dbn.py 文件源码

def load_finetuned_dbn(path):
    """
    Load a fine tuned Deep Belief Net from file
    :param path: path to deep belief net parameters
    :return: deep belief net
    """
    dbn = NeuralNet(
        layers=[
            ('input', las.layers.InputLayer),
            ('l1', las.layers.DenseLayer),
            ('l2', las.layers.DenseLayer),
            ('l3', las.layers.DenseLayer),
            ('l4', las.layers.DenseLayer),
            ('l5', las.layers.DenseLayer),
            ('l6', las.layers.DenseLayer),
            ('l7', las.layers.DenseLayer),
            ('output', las.layers.DenseLayer)
        ],
        input_shape=(None, 1200),
        l1_num_units=2000, l1_nonlinearity=sigmoid,
        l2_num_units=1000, l2_nonlinearity=sigmoid,
        l3_num_units=500, l3_nonlinearity=sigmoid,
        l4_num_units=50, l4_nonlinearity=linear,
        l5_num_units=500, l5_nonlinearity=sigmoid,
        l6_num_units=1000, l6_nonlinearity=sigmoid,
        l7_num_units=2000, l7_nonlinearity=sigmoid,
        output_num_units=1200, output_nonlinearity=linear,
        update=nesterov_momentum,
        update_learning_rate=0.001,
        update_momentum=0.5,
        objective_l2=0.005,
        verbose=1,
        regression=True
    )
    with open(path, 'rb') as f:
        pretrained_nn = pickle.load(f)
    if pretrained_nn is not None:
        dbn.load_params_from(path)
    return dbn