请问为什么添加了batchnormal层后会报错啊,去掉这一层可以正常运行

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标签: #<Tag:0x00007fc4dde68df0>

(heros979) #1

在添加了batchnormal层后会报如下错误:
ConstructorError Traceback (most recent call last) <ipython-input-19-3dc906dfad22> in <module>()
509 epochs=5,
510 n_gpus=1, -->
511 verbose=‘2:每个epoch输出一行记录’
512 )
513 ConstructorError: could not determine a constructor for the tag ‘tag:yaml.org,2002:python/object/apply:tensorflow.python.training.tracking.data_structures.ListWrapper’ in “<unicode string>”, line 55, column 13: axis: !!python/object/apply:tensorflow …

策略:

克隆策略

策略简介

因子:样例因子(7个)

因子是否标准化:是

标注:未来5日收益(不做离散化)

算法:DNN

类型:回归问题

训练集:10-15年

测试集:16-19年

选股依据:根据预测值降序排序买入

持股数:30

持仓天数:5

模型结构

输入层 7 - 因子数量

全连接层 256 激活函数为relu

dropout 0.1

全连接层 128 激活函数为relu

全连接层 1 激活函数为linear - 预测输出

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    In [ ]:
    # 本代码由可视化策略环境自动生成 2020年3月13日 18:45
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m24_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        pred_label = input_1.read_pickle()
        df = input_2.read_df()
        df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
        df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
        return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 20
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.options['hold_days'] = 10
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2019-04-15',
        end_date='2019-12-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    where(shift(close, -10) / close -1>0,1,0)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    #all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=False
    )
    
    m3 = M.input_features.v1(
        features="""close_0
    turn_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m32 = M.winsorize.v6(
        input_data=m16.data,
        features=m3.data,
        columns_input='',
        median_deviate=3
    )
    
    m14 = M.standardlize.v8(
        input_1=m32.data,
        input_2=m3.data,
        columns_input=''
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m14.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.aa.v5(
        input_data=m7.data,
        day_number=50
    )
    
    m26 = M.dl_convert_to_bin.v2(
        input_data=m13.data,
        features=m3.data,
        window_size=60,
        feature_clip=5,
        flatten=False,
        window_along_col='instrument'
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-12-01'),
        end_date=T.live_run_param('trading_date', '2020-03-12'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m12 = M.winsorize.v6(
        input_data=m18.data,
        features=m3.data,
        columns_input='',
        median_deviate=3
    )
    
    m25 = M.standardlize.v8(
        input_1=m12.data,
        input_2=m3.data,
        columns_input=''
    )
    
    m10 = M.advanced_auto_labeler.v2(
        instruments=m9.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    where(shift(close, -10) / close -1>0,1,0)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    
    # 将分数映射到分类,这里使用20个分类
    #all_wbins(label, 20)
    
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=False
    )
    
    m28 = M.join.v3(
        data1=m10.data,
        data2=m25.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m33 = M.aa.v5(
        input_data=m28.data,
        day_number=50
    )
    
    m29 = M.dl_convert_to_bin.v2(
        input_data=m33.data,
        features=m3.data,
        window_size=60,
        feature_clip=5,
        flatten=False,
        window_along_col='instrument'
    )
    
    m6 = M.dl_layer_input.v1(
        shape='60,2',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m36 = M.dl_layer_locallyconnected1d.v1(
        inputs=m6.data,
        filters=64,
        kernel_size='5',
        strides='2',
        padding='valid',
        data_format='channels_last',
        activation='None',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m4 = M.dl_layer_batchnormalization.v1(
        inputs=m36.data,
        axis=-1,
        momentum=0.99,
        epsilon=0.001,
        center=True,
        scale=True,
        beta_initializer='Zeros',
        gamma_initializer='Zeros',
        moving_mean_initializer='Zeros',
        moving_variance_initializer='Zeros',
        beta_regularizer='None',
        beta_regularizer_l1=0,
        beta_regularizer_l2=0,
        gamma_regularizer='None',
        gamma_regularizer_l1=0,
        gamma_regularizer_l2=0,
        beta_constraint='None',
        gamma_constraint='None',
        name=''
    )
    
    m21 = M.dl_layer_activation.v1(
        inputs=m4.data,
        activation='relu',
        name=''
    )
    
    m22 = M.dl_layer_locallyconnected1d.v1(
        inputs=m21.data,
        filters=64,
        kernel_size='5',
        strides='1',
        padding='valid',
        data_format='channels_last',
        activation='None',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m41 = M.dl_layer_batchnormalization.v1(
        inputs=m22.data,
        axis=-1,
        momentum=0.99,
        epsilon=0.001,
        center=True,
        scale=True,
        beta_initializer='Zeros',
        gamma_initializer='Zeros',
        moving_mean_initializer='Zeros',
        moving_variance_initializer='Zeros',
        beta_regularizer='None',
        beta_regularizer_l1=0,
        beta_regularizer_l2=0,
        gamma_regularizer='None',
        gamma_regularizer_l1=0,
        gamma_regularizer_l2=0,
        beta_constraint='None',
        gamma_constraint='None',
        name=''
    )
    
    m42 = M.dl_layer_activation.v1(
        inputs=m41.data,
        activation='relu',
        name=''
    )
    
    m40 = M.dl_layer_maxpooling1d.v1(
        inputs=m42.data,
        pool_size=2,
        padding='valid',
        name=''
    )
    
    m37 = M.dl_layer_locallyconnected1d.v1(
        inputs=m40.data,
        filters=128,
        kernel_size='3',
        strides='1',
        padding='valid',
        data_format='channels_last',
        activation='None',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m38 = M.dl_layer_batchnormalization.v1(
        inputs=m37.data,
        axis=-1,
        momentum=0.99,
        epsilon=0.001,
        center=True,
        scale=True,
        beta_initializer='Zeros',
        gamma_initializer='Zeros',
        moving_mean_initializer='Zeros',
        moving_variance_initializer='Zeros',
        beta_regularizer='None',
        beta_regularizer_l1=0,
        beta_regularizer_l2=0,
        gamma_regularizer='None',
        gamma_regularizer_l1=0,
        gamma_regularizer_l2=0,
        beta_constraint='None',
        gamma_constraint='None',
        name=''
    )
    
    m39 = M.dl_layer_activation.v1(
        inputs=m38.data,
        activation='relu',
        name=''
    )
    
    m43 = M.dl_layer_locallyconnected1d.v1(
        inputs=m39.data,
        filters=128,
        kernel_size='3',
        strides='1',
        padding='valid',
        data_format='channels_last',
        activation='None',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m44 = M.dl_layer_batchnormalization.v1(
        inputs=m43.data,
        axis=-1,
        momentum=0.99,
        epsilon=0.001,
        center=True,
        scale=True,
        beta_initializer='Zeros',
        gamma_initializer='Zeros',
        moving_mean_initializer='Zeros',
        moving_variance_initializer='Zeros',
        beta_regularizer='None',
        beta_regularizer_l1=0,
        beta_regularizer_l2=0,
        gamma_regularizer='None',
        gamma_regularizer_l1=0,
        gamma_regularizer_l2=0,
        beta_constraint='None',
        gamma_constraint='None',
        name=''
    )
    
    m45 = M.dl_layer_activation.v1(
        inputs=m44.data,
        activation='relu',
        name=''
    )
    
    m23 = M.dl_layer_maxpooling1d.v1(
        inputs=m45.data,
        pool_size=2,
        padding='valid',
        name=''
    )
    
    m27 = M.dl_layer_flatten.v1(
        inputs=m23.data,
        name=''
    )
    
    m30 = M.dl_layer_dense.v1(
        inputs=m27.data,
        units=256,
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m8 = M.dl_layer_dropout.v1(
        inputs=m30.data,
        rate=0.2,
        noise_shape='',
        name=''
    )
    
    m31 = M.dl_layer_dense.v1(
        inputs=m8.data,
        units=128,
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m20 = M.dl_layer_dropout.v1(
        inputs=m31.data,
        rate=0.2,
        noise_shape='',
        name=''
    )
    
    m34 = M.dl_layer_dense.v1(
        inputs=m20.data,
        units=1,
        activation='sigmoid',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m35 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m34.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m35.data,
        training_data=m26.data,
        validation_data=m29.data,
        optimizer='Adam',
        loss='binary_crossentropy',
        metrics='acc',
        batch_size=2048,
        epochs=3,
        n_gpus=1,
        verbose='2:每个epoch输出一行记录'
    )
    
    m11 = M.dl_model_predict.v1(
        trained_model=m5.data,
        input_data=m29.data,
        batch_size=1024,
        n_gpus=1,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.cached.v3(
        input_1=m11.data,
        input_2=m33.data,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m24.data_1,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    Train on 537731 samples, validate on 233662 samples
    Epoch 1/3
    537731/537731 - 481s - loss: 0.6342 - acc: 0.6423 - val_loss: 0.8817 - val_acc: 0.5289
    Epoch 2/3
    537731/537731 - 461s - loss: 0.6115 - acc: 0.6661 - val_loss: 0.9844 - val_acc: 0.5403
    Epoch 3/3
    

    (iQuant) #2

    收到反馈 我们在修复


    (heros979) #3

    我实盘的时候又报了这个错误


    (达达) #4

    如果回测还没能跑通,实盘无法跑通


    (heros979) #5

    回测不知道为啥莫名其妙跑通了,实盘如果加batchnormal就会报这个错误,去掉batchnormal不报这个错误