()
return clips
Loads c.BATCH_SIZE clips from the database of preprocessed training clips.

    @return: An array of shape
            [c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))].
Loads c.BATCH_SIZE clips from the database of preprocessed training clips.
Loads c . BATCH_SIZE clips from the database of preprocessed training clips .
def get_train_batch():
    """
    Loads c.BATCH_SIZE clips from the database of preprocessed training clips.

    @return: An array of shape
            [c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))].
    """
    clips = np.empty([c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))],
                     dtype=np.float32)
    for i in xrange(c.BATCH_SIZE):
        path = c.TRAIN_DIR_CLIPS + str(np.random.choice(c.NUM_CLIPS)) + '.npz'
        clip = np.load(path)['arr_0']

        clips[i] = clip

    return clips
def get_train_batch ( ) : clips = np . empty ( [ c . BATCH_SIZE , c . TRAIN_HEIGHT , c . TRAIN_WIDTH , ( 3 * ( c . HIST_LEN + 1 ) ) ] , dtype = np . float32 ) for i in xrange ( c . BATCH_SIZE ) : path = c . TRAIN_DIR_CLIPS + str ( np . random . choice ( c . NUM_CLIPS ) ) + '.npz' clip = np . load ( path ) [ 'arr_0' ] clips [ i ] = clip return clips