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DeepLearning

  

ApplyOperation

  

apply an operation

 

Calling Sequence

Parameters

Options

Description

Examples

Compatibility

Calling Sequence

ApplyOperation(oper,t1,...,tn,opts)

Parameters

oper

-

string

t1,...,tn

-

numeric or Tensor; inputs to be passed to oper

opts

-

options to be passed to oper

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 ApplyOperation(oper,t1,...,tn,opts) command applies the operation oper to the arguments t1,...,tn with options opts

• 

The arguments t1,...,tn may be complex numerics or Tensor objects.

• 

The operation oper must be a valid, fully-qualified, function name in the TensorFlow Python API. For a list of functions, consult the TensorFlow Python API Documentation.

• 

For convenience the abbreviated syntax "tf." is also supported.

• 

This function is part of the DeepLearning package, so it can be used in the short form ApplyOperation(..) only after executing the command with(DeepLearning). However, it can always be accessed through the long form of the command by using DeepLearning[ApplyOperation](..).

Examples

withDeepLearning:

t1Constant1.,2.,datatype=float4

t1DeepLearning TensorName: Const:0Shape: [2]Data Type: float[4]

(1)

t2Constant3.,4.,datatype=float4

t2DeepLearning TensorName: Const_1:0Shape: [2]Data Type: float[4]

(2)

Add two tensors using standard Maple operators.

t3t1+t2

t3DeepLearning TensorName: Add:0Shape: [2]Data Type: float[4]

(3)

Now perform the same operation using ApplyOperation.

t4ApplyOperationtf.add,t1,t2

t4DeepLearning TensorName: Add_1:0Shape: [2]Data Type: float[4]

(4)

Compatibility

• 

The DeepLearning[ApplyOperation] command was introduced in Maple 2018.

• 

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

See Also

DeepLearning Overview