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API Reference

  • Modules Overview
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  • Benchmark
    • Compilation benchmark
  • Dataset
    • Dataset balancing
    • Cached dataset
    • Quantum circuit dataset
    • Config dataset
    • Dataset helper functions
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  • Inference
    • Evaluation metrics
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    • Transformers
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      • Transformers
  • Pipeline
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    • Metrics
    • Multimodal Diffusion Pipeline
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  • Platform
    • Circuits dataset generation functions
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    • Simulation backend
    • Backends
      • Base backend
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      • Pennylane circuits backend
      • Qiskit circuits backend
    • Tokenizer
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  • Scheduler
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    • DPM Scheduler
  • Utils
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    • Config loader
    • Math and algorithms
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On this page

  • Metric
  • Mean
  • Accuracy
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  1. Pipeline
  2. Metrics

Metrics

Definition of metrics used during training.

source

Metric

 Metric (name:str, device)

Base metric class.


source

Mean

 Mean (name:str, device)

Mean metric, used for loss.


source

Accuracy

 Accuracy (name:str, device)

Accuracy metric.

Example usage:

a = Accuracy("mean", "cpu")
print(a, a.empty)

a.update_state(torch.Tensor([3,2,2,1]), torch.Tensor([1,2,2,1]))
print(a, a.empty)

a.update_state(torch.Tensor([1,2,2,3]), torch.Tensor([1,2,2,3]))
print(a, a.empty)

a.reset_state()
print(a, a.empty)
mean=nan True
mean=0.75 False
mean=0.875 False
mean=nan True
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Diffusion Pipeline Special
Multimodal Diffusion Pipeline
 

Copyright 2025, Florian Fürrutter

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