TypeError: Input 'filename' of 'ReadFile' Op has type float32 that does not match expected type of string
Simplest way I found is to create a subfolder and copy the files to that subfolder. i.e. Lets assume your files are 0.jpg, 1.jpg,2.jpg....2000.jpg and in directory named "patterns".
Seems like the Keras API does not accept it as the files are named by numbers and for Keras it is in float32.
To overcome this issue, either you can rename the files as one answer suggests, or you can simply create a subfolder under "patterns" (i.e. "patterndir"). So now your image files are under ...\patterns\patterndir
Keras (internally) possibly using the subdirectory name and may be attaching it in front of the image file thus making it a string (sth like patterndir_01.jpg, patterndir_02.jpg) [Note this is my interpretation, does not mean that it is true]
When you compile it this time, you will see that it works and you will get a compiler message as:
Found 2001 files belonging to 1 classes. Using 1601 files for training. Found 2001 files belonging to 1 classes. Using 400 files for validation.
My code looks like this
import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers #Generate a dataset image_size = (28, 28) batch_size = 32 train_ds = tf.keras.preprocessing.image_dataset_from_directory( "patterns", validation_split=0.2, subset="training", seed=1337, image_size=image_size, batch_size=batch_size, ) val_ds = tf.keras.preprocessing.image_dataset_from_directory( "patterns", validation_split=0.2, subset="validation", seed=1337, image_size=image_size, batch_size=batch_size, )
In my case, I simply did not have enough samples in the training directories. There was one per category and I got the error.
The names of the files are in the float32 format.
Renaming all the images in the dataset solves the problem.
Loop over all the files with