Why doesn't it learn?
You think it's a simple problem to train an RNN on, but actually your setup isn't easy for the network at all:
As already mentioned, there's lack of important samples. You throw so much data into it (300 * 2000 points), but the actual target (frequency) is seen only once by the network. Even if the network does learn something, there's high chance it will overfit.
Inconsistent data. Remember that RNNs are good at capturing similar patterns in the series data. For instance, in NLP all sentences in the corpus are governed by the same language rules and more sentences help RNN to understand these rules better, i.e., more data helps.
In your case, the series with different frequencies aren't very much alike: compare the sine with frequency=1
and frequency=100
. This kind of diversity in the data makes it harder to learn, not easier. It doesn't mean that the frequency is impossible for an RNN to learn, it simply means that you shouldn't be surprised that a trivial RNN like yours has hard time.
Data scale. Changing the frequency from 1 to 300, changes the scale of both x
and y
by two orders of magnitude, which may be problematic for any neural network.
Solution
Since your goal is rather educational, I solved the second and third items simply by limiting the target frequency to 10, so that scaling and distribution diversity isn't much of an issue (you are welcome to try different values here: you should see that increasing this one parameter to, say, 50 makes the task much more complex).
The first item is solved by giving the RNN 10 examples of each frequency, instead of just one. I've also added one more hidden layer to increase network flexibility, plus a simple regularizer (Dropout
layer).
The complete code:
import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, Dropout, LSTM
max_freq = 10
time_steps = 100
def create_sine(frequency, offset):
return np.sin(frequency * np.linspace(offset, 2 * np.pi + offset, time_steps))
train_y = list(range(1, max_freq)) * 10
train_x = np.array([create_sine(freq, np.random.uniform(0,1)) for freq in train_y])
train_y = np.array(train_y)
input_series = Input(shape=(time_steps, 1), name='Input')
lstm = LSTM(units=100)(input_series)
hidden = Dense(units=100, activation='relu')(lstm)
dropout = Dropout(rate=0.1)(hidden)
output = Dense(units=1, activation='relu')(dropout)
model = Model(input_series, output)
model.compile('adam', 'mean_squared_error')
model.fit(train_x.reshape(-1, time_steps, 1), train_y, epochs=200)
# Trying the network on the same data
test_x = train_x.reshape(-1, time_steps, 1)
test_y = train_y
predicted = model.predict(test_x).reshape([-1])
print()
print((predicted - train_y)[:12])
print(np.mean(np.abs(predicted - train_y)))
The output:
max_freq=10
[-0.05612183 -0.01982236 -0.03744316 -0.02568841 -0.11959982 -0.0770483
0.04643679 0.12057972 -0.00625324 -0.00724655 -0.16919005 -0.04512954]
0.0503574344847
max_freq=20 (everything else is the same)
[ 0.51365542 0.09269333 -0.009691 0.0619092 0.09852839 0.04378462
0.01430321 -0.01953268 0.00722599 0.02558327 -0.04520988 -0.0614748 ]
0.146024380232
max_freq=30 (everything else is the same)
[-0.28205156 -0.28922796 -0.00569081 -0.21314907 0.1068716 0.23497915
0.23975039 0.25955486 0.26333141 0.24235058 0.08320332 -0.03686047]
0.406703719805
Note that results are random and actually increasing the max_freq
increases the changes of divergence. But even when it converges, the performance doesn't improve despite having more data, instead gets worse and pretty fast.