Classes¶
Summary¶
class |
class parent |
truncated documentation |
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Implements a square loss |
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Base class for optimizers. Implements common methods such __repr__. |
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Class handling the loss for class |
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Class handling ONNX function to manipulate OrtValue. Base class for |
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Class handling the penalty on the coefficients for class |
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Class handling the learning rate update after every iteration of a gradient. Two methods need to be overwritten … |
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Bases class with common functions to handle attributes in classes owning ONNX graphs. |
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List of events. |
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Raised when a learning algorithm failed to converge. |
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Raised when a learning algorithm failed to converge. |
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Options defining how to build the onnx graph of the gradients. |
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Implements a square loss |
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Implements a L1 or L2 regularization on weights. |
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Raised when an evaluation failed. |
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Implements the learning the same way as |
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Implements the learning the same way as |
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Implements a negative log loss ‘log(yt, yp) = -(1-yt)log(1-yp) - ytlog(yp), this only works for a binary classification … |
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No regularization. |
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Draws consecutive random observations from a dataset by batch. It iterates over the datasets by drawing batch_size … |
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Implements forward backward mechanism assuming the function to train is defined by an ONNX graph. |
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Ancestor for a class implementing forward and backward and dynamically created by |
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Implements a simple Stochastic Gradient Descent with onnxruntime-training. It leverages class @see … |
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Implements a simple Stochastic Gradient Descent with onnxruntime-training. |
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Raised when an input is not on the expected device (CPU, GPU). |
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Implements a square loss |
where Y is the output and Z the expected output. See 
where Y is the output and Z the expected …
where Y is the output and Z the expected output. See