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imageInputLayer

Image input layer

Description

An image input layer inputs 2-D images to a network and applies data normalization.

For 3-D image input, use image3dInputLayer.

Creation

Description

layer = imageInputLayer(inputSize) returns an image input layer and specifies the InputSize property.

example

layer = imageInputLayer(inputSize,Name,Value) sets the optional Normalization, NormalizationDimension, Mean, StandardDeviation, Min, Max, SplitComplexInputs, and Name properties using one or more name-value arguments. Enclose the property names in quotes.

Properties

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Image Input

This property is read-only.

Size of the input data, specified as a row vector of integers [h w c], where h, w, and c correspond to the height, width, and number of channels respectively.

  • For grayscale images, specify a vector with c equal to 1.

  • For RGB images, specify a vector with c equal to 3.

  • For multispectral or hyperspectral images, specify a vector with c equal to the number of channels.

For 3-D image or volume input, use image3dInputLayer.

Example: [224 224 3]

This property is read-only.

Data normalization to apply every time data is forward propagated through the input layer, specified as one of the following:

  • 'zerocenter' — Subtract the mean specified by Mean.

  • 'zscore' — Subtract the mean specified by Mean and divide by StandardDeviation.

  • 'rescale-symmetric' — Rescale the input to be in the range [-1, 1] using the minimum and maximum values specified by Min and Max, respectively.

  • 'rescale-zero-one' — Rescale the input to be in the range [0, 1] using the minimum and maximum values specified by Min and Max, respectively.

  • 'none' — Do not normalize the input data.

  • function handle — Normalize the data using the specified function. The function must be of the form Y = func(X), where X is the input data and the output Y is the normalized data.

Tip

The software, by default, automatically calculates the normalization statistics when using the trainNetwork function. To save time when training, specify the required statistics for normalization and set the ResetInputNormalization option in trainingOptions to 0 (fasle).

Normalization dimension, specified as one of the following:

  • 'auto' – If the training option is false and you specify any of the normalization statistics (Mean, StandardDeviation, Min, or Max), then normalize over the dimensions matching the statistics. Otherwise, recalculate the statistics at training time and apply channel-wise normalization.

  • 'channel' – Channel-wise normalization.

  • 'element' – Element-wise normalization.

  • 'all' – Normalize all values using scalar statistics.

Data Types: char | string

Mean for zero-center and z-score normalization, specified as a h-by-w-by-c array, a 1-by-1-by-c array of means per channel, a numeric scalar, or [], where h, w, and c correspond to the height, width, and the number of channels of the mean, respectively.

If you specify the Mean property, then Normalization must be 'zerocenter' or 'zscore'. If Mean is [], then the trainNetwork function calculates the mean. To train a dlnetwork object using a custom training loop or assemble a network without training it using the assembleNetwork function, you must set the Mean property to a numeric scalar or a numeric array.

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

Standard deviation for z-score normalization, specified as a h-by-w-by-c array, a 1-by-1-by-c array of means per channel, a numeric scalar, or [], where h, w, and c correspond to the height, width, and the number of channels of the standard deviation, respectively.

If you specify the StandardDeviation property, then Normalization must be 'zscore'. If StandardDeviation is [], then the trainNetwork function calculates the standard deviation. To train a dlnetwork object using a custom training loop or assemble a network without training it using the assembleNetwork function, you must set the StandardDeviation property to a numeric scalar or a numeric array.

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

Minimum value for rescaling, specified as a h-by-w-by-c array, a 1-by-1-by-c array of minima per channel, a numeric scalar, or [], where h, w, and c correspond to the height, width, and the number of channels of the minima, respectively.

If you specify the Min property, then Normalization must be 'rescale-symmetric' or 'rescale-zero-one'. If Min is [], then the trainNetwork function calculates the minima. To train a dlnetwork object using a custom training loop or assemble a network without training it using the assembleNetwork function, you must set the Min property to a numeric scalar or a numeric array.

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

Maximum value for rescaling, specified as a h-by-w-by-c array, a 1-by-1-by-c array of maxima per channel, a numeric scalar, or [], where h, w, and c correspond to the height, width, and the number of channels of the maxima, respectively.

If you specify the Max property, then Normalization must be 'rescale-symmetric' or 'rescale-zero-one'. If Max is [], then the trainNetwork function calculates the maxima. To train a dlnetwork object using a custom training loop or assemble a network without training it using the assembleNetwork function, you must set the Max property to a numeric scalar or a numeric array.

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

This property is read-only.

Flag to split input data into real and imaginary components specified as one of these values:

  • 0 (false) – Do not split input data.

  • 1 (true) – Split data into real and imaginary components.

When SplitComplexInputs is 1, then the layer outputs twice as many channels as the input data. For example, if the input data is complex-values with numChannels channels, then the layer outputs data with 2*numChannels channels, where channels 1 through numChannels contain the real components of the input data and numChannels+1 through 2*numChannels contain the imaginary components of the input data. If the input data is real, then channels numChannels+1 through 2*numChannels are all zero.

To input complex-valued data into a network, the SplitComplexInputs option of the input layer must be 1.

For an example showing how to train a network with complex-valued data, see Train Network with Complex-Valued Data.

This property is read-only.

Note

The DataAugmentation property is not recommended. To preprocess images with cropping, reflection, and other geometric transformations, use augmentedImageDatastore instead.

Data augmentation transforms to use during training, specified as one of the following.

  • 'none' — No data augmentation

  • 'randcrop' — Take a random crop from the training image. The random crop has the same size as the input size.

  • 'randfliplr' — Randomly flip the input images horizontally with a 50% chance.

  • Cell array of 'randcrop' and 'randfliplr'. The software applies the augmentation in the order specified in the cell array.

Augmentation of image data is another way of reducing overfitting [1], [2].

Data Types: string | char | cell

Layer

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

This property is read-only.

Number of inputs of the layer. The layer has no inputs.

Data Types: double

This property is read-only.

Input names of the layer. The layer has no inputs.

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

Examples

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Create an image input layer for 28-by-28 color images with name 'input'. By default, the layer performs data normalization by subtracting the mean image of the training set from every input image.

inputlayer = imageInputLayer([28 28 3],'Name','input')
inputlayer = 
  ImageInputLayer with properties:

                      Name: 'input'
                 InputSize: [28 28 3]
        SplitComplexInputs: 0

   Hyperparameters
          DataAugmentation: 'none'
             Normalization: 'zerocenter'
    NormalizationDimension: 'auto'
                      Mean: []

Include an image input layer in a Layer array.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer]
layers = 
  7x1 Layer array with layers:

     1   ''   Image Input             28x28x1 images with 'zerocenter' normalization
     2   ''   2-D Convolution         20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                    ReLU
     4   ''   2-D Max Pooling         2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     5   ''   Fully Connected         10 fully connected layer
     6   ''   Softmax                 softmax
     7   ''   Classification Output   crossentropyex

References

[1] Krizhevsky, A., I. Sutskever, and G. E. Hinton. "ImageNet Classification with Deep Convolutional Neural Networks". Advances in Neural Information Processing Systems. Vol 25, 2012.

[2] Cireşan, D., U. Meier, J. Schmidhuber. "Multi-column Deep Neural Networks for Image Classification". IEEE Conference on Computer Vision and Pattern Recognition, 2012.

Extended Capabilities

Version History

Introduced in R2016a

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