flattenLayer

Flatten layer

Description

A flatten layer collapses the spatial dimensions of the input into the channel dimension.

For example, if the input to the layer is an H-by-W-by-C-by-N-by-S array (sequences of images), then the flattened output is an (H*W*C)-by-N-by-S array.

Creation

Syntax

layer = flattenLayer

layer = flattenLayer('Name',Name)

Description

layer = flattenLayer creates a flatten layer.

layer = flattenLayer('Name',Name) sets the optional Name property using a name-value pair. For example, flattenLayer('Name','flatten1') creates a flatten layer with name 'flatten1'.

Properties

Name — Layer name

'' (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainNetwork, assembleNetwork, layerGraph, and dlnetwork functions automatically assign names to layers with the name ''.

Data Types: char | string

NumInputs — Number of inputs

1 (default)

This property is read-only.

Number of inputs of the layer. This layer accepts a single input only.

Data Types: double

InputNames — Input names

{'in'} (default)

This property is read-only.

Input names of the layer. This layer accepts a single input only.

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

Number of outputs of the layer. This layer has a single output only.

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names of the layer. This layer has a single output only.

Data Types: cell

Object Functions

Examples

Create Flatten Layer

Create a flatten layer with the name 'flatten1'.

layer = flattenLayer('Name','flatten1')

layer = 
  FlattenLayer with properties:

    Name: 'flatten1'

Create Network for Video Classification

Create a deep learning network for data containing sequences of images, such as video and medical image data.

• To input sequences of images into a network, use a sequence input layer.

• To apply convolutional operations independently to each time step, first convert the sequences of images to an array of images using a sequence folding layer.

• To restore the sequence structure after performing these operations, convert this array of images back to image sequences using a sequence unfolding layer.

• To convert images to feature vectors, use a flatten layer.

You can then input vector sequences into LSTM and BiLSTM layers.

Define Network Architecture

Create a classification LSTM network that classifies sequences of 28-by-28 grayscale images into 10 classes.

Define the following network architecture:

• A sequence input layer with an input size of [28 28 1].

• A convolution, batch normalization, and ReLU layer block with 20 5-by-5 filters.

• An LSTM layer with 200 hidden units that outputs the last time step only.

• A fully connected layer of size 10 (the number of classes) followed by a softmax layer and a classification layer.

To perform the convolutional operations on each time step independently, include a sequence folding layer before the convolutional layers. LSTM layers expect vector sequence input. To restore the sequence structure and reshape the output of the convolutional layers to sequences of feature vectors, insert a sequence unfolding layer and a flatten layer between the convolutional layers and the LSTM layer.

inputSize = [28 28 1];
filterSize = 5;
numFilters = 20;
numHiddenUnits = 200;
numClasses = 10;

layers = [ ...
    sequenceInputLayer(inputSize,'Name','input')
    
    sequenceFoldingLayer('Name','fold')
    
    convolution2dLayer(filterSize,numFilters,'Name','conv')
    batchNormalizationLayer('Name','bn')
    reluLayer('Name','relu')
    
    sequenceUnfoldingLayer('Name','unfold')
    flattenLayer('Name','flatten')
    
    lstmLayer(numHiddenUnits,'OutputMode','last','Name','lstm')
    
    fullyConnectedLayer(numClasses, 'Name','fc')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classification')];

Convert the layers to a layer graph and connect the miniBatchSize output of the sequence folding layer to the corresponding input of the sequence unfolding layer.

lgraph = layerGraph(layers);
lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize');

View the final network architecture using the plot function.

figure
plot(lgraph)

Algorithms

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 consists of one or more of these characters:

• "S" — Spatial

• "C" — Channel

• "B" — Batch

• "T" — Time

• "U" — Unspecified

For example, 2-D image data represented as a 4-D 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, can be described as having the format "SSCB" (spatial, spatial, channel, batch).

You can interact with these dlarray objects in automatic differentiation workflows such as 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 FlattenLayer objects and the corresponding output format. If the output of the layer is passed 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 corresponding to the formats in this table.

Input Format	Output Format
"CB" (channel, batch)	"CB" (channel, batch)
"SCB" (spatial, channel, batch)
"SSCB" (spatial, spatial, channel, batch)
"SSSCB" (spatial, spatial, channel, batch)
"CBT" (channel, batch, time)	"CBT" (channel, batch, time)
"SCBT" (spatial, channel, batch, time)
"SSCBT" (spatial, spatial, channel, batch, time)
"SSSCBT" (spatial, spatial, spatial, channel, batch, time)

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Version History

Introduced in R2019a