By : The Chee
Date : November 21 2020, 11:01 PM

This might help you The idea in multiclass variant of the Perceptron algorithm is pretty much the same as in the binary classification except for a few minor differences. In the multiclass classification with K classes, we will maintain a set of K weight vectors W_{1},...,W_{K} (each weight vector is of size D where D is the number of features). The prediction (both at training and test time) would change to: code :
\widehat{y}_{n} = arg max_{k}(W_{k}^{T} x_{n} + b)
if(\widehat{y}_{n} != y_{n})
W_{\widehat{y}_{n}} = W_{\widehat{y}_{n}}  X_{n}
W_{\widehat{y}_{n}} = W_{\widehat{y}_{n}} + X_{n}
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perceptron classification and R
By : user3091917
Date : March 29 2020, 07:55 AM
I hope this helps you . I have four points: a=(0.5, 0.5) b=(0.5, 0.5) c=(0.3, 0.5) d=(0.0, 1.0) , It would help to show that you've done some homework ... code :
library(sos)
findFn("perceptron")
RSiteSearch("perceptron") ## after running this, click on some buttons on the web page to expand the search

Creating a basic feed forward perceptron neural network for multiclass classification
By : Avinash Raghav
Date : March 29 2020, 07:55 AM

How to use Keras' multi layer perceptron for multiclass classification
By : DavidC
Date : March 29 2020, 07:55 AM
this one helps. This is a pretty common beginner's mistake with Keras. Unlike other Deep Learning frameworks, Keras does not use integer labels for the usual crossentropy loss, instead it expects a binary vector (called "onehot"), where the vector is just 0's and a 1 over the index of the right class. You can easily convert your labels to this format with the following code: code :
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

Classification perceptron implementation
By : Waqas Ahmed
Date : March 29 2020, 07:55 AM
this one helps. I looked at your code and the video and I believe the way your code is written, the points start out as green, if their guess matches their target they turn red and if their guess doesn't match the target they turn blue. This repeats with the remaining blue eventually turning red as their guess matches the target. (The changing weights may turn a red to blue but eventually it will be corrected.) Below is my rework of your code that slows down the process by: adding more points; only processing one point per frame instead of all of them: code :
import random as rnd
import matplotlib.pyplot as plt
import matplotlib.animation as animation
NUM_POINTS = 100
LEARNING_RATE = 0.1
X, Y = 0, 1
fig = plt.figure() # an empty figure with no axes
ax1 = fig.add_subplot(1, 1, 1)
plt.xlim(0, 120)
plt.ylim(0, 120)
plt.plot([x for x in range(100)], [y for y in range(100)])
weights = [rnd.uniform(1, 1), rnd.uniform(1, 1)]
points = []
circles = []
for i in range(NUM_POINTS):
x = rnd.uniform(1, 100)
y = rnd.uniform(1, 100)
points.append((x, y))
circle = plt.Circle((x, y), radius=1, fill=False, color='g')
circles.append(circle)
ax1.add_patch(circle)
def activation(val):
if val >= 0:
return 1
return 1
def guess(point):
vsum = 0
# x and y and bias weights
vsum += point[X] * weights[X]
vsum += point[Y] * weights[Y]
return activation(vsum)
def train(point, error):
# adjust weights
weights[X] += point[X] * error * LEARNING_RATE
weights[Y] += point[Y] * error * LEARNING_RATE
point_index = 0
def animate(frame):
global point_index
point = points[point_index]
if point[X] > point[Y]:
answer = 1 # group A (X > Y)
else:
answer = 1 # group B (Y > X)
guessed = guess(point)
if answer == guessed:
circles[point_index].set_color('r')
else:
circles[point_index].set_color('b')
train(point, answer  guessed)
point_index = (point_index + 1) % NUM_POINTS
ani = animation.FuncAnimation(fig, animate, interval=100)
plt.show()
if answer == guessed:
circles[point_index].set_color('r' if answer == 1 else 'b')
else:
circles[point_index].set_color('g')
train(point, answer  guessed)

classification using multilayer perceptron
By : Jeffrey Lin
Date : March 29 2020, 07:55 AM
I wish this help you You should represent your 5 classes by 5 binary outputs. This is known as 1ofC encoding, onehot encoding, dummy variables, indicators, ... Then you need a softmax activation function in the output layer which will give you class probabilities as outputs. In addition, you should use the crossentropy (CE) error function. Softmax+CE will give you the same gradient as identity+SSE in the output layer: dE/da_i = y_i  t_i. Softmax+CE has been used for up to 20,000 classes in the ImageNet dataset.



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