I have a question about deep learning with keras. I have programmed a custom data generator because I was running out of memory and I need to load x by x samples for training because I am using nifty images with a big size. I tried several solutions of this forum, but as they are 3d images, they cannot be used in my model. The problem comes in the training command (fit) that throws an error:
ValueError: Layer "3dcnn" expects 1 input(s), but it received 16 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:2' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:3' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:4' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:5' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:6' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:7' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:8' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:9' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:10' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:11' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:12' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:13' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:14' shape=(None, 208, 150, 10) dtype=float32>, <tf.Tensor 'IteratorGetNext:15' shape=(None, 208, 150, 10) dtype=float32>]
The code is as follows:

def get_model(width=208, height=150, depth=50):
"""Build a 3D convolutional neural network model."""

inputs = keras.Input((width, height, depth, 1))

x = layers.Conv3D(filters=64, kernel_size=3, activation="relu")(inputs)
x = layers.MaxPool3D(pool_size=2)(x)
x = tf.keras.layers.BatchNormalization()(x)

x = layers.Conv3D(filters=64, kernel_size=3, activation="relu")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = tf.keras.layers.BatchNormalization()(x)

x = layers.Conv3D(filters=128, kernel_size=3, activation="relu")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = tf.keras.layers.BatchNormalization()(x)

x = layers.Conv3D(filters=256, kernel_size=3, activation="relu")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = tf.keras.layers.BatchNormalization()(x)

x = layers.GlobalAveragePooling3D()(x)
x = tf.keras.layers.Dense(units=512, activation="relu")(x)
x = layers.Dropout(0.3)(x)

outputs = tf.keras.layers.Dense(units=3, activation="softmax")(x)

# Define the model.
model = keras.Model(inputs, outputs, name="3dcnn")
return model


#Get ALL the training images to batch/split/iterate from batch size to batch size 
train_data_generator = CustomDataGenerator(
    batch_size = 16, 
    #dataset_directory = "E:\\NIFTI_train_codegenerator"
    dataset_directory = "NIFTI_train_codegenerator"
)
 
# get a batch of images
train_images,labels = next(iter(train_data_generator))

#validation_split=0.2,
epochs = 100
model.fit(
    train_images,
    labels,
    batch_size=16,
    epochs=epochs,
    shuffle=True,
    verbose=2,
    callbacks=[checkpoint_cb, early_stopping_cb],
)

Thank you in advance