running a Dense feed-forward neural net in Keras. there are class_weights for two outputs, and sample_weights for a third output. fore some reason it prints the progress ver
Import the library:
from tqdm.keras import TqdmCallback
Specify using tqdm library:
model.fit(xs, ys, epochs=10000, verbose=0, callbacks=[TqdmCallback(verbose=1)])
Result:
100%|██████████████| 10000/10000 [01:34<00:00, 105.57epoch/s, loss=1.56e+3]
29%|███▌ | 2891/10000 [00:28<01:05, 108.02epoch/s, loss=1.57e+3]
For my case, verbose=1 is what really works for me. My answer is based on casper.dcl's answer.