input_layer = layers.Input(shape=(150, 150, 3), name="model_input")
conv_base = VGG16(weights="imagenet", include_top=False, input_tensor=input_layer)

cust_model = conv_base(input_layer)
cust_model = layers.Flatten()(cust_model)
cust_model = layers.Dense(256, activation="relu")(cust_model)
cust_model = layers.Dense(1, activation="sigmoid")(cust_model)

final_model = models.Model(input=input_layer, output=cust_model)
...  # model training etc. (works fine)
final_model.save("models/custom_vgg16.h5")

model_vgg16 = load_model("models/custom_vgg16.h5")

layer_input = model_vgg16.get_layer("model_input").input
layer_outputs = [layer.output for layer in model_vgg16.get_layer("vgg16").layers[:]]
activation_model = models.Model(inputs=layer_input, outputs=layer_outputs)

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("model_input_1:0", shape=(?, 150, 150, 3), dtype=float32) at layer "model_input". The following previous layers were accessed without issue: []