Supported neural network layers.
Each layer in a neural network configuration represents a unit of hidden units. When layers are stacked together, they represent a deep neural network.
All layers available in Eclipse Deeplearning4j can be used either in a MultiLayerNetwork or ComputationGraph. When configuring a neural network, you pass the layer configuration and the network will instantiate the layer for you.
If you are configuring complex networks such as InceptionV4, you will need to use the ComputationGraph API and join different branches together using vertices. Check the vertices for more information.
Activation layer is a simple layer that applies the specified activation function to the input activations
clone
param activation Activation function for the layer
activation
Activation function for the layer
activation
param activationFunction Activation function for the layer
activation
param activation Activation function for the layer
Dense layer: a standard fully connected feed forward layer
hasBias
If true (default): include bias parameters in the model. False: no bias.
hasLayerNorm
If true (default = false): enable layer normalization on this layer
Dropout layer. This layer simply applies dropout at training time, and passes activations through unmodified at test
build
Create a dropout layer with standard {- link Dropout}, with the specified probability of retaining the input activation. See {- link Dropout} for the full details
param dropout Activation retain probability.
Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to the equivalent one-hot representation. Mathematically, EmbeddingLayer is equivalent to using a DenseLayer with a one-hot representation for the input; however, it can be much more efficient with a large number of classes (as a dense layer + one-hot input does a matrix multiply with all but one value being zero). Note: can only be used as the first layer for a network Note 2: For a given example index i, the output is activationFunction(weights.getRow(i) + bias), hence the weight rows can be considered a vector/embedding for each example. Note also that embedding layer has an activation function (set to IDENTITY to disable) and optional bias (which is disabled by default)
hasBias
If true: include bias parameters in the layer. False (default): no bias.
weightInit
Initialize the embedding layer using the specified EmbeddingInitializer - such as a Word2Vec instance
param embeddingInitializer Source of the embedding layer weights
weightInit
Initialize the embedding layer using values from the specified array. Note that the array should have shape [vocabSize, vectorSize]. After copying values from the array to initialize the network parameters, the input array will be discarded (so that, if necessary, it can be garbage collected)
param vectors Vectors to initialize the embedding layer with
Embedding layer for sequences: feed-forward layer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1. This input thus has shape [numExamples, inputLength] or shape [numExamples, 1, inputLength]. The output of this layer is 3D (sequence/time series), namely of shape [numExamples, nOut, inputLength]. Note: can only be used as the first layer for a network Note 2: For a given example index i, the output is activationFunction(weights.getRow(i) + bias), hence the weight rows can be considered a vector/embedding of each index. Note also that embedding layer has an activation function (set to IDENTITY to disable) and optional bias (which is disabled by default)
hasBias
If true: include bias parameters in the layer. False (default): no bias.
inputLength
Set input sequence length for this embedding layer.
param inputLength input sequence length
return Builder
inferInputLength
Set input sequence inference mode for embedding layer.
param inferInputLength whether to infer input length
return Builder
weightInit
Initialize the embedding layer using the specified EmbeddingInitializer - such as a Word2Vec instance
param embeddingInitializer Source of the embedding layer weights
weightInit
Initialize the embedding layer using values from the specified array. Note that the array should have shape [vocabSize, vectorSize]. After copying values from the array to initialize the network parameters, the input array will be discarded (so that, if necessary, it can be garbage collected)
param vectors Vectors to initialize the embedding layer with
Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs. Supports the following
Global pooling layer can also handle mask arrays when dealing with variable length inputs. Mask arrays are assumed to be 2d, and are fed forward through the network during training or post-training forward pass:
Time series: mask arrays are shape [miniBatchSize, maxTimeSeriesLength] and contain values 0 or 1 only
CNNs: mask have shape [miniBatchSize, height] or [miniBatchSize, width]. Important: the current implementation assumes that for CNNs + variable length (masking), the input shape is [miniBatchSize, channels, height, 1] or [miniBatchSize, channels, 1, width] respectively. This is the case with global pooling in architectures like CNN for sentence classification.
Behaviour with default settings:
3d (time series) input with shape [miniBatchSize, vectorSize, timeSeriesLength] -> 2d output [miniBatchSize, vectorSize]
4d (CNN) input with shape [miniBatchSize, channels, height, width] -> 2d output [miniBatchSize, channels]
5d (CNN3D) input with shape [miniBatchSize, channels, depth, height, width] -> 2d output [miniBatchSize, channels]
Alternatively, by setting collapseDimensions = false in the configuration, it is possible to retain the reduced dimensions as 1s: this gives
[miniBatchSize, vectorSize, 1] for RNN output,
[miniBatchSize, channels, 1, 1] for CNN output, and
[miniBatchSize, channels, 1, 1, 1] for CNN3D output.
poolingDimensions
Pooling type for global pooling
poolingType
param poolingType Pooling type for global pooling
collapseDimensions
Whether to collapse dimensions when pooling or not. Usually you do want to do this. Default: true. If true:
3d (time series) input with shape [miniBatchSize, vectorSize, timeSeriesLength] -> 2d output [miniBatchSize, vectorSize]
4d (CNN) input with shape [miniBatchSize, channels, height, width] -> 2d output [miniBatchSize, channels]
5d (CNN3D) input with shape [miniBatchSize, channels, depth, height, width] -> 2d output [miniBatchSize, channels]
If false:
3d (time series) input with shape [miniBatchSize, vectorSize, timeSeriesLength] -> 3d output [miniBatchSize, vectorSize, 1]
4d (CNN) input with shape [miniBatchSize, channels, height, width] -> 2d output [miniBatchSize, channels, 1, 1]
5d (CNN3D) input with shape [miniBatchSize, channels, depth, height, width] -> 2d output [miniBatchSize, channels, 1, 1, 1]
param collapseDimensions Whether to collapse the dimensions or not
pnorm
P-norm constant. Only used if using {- link PoolingType#PNORM} for the pooling type
param pnorm P-norm constant
Local response normalization layer See section 3.3 of http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
k
LRN scaling constant k. Default: 2
n
Number of adjacent kernel maps to use when doing LRN. default: 5
param n Number of adjacent kernel maps
alpha
LRN scaling constant alpha. Default: 1e-4
param alpha Scaling constant
beta
Scaling constant beta. Default: 0.75
param beta Scaling constant
cudnnAllowFallback
When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user. If false, the built-in (non-CuDNN) implementation for BatchNormalization will be used
param allowFallback Whether fallback to non-CuDNN implementation should be used
SameDiff version of a 1D locally connected layer.
nIn
Number of inputs to the layer (input size)
nOut
param nOut Number of outputs (output size)
activation
param activation Activation function for the layer
kernelSize
param k Kernel size for the layer
stride
param s Stride for the layer
padding
param p Padding for the layer. Not used if {- link ConvolutionMode#Same} is set
convolutionMode
param cm Convolution mode for the layer. See {- link ConvolutionMode} for details
dilation
param d Dilation for the layer
hasBias
param hasBias If true (default is false) the layer will have a bias
setInputSize
Set input filter size for this locally connected 1D layer
param inputSize height of the input filters
return Builder
SameDiff version of a 2D locally connected layer.
setKernel
Number of inputs to the layer (input size)
setStride
param stride Stride for the layer. Must be 2 values (height/width)
setPadding
param padding Padding for the layer. Not used if {- link ConvolutionMode#Same} is set. Must be 2 values (height/width)
setDilation
param dilation Dilation for the layer. Must be 2 values (height/width)
nIn
param nIn Number of inputs to the layer (input size)
nOut
param nOut Number of outputs (output size)
activation
param activation Activation function for the layer
kernelSize
param k Kernel size for the layer. Must be 2 values (height/width)
stride
param s Stride for the layer. Must be 2 values (height/width)
padding
param p Padding for the layer. Not used if {- link ConvolutionMode#Same} is set. Must be 2 values (height/width)
convolutionMode
param cm Convolution mode for the layer. See {- link ConvolutionMode} for details
dilation
param d Dilation for the layer. Must be 2 values (height/width)
hasBias
param hasBias If true (default is false) the layer will have a bias
setInputSize
Set input filter size (h,w) for this locally connected 2D layer
param inputSize pair of height and width of the input filters to this layer
return Builder
LossLayer is a flexible output layer that performs a loss function on an input without MLP logic. LossLayer is does not have any parameters. Consequently, setting nIn/nOut isn’t supported - the output size is the same size as the input activations.
nIn
param lossFunction Loss function for the loss layer
Output layer used for training via backpropagation based on labels and a specified loss function. Can be configured for both classification and regression. Note that OutputLayer has parameters - it contains a fully-connected layer (effectively contains a DenseLayer) internally. This allows the output size to be different to the layer input size.
build
param lossFunction Loss function for the output layer
Supports the following pooling types: MAX, AVG, SUM, PNORM, NONE
Supports the following pooling types: MAX, AVG, SUM, PNORM, NONE
sequenceLength]}. This layer accepts RNN InputTypes instead of CNN InputTypes.
Supports the following pooling types: MAX, AVG, SUM, PNORM
setKernelSize
Kernel size
param kernelSize kernel size
setStride
Stride
param stride stride value
setPadding
Padding
param padding padding value
sequenceLength]} Example:
size
Upsampling size
param size upsampling size in single spatial dimension of this 1D layer
size
Upsampling size int array with a single element. Array must be length 1
param size upsampling size in single spatial dimension of this 1D layer
Upsampling 2D layer Repeats each value (or rather, set of depth values) in the height and width dimensions by
size
Upsampling size int, used for both height and width
param size upsampling size in height and width dimensions
size
Upsampling size array
param size upsampling size in height and width dimensions
Upsampling 3D layer Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and [minibatch, channels, size[0] depth, size[1] height, size[2] width]}
size
Upsampling size as int, so same upsampling size is used for depth, width and height
param size upsampling size in height, width and depth dimensions
size
Upsampling size as int, so same upsampling size is used for depth, width and height
param size upsampling size in height, width and depth dimensions
Zero padding 1D layer for convolutional neural networks. Allows padding to be done separately for top and bottom.
setPadding
Padding value for left and right. Must be length 2 array
build
param padding Padding for both the left and right
Zero padding 3D layer for convolutional neural networks. Allows padding to be done separately for “left” and “right” in all three spatial dimensions.
setPadding
[padLeftD, padRightD, padLeftH, padRightH, padLeftW, padRightW]
build
param padding Padding for both the left and right in all three spatial dimensions
Zero padding layer for convolutional neural networks (2D CNNs). Allows padding to be done separately for top/bottom/left/right
setPadding
Padding value for top, bottom, left, and right. Must be length 4 array
build
param padHeight Padding for both the top and bottom
param padWidth Padding for both the left and right
is a learnable weight vector of length nOut
“.” is element-wise multiplication
b is a bias vector
Note that the input and output sizes of the element-wise layer are the same for this layer
created by jingshu
getMemoryReport
This is a report of the estimated memory consumption for the given layer
param inputType Input type to the layer. Memory consumption is often a function of the input type
return Memory report for the layer
RepeatVector layer configuration.
RepeatVector takes a mini-batch of vectors of shape (mb, length) and a repeat factor n and outputs a 3D tensor of shape (mb, n, length) in which x is repeated n times.
getRepetitionFactor
Set repetition factor for RepeatVector layer
setRepetitionFactor
Set repetition factor for RepeatVector layer
param n upsampling size in height and width dimensions
repetitionFactor
Set repetition factor for RepeatVector layer
param n upsampling size in height and width dimensions
Output (loss) layer for YOLOv2 object detection model, based on the papers: YOLO9000: Better, Faster, Stronger - Redmon & Farhadi (2016) - https://arxiv.org/abs/1612.08242 and You Only Look Once: Unified, Real-Time Object Detection - Redmon et al. (2016) - http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf This loss function implementation is based on the YOLOv2 version of the paper. However, note that it doesn’t currently support simultaneous training on both detection and classification datasets as described in the YOlO9000 paper.
Note: Input activations to the Yolo2OutputLayer should have shape: [minibatch, b(5+c), H, W], where: b = number of bounding boxes (determined by config - see papers for details) c = number of classes H = output/label height W = output/label width
Important: In practice, this means that the last convolutional layer before your Yolo2OutputLayer should have output depth of b(5+c). Thus if you change the number of bounding boxes, or change the number of object classes, the number of channels (nOut of the last convolution layer) needs to also change. Label format: [minibatch, 4+C, H, W] Order for labels depth: [x1,y1,x2,y2,(class labels)] x1 = box top left position y1 = as above, y axis x2 = box bottom right position y2 = as above y axis Note: labels are represented as a multiple of grid size - for a 13x13 grid, (0,0) is top left, (13,13) is bottom right Note also that mask arrays are not required - this implementation infers the presence or absence of objects in each grid cell from the class labels (which should be 1-hot if an object is present, or all 0s otherwise).
lambdaCoord
Loss function coefficient for position and size/scale components of the loss function. Default (as per paper): 5
lambbaNoObj
Loss function coefficient for the “no object confidence” components of the loss function. Default (as per paper): 0.5
param lambdaNoObj Lambda value for no-object (confidence) component of the loss function
lossPositionScale
Loss function for position/scale component of the loss function
param lossPositionScale Loss function for position/scale
lossClassPredictions
Loss function for the class predictions - defaults to L2 loss (i.e., sum of squared errors, as per the paper), however Loss MCXENT could also be used (which is more common for classification).
param lossClassPredictions Loss function for the class prediction error component of the YOLO loss function
boundingBoxPriors
Bounding box priors dimensions [width, height]. For N bounding boxes, input has shape [rows, columns] = [N, 2] Note that dimensions should be specified as fraction of grid size. For example, a network with 13x13 output, a value of 1.0 would correspond to one grid cell; a value of 13 would correspond to the entire image.
param boundingBoxes Bounding box prior dimensions (width, height)
MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer. It can be used with 2d (feed-forward), 3d (time series) or 4d (CNN) activations.
Wrapper which masks timesteps with activation equal to the specified masking value (0.0 default). Assumes that the input shape is [batch_size, input_size, timesteps].