DeepLearning
ApplyOperation
apply an operation
Calling Sequence
Parameters
Options
Description
Examples
Compatibility
ApplyOperation(oper,t1,...,tn,opts)
oper
-
string
t1,...,tn
numeric or Tensor; inputs to be passed to oper
opts
options to be passed to oper
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.
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](..).
with⁡DeepLearning:
t1≔Constant⁡1.,2.,datatype=float4
t1≔DeepLearning TensorShape: [2]Data Type: float[4]
t2≔Constant⁡3.,4.,datatype=float4
t2≔DeepLearning TensorShape: [2]Data Type: float[4]
Add two tensors using standard Maple operators.
t3≔t1+t2
t3≔DeepLearning TensorShape: [2]Data Type: float[4]
Now perform the same operation using ApplyOperation.
t4≔ApplyOperation⁡tf.add,t1,t2
t4≔DeepLearning TensorShape: [2]Data Type: float[4]
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
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