Returns category names of 1000 ImageNet classes.
545461
def category_names(self):
        '''
        
        '''
        if hasattr(self, '_category_names'):
            return self._category_names
        with open(os.path.join(os.path.dirname(__file__), 'category_names.txt'), 'r') as fd:
            self._category_names = fd.read().splitlines()
        return self._category_names
354045
def data():
    
    from keras.datasets import mnist
    from keras.utils import np_utils
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(60000, 784)
    x_test = x_test.reshape(10000, 784)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    nb_classes = 10
    y_train = np_utils.to_categorical(y_train, nb_classes)
    y_test = np_utils.to_categorical(y_test, nb_classes)
    return x_train, y_train, x_test, y_test
324187
public function getMetaImageWrapperClassNames()
    {
        $classes = [$this->owner->MetaImageCaptionShown ? 'captionImage' : 'image'];
        
        if ($alignment = $this->owner->getMetaImageAlignment()) {
            $classes[] = $alignment;
        }
        
        return $classes;
    }
1318483
def categories(self):
        
        r = [ch for ch in self._channels.values() if isinstance(ch, CategoryChannel)]
        r.sort(key=lambda c: (c.position, c.id))
        return r
6935
def image_list(self, name=None):
        '''
        
        '''
        nt_ks = self.compute_conn
        ret = {}
        for image in nt_ks.images.list():
            links = {}
            for link in image.links:
                links[link['rel']] = link['href']
            ret[image.name] = {
                'name': image.name,
                'id': image.id,
                'status': image.status,
                'progress': image.progress,
                'created': image.created,
                'updated': image.updated,
                'metadata': image.metadata,
                'links': links,
            }
            if hasattr(image, 'minDisk'):
                ret[image.name]['minDisk'] = image.minDisk
            if hasattr(image, 'minRam'):
                ret[image.name]['minRam'] = image.minRam
        if name:
            return {name: ret[name]}
        return ret
1368989
def get_category(filename):
    
    return '/'.join(os.path.dirname(filename).split(os.sep))
426530
def get_curent_module_classes(module):
    
    classes = []
    for name, obj in inspect.getmembers(module):
        if inspect.isclass(obj):
            classes.append(obj)
    return classes
641683
def category_count(self):
        """"""
        category_dict = self.categories
        count_dict = {category: len(
            category_dict[category]) for category in category_dict}
        return count_dict
58437
def _repr_categories(self):
        
        max_categories = (10 if get_option("display.max_categories") == 0 else
                          get_option("display.max_categories"))
        from pandas.io.formats import format as fmt
        if len(self.categories) > max_categories:
            num = max_categories // 2
            head = fmt.format_array(self.categories[:num], None)
            tail = fmt.format_array(self.categories[-num:], None)
            category_strs = head + ["..."] + tail
        else:
            category_strs = fmt.format_array(self.categories, None)

        # Strip all leading spaces, which format_array adds for columns...
        category_strs = [x.strip() for x in category_strs]
        return category_strs
829541
def Categories(unicode_dir=_UNICODE_DIR):
  

  categories = {}

  def DoLine(codes, fields):
    """Process single UnicodeData.txt line, updating categories."""
    category = fields[2]
    categories.setdefault(category, []).extend(codes)
    # Add codes from Lu into L, etc.
    if len(category) > 1:
      short = category[0]
      categories.setdefault(short, []).extend(codes)

  ReadUnicodeTable(unicode_dir+"/UnicodeData.txt", 15, DoLine)
  return categories
26442
def get_annotations(fname, prefix=None):
    ""
    annot_dict = json.load(open(fname))
    id2images, id2bboxes, id2cats = {}, collections.defaultdict(list), collections.defaultdict(list)
    classes = {}
    for o in annot_dict['categories']:
        classes[o['id']] = o['name']
    for o in annot_dict['annotations']:
        bb = o['bbox']
        id2bboxes[o['image_id']].append([bb[1],bb[0], bb[3]+bb[1], bb[2]+bb[0]])
        id2cats[o['image_id']].append(classes[o['category_id']])
    for o in annot_dict['images']:
        if o['id'] in id2bboxes:
            id2images[o['id']] = ifnone(prefix, '') + o['file_name']
    ids = list(id2images.keys())
    return [id2images[k] for k in ids], [[id2bboxes[k], id2cats[k]] for k in ids]
1115657
def vgg11(num_classes=1000, pretrained='imagenet'):
    
    model = models.vgg11(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg11'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_vggs(model)
    return model
16307
def get_categories(self):
        
        sdb_resp = get_with_retry(self.cerberus_url + '/v1/category',
                                headers=self.HEADERS)

        throw_if_bad_response(sdb_resp)
        return sdb_resp.json()
457826
def get_classification(self):
        
        classification_dictionary = {}
        for item in self.classification:
            classification_dictionary[item['value']] = item['name']

        classification_dictionary = OrderedDict(
            sorted(classification_dictionary.items()))

        return classification_dictionary
219834
def categories(self):
        
        if self._categories is None:
            self._categories = list()
            self.__pull_combined_properties()
        return self._categories
41928
def _load_class_names(self, filename, dirname):
        
        full_path = osp.join(dirname, filename)
        classes = []
        with open(full_path, 'r') as f:
            classes = [l.strip() for l in f.readlines()]
        return classes
1066939
def _descriptor_names(self):
        

        descriptor_names = []

        for name in dir(self):
            try:
                attr = getattr(type(self), name)

                if isinstance(attr, DJANGO_RELATED_FIELD_DESCRIPTOR_CLASSES):
                    descriptor_names.append(name)

            except AttributeError:
                pass

        return descriptor_names