如何将tensorflow2.0数据集与keras ImageDataGenerator一起使用
我使用的是TensorFlow2.0API,我从所有图像路径创建了一个数据集,如下面的示例所示如何将tensorflow2.0数据集与keras ImageDataGenerator一起使用,keras,tensorflow2.0,Keras,Tensorflow2.0,我使用的是TensorFlow2.0API,我从所有图像路径创建了一个数据集,如下面的示例所示 X_train, X_test, y_train, y_test = train_test_split(all_image_paths, all_image_labels, test_size=0.20, random_state=32) path_train_ds = tf.data.Dataset.from_tensor_slices(X_train) image_train_ds = path
X_train, X_test, y_train, y_test = train_test_split(all_image_paths, all_image_labels, test_size=0.20, random_state=32)
path_train_ds = tf.data.Dataset.from_tensor_slices(X_train)
image_train_ds = path_train_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
然而,当我运行此代码以使用keras ImageDataGenerator应用某些Aguentiation时,我遇到了错误
datagen=tf.keras.preprocessing.image.ImageDataGenerator(featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
datagen.fit(image_train_ds)
错误:
/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py in fit(self, x, augment, rounds, seed)
907 seed: Int (default: None). Random seed.
908 """
--> 909 x = np.asarray(x, dtype=self.dtype)
910 if x.ndim != 4:
911 raise ValueError('Input to `.fit()` should have rank 4. '
/usr/local/lib/python3.6/dist-packages/numpy/core/numeric.py in asarray(a, dtype, order)
499
500 """
--> 501 return array(a, dtype, copy=False, order=order)
502
503
TypeError: float() argument must be a string or a number, not 'ParallelMapDataset'
tf.keras.preprocessing.image.ImageDataGenerator
不适用于tf.data.Dataset
对象,它被设计用于处理普通的旧图像
如果要应用增强,必须使用
tf.data.Dataset
对象本身(通过各种.map
调用),或者在使用tf.keras.preprocessing.image.ImageDataGenerator
创建增强数据集后,可以创建tf.data.Dataset
对象,如果为true,这是一个令人难以置信的愚蠢的设计选择。