Ludwig
rpi.analyticsdojo.com
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
</div>
<div class="output_wrapper" markdown="1">
<div class="output_subarea" markdown="1">
{:.output_stream}
Collecting ludwig [?25l Downloading https://files.pythonhosted.org/packages/cd/a2/9f7f1952398e5aeb2f39579616fab8c3fada84a956ba6c855e6bc30a99f1/ludwig-0.1.1.tar.gz (129kB) [K 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) [K 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) [K 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) [K 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) [K 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
</div>
</div>
</div>
<div markdown="1" class="cell code_cell">
<div class="input_area" markdown="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
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
</div>
<div class="output_wrapper" markdown="1">
<div class="output_subarea" markdown="1">
{:.output_stream}
–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 100%[===================>] 524 –.-KB/s in 0s
2019-04-15 14:41:17 (83.2 MB/s) - ‘model_definition.yaml’ saved [524/524]
</div>
</div>
</div>
<div markdown="1" class="cell code_cell">
<div class="input_area" markdown="1">
```!cat model_definition.yaml
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
</div>
<div class="output_wrapper" markdown="1">
<div class="output_subarea" markdown="1">
{:.output_stream}
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’: ‘
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):
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
</div>
</div>
</div>
<div markdown="1" class="cell code_cell">
<div class="input_area" markdown="1">
!ludwig visualize –visualization learning_curves –training_statistics results/experiment_run_0/training_statistics.json
</div>
<div class="output_wrapper" markdown="1">
<div class="output_subarea" markdown="1">
{:.output_stream}
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>
</div>
</div>
</div>
<div markdown="1" class="cell code_cell">
<div class="input_area" markdown="1">
```!cd results/experiment_run_0/ &&ls
```!ludwig predict –data_csv train.csv –model_path results/experiment_run_0/model/
</div>
<div class="output_wrapper" markdown="1">
<div class="output_subarea" markdown="1">
{:.output_stream}
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):
===== 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
</div>
</div>
</div>
<div markdown="1" class="cell code_cell">
<div class="input_area" markdown="1">
```!ls results/experiment_run_0/model/