.. battelle.nn

.. currentmodule:: battelle.nn

Neural Network subpackage (battelle.nn)
========================================

.. autosummary::
   :toctree: generated/

   Neuron
   Layer
   NeuralNetwork
   ActivationFunction

   .. automethod::
      
      NeuralNetwork.train
      NeuralNetwork.predict
      NeuralNetwork.get_metrics

This module allows the user to create, train and visualize simple neural networks.

Here is a simple example of binary classification:

.. code-block:: python 

   import numpy as np
   import matplotlib.pyplot as plt

   from battelle.nn import *


   # List of features
   x = np.random.random(size=(300, 2)) - 0.5
   x = [list(xi) for xi in x]

   # List of corresponding labels (xor)
   y = np.array([np.sign(np.prod(k)) for k in x])

   # Define neural network
   nn = NeuralNetwork()
   nn.add_layer(Layer(5, "tanh", input_dim=2))
   nn.add_layer(Layer(4, "tanh"))
   nn.add_layer(Layer(1, "tanh"))

   # Train the neural network in epochs
   NUM_EPOCHS = 100
   for i in range(NUM_EPOCHS):
       print(f"Epoch {i+1}/{NUM_EPOCHS}")
       nn.train(x, y, learning_rate=0.03)
       print(f"Loss: {np.mean([(nn.predict(x[k]) - y[k])**2 for k in range(len(x))])}")

   # Plot training data
   plt.subplot(1, 2, 1)
   for i, xi in enumerate(x):
       if y[i] == 1:
           plt.plot(xi[0], xi[1], "xr")
       else:
           plt.plot(xi[0], xi[1], "xb")
   plt.title("Testing data")

   # Plot neural net result
   plt.subplot(1, 2, 2)
   xrange = np.linspace(-0.5, 0.5, 20)
   yrange = np.linspace(-0.5, 0.5, 20)
   for x in xrange:
       for y in yrange:
           if nn.predict([x, y]) > 0:
               plt.plot(x, y, "xr")
           else:
               plt.plot(x, y, "xb")

   plt.title("Neural net result")
   plt.show()


This code creates the following graph:

   
.. figure:: images/xor_nn.png