I am kinda stuck here. Also , x = base_model(image_batch, training=False) correct ?prediction_layer = tfl.Dense(1, activation=‘linear’)(x) are these correct parameters? Thanks in advance.

Hi Suhas,

Your definition of prediction_layer consists of an application of a Dense layer to a tensor. In result, prediction_layer becomes a tensor object rather than a layer. This is why you get the error ‘…Tensor’ object is not callable when trying to use prediction_layer as a layer.

So you have to redefine prediction_layer in such a way that the Dense layer is not yet applied to a tensor.

Same error message. is there any hint?

Hi reeshi80,

If you get the same error message the hint is

i get this error now

“Tensor.op is meaningless when eager execution is enabled.”

def alpaca_model(image_shape=IMG_SIZE, data_augmentation=data_augmenter()):

‘’’ Define a tf.keras model for binary classification out of the MobileNetV2 model

Arguments:

image_shape – Image width and height

data_augmentation – data augmentation function

Returns:

Returns:

tf.keras.model

‘’’

Hi reeshi,

data augmentation should be applied to all the inputs. And the base_model should be applied to the entire augmented dataset.

Thank you. it worked.

Great!

Could you please remove your code? Many thanks and good luck with the rest of the course!

I have the exact same problem, would you elaborate on how you solved it?