import tensorflow as tf

print(sess.run(tf.matrix_inverse(D)))

print('\ndeterminant(D)={:.1f}'.format(sess.run(tf.matrix_determinant(D))))

print('\ncholesky(D):')
print(sess.run(tf.cholesky(identity_matrix)))

print('\nselfAdjointEig(D):')
print(sess.run(tf.self_adjoint_eig(D)))

print(sess.run(tf.div(13, 4)))
print(sess.run(tf.truediv(13, 4)))
print(sess.run(tf.floordiv(13, 4)))
print(sess.run(tf.mod(13.2, 4)))

print(sess.run(tf.cross([1, 0, 0], [0, 1, 0])))
print(sess.run(tf.square([1, 2, 3])))


def custom_polynomial(local_tf, value):
    return local_tf.subtract(3 * local_tf.square(value), value) + 10


print((sess.run(custom_polynomial(tf, 11))))

alpha = 0.1
