Modifier and Type | Class and Description |
---|---|
class |
ActivationLayer
Activation Layer
Used to apply activation on input and corresponding derivative on epsilon.
|
class |
BaseLayer<LayerConfT extends BaseLayer>
A layer with parameters
|
class |
BaseOutputLayer<LayerConfT extends BaseOutputLayer>
Output layer with different objective
in co-occurrences for different objectives.
|
class |
BasePretrainNetwork<LayerConfT extends BasePretrainNetwork>
Baseline class for any Neural Network used
as a layer in a deep network *
|
class |
DropoutLayer
Created by davekale on 12/7/16.
|
class |
LossLayer
LossLayer is a flexible output "layer" that performs a loss function on
an input without MLP logic.
|
class |
OutputLayer
Output layer with different objective
incooccurrences for different objectives.
|
class |
RepeatVector
RepeatVector layer.
|
Modifier and Type | Class and Description |
---|---|
class |
Cnn3DLossLayer
3D Convolutional Neural Network Loss Layer.
|
class |
CnnLossLayer
Convolutional Neural Network Loss Layer.
|
class |
Convolution1DLayer
1D (temporal) convolutional layer.
|
class |
Convolution3DLayer
3D convolution layer implementation.
|
class |
ConvolutionLayer
Convolution layer
|
class |
Cropping1DLayer
Zero cropping layer for 1D convolutional neural networks.
|
class |
Cropping2DLayer
Zero cropping layer for convolutional neural networks.
|
class |
Cropping3DLayer
Cropping layer for 3D convolutional neural networks.
|
class |
Deconvolution2DLayer
2D deconvolution layer implementation.
|
class |
Deconvolution3DLayer
3D deconvolution layer implementation.
|
class |
DepthwiseConvolution2DLayer
2D depth-wise convolution layer configuration.
|
class |
SeparableConvolution2DLayer
2D Separable convolution layer implementation
Separable convolutions split a regular convolution operation into two
simpler operations, which are usually computationally more efficient.
|
class |
SpaceToBatch
Space to batch utility layer for convolutional input types.
|
class |
SpaceToDepth
Space to channels utility layer for convolutional input types.
|
class |
ZeroPadding1DLayer
Zero padding 1D layer for convolutional neural networks.
|
class |
ZeroPadding3DLayer
Zero padding 3D layer for convolutional neural networks.
|
class |
ZeroPaddingLayer
Zero padding layer for convolutional neural networks.
|
Modifier and Type | Class and Description |
---|---|
class |
Subsampling1DLayer
1D (temporal) subsampling layer.
|
class |
Subsampling3DLayer
Subsampling 3D layer, used for downsampling a 3D convolution
|
class |
SubsamplingLayer
Subsampling layer.
|
Modifier and Type | Class and Description |
---|---|
class |
Upsampling1D
1D Upsampling layer.
|
class |
Upsampling2D
2D Upsampling layer.
|
class |
Upsampling3D
3D Upsampling layer.
|
Modifier and Type | Class and Description |
---|---|
class |
PReLU
Parametrized Rectified Linear Unit (PReLU)
f(x) = alpha * x for x < 0, f(x) = x for x >= 0
alpha has the same shape as x and is a learned parameter.
|
Modifier and Type | Class and Description |
---|---|
class |
AutoEncoder
Autoencoder.
|
Modifier and Type | Class and Description |
---|---|
class |
DenseLayer |
Modifier and Type | Class and Description |
---|---|
class |
ElementWiseMultiplicationLayer
Elementwise multiplication layer with weights: implements out = activationFn(input .* w + b) where:
- w 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 |
Modifier and Type | Class and Description |
---|---|
class |
EmbeddingLayer
Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1)
as input.
|
class |
EmbeddingSequenceLayer
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.
|
Modifier and Type | Class and Description |
---|---|
class |
BatchNormalization
Batch normalization layer.
|
class |
LocalResponseNormalization
Deep neural net normalization approach normalizes activations between layers
"brightness normalization"
Used for nets like AlexNet
For a^i_{x,y} the activity of a neuron computed by applying kernel i
at position (x,y) and applying ReLU nonlinearity, the response
normalized activation b^i_{x,y} is given by:
x^2 = (a^j_{x,y})^2
unitScale = (k + alpha * sum_{j=max(0, i - n/2)}^{max(N-1, i + n/2)} (a^j_{x,y})^2 )
y = b^i_{x,y} = x * unitScale**-beta
gy = epsilon (aka deltas from previous layer)
sumPart = sum(a^j_{x,y} * gb^j_{x,y})
gx = gy * unitScale**-beta - 2 * alpha * beta * sumPart/unitScale * a^i_{x,y}
Reference:
http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf https://github.com/vlfeat/matconvnet/issues/10 Created by nyghtowl on 10/29/15. |
Modifier and Type | Class and Description |
---|---|
class |
Yolo2OutputLayer
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. |
Modifier and Type | Class and Description |
---|---|
class |
OCNNOutputLayer
Layer implementation for
OCNNOutputLayer
See OCNNOutputLayer
for details. |
Modifier and Type | Class and Description |
---|---|
class |
GlobalPoolingLayer
Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs.
|
Modifier and Type | Class and Description |
---|---|
class |
BaseRecurrentLayer<LayerConfT extends BaseRecurrentLayer> |
class |
GravesBidirectionalLSTM
RNN tutorial: https://deeplearning4j.konduit.ai/models/recurrent
READ THIS FIRST
Bdirectional LSTM layer implementation.
|
class |
GravesLSTM
Deprecated.
Will be eventually removed. Use
LSTM instead, which has similar prediction accuracy, but supports
CuDNN for faster network training on CUDA (Nvidia) GPUs |
class |
LSTM
LSTM layer implementation.
|
class |
RnnLossLayer
Recurrent Neural Network Loss Layer.
|
class |
RnnOutputLayer
Recurrent Neural Network Output Layer.
|
class |
SimpleRnn
Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.
|
Modifier and Type | Class and Description |
---|---|
class |
SameDiffLayer |
class |
SameDiffOutputLayer |
Modifier and Type | Class and Description |
---|---|
class |
CenterLossOutputLayer
Center loss is similar to triplet loss except that it enforces
intraclass consistency and doesn't require feed forward of multiple
examples.
|
Modifier and Type | Class and Description |
---|---|
class |
MaskLayer
MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through
this layer.
|
Modifier and Type | Class and Description |
---|---|
class |
TFOpLayerImpl |
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