You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
In the chunk for generation of the test set (Data Generation — Test) the full_testis derived from the points data structure, which are used for training, not from the test_points.
I do not think that is intended, so there is a simple correction possible:
full_test = torch.as_tensor(test_points).float()
Based on that change we get different performance figures.
Loss:
and another figures prediction:
with 8 of 10 sequences with "clashing" points.
If my results are right, this text chunk needs some adaption as well:
The results are, at the same time, very good and very bad. In half of the sequences,
the predicted coordinates are quite close to the actual ones. But, in the other half,
the predicted coordinates are overlapping with each other and close to the
midpoint between the actual coordinates.
For whatever reason, the model learned
to make good predictions whenever the first corner is on the right edge of the
square, but really bad ones otherwise.
See sequence pictures, these statements needs to be adapted. Especially the second.
Same issue can be found in the final putting it all together section:
The differences caused by the needed change in the full_testdata result in significant different results also in the
Encoder + Decoder + PE
section.
I see now this loss graph (when running the code with the independent test_point data):
quite different from the original (where the test set is filled with the same points as the training set):
and now in the updated test setup, we have one clashing points sequence:
please compare to the sequences based on the original code:
doubling the size of the training set from 128 to 256 sequences, will give results nearer to the expectation: points, directions = generate_sequences(n=256, seed=13) (has to be changed at several places)
and the selected 10 validation sequences are good (depending on seed, this was run with 13):
In the chunk for generation of the test set (Data Generation — Test) the
full_test
is derived from thepoints
data structure, which are used for training, not from thetest_points
.I do not think that is intended, so there is a simple correction possible:
Based on that change we get different performance figures.
Loss:
and another figures prediction:
with 8 of 10 sequences with "clashing" points.
If my results are right, this text chunk needs some adaption as well:
See sequence pictures, these statements needs to be adapted. Especially the second.
Same issue can be found in the final putting it all together section:
All based on your 1.1 revision, if I did not make any mistakes in updating by
git pull
.The text was updated successfully, but these errors were encountered: