There is no easy way to just plug it in with one line of code, you have to write your summaries by hand.

The good news is that it's not difficult and you can use the TensorBoard callback code in Keras as a reference. (There is also a version 2 ready for TensorFlow 2.x.)

Basically, write a function e.g. write_summaries(model) and call it whenever you want to write your summaries (e.g. just after your fit_generator())

Inside your write_summaries(model) function use tf.summary, histogram_summary and other summary functions to log data you want to see on tensorboard.

If you don't know exactly how to check official tutorial: and this great example of MNIST with summaries.

I believe bartgras's explanation is superseded in more recent versions of Keras (I'm using Keras 2.2.2). To get histograms in Tensorboard all I did was the following, (where bg is a data wrangling class which exposes a generator for gb.training_batch(); gb.validation_batch() however is NOT a generator):

NAME = "Foo_{}".format(datetime.now().isoformat(timespec='seconds')).replace(':', '-')

tensorboard = keras.callbacks.TensorBoard(
    log_dir="logs/{}".format(NAME),
    histogram_freq=1,
    write_images=True)

callbacks = [
    tensorboard
]

history = model.fit_generator(
    bg.training_batch(),
    validation_data=bg.validation_batch(),
    epochs=EPOCHS,
    steps_per_epoch=bg.steps_per_epoch,
    validation_steps=bg.validation_steps,
    verbose=1,
    shuffle=False,
    callbacks=callbacks)