小编Shu*_*hal的帖子

我根据官方TensorFlow教程使用数据增强。首先，我创建一个具有增强层的顺序模型：

def _getAugmentationFunction(self):
    if not self.augmentation:
        return None
    pipeline = []
    
    pipeline.append(layers.RandomFlip('horizontal_and_vertical'))
    pipeline.append(layers.RandomRotation(30))
    pipeline.append(layers.RandomTranslation(0.1, 0.1, fill_mode='nearest'))
    pipeline.append(layers.RandomBrightness(0.1, value_range=(0.0, 1.0)))

    model =  Sequential(pipeline)
    return lambda x, y: (model(x, training=True), y)

然后，我在数据集上使用映射函数：

data_augmentation = self._getAugmentationFunction()
self.train_data = self.train_data.map(data_augmentation,
                                      num_parallel_calls=AUTOTUNE)

该代码按预期工作，但我收到以下警告：

WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2
WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip
WARNING:tensorflow:Using a while_loop for converting Bitcast
WARNING:tensorflow:Using a while_loop for …

我使用此 API 从目录加载了一个数据集

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.3,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

我想更改数据类型并使训练更快

我尝试过，但没有成功

for image_batch, labels_batch in train_ds:
  image_batch = tf.cast(image_batch,tf.int16)