Neural Network cannot learn nonlinear function I am currently creating a neural network to learn a function of the following form Data that I want to learn x corresponds to x axis and y to y axis(one dependent and one independent variable)



I am using both keras and tensorflow and with both scripts I get the following result 


I am currently creating a neural network to learn a function of the following form Data that I want to learn x corresponds to x axis and y to y axis(one dependent and one independent variable)

I am using both keras and tensorflow and with both scripts I get the following result Predictions orange line Data blue line. Somehow my neural network doesn't capture the non-linearity of the data and only tries to fit a linear function. Do you maybe have a suggestion what I am doing wrong? Also is the architecture appropriate for the following task or there exists some problems.

Additionally as an information I also include a snippet of the architecture that I am using in keras

    def individual_model(keys, labels, config):
model = Sequential()
model.add(Dense(32, input_dim=1))
model.add(LeakyReLU())
for i in range(2):
    # if str(i) not in config:
    #     break
    model.add(Dense(32))
    model.add(LeakyReLU())
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse', metrics=[
              max_absolute_error, 'mse', 'mae'])
model.fit(keys, labels, epochs=100, batch_size=32, verbose=1) 
return model
You should try using a sigmoid activation function, as leakyRelu is very close to a linear function, and remove one hidden layer. Just one hidden layer should be engough as you have only one input, with a very small length, maybe 3 or 5 neurons.