com.intel.analytics.bigdl.tensor.DnnTensor

DnnTensorUnsupportOperations

class DnnTensorUnsupportOperations[T] extends Tensor[T]

Linear Supertypes
Tensor[T], Activity, TensorMath[T], Serializable, AnyRef, Any
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  1. DnnTensorUnsupportOperations
  2. Tensor
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  4. TensorMath
  5. Serializable
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Instance Constructors

  1. new DnnTensorUnsupportOperations()(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. def *(t: Tensor[T]): Tensor[T]

    Multiply a Tensor by another one, return the result in new allocated memory.

    Multiply a Tensor by another one, return the result in new allocated memory. The number of elements in the Tensors must match, but the sizes do not matter. The size of the returned Tensor will be the size of the first Tensor

    t
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  5. def *(s: T): Tensor[T]

    multiply all elements of this with value not in place.

    multiply all elements of this with value not in place. It will allocate new memory.

    s
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  6. def +(t: Tensor[T]): Tensor[T]

    Add a Tensor to another one, return the result in new allocated memory.

    Add a Tensor to another one, return the result in new allocated memory. The number of elements in the Tensors must match, but the sizes do not matter. The size of the returned Tensor will be the size of the first Tensor

    t
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  7. def +(s: T): Tensor[T]

    Add all elements of this with value not in place.

    Add all elements of this with value not in place. It will allocate new memory.

    s
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  8. def +(e: Either[Tensor[T], T]): Tensor[T]

    Definition Classes
    TensorMath
  9. def -(t: Tensor[T]): Tensor[T]

    Subtract a Tensor from another one, return the result in new allocated memory.

    Subtract a Tensor from another one, return the result in new allocated memory. The number of elements in the Tensors must match, but the sizes do not matter. The size of the returned Tensor will be the size of the first Tensor

    t
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  10. def -(s: T): Tensor[T]

    subtract all elements of this with the value not in place.

    subtract all elements of this with the value not in place. It will allocate new memory.

    s
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  11. def /(t: Tensor[T]): Tensor[T]

    Divide a Tensor by another one, return the result in new allocated memory.

    Divide a Tensor by another one, return the result in new allocated memory. The number of elements in the Tensors must match, but the sizes do not matter. The size of the returned Tensor will be the size of the first Tensor

    t
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  12. def /(s: T): Tensor[T]

    divide all elements of this with value not in place.

    divide all elements of this with value not in place. It will allocate new memory.

    s
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  13. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  14. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  15. def abs(x: Tensor[T]): Tensor[T]

  16. def abs(): Tensor[T]

    replaces all elements in-place with the absolute values of the elements of this.

    replaces all elements in-place with the absolute values of the elements of this.

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  17. def add(x: Tensor[T], y: Tensor[T]): Tensor[T]

  18. def add(value: T): Tensor[T]

    x.

    x.add(value) : add value to all elements of x in place.

    value
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  19. def add(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T]

    z.

    z.add(x, value, y) puts the result of x + value * y in z.

    x
    value
    y
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  20. def add(y: Tensor[T]): Tensor[T]

    accumulates all elements of y into this

    accumulates all elements of y into this

    y

    other tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  21. def add(value: T, y: Tensor[T]): Tensor[T]

    x.

    x.add(value,y) multiply-accumulates values of y into x.

    value

    scalar

    y

    other tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  22. def addSingletonDimension(t: Tensor[T], dim: Int): Tensor[T]

    view this.

    view this.tensor and add a Singleton Dimension to dim dimension

    t

    source tensor

    dim

    the specific dimension, default is 1

    returns

    this

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  23. def addcdiv(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T]

    Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to x.

    Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to x. The number of elements must match, but sizes do not matter.

    value
    tensor1
    tensor2
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  24. def addcmul(tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T]

  25. def addcmul(value: T, tensor1: Tensor[T], tensor2: Tensor[T]): Tensor[T]

    Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the scalar value (1 if not present) and add it to x.

    Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the scalar value (1 if not present) and add it to x. The number of elements must match, but sizes do not matter.

    value
    tensor1
    tensor2

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  26. def addmm(v1: T, v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]

    res = v1 * res + v2 * mat1*mat2

    res = v1 * res + v2 * mat1*mat2

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  27. def addmm(v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]

    res = res + v2 * mat1 * mat2

    res = res + v2 * mat1 * mat2

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  28. def addmm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]

    res = res + mat1 * mat2

    res = res + mat1 * mat2

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  29. def addmm(M: Tensor[T], mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]

    res = M + (mat1*mat2)

    res = M + (mat1*mat2)

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  30. def addmm(v1: T, M: Tensor[T], v2: T, mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]

    Performs a matrix-matrix multiplication between mat1 (2D tensor) and mat2 (2D tensor).

    Performs a matrix-matrix multiplication between mat1 (2D tensor) and mat2 (2D tensor). Optional values v1 and v2 are scalars that multiply M and mat1 * mat2 respectively. Optional value beta is a scalar that scales the result tensor, before accumulating the result into the tensor. Defaults to 1.0. If mat1 is a n x m matrix, mat2 a m x p matrix, M must be a n x p matrix.

    res = (v1 * M) + (v2 * mat1*mat2)

    v1
    M
    v2
    mat1
    mat2

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  31. def addmv(alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T]

    res = res + alpha * (mat * vec2)

    res = res + alpha * (mat * vec2)

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  32. def addmv(beta: T, alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T]

    res = beta * res + alpha * (mat * vec2)

    res = beta * res + alpha * (mat * vec2)

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  33. def addmv(beta: T, vec1: Tensor[T], alpha: T, mat: Tensor[T], vec2: Tensor[T]): Tensor[T]

    Performs a matrix-vector multiplication between mat (2D Tensor) and vec2 (1D Tensor) and add it to vec1.

    Performs a matrix-vector multiplication between mat (2D Tensor) and vec2 (1D Tensor) and add it to vec1. Optional values v1 and v2 are scalars that multiply vec1 and vec2 respectively.

    In other words, res = (beta * vec1) + alpha * (mat * vec2)

    Sizes must respect the matrix-multiplication operation: if mat is a n × m matrix, vec2 must be vector of size m and vec1 must be a vector of size n.

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  34. def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T], t3: Tensor[T]): Tensor[T]

    Performs the outer-product between vec1 (1D Tensor) and vec2 (1D Tensor).

    Performs the outer-product between vec1 (1D Tensor) and vec2 (1D Tensor). Optional values v1 and v2 are scalars that multiply mat and vec1 [out] vec2 respectively. In other words,res_ij = (v1 * mat_ij) + (v2 * vec1_i * vec2_j)

    v1
    t1
    v2
    t2
    t3
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  35. def addr(v1: T, t1: Tensor[T], v2: T, t2: Tensor[T]): Tensor[T]

  36. def addr(v1: T, t1: Tensor[T], t2: Tensor[T]): Tensor[T]

  37. def addr(t1: Tensor[T], t2: Tensor[T]): Tensor[T]

    Performs the outer-product between vec1 (1D tensor) and vec2 (1D tensor).

    Performs the outer-product between vec1 (1D tensor) and vec2 (1D tensor). Optional values v1 and v2 are scalars that multiply mat and vec1 [out] vec2 respectively. In other words, res_ij = (v1 * mat_ij) + (v2 * vec1_i * vec2_j)

    t1
    t2
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  38. def almostEqual(other: Tensor[T], delta: Double): Boolean

    Compare with other tensor.

    Compare with other tensor. The shape of the other tensor must be same with this tensor. If element wise difference is less than delta, return true.

    other
    delta
    returns

    Definition Classes
    Tensor
  39. def apply(t: Table): Tensor[T]

    Subset the tensor by apply the elements of the given table to the corresponding dimension of the tensor.

    Subset the tensor by apply the elements of the given table to the corresponding dimension of the tensor. The elements of the given table can be an Int or another Table. An Int means select on current dimension; A table means narrow on current dimension, the table should have two elements, of which the first is the start index and the second is the end index. An empty table is equal to Table(1, size_of_current_dimension) If the table length is less than the tensor dimension, each missing dimension is token up by an empty table

    t

    The table length should be less than or equal to the tensor dimensions

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
    See also

    narrow

    select

  40. def apply(indexes: Array[Int]): T

    Query the value on a given index.

    Query the value on a given index. Tensor should not be empty

    indexes

    the indexes length should be same as the tensor dimension length and each value count from 1

    returns

    the value on the given index

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  41. def apply(index: Int): Tensor[T]

    Query tensor on a given index.

    Query tensor on a given index. Tensor should not be empty

    index

    count from 1

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  42. def apply1(func: (T) ⇒ T): Tensor[T]

    Apply a function to each element of the tensor and modified it value if it return a double

    Apply a function to each element of the tensor and modified it value if it return a double

    func

    applied function

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  43. def applyFun[A](t: Tensor[A], func: (A) ⇒ T)(implicit arg0: ClassManifest[A]): Tensor[T]

    Apply a function to each element of the tensor t and set each value to self

    Apply a function to each element of the tensor t and set each value to self

    t

    tensor to be modified

    func

    applied function

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  44. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  45. def baddbmm(alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T]

    res_i = res_i + (alpha * batch1_i * batch2_i)

    res_i = res_i + (alpha * batch1_i * batch2_i)

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  46. def baddbmm(beta: T, alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T]

    res_i = (beta * res_i) + (alpha * batch1_i * batch2_i)

    res_i = (beta * res_i) + (alpha * batch1_i * batch2_i)

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  47. def baddbmm(beta: T, M: Tensor[T], alpha: T, batch1: Tensor[T], batch2: Tensor[T]): Tensor[T]

    Perform a batch matrix matrix multiplication of matrices and stored in batch1 and batch2 with batch add.

    Perform a batch matrix matrix multiplication of matrices and stored in batch1 and batch2 with batch add. batch1 and batch2 must be 3D Tensors each containing the same number of matrices. If batch1 is a b × n × m Tensor, batch2 a b × m × p Tensor, res will be a b × n × p Tensor.

    In other words, res_i = (beta * M_i) + (alpha * batch1_i * batch2_i)

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  48. def bernoulli(p: Double): Tensor[T]

    Fill with random value(bernoulli distribution).

    Fill with random value(bernoulli distribution). It will change the value of the current tensor and return itself

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  49. def bmm(batch1: Tensor[T], batch2: Tensor[T]): Tensor[T]

    res_i = res_i + batch1_i * batch2_i

    res_i = res_i + batch1_i * batch2_i

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  50. def cast[D](castTensor: Tensor[D])(implicit arg0: ClassManifest[D], ev: TensorNumeric[D]): Tensor[D]

    Cast the currenct tensor to a tensor with tensor numeric type D and set cast value to castTensor

    Cast the currenct tensor to a tensor with tensor numeric type D and set cast value to castTensor

    D

    new numeric type

    castTensor

    the cast value set to this tensor

    returns

    return castTensort

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  51. def cdiv(x: Tensor[T], y: Tensor[T]): Tensor[T]

    Element-wise divide z.

    Element-wise divide z.cdiv(x, y) means z = x / y

    x

    tensor

    y

    tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  52. def cdiv(y: Tensor[T]): Tensor[T]

    Element-wise divide x.

    Element-wise divide x.cdiv(y) all elements of x divide all elements of y. x = x / y

    y

    tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  53. def ceil(): Tensor[T]

    Replaces all elements in-place with the ceil result of elements

    Replaces all elements in-place with the ceil result of elements

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  54. def clamp(min: Double, max: Double): Tensor[T]

  55. def clone(): Tensor[T]

    Get a new tensor with same value and different storage

    Get a new tensor with same value and different storage

    returns

    new tensor

    Definition Classes
    Tensor → AnyRef
  56. def cmax(x: Tensor[T], y: Tensor[T]): Tensor[T]

    stores the element-wise maximum of x and y in z.

    stores the element-wise maximum of x and y in z. z.cmax(x, y) means z = max(x, y)

    x

    tensor

    y

    tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  57. def cmax(y: Tensor[T]): Tensor[T]

    stores the element-wise maximum of x and y in x.

    stores the element-wise maximum of x and y in x. x.cmax(y) = max(x, y)

    y

    tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  58. def cmax(value: T): Tensor[T]

    For each elements of the tensor, performs the max operation compared with the given value vector.

    For each elements of the tensor, performs the max operation compared with the given value vector.

    value
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  59. def cmin(x: Tensor[T], y: Tensor[T]): Tensor[T]

    stores the element-wise maximum of x and y in z.

    stores the element-wise maximum of x and y in z. z.cmin(x, y) means z = min(x, y)

    x

    tensor

    y

    tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  60. def cmin(y: Tensor[T]): Tensor[T]

    stores the element-wise maximum of x and y in x.

    stores the element-wise maximum of x and y in x. x.cmin(y) = min(x, y)

    y

    tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  61. def cmul(x: Tensor[T], y: Tensor[T]): Tensor[T]

    Element-wise multiply z.

    Element-wise multiply z.cmul(x, y) equals z = x * y

    x

    tensor

    y

    tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  62. def cmul(y: Tensor[T]): Tensor[T]

    Element-wise multiply x.

    Element-wise multiply x.cmul(y) multiplies all elements of x with corresponding elements of y. x = x * y

    y

    tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  63. def contiguous(): Tensor[T]

    Get a contiguous tensor from current tensor

    Get a contiguous tensor from current tensor

    returns

    the current tensor if it's contiguous; or a new contiguous tensor with separated storage

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  64. def conv2(kernel: Tensor[T], vf: Char): Tensor[T]

    This function computes 2 dimensional convolution of a single image with a single kernel (2D output).

    This function computes 2 dimensional convolution of a single image with a single kernel (2D output). the dimensions of input and kernel need to be 2, and Input image needs to be bigger than kernel. The last argument controls if the convolution is a full ('F') or valid ('V') convolution. The default is valid convolution.

    kernel
    vf

    full ('F') or valid ('V') convolution.

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  65. def copy(other: Tensor[T]): Tensor[T]

    Copy the value of the given tensor to the current.

    Copy the value of the given tensor to the current. They should have same size. It will use the old storage

    other

    source tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  66. def diff(other: Tensor[T], count: Int, reverse: Boolean): Boolean

    Compare and print differences between two tensors

    Compare and print differences between two tensors

    other
    count
    returns

    true if there's difference, vice versa

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  67. def digamma(): Tensor[T]

    Computes Psi, the derivative of Lgamma (the log of the absolute value of Gamma(x)), element-wise and update the content inplace

    Computes Psi, the derivative of Lgamma (the log of the absolute value of Gamma(x)), element-wise and update the content inplace

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  68. def dim(): Int

    A shortcut of nDimension()

    A shortcut of nDimension()

    Definition Classes
    DnnTensorUnsupportOperationsTensor
    See also

    nDimension()

  69. def dist(y: Tensor[T], norm: Int): T

    Performs the p-norm distance calculation between two tensors

    Performs the p-norm distance calculation between two tensors

    y

    the secode Tensor

    norm

    the norm of distance

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  70. def div(y: Tensor[T]): Tensor[T]

    Element-wise divide x.

    Element-wise divide x.div(y) all elements of x divide all elements of y. x = x / y

    y

    tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  71. def div(value: T): Tensor[T]

    divide all elements of this with value in-place.

    divide all elements of this with value in-place.

    value
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  72. def dot(y: Tensor[T]): T

    Performs the dot product.

    Performs the dot product. The number of elements must match: both Tensors are seen as a 1D vector.

    y
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  73. def emptyInstance(): Tensor[T]

    return a new empty tensor of the same type

    return a new empty tensor of the same type

    returns

    new tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  74. def eq(x: Tensor[T], y: T): Tensor[T]

    Implements == operator comparing each element in x with y

    Implements == operator comparing each element in x with y

    y
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  75. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  76. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  77. def erf(): Tensor[T]

    Computes the reciprocal of this tensor element-wise and update the content inplace

    Computes the reciprocal of this tensor element-wise and update the content inplace

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  78. def erfc(): Tensor[T]

    Computes the reciprocal of this tensor element-wise and update the content inplace

    Computes the reciprocal of this tensor element-wise and update the content inplace

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  79. def exp(): Tensor[T]

  80. def exp(y: Tensor[T]): Tensor[T]

  81. def expand(sizes: Array[Int]): Tensor[T]

    Expanding a tensor allocates new memory, tensor where singleton dimensions can be expanded to multiple ones by setting the stride to 0.

    Expanding a tensor allocates new memory, tensor where singleton dimensions can be expanded to multiple ones by setting the stride to 0. Any dimension that has size 1 can be expanded to arbitrary value with new memory allocation. Attempting to expand along a dimension that does not have size 1 will result in an error.

    sizes

    the size that tensor will expend to

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  82. def expandAs(template: Tensor[T]): Tensor[T]

    This is equivalent to this.

    This is equivalent to this.expand(template.size())

    template

    the given tensor

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  83. def fill(v: T): Tensor[T]

    Fill with a given value.

    Fill with a given value. It will change the value of the current tensor and return itself

    v

    value to fill the tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  84. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  85. def floor(): Tensor[T]

    Replaces all elements in-place with the floor result of elements

    Replaces all elements in-place with the floor result of elements

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  86. def floor(y: Tensor[T]): Tensor[T]

    Populate the given tensor with the floor result of elements

    Populate the given tensor with the floor result of elements

    y
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  87. def forceFill(v: Any): Tensor[T]

    Fill with a given value.

    Fill with a given value. It will change the value of the current tensor and return itself

    Note the value should be an instance of T

    v

    value to fill the tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  88. def gather(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T]

    change this tensor with values from the original tensor by gathering a number of values from each "row", where the rows are along the dimension dim.

    change this tensor with values from the original tensor by gathering a number of values from each "row", where the rows are along the dimension dim.

    dim
    index
    src
    returns

    this

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  89. def ge(x: Tensor[T], value: Double): Tensor[T]

    Implements >= operator comparing each element in x with value

    Implements >= operator comparing each element in x with value

    x
    value
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  90. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  91. def getTensorNumeric(): TensorNumeric[T]

    Return tensor numeric

    Return tensor numeric

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  92. def getTensorType: TensorType

    Return tensor type

    Return tensor type

    returns

    Dense / Quant

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  93. def getType(): TensorDataType

    return the tensor datatype( DoubleType or FloatType)

    return the tensor datatype( DoubleType or FloatType)

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  94. def gt(x: Tensor[T], y: Tensor[T]): Tensor[T]

    Implements > operator comparing each element in x with y

    Implements > operator comparing each element in x with y

    x
    y
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  95. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  96. def index(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T]

    Accumulate the elements of tensor into the original tensor by adding to the indices in the order given in index.

    Accumulate the elements of tensor into the original tensor by adding to the indices in the order given in index. The shape of tensor must exactly match the elements indexed or an error will be thrown.

    dim
    index
    y
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  97. def indexAdd(dim: Int, index: Tensor[T], y: Tensor[T]): Tensor[T]

    Accumulate the elements of tensor into the original tensor by adding to the indices in the order given in index.

    Accumulate the elements of tensor into the original tensor by adding to the indices in the order given in index. The shape of tensor must exactly match the elements indexed or an error will be thrown.

    dim
    index
    y
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  98. def inv(): Tensor[T]

    Computes the reciprocal of this tensor element-wise and update the content inplace

    Computes the reciprocal of this tensor element-wise and update the content inplace

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  99. def isContiguous(): Boolean

    Check if the tensor is contiguous on the storage

    Check if the tensor is contiguous on the storage

    returns

    true if it's contiguous

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  100. def isEmpty: Boolean

    returns

    whether this tensor is an empty tensor. Note that nDimension == 0 is not sufficient to determine a tensor is empty, because a scalar tensor's nDimension is also 0.

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  101. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  102. def isSameSizeAs(other: Tensor[_]): Boolean

    Check if the size is same with the give tensor

    Check if the size is same with the give tensor

    other

    tensor to be compared

    returns

    true if they have same size

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  103. def isScalar: Boolean

    returns

    whether this tensor is a scalar

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  104. def isTable: Boolean

    Return false because it's not a Table

    Return false because it's not a Table

    returns

    false

    Definition Classes
    TensorActivity
  105. def isTensor: Boolean

    Return true because it's a Tensor implemented from Activity

    Return true because it's a Tensor implemented from Activity

    returns

    true

    Definition Classes
    TensorActivity
  106. def le(x: Tensor[T], y: Tensor[T]): Tensor[T]

    Implements <= operator comparing each element in x with y

    Implements <= operator comparing each element in x with y

    x
    y
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  107. def log(): Tensor[T]

  108. def log(y: Tensor[T]): Tensor[T]

    Replaces all elements in-place with the elements of lnx

    Replaces all elements in-place with the elements of lnx

    y
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  109. def log1p(): Tensor[T]

  110. def log1p(y: Tensor[T]): Tensor[T]

  111. def logGamma(): Tensor[T]

    Computes the log of the absolute value of Gamma(x) element-wise, and update the content inplace

    Computes the log of the absolute value of Gamma(x) element-wise, and update the content inplace

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  112. def lt(x: Tensor[T], y: Tensor[T]): Tensor[T]

    Implements < operator comparing each element in x with y

    Implements < operator comparing each element in x with y

    x
    y
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  113. def map(other: Tensor[T], func: (T, T) ⇒ T): Tensor[T]

    Map value of another tensor to corresponding value of current tensor and apply function on the two value and change the value of the current tensor The another tensor should has the same size of the current tensor

    Map value of another tensor to corresponding value of current tensor and apply function on the two value and change the value of the current tensor The another tensor should has the same size of the current tensor

    other

    another tensor

    func

    applied function

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  114. def maskedCopy(mask: Tensor[T], y: Tensor[T]): Tensor[T]

    Copies the elements of tensor into mask locations of itself.

    Copies the elements of tensor into mask locations of itself.

    mask
    y
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  115. def maskedFill(mask: Tensor[T], e: T): Tensor[T]

    Fills the masked elements of itself with value val

    Fills the masked elements of itself with value val

    mask
    e
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  116. def maskedSelect(mask: Tensor[T], y: Tensor[T]): Tensor[T]

    Returns a new Tensor which contains all elements aligned to a 1 in the corresponding mask.

    Returns a new Tensor which contains all elements aligned to a 1 in the corresponding mask.

    mask
    y
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  117. def max(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T])

    performs the max operation over the dimension n

    performs the max operation over the dimension n

    values
    indices
    dim
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  118. def max(dim: Int): (Tensor[T], Tensor[T])

    performs the max operation over the dimension n

    performs the max operation over the dimension n

    dim
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  119. def max(): T

    returns the single biggest element of x

    returns the single biggest element of x

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  120. def mean(dim: Int): Tensor[T]

    performs the mean operation over the dimension dim.

    performs the mean operation over the dimension dim.

    dim
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  121. def mean(): T

    returns the mean of all elements of this.

    returns the mean of all elements of this.

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  122. def min(values: Tensor[T], indices: Tensor[T], dim: Int): (Tensor[T], Tensor[T])

    performs the min operation over the dimension n

    performs the min operation over the dimension n

    values
    indices
    dim
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  123. def min(dim: Int): (Tensor[T], Tensor[T])

    performs the min operation over the dimension n

    performs the min operation over the dimension n

    dim
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  124. def min(): T

    returns the single minimum element of x

    returns the single minimum element of x

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  125. def mm(mat1: Tensor[T], mat2: Tensor[T]): Tensor[T]

    res = mat1*mat2

    res = mat1*mat2

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  126. def mul(x: Tensor[T], value: T): Tensor[T]

    put the result of x * value in current tensor

    put the result of x * value in current tensor

    value
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  127. def mul(value: T): Tensor[T]

    multiply all elements of this with value in-place.

    multiply all elements of this with value in-place.

    value
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  128. def mv(mat: Tensor[T], vec2: Tensor[T]): Tensor[T]

    res = res + (mat * vec2)

    res = res + (mat * vec2)

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  129. def nDimension(): Int

    Dimension number of the tensor.

    Dimension number of the tensor. For empty tensor, its dimension number is 0

    returns

    dimension number

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  130. def nElement(): Int

    Element number

    Element number

    returns

    element number

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  131. def narrow(dim: Int, index: Int, size: Int): Tensor[T]

    Get a subset of the tensor on dim-th dimension.

    Get a subset of the tensor on dim-th dimension. The offset is given by index, and length is given by size. The important difference with select is that it will not reduce the dimension number. For Instance tensor = 1 2 3 4 5 6 tensor.narrow(1, 1, 1) is [1 2 3] tensor.narrow(2, 2, 2) is 2 3 5 6

    dim
    index
    size
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  132. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  133. def negative(x: Tensor[T]): Tensor[T]

    Computes numerical negative value element-wise.

    Computes numerical negative value element-wise. y = -x

    x
    returns

    this tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  134. def norm(value: Int): T

    returns the sum of the n-norms on the Tensor x

    returns the sum of the n-norms on the Tensor x

    value

    the n-norms

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  135. def norm(y: Tensor[T], value: Int, dim: Int): Tensor[T]

    returns the p-norms of the Tensor x computed over the dimension dim.

    returns the p-norms of the Tensor x computed over the dimension dim.

    y

    result buffer

    value
    dim
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  136. def notEqualValue(value: Double): Boolean

    Element wise inequality between tensor and given value

    Element wise inequality between tensor and given value

    value
    returns

    Definition Classes
    Tensor
  137. final def notify(): Unit

    Definition Classes
    AnyRef
  138. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  139. def numNonZeroByRow(): Array[Int]

    Count the number of non-zero elements in first dimension.

    Count the number of non-zero elements in first dimension. For SparseTensor only.

    returns

    an array number of non-zero elements in first dimension.

    Definition Classes
    Tensor
  140. def pow(n: T): Tensor[T]

  141. def pow(y: Tensor[T], n: T): Tensor[T]

    Replaces all elements in-place with the elements of x to the power of n

    Replaces all elements in-place with the elements of x to the power of n

    y
    n
    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  142. def prod(x: Tensor[T], dim: Int): Tensor[T]

  143. def prod(): T

    returns the product of the elements of this

    returns the product of the elements of this

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  144. def rand(lowerBound: Double, upperBound: Double): Tensor[T]

    Fill with random value(uniform distribution between [lowerBound, upperBound]) It will change the value of the current tensor and return itself

    Fill with random value(uniform distribution between [lowerBound, upperBound]) It will change the value of the current tensor and return itself

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  145. def rand(): Tensor[T]

    Fill with random value(uniform distribution).

    Fill with random value(uniform distribution). It will change the value of the current tensor and return itself

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  146. def randn(mean: Double, stdv: Double): Tensor[T]

    Fill with random value(normal gaussian distribution with the specified mean and stdv).

    Fill with random value(normal gaussian distribution with the specified mean and stdv). It will change the value of the current tensor and return itself

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  147. def randn(): Tensor[T]

    Fill with random value(normal gaussian distribution).

    Fill with random value(normal gaussian distribution). It will change the value of the current tensor and return itself

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  148. def range(xmin: Double, xmax: Double, step: Int): Tensor[T]

    resize this tensor size to floor((xmax - xmin) / step) + 1 and set values from xmin to xmax with step (default to 1).

    resize this tensor size to floor((xmax - xmin) / step) + 1 and set values from xmin to xmax with step (default to 1).

    xmin
    xmax
    step
    returns

    this tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  149. def reduce(dim: Int, result: Tensor[T], reducer: (T, T) ⇒ T): Tensor[T]

    Reduce along the given dimension with the given reducer, and copy the result to the result tensor

    Reduce along the given dimension with the given reducer, and copy the result to the result tensor

    dim
    result
    reducer

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  150. def repeatTensor(sizes: Array[Int]): Tensor[T]

    Repeating a tensor allocates new memory, unless result is provided, in which case its memory is resized.

    Repeating a tensor allocates new memory, unless result is provided, in which case its memory is resized. sizes specify the number of times the tensor is repeated in each dimension.

    sizes
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  151. def reshape(sizes: Array[Int]): Tensor[T]

    create a new tensor without any change of the tensor

    create a new tensor without any change of the tensor

    sizes

    the size of the new Tensor

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  152. def resize(size1: Int, size2: Int, size3: Int, size4: Int, size5: Int): Tensor[T]

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  153. def resize(size1: Int, size2: Int, size3: Int, size4: Int): Tensor[T]

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  154. def resize(size1: Int, size2: Int, size3: Int): Tensor[T]

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  155. def resize(size1: Int, size2: Int): Tensor[T]

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  156. def resize(size1: Int): Tensor[T]

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  157. def resize(sizes: Array[Int], strides: Array[Int]): Tensor[T]

    Resize the current tensor to the give shape

    Resize the current tensor to the give shape

    sizes

    Array describe the size

    strides

    Array describe the jumps

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  158. def resize(sizes: Array[Int], nElement: Int): Tensor[T]

    Definition Classes
    Tensor
  159. def resizeAs(src: Tensor[_]): Tensor[T]

    Resize the current tensor to the same size of the given tensor.

    Resize the current tensor to the same size of the given tensor. It will still use the same storage if the storage is sufficient for the new size

    src

    target tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  160. def save(path: String, overWrite: Boolean): DnnTensorUnsupportOperations.this.type

    Save the tensor to given path

    Save the tensor to given path

    path
    overWrite
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  161. def scatter(dim: Int, index: Tensor[T], src: Tensor[T]): Tensor[T]

    Writes all values from tensor src into this tensor at the specified indices

    Writes all values from tensor src into this tensor at the specified indices

    dim
    index
    src
    returns

    this

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  162. def select(dim: Int, index: Int): Tensor[T]

    Remove the dim-th dimension and return the subset part.

    Remove the dim-th dimension and return the subset part. For instance tensor = 1 2 3 4 5 6 tensor.select(1, 1) is [1 2 3] tensor.select(1, 2) is [4 5 6] tensor.select(2, 3) is [3 6]

    dim
    index
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  163. def set(): Tensor[T]

    Shrunk the size of the storage to 0, and also the tensor size

    Shrunk the size of the storage to 0, and also the tensor size

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  164. def set(storage: Storage[T], storageOffset: Int, sizes: Array[Int], strides: Array[Int]): Tensor[T]

    The Tensor is now going to "view" the given storage, starting at position storageOffset (>=1) with the given dimension sizes and the optional given strides.

    The Tensor is now going to "view" the given storage, starting at position storageOffset (>=1) with the given dimension sizes and the optional given strides. As the result, any modification in the elements of the Storage will have an impact on the elements of the Tensor, and vice-versa. This is an efficient method, as there is no memory copy!

    If only storage is provided, the whole storage will be viewed as a 1D Tensor.

    storage
    storageOffset
    sizes
    strides
    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  165. def set(other: Tensor[T]): Tensor[T]

    The Tensor is now going to "view" the same storage as the given tensor.

    The Tensor is now going to "view" the same storage as the given tensor. As the result, any modification in the elements of the Tensor will have an impact on the elements of the given tensor, and vice-versa. This is an efficient method, as there is no memory copy!

    other

    the given tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  166. def setValue(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int, value: T): DnnTensorUnsupportOperations.this.type

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  167. def setValue(d1: Int, d2: Int, d3: Int, d4: Int, value: T): DnnTensorUnsupportOperations.this.type

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  168. def setValue(d1: Int, d2: Int, d3: Int, value: T): DnnTensorUnsupportOperations.this.type

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  169. def setValue(d1: Int, d2: Int, value: T): DnnTensorUnsupportOperations.this.type

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  170. def setValue(d1: Int, value: T): DnnTensorUnsupportOperations.this.type

    Write the value on a given position.

    Write the value on a given position. The number of parameters should be equal to the dimension number of the tensor.

    value

    the written value

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  171. def setValue(value: T): DnnTensorUnsupportOperations.this.type

    Set value for a scalar tensor

    Set value for a scalar tensor

    value

    the written value

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  172. def shallowClone(): Tensor[T]

    Get a new tensor with same storage.

    Get a new tensor with same storage.

    returns

    new tensor

    Definition Classes
    Tensor
  173. def sign(): Tensor[T]

    returns a new Tensor with the sign (+/- 1 or 0) of the elements of x.

    returns a new Tensor with the sign (+/- 1 or 0) of the elements of x.

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  174. def size(dim: Int): Int

    size of the tensor on the given dimension

    size of the tensor on the given dimension

    dim

    dimension, count from 1

    returns

    size

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  175. def size(): Array[Int]

    Size of tensor.

    Size of tensor. Return an array of which each value represents the size on the dimension(i + 1), i is the index of the corresponding value. It will generate a new array each time method is invoked.

    returns

    size array

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  176. def split(dim: Int): Array[Tensor[T]]

    spilt one tensor into multi tensor along the dim dimension

    spilt one tensor into multi tensor along the dim dimension

    dim

    the specific dimension

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  177. def split(size: Int, dim: Int): Array[Tensor[T]]

    Splits current tensor along dimension dim into a result table of Tensors of size size (a number) or less (in the case of the last Tensor).

    Splits current tensor along dimension dim into a result table of Tensors of size size (a number) or less (in the case of the last Tensor). The sizes of the non-dim dimensions remain unchanged. Internally, a series of narrows are performed along dimensions dim. Argument dim defaults to 1.

    size
    dim
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  178. def sqrt(y: Tensor[T]): Tensor[T]

  179. def sqrt(): Tensor[T]

    replaces all elements in-place with the square root of the elements of this.

    replaces all elements in-place with the square root of the elements of this.

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  180. def square(): Tensor[T]

    Replaces all elements in-place with the elements of x squared

    Replaces all elements in-place with the elements of x squared

    returns

    current tensor reference

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  181. def squeeze(dim: Int): Tensor[T]

    Removes given dimensions of the tensor if it's singleton

    Removes given dimensions of the tensor if it's singleton

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  182. def squeeze(): Tensor[T]

    Removes all singleton dimensions of the tensor

    Removes all singleton dimensions of the tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  183. def squeezeNewTensor(): Tensor[T]

    Create a new tensor that removes all singleton dimensions of the tensor

    Create a new tensor that removes all singleton dimensions of the tensor

    returns

    create a new tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  184. def storage(): Storage[T]

    Get the storage

    Get the storage

    returns

    storage

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  185. def storageOffset(): Int

    tensor offset on the storage

    tensor offset on the storage

    returns

    storage offset, count from 1

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  186. def stride(dim: Int): Int

    Jumps between elements on the given dimension in the storage.

    Jumps between elements on the given dimension in the storage.

    dim

    dimension, count from 1

    returns

    jump

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  187. def stride(): Array[Int]

    Jumps between elements on the each dimension in the storage.

    Jumps between elements on the each dimension in the storage. It will generate a new array each time method is invoked.

    returns

    strides array

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  188. def sub(value: T): Tensor[T]

  189. def sub(x: Tensor[T], y: Tensor[T]): Tensor[T]

  190. def sub(y: Tensor[T]): Tensor[T]

    subtracts all elements of y from this

    subtracts all elements of y from this

    y

    other tensor

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  191. def sub(x: Tensor[T], value: T, y: Tensor[T]): Tensor[T]

  192. def sub(value: T, y: Tensor[T]): Tensor[T]

  193. def sum(x: Tensor[T], dim: Int): Tensor[T]

  194. def sum(dim: Int): Tensor[T]

    performs the sum operation over the dimension dim

    performs the sum operation over the dimension dim

    dim
    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  195. def sum(): T

    returns the sum of the elements of this

    returns the sum of the elements of this

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  196. def sumSquare(): T

  197. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  198. def t(): Tensor[T]

    Shortcut of transpose(1, 2) for 2D tensor

    Shortcut of transpose(1, 2) for 2D tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
    See also

    transpose()

  199. def tanh(y: Tensor[T]): Tensor[T]

  200. def tanh(): Tensor[T]

    replaces all elements in-place with the tanh root of the elements of this.

    replaces all elements in-place with the tanh root of the elements of this.

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  201. def toArray(): Array[T]

    Convert 1D tensor to an array.

    Convert 1D tensor to an array. If the tensor is not 1D, an exception will be thrown out.

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  202. def toBreezeMatrix(): DenseMatrix[T]

    convert the tensor to BreezeMatrix, the dimension of the tensor need to be 2.

    convert the tensor to BreezeMatrix, the dimension of the tensor need to be 2.

    returns

    BrzDenseMatrix

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  203. def toBreezeVector(): DenseVector[T]

    convert the tensor to BreezeVector, the dimension of the tensor need to be 1.

    convert the tensor to BreezeVector, the dimension of the tensor need to be 1.

    returns

    BrzDenseVector

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  204. def toMLlibMatrix(): Matrix

    convert the tensor to MLlibMatrix, the dimension of the tensor need to be 2, and tensor need to be continuous.

    convert the tensor to MLlibMatrix, the dimension of the tensor need to be 2, and tensor need to be continuous.

    returns

    Matrix

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  205. def toMLlibVector(): Vector

    convert the tensor to MLlibVector, the dimension of the tensor need to be 1, and tensor need to be continuous.

    convert the tensor to MLlibVector, the dimension of the tensor need to be 1, and tensor need to be continuous.

    returns

    Vector

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  206. def toString(): String

    Definition Classes
    AnyRef → Any
  207. def toTable: Table

    Definition Classes
    TensorActivity
  208. def toTensor[D](implicit ev: TensorNumeric[D]): Tensor[D]

    Definition Classes
    DnnTensorUnsupportOperationsActivity
  209. def topk(k: Int, dim: Int, increase: Boolean, result: Tensor[T], indices: Tensor[T], sortedResult: Boolean): (Tensor[T], Tensor[T])

    Get the top k smallest values and their indices.

    Get the top k smallest values and their indices.

    k
    dim

    dimension, default is the last dimension

    increase

    sort order, set it to true if you want to get the smallest top k values

    result

    result buffer

    indices

    indices buffer

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  210. def transpose(dim1: Int, dim2: Int): Tensor[T]

    * Create a new tensor which exchanges the given dimensions of the current tensor

    * Create a new tensor which exchanges the given dimensions of the current tensor

    dim1

    dimension to be exchanged, count from one

    dim2

    dimension to be exchanged, count from one

    returns

    new tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  211. def unary_-(): Tensor[T]

  212. def unfold(dim: Int, size: Int, step: Int): Tensor[T]

    Returns a tensor which contains all slices of size @param size in the dimension @param dim.

    Returns a tensor which contains all slices of size @param size in the dimension @param dim. Step between two slices is given by @param step.

    dim
    size
    step

    Step between two slices

    returns

    new tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  213. def uniform(args: T*): T

    return pseudo-random numbers, require 0<=args.

    return pseudo-random numbers, require 0<=args.length<=2 if args.length = 0, return [0, 1) if args.length = 1, return [1, args(0)] or [args(0), 1] if args.length = 2, return [args(0), args(1)]

    args

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  214. def update(filter: (T) ⇒ Boolean, value: T): Unit

    Update the value meeting the filter criteria with the give value

    Update the value meeting the filter criteria with the give value

    filter

    filter

    value

    value to update

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  215. def update(t: Table, src: Tensor[T]): Unit

    Copy the given tensor values to the selected subset of the current tensor Each element of the given table can be an Int or another Table.

    Copy the given tensor values to the selected subset of the current tensor Each element of the given table can be an Int or another Table. An Int means select on current dimension; A table means narrow on current dimension, the table should has two elements, of which the first is start index and the second is the end index. An empty table is equal to Table(1, size_of_current_dimension). If the table's length is smaller than the tensor's dimension, the missing dimension is applied by an empty table.

    t

    subset table

    src

    tensor to copy

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  216. def update(t: Table, value: T): Unit

    Fill the select subset of the current tensor with the given value.

    Fill the select subset of the current tensor with the given value. The element of the given table can be an Int or another Table. An Int means select on current dimension; A table means narrow on the current dimension, the table should has two elements, of which the first is the start index and the second is the end index. An empty table is equal to Table(1, size_of_current_dimension) If the table length is less than the tensor dimension, each missing dimension is applied by an empty table

    t

    subset table

    value

    value to write

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  217. def update(indexes: Array[Int], value: T): Unit

    Write the value to the positions indexed by the given index array

    Write the value to the positions indexed by the given index array

    indexes

    index array. It should has same length with the tensor dimension

    value

    value to write

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  218. def update(index: Int, src: Tensor[T]): Unit

    Copy the give tensor value to the select subset of the current tensor by the given index.

    Copy the give tensor value to the select subset of the current tensor by the given index. The subset should have the same size of the given tensor

    index

    index

    src

    tensor to write

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  219. def update(index: Int, value: T): Unit

    For tensor(i) = value.

    For tensor(i) = value. If tensor(i) is another tensor, it will fill the selected subset by the given value

    index

    index

    value

    value to write

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  220. def value(): T

    returns

    the value of a scalar. Requires the tensor to be a scalar.

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  221. def valueAt(d1: Int, d2: Int, d3: Int, d4: Int, d5: Int): T

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  222. def valueAt(d1: Int, d2: Int, d3: Int, d4: Int): T

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  223. def valueAt(d1: Int, d2: Int, d3: Int): T

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  224. def valueAt(d1: Int, d2: Int): T

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  225. def valueAt(d1: Int): T

    Query the value on a given position.

    Query the value on a given position. The number of parameters should be equal to the dimension number of the tensor. Tensor should not be empty.

    returns

    the value on a given position

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  226. def view(sizes: Array[Int]): Tensor[T]

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  227. def view(sizes: Int*): Tensor[T]

    Return a new tensor with specified sizes.

    Return a new tensor with specified sizes. The input tensor must be contiguous, and the elements number in the given sizes must be equal to the current tensor

    sizes
    returns

    new tensor

    Definition Classes
    Tensor
  228. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  229. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  230. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  231. def xcorr2(kernel: Tensor[T], vf: Char): Tensor[T]

    This function operates with same options and input/output configurations as conv2, but performs cross-correlation of the input with the kernel k.

    This function operates with same options and input/output configurations as conv2, but performs cross-correlation of the input with the kernel k.

    kernel
    vf

    full ('F') or valid ('V') convolution.

    returns

    Definition Classes
    DnnTensorUnsupportOperationsTensorMath
  232. def zero(): Tensor[T]

    Fill with zero.

    Fill with zero. It will change the value of the current tensor and return itself

    returns

    current tensor

    Definition Classes
    DnnTensorUnsupportOperationsTensor
  233. def zipWith[A, B](t1: Tensor[A], t2: Tensor[B], func: (A, B) ⇒ T)(implicit arg0: ClassManifest[A], arg1: ClassManifest[B]): Tensor[T]

    Zip values of two other tensors with applying the function func on each two values element-wisely and assign the result value to the current tensor

    Zip values of two other tensors with applying the function func on each two values element-wisely and assign the result value to the current tensor

    The two given tensors should has the same size of the current tensor

    A

    numeric type of tensor 1

    B

    numeric type of tensor 2

    t1

    tensor 1

    t2

    tensor 2

    func

    zip with the function

    returns

    self

    Definition Classes
    DnnTensorUnsupportOperationsTensor

Inherited from Tensor[T]

Inherited from Activity

Inherited from TensorMath[T]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped