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

With these changes:

• Activations to relu
• Remove kernel_initializer (i.e. leave the default 'glorot_uniform')
• Adam optimizer
• 100 epochs

i.e.

regressor = Sequential()
regressor.add(Dense(units=20, activation='relu', input_dim=1)) 
regressor.add(Dense(units=20, activation='relu')) 
regressor.add(Dense(units=20, activation='relu')) 
regressor.add(Dense(units=1))
regressor.compile(loss='mean_squared_error', optimizer='adam')

regressor.fit(X, Y, epochs=100, verbose=1, batch_size=32)

and the rest of your code unchanged, here is the result:

Tinker, again and again...