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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
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
add a comment |
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
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 iny = mx+b
? Two guesses: 1) your perceptron output isfit_data[i] * weights
, there is nob
. 2) you are trying to visualiseweights + fit_data[i] * error * lr
? Is there the original code/tutorial link that you are using as source?
– EPo
Mar 7 at 6:56
add a comment |
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
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
python-3.x neural-network google-colaboratory perceptron
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 iny = mx+b
? Two guesses: 1) your perceptron output isfit_data[i] * weights
, there is nob
. 2) you are trying to visualiseweights + fit_data[i] * error * lr
? Is there the original code/tutorial link that you are using as source?
– EPo
Mar 7 at 6:56
add a comment |
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 iny = mx+b
? Two guesses: 1) your perceptron output isfit_data[i] * weights
, there is nob
. 2) you are trying to visualiseweights + 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
add a comment |
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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 isfit_data[i] * weights
, there is nob
. 2) you are trying to visualiseweights + fit_data[i] * error * lr
? Is there the original code/tutorial link that you are using as source?– EPo
Mar 7 at 6:56