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
layer = flattenLayer('Name',Name)
NumInputs— Number of inputs
Number of inputs of the layer. This layer accepts a single input only.
InputNames— Input names
Input names of the layer. This layer accepts a single input only.
NumOutputs— Number of outputs
Number of outputs of the layer. This layer has a single output only.
OutputNames— Output names
Output names of the layer. This layer has a single output only.
Create a flatten layer with the name
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 = [ ... 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