python keras.utils.Sequence

The `keras.utils.Sequence` class is an abstract base class that allows you to create a custom data generator for training neural networks with Keras. It provides a convenient way to load and preprocess large amounts of data directly from disk, without having to keep the entire dataset in memory at once.

To use `Sequence`, you need to subclass it and implement two methods:
- `__len__(self)`: returns the total number of batches in your dataset
- `__getitem__(self, index)`: returns the `index`-th batch of data

You can also optionally implement the `on_epoch_end(self)` method, which is called at the end of each epoch to perform any required actions (e.g., shuffling the data).

Here's an example of how to use `Sequence` to create a custom data generator:

from keras.utils import Sequence

class MyDataGenerator(Sequence):
    def __init__(self, data_dir, batch_size):
        self.data_dir = data_dir
        self.batch_size = batch_size
        self.data_files = os.listdir(data_dir)

    def __len__(self):
        return len(self.data_files) // self.batch_size

    def __getitem__(self, index):
        batch_files = self.data_files[index*self.batch_size:(index+1)*self.batch_size]
        batch_data = []
        for file_name in batch_files:
            data = load_data(os.path.join(self.data_dir, file_name))
            preprocessed_data = preprocess_data(data)
            batch_data.append(preprocessed_data)
        return np.array(batch_data)

    def on_epoch_end(self):
        np.random.shuffle(self.data_files)

In this example, `MyDataGenerator` takes a directory containing data files and a batch size as input. `__len__` returns the number of batches (i.e., the number of files divided by the batch size), and `__getitem__` loads and preprocesses each batch of data. `on_epoch_end` shuffles the list of data files at the end of each epoch.

Once you've defined your custom data generator, you can use it to train your Keras model like this:

model.fit_generator(generator=MyDataGenerator(data_dir='data/', batch_size=32),
                    steps_per_epoch=1000,
                    epochs=10)