Train data shape: (49000, 3073) Train labels shape: (49000,) Validation data shape: (1000, 3073) Validation labels shape: (1000,) Test data shape: (1000, 3073) Test labels shape: (1000,) dev data shape: (500, 3073) dev labels shape: (500,)
loss: 2.374488 sanity check: 2.302585
numerical: 1.055128 analytic: 1.055127, relative error: 8.054454e-08 numerical: 1.799979 analytic: 1.799979, relative error: 3.548119e-08 numerical: 3.057507 analytic: 3.057507, relative error: 4.628624e-09 numerical: 3.042005 analytic: 3.042005, relative error: 1.489120e-08 numerical: 3.134774 analytic: 3.134774, relative error: 1.959762e-08 numerical: 3.469694 analytic: 3.469694, relative error: 1.521055e-09 numerical: -0.821283 analytic: -0.821283, relative error: 4.904056e-09 numerical: 0.033736 analytic: 0.033736, relative error: 2.233549e-07 numerical: -0.249902 analytic: -0.249903, relative error: 1.138011e-07 numerical: 0.259507 analytic: 0.259507, relative error: 5.076593e-08 numerical: -4.654762 analytic: -468.625947, relative error: 9.803298e-01 numerical: -3.464832 analytic: -349.169539, relative error: 9.803489e-01 numerical: 0.225706 analytic: 22.937998, relative error: 9.805121e-01 numerical: 0.458898 analytic: 45.620289, relative error: 9.800822e-01 numerical: -2.204595 analytic: -221.769293, relative error: 9.803138e-01 numerical: -0.125210 analytic: -13.301957, relative error: 9.813498e-01 numerical: 2.005059 analytic: 201.274224, relative error: 9.802729e-01 numerical: 1.269539 analytic: 129.611320, relative error: 9.806001e-01 numerical: -0.056690 analytic: -6.611941, relative error: 9.829980e-01 numerical: -0.262966 analytic: -27.175883, relative error: 9.808326e-01
naive loss: 2.374488e+00 computed in 0.211975s vectorized loss: 2.374488e+00 computed in 0.009088s Loss difference: 0.000000 Gradient difference: 0.000000
iteration 0 / 1500: loss 782.193431 iteration 100 / 1500: loss 287.369345 iteration 200 / 1500: loss 106.520535 iteration 300 / 1500: loss 40.317201 iteration 400 / 1500: loss 16.090033 iteration 500 / 1500: loss 7.234079 iteration 600 / 1500: loss 3.996430 iteration 700 / 1500: loss 2.795388 iteration 800 / 1500: loss 2.292711 iteration 900 / 1500: loss 2.170212 iteration 1000 / 1500: loss 2.175318 iteration 1100 / 1500: loss 2.168804 iteration 1200 / 1500: loss 2.132038 iteration 1300 / 1500: loss 2.146590 iteration 1400 / 1500: loss 2.076369 iteration 0 / 1500: loss 1533014.675630 iteration 100 / 1500: loss nan iteration 200 / 1500: loss nan iteration 300 / 1500: loss nan iteration 400 / 1500: loss nan iteration 500 / 1500: loss nan iteration 600 / 1500: loss nan iteration 700 / 1500: loss nan iteration 800 / 1500: loss nan iteration 900 / 1500: loss nan iteration 1000 / 1500: loss nan iteration 1100 / 1500: loss nan iteration 1200 / 1500: loss nan iteration 1300 / 1500: loss nan iteration 1400 / 1500: loss nan iteration 0 / 1500: loss 768.536932 iteration 100 / 1500: loss 6.860303 iteration 200 / 1500: loss 2.084644 iteration 300 / 1500: loss 2.097076 iteration 400 / 1500: loss 2.101419 iteration 500 / 1500: loss 2.098051 iteration 600 / 1500: loss 2.126691 iteration 700 / 1500: loss 2.055123 iteration 800 / 1500: loss 2.072236 iteration 900 / 1500: loss 2.111768 iteration 1000 / 1500: loss 2.136510 iteration 1100 / 1500: loss 2.105484 iteration 1200 / 1500: loss 2.069053 iteration 1300 / 1500: loss 2.135619 iteration 1400 / 1500: loss 2.033488 iteration 0 / 1500: loss 1550818.358399 iteration 100 / 1500: loss nan iteration 200 / 1500: loss nan iteration 300 / 1500: loss nan iteration 400 / 1500: loss nan iteration 500 / 1500: loss nan iteration 600 / 1500: loss nan iteration 700 / 1500: loss nan iteration 800 / 1500: loss nan iteration 900 / 1500: loss nan iteration 1000 / 1500: loss nan iteration 1100 / 1500: loss nan iteration 1200 / 1500: loss nan iteration 1300 / 1500: loss nan iteration 1400 / 1500: loss nan lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.327531 val accuracy: 0.353000 lr 1.000000e-07 reg 1.000000e+08 train accuracy: 0.100265 val accuracy: 0.087000 lr 5.000000e-07 reg 5.000000e+04 train accuracy: 0.330939 val accuracy: 0.331000 lr 5.000000e-07 reg 1.000000e+08 train accuracy: 0.100265 val accuracy: 0.087000 best validation accuracy achieved during cross-validation: 0.353000
softmax on raw pixels final test set accuracy: 0.340000
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