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import numpy as np | |
def loss_function(prediction, ground_truth): #mean square error with batch size = 1 | |
cost = (ground_truth-prediction)**2 | |
return cost | |
def prediction(x,current_weights,current_bias): | |
y_predicted = np.sum((current_weights * x)) + current_bias | |
return y_predicted | |
def gradient_descent(x, y, current_weights, current_bias, learning_rate): | |
# Making predictions | |
y_predicted = prediction(x,current_weights, current_bias) | |
# Calculationg the current cost | |
current_cost = loss_function(y, y_predicted) | |
print("current_cost ", current_cost) | |
# Calculating the gradients | |
weight_derivative = -2 * sum(x * (y-y_predicted)) | |
bias_derivative = -2 * sum(y-y_predicted) | |
# Updating weights and bias | |
updated_weight = current_weights - (learning_rate * weight_derivative) | |
updated_bias = current_bias - (learning_rate * bias_derivative) | |
return updated_weight, updated_bias | |
""" Dataset """ | |
v1 = np.array([1, 1, 0, 0]) | |
v2 = np.array([1, 0, 0, 0]) | |
v3 = np.array([0, 0, 0, 1]) | |
v4 = np.array([0, 0, 1, 1]) | |
inputs = [v1, v2, v3, v4] | |
ground_truth = [0, 0 ,1 , 1] | |
dataset_length = len(inputs) | |
print(dataset_length) | |
""" Training """ | |
iteration_number = 10 | |
learning_rate = 0.01 | |
current_weights = [0.09, 0.2, 0.5, 0.95] | |
current_bias = [0.02] | |
for i in range(iteration_number): | |
for k in range(dataset_length): | |
current_weights, current_bias = gradient_descent(inputs[k], ground_truth[k], current_weights, current_bias, learning_rate) | |
print("Iteration :", i, "current weights", current_weights, "current bias", current_bias) | |
""" Test """ | |
for k in range(dataset_length): | |
p_ = prediction(inputs[k], current_weights, current_bias) | |
print("data:", inputs[k], "prediction", p_ ) |
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