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Python Training a logistic regression model


New Coder
Hello ! I wanted to know please if i missed something in my training function for binary class logistic regression

def logistic_regression_train(data, labels, max_iters=10, lr=0.001,
                              print_period=1000, plot_period=1000):
    # Initialize the weights randomly according to a Gaussian distribution
    weights = np.random.normal(0., 0.1, [data.shape[1],])
    for it in range(max_iters):
        #The gradient and do a gradient step
        gradient = gradient_cross_entropy(data, labels, weights)
        weights = weights - lr * gradient
        # If we reach 100% accuracy, we can stop training immediately
        predictions = logistic_regression_predict(data, weights)#on recalcule avec les nouv w
        if accuracy_fn(predictions, labels) == 100:
        # logging
        if print_period and it % print_period == 0:
            print('loss at iteration', it, ":", cross_entropy(data, labels, weights))
    return weights

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