tensor/tensor_math_cpu library

Functions

add(Tensor<Scalar> a, Tensor<Scalar> b) → Tensor<Scalar>

add3D(Tensor<Tensor3D> a, Tensor<Tensor3D> b) → Tensor<Tensor3D>

addMatrix(Tensor<Matrix> a, Tensor<Matrix> b) → Tensor<Matrix>

addMatrixAndVector(Tensor<Matrix> m, Tensor<Vector> v) → Tensor<Matrix>

addScalar(Tensor<Vector> v, double s) → Tensor<Vector>

addScalarToMatrix(Tensor<Matrix> m, Tensor<Scalar> s) → Tensor<Matrix>

addVector(Tensor<Vector> a, Tensor<Vector> b) → Tensor<Vector>

avgPool1d(Tensor<Vector> input, int poolSize, int stride) → Tensor<Vector>

avgPool2d(Tensor<Matrix> input, int poolSize, int stride) → Tensor<Matrix>

batchNorm1dMath(Tensor<Vector> x, Tensor<Vector> gamma, Tensor<Vector> beta, Vector runningMean, Vector runningVariance, int numFeatures, bool isTraining, double momentum, double epsilon) → Tensor<Vector>

batchNorm2dMath(Tensor<Tensor3D> x, Tensor<Vector> gamma, Tensor<Vector> beta, Vector runningMean, Vector runningVariance, int numChannels, bool isTraining, double momentum, double epsilon) → Tensor<Tensor3D>

binaryCrossEntropy(Tensor<Scalar> prediction, Tensor<Scalar> target) → Tensor<Scalar>

concatenate(Tensor<Vector> a, Tensor<Vector> b) → Tensor<Vector>

concatenate3D(Tensor<Tensor3D> a, Tensor<Tensor3D> b) → Tensor<Tensor3D>

concatenateMatricesByColumn(List<Tensor<Matrix>> matrices) → Tensor<Matrix>

conv2d(Tensor<Matrix> input, Tensor<Matrix> kernel, {String padding = 'valid'}) → Tensor<Matrix>

dot(Tensor<Vector> a, Tensor<Vector> b) → Tensor<Scalar>

dropoutMatrixMath(Tensor<Matrix> input, double rate, bool isTraining) → Tensor<Matrix>

dropoutVectorMath(Tensor<Vector> input, double rate, bool isTraining) → Tensor<Vector>

elementWiseMultiply(Tensor<Vector> a, Tensor<Vector> b) → Tensor<Vector>

elementWiseMultiply3D(Tensor<Tensor3D> a, Tensor<Tensor3D> b) → Tensor<Tensor3D>

elementWiseMultiplyMatrix(Tensor<Matrix> a, Tensor<Matrix> b) → Tensor<Matrix>

eluMatrix(Tensor<Matrix> m, double alpha) → Tensor<Matrix>

eluVector(Tensor<Vector> v, double alpha) → Tensor<Vector>

globalAveragePooling(Tensor<Matrix> input) → Tensor<Vector>

leakyReluMatrix(Tensor<Matrix> m, double alpha) → Tensor<Matrix>

leakyReluVector(Tensor<Vector> v, double alpha) → Tensor<Vector>

matMul(Tensor<Matrix> a, Tensor<Matrix> b) → Tensor<Matrix>

matVecMul(Tensor<Matrix> M, Tensor<Vector> v) → Tensor<Vector>

maxPool1d(Tensor<Vector> input, int poolSize, int stride) → Tensor<Vector>

maxPool2d(Tensor<Matrix> input, int poolSize, int stride) → Tensor<Matrix>

mishMatrix(Tensor<Matrix> m) → Tensor<Matrix>

mishVector(Tensor<Vector> v) → Tensor<Vector>

mse(Tensor<Vector> predictions, Tensor<Vector> targets) → Tensor<Scalar>

mseMatrix(Tensor<Matrix> predictions, Tensor<Matrix> targets) → Tensor<Scalar>

multiply(Tensor<Scalar> a, Tensor<Scalar> b) → Tensor<Scalar>

padMatrix(Matrix input, int padding) → Matrix

relu(Tensor<Vector> v) → Tensor<Vector>

reluMatrix(Tensor<Matrix> m) → Tensor<Matrix>

reshapeVectorToMatrix(Tensor<Vector> v, int numRows, int numCols) → Tensor<Matrix>

scaleMatrix(Tensor<Matrix> m, double s) → Tensor<Matrix>

selectRow(Tensor<Matrix> m, int rowIndex) → Tensor<Vector>

sigmoid(Tensor<Vector> v) → Tensor<Vector>

sigmoidMatrix(Tensor<Matrix> m) → Tensor<Matrix>

sigmoidScalar(Tensor<Scalar> s) → Tensor<Scalar>

softmaxMatrix(Tensor<Matrix> m) → Tensor<Matrix>

softmaxVector(Tensor<Vector> v) → Tensor<Vector>

softplus(Tensor<Vector> v) → Tensor<Vector>

stackMatricesTo3D(List<Tensor<Matrix>> matrices) → Tensor<Tensor3D>

sum(Tensor<Vector> v) → Tensor<Scalar>

sumMatrix(Tensor<Matrix> m) → Tensor<Scalar>

swishMatrix(Tensor<Matrix> m) → Tensor<Matrix>

swishVector(Tensor<Vector> v) → Tensor<Vector>

tanhMatrix(Tensor<Matrix> m) → Tensor<Matrix>

transpose(Tensor<Matrix> a) → Tensor<Matrix>

vectorExp(Tensor<Vector> v) → Tensor<Vector>

vectorLog(Tensor<Vector> v) → Tensor<Vector>

vectorTanh(Tensor<Vector> v) → Tensor<Vector>