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Re: Researchers unveil a pruning algorithm to shrink deep le... by cs702 on Friday May 08, 2020 @10:08PM (#23121575)
You could. But that's not the hard part. The hard part, for the foreseeable future, is that if you write your code like that, you will find it very difficult to get decent performance out of existing frameworks (PyTorch/TensorFlow) and accelerated hardware (GPUs/TPUs) which operate on neurons arranged as arrays of the same shape. (Oversimplifying a bit.)
Re: Researchers unveil a pruning algorithm to shrink deep le... by gavinray on Saturday May 09, 2020 @12:28AM (#23122324)
But the Neuron and Connections are just metadata objects like pointers that hold arrays, I don't understand.

Surely in PyTorch/TF, the ND-Arrays of NN's must also be associated to an object reference?

> existing frameworks (PyTorch/TensorFlow) and accelerated hardware (GPUs/TPUs) which operate on neurons arranged as arrays of the same shape.

    const dot = (inputs: number[], weights: number[]) => {
      let total = 0;
      const length = Math.min(inputs.length, weights.length);
      for (let i = 0; i < length; i++) {
        total += inputs[i] * weights[i];
      }
      return total;
    };

  activate(input?: number): number {
    if (!this.incoming.length) {
      this.output = input;
    } else {
      const inputs = this.incoming.map(connection => connection.from.output)
      const weights = this.incoming.map(connection => connection.weight);
      this.output = squash(dot(inputs, weights) + this.bias);
    }
    return this.output;
  }
This is on principle the same concept right?
Re: Researchers unveil a pruning algorithm to shrink deep le... by cs702 on Saturday May 09, 2020 @11:48AM (#23125688)
Unfortunately, the problem is more complex than that: https://dl.acm.org/doi/pdf/10.1145/3317550.3321441?download=... (I oversimplified things in my comment above).
Re: Researchers unveil a pruning algorithm to shrink deep le... by goatlover on Saturday May 09, 2020 @12:00AM (#23122195)
Could there be a transpiling step that converts the objects to arrays, targeting PyTorch or TensorFlow?