 DeepLearning/Tensor/abs - Maple Programming Help

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DeepLearning/Tensor/abs

compute the absolute value of entries in a Tensor

DeepLearning/Tensor/ceil

compute the ceiling of entries in a Tensor

DeepLearning/Tensor/erf

compute the erf of entries in a Tensor

DeepLearning/Tensor/erfc

compute the erfc of entries in a Tensor

DeepLearning/Tensor/exp

compute the exponential of entries in a Tensor

DeepLearning/Tensor/floor

compute the floor of entries in a Tensor

DeepLearning/Tensor/log

compute the logarithm of entries in a Tensor

DeepLearning/Tensor/lnGAMMA

compute the lnGAMMA of entries in a Tensor

DeepLearning/Tensor/Psi

compute the Psi of entries in a Tensor

DeepLearning/Tensor/round

compute the rounded value of entries in a Tensor

DeepLearning/Tensor/sign

compute the sign of entries in a Tensor

DeepLearning/Tensor/sqrt

compute the square root of entries in a Tensor

DeepLearning/Tensor/Zeta

compute the Hurwitz zeta function on entries in a Tensor

 Calling Sequence abs(t,opts)    ceil(t,opts)     erf(t,opts) erfc(t,opts)   exp(t,opts)      floor(t,opts) log(t,opts)    lnGAMMA(t,opts)  Psi(t,opts) round(t,opts)  sign(t,opts)     sqrt(t,opts) Zeta(t,opts)

Parameters

 t - Tensor opts - zero or more options as specified below

Options

 • name=string

The value of option name specifies an optional name for this Tensor, to be displayed in output and when visualizing the dataflow graph.

Description

 • The abs(t) command computes the abs of entries in a Tensor.
 • The ceil(t) command computes the complex ceil of entries in a Tensor.
 • The erf(t) command computes the erf of entries in a Tensor.
 • The erfc(t) command computes the erfc of entries in a Tensor.
 • The exp(t) command computes the exponential of entries in a Tensor.
 • The floor(t) command computes the floor of entries in a Tensor.
 • The log(t) command computes the logarithm of entries in a Tensor.
 • The lnGAMMA(t) command computes the lnGAMMA of entries in a Tensor.
 • The Psi(t) command computes the Psi of entries in a Tensor.
 • The round(t) command computes the rounded value of entries in a Tensor.
 • The sign(t) command computes the sign of entries in a Tensor.
 • The sqrt(t) command computes the square root of entries in a Tensor.
 • The Zeta(t) command computes the Hurwitz zeta of entries in a Tensor.

Examples

 > $\mathrm{with}\left(\mathrm{DeepLearning}\right):$
 > $V≔\mathrm{Matrix}\left(\left[\left[11.0,18.3\right],\left[12.1,20.3\right]\right],\mathrm{datatype}=\mathrm{float}\left[8\right]\right)$
 ${V}{≔}\left[\begin{array}{cc}{11.}& {18.3000000000000}\\ {12.1000000000000}& {20.3000000000000}\end{array}\right]$ (1)
 > $t≔\mathrm{Tensor}\left(V\right)$
 ${t}{≔}\left[\begin{array}{c}{\mathrm{DeepLearning Tensor}}\\ {\mathrm{Name: Const:0}}\\ {\mathrm{Shape: \left[2, 2\right]}}\\ {\mathrm{Data Type: float\left[8\right]}}\end{array}\right]$ (2)
 > $\mathrm{value}\left(\mathrm{abs}\left(t\right)\right)$
 $\left[\begin{array}{cc}{11.}& {18.3000000000000}\\ {12.1000000000000}& {20.3000000000000}\end{array}\right]$ (3)
 > $\mathrm{value}\left(\mathrm{ceil}\left(t\right)\right)$
 $\left[\begin{array}{cc}{11.}& {19.}\\ {13.}& {21.}\end{array}\right]$ (4)
 > $\mathrm{value}\left(\mathrm{erf}\left(t\right)\right)$
 $\left[\begin{array}{cc}{1.}& {1.}\\ {1.}& {1.}\end{array}\right]$ (5)
 > $\mathrm{value}\left(\mathrm{exp}\left(t\right)\right)$
 $\left[\begin{array}{cc}{59874.1417151978}& {8.86316876451942}{}{{10}}^{{7}}\\ {179871.862253751}& {6.54904512153239}{}{{10}}^{{8}}\end{array}\right]$ (6)
 > $\mathrm{value}\left(\mathrm{round}\left(t\right)\right)$
 $\left[\begin{array}{cc}{11.}& {18.}\\ {12.}& {20.}\end{array}\right]$ (7)

Compatibility

 • The DeepLearning/Tensor/abs, DeepLearning/Tensor/ceil, DeepLearning/Tensor/erf, DeepLearning/Tensor/erfc, DeepLearning/Tensor/exp, DeepLearning/Tensor/floor, DeepLearning/Tensor/log, DeepLearning/Tensor/lnGAMMA, DeepLearning/Tensor/Psi, DeepLearning/Tensor/round, DeepLearning/Tensor/sign, DeepLearning/Tensor/sqrt and DeepLearning/Tensor/Zeta commands were introduced in Maple 2018.
 • For more information on Maple 2018 changes, see Updates in Maple 2018.

 See Also