Author: Rodolfo Ferro Pérez
Email: ferro@cimat.mx
Twitter: @FerroRodolfo
GitHub: RodolfoFerro
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.
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
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
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))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])))- 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.
Copyright (c) 2018 Rodolfo Ferro