def load_test_data(self):
        dirname = 'cifar-10-batches-py'
        path = os.path.join(self.data_dir, dirname)
        fpath = os.path.join(path, 'test_batch')
        x_test, y_test = self._load_batch(fpath)

        if K.image_data_format() == 'channels_last':
            x_test = x_test.transpose(0, 2, 3, 1)

        y_test_labels = one_hot_encoded(y_test, num_classes=self.n_classes)
        return x_test, np.array(y_test), y_test_labels
 def load_training_data(self):
     dirname = 'cifar-10-batches-py'
     path = os.path.join(self.data_dir, dirname)
     n_train_batchs = 5
     x_train = np.zeros((0, self.depth, self.width, self.height))
     y_train = []
     for batch in range(n_train_batchs):
         fpath = os.path.join(path, 'data_batch_' + str(batch + 1))
         cur_data, cur_labels = self._load_batch(fpath)
         x_train = np.concatenate((cur_data, x_train), axis=0)
         y_train = cur_labels + y_train
     x_train = x_train.astype(np.uint8)
     if K.image_data_format() == 'channels_last':
         x_train = x_train.transpose(0, 2, 3, 1)
     y_train_labels = one_hot_encoded(y_train, num_classes=self.n_classes)
     return x_train, np.array(y_train), y_train_labels
Пример #3
0
    def load_test_data(self):
        dirname = 'cifar-100-python'
        path = os.path.join(self.data_dir, dirname)
        fpath = os.path.join(path, 'test')
        x_test, y_test_fine = self._load_batch(fpath, 'fine_labels')
        data_size = len(y_test_fine)

        if K.image_data_format() == 'channels_last':
            x_test = x_test.transpose(0, 2, 3, 1)
            
        relevant_idxes = [i for i in range(data_size) if y_test_fine[i] in self.subsets_idxes]
        x_test = x_test[relevant_idxes, :, :, :]
        y_test = np.asarray(y_test_fine)[relevant_idxes]
        y_test_values = sorted(list(set(y_test)))
        assert(len(y_test_values) == self.n_classes)
        map_dict = {val: i for i, val in enumerate(y_test_values)}
        for i, y in enumerate(y_test):
            y_test[i] = map_dict[y]

        y_test_labels = one_hot_encoded(y_test, num_classes=self.n_classes)
        return x_test, y_test, y_test_labels
    def load_training_data(self):
        dirname = 'cifar-100-python'
        path = os.path.join(self.data_dir, dirname)
        fpath = os.path.join(path, 'train')
        x_train, y_train_fine = self._load_batch(fpath, 'fine_labels')
        _, y_train_coarse = self._load_batch(fpath, 'coarse_labels')

        if K.image_data_format() == 'channels_last':
            x_train = x_train.transpose(0, 2, 3, 1)

        curr_superclass_idxes = [i for i in range(len(y_train_fine)) if y_train_coarse[i] == self.superclass_idx]
        x_train = x_train[curr_superclass_idxes]
        y_train = np.asarray(y_train_fine)[curr_superclass_idxes]
        y_train_values = sorted(list(set(y_train)))
        assert(len(y_train_values) == self.n_classes)
        map_dict = {val: i for i, val in enumerate(y_train_values)}
        for i, y in enumerate(y_train):
            y_train[i] = map_dict[y]

        y_train_labels = one_hot_encoded(y_train, num_classes=self.n_classes)
        return x_train, y_train, y_train_labels