replaceLayer

Replace layer in layer graph or network

Syntax

lgraphUpdated = replaceLayer(lgraph,layerName,larray)

netUpdated = replaceLayer(net,layerName,larray)

___ = replaceLayer(___,'ReconnectBy',mode)

Description

lgraphUpdated = replaceLayer(lgraph,layerName,larray) replaces the layer layerName in the layer graph lgraph with the layers in larray.

replaceLayer connects the layers in larray sequentially and connects larray into the layer graph.

netUpdated = replaceLayer(net,layerName,larray) replaces the layer layerName in the dlnetwork object net with the layers in larray.

replaceLayer connects the layers in larray sequentially and connects larray into the network.

___ = replaceLayer(___,'ReconnectBy',mode) additionally specifies the method of reconnecting layers.

Examples

Replace Layer in Layer Graph

Define a simple network architecture and plot it.

layers = [
    imageInputLayer([28 28 1],'Name','input')
    convolution2dLayer(3,16,'Padding','same')
    reluLayer('Name','relu_1')
    additionLayer(2,'Name','add')
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

lgraph = layerGraph(layers);
lgraph = connectLayers(lgraph,'input','add/in2');

figure
plot(lgraph)

Replace the ReLU layer in the network with a batch normalization layer followed by a leaky ReLU layer.

layers = [
    batchNormalizationLayer
    leakyReluLayer(0.1)];
lgraph = replaceLayer(lgraph,'relu_1',layers);

plot(lgraph)

Assemble Network from Pretrained Keras Layers

This example shows how to import the layers from a pretrained Keras network, replace the unsupported layers with custom layers, and assemble the layers into a network ready for prediction.

Import Keras Network

Import the layers from a Keras network model. The network in 'digitsDAGnetwithnoise.h5' classifies images of digits.

filename = 'digitsDAGnetwithnoise.h5';
lgraph = importKerasLayers(filename,'ImportWeights',true);

Warning: 'importKerasLayers' is not recommended and will be removed in a future release. To import TensorFlow-Keras models, save using the SavedModel format and use importNetworkFromTensorFlow function.

Warning: Unable to import some Keras layers, because they are not supported by the Deep Learning Toolbox. They have been replaced by placeholder layers. To find these layers, call the function findPlaceholderLayers on the returned object.

The Keras network contains some layers that are not supported by Deep Learning Toolbox. The importKerasLayers function displays a warning and replaces the unsupported layers with placeholder layers.

Plot the layer graph using plot.

figure
plot(lgraph)
title("Imported Network")

Replace Placeholder Layers

To replace the placeholder layers, first identify the names of the layers to replace. Find the placeholder layers using findPlaceholderLayers.

placeholderLayers = findPlaceholderLayers(lgraph)

placeholderLayers = 
  2x1 PlaceholderLayer array with layers:

     1   'gaussian_noise_1'   GaussianNoise   Placeholder for 'GaussianNoise' Keras layer
     2   'gaussian_noise_2'   GaussianNoise   Placeholder for 'GaussianNoise' Keras layer

Display the Keras configurations of these layers.

placeholderLayers.KerasConfiguration

ans = struct with fields:
        trainable: 1
             name: 'gaussian_noise_1'
           stddev: 1.5000
    inbound_nodes: {{1x1 cell}}

ans = struct with fields:
        trainable: 1
             name: 'gaussian_noise_2'
           stddev: 0.7000
    inbound_nodes: {{1x1 cell}}

Create two Gaussian noise layers with the same configurations as the imported Keras layers using the helper gaussianNoiseLayer function.

gnLayer1 = gaussianNoiseLayer(1.5,'new_gaussian_noise_1');
gnLayer2 = gaussianNoiseLayer(0.7,'new_gaussian_noise_2');

Replace the placeholder layers with the custom layers using replaceLayer.

lgraph = replaceLayer(lgraph,'gaussian_noise_1',gnLayer1);
lgraph = replaceLayer(lgraph,'gaussian_noise_2',gnLayer2);

Plot the updated layer graph using plot.

figure
plot(lgraph)
title("Network with Replaced Layers")

Specify Class Names

If the imported classification layer does not contain the classes, then you must specify these before prediction. If you do not specify the classes, then the software automatically sets the classes to 1, 2, ..., N, where N is the number of classes.

Find the index of the classification layer by viewing the Layers property of the layer graph.

lgraph.Layers

ans = 
  15x1 Layer array with layers:

     1   'input_1'                            Image Input             28x28x1 images
     2   'conv2d_1'                           2-D Convolution         20 7x7x1 convolutions with stride [1  1] and padding 'same'
     3   'conv2d_1_relu'                      ReLU                    ReLU
     4   'conv2d_2'                           2-D Convolution         20 3x3x1 convolutions with stride [1  1] and padding 'same'
     5   'conv2d_2_relu'                      ReLU                    ReLU
     6   'new_gaussian_noise_1'               Gaussian Noise          Gaussian noise with standard deviation 1.5
     7   'new_gaussian_noise_2'               Gaussian Noise          Gaussian noise with standard deviation 0.7
     8   'max_pooling2d_1'                    2-D Max Pooling         2x2 max pooling with stride [2  2] and padding 'same'
     9   'max_pooling2d_2'                    2-D Max Pooling         2x2 max pooling with stride [2  2] and padding 'same'
    10   'flatten_1'                          Keras Flatten           Flatten activations into 1-D assuming C-style (row-major) order
    11   'flatten_2'                          Keras Flatten           Flatten activations into 1-D assuming C-style (row-major) order
    12   'concatenate_1'                      Depth concatenation     Depth concatenation of 2 inputs
    13   'dense_1'                            Fully Connected         10 fully connected layer
    14   'activation_1'                       Softmax                 softmax
    15   'ClassificationLayer_activation_1'   Classification Output   crossentropyex

The classification layer has the name 'ClassificationLayer_activation_1'. View the classification layer and check the Classes property.

cLayer = lgraph.Layers(end)

cLayer = 
  ClassificationOutputLayer with properties:

            Name: 'ClassificationLayer_activation_1'
         Classes: 'auto'
    ClassWeights: 'none'
      OutputSize: 'auto'

   Hyperparameters
    LossFunction: 'crossentropyex'

Because the Classes property of the layer is 'auto', you must specify the classes manually. Set the classes to 0, 1, ..., 9, and then replace the imported classification layer with the new one.

cLayer.Classes = string(0:9)

cLayer = 
  ClassificationOutputLayer with properties:

            Name: 'ClassificationLayer_activation_1'
         Classes: [0    1    2    3    4    5    6    7    8    9]
    ClassWeights: 'none'
      OutputSize: 10

   Hyperparameters
    LossFunction: 'crossentropyex'

lgraph = replaceLayer(lgraph,'ClassificationLayer_activation_1',cLayer);

Assemble Network

Assemble the layer graph using assembleNetwork. The function returns a DAGNetwork object that is ready to use for prediction.

net = assembleNetwork(lgraph)

net = 
  DAGNetwork with properties:

         Layers: [15x1 nnet.cnn.layer.Layer]
    Connections: [15x2 table]
     InputNames: {'input_1'}
    OutputNames: {'ClassificationLayer_activation_1'}

Input Arguments

lgraph — Layer graph

LayerGraph object

Layer graph, specified as a LayerGraph object. To create a layer graph, use layerGraph.

net — Neural network

dlnetwork object

Neural network, specified as a dlnetwork object.

layerName — Name of layer to replace

string scalar | character vector

Name of the layer to replace, specified as a string scalar or a character vector.

larray — Network layers

Layer array

Network layers, specified as a Layer array.

For a list of built-in layers, see List of Deep Learning Layers.

mode — Method to reconnect layers

'name' (default) | 'order'

Method to reconnect layers specified as one of the following:

• 'name' – Reconnect larray using the input and output names of the replaced layer. For each layer connected to an input of the replaced layer, reconnect the layer to the input of the same input name of larray(1). For each layer connected to an output of the replaced layer, reconnect the layer to the output of the same output name of larray(end).

• 'order' – Reconnect larray using the order of the input names of larray(1) and the output names of larray(end). Reconnect the layer connected to the ith input of the replaced layer to the ith input of larray(1). Reconnect the layer connected to the jth output of the replaced layer to the jth output of larray(end).

Data Types: char | string

Output Arguments

lgraphUpdated — Updated layer graph

LayerGraph object

Updated layer graph, returned as a LayerGraph object.

netUpdated — Updated network

dlnetwork object

Updated network, returned as an uninitialized dlnetwork object.

To initialize the learnable parameters of a dlnetwork object, use the initialize function.

The replaceLayer function does not preserve quantization information. If the input network is a quantized network, then the output network does not contain quantization information.

Version History

Introduced in R2018b