Overview#
Perceptron (MLP) is a class of Artificial Neuron that takes takes several binary inputs, x1,x2,…x1,x2,…, and produces a single binary output based on the bias.Perceptron are sometimes colloquially referred to as "vanilla" Artificial Neural networks, especially when they have a single Hidden layer
Perceptrons are not in common usage in Artificial Neural networks as the use of more sophisticated functions.
Perceptron in the context of a binary classification task where we refer to our two classifications as 1 (positive class) and -1 (negative class) for simplicity. We can then define a Mapping function: Z = w(transform)T x
Perceptron in Python#
import numpy as np class Perceptron(object): """Perceptron classifier. Parameters ------------ eta : float Learning rate (between 0.0 and 1.0) n_iter : int Passes over the training dataset. random_state : int Random number generator seed for random weight initialization. Attributes ----------- w_ : 1d-array Weights after fitting. errors_ : list Number of misclassifications (updates) in each epoch. """ def __init__(self, eta=0.01, n_iter=50, random_state=1): self.eta = eta self.n_iter = n_iter self.random_state = random_state def fit(self, X, y): """Fit training data. Parameters ---------- X : {array-like}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. Returns ------- self : object """ rgen = np.random.RandomState(self.random_state) self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1]) self.errors_ = [] for _ in range(self.n_iter): errors = 0 for xi, target in zip(X, y): update = self.eta * (target - self.predict(xi)) self.w_[1:] += update * xi self.w_[0] += update errors += int(update != 0.0) self.errors_.append(errors) return self def net_input(self, X): """Calculate net input""" return np.dot(X, self.w_[1:]) + self.w_[0] def predict(self, X): """Return class label after unit step""" return np.where(self.net_input(X) >= 0.0, 1, -1)
More Information#
There might be more information for this subject on one of the following:- [#1] - Multilayer_perceptron
- based on information obtained 2017-11-24-