├── .gitignore ├── LICENSE ├── README.md ├── data.py ├── data └── mnist.npz ├── nn.py ├── poetry.lock └── pyproject.toml /.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 | 131 | .idea/ 132 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Bot Academy 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | Logo 3 |
4 |
5 | MIT License 6 | Code style: black 7 |
8 | Twitter 9 | Discord 10 | Code style: black 11 |
12 |
13 | Minimalistic implementation of a simple feedforward Neural Network 14 |
15 | Video 16 |

17 |
18 | 19 | ## Setup 20 | This project requires [Poetry](https://python-poetry.org/) to install the required dependencies. 21 | Check out [this link](https://python-poetry.org/docs/) to install Poetry on your operating system. 22 | 23 | Make sure you have installed [Python](https://www.python.org/downloads/) 3.8 or higher! Otherwise Step 3 will let you know that you have no compatible Python version installed. 24 | 25 | 1. Clone/Download this repository 26 | 2. Navigate to the root of the repository 27 | 3. Run ```poetry install``` to create a virtual environment with Poetry 28 | 4. Run ```poetry run python nn.py``` to run the program. Alternatively you can run ```poetry shell``` followed by ```python nn.py``` 29 | 5. Enjoy and Subscribe :) 30 | 31 | ## Troubleshooting 32 | Nothing in here so far! 33 | -------------------------------------------------------------------------------- /data.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pathlib 3 | 4 | 5 | def get_mnist(): 6 | with np.load(f"{pathlib.Path(__file__).parent.absolute()}/data/mnist.npz") as f: 7 | images, labels = f["x_train"], f["y_train"] 8 | images = images.astype("float32") / 255 9 | images = np.reshape(images, (images.shape[0], images.shape[1] * images.shape[2])) 10 | labels = np.eye(10)[labels] 11 | return images, labels 12 | -------------------------------------------------------------------------------- /data/mnist.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bot-Academy/NeuralNetworkFromScratch/9c1b4791ec934176c073ee99511e9cdf7c90c3d1/data/mnist.npz -------------------------------------------------------------------------------- /nn.py: -------------------------------------------------------------------------------- 1 | from data import get_mnist 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | 5 | 6 | """ 7 | w = weights, b = bias, i = input, h = hidden, o = output, l = label 8 | e.g. w_i_h = weights from input layer to hidden layer 9 | """ 10 | images, labels = get_mnist() 11 | w_i_h = np.random.uniform(-0.5, 0.5, (20, 784)) 12 | w_h_o = np.random.uniform(-0.5, 0.5, (10, 20)) 13 | b_i_h = np.zeros((20, 1)) 14 | b_h_o = np.zeros((10, 1)) 15 | 16 | learn_rate = 0.01 17 | nr_correct = 0 18 | epochs = 3 19 | for epoch in range(epochs): 20 | for img, l in zip(images, labels): 21 | img.shape += (1,) 22 | l.shape += (1,) 23 | # Forward propagation input -> hidden 24 | h_pre = b_i_h + w_i_h @ img 25 | h = 1 / (1 + np.exp(-h_pre)) 26 | # Forward propagation hidden -> output 27 | o_pre = b_h_o + w_h_o @ h 28 | o = 1 / (1 + np.exp(-o_pre)) 29 | 30 | # Cost / Error calculation 31 | e = 1 / len(o) * np.sum((o - l) ** 2, axis=0) 32 | nr_correct += int(np.argmax(o) == np.argmax(l)) 33 | 34 | # Backpropagation output -> hidden (cost function derivative) 35 | delta_o = o - l 36 | w_h_o += -learn_rate * delta_o @ np.transpose(h) 37 | b_h_o += -learn_rate * delta_o 38 | # Backpropagation hidden -> input (activation function derivative) 39 | delta_h = np.transpose(w_h_o) @ delta_o * (h * (1 - h)) 40 | w_i_h += -learn_rate * delta_h @ np.transpose(img) 41 | b_i_h += -learn_rate * delta_h 42 | 43 | # Show accuracy for this epoch 44 | print(f"Acc: {round((nr_correct / images.shape[0]) * 100, 2)}%") 45 | nr_correct = 0 46 | 47 | # Show results 48 | while True: 49 | index = int(input("Enter a number (0 - 59999): ")) 50 | img = images[index] 51 | plt.imshow(img.reshape(28, 28), cmap="Greys") 52 | 53 | img.shape += (1,) 54 | # Forward propagation input -> hidden 55 | h_pre = b_i_h + w_i_h @ img.reshape(784, 1) 56 | h = 1 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