taking VGG feature maps as an example :
VGG_model= Model(inputs=VGG_model.input, outputs=VGG_model.get_layer('block1_conv2').output)  # to take the features of the first VGG block
features_maps = VGG_model.predict(img)         # extracting feature maps from the img
single_fm= features_maps[1]          # taking single feature map from the generated feature_maps 
maxval= np.max(single_fm)
minval= np.min(single_fm)

in the above example , i can perform numpy operations like (max/min) coz the result of VGG_model.predictis numpy array by default.
However, if i need to apply this code in between the layers of my model (as stated in post above), the type of each layer is kerastensor. Therefore, i need to convert kerastensor to numpy  to do the max/min operations on each individual feature map.
hope this is clear to you