Table Of Contents
Table Of Contents

mx.symbol.dot

Description

Dot product of two arrays.

dot’s behavior depends on the input array dimensions:

  • 1-D arrays: inner product of vectors

  • 2-D arrays: matrix multiplication

  • N-D arrays: a sum product over the last axis of the first input and the first

axis of the second input

For example, given 3-D x with shape (n,m,k) and y with shape (k,r,s), the

result array will have shape (n,m,r,s). It is computed by:

dot(x,y)[i,j,a,b] = sum(x[i,j,:]*y[:,a,b])

Example:

 x = reshape([0,1,2,3,4,5,6,7], shape=(2,2,2))
 y = reshape([7,6,5,4,3,2,1,0], shape=(2,2,2))
 dot(x,y)[0,0,1,1] = 0
 sum(x[0,0,:]*y[:,1,1]) = 0

 The storage type of ``dot`` output depends on storage types of inputs, transpose option and
 forward_stype option for output storage type. Implemented sparse operations include:

- dot(default, default, transpose_a=True/False, transpose_b=True/False) = default
- dot(csr, default, transpose_a=True) = default
- dot(csr, default, transpose_a=True) = row_sparse
- dot(csr, default) = default
- dot(csr, row_sparse) = default
- dot(default, csr) = csr (CPU only)
- dot(default, csr, forward_stype='default') = default
- dot(default, csr, transpose_b=True, forward_stype='default') = default

 If the combination of input storage types and forward_stype does not match any of the
 above patterns, ``dot`` will fallback and generate output with default storage.

Note

If the storage type of the lhs is “csr”, the storage type of gradient w.r.t rhs will be “row_sparse”. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad and Adam. Note that by default lazy updates is turned on, which may perform differently from standard updates. For more details, please check the Optimization API at: https://mxnet.incubator.apache.org/api/python/optimization/optimization.html

Usage

mx.symbol.dot(...)

Arguments

Argument

Description

lhs

NDArray-or-Symbol.

The first input

rhs

NDArray-or-Symbol.

The second input

transpose.a

boolean, optional, default=0.

If true then transpose the first input before dot.

transpose.b

boolean, optional, default=0.

If true then transpose the second input before dot.

forward.stype

{None, ‘csr’, ‘default’, ‘row_sparse’},optional, default=’None’.

The desired storage type of the forward output given by user, if thecombination of input storage types and this hint does not matchany implemented ones, the dot operator will perform fallback operationand still produce an output of the desired storage type.

name

string, optional.

Name of the resulting symbol.