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CUDA Toolkit
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Updated
Clip
Description
Clip operator limits the given input within an interval. The interval is specified by the inputs βminβ and βmaxβ. They default to numeric_limits::lowest() and numeric_limits::max(), respectively. When βminβ is greater than βmaxβ, the clip operator sets all the βinputβ values to the value of βmaxβ. Thus, this is equivalent to βMin(max, Max(input, min))β.
Β
Input parameters
specified_outputs_name :Β array, this parameter lets you manually assign custom names to the output tensors of a node.
Β Graphs in :Β cluster, ONNX model architecture.
inputΒ (heterogeneous) –Β T : object, input tensor whose elements to be clipped. min (optional, heterogeneous) – T : object, minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape). max (optional, heterogeneous) – T : object, maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).
Β Parameters : cluster,
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value βTrueβ.
Β lda coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value β1β.
Β name (optional) :Β string, name of the node.
Output parameters
output (heterogeneous) – T : object, output tensor with clipped input elements.
Type Constraints
T in (tensor(bfloat16),Β tensor(double),Β tensor(float),Β tensor(float16),Β tensor(int16),Β tensor(int32),Β tensor(int64),Β tensor(int8),Β tensor(uint16),Β tensor(uint32),Β tensor(uint64),Β tensor(uint8)) : Constrain input and output types to all numeric tensors.
Example
All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).
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