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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'activation', 'init_seed', 'dataset_seed', 'init_scale'})
This happened while the csv dataset builder was generating data using
hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence/cifar_relu_b256_long_a10g/cifar_training_trace.csv (at revision 5eaad73ce8497f98189b4fff8d5d8b55bba871b3), ['hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_real_hf_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_relu_b256_long_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_relu_b4_long_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_silu_seeded_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_source_aligned_incremental_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_source_endpoint_long_a10g/cifar_training_trace.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
arch: string
optimizer: string
batch: int64
seed: int64
dataset_seed: int64
init_seed: int64
steps: int64
eta: double
beta: double
n_train: int64
sharpness_every: int64
sharpness_batches: int64
hessian_iters: int64
init_scale: double
activation: string
step: int64
loss: double
tail_loss: double
batch_sharpness: double
elapsed_s: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2591
to
{'arch': Value('string'), 'optimizer': Value('string'), 'batch': Value('int64'), 'seed': Value('int64'), 'steps': Value('int64'), 'eta': Value('float64'), 'beta': Value('float64'), 'n_train': Value('int64'), 'sharpness_every': Value('int64'), 'sharpness_batches': Value('int64'), 'hessian_iters': Value('int64'), 'step': Value('int64'), 'loss': Value('float64'), 'tail_loss': Value('float64'), 'batch_sharpness': Value('float64'), 'elapsed_s': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'activation', 'init_seed', 'dataset_seed', 'init_scale'})
This happened while the csv dataset builder was generating data using
hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence/cifar_relu_b256_long_a10g/cifar_training_trace.csv (at revision 5eaad73ce8497f98189b4fff8d5d8b55bba871b3), ['hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_real_hf_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_relu_b256_long_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_relu_b4_long_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_silu_seeded_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_source_aligned_incremental_a10g/cifar_training_trace.csv', 'hf://datasets/Srishti280992/repro-momentum-cifar10-real-evidence@5eaad73ce8497f98189b4fff8d5d8b55bba871b3/cifar_source_endpoint_long_a10g/cifar_training_trace.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
arch string | optimizer string | batch int64 | seed int64 | steps int64 | eta float64 | beta float64 | n_train int64 | sharpness_every int64 | sharpness_batches int64 | hessian_iters int64 | step int64 | loss float64 | tail_loss float64 | batch_sharpness float64 | elapsed_s float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mlp | sgdm | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.287802 | 2.287802 | 7.950032 | 0.599467 |
mlp | sgdm | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.911976 | 1.869683 | 29.991751 | 2.157815 |
mlp | sgdm | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.767475 | 1.8773 | 43.476223 | 3.767983 |
mlp | sgd | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.287802 | 2.287802 | 7.950032 | 0.20136 |
mlp | sgd | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.764738 | 1.829029 | 42.61293 | 1.647477 |
mlp | sgd | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.983781 | 1.846409 | 55.858284 | 3.130589 |
mlp | sgdn | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.287802 | 2.287802 | 7.88168 | 0.140598 |
mlp | sgdn | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.951566 | 1.880475 | 26.024153 | 1.614847 |
mlp | sgdn | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.826904 | 1.868671 | 65.573219 | 3.159105 |
mlp | sgdm | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.302637 | 2.302637 | 1.764935 | 0.209902 |
mlp | sgdm | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.466767 | 1.574543 | 31.533764 | 9.807936 |
mlp | sgdm | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.048708 | 1.29557 | 48.063316 | 19.257135 |
mlp | sgdn | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.302637 | 2.302637 | 1.77464 | 0.218005 |
mlp | sgdn | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.46813 | 1.57444 | 30.414589 | 9.611737 |
mlp | sgdn | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.053406 | 1.294589 | 50.346291 | 19.276741 |
mlp | sgdm | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.305123 | 2.305123 | 1.97845 | 0.397365 |
mlp | sgdm | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.466239 | 1.525224 | 22.819078 | 34.570677 |
mlp | sgdm | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.196367 | 1.174581 | 56.948971 | 69.551007 |
mlp | sgd | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.305123 | 2.305123 | 1.97845 | 0.346725 |
mlp | sgd | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.681422 | 1.722652 | 14.227516 | 35.196641 |
mlp | sgd | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.505379 | 1.498147 | 23.700413 | 70.501004 |
mlp | sgdn | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.305123 | 2.305123 | 1.942028 | 0.385205 |
mlp | sgdn | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.468194 | 1.524385 | 22.611752 | 34.149874 |
mlp | sgdn | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.198184 | 1.173867 | 57.394524 | 69.836637 |
cnn | sgdm | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.276296 | 2.276296 | 8.061407 | 0.303874 |
cnn | sgdm | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.565002 | 1.816156 | 63.897942 | 2.025683 |
cnn | sgdm | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.569349 | 1.668934 | 80.318497 | 3.724908 |
cnn | sgd | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.276296 | 2.276296 | 8.061404 | 0.157188 |
cnn | sgd | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.658436 | 1.907909 | 36.642666 | 1.82543 |
cnn | sgd | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.864659 | 1.753736 | 106.447716 | 3.589586 |
cnn | sgdn | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.276296 | 2.276296 | 8.05274 | 0.153899 |
cnn | sgdn | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.510211 | 1.828465 | 62.589802 | 1.89609 |
cnn | sgdn | 4 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.55218 | 1.670687 | 79.824593 | 3.658876 |
cnn | sgdm | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.310882 | 2.310882 | 2.992163 | 0.264538 |
cnn | sgdm | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.489121 | 1.524065 | 105.378365 | 9.708991 |
cnn | sgdm | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.148698 | 1.23595 | 96.653564 | 19.881616 |
cnn | sgdn | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.310882 | 2.310882 | 3.010429 | 0.224711 |
cnn | sgdn | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.501995 | 1.526994 | 100.093346 | 9.644178 |
cnn | sgdn | 64 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.136927 | 1.247719 | 93.41864 | 19.026325 |
cnn | sgdm | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.304807 | 2.304807 | 2.787522 | 0.441988 |
cnn | sgdm | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.345092 | 1.439516 | 148.530029 | 36.881465 |
cnn | sgdm | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.081047 | 1.114883 | 155.563599 | 71.995427 |
cnn | sgd | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.304807 | 2.304807 | 2.787522 | 0.363218 |
cnn | sgd | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.651731 | 1.704497 | 128.098251 | 35.322319 |
cnn | sgd | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.352519 | 1.413038 | 182.359161 | 70.809686 |
cnn | sgdn | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1 | 2.304807 | 2.304807 | 2.788348 | 0.438604 |
cnn | sgdn | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 600 | 1.351043 | 1.458046 | 121.472931 | 35.47572 |
cnn | sgdn | 256 | 0 | 1,200 | 0.01 | 0.5 | 8,192 | 600 | 1 | 4 | 1,200 | 1.088939 | 1.144075 | 115.419571 | 69.958058 |
mlp | sgdm | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 1 | 2.301992 | 2.301992 | 0.070959 | 0.767908 |
mlp | sgdm | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 3,000 | 0.473875 | 0.454396 | 78.842406 | 170.260319 |
mlp | sgdm | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 6,000 | 0.016975 | 0.019959 | 45.937616 | 341.151713 |
mlp | sgdm | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 9,000 | 0.006645 | 0.006561 | 17.574509 | 509.659902 |
mlp | sgdm | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 12,000 | 0.0032 | 0.003621 | 7.706237 | 678.530289 |
mlp | sgd | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 1 | 2.301992 | 2.301992 | 0.070959 | 0.61342 |
mlp | sgd | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 3,000 | 1.277377 | 1.280656 | 11.698023 | 173.493915 |
mlp | sgd | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 6,000 | 0.325565 | 0.408774 | 55.659024 | 346.306958 |
mlp | sgd | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 9,000 | 0.058997 | 0.060288 | 73.821364 | 521.335366 |
mlp | sgd | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 3,000 | 8 | 1 | 12,000 | 0.017051 | 0.019255 | 28.827293 | 696.339602 |
mlp | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 1 | 2.30727 | 2.30727 | 0.274124 | 0.554506 |
mlp | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 12,000 | 3.822967 | 1.38353 | 36.363084 | 31.635456 |
mlp | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 24,000 | 1.035077 | 1.047735 | 124.877458 | 64.000466 |
mlp | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 36,000 | 0.052703 | 0.778476 | 159.900398 | 96.078648 |
mlp | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 48,000 | 0.703485 | 0.728492 | 123.921255 | 128.108397 |
mlp | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 1 | 2.30727 | 2.30727 | 0.274124 | 0.212285 |
mlp | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 12,000 | 3.611848 | 1.328202 | 104.566055 | 31.258343 |
mlp | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 24,000 | 0.798075 | 0.826674 | 128.988354 | 61.646353 |
mlp | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 36,000 | 0.04066 | 0.291473 | 248.109404 | 92.360497 |
mlp | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 48,000 | 0.036577 | 0.169883 | 349.562912 | 123.646617 |
cnn | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 1 | 2.302533 | 2.302533 | 0.10482 | 0.38534 |
cnn | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 12,000 | 1.506763 | 1.024484 | 97.029202 | 40.637941 |
cnn | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 24,000 | 0.005443 | 0.174406 | 189.251941 | 80.798128 |
cnn | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 36,000 | 0.4573 | 0.213409 | 222.026621 | 121.300012 |
cnn | sgdm | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 48,000 | 0 | 0.049368 | 10.257809 | 161.7274 |
cnn | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 1 | 2.302533 | 2.302533 | 0.10482 | 0.258909 |
cnn | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 12,000 | 1.596571 | 1.317375 | 46.101499 | 38.804445 |
cnn | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 24,000 | 0.081358 | 0.221403 | 245.776946 | 77.548339 |
cnn | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 36,000 | 0.001835 | 0.110811 | 293.001078 | 116.272452 |
cnn | sgd | 4 | 0 | 48,000 | 0.01 | 0.5 | 8,192 | 12,000 | 8 | 1 | 48,000 | 0.002205 | 0.01996 | 38.048957 | 155.085521 |
mlp | sgdm | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 1 | 2.302011 | 2.302011 | 0.078933 | 0.505621 |
mlp | sgdm | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 4,000 | 1.121063 | 1.63417 | 15.473057 | 10.761952 |
mlp | sgdm | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 8,000 | 1.175075 | 1.473832 | 26.794599 | 21.003648 |
mlp | sgdm | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 12,000 | 0.725914 | 1.207526 | 46.526913 | 31.249772 |
mlp | sgd | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 1 | 2.302011 | 2.302011 | 0.078933 | 0.206514 |
mlp | sgd | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 4,000 | 1.23749 | 1.664724 | 14.810814 | 10.215568 |
mlp | sgd | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 8,000 | 1.360279 | 1.502766 | 25.498218 | 20.266818 |
mlp | sgd | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 12,000 | 1.024258 | 1.272502 | 33.451621 | 30.248631 |
mlp | sgdn | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 1 | 2.302011 | 2.302011 | 0.075781 | 0.216676 |
mlp | sgdn | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 4,000 | 1.134265 | 1.631749 | 15.909806 | 10.600686 |
mlp | sgdn | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 8,000 | 1.195128 | 1.47443 | 27.491019 | 21.044863 |
mlp | sgdn | 4 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 12,000 | 0.671844 | 1.210677 | 46.102801 | 31.507868 |
mlp | sgdm | 64 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 1 | 2.301212 | 2.301212 | 0.035828 | 0.354839 |
mlp | sgdm | 64 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 4,000 | 0.793759 | 0.925322 | 66.066306 | 64.442489 |
mlp | sgdm | 64 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 8,000 | 0.185969 | 0.121838 | 219.717706 | 127.745445 |
mlp | sgdm | 64 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 12,000 | 0.003652 | 0.004824 | 20.882234 | 191.144546 |
mlp | sgdn | 64 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 1 | 2.301212 | 2.301212 | 0.034104 | 0.287214 |
mlp | sgdn | 64 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 4,000 | 0.822168 | 0.931457 | 67.683625 | 63.713008 |
mlp | sgdn | 64 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 8,000 | 0.063845 | 0.111323 | 102.442347 | 126.865192 |
mlp | sgdn | 64 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 12,000 | 0.003787 | 0.005088 | 21.503445 | 189.62393 |
mlp | sgdm | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 1 | 2.301132 | 2.301132 | 0.025698 | 0.5392 |
mlp | sgdm | 256 | 0 | 12,000 | 0.01 | 0.5 | 8,192 | 4,000 | 8 | 1 | 4,000 | 0.578563 | 0.603346 | 103.976654 | 227.146865 |
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