如何使用 tf.keras.utils.image_dataset_from_directory 应用 kfold 交叉验证

我的目标是应用 k 折交叉验证来训练 VGG19 模型。为此，我使用以下代码从目录中读取图像：

DIR = "/Images"
data_dir = pathlib.Path(os.getcwd() + '\\Images')

train_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(224, 224),
  batch_size=32)

val_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(224, 224),
  batch_size=32)

并且在不使用 kfold 交叉验证的情况下它也能正常工作。但是当我想使用 K 折交叉验证时，我必须有标签和图像train_ds单独地，我找不到解决方案，除了我需要使用另一种方法读取图像。因此，我决定使用读取图像ImageDataGenerator and flow_from_directory。但据我了解，为了使用加载图像flow_from_directory，我必须有两个独立的子集traning and test在图像中，虽然我没有traning and test以我为例。这两种方法有什么解决方案吗？

此外，使用第一种方法，即tf.keras.utils.image_dataset_from_directory，将找到的图像数量不同于flow_from_directory。 这是第一个方法的输出：

找到属于 4 个类别的 1060 个文件。 使用848个文件进行训练。

这是第二种方法的输出：

img_gen = tf.keras.preprocessing.image.ImageDataGenerator(
    rescale=1.0 / 255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    vertical_flip=True)
Wheat_data = img_gen.flow_from_directory(data_dir,
                                         subset="training",
                                         seed=123)

找到属于 4 个类别的 849 张图像。

您可以将数据集转换为numpy数组，它应该像往常一样工作：

import tensorflow as tf
import pathlib
import numpy as np
from sklearn.model_selection import KFold

dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

batch_size = 32

train_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(180, 180),
  batch_size=batch_size)

val_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(180, 180),
  batch_size=batch_size)

train_images = np.concatenate(list(train_ds.map(lambda x, y:x)))
train_labels = np.concatenate(list(train_ds.map(lambda x, y:y)))

val_images = np.concatenate(list(val_ds.map(lambda x, y:x)))
val_labels = np.concatenate(list(val_ds.map(lambda x, y:y)))

inputs = np.concatenate((train_images, val_images), axis=0)
targets = np.concatenate((train_labels, val_labels), axis=0)

kfold = KFold(n_splits=5, shuffle=True)

for train, test in kfold.split(inputs, targets):
  
  model = tf.keras.Sequential([
  tf.keras.layers.Rescaling(1./255, input_shape=(180, 180, 3)),
  tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(5)])

  model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
  history = model.fit(inputs[train], targets[train],
              batch_size=batch_size,
              epochs=2)
  scores = model.evaluate(inputs[test], targets[test], verbose=0)

或者你可以使用tf.keras.utils.image_dataset_from_directory批量大小为 1 且shuffle=False但它不是那么有效：

import tensorflow as tf
import pathlib
import numpy as np
from sklearn.model_selection import KFold

dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

batch_size = 1

train_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(180, 180),
  batch_size=batch_size,
  shuffle = False)

val_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(180, 180),
  batch_size=batch_size,
  shuffle = False)

ds = train_ds.concatenate(val_ds)

kfold = KFold(n_splits=5, shuffle=True)

for train, test in kfold.split(np.arange(len(ds))):
  train = [x+1 for x in train]
  test = [x+1 for x in test]
  train_ds = tf.data.Dataset.from_tensor_slices([ds.skip(t-1).take(t) for t in train]).flat_map(lambda x: x).map(lambda x, y: (x[0, ...], y[0, ...]))
  test_ds = tf.data.Dataset.from_tensor_slices([ds.skip(t-1).take(t) for t in test]).flat_map(lambda x: x).map(lambda x, y: (x[0, ...], y[0, ...]))
  train_ds = train_ds.take(len(train)).batch(64, drop_remainder=True)
  test_ds = test_ds.take(len(test)).batch(64, drop_remainder=True)
  
  model = tf.keras.Sequential([
  tf.keras.layers.Rescaling(1./255, input_shape=(180, 180, 3)),
  tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(5)])

  model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
  history = model.fit(train_ds,
              epochs=2)
  scores = model.evaluate(test_ds, verbose=0)

另一种选择是使用字典来存储索引和张量：

#...
ds = train_ds.concatenate(val_ds)

lookup_images = {}
lookup_labels = {}
for i, (x, y) in enumerate(ds):
  lookup_images[i] = x
  lookup_labels[i] = y

kfold = KFold(n_splits=5, shuffle=True)

for train, test in kfold.split(np.arange(len(ds))):

  images_train = np.concatenate(list(map(lookup_images.get, train)))
  labels_train = np.concatenate(list(map(lookup_labels.get, train)))

  images_test = np.concatenate(list(map(lookup_images.get, test)))
  labels_test = np.concatenate(list(map(lookup_labels.get, test)))

  model = tf.keras.Sequential([
  tf.keras.layers.Rescaling(1./255, input_shape=(180, 180, 3)),
  tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(5)])

  model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
  history = model.fit(images_train, labels_train, epochs=2)
  scores = model.evaluate(images_test, labels_test, verbose=0)