python - scipy.optimize.fmin_cg: "'Desired error not necessarily achieved due to precision loss.' -

i using scipy.optimize.fmin_cg minimize function. function takes various data sets , fmin_cg returns values lot of data sets, except first 3 fail:

dataset:  0 warning: desired error not achieved due precision loss.          current function value: 2.988730          iterations: 1          function evaluations: 32          gradient evaluations: 5 [ 500.00011672   -0.63965932]  dataset:  1 warning: desired error not achieved due precision loss.          current function value: 3.076145          iterations: 1          function evaluations: 32          gradient evaluations: 5 [ 500.00013434   -0.58092425]  dataset:  2 warning: desired error not achieved due precision loss.          current function value: 3.160507          iterations: 1          function evaluations: 32          gradient evaluations: 5 [ 500.00014962   -0.52933729]  dataset:  3 optimization terminated successfully.          current function value: 4.000000          iterations: 1          function evaluations: 8          gradient evaluations: 2 [ 500.00729686   23.29306024]  dataset:  4 optimization terminated successfully.          current function value: 4.000000          iterations: 1          function evaluations: 8          gradient evaluations: 2 [ 500.00915456   30.21053839]  dataset:  5 optimization terminated successfully.          current function value: 4.000000          iterations: 1          function evaluations: 8          gradient evaluations: 2 [ 500.01103431   37.37704849]  dataset:  6 optimization terminated successfully.          current function value: 4.000000          iterations: 1          function evaluations: 8          gradient evaluations: 2 [ 500.03064942  118.1983465 ]  dataset:  7 optimization terminated successfully.          current function value: 4.000000          iterations: 1          function evaluations: 8          gradient evaluations: 2 [ 500.03454471  135.11401129]  dataset:  8 optimization terminated successfully.          current function value: 4.000000          iterations: 1          function evaluations: 8          gradient evaluations: 2 [ 500.03848004  152.4157083 ] 

etc....................

the optimised results begin x0 = [500, -1] initial guesses, lowering 500 around 300 results in successes, no matter value picked results don't tend anywhere near expected. (there should large difference, minute variations, when ratios of 4 should seen between of them. however, second value in returned array behaves expected)