tmp_model.py 文件源码

def build_combination(input_var, output_nodes, input_size, stocks, period, feature_types):
    # Input layer
    input_layer = InputLayer(shape=(None, 1, input_size), input_var=input_var)
    assert input_size == stocks * period * feature_types
    input_layer = ReshapeLayer(input_layer, (([0], stocks, period, feature_types)))

    #slice for partition
    stock_feature_type_layers = []
    for ix in range(stocks):
        stock_layer = SliceLayer(input_layer, indices=ix, axis=1)
        this_stock_feature_type_layers = []
        for rx in range(feature_types):
            this_stock_feature_type_layers.append(SliceLayer(stock_layer, indices=rx, axis=1))
        stock_feature_type_layers.append(this_stock_feature_type_layers)

    stock_networks = []
    for this_stock_feature_type_layers in stock_feature_type_layers:
        this_stock_networks = []
        for feature_type_layer in this_stock_feature_type_layers:
            tmp = DenseLayer(dropout(feature_type_layer, p=.2),
                num_units=10, nonlinearity=tanh)
            tmp = DenseLayer(dropout(tmp, p=.5), num_units=1, nonlinearity=tanh)
            this_stock_networks.append(tmp)

        this_stock_network = ConcatLayer(this_stock_networks)

        stock_network = DenseLayer(dropout(this_stock_network, p=.5),
                num_units=1, nonlinearity=tanh)

        stock_networks.append(stock_network)

    network = ConcatLayer(stock_networks)
    network = DenseLayer(dropout(network, p=.5),
                num_units=output_nodes, nonlinearity=sigmoid)

    return network, stock_networks