# RNN learning sine waves of different frequencies

As a warm up with recurrent neural networks, I'm trying to predict a sine wave from another sine wave of another frequency.

My model is a simple RNN, its forward pass can be expressed as follow:

\begin{aligned} r_t &= \sigma(W_{in} \cdot x_t + W_{rec} \cdot r_{t-1}))\\ z_t &= W_{out} \cdot r_t \end{aligned} where $\sigma$ is the sigmoïd function.

When both input the input and expected output are two sine waves of the same frequency but with (possibly) a phase shift, the model is able to properly converge to a reasonable approximation.

However, in the following case, the model converge to a local minima and predicts zero all the time:

• input: $x = sin(t)$
• expected output: $y = sin(\frac{t}{2})$

Here's what the network predicts when given the full input sequence after 10 epochs of training, using mini-batches of size 16, a learning rate of 0.01, a sequence length of 16 and hidden layers of size 32:

Which leads me to think the network is unable to learn through time and relies only on the current input to make its prediction.

I tried to tune the learning rate, sequences length and hidden layers size without much success.

I'm having the exact same issue with an LSTM. I don't want to believe these architectures are that flawed, any hints on what am I doing wrong ?

I'm using an rnn package for Torch, the code is in a Gist.

Your data basically cannot be learned with an RNN trained that way. Your input is $\sin(t)$ is $2\pi$-periodic $\sin(t) = \sin(t+2\pi)$
but your target $\sin(t/2)$ is $4\pi$-periodic and $\sin(t/2) = -\sin(t+2\pi)$