Ludwig
rpi.analyticsdojo.com
76. Ludwig#
Ludwig, a project of Uber, provides a new data type-based approach to deep learning model design that makes the tool suited for many different applications. Rather than building out the architecture, you just need to specify the data.
First let’s install ludwig and grab some data.
!pip install ludwig
Collecting ludwig
?25l Downloading https://files.pythonhosted.org/packages/cd/a2/9f7f1952398e5aeb2f39579616fab8c3fada84a956ba6c855e6bc30a99f1/ludwig-0.1.1.tar.gz (129kB)
100% |████████████████████████████████| 133kB 3.7MB/s
?25hRequirement already satisfied: Cython>=0.25 in /usr/local/lib/python3.6/dist-packages (from ludwig) (0.29.6)
Requirement already satisfied: h5py>=2.6 in /usr/local/lib/python3.6/dist-packages (from ludwig) (2.8.0)
Requirement already satisfied: matplotlib>=3.0 in /usr/local/lib/python3.6/dist-packages (from ludwig) (3.0.3)
Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from ludwig) (1.16.2)
Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.6/dist-packages (from ludwig) (0.23.4)
Requirement already satisfied: scipy>=0.18 in /usr/local/lib/python3.6/dist-packages (from ludwig) (1.2.1)
Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from ludwig) (0.20.3)
Requirement already satisfied: scikit-image==0.14.2 in /usr/local/lib/python3.6/dist-packages (from ludwig) (0.14.2)
Requirement already satisfied: seaborn>=0.7 in /usr/local/lib/python3.6/dist-packages (from ludwig) (0.7.1)
Collecting spacy>=2.1 (from ludwig)
?25l Downloading https://files.pythonhosted.org/packages/52/da/3a1c54694c2d2f40df82f38a19ae14c6eb24a5a1a0dae87205ebea7a84d8/spacy-2.1.3-cp36-cp36m-manylinux1_x86_64.whl (27.7MB)
100% |████████████████████████████████| 27.7MB 1.3MB/s
?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from ludwig) (4.28.1)
Requirement already satisfied: tabulate>=0.7 in /usr/local/lib/python3.6/dist-packages (from ludwig) (0.8.3)
Requirement already satisfied: tensorflow==1.13.1 in /usr/local/lib/python3.6/dist-packages (from ludwig) (1.13.1)
Requirement already satisfied: PyYAML>=3.12 in /usr/local/lib/python3.6/dist-packages (from ludwig) (3.13)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py>=2.6->ludwig) (1.11.0)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0->ludwig) (0.10.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0->ludwig) (1.0.1)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0->ludwig) (2.5.3)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0->ludwig) (2.4.0)
Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->ludwig) (2018.9)
Requirement already satisfied: cloudpickle>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from scikit-image==0.14.2->ludwig) (0.6.1)
Requirement already satisfied: networkx>=1.8 in /usr/local/lib/python3.6/dist-packages (from scikit-image==0.14.2->ludwig) (2.2)
Requirement already satisfied: dask[array]>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image==0.14.2->ludwig) (1.1.5)
Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image==0.14.2->ludwig) (4.3.0)
Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image==0.14.2->ludwig) (1.0.3)
Requirement already satisfied: plac<1.0.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.1->ludwig) (0.9.6)
Collecting srsly<1.1.0,>=0.0.5 (from spacy>=2.1->ludwig)
?25l Downloading https://files.pythonhosted.org/packages/6b/97/47753e3393aa4b18de9f942fac26f18879d1ae950243a556888f389d1398/srsly-0.0.5-cp36-cp36m-manylinux1_x86_64.whl (180kB)
100% |████████████████████████████████| 184kB 11.4MB/s
?25hRequirement already satisfied: jsonschema<3.0.0,>=2.6.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.1->ludwig) (2.6.0)
Collecting wasabi<1.1.0,>=0.2.0 (from spacy>=2.1->ludwig)
Downloading https://files.pythonhosted.org/packages/76/6c/0376977df1ba9f0ec27835d80456d9284c79737cb5205649451db1181f01/wasabi-0.2.1-py3-none-any.whl
Collecting blis<0.3.0,>=0.2.2 (from spacy>=2.1->ludwig)
?25l Downloading https://files.pythonhosted.org/packages/34/46/b1d0bb71d308e820ed30316c5f0a017cb5ef5f4324bcbc7da3cf9d3b075c/blis-0.2.4-cp36-cp36m-manylinux1_x86_64.whl (3.2MB)
100% |████████████████████████████████| 3.2MB 9.6MB/s
?25hRequirement already satisfied: preshed<2.1.0,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.1->ludwig) (2.0.1)
Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.1->ludwig) (2.0.2)
Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.1->ludwig) (1.0.2)
Collecting thinc<7.1.0,>=7.0.2 (from spacy>=2.1->ludwig)
?25l Downloading https://files.pythonhosted.org/packages/a9/f1/3df317939a07b2fc81be1a92ac10bf836a1d87b4016346b25f8b63dee321/thinc-7.0.4-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)
100% |████████████████████████████████| 2.1MB 11.3MB/s
?25hRequirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.1->ludwig) (2.18.4)
Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (1.0.7)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (0.33.1)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (0.7.1)
Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (1.13.0)
Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (3.7.1)
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (1.15.0)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (0.2.2)
Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (0.7.1)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (1.0.9)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (1.1.0)
Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ludwig) (1.13.1)
Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.0->ludwig) (40.9.0)
Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=1.8->scikit-image==0.14.2->ludwig) (4.4.0)
Requirement already satisfied: toolz>=0.7.3; extra == "array" in /usr/local/lib/python3.6/dist-packages (from dask[array]>=1.0.0->scikit-image==0.14.2->ludwig) (0.9.0)
Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.3.0->scikit-image==0.14.2->ludwig) (0.46)
Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.1->ludwig) (1.22)
Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.1->ludwig) (2.6)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.1->ludwig) (3.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.1->ludwig) (2019.3.9)
Requirement already satisfied: mock>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow==1.13.1->ludwig) (2.0.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1->ludwig) (3.1)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1->ludwig) (0.15.2)
Requirement already satisfied: pbr>=0.11 in /usr/local/lib/python3.6/dist-packages (from mock>=2.0.0->tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow==1.13.1->ludwig) (5.1.3)
Building wheels for collected packages: ludwig
Building wheel for ludwig (setup.py) ... ?25ldone
?25h Stored in directory: /root/.cache/pip/wheels/95/e6/05/fa2b84191f6635508ed189ff80d40a641b4c42bc9709194c4d
Successfully built ludwig
Installing collected packages: srsly, wasabi, blis, thinc, spacy, ludwig
Found existing installation: thinc 6.12.1
Uninstalling thinc-6.12.1:
Successfully uninstalled thinc-6.12.1
Found existing installation: spacy 2.0.18
Uninstalling spacy-2.0.18:
Successfully uninstalled spacy-2.0.18
Successfully installed blis-0.2.4 ludwig-0.1.1 spacy-2.1.3 srsly-0.0.5 thinc-7.0.4 wasabi-0.2.1
!wget https://raw.githubusercontent.com/rpi-techfundamentals/fall2018-materials/master/input/train.csv && wget https://raw.githubusercontent.com/rpi-techfundamentals/fall2018-materials/master/input/test.csv
--2019-04-15 14:41:15-- https://raw.githubusercontent.com/rpi-techfundamentals/fall2018-materials/master/input/train.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 61194 (60K) [text/plain]
Saving to: ‘train.csv’
train.csv 0%[ ] 0 --.-KB/s
train.csv 100%[===================>] 59.76K --.-KB/s in 0.02s
2019-04-15 14:41:15 (2.34 MB/s) - ‘train.csv’ saved [61194/61194]
--2019-04-15 14:41:15-- https://raw.githubusercontent.com/rpi-techfundamentals/fall2018-materials/master/input/test.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 28629 (28K) [text/plain]
Saving to: ‘test.csv’
test.csv 100%[===================>] 27.96K --.-KB/s in 0.01s
2019-04-15 14:41:15 (2.28 MB/s) - ‘test.csv’ saved [28629/28629]
76.1. Model Definition File#
Here in order to describe the model, we need to create/download a model definition file. This is a simple file that describes the data.
input_features:
-
name: text
type: text
level: word
encoder: parallel_cnn
output_features:
-
name: class
type: category
!wget https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/13-deep-learning3/model_definition.yaml
--2019-04-15 14:41:17-- https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/13-deep-learning3/model_definition.yaml
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 524 [text/plain]
Saving to: ‘model_definition.yaml’
model_definition.ya 0%[ ] 0 --.-KB/s
model_definition.ya 100%[===================>] 524 --.-KB/s in 0s
2019-04-15 14:41:17 (83.2 MB/s) - ‘model_definition.yaml’ saved [524/524]
!cat model_definition.yaml
input_features:
-
name: Pclass
type: category
-
name: Sex
type: category
-
name: Age
type: numerical
missing_value_strategy: fill_with_mean
-
name: SibSp
type: numerical
-
name: Parch
type: numerical
-
name: Fare
type: numerical
missing_value_strategy: fill_with_mean
-
name: Embarked
type: category
output_features:
-
name: Survived
type: binary
76.2. Training the Model#
We are good to now train the model.
While previously we have always done splits and done all of our training in core python. Here we are just going to call the ludwig command line tool.
ludwig experiment
–data_csv reuters-allcats.csv
–model_definition_file model_definition.yaml
!ludwig experiment --data_csv train.csv \
--model_definition_file model_definition.yaml
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
_ _ _
| |_ _ __| |_ __ _(_)__ _
| | || / _` \ V V / / _` |
|_|\_,_\__,_|\_/\_/|_\__, |
|___/
ludwig v0.1.1 - Experiment
Experiment name: experiment
Model name: run
Output path: results/experiment_run_0
ludwig_version: '0.1.1'
command: ('/usr/local/bin/ludwig experiment --data_csv train.csv '
'--model_definition_file model_definition.yaml')
dataset_type: 'generic'
random_seed: 42
input_data: 'train.csv'
model_definition: { 'combiner': {'type': 'concat'},
'input_features': [ { 'name': 'Pclass',
'tied_weights': None,
'type': 'category'},
{ 'name': 'Sex',
'tied_weights': None,
'type': 'category'},
{ 'missing_value_strategy': 'fill_with_mean',
'name': 'Age',
'tied_weights': None,
'type': 'numerical'},
{ 'name': 'SibSp',
'tied_weights': None,
'type': 'numerical'},
{ 'name': 'Parch',
'tied_weights': None,
'type': 'numerical'},
{ 'missing_value_strategy': 'fill_with_mean',
'name': 'Fare',
'tied_weights': None,
'type': 'numerical'},
{ 'name': 'Embarked',
'tied_weights': None,
'type': 'category'}],
'output_features': [ { 'dependencies': [],
'loss': { 'confidence_penalty': 0,
'robust_lambda': 0,
'threshold': 0.5,
'weight': 1},
'name': 'Survived',
'reduce_dependencies': 'sum',
'reduce_input': 'sum',
'threshold': 0.5,
'type': 'binary',
'weight': 1}],
'preprocessing': { 'bag': { 'fill_value': '',
'format': 'space',
'lowercase': False,
'missing_value_strategy': 'fill_with_const',
'most_common': 10000},
'binary': { 'fill_value': 0,
'missing_value_strategy': 'fill_with_const'},
'category': { 'fill_value': '<UNK>',
'lowercase': False,
'missing_value_strategy': 'fill_with_const',
'most_common': 10000},
'force_split': False,
'image': { 'in_memory': True,
'missing_value_strategy': 'backfill',
'resize_method': 'crop_or_pad'},
'numerical': { 'fill_value': 0,
'missing_value_strategy': 'fill_with_const'},
'sequence': { 'fill_value': '',
'format': 'space',
'lowercase': False,
'missing_value_strategy': 'fill_with_const',
'most_common': 20000,
'padding': 'right',
'padding_symbol': '<PAD>',
'sequence_length_limit': 256,
'unknown_symbol': '<UNK>'},
'set': { 'fill_value': '',
'format': 'space',
'lowercase': False,
'missing_value_strategy': 'fill_with_const',
'most_common': 10000},
'split_probabilities': (0.7, 0.1, 0.2),
'stratify': None,
'text': { 'char_format': 'characters',
'char_most_common': 70,
'char_sequence_length_limit': 1024,
'fill_value': '',
'lowercase': True,
'missing_value_strategy': 'fill_with_const',
'padding': 'right',
'padding_symbol': '<PAD>',
'unknown_symbol': '<UNK>',
'word_format': 'space_punct',
'word_most_common': 20000,
'word_sequence_length_limit': 256},
'timeseries': { 'fill_value': '',
'format': 'space',
'missing_value_strategy': 'fill_with_const',
'padding': 'right',
'padding_value': 0,
'timeseries_length_limit': 256}},
'training': { 'batch_size': 128,
'bucketing_field': None,
'decay': False,
'decay_rate': 0.96,
'decay_steps': 10000,
'dropout_rate': 0.0,
'early_stop': 5,
'epochs': 100,
'eval_batch_size': 0,
'gradient_clipping': None,
'increase_batch_size_on_plateau': 0,
'increase_batch_size_on_plateau_max': 512,
'increase_batch_size_on_plateau_patience': 5,
'increase_batch_size_on_plateau_rate': 2,
'learning_rate': 0.001,
'learning_rate_warmup_epochs': 5,
'optimizer': { 'beta1': 0.9,
'beta2': 0.999,
'epsilon': 1e-08,
'type': 'adam'},
'reduce_learning_rate_on_plateau': 0,
'reduce_learning_rate_on_plateau_patience': 5,
'reduce_learning_rate_on_plateau_rate': 0.5,
'regularization_lambda': 0,
'regularizer': 'l2',
'staircase': False,
'validation_field': 'combined',
'validation_measure': 'loss'}}
Using full raw csv, no hdf5 and json file with the same name have been found
Building dataset (it may take a while)
/usr/local/lib/python3.6/dist-packages/ludwig/features/numerical_feature.py:63: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
np.float32).as_matrix()
/usr/local/lib/python3.6/dist-packages/ludwig/features/binary_feature.py:62: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
np.bool_).as_matrix()
Writing dataset
Writing train set metadata with vocabulary
Training set: 630
Validation set: 81
Test set: 180
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
embedding_size (50) is greater than vocab_size (4). Setting embedding size to be equal to vocab_size.
embedding_size (50) is greater than vocab_size (3). Setting embedding size to be equal to vocab_size.
embedding_size (50) is greater than vocab_size (5). Setting embedding size to be equal to vocab_size.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
╒══════════╕
│ TRAINING │
╘══════════╛
2019-04-15 14:41:29.343441: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
2019-04-15 14:41:29.346608: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2020d60 executing computations on platform Host. Devices:
2019-04-15 14:41:29.346694: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
Epoch 1
Training: 100% 5/5 [00:00<00:00, 10.08it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 110.90it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 614.91it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 729.38it/s]
Took 0.5719s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 7.9026 │ 0.3968 │
├────────────┼────────┼────────────┤
│ vali │ 7.5711 │ 0.3827 │
├────────────┼────────┼────────────┤
│ test │ 8.8524 │ 0.3667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 2
Training: 100% 5/5 [00:00<00:00, 276.59it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 732.60it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 755.87it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 843.92it/s]
Took 0.3235s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 7.6934 │ 0.3968 │
├────────────┼────────┼────────────┤
│ vali │ 7.3628 │ 0.3827 │
├────────────┼────────┼────────────┤
│ test │ 8.6174 │ 0.3667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 3
Training: 100% 5/5 [00:00<00:00, 443.98it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 771.10it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 710.66it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 322.44it/s]
Took 0.2996s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 7.4851 │ 0.3968 │
├────────────┼────────┼────────────┤
│ vali │ 7.1558 │ 0.3827 │
├────────────┼────────┼────────────┤
│ test │ 8.3837 │ 0.3611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 4
Training: 100% 5/5 [00:00<00:00, 455.84it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 773.37it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 834.36it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 865.97it/s]
Took 0.2475s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 7.2782 │ 0.3968 │
├────────────┼────────┼────────────┤
│ vali │ 6.9506 │ 0.3827 │
├────────────┼────────┼────────────┤
│ test │ 8.1514 │ 0.3611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 5
Training: 100% 5/5 [00:00<00:00, 445.86it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 814.71it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 660.52it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 837.94it/s]
Took 0.2481s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 7.0727 │ 0.3984 │
├────────────┼────────┼────────────┤
│ vali │ 6.7476 │ 0.3951 │
├────────────┼────────┼────────────┤
│ test │ 7.9209 │ 0.3611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 6
Training: 100% 5/5 [00:00<00:00, 461.45it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 784.69it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 843.08it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 882.45it/s]
Took 0.2411s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 6.8693 │ 0.4000 │
├────────────┼────────┼────────────┤
│ vali │ 6.5471 │ 0.3951 │
├────────────┼────────┼────────────┤
│ test │ 7.6929 │ 0.3667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 7
Training: 100% 5/5 [00:00<00:00, 454.23it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 779.84it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 816.97it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 831.46it/s]
Took 0.2468s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 6.6683 │ 0.4016 │
├────────────┼────────┼────────────┤
│ vali │ 6.3495 │ 0.4074 │
├────────────┼────────┼────────────┤
│ test │ 7.4677 │ 0.3722 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 8
Training: 100% 5/5 [00:00<00:00, 430.87it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 746.34it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 778.74it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 656.75it/s]
Took 0.2715s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 6.4704 │ 0.4000 │
├────────────┼────────┼────────────┤
│ vali │ 6.1552 │ 0.4074 │
├────────────┼────────┼────────────┤
│ test │ 7.2459 │ 0.3778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 9
Training: 100% 5/5 [00:00<00:00, 413.70it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 746.18it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 744.86it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 817.68it/s]
Took 0.2651s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 6.2761 │ 0.4032 │
├────────────┼────────┼────────────┤
│ vali │ 5.9643 │ 0.4074 │
├────────────┼────────┼────────────┤
│ test │ 7.0279 │ 0.3833 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 10
Training: 100% 5/5 [00:00<00:00, 440.89it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 778.60it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 786.33it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 851.46it/s]
Took 0.2500s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 6.0861 │ 0.4127 │
├────────────┼────────┼────────────┤
│ vali │ 5.7773 │ 0.4198 │
├────────────┼────────┼────────────┤
│ test │ 6.8142 │ 0.3889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 11
Training: 100% 5/5 [00:00<00:00, 411.29it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 772.66it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 789.29it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 814.82it/s]
Took 0.2603s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 5.9009 │ 0.4095 │
├────────────┼────────┼────────────┤
│ vali │ 5.5945 │ 0.4198 │
├────────────┼────────┼────────────┤
│ test │ 6.6052 │ 0.3889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 12
Training: 100% 5/5 [00:00<00:00, 436.50it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 758.24it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 762.60it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 867.58it/s]
Took 0.2527s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 5.7210 │ 0.4190 │
├────────────┼────────┼────────────┤
│ vali │ 5.4165 │ 0.4198 │
├────────────┼────────┼────────────┤
│ test │ 6.4013 │ 0.3944 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 13
Training: 100% 5/5 [00:00<00:00, 476.17it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 777.59it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 787.51it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 810.57it/s]
Took 0.2433s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 5.5470 │ 0.4270 │
├────────────┼────────┼────────────┤
│ vali │ 5.2436 │ 0.4198 │
├────────────┼────────┼────────────┤
│ test │ 6.2030 │ 0.3944 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 14
Training: 100% 5/5 [00:00<00:00, 452.28it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 789.50it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 789.59it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 843.33it/s]
Took 0.2463s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 5.3792 │ 0.4365 │
├────────────┼────────┼────────────┤
│ vali │ 5.0764 │ 0.4198 │
├────────────┼────────┼────────────┤
│ test │ 6.0107 │ 0.4111 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 15
Training: 100% 5/5 [00:00<00:00, 451.66it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 579.71it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 820.80it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 861.08it/s]
Took 0.2671s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 5.2179 │ 0.4444 │
├────────────┼────────┼────────────┤
│ vali │ 4.9153 │ 0.4321 │
├────────────┼────────┼────────────┤
│ test │ 5.8247 │ 0.4278 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 16
Training: 100% 5/5 [00:00<00:00, 448.99it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 712.52it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 780.92it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 810.89it/s]
Took 0.2627s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 5.0633 │ 0.4508 │
├────────────┼────────┼────────────┤
│ vali │ 4.7606 │ 0.4444 │
├────────────┼────────┼────────────┤
│ test │ 5.6452 │ 0.4444 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 17
Training: 100% 5/5 [00:00<00:00, 404.22it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 765.66it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 835.35it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 835.94it/s]
Took 0.2625s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 4.9156 │ 0.4476 │
├────────────┼────────┼────────────┤
│ vali │ 4.6128 │ 0.4691 │
├────────────┼────────┼────────────┤
│ test │ 5.4724 │ 0.4611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 18
Training: 100% 5/5 [00:00<00:00, 447.81it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 695.34it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 667.46it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 791.45it/s]
Took 0.2608s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 4.7748 │ 0.4540 │
├────────────┼────────┼────────────┤
│ vali │ 4.4719 │ 0.5062 │
├────────────┼────────┼────────────┤
│ test │ 5.3063 │ 0.4778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 19
Training: 100% 5/5 [00:00<00:00, 391.12it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 680.52it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 670.45it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 711.80it/s]
Took 0.2840s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 4.6408 │ 0.4683 │
├────────────┼────────┼────────────┤
│ vali │ 4.3382 │ 0.5309 │
├────────────┼────────┼────────────┤
│ test │ 5.1471 │ 0.4944 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 20
Training: 100% 5/5 [00:00<00:00, 443.24it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 772.83it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 778.74it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 824.35it/s]
Took 0.2519s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 4.5136 │ 0.4714 │
├────────────┼────────┼────────────┤
│ vali │ 4.2120 │ 0.5432 │
├────────────┼────────┼────────────┤
│ test │ 4.9946 │ 0.5111 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 21
Training: 100% 5/5 [00:00<00:00, 456.19it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 770.22it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 744.46it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 864.98it/s]
Took 0.2458s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 4.3929 │ 0.4794 │
├────────────┼────────┼────────────┤
│ vali │ 4.0931 │ 0.5432 │
├────────────┼────────┼────────────┤
│ test │ 4.8489 │ 0.5111 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 22
Training: 100% 5/5 [00:00<00:00, 447.02it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 742.78it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 700.22it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 764.69it/s]
Took 0.2560s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 4.2788 │ 0.4905 │
├────────────┼────────┼────────────┤
│ vali │ 3.9815 │ 0.5309 │
├────────────┼────────┼────────────┤
│ test │ 4.7099 │ 0.5111 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 23
Training: 100% 5/5 [00:00<00:00, 462.39it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 802.34it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 839.87it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 838.19it/s]
Took 0.2412s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 4.1710 │ 0.5127 │
├────────────┼────────┼────────────┤
│ vali │ 3.8767 │ 0.5556 │
├────────────┼────────┼────────────┤
│ test │ 4.5776 │ 0.5222 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 24
Training: 100% 5/5 [00:00<00:00, 438.30it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 770.90it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 829.73it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 848.36it/s]
Took 0.2502s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 4.0692 │ 0.5238 │
├────────────┼────────┼────────────┤
│ vali │ 3.7784 │ 0.5679 │
├────────────┼────────┼────────────┤
│ test │ 4.4520 │ 0.5444 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 25
Training: 100% 5/5 [00:00<00:00, 419.62it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 783.22it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 736.49it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 844.01it/s]
Took 0.2558s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.9733 │ 0.5349 │
├────────────┼────────┼────────────┤
│ vali │ 3.6860 │ 0.5926 │
├────────────┼────────┼────────────┤
│ test │ 4.3329 │ 0.5556 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 26
Training: 100% 5/5 [00:00<00:00, 486.97it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 792.13it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 666.82it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 836.27it/s]
Took 0.2403s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.8830 │ 0.5413 │
├────────────┼────────┼────────────┤
│ vali │ 3.5990 │ 0.5926 │
├────────────┼────────┼────────────┤
│ test │ 4.2202 │ 0.5722 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 27
Training: 100% 5/5 [00:00<00:00, 437.38it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 752.83it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 726.66it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 827.28it/s]
Took 0.2552s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.7980 │ 0.5571 │
├────────────┼────────┼────────────┤
│ vali │ 3.5170 │ 0.6173 │
├────────────┼────────┼────────────┤
│ test │ 4.1136 │ 0.5944 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 28
Training: 100% 5/5 [00:00<00:00, 473.07it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 713.32it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 832.70it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 814.67it/s]
Took 0.2473s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.7180 │ 0.5587 │
├────────────┼────────┼────────────┤
│ vali │ 3.4395 │ 0.6049 │
├────────────┼────────┼────────────┤
│ test │ 4.0128 │ 0.5944 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 29
Training: 100% 5/5 [00:00<00:00, 454.26it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 782.78it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 830.88it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 813.80it/s]
Took 0.2479s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.6428 │ 0.5683 │
├────────────┼────────┼────────────┤
│ vali │ 3.3662 │ 0.6049 │
├────────────┼────────┼────────────┤
│ test │ 3.9176 │ 0.6056 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 30
Training: 100% 5/5 [00:00<00:00, 416.43it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 780.74it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 793.32it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 820.32it/s]
Took 0.2561s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.5719 │ 0.5651 │
├────────────┼────────┼────────────┤
│ vali │ 3.2969 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 3.8276 │ 0.6000 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 31
Training: 100% 5/5 [00:00<00:00, 454.96it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 812.00it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 854.41it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 850.60it/s]
Took 0.2407s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.5053 │ 0.5746 │
├────────────┼────────┼────────────┤
│ vali │ 3.2311 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 3.7425 │ 0.6000 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 32
Training: 100% 5/5 [00:00<00:00, 464.45it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 773.60it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 701.98it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 806.05it/s]
Took 0.2474s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.4424 │ 0.5841 │
├────────────┼────────┼────────────┤
│ vali │ 3.1687 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 3.6620 │ 0.6056 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 33
Training: 100% 5/5 [00:00<00:00, 453.83it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 757.97it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 718.20it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 551.23it/s]
Took 0.2634s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.3830 │ 0.5857 │
├────────────┼────────┼────────────┤
│ vali │ 3.1093 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 3.5857 │ 0.6056 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 34
Training: 100% 5/5 [00:00<00:00, 440.54it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 778.19it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 722.91it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 799.68it/s]
Took 0.2574s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.3268 │ 0.5857 │
├────────────┼────────┼────────────┤
│ vali │ 3.0528 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 3.5134 │ 0.6167 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 35
Training: 100% 5/5 [00:00<00:00, 466.61it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 784.80it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 818.40it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 873.90it/s]
Took 0.2424s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.2734 │ 0.5952 │
├────────────┼────────┼────────────┤
│ vali │ 2.9989 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 3.4448 │ 0.6222 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 36
Training: 100% 5/5 [00:00<00:00, 486.31it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 796.12it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 785.01it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 845.20it/s]
Took 0.2379s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.2225 │ 0.5984 │
├────────────┼────────┼────────────┤
│ vali │ 2.9473 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 3.3795 │ 0.6333 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 37
Training: 100% 5/5 [00:00<00:00, 444.96it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 758.55it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 722.78it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 819.68it/s]
Took 0.2551s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.1740 │ 0.5937 │
├────────────┼────────┼────────────┤
│ vali │ 2.8980 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 3.3173 │ 0.6389 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 38
Training: 100% 5/5 [00:00<00:00, 420.89it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 715.48it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 774.43it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 830.64it/s]
Took 0.2631s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.1275 │ 0.5952 │
├────────────┼────────┼────────────┤
│ vali │ 2.8507 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 3.2579 │ 0.6500 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 39
Training: 100% 5/5 [00:00<00:00, 435.36it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 769.40it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 820.80it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 863.11it/s]
Took 0.2513s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.0828 │ 0.5952 │
├────────────┼────────┼────────────┤
│ vali │ 2.8052 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 3.2010 │ 0.6444 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 40
Training: 100% 5/5 [00:00<00:00, 469.51it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 747.19it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 777.30it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 871.63it/s]
Took 0.2456s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 3.0398 │ 0.5968 │
├────────────┼────────┼────────────┤
│ vali │ 2.7613 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 3.1465 │ 0.6556 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 41
Training: 100% 5/5 [00:00<00:00, 469.54it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 796.40it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 780.92it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 790.63it/s]
Took 0.2431s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.9982 │ 0.5984 │
├────────────┼────────┼────────────┤
│ vali │ 2.7190 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 3.0941 │ 0.6556 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 42
Training: 100% 5/5 [00:00<00:00, 430.72it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 731.10it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 704.45it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 851.98it/s]
Took 0.2605s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.9580 │ 0.5952 │
├────────────┼────────┼────────────┤
│ vali │ 2.6781 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 3.0437 │ 0.6556 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 43
Training: 100% 5/5 [00:00<00:00, 351.76it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 660.10it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 752.07it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 560.36it/s]
Took 0.3093s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.9190 │ 0.5952 │
├────────────┼────────┼────────────┤
│ vali │ 2.6384 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 2.9950 │ 0.6556 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 44
Training: 100% 5/5 [00:00<00:00, 436.36it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 725.21it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 770.87it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 806.67it/s]
Took 0.2577s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.8810 │ 0.5952 │
├────────────┼────────┼────────────┤
│ vali │ 2.5999 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 2.9479 │ 0.6667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 45
Training: 100% 5/5 [00:00<00:00, 432.57it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 765.41it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 795.13it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 815.93it/s]
Took 0.2563s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.8441 │ 0.5952 │
├────────────┼────────┼────────────┤
│ vali │ 2.5625 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 2.9023 │ 0.6556 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 46
Training: 100% 5/5 [00:00<00:00, 466.43it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 804.96it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 814.27it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 849.74it/s]
Took 0.2414s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.8080 │ 0.5952 │
├────────────┼────────┼────────────┤
│ vali │ 2.5261 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 2.8581 │ 0.6556 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 47
Training: 100% 5/5 [00:00<00:00, 456.46it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 793.74it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 790.93it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 823.14it/s]
Took 0.2458s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.7728 │ 0.6000 │
├────────────┼────────┼────────────┤
│ vali │ 2.4907 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 2.8150 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 48
Training: 100% 5/5 [00:00<00:00, 413.87it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 769.88it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 783.69it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 816.17it/s]
Took 0.2590s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.7383 │ 0.6016 │
├────────────┼────────┼────────────┤
│ vali │ 2.4560 │ 0.6420 │
├────────────┼────────┼────────────┤
│ test │ 2.7732 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 49
Training: 100% 5/5 [00:00<00:00, 467.02it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 764.30it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 827.28it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 856.24it/s]
Took 0.2435s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.7044 │ 0.6016 │
├────────────┼────────┼────────────┤
│ vali │ 2.4222 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 2.7323 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 50
Training: 100% 5/5 [00:00<00:00, 436.84it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 797.37it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 840.37it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 858.52it/s]
Took 0.2484s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.6712 │ 0.6000 │
├────────────┼────────┼────────────┤
│ vali │ 2.3891 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 2.6924 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 51
Training: 100% 5/5 [00:00<00:00, 328.96it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 792.69it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 830.06it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 841.38it/s]
Took 0.2872s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.6385 │ 0.6016 │
├────────────┼────────┼────────────┤
│ vali │ 2.3566 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 2.6534 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 52
Training: 100% 5/5 [00:00<00:00, 243.78it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 790.96it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 726.66it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 863.38it/s]
Took 0.3631s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.6063 │ 0.6016 │
├────────────┼────────┼────────────┤
│ vali │ 2.3248 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 2.6153 │ 0.6667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 53
Training: 100% 5/5 [00:00<00:00, 413.96it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 732.76it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 759.70it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 834.11it/s]
Took 0.2628s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.5745 │ 0.6016 │
├────────────┼────────┼────────────┤
│ vali │ 2.2935 │ 0.6296 │
├────────────┼────────┼────────────┤
│ test │ 2.5779 │ 0.6667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 54
Training: 100% 5/5 [00:00<00:00, 465.53it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 769.37it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 813.95it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 859.14it/s]
Took 0.2446s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.5432 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 2.2627 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.5412 │ 0.6667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 55
Training: 100% 5/5 [00:00<00:00, 437.41it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 794.35it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 796.19it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 832.45it/s]
Took 0.2502s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.5122 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 2.2324 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.5052 │ 0.6667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 56
Training: 100% 5/5 [00:00<00:00, 274.75it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 759.95it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 731.99it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 769.46it/s]
Took 0.3527s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.4816 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 2.2026 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.4698 │ 0.6667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 57
Training: 100% 5/5 [00:00<00:00, 289.87it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 646.33it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 824.68it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 797.85it/s]
Took 0.3365s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.4513 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 2.1732 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.4349 │ 0.6667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 58
Training: 100% 5/5 [00:00<00:00, 470.30it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 807.34it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 803.51it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 825.89it/s]
Took 0.2415s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.4213 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 2.1442 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.4007 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 59
Training: 100% 5/5 [00:00<00:00, 450.07it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 746.18it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 740.00it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 796.26it/s]
Took 0.2662s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.3916 │ 0.6016 │
├────────────┼────────┼────────────┤
│ vali │ 2.1155 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.3669 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 60
Training: 100% 5/5 [00:00<00:00, 460.15it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 760.75it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 764.27it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 841.05it/s]
Took 0.2500s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.3621 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 2.0872 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.3337 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 61
Training: 100% 5/5 [00:00<00:00, 403.43it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 710.10it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 840.21it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 632.34it/s]
Took 0.2807s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.3328 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 2.0592 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.3009 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 62
Training: 100% 5/5 [00:00<00:00, 428.22it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 668.14it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 777.15it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 669.21it/s]
Took 0.2748s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.3038 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 2.0315 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.2685 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 63
Training: 100% 5/5 [00:00<00:00, 454.09it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 797.18it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 780.63it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 845.20it/s]
Took 0.2465s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.2750 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 2.0042 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.2366 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 64
Training: 100% 5/5 [00:00<00:00, 453.88it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 608.81it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 757.92it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 833.77it/s]
Took 0.2652s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.2464 │ 0.6016 │
├────────────┼────────┼────────────┤
│ vali │ 1.9770 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.2051 │ 0.6611 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 65
Training: 100% 5/5 [00:00<00:00, 412.13it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 727.52it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 776.29it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 792.42it/s]
Took 0.2643s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.2180 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 1.9502 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.1739 │ 0.6667 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 66
Training: 100% 5/5 [00:00<00:00, 427.18it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 747.65it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 807.68it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 777.30it/s]
Took 0.2586s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.1898 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 1.9236 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.1431 │ 0.6722 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 67
Training: 100% 5/5 [00:00<00:00, 471.89it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 803.51it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 832.86it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 880.42it/s]
Took 0.2424s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.1618 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 1.8973 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.1127 │ 0.6722 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 68
Training: 100% 5/5 [00:00<00:00, 298.62it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 796.73it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 829.08it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 844.77it/s]
Took 0.3015s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.1339 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 1.8712 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.0826 │ 0.6722 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 69
Training: 100% 5/5 [00:00<00:00, 447.07it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 346.37it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 766.08it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 860.72it/s]
Took 0.3286s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.1062 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 1.8453 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.0529 │ 0.6722 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 70
Training: 100% 5/5 [00:00<00:00, 308.59it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 674.74it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 823.38it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 851.89it/s]
Took 0.3164s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.0787 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 1.8196 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 2.0235 │ 0.6722 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 71
Training: 100% 5/5 [00:00<00:00, 460.36it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 403.67it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 713.68it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 831.71it/s]
Took 0.3091s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.0514 │ 0.6032 │
├────────────┼────────┼────────────┤
│ vali │ 1.7942 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 1.9944 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 72
Training: 100% 5/5 [00:00<00:00, 453.82it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 417.22it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 690.42it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 848.45it/s]
Took 0.3130s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 2.0242 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 1.7690 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 1.9655 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 73
Training: 100% 5/5 [00:00<00:00, 450.93it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 742.78it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 693.50it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 805.05it/s]
Took 0.2543s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.9972 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 1.7440 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 1.9370 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 74
Training: 100% 5/5 [00:00<00:00, 451.47it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 825.81it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 801.51it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 789.59it/s]
Took 0.2448s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.9703 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 1.7192 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 1.9088 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 75
Training: 100% 5/5 [00:00<00:00, 449.40it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 767.51it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 843.08it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 845.71it/s]
Took 0.2527s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.9436 │ 0.6048 │
├────────────┼────────┼────────────┤
│ vali │ 1.6946 │ 0.6543 │
├────────────┼────────┼────────────┤
│ test │ 1.8808 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 76
Training: 100% 5/5 [00:00<00:00, 320.58it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 734.19it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 723.90it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 826.30it/s]
Took 0.3138s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.9171 │ 0.6063 │
├────────────┼────────┼────────────┤
│ vali │ 1.6703 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.8532 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 77
Training: 100% 5/5 [00:00<00:00, 466.08it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 818.85it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 748.45it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 858.17it/s]
Took 0.2412s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.8907 │ 0.6079 │
├────────────┼────────┼────────────┤
│ vali │ 1.6461 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.8258 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 78
Training: 100% 5/5 [00:00<00:00, 421.08it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 761.99it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 858.61it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 865.34it/s]
Took 0.2541s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.8645 │ 0.6079 │
├────────────┼────────┼────────────┤
│ vali │ 1.6221 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.7986 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 79
Training: 100% 5/5 [00:00<00:00, 452.93it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 779.99it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 815.38it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 813.24it/s]
Took 0.2471s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.8385 │ 0.6095 │
├────────────┼────────┼────────────┤
│ vali │ 1.5984 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.7718 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 80
Training: 100% 5/5 [00:00<00:00, 442.53it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 768.78it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 770.30it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 761.42it/s]
Took 0.2552s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.8127 │ 0.6095 │
├────────────┼────────┼────────────┤
│ vali │ 1.5749 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.7451 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 81
Training: 100% 5/5 [00:00<00:00, 414.58it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 758.85it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 840.54it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 777.88it/s]
Took 0.2594s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.7870 │ 0.6095 │
├────────────┼────────┼────────────┤
│ vali │ 1.5515 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.7188 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 82
Training: 100% 5/5 [00:00<00:00, 391.73it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 751.61it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 690.99it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 841.89it/s]
Took 0.2698s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.7615 │ 0.6127 │
├────────────┼────────┼────────────┤
│ vali │ 1.5284 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.6927 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 83
Training: 100% 5/5 [00:00<00:00, 301.75it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 746.69it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 874.91it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 819.52it/s]
Took 0.3045s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.7362 │ 0.6127 │
├────────────┼────────┼────────────┤
│ vali │ 1.5055 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.6668 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 84
Training: 100% 5/5 [00:00<00:00, 369.40it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 655.20it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 774.43it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 786.19it/s]
Took 0.2939s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.7110 │ 0.6127 │
├────────────┼────────┼────────────┤
│ vali │ 1.4828 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.6412 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 85
Training: 100% 5/5 [00:00<00:00, 475.28it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 819.07it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 818.72it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 850.60it/s]
Took 0.2376s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.6861 │ 0.6111 │
├────────────┼────────┼────────────┤
│ vali │ 1.4603 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.6158 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 86
Training: 100% 5/5 [00:00<00:00, 422.43it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 795.88it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 786.04it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 720.98it/s]
Took 0.2584s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.6613 │ 0.6111 │
├────────────┼────────┼────────────┤
│ vali │ 1.4381 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.5907 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 87
Training: 100% 5/5 [00:00<00:00, 440.43it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 795.64it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 722.41it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 877.65it/s]
Took 0.2490s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.6367 │ 0.6111 │
├────────────┼────────┼────────────┤
│ vali │ 1.4161 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.5658 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 88
Training: 100% 5/5 [00:00<00:00, 421.30it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 774.97it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 791.08it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 698.35it/s]
Took 0.3407s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.6124 │ 0.6111 │
├────────────┼────────┼────────────┤
│ vali │ 1.3942 │ 0.6667 │
├────────────┼────────┼────────────┤
│ test │ 1.5412 │ 0.6778 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 89
Training: 100% 5/5 [00:00<00:00, 430.04it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 790.39it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 582.62it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 813.48it/s]
Took 0.2596s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.5882 │ 0.6111 │
├────────────┼────────┼────────────┤
│ vali │ 1.3727 │ 0.6790 │
├────────────┼────────┼────────────┤
│ test │ 1.5168 │ 0.6833 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 90
Training: 100% 5/5 [00:00<00:00, 437.97it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 767.60it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 649.37it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 787.81it/s]
Took 0.2602s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.5642 │ 0.6159 │
├────────────┼────────┼────────────┤
│ vali │ 1.3513 │ 0.6790 │
├────────────┼────────┼────────────┤
│ test │ 1.4927 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 91
Training: 100% 5/5 [00:00<00:00, 457.56it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 743.46it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 829.24it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 845.71it/s]
Took 0.2483s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.5405 │ 0.6175 │
├────────────┼────────┼────────────┤
│ vali │ 1.3302 │ 0.6914 │
├────────────┼────────┼────────────┤
│ test │ 1.4688 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 92
Training: 100% 5/5 [00:00<00:00, 440.29it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 706.11it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 718.57it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 856.50it/s]
Took 0.2597s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.5169 │ 0.6365 │
├────────────┼────────┼────────────┤
│ vali │ 1.3093 │ 0.7037 │
├────────────┼────────┼────────────┤
│ test │ 1.4452 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 93
Training: 100% 5/5 [00:00<00:00, 384.35it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 760.47it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 800.44it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 839.62it/s]
Took 0.2693s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.4936 │ 0.6524 │
├────────────┼────────┼────────────┤
│ vali │ 1.2886 │ 0.7160 │
├────────────┼────────┼────────────┤
│ test │ 1.4218 │ 0.6944 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 94
Training: 100% 5/5 [00:00<00:00, 459.83it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 770.96it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 817.44it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 846.82it/s]
Took 0.2464s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.4705 │ 0.6524 │
├────────────┼────────┼────────────┤
│ vali │ 1.2682 │ 0.7160 │
├────────────┼────────┼────────────┤
│ test │ 1.3987 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 95
Training: 100% 5/5 [00:00<00:00, 468.25it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 748.80it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 863.56it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 848.36it/s]
Took 0.2432s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.4476 │ 0.6540 │
├────────────┼────────┼────────────┤
│ vali │ 1.2481 │ 0.7160 │
├────────────┼────────┼────────────┤
│ test │ 1.3758 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 96
Training: 100% 5/5 [00:00<00:00, 444.56it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 790.66it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 829.73it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 729.89it/s]
Took 0.2514s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.4249 │ 0.6524 │
├────────────┼────────┼────────────┤
│ vali │ 1.2282 │ 0.7160 │
├────────────┼────────┼────────────┤
│ test │ 1.3531 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 97
Training: 100% 5/5 [00:00<00:00, 433.12it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 751.99it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 810.65it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 832.29it/s]
Took 0.2544s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.4025 │ 0.6508 │
├────────────┼────────┼────────────┤
│ vali │ 1.2085 │ 0.7160 │
├────────────┼────────┼────────────┤
│ test │ 1.3308 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 98
Training: 100% 5/5 [00:00<00:00, 475.64it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 804.34it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 804.59it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 834.60it/s]
Took 0.2385s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.3804 │ 0.6540 │
├────────────┼────────┼────────────┤
│ vali │ 1.1891 │ 0.7160 │
├────────────┼────────┼────────────┤
│ test │ 1.3087 │ 0.6944 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 99
Training: 100% 5/5 [00:00<00:00, 468.49it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 713.03it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 772.57it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 819.92it/s]
Took 0.2515s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.3584 │ 0.6540 │
├────────────┼────────┼────────────┤
│ vali │ 1.1699 │ 0.7160 │
├────────────┼────────┼────────────┤
│ test │ 1.2868 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Epoch 100
Training: 100% 5/5 [00:00<00:00, 412.88it/s]
Evaluation train: 100% 5/5 [00:00<00:00, 788.17it/s]
Evaluation vali : 100% 1/1 [00:00<00:00, 792.72it/s]
Evaluation test : 100% 2/2 [00:00<00:00, 849.48it/s]
Took 0.2582s
╒════════════╤════════╤════════════╕
│ Survived │ loss │ accuracy │
╞════════════╪════════╪════════════╡
│ train │ 1.3368 │ 0.6540 │
├────────────┼────────┼────────────┤
│ vali │ 1.1510 │ 0.7160 │
├────────────┼────────┼────────────┤
│ test │ 1.2652 │ 0.6889 │
╘════════════╧════════╧════════════╛
Validation loss on combined improved, model saved
Best validation model epoch: 100
Best validation model loss on validation set combined: 1.1510242415063174
Best validation model loss on test set combined: 1.2652276780870226
╒═════════╕
│ PREDICT │
╘═════════╛
Evaluation: 100% 2/2 [00:00<00:00, 50.27it/s]
===== Survived =====
accuracy: 0.6888888888888889
average_precision_macro: 0.6052279888757147
average_precision_micro: 0.6052279888757147
average_precision_samples: 0.6052279888757147
loss: 1.2652276780870226
overall_stats: { 'avg_f1_score_macro': 0.68,
'avg_f1_score_micro': 0.6888888888888889,
'avg_f1_score_weighted': 0.6948148148148149,
'avg_precision_macro': 0.681733746130031,
'avg_precision_micro': 0.6888888888888889,
'avg_precision_weighted': 0.6888888888888889,
'avg_recall_macro': 0.6963210702341137,
'avg_recall_micro': 0.6888888888888889,
'avg_recall_weighted': 0.6888888888888889,
'kappa_score': 0.36802507836990594,
'overall_accuracy': 0.6888888888888889}
per_class_stats: {False: { 'accuracy': 0.6888888888888889,
'f1_score': 0.7333333333333334,
'fall_out': 0.44705882352941173,
'false_discovery_rate': 0.33043478260869563,
'false_negative_rate': 0.18947368421052635,
'false_negatives': 18,
'false_omission_rate': 0.27692307692307694,
'false_positive_rate': 0.44705882352941173,
'false_positives': 38,
'hit_rate': 0.8105263157894737,
'informedness': 0.36346749226006203,
'markedness': 0.39264214046822743,
'matthews_correlation_coefficient': 0.377773284062822,
'miss_rate': 0.18947368421052635,
'negative_predictive_value': 0.7230769230769231,
'positive_predictive_value': 0.6695652173913044,
'precision': 0.6695652173913044,
'recall': 0.8105263157894737,
'sensitivity': 0.8105263157894737,
'specificity': 0.5529411764705883,
'true_negative_rate': 0.5529411764705883,
'true_negatives': 47,
'true_positive_rate': 0.8105263157894737,
'true_positives': 77},
True: { 'accuracy': 0.6888888888888889,
'f1_score': 0.6266666666666667,
'fall_out': 0.18947368421052635,
'false_discovery_rate': 0.27692307692307694,
'false_negative_rate': 0.44705882352941173,
'false_negatives': 38,
'false_omission_rate': 0.33043478260869563,
'false_positive_rate': 0.18947368421052635,
'false_positives': 18,
'hit_rate': 0.5529411764705883,
'informedness': 0.36346749226006203,
'markedness': 0.39264214046822743,
'matthews_correlation_coefficient': 0.377773284062822,
'miss_rate': 0.44705882352941173,
'negative_predictive_value': 0.6695652173913044,
'positive_predictive_value': 0.7230769230769231,
'precision': 0.7230769230769231,
'recall': 0.5529411764705883,
'sensitivity': 0.5529411764705883,
'specificity': 0.8105263157894737,
'true_negative_rate': 0.8105263157894737,
'true_negatives': 77,
'true_positive_rate': 0.5529411764705883,
'true_positives': 47}}
precision_recall_curve: { 'precisions': [ 0.39156626506024095,
0.3878787878787879,
0.3902439024390244,
0.39263803680981596,
0.3950617283950617,
0.39751552795031053,
0.4,
0.4025157232704403,
0.4050632911392405,
0.40764331210191085,
0.40384615384615385,
0.4064516129032258,
0.4090909090909091,
0.4117647058823529,
0.4144736842105263,
0.41721854304635764,
0.42,
0.4161073825503356,
0.4189189189189189,
0.4217687074829932,
0.4246575342465753,
0.42758620689655175,
0.4305555555555556,
0.43356643356643354,
0.43661971830985913,
0.4326241134751773,
0.4357142857142857,
0.43884892086330934,
0.4420289855072464,
0.44525547445255476,
0.4485294117647059,
0.45185185185185184,
0.4552238805970149,
0.45864661654135336,
0.4621212121212121,
0.4580152671755725,
0.46153846153846156,
0.46511627906976744,
0.46875,
0.47244094488188976,
0.47619047619047616,
0.48,
0.4838709677419355,
0.4878048780487805,
0.4918032786885246,
0.49586776859504134,
0.49166666666666664,
0.4957983193277311,
0.5,
0.5042735042735043,
0.5086206896551724,
0.5130434782608696,
0.5175438596491229,
0.5132743362831859,
0.5178571428571429,
0.5135135135135135,
0.509090909090909,
0.5045871559633027,
0.5092592592592593,
0.514018691588785,
0.5094339622641509,
0.5142857142857142,
0.5192307692307693,
0.5242718446601942,
0.5196078431372549,
0.5247524752475248,
0.53,
0.5353535353535354,
0.5408163265306123,
0.5463917525773195,
0.5520833333333334,
0.5473684210526316,
0.5425531914893617,
0.5483870967741935,
0.5604395604395604,
0.5555555555555556,
0.550561797752809,
0.5568181818181818,
0.5517241379310345,
0.5581395348837209,
0.5529411764705883,
0.5476190476190477,
0.5542168674698795,
0.5609756097560976,
0.5679012345679012,
0.575,
0.569620253164557,
0.5641025641025641,
0.5584415584415584,
0.5526315789473685,
0.56,
0.5833333333333334,
0.5915492957746479,
0.5857142857142857,
0.5942028985507246,
0.5970149253731343,
0.6060606060606061,
0.6,
0.59375,
0.6031746031746031,
0.5967741935483871,
0.5901639344262295,
0.6,
0.5932203389830508,
0.603448275862069,
0.5964912280701754,
0.6071428571428571,
0.6181818181818182,
0.6296296296296297,
0.6153846153846154,
0.6078431372549019,
0.62,
0.6122448979591837,
0.6041666666666666,
0.6170212765957447,
0.6086956521739131,
0.6,
0.6136363636363636,
0.627906976744186,
0.6190476190476191,
0.6097560975609756,
0.625,
0.6153846153846154,
0.631578947368421,
0.6216216216216216,
0.6111111111111112,
0.6285714285714286,
0.6470588235294118,
0.6363636363636364,
0.625,
0.6451612903225806,
0.6333333333333333,
0.6206896551724138,
0.6428571428571429,
0.6666666666666666,
0.6538461538461539,
0.64,
0.625,
0.6521739130434783,
0.6363636363636364,
0.6190476190476191,
0.65,
0.631578947368421,
0.6111111111111112,
0.6470588235294118,
0.625,
0.6666666666666666,
0.7142857142857143,
0.7692307692307693,
0.75,
0.8181818181818182,
0.8,
0.7777777777777778,
0.75,
0.7142857142857143,
1.0],
'recalls': [ 1.0,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.8923076923076924,
0.8923076923076924,
0.8769230769230769,
0.8615384615384616,
0.8461538461538461,
0.8461538461538461,
0.8461538461538461,
0.8307692307692308,
0.8307692307692308,
0.8307692307692308,
0.8307692307692308,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8,
0.7846153846153846,
0.7846153846153846,
0.7846153846153846,
0.7692307692307693,
0.7538461538461538,
0.7538461538461538,
0.7384615384615385,
0.7384615384615385,
0.7230769230769231,
0.7076923076923077,
0.7076923076923077,
0.7076923076923077,
0.7076923076923077,
0.7076923076923077,
0.6923076923076923,
0.676923076923077,
0.6615384615384615,
0.6461538461538462,
0.6461538461538462,
0.6461538461538462,
0.6461538461538462,
0.6307692307692307,
0.6307692307692307,
0.6153846153846154,
0.6153846153846154,
0.6,
0.5846153846153846,
0.5846153846153846,
0.5692307692307692,
0.5538461538461539,
0.5538461538461539,
0.5384615384615384,
0.5384615384615384,
0.5230769230769231,
0.5230769230769231,
0.5230769230769231,
0.5230769230769231,
0.49230769230769234,
0.47692307692307695,
0.47692307692307695,
0.46153846153846156,
0.4461538461538462,
0.4461538461538462,
0.4307692307692308,
0.4153846153846154,
0.4153846153846154,
0.4153846153846154,
0.4,
0.38461538461538464,
0.38461538461538464,
0.36923076923076925,
0.36923076923076925,
0.35384615384615387,
0.3384615384615385,
0.3384615384615385,
0.3384615384615385,
0.3230769230769231,
0.3076923076923077,
0.3076923076923077,
0.2923076923076923,
0.27692307692307694,
0.27692307692307694,
0.27692307692307694,
0.26153846153846155,
0.24615384615384617,
0.23076923076923078,
0.23076923076923078,
0.2153846153846154,
0.2,
0.2,
0.18461538461538463,
0.16923076923076924,
0.16923076923076924,
0.15384615384615385,
0.15384615384615385,
0.15384615384615385,
0.15384615384615385,
0.13846153846153847,
0.13846153846153847,
0.12307692307692308,
0.1076923076923077,
0.09230769230769231,
0.07692307692307693,
0.0]}
roc_auc_macro: 0.7635451505016722
roc_auc_micro: 0.7635451505016722
Finished: experiment_run
Saved to: results/experiment_run_0
!ludwig visualize --visualization learning_curves --training_statistics results/experiment_run_0/training_statistics.json
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
<Figure size 800x550 with 1 Axes>
<Figure size 800x550 with 1 Axes>
<Figure size 800x550 with 1 Axes>
<Figure size 800x550 with 1 Axes>
!cd results/experiment_run_0/ &&ls
description.json Survived_predictions.npy
model Survived_probabilities.csv
prediction_statistics.json Survived_probabilities.npy
Survived_predictions.csv training_statistics.json
!ludwig predict --data_csv train.csv --model_path results/experiment_run_0/model/
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
_ _ _
| |_ _ __| |_ __ _(_)__ _
| | || / _` \ V V / / _` |
|_|\_,_\__,_|\_/\_/|_\__, |
|___/
ludwig v0.1.1 - Predict
Dataset type: generic
Dataset path: train.csv
Model path: results/experiment_run_0/model/
Output path: results_0
Found hdf5 with the same filename of the csv, using it instead
Loading metadata from: results/experiment_run_0/model/train_set_metadata.json
Loading data from: train.hdf5
╒═══════════════╕
│ LOADING MODEL │
╘═══════════════╛
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
embedding_size (50) is greater than vocab_size (4). Setting embedding size to be equal to vocab_size.
embedding_size (50) is greater than vocab_size (3). Setting embedding size to be equal to vocab_size.
embedding_size (50) is greater than vocab_size (5). Setting embedding size to be equal to vocab_size.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
╒═════════╕
│ PREDICT │
╘═════════╛
2019-04-15 15:03:29.611185: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
2019-04-15 15:03:29.611485: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2bfcc00 executing computations on platform Host. Devices:
2019-04-15 15:03:29.611522: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from results/experiment_run_0/model/model_weights
Restoring parameters from results/experiment_run_0/model/model_weights
Evaluation: 100% 2/2 [00:00<00:00, 64.62it/s]
===== Survived =====
accuracy: 0.6888888888888889
average_precision_macro: 0.6052279888757147
average_precision_micro: 0.6052279888757147
average_precision_samples: 0.6052279888757147
loss: 1.2652276780870226
overall_stats: { 'avg_f1_score_macro': 0.68,
'avg_f1_score_micro': 0.6888888888888889,
'avg_f1_score_weighted': 0.6948148148148149,
'avg_precision_macro': 0.681733746130031,
'avg_precision_micro': 0.6888888888888889,
'avg_precision_weighted': 0.6888888888888889,
'avg_recall_macro': 0.6963210702341137,
'avg_recall_micro': 0.6888888888888889,
'avg_recall_weighted': 0.6888888888888889,
'kappa_score': 0.36802507836990594,
'overall_accuracy': 0.6888888888888889}
per_class_stats: {False: { 'accuracy': 0.6888888888888889,
'f1_score': 0.7333333333333334,
'fall_out': 0.44705882352941173,
'false_discovery_rate': 0.33043478260869563,
'false_negative_rate': 0.18947368421052635,
'false_negatives': 18,
'false_omission_rate': 0.27692307692307694,
'false_positive_rate': 0.44705882352941173,
'false_positives': 38,
'hit_rate': 0.8105263157894737,
'informedness': 0.36346749226006203,
'markedness': 0.39264214046822743,
'matthews_correlation_coefficient': 0.377773284062822,
'miss_rate': 0.18947368421052635,
'negative_predictive_value': 0.7230769230769231,
'positive_predictive_value': 0.6695652173913044,
'precision': 0.6695652173913044,
'recall': 0.8105263157894737,
'sensitivity': 0.8105263157894737,
'specificity': 0.5529411764705883,
'true_negative_rate': 0.5529411764705883,
'true_negatives': 47,
'true_positive_rate': 0.8105263157894737,
'true_positives': 77},
True: { 'accuracy': 0.6888888888888889,
'f1_score': 0.6266666666666667,
'fall_out': 0.18947368421052635,
'false_discovery_rate': 0.27692307692307694,
'false_negative_rate': 0.44705882352941173,
'false_negatives': 38,
'false_omission_rate': 0.33043478260869563,
'false_positive_rate': 0.18947368421052635,
'false_positives': 18,
'hit_rate': 0.5529411764705883,
'informedness': 0.36346749226006203,
'markedness': 0.39264214046822743,
'matthews_correlation_coefficient': 0.377773284062822,
'miss_rate': 0.44705882352941173,
'negative_predictive_value': 0.6695652173913044,
'positive_predictive_value': 0.7230769230769231,
'precision': 0.7230769230769231,
'recall': 0.5529411764705883,
'sensitivity': 0.5529411764705883,
'specificity': 0.8105263157894737,
'true_negative_rate': 0.8105263157894737,
'true_negatives': 77,
'true_positive_rate': 0.5529411764705883,
'true_positives': 47}}
precision_recall_curve: { 'precisions': [ 0.39156626506024095,
0.3878787878787879,
0.3902439024390244,
0.39263803680981596,
0.3950617283950617,
0.39751552795031053,
0.4,
0.4025157232704403,
0.4050632911392405,
0.40764331210191085,
0.40384615384615385,
0.4064516129032258,
0.4090909090909091,
0.4117647058823529,
0.4144736842105263,
0.41721854304635764,
0.42,
0.4161073825503356,
0.4189189189189189,
0.4217687074829932,
0.4246575342465753,
0.42758620689655175,
0.4305555555555556,
0.43356643356643354,
0.43661971830985913,
0.4326241134751773,
0.4357142857142857,
0.43884892086330934,
0.4420289855072464,
0.44525547445255476,
0.4485294117647059,
0.45185185185185184,
0.4552238805970149,
0.45864661654135336,
0.4621212121212121,
0.4580152671755725,
0.46153846153846156,
0.46511627906976744,
0.46875,
0.47244094488188976,
0.47619047619047616,
0.48,
0.4838709677419355,
0.4878048780487805,
0.4918032786885246,
0.49586776859504134,
0.49166666666666664,
0.4957983193277311,
0.5,
0.5042735042735043,
0.5086206896551724,
0.5130434782608696,
0.5175438596491229,
0.5132743362831859,
0.5178571428571429,
0.5135135135135135,
0.509090909090909,
0.5045871559633027,
0.5092592592592593,
0.514018691588785,
0.5094339622641509,
0.5142857142857142,
0.5192307692307693,
0.5242718446601942,
0.5196078431372549,
0.5247524752475248,
0.53,
0.5353535353535354,
0.5408163265306123,
0.5463917525773195,
0.5520833333333334,
0.5473684210526316,
0.5425531914893617,
0.5483870967741935,
0.5604395604395604,
0.5555555555555556,
0.550561797752809,
0.5568181818181818,
0.5517241379310345,
0.5581395348837209,
0.5529411764705883,
0.5476190476190477,
0.5542168674698795,
0.5609756097560976,
0.5679012345679012,
0.575,
0.569620253164557,
0.5641025641025641,
0.5584415584415584,
0.5526315789473685,
0.56,
0.5833333333333334,
0.5915492957746479,
0.5857142857142857,
0.5942028985507246,
0.5970149253731343,
0.6060606060606061,
0.6,
0.59375,
0.6031746031746031,
0.5967741935483871,
0.5901639344262295,
0.6,
0.5932203389830508,
0.603448275862069,
0.5964912280701754,
0.6071428571428571,
0.6181818181818182,
0.6296296296296297,
0.6153846153846154,
0.6078431372549019,
0.62,
0.6122448979591837,
0.6041666666666666,
0.6170212765957447,
0.6086956521739131,
0.6,
0.6136363636363636,
0.627906976744186,
0.6190476190476191,
0.6097560975609756,
0.625,
0.6153846153846154,
0.631578947368421,
0.6216216216216216,
0.6111111111111112,
0.6285714285714286,
0.6470588235294118,
0.6363636363636364,
0.625,
0.6451612903225806,
0.6333333333333333,
0.6206896551724138,
0.6428571428571429,
0.6666666666666666,
0.6538461538461539,
0.64,
0.625,
0.6521739130434783,
0.6363636363636364,
0.6190476190476191,
0.65,
0.631578947368421,
0.6111111111111112,
0.6470588235294118,
0.625,
0.6666666666666666,
0.7142857142857143,
0.7692307692307693,
0.75,
0.8181818181818182,
0.8,
0.7777777777777778,
0.75,
0.7142857142857143,
1.0],
'recalls': [ 1.0,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9846153846153847,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9692307692307692,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9538461538461539,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9384615384615385,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9230769230769231,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.9076923076923077,
0.8923076923076924,
0.8923076923076924,
0.8769230769230769,
0.8615384615384616,
0.8461538461538461,
0.8461538461538461,
0.8461538461538461,
0.8307692307692308,
0.8307692307692308,
0.8307692307692308,
0.8307692307692308,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8153846153846154,
0.8,
0.7846153846153846,
0.7846153846153846,
0.7846153846153846,
0.7692307692307693,
0.7538461538461538,
0.7538461538461538,
0.7384615384615385,
0.7384615384615385,
0.7230769230769231,
0.7076923076923077,
0.7076923076923077,
0.7076923076923077,
0.7076923076923077,
0.7076923076923077,
0.6923076923076923,
0.676923076923077,
0.6615384615384615,
0.6461538461538462,
0.6461538461538462,
0.6461538461538462,
0.6461538461538462,
0.6307692307692307,
0.6307692307692307,
0.6153846153846154,
0.6153846153846154,
0.6,
0.5846153846153846,
0.5846153846153846,
0.5692307692307692,
0.5538461538461539,
0.5538461538461539,
0.5384615384615384,
0.5384615384615384,
0.5230769230769231,
0.5230769230769231,
0.5230769230769231,
0.5230769230769231,
0.49230769230769234,
0.47692307692307695,
0.47692307692307695,
0.46153846153846156,
0.4461538461538462,
0.4461538461538462,
0.4307692307692308,
0.4153846153846154,
0.4153846153846154,
0.4153846153846154,
0.4,
0.38461538461538464,
0.38461538461538464,
0.36923076923076925,
0.36923076923076925,
0.35384615384615387,
0.3384615384615385,
0.3384615384615385,
0.3384615384615385,
0.3230769230769231,
0.3076923076923077,
0.3076923076923077,
0.2923076923076923,
0.27692307692307694,
0.27692307692307694,
0.27692307692307694,
0.26153846153846155,
0.24615384615384617,
0.23076923076923078,
0.23076923076923078,
0.2153846153846154,
0.2,
0.2,
0.18461538461538463,
0.16923076923076924,
0.16923076923076924,
0.15384615384615385,
0.15384615384615385,
0.15384615384615385,
0.15384615384615385,
0.13846153846153847,
0.13846153846153847,
0.12307692307692308,
0.1076923076923077,
0.09230769230769231,
0.07692307692307693,
0.0]}
roc_auc_macro: 0.7635451505016722
roc_auc_micro: 0.7635451505016722
Saved to: results_0
!ls results/experiment_run_0/model/
checkpoint model_weights_progress.data-00000-of-00001
log model_weights_progress.index
model_hyperparameters.json model_weights_progress.meta
model_weights.data-00000-of-00001 training_progress.p
model_weights.index train_set_metadata.json
model_weights.meta