Can a neural network approximate non linear and periodic functions

I recently found a few assertions in forums (not this one) saying that neural networks cannot approximate non linear functions and, in particular, periodic functions.

I wondered if I could answer this on my own by using what we’ve seen so far in the course. So I extended the “Hello world” example obtaining these results.

Quadratic approximation


Sinusoidal approximation (in the domain of the training)


Sinusoidal approximation (predicting further from training points)


I think that the results speak for themselves:

  1. neural networks can easily learn non linear functions,
  2. they can be pretty accurate as soon as they are trained close to the points they make predictions for.

The sources used for the charts can be found here.

This is false. Non-linear functions are a particular specialty of neural networks. It’s because they have a non-linear activation in the hidden layer.

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Thanks TMosh! You are absolutely right. I’m happy that I already developed the tools to figure it out on my own. What you are pointing out is what I imagined in the first place but I still don’t have enough theoretical knowledge to answer with confidence.

By the way, as a physics student (once I was) I wanted to see if I could use a neural network to guess the explicit parameters (phase and angular velocity) of a sinusoidal signal using a neural network and it seems like it’s quite easy with a custom layer:

class Cosine(tf.keras.layers.Layer):
    def __init__(self, units=32, input_dim=32):
        self.w = self.add_weight(
            shape=(input_dim, units),
        self.b = self.add_weight(

    def call(self, inputs):
        return tf.math.cos(tf.matmul(inputs, self.w) + self.b)

I still have to do more testing but it seems to work quite well even for predicting points that are pretty far from the training set. :heart_eyes:

Too much to learn yet! Too much fun to have!