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
- RandomNormal
- RandomNormalLike
- RandomUniform
- 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
- SequenceEmpty
- SequenceErase
- SequenceInsert
- SequenceLength
- SequenceMap
- Shape
- Shrink
- Sign
- Sin
- Sinh
- Size
- SkipGroupNorm
- Show All Articles ( 234 ) 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
Metric resume

In this section you’ll find a list of all metric fonctionalities.
ICONS | RESUME | |
Accuracy | ![]() |
Calculates how often predictions equal labels. |
BinaryAccuracy | ![]() |
Calculates how often predictions match binary labels. |
BinaryCrossentropy | ![]() |
Computes the crossentropy metric between the labels and predictions. |
BinaryIoU | ![]() |
Computes the Intersection-Over-Union metric for class 0 and/or 1. |
CategoricalAccuracy | ![]() |
Calculates how often predictions match one-hot labels. |
CategoricalCrossentropy | ![]() |
Computes the crossentropy metric between the labels and predictions. |
CategoricalHinge | ![]() |
Computes the categorical hinge metric between y_true and y_pred. |
CosineSimilarity | ![]() |
Computes the cosine similarity between the labels and predictions. |
FalseNegatives | ![]() |
Calculates the number of false negatives. |
FalsePositives | ![]() |
Calculates the number of false positives. |
Hinge | ![]() |
Computes the hinge metric between y_true and y_pred. |
Huber | ![]() |
Computes the huber metrics between y_true and y_pred. |
IoU | ![]() |
Computes the Intersection-Over-Union metric for specific target classes. |
KLDivergence | ![]() |
Computes Kullback-Leibler divergence metric between y_true and y_pred. |
LogCoshError | ![]() |
Computes the logarithm of the hyperbolic cosine of the prediction error. |
Mean | ![]() |
Computes the mean of the given values. |
MeanAbsoluteError | ![]() |
Computes the mean absolute error between the labels and predictions. |
MeanAbsolutePercentageError | ![]() |
Computes the mean absolute percentage error between y_true and y_pred. |
MeanIoU | ![]() |
Computes the mean Intersection-Over-Union metric. |
MeanRelativeError | ![]() |
Computes the mean relative error by normalizing with the given values. |
MeanSquaredError | ![]() |
Computes the mean squared error between y_true and y_pred. |
MeanSquaredLogarithmicError | ![]() |
Computes the mean squared logarithmic error between y_true and y_pred. |
MeanTensor | ![]() |
Computes the element-wise mean of the given tensors. |
OneHotIoU | ![]() |
Computes the Intersection-Over-Union metric for one-hot encoded labels. |
OneHotMeanIoU | ![]() |
Computes mean Intersection-Over-Union metric for one-hot encoded labels. |
Poisson | ![]() |
Computes the poisson metric between y_true and y_pred. |
Precision | ![]() |
Computes the precision of the predictions with respect to the labels. |
PrecisionAtRecall | ![]() |
Computes best precision where recall is > specified value. |
Recall | ![]() |
Computes the recall of the predictions with respect to the labels. |
RecallAtPrecision | ![]() |
Computes best recall where precision is > specified value. |
RootMeanSquaredError | ![]() |
Computes root mean squared error metric between y_true and y_pred. |
SensitivityAtSpecificity | ![]() |
Computes best sensitivity where specificity is > specified value. |
SparseCategoricalAccuracy | ![]() |
Calculates how often predictions match integer labels. |
SparseCategoricalCrossentropy | ![]() |
Computes the crossentropy metric between the labels and predictions. |
SparseTopKCategoricalAccuracy | ![]() |
Computes how often integer targets are in the top K predictions. |
Specificity | ![]() |
Computes the specificity of the predictions with respect to the labels. |
SpecificityAtSensitivity | ![]() |
Computes best specificity where sensitivity is > specified value. |
SquaredHinge | ![]() |
Computes the squared hinge metric between y_true and y_pred. |
Sum | ![]() |
Computes the sum of the given values. |
TopKCategoricalAccuracy | ![]() |
Computes how often targets are in the top K predictions. |
TrueNegatives | ![]() |
Calculates the number of true negatives. |
TruePositives | ![]() |
Calculates the number of true positives. |
Table of Contents