<p>下面是一个遵循@nuric指示的有效示例:</p>
<pre><code>from tensorflow.python.keras.callbacks import ReduceLROnPlateau
from tensorflow.python.platform import tf_logging as logging
class ReduceLRBacktrack(ReduceLROnPlateau):
def __init__(self, best_path, *args, **kwargs):
super(ReduceLRBacktrack, self).__init__(*args, **kwargs)
self.best_path = best_path
def on_epoch_end(self, epoch, logs=None):
current = logs.get(self.monitor)
if current is None:
logging.warning('Reduce LR on plateau conditioned on metric `%s` '
'which is not available. Available metrics are: %s',
self.monitor, ','.join(list(logs.keys())))
if not self.monitor_op(current, self.best): # not new best
if not self.in_cooldown(): # and we're not in cooldown
if self.wait+1 >= self.patience: # going to reduce lr
# load best model so far
print("Backtracking to best model before reducting LR")
self.model.load_weights(self.best_path)
super().on_epoch_end(epoch, logs) # actually reduce LR
</code></pre>
<p>ModelCheckpoint回调可用于更新最佳模型转储。e、 g.将以下两个回调传递给model fit:</p>
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