idx int64 0 41.2k | question stringlengths 73 5.81k | target stringlengths 5 918 |
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19,600 | public static long numVectors ( INDArray arr ) { if ( arr . rank ( ) == 1 ) return 1 ; else if ( arr . rank ( ) == 2 ) return arr . size ( 0 ) ; else { int prod = 1 ; for ( int i = 0 ; i < arr . rank ( ) - 1 ; i ++ ) { prod *= arr . size ( i ) ; } return prod ; } } | Return the number of vectors for an array the number of vectors for an array |
19,601 | public static long sliceOffsetForTensor ( int index , INDArray arr , int [ ] tensorShape ) { long tensorLength = ArrayUtil . prodLong ( tensorShape ) ; long lengthPerSlice = NDArrayMath . lengthPerSlice ( arr ) ; long offset = index * tensorLength / lengthPerSlice ; return offset ; } | calculates the offset for a tensor |
19,602 | public static int mapIndexOntoTensor ( int index , INDArray arr , int ... rank ) { int ret = index * ArrayUtil . prod ( ArrayUtil . removeIndex ( arr . shape ( ) , rank ) ) ; return ret ; } | This maps an index of a vector on to a vector in the matrix that can be used for indexing in to a tensor |
19,603 | public HashMap < Integer , List < Integer > > getPair_PositionList_Table ( byte [ ] fingerprint ) { List < int [ ] > pairPositionList = getPairPositionList ( fingerprint ) ; HashMap < Integer , List < Integer > > pair_positionList_table = new HashMap < > ( ) ; for ( int [ ] pair_position : pairPositionList ) { if ( pair_positionList_table . containsKey ( pair_position [ 0 ] ) ) { pair_positionList_table . get ( pair_position [ 0 ] ) . add ( pair_position [ 1 ] ) ; } else { List < Integer > positionList = new LinkedList < > ( ) ; positionList . add ( pair_position [ 1 ] ) ; pair_positionList_table . put ( pair_position [ 0 ] , positionList ) ; } } return pair_positionList_table ; } | Get a pair - positionList table It s a hash map which the key is the hashed pair and the value is list of positions That means the table stores the positions which have the same hashed pair |
19,604 | public void fit ( INDArray x ) { this . data = x ; for ( int i = 0 ; i < numTrees ; i ++ ) { RPTree tree = new RPTree ( data . columns ( ) , maxSize , similarityFunction ) ; tree . buildTree ( x ) ; trees . add ( tree ) ; } } | Build the trees from the given dataset |
19,605 | public INDArray queryAll ( INDArray toQuery , int n ) { return RPUtils . queryAll ( toQuery , data , trees , n , similarityFunction ) ; } | Query results up to length n nearest neighbors |
19,606 | public static Tree toTree ( TreebankNode node , Pair < String , MultiDimensionalMap < Integer , Integer , String > > labels ) throws Exception { List < String > tokens = tokens ( node ) ; Tree ret = new Tree ( tokens ) ; ret . setValue ( node . getNodeValue ( ) ) ; ret . setLabel ( node . getNodeType ( ) ) ; ret . setType ( node . getNodeType ( ) ) ; ret . setBegin ( node . getBegin ( ) ) ; ret . setEnd ( node . getEnd ( ) ) ; ret . setParse ( TreebankNodeUtil . toTreebankString ( node ) ) ; if ( node . getNodeTags ( ) != null ) ret . setTags ( tags ( node ) ) ; else ret . setTags ( Arrays . asList ( node . getNodeType ( ) ) ) ; return ret ; } | Converts a treebank node to a tree |
19,607 | public static int getVIntSize ( long i ) { if ( i >= - 112 && i <= 127 ) { return 1 ; } if ( i < 0 ) { i ^= - 1L ; } int dataBits = Long . SIZE - Long . numberOfLeadingZeros ( i ) ; return ( dataBits + 7 ) / 8 + 1 ; } | Get the encoded length if an integer is stored in a variable - length format |
19,608 | public static < T extends Enum < T > > T readEnum ( DataInput in , Class < T > enumType ) throws IOException { return T . valueOf ( enumType , Text . readString ( in ) ) ; } | Read an Enum value from DataInput Enums are read and written using String values . |
19,609 | public static void writeEnum ( DataOutput out , Enum < ? > enumVal ) throws IOException { Text . writeString ( out , enumVal . name ( ) ) ; } | writes String value of enum to DataOutput . |
19,610 | public static byte [ ] toByteArray ( Writable ... writables ) { final DataOutputBuffer out = new DataOutputBuffer ( ) ; try { for ( Writable w : writables ) { w . write ( out ) ; } out . close ( ) ; } catch ( IOException e ) { throw new RuntimeException ( "Fail to convert writables to a byte array" , e ) ; } return out . getData ( ) ; } | Convert writables to a byte array |
19,611 | public void broadcast ( INDArray array ) { if ( array == null ) return ; Nd4j . getExecutioner ( ) . commit ( ) ; val config = OpProfiler . getInstance ( ) . getConfig ( ) ; val locality = config . isCheckLocality ( ) ; if ( locality ) config . setCheckLocality ( false ) ; int numDevices = Nd4j . getAffinityManager ( ) . getNumberOfDevices ( ) ; for ( int i = 0 ; i < numDevices ; i ++ ) { if ( Nd4j . getAffinityManager ( ) . getDeviceForCurrentThread ( ) == i ) { set ( i , array ) ; } else { set ( i , Nd4j . getAffinityManager ( ) . replicateToDevice ( i , array ) ) ; } } config . setCheckLocality ( locality ) ; } | This method duplicates array and stores it to all devices |
19,612 | public void synchronizeToHost ( AllocationPoint point ) { if ( ! point . isActualOnHostSide ( ) ) { CudaContext context = ( CudaContext ) allocator . getDeviceContext ( ) . getContext ( ) ; if ( ! point . isConstant ( ) ) waitTillFinished ( point ) ; if ( point . getAllocationStatus ( ) == AllocationStatus . DEVICE && ! point . isActualOnHostSide ( ) ) { long perfD = PerformanceTracker . getInstance ( ) . helperStartTransaction ( ) ; if ( nativeOps . memcpyAsync ( point . getHostPointer ( ) , point . getDevicePointer ( ) , AllocationUtils . getRequiredMemory ( point . getShape ( ) ) , CudaConstants . cudaMemcpyDeviceToHost , context . getSpecialStream ( ) ) == 0 ) throw new IllegalStateException ( "MemcpyAsync failed: " + point . getShape ( ) ) ; commitTransfer ( context . getSpecialStream ( ) ) ; PerformanceTracker . getInstance ( ) . helperRegisterTransaction ( point . getDeviceId ( ) , perfD , point . getNumberOfBytes ( ) , MemcpyDirection . DEVICE_TO_HOST ) ; } point . tickHostRead ( ) ; } } | This method makes sure HOST memory contains latest data from GPU |
19,613 | public MixtureDensityComponents extractComponents ( INDArray output ) { long outputSize = output . size ( 1 ) ; if ( outputSize != ( mLabelWidth + 2 ) * mMixtures ) { throw new IllegalArgumentException ( "Network output size " + outputSize + " must be (labels+2)*mixtures where labels = " + mLabelWidth + " and mixtures = " + mMixtures ) ; } MixtureDensityComponents mdc = new MixtureDensityComponents ( ) ; mdc . alpha = output . get ( NDArrayIndex . all ( ) , NDArrayIndex . interval ( 0 , mMixtures ) ) ; mdc . sigma = output . get ( NDArrayIndex . all ( ) , NDArrayIndex . interval ( mMixtures , 2 * mMixtures ) ) ; mdc . mu = output . get ( NDArrayIndex . all ( ) , NDArrayIndex . interval ( 2 * mMixtures , ( mLabelWidth + 2 ) * mMixtures ) ) . reshape ( output . size ( 0 ) , mMixtures , mLabelWidth ) ; mdc . alpha = Nd4j . getExecutioner ( ) . exec ( new OldSoftMax ( mdc . alpha ) ) ; mdc . sigma = Transforms . exp ( mdc . sigma ) ; return mdc ; } | through Nd4j operations in order to increase performance . |
19,614 | public INDArray computeScoreArray ( INDArray labels , INDArray preOutput , IActivation activationFn , INDArray mask ) { labels = labels . castTo ( preOutput . dataType ( ) ) ; INDArray output = activationFn . getActivation ( preOutput . dup ( ) , false ) ; MixtureDensityComponents mdc = extractComponents ( output ) ; INDArray scoreArr = negativeLogLikelihood ( labels , mdc . alpha , mdc . mu , mdc . sigma ) ; if ( mask != null ) { LossUtil . applyMask ( scoreArr , mask ) ; } return scoreArr ; } | This method returns the score for each of the given outputs against the given set of labels . For a mixture density network this is done by extracting the alpha mu and sigma components of each gaussian and computing the negative log likelihood that the labels fall within a linear combination of these gaussian distributions . The smaller the negative log likelihood the higher the probability that the given labels actually would fall within the distribution . Therefore by minimizing the negative log likelihood we get to a position of highest probability that the gaussian mixture explains the phenomenon . |
19,615 | public static List < Pair < Double , Integer > > sortCandidates ( INDArray x , INDArray X , List < Integer > candidates , String similarityFunction ) { int prevIdx = - 1 ; List < Pair < Double , Integer > > ret = new ArrayList < > ( ) ; for ( int i = 0 ; i < candidates . size ( ) ; i ++ ) { if ( candidates . get ( i ) != prevIdx ) { ret . add ( Pair . of ( computeDistance ( similarityFunction , X . slice ( candidates . get ( i ) ) , x ) , candidates . get ( i ) ) ) ; } prevIdx = i ; } Collections . sort ( ret , new Comparator < Pair < Double , Integer > > ( ) { public int compare ( Pair < Double , Integer > doubleIntegerPair , Pair < Double , Integer > t1 ) { return Doubles . compare ( doubleIntegerPair . getFirst ( ) , t1 . getFirst ( ) ) ; } } ) ; return ret ; } | Get the sorted distances given the query vector input data given the list of possible search candidates |
19,616 | public static RPNode query ( RPNode from , RPHyperPlanes planes , INDArray x , String similarityFunction ) { if ( from . getLeft ( ) == null && from . getRight ( ) == null ) { return from ; } INDArray hyperPlane = planes . getHyperPlaneAt ( from . getDepth ( ) ) ; double dist = computeDistance ( similarityFunction , x , hyperPlane ) ; if ( dist <= from . getMedian ( ) ) { return query ( from . getLeft ( ) , planes , x , similarityFunction ) ; } else { return query ( from . getRight ( ) , planes , x , similarityFunction ) ; } } | Query the tree starting from the given node using the given hyper plane and similarity function |
19,617 | public static void buildTree ( RPTree tree , RPNode from , RPHyperPlanes planes , INDArray X , int maxSize , int depth , String similarityFunction ) { if ( from . getIndices ( ) . size ( ) <= maxSize ) { slimNode ( from ) ; return ; } List < Double > distances = new ArrayList < > ( ) ; RPNode left = new RPNode ( tree , depth + 1 ) ; RPNode right = new RPNode ( tree , depth + 1 ) ; if ( planes . getWholeHyperPlane ( ) == null || depth >= planes . getWholeHyperPlane ( ) . rows ( ) ) { planes . addRandomHyperPlane ( ) ; } INDArray hyperPlane = planes . getHyperPlaneAt ( depth ) ; for ( int i = 0 ; i < from . getIndices ( ) . size ( ) ; i ++ ) { double cosineSim = computeDistance ( similarityFunction , hyperPlane , X . slice ( from . getIndices ( ) . get ( i ) ) ) ; distances . add ( cosineSim ) ; } Collections . sort ( distances ) ; from . setMedian ( distances . get ( distances . size ( ) / 2 ) ) ; for ( int i = 0 ; i < from . getIndices ( ) . size ( ) ; i ++ ) { double cosineSim = computeDistance ( similarityFunction , hyperPlane , X . slice ( from . getIndices ( ) . get ( i ) ) ) ; if ( cosineSim <= from . getMedian ( ) ) { left . getIndices ( ) . add ( from . getIndices ( ) . get ( i ) ) ; } else { right . getIndices ( ) . add ( from . getIndices ( ) . get ( i ) ) ; } } if ( left . getIndices ( ) . isEmpty ( ) || right . getIndices ( ) . isEmpty ( ) ) { slimNode ( from ) ; return ; } from . setLeft ( left ) ; from . setRight ( right ) ; slimNode ( from ) ; buildTree ( tree , left , planes , X , maxSize , depth + 1 , similarityFunction ) ; buildTree ( tree , right , planes , X , maxSize , depth + 1 , similarityFunction ) ; } | Initialize the tree given the input parameters |
19,618 | public static void slimNode ( RPNode node ) { if ( node . getRight ( ) != null && node . getLeft ( ) != null ) { node . getIndices ( ) . clear ( ) ; } } | Prune indices from the given node when it s a leaf |
19,619 | private < T extends TokenBase > List < T > createTokenList ( int offset , String text ) { ArrayList < T > result = new ArrayList < > ( ) ; ViterbiLattice lattice = viterbiBuilder . build ( text ) ; List < ViterbiNode > bestPath = viterbiSearcher . search ( lattice ) ; for ( ViterbiNode node : bestPath ) { int wordId = node . getWordId ( ) ; if ( node . getType ( ) == ViterbiNode . Type . KNOWN && wordId == - 1 ) { continue ; } @ SuppressWarnings ( "unchecked" ) T token = ( T ) tokenFactory . createToken ( wordId , node . getSurface ( ) , node . getType ( ) , offset + node . getStartIndex ( ) , dictionaryMap . get ( node . getType ( ) ) ) ; result . add ( token ) ; } return result ; } | Tokenize input sentence . |
19,620 | public void destroyWorkspace ( MemoryWorkspace workspace ) { if ( workspace == null || workspace instanceof DummyWorkspace ) return ; backingMap . get ( ) . remove ( workspace . getId ( ) ) ; } | This method destroys given workspace |
19,621 | public void destroyWorkspace ( ) { ensureThreadExistense ( ) ; MemoryWorkspace workspace = backingMap . get ( ) . get ( MemoryWorkspace . DEFAULT_ID ) ; backingMap . get ( ) . remove ( MemoryWorkspace . DEFAULT_ID ) ; } | This method destroy default workspace if any |
19,622 | public void destroyAllWorkspacesForCurrentThread ( ) { ensureThreadExistense ( ) ; List < MemoryWorkspace > workspaces = new ArrayList < > ( ) ; workspaces . addAll ( backingMap . get ( ) . values ( ) ) ; for ( MemoryWorkspace workspace : workspaces ) { destroyWorkspace ( workspace ) ; } Nd4j . getMemoryManager ( ) . invokeGc ( ) ; } | This method destroys all workspaces allocated in current thread |
19,623 | public static void scaleByMax ( INDArray toScale ) { INDArray scale = toScale . max ( 1 ) ; for ( int i = 0 ; i < toScale . rows ( ) ; i ++ ) { double scaleBy = scale . getDouble ( i ) ; toScale . putRow ( i , toScale . getRow ( i ) . divi ( scaleBy ) ) ; } } | Divides each row by its max |
19,624 | public String decodePredictions ( INDArray predictions ) { Preconditions . checkState ( predictions . size ( 1 ) == predictionLabels . size ( ) , "Invalid input array:" + " expected array with size(1) equal to numLabels (%s), got array with shape %s" , predictionLabels . size ( ) , predictions . shape ( ) ) ; String predictionDescription = "" ; int [ ] top5 = new int [ 5 ] ; float [ ] top5Prob = new float [ 5 ] ; int i = 0 ; for ( int batch = 0 ; batch < predictions . size ( 0 ) ; batch ++ ) { predictionDescription += "Predictions for batch " ; if ( predictions . size ( 0 ) > 1 ) { predictionDescription += String . valueOf ( batch ) ; } predictionDescription += " :" ; INDArray currentBatch = predictions . getRow ( batch ) . dup ( ) ; while ( i < 5 ) { top5 [ i ] = Nd4j . argMax ( currentBatch , 1 ) . getInt ( 0 ) ; top5Prob [ i ] = currentBatch . getFloat ( batch , top5 [ i ] ) ; currentBatch . putScalar ( 0 , top5 [ i ] , 0 ) ; predictionDescription += "\n\t" + String . format ( "%3f" , top5Prob [ i ] * 100 ) + "%, " + predictionLabels . get ( top5 [ i ] ) ; i ++ ; } } return predictionDescription ; } | Given predictions from the trained model this method will return a string listing the top five matches and the respective probabilities |
19,625 | public void load ( File ... files ) throws IOException { setFeatureStats ( DistributionStats . load ( files [ 0 ] , files [ 1 ] ) ) ; if ( isFitLabel ( ) ) { setLabelStats ( DistributionStats . load ( files [ 2 ] , files [ 3 ] ) ) ; } } | Load the means and standard deviations from the file system |
19,626 | public static File getDirectory ( ResourceType resourceType , String resourceName ) { File f = new File ( baseDirectory , resourceType . resourceName ( ) ) ; f = new File ( f , resourceName ) ; f . mkdirs ( ) ; return f ; } | Get the storage location for the specified resource type and resource name |
19,627 | public void finishTraining ( long originatorId , long taskId ) { if ( params != null && stepFunction != null ) { if ( hasSomething . get ( ) ) { stepFunction . step ( params , updates ) ; updates . assign ( 0.0 ) ; } } } | This method is used on Master only applies buffered updates to params |
19,628 | private Mat streamToMat ( InputStream is ) throws IOException { if ( buffer == null ) { buffer = IOUtils . toByteArray ( is ) ; bufferMat = new Mat ( buffer ) ; return bufferMat ; } else { int numReadTotal = is . read ( buffer ) ; if ( numReadTotal < buffer . length ) { bufferMat . data ( ) . put ( buffer , 0 , numReadTotal ) ; bufferMat . cols ( numReadTotal ) ; return bufferMat ; } int numReadCurrent = numReadTotal ; while ( numReadCurrent != - 1 ) { byte [ ] oldBuffer = buffer ; if ( oldBuffer . length == Integer . MAX_VALUE ) { throw new IllegalStateException ( "Cannot read more than Integer.MAX_VALUE bytes" ) ; } long increase = Math . max ( buffer . length , MIN_BUFFER_STEP_SIZE ) ; int newBufferLength = ( int ) Math . min ( Integer . MAX_VALUE , buffer . length + increase ) ; buffer = new byte [ newBufferLength ] ; System . arraycopy ( oldBuffer , 0 , buffer , 0 , oldBuffer . length ) ; numReadCurrent = is . read ( buffer , oldBuffer . length , buffer . length - oldBuffer . length ) ; if ( numReadCurrent > 0 ) { numReadTotal += numReadCurrent ; } } bufferMat = new Mat ( buffer ) ; return bufferMat ; } } | Read the stream to the buffer and return the number of bytes read |
19,629 | public ImageWritable asWritable ( File f ) throws IOException { try ( BufferedInputStream bis = new BufferedInputStream ( new FileInputStream ( f ) ) ) { Mat mat = streamToMat ( bis ) ; Mat image = imdecode ( mat , IMREAD_ANYDEPTH | IMREAD_ANYCOLOR ) ; if ( image == null || image . empty ( ) ) { PIX pix = pixReadMem ( mat . data ( ) , mat . cols ( ) ) ; if ( pix == null ) { throw new IOException ( "Could not decode image from input stream" ) ; } image = convert ( pix ) ; pixDestroy ( pix ) ; } ImageWritable writable = new ImageWritable ( converter . convert ( image ) ) ; return writable ; } } | Convert a file to a INDArray |
19,630 | public INDArray asMatrix ( ImageWritable writable ) throws IOException { Mat image = converter . convert ( writable . getFrame ( ) ) ; return asMatrix ( image ) ; } | Convert ImageWritable to INDArray |
19,631 | public Frame asFrame ( INDArray array , int dataType ) { return converter . convert ( asMat ( array , OpenCVFrameConverter . getMatDepth ( dataType ) ) ) ; } | Converts an INDArray to a JavaCV Frame . Only intended for images with rank 3 . |
19,632 | private INDArray asMatrix ( BytePointer bytes , long length ) throws IOException { PIXA pixa ; pixa = pixaReadMemMultipageTiff ( bytes , length ) ; INDArray data ; INDArray currentD ; INDArrayIndex [ ] index = null ; switch ( this . multiPageMode ) { case MINIBATCH : data = Nd4j . create ( pixa . n ( ) , 1 , pixa . pix ( 0 ) . h ( ) , pixa . pix ( 0 ) . w ( ) ) ; break ; case CHANNELS : data = Nd4j . create ( 1 , pixa . n ( ) , pixa . pix ( 0 ) . h ( ) , pixa . pix ( 0 ) . w ( ) ) ; break ; case FIRST : data = Nd4j . create ( 1 , 1 , pixa . pix ( 0 ) . h ( ) , pixa . pix ( 0 ) . w ( ) ) ; PIX pix = pixa . pix ( 0 ) ; currentD = asMatrix ( convert ( pix ) ) ; pixDestroy ( pix ) ; index = new INDArrayIndex [ ] { NDArrayIndex . point ( 0 ) , NDArrayIndex . point ( 0 ) , NDArrayIndex . all ( ) , NDArrayIndex . all ( ) } ; data . put ( index , currentD . get ( NDArrayIndex . all ( ) , NDArrayIndex . all ( ) , NDArrayIndex . all ( ) ) ) ; return data ; default : throw new UnsupportedOperationException ( "Unsupported MultiPageMode: " + multiPageMode ) ; } for ( int i = 0 ; i < pixa . n ( ) ; i ++ ) { PIX pix = pixa . pix ( i ) ; currentD = asMatrix ( convert ( pix ) ) ; pixDestroy ( pix ) ; switch ( this . multiPageMode ) { case MINIBATCH : index = new INDArrayIndex [ ] { NDArrayIndex . point ( i ) , NDArrayIndex . all ( ) , NDArrayIndex . all ( ) , NDArrayIndex . all ( ) } ; break ; case CHANNELS : index = new INDArrayIndex [ ] { NDArrayIndex . all ( ) , NDArrayIndex . point ( i ) , NDArrayIndex . all ( ) , NDArrayIndex . all ( ) } ; break ; default : throw new UnsupportedOperationException ( "Unsupported MultiPageMode: " + multiPageMode ) ; } data . put ( index , currentD . get ( NDArrayIndex . all ( ) , NDArrayIndex . all ( ) , NDArrayIndex . all ( ) ) ) ; } return data ; } | Read multipage tiff and load into INDArray |
19,633 | public void resetLayerDefaultConfig ( ) { this . setIUpdater ( null ) ; this . setWeightInitFn ( null ) ; this . setBiasInit ( Double . NaN ) ; this . setGainInit ( Double . NaN ) ; this . regularization = null ; this . regularizationBias = null ; this . setGradientNormalization ( GradientNormalization . None ) ; this . setGradientNormalizationThreshold ( 1.0 ) ; this . iUpdater = null ; this . biasUpdater = null ; } | Reset the learning related configs of the layer to default . When instantiated with a global neural network configuration the parameters specified in the neural network configuration will be used . For internal use with the transfer learning API . Users should not have to call this method directly . |
19,634 | public static void exec ( String code ) { code = getFunctionalCode ( "__f_" + Thread . currentThread ( ) . getId ( ) , code ) ; acquireGIL ( ) ; log . info ( "CPython: PyRun_SimpleStringFlag()" ) ; log . info ( code ) ; int result = PyRun_SimpleStringFlags ( code , null ) ; if ( result != 0 ) { PyErr_Print ( ) ; throw new RuntimeException ( "exec failed" ) ; } log . info ( "Exec done" ) ; releaseGIL ( ) ; } | Executes python code . Also manages python thread state . |
19,635 | public static void assertSameLength ( INDArray x , INDArray z ) { val lengthX = x . length ( ) ; val lengthZ = z . length ( ) ; if ( lengthX != lengthZ && lengthX != 1 && lengthZ != 1 ) throw new IllegalStateException ( "Mis matched lengths: [" + x . length ( ) + "] != [" + z . length ( ) + "] - " + "Array 1 shape: " + Arrays . toString ( x . shape ( ) ) + ", array 2 shape: " + Arrays . toString ( z . shape ( ) ) ) ; } | Asserts both arrays be the same length |
19,636 | public TF_Session loadSavedModel ( SavedModelConfig savedModelConfig , TF_SessionOptions options , TF_Buffer runOptions , TF_Graph graph , Map < String , String > inputsMap , Map < String , String > outputsMap , TF_Status status ) { TF_Buffer metaGraph = TF_Buffer . newBuffer ( ) ; TF_Session session = TF_LoadSessionFromSavedModel ( options , runOptions , new BytePointer ( savedModelConfig . getSavedModelPath ( ) ) , new BytePointer ( savedModelConfig . getModelTag ( ) ) , 1 , graph , metaGraph , status ) ; if ( TF_GetCode ( status ) != TF_OK ) { throw new IllegalStateException ( "ERROR: Unable to import model " + TF_Message ( status ) . getString ( ) ) ; } MetaGraphDef metaGraphDef ; try { metaGraphDef = MetaGraphDef . parseFrom ( metaGraph . data ( ) . capacity ( metaGraph . length ( ) ) . asByteBuffer ( ) ) ; } catch ( InvalidProtocolBufferException ex ) { throw new IllegalStateException ( "ERROR: Unable to import model " + ex ) ; } Map < String , SignatureDef > signatureDefMap = metaGraphDef . getSignatureDefMap ( ) ; SignatureDef signatureDef = signatureDefMap . get ( savedModelConfig . getSignatureKey ( ) ) ; Map < String , TensorInfo > inputs = signatureDef . getInputsMap ( ) ; for ( Map . Entry < String , TensorInfo > e : inputs . entrySet ( ) ) { inputsMap . put ( e . getKey ( ) , e . getValue ( ) . getName ( ) ) ; } Map < String , TensorInfo > outputs = signatureDef . getOutputsMap ( ) ; for ( Map . Entry < String , TensorInfo > e : outputs . entrySet ( ) ) { outputsMap . put ( e . getKey ( ) , e . getValue ( ) . getName ( ) ) ; } return session ; } | Load a session based on the saved model |
19,637 | public void leverageTo ( String id ) { if ( fwdPassOutput != null ) fwdPassOutput = fwdPassOutput . leverageTo ( id ) ; if ( fwdPassOutputAsArrays != null ) for ( int i = 0 ; i < fwdPassOutputAsArrays . length ; i ++ ) fwdPassOutputAsArrays [ i ] = fwdPassOutputAsArrays [ i ] . leverageTo ( id ) ; if ( memCellState != null ) for ( int i = 0 ; i < memCellState . length ; i ++ ) memCellState [ i ] = memCellState [ i ] . leverageTo ( id ) ; if ( memCellActivations != null ) for ( int i = 0 ; i < memCellActivations . length ; i ++ ) memCellActivations [ i ] = memCellActivations [ i ] . leverageTo ( id ) ; if ( fwdPassOutputAsArrays != null ) for ( int i = 0 ; i < fwdPassOutputAsArrays . length ; i ++ ) fwdPassOutputAsArrays [ i ] = fwdPassOutputAsArrays [ i ] . leverageTo ( id ) ; if ( iz != null ) for ( int i = 0 ; i < iz . length ; i ++ ) iz [ i ] = iz [ i ] . leverageTo ( id ) ; if ( ia != null ) for ( int i = 0 ; i < ia . length ; i ++ ) ia [ i ] = ia [ i ] . leverageTo ( id ) ; if ( fa != null ) for ( int i = 0 ; i < fa . length ; i ++ ) fa [ i ] = fa [ i ] . leverageTo ( id ) ; if ( oa != null ) for ( int i = 0 ; i < oa . length ; i ++ ) oa [ i ] = oa [ i ] . leverageTo ( id ) ; if ( ga != null ) for ( int i = 0 ; i < ga . length ; i ++ ) ga [ i ] = ga [ i ] . leverageTo ( id ) ; if ( fz != null ) for ( int i = 0 ; i < fz . length ; i ++ ) fz [ i ] = fz [ i ] . leverageTo ( id ) ; if ( oz != null ) for ( int i = 0 ; i < oz . length ; i ++ ) oz [ i ] = oz [ i ] . leverageTo ( id ) ; if ( gz != null ) for ( int i = 0 ; i < gz . length ; i ++ ) gz [ i ] = gz [ i ] . leverageTo ( id ) ; if ( lastAct != null ) lastAct = lastAct . leverageTo ( id ) ; if ( lastMemCell != null ) lastMemCell = lastMemCell . leverageTo ( id ) ; } | This method is OPTIONAL and written mostly for future use |
19,638 | public List < ViterbiNode > search ( ViterbiLattice lattice ) { ViterbiNode [ ] [ ] endIndexArr = calculatePathCosts ( lattice ) ; LinkedList < ViterbiNode > result = backtrackBestPath ( endIndexArr [ 0 ] [ 0 ] ) ; return result ; } | Find best path from input lattice . |
19,639 | protected VocabCache < ShallowSequenceElement > buildShallowVocabCache ( Counter < Long > counter ) { VocabCache < ShallowSequenceElement > vocabCache = new AbstractCache < > ( ) ; for ( Long id : counter . keySet ( ) ) { ShallowSequenceElement shallowElement = new ShallowSequenceElement ( counter . getCount ( id ) , id ) ; vocabCache . addToken ( shallowElement ) ; } Huffman huffman = new Huffman ( vocabCache . vocabWords ( ) ) ; huffman . build ( ) ; huffman . applyIndexes ( vocabCache ) ; return vocabCache ; } | This method builds shadow vocabulary and huffman tree |
19,640 | public INDArray storeAndAllocateNewArray ( ) { Preconditions . checkState ( variableType == VariableType . VARIABLE , "Unable to allocate and store array for variable of type %s: only" + " VARIABLE type variables can be initialized using this method" , variableType ) ; if ( ! sameDiff . arrayAlreadyExistsForVarName ( varName ) ) { long [ ] shape = getShape ( ) ; INDArray arr = getWeightInitScheme ( ) . create ( dataType ( ) , shape ) ; sameDiff . associateArrayWithVariable ( arr , this ) ; if ( log . isTraceEnabled ( ) ) { log . trace ( "Generated and stored new array for variable \"{}\": shape {}" , getVarName ( ) , Arrays . toString ( arr . shape ( ) ) ) ; } return arr ; } INDArray ret = getArr ( ) ; return ret ; } | Allocate and return a new array based on the vertex id and weight initialization . |
19,641 | public long [ ] getShape ( ) { if ( variableType == VariableType . PLACEHOLDER && getArr ( ) == null ) { if ( shape != null ) return shape ; else return new long [ 0 ] ; } long [ ] initialShape = sameDiff . getShapeForVarName ( getVarName ( ) ) ; if ( initialShape == null ) { val arr = getArr ( ) ; if ( arr != null ) return arr . shape ( ) ; } return initialShape ; } | Returns the shape of this variable |
19,642 | public NearestNeighborsResults knnNew ( int k , INDArray arr ) throws Exception { Base64NDArrayBody base64NDArrayBody = Base64NDArrayBody . builder ( ) . k ( k ) . ndarray ( Nd4jBase64 . base64String ( arr ) ) . build ( ) ; HttpRequestWithBody req = Unirest . post ( url + "/knnnew" ) ; req . header ( "accept" , "application/json" ) . header ( "Content-Type" , "application/json" ) . body ( base64NDArrayBody ) ; addAuthHeader ( req ) ; NearestNeighborsResults ret = req . asObject ( NearestNeighborsResults . class ) . getBody ( ) ; return ret ; } | Run a k nearest neighbors search on a NEW data point |
19,643 | protected HttpRequest addAuthHeader ( HttpRequest request ) { if ( authToken != null ) { request . header ( "authorization" , "Bearer " + authToken ) ; } return request ; } | Add the specified authentication header to the specified HttpRequest |
19,644 | public SDVariable localResponseNormalization ( SDVariable input , LocalResponseNormalizationConfig lrnConfig ) { LocalResponseNormalization lrn = LocalResponseNormalization . builder ( ) . inputFunctions ( new SDVariable [ ] { input } ) . sameDiff ( sameDiff ( ) ) . config ( lrnConfig ) . build ( ) ; return lrn . outputVariable ( ) ; } | Local response normalization operation . |
19,645 | public SDVariable conv1d ( SDVariable input , SDVariable weights , Conv1DConfig conv1DConfig ) { Conv1D conv1D = Conv1D . builder ( ) . inputFunctions ( new SDVariable [ ] { input , weights } ) . sameDiff ( sameDiff ( ) ) . config ( conv1DConfig ) . build ( ) ; return conv1D . outputVariable ( ) ; } | Conv1d operation . |
19,646 | public SDVariable avgPooling2d ( SDVariable input , Pooling2DConfig pooling2DConfig ) { AvgPooling2D avgPooling2D = AvgPooling2D . builder ( ) . input ( input ) . sameDiff ( sameDiff ( ) ) . config ( pooling2DConfig ) . build ( ) ; return avgPooling2D . outputVariable ( ) ; } | Average pooling 2d operation . |
19,647 | public SDVariable maxPooling2d ( SDVariable input , Pooling2DConfig pooling2DConfig ) { MaxPooling2D maxPooling2D = MaxPooling2D . builder ( ) . input ( input ) . sameDiff ( sameDiff ( ) ) . config ( pooling2DConfig ) . build ( ) ; return maxPooling2D . outputVariable ( ) ; } | Max pooling 2d operation . |
19,648 | public SDVariable avgPooling3d ( SDVariable input , Pooling3DConfig pooling3DConfig ) { pooling3DConfig . setType ( Pooling3D . Pooling3DType . AVG ) ; return pooling3d ( input , pooling3DConfig ) ; } | Avg pooling 3d operation . |
19,649 | public SDVariable maxPooling3d ( SDVariable input , Pooling3DConfig pooling3DConfig ) { pooling3DConfig . setType ( Pooling3D . Pooling3DType . MAX ) ; return pooling3d ( input , pooling3DConfig ) ; } | Max pooling 3d operation . |
19,650 | public SDVariable conv3d ( SDVariable [ ] inputs , Conv3DConfig conv3DConfig ) { Conv3D conv3D = Conv3D . builder ( ) . inputFunctions ( inputs ) . conv3DConfig ( conv3DConfig ) . sameDiff ( sameDiff ( ) ) . build ( ) ; val outputVars = conv3D . outputVariables ( ) ; return outputVars [ 0 ] ; } | Conv3d operation . |
19,651 | public SDVariable matchCondition ( SDVariable in , Condition condition ) { return new MatchConditionTransform ( sameDiff ( ) , in , condition ) . outputVariable ( ) ; } | Returns a boolean mask of equal shape to the input where the condition is satisfied |
19,652 | public static SingleCSVRecord fromRow ( DataSet row ) { if ( ! row . getFeatures ( ) . isVector ( ) && ! row . getFeatures ( ) . isScalar ( ) ) throw new IllegalArgumentException ( "Passed in dataset must represent a scalar or vector" ) ; if ( ! row . getLabels ( ) . isVector ( ) && ! row . getLabels ( ) . isScalar ( ) ) throw new IllegalArgumentException ( "Passed in dataset labels must be a scalar or vector" ) ; SingleCSVRecord record ; int idx = 0 ; if ( row . getLabels ( ) . sumNumber ( ) . doubleValue ( ) == 1.0 ) { String [ ] values = new String [ row . getFeatures ( ) . columns ( ) + 1 ] ; for ( int i = 0 ; i < row . getFeatures ( ) . length ( ) ; i ++ ) { values [ idx ++ ] = String . valueOf ( row . getFeatures ( ) . getDouble ( i ) ) ; } int maxIdx = 0 ; for ( int i = 0 ; i < row . getLabels ( ) . length ( ) ; i ++ ) { if ( row . getLabels ( ) . getDouble ( maxIdx ) < row . getLabels ( ) . getDouble ( i ) ) { maxIdx = i ; } } values [ idx ++ ] = String . valueOf ( maxIdx ) ; record = new SingleCSVRecord ( values ) ; } else { String [ ] values = new String [ row . getFeatures ( ) . columns ( ) + row . getLabels ( ) . columns ( ) ] ; for ( int i = 0 ; i < row . getFeatures ( ) . length ( ) ; i ++ ) { values [ idx ++ ] = String . valueOf ( row . getFeatures ( ) . getDouble ( i ) ) ; } for ( int i = 0 ; i < row . getLabels ( ) . length ( ) ; i ++ ) { values [ idx ++ ] = String . valueOf ( row . getLabels ( ) . getDouble ( i ) ) ; } record = new SingleCSVRecord ( values ) ; } return record ; } | Instantiate a csv record from a vector given either an input dataset and a one hot matrix the index will be appended to the end of the record or for regression it will append all values in the labels |
19,653 | private URL getUrl ( ) { ClassLoader loader = null ; try { loader = Thread . currentThread ( ) . getContextClassLoader ( ) ; } catch ( Exception e ) { } if ( loader == null ) { loader = ClassPathResource . class . getClassLoader ( ) ; } URL url = loader . getResource ( this . resourceName ) ; if ( url == null ) { if ( this . resourceName . startsWith ( "/" ) ) { url = loader . getResource ( this . resourceName . replaceFirst ( "[\\\\/]" , "" ) ) ; if ( url != null ) return url ; } else { url = loader . getResource ( "/" + this . resourceName ) ; if ( url != null ) return url ; } throw new IllegalStateException ( "Resource '" + this . resourceName + "' cannot be found." ) ; } return url ; } | Returns URL of the requested resource |
19,654 | public File getFile ( ) throws FileNotFoundException { URL url = this . getUrl ( ) ; if ( isJarURL ( url ) ) { try { url = extractActualUrl ( url ) ; File file = File . createTempFile ( "canova_temp" , "file" ) ; file . deleteOnExit ( ) ; ZipFile zipFile = new ZipFile ( url . getFile ( ) ) ; ZipEntry entry = zipFile . getEntry ( this . resourceName ) ; if ( entry == null ) { if ( this . resourceName . startsWith ( "/" ) ) { entry = zipFile . getEntry ( this . resourceName . replaceFirst ( "/" , "" ) ) ; if ( entry == null ) { throw new FileNotFoundException ( "Resource " + this . resourceName + " not found" ) ; } } else throw new FileNotFoundException ( "Resource " + this . resourceName + " not found" ) ; } long size = entry . getSize ( ) ; InputStream stream = zipFile . getInputStream ( entry ) ; FileOutputStream outputStream = new FileOutputStream ( file ) ; byte [ ] array = new byte [ 1024 ] ; int rd = 0 ; long bytesRead = 0 ; do { rd = stream . read ( array ) ; outputStream . write ( array , 0 , rd ) ; bytesRead += rd ; } while ( bytesRead < size ) ; outputStream . flush ( ) ; outputStream . close ( ) ; stream . close ( ) ; zipFile . close ( ) ; return file ; } catch ( Exception e ) { throw new RuntimeException ( e ) ; } } else { try { URI uri = new URI ( url . toString ( ) . replaceAll ( " " , "%20" ) ) ; return new File ( uri . getSchemeSpecificPart ( ) ) ; } catch ( URISyntaxException e ) { return new File ( url . getFile ( ) ) ; } } } | Returns requested ClassPathResource as File object |
19,655 | public void putFunctionForId ( String id , DifferentialFunction function ) { if ( ops . containsKey ( id ) && ops . get ( id ) . getOp ( ) == null ) { throw new ND4JIllegalStateException ( "Function by id already exists!" ) ; } else if ( function instanceof SDVariable ) { throw new ND4JIllegalStateException ( "Function must not be a variable!" ) ; } if ( ops . containsKey ( id ) ) { } else { ops . put ( id , SameDiffOp . builder ( ) . name ( id ) . op ( function ) . build ( ) ) ; } } | Put the function for the given id |
19,656 | public void putShapeForVarName ( String varName , long [ ] shape ) { if ( shape == null ) { throw new ND4JIllegalStateException ( "Shape must not be null!" ) ; } if ( variableNameToShape . containsKey ( varName ) ) { throw new ND4JIllegalStateException ( "Shape for " + varName + " already exists!" ) ; } variableNameToShape . put ( varName , shape ) ; } | Associate a vertex id with the given shape . |
19,657 | public void putOrUpdateShapeForVarName ( String varName , long [ ] shape , boolean clearArrayOnShapeMismatch ) { Preconditions . checkNotNull ( shape , "Cannot put null shape for variable: %s" , varName ) ; if ( variableNameToShape . containsKey ( varName ) ) { } else { putShapeForVarName ( varName , shape ) ; } } | Put or update the shape for the given variable name . Optionally supports clearing the specified variable s INDArray if it s shape does not match the new shape |
19,658 | public Map < String , SDVariable > variableMap ( ) { Map < String , SDVariable > ret = new LinkedHashMap < > ( ) ; for ( Variable v : variables . values ( ) ) { ret . put ( v . getName ( ) , v . getVariable ( ) ) ; } return ret ; } | Return a copy of the internal variable map |
19,659 | public boolean hasArgs ( DifferentialFunction function ) { List < String > vertexIdArgs = ops . get ( function . getOwnName ( ) ) . getInputsToOp ( ) ; return vertexIdArgs != null && vertexIdArgs . size ( ) > 0 ; } | Returns true if this function already has defined arguments |
19,660 | public DifferentialFunction [ ] functions ( ) { List < DifferentialFunction > out = new ArrayList < > ( ops . size ( ) ) ; for ( SameDiffOp op : ops . values ( ) ) { out . add ( op . getOp ( ) ) ; } return out . toArray ( new DifferentialFunction [ out . size ( ) ] ) ; } | Get an array of differential functions that have been defined for this SameDiff instance |
19,661 | public SDVariable one ( String name , org . nd4j . linalg . api . buffer . DataType dataType , int ... shape ) { return var ( name , new ConstantInitScheme ( 'f' , 1.0 ) , dataType , ArrayUtil . toLongArray ( shape ) ) ; } | Create a new variable with the specified shape with all values initialized to 1 . 0 |
19,662 | public SDVariable zero ( String name , org . nd4j . linalg . api . buffer . DataType dataType , int ... shape ) { return var ( name , new ZeroInitScheme ( ) , dataType , ArrayUtil . toLongArray ( shape ) ) ; } | Create a new variable with the specified shape with all values initialized to 0 |
19,663 | public void removeArgFromFunction ( String varName , DifferentialFunction function ) { val args = function . args ( ) ; for ( int i = 0 ; i < args . length ; i ++ ) { if ( args [ i ] . getVarName ( ) . equals ( varName ) ) { List < String > reverseArgs = ops . get ( function . getOwnName ( ) ) . getInputsToOp ( ) ; val newArgs = new ArrayList < String > ( args . length - 1 ) ; for ( int arg = 0 ; arg < args . length ; arg ++ ) { if ( ! reverseArgs . get ( arg ) . equals ( varName ) ) { newArgs . add ( reverseArgs . get ( arg ) ) ; } } ops . get ( function . getOwnName ( ) ) . setInputsToOp ( newArgs ) ; break ; } } } | Remove an argument for a function . Note that if this function does not contain the argument it will just be a no op . |
19,664 | public SDVariable getVariable ( String name ) { Variable v = variables . get ( name ) ; return v == null ? null : v . getVariable ( ) ; } | Get the variable based on the opName |
19,665 | public void setGradientForVariableName ( String variableName , SDVariable variable ) { Preconditions . checkState ( variables . containsKey ( variableName ) , "No variable exists with name \"%s\"" , variableName ) ; if ( variable == null ) { throw new ND4JIllegalStateException ( "Unable to set null gradient for variable name " + variableName ) ; } variables . get ( variableName ) . setGradient ( variable ) ; } | Assign a SDVariable to represent the gradient of the SDVariable with the specified name |
19,666 | public SDVariable addVariable ( SDVariable variable ) { Preconditions . checkState ( variable . getSameDiff ( ) == this , "Samediff instance must be the same." ) ; if ( variables . containsKey ( variable . getVarName ( ) ) && ! variables . get ( variable . getVarName ( ) ) . getVariable ( ) . equals ( variable ) ) { throw new IllegalArgumentException ( "Variable already found with variable opName " + variable . getVarName ( ) ) ; } Preconditions . checkState ( variable . getSameDiff ( ) == this , "Same diff instance for variable must be the same!" ) ; variables . put ( variable . getVarName ( ) , Variable . builder ( ) . name ( variable . getVarName ( ) ) . variable ( variable ) . build ( ) ) ; return variable ; } | Add the specified variable to this SameDiff instance |
19,667 | public SDVariable [ ] updateVariableNamesAndReferences ( SDVariable [ ] variablesToUpdate , String [ ] newVariableNames ) { int numVariables = variablesToUpdate . length ; SDVariable [ ] updatedVariables = new SDVariable [ numVariables ] ; for ( int i = 0 ; i < numVariables ; i ++ ) { SDVariable varToUpdate = variablesToUpdate [ i ] ; String name = newVariableNames == null ? null : newVariableNames [ i ] ; updatedVariables [ i ] = updateVariableNameAndReference ( varToUpdate , name ) ; } return updatedVariables ; } | Updates the variable name property on the passed in variables its reference in samediff and returns the variable . |
19,668 | public FlatGraph asFlatGraph ( long graphId , ExecutorConfiguration configuration ) { return FlatGraph . getRootAsFlatGraph ( asFlatBuffers ( graphId , configuration ) ) ; } | This method returns FlatGraph structure |
19,669 | public void saveWithTrainingConfig ( TrainingConfig trainingConfig , OutputStream outputStream ) throws IOException { ObjectMapper objectMapper = ObjectMapperHolder . getJsonMapper ( ) ; String configJson = objectMapper . writeValueAsString ( trainingConfig ) ; ZipOutputStream zipfile = new ZipOutputStream ( new CloseShieldOutputStream ( outputStream ) ) ; ZipEntry config = new ZipEntry ( TRAINING_CONFIG_JSON_ZIP_ENTRY_NAME ) ; zipfile . putNextEntry ( config ) ; zipfile . write ( configJson . getBytes ( ) ) ; ZipEntry sameDiff = new ZipEntry ( SAMEDIFF_FILE_ENTRY_NAME ) ; zipfile . putNextEntry ( sameDiff ) ; val fb = asFlatBuffers ( ) ; val offset = fb . position ( ) ; val array = fb . array ( ) ; try ( BufferedOutputStream zipFileOutputStream = new BufferedOutputStream ( zipfile ) ; val dos = new DataOutputStream ( zipFileOutputStream ) ) { dos . write ( array , offset , array . length - offset ) ; } } | Save this samediff instance as a zip file with the training configuration |
19,670 | public String serializeTransformList ( List < Transform > list ) { ObjectMapper om = getObjectMapper ( ) ; try { return om . writeValueAsString ( new ListWrappers . TransformList ( list ) ) ; } catch ( Exception e ) { throw new RuntimeException ( e ) ; } } | Serialize a list of Transforms |
19,671 | public String serializeFilterList ( List < Filter > list ) { ObjectMapper om = getObjectMapper ( ) ; try { return om . writeValueAsString ( new ListWrappers . FilterList ( list ) ) ; } catch ( Exception e ) { throw new RuntimeException ( e ) ; } } | Serialize a list of Filters |
19,672 | public String serializeConditionList ( List < Condition > list ) { ObjectMapper om = getObjectMapper ( ) ; try { return om . writeValueAsString ( new ListWrappers . ConditionList ( list ) ) ; } catch ( Exception e ) { throw new RuntimeException ( e ) ; } } | Serialize a list of Conditions |
19,673 | public String serializeReducerList ( List < IAssociativeReducer > list ) { ObjectMapper om = getObjectMapper ( ) ; try { return om . writeValueAsString ( new ListWrappers . ReducerList ( list ) ) ; } catch ( Exception e ) { throw new RuntimeException ( e ) ; } } | Serialize a list of IReducers |
19,674 | public String serializeSequenceComparatorList ( List < SequenceComparator > list ) { ObjectMapper om = getObjectMapper ( ) ; try { return om . writeValueAsString ( new ListWrappers . SequenceComparatorList ( list ) ) ; } catch ( Exception e ) { throw new RuntimeException ( e ) ; } } | Serialize a list of SequenceComparators |
19,675 | public String serializeDataActionList ( List < DataAction > list ) { ObjectMapper om = getObjectMapper ( ) ; try { return om . writeValueAsString ( new ListWrappers . DataActionList ( list ) ) ; } catch ( Exception e ) { throw new RuntimeException ( e ) ; } } | Serialize a list of DataActions |
19,676 | public < M extends Model > M initPretrained ( PretrainedType pretrainedType ) throws IOException { String remoteUrl = pretrainedUrl ( pretrainedType ) ; if ( remoteUrl == null ) throw new UnsupportedOperationException ( "Pretrained " + pretrainedType + " weights are not available for this model." ) ; String localFilename = new File ( remoteUrl ) . getName ( ) ; File rootCacheDir = DL4JResources . getDirectory ( ResourceType . ZOO_MODEL , modelName ( ) ) ; File cachedFile = new File ( rootCacheDir , localFilename ) ; if ( ! cachedFile . exists ( ) ) { log . info ( "Downloading model to " + cachedFile . toString ( ) ) ; FileUtils . copyURLToFile ( new URL ( remoteUrl ) , cachedFile ) ; } else { log . info ( "Using cached model at " + cachedFile . toString ( ) ) ; } long expectedChecksum = pretrainedChecksum ( pretrainedType ) ; if ( expectedChecksum != 0L ) { log . info ( "Verifying download..." ) ; Checksum adler = new Adler32 ( ) ; FileUtils . checksum ( cachedFile , adler ) ; long localChecksum = adler . getValue ( ) ; log . info ( "Checksum local is " + localChecksum + ", expecting " + expectedChecksum ) ; if ( expectedChecksum != localChecksum ) { log . error ( "Checksums do not match. Cleaning up files and failing..." ) ; cachedFile . delete ( ) ; throw new IllegalStateException ( "Pretrained model file failed checksum. If this error persists, please open an issue at https://github.com/deeplearning4j/deeplearning4j." ) ; } } if ( modelType ( ) == MultiLayerNetwork . class ) { return ( M ) ModelSerializer . restoreMultiLayerNetwork ( cachedFile ) ; } else if ( modelType ( ) == ComputationGraph . class ) { return ( M ) ModelSerializer . restoreComputationGraph ( cachedFile ) ; } else { throw new UnsupportedOperationException ( "Pretrained models are only supported for MultiLayerNetwork and ComputationGraph." ) ; } } | Returns a pretrained model for the given dataset if available . |
19,677 | public static boolean getUnrollRecurrentLayer ( KerasLayerConfiguration conf , Map < String , Object > layerConfig ) throws InvalidKerasConfigurationException { Map < String , Object > innerConfig = KerasLayerUtils . getInnerLayerConfigFromConfig ( layerConfig , conf ) ; if ( ! innerConfig . containsKey ( conf . getLAYER_FIELD_UNROLL ( ) ) ) throw new InvalidKerasConfigurationException ( "Keras LSTM layer config missing " + conf . getLAYER_FIELD_UNROLL ( ) + " field" ) ; return ( boolean ) innerConfig . get ( conf . getLAYER_FIELD_UNROLL ( ) ) ; } | Get unroll parameter to decide whether to unroll RNN with BPTT or not . |
19,678 | public static double getRecurrentDropout ( KerasLayerConfiguration conf , Map < String , Object > layerConfig ) throws UnsupportedKerasConfigurationException , InvalidKerasConfigurationException { Map < String , Object > innerConfig = KerasLayerUtils . getInnerLayerConfigFromConfig ( layerConfig , conf ) ; double dropout = 1.0 ; if ( innerConfig . containsKey ( conf . getLAYER_FIELD_DROPOUT_U ( ) ) ) try { dropout = 1.0 - ( double ) innerConfig . get ( conf . getLAYER_FIELD_DROPOUT_U ( ) ) ; } catch ( Exception e ) { int kerasDropout = ( int ) innerConfig . get ( conf . getLAYER_FIELD_DROPOUT_U ( ) ) ; dropout = 1.0 - ( double ) kerasDropout ; } if ( dropout < 1.0 ) throw new UnsupportedKerasConfigurationException ( "Dropout > 0 on recurrent connections not supported." ) ; return dropout ; } | Get recurrent weight dropout from Keras layer configuration . Non - zero dropout rates are currently not supported . |
19,679 | public static void download ( String name , URL url , File f , String targetMD5 , int maxTries ) throws IOException { download ( name , url , f , targetMD5 , maxTries , 0 ) ; } | Download the specified URL to the specified file and verify that the target MD5 matches |
19,680 | public static boolean checkMD5OfFile ( String targetMD5 , File file ) throws IOException { InputStream in = FileUtils . openInputStream ( file ) ; String trueMd5 = DigestUtils . md5Hex ( in ) ; IOUtils . closeQuietly ( in ) ; return ( targetMD5 . equals ( trueMd5 ) ) ; } | Check the MD5 of the specified file |
19,681 | public void addUpdate ( NDArrayMessage array ) { UnsafeBuffer directBuffer = ( UnsafeBuffer ) NDArrayMessage . toBuffer ( array ) ; byte [ ] data = directBuffer . byteArray ( ) ; if ( data == null ) { data = new byte [ directBuffer . capacity ( ) ] ; directBuffer . getBytes ( 0 , data , 0 , data . length ) ; } byte [ ] key = ByteBuffer . allocate ( 4 ) . putInt ( size ) . array ( ) ; try { db . put ( key , data ) ; } catch ( RocksDBException e ) { throw new RuntimeException ( e ) ; } size ++ ; } | Add an ndarray to the storage |
19,682 | public void clear ( ) { RocksIterator iterator = db . newIterator ( ) ; while ( iterator . isValid ( ) ) try { db . remove ( iterator . key ( ) ) ; } catch ( RocksDBException e ) { throw new RuntimeException ( e ) ; } iterator . close ( ) ; size = 0 ; } | Clear the array storage |
19,683 | public NDArrayMessage doGetUpdate ( int index ) { byte [ ] key = ByteBuffer . allocate ( 4 ) . putInt ( index ) . array ( ) ; try { UnsafeBuffer unsafeBuffer = new UnsafeBuffer ( db . get ( key ) ) ; return NDArrayMessage . fromBuffer ( unsafeBuffer , 0 ) ; } catch ( RocksDBException e ) { throw new RuntimeException ( e ) ; } } | A method for actually performing the implementation of retrieving the ndarray |
19,684 | private long numDepthWiseParams ( SeparableConvolution2D layerConf ) { int [ ] kernel = layerConf . getKernelSize ( ) ; val nIn = layerConf . getNIn ( ) ; val depthMultiplier = layerConf . getDepthMultiplier ( ) ; return nIn * depthMultiplier * kernel [ 0 ] * kernel [ 1 ] ; } | For each input feature we separately compute depthMultiplier many output maps for the given kernel size |
19,685 | public static String [ ] textToWordSequence ( String text , String filters , boolean lower , String split ) { if ( lower ) text = text . toLowerCase ( ) ; for ( String filter : filters . split ( "" ) ) { text = text . replace ( filter , split ) ; } String [ ] sequences = text . split ( split ) ; List < String > seqList = new ArrayList ( Arrays . asList ( sequences ) ) ; seqList . removeAll ( Arrays . asList ( "" , null ) ) ; return seqList . toArray ( new String [ seqList . size ( ) ] ) ; } | Turns a String text into a sequence of tokens . |
19,686 | public void fitOnTexts ( String [ ] texts ) { String [ ] sequence ; for ( String text : texts ) { if ( documentCount == null ) documentCount = 1 ; else documentCount += 1 ; if ( charLevel ) { if ( lower ) text = text . toLowerCase ( ) ; sequence = text . split ( "" ) ; } else { sequence = textToWordSequence ( text , filters , lower , split ) ; } for ( String word : sequence ) { if ( wordCounts . containsKey ( word ) ) wordCounts . put ( word , wordCounts . get ( word ) + 1 ) ; else wordCounts . put ( word , 1 ) ; } Set < String > sequenceSet = new HashSet < > ( Arrays . asList ( sequence ) ) ; for ( String word : sequenceSet ) { if ( wordDocs . containsKey ( word ) ) wordDocs . put ( word , wordDocs . get ( word ) + 1 ) ; else wordDocs . put ( word , 1 ) ; } } Map < String , Integer > sortedWordCounts = reverseSortByValues ( ( HashMap ) wordCounts ) ; ArrayList < String > sortedVocabulary = new ArrayList < > ( ) ; if ( outOfVocabularyToken != null ) sortedVocabulary . add ( outOfVocabularyToken ) ; for ( String word : sortedWordCounts . keySet ( ) ) { sortedVocabulary . add ( word ) ; } for ( int i = 0 ; i < sortedVocabulary . size ( ) ; i ++ ) wordIndex . put ( sortedVocabulary . get ( i ) , i + 1 ) ; for ( String key : wordIndex . keySet ( ) ) { indexWord . put ( wordIndex . get ( key ) , key ) ; } for ( String key : wordDocs . keySet ( ) ) indexDocs . put ( wordIndex . get ( key ) , wordDocs . get ( key ) ) ; } | Fit this tokenizer on a corpus of texts . |
19,687 | private static HashMap reverseSortByValues ( HashMap map ) { List list = new LinkedList ( map . entrySet ( ) ) ; Collections . sort ( list , new Comparator ( ) { public int compare ( Object o1 , Object o2 ) { return ( ( Comparable ) ( ( Map . Entry ) ( o1 ) ) . getValue ( ) ) . compareTo ( ( ( Map . Entry ) ( o2 ) ) . getValue ( ) ) ; } } ) ; HashMap sortedHashMap = new LinkedHashMap ( ) ; for ( Iterator it = list . iterator ( ) ; it . hasNext ( ) ; ) { Map . Entry entry = ( Map . Entry ) it . next ( ) ; sortedHashMap . put ( entry . getKey ( ) , entry . getValue ( ) ) ; } return sortedHashMap ; } | Sort HashMap by values in reverse order |
19,688 | public void fitOnSequences ( Integer [ ] [ ] sequences ) { documentCount += 1 ; for ( Integer [ ] sequence : sequences ) { Set < Integer > sequenceSet = new HashSet < > ( Arrays . asList ( sequence ) ) ; for ( Integer index : sequenceSet ) indexDocs . put ( index , indexDocs . get ( index ) + 1 ) ; } } | Fit this tokenizer on a corpus of word indices |
19,689 | public Integer [ ] [ ] textsToSequences ( String [ ] texts ) { Integer oovTokenIndex = wordIndex . get ( outOfVocabularyToken ) ; String [ ] wordSequence ; ArrayList < Integer [ ] > sequences = new ArrayList < > ( ) ; for ( String text : texts ) { if ( charLevel ) { if ( lower ) { text = text . toLowerCase ( ) ; } wordSequence = text . split ( "" ) ; } else { wordSequence = textToWordSequence ( text , filters , lower , split ) ; } ArrayList < Integer > indexVector = new ArrayList < > ( ) ; for ( String word : wordSequence ) { if ( wordIndex . containsKey ( word ) ) { int index = wordIndex . get ( word ) ; if ( numWords != null && index >= numWords ) { if ( oovTokenIndex != null ) indexVector . add ( oovTokenIndex ) ; } else { indexVector . add ( index ) ; } } else if ( oovTokenIndex != null ) { indexVector . add ( oovTokenIndex ) ; } } Integer [ ] indices = indexVector . toArray ( new Integer [ indexVector . size ( ) ] ) ; sequences . add ( indices ) ; } return sequences . toArray ( new Integer [ sequences . size ( ) ] [ ] ) ; } | Transforms a bunch of texts into their index representations . |
19,690 | public String [ ] sequencesToTexts ( Integer [ ] [ ] sequences ) { Integer oovTokenIndex = wordIndex . get ( outOfVocabularyToken ) ; ArrayList < String > texts = new ArrayList < > ( ) ; for ( Integer [ ] sequence : sequences ) { ArrayList < String > wordVector = new ArrayList < > ( ) ; for ( Integer index : sequence ) { if ( indexWord . containsKey ( index ) ) { String word = indexWord . get ( index ) ; if ( numWords != null && index >= numWords ) { if ( oovTokenIndex != null ) { wordVector . add ( indexWord . get ( oovTokenIndex ) ) ; } else { wordVector . add ( word ) ; } } } else if ( oovTokenIndex != null ) { wordVector . add ( indexWord . get ( oovTokenIndex ) ) ; } } StringBuilder builder = new StringBuilder ( ) ; for ( String word : wordVector ) { builder . append ( word + split ) ; } String text = builder . toString ( ) ; texts . add ( text ) ; } return texts . toArray ( new String [ texts . size ( ) ] ) ; } | Turns index sequences back into texts |
19,691 | public void setArray ( INDArray arr ) { if ( this . arr . get ( ) == null ) this . arr . set ( arr ) ; } | Set the ndarray |
19,692 | public static TFImportStatus checkAllModelsForImport ( File directory ) throws IOException { Preconditions . checkState ( directory . isDirectory ( ) , "Specified directory %s is not actually a directory" , directory ) ; Collection < File > files = FileUtils . listFiles ( directory , new String [ ] { "pb" } , true ) ; Preconditions . checkState ( ! files . isEmpty ( ) , "No .pb files found in directory %s" , directory ) ; TFImportStatus status = null ; for ( File f : files ) { if ( status == null ) { status = checkModelForImport ( f ) ; } else { status = status . merge ( checkModelForImport ( f ) ) ; } } return status ; } | Recursively scan the specified directory for . pb files and evaluate |
19,693 | public < T > T output ( long graphId , T value , OperandsAdapter < T > adapter ) { return adapter . output ( this . output ( graphId , adapter . input ( value ) ) ) ; } | This method is suited for use of custom OperandsAdapters |
19,694 | public INDArray [ ] output ( long graphId , Pair < String , INDArray > ... inputs ) { val operands = new Operands ( ) ; for ( val in : inputs ) operands . addArgument ( in . getFirst ( ) , in . getSecond ( ) ) ; return output ( graphId , operands ) . asArray ( ) ; } | This method sends inference request to the GraphServer instance and returns result as array of INDArrays |
19,695 | public void dropGraph ( long graphId ) { val builder = new FlatBufferBuilder ( 128 ) ; val off = FlatDropRequest . createFlatDropRequest ( builder , graphId ) ; builder . finish ( off ) ; val req = FlatDropRequest . getRootAsFlatDropRequest ( builder . dataBuffer ( ) ) ; val v = blockingStub . forgetGraph ( req ) ; if ( v . status ( ) != 0 ) throw new ND4JIllegalStateException ( "registerGraph() gRPC call failed" ) ; } | This method allows to remove graph from the GraphServer instance |
19,696 | public static TimeSource getInstance ( String className ) { try { Class < ? > c = Class . forName ( className ) ; Method m = c . getMethod ( "getInstance" ) ; return ( TimeSource ) m . invoke ( null ) ; } catch ( Exception e ) { throw new RuntimeException ( "Error getting TimeSource instance for class \"" + className + "\"" , e ) ; } } | Get a specific TimeSource by class name |
19,697 | public static int [ ] getDeconvolutionOutputSize ( INDArray inputData , int [ ] kernel , int [ ] strides , int [ ] padding , ConvolutionMode convolutionMode , int [ ] dilation ) { int hIn = ( int ) inputData . size ( 2 ) ; int wIn = ( int ) inputData . size ( 3 ) ; int [ ] eKernel = effectiveKernelSize ( kernel , dilation ) ; if ( convolutionMode == ConvolutionMode . Same ) { int hOut = strides [ 0 ] * hIn ; int wOut = strides [ 1 ] * wIn ; return new int [ ] { hOut , wOut } ; } int hOut = strides [ 0 ] * ( hIn - 1 ) + eKernel [ 0 ] - 2 * padding [ 0 ] ; int wOut = strides [ 1 ] * ( wIn - 1 ) + eKernel [ 1 ] - 2 * padding [ 1 ] ; return new int [ ] { hOut , wOut } ; } | Get the output size of a deconvolution operation for given input data . In deconvolution we compute the inverse of the shape computation of a convolution . |
19,698 | public static int [ ] getHeightAndWidth ( NeuralNetConfiguration conf ) { return getHeightAndWidth ( ( ( org . deeplearning4j . nn . conf . layers . ConvolutionLayer ) conf . getLayer ( ) ) . getKernelSize ( ) ) ; } | Get the height and width from the configuration |
19,699 | public static int [ ] getHeightAndWidth ( int [ ] shape ) { if ( shape . length < 2 ) throw new IllegalArgumentException ( "No width and height able to be found: array must be at least length 2" ) ; return new int [ ] { shape [ shape . length - 1 ] , shape [ shape . length - 2 ] } ; } | Get the height and width for an image |
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