Function regression

examples/nnet_mlp.py:59–90

()

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()))


if __name__ == "__main__":