訓練模型以對視訊進行分類

對於這個例子，讓 model 為 Keras 模型，用於對視訊輸入進行分類，讓 X 為視訊輸入的大資料集，形狀為 （樣本，幀，通道，行，列） ，讓 Y 為相應的資料集單熱編碼標籤，形狀為 （樣本，類） 。兩個資料集都儲存在名為 video_data.h5 的 HDF5 檔案中。HDF5 檔案還具有樣本數量的 sample_count 屬性。

以下是使用 fit_generator 訓練模型的功能

def train_model(model, video_data_fn="video_data.h5", validation_ratio=0.3, batch_size=32):
    """ Train the video classification model
    """
    with h5py.File(video_data_fn, "r") as video_data:
         sample_count = int(video_data.attrs["sample_count"])
         sample_idxs = range(0, sample_count)
         sample_idxs = np.random.permutation(sample_idxs)
         training_sample_idxs = sample_idxs[0:int((1-validation_ratio)*sample_count)]
         validation_sample_idxs = sample_idxs[int((1-validation_ratio)*sample_count):]
         training_sequence_generator = generate_training_sequences(batch_size=batch_size,
                                                                   video_data=video_data,
                                                                   training_sample_idxs=training_sample_idxs)
         validation_sequence_generator = generate_validation_sequences(batch_size=batch_size,
                                                                       video_data=video_data,
                                                                       validation_sample_idxs=validation_sample_idxs)
         model.fit_generator(generator=training_sequence_generator,
                             validation_data=validation_sequence_generator,
                             samples_per_epoch=len(training_sample_idxs),
                             nb_val_samples=len(validation_sample_idxs),
                             nb_epoch=100,
                             max_q_size=1,
                             verbose=2,
                             class_weight=None,
                             nb_worker=1)

以下是培訓和驗證序列生成器

def generate_training_sequences(batch_size, video_data, training_sample_idxs):
    """ Generates training sequences on demand
    """
    while True:
        # generate sequences for training
        training_sample_count = len(training_sample_idxs)
        batches = int(training_sample_count/batch_size)
        remainder_samples = training_sample_count%batch_size
        if remainder_samples:
            batches = batches + 1
        # generate batches of samples
        for idx in xrange(0, batches):
            if idx == batches - 1:
                batch_idxs = training_sample_idxs[idx*batch_size:]
            else:
                batch_idxs = training_sample_idxs[idx*batch_size:idx*batch_size+batch_size]
            batch_idxs = sorted(batch_idxs)

            X = video_data["X"][batch_idxs]
            Y = video_data["Y"][batch_idxs]

            yield (np.array(X), np.array(Y))

def generate_validation_sequences(batch_size, video_data, validation_sample_idxs):
    """ Generates validation sequences on demand
    """
    while True:
        # generate sequences for validation
        validation_sample_count = len(validation_sample_idxs)
        batches = int(validation_sample_count/batch_size)
        remainder_samples = validation_sample_count%batch_size
        if remainder_samples:
            batches = batches + 1
        # generate batches of samples
        for idx in xrange(0, batches):
            if idx == batches - 1:
                batch_idxs = validation_sample_idxs[idx*batch_size:]
            else:
                batch_idxs = validation_sample_idxs[idx*batch_size:idx*batch_size+batch_size]
            batch_idxs = sorted(batch_idxs)

            X = video_data["X"][batch_idxs]
            Y = video_data["Y"][batch_idxs]

            yield (np.array(X), np.array(Y))