FLISoL 2018 - Intro to ANNs w/Keras

Thursday. April 26, 2018 - 4 mins

Author: Rodolfo Ferro Pérez
Email: ferro@cimat.mx
Twitter: @FerroRodolfo
GitHub: RodolfoFerro

About 🕹

FLISoL is the largest Free Software dissemination event in Latin America and is aimed at all types of audiences: students, academics, businessmen, workers, public officials, enthusiasts and even people who do not have much computer knowledge.

This is a Python 🐍 workshop for the ‘Festival Latinoamericano de Instalación de Software Libre 2018’ (FLISoL) at the Instituto Tecnológico de León, for which I was invited.

It is basically a 101 workshop about Artificial Neural Networks using Keras.

Checkout the slides of the workshop:

Intro a ANNs c/Keras from Rodolfo Ferro

Setup ⚙️

We’ll be working on Azure Notebooks for which you can create and use a free account to train ANN models online.

A new library will be needed, and for this, you can import all the code from this repo: https://github.com/RodolfoFerro/FLISoL18

Content 👾

All repo content is contained inside the main folder, in which you’ll find a set of Jupyter Notebooks with the ANN code, along with a pre-trained model and a set of images use in the Notebooks.

Demo Gist for PerceptronFLISoL18.py and SigmoidFLISoL18.py: https://gist.github.com/RodolfoFerro/46dc7ba3dded4cd6a3a9d58e1284557a

PerceptronFLISoL18

import numpy as np


class PerceptronFLISoL():
    def __init__(self, entradas, pesos):
        """Constructor de la clase."""
        self.n = len(entradas)
        self.entradas = np.array(entradas)
        self.pesos = np.array(pesos)

    def voy_no_voy(self, umbral):
        """Calcula el output deseado."""
        si_no = (self.entradas @ self.pesos) >= umbral
        if si_no:
            return "Sí voy."
        else:
            return "No voy."


if __name__ == '__main__':
    entradas = [1, 1, 1, 1]
    pesos = [-4, 3, 1, 2]

    dev = PerceptronFLISoL(entradas, pesos)
    print(dev.voy_no_voy(3))

SigmoidFLISoL18

import numpy as np


class SigmoidNeuron():
    def __init__(self, n):
        np.random.seed(123)
        self.synaptic_weights = 2 * np.random.random((n, 1)) - 1

    def __sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def __sigmoid_derivative(self, x):
        return x * (1 - x)

    def train(self, training_inputs, training_output, iterations):
        for iteration in range(iterations):
            output = self.predict(training_inputs)
            error = training_output.reshape((len(training_inputs), 1)) - output
            adjustment = np.dot(training_inputs.T, error *
                                self.__sigmoid_derivative(output))
            self.synaptic_weights += adjustment

    def predict(self, inputs):
        return self.__sigmoid(np.dot(inputs, self.synaptic_weights))


if __name__ == '__main__':
    # Initialize Sigmoid Neuron:
    sigmoid = SigmoidNeuron(2)
    print("Inicialización de pesos aleatorios:")
    print(sigmoid.synaptic_weights)

    # Datos de entrenamiento:
    training_inputs = np.array([[1, 0], [0, 0], [0, 1]])
    training_output = np.array([1, 0, 1]).T.reshape((3, 1))

    # Entrenamos la neurona (100,000 iteraciones):
    sigmoid.train(training_inputs, training_output, 100000)
    print("Nuevos pesos sinápticos luego del entrenamiento: ")
    print(sigmoid.synaptic_weights)

    # Predecimos para probar la red:
    print("Predicción para [1, 1]: ")
    print(sigmoid.predict(np.array([1, 1])))

ABOUT COPYING OR USING PARTIAL INFORMATION: 🔐

These documents were originally created by the author.
Any usage of these documents or their contents is granted according to the provided license and its conditions.
For any question, you can contact the author via email or Twitter.

Rodolfo Ferro

With great power comes great responsibility.