Uses of Class
org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Packages that use ComputationGraphConfiguration.GraphBuilder

Package	Description
org.deeplearning4j.nn.conf

Uses of ComputationGraphConfiguration.GraphBuilder in org.deeplearning4j.nn.conf

Methods in org.deeplearning4j.nn.conf that return ComputationGraphConfiguration.GraphBuilder

Modifier and Type	Method and Description
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.addInputs(String... inputNames) Specify the inputs to the network, and their associated labels.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.addLayer(String layerName, Layer layer, InputPreProcessor preProcessor, String... layerInputs) Add a layer and an InputPreProcessor, with the specified name and specified inputs.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.addLayer(String layerName, Layer layer, String... layerInputs) Add a layer, with no InputPreProcessor, with the specified name and specified inputs.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.addVertex(String vertexName, GraphVertex vertex, String... vertexInputs) Add a GraphVertex to the network configuration.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.backprop(boolean backprop) Whether to do back prop (standard supervised learning) or not
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.backpropType(BackpropType type) The type of backprop.
ComputationGraphConfiguration.GraphBuilder	NeuralNetConfiguration.Builder.graphBuilder() Create a GraphBuilder (for creating a ComputationGraphConfiguration).
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.inputPreProcessor(String layer, InputPreProcessor processor) Specify the processors for a given layer These are used at each layer for doing things like normalization and shaping of input. Note: preprocessors can also be defined using the addLayer(String, Layer, InputPreProcessor, String...) method.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.pretrain(boolean pretrain) Whether to do layerwise pre training or not
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.redistributeParams(boolean redistributeParams) Deprecated. As of 0.6.0
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.setInputTypes(InputType... inputTypes) Specify the types of inputs to the network, so that: (a) preprocessors can be automatically added, and (b) the nIns (input size) for each layer can be automatically calculated and set The order here is the same order as .addInputs().
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.setOutputs(String... outputNames) Set the network output labels.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.tBPTTBackwardLength(int backwardLength) When doing truncated BPTT: how many steps of backward should we do? Only applicable when doing backpropType(BackpropType.TruncatedBPTT) This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.tBPTTForwardLength(int forwardLength) When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop? Only applicable when doing backpropType(BackpropType.TruncatedBPTT) Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller) Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf