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Commit 2c5d5b52 authored by tomrink's avatar tomrink
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parent 18584809
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...@@ -285,19 +285,21 @@ class Trainer(object): ...@@ -285,19 +285,21 @@ class Trainer(object):
for _step in decay_steps.copy(): for _step in decay_steps.copy():
if num_step >= _step: if num_step >= _step:
decay_steps.pop(0) decay_steps.pop(0)
g_current_lr = self.strategy.reduce( # TDR, Let's don't print this out, causes an error in the next line
tf.distribute.ReduceOp.MEAN, # g_current_lr = self.strategy.reduce(
G_optimizer.learning_rate, axis=None) # tf.distribute.ReduceOp.MEAN,
# G_optimizer.learning_rate, axis=None)
d_current_lr = self.strategy.reduce( #
tf.distribute.ReduceOp.MEAN, # d_current_lr = self.strategy.reduce(
D_optimizer.learning_rate, axis=None) # tf.distribute.ReduceOp.MEAN,
# D_optimizer.learning_rate, axis=None)
logging.debug( #
"Current LR: G = %s, D = %s" % # logging.debug(
(g_current_lr, d_current_lr)) # "Current LR: G = %s, D = %s" %
logging.debug( # (g_current_lr, d_current_lr))
"[Phase 2] Decayed Learing Rate by %f." % decay_factor) # logging.debug(
# "[Phase 2] Decayed Learing Rate by %f." % decay_factor)
G_optimizer.learning_rate.assign(G_optimizer.learning_rate * decay_factor) G_optimizer.learning_rate.assign(G_optimizer.learning_rate * decay_factor)
D_optimizer.learning_rate.assign(D_optimizer.learning_rate * decay_factor) D_optimizer.learning_rate.assign(D_optimizer.learning_rate * decay_factor)
......
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