Using tf.keras.utils.image_dataset_from_directory with label list

from the document image_dataset_from_directory it specifically required a label as inferred and none when used but the directory structures are specific to the label name. I am using the cats and dogs image to categorize where cats are labeled '0' and dog is the next label.

[ Sample ]:

import os
import tensorflow as tf

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
PATH = 'F:\\datasets\\downloads\\sample\\cats_dogs\\training'
train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')

BATCH_SIZE = 1                                              # 32
IMG_SIZE = (32, 32)

train_dataset = tf.keras.utils.image_dataset_from_directory(train_dir,
                                                            shuffle=True,
                                                            batch_size=BATCH_SIZE,
                                                            image_size=IMG_SIZE)
                                                            
validation_dataset = tf.keras.utils.image_dataset_from_directory(validation_dir,
                                                                 shuffle=True,
                                                                 batch_size=BATCH_SIZE,
                                                                 image_size=IMG_SIZE)
                                                            
class_names = train_dataset.class_names

print('Number of training batches: %d' % tf.data.experimental.cardinality(train_dataset).numpy())
print('Number of validation batches: %d' % tf.data.experimental.cardinality(validation_dataset).numpy())

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
AUTOTUNE = tf.data.AUTOTUNE
train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=( 32, 32, 3 )),
    tf.keras.layers.Reshape((32, 32 * 3)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM( 32, return_sequences=True, return_state=False )),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM( 32 )),
    tf.keras.layers.Dense( 256 ),
    tf.keras.layers.Dropout(.2),
    tf.keras.layers.Dense( 256 ),

])
        
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10))
model.summary()

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
    learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
    name='Nadam'
) # 0.00001

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
# 1
# lossfn = tf.keras.losses.MeanSquaredLogarithmicError(reduction=tf.keras.losses.Reduction.AUTO, name='mean_squared_logarithmic_error')

# 2
lossfn = tf.keras.losses.SparseCategoricalCrossentropy( from_logits=False, reduction=tf.keras.losses.Reduction.AUTO, name='sparse_categorical_crossentropy')

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy', tf.keras.metrics.CategoricalAccuracy()])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit(train_dataset, epochs=15000 ,validation_data=(validation_dataset))
    
input("Press Any Key!")

[ Output ]:

Epoch 1233/15000
1/14 [=>............................] - ETA: 0s - loss: 1.2278e-05 - accuracy: 1.0000 - categorical_accuracy: 0.0000e+0 
3/14 [=====>........................] - ETA: 0s - loss: 0.7675 - accuracy: 1.0000 - categorical_accuracy: 0.3333       
14/14 [==============================] - 1s 40ms/step - loss: 1.3322 - accuracy: 0.7857 - categorical_accuracy: 0.5000 - val_loss: 1.1513 - val_accuracy: 0.7857 - val_categorical_accuracy: 0.5000

Your data folder probably does not have the right structure. Try something like this:

import numpy
from PIL import Image
import tensorflow as tf

samples = 10
for idx, c in enumerate(['/content/data/class1/', '/content/data/class2/']*samples):
  imarray = numpy.random.rand(100,100,3) * 255
  im = Image.fromarray(imarray.astype('uint8')).convert('RGB')
  im.save('{}result_image{}.png'.format(c, idx))

train_labels = [0]*samples + [1]*samples
train_ds = tf.keras.utils.image_dataset_from_directory(
  '/content/data',
  label_mode='int',
  labels = train_labels,
  shuffle=False,
  seed=123,
  image_size=(100, 100),
  batch_size=4)

for x, y in train_ds.take(1):
  print(x.shape, y)

Found 20 files belonging to 2 classes.
(4, 100, 100, 3) tf.Tensor([0 0 0 0], shape=(4,), dtype=int32)

Your folder structure should look like this:

├── data
│   ├── class2
│   │   ├── result_image5.png
│   │   ├── result_image9.png
│   │   ├── result_image15.png
│   │   ├── result_image13.png
│   │   ├── result_image1.png
│   │   ├── result_image3.png
│   │   ├── result_image11.png
│   │   ├── result_image19.png
│   │   ├── result_image7.png
│   │   └── result_image17.png
│   └── class1
│       ├── result_image14.png
│       ├── result_image8.png
│       ├── result_image12.png
│       ├── result_image18.png
│       ├── result_image16.png
│       ├── result_image6.png
│       ├── result_image2.png
│       ├── result_image10.png
│       ├── result_image4.png
│       └── result_image0.png