Welcome to our Support Center
   			
		-                 
		Deep Learning
- 
							 			- 
						 
		- 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
- Show All Articles (63) Collapse Articles
 
- 
						 
		 			- 
						 
		 			
- 
						 			- 
						 
		- 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
- Show All Articles (65) Collapse Articles
 
 
- 
						 
		
- 
						 			- 
						 
		- 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
- Show All Articles (134) Collapse Articles
 
- 
						 
		- 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
- Show All Articles (15) Collapse Articles
 
 
- 
						 
		
 
 
- 
						 
		 			
- 
						 
		 			
- 
						 			
- 
						 			
 
- 
						 
		
- 
							 
		 			
- 
							 
		 			- 
						 
		 			- 
						 
		- 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
- Show All Articles (22) Collapse Articles
 
- 
						 
		- 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
- Show All Articles (21) Collapse Articles
 
 
- 
						 
		
- 
						 
		- 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
- Show All Articles (12) Collapse Articles
 
- 
						 
		- 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
- Show All Articles (27) Collapse Articles
 
 
- 
						 
		 			
 
-                 
		Accelerator
-                 
		Constant
-                 
		Generator
-                 
		Full Train Step
-                 
		Eval Step
-                 
		Train Step
		UpdatedOctober 24, 2025		
 2D
Description
Execute forward with 2D Float Data (Inference Session).
 
			Input parameters
 Β Inference inΒ :Β object,Β inference session.
Β Inference inΒ :Β object,Β inference session. 2D Input Data : array, 2D array of data with any type : integers (signed/unsigned), floats, doubles, booleans, or strings.
 2D Input Data : array, 2D array of data with any type : integers (signed/unsigned), floats, doubles, booleans, or strings.
Output parameters
 Β Inference outΒ :Β object,Β inference session.
Β Inference outΒ :Β object,Β inference session.
Example
All these exemples 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).
Table of Contents

