manually terminate threads process returned by `generator_queue()` (#4101)
manually terminate threads process returned by `generator_queue()` (#4101)

* manually terminate threads process returned by `generator_queue()`

Recently I custum a video sequence DataGenerator (based on ImageDataGenerator) for experiment. When I use model.fit_generator as following:
>history = model.fit_generator(train_data_generator, samples_per_epoch=train_data_generator.nb_sample,
                              nb_epoch=nb_epoch, verbose=1, callbacks=[early_stopping, model_checkpoint],
                              validation_data=test_data_generator, nb_val_samples=test_data_generator.nb_sample,
                              max_q_size=10, nb_worker=8, pickle_safe=True)
I found that the validation process consumes much longer time than training despite it contains less data.
I read the code and changed the `self.evaluate_generator()` (line 1482) in `fit_generator' to use a multiprocessing approach as training process did. However, the memory usage quikly increases and it only last for a few epoches. 
Through analysis, I think it is caused by the processes weren't freed after the `evaluate_generator` accomplished. Thus I suggest returning `generator_threads` from function `generator_queue()` and manually terminate these threads in `fit_generator`, `evaluate_generator`, `predict_generator`.

* stastify the PEP style

* correct the PEP8's E128 error

dolaameng/keras,keras-team/keras,keras-team/keras