n_samples, h, w = lfw_people.images.shape # for machine learning we use the 2 data directly (as relative pixel # positions info is ignored by this model) X = lfw_people.data n_features = X.shape[1] # the label to predict is the id of the person y = lfw_people.target # y = self.translate_to_binary_array(y) target_names = lfw_people.target_names n_classes = target_names.shape[0] # split into a training and testing set X_train, X_test, y_train, y_test = sklearn_train_test_split( X, y, test_size=0.25) y_pred = None y_test = None with np.load('target-predicted-info-file-npz-exp-1.npz') as data: y_pred = data['arr_1'] y_test = data['arr_0'] learning_rates = None with np.load('learning-rates-info-file-npz-exp-1.npz') as data: learning_rates = data['arr_0'] plot_learning_rates_versus_epochs(1, False, learning_rates) prediction_titles = [title(y_pred, y_test, target_names, i)
# for machine learning we use the 2 data directly (as relative pixel # positions info is ignored by this model) X = lfw_people.data n_features = X.shape[1] # the label to predict is the id of the person y = lfw_people.target # y = self.translate_to_binary_array(y) target_names = lfw_people.target_names n_classes = target_names.shape[0] # split into a training and testing set X_train, X_test, y_train, y_test = sklearn_train_test_split(X, y, test_size=0.25) y_pred = None y_test = None with np.load('target-predicted-info-file-npz-exp-1.npz') as data: y_pred = data['arr_1'] y_test = data['arr_0'] learning_rates = None with np.load('learning-rates-info-file-npz-exp-1.npz') as data: learning_rates = data['arr_0'] plot_learning_rates_versus_epochs(1, False, learning_rates) prediction_titles = [