dbn.py 文件源码

def load_encoder(path):
    """
        load a pretrained dbn from path
        :param path: path to the .mat dbn
        :return: pretrained unrolled encoder
        """
    # create the network using weights from pretrain_nn.mat
    nn = sio.loadmat(path)
    w1 = nn['w1']
    w2 = nn['w2']
    w3 = nn['w3']
    w4 = nn['w4']
    b1 = nn['b1'][0]
    b2 = nn['b2'][0]
    b3 = nn['b3'][0]
    b4 = nn['b4'][0]

    encoder = NeuralNet(
        layers=[
            (InputLayer, {'name': 'input', 'shape': (None, 1200)}),
            (DenseLayer, {'name': 'l1', 'num_units': 2000, 'nonlinearity': sigmoid,
                          'W': w1, 'b': b1}),
            (DenseLayer, {'name': 'l2', 'num_units': 1000, 'nonlinearity': sigmoid,
                          'W': w2, 'b': b2}),
            (DenseLayer, {'name': 'l3', 'num_units': 500, 'nonlinearity': sigmoid,
                          'W': w3, 'b': b3}),
            (DenseLayer, {'name': 'l4', 'num_units': 50, 'nonlinearity': linear,
                          'W': w4, 'b': b4}),
        ],
        update=nesterov_momentum,
        update_learning_rate=0.001,
        update_momentum=0.5,
        objective_l2=0.005,
        verbose=1,
        regression=True
    )
    encoder.initialize()
    return encoder