---
base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:645861
- loss:ContrastiveLoss
widget:
- source_sentence: There was an Eye OS alert.
sentences:
- i see lots of tubes
- On the door is lima mike zero twenty three north exit
- EyeOS, that’s some kind of tech, right
- source_sentence: how to use
sentences:
- how do i use it
- This fallen panel might lead to the control room.
- The rings appear to be completely unmoving now.
- source_sentence: I'm unsure about this room's name how do I find out?
sentences:
- How do I identify the room I'm in without any obvious signs?
- The door shows l m zero twenty three north exit
- it reads Cryochamber Medical Support Systems
- source_sentence: i see Cryochamber Atmospheric Sealing
sentences:
- Can you guide me on how to identify this room?
- it's Laboratory Chemical Storage
- it reads Cryochamber Atmospheric Sealing
- source_sentence: floating up
sentences:
- All indicators are blue.
- i can see an interface
- Found a narrow corridor leading somewhere.
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: sts dev
type: sts-dev
metrics:
- type: cosine_accuracy
value: 0.9002097965885251
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.4494956135749817
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8908462575859745
name: Cosine F1
- type: cosine_f1_threshold
value: 0.41577932238578796
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8739044154126013
name: Cosine Precision
- type: cosine_recall
value: 0.908457968024755
name: Cosine Recall
- type: cosine_ap
value: 0.9618224590785398
name: Cosine Ap
- type: dot_accuracy
value: 0.9002097965885251
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.4494956135749817
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.8908462575859745
name: Dot F1
- type: dot_f1_threshold
value: 0.4157792925834656
name: Dot F1 Threshold
- type: dot_precision
value: 0.8739044154126013
name: Dot Precision
- type: dot_recall
value: 0.908457968024755
name: Dot Recall
- type: dot_ap
value: 0.961822458350164
name: Dot Ap
- type: manhattan_accuracy
value: 0.8989979280958028
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 22.644113540649414
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.8901100449479366
name: Manhattan F1
- type: manhattan_f1_threshold
value: 23.330610275268555
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.8757104438714686
name: Manhattan Precision
- type: manhattan_recall
value: 0.9049911179875079
name: Manhattan Recall
- type: manhattan_ap
value: 0.9615309074220045
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9002097965885251
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 1.0492897033691406
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.8908462575859745
name: Euclidean F1
- type: euclidean_f1_threshold
value: 1.080944538116455
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.8739044154126013
name: Euclidean Precision
- type: euclidean_recall
value: 0.908457968024755
name: Euclidean Recall
- type: euclidean_ap
value: 0.9618224553002042
name: Euclidean Ap
- type: max_accuracy
value: 0.9002097965885251
name: Max Accuracy
- type: max_accuracy_threshold
value: 22.644113540649414
name: Max Accuracy Threshold
- type: max_f1
value: 0.8908462575859745
name: Max F1
- type: max_f1_threshold
value: 23.330610275268555
name: Max F1 Threshold
- type: max_precision
value: 0.8757104438714686
name: Max Precision
- type: max_recall
value: 0.908457968024755
name: Max Recall
- type: max_ap
value: 0.9618224590785398
name: Max Ap
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("IconicAI/all-mpnet-base-v2-anteater")
# Run inference
sentences = [
'floating up',
'i can see an interface',
'All indicators are blue.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `sts-dev`
* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9002 |
| cosine_accuracy_threshold | 0.4495 |
| cosine_f1 | 0.8908 |
| cosine_f1_threshold | 0.4158 |
| cosine_precision | 0.8739 |
| cosine_recall | 0.9085 |
| cosine_ap | 0.9618 |
| dot_accuracy | 0.9002 |
| dot_accuracy_threshold | 0.4495 |
| dot_f1 | 0.8908 |
| dot_f1_threshold | 0.4158 |
| dot_precision | 0.8739 |
| dot_recall | 0.9085 |
| dot_ap | 0.9618 |
| manhattan_accuracy | 0.899 |
| manhattan_accuracy_threshold | 22.6441 |
| manhattan_f1 | 0.8901 |
| manhattan_f1_threshold | 23.3306 |
| manhattan_precision | 0.8757 |
| manhattan_recall | 0.905 |
| manhattan_ap | 0.9615 |
| euclidean_accuracy | 0.9002 |
| euclidean_accuracy_threshold | 1.0493 |
| euclidean_f1 | 0.8908 |
| euclidean_f1_threshold | 1.0809 |
| euclidean_precision | 0.8739 |
| euclidean_recall | 0.9085 |
| euclidean_ap | 0.9618 |
| max_accuracy | 0.9002 |
| max_accuracy_threshold | 22.6441 |
| max_f1 | 0.8908 |
| max_f1_threshold | 23.3306 |
| max_precision | 0.8757 |
| max_recall | 0.9085 |
| **max_ap** | **0.9618** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 645,861 training samples
* Columns: example1, example2, and label
* Approximate statistics based on the first 1000 samples:
| | example1 | example2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | - min: 3 tokens
- mean: 9.02 tokens
- max: 25 tokens
| - min: 3 tokens
- mean: 9.19 tokens
- max: 23 tokens
| |
* Samples:
| example1 | example2 | label |
|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|:---------------|
| Drones are present all around here. | What are those drones doing buzzing around here? | 1 |
| am i the only one | am i the only one alive on this ship | 1 |
| I’m in a room with a door in front of me and a terminal on the wall | mechanics room | 1 |
* Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 1.0,
"size_average": true
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 76,741 evaluation samples
* Columns: example1, example2, and label
* Approximate statistics based on the first 1000 samples:
| | example1 | example2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | - min: 3 tokens
- mean: 9.25 tokens
- max: 21 tokens
| - min: 3 tokens
- mean: 9.15 tokens
- max: 19 tokens
| |
* Samples:
| example1 | example2 | label |
|:----------------------------------------------|:----------------------------------------------------------|:---------------|
| Not much, how about you? | Nothing, you? | 1 |
| Rings stopped moving. | I notice the rings are not spinning anymore. | 1 |
| it's Laboratory Chemical Storage | the switch is Laboratory Chemical Storage | 1 |
* Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 1.0,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 1e-07
- `weight_decay`: 0.01
- `max_grad_norm`: 0.02
- `num_train_epochs`: 5
- `warmup_steps`: 100
- `bf16`: True
- `eval_on_start`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-07
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 0.02
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 100
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: True
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | loss | sts-dev_max_ap |
|:------:|:-----:|:-------------:|:------:|:--------------:|
| 0 | 0 | - | 0.0764 | 0.9175 |
| 0.0040 | 10 | 0.0772 | - | - |
| 0.0079 | 20 | 0.0783 | - | - |
| 0.0119 | 30 | 0.0775 | - | - |
| 0.0159 | 40 | 0.0756 | - | - |
| 0.0198 | 50 | 0.075 | - | - |
| 0.0238 | 60 | 0.0777 | - | - |
| 0.0277 | 70 | 0.0784 | - | - |
| 0.0317 | 80 | 0.0721 | - | - |
| 0.0357 | 90 | 0.0755 | - | - |
| 0.0396 | 100 | 0.0778 | - | - |
| 0.0436 | 110 | 0.0735 | - | - |
| 0.0476 | 120 | 0.0753 | - | - |
| 0.0515 | 130 | 0.0741 | - | - |
| 0.0555 | 140 | 0.0791 | - | - |
| 0.0595 | 150 | 0.0753 | - | - |
| 0.0634 | 160 | 0.0748 | - | - |
| 0.0674 | 170 | 0.0709 | - | - |
| 0.0713 | 180 | 0.0738 | - | - |
| 0.0753 | 190 | 0.0759 | - | - |
| 0.0793 | 200 | 0.0703 | - | - |
| 0.0832 | 210 | 0.0724 | - | - |
| 0.0872 | 220 | 0.0726 | - | - |
| 0.0912 | 230 | 0.0734 | - | - |
| 0.0951 | 240 | 0.0718 | - | - |
| 0.0991 | 250 | 0.0776 | - | - |
| 0.1031 | 260 | 0.0757 | - | - |
| 0.1070 | 270 | 0.0722 | - | - |
| 0.1110 | 280 | 0.0746 | - | - |
| 0.1149 | 290 | 0.0718 | - | - |
| 0.1189 | 300 | 0.0733 | - | - |
| 0.1229 | 310 | 0.0725 | - | - |
| 0.1268 | 320 | 0.0724 | - | - |
| 0.1308 | 330 | 0.0681 | - | - |
| 0.1348 | 340 | 0.0735 | - | - |
| 0.1387 | 350 | 0.0716 | - | - |
| 0.1427 | 360 | 0.0698 | - | - |
| 0.1467 | 370 | 0.072 | - | - |
| 0.1506 | 380 | 0.071 | - | - |
| 0.1546 | 390 | 0.0713 | - | - |
| 0.1585 | 400 | 0.073 | - | - |
| 0.1625 | 410 | 0.077 | - | - |
| 0.1665 | 420 | 0.072 | - | - |
| 0.1704 | 430 | 0.0689 | - | - |
| 0.1744 | 440 | 0.0708 | - | - |
| 0.1784 | 450 | 0.0687 | - | - |
| 0.1823 | 460 | 0.0692 | - | - |
| 0.1863 | 470 | 0.0715 | - | - |
| 0.1902 | 480 | 0.0707 | - | - |
| 0.1942 | 490 | 0.0671 | - | - |
| 0.1982 | 500 | 0.0741 | 0.0703 | 0.9245 |
| 0.2021 | 510 | 0.0681 | - | - |
| 0.2061 | 520 | 0.0749 | - | - |
| 0.2101 | 530 | 0.0718 | - | - |
| 0.2140 | 540 | 0.0689 | - | - |
| 0.2180 | 550 | 0.0733 | - | - |
| 0.2220 | 560 | 0.067 | - | - |
| 0.2259 | 570 | 0.0685 | - | - |
| 0.2299 | 580 | 0.07 | - | - |
| 0.2338 | 590 | 0.0683 | - | - |
| 0.2378 | 600 | 0.0693 | - | - |
| 0.2418 | 610 | 0.0705 | - | - |
| 0.2457 | 620 | 0.0707 | - | - |
| 0.2497 | 630 | 0.0703 | - | - |
| 0.2537 | 640 | 0.068 | - | - |
| 0.2576 | 650 | 0.0682 | - | - |
| 0.2616 | 660 | 0.0654 | - | - |
| 0.2656 | 670 | 0.0682 | - | - |
| 0.2695 | 680 | 0.0698 | - | - |
| 0.2735 | 690 | 0.0701 | - | - |
| 0.2774 | 700 | 0.0674 | - | - |
| 0.2814 | 710 | 0.0669 | - | - |
| 0.2854 | 720 | 0.0677 | - | - |
| 0.2893 | 730 | 0.0674 | - | - |
| 0.2933 | 740 | 0.0682 | - | - |
| 0.2973 | 750 | 0.0677 | - | - |
| 0.3012 | 760 | 0.0661 | - | - |
| 0.3052 | 770 | 0.0634 | - | - |
| 0.3092 | 780 | 0.0658 | - | - |
| 0.3131 | 790 | 0.0687 | - | - |
| 0.3171 | 800 | 0.069 | - | - |
| 0.3210 | 810 | 0.0665 | - | - |
| 0.3250 | 820 | 0.0648 | - | - |
| 0.3290 | 830 | 0.0656 | - | - |
| 0.3329 | 840 | 0.0672 | - | - |
| 0.3369 | 850 | 0.0663 | - | - |
| 0.3409 | 860 | 0.0666 | - | - |
| 0.3448 | 870 | 0.0644 | - | - |
| 0.3488 | 880 | 0.065 | - | - |
| 0.3528 | 890 | 0.0666 | - | - |
| 0.3567 | 900 | 0.0657 | - | - |
| 0.3607 | 910 | 0.0636 | - | - |
| 0.3646 | 920 | 0.0681 | - | - |
| 0.3686 | 930 | 0.0671 | - | - |
| 0.3726 | 940 | 0.0653 | - | - |
| 0.3765 | 950 | 0.0643 | - | - |
| 0.3805 | 960 | 0.0637 | - | - |
| 0.3845 | 970 | 0.066 | - | - |
| 0.3884 | 980 | 0.0645 | - | - |
| 0.3924 | 990 | 0.0628 | - | - |
| 0.3964 | 1000 | 0.0627 | 0.0653 | 0.9325 |
| 0.4003 | 1010 | 0.0647 | - | - |
| 0.4043 | 1020 | 0.0649 | - | - |
| 0.4082 | 1030 | 0.0637 | - | - |
| 0.4122 | 1040 | 0.0648 | - | - |
| 0.4162 | 1050 | 0.0647 | - | - |
| 0.4201 | 1060 | 0.0646 | - | - |
| 0.4241 | 1070 | 0.0659 | - | - |
| 0.4281 | 1080 | 0.0641 | - | - |
| 0.4320 | 1090 | 0.0609 | - | - |
| 0.4360 | 1100 | 0.0642 | - | - |
| 0.4400 | 1110 | 0.0614 | - | - |
| 0.4439 | 1120 | 0.0603 | - | - |
| 0.4479 | 1130 | 0.0613 | - | - |
| 0.4518 | 1140 | 0.0646 | - | - |
| 0.4558 | 1150 | 0.0619 | - | - |
| 0.4598 | 1160 | 0.0611 | - | - |
| 0.4637 | 1170 | 0.0638 | - | - |
| 0.4677 | 1180 | 0.0636 | - | - |
| 0.4717 | 1190 | 0.0647 | - | - |
| 0.4756 | 1200 | 0.0622 | - | - |
| 0.4796 | 1210 | 0.0642 | - | - |
| 0.4836 | 1220 | 0.0607 | - | - |
| 0.4875 | 1230 | 0.0623 | - | - |
| 0.4915 | 1240 | 0.0614 | - | - |
| 0.4954 | 1250 | 0.0643 | - | - |
| 0.4994 | 1260 | 0.0614 | - | - |
| 0.5034 | 1270 | 0.0599 | - | - |
| 0.5073 | 1280 | 0.0615 | - | - |
| 0.5113 | 1290 | 0.0595 | - | - |
| 0.5153 | 1300 | 0.061 | - | - |
| 0.5192 | 1310 | 0.0623 | - | - |
| 0.5232 | 1320 | 0.0646 | - | - |
| 0.5272 | 1330 | 0.0621 | - | - |
| 0.5311 | 1340 | 0.0606 | - | - |
| 0.5351 | 1350 | 0.0597 | - | - |
| 0.5390 | 1360 | 0.0621 | - | - |
| 0.5430 | 1370 | 0.0586 | - | - |
| 0.5470 | 1380 | 0.0618 | - | - |
| 0.5509 | 1390 | 0.0601 | - | - |
| 0.5549 | 1400 | 0.0578 | - | - |
| 0.5589 | 1410 | 0.0628 | - | - |
| 0.5628 | 1420 | 0.0595 | - | - |
| 0.5668 | 1430 | 0.0576 | - | - |
| 0.5707 | 1440 | 0.0606 | - | - |
| 0.5747 | 1450 | 0.0618 | - | - |
| 0.5787 | 1460 | 0.0591 | - | - |
| 0.5826 | 1470 | 0.0598 | - | - |
| 0.5866 | 1480 | 0.0611 | - | - |
| 0.5906 | 1490 | 0.0594 | - | - |
| 0.5945 | 1500 | 0.0616 | 0.0619 | 0.9393 |
| 0.5985 | 1510 | 0.0592 | - | - |
| 0.6025 | 1520 | 0.0597 | - | - |
| 0.6064 | 1530 | 0.0619 | - | - |
| 0.6104 | 1540 | 0.0595 | - | - |
| 0.6143 | 1550 | 0.0598 | - | - |
| 0.6183 | 1560 | 0.0609 | - | - |
| 0.6223 | 1570 | 0.059 | - | - |
| 0.6262 | 1580 | 0.0601 | - | - |
| 0.6302 | 1590 | 0.0595 | - | - |
| 0.6342 | 1600 | 0.059 | - | - |
| 0.6381 | 1610 | 0.0606 | - | - |
| 0.6421 | 1620 | 0.0591 | - | - |
| 0.6461 | 1630 | 0.0617 | - | - |
| 0.6500 | 1640 | 0.0592 | - | - |
| 0.6540 | 1650 | 0.0588 | - | - |
| 0.6579 | 1660 | 0.0587 | - | - |
| 0.6619 | 1670 | 0.0585 | - | - |
| 0.6659 | 1680 | 0.0558 | - | - |
| 0.6698 | 1690 | 0.057 | - | - |
| 0.6738 | 1700 | 0.0598 | - | - |
| 0.6778 | 1710 | 0.0567 | - | - |
| 0.6817 | 1720 | 0.0555 | - | - |
| 0.6857 | 1730 | 0.0604 | - | - |
| 0.6897 | 1740 | 0.0558 | - | - |
| 0.6936 | 1750 | 0.0572 | - | - |
| 0.6976 | 1760 | 0.0577 | - | - |
| 0.7015 | 1770 | 0.0587 | - | - |
| 0.7055 | 1780 | 0.0589 | - | - |
| 0.7095 | 1790 | 0.0598 | - | - |
| 0.7134 | 1800 | 0.0583 | - | - |
| 0.7174 | 1810 | 0.058 | - | - |
| 0.7214 | 1820 | 0.0564 | - | - |
| 0.7253 | 1830 | 0.0589 | - | - |
| 0.7293 | 1840 | 0.0557 | - | - |
| 0.7333 | 1850 | 0.0586 | - | - |
| 0.7372 | 1860 | 0.0601 | - | - |
| 0.7412 | 1870 | 0.0556 | - | - |
| 0.7451 | 1880 | 0.0572 | - | - |
| 0.7491 | 1890 | 0.0574 | - | - |
| 0.7531 | 1900 | 0.0583 | - | - |
| 0.7570 | 1910 | 0.0573 | - | - |
| 0.7610 | 1920 | 0.0555 | - | - |
| 0.7650 | 1930 | 0.0561 | - | - |
| 0.7689 | 1940 | 0.0579 | - | - |
| 0.7729 | 1950 | 0.0557 | - | - |
| 0.7769 | 1960 | 0.0558 | - | - |
| 0.7808 | 1970 | 0.0589 | - | - |
| 0.7848 | 1980 | 0.0572 | - | - |
| 0.7887 | 1990 | 0.0572 | - | - |
| 0.7927 | 2000 | 0.0549 | 0.0592 | 0.9444 |
| 0.7967 | 2010 | 0.0548 | - | - |
| 0.8006 | 2020 | 0.0569 | - | - |
| 0.8046 | 2030 | 0.058 | - | - |
| 0.8086 | 2040 | 0.0581 | - | - |
| 0.8125 | 2050 | 0.0585 | - | - |
| 0.8165 | 2060 | 0.0542 | - | - |
| 0.8205 | 2070 | 0.0558 | - | - |
| 0.8244 | 2080 | 0.0569 | - | - |
| 0.8284 | 2090 | 0.0564 | - | - |
| 0.8323 | 2100 | 0.0552 | - | - |
| 0.8363 | 2110 | 0.0559 | - | - |
| 0.8403 | 2120 | 0.0534 | - | - |
| 0.8442 | 2130 | 0.0543 | - | - |
| 0.8482 | 2140 | 0.0573 | - | - |
| 0.8522 | 2150 | 0.0546 | - | - |
| 0.8561 | 2160 | 0.0554 | - | - |
| 0.8601 | 2170 | 0.0568 | - | - |
| 0.8641 | 2180 | 0.0544 | - | - |
| 0.8680 | 2190 | 0.0547 | - | - |
| 0.8720 | 2200 | 0.0549 | - | - |
| 0.8759 | 2210 | 0.0544 | - | - |
| 0.8799 | 2220 | 0.058 | - | - |
| 0.8839 | 2230 | 0.0557 | - | - |
| 0.8878 | 2240 | 0.0551 | - | - |
| 0.8918 | 2250 | 0.0558 | - | - |
| 0.8958 | 2260 | 0.0554 | - | - |
| 0.8997 | 2270 | 0.053 | - | - |
| 0.9037 | 2280 | 0.0552 | - | - |
| 0.9076 | 2290 | 0.0549 | - | - |
| 0.9116 | 2300 | 0.0533 | - | - |
| 0.9156 | 2310 | 0.0543 | - | - |
| 0.9195 | 2320 | 0.0531 | - | - |
| 0.9235 | 2330 | 0.0553 | - | - |
| 0.9275 | 2340 | 0.0542 | - | - |
| 0.9314 | 2350 | 0.0537 | - | - |
| 0.9354 | 2360 | 0.0536 | - | - |
| 0.9394 | 2370 | 0.055 | - | - |
| 0.9433 | 2380 | 0.0551 | - | - |
| 0.9473 | 2390 | 0.0532 | - | - |
| 0.9512 | 2400 | 0.0556 | - | - |
| 0.9552 | 2410 | 0.0548 | - | - |
| 0.9592 | 2420 | 0.0533 | - | - |
| 0.9631 | 2430 | 0.0536 | - | - |
| 0.9671 | 2440 | 0.0549 | - | - |
| 0.9711 | 2450 | 0.0548 | - | - |
| 0.9750 | 2460 | 0.0557 | - | - |
| 0.9790 | 2470 | 0.055 | - | - |
| 0.9830 | 2480 | 0.0535 | - | - |
| 0.9869 | 2490 | 0.0564 | - | - |
| 0.9909 | 2500 | 0.0526 | 0.0572 | 0.9482 |
| 0.9948 | 2510 | 0.0547 | - | - |
| 0.9988 | 2520 | 0.054 | - | - |
| 1.0028 | 2530 | 0.0527 | - | - |
| 1.0067 | 2540 | 0.0522 | - | - |
| 1.0107 | 2550 | 0.0535 | - | - |
| 1.0147 | 2560 | 0.0557 | - | - |
| 1.0186 | 2570 | 0.052 | - | - |
| 1.0226 | 2580 | 0.055 | - | - |
| 1.0266 | 2590 | 0.0542 | - | - |
| 1.0305 | 2600 | 0.0539 | - | - |
| 1.0345 | 2610 | 0.0523 | - | - |
| 1.0384 | 2620 | 0.0507 | - | - |
| 1.0424 | 2630 | 0.0517 | - | - |
| 1.0464 | 2640 | 0.0543 | - | - |
| 1.0503 | 2650 | 0.0543 | - | - |
| 1.0543 | 2660 | 0.054 | - | - |
| 1.0583 | 2670 | 0.0536 | - | - |
| 1.0622 | 2680 | 0.0531 | - | - |
| 1.0662 | 2690 | 0.0537 | - | - |
| 1.0702 | 2700 | 0.0521 | - | - |
| 1.0741 | 2710 | 0.054 | - | - |
| 1.0781 | 2720 | 0.0513 | - | - |
| 1.0820 | 2730 | 0.0496 | - | - |
| 1.0860 | 2740 | 0.0519 | - | - |
| 1.0900 | 2750 | 0.0529 | - | - |
| 1.0939 | 2760 | 0.0542 | - | - |
| 1.0979 | 2770 | 0.0526 | - | - |
| 1.1019 | 2780 | 0.051 | - | - |
| 1.1058 | 2790 | 0.0531 | - | - |
| 1.1098 | 2800 | 0.0539 | - | - |
| 1.1138 | 2810 | 0.0521 | - | - |
| 1.1177 | 2820 | 0.0539 | - | - |
| 1.1217 | 2830 | 0.0505 | - | - |
| 1.1256 | 2840 | 0.0513 | - | - |
| 1.1296 | 2850 | 0.0521 | - | - |
| 1.1336 | 2860 | 0.0537 | - | - |
| 1.1375 | 2870 | 0.0514 | - | - |
| 1.1415 | 2880 | 0.0511 | - | - |
| 1.1455 | 2890 | 0.0495 | - | - |
| 1.1494 | 2900 | 0.0505 | - | - |
| 1.1534 | 2910 | 0.0517 | - | - |
| 1.1574 | 2920 | 0.0509 | - | - |
| 1.1613 | 2930 | 0.0507 | - | - |
| 1.1653 | 2940 | 0.0535 | - | - |
| 1.1692 | 2950 | 0.0511 | - | - |
| 1.1732 | 2960 | 0.0507 | - | - |
| 1.1772 | 2970 | 0.052 | - | - |
| 1.1811 | 2980 | 0.0494 | - | - |
| 1.1851 | 2990 | 0.0524 | - | - |
| 1.1891 | 3000 | 0.052 | 0.0555 | 0.9512 |
| 1.1930 | 3010 | 0.0536 | - | - |
| 1.1970 | 3020 | 0.0502 | - | - |
| 1.2010 | 3030 | 0.0504 | - | - |
| 1.2049 | 3040 | 0.0532 | - | - |
| 1.2089 | 3050 | 0.0529 | - | - |
| 1.2128 | 3060 | 0.0514 | - | - |
| 1.2168 | 3070 | 0.0504 | - | - |
| 1.2208 | 3080 | 0.0501 | - | - |
| 1.2247 | 3090 | 0.0493 | - | - |
| 1.2287 | 3100 | 0.0507 | - | - |
| 1.2327 | 3110 | 0.0501 | - | - |
| 1.2366 | 3120 | 0.0502 | - | - |
| 1.2406 | 3130 | 0.0491 | - | - |
| 1.2446 | 3140 | 0.0495 | - | - |
| 1.2485 | 3150 | 0.051 | - | - |
| 1.2525 | 3160 | 0.0495 | - | - |
| 1.2564 | 3170 | 0.0534 | - | - |
| 1.2604 | 3180 | 0.0483 | - | - |
| 1.2644 | 3190 | 0.049 | - | - |
| 1.2683 | 3200 | 0.0532 | - | - |
| 1.2723 | 3210 | 0.0481 | - | - |
| 1.2763 | 3220 | 0.0496 | - | - |
| 1.2802 | 3230 | 0.0504 | - | - |
| 1.2842 | 3240 | 0.0477 | - | - |
| 1.2881 | 3250 | 0.0483 | - | - |
| 1.2921 | 3260 | 0.0493 | - | - |
| 1.2961 | 3270 | 0.0491 | - | - |
| 1.3000 | 3280 | 0.0489 | - | - |
| 1.3040 | 3290 | 0.0493 | - | - |
| 1.3080 | 3300 | 0.0507 | - | - |
| 1.3119 | 3310 | 0.0482 | - | - |
| 1.3159 | 3320 | 0.0506 | - | - |
| 1.3199 | 3330 | 0.0486 | - | - |
| 1.3238 | 3340 | 0.0487 | - | - |
| 1.3278 | 3350 | 0.0482 | - | - |
| 1.3317 | 3360 | 0.0492 | - | - |
| 1.3357 | 3370 | 0.049 | - | - |
| 1.3397 | 3380 | 0.0485 | - | - |
| 1.3436 | 3390 | 0.0501 | - | - |
| 1.3476 | 3400 | 0.0505 | - | - |
| 1.3516 | 3410 | 0.0508 | - | - |
| 1.3555 | 3420 | 0.0481 | - | - |
| 1.3595 | 3430 | 0.049 | - | - |
| 1.3635 | 3440 | 0.0495 | - | - |
| 1.3674 | 3450 | 0.0507 | - | - |
| 1.3714 | 3460 | 0.0478 | - | - |
| 1.3753 | 3470 | 0.0522 | - | - |
| 1.3793 | 3480 | 0.0505 | - | - |
| 1.3833 | 3490 | 0.0489 | - | - |
| 1.3872 | 3500 | 0.0504 | 0.0541 | 0.9537 |
| 1.3912 | 3510 | 0.0492 | - | - |
| 1.3952 | 3520 | 0.0469 | - | - |
| 1.3991 | 3530 | 0.0495 | - | - |
| 1.4031 | 3540 | 0.0486 | - | - |
| 1.4071 | 3550 | 0.0506 | - | - |
| 1.4110 | 3560 | 0.0506 | - | - |
| 1.4150 | 3570 | 0.0475 | - | - |
| 1.4189 | 3580 | 0.0483 | - | - |
| 1.4229 | 3590 | 0.0471 | - | - |
| 1.4269 | 3600 | 0.0477 | - | - |
| 1.4308 | 3610 | 0.0494 | - | - |
| 1.4348 | 3620 | 0.0481 | - | - |
| 1.4388 | 3630 | 0.0484 | - | - |
| 1.4427 | 3640 | 0.0505 | - | - |
| 1.4467 | 3650 | 0.0498 | - | - |
| 1.4507 | 3660 | 0.0482 | - | - |
| 1.4546 | 3670 | 0.0488 | - | - |
| 1.4586 | 3680 | 0.0458 | - | - |
| 1.4625 | 3690 | 0.0479 | - | - |
| 1.4665 | 3700 | 0.0474 | - | - |
| 1.4705 | 3710 | 0.0471 | - | - |
| 1.4744 | 3720 | 0.0498 | - | - |
| 1.4784 | 3730 | 0.0495 | - | - |
| 1.4824 | 3740 | 0.0505 | - | - |
| 1.4863 | 3750 | 0.0487 | - | - |
| 1.4903 | 3760 | 0.0485 | - | - |
| 1.4943 | 3770 | 0.0479 | - | - |
| 1.4982 | 3780 | 0.0475 | - | - |
| 1.5022 | 3790 | 0.0462 | - | - |
| 1.5061 | 3800 | 0.0487 | - | - |
| 1.5101 | 3810 | 0.0476 | - | - |
| 1.5141 | 3820 | 0.0485 | - | - |
| 1.5180 | 3830 | 0.0489 | - | - |
| 1.5220 | 3840 | 0.0475 | - | - |
| 1.5260 | 3850 | 0.0484 | - | - |
| 1.5299 | 3860 | 0.0465 | - | - |
| 1.5339 | 3870 | 0.0491 | - | - |
| 1.5379 | 3880 | 0.0477 | - | - |
| 1.5418 | 3890 | 0.0475 | - | - |
| 1.5458 | 3900 | 0.0489 | - | - |
| 1.5497 | 3910 | 0.0459 | - | - |
| 1.5537 | 3920 | 0.0488 | - | - |
| 1.5577 | 3930 | 0.0475 | - | - |
| 1.5616 | 3940 | 0.049 | - | - |
| 1.5656 | 3950 | 0.0469 | - | - |
| 1.5696 | 3960 | 0.0493 | - | - |
| 1.5735 | 3970 | 0.0481 | - | - |
| 1.5775 | 3980 | 0.0478 | - | - |
| 1.5815 | 3990 | 0.0456 | - | - |
| 1.5854 | 4000 | 0.047 | 0.0528 | 0.9556 |
| 1.5894 | 4010 | 0.0481 | - | - |
| 1.5933 | 4020 | 0.0468 | - | - |
| 1.5973 | 4030 | 0.0467 | - | - |
| 1.6013 | 4040 | 0.0448 | - | - |
| 1.6052 | 4050 | 0.0491 | - | - |
| 1.6092 | 4060 | 0.0476 | - | - |
| 1.6132 | 4070 | 0.0459 | - | - |
| 1.6171 | 4080 | 0.0456 | - | - |
| 1.6211 | 4090 | 0.0476 | - | - |
| 1.6250 | 4100 | 0.0443 | - | - |
| 1.6290 | 4110 | 0.0477 | - | - |
| 1.6330 | 4120 | 0.0476 | - | - |
| 1.6369 | 4130 | 0.0466 | - | - |
| 1.6409 | 4140 | 0.0457 | - | - |
| 1.6449 | 4150 | 0.0468 | - | - |
| 1.6488 | 4160 | 0.0462 | - | - |
| 1.6528 | 4170 | 0.0476 | - | - |
| 1.6568 | 4180 | 0.0464 | - | - |
| 1.6607 | 4190 | 0.0467 | - | - |
| 1.6647 | 4200 | 0.0455 | - | - |
| 1.6686 | 4210 | 0.0455 | - | - |
| 1.6726 | 4220 | 0.0474 | - | - |
| 1.6766 | 4230 | 0.0469 | - | - |
| 1.6805 | 4240 | 0.0453 | - | - |
| 1.6845 | 4250 | 0.0464 | - | - |
| 1.6885 | 4260 | 0.0448 | - | - |
| 1.6924 | 4270 | 0.0448 | - | - |
| 1.6964 | 4280 | 0.0461 | - | - |
| 1.7004 | 4290 | 0.0444 | - | - |
| 1.7043 | 4300 | 0.045 | - | - |
| 1.7083 | 4310 | 0.047 | - | - |
| 1.7122 | 4320 | 0.0473 | - | - |
| 1.7162 | 4330 | 0.0453 | - | - |
| 1.7202 | 4340 | 0.0461 | - | - |
| 1.7241 | 4350 | 0.0464 | - | - |
| 1.7281 | 4360 | 0.0474 | - | - |
| 1.7321 | 4370 | 0.0444 | - | - |
| 1.7360 | 4380 | 0.0465 | - | - |
| 1.7400 | 4390 | 0.0454 | - | - |
| 1.7440 | 4400 | 0.045 | - | - |
| 1.7479 | 4410 | 0.0444 | - | - |
| 1.7519 | 4420 | 0.0451 | - | - |
| 1.7558 | 4430 | 0.0454 | - | - |
| 1.7598 | 4440 | 0.0471 | - | - |
| 1.7638 | 4450 | 0.0467 | - | - |
| 1.7677 | 4460 | 0.0466 | - | - |
| 1.7717 | 4470 | 0.0452 | - | - |
| 1.7757 | 4480 | 0.0466 | - | - |
| 1.7796 | 4490 | 0.046 | - | - |
| 1.7836 | 4500 | 0.0462 | 0.0518 | 0.9570 |
| 1.7876 | 4510 | 0.0459 | - | - |
| 1.7915 | 4520 | 0.0455 | - | - |
| 1.7955 | 4530 | 0.0456 | - | - |
| 1.7994 | 4540 | 0.0476 | - | - |
| 1.8034 | 4550 | 0.0465 | - | - |
| 1.8074 | 4560 | 0.0447 | - | - |
| 1.8113 | 4570 | 0.0438 | - | - |
| 1.8153 | 4580 | 0.0463 | - | - |
| 1.8193 | 4590 | 0.0452 | - | - |
| 1.8232 | 4600 | 0.0454 | - | - |
| 1.8272 | 4610 | 0.0459 | - | - |
| 1.8312 | 4620 | 0.044 | - | - |
| 1.8351 | 4630 | 0.0445 | - | - |
| 1.8391 | 4640 | 0.0435 | - | - |
| 1.8430 | 4650 | 0.0435 | - | - |
| 1.8470 | 4660 | 0.0442 | - | - |
| 1.8510 | 4670 | 0.0424 | - | - |
| 1.8549 | 4680 | 0.0438 | - | - |
| 1.8589 | 4690 | 0.0451 | - | - |
| 1.8629 | 4700 | 0.0451 | - | - |
| 1.8668 | 4710 | 0.0455 | - | - |
| 1.8708 | 4720 | 0.0441 | - | - |
| 1.8748 | 4730 | 0.0432 | - | - |
| 1.8787 | 4740 | 0.0445 | - | - |
| 1.8827 | 4750 | 0.0482 | - | - |
| 1.8866 | 4760 | 0.045 | - | - |
| 1.8906 | 4770 | 0.0443 | - | - |
| 1.8946 | 4780 | 0.0451 | - | - |
| 1.8985 | 4790 | 0.0446 | - | - |
| 1.9025 | 4800 | 0.0432 | - | - |
| 1.9065 | 4810 | 0.0432 | - | - |
| 1.9104 | 4820 | 0.0465 | - | - |
| 1.9144 | 4830 | 0.0462 | - | - |
| 1.9184 | 4840 | 0.0443 | - | - |
| 1.9223 | 4850 | 0.0447 | - | - |
| 1.9263 | 4860 | 0.0459 | - | - |
| 1.9302 | 4870 | 0.043 | - | - |
| 1.9342 | 4880 | 0.0456 | - | - |
| 1.9382 | 4890 | 0.0444 | - | - |
| 1.9421 | 4900 | 0.0455 | - | - |
| 1.9461 | 4910 | 0.0427 | - | - |
| 1.9501 | 4920 | 0.0461 | - | - |
| 1.9540 | 4930 | 0.0454 | - | - |
| 1.9580 | 4940 | 0.0447 | - | - |
| 1.9620 | 4950 | 0.0434 | - | - |
| 1.9659 | 4960 | 0.0444 | - | - |
| 1.9699 | 4970 | 0.0451 | - | - |
| 1.9738 | 4980 | 0.044 | - | - |
| 1.9778 | 4990 | 0.0444 | - | - |
| 1.9818 | 5000 | 0.0439 | 0.0508 | 0.9581 |
| 1.9857 | 5010 | 0.0427 | - | - |
| 1.9897 | 5020 | 0.0439 | - | - |
| 1.9937 | 5030 | 0.0427 | - | - |
| 1.9976 | 5040 | 0.0435 | - | - |
| 2.0016 | 5050 | 0.0445 | - | - |
| 2.0055 | 5060 | 0.0433 | - | - |
| 2.0095 | 5070 | 0.0433 | - | - |
| 2.0135 | 5080 | 0.0435 | - | - |
| 2.0174 | 5090 | 0.0438 | - | - |
| 2.0214 | 5100 | 0.0431 | - | - |
| 2.0254 | 5110 | 0.0422 | - | - |
| 2.0293 | 5120 | 0.0436 | - | - |
| 2.0333 | 5130 | 0.0455 | - | - |
| 2.0373 | 5140 | 0.044 | - | - |
| 2.0412 | 5150 | 0.0423 | - | - |
| 2.0452 | 5160 | 0.045 | - | - |
| 2.0491 | 5170 | 0.0422 | - | - |
| 2.0531 | 5180 | 0.0435 | - | - |
| 2.0571 | 5190 | 0.0419 | - | - |
| 2.0610 | 5200 | 0.0427 | - | - |
| 2.0650 | 5210 | 0.0447 | - | - |
| 2.0690 | 5220 | 0.0443 | - | - |
| 2.0729 | 5230 | 0.0429 | - | - |
| 2.0769 | 5240 | 0.0436 | - | - |
| 2.0809 | 5250 | 0.0436 | - | - |
| 2.0848 | 5260 | 0.0439 | - | - |
| 2.0888 | 5270 | 0.0433 | - | - |
| 2.0927 | 5280 | 0.0434 | - | - |
| 2.0967 | 5290 | 0.0428 | - | - |
| 2.1007 | 5300 | 0.0431 | - | - |
| 2.1046 | 5310 | 0.0441 | - | - |
| 2.1086 | 5320 | 0.0443 | - | - |
| 2.1126 | 5330 | 0.0442 | - | - |
| 2.1165 | 5340 | 0.044 | - | - |
| 2.1205 | 5350 | 0.0431 | - | - |
| 2.1245 | 5360 | 0.0432 | - | - |
| 2.1284 | 5370 | 0.0421 | - | - |
| 2.1324 | 5380 | 0.0439 | - | - |
| 2.1363 | 5390 | 0.0436 | - | - |
| 2.1403 | 5400 | 0.0428 | - | - |
| 2.1443 | 5410 | 0.044 | - | - |
| 2.1482 | 5420 | 0.0428 | - | - |
| 2.1522 | 5430 | 0.0428 | - | - |
| 2.1562 | 5440 | 0.0418 | - | - |
| 2.1601 | 5450 | 0.0439 | - | - |
| 2.1641 | 5460 | 0.0415 | - | - |
| 2.1681 | 5470 | 0.0415 | - | - |
| 2.1720 | 5480 | 0.0418 | - | - |
| 2.1760 | 5490 | 0.042 | - | - |
| 2.1799 | 5500 | 0.0418 | 0.0500 | 0.9591 |
| 2.1839 | 5510 | 0.0434 | - | - |
| 2.1879 | 5520 | 0.0424 | - | - |
| 2.1918 | 5530 | 0.0425 | - | - |
| 2.1958 | 5540 | 0.0427 | - | - |
| 2.1998 | 5550 | 0.0418 | - | - |
| 2.2037 | 5560 | 0.04 | - | - |
| 2.2077 | 5570 | 0.0426 | - | - |
| 2.2117 | 5580 | 0.0413 | - | - |
| 2.2156 | 5590 | 0.0429 | - | - |
| 2.2196 | 5600 | 0.0428 | - | - |
| 2.2235 | 5610 | 0.044 | - | - |
| 2.2275 | 5620 | 0.0423 | - | - |
| 2.2315 | 5630 | 0.0398 | - | - |
| 2.2354 | 5640 | 0.0427 | - | - |
| 2.2394 | 5650 | 0.0419 | - | - |
| 2.2434 | 5660 | 0.0424 | - | - |
| 2.2473 | 5670 | 0.0422 | - | - |
| 2.2513 | 5680 | 0.0426 | - | - |
| 2.2553 | 5690 | 0.0434 | - | - |
| 2.2592 | 5700 | 0.044 | - | - |
| 2.2632 | 5710 | 0.0427 | - | - |
| 2.2671 | 5720 | 0.0431 | - | - |
| 2.2711 | 5730 | 0.0416 | - | - |
| 2.2751 | 5740 | 0.0428 | - | - |
| 2.2790 | 5750 | 0.0418 | - | - |
| 2.2830 | 5760 | 0.0418 | - | - |
| 2.2870 | 5770 | 0.0421 | - | - |
| 2.2909 | 5780 | 0.041 | - | - |
| 2.2949 | 5790 | 0.0419 | - | - |
| 2.2989 | 5800 | 0.0422 | - | - |
| 2.3028 | 5810 | 0.0428 | - | - |
| 2.3068 | 5820 | 0.0432 | - | - |
| 2.3107 | 5830 | 0.043 | - | - |
| 2.3147 | 5840 | 0.0424 | - | - |
| 2.3187 | 5850 | 0.0396 | - | - |
| 2.3226 | 5860 | 0.0433 | - | - |
| 2.3266 | 5870 | 0.0413 | - | - |
| 2.3306 | 5880 | 0.0436 | - | - |
| 2.3345 | 5890 | 0.0399 | - | - |
| 2.3385 | 5900 | 0.0426 | - | - |
| 2.3424 | 5910 | 0.0405 | - | - |
| 2.3464 | 5920 | 0.0423 | - | - |
| 2.3504 | 5930 | 0.0409 | - | - |
| 2.3543 | 5940 | 0.0412 | - | - |
| 2.3583 | 5950 | 0.0401 | - | - |
| 2.3623 | 5960 | 0.042 | - | - |
| 2.3662 | 5970 | 0.0397 | - | - |
| 2.3702 | 5980 | 0.0422 | - | - |
| 2.3742 | 5990 | 0.0416 | - | - |
| 2.3781 | 6000 | 0.0422 | 0.0493 | 0.9599 |
| 2.3821 | 6010 | 0.041 | - | - |
| 2.3860 | 6020 | 0.0404 | - | - |
| 2.3900 | 6030 | 0.0404 | - | - |
| 2.3940 | 6040 | 0.0412 | - | - |
| 2.3979 | 6050 | 0.0424 | - | - |
| 2.4019 | 6060 | 0.043 | - | - |
| 2.4059 | 6070 | 0.0416 | - | - |
| 2.4098 | 6080 | 0.0405 | - | - |
| 2.4138 | 6090 | 0.0408 | - | - |
| 2.4178 | 6100 | 0.0413 | - | - |
| 2.4217 | 6110 | 0.0408 | - | - |
| 2.4257 | 6120 | 0.0407 | - | - |
| 2.4296 | 6130 | 0.041 | - | - |
| 2.4336 | 6140 | 0.0387 | - | - |
| 2.4376 | 6150 | 0.0408 | - | - |
| 2.4415 | 6160 | 0.0413 | - | - |
| 2.4455 | 6170 | 0.0429 | - | - |
| 2.4495 | 6180 | 0.0394 | - | - |
| 2.4534 | 6190 | 0.041 | - | - |
| 2.4574 | 6200 | 0.0419 | - | - |
| 2.4614 | 6210 | 0.0395 | - | - |
| 2.4653 | 6220 | 0.0405 | - | - |
| 2.4693 | 6230 | 0.0412 | - | - |
| 2.4732 | 6240 | 0.0439 | - | - |
| 2.4772 | 6250 | 0.0423 | - | - |
| 2.4812 | 6260 | 0.0423 | - | - |
| 2.4851 | 6270 | 0.0406 | - | - |
| 2.4891 | 6280 | 0.0402 | - | - |
| 2.4931 | 6290 | 0.0428 | - | - |
| 2.4970 | 6300 | 0.0422 | - | - |
| 2.5010 | 6310 | 0.0399 | - | - |
| 2.5050 | 6320 | 0.0409 | - | - |
| 2.5089 | 6330 | 0.0412 | - | - |
| 2.5129 | 6340 | 0.0403 | - | - |
| 2.5168 | 6350 | 0.04 | - | - |
| 2.5208 | 6360 | 0.0412 | - | - |
| 2.5248 | 6370 | 0.0424 | - | - |
| 2.5287 | 6380 | 0.0409 | - | - |
| 2.5327 | 6390 | 0.0409 | - | - |
| 2.5367 | 6400 | 0.0418 | - | - |
| 2.5406 | 6410 | 0.0403 | - | - |
| 2.5446 | 6420 | 0.0413 | - | - |
| 2.5486 | 6430 | 0.038 | - | - |
| 2.5525 | 6440 | 0.0414 | - | - |
| 2.5565 | 6450 | 0.0409 | - | - |
| 2.5604 | 6460 | 0.0407 | - | - |
| 2.5644 | 6470 | 0.0406 | - | - |
| 2.5684 | 6480 | 0.0392 | - | - |
| 2.5723 | 6490 | 0.0417 | - | - |
| 2.5763 | 6500 | 0.0391 | 0.0487 | 0.9605 |
| 2.5803 | 6510 | 0.039 | - | - |
| 2.5842 | 6520 | 0.0414 | - | - |
| 2.5882 | 6530 | 0.0411 | - | - |
| 2.5922 | 6540 | 0.0395 | - | - |
| 2.5961 | 6550 | 0.0405 | - | - |
| 2.6001 | 6560 | 0.0392 | - | - |
| 2.6040 | 6570 | 0.041 | - | - |
| 2.6080 | 6580 | 0.0387 | - | - |
| 2.6120 | 6590 | 0.0409 | - | - |
| 2.6159 | 6600 | 0.0416 | - | - |
| 2.6199 | 6610 | 0.0399 | - | - |
| 2.6239 | 6620 | 0.0395 | - | - |
| 2.6278 | 6630 | 0.0416 | - | - |
| 2.6318 | 6640 | 0.0397 | - | - |
| 2.6358 | 6650 | 0.041 | - | - |
| 2.6397 | 6660 | 0.0422 | - | - |
| 2.6437 | 6670 | 0.0404 | - | - |
| 2.6476 | 6680 | 0.0405 | - | - |
| 2.6516 | 6690 | 0.0413 | - | - |
| 2.6556 | 6700 | 0.0405 | - | - |
| 2.6595 | 6710 | 0.04 | - | - |
| 2.6635 | 6720 | 0.0383 | - | - |
| 2.6675 | 6730 | 0.0412 | - | - |
| 2.6714 | 6740 | 0.0416 | - | - |
| 2.6754 | 6750 | 0.0405 | - | - |
| 2.6793 | 6760 | 0.0423 | - | - |
| 2.6833 | 6770 | 0.0419 | - | - |
| 2.6873 | 6780 | 0.0405 | - | - |
| 2.6912 | 6790 | 0.0409 | - | - |
| 2.6952 | 6800 | 0.04 | - | - |
| 2.6992 | 6810 | 0.0397 | - | - |
| 2.7031 | 6820 | 0.039 | - | - |
| 2.7071 | 6830 | 0.0393 | - | - |
| 2.7111 | 6840 | 0.0413 | - | - |
| 2.7150 | 6850 | 0.039 | - | - |
| 2.7190 | 6860 | 0.04 | - | - |
| 2.7229 | 6870 | 0.0409 | - | - |
| 2.7269 | 6880 | 0.0403 | - | - |
| 2.7309 | 6890 | 0.0397 | - | - |
| 2.7348 | 6900 | 0.0404 | - | - |
| 2.7388 | 6910 | 0.0396 | - | - |
| 2.7428 | 6920 | 0.04 | - | - |
| 2.7467 | 6930 | 0.0397 | - | - |
| 2.7507 | 6940 | 0.0393 | - | - |
| 2.7547 | 6950 | 0.037 | - | - |
| 2.7586 | 6960 | 0.0383 | - | - |
| 2.7626 | 6970 | 0.04 | - | - |
| 2.7665 | 6980 | 0.0406 | - | - |
| 2.7705 | 6990 | 0.0394 | - | - |
| 2.7745 | 7000 | 0.0385 | 0.0482 | 0.9609 |
| 2.7784 | 7010 | 0.0383 | - | - |
| 2.7824 | 7020 | 0.0403 | - | - |
| 2.7864 | 7030 | 0.04 | - | - |
| 2.7903 | 7040 | 0.0395 | - | - |
| 2.7943 | 7050 | 0.039 | - | - |
| 2.7983 | 7060 | 0.0398 | - | - |
| 2.8022 | 7070 | 0.0401 | - | - |
| 2.8062 | 7080 | 0.0401 | - | - |
| 2.8101 | 7090 | 0.0395 | - | - |
| 2.8141 | 7100 | 0.0396 | - | - |
| 2.8181 | 7110 | 0.0395 | - | - |
| 2.8220 | 7120 | 0.0411 | - | - |
| 2.8260 | 7130 | 0.0386 | - | - |
| 2.8300 | 7140 | 0.0382 | - | - |
| 2.8339 | 7150 | 0.0386 | - | - |
| 2.8379 | 7160 | 0.0389 | - | - |
| 2.8419 | 7170 | 0.0396 | - | - |
| 2.8458 | 7180 | 0.0394 | - | - |
| 2.8498 | 7190 | 0.04 | - | - |
| 2.8537 | 7200 | 0.0401 | - | - |
| 2.8577 | 7210 | 0.0412 | - | - |
| 2.8617 | 7220 | 0.0383 | - | - |
| 2.8656 | 7230 | 0.0392 | - | - |
| 2.8696 | 7240 | 0.0394 | - | - |
| 2.8736 | 7250 | 0.0399 | - | - |
| 2.8775 | 7260 | 0.0403 | - | - |
| 2.8815 | 7270 | 0.0384 | - | - |
| 2.8855 | 7280 | 0.0397 | - | - |
| 2.8894 | 7290 | 0.0407 | - | - |
| 2.8934 | 7300 | 0.0386 | - | - |
| 2.8973 | 7310 | 0.0385 | - | - |
| 2.9013 | 7320 | 0.0405 | - | - |
| 2.9053 | 7330 | 0.0389 | - | - |
| 2.9092 | 7340 | 0.0362 | - | - |
| 2.9132 | 7350 | 0.0397 | - | - |
| 2.9172 | 7360 | 0.0393 | - | - |
| 2.9211 | 7370 | 0.0397 | - | - |
| 2.9251 | 7380 | 0.0386 | - | - |
| 2.9291 | 7390 | 0.0388 | - | - |
| 2.9330 | 7400 | 0.0366 | - | - |
| 2.9370 | 7410 | 0.0394 | - | - |
| 2.9409 | 7420 | 0.0396 | - | - |
| 2.9449 | 7430 | 0.0393 | - | - |
| 2.9489 | 7440 | 0.0401 | - | - |
| 2.9528 | 7450 | 0.0391 | - | - |
| 2.9568 | 7460 | 0.0388 | - | - |
| 2.9608 | 7470 | 0.0386 | - | - |
| 2.9647 | 7480 | 0.0391 | - | - |
| 2.9687 | 7490 | 0.037 | - | - |
| 2.9727 | 7500 | 0.0386 | 0.0477 | 0.9613 |
| 2.9766 | 7510 | 0.0392 | - | - |
| 2.9806 | 7520 | 0.0399 | - | - |
| 2.9845 | 7530 | 0.0385 | - | - |
| 2.9885 | 7540 | 0.0381 | - | - |
| 2.9925 | 7550 | 0.0392 | - | - |
| 2.9964 | 7560 | 0.0386 | - | - |
| 3.0004 | 7570 | 0.0394 | - | - |
| 3.0044 | 7580 | 0.0401 | - | - |
| 3.0083 | 7590 | 0.0404 | - | - |
| 3.0123 | 7600 | 0.0384 | - | - |
| 3.0163 | 7610 | 0.0381 | - | - |
| 3.0202 | 7620 | 0.0383 | - | - |
| 3.0242 | 7630 | 0.0389 | - | - |
| 3.0281 | 7640 | 0.0364 | - | - |
| 3.0321 | 7650 | 0.0399 | - | - |
| 3.0361 | 7660 | 0.0383 | - | - |
| 3.0400 | 7670 | 0.0401 | - | - |
| 3.0440 | 7680 | 0.0388 | - | - |
| 3.0480 | 7690 | 0.0389 | - | - |
| 3.0519 | 7700 | 0.036 | - | - |
| 3.0559 | 7710 | 0.0403 | - | - |
| 3.0598 | 7720 | 0.0376 | - | - |
| 3.0638 | 7730 | 0.0387 | - | - |
| 3.0678 | 7740 | 0.0405 | - | - |
| 3.0717 | 7750 | 0.0399 | - | - |
| 3.0757 | 7760 | 0.0382 | - | - |
| 3.0797 | 7770 | 0.0376 | - | - |
| 3.0836 | 7780 | 0.0393 | - | - |
| 3.0876 | 7790 | 0.0388 | - | - |
| 3.0916 | 7800 | 0.0395 | - | - |
| 3.0955 | 7810 | 0.0391 | - | - |
| 3.0995 | 7820 | 0.0392 | - | - |
| 3.1034 | 7830 | 0.0371 | - | - |
| 3.1074 | 7840 | 0.039 | - | - |
| 3.1114 | 7850 | 0.0395 | - | - |
| 3.1153 | 7860 | 0.0385 | - | - |
| 3.1193 | 7870 | 0.0362 | - | - |
| 3.1233 | 7880 | 0.0375 | - | - |
| 3.1272 | 7890 | 0.0376 | - | - |
| 3.1312 | 7900 | 0.0384 | - | - |
| 3.1352 | 7910 | 0.0378 | - | - |
| 3.1391 | 7920 | 0.0393 | - | - |
| 3.1431 | 7930 | 0.0378 | - | - |
| 3.1470 | 7940 | 0.0404 | - | - |
| 3.1510 | 7950 | 0.0361 | - | - |
| 3.1550 | 7960 | 0.0369 | - | - |
| 3.1589 | 7970 | 0.0396 | - | - |
| 3.1629 | 7980 | 0.0404 | - | - |
| 3.1669 | 7990 | 0.0386 | - | - |
| 3.1708 | 8000 | 0.038 | 0.0473 | 0.9616 |
| 3.1748 | 8010 | 0.0372 | - | - |
| 3.1788 | 8020 | 0.0373 | - | - |
| 3.1827 | 8030 | 0.0369 | - | - |
| 3.1867 | 8040 | 0.0371 | - | - |
| 3.1906 | 8050 | 0.0386 | - | - |
| 3.1946 | 8060 | 0.038 | - | - |
| 3.1986 | 8070 | 0.0366 | - | - |
| 3.2025 | 8080 | 0.0378 | - | - |
| 3.2065 | 8090 | 0.0379 | - | - |
| 3.2105 | 8100 | 0.038 | - | - |
| 3.2144 | 8110 | 0.0374 | - | - |
| 3.2184 | 8120 | 0.0388 | - | - |
| 3.2224 | 8130 | 0.038 | - | - |
| 3.2263 | 8140 | 0.0363 | - | - |
| 3.2303 | 8150 | 0.0369 | - | - |
| 3.2342 | 8160 | 0.0371 | - | - |
| 3.2382 | 8170 | 0.0377 | - | - |
| 3.2422 | 8180 | 0.0364 | - | - |
| 3.2461 | 8190 | 0.0372 | - | - |
| 3.2501 | 8200 | 0.0403 | - | - |
| 3.2541 | 8210 | 0.0385 | - | - |
| 3.2580 | 8220 | 0.0385 | - | - |
| 3.2620 | 8230 | 0.0386 | - | - |
| 3.2660 | 8240 | 0.0369 | - | - |
| 3.2699 | 8250 | 0.039 | - | - |
| 3.2739 | 8260 | 0.0365 | - | - |
| 3.2778 | 8270 | 0.0382 | - | - |
| 3.2818 | 8280 | 0.0354 | - | - |
| 3.2858 | 8290 | 0.0393 | - | - |
| 3.2897 | 8300 | 0.0387 | - | - |
| 3.2937 | 8310 | 0.0366 | - | - |
| 3.2977 | 8320 | 0.0391 | - | - |
| 3.3016 | 8330 | 0.0382 | - | - |
| 3.3056 | 8340 | 0.0377 | - | - |
| 3.3096 | 8350 | 0.0369 | - | - |
| 3.3135 | 8360 | 0.0384 | - | - |
| 3.3175 | 8370 | 0.0379 | - | - |
| 3.3214 | 8380 | 0.0372 | - | - |
| 3.3254 | 8390 | 0.0391 | - | - |
| 3.3294 | 8400 | 0.0378 | - | - |
| 3.3333 | 8410 | 0.0393 | - | - |
| 3.3373 | 8420 | 0.0373 | - | - |
| 3.3413 | 8430 | 0.0394 | - | - |
| 3.3452 | 8440 | 0.0367 | - | - |
| 3.3492 | 8450 | 0.0373 | - | - |
| 3.3532 | 8460 | 0.0362 | - | - |
| 3.3571 | 8470 | 0.0372 | - | - |
| 3.3611 | 8480 | 0.0396 | - | - |
| 3.3650 | 8490 | 0.0392 | - | - |
| 3.3690 | 8500 | 0.0374 | 0.0470 | 0.9616 |
| 3.3730 | 8510 | 0.0378 | - | - |
| 3.3769 | 8520 | 0.0385 | - | - |
| 3.3809 | 8530 | 0.0375 | - | - |
| 3.3849 | 8540 | 0.0392 | - | - |
| 3.3888 | 8550 | 0.0378 | - | - |
| 3.3928 | 8560 | 0.0366 | - | - |
| 3.3967 | 8570 | 0.0383 | - | - |
| 3.4007 | 8580 | 0.0372 | - | - |
| 3.4047 | 8590 | 0.038 | - | - |
| 3.4086 | 8600 | 0.0384 | - | - |
| 3.4126 | 8610 | 0.0359 | - | - |
| 3.4166 | 8620 | 0.0377 | - | - |
| 3.4205 | 8630 | 0.0387 | - | - |
| 3.4245 | 8640 | 0.0365 | - | - |
| 3.4285 | 8650 | 0.0359 | - | - |
| 3.4324 | 8660 | 0.0358 | - | - |
| 3.4364 | 8670 | 0.0366 | - | - |
| 3.4403 | 8680 | 0.0369 | - | - |
| 3.4443 | 8690 | 0.0365 | - | - |
| 3.4483 | 8700 | 0.0366 | - | - |
| 3.4522 | 8710 | 0.0357 | - | - |
| 3.4562 | 8720 | 0.036 | - | - |
| 3.4602 | 8730 | 0.0365 | - | - |
| 3.4641 | 8740 | 0.0381 | - | - |
| 3.4681 | 8750 | 0.0399 | - | - |
| 3.4721 | 8760 | 0.0388 | - | - |
| 3.4760 | 8770 | 0.0366 | - | - |
| 3.4800 | 8780 | 0.0346 | - | - |
| 3.4839 | 8790 | 0.0371 | - | - |
| 3.4879 | 8800 | 0.0376 | - | - |
| 3.4919 | 8810 | 0.0374 | - | - |
| 3.4958 | 8820 | 0.0354 | - | - |
| 3.4998 | 8830 | 0.0363 | - | - |
| 3.5038 | 8840 | 0.0374 | - | - |
| 3.5077 | 8850 | 0.0373 | - | - |
| 3.5117 | 8860 | 0.0347 | - | - |
| 3.5157 | 8870 | 0.0374 | - | - |
| 3.5196 | 8880 | 0.0349 | - | - |
| 3.5236 | 8890 | 0.0376 | - | - |
| 3.5275 | 8900 | 0.0363 | - | - |
| 3.5315 | 8910 | 0.036 | - | - |
| 3.5355 | 8920 | 0.0378 | - | - |
| 3.5394 | 8930 | 0.0376 | - | - |
| 3.5434 | 8940 | 0.039 | - | - |
| 3.5474 | 8950 | 0.0373 | - | - |
| 3.5513 | 8960 | 0.0361 | - | - |
| 3.5553 | 8970 | 0.0356 | - | - |
| 3.5593 | 8980 | 0.0357 | - | - |
| 3.5632 | 8990 | 0.0371 | - | - |
| 3.5672 | 9000 | 0.0374 | 0.0468 | 0.9617 |
| 3.5711 | 9010 | 0.0372 | - | - |
| 3.5751 | 9020 | 0.0369 | - | - |
| 3.5791 | 9030 | 0.0362 | - | - |
| 3.5830 | 9040 | 0.0367 | - | - |
| 3.5870 | 9050 | 0.0388 | - | - |
| 3.5910 | 9060 | 0.0369 | - | - |
| 3.5949 | 9070 | 0.0375 | - | - |
| 3.5989 | 9080 | 0.0374 | - | - |
| 3.6029 | 9090 | 0.0365 | - | - |
| 3.6068 | 9100 | 0.0363 | - | - |
| 3.6108 | 9110 | 0.0396 | - | - |
| 3.6147 | 9120 | 0.0372 | - | - |
| 3.6187 | 9130 | 0.0363 | - | - |
| 3.6227 | 9140 | 0.0363 | - | - |
| 3.6266 | 9150 | 0.0366 | - | - |
| 3.6306 | 9160 | 0.0352 | - | - |
| 3.6346 | 9170 | 0.038 | - | - |
| 3.6385 | 9180 | 0.0359 | - | - |
| 3.6425 | 9190 | 0.0374 | - | - |
| 3.6465 | 9200 | 0.0363 | - | - |
| 3.6504 | 9210 | 0.0356 | - | - |
| 3.6544 | 9220 | 0.0354 | - | - |
| 3.6583 | 9230 | 0.0377 | - | - |
| 3.6623 | 9240 | 0.0361 | - | - |
| 3.6663 | 9250 | 0.0374 | - | - |
| 3.6702 | 9260 | 0.0373 | - | - |
| 3.6742 | 9270 | 0.0357 | - | - |
| 3.6782 | 9280 | 0.0359 | - | - |
| 3.6821 | 9290 | 0.037 | - | - |
| 3.6861 | 9300 | 0.0366 | - | - |
| 3.6901 | 9310 | 0.0374 | - | - |
| 3.6940 | 9320 | 0.0376 | - | - |
| 3.6980 | 9330 | 0.0373 | - | - |
| 3.7019 | 9340 | 0.0363 | - | - |
| 3.7059 | 9350 | 0.0381 | - | - |
| 3.7099 | 9360 | 0.0353 | - | - |
| 3.7138 | 9370 | 0.0363 | - | - |
| 3.7178 | 9380 | 0.0377 | - | - |
| 3.7218 | 9390 | 0.0364 | - | - |
| 3.7257 | 9400 | 0.0378 | - | - |
| 3.7297 | 9410 | 0.0376 | - | - |
| 3.7337 | 9420 | 0.0376 | - | - |
| 3.7376 | 9430 | 0.0368 | - | - |
| 3.7416 | 9440 | 0.0381 | - | - |
| 3.7455 | 9450 | 0.0358 | - | - |
| 3.7495 | 9460 | 0.0362 | - | - |
| 3.7535 | 9470 | 0.038 | - | - |
| 3.7574 | 9480 | 0.0371 | - | - |
| 3.7614 | 9490 | 0.0371 | - | - |
| 3.7654 | 9500 | 0.0353 | 0.0465 | 0.9617 |
| 3.7693 | 9510 | 0.0381 | - | - |
| 3.7733 | 9520 | 0.0362 | - | - |
| 3.7772 | 9530 | 0.0352 | - | - |
| 3.7812 | 9540 | 0.0363 | - | - |
| 3.7852 | 9550 | 0.0352 | - | - |
| 3.7891 | 9560 | 0.0367 | - | - |
| 3.7931 | 9570 | 0.035 | - | - |
| 3.7971 | 9580 | 0.0367 | - | - |
| 3.8010 | 9590 | 0.0369 | - | - |
| 3.8050 | 9600 | 0.0365 | - | - |
| 3.8090 | 9610 | 0.0369 | - | - |
| 3.8129 | 9620 | 0.0359 | - | - |
| 3.8169 | 9630 | 0.0367 | - | - |
| 3.8208 | 9640 | 0.0384 | - | - |
| 3.8248 | 9650 | 0.0359 | - | - |
| 3.8288 | 9660 | 0.0368 | - | - |
| 3.8327 | 9670 | 0.0363 | - | - |
| 3.8367 | 9680 | 0.0374 | - | - |
| 3.8407 | 9690 | 0.0372 | - | - |
| 3.8446 | 9700 | 0.0361 | - | - |
| 3.8486 | 9710 | 0.0381 | - | - |
| 3.8526 | 9720 | 0.0342 | - | - |
| 3.8565 | 9730 | 0.0348 | - | - |
| 3.8605 | 9740 | 0.0372 | - | - |
| 3.8644 | 9750 | 0.0377 | - | - |
| 3.8684 | 9760 | 0.0356 | - | - |
| 3.8724 | 9770 | 0.0365 | - | - |
| 3.8763 | 9780 | 0.0368 | - | - |
| 3.8803 | 9790 | 0.0366 | - | - |
| 3.8843 | 9800 | 0.0383 | - | - |
| 3.8882 | 9810 | 0.0353 | - | - |
| 3.8922 | 9820 | 0.0377 | - | - |
| 3.8962 | 9830 | 0.0364 | - | - |
| 3.9001 | 9840 | 0.0362 | - | - |
| 3.9041 | 9850 | 0.0351 | - | - |
| 3.9080 | 9860 | 0.0381 | - | - |
| 3.9120 | 9870 | 0.0368 | - | - |
| 3.9160 | 9880 | 0.0361 | - | - |
| 3.9199 | 9890 | 0.0356 | - | - |
| 3.9239 | 9900 | 0.035 | - | - |
| 3.9279 | 9910 | 0.0345 | - | - |
| 3.9318 | 9920 | 0.0378 | - | - |
| 3.9358 | 9930 | 0.036 | - | - |
| 3.9398 | 9940 | 0.0367 | - | - |
| 3.9437 | 9950 | 0.0356 | - | - |
| 3.9477 | 9960 | 0.034 | - | - |
| 3.9516 | 9970 | 0.0377 | - | - |
| 3.9556 | 9980 | 0.0379 | - | - |
| 3.9596 | 9990 | 0.0388 | - | - |
| 3.9635 | 10000 | 0.0362 | 0.0463 | 0.9618 |