Month: December 2021

Browsing through freely available sources I find both statements: DQN is good / is not good for stochastic environments.

As far as I understand it, the Q-Network predicts the expected return of an action in a state, which can then be used to decide e.g. greedily; and training makes that prediction better. If the environment is stochastic, repeated learning should nudge the prediction to the distribution center as the loss minimum.

So it should work, but might need a lot of time to get there (law of great numbers), especially since the game is being played by 2 agents suffering from the same problem, and being part of the “environment” stochastic behaviour for the opponent!

Maybe there is another technique in Deep Learning / Reinforcement Learning much better suited for such a strongly stochastic environment? Any advices?

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Please comment below or DM me for more details

Hi,
I’m working on a project that uses Edge TPU and I need to use appropriate models[1] converted from Tensorflow form. I need a face recognition and decided to use FaceNet implementation and took the model from here: [2]. I have it working on my PC, but when I tried to convert the model to tflite and compile it for edgetpu (using steps presented in [3]), I ended up with all of the resulting embeddings (output tensors) to be the same. They are in proper form (128D vector with uint8 values (as opposed to float32 values of TF model)), but they are all the same.

Does anyone has any idea what might be the reason of that? Is such conversion impossible on already pre-trained model or am I missing something obvious?

References:

[1] https://coral.ai/docs/edgetpu/models-intro/

[2] https://github.com/nyoki-mtl/keras-facenet

[3] https://www.tensorflow.org/lite/performance/post_training_integer_quant

I have a somewhat unique issue that I cannot solve because nothing on Google comes up.

My data are one-hot encoded DNA sequences of VARYING length. This is easily stored in a jagged NumPy array (4 x n x m), where n = number of samples, m = length of sequence (may vary.) However, the size requirements after zero-padding the entire array (padded by max sequence length) is insane and I need to avoid doing that.

The solution I have thought up is as follows:

1. Generated jagged numpy array (varying input lengths)
2. Extract k sequences from this large array where k = batch size
3. Zero-pad the batch
4. Pass to model
5. Repeat from step 2

Any help would be greatly appreciated. Thanks!