A model that applies a 1D convolution over an input signal composed of several input planes
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Description
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Full Usage:
Conv1d(inChannels, outChannels, kernelSize, ?stride, ?padding, ?dilation, ?bias)
Parameters:
int
outChannels : int
kernelSize : int
?stride : int
?padding : int
?dilation : int
?bias : bool
Returns: Conv1d
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| Instance member |
Description
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Get or set the bias parameter of the model
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Get or set the weight parameter of the model
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ModelBase
| Instance member |
Description
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TBD
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Full Usage:
this.eval ()
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TBD |
Full Usage:
this.forwardDiff (derivatives, ?nestingTag)
Parameters:
ParameterDict
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The derivatives of the parameters
?nestingTag : uint32
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The level tag for nested differentiation. Defaults to the current global nesting level
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Adjust the parameters of the model to initiate a new level of forward-mode automatic differentiation. After this call the current parameters of the model will have attached derivatives for forward mode differentiation.
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Full Usage:
this.noDiff ()
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TBD |
Full Usage:
this.reverseDiff ?nestingTag
Parameters:
uint32
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The level tag for nested differentiation. Defaults to the current global nesting level
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Adjust the parameters of the model to initiate a new level of reverse-mode automatic differentiation.
After this call the current parameters of the model will support reverse-mode differentiation. After the completion
of the corresponding
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Full Usage:
this.summary ()
Returns: string
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TBD
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Full Usage:
this.train ()
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TBD |
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TBD
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TBD
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Full Usage:
this.hasOwnParameters
Returns: bool
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TBD
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Full Usage:
this.isNoDiff
Returns: bool
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Full Usage:
this.hasOwnBuffers
Returns: bool
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TBD
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TBD
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Full Usage:
this.isReverseDiff
Returns: bool
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Full Usage:
this.nstate
Returns: int
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TBD
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Full Usage:
this.nparameters
Returns: int
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Gets the number of parameters of the Model
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Full Usage:
this.hasOwnState
Returns: bool
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TBD
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TBD
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Full Usage:
this.isForwardDiff
Returns: bool
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TBD
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TBD
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Full Usage:
this.nbuffers
Returns: int
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TBD
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TBD
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TBD
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