Neural network sine approximation

Question

After spending days failing to use neural network for Q learning, I decided to go back to the basics and do a simple function approximation to see if everything was working

Answer 1

A more concise version of your code that works:

def data_gen():
    while True:
        x = (np.random.random([1024])-0.5) * 10 
        y = np.sin(x)
        yield (x,y)

regressor = Sequential()
regressor.add(Dense(units=20, activation='tanh', input_dim=1))
regressor.add(Dense(units=20, activation='tanh'))
regressor.add(Dense(units=20, activation='tanh'))
regressor.add(Dense(units=1, activation='linear'))
regressor.compile(loss='mse', optimizer='adam')

regressor.fit_generator(data_gen(), epochs=3, steps_per_epoch=128)

x = (np.random.random([1024])-0.5)*10
x = np.sort(x)
y = np.sin(x)

plt.plot(x, y)
plt.plot(x, regressor.predict(x))
plt.show()

Changes made: replacing low layer activations with hyperbolic tangents, replacing the static dataset with a random generator, replacing sgd with adam. That said, there still are problems with other parts of your code that I haven't been able to locate yet (most likely your scaler and random process).