ClassificationNN.py 文件源码

def __init__(self, isTrain, isOutlierRemoval, isNN=1):
        super(ClassificationNN, self).__init__(isTrain, isOutlierRemoval, isNN=1)
        # data preprocessing
        self.dataPreprocessing()

        self.net1 = NeuralNet(
                        layers=[  # three layers: one hidden layer
                            ('input', layers.InputLayer),
                            ('hidden', layers.DenseLayer),
                            #('hidden2', layers.DenseLayer),
                            ('output', layers.DenseLayer),
                            ],
                        # layer parameters:
                        input_shape=(None, 12),  # inut dimension is 12
                        hidden_num_units=6,  # number of units in hidden layer
                        #hidden2_num_units=3,  # number of units in hidden layer
                        output_nonlinearity=lasagne.nonlinearities.sigmoid,  # output layer uses sigmoid function
                        output_num_units=1,  # output dimension is 1

                        # optimization method:
                        update=nesterov_momentum,
                        update_learning_rate=0.002,
                        update_momentum=0.9,

                        regression=True,  # flag to indicate we're dealing with regression problem
                        max_epochs=25,  # we want to train this many epochs
                        verbose=0,
                        )