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  • Anyone using the Tensorflow functional API from inside q(embedpy)?

    Posted by krish240574 on February 17, 2022 at 12:00 am

    Hello,

    I’m trying to write this code inside q:

    def get_model(input_shape, time2vec_dim = 3):
    inp = Input(input_shape)
    x = inp
    
    time_embedding = keras.layers.TimeDistributed(Time2Vec(time2vec_dim - 1))(x) /*******************/
    
    Equivalent q code is :
    
    lyr:.p.import`keras.layers
    
    x:lyr[`:Input;<;65;16]  /create Input(65,16)
    
    time_embedding:lyr[`:TimeDistributed;<;.p.eval("Time2Vec(2)")] / Time2Vec is my own class, defined in a .p file

    Now, how do I pass the final (x) in the above line? (marked /******************/)

    I tried time_embedding[x], it gives me a “rank” error.

    What is the semantic meaning of a (x) passed in the functional API? Code-wise, it seems that we’re passing (x) to an object of TimeDistributed above. Doesn’t quite make sense, could someone please shed light on this issue?

    Thanks,

    Krishna

    krish240574 replied 1 month, 1 week ago 2 Members · 2 Replies
  • 2 Replies
  • rocuinneagain

    Member
    February 18, 2022 at 12:00 am

    You could load the function in to q rather than redefine it line by line.

     

    Example file.p

    def get_model(input_shape, time2vec_dim = 3): inp = Input(input_shape) x = inp time_embedding = keras.layers.TimeDistributed(Time2Vec(time2vec_dim – 1))(x)

    Load it and pull in the function:

    q)l file.p 
    q)get_model:.p.get`get_model 
    q)get_model[(65;16)]

    Another example of this: https://github.com/rianoc/qparquet

     

    Looking to replicate an example from https://keras.io/api/layers/recurrent_layers/time_distributed/

    q)lyr:.p.import`keras.layers 
    q)x:lyr[`:Input;`shape pykw (10, 128, 128, 3)] 
    q)conv_2d_layer:lyr[`:Conv2D;64;(3;3)] 
    q)time_embedding:lyr[`:TimeDistributed;conv_2d_layer] q
    )outputs:time_embedding[x] 
    q)print outputs[`:shape] (None, 10, 126, 126, 64)

     

  • krish240574

    Member
    February 18, 2022 at 12:00 am

    Thanks a ton for the detailed replies, @rocuinneagain. I see that I was making a couple of errors:

    1. Using all the tensorflow function calls with “<” included, i.e intending to get them to return q. I’m much better off leaving them without the “<“, – using them as a default embedpy  , as mentioned here :

    https://code.kx.com/q/ml/embedpy/userguide/#function-calls

    2. Doing a .p.eval”Time2Vec(2)” – causes the following warning:

    WARNING:tensorflow:AutoGraph could not transform <bound method Time2Vec.call of <__main__.Time2Vec object at 0x7fbf09ad6160>> and will run it as-is.
    Cause: Unable to locate the source code of <bound method Time2Vec.call of <__main__.Time2Vec object at 0x7fbf09ad6160>>. Note that functions defined in certain environments, like the interactive Python shell, do not expose their source code. If that is the case, you should define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.experimental.do_not_convert. Original error: source code not available
    To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert

     

    Which I avoided using the following:(

    (.p.get`Time2Vec)[2] – seems strange, that the .p.eval should cause that warning, which the .p.get `Time2Vec does not.

    I

    I’ll read the documentation again.

     

    Cheers,

    Krishna

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