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Flatten layer


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.

This layer supports sequence input only.



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'.


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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 Name set to ''.

Data Types: char | string

This property is read-only.

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

Data Types: double

This property is read-only.

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

Data Types: cell

This property is read-only.

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

Data Types: double

This property is read-only.

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

Data Types: cell

Object Functions


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Create a flatten layer with the name 'flatten1'.

layer = flattenLayer('Name','flatten1')
layer = 
  FlattenLayer with properties:

    Name: 'flatten1'

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 = [ ...
    fullyConnectedLayer(numClasses, 'Name','fc')

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 contains an axes object. The axes object contains an object of type graphplot.

Extended Capabilities

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

Introduced in R2019a