module `onnx_conv.onnx_ops.onnx_gradient_op`#

Short summary#

module mlprodict.onnx_conv.onnx_ops.onnx_gradient_op

Custom operators for gradient numbers.

Classes#

class	truncated documentation
`OnnxBroadcastGradientArgs_1`	Defines a custom operator for BroadcastGradientArgs. Returns the reduction axes for computing gradients of s0 op s1 …
`OnnxBroadcastGradientArgs_1`	Defines a custom operator for BroadcastGradientArgs. Returns the reduction axes for computing gradients of s0 op s1 …
`OnnxFusedMatMul_1`	MatMul and Gemm without a C.
`OnnxFusedMatMul_1`	MatMul and Gemm without a C.
`OnnxSoftmaxGrad_13`	Gradient of Softmax. SoftmaxGrad computes $Y * ( dY - ReduceSum(Y * dY))$ . ONNX does not have a dot product, …
`OnnxSoftmaxGrad_13`	Gradient of Softmax. SoftmaxGrad computes $Y * ( dY - ReduceSum(Y * dY))$ . ONNX does not have a dot product, …
`OnnxYieldOp_1`	Defines a custom operator for YieldOp.
`OnnxYieldOp_1`	Defines a custom operator for YieldOp.

Properties#

property	truncated documentation
`onnx_prefix`
`onnx_prefix`
`onnx_prefix`
`onnx_prefix`
`onnx_prefix`
`onnx_prefix`
`onnx_prefix`
`onnx_prefix`
`outputs`	Returns the outputs of the node.
`outputs`	Returns the outputs of the node.
`outputs`	Returns the outputs of the node.
`outputs`	Returns the outputs of the node.
`outputs`	Returns the outputs of the node.
`outputs`	Returns the outputs of the node.
`outputs`	Returns the outputs of the node.
`outputs`	Returns the outputs of the node.

Methods#

method	truncated documentation
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`

Documentation#

Custom operators for gradient numbers.

source on GitHub

mlprodict.onnx_conv.onnx_ops.onnx_gradient_op.OnnxBroadcastGradientArgs#: alias of OnnxBroadcastGradientArgs_1

class mlprodict.onnx_conv.onnx_ops.onnx_gradient_op.OnnxBroadcastGradientArgs_1(a_shape, b_shape, op_version=None, **kwargs)#

Bases: OnnxOperator

Defines a custom operator for BroadcastGradientArgs. Returns the reduction axes for computing gradients of s0 op s1 with broadcast. The ouput axes are deterministic from last to first. Output is an empty vector when no reduction is necessary for the corresponding input.

source on GitHub

Parameters:

a_shape – The 1st input shape as Tensor.
b_shape – The 2nds input shape as Tensor.
op_version – opset version
kwargs – additional parameter

source on GitHub

__init__(a_shape, b_shape, op_version=None, **kwargs)#

Parameters:

a_shape – The 1st input shape as Tensor.
b_shape – The 2nds input shape as Tensor.
op_version – opset version
kwargs – additional parameter

source on GitHub

mlprodict.onnx_conv.onnx_ops.onnx_gradient_op.OnnxFusedMatMul#: alias of OnnxFusedMatMul_1

class mlprodict.onnx_conv.onnx_ops.onnx_gradient_op.OnnxFusedMatMul_1(X, Y, transA=0, transB=0, op_version=None, **kwargs)#

Bases: OnnxOperator

MatMul and Gemm without a C.

source on GitHub

Parameters:

X – first matrix
Y – second matrix
transA – transpose first matrix
transB – transpose second matrix
op_version – opset version
kwargs – additional parameter

source on GitHub

__init__(X, Y, transA=0, transB=0, op_version=None, **kwargs)#

Parameters:

X – first matrix
Y – second matrix
transA – transpose first matrix
transB – transpose second matrix
op_version – opset version
kwargs – additional parameter

source on GitHub

mlprodict.onnx_conv.onnx_ops.onnx_gradient_op.OnnxSoftmaxGrad#: alias of OnnxSoftmaxGrad_13

class mlprodict.onnx_conv.onnx_ops.onnx_gradient_op.OnnxSoftmaxGrad_13(grad, prob, op_version=None, **kwargs)#

Bases: OnnxOperator

Gradient of Softmax. SoftmaxGrad computes $Y * ( dY - ReduceSum(Y * dY))$ . ONNX does not have a dot product, which can be simulated as a pointwise-multiplication (“Mul”), followed by a “ReduceSum”. Unfortunately, the treatment of “axis” is different in “SoftmaxGrad” and “ReduceSum”. If axis=k for SoftmaxGrad, we need to specify [k, …, n-1] as the axes of reduction for “ReduceSum”, after accounting for negative-axis specification. An alternative solution would be to Flatten inputs to 2D and then reshape output back to original shape. Hopefully, many of these ops can be optimized away in the common-case of statically-known shapes.

source on GitHub

Parameters:

grad – gradient
prob – probablities
op_version – opset version
kwargs – additional parameter

source on GitHub

__init__(grad, prob, op_version=None, **kwargs)#

Parameters:

grad – gradient
prob – probablities
op_version – opset version
kwargs – additional parameter

source on GitHub

mlprodict.onnx_conv.onnx_ops.onnx_gradient_op.OnnxYieldOp#: alias of OnnxYieldOp_1

class mlprodict.onnx_conv.onnx_ops.onnx_gradient_op.OnnxYieldOp_1(X, non_differentiable_outputs=None, full_shape_outputs=None, op_version=None, **kwargs)#

Bases: OnnxOperator

Defines a custom operator for YieldOp.

source on GitHub

Parameters:

X – array or OnnxOperatorMixin
non_differentiable_outputs – the indices of the module outputs that doesn’t have a gradient.
full_shape_outputs – the indices of the module outputs that must have full shape.
op_version – opset version
kwargs – additional parameter

source on GitHub

__init__(X, non_differentiable_outputs=None, full_shape_outputs=None, op_version=None, **kwargs)#

Parameters:

X – array or OnnxOperatorMixin
non_differentiable_outputs – the indices of the module outputs that doesn’t have a gradient.
full_shape_outputs – the indices of the module outputs that must have full shape.
op_version – opset version
kwargs – additional parameter

source on GitHub

module onnx_conv.onnx_ops.onnx_gradient_op#

Short summary#

Classes#

Properties#

Methods#

Documentation#

module `onnx_conv.onnx_ops.onnx_gradient_op`#