Kaggle competition: Flower Classification with TPUs
clf_nn = MLPClassifier(hidden_layer_sizes=(5,3), max_iter=300, learning_rate_init=0.01, random_state=101)
 scores = cross_val_score(clf_nn, X=X_train, y=y_train, cv=cv_value)
 model_results['Neural Network'] = np.mean(scores)
 np.mean(scores )<compute_train_metric>
submission = pd.read_csv('/kaggle/input/104-flowers-submissions/0.96523.csv')
 submission
clf_xgb = XGBClassifier(learning_rate=0.01)
 scores = cross_val_score(clf_xgb, X=X_train, y=y_train, cv=cv_value)
 model_results['XGBoost'] = np.mean(scores)
 np.mean(scores )<create_dataframe>
submission.to_csv('submission.csv', index=False )
<train_model><EOS> 
submission.to_csv('submission.csv', index=False )