Multiply Tensorflow sequential layer by fixed integers I'm trying to make a simple reinforcement learning model that makes one of three decisions, A, B, or C.  However, C will be effectively a "do nothing" decision.  Because C will never result in an action, it should always have a value of 0.

So all I want to do is multiply the 3 output nodes by 1, 1, 0.  That is, I want to do an element-wise multiplication by a [1,1,0] tensor so that the A and B nodes retain their values from the previous layer and the C node always has a value of 0.

I see that there's a Multiply layers class, which I think might work, but I'm seeing examples with the functional API.  Is there another way to do this with the Sequential API?
This turned out to be really simple.  I just used a Lambda layer to multiply the previous output layer by the multiplication vector.

model.add(tf.keras.layers.Dense(3)) # previous output layer
model.add(tf.keras.layers.Lambda(lambda x: x * [1,1,0])) # new output layer


It appears to be working correctly.