Function random_search

ann_class2/random_search.py:18–72

()

def random_search():
  # get the data and split into train/test
  X, Y = get_spiral()
  # X, Y = get_clouds()
  X, Y = shuffle(X, Y)
  Ntrain = int(0.7*len(X))
  Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]
  Xtest, Ytest = X[Ntrain:], Y[Ntrain:]

  # starting hyperparameters
  M = 20
  nHidden = 2
  log_lr = -4
  log_l2 = -2 # since we always want it to be positive
  max_tries = 30
  

  # loop through all possible hyperparameter settings
  best_validation_rate = 0
  best_hls = None
  best_lr = None
  best_l2 = None
  for _ in range(max_tries):
    model = ANN([M]*nHidden)
    model.fit(
      Xtrain, Ytrain,
      learning_rate=10**log_lr, reg=10**log_l2,
      mu=0.99, epochs=3000, show_fig=False
    )
    validation_accuracy = model.score(Xtest, Ytest)
    train_accuracy = model.score(Xtrain, Ytrain)
    print(
      "validation_accuracy: %.3f, train_accuracy: %.3f, settings: %s, %s, %s" %
        (validation_accuracy, train_accuracy, [M]*nHidden, log_lr, log_l2)
    )
    if validation_accuracy > best_validation_rate:
      best_validation_rate = validation_accuracy
      best_M = M
      best_nHidden = nHidden
      best_lr = log_lr
      best_l2 = log_l2

    # select new hyperparams
    nHidden = best_nHidden + np.random.randint(-1, 2) # -1, 0, or 1
    nHidden = max(1, nHidden)
    M = best_M + np.random.randint(-1, 2)*10
    M = max(10, M)
    log_lr = best_lr + np.random.randint(-1, 2)
    log_l2 = best_l2 + np.random.randint(-1, 2)
  print("Best validation_accuracy:", best_validation_rate)
  print("Best settings:")
  print("best_M:", best_M)
  print("best_nHidden:", best_nHidden)
  print("learning_rate:", best_lr)
  print("l2:", best_l2)


if __name__ == '__main__':