Package org.deeplearning4j.nn.api
Interface ParamInitializer
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- All Known Implementing Classes:
BatchNormalizationParamInitializer,BidirectionalParamInitializer,CenterLossParamInitializer,Convolution3DParamInitializer,ConvolutionParamInitializer,Deconvolution3DParamInitializer,DeconvolutionParamInitializer,DefaultParamInitializer,DepthwiseConvolutionParamInitializer,ElementWiseParamInitializer,EmbeddingLayerParamInitializer,EmptyParamInitializer,FrozenLayerParamInitializer,FrozenLayerWithBackpropParamInitializer,GravesBidirectionalLSTMParamInitializer,GravesLSTMParamInitializer,LSTMParamInitializer,OCNNParamInitializer,PReLUParamInitializer,PretrainParamInitializer,SameDiffParamInitializer,SeparableConvolutionParamInitializer,SimpleRnnParamInitializer,VariationalAutoencoderParamInitializer,WrapperLayerParamInitializer
public interface ParamInitializerParam initializer for a layer- Author:
- Adam Gibson
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description List<String>biasKeys(Layer layer)Bias parameter keys given the layer configurationMap<String,INDArray>getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView)Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array.Map<String,INDArray>init(NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams)Initialize the parametersbooleanisBiasParam(Layer layer, String key)Is the specified parameter a bias?booleanisWeightParam(Layer layer, String key)Is the specified parameter a weight?longnumParams(Layer layer)longnumParams(NeuralNetConfiguration conf)List<String>paramKeys(Layer layer)Get a list of all parameter keys given the layer configurationList<String>weightKeys(Layer layer)Weight parameter keys given the layer configuration
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Method Detail
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numParams
long numParams(NeuralNetConfiguration conf)
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numParams
long numParams(Layer layer)
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paramKeys
List<String> paramKeys(Layer layer)
Get a list of all parameter keys given the layer configuration- Parameters:
layer- Layer- Returns:
- All parameter keys
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weightKeys
List<String> weightKeys(Layer layer)
Weight parameter keys given the layer configuration- Parameters:
layer- Layer- Returns:
- Weight parameter keys
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biasKeys
List<String> biasKeys(Layer layer)
Bias parameter keys given the layer configuration- Parameters:
layer- Layer- Returns:
- Bias parameter keys
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isWeightParam
boolean isWeightParam(Layer layer, String key)
Is the specified parameter a weight?- Parameters:
layer- Layerkey- Key to check- Returns:
- True if parameter is a weight
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isBiasParam
boolean isBiasParam(Layer layer, String key)
Is the specified parameter a bias?- Parameters:
layer- Layerkey- Key to check- Returns:
- True if parameter is a bias
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init
Map<String,INDArray> init(NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams)
Initialize the parameters- Parameters:
conf- the configurationparamsView- a view of the full network (backprop) parametersinitializeParams- if true: initialize the parameters according to the configuration. If false: don't modify the values in the paramsView array (but do select out the appropriate subset, reshape etc as required)- Returns:
- Map of parameters keyed by type (view of the 'paramsView' array)
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getGradientsFromFlattened
Map<String,INDArray> getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView)
Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array. The idea is that operates in exactly the same way as the paramsView does in#init(Map, NeuralNetConfiguration, INDArray); thus the position in the view (and, the array orders) must match those of the parameters- Parameters:
conf- ConfigurationgradientView- The flattened gradients array, as a view of the larger array- Returns:
- A map containing an array by parameter type, that is a view of the full network gradients array
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