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SOTA
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Accelerator Toolkit
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Deep Learning Toolkit
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- Resume
- Add
- AlphaDropout
- AdditiveAttention
- Attention
- Average
- AvgPool1D
- AvgPool2D
- AvgPool3D
- BatchNormalization
- Bidirectional
- Concatenate
- Conv1D
- Conv1DTranspose
- Conv2D
- Conv2DTranspose
- Conv3D
- Conv3DTranspose
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Dense
- Cropping1D
- Cropping2D
- Cropping3D
- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
- ELU
- Exponential
- GaussianDropout
- GaussianNoise
- GlobalAvgPool1D
- GlobalAvgPool2D
- GlobalAvgPool3D
- GlobalMaxPool1D
- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- GELU
- Input
- LayerNormalization
- LSTM
- MaxPool1D
- MaxPool2D
- MaxPool3D
- MultiHeadAttention
- HardSigmoid
- LeakyReLU
- Linear
- Multiply
- Permute3D
- Reshape
- RNN
- PReLU
- ReLU
- SELU
- Output Predict
- Output Train
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- SpatialDropout
- Sigmoid
- SoftMax
- SoftPlus
- SoftSign
- Split
- UpSampling1D
- UpSampling2D
- UpSampling3D
- ZeroPadding1D
- ZeroPadding2D
- ZeroPadding3D
- Swish
- TanH
- ThresholdedReLU
- Substract
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- Exp
- Identity
- Abs
- Acos
- Acosh
- ArgMax
- ArgMin
- Asin
- Asinh
- Atan
- Atanh
- AveragePool
- Bernouilli
- BitwiseNot
- BlackmanWindow
- Cast
- Ceil
- Celu
- ConcatFromSequence
- Cos
- Cosh
- DepthToSpace
- Det
- DynamicTimeWarping
- Erf
- EyeLike
- Flatten
- Floor
- GlobalAveragePool
- GlobalLpPool
- GlobalMaxPool
- HammingWindow
- HannWindow
- HardSwish
- HardMax
- lrfft
- lsNaN
- Log
- LogSoftmax
- LpNormalization
- LpPool
- LRN
- MeanVarianceNormalization
- MicrosoftGelu
- Mish
- Multinomial
- MurmurHash3
- Neg
- NhwcMaxPool
- NonZero
- Not
- OptionalGetElement
- OptionalHasElement
- QuickGelu
- RandomNormalLike
- RandomUniformLike
- RawConstantOfShape
- Reciprocal
- ReduceSumInteger
- RegexFullMatch
- Rfft
- Round
- SampleOp
- Shape
- SequenceLength
- Shrink
- Sin
- Sign
- Sinh
- Size
- SpaceToDepth
- Sqrt
- StringNormalizer
- Tan
- TfldfVectorizer
- Tokenizer
- Transpose
- UnfoldTensor
- lslnf
- ImageDecoder
- Inverse
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- Add
- AffineGrid
- And
- BiasAdd
- BiasGelu
- BiasSoftmax
- BiasSplitGelu
- BitShift
- BitwiseAnd
- BitwiseOr
- BitwiseXor
- CastLike
- CDist
- CenterCropPad
- Clip
- Col2lm
- ComplexMul
- ComplexMulConj
- Compress
- ConvInteger
- Conv
- ConvTranspose
- ConvTransposeWithDynamicPads
- CropAndResize
- CumSum
- DeformConv
- DequantizeBFP
- DequantizeLinear
- DequantizeWithOrder
- DFT
- Div
- DynamicQuantizeMatMul
- Equal
- Expand
- ExpandDims
- FastGelu
- FusedConv
- FusedGemm
- FusedMatMul
- FusedMatMulActivation
- GatedRelativePositionBias
- Gather
- GatherElements
- GatherND
- Gemm
- GemmFastGelu
- GemmFloat8
- Greater
- GreaterOrEqual
- GreedySearch
- GridSample
- GroupNorm
- InstanceNormalization
- Less
- LessOrEqual
- LongformerAttention
- MatMul
- MatMulBnb4
- MatMulFpQ4
- MatMulInteger
- MatMulInteger16
- MatMulIntergerToFloat
- MatMulNBits
- MaxPoolWithMask
- MaxRoiPool
- MaxUnPool
- MelWeightMatrix
- MicrosoftDequantizeLinear
- MicrosoftGatherND
- MicrosoftGridSample
- MicrosoftPad
- MicrosoftQLinearConv
- MicrosoftQuantizeLinear
- MicrosoftRange
- MicrosoftTrilu
- Mod
- MoE
- Mul
- MulInteger
- NegativeLogLikelihoodLoss
- NGramRepeatBlock
- NhwcConv
- NhwcFusedConv
- NonMaxSuppression
- OneHot
- Or
- PackedAttention
- PackedMultiHeadAttention
- Pad
- Pow
- QGemm
- QLinearAdd
- QLinearAveragePool
- QLinearConcat
- QLinearConv
- QLinearGlobalAveragePool
- QLinearLeakyRelu
- QLinearMatMul
- QLinearMul
- QLinearReduceMean
- QLinearSigmoid
- QLinearSoftmax
- QLinearWhere
- QMoE
- QOrderedAttention
- QOrderedGelu
- QOrderedLayerNormalization
- QOrderedLongformerAttention
- QOrderedMatMul
- QuantizeLinear
- QuantizeWithOrder
- Range
- ReduceL1
- ReduceL2
- ReduceLogSum
- ReduceLogSumExp
- ReduceMax
- ReduceMean
- ReduceMin
- ReduceProd
- ReduceSum
- ReduceSumSquare
- RelativePositionBias
- Reshape
- Resize
- RestorePadding
- ReverseSequence
- RoiAlign
- RotaryEmbedding
- ScatterElements
- ScatterND
- SequenceAt
- SequenceErase
- SequenceInsert
- Sinh
- Slice
- SparseToDenseMatMul
- SplitToSequence
- Squeeze
- STFT
- StringConcat
- Sub
- Tile
- TorchEmbedding
- TransposeMatMul
- Trilu
- Unsqueeze
- Where
- WordConvEmbedding
- Xor
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- Attention
- AttnLSTM
- BatchNormalization
- BiasDropout
- BifurcationDetector
- BitmaskBiasDropout
- BitmaskDropout
- DecoderAttention
- DecoderMaskedMultiHeadAttention
- DecoderMaskedSelfAttention
- Dropout
- DynamicQuantizeLinear
- DynamicQuantizeLSTM
- EmbedLayerNormalization
- GemmaRotaryEmbedding
- GroupQueryAttention
- GRU
- LayerNormalization
- LSTM
- MicrosoftMultiHeadAttention
- QAttention
- RemovePadding
- RNN
- Sampling
- SkipGroupNorm
- SkipLayerNormalization
- SkipSimplifiedLayerNormalization
- SoftmaxCrossEntropyLoss
- SparseAttention
- TopK
- WhisperBeamSearch
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- AdditiveAttention
- Attention
- BatchNormalization
- Bidirectional
- Conv1D
- Conv2D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Dense
- DepthwiseConv2D
- Embedding
- LayerNormalization
- GRU
- LSTM
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- MutiHeadAttention
- SeparableConv1D
- SeparableConv2D
- MultiHeadAttention
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
- 1D
- 2D
- 3D
- 4D
- 5D
- 6D
- Scalar
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- AdditiveAttention
- Attention
- BatchNormalization
- Conv1D
- Conv2D
- Conv1DTranspose
- Conv2DTranspose
- Bidirectional
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv3DTranspose
- DepthwiseConv2D
- Dense
- Embedding
- LayerNormalization
- GRU
- PReLU 2D
- PReLU 3D
- PReLU 4D
- MultiHeadAttention
- LSTM
- PReLU 5D
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- 1D
- 2D
- 3D
- 4D
- 5D
- 6D
- Scalar
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- AdditiveAttention
- Attention
- BatchNormalization
- Bidirectional
- Conv1D
- Conv2D
- Conv3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Dense
- DepthwiseConv2D
- Embedding
- GRU
- LayerNormalization
- LSTM
- MultiHeadAttention
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Resume
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
- PReLU 4D
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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
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- Accuracy
- BinaryAccuracy
- BinaryCrossentropy
- BinaryIoU
- CategoricalAccuracy
- CategoricalCrossentropy
- CategoricalHinge
- CosineSimilarity
- FalseNegatives
- FalsePositives
- Hinge
- Huber
- IoU
- KLDivergence
- LogCoshError
- Mean
- MeanAbsoluteError
- MeanAbsolutePercentageError
- MeanIoU
- MeanRelativeError
- MeanSquaredError
- MeanSquaredLogarithmicError
- MeanTensor
- OneHotIoU
- OneHotMeanIoU
- Poisson
- Precision
- PrecisionAtRecall
- Recall
- RecallAtPrecision
- RootMeanSquaredError
- SensitivityAtSpecificity
- SparseCategoricalAccuracy
- SparseCategoricalCrossentropy
- SparseTopKCategoricalAccuracy
- Specificity
- SpecificityAtSensitivity
- SquaredHinge
- Sum
- TopKCategoricalAccuracy
- TrueNegatives
- TruePositives
- Resume
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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
- BatchNormalization
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Computer Vision Toolkit
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CUDA Toolkit
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- Resume
- Array size
- Index Array
- Replace Subset
- Insert Into Array
- Delete From Array
- Initialize Array
- Build Array
- Concatenate Array
- Array Subset
- Min & Max
- Reshape Array
- Short Array
- Reverse 1D array
- Shuffle array
- Search In Array
- Split 1D Array
- Split 2D Array
- Rotate 1D Array
- Increment Array Element
- Decrement Array Element
- Interpolate 1D Array
- Threshold 1D Array
- Interleave 1D Array
- Decimate 1D Array
- Transpose Array
- Remove Duplicate From 1D Array
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Min & Max
Description
Returns the maximum and minimum values found in array, along with the indexes for each value. Type : Polyporphic.Β

Input parameters
array : class, n-dimensional tensor.
Output parameters
max value : float, is of the same data type and structure as the elements in array.
max index(es) : array, index for the first max value. If array is multidimensional, max index(es) is an array whose elements are the indexes for the first maximum value in array.Β
min value : float, is of the same data type and structure as the elements in array.
min index(es) : array, index for the first max value. If array is multidimensional, max index(es) is an array whose elements are the indexes for the first minimum value in array.Β
Examples
All these examples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Accelerator library to run it).
