoolSize
.
Include an average pooling layer with overlapping pooling regions in a Layer
array.
layers = [ ... imageInputLayer([28 28 1]) convolution2dLayer(5,20) reluLayer averagePooling2dLayer([3 2],'Stride',2) fullyConnectedLayer(10) softmaxLayer]
layers = 6x1 Layer array with layers: 1 '' Image Input 28x28x1 images with 'zerocenter' normalization 2 '' 2D Convolution 20 5x5 convolutions with stride [1 1] and padding [0 0 0 0] 3 '' ReLU ReLU 4 '' 2D Average Pooling 3x2 average pooling with stride [2 2] and padding [0 0 0 0] 5 '' Fully Connected 10 fully connected layer 6 '' Softmax softmax
Algorithms
2D Average Pooling Layer
A 2D average pooling layer performs downsampling by dividing the input into rectangular pooling regions, then computing the average of each region.
The dimensions that the layer pools over depends on the layer input:
For 2D image input (data with four dimensions corresponding to pixels in two spatial dimensions, the channels, and the observations), the layer pools over the spatial dimensions.
For 2D image sequence input (data with five dimensions corresponding to the pixels in two spatial dimensions, the channels, the observations, and the time steps), the layer pools over the spatial dimensions.
For 1D image sequence input (data with four dimensions corresponding to the pixels in one spatial dimension, the channels, the observations, and the time steps), the layer pools over the spatial and time dimensions.
Layer Input and Output Formats
Layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray
objects.
The format of a dlarray
object is a string of characters in which each
character describes the corresponding dimension of the data. The formats consist of one or
more of these characters:
"S"
— Spatial"C"
— Channel"B"
— Batch"T"
— Time"U"
— Unspecified
For example, you can describe 2D image data that is represented as a 4D array, where the
first two dimensions correspond to the spatial dimensions of the images, the third
dimension corresponds to the channels of the images, and the fourth dimension
corresponds to the batch dimension, as having the format "SSCB"
(spatial, spatial, channel, batch).
You can interact with these dlarray
objects in automatic differentiation
workflows, such as those for developing a custom layer, using a functionLayer
object, or using the forward
and predict
functions with
dlnetwork
objects.
This table shows the supported input formats of AveragePooling2DLayer
objects and the
corresponding output format. If the software passes the output of the layer to a custom
layer that does not inherit from the nnet.layer.Formattable
class, or a
FunctionLayer
object with the Formattable
property
set to 0
(false
), then the layer receives an
unformatted dlarray
object with dimensions ordered according to the formats
in this table. The formats listed here are only a subset. The layer may support additional
formats such as formats with additional "S"
(spatial) or
"U"
(unspecified) dimensions.
Input Format  Output Format 



















References
[1] Nagi, J., F. Ducatelle, G. A. Di Caro, D. Ciresan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, L. M. Gambardella. ''MaxPooling Convolutional Neural Networks for Visionbased Hand Gesture Recognition''. IEEE International Conference on Signal and Image Processing Applications (ICSIPA2011), 2011.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
You can generate C/C++ code using
"mean"
setting forPaddingValue
property.For Simulink^{®} models that implement deep learning functionality using MATLAB Function block, simulation errors out if the network contains an average pooling layer with nonzero padding value. In such cases, use the blocks from the Deep Neural Networks library instead of a MATLAB Function to implement the deep learning functionality.
Code generation does not support passing
dlarray
objects with unspecified (U) dimensions to this layer.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
You can generate code using the NVIDIA^{®} cuDNN and TensorRT libraries by using the
"mean"
setting for thePaddingValue
property if the padding size is symmetric. For example, specifyPaddingSize
property as the vector[2 2 3 3]
to add two rows of padding to the top and bottom, and three columns of padding to the left and right of the input.For Simulink models that implement deep learning functionality using MATLAB Function block, simulation errors out if the network contains an average pooling layer with nonzero padding value. In such cases, use the blocks from the Deep Neural Networks library instead of a MATLAB Function to implement the deep learning functionality.
Code generation does not support passing
dlarray
objects with unspecified (U) dimensions to this layer.
Version History
Introduced in R2016a
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