ONNX 0.1 documentation

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ONNX operators
- Function expect
- Abs
- Acos
- Acosh
- Add
- And
- ArgMax
- ArgMin
- Asin
- Asinh
- Atan
- Atanh
- AveragePool
- BatchNormalization
  - BatchNormalization - 14 vs 15
  - BatchNormalization - 9 vs 14
  - BatchNormalization - 7 vs 9
  - BatchNormalization - 6 vs 7
  - BatchNormalization - 1 vs 6
- Bernoulli
- BitShift
- BlackmanWindow
- Cast
- CastLike
- Ceil
- Celu
- CenterCropPad
- Clip
- Col2Im
- Compress
- Concat
- ConcatFromSequence
- Constant
- ConstantOfShape
- Conv
- ConvInteger
- ConvTranspose
- Cos
- Cosh
- CumSum
- DFT
- DepthToSpace
- DequantizeLinear
- Det
- Div
- Dropout
- DynamicQuantizeLinear
- Einsum
- Elu
- Equal
- Erf
- Exp
- Expand
- EyeLike
- Flatten
- Floor
- GRU
- Gather
- GatherElements
- GatherND
- Gemm
- GlobalAveragePool
- GlobalLpPool
- GlobalMaxPool
- Greater
- GreaterOrEqual
- GridSample
- HammingWindow
- HannWindow
- HardSigmoid
- HardSwish
- Hardmax
- Identity
- If
- InstanceNormalization
- IsInf
- IsNaN
- LRN
- LSTM
- LayerNormalization
- LeakyRelu
- Less
- LessOrEqual
- Log
- LogSoftmax
- Loop
- LpNormalization
- LpPool
- MatMul
- MatMulInteger
- Max
- MaxPool
- MaxRoiPool
- MaxUnpool
- Mean
- MeanVarianceNormalization
- MelWeightMatrix
- Min
- Mish
- Mod
- Mul
- Multinomial
- Neg
- NegativeLogLikelihoodLoss
- NonMaxSuppression
- NonZero
- Not
- OneHot
- Optional
- OptionalGetElement
- OptionalHasElement
- Or
- PRelu
- Pad
- Pow
- QLinearConv
- QLinearMatMul
- QuantizeLinear
- RNN
- RandomNormal
- RandomNormalLike
- RandomUniform
- RandomUniformLike
- Range
- Reciprocal
- ReduceL1
- ReduceL2
- ReduceLogSum
- ReduceLogSumExp
- ReduceMax
- ReduceMean
- ReduceMin
- ReduceProd
- ReduceSum
- ReduceSumSquare
- Relu
- Reshape
- Resize
- ReverseSequence
- RoiAlign
- Round
- STFT
- Scan
- Scatter
- ScatterElements
- ScatterND
- Selu
- SequenceAt
- SequenceConstruct
- SequenceEmpty
- SequenceErase
- SequenceInsert
- SequenceLength
- SequenceMap
- Shape
- Shrink
- Sigmoid
- Sign
- Sin
- Sinh
- Size
- Slice
- Softmax
- SoftmaxCrossEntropyLoss
- Softplus
- Softsign
- SpaceToDepth
- Split
- SplitToSequence
- Sqrt
- Squeeze
- StringNormalizer
- Sub
- Sum
- Tan
- Tanh
- TfIdfVectorizer
- ThresholdedRelu
- Tile
- TopK
- Transpose
- Trilu
- Unique
- Unsqueeze
- Upsample
- Where
- Xor
- ai.onnx.ml - ArrayFeatureExtractor
- ai.onnx.ml - Binarizer
- ai.onnx.ml - CastMap
- ai.onnx.ml - CategoryMapper
- ai.onnx.ml - DictVectorizer
- ai.onnx.ml - FeatureVectorizer
- ai.onnx.ml - Imputer
- ai.onnx.ml - LabelEncoder
- ai.onnx.ml - LinearClassifier
- ai.onnx.ml - LinearRegressor
- ai.onnx.ml - Normalizer
- ai.onnx.ml - OneHotEncoder
- ai.onnx.ml - SVMClassifier
- ai.onnx.ml - SVMRegressor
- ai.onnx.ml - Scaler
- ai.onnx.ml - TreeEnsembleClassifier
- ai.onnx.ml - TreeEnsembleRegressor
- ai.onnx.ml - ZipMap
- ai.onnx.preview.training - Adagrad
- ai.onnx.preview.training - Adam
- ai.onnx.preview.training - Gradient
- ai.onnx.preview.training - Momentum
- operator table for domain main
- operator table for domain ai.onnx.ml
- operator table for domain ai.onnx.preview.training
onnx API Overview

BatchNormalization - 9 vs 14¶

<BatchNormalization - 14 vs 15

BatchNormalization - 7 vs 9>

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