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QLinearSoftmax

Description

QLinearSoftmax computes the normalized exponential values for the given input: Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) The input does not need to explicitly be a 2D vector. The “axis” attribute indicates the dimension along which QLinearSoftmax will be performed for onnx v.13+. or the dimension coerced to NxD Matrix for onnx v.12-. The output tensor has the same shape.

 

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

 Graphs in : cluster, ONNX model architecture.

 X (heterogeneous) – T : object, input tensor.
 X_scale (heterogeneous) – tensor(float) : object, scale of quantized input ‘X’. It must be a scalar.
 X_zero_point (optional, heterogeneous) – T : object, zero point tensor for input ‘X’.It must be a scalar.
 Y_scale (heterogeneous) – tensor(float) : object, scale of quantized output ‘Y’. It must be a scalar.
 Y_zero_point (heterogeneous) – T : object, zero point tensor for output ‘Y’. It must be a scalar.

 Parameters : cluster,

axis : integer, apply softmax to elements for dimensions axis,or all dims along with axis according to op-version.
Default value “0”.
opset : integer, opset version of corresponding SoftMax.
Default value “17”.
 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : 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 “1”.

 name (optional) : string, name of the node.

Output parameters

 

Y (heterogeneous) – T : object, output data tensor from pooling across the input tensor. The output tensor has the same rank as the input.

Type Constraints

T in (tensor(uint8), tensor(int8)) : Constrain input and output types to signed/unsigned int8 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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