module `utils.orttraining_helper`#

Short summary#

module onnxcustom.utils.orttraining_helper

ONNX manipulations to help build ONNX gradient graphs.

Functions#

function	truncated documentation
`_loss_elastic`	Implements mixture of losses l1 and l2.
`_loss_l1`	Implements loss l1.
`_loss_l2`	Implements loss l2.
`_loss_log`	This only works for a binary classification. The log loss is ‘log(yt, yp) = (1-yt)log(1-yp) - ytlog(yp), this …
`_rewrite_op_no_grad`	Rewrites operators with no gradient.
`_unique_name`	Returns a name different from any name in existing_names.
`add_loss_output`	Modifies an ONNX graph to add operators to score and allow training.
`get_train_initializer`	Returns the list of initializers to train.
`penalty_loss_onnx`	Returns onnx nodes to compute $\|w\| \alpha + w^2 \beta$ where $\alpha=l1$ and $\beta=l2$ .

Documentation#

ONNX manipulations to help build ONNX gradient graphs.

source on GitHub

onnxcustom.utils.orttraining_helper._loss_elastic(existing_names, elem, shape, output_name, label_name, weight_name, loss_name, l1_weight=0.5, l2_weight=0.5)#

Implements mixture of losses l1 and l2.

source on GitHub

onnxcustom.utils.orttraining_helper._loss_l1(existing_names, elem, shape, output_name, label_name, weight_name, loss_name)#

Implements loss l1.

source on GitHub

onnxcustom.utils.orttraining_helper._loss_l2(existing_names, elem, shape, output_name, label_name, weight_name, loss_name)#

Implements loss l2.

source on GitHub

onnxcustom.utils.orttraining_helper._loss_log(existing_names, elem, shape, output_name, label_name, weight_name, loss_name, eps=1e-06)#

This only works for a binary classification. The log loss is ‘log(yt, yp) = (1-yt)log(1-yp) - ytlog(yp), this only works for a binary classification where yp is the predicted probability, yt is the expected probability. yt is expected to be binary, yp is a matrix with two columns, the sum on every line is 1. Parameter eps is used to avoid computing log(0).

source on GitHub

onnxcustom.utils.orttraining_helper._rewrite_op_no_grad(onx)#

Rewrites operators with no gradient.

source on GitHub

onnxcustom.utils.orttraining_helper._unique_name(existing_names, name)#

Returns a name different from any name in existing_names.

Parameters:

existing_names – set of names
name – current

Returns:

unique name

source on GitHub

onnxcustom.utils.orttraining_helper.add_loss_output(onx, score_name='squared_error', loss_name='loss', label_name='label', weight_name=None, penalty=None, output_index=None, **kwargs)#

Modifies an ONNX graph to add operators to score and allow training.

Parameters:

onx – onx graph
score_name – name of the score
loss_name – name of the output loss
label_name – name of the label input
weight_name – None or any value to consider weight while computing loss
penalty – dictionary similar to the following one { weight_name: {‘l1’: alpha, ‘l2’: beta} } or { weight_name: beta}, it adds a L1 and/or L2 penalty to one input or initializer, penalty = $|w| \alpha + w^2 \beta$
output_index – the output used to compute the loss, if None, the function assumes there is only one output, it must be specified if there are more than 1, it can be an integer or a string (output name)
kwargs – additional arguments for losses (see below)

Returns:

modified graph

Possible values for score_name:

‘squared_error’ or ‘l2’: $\sum_i{(f(x_i)-y_i)^2}$ or $\sum_i{w_i (f(x_i)-y_i)^2}$ if weight_name is not None
‘absolute_error’ or ‘l1’: $\sum_i{|f(x_i)-y_i|}$ or $\sum_i{w_i |f(x_i)-y_i|}$ if weight_name is not None
‘elastic’: mixture of losses, kwargs must define l1_weight and l2_weight, undefined, default value are 0.5
‘log’: log loss $(1-yt)\log(1-yp) - yt\log(yp)$ ,
this only works for a binary classification where yp is the predicted probability, yt is the expected probability. yt is expected to be binary, yp is a matrix with two columns, the sum on every line is 1.

See example Train a scikit-learn neural network with onnxruntime-training on GPU. Next example shows the loss with L1 and L2 loss.

Next example shows how to add a L2 loss with L1 and L2 penalties on the coefficients.

source on GitHub

onnxcustom.utils.orttraining_helper.get_train_initializer(onx)#

Returns the list of initializers to train.

Returns:: dictionary {name: (value, tensor)}

The function walk through the list of initializers and returns all tensors with elements from types float or double.

source on GitHub

onnxcustom.utils.orttraining_helper.penalty_loss_onnx(name, dtype, l1=None, l2=None, existing_names=None)#

Returns onnx nodes to compute $|w| \alpha + w^2 \beta$ where $\alpha=l1$ and $\beta=l2$ .

Parameters:

name – name of weights
dtype – numpy dtype
l1 – coefficient for L1 norm
l2 – coefficient for L2 norm
existing_names – names already taken in the ONNX graph

Returns:

initializer, nodes

source on GitHub

module utils.orttraining_helper#

Short summary#

Functions#

Documentation#

module `utils.orttraining_helper`#