org.deeplearning4j.nn.conf.graph.GraphVertex Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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package org.deeplearning4j.nn.conf.graph;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public abstract class GraphVertex implements Cloneable, Serializable {
@Override
public abstract GraphVertex clone();
@Override
public abstract boolean equals(Object o);
@Override
public abstract int hashCode();
public abstract long numParams(boolean backprop);
/**
* @return The Smallest valid number of inputs to this vertex
*/
public abstract int minVertexInputs();
/**
* @return The largest valid number of inputs to this vertex
*/
public abstract int maxVertexInputs();
/**
* Create a {@link org.deeplearning4j.nn.graph.vertex.GraphVertex} instance, for the given computation graph,
* given the configuration instance.
*
* @param graph The computation graph that this GraphVertex is to be part of
* @param name The name of the GraphVertex object
* @param idx The index of the GraphVertex
* @param paramsView A view of the full parameters array
* @param initializeParams If true: initialize the parameters. If false: make no change to the values in the paramsView array
* @param networkDatatype
* @return The implementation GraphVertex object (i.e., implementation, no the configuration)
*/
public abstract org.deeplearning4j.nn.graph.vertex.GraphVertex instantiate(ComputationGraph graph, String name,
int idx, INDArray paramsView, boolean initializeParams, DataType networkDatatype);
/**
* Determine the type of output for this GraphVertex, given the specified inputs. Given that a GraphVertex may do arbitrary
* processing or modifications of the inputs, the output types can be quite different to the input type(s).
* This is generally used to determine when to add preprocessors, as well as the input sizes etc for layers
*
* @param layerIndex The index of the layer (if appropriate/necessary).
* @param vertexInputs The inputs to this vertex
* @return The type of output for this vertex
* @throws InvalidInputTypeException If the input type is invalid for this type of GraphVertex
*/
public abstract InputType getOutputType(int layerIndex, InputType... vertexInputs) throws InvalidInputTypeException;
/**
* This is a report of the estimated memory consumption for the given vertex
*
* @param inputTypes Input types to the vertex. Memory consumption is often a function of the input type
* @return Memory report for the vertex
*/
public abstract MemoryReport getMemoryReport(InputType... inputTypes);
public void setDataType(DataType dataType) {
//No-op for most layers
}
}