com.intel.analytics.bigdl.tensor
TensorMath
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package tensor
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
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.
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
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.
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
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.
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
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.
replaces all elements in-place with the absolute values of the elements of this.
x.add(value) : add value to all elements of x in place.
z.add(x, value, y) puts the result of x + value * y in z.
accumulates all elements of y into this
accumulates all elements of y into this
other tensor
current tensor
x.add(value,y) multiply-accumulates values of y into x.
x.add(value,y) multiply-accumulates values of y into x.
scalar
other tensor
current tensor
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.
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.
res = v1 * res + v2 * mat1*mat2
res = res + v2 * mat1 * mat2
res = res + mat1 * mat2
res = M + (mat1*mat2)
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)
res = res + alpha * (mat * vec2)
res = beta * res + alpha * (mat * vec2)
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.
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)
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)
res_i = res_i + (alpha * batch1_i * batch2_i)
res_i = (beta * res_i) + (alpha * batch1_i * batch2_i)
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)
res_i = res_i + batch1_i * batch2_i
Element-wise divide z.cdiv(x, y) means z = x / y
Element-wise divide z.cdiv(x, y) means z = x / y
tensor
tensor
current tensor
Element-wise divide x.cdiv(y) all elements of x divide all elements of y.
Element-wise divide x.cdiv(y) all elements of x divide all elements of y. x = x / y
tensor
current tensor
Replaces all elements in-place with the ceil result of elements
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)
tensor
tensor
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)
tensor
current tensor
For each elements of the tensor, performs the max operation compared with the given value vector.
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)
tensor
tensor
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)
tensor
current tensor
Element-wise multiply z.cmul(x, y) equals z = x * y
Element-wise multiply z.cmul(x, y) equals z = x * y
tensor
tensor
current tensor
Element-wise multiply x.cmul(y) multiplies all elements of x with corresponding elements of y.
Element-wise multiply x.cmul(y) multiplies all elements of x with corresponding elements of y. x = x * y
tensor
current tensor
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.
full ('F') or valid ('V') convolution.
Computes Psi, the derivative of Lgamma (the log of the absolute value of
Gamma(x)), element-wise and update the content inplace
Performs the p-norm distance calculation between two tensors
Performs the p-norm distance calculation between two tensors
the secode Tensor
the norm of distance
Element-wise divide x.div(y) all elements of x divide all elements of y.
Element-wise divide x.div(y) all elements of x divide all elements of y. x = x / y
tensor
current tensor
divide all elements of this with value in-place.
Performs the dot product.
Performs the dot product. The number of elements must match: both Tensors are seen as a 1D vector.
Implements == operator comparing each element in x with y
Implements == operator comparing each element in x with y
current tensor reference
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
Replaces all elements in-place with the floor result of elements
Populate the given tensor with the floor result of elements
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.
this
Implements >= operator comparing each element in x with value
Implements > operator comparing each element in x with y
Implements > operator comparing each element in x with y
current tensor reference
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.
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.
Computes the reciprocal of this tensor element-wise and update the content inplace
Implements <= operator comparing each element in x with y
Implements <= operator comparing each element in x with y
current tensor reference
Replaces all elements in-place with the elements of lnx
Replaces all elements in-place with the elements of lnx
current tensor reference
Computes the log of the absolute value of Gamma(x) element-wise,
and update the content inplace
Implements < operator comparing each element in x with y
Implements < operator comparing each element in x with y
current tensor reference
Copies the elements of tensor into mask locations of itself.
Copies the elements of tensor into mask locations of itself.
current tensor reference
Fills the masked elements of itself with value val
Fills the masked elements of itself with value val
current tensor reference
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.
current tensor reference
performs the max operation over the dimension n
performs the max operation over the dimension n
returns the single biggest element of x
performs the mean operation over the dimension dim.
returns the mean of all elements of this.
performs the min operation over the dimension n
performs the min operation over the dimension n
returns the single minimum element of x
res = mat1*mat2
put the result of x * value in current tensor
multiply all elements of this with value in-place.
res = res + (mat * vec2)
Computes numerical negative value element-wise.
Computes numerical negative value element-wise. y = -x
this tensor
returns the sum of the n-norms on the Tensor x
returns the sum of the n-norms on the Tensor x
the n-norms
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.
result buffer
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
current tensor reference
returns the product of the elements of this
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).
this tensor
Reduce along the given dimension with the given reducer, and copy the result to the result tensor
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
this
returns a new Tensor with the sign (+/- 1 or 0) of the elements of x.
replaces all elements in-place with the square root of the elements of this.
Replaces all elements in-place with the elements of x squared
Replaces all elements in-place with the elements of x squared
current tensor reference
subtracts all elements of y from this
subtracts all elements of y from this
other tensor
current tensor
performs the sum operation over the dimension dim
returns the sum of the elements of this
replaces all elements in-place with the tanh root of the elements of this.
Get the top k smallest values and their indices.
Get the top k smallest values and their indices.
dimension, default is the last dimension
sort order, set it to true if you want to get the smallest top k values
result buffer
indices buffer
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)]
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.
full ('F') or valid ('V') convolution.
It provides multiple math operation functions for manipulating Tensor objects. All functions support both allocating a new Tensor to return the result and treating the caller as a target Tensor, in which case the target Tensor(s) will be resized accordingly and filled with the result. This property is especially useful when one wants to have tight control over when memory is allocated.
should be double or float