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Thank you for your contribution, I learned much from it. But there is a thing that I can not come up with.
There is a code in optimize method in model.py, where:
with tf.control_dependencies([G_optimizer, D_Y_optimizer, F_optimizer, D_X_optimizer]): return tf.no_op(name='optimizers')
What the control dependencies actually mean here? And why do we need tf.no_op? What the side-effect could arise if we drop it?
Thanks in advance!
The text was updated successfully, but these errors were encountered:
Thank you for your contribution, I learned much from it. But there is a thing that I can not come up with.
There is a code in optimize method in model.py, where:
with tf.control_dependencies([G_optimizer, D_Y_optimizer, F_optimizer, D_X_optimizer]): return tf.no_op(name='optimizers')
What the control dependencies actually mean here? And why do we need tf.no_op? What the side-effect could arise if we drop it?
Thanks in advance!
The text was updated successfully, but these errors were encountered: