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back propagation python

Innocent Iguana answered on April 15, 2024 Popularity 3/10 Helpfulness 2/10

answer back propagation python

back propagation python

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weights = np.array([0, 2, 1])
input_data = np.array([1, 2, 3])
target = 0

def get_slope(input_data, target, weights):
    error = get_error(input_data, target, weights)
    slope = 2 * input_data * error
    return(slope)

def get_error(input_data, target, weights):
    preds = (weights * input_data).sum()
    error = preds - target
    return(error)

def get_mse(input_data, target, weights):
    errors = get_error(input_data, target, weights)
    mse = np.mean(errors**2)
    return(mse)

import matplotlib.pyplot as plt
n_updates = 20 # epochs
learning_rate = 0.01
mse_hist = []

# Iterate over the number of updates
for i in range(n_updates):
    # Calculate the slope: slope
    slope = get_slope(input_data, target, weights)
    
    # Update the weights: weights
    weights = weights - learning_rate * slope
    
    # Calculate mse with new weights: mse
    mse = get_mse(input_data, target, weights)
    
    # Append the mse to mse_hist
    mse_hist.append(mse)

# Plot the mse history
plt.plot(mse_hist)
plt.xlabel('Iterations')
plt.ylabel('Mean Squared Error')
plt.show()

Popularity 3/10 Helpfulness 2/10 Language python

Source: Grepper

Tags: back propagation python

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Contributed on Apr 15 2024

Innocent Iguana

0 Answers Avg Quality 2/10

back propagation python

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