finish train

Author: Germey

.gitattributes

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+# Created by .ignore support plugin (hsz.mobi)
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+.idea/
+scores/
+# C extensions
+*.so
+checkpoints/
+notebooks/.ipynb_checkpoints
+debug.log
+# Distribution / packaging
+.Python
+build/
+examples/checkpoints/
+examples/events/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
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+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+*.pyc
+# PyInstaller
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
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+# Installer logs
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+
+# Unit test / coverage reports
+htmlcov/
+.tox/
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+.coverage.*
+.cache
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+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+events/
+checkpoints/
+
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+.spyderproject
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+# Rope project settings
+.ropeproject
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+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+

.gitignore

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+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+absl-py = "==0.5.0"
+astor = "==0.7.1"
+cycler = "==0.10.0"
+face-recognition = "*"
+gast = "==0.2.0"
+grpcio = "==1.15.0"
+"h5py" = "==2.8.0"
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+[dev-packages]
+
+[requires]
+python_version = "3.6"

Pipfile

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+# Emotion Recognition
+
+Emotion Recognition Implemented by [ModelZoo](https://github.com/ModelZoo/ModelZoo).
+
+## Usage
+
+Firstly, you need to clone this repository and download training data:
+
+```
+git clone https://github.com/ModelZoo/EmotionRecognition.git
+cd EmotionRecognition
+git lfs pull
+```
+
+Next, install the dependencies using pip:
+
+```
+pip3 install -r requirements.txt
+```
+
+Finally, just run training:
+
+```
+python3 train.py
+```
+
+If you want to continue training your model, you need to define `checkpoint_restore` flag in `train.py`:
+
+```python
+tf.flags.DEFINE_bool('checkpoint_restore', True, help='Model restore')
+```
+
+And you can define the specific model with `checkpoint_name` which you want to continue training with:
+
+```python
+tf.flags.DEFINE_string('checkpoint_name', 'model-178.ckpt', help='Model name')
+```
+
+
+## TensorBoard
+
+After training, you can see the transition of loss in TensorBoard.
+
+```
+cd events
+tensorboard --logdir=.
+```
+
+![](https://ws3.sinaimg.cn/large/006tNbRwgy1fw37u664tzj319d0mumym.jpg)
+
+The best accuracy is 65.64% from step 178.
+
+## Predict
+
+Next, we can use our model to recognize the emotion.
+
+Here are the test pictures we picked from the website:
+
+![](https://ws4.sinaimg.cn/large/006tNbRwgy1fw3f6am6jpj310405cwf8.jpg)
+
+Then put them to the folder named `tests` and define the
+ model path and test folder in `infer.py`:
+
+```python
+tf.flags.DEFINE_string('checkpoint_name', 'model.ckpt-178', help='Model name')
+tf.flags.DEFINE_string('test_dir', 'tests/', help='Dir of test data')
+```
+
+Then just run inference using this cmd:
+
+```
+python3 infer.py
+```
+
+We can get the result of emotion recognition and probabilities of each emotion:
+
+```
+Image Path: test1.png
+Predict Result: Happy
+Emotion Distribution: {'Angry': 0.0, 'Disgust': 0.0, 'Fear': 0.0, 'Happy': 1.0, 'Sad': 0.0, 'Surprise': 0.0, 'Neutral': 0.0}
+====================
+Image Path: test2.png
+Predict Result: Happy
+Emotion Distribution: {'Angry': 0.0, 'Disgust': 0.0, 'Fear': 0.0, 'Happy': 0.998, 'Sad': 0.0, 'Surprise': 0.0, 'Neutral': 0.002}
+====================
+Image Path: test3.png
+Predict Result: Surprise
+Emotion Distribution: {'Angry': 0.0, 'Disgust': 0.0, 'Fear': 0.0, 'Happy': 0.0, 'Sad': 0.0, 'Surprise': 1.0, 'Neutral': 0.0}
+====================
+Image Path: test4.png
+Predict Result: Angry
+Emotion Distribution: {'Angry': 1.0, 'Disgust': 0.0, 'Fear': 0.0, 'Happy': 0.0, 'Sad': 0.0, 'Surprise': 0.0, 'Neutral': 0.0}
+====================
+Image Path: test5.png
+Predict Result: Fear
+Emotion Distribution: {'Angry': 0.04, 'Disgust': 0.002, 'Fear': 0.544, 'Happy': 0.03, 'Sad': 0.036, 'Surprise': 0.31, 'Neutral': 0.039}
+====================
+Image Path: test6.png
+Predict Result: Sad
+Emotion Distribution: {'Angry': 0.005, 'Disgust': 0.0, 'Fear': 0.027, 'Happy': 0.002, 'Sad': 0.956, 'Surprise': 0.0, 'Neutral': 0.009}
+```
+
+Emmm, looks good!
+
+## Pretrained Model
+
+Looking for pretrained model?
+
+just go to `checkpoints` folder, here is the model with best performance at step 178.