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QLinearSigmoid

Description

QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces one output data (Tensor) where the function f(x) = quantize(Sigmoid(dequantize(x))), is applied to the data tensor elementwise. Wwhere the function Sigmoid(x) = 1 / (1 + exp(-x))

 

 

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, input X’s scale. It’s a scalar, which means a per-tensor/layer quantization.
 X_zero_point (optional, heterogeneous) – T : object, input X’s zero point. Default value is 0 if it’s not specified. It’s a scalar, which means a per-tensor/layer quantization.
 Y_scale (heterogeneous) – tensor(float) : object, output Y’s scale. It’s a scalar, which means a per-tensor/layer quantization.
 Y_zero_point (optional, heterogeneous) – T : object, output Y’s zero point. Default value is 0 if it’s not specified. It’s a scalar, which means a per-tensor/layer quantization.

 Parameters : cluster,

 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 tensor.

Type Constraints

T in (tensor(uint8), tensor(int8)) : Constrain input and output types to 8 bit 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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