QLinearLeakyRelu
Description
QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for output, and produces one output data (Tensor) where the function f(x) = quantize(alpha * dequantize(x)) for dequantize(x) < 0, f(x) = quantize(dequantize(x)) for dequantize(x) >= 0, is applied to the data tensor elementwise.
Documentation illustration (illustration unavailable in the archive).
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.
Documentation illustration (illustration unavailable in the archive).
Parameters : cluster,
alpha : float, coefficient of leakage.
Default value “0”.
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.
Documentation illustration (illustration unavailable in the archive).
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).