nnet_mlp.py 文件源码

def regression():
    # Generate a random regression problem
    X, y = make_regression(n_samples=5000, n_features=25, n_informative=25,
                           n_targets=1, random_state=100, noise=0.05)
    y *= 0.01
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
                                                        random_state=1111)

    model = NeuralNet(
        layers=[
            Dense(64, Parameters(init='normal')),
            Activation('linear'),
            Dense(32, Parameters(init='normal')),
            Activation('linear'),
            Dense(1),
        ],
        loss='mse',
        optimizer=Adam(),
        metric='mse',
        batch_size=256,
        max_epochs=15,
    )
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print("regression mse", mean_squared_error(y_test, predictions.flatten()))