ProgBar has poor performance in jupyter notebook

When I use model.fit() in jupyter notebook (with verbose) the chrome which host jupyter client frozen. the output shows:

Epoch 1/10
5000/5000 [==============================] - 1s - loss: 0.3613 - acc: 0.9016     
Epoch 2/10
5000/5000 [==============================] - 1s - loss: 0.3593 - acc: 0.9026     
Epoch 3/10
5000/5000 [========================>.....] - ETA: 0s - loss: 0.3447 - acc: 0.9059

I dump it with xxd and find there’s lots of ‘\x08’ at the end of line.

00000000: 4570 6f63 6820 312f 3130 0a35 3030 302f  Epoch 1/10.5000/
00000010: 3530 3030 205b 3d3d 3d3d 3d3d 3d3d 3d3d  5000 [==========
00000020: 3d3d 3d3d 3d3d 3d3d 3d3d 3d3d 3d3d 3d3d  ================
00000030: 3d3d 3d3d 5d20 2d20 3173 202d 206c 6f73  ====] - 1s - los
00000040: 733a 2030 2e33 3631 3320 2d20 6163 633a  s: 0.3613 - acc:
00000050: 2030 2e39 3031 3620 2020 2020 0808 0808   0.9016     ....
00000060: 0808 0808 0808 0808 0808 0808 0808 0808  ................
00000070: 0808 0808 0808 0808 0808 0808 0808 0808  ................
00000080: 0808 0808 0808 0808 0808 0808 0808 0808  ................
00000090: 0808 0808 0808 0808 0808 0808 0808 0808  ................
000000a0: 0808 0808 0808 0808 0808 0808 0808 0808  ................
000000b0: 0808 0808 0808 0808 0808 0808 0808 0808  ................
000000c0: 0808 0808 0808 0808 0808 0808 0808 0808  ................
000000d0: 0808 0808 0808 0808 0808 0808 0808 0808  ................
000000e0: 0808 0808 0808 0808 0808 0808 0808 0808  ................
000000f0: 0808 0808 0808 0808 0808 0808 0808 0808  ................
00000100: 0808 0808 0808 0808 0808 0808 0808 0808  ................
00000110: 0808 0808 0808 0808 0808 0808 0808 0808  ................
00000120: 0808 0808 0808 0808 0808 0808 0808 0808  ................
00000130: 0808 0808 0808 0808 0808 0808 0808 0808  ................
00000140: 0808 0808 0808 0808 0808 0808 0808 0808  ................

I think we could use ipywidgets to improve the performance. Here’s an example The code snippets I’m using is:

from keras.layers import Dense, Activation
from keras.models import Sequential

model = Sequential()
model.add(Dense(output_dim=1024, input_dim=image_size*image_size))
model.add(Activation("relu"))
model.add(Dense(output_dim=10))
model.add(Activation("softmax"))

model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])

model.fit(train_dataset[:5000], train_labels[:5000], nb_epoch=10, batch_size=128)

Any ideas?

[bstriner]

I just put together a module for keras/tqdm integration. Try this out:

https://github.com/bstriner/keras-tqdm/blob/master/examples/keras_progress_bars.ipynb https://github.com/bstriner/keras-tqdm

After some fiddling, the TQDM display looks really good

Please let me know if it works for you. I’d be interested in how the performance is in comparison.

To use it, set verbose=0 to stop the built-in keras progress bar, and set callbacks=[TQDMCallback()] to use the TQDM progress bar.

Cheers, Ben

[clouds56]

Hi all, @bstriner have implement a progress bar using tqdm, please checkout https://github.com/bstriner/keras-tqdm.

#!/usr/bin/bash
pip install keras-tqdm

#!/usr/bin/python3
from keras_tqdm import TQDMNotebookCallback
# keras, model definition...
model.fit(X_train, Y_train, verbose=0, callbacks=[TQDMNotebookCallback()])

It is a fancy package, worth a try!