for epoch in range(5): clf.fit(x_train, y_train, steps=500) y_pred = clf.predict(x_test) print('training {}'.format(clf.evaluate(x=x_train, y=y_train))) print('validation {}'.format(clf.evaluate(x=x_test, y=y_test))) # print('all {}'.format(clf.evaluate(x=x_all, y=y_all))) print(metrics.classification_report(y_test, y_pred)) print('confusion matrix\n', metrics.confusion_matrix(y_test, y_pred)) return clf #clf = TrainLinear(x_train, y_train, x_test, y_test) clf = TrainLinear(x_all, y_all, x_all, y_all) clf.get_variable_names() clf.get_variable_value('linear/_weight/Adagrad') df_goblin = df[df.type == 'Goblin'] sns.pairplot(df.drop(['id', 'color'], axis=1), hue="type", diag_kind='kde') sns.pairplot(df_goblin.drop(['id'], axis=1), hue="color", diag_kind='kde') sns.pairplot(df_goblin.drop(['id'], axis=1), hue="color", diag_kind='hist') df3 = AddMahalanobis(df) df3['type'][np.array(id_test)[(y_test == 2) & (y_pred == 1)]] = 'test_Goblin_pred_Ghoul' df3['type'][np.array(id_test)[(y_test == 2) & (y_pred == 0)]] = 'test_Goblin_pred_Ghost' sns.pairplot(df3.drop(['id'], axis=1), hue="type") from sklearn import metrics tf.logging.set_verbosity(tf.logging.ERROR)