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DeepLearning

  

Fill

  

fill compute min over entries of a Tensor

 

Calling Sequence

Parameters

Options

Description

Examples

Compatibility

Calling Sequence

Fill(shape,val,opts)

Ones(shape,opts)

Ones(t,opts)

Zeros(shape,opts)

Zeros(t,opts)

Parameters

val

-

numeric quantity

shape

-

list, dimensions of the output Tensor

t

-

Tensor

opts

-

(optional) options as specified below

Options

• 

datatype=one of string,truefalse,integer[4],integer[8],float[4],float[8],complex[4], or complex[8]

  

The value of option datatype specifies the type of data this Tensor will hold. If a Tensor t was provided as input, the default value of datatype is the datatype of t; otherwise, the default is float[4].

• 

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 Fill(shape,val,opts) command creates a Tensor in the active dataflow graph with shape shape filled with the scalar value val.

• 

The Ones(shape,opts) and Zeros(shape,opts) commands behave similarly but create Tensors whose whose entries are 1 and 0, respectively. If x is a Tensor, the val of the resulting Tensor matches x.

• 

If t is a Tensor, the commands Ones(t,opts) and Zeros(t,opts) create Tensors whose whose shape is identical to t but whose entries are 1 and 0, respectively.

• 

These functions are part of the DeepLearning package, so they can can be used in the short form Fill(..) only after executing the command with(DeepLearning). However,  can always be accessed through the long form of the command by using DeepLearning[Fill](..).

Examples

withDeepLearning:

T1Fill2,3,0.75

T1DeepLearning TensorName: Fill:0Shape: [2, 3]Data Type: float[4]

(1)

T2OnesT1

T2DeepLearning TensorName: ones_like:0Shape: [2, 3]Data Type: float[4]

(2)

T3Zeros3,4,datatype=float8

T3DeepLearning TensorName: zeros:0Shape: [3, 4]Data Type: float[8]

(3)

Compatibility

• 

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

• 

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

See Also

DeepLearning Overview