# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations (test_iter: 100) ,
# covering the full 10,000 testing images.
I want to know if it is different that I set batch size =1 & test_iter=10,000 between batch size =100 & test_iter=100 when I train caffe model? Does it has different accuracy result or computational situation?
Thank you all.
I1110 16:04:32.444972 9722 solver.cpp:337] Iteration 10, Testing net (#0)
I1110 16:05:11.255198 9722 solver.cpp:404] Test net output #0: accuracy = 0.518889
I1110 16:05:11.255300 9722 solver.cpp:404] Test net output #1: loss = 0.696062 (* 1 = 0.696062 loss)
I1110 16:05:17.068862 9722 solver.cpp:337] Iteration 15, Testing net (#0)
I1110 16:05:55.884680 9722 solver.cpp:404] Test net output #0: accuracy = 0.537222
I1110 16:05:55.884776 9722 solver.cpp:404] Test net output #1: loss = 0.691227 (* 1 = 0.691227 loss)
I1110 16:06:01.924124 9722 solver.cpp:337] Iteration 20, Testing net (#0)
I1110 16:06:40.695374 9722 solver.cpp:404] Test net output #0: accuracy = 0.552778
I1110 16:06:40.695482 9722 solver.cpp:404] Test net output #1: loss = 0.684588 (* 1 = 0.684588 loss)
I1110 16:40:41.339869 9859 solver.cpp:337] Iteration 10, Testing net (#0)
I1110 16:40:44.912143 9859 solver.cpp:404] Test net output #0: accuracy = 0.518889
I1110 16:40:44.912185 9859 solver.cpp:404] Test net output #1: loss = 0.696061 (* 1 = 0.696061 loss)
I1110 16:40:51.390815 9859 solver.cpp:337] Iteration 15, Testing net (#0)
I1110 16:40:54.948755 9859 solver.cpp:404] Test net output #0: accuracy = 0.537222
I1110 16:40:54.948791 9859 solver.cpp:404] Test net output #1: loss = 0.691227 (* 1 = 0.691227 loss)
I1110 16:41:01.446266 9859 solver.cpp:337] Iteration 20, Testing net (#0)
I1110 16:41:05.033687 9859 solver.cpp:404] Test net output #0: accuracy = 0.552778
I1110 16:41:05.033730 9859 solver.cpp:404] Test net output #1: loss = 0.684588 (* 1 = 0.684588 loss)