train.py 文件源码

def train(self, n_iterations):
        print("Training network...")        

        # Recover losses from previous run
        if (self.option == 'continue'):
            with open("{0}_{1}_loss.json".format(self.root, self.depth), 'r') as f:
                losses = json.load(f)
        else:
            losses = []

        self.checkpointer = ModelCheckpoint(filepath="{0}_{1}_weights.hdf5".format(self.root, self.depth), verbose=1, save_best_only=True)
        self.history = LossHistory(self.root, self.depth, losses, {'name': '{0}_{1}'.format(self.root, self.depth), 'init_t': time.asctime()})

        self.reduce_lr = LearningRateScheduler(self.learning_rate)

        self.metrics = self.model.fit_generator(self.training_generator(), self.batchs_per_epoch_training, epochs=n_iterations, 
            callbacks=[self.checkpointer, self.history, self.reduce_lr], validation_data=self.validation_generator(), validation_steps=self.batchs_per_epoch_validation)

        self.history.finalize()