yoshihikoueno/fix_unresolved_object_checkpoint

## fix_unresolved_object_checkpoint
# workaround to fix opimizer bug in tensorflow
optimizer = tf.keras.optimizers.Adam(
    learning_rate=tf.Variable(0.001),
    beta_1=tf.Variable(0.9),
    beta_2=tf.Variable(0.999),
    epsilon=tf.Variable(1e-7),
)
optimizer.iterations  # this access will invoke optimizer._iterations method and create optimizer.iter attribute
optimizer.decay = tf.Variable(0.0)  # Adam.__init__ assumes ``decay`` is a float object, so this needs to be converted to tf.Variable **after** __init__ method.


///
The root problem is that Adam.__init__ will initialize variables with python float objects which will not be tracked by tensorflow.
We need to let them tracked and make them appear in Adam._checkpoint_dependencies in order to load weights without actually calling the optimizer itself.
By converting Python float to tf.Variable, they will be tracked because tf.Variable is a subclass of ``trackable.Trackable``.
///
	# workaround to fix opimizer bug in tensorflow
	optimizer = tf.keras.optimizers.Adam(
	learning_rate=tf.Variable(0.001),
	beta_1=tf.Variable(0.9),
	beta_2=tf.Variable(0.999),
	epsilon=tf.Variable(1e-7),
	)
	optimizer.iterations # this access will invoke optimizer._iterations method and create optimizer.iter attribute
	optimizer.decay = tf.Variable(0.0) # Adam.__init__ assumes ``decay`` is a float object, so this needs to be converted to tf.Variable after __init__ method.


	///
	The root problem is that Adam.__init__ will initialize variables with python float objects which will not be tracked by tensorflow.
	We need to let them tracked and make them appear in Adam._checkpoint_dependencies in order to load weights without actually calling the optimizer itself.
	By converting Python float to tf.Variable, they will be tracked because tf.Variable is a subclass of ``trackable.Trackable``.
	///