# Place the logs in a timestamped subdirectory
# This allows to easy select different training runs
# In order not to overwrite some data, it is useful to have a name with a timestamp
log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
# Specify the callback object
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

# tf.keras.callback.TensorBoard ensures that logs are created and stored
# We need to pass callback object to the fit method
# The way to do this is by passing the list of callback objects, which is in our case just one

model.fit(x_train, y=y_train, 
          epochs=20, 
          validation_data=(x_test, y_test), 
          callbacks=[tensorboard_callback],
          verbose=0)

%tensorboard --logdir logs/fit