Metadata-Version: 2.1
Name: DeepNeuralBranchNet
Version: 0.0.12
Summary: Neural network module with branching output
Home-page: https://github.com/hamzahshabbir96/Neural-network-with-branching-output
Author: Hamzah Shabbir
Author-email: hamzahshabbir7@gmail.com
License: UNKNOWN
Description: 
        # Neural network with branching output
        This package can be used to train neural network for classification with option of multiple outputs by branching at different layers. 
        
        
        __Features available__
        - Customizable layers with option of selecting activation function
        - No limits on adding number of layers and number of neurons in each layers
        - By default gradient descent for optimization but in further version, other optimization methods are to be included
        - Multiple outputs can be selected at different layers with just one method call
        
        ## Requirements
        This package is developed at very low-level python coding so there is no much requirement other than numpy library.
        [Numpy](https://numpy.org/)
        
        
        
        ## Installation
        To install a stable version, use the following command
        
        ```
        pip install DeepNeuralBranchNet
        ```
        
        
        ## Example of how to use
        
        Import module 
        ```
        from DeepNeuralBranchNet import neuralnet
        ```
        Initiate the neural network object class
        
        ```
        example=NeuralNet()
        ```
        Add input by passing number of nput features as parameter
        ```
        example.add_input(input_length=16)
        ```
        
        Add layer sequentially by passing number of neurons and activation function
        ```
        example.add_layer(10,activation_function="relu")
        ```
        If you want to add multiple output(or opting for branching), then trigger branching by following line of code and then keep adding layers
        ```
        example.do_branching()
        ```
        
        Run model to update parameters such as weights and bias and for training.
        - input_array : Input matrix in the form of array
        - output_array : Output matrix in the form of array
        - number_of_iterations : Total number of times you want to run back propagations to update weights
        - multiple_output : If you have branches then set it to True
        ```
        example.run_model(input_array,output_array, number_of_iterations=1000, learning_rate=0.001, multiple_output=False)
        ```
        Finally predict by calling predict method
        ```
        example.predict()
        ```
        
Keywords: Neural Network,Classification,Python,Neurons,layers
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Life
Description-Content-Type: text/markdown
