Quickprop example

quickprop.py

from sklearn.datasets import make_classification

import numpy as np

# Set random seed (for reproducibility)
np.random.seed(1000)

def sigmoid(arg):
    return 1.0 / (1.0 + np.exp(-arg))

# Create random dataset
nb_samples = 500
nb_features = 2

X, Y = make_classification(n_samples=nb_samples, 
                           n_features=nb_features, 
                           n_informative=nb_features, 
                           n_redundant=0, 
                           random_state=4)

# Prepreprocess the dataset
Xt = np.ones(shape=(nb_samples, nb_features+1))
Xt[:, 0:2] = X
Yt = Y.reshape((nb_samples, 1))

# Initial values
W = np.random.uniform(-0.01, 0.01, size=(1, nb_features+1))
dL = np.zeros((1, nb_features + 1))
dL_prev = np.random.uniform(-0.001, 0.001, size=(1, nb_features + 1))
dW_prec = np.ones((1, nb_features + 1))
W_prev = np.zeros((1, nb_features + 1))

learning_rate = 0.0001
tolerance = 1e-6

# Run a training process
i = 0

while np.linalg.norm(W_prev - W, ord=1) > tolerance:
    i += 1
    
    sZ = sigmoid(np.dot(Xt, W.T))
    
    dL = np.sum((Yt - sZ) * Xt, axis=0)
    delta = dL / (dL_prev - dL)
    dL_prev = dL.copy()
    
    dW = dW_prec * delta
    dW_prec = dW.copy()
    
    W_prev = W.copy()
    W += learning_rate * dW
    
print('Converged after {} iterations'.format(i))

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