keras_bug.py

import keras.layers as L
import keras.backend as K
from keras.engine import Model

import tensorflow as tf


def unexpected_initialization():
    sess = tf.Session()
    K.set_session(sess)

    var_global_step = tf.Variable(0, trainable=False)

    x = L.Input((8,))
    y = L.Dense(2)(x)
    model = Model(x, y)

    sess.run(tf.global_variables_initializer())

    step_initial = sess.run(var_global_step)
    print(' initial step: {}'.format(step_initial)) # -> outputs 0

    sess.run(var_global_step.assign(16))

    step_assigned = sess.run(var_global_step)
    print('assigned step: {}'.format(step_assigned)) # -> outputs 16 

    model.save('model.h5')

    step_saved = sess.run(var_global_step)
    print('   saved step: {}'.format(step_saved)) # -> outputs 0 !!!!!!



def expected_behaviour():
    sess = tf.Session()
    K.set_session(sess)

    var_global_step = tf.Variable(0, trainable=False)

    x = L.Input((8,))
    model = Model(x, x)

    sess.run(tf.global_variables_initializer())

    step_initial = sess.run(var_global_step)
    print(' initial step: {}'.format(step_initial)) # -> outputs 0

    sess.run(var_global_step.assign(16))

    step_assigned = sess.run(var_global_step)
    print('assigned step: {}'.format(step_assigned)) # -> outputs 16

    model.save('model.h5')

    step_saved = sess.run(var_global_step)
    print('   saved step: {}'.format(step_saved)) # -> outputs 16

if __name__ == '__main__':
    print('bug case')
    unexpected_initialization()
    print('')
    print('not bugged case')
    expected_behaviour()

outputs

bug case
 initial step: 0
assigned step: 16
   saved step: 0

not bugged case
 initial step: 0
assigned step: 16
   saved step: 16