often recognize 'u' wrongly

[kojit]

Hello,

I trained your model with mjsynth dataset and default parameter settings over 1000000 steps. I found that the model often wrongly recognizes character 'u'. It seems as if there is no 'u' class. Do you have any thoughts about what the cause might be?

[kojit]

I've done training the model 2**21 steps. But it still cannot recognize 'u' correctly, I also found that it doesn't recognize 'q' at all. I tested with several images and found that the probability of 'u' and 'q' before the CTC layer are always 0. Does anyone have such experiences?

[kojit]

It's wired though, after I only changed the CNN model to Shi et al.'s CRNN architecture version, then it recognizes 'u'.

[weinman]

What is the training loss? Validation loss? What values does test.py report on the test data?

The default training parameters sometimes can get stuck (quite early in training) in a poor local minimum. I've never investigated specific character-level confusions/probabilities, but I definitely don't see this behavior in my own experiences.

To avoid local minima, I have set up an alternative training schedule that starts with a small batch size and increases from 16 to 128 as the step size (no staircase) decreases from .0001 down to .000003. (See Takase et al.)

[kojit]

Thanks for your reply.

test.py shows as follows although I didn't use the entire test set because it's too slow.

{'total_num_labels': 144942, 'total_num_sequence_errs': 3892, 'total_num_label_errors': 6711, 'mean_label_error': 0.04630127913234259, 'loss': 1.5078024, 'total_num_sequences': 17837, 'mean_sequence_error': 0.21819812748780623, 'global_step': 2097152}

I understand that you've never seen this problem and you think it's a local minimum. I'd like to try changing battch size.

[weinman]

Those label error rates and sequence error rates seem pretty reasonable. Maybe its not a local minimum.

That loss seems a bit high, but I just realized that test.py probably reports only the last test batch's loss (rather than a cumulative average, which it should).

What's the smoothed training loss (i.e., as reported in tensorboard)? (Say with a smoothing factor of something like 0.95.)

My training schedule is as follows:

Batch Size	Learning Rate	Steps (Cumulative)
16	1e-4	2^16
32	3e-5	2^18
64	3e-5	2^19
128	1e-5	2^19 + 2^18
128	3e-6	2^20

[kojit]

Smoothed training loss is 1.072.

[weinman]

Oh yeah that's probably not very good. You want it down around 0.4–0.5.

The colors below indicate the training sessions in the table above.

[kojit]

I've done training the model 2**21 steps. What was wrong...?

[weinman]

@kojit I forgot to add, I set --decay_rate=1.0 so the learning rate was fixed at each stage of training.

I recommend you read the recent Neural Computation paper I cited above to get a sense of why it's not the number of steps but the batch size that can have an overriding performance impact.

[kojit]

Thanks. I will try with that and report later.

[sahilbandar]

Same here, I've trained this for the 1^21 epochs, It is not able to recognise 8 and 9. Is anything there which I've to modify in training hyper parameters settings?

[weinman]

@sahilbandar Just decay rate=1, as well as the batch size, learning rate, max number of steps, (and tune from) to set the schedule noted above.