Function classification

examples/nnet_mlp.py:23–56

()

def classification():
    # Generate a random binary classification problem.
    X, y = make_classification(
        n_samples=1000,
        n_features=100,
        n_informative=75,
        random_state=1111,
        n_classes=2,
        class_sep=2.5,
    )
    y = one_hot(y)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.15, random_state=1111
    )

    model = NeuralNet(
        layers=[
            Dense(256, Parameters(init="uniform", regularizers={"W": L2(0.05)})),
            Activation("relu"),
            Dropout(0.5),
            Dense(128, Parameters(init="normal", constraints={"W": MaxNorm()})),
            Activation("relu"),
            Dense(2),
            Activation("softmax"),
        ],
        loss="categorical_crossentropy",
        optimizer=Adadelta(),
        metric="accuracy",
        batch_size=64,
        max_epochs=25,
    )
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print("classification accuracy", roc_auc_score(y_test[:, 0], predictions[:, 0]))


def regression():