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resnetNetwork

2-D residual neural network

Since R2024a

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    Syntax

    net = resnetNetwork(inputSize,numClasses)
    net = resnetNetwork(inputSize,numClasses,Name=Value)

    Description

    net = resnetNetwork(inputSize,numClasses) creates a 2-D residual neural network with the specified image input size and number of classes.

    To create a 3-D residual network, use resnet3dNetwork.

    example

    net = resnetNetwork(inputSize,numClasses,Name=Value) specifies additional options using one or more name-value arguments. For example, BottleneckType="none" returns a 2-D residual neural network without bottleneck components.

    Tip

    To load a pretrained ResNet neural network, use the imagePretrainedNetwork function.

    example

    Examples

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    Open Live Script

    Create a residual network with a bottleneck architecture.

    imageSize = [224 224 3];
numClasses = 10;

net = resnetNetwork(imageSize,numClasses)
    net = 
  dlnetwork with properties:

         Layers: [176x1 nnet.cnn.layer.Layer]
    Connections: [191x2 table]
     Learnables: [214x3 table]
          State: [106x3 table]
     InputNames: {'input'}
    OutputNames: {'softmax'}
    Initialized: 1

  View summary with summary.

    Analyze the network using the analyzeNetwork function. Note that this network is equivalent to a ResNet-50 residual neural network.

    analyzeNetwork(net)

    Open Live Script

    Create a ResNet-101 network using a custom stack depth. 

    imageSize = [224 224 3];
numClasses = 10;

stackDepth = [3 4 23 3];
numFilters = [64 128 256 512];

net = resnetNetwork(imageSize,numClasses, ...
    StackDepth=stackDepth, ...
    NumFilters=numFilters)
    net = 
  dlnetwork with properties:

         Layers: [346x1 nnet.cnn.layer.Layer]
    Connections: [378x2 table]
     Learnables: [418x3 table]
          State: [208x3 table]
     InputNames: {'input'}
    OutputNames: {'softmax'}
    Initialized: 1

  View summary with summary.

    Analyze the network.

    analyzeNetwork(net)

    Input Arguments

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    Network image input size, specified as one of these values:

    • Vector of positive integers of the form [h w] — Input has a height and width of h and w, respectively.

    • Vector of positive integers of the form [h w c] — Input has a height, width, and number of channels of h, w, and c, respectively. For RGB images, c is 3, and for grayscale images, c is 1.

    The values of inputSize depend on the InitialPoolingLayer argument:

    • If InitialPoolingLayer is "max" or "average", then the spatial dimension sizes must be greater than or equal to k*2^(D+1), where k is the value of InitialStride in the first convolutional layer in the corresponding direction, and D is the number of downsampling blocks.

    • If InitialPoolingLayer is "none", then the spatial dimension sizes must be greater than or equal to k*2^D, where k is the value of InitialStride in the first convolutional layer in the corresponding direction.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Number of classes for classification tasks, specified as a positive integer.

    The function returns a neural network for classification tasks with the specified number of classes by setting the output size of the last fully connected layer to numClasses.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Name-Value Arguments

    Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

    Example: net = resnetNetwork(inputSize,numClasses,BottleneckType="none") returns a 2-D residual neural network without bottleneck components.

    Initial Layers

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    Filter size in the first convolutional layer, specified as one of these values:

    • Positive integer — First convolutional layer has filters with a height and width of the specified value.

    • Vector of positive integers of the form [h w] — First convolutional layer has filters with a height and width of h and w, respectively.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Number of filters in the first convolutional layer, specified as a positive integer. The number of initial filters determines the number of channels (feature maps) in the output of the first convolutional layer in the residual network.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Stride in the first convolutional layer, specified as one of these values:

    • Positive integer — First convolutional layer has a vertical and horizontal stride of the specified value.

    • Vector of positive integers of the form [h w] — First convolutional layer has a vertical and horizontal stride of h and w, respectively.

    The stride defines the step size for traversing the input vertically and horizontally.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    First pooling layer before the initial residual block, specified as one of these values:

    • "max" — Use a max pooling layer before the initial residual block. For more information, see maxPooling2dLayer.

    • "average" — Use an average pooling layer before the initial residual block. For more information, see averagePooling2dLayer.

    • "none"— Do not use a pooling layer before the initial residual block.

    Network Architecture

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    Residual block type, specified as one of these values:

    • "batchnorm-before-add" — Include the batch normalization layer before the addition layer in the residual blocks [1].

    • "batchnorm-after-add" — Include the batch normalization layer after the addition layer in the residual blocks [2].

    The ResidualBlockType argument specifies the location of the batch normalization layer in the standard and downsampling residual blocks. For more information, see Residual Network.

    Block bottleneck type, specified as one of these values:

    • "downsample-first-conv" — Use bottleneck residual blocks that perform downsampling, using a stride of 2, in the first convolutional layer of the downsampling residual blocks. A bottleneck residual block consists of three layers: a convolutional layer with filters of size 1 for downsampling the channel dimension, a convolutional layer with filters of size 3, and a convolutional layer with filters of size 1 for upsampling the channel dimension.

      The number of filters in the final convolutional layer is four times that in the first two convolutional layers.

    • "none" — Do not use bottleneck residual blocks. The residual blocks consist of two convolutional layers with filters of size 3.

    A bottleneck block reduces the number of channels by a factor of four by performing a convolution with filters of size 1 before performing convolution with filters of size 3. Networks with and without bottleneck blocks have a similar level of computational complexity, but the total number of features propagating in the residual connections is four times larger when you use bottleneck units. Therefore, using a bottleneck increases the efficiency of the network [1].

    For more information on the layers in each residual block, see Residual Network.

    Number of residual blocks in each stack, specified as a vector of positive integers.

    For example, if the stack depth is [3 4 6 3], the network has four stacks, with three blocks, four blocks, six blocks, and three blocks.

    Specify the number of filters in the convolutional layers of each stack using the NumFilters argument. StackDepth must have the same number of elements as NumFilters.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Number of filters in the convolutional layers of each stack, specified as a vector of positive integers.

    • If BottleneckType is "downsample-first-conv", then the number of filters in each of the first two convolutional layers in each block of each stack is NumFilters. The final convolutional layer has four times the number of filters in each of the first two convolutional layers.

      For example, if NumFilters is [4 5] and BottleneckType is "downsample-first-conv", then in the first stack, the first two convolutional layers in each block have 4 filters and the final convolutional layer in each block has 16 filters. In the second stack, the first two convolutional layers in each block have 5 filters and the final convolutional layer has 20 filters.

    • If BottleneckType is "none", then the number of filters in each convolutional layer in each stack is NumFilters.

    NumFilters must have the same number of elements as StackDepth.

    The NumFilters value determines the layers on the residual connection in the initial residual block. The residual connection has a convolutional layer when you meet one of these conditions:

    • BottleneckType is "downsample-first-conv", and InitialNumFilters is not equal to four times the first element of NumFilters.

    • BottleneckType is "none", and InitialNumFilters is not equal to the first element of NumFilters.

    For more information about the layers in each residual block, see Residual Network.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Data normalization to apply every time data forward-propagates through the input layer, specified as one of these options:

    • "zerocenter" — Subtract the mean of the training data.

    • "zscore" — Subtract the mean and then divide by the standard deviation of the training data.

    The trainnet function automatically calculates the mean and standard deviation of the training data.

    Flag to initialize learnable parameters, specified as a logical 1 (true) or 0 (false).

    Output Arguments

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    Residual neural network, returned as a dlnetwork object.

    More About

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    Residual Network

    Residual networks (ResNets) are a type of deep network that consists of building blocks that have residual connections (also known as skip or shortcut connections). These connections allow the input to skip the convolutional units of the main branch, thus providing a simpler path through the network. By allowing the parameter gradients to flow more easily from the final layers to the earlier layers of the network, residual connections mitigate the problem of vanishing gradients during early training.

    The structure of a residual network is flexible. The key component is the inclusion of the residual connections within residual blocks. A group of residual blocks is called a stack. A ResNet architecture consists of initial layers, followed by stacks containing residual blocks, and then the final layers. A network has three types of residual blocks:

    • Initial residual block — This block occurs at the start of the first stack. The layers in the residual connection of the initial residual block determine if the block preserves the activation sizes or performs downsampling.

    • Standard residual block — This block occurs multiple times in each stack, after the first downsampling residual block. The standard residual block preserves the activation sizes.

    • Downsampling residual block — This block occurs once, at the start of each stack. The first convolutional unit in the downsampling block downsamples the spatial dimensions by a factor of two.

    A typical stack has a downsampling residual block, followed by m standard residual blocks, where m is a positive integer. The first stack is the only stack that begins with an initial residual block.

    Diagram showing N stacks connected in series.

    The initial, standard, and downsampling residual blocks can be bottleneck or nonbottleneck blocks.

    A bottleneck block reduces the number of channels by a factor of four by performing a convolution with filters of size 1 before performing convolution with filters of size 3. Networks with and without bottleneck blocks have a similar level of computational complexity, but the total number of features propagating in the residual connections is four times larger when you use bottleneck units. Therefore, using a bottleneck increases the efficiency of the network [1].

    The options you set determine the layers inside each block.

    Block Layers

    NameInitial LayersInitial Residual BlockStandard Residual Block (BottleneckType="downsample-first-conv")Standard Residual Block (BottleneckType="none")Downsampling Residual BlockFinal Layers
    Description

    A residual network starts with these layers, in order:

    The main branch of the initial residual block has the same layers as a standard residual block.

    The InitialNumFilters and NumFilters values determine the layers on the residual connection. The residual connection has a convolutional layer with filters and a stride of size 1 when you meet one of these conditions:

    • BottleneckType is "downsample-first-conv" and InitialNumFilters is not equal to four times the first element of NumFilters.

    • BottleneckType is "none" and InitialNumFilters is not equal to the first element of NumFilters.

    If ResidualBlockType is "batchnorm-before-add", then the residual connection also has a batch normalization layer.

    The standard residual block with bottleneck units has these layers, in order:

    The standard block has a residual connection from the output of the previous block to the addition layer.

    Set the position of the addition layer using the ResidualBlockType argument.

    The standard residual block without bottleneck units has these layers, in order:

    The standard block has a residual connection from the output of the previous block to the addition layer.

    Set the position of the addition layer using the ResidualBlockType argument.

    The downsampling residual block is the same as the standard block (either with or without the bottleneck) but with a stride of size 2 in the first convolutional layer and additional layers on the residual connection.

    The layers on the residual connection depend on the value of ResidualBlockType.

    The downsampling block halves the height and width of the input, and increases the number of channels.

    A residual network ends with these layers, in order:

    Example Visualization

    Initial layers of a residual network.

    Example of an initial residual block for a network without a bottleneck and with the batch normalization layer before the addition layer.

    Example of an initial residual block in a residual network.

    Example of the standard residual block for a network with a bottleneck and with the batch normalization layer before the addition layer.

    Example of a standard residual block in a residual network with bottleneck units.

    Example of the standard residual block for a network without a bottleneck and with the batch normalization layer before the addition layer.

    Example of a standard residual block in a residual network without bottleneck units.

    Example of a downsampling residual block for a network without a bottleneck and with the batch normalization layer before the addition layer.

    Example of a downsampling residual block in a residual network without bottleneck units.

    Final layers of a residual network.

    The convolution and fully connected layer weights are initialized using the He weight initialization method [3].

    Tips

    • When working with small images, set the InitialPoolingLayer option to "none" to remove the initial pooling layer and reduce the amount of downsampling.

    • Residual networks are usually named ResNet-X, where X is the depth of the network. The depth of a network is defined as the largest number of sequential convolutional or fully connected layers on a path from the network input to the network output. You can use this formula to compute the depth of your network:

      depth = {1+2∑i=1Nsi+1       If no bottleneck1+3∑i=1Nsi+1            If bottleneck     ,

      where si is the depth of stack i.

    References

    [1] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Deep Residual Learning for Image Recognition.” Preprint, submitted December 10, 2015. https://arxiv.org/abs/1512.03385.

    [2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Identity Mappings in Deep Residual Networks.” Preprint, submitted July 25, 2016. https://arxiv.org/abs/1603.05027.

    [3] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification." In Proceedings of the 2015 IEEE International Conference on Computer Vision, 1026–34. Washington, DC: IEEE Computer Vision Society, 2015.

    Version History

    Introduced in R2024a

    See Also

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