def main():
"""Run perceptron algorithm on AND dataset.
"""
# construct the AND dataset
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [0], [0], [1]])
# define our perceptron and train it
print("[INFO] training perceptron...")
perceptron = Perceptron(X.shape[1], alpha=0.1)
perceptron.fit(X, y, epochs=20)
# now that our perceptron is trained we can evaluate it
print("[INFO] testing perceptron...")
# now that our network is trained, loop over the data points
for (value, target) in zip(X, y):
# make a prediction on the data point and display the result to our console
pred = perceptron.predict(value)
print("[INFO] data={}, ground-truth={}, pred={}".format(
value, target[0], pred))
# -*- coding: utf-8 -*-
# perceptron_or.py
import numpy as np
from pyimagesearch.nn import Perceptron
# construct the OR dataset
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [1]])
# define our perceptron and train it
print("[INFO] training perceptron...")
p = Perceptron(X.shape[1], alpha=0.1)
p.fit(X, y, epochs=20)
# now that out perceptron is trained we can evaluate it
print("[INFO] testing perceptron...")
# now that out network is trained, loop over the data points
for (x, target) in zip(X, y):
# make a prediction on the data point and display the
# result to our console
pred = p.predict(x)
print("[INFO] data = {}, ground-truth = {}, pred = {}".format(
x, target[0], pred))
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 9 10:50:58 2020
@author: Porto
"""
# import the necessary packages
from pyimagesearch.nn import Perceptron
import numpy as np
# construct the AND dataset
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [0], [0], [1]])
# define our perceptron and train it
print("[INFO] training perceptron...")
p = Perceptron.Perceptron(X.shape[1], alpha=0.1)
p.fit(X, y, epochs=20)
# now that our perceptron is trained we can evaluate it
print("[INFO] testing perceptron...")
# now that our network is trained, loop over the data points
for (x, target) in zip(X, y):
# make a prediction on the data point and display the result
# to our console
pred = p.predict(x)
print("[INFO] data={}, ground-truth={}, pred={}".format(
x, target[0], pred))
# USAGE
# python perceptron_and.py
# import the necessary packages
from pyimagesearch.nn import Perceptron
import numpy as np
# construct the AND dataset
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [0], [0], [1]])
# define our perceptron and train it
print("[INFO] training perceptron...")
p = Perceptron(X.shape[1], alpha=0.1)
p.fit(X, y, epochs=20)
# now that our perceptron is trained we can evaluate it
print("[INFO] testing perceptron...")
# now that our network is trained, loop over the data points
for (x, target) in zip(X, y):
# make a prediction on the data point and display the result
# to our console
pred = p.predict(x)
print("[INFO] data={}, ground-truth={}, pred={}".format(
x, target[0], pred))