self-driving-car | Udacity Self-Driving Car Engineer Nanodegree projects | Robotics library
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Lines of Code : 57License : Permissive (MIT License)
Copydef lrcn(self):
"""Build a CNN into RNN.
Starting version from:
https://github.com/udacity/self-driving-car/blob/master/
steering-models/community-models/chauffeur/models.py
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Trending Discussions on self-driving-car
QUESTION
I run drive.py program from Code Project | A Complete guide to self driving car
but when i start program i have error:
Not creating XLA devices, tf_xla_enable_xla_devices not set
Does anyone know how I can fix this problem? What should I download or reinstall?
I use:
Python 3.8.7
CUDA 11.0
tensorflow 2.4.1 On http://0.0.0.0:4567/ of course I see nothing
...ANSWER
Answered 2021-Jan-27 at 08:34Usually this message should not interfere.
Please try
QUESTION
This is my code:
...ANSWER
Answered 2018-Feb-07 at 06:06Lets break it down a bit. (A.array() == A.array()) represents the (2D) array with a boolean showing element-wise equality. If you were to write
QUESTION
How to prevent a lazy Convolutional Neural Network? I end with a ‘lazy CNN’ after training it with KERAS. Whatever the input is, the output is constant. What do you think the problem is?
I try to repeat an experiment of NVIDIA’s End to End Learning for Self-Driving Cars the paper. Absolutely, I do not have a real car but a Udacity’s simulator . The simulator generates figures about the foreground of a car.
A CNN receives the figure, and it gives the steering angle to keep the car in the track. The rule of the game is to keep the simulated car runs in the track safely. It is not very difficult.
The strange thing is sometimes I end with a lazy CNN after training it with KERAS, which gives constant steering angles. The simulated car will go off the trick, but the output of the CNN has no change. Especially the layer gets deeper, e.g. the CNN in the paper.
If I use a CNN like this, I can get a useful model after training.
...ANSWER
Answered 2017-Dec-22 at 15:12I can't run your model, because neither the question not the GitHub repo contains the data. That's why I am 90% sure of my answer.
But I think the main problem of your network is the sigmoid activation function after dense layers. I assume, it will train well when there's just two of them, but four is too much.
Unfortunately, NVidia's End to End Learning for Self-Driving Cars paper doesn't specify it explicitly, but these days the default activation is no longer sigmoid (as it once was), but relu. See this discussion if you're interested why that is so. So the solution I'm proposing is try this model:
QUESTION
in udacity self driving https://github.com/udacity/self-driving-car/tree/master/vehicle-detection/u-net the method get_mask_seg(img, bb_boxes_f) gives slice indices must be integers or none or have an index method
...ANSWER
Answered 2017-Nov-26 at 04:17This code is breaking due to a relatively recent change in NumPy. To fix it, you'll need to ensure that the index array bb_box_i is an integer array. The easiest way to do that is probably to add a line of code that does bb_box_i = bb_box_i.astype('int') before indexing into the img_mask array.
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