Hesham Eraqi - 7 months ago 51

Python Question

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)))`

- Is it correct? Does x here means the net as I expect ?
- I get an error: "In Lambda, must be a list, a tuple, or a function".
`output_shape`

Answer

`output_shape=(1)`

should be `output_shape=(1,)`

.

BTW, I consider following alternatives being better:

Clip output to

`[0.0, 100.0]`

.`#... model.add(Dense(1)) #-2nd line from code in question model.add(Lambda(lambda x: max(0., min(x,100.)), output_shape=(1,)))`

This is a continuous function as opposed to mod 100.

Use scaled sigmoid output layer.

`#... model.add(Dense(1), activation='sigmoid') model.add(Lambda(lambda x:x*100., output_shape=(1,)))`

This is differentiable, being friendly to SGD.

Source (Stackoverflow)