Welcome to our Support Center
-
Quick start
-
API
-
-
- Resume
- Add
- AdditiveAttention
- AlphaDropout
- Attention
- Average
- AvgPool1D
- AvgPool2D
- AvgPool3D
- BatchNormalization
- Bidirectional
- Concatenate
- Conv1D
- Conv1DTranspose
- Conv2D
- Conv2DTranspose
- Conv3D
- Conv3DTranspose
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Cropping1D
- Cropping2D
- Cropping3D
- Dense
- DepthwiseConv2D
- Dropout
- ELU
- Embedding
- Exponential
- Flatten
- GaussianDropout
- GaussianNoise
- GELU
- GlobalAvgPool1D
- GlobalAvgPool2D
- GlobalAvgPool3D
- GlobalMaxPool1D
- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- HardSigmoid
- Input
- LayerNormalization
- LeakyReLU
- Linear
- LSTM
- MaxPool1D
- MaxPool2D
- MaxPool3D
- MultiHeadAttention
- Multiply
- Output Predict
- Output Train
- Permute3D
- PReLU
- ReLU
- Reshape
- RNN
- SELU
- SeparableConv1D
- SeparableConv2D
- Sigmoid
- SimpleRNN
- SoftMax
- SoftPlus
- SoftSign
- SpatialDropout
- Split
- Substract
- Swish
- TanH
- ThresholdedReLU
- UpSampling1D
- UpSampling2D
- UpSampling3D
- ZeroPadding1D
- ZeroPadding2D
- ZeroPadding3D
- Show All Articles ( 64 ) Collapse Articles
-
-
- Abs
- Acos
- Acosh
- Add
- AffineGrid
- And
- ArgMax
- ArgMin
- Asin
- Asinh
- Atan
- Atanh
- Attention
- AttnLSTM
- AveragePool
- BatchNormalization
- Bernouilli
- BiasAdd
- BiasDropout
- BiasGelu
- BiasSoftmax
- BiasSplitGelu
- BifurcationDetector
- BitmaskBiasDropout
- BitmaskDropout
- BitShift
- BitwiseAnd
- BitwiseNot
- BitwiseOr
- BitwiseXor
- BlackmanWindow
- Cast
- CastLike
- CDist
- Ceil
- Celu
- CenterCropPad
- Clip
- Col2lm
- ComplexMul
- ComplexMulConj
- Compress
- Concat
- ConcatFromSequence
- Conv
- ConvInteger
- ConvTranspose
- ConvTransposeWithDynamicPads
- Cos
- Cosh
- CropAndResize
- CumSum
- DecoderAttention
- DecoderMaskedMultiHeadAttention
- DecoderMaskedSelfAttention
- DeformConv
- DepthToSpace
- DequantizeBFP
- DequantizeLinear
- DequantizeWithOrder
- Det
- DFT
- Div
- Dropout
- DynamicQuantizeLinear
- DynamicQuantizeLSTM
- DynamicQuantizeMatMul
- DynamicTimeWarping
- Einsum
- EmbedLayerNormalization
- EPContext
- Equal
- Erf
- Exp
- Expand
- ExpandDims
- EyeLike
- FastGelu
- Flatten
- Floor
- FusedConv
- FusedGemm
- FusedMatMul
- FusedMatMulActivation
- GatedRelativePositionBias
- Gather
- GatherElements
- GatherND
- Gemm
- GemmaRotaryEmbedding
- GemmFastGelu
- GemmFloat8
- GlobalAveragePool
- GlobalLpPool
- GlobalMaxPool
- Greater
- GreaterOrEqual
- GreedySearch
- GridSample
- GroupNorm
- GroupQueryAttention
- GRU
- HammingWindow
- HannWindow
- HardMax
- HardSwish
- Identity
- If
- ImageDecoder
- InstanceNormalization
- Inverse
- lrfft
- lslnf
- lsNaN
- LayerNormalization
- Less
- LessOrEqual
- Log
- LogSoftmax
- LongformerAttention
- Loop
- LpNormalization
- LpPool
- LRN
- LSTM
- MatMul
- MatMulBnb4
- MatMulFpQ4
- MatMulInteger
- MatMulInteger16
- MatMulIntergerToFloat
- MatMulNBits
- Max
- MaxPool
- MaxPoolWithMask
- MaxRoiPool
- MaxUnPool
- Mean
- MeanVarianceNormalization
- MelWeightMatrix
- MicrosoftDequantizeLinear
- MicrosoftGatherND
- MicrosoftGelu
- MicrosoftGridSample
- MicrosoftMultiHeadAttention
- MicrosoftPad
- MicrosoftQLinearConv
- MicrosoftQuantizeLinear
- MicrosoftRange
- MicrosoftTrilu
- MicrosoftUnique
- Min
- Mish
- Mod
- MoE
- Mul
- MulInteger
- Multinomial
- MurmurHash3
- Neg
- NegativeLogLikelihoodLoss
- NGramRepeatBlock
- NhwcConv
- NhwcFusedConv
- NhwcMaxPool
- NonMaxSuppression
- NonZero
- Not
- OneHot
- OptionalGetElement
- OptionalHasElement
- Or
- PackedAttention
- PackedMultiHeadAttention
- Pad
- Pow
- PRelu
- QAttention
- QGemm
- QLinearAdd
- QLinearAveragePool
- QLinearConcat
- QLinearConv
- QLinearGlobalAveragePool
- QLinearLeakyRelu
- QLinearMatMul
- QLinearMul
- QLinearReduceMean
- QLinearSigmoid
- QLinearSoftmax
- QLinearWhere
- QMoE
- QOrderedAttention
- QOrderedGelu
- QOrderedLayerNormalization
- QOrderedLongformerAttention
- QOrderedMatMul
- QuantizeBFP
- QuantizeLinear
- QuantizeWithOrder
- QuickGelu
- RandomNormalLike
- RandomUniformLike
- Range
- Reciprocal
- ReduceL1
- ReduceL2
- ReduceLogSum
- ReduceLogSumExp
- ReduceMax
- ReduceMean
- ReduceMin
- ReduceProd
- ReduceSum
- ReduceSumInteger
- ReduceSumSquare
- RegexFullMatch
- RelativePositionBias
- RemovePadding
- Reshape
- Resize
- RestorePadding
- ReverseSequence
- Rfft
- RNN
- RoiAlign
- RotaryEmbedding
- Round
- SampleOp
- Sampling
- Scan
- ScatterElements
- ScatterND
- SequenceAt
- SequenceConstruct
- SequenceErase
- SequenceInsert
- SequenceLength
- SequenceMap
- Shape
- Shrink
- Sign
- Sin
- Sinh
- Size
- SkipGroupNorm
- SkipLayerNormalization
- SkipSimplifiedLayerNormalization
- Slice
- Snpe
- SoftmaxCrossEntropyLoss
- SpaceToDepth
- SparseAttention
- SparseToDenseMatMul
- Split
- SplitToSequence
- Sqrt
- Squeeze
- STFT
- StringConcat
- StringNormalizer
- StringSplit
- Sub
- Sum
- Tan
- TfldfVectorizer
- Tile
- Tokenizer
- TopK
- TorchEmbedding
- Transpose
- TransposeMatMul
- Trilu
- UnfoldTensor
- Unique
- Unsqueeze
- Where
- WhisperBeamSearch
- WordConvEmbedding
- Xor
- Elu
- Gelu
- HardSigmoid
- LeakyReLU
- Relu
- Selu
- Sigmoid
- Softmax
- Softplus
- Softsign
- Swish
- Tanh
- ThresholdedRelu
- Show All Articles ( 278 ) Collapse Articles
-
-
-
-
-
- Resume
- Constant
- GlorotNormal
- GlorotUniform
- HeNormal
- HeUniform
- Identity
- LecunNormal
- LecunUniform
- Ones
- Orthogonal
- RandomNormal
- RandomUnifom
- TruncatedNormal
- VarianceScaling
- Zeros
- Show All Articles ( 1 ) Collapse Articles
-
- Resume
- BinaryCrossentropy
- CategoricalCrossentropy
- CategoricalHinge
- CosineSimilarity
- Hinge
- Huber
- KLDivergence
- LogCosh
- MeanAbsoluteError
- MeanAbsolutePercentageError
- MeanSquaredError
- MeanSquaredLogarithmicError
- Poisson
- SquaredHinge
- Custom
- Show All Articles ( 1 ) Collapse Articles
-
-
-
-
-
- Dense
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MutiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
- Show All Articles ( 12 ) Collapse Articles
-
- Dense
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Embedding
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
- Show All Articles ( 12 ) Collapse Articles
-
-
- Resume
- Dense
- AdditiveAttention
- Attention
- MultiHeadAttention
- BatchNormalization
- LayerNormalization
- Bidirectional
- GRU
- LSTM
- SimpleRNN
- Conv1D
- Conv2D
- Conv3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- Embedding
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Show All Articles ( 13 ) Collapse Articles
-
-
- 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
- Show All Articles ( 15 ) Collapse Articles
-
- 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
- Show All Articles ( 15 ) Collapse Articles
-
-
-
- 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
- Show All Articles ( 15 ) Collapse Articles
-
- 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
- Show All Articles ( 15 ) Collapse Articles
-
-
- Resume
- 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
- Show All Articles ( 28 ) Collapse Articles
-
-
Updated
type
Β type :Β enum,Β defines how the initializer behaves when the graph is used in training mode. It determines whether the tensor is treated as a constant, as trainable weights, or as frozen weights.Β During inference, it makes no difference whether the type isΒ
Constant
,Β Train Weights
, orΒ Frozen Weights
.
Constant
The initializer is treated as a fixed, non-trainable variable (e.g., a constant coefficient in a multiplication).
Train Weight
The initializer is treated as trainable weights during training (it will be updated by the optimizer).
Frozen Weight
The initializer is treated as weights, but they remain frozen (not updated) during training.
Tags:
Table of Contents