@darshan_deshpande
i am trying to get the activation maps of conv1, convert it to numpy array to do some computations and then convert them back to kerasTensor and feed them to conv2
using below example, could you please help me  
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   ####
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)    

please help…