Demonstrates correct behavior of TensorBoard callback on model re-training.

model_retraining.py

import numpy as np
from keras import Input
from keras.layers import Dense
from keras.models import Model
from keras import callbacks
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

def build_model(shape, name=None):
    x = Input(shape)
    y = Dense(128, activation='relu')(x)
    model = Model(inputs=x, outputs=y, name=name)
    model.compile('adam', 'sparse_categorical_crossentropy')
    return model

x, y = load_digits(return_X_y=True)
x, x_test, y, y_test = train_test_split(x, y)

samples, features = x.shape
models = [build_model([features], name=name) for name in ('alpha', 'beta')]

for model in models:
    # Training "fresh" models.
    model.fit(x, y,
              epochs=2,
              batch_size=None,
              validation_data=(x_test, y_test),
              callbacks=[callbacks.TensorBoard('./tf-log/' + model.name)])

for model in models:
    # Training "trained" models.
    model.fit(x, y,
              epochs=4,
              batch_size=None,
              validation_data=(x_test, y_test),
              callbacks=[callbacks.TensorBoard('./tf-log/' + model.name)],
              initial_epoch=2)

for model in models:
    # Training "fresh, but trained" models.
    model = Model(model.inputs, model.outputs, name=model.name)
    model.compile('adam', 'sparse_categorical_crossentropy')
    model.fit(x, y,
              epochs=6,
              batch_size=None,
              validation_data=(x_test, y_test),
              callbacks=[callbacks.TensorBoard('./tf-log/' + model.name)],
              initial_epoch=4)