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Amen. I'd love to find a way to access Tensorflow directly from a native compiled Common Lisp like CCL or SBCL. This would be ideal for combining symbolic AI wi

Amen. I'd love to find a way to access Tensorflow directly from a native compiled Common Lisp like CCL or SBCL. This would be ideal for combining symbolic AI with DL.

mark_l_watson 8y ago
I would like the same. I like TensorFlow with Keras much more than using DeepLearning4j, but DL4J is callable from Armed Bear Common Lisp. I have simple examples in my book that you can read free online: https://leanpub.com/lovinglisp https://leanpub.com/lovinglisp Unfortunately the example in the book is a little weak because I wrote the deep learning code in Java and just called my Java code from Common Lisp. I should modify the example to build models, train, and evaluate using the DL4J APIs directly from Lisp. EDIT: in the book example, I just call train and evaluate entry points in my Java code from Lisp so I can train new models from Lisp but can’t create new models with new architectures without modifying the Java code. I really should change that.
- agibsonccc 8y ago
  Hi Mark, I want to correct one thing. My company is behind dl4j. We are also the second largest contributor to keras. We have near perfect model import (with keras contributors maintaining it). Dl4j also has TF import and a native graph runner: https://github.com/deeplearning4j/dl4j-examples/tree/master/tf-import-examples https://github.com/deeplearning4j/dl4j-examples/tree/master/... Java itself tends to be fairly verbose I know, but not only are there wrappers but for inference if you want to use python for basics and then us for inference. We enable several ways to do that. Folks tend to oversensationlize these "us vs them" framework "fights". Rather than do that, why not just use what's good for each use case? Python for training, java for deployment? Beyond that, you could also consider building your own wrapper maybe? In clojure, there's jutsu.ai for example: https://github.com/hswick/jutsu.ai https://github.com/hswick/jutsu.ai
  - mark_l_watson 8y ago
    Thanks for the info! Sorry if I sounded like I was knocking DL4J: I have used it in examples for two books I have written. I use Keras at work so that is why I prefer it - I am very used to Keras, and to a lesser degree TensorFlow. Just tonight I was experimenting with jutsu.ai, and your advice of training on Keras and running on DL4J is good advice. I hacked up a little code to import Keras weights into Racket, but I can only process dense layers, and not RNN or complex architectures.
    - agibsonccc 8y ago
      Thanks! Usually dl4j is targeted at data engineers, not python folks. We actually have the end to end system working.

Amen. I'd love to find a way to access Tensorflow directly from a native compiled Common Lisp like CCL or SBCL. This would be ideal for combining symbolic AI with DL.

mark_l_watson 8y ago

I would like the same. I like TensorFlow with Keras much more than using DeepLearning4j, but DL4J is callable from Armed Bear Common Lisp. I have simple examples in my book that you can read free online: https://leanpub.com/lovinglisp https://leanpub.com/lovinglisp Unfortunately the example in the book is a little weak because I wrote the deep learning code in Java and just called my Java code from Common Lisp. I should modify the example to build models, train, and evaluate using the DL4J APIs directly from Lisp. EDIT: in the book example, I just call train and evaluate entry points in my Java code from Lisp so I can train new models from Lisp but can’t create new models with new architectures without modifying the Java code. I really should change that.

agibsonccc 8y ago
Hi Mark, I want to correct one thing. My company is behind dl4j. We are also the second largest contributor to keras. We have near perfect model import (with keras contributors maintaining it). Dl4j also has TF import and a native graph runner: https://github.com/deeplearning4j/dl4j-examples/tree/master/tf-import-examples https://github.com/deeplearning4j/dl4j-examples/tree/master/... Java itself tends to be fairly verbose I know, but not only are there wrappers but for inference if you want to use python for basics and then us for inference. We enable several ways to do that. Folks tend to oversensationlize these "us vs them" framework "fights". Rather than do that, why not just use what's good for each use case? Python for training, java for deployment? Beyond that, you could also consider building your own wrapper maybe? In clojure, there's jutsu.ai for example: https://github.com/hswick/jutsu.ai https://github.com/hswick/jutsu.ai
- mark_l_watson 8y ago
  Thanks for the info! Sorry if I sounded like I was knocking DL4J: I have used it in examples for two books I have written. I use Keras at work so that is why I prefer it - I am very used to Keras, and to a lesser degree TensorFlow. Just tonight I was experimenting with jutsu.ai, and your advice of training on Keras and running on DL4J is good advice. I hacked up a little code to import Keras weights into Racket, but I can only process dense layers, and not RNN or complex architectures.
  - agibsonccc 8y ago
    Thanks! Usually dl4j is targeted at data engineers, not python folks. We actually have the end to end system working.