behavioral-cloning | Neural Network to clone the own driving behaviour | Machine Learning library
kandi X-RAY | behavioral-cloning Summary
kandi X-RAY | behavioral-cloning Summary
This is a short walkthrough of the training approach used to solve the problem of Udacity’s third project "Behavioral Cloning". The logic is based in model.py while drive.py handles the driving.
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Top functions reviewed by kandi - BETA
- Generate training images
- Randomize image
- Process an image
- Translates an image
- Sample a batch of data
- Telemetry
- Send a steering control
- Nvidia model
- Remove rows with low steering
- Generate valid data
- Connect to a socket
behavioral-cloning Key Features
behavioral-cloning Examples and Code Snippets
Community Discussions
Trending Discussions on behavioral-cloning
QUESTION
While trying the project of car behavioral cloning, I successfully trained my model and generated the file model-001.h5. now in order to test the model, it must successfully drive the vehicle in Udacity self-driving simulator this is done by running the drive.py and the generated model-001.h5 file
...ANSWER
Answered 2020-Dec-12 at 15:22It'd be helpful to see on what line you get an error.
It's not obvious to me where the "if statement to change the incoming data to float format" that you refer to is. I see that the model is called on whatever image is loaded, in whatever datatype it comes from. I predict that the image is stored as uint8, the most efficient storage for a (0,255) colour representation.
So try:
image = np.asarray(image, dtype=np.float32)
To create an array of the correct datatype for your model, which I am presuming is tf.float32.
QUESTION
I created a CSV file (driving_log.csv) for the filepath of each image that my simulator created but I was using my brother's Windows computer when I did that so now the file path looks like this for each image (and there is almost 14000 of them). I get an error when invoking it from my file.py, which is in the same directory as the CSV file.
ANSWER
Answered 2020-Feb-19 at 03:28If you are running your code on a *nix machine, you can use the PureWindowsPath class:
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
As a beginner of bash script, I wrote a simple script to change the directory. Here it is my source code:
ANSWER
Answered 2017-Aug-14 at 14:53Your ./start call creates a sub shell. Run source start or . start (. is an abbreviation of source) instead to execute your script directly in your command line, not in a nested container.
QUESTION
In order to use a VGG16 network for a regression task, I extend it in the following way:
...ANSWER
Answered 2017-Jan-18 at 14:57Worked for me:
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Install behavioral-cloning
You can use behavioral-cloning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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