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DeepLearning,Tensor,betainc

compute the incomplete beta function on entries in a Tensor

DeepLearning,Tensor,expm1

compute the expm1 of entries in a Tensor

DeepLearning,Tensor,lbeta

compute the lbeta of entries in a Tensor

DeepLearning,Tensor,lgamma

compute the lgamma of entries in a Tensor

DeepLearning,Tensor,log1p

compute the log1p of entries in a Tensor

DeepLearning,Tensor,log_sigmoid

compute the log_sigmoid of entries in a Tensor

DeepLearning,Tensor,rsqrt

compute the rsqrt of entries in a Tensor

DeepLearning,Tensor,sigmoid

compute the sigmoid of entries in a Tensor

 

Calling Sequence

Parameters

Options

Description

Examples

Compatibility

Calling Sequence

betainc(t,u,v,opts)   expm1(t,opts)

lbeta(t,opts)         lgamma(t,opts)

log1p(t,opts)         log_sigmoid(t,opts)

rsqrt(t,opts)         sigmoid(t,opts)

Parameters

t

-

Tensor

u

-

Tensor

v

-

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 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.

Examples

withDeepLearning:

XMatrix11.0,18.3,12.1,20.3,datatype=float8

X11.18.300000000000012.100000000000020.3000000000000

(1)

YMatrix96.0,12.8,8.7,27.6,datatype=float8

Y96.12.80000000000008.7000000000000027.6000000000000

(2)

ZMatrix1.,1.,1.,1.,datatype=float8

Z1.1.1.1.

(3)

t1ConstantX

t1DeepLearning TensorName: Const:0Shape: [2, 2]Data Type: float[8]

(4)

t2ConstantY

t2DeepLearning TensorName: Const_1:0Shape: [2, 2]Data Type: float[8]

(5)

t3ConstantZ

t3DeepLearning TensorName: Const_2:0Shape: [2, 2]Data Type: float[8]

(6)

valuebetainct1,t2,t3

betaincDeepLearning TensorName: Const:0Shape: [2, 2]Data Type: float[8],DeepLearning TensorName: Const_1:0Shape: [2, 2]Data Type: float[8],DeepLearning TensorName: Const_2:0Shape: [2, 2]Data Type: float[8]

(7)

valueexpm1t1

expm1DeepLearning TensorName: Const:0Shape: [2, 2]Data Type: float[8]

(8)

valuelbetat1

lbetaDeepLearning TensorName: Const:0Shape: [2, 2]Data Type: float[8]

(9)

valuelgammat1

lgammaDeepLearning TensorName: Const:0Shape: [2, 2]Data Type: float[8]

(10)

valuelog1pt1

log1pDeepLearning TensorName: Const:0Shape: [2, 2]Data Type: float[8]

(11)

valuelog_sigmoidt2

log_sigmoidDeepLearning TensorName: Const_1:0Shape: [2, 2]Data Type: float[8]

(12)

valuersqrtt2

rsqrtDeepLearning TensorName: Const_1:0Shape: [2, 2]Data Type: float[8]

(13)

valuesigmoidt2

sigmoidDeepLearning TensorName: Const_1:0Shape: [2, 2]Data Type: float[8]

(14)

Compatibility

• 

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.

See Also

DeepLearning Overview

Tensor