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The betainc(t,u,v,opts) command computes the incomplete Beta function of entries in a Tensor.

The expm1(t,opts) command computes the complex expm1 of entries in a Tensor.

The lbeta(t,opts) command computes the lbeta of entries in a Tensor.

The lgamma(t,opts) command computes the lgamma of entries in a Tensor.

The log1p(t,opts) command computes the log1p of entries in a Tensor.

The log_sigmoid(t,opts) command computes the log-sigmoid of entries in a Tensor.

The rsqrt(t,opts) command computes the reciprocal of the square root of entries in a Tensor.

The sigmoid(t,opts) command computes the sigmoid of entries in a Tensor.

$\mathrm{with}\left(\mathrm{DeepLearning}\right)\:$

$X≔\mathrm{Matrix}\left(\left[\left[11.0\,18.3\right]\,\left[12.1\,20.3\right]\right]\,\mathrm{datatype}=\mathrm{float}\left[8\right]\right)$

$X≔\left[\begin{array}{cc}11.& 18.3000000000000\\ 12.1000000000000& 20.3000000000000\end{array}\right]$

$Y≔\mathrm{Matrix}\left(\left[\left[96.0\,12.8\right]\,\left[8.7\,27.6\right]\right]\,\mathrm{datatype}=\mathrm{float}\left[8\right]\right)$

$Y≔\left[\begin{array}{cc}96.& 12.8000000000000\\ 8.70000000000000& 27.6000000000000\end{array}\right]$

$Z≔\mathrm{Matrix}\left(\left[\left[1.\,1.\right]\,\left[1.\,1.\right]\right]\,\mathrm{datatype}=\mathrm{float}\left[8\right]\right)$

$Z≔\left[\begin{array}{cc}1.& 1.\\ 1.& 1.\end{array}\right]$

$\mathrm{t1}≔\mathrm{Constant}\left(X\right)$

$\mathrm{t1}≔\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]$

$\mathrm{t2}≔\mathrm{Constant}\left(Y\right)$

$\mathrm{t2}≔\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]$

$\mathrm{t3}≔\mathrm{Constant}\left(Z\right)$

$\mathrm{t3}≔\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]$

$\mathrm{betainc}\left(\mathrm{t1}\,\mathrm{t2}\,\mathrm{t3}\right)$

$\mathrm{betainc}\left(\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\,\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\,\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right)$

$\mathrm{expm1}\left(\mathrm{t1}\right)$

$\mathrm{expm1}\left(\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right)$

$\mathrm{lbeta}\left(\mathrm{t1}\right)$

$\mathrm{lbeta}\left(\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right)$

$\mathrm{lgamma}\left(\mathrm{t1}\right)$

$\mathrm{lgamma}\left(\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right)$

$\mathrm{log1p}\left(\mathrm{t1}\right)$

$\mathrm{log1p}\left(\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right)$

$\mathrm{log\_sigmoid}\left(\mathrm{t2}\right)$

$\mathrm{log\_sigmoid}\left(\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right)$

$\mathrm{rsqrt}\left(\mathrm{t2}\right)$

$\mathrm{rsqrt}\left(\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right)$

$\mathrm{sigmoid}\left(\mathrm{t2}\right)$

$\mathrm{sigmoid}\left(\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [2,\; 2]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right)$

The DeepLearning,Tensor,betainc, DeepLearning,Tensor,expm1, DeepLearning,Tensor,lbeta, DeepLearning,Tensor,lgamma, DeepLearning,Tensor,log1p, DeepLearning,Tensor,log_sigmoid, DeepLearning,Tensor,rsqrt and DeepLearning,Tensor,sigmoid commands were introduced in Maple 2018.

For more information on Maple 2018 changes, see Updates in Maple 2018.