I'm currently working on a project of a nn to play a game similar to atari games (more details in the link). I'm having trouble with the indexing. perhaps anyone knows what could be the problem? because I cant seem to find it. Thank you for your time. here's my code (click on the link) and here's the full traceback. the problem starts from the way I call
history = network.fit(state, epochs=10, batch_size=10) // in line 82
See this post: Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
As said in the correct answer,
Modern CPUs provide a lot of low-level instructions, besides the usual arithmetic and logic, known as extensions, e.g. SSE2, SSE4, AVX, etc. From the Wikipedia:
The warning states that your CPU does support AVX (hooray!).
Pretty much, AVX speeds up your training, etc. Sadly, tensorflow is saying that they aren't going to use it... Why?
Because tensorflow default distribution is built without CPU extensions, such as SSE4.1, SSE4.2, AVX, AVX2, FMA, etc. The default builds (ones from pip install tensorflow) are intended to be compatible with as many CPUs as possible. Another argument is that even with these extensions CPU is a lot slower than a GPU, and it's expected for medium- and large-scale machine-learning training to be performed on a GPU.
If you have a GPU, you shouldn't care about AVX support, because most expensive ops will be dispatched on a GPU device (unless explicitly set not to). In this case, you can simply ignore this warning by:
# Just disables the warning, doesn't enable AVX/FMA
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
If you don't have a GPU and want to utilize CPU as much as possible, you should build tensorflow from the source optimized for your CPU with AVX, AVX2, and FMA enabled if your CPU supports them. It's been discussed in this question and also this GitHub issue. Tensorflow uses an ad-hoc build system called bazel and building it is not that trivial, but is certainly doable. After this, not only will the warning disappear, tensorflow performance should also improve.
You can find all the details and comments in this StackOverflow question.
NOTE: This answer is a product of my professional copy-and-pasting.
Happy coding,
Bobbay
Has the code been debugged line by line ? as this would trace to the line causing error.
I assume the index error crops up from the below one - where "i" and further targets[i] , outs[i] can be checked for the values they have -
per_sample_losses = loss_fn.call(targets[i], outs[i])
