[FIXED] Keras Model.fit Verbose Formatting

Issue

I'm running Keras model.fit() in Jupyter notebook, and the output is very messy if verbose is set to 1:

    Train on 6400 samples, validate on 800 samples
    Epoch 1/200
    2080/6400 [========>.....................] - ETA: 39s - loss: 0.4383 - acc: 0.79 
    - ETA: 34s - loss: 0.3585 - acc: 0.84 - ETA: 33s - loss: 0.3712 - acc: 0.84 
    - ETA: 34s - loss: 0.3716 - acc: 0.84 - ETA: 33s - loss: 0.3675 - acc: 0.84 
    - ETA: 33s - loss: 0.3650 - acc: 0.84 - ETA: 34s - loss: 0.3759 - acc: 0.83 
    - ETA: 34s - loss: 0.3933 - acc: 0.82 - ETA: 34s - loss: 0.3985 - acc: 0.82 
    - ETA: 34s - loss: 0.4057 - acc: 0.82 - ETA: 33s - loss: 0.4071 - acc: 0.81 
    ....

As you can see, the ETA, loss, acc outputs kept appending to the log, instead of replacing the original ETA/loss/acc values within the first line, just like how the progress bar works.

How do I fix it it so that only 1 line of progress bar, ETA, loss & acc are shown per epoch? Right now, my cell output has tons of these lines as the training continues.

I'm running Python 3.6.1 on Windows 10, with the following module versions:

jupyter                            1.0.0
jupyter-client                     5.0.1
jupyter-console                    5.1.0
jupyter-core                       4.3.0
jupyterthemes                      0.19.0
Keras                              2.2.0
Keras-Applications                 1.0.2
Keras-Preprocessing                1.0.1
tensorflow-gpu                     1.7.0

Thank you.

Solution

You can try the Keras-adapted version of the TQDM progress bar library.

• The original TQDM library: https://github.com/tqdm/tqdm
• The Keras version of TQDM: https://github.com/bstriner/keras-tqdm

The usage instructions can be brought down to:

1. install e.g. per pip install keras-tqdm (stable) or pip install git+https://github.com/bstriner/keras-tqdm.git (for latest dev-version)

2. import the callback function with from keras_tqdm import TQDMNotebookCallback

3. run Keras' fit or fit_generator with verbose=0 or verbose=2 settings, but with a callback to the imported TQDMNotebookCallback, e.g. model.fit(X_train, Y_train, verbose=0, callbacks=[TQDMNotebookCallback()])

The result:

Answered By - Alaroff