im building an mlp with scikit learn. Is there a way I can access weights and biases of the output layer per iteration? There is an option mlp.coefs_ But it outputs the train weights when the model is compiled

  • $\begingroup$ clf.coefs_ contains the weight matrices that constitute the model parameters: $\endgroup$ Oct 31 '19 at 16:35
  • $\begingroup$ @SachinYadav but it accessible only when the model is compiled. I want to access the weights which are updated in every iteration $\endgroup$ Oct 31 '19 at 17:09

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