Metadata-Version: 2.1
Name: aunnl
Version: 3.0.2
Summary: Another unnecessary neural network library
Home-page: https://github.com/adityaradk/aunnl
Author: Aditya Radhakrishnan
Author-email: adityaradk@gmail.com
License: MIT
Description: ![](https://i.ibb.co/ypC5ZVv/aunnl.png)
        ___
        
        AUNNL is another unnecessary neural network library for Python 3.x. It is intended to help create and train basic neural networks very easily.
        
        ## Getting Started
        
        ### Installation
        
        It is recommended you install via pip for Python 3:
        
        ```
        pip install aunnl
        ```
        
        After this, you can import it into your python program with:
        
        ```
        import aunnl
        ```
        
        ### Basic Example
        
        The following example trains a neural network to classify handwritten digits from the MNIST dataset. The dataset is loaded using the `mnist_web` module, which is not packaged with AUNNL. Download and install it with the command `pip install mnist_web`.
        
        ```
        import aunnl
        
        from mnist_web import mnist
        data, labels, _, _ = mnist(path="dataset")
        
        model = aunnl.NeuralNetwork([784, 256, 10], ["relu", "sigmoid"])
        
        epochs, lr, batch_size = 1, 0.1, 64
        
        model.fit(data, labels, epochs, batch_size, lr, aunnl.losses.MSE)
        model.save("mnist.aunn")
        ```
        
        In the above example, a neural network with a hidden layer of 256 neurons is trained - its activation being ReLU and the output layer activation being sigmoid. The model, which is an `aunnl.NeuralNetwork` object, is then saved to the file `mnist.aunn`. The model can be loaded from the file with `aunnl.loadModel('mnist.aunn')`.
        
        To use the model, simply pass the image in the form of a flat numpy array (denoted here as `img_arr`) to the model with `model.feedForward(img_arr)`. The `feedForward` function returns a list of the values outputted by the model.
        
Keywords: DEEP LEARNING,MACHINE LEARNING,NEURAL NETWORK
Platform: UNKNOWN
Description-Content-Type: text/markdown
