thanks for your reply!! I tried several ways to load my data with tf.data.Dataset but no luck😿
I have all resized images saved as .npy files and I was trying these:
def map_func(feature_path):
  feature = np.load(feature_path)
  return feature

feature_paths = glob.glob('./*.np[yz]')

dataset = tf.data.Dataset.from_tensor_slices(feature_paths)

# Use map to load the numpy files in parallel
dataset = dataset.map(lambda item: tf.numpy_function(
          map_func, [item], tf.float16),
          num_parallel_calls=tf.data.AUTOTUNE)

print(dataset)

but can’t really understand how would i fit such a dataset to the model? I have labels for each .npy file in separate array but as I understand labels should be included to dataset somehow(?) because when I’m trying to add it usual way it throughs an error: ValueError: y argument is not supported when using dataset as input.
and without labels I’ve got ValueError: No gradients provided for any variable: ['dense/kernel:0', 'dense/bias:0', 'dense_1/kernel:0', 'dense_1/bias:0', 'dense_2/kernel:0', 'dense_2/bias:0'].
Could you please advise on how to add labels to the dataset properly?