├── .gitignore ├── LICENSE ├── README.md ├── angular_grad.py ├── requirements.txt └── test.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | # AngularGrad-tf 2 | 3 | Tensorflow implementation of [AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks](http://arxiv.org/abs/2105.10190). 4 | 5 | The official implementation of AngularGrad is [mhaut/AngularGrad](https://github.com/mhaut/AngularGrad). 6 | 7 | ## How to use 8 | 9 | You can import the optimizer as follows: 10 | 11 | ### AngularGrad(cos) 12 | ```python 13 | from angular_grad import AngularGrad 14 | ... 15 | model = YourModel() 16 | ... 17 | model.compile(optimizer=AngularGrad("cos"), ...) 18 | ... 19 | ``` 20 | 21 | Or you can omit a value "cos". 22 | ```python 23 | ... 24 | model.compile(optimizer=AngularGrad(), ...) 25 | ... 26 | ``` 27 | 28 | ### AngularGrad(tan) 29 | ```python 30 | from angular_grad import AngularGrad 31 | ... 32 | model = YourModel() 33 | ... 34 | model.compile(optimizer=AngularGrad("tan"), ...) 35 | ... 36 | ``` 37 | 38 | 39 | ## Params 40 | ```python 41 | AngularGrad( 42 | method_angle: str = "cos", 43 | learning_rate=1e-3, 44 | beta_1=0.9, 45 | beta_2=0.999, 46 | eps=1e-7, 47 | name: str = "AngularGrad", 48 | **kwargs 49 | ) 50 | ``` 51 | 52 | ## Tested version 53 | - Python 3.6.9 54 | - Tensorflow 2.5.0 55 | 56 | Developed by Eunchan Lee(eunchan@linewalks.com), 2021 [Linewalks](https://linewalks.com). 57 | 58 | If there is any problem in this repository, please feel free to contact us at the above email address. 59 | -------------------------------------------------------------------------------- /angular_grad.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import math 3 | 4 | 5 | class AngularGrad(tf.keras.optimizers.Optimizer): 6 | def __init__( 7 | self, 8 | method_angle: str = "cos", 9 | learning_rate=1e-3, 10 | beta_1=0.9, 11 | beta_2=0.999, 12 | eps=1e-7, 13 | name: str = "AngularGrad", 14 | **kwargs, 15 | ): 16 | super().__init__(name, **kwargs) 17 | 18 | self.method_angle = method_angle 19 | self._set_hyper("learning_rate", kwargs.get("lr", learning_rate)) 20 | self._set_hyper("beta_1", beta_1) 21 | self._set_hyper("beta_2", beta_2) 22 | self._set_hyper("eps", eps) 23 | self.eps = eps or tf.keras.backend.epsilon() 24 | 25 | def _create_slots(self, var_list): 26 | for var in var_list: 27 | self.add_slot(var, "exp_avg") 28 | self.add_slot(var, "exp_avg_sq") 29 | self.add_slot(var, "previous_grad") 30 | self.add_slot(var, "min", initializer=tf.keras.initializers.Constant(value=math.pi / 2)) 31 | self.add_slot(var, "final_angle_function_theta") 32 | 33 | def _resource_apply_dense(self, grad, var): 34 | var_dtype = var.dtype.base_dtype 35 | 36 | lr = self._get_hyper("learning_rate", var_dtype) 37 | beta_1 = self._get_hyper("beta_1", var_dtype) 38 | beta_2 = self._get_hyper("beta_2", var_dtype) 39 | eps = self._get_hyper("eps", var_dtype) 40 | 41 | exp_avg = self.get_slot(var, "exp_avg") 42 | exp_avg_sq = self.get_slot(var, "exp_avg_sq") 43 | previous_grad = self.get_slot(var, "previous_grad") 44 | min = self.get_slot(var, "min") 45 | final_angle_function_theta = self.get_slot(var, "final_angle_function_theta") 46 | 47 | step = tf.cast(self.iterations + 1, var_dtype) 48 | beta_1_power = tf.pow(beta_1, step) 49 | beta_2_power = tf.pow(beta_2, step) 50 | 51 | new_exp_avg = exp_avg.assign( 52 | beta_1 * exp_avg + (1.0 - beta_1) * grad, 53 | use_locking=self._use_locking 54 | ) 55 | exp_avg_corrected = new_exp_avg / (1.0 - beta_1_power) 56 | 57 | new_exp_avg_sq = exp_avg_sq.assign( 58 | beta_2 * exp_avg_sq + (1.0 - beta_2) * tf.square(grad), 59 | use_locking=self._use_locking, 60 | ) 61 | exp_avg_sq_corrected = new_exp_avg_sq / (1.0 - beta_2_power) 62 | 63 | tan_theta = tf.abs((previous_grad - grad) / (1 + previous_grad * grad)) 64 | cos_theta = 1 / tf.sqrt(1 + tf.square(tan_theta)) 65 | 66 | angle = tf.atan(tan_theta) * (180 / math.pi) 67 | ans = tf.greater(angle, min) 68 | mean_ans = tf.reduce_mean(tf.cast(ans, tf.float32)) 69 | 70 | def true_fn(): 71 | new_min = min.assign(angle, use_locking=self._use_locking) 72 | new_final_angle_function_theta = final_angle_function_theta.assign( 73 | tf.identity(tan_theta if self.method_angle == "tan" else cos_theta), 74 | use_locking=self._use_locking 75 | ) 76 | return new_min, new_final_angle_function_theta 77 | 78 | def false_fn(): 79 | return min, final_angle_function_theta 80 | 81 | new_min, new_final_angle_function_theta = tf.cond(tf.less(mean_ans, 0.5), true_fn, false_fn) 82 | angular_coeff = tf.tanh(tf.abs(final_angle_function_theta)) * 0.5 + 0.5 83 | 84 | var_update = var.assign_sub( 85 | lr * exp_avg_corrected * angular_coeff / (tf.sqrt(exp_avg_sq_corrected) + eps), 86 | use_locking=self._use_locking 87 | ) 88 | 89 | new_previous_grad = previous_grad.assign(grad, use_locking=self._use_locking) 90 | 91 | updates = [var_update, new_exp_avg, new_exp_avg_sq, new_min, new_previous_grad, new_final_angle_function_theta] 92 | return tf.group(*updates) 93 | 94 | def _resource_apply_sparse(self, grad, var, indices): 95 | raise NotImplementedError 96 | 97 | def get_config(self): 98 | config = super().get_config() 99 | config.update( 100 | { 101 | "learning_rate": self._serialize_hyperparameter("learning_rate"), 102 | "beta_1": self._serialize_hyperparameter("beta_1"), 103 | "beta_2": self._serialize_hyperparameter("beta_2"), 104 | "eps": self._serialize_hyperparameter("eps") 105 | } 106 | ) 107 | return config 108 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==0.12.0 2 | argon2-cffi==20.1.0 3 | asn1crypto==0.24.0 4 | astunparse==1.6.3 5 | async-generator==1.10 6 | attrs==21.2.0 7 | backcall==0.2.0 8 | bleach==3.3.0 9 | cached-property==1.5.2 10 | cachetools==4.2.2 11 | certifi==2020.12.5 12 | cffi==1.14.5 13 | chardet==4.0.0 14 | cryptography==2.1.4 15 | cycler==0.10.0 16 | dataclasses==0.8 17 | decorator==5.0.7 18 | defusedxml==0.7.1 19 | entrypoints==0.3 20 | flatbuffers==1.12 21 | gast==0.4.0 22 | google-auth==1.30.0 23 | google-auth-oauthlib==0.4.4 24 | google-pasta==0.2.0 25 | grpcio==1.34.1 26 | h5py==3.1.0 27 | idna==2.6 28 | importlib-metadata==4.0.1 29 | ipykernel==5.1.1 30 | ipython==7.16.1 31 | ipython-genutils==0.2.0 32 | ipywidgets==7.6.3 33 | jedi==0.18.0 34 | Jinja2==3.0.0 35 | jsonschema==3.2.0 36 | jupyter==1.0.0 37 | jupyter-client==6.1.12 38 | jupyter-console==6.4.0 39 | jupyter-core==4.7.1 40 | jupyter-http-over-ws==0.0.8 41 | jupyterlab-pygments==0.1.2 42 | jupyterlab-widgets==1.0.0 43 | keras-nightly==2.5.0.dev2021032900 44 | Keras-Preprocessing==1.1.2 45 | keyring==10.6.0 46 | keyrings.alt==3.0 47 | kiwisolver==1.3.1 48 | Markdown==3.3.4 49 | MarkupSafe==2.0.0 50 | matplotlib==3.3.4 51 | mistune==0.8.4 52 | nbclient==0.5.3 53 | nbconvert==6.0.7 54 | nbformat==4.4.0 55 | nest-asyncio==1.5.1 56 | notebook==6.3.0 57 | numpy==1.19.5 58 | oauthlib==3.1.0 59 | opt-einsum==3.3.0 60 | packaging==20.9 61 | pandocfilters==1.4.3 62 | parso==0.8.2 63 | pexpect==4.8.0 64 | pickleshare==0.7.5 65 | Pillow==8.2.0 66 | prometheus-client==0.10.1 67 | prompt-toolkit==3.0.18 68 | protobuf==3.17.0 69 | ptyprocess==0.7.0 70 | pyasn1==0.4.8 71 | pyasn1-modules==0.2.8 72 | pycparser==2.20 73 | pycrypto==2.6.1 74 | Pygments==2.9.0 75 | pygobject==3.26.1 76 | pyparsing==2.4.7 77 | pyrsistent==0.17.3 78 | python-apt==1.6.5+ubuntu0.5 79 | python-dateutil==2.8.1 80 | pyxdg==0.25 81 | pyzmq==22.0.3 82 | qtconsole==5.1.0 83 | QtPy==1.9.0 84 | requests==2.25.1 85 | requests-oauthlib==1.3.0 86 | rsa==4.7.2 87 | SecretStorage==2.3.1 88 | Send2Trash==1.5.0 89 | six==1.15.0 90 | tensorboard==2.5.0 91 | tensorboard-data-server==0.6.1 92 | tensorboard-plugin-wit==1.8.0 93 | tensorflow==2.5.0 94 | tensorflow-estimator==2.5.0rc0 95 | termcolor==1.1.0 96 | terminado==0.9.5 97 | testpath==0.4.4 98 | tornado==6.1 99 | traitlets==4.3.3 100 | typing-extensions==3.7.4.3 101 | urllib3==1.26.4 102 | wcwidth==0.2.5 103 | webencodings==0.5.1 104 | Werkzeug==2.0.0 105 | widgetsnbextension==3.5.1 106 | wrapt==1.12.1 107 | zipp==3.4.1 108 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | # Codes from https://www.tensorflow.org/tutorials/keras/classification 2 | # TensorFlow and tf.keras 3 | import tensorflow as tf 4 | 5 | from angular_grad import AngularGrad 6 | 7 | fashion_mnist = tf.keras.datasets.fashion_mnist 8 | (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() 9 | train_images = train_images / 255.0 10 | test_images = test_images / 255.0 11 | 12 | model = tf.keras.Sequential([ 13 | tf.keras.layers.Flatten(input_shape=(28, 28)), 14 | tf.keras.layers.Dense(128, activation="relu"), 15 | tf.keras.layers.Dense(10) 16 | ]) 17 | 18 | # AngularGrad(cos) 19 | optimizer = AngularGrad("cos") 20 | 21 | # AngularGrad(tan) 22 | # optimizer = AngularGrad("tan") 23 | 24 | model.compile(optimizer=optimizer, 25 | loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), 26 | metrics=["accuracy"]) 27 | 28 | model.fit(train_images, train_labels, epochs=10) 29 | 30 | test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) 31 | print("\nTest accuracy:", test_acc) --------------------------------------------------------------------------------