Keras training progress bar on one line with epoch number

Question

When I use Keras to train a model with model.fit(), I see a progress bar that looks like this:

Epoch 1/10
8000/8000 [==========] - 55s 7ms/step - loss: 0.9318 - acc: 0.0783 - val_loss: 0.8631 - val_acc: 0.1180
Epoch 2/10
8000/8000 [==========] - 55s 7ms/step - loss: 0.6587 - acc: 0.1334 - val_loss: 0.7052 - val_acc: 0.1477
Epoch 3/10
8000/8000 [==========] - 54s 7ms/step - loss: 0.5701 - acc: 0.1526 - val_loss: 0.6445 - val_acc: 0.1632

To improve readability, I would like to have the epoch number on the same line as the progress bar, like this:

Epoch 1/10: 8000/8000 [==========] - 55s 7ms/step - loss: 0.9318 - acc: 0.0783 - val_loss: 0.8631 - val_acc: 0.1180
Epoch 2/10: 8000/8000 [==========] - 55s 7ms/step - loss: 0.6587 - acc: 0.1334 - val_loss: 0.7052 - val_acc: 0.1477
Epoch 3/10: 8000/8000 [==========] - 54s 7ms/step - loss: 0.5701 - acc: 0.1526 - val_loss: 0.6445 - val_acc: 0.1632

How can I make that change? I know that Keras has callbacks that can be invoked during training, but I am not familiar with how that works.

Answer 1

If you want to use an alternative, you could use tqdm (version >= 4.41.0):

from tqdm.keras import TqdmCallback
...
model.fit(..., verbose=0, callbacks=[TqdmCallback(verbose=2)])

This turns off keras' progress (verbose=0), and uses tqdm instead. For the callback, verbose=2 means separate progressbars for epochs and batches. 1 means clear batch bars when done. 0 means only show epochs (never show batch bars).

Answer 2

Yes, you can use callbacks (https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/Callback). For example:

import tensorflow as tf

class PrintLogs(tf.keras.callbacks.Callback):
    def __init__(self, epochs):
        self.epochs = epochs

    def set_params(self, params):
        params['epochs'] = 0

    def on_epoch_begin(self, epoch, logs=None):
        print('Epoch %d/%d' % (epoch + 1, self.epochs), end='')


mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
epochs = 5
model.fit(x_train, y_train,
          epochs=epochs, 
          validation_split=0.2, 
          verbose = 2, 
          callbacks=[PrintLogs(epochs)])

output:

Train on 48000 samples, validate on 12000 samples
Epoch 1/5 - 10s - loss: 0.0306 - acc: 0.9901 - val_loss: 0.0837 - val_acc: 0.9786
Epoch 2/5 - 9s - loss: 0.0269 - acc: 0.9910 - val_loss: 0.0839 - val_acc: 0.9788
Epoch 3/5 - 9s - loss: 0.0253 - acc: 0.9915 - val_loss: 0.0895 - val_acc: 0.9781
Epoch 4/5 - 9s - loss: 0.0201 - acc: 0.9930 - val_loss: 0.0871 - val_acc: 0.9792
Epoch 5/5 - 9s - loss: 0.0206 - acc: 0.9931 - val_loss: 0.0917 - val_acc: 0.9793