test_classification_accuracy.py 文件源码

def test_mlp():
    y_train_onehot = one_hot(y_train)
    y_test_onehot = one_hot(y_test)

    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_onehot)
    predictions = model.predict(X_test)
    assert roc_auc_score(y_test_onehot[:, 0], predictions[:, 0]) >= 0.95