A question to experts
is it possible to extract feature maps right after each conv layer as numpy arrays and do computations on them then convert the resulted feature maps arrays back to tensors to feed them to the next layer in the model?
If that is possible, please let me know, coz i am hopeless as i tried to do that many time and i failed.
To make it clear,  please have a look at the following example :
inputs = Input(shape=(48,48,3))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)

####   here i need to get the activation maps of conv1 as numpy arrays   ####

pool1 = MaxPooling2D((2, 2))(conv1)    

#shape=(None, 64, 24, 24)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D((2, 2))(conv2)  

Note: i am using
tf 2.7 ,
Keras 2.6,
python 3.8.1