コード例 #1
0
ファイル: perceptron_and.py プロジェクト: the-fenrir/pis_code
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))
コード例 #2
0
# -*- 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))
コード例 #3
0
ファイル: perceptron_and.py プロジェクト: ritika26/test-repo
# 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))