如何使用我的keras TensorFlow模型减少损失？

Question

So I have a dataset of about 140,000 samples with 5 inputs, the velocity of the car, the acceleration of the car, the velocity of the lead car gathered with radar, the distance of the lead car, and the acceleration of the lead car. The output is from 0 to 1, 0 being maximum brake, and 1 being max acceleration.

I'm a beginner to neural nets, so I'm having trouble optimizing my model to get the best accuracy/loss for this data. I've been experimenting with changing the optimizer, activation function, number of hidden layers, number of nodes in the layers, etc but nothing seems to be lowering the loss over time.

Here's my current model:

opt = keras.optimizers.Adam(lr=0.001, decay=1e-6)
model = Sequential()
model.add(Dense(5, activation="tanh", input_shape=(x_train.shape[1:])))
for i in range(40):
    model.add(Dense(60, activation="relu"))
model.add(Dense(1))

I'm not too worried about overfitting my data right now as I can work on that later, I'm just trying to basically memorize the data to see how low I can get the loss, just to see if I can improve it. And predict on data that was trained to make sure it can return the correct output. However the lowest I've gotten the validation loss is around 0.015 , which definitely isn't returning the current output in my tests, it's about 90% accurate.

Is there anything I'm doing wrong? Should I be increasing my model size, or decreasing it? Nothing I've tried seems to be working. I've also made sure to normalize my 5 inputs and 1 output independently. It seems it never learns anything after a few epochs.

Thanks if anyone decides to help me on this very specific problem.

Answer 1

I'd like to help but there's not too much info there.

First what's the aim of the network? It's hard to tell what you're trying to reduce the loss of . What's your loss function? What are your labels? This seems like a classic reinforcement learning question, rather than a traditional supervised learning problem. How's the data structured? I'm assuming they're "runs" with some sort of score?

A blanket thing, that's probably too many layers.

Most of the time with networks the biggest improvement comes with cleaning your data (which we'll assume is fine) and messing with your loss function. It's best to start with a simple model and figure out if you need more.

There's also this, which is reinforcement learning, but it's a good example https://github.com/lexfridman/deeptraffic