I am trying to get the activation maps of conv1, convert them to numpy array to do some computations and then convert them back to kerasTensor to feed them to conv2
using below example, could you please help me :pray: :pray:
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)
i tried .numpy() and eval() but none of them worked, also i tried to disable eager_execution and also didn't succeed.
Please help.