Lambda output layer

I’ve a sequential model as follows with a linear activation function (Keras default) for the single output neuron:

model = Sequential()
model.add( ...
...
model.add(Dense(100, activation='relu'))
model.add(Dense(1))

I need the final number to be bounded by 100, so I modified the last line of code above to be: model.add(Lambda(lambda x: x%100, output_shape=(1))) 1- Is it correct? Does x here means the net as I expect ? 2- I get an error: “In Lambda, output_shape must be a list, a tuple, or a function”.

[farizrahman4u]

(Notice the comma after 1)

model.add(Lambda(lambda x: x%100, output_shape=(1,))

You can also igonre the output_shape argument in this case.

model.add(Lambda(lambda x: x%100))

For bounding by 100, I think you should use K.clip.

[kgrm]

For an example of a function that takes an arbitrary-shape tensor and outputs a scalar, see K.sum().