kerasext.py 文件源码

def on_epoch_begin(self, epoch, logs=None):
        super(MyLearningRateScheduler, self).on_epoch_begin(epoch, logs=logs)

        if epoch > self.epoch_unfreeze:
            for i, layer in enumerate(self.model.layers[1:]):
                layer.trainable = i >= self.num_layers_to_freeze
        else:
            for layer in self.model.layers[1:-1]:
                layer.trainable = False
            self.model.layers[-1].trainable = True

        if not self.recompiled_first or (not self.recompiled and epoch > self.epoch_unfreeze):
            adam = keras.optimizers.Adam(lr=self.step_decay(epoch))
            self.model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy',
                                                                                    custom_metrics.f2score_samples])
            print self.model.summary()
            if not self.recompiled_first:
                self.recompiled_first = True
            else:
                self.recompiled = True