rich7420 commented on PR #1088:
URL: https://github.com/apache/mahout/pull/1088#issuecomment-3950519931
results in my local environment
our throuphput is better
```
uv run python
benchmark/encoding_benchmarks/pennylane_baseline/iris_amplitude.py --data-file
benchmark/encoding_benchmarks/pennylane_baseline/data/iris_classes1and2_scaled.txt
--optimizer nesterov --lr 0.01 --layers 6 --trials 3 --iters 80 --early-stop 0
2>&1
Iris amplitude baseline (PennyLane) — 2-class variational classifier
Data: official file (2 features):
benchmark/encoding_benchmarks/pennylane_baseline/data/iris_classes1and2_scaled.txt
→ L2 norm → get_angles (n=100; 2-class Iris = 100 samples)
Iters: 80, batch_size: 5, layers: 6, lr: 0.01, optimizer: nesterov
Trial 1:
Compile: 0.0121 s
Train: 1.4786 s
Train acc: 1.0000 (n=75)
Test acc: 1.0000 (n=25)
Throughput: 270.5 samples/s
Trial 2:
Compile: 0.0101 s
Train: 1.5911 s
Train acc: 1.0000 (n=75)
Test acc: 1.0000 (n=25)
Throughput: 251.4 samples/s
Trial 3:
Compile: 0.0102 s
Train: 1.8057 s
Train acc: 1.0000 (n=75)
Test acc: 1.0000 (n=25)
Throughput: 221.5 samples/s
Best test accuracy: 1.0000 (median: 1.0000, min: 1.0000, max: 1.0000)
→ Target ≥0.9 achieved.
uv run python benchmark/encoding_benchmarks/qdp_pipeline/iris_amplitude.py
--data-file
benchmark/encoding_benchmarks/pennylane_baseline/data/iris_classes1and2_scaled.txt
--optimizer nesterov --lr 0.01 --layers 6 --trials 3 --iters 80 --early-stop 0
2>&1
Iris amplitude (QDP encoding) — 2-class variational classifier
Data: official file (2 features):
benchmark/encoding_benchmarks/pennylane_baseline/data/iris_classes1and2_scaled.txt
→ QDP amplitude (n=100; 2-class Iris = 100 samples)
Iters: 80, batch_size: 5, layers: 6, lr: 0.01, optimizer: nesterov
Trial 1:
QML device: cpu
Compile: 0.0117 s
Train: 1.2235 s
Train acc: 0.9867 (n=75)
Test acc: 1.0000 (n=25)
Throughput: 326.9 samples/s
Trial 2:
QML device: cpu
Compile: 0.0081 s
Train: 1.2546 s
Train acc: 1.0000 (n=75)
Test acc: 1.0000 (n=25)
Throughput: 318.8 samples/s
Trial 3:
QML device: cpu
Compile: 0.0081 s
Train: 1.3195 s
Train acc: 0.9867 (n=75)
Test acc: 1.0000 (n=25)
Throughput: 303.1 samples/s
Best test accuracy: 1.0000 (median: 1.0000, min: 1.0000, max: 1.0000)
→ Target ≥0.9 achieved.
```
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
