496e3d795c1f33010899f34c2e171a76a3d8e75c,codes/2-basics_in_machine_learning/linear_svm/code/linear_SVM.py,,,#,70

Before Change

    // Displaying the desired values.
    if (step_idx+1) % FLAGS.log_step == 0:
        print("Step //%d, Loss= %f, training accuracy= %f, testing accuracy= %f " % (step_idx+1, loss_step, train_acc_step, test_acc_step))

if FLAGS.is_evaluation:
    [[w1], [w2]] = sess.run(W)

After Change

    // coor_neg_list = [negative_X, negative_y]

    for index, data in enumerate(X):
        if y[index] == 1:
            positive_X = data[1]
            positive_y = data[0]
        elif y[index] == 0:
            negative_X = data[1]
            negative_y = data[0]
        else:
            sys.exit("Invalid label!")

    // Plotting the SVM decision boundary.
    plt.plot(positive_X, positive_y, "+", label="Positive")
    plt.plot(negative_X, negative_y, "o", label="Negative")
    plt.plot(x_line, line, "r-", label="Separator", linewidth=3)
    plt.legend(loc="best")

Instances