skip InputLayer and MaskingLayer in read_activations

keras_visualize_activations/read_activations.py

 import keras.backend as K
 
+from keras.engine.input_layer import InputLayer
+from keras.layers.core import Masking
 
 def get_activations(model, model_inputs, print_shape_only=False, layer_name=None):
     print('----- activations -----')
@@ -12,8 +14,12 @@ def get_activations(model, model_inputs, print_shape_only=False, layer_name=None
         inp = [inp]
         model_multi_inputs_cond = False
 
+    # all layer outputs
+    # skip input and masking layers
     outputs = [layer.output for layer in model.layers if
-               layer.name == layer_name or layer_name is None]  # all layer outputs
+               (layer.name == layer_name or layer_name is None)
+               and not isinstance(layer, InputLayer)
+               and not isinstance(layer, Masking)]
 
     funcs = [K.function(inp + [K.learning_phase()], [out]) for out in outputs]  # evaluation functions