module onnxrt.ops_cpu.op_relu

Short summary

module mlprodict.onnxrt.ops_cpu.op_relu

Runtime operator.

source on GitHub

Classes

class	truncated documentation
Relu	Relu ==== Relu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the rectified linear function, …
ThresholdedRelu	ThresholdedRelu =============== ThresholdedRelu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) …

Properties

property	truncated documentation
args_default	Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …
args_default	Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …
args_default_modified	Returns the list of modified parameters.
args_default_modified	Returns the list of modified parameters.
args_mandatory	Returns the list of optional arguments.
args_mandatory	Returns the list of optional arguments.
args_optional	Returns the list of optional arguments.
args_optional	Returns the list of optional arguments.
atts_value	Returns all parameters in a dictionary.
atts_value	Returns all parameters in a dictionary.

Methods

method	truncated documentation
__init__
__init__
_run
_run
_run_inplace
_run_inplace
to_python
to_python

Documentation

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_relu.Relu(onnx_node, desc=None, **options)

Bases: OpRunUnaryNum

---

Relu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the rectified linear function, y = max(0, x), is applied to the tensor elementwise.

Inputs

• X (heterogeneous)T: Input tensor

Outputs

• Y (heterogeneous)T: Output tensor

Type Constraints

• T tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16): Constrain input and output types to signed numeric tensors.

Version

Onnx name: Relu

This version of the operator has been available since version 14.

Runtime implementation: Relu

__init__(onnx_node, desc=None, **options)

_run(x, attributes=None, verbose=0, fLOG=None)

Should be overwritten.

source on GitHub

_run_inplace(x)

to_python(inputs)

Returns a python code equivalent to this operator.

Parameters:

inputs – inputs name

Returns:

imports, python code, both as strings

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_relu.ThresholdedRelu(onnx_node, desc=None, **options)

Bases: OpRunUnaryNum

---

ThresholdedRelu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise.

Attributes

• alpha: Threshold value Default value is namealphaf1.0typeFLOAT (FLOAT)

Inputs

• X (heterogeneous)T: Input tensor

Outputs

• Y (heterogeneous)T: Output tensor

Type Constraints

• T tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Version

Onnx name: ThresholdedRelu

This version of the operator has been available since version 10.

Runtime implementation: ThresholdedRelu

__init__(onnx_node, desc=None, **options)

_run(x, attributes=None, verbose=0, fLOG=None)

Should be overwritten.

source on GitHub

_run_inplace(x)

to_python(inputs)

Returns a python code equivalent to this operator.

Parameters:

inputs – inputs name

Returns:

imports, python code, both as strings

source on GitHub