I’m trying to create and train an image classifier (with tensorflow 2.16.1, in pycharm).
I processed the data as follows:

def data_processing(train_dir, validation_dir, test_dir ):
    # Data augmentation for training
    train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
        rescale=1. / 255,
        rotation_range=20,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest',
        )

    # For validation and testing, just rescale
    validation_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255)

    # Load training images
    train_generator, train_labels = train_datagen.flow_from_directory(
        train_dir,
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary',
    )

    # Load validation images
    validation_generator = validation_datagen.flow_from_directory(
        validation_dir,
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary',
        shuffle=False
    )

    test_generator = validation_datagen.flow_from_directory(
        test_dir,
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary',
        shuffle= False
    )

    return train_generator, validation_generator, test_generator```

And then created and trained the model like this:

def create_model():
    #building the model (classic CNN, 3 layers of Conv2D + MaxPooling + fully connected (with dropout)
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu'))
    model.add(tf.keras.layers.MaxPooling2D(2, 2))

    model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(tf.keras.layers.MaxPooling2D(2, 2))

    model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))
    model.add(tf.keras.layers.MaxPooling2D(2, 2))

    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(512, activation='relu'))
    model.add(tf.keras.layers.Dropout(0.5)) #dropt common rule of thumb

    # Output Layer
    model.add(tf.keras.layers.Dense(1, activation='sigmoid')) # For binary classification

    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) #adam optimizer for adaptive learning rate

    return model


def train_model(model, train_generator, validation_generator, epochs):
    # Train the model
    # Set the number of steps (batches) per epoch

    steps_per_epoch = math.ceil(train_generator.samples / train_generator.batch_size)
    validation_steps = math.ceil(validation_generator.samples / validation_generator.batch_size)
    print(steps_per_epoch, validation_steps)


    history = model.fit(
        train_generator,
        steps_per_epoch=steps_per_epoch,
        epochs=epochs,
        validation_data=validation_generator,
        validation_steps=validation_steps,
    )

I’m getting the following warning: UserWarning: Your PyDataset class should call super().__init__(**kwargs) in its constructor. **kwargs can include workers, use_multiprocessing, max_queue_size. Do not pass these arguments to fit(), as they will be ignored.
self._warn_if_super_not_called() which I do not succeed to trace back th issue

and also encounter UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches. You may need to use the .repeat() function when building your dataset.
self.gen.throw(value) which does not make sense to me:
train data is of 4704 images and validation data is of 496 images.
step size are calculated according to the batch size.
what am I missing?

tried to change the batch size to 1 and the stps to the dataset size
tried to move to tensorflow 2.17