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        "author": "Miles Granger",
        "author_email": "miles59923@gmail.com",
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            "Intended Audience :: Developers",
            "Natural Language :: English",
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        "description": "# pyrus-nn\n\n[![Build Status](https://milesgranger.visualstudio.com/builds/_apis/build/status/pyrus-nn?branchName=master)](https://milesgranger.visualstudio.com/builds/_build/latest?definitionId=1&branchName=master)\n[![Dependabot Status](https://api.dependabot.com/badges/status?host=github&repo=milesgranger/black-jack)](https://dependabot.com)\n[![crates.io](http://meritbadge.herokuapp.com/pyrus-nn)](https://crates.io/crates/pyrus-nn)\n\n[Rust API Documentation](https://docs.rs/pyrus-nn)\n\nLightweight neural network framework written in Rust, with _thin_ python bindings.\n\n- Features:\n    - Serialize networks into/from YAML & JSON!\n        - Rust -> serde compatible\n        - Python -> `network.to_dict()` & `Sequential.from_dict()`\n    - Python install requires _zero_ dependencies\n    - No external system libs to install \n\n- Draw backs:\n    - Only supports generic gradient descent. \n    - Fully connected (Dense) layers only so far\n    - Activation functions limited to linear, tanh, sigmoid and softmax\n    - Cost functions limited to MSE, MAE, Cross Entropy and Accuracy\n\n### Install:\n\nPython:\n```\npip install pyrus-nn  # Has ZERO dependencies!\n```\n\nRust:\n```toml\n[dependencies]\npyrus-nn = \"0.2.1\"\n```\n\n\n\n### From Python\n```python\nfrom pyrus_nn.models import Sequential\nfrom pyrus_nn.layers import Dense\n\nmodel = Sequential(lr=0.001, n_epochs=10)\nmodel.add(Dense(n_input=12, n_output=24, activation='sigmoid'))\nmodel.add(Dense(n_input=24, n_output=1, activation='sigmoid'))\n\n# Create some X and y, each of which must be 2d\nX = [list(range(12)) for _ in range(10)]\ny = [[i] for i in range(10)]  \n\nmodel.fit(X, y)\nout = model.predict(X)\n\n```\n\n---\n\n### From Rust\n```rust\nuse ndarray::Array2;\nuse pyrus_nn::{network::Sequential, layers::Dense};\n\n\n// Network with 4 inputs and 1 output.\nfn main() {\n    let mut network = Sequential::new(0.001, 100, 32, CostFunc::CrossEntropy);\n    assert!(\n        network.add(Dense::new(4, 5)).is_ok()\n    );\n    assert!(\n        network.add(Dense::new(5, 6)).is_ok()\n    );\n    assert!(\n        network.add(Dense::new(6, 4)).is_ok()\n    );\n    assert!(\n        network.add(Dense::new(4, 1)).is_ok()\n    );\n\n    let X: Array2<f32> = ...\n    let y: Array2<f32> = ...\n\n    network.fit(X.view(), y.view());\n\n    let yhat: Array2<f32> = network.predict(another_x.view());\n}\n\n```\n\n",
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