public class ConvolutionLayer extends BaseLayer<ConvolutionLayer>
Layer.TrainingMode, Layer.Type
Modifier and Type | Field and Description |
---|---|
protected ConvolutionMode |
convolutionMode |
protected ConvolutionHelper |
helper |
protected static org.slf4j.Logger |
log |
conf, dropoutApplied, dropoutMask, gradient, gradientsFlattened, gradientViews, index, input, iterationListeners, maskArray, maskState, optimizer, params, paramsFlattened, score, solver
Constructor and Description |
---|
ConvolutionLayer(NeuralNetConfiguration conf) |
ConvolutionLayer(NeuralNetConfiguration conf,
org.nd4j.linalg.api.ndarray.INDArray input) |
Modifier and Type | Method and Description |
---|---|
org.nd4j.linalg.api.ndarray.INDArray |
activate(boolean training)
Trigger an activation with the last specified input
|
Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray> |
backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon)
Calculate the gradient relative to the error in the next layer
|
Gradient |
calcGradient(Gradient layerError,
org.nd4j.linalg.api.ndarray.INDArray indArray)
Calculate the gradient
|
double |
calcL1(boolean backpropParamsOnly)
Calculate the l1 regularization term
0.0 if regularization is not used. |
double |
calcL2(boolean backpropParamsOnly)
Calculate the l2 regularization term
0.0 if regularization is not used. |
void |
fit(org.nd4j.linalg.api.ndarray.INDArray input)
Fit the model to the given data
|
boolean |
isPretrainLayer()
Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)
|
void |
merge(Layer layer,
int batchSize)
Averages the given logistic regression from a mini batch into this layer
|
org.nd4j.linalg.api.ndarray.INDArray |
params()
Returns the parameters of the neural network as a flattened row vector
|
org.nd4j.linalg.api.ndarray.INDArray |
preOutput(boolean training) |
protected org.nd4j.linalg.api.ndarray.INDArray |
preOutput4d(boolean training)
preOutput4d: Used so that ConvolutionLayer subclasses (such as Convolution1DLayer) can maintain their standard
non-4d preOutput method, while overriding this to return 4d activations (for use in backprop) without modifying
the public API
|
void |
setParams(org.nd4j.linalg.api.ndarray.INDArray params)
Set the parameters for this model.
|
Layer |
transpose()
Return a transposed copy of the weights/bias
(this means reverse the number of inputs and outputs on the weights)
|
Layer.Type |
type()
Returns the layer type
|
accumulateScore, activate, activate, activate, activate, activate, activationMean, applyDropOutIfNecessary, applyLearningRateScoreDecay, applyMask, batchSize, clear, clone, computeGradientAndScore, conf, createGradient, derivativeActivation, error, feedForwardMaskArray, fit, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, getOptimizer, getParam, gradient, gradientAndScore, init, initParams, input, iterate, layerConf, layerNameAndIndex, numParams, numParams, paramTable, paramTable, preOutput, preOutput, preOutput, score, setBackpropGradientsViewArray, setConf, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, setParam, setParams, setParamsViewArray, setParamTable, setScoreWithZ, toString, update, update, validateInput
protected static final org.slf4j.Logger log
protected ConvolutionHelper helper
protected ConvolutionMode convolutionMode
public ConvolutionLayer(NeuralNetConfiguration conf)
public ConvolutionLayer(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)
public double calcL2(boolean backpropParamsOnly)
Layer
calcL2
in interface Layer
calcL2
in class BaseLayer<ConvolutionLayer>
backpropParamsOnly
- If true: calculate L2 based on backprop params only. If false: calculate
based on all params (including pretrain params, if any)public double calcL1(boolean backpropParamsOnly)
Layer
calcL1
in interface Layer
calcL1
in class BaseLayer<ConvolutionLayer>
backpropParamsOnly
- If true: calculate L1 based on backprop params only. If false: calculate
based on all params (including pretrain params, if any)public Layer.Type type()
Layer
type
in interface Layer
type
in class BaseLayer<ConvolutionLayer>
public Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray> backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon)
Layer
backpropGradient
in interface Layer
backpropGradient
in class BaseLayer<ConvolutionLayer>
epsilon
- w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C
is cost function a=sigma(z) is activation.protected org.nd4j.linalg.api.ndarray.INDArray preOutput4d(boolean training)
public org.nd4j.linalg.api.ndarray.INDArray preOutput(boolean training)
preOutput
in class BaseLayer<ConvolutionLayer>
public org.nd4j.linalg.api.ndarray.INDArray activate(boolean training)
Layer
activate
in interface Layer
activate
in class BaseLayer<ConvolutionLayer>
training
- training or test modepublic Layer transpose()
Layer
transpose
in interface Layer
transpose
in class BaseLayer<ConvolutionLayer>
public boolean isPretrainLayer()
Layer
public Gradient calcGradient(Gradient layerError, org.nd4j.linalg.api.ndarray.INDArray indArray)
Layer
calcGradient
in interface Layer
calcGradient
in class BaseLayer<ConvolutionLayer>
layerError
- the layer errorpublic void fit(org.nd4j.linalg.api.ndarray.INDArray input)
Model
fit
in interface Model
fit
in class BaseLayer<ConvolutionLayer>
input
- the data to fit the model topublic void merge(Layer layer, int batchSize)
BaseLayer
merge
in interface Layer
merge
in class BaseLayer<ConvolutionLayer>
layer
- the logistic regression layer to average into this layerbatchSize
- the batch sizepublic org.nd4j.linalg.api.ndarray.INDArray params()
BaseLayer
params
in interface Model
params
in class BaseLayer<ConvolutionLayer>
public void setParams(org.nd4j.linalg.api.ndarray.INDArray params)
Model
setParams
in interface Model
setParams
in class BaseLayer<ConvolutionLayer>
params
- the parameters for the model