Tensorflow 中 model.fit 的 InvalidArgumentError

Question

使用 CNN 进行图像分类。当 model.fit() 被调用时，它开始训练模型一段时间，并在执行过程中被中断并且 returns 一条错误消息。

错误信息如下

InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  Input size should match (header_size + row_size * abs_height) but they differ by 2
     [[{{node decode_image/DecodeImage}}]]
     [[IteratorGetNext]]
     [[IteratorGetNext/_4]]
  (1) Invalid argument:  Input size should match (header_size + row_size * abs_height) but they differ by 2
     [[{{node decode_image/DecodeImage}}]]
     [[IteratorGetNext]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_8873]

Function call stack:
train_function -> train_function

更新：我的建议是检查数据集的元数据。它帮助解决了我的问题。

Answer 1

您没有指定参数 label_mode 。为了使用 SparseCategoricalCrossentropy 作为损失函数，您需要将其设置为 int。如果您不指定它，那么它将设置为 None as per the documentation.

您还需要根据您从中读取图像的目录结构将参数labels指定为inferred。

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  labels="inferred",
  label_mode="int",
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)
  
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  labels="inferred",
  label_mode="int",
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

Tensorflow 中 model.fit 的 InvalidArgumentError

InvalidArgumentError with model.fit in Tensorflow

python

deep-learning

keras

tensorflow

google-colaboratory