创建一个Keras回调,每n个周期激活一次 [英] Creating a Keras Callback that activates every n epochs
问题描述
我想在Keras中创建自己的Callback,但我不太了解该怎么做.
I want to create my own Callback in Keras but don't really understand how to do it.
我想做的是创建一个回调,每个 n
(例如 n = 10
)时代都调用一个函数.根据Keras文档( https://keras.io/callbacks/),基类keras.callbacks.Callback
具有属性 params
.
What I want to do is to create a Callback that every n
(e.g. n=10
) epochs calls a function.
According to the Keras documentation (https://keras.io/callbacks/) the base class keras.callbacks.Callback
has a property params
.
此属性是否包括当前纪元?如果是这样,如何使用/调用它?
Does this property include the current epoch? And if so, how can it be used/called?
或者是否还有其他好的方法来每隔 n
个纪元调用一个函数?
Or is there any other good way to call a function every n
epochs?
我想到了一个回调,当使用参数 period = 10
时,它的作用类似于 keras.callbacks.ModelCheckpoint
.
I had a Callback in mind that works similar as keras.callbacks.ModelCheckpoint
when using the argument period=10
.
我们将非常感谢您的帮助.谢谢:)
Help would be really appreciated. Thank you :)
经过一番阅读后,我想到了这一点,它似乎可以正常工作(仍然需要对其进行正确的测试)
After quite some reading I came up with this and it seems to work (still needs to test it properly)
class Test1(callbacks.Callback):
def on_epoch_end(self, epochs, logs={})
if epochs == 10:
print('abc') #a random function
callb = Test1()
model = networks.compute_network(layers=layers, batch_size=batch_size, epochs=epochs, call_list=[callb])
# compute_network in my case loads all the data, trains the network, and then returns it
一个网站特别帮助我更好地了解了回调: https://keunwoochoi.wordpress.com/2016/07/16/keras-callbacks/
One site helped me in particular to understand Callbacks better: https://keunwoochoi.wordpress.com/2016/07/16/keras-callbacks/
推荐答案
正如@HMK所说, on_epoch_begin
和 on_epoch_end
方法提供了当前纪元.例如,要每隔10个时间应用一个函数,您可以像这样编辑自定义回调:
As @HMK said, on_epoch_begin
and on_epoch_end
methods provide the current epoch. For example, to apply a function every 10th epochs you can edit your custom callback like that:
class myCallback(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None)
if epoch % 10 == 0:
your_func()
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