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Is each architecture given one set of random weights? Or is the architecture of the net tested against a bunch of random weights so that it performs well indep

by TekMol 7y ago

Is each architecture given one set of random weights? Or is the architecture of the net tested against a bunch of random weights so that it performs well independently of the weights?

reiinakano 7y ago
Neither. The architecture performs well independently of weights BUT all weights must be the same e.g. Same network works well when all weights are 5.0 or when all weights are -3.0
- TekMol 7y ago
  Hmm... that collides with what patresh said.
  - reiinakano 7y ago
    No, it doesn't. Shared weight means all weights are the same value, as I originally mentioned.
patresh 7y ago
Each architecture is tested multiple times against different samples of the shared weight From the paper : (1) An initial population of minimal neural network topologies is created (2) each network is evaluated over multiple rollouts, with a different shared weight value assigned at each rollout (3) networks are ranked according to their performance and complexity (4) a new population is created by varying the highest ranked network topologies, chosen probabilistically through tournament selection
- TekMol 7y ago
  What is a "shared weight"?
  - patresh 7y ago
    Usually in neural networks, each neuron has their own weights with different values that will be tuned during training. Shared weight here means that every neuron in the network shares the exact same weight value.