Issue retrieving error when adding a classifier to a MobileNet model - tensorflow

Question

I have the following code, I am retrieving error when I try to add my own classifier.
import keras
from keras import layers,Model
from keras.layers import Input,GlobalAveragePooling2D,Flatten,Dense
MobileNetV2_model= tf.keras.applications.MobileNetV2(input_shape=None, alpha=1.0, include_top=False,
weights='imagenet')
#MobileNetV2_model.summary()
x= MobileNetV2_model.output
x = layers.GlobalAveragePooling2D()(x)
final_output=layers.Dense(2, activation='sigmoid')(x)
model = keras.Model(inputs=MobileNetV2.input, outputs = final_output)
model.compile(optimizer="adam", loss='BinaryCrossentropy', metrics=['accuracy'],loss_weights=0.1)
Error
TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that
you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying
to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.

Answer 1

You should never mix keras and tf.keras. You can refer working code as shown below
import tensorflow as tf
from tensorflow.keras import layers, Model
MobileNetV2_model= tf.keras.applications.MobileNetV2(input_shape=(224,224,3), alpha=1.0, include_top=False, weights='imagenet')
#MobileNetV2_model.summary()
x= MobileNetV2_model.output
x = layers.GlobalAveragePooling2D()(x)
final_output=layers.Dense(2, activation='sigmoid')(x)
model = Model(inputs=MobileNetV2_model.input, outputs = final_output)
model.compile(optimizer="adam", loss='BinaryCrossentropy', metrics=['accuracy'],loss_weights=0.1)