Update README.md

README.md

@@ -61,6 +61,55 @@ The network might take several hours to converge (depending on the speed of your
 
 The results are saved into the *output_folder* specified in the cfg file. In this folder, you can find a file (*res.res*) summarizing training and test error rates. The model *model_raw.pkl* is the SincNet model saved after the last iteration. 
 Using the cfg file specified above, we obtain the following results:
+```
+epoch 0, loss_tr=6.309807 err_tr=0.997656 loss_te=6.332657 err_te=0.997605 err_te_snt=0.996392
+epoch 0, loss_tr=5.542032 err_tr=0.984189 loss_te=4.996982 err_te=0.969038 err_te_snt=0.919913
+epoch 8, loss_tr=1.693487 err_tr=0.434424 loss_te=2.735717 err_te=0.612260 err_te_snt=0.069264
+epoch 16, loss_tr=0.861834 err_tr=0.229424 loss_te=2.465258 err_te=0.520276 err_te_snt=0.038240
+epoch 24, loss_tr=0.528619 err_tr=0.144375 loss_te=2.948707 err_te=0.534053 err_te_snt=0.062049
+epoch 32, loss_tr=0.362914 err_tr=0.100518 loss_te=2.530276 err_te=0.469060 err_te_snt=0.015152
+epoch 40, loss_tr=0.267921 err_tr=0.076445 loss_te=2.761606 err_te=0.464799 err_te_snt=0.023088
+epoch 48, loss_tr=0.215479 err_tr=0.061406 loss_te=2.737486 err_te=0.453493 err_te_snt=0.010823
+epoch 56, loss_tr=0.173690 err_tr=0.050732 loss_te=2.812427 err_te=0.443322 err_te_snt=0.011544
+epoch 64, loss_tr=0.145256 err_tr=0.043594 loss_te=2.917569 err_te=0.438507 err_te_snt=0.009380
+epoch 72, loss_tr=0.128894 err_tr=0.038486 loss_te=3.009008 err_te=0.438005 err_te_snt=0.019481
+epoch 80, loss_tr=0.111940 err_tr=0.033389 loss_te=2.925527 err_te=0.428739 err_te_snt=0.011544
+epoch 88, loss_tr=0.101788 err_tr=0.031016 loss_te=3.050507 err_te=0.438099 err_te_snt=0.011544
+epoch 96, loss_tr=0.089672 err_tr=0.027451 loss_te=3.212288 err_te=0.445679 err_te_snt=0.011544
+epoch 104, loss_tr=0.085366 err_tr=0.026445 loss_te=3.226385 err_te=0.431996 err_te_snt=0.012266
+epoch 112, loss_tr=0.077404 err_tr=0.023564 loss_te=3.341498 err_te=0.433145 err_te_snt=0.010101
+epoch 120, loss_tr=0.073497 err_tr=0.022861 loss_te=3.858381 err_te=0.472951 err_te_snt=0.028139
+epoch 128, loss_tr=0.067383 err_tr=0.020527 loss_te=3.474988 err_te=0.431545 err_te_snt=0.008658
+epoch 136, loss_tr=0.064087 err_tr=0.019961 loss_te=3.341287 err_te=0.436171 err_te_snt=0.007215
+epoch 144, loss_tr=0.062003 err_tr=0.019160 loss_te=3.412609 err_te=0.426363 err_te_snt=0.009380
+epoch 152, loss_tr=0.058740 err_tr=0.018281 loss_te=3.815553 err_te=0.443672 err_te_snt=0.010823
+epoch 160, loss_tr=0.055162 err_tr=0.017314 loss_te=3.784261 err_te=0.446239 err_te_snt=0.008658
+epoch 168, loss_tr=0.053430 err_tr=0.016279 loss_te=3.397493 err_te=0.427959 err_te_snt=0.009380
+epoch 176, loss_tr=0.052093 err_tr=0.016064 loss_te=3.777609 err_te=0.442838 err_te_snt=0.011544
+epoch 184, loss_tr=0.050022 err_tr=0.015605 loss_te=3.615857 err_te=0.431436 err_te_snt=0.009380
+epoch 192, loss_tr=0.048606 err_tr=0.014844 loss_te=4.254653 err_te=0.458577 err_te_snt=0.020924
+epoch 200, loss_tr=0.045252 err_tr=0.014209 loss_te=3.809854 err_te=0.437975 err_te_snt=0.010101
+epoch 208, loss_tr=0.046115 err_tr=0.014219 loss_te=3.525989 err_te=0.416244 err_te_snt=0.010823
+epoch 216, loss_tr=0.046525 err_tr=0.013945 loss_te=3.731409 err_te=0.428357 err_te_snt=0.010101
+epoch 224, loss_tr=0.043378 err_tr=0.013350 loss_te=4.014791 err_te=0.430589 err_te_snt=0.013709
+epoch 232, loss_tr=0.042941 err_tr=0.013203 loss_te=3.774163 err_te=0.415966 err_te_snt=0.010101
+epoch 240, loss_tr=0.040990 err_tr=0.012598 loss_te=3.788815 err_te=0.416591 err_te_snt=0.010823
+epoch 248, loss_tr=0.039575 err_tr=0.011924 loss_te=3.918533 err_te=0.427865 err_te_snt=0.008658
+epoch 256, loss_tr=0.038113 err_tr=0.011924 loss_te=3.933329 err_te=0.432080 err_te_snt=0.008658
+epoch 264, loss_tr=0.038549 err_tr=0.011914 loss_te=3.887040 err_te=0.416849 err_te_snt=0.010823
+epoch 272, loss_tr=0.039867 err_tr=0.012109 loss_te=4.017699 err_te=0.430378 err_te_snt=0.008658
+epoch 280, loss_tr=0.037822 err_tr=0.011914 loss_te=4.395680 err_te=0.453985 err_te_snt=0.014430
+epoch 288, loss_tr=0.036721 err_tr=0.011250 loss_te=4.222330 err_te=0.442820 err_te_snt=0.010101
+epoch 296, loss_tr=0.035290 err_tr=0.010723 loss_te=3.918045 err_te=0.410693 err_te_snt=0.007937
+epoch 304, loss_tr=0.034258 err_tr=0.010225 loss_te=4.165709 err_te=0.434250 err_te_snt=0.007215
+epoch 312, loss_tr=0.034672 err_tr=0.010830 loss_te=4.313679 err_te=0.445955 err_te_snt=0.014430
+epoch 320, loss_tr=0.033052 err_tr=0.009639 loss_te=4.076542 err_te=0.416710 err_te_snt=0.006494
+epoch 328, loss_tr=0.033344 err_tr=0.010117 loss_te=3.928874 err_te=0.415024 err_te_snt=0.007215
+epoch 336, loss_tr=0.033228 err_tr=0.010166 loss_te=4.030224 err_te=0.410034 err_te_snt=0.005051
+epoch 344, loss_tr=0.033313 err_tr=0.010166 loss_te=4.402949 err_te=0.428691 err_te_snt=0.009380
+epoch 352, loss_tr=0.031828 err_tr=0.009238 loss_te=4.080747 err_te=0.414066 err_te_snt=0.006494
+epoch 360, loss_tr=0.033095 err_tr=0.009600 loss_te=4.254683 err_te=0.419954 err_te_snt=0.005772
+``` 
 
 ## Where SincNet is implemented?
 To take a look into the SincNet implementation you should open the file *dnn_models.py* and read the classes *SincNet*, *sinc_conv* and the function *sinc*.