model.py 文件源码

def build_dnn(input_var, output_nodes, n_chanels, input_size, reshaped_input_size, activity):
    """
    Builds the complete network with 1D-conv1d layer to integrate time from sequences of EEG images.

    :param input_vars: list of EEG images (one image per time window)
    :param output_nodes: number of classes
    :return: a pointer to the output of last layer
    """
    # Input layer
    network = InputLayer(shape=(None, 1, input_size), input_var=input_var)

    #network = ReshapeLayer(network, (([0], n_chanels, reshaped_input_size)))

    network = batch_norm(DenseLayer(network, num_units=7680, nonlinearity=tanh))

    network = batch_norm(DenseLayer(network, num_units=3840, nonlinearity=tanh))

    network = batch_norm(DenseLayer(network, num_units=1920, nonlinearity=tanh))

    network = batch_norm(DenseLayer(network, num_units=960, nonlinearity=tanh))

    network = batch_norm(DenseLayer(network, num_units=output_nodes, nonlinearity=activity))

    return network