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How to plot slope from a simple perceptron



2019 Community Moderator ElectionHow can I represent an 'Enum' in Python?How to flush output of print function?Perceptron learning algorithm not converging to 0multi-layer perceptron (MLP) architecture: criteria for choosing number of hidden layers and size of the hidden layer?simple perceptron model and XORError with PerceptronHyperplane in perceptronsSingle Layer PerceptronSimple Perceptron In Javascript for XOR gateMultilayer Perceptron questions










0















I want to plot the slope y = mx+b when the weights change but i don't know how to get the value of m and b



Here is the code:



import numpy as np
import matplotlib.pyplot as plt

def sign(x):
return 1 if x > 0 else 0

def get_mse(predicted_data, targets):
error = predicted_data - targets
return np.square(error).sum()/len(targets)

fit_data = np.array([[1,0,0],
[1,0,1],
[1,1,0],
[1,1,1],])

targets = np.array([0,0,0,1])
weights = np.array([0,0,0])
lr = 1
epochs = 10

mse_hist = []

for _ in range(epochs):
model_outputs = []
for i in range(len(targets)):
y = (fit_data[i] * weights).sum()
y = sign(y)
error = targets[i] - y
weights = weights + fit_data[i] * error * lr

model_outputs.append(y)
mse = get_mse(model_outputs, targets)
mse_hist.append(mse)


Here the plot of the slope should be, at least that's what i think so



 print("Weights:", weights)
print("Mse:", mse_hist[-1])
plt.plot(mse_hist)
plt.xlabel('Iterations')
plt.ylabel('Mean Squared Error')
plt.pause(0.5)

plt.show()

input_data = ([1,1,1])
prediction = (input_data * weights).sum()
prediction = sign(prediction)
print("Prediction:", prediction)


Thanks for the help










share|improve this question
























  • Please fix indentation at bottom of first snippet. I'm guessing you want to indent into the loop, but I am unsure.

    – kabanus
    Mar 6 at 8:04











  • oh, sorry i didn't notice it, i have fixed it Thanks

    – Rodrigo Arce Villa
    Mar 7 at 5:17











  • What do you consider as variabels in y = mx+b? Two guesses: 1) your perceptron output is fit_data[i] * weights, there is no b. 2) you are trying to visualise weights + fit_data[i] * error * lr? Is there the original code/tutorial link that you are using as source?

    – EPo
    Mar 7 at 6:56















0















I want to plot the slope y = mx+b when the weights change but i don't know how to get the value of m and b



Here is the code:



import numpy as np
import matplotlib.pyplot as plt

def sign(x):
return 1 if x > 0 else 0

def get_mse(predicted_data, targets):
error = predicted_data - targets
return np.square(error).sum()/len(targets)

fit_data = np.array([[1,0,0],
[1,0,1],
[1,1,0],
[1,1,1],])

targets = np.array([0,0,0,1])
weights = np.array([0,0,0])
lr = 1
epochs = 10

mse_hist = []

for _ in range(epochs):
model_outputs = []
for i in range(len(targets)):
y = (fit_data[i] * weights).sum()
y = sign(y)
error = targets[i] - y
weights = weights + fit_data[i] * error * lr

model_outputs.append(y)
mse = get_mse(model_outputs, targets)
mse_hist.append(mse)


Here the plot of the slope should be, at least that's what i think so



 print("Weights:", weights)
print("Mse:", mse_hist[-1])
plt.plot(mse_hist)
plt.xlabel('Iterations')
plt.ylabel('Mean Squared Error')
plt.pause(0.5)

plt.show()

input_data = ([1,1,1])
prediction = (input_data * weights).sum()
prediction = sign(prediction)
print("Prediction:", prediction)


Thanks for the help










share|improve this question
























  • Please fix indentation at bottom of first snippet. I'm guessing you want to indent into the loop, but I am unsure.

    – kabanus
    Mar 6 at 8:04











  • oh, sorry i didn't notice it, i have fixed it Thanks

    – Rodrigo Arce Villa
    Mar 7 at 5:17











  • What do you consider as variabels in y = mx+b? Two guesses: 1) your perceptron output is fit_data[i] * weights, there is no b. 2) you are trying to visualise weights + fit_data[i] * error * lr? Is there the original code/tutorial link that you are using as source?

    – EPo
    Mar 7 at 6:56













0












0








0








I want to plot the slope y = mx+b when the weights change but i don't know how to get the value of m and b



Here is the code:



import numpy as np
import matplotlib.pyplot as plt

def sign(x):
return 1 if x > 0 else 0

def get_mse(predicted_data, targets):
error = predicted_data - targets
return np.square(error).sum()/len(targets)

fit_data = np.array([[1,0,0],
[1,0,1],
[1,1,0],
[1,1,1],])

targets = np.array([0,0,0,1])
weights = np.array([0,0,0])
lr = 1
epochs = 10

mse_hist = []

for _ in range(epochs):
model_outputs = []
for i in range(len(targets)):
y = (fit_data[i] * weights).sum()
y = sign(y)
error = targets[i] - y
weights = weights + fit_data[i] * error * lr

model_outputs.append(y)
mse = get_mse(model_outputs, targets)
mse_hist.append(mse)


Here the plot of the slope should be, at least that's what i think so



 print("Weights:", weights)
print("Mse:", mse_hist[-1])
plt.plot(mse_hist)
plt.xlabel('Iterations')
plt.ylabel('Mean Squared Error')
plt.pause(0.5)

plt.show()

input_data = ([1,1,1])
prediction = (input_data * weights).sum()
prediction = sign(prediction)
print("Prediction:", prediction)


Thanks for the help










share|improve this question
















I want to plot the slope y = mx+b when the weights change but i don't know how to get the value of m and b



Here is the code:



import numpy as np
import matplotlib.pyplot as plt

def sign(x):
return 1 if x > 0 else 0

def get_mse(predicted_data, targets):
error = predicted_data - targets
return np.square(error).sum()/len(targets)

fit_data = np.array([[1,0,0],
[1,0,1],
[1,1,0],
[1,1,1],])

targets = np.array([0,0,0,1])
weights = np.array([0,0,0])
lr = 1
epochs = 10

mse_hist = []

for _ in range(epochs):
model_outputs = []
for i in range(len(targets)):
y = (fit_data[i] * weights).sum()
y = sign(y)
error = targets[i] - y
weights = weights + fit_data[i] * error * lr

model_outputs.append(y)
mse = get_mse(model_outputs, targets)
mse_hist.append(mse)


Here the plot of the slope should be, at least that's what i think so



 print("Weights:", weights)
print("Mse:", mse_hist[-1])
plt.plot(mse_hist)
plt.xlabel('Iterations')
plt.ylabel('Mean Squared Error')
plt.pause(0.5)

plt.show()

input_data = ([1,1,1])
prediction = (input_data * weights).sum()
prediction = sign(prediction)
print("Prediction:", prediction)


Thanks for the help







python-3.x neural-network google-colaboratory perceptron






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Mar 7 at 5:17







Rodrigo Arce Villa

















asked Mar 6 at 8:00









Rodrigo Arce VillaRodrigo Arce Villa

33




33












  • Please fix indentation at bottom of first snippet. I'm guessing you want to indent into the loop, but I am unsure.

    – kabanus
    Mar 6 at 8:04











  • oh, sorry i didn't notice it, i have fixed it Thanks

    – Rodrigo Arce Villa
    Mar 7 at 5:17











  • What do you consider as variabels in y = mx+b? Two guesses: 1) your perceptron output is fit_data[i] * weights, there is no b. 2) you are trying to visualise weights + fit_data[i] * error * lr? Is there the original code/tutorial link that you are using as source?

    – EPo
    Mar 7 at 6:56

















  • Please fix indentation at bottom of first snippet. I'm guessing you want to indent into the loop, but I am unsure.

    – kabanus
    Mar 6 at 8:04











  • oh, sorry i didn't notice it, i have fixed it Thanks

    – Rodrigo Arce Villa
    Mar 7 at 5:17











  • What do you consider as variabels in y = mx+b? Two guesses: 1) your perceptron output is fit_data[i] * weights, there is no b. 2) you are trying to visualise weights + fit_data[i] * error * lr? Is there the original code/tutorial link that you are using as source?

    – EPo
    Mar 7 at 6:56
















Please fix indentation at bottom of first snippet. I'm guessing you want to indent into the loop, but I am unsure.

– kabanus
Mar 6 at 8:04





Please fix indentation at bottom of first snippet. I'm guessing you want to indent into the loop, but I am unsure.

– kabanus
Mar 6 at 8:04













oh, sorry i didn't notice it, i have fixed it Thanks

– Rodrigo Arce Villa
Mar 7 at 5:17





oh, sorry i didn't notice it, i have fixed it Thanks

– Rodrigo Arce Villa
Mar 7 at 5:17













What do you consider as variabels in y = mx+b? Two guesses: 1) your perceptron output is fit_data[i] * weights, there is no b. 2) you are trying to visualise weights + fit_data[i] * error * lr? Is there the original code/tutorial link that you are using as source?

– EPo
Mar 7 at 6:56





What do you consider as variabels in y = mx+b? Two guesses: 1) your perceptron output is fit_data[i] * weights, there is no b. 2) you are trying to visualise weights + fit_data[i] * error * lr? Is there the original code/tutorial link that you are using as source?

– EPo
Mar 7 at 6:56












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