Function grid_search

ann_class2/grid_search.py:18–61

()

def grid_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:]

  # hyperparameters to try
  hidden_layer_sizes = [
    [300],
    [100,100],
    [50,50,50],
  ]
  learning_rates = [1e-4, 1e-3, 1e-2]
  l2_penalties = [0., 0.1, 1.0]

  # loop through all possible hyperparameter settings
  best_validation_rate = 0
  best_hls = None
  best_lr = None
  best_l2 = None
  for hls in hidden_layer_sizes:
    for lr in learning_rates:
      for l2 in l2_penalties:
        model = ANN(hls)
        model.fit(Xtrain, Ytrain, learning_rate=lr, reg=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, hls, lr, l2)
        )
        if validation_accuracy > best_validation_rate:
          best_validation_rate = validation_accuracy
          best_hls = hls
          best_lr = lr
          best_l2 = l2
  print("Best validation_accuracy:", best_validation_rate)
  print("Best settings:")
  print("hidden_layer_sizes:", best_hls)
  print("learning_rate:", best_lr)
  print("l2:", best_l2)


# def one():