Keras 资料

http://www.360doc.com/content/17/0415/12/1489589_645772879.shtml

http://adventuresinmachinelearning.com/keras-tutorial-cnn-11-lines/

https://www.zhihu.com/question/42139290

To create a callback we create an inherited class which inherits from keras.callbacks.Callback:

class AccuracyHistory(keras.callbacks.Callback):

    def on_train_begin(self, logs={}):

        self.acc = []

    def on_epoch_end(self, batch, logs={}):

        self.acc.append(logs.get('acc'))

The Callback super class that the code above inherits from has a number of methods that can be overridden in our callback definition such as on_train_begin, on_epoch_end, on_batch_begin and on_batch_end. The name of these methods are fairly self explanatory, and represent moments in the training process where we can “do stuff”. In the code above, at the beginning of training we initialise a list self.acc = [] to store our accuracy results. Using the on_epoch_end() method, we can extract the variable we want from the logs, which is a dictionary that holds, as a default, the loss and accuracy during training. We then instantiate this callback like so:

history = AccuracyHistory()

Now we can pass history to the .fit() function using the callback parameter name. Note that .fit() takes a list for the callback parameter, so you have to pass it history like this: [history]. To access the accuracy list that we created after the training is complete, you can simply call history.acc, which I then also plotted:

plt.plot(range(1,11), history.acc)

plt.xlabel('Epochs')

plt.ylabel('Accuracy')

plt.show()

Hope that helps. Have fun using Keras.

巴特西

Keras 资料

最新文章

热门文章