【深度学习】如何验证你的方法?

机器学习
深度学习
标签: #<Tag:0x00007fc06770c3b0> #<Tag:0x00007fc06770c270>

(sszy) #1

深度学习里有个重要参数epoch如何调整可以达到最好的学习效果?如何验证模型是否有效/会不会过拟合or欠拟合?采取适当的可视化验证方法可以很好地解决上述问题。

1 验证思路

  • 划分训练集和验证集。模型良好的泛化能力是我们的目标,因此为了在训练过程中监控模型在前所未见的数据上的效果,可以将原始训练数据留出一部分样本作为验证集。也就是说,用训练集数据训练模型,用验证集数据监控模型在新数据上的效果。
  • 训练模型。用训练集数据训练模型,然后用验证集数据预测。
  • 绘制训练/验证的损失/误差图。将训练集和验证集效果进行对比,以优化模型。
  • 分析结果,优化模型

2 基础知识

神经网络中,各神经元的权重是通过训练自动得出的。所谓训练,就是神经网络在训练集数据上跑一遍,看看损失函数的值是否够小:如果损失函数的值小到符合我们的要求,那就说明神经网络拟合得不错,可以停止训练;如果损失函数得值仍然较大,那就要再次训练,也就是迭代(iteration)。易得,迭代次数越少,说明神经网络的训练效率越高。

但是,如果使用的数据集很大,那每次利用全样本迭代就会耗费非常长的时间。为了减少计算量、缩短训练时间,一个方法就是使用部分样本(batch),训练样本的样本数量就是batch size

然而,batch size会影响迭代次数,使得不同batch size的神经网络不好比较优劣。epoch是调整后的迭代次数,是针对总体样本的迭代次数,统一了比较标准。

举个例子
总体样本数为10000,DNN A的batch size为500,需要迭代12次达到目标函数要求;DNN B的batch size为2000,需要迭代4次达到目标函数要求;
那么,A的epoch为 $\frac{12}{10000/500}=\frac{3}{5}$,B的epoch为$\frac{4}{10000/2000}=\frac{4}{5}$
也就是说,虽然看起来A的iteration比较多,但实际上epoch更小,训练的效率高一点

我们可以得出结论,利用epoch可以衡量神经网络的训练效率。选择合适的epoch能够在训练速度和训练效果之间达到均衡。

3 验证流程

3.1 划分训练集和验证集


2/3的样本为训练集,1/3的样本为验证集。

3.2 训练模型

3.3 绘制训练/验证的损失/误差图

平台的深度学习(训练)模块可以输出训练history,得到每一次迭代的loss和mae等指标。
据此,画出训练/验证的损失/误差图,具体代码见“策略”。

3.4 分析结果,优化模型

根据上图,训练集上的loss&mae逐步下降,验证集上的loss&mae先下降,但随着迭代次数(epoch)的增多,又有所上升。这意味着,迭代次数较少时,模型出于欠拟合状态,增加迭代次数可以提高模型精度;但是,当迭代次数大于5次之后,模型得到过度训练,没有使训练集的loss&mae充分减少,反倒影响了验证集的表现。

由此,可以看出最佳迭代次数在5次,可以修改模型的参数重新训练。

克隆策略

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pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"后复权","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-141"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-141"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-141","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"Comment":"","CommentCollapsed":true},{"Id":"-160","ModuleId":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","ModuleParameters":[{"Name":"shape","Value":"7,5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_shape","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dtype","Value":"float32","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sparse","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"name","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"inputs","NodeId":"-160"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-160","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":6,"Comment":"","CommentCollapsed":true},{"Id":"-1098","ModuleId":"BigQuantSpace.dl_model_train.dl_model_train-v1","ModuleParameters":[{"Name":"optimizer","Value":"RMSprop","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_optimizer","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"loss","Value":"mean_squared_error","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_loss","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"metrics","Value":"mae","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"batch_size","Value":"256","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"epochs","Value":"10","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_model","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"training_data","NodeId":"-1098"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"validation_data","NodeId":"-1098"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1098","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"Comment":"","CommentCollapsed":true},{"Id":"-1540","ModuleId":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","ModuleParameters":[{"Name":"batch_size","Value":"1024","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"n_gpus","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"verbose","Value":"2:每个epoch输出一行记录","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trained_model","NodeId":"-1540"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1540"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1540","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"Comment":"","CommentCollapsed":true},{"Id":"-2431","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n pred_label = input_1.read_pickle()\n df = input_2.read_df()\n df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})\n df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])\n return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 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。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-2431"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-2431"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-2431","OutputType":null},{"Name":"data_2","NodeId":"-2431","OutputType":null},{"Name":"data_3","NodeId":"-2431","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":24,"Comment":"","CommentCollapsed":true},{"Id":"-768","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-768"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-768"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-768","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"Comment":"","CommentCollapsed":true},{"Id":"-773","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"label","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-773"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-773"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-773","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"Comment":"","CommentCollapsed":true},{"Id":"-778","ModuleId":"BigQuantSpace.standardlize.standardlize-v8","ModuleParameters":[{"Name":"columns_input","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-778"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-778"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-778","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"Comment":"","CommentCollapsed":true},{"Id":"-243","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":5,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-243"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-243"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-243","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"Comment":"","CommentCollapsed":true},{"Id":"-251","ModuleId":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","ModuleParameters":[{"Name":"window_size","Value":"5","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_clip","Value":5,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"flatten","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"window_along_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-251"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-251"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-251","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"Comment":"","CommentCollapsed":true},{"Id":"-2680","ModuleId":"BigQuantSpace.dl_layer_conv1d.dl_layer_conv1d-v1","ModuleParameters":[{"Name":"filters","Value":"20","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"kernel_size","Value":"3","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"strides","Value":"1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"padding","Value":"valid","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"dilation_rate","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"activation","Value":"relu","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_activation","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{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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\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read_pickle()\n feature_len = len(input_2.read_pickle()) \n \n df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))\n flag = int(len(df['y'])/3)\n print(\"flag:\", flag)\n val_x = df['x'][:flag]\n val_y = df['y'][:flag]\n partial_x = df['x'][flag:]\n partial_y = df['y'][flag:]\n val = {'x':val_x, 'y':val_y}\n train = {'x':partial_x, 'y':partial_y}\n \n # 划分2/3训练集和1/3验证集\n data_1 = DataSource.write_pickle(train)\n data_2 = DataSource.write_pickle(val)\n return Outputs(data_1=data_1, data_2=data_2)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 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。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-3895"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3895"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3895"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-3895","OutputType":null},{"Name":"data_2","NodeId":"-3895","OutputType":null},{"Name":"data_3","NodeId":"-3895","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"Comment":"调整数据+\n训练集和验证集划分","CommentCollapsed":false},{"Id":"-3907","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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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\ndef bigquant_run(input_1):\n import matplotlib.pyplot as plt\n plt.style.use('seaborn')\n\n history = input_1.read()['history']\n loss = history['loss']\n val_loss = history['val_loss']\n mae = history['mean_absolute_error']\n val_mae = history['val_mean_absolute_error']\n epochs = range(1, len(loss) + 1)\n\n plt.figure(1)\n plt.plot(epochs, loss, 'bo', label='Training loss')\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\n plt.title('Training and validation loss')\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.legend()\n plt.show()\n\n plt.figure(2)\n plt.plot(epochs, mae, 'bo', label='Training mae')\n plt.plot(epochs, val_mae, 'b', label='Validation mae')\n plt.title('Training and validation mean absolute error')\n plt.xlabel('Epochs')\n plt.ylabel('MAE')\n plt.legend()\n plt.show()\n \n return Outputs()\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 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    In [1]:
    # 本代码由可视化策略环境自动生成 2019年7月29日 19:32
    # 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 m4_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle()) 
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        flag = int(len(df['y'])/3)
        print("flag:", flag)
        val_x = df['x'][:flag]
        val_y = df['y'][:flag]
        partial_x = df['x'][flag:]
        partial_y = df['y'][flag:]
        val = {'x':val_x, 'y':val_y}
        train = {'x':partial_x, 'y':partial_y}
        
        # 划分2/3训练集和1/3验证集
        data_1 = DataSource.write_pickle(train)
        data_2 = DataSource.write_pickle(val)
        return Outputs(data_1=data_1, data_2=data_2)
    
    # 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 m4_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m8_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(df)
        return Outputs(data_1=data_1)
    
    # 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 m8_post_run_bigquant_run(outputs):
        return outputs
    
    # 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'] = 2
    
    # 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
    
    # 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 m20_run_bigquant_run(input_1):
        import matplotlib.pyplot as plt
        plt.style.use('seaborn')
    
        history = input_1.read()['history']
        loss = history['loss']
        val_loss = history['val_loss']
        mae = history['mean_absolute_error']
        val_mae = history['val_mean_absolute_error']
        epochs = range(1, len(loss) + 1)
    
        plt.figure(1)
        plt.plot(epochs, loss, 'bo', label='Training loss')
        plt.plot(epochs, val_loss, 'b', label='Validation loss')
        plt.title('Training and validation loss')
        plt.xlabel('Epochs')
        plt.ylabel('Loss')
        plt.legend()
        plt.show()
    
        plt.figure(2)
        plt.plot(epochs, mae, 'bo', label='Training mae')
        plt.plot(epochs, val_mae, 'b', label='Validation mae')
        plt.title('Training and validation mean absolute error')
        plt.xlabel('Epochs')
        plt.ylabel('MAE')
        plt.legend()
        plt.show()
        
        return Outputs()
    
    # 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 m20_post_run_bigquant_run(outputs):
        return outputs
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-01-01',
        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)
    shift(close, -2) / shift(open, -1)-1
    
    # 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))
    
    # 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
    )
    
    m13 = M.standardlize.v8(
        input_1=m2.data,
        columns_input='label'
    )
    
    m3 = M.input_features.v1(
        features="""close_0/mean(close_0,5)
    close_0/mean(close_0,10)
    close_0/mean(close_0,20)
    close_0/open_0
    open_0/mean(close_0,5)
    open_0/mean(close_0,10)
    open_0/mean(close_0,20)"""
    )
    
    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
    )
    
    m14 = M.standardlize.v8(
        input_1=m16.data,
        input_2=m3.data,
        columns_input=''
    )
    
    m7 = M.join.v3(
        data1=m13.data,
        data2=m14.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m26 = M.dl_convert_to_bin.v2(
        input_data=m7.data,
        features=m3.data,
        window_size=5,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m4 = M.cached.v3(
        input_1=m26.data,
        input_2=m3.data,
        run=m4_run_bigquant_run,
        post_run=m4_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        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
    )
    
    m25 = M.standardlize.v8(
        input_1=m18.data,
        input_2=m3.data,
        columns_input=''
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m25.data,
        features=m3.data,
        window_size=5,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m8 = M.cached.v3(
        input_1=m27.data,
        input_2=m3.data,
        run=m8_run_bigquant_run,
        post_run=m8_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = M.dl_layer_input.v1(
        shape='7,5',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m10 = M.dl_layer_conv1d.v1(
        inputs=m6.data,
        filters=20,
        kernel_size='3',
        strides='1',
        padding='valid',
        dilation_rate=1,
        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=''
    )
    
    m12 = M.dl_layer_maxpooling1d.v1(
        inputs=m10.data,
        pool_size=1,
        padding='valid',
        name=''
    )
    
    m32 = M.dl_layer_conv1d.v1(
        inputs=m12.data,
        filters=20,
        kernel_size='3',
        strides='1',
        padding='valid',
        dilation_rate=1,
        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=''
    )
    
    m33 = M.dl_layer_maxpooling1d.v1(
        inputs=m32.data,
        pool_size=1,
        padding='valid',
        name=''
    )
    
    m28 = M.dl_layer_globalmaxpooling1d.v1(
        inputs=m33.data,
        name=''
    )
    
    m30 = M.dl_layer_dense.v1(
        inputs=m28.data,
        units=1,
        activation='linear',
        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=''
    )
    
    m34 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m30.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m34.data,
        training_data=m4.data_1,
        validation_data=m4.data_2,
        optimizer='RMSprop',
        loss='mean_squared_error',
        metrics='mae',
        batch_size=256,
        epochs=10,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m11 = M.dl_model_predict.v1(
        trained_model=m5.data,
        input_data=m8.data_1,
        batch_size=1024,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.cached.v3(
        input_1=m11.data,
        input_2=m18.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'
    )
    
    m20 = M.cached.v3(
        input_1=m5.data,
        run=m20_run_bigquant_run,
        post_run=m20_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    flag: 861326
    
    Using TensorFlow backend.
    
    Train on 1722654 samples, validate on 861326 samples
    Epoch 1/10
     - 85s - loss: 0.9903 - mean_absolute_error: 0.7221 - val_loss: 0.9936 - val_mean_absolute_error: 0.7399
    Epoch 2/10
     - 82s - loss: 0.9868 - mean_absolute_error: 0.7208 - val_loss: 0.9940 - val_mean_absolute_error: 0.7449
    Epoch 3/10
     - 82s - loss: 0.9860 - mean_absolute_error: 0.7204 - val_loss: 0.9937 - val_mean_absolute_error: 0.7434
    Epoch 4/10
     - 87s - loss: 0.9855 - mean_absolute_error: 0.7203 - val_loss: 0.9930 - val_mean_absolute_error: 0.7424
    Epoch 5/10
     - 98s - loss: 0.9852 - mean_absolute_error: 0.7202 - val_loss: 0.9914 - val_mean_absolute_error: 0.7389
    Epoch 6/10
     - 75s - loss: 0.9849 - mean_absolute_error: 0.7200 - val_loss: 0.9926 - val_mean_absolute_error: 0.7391
    Epoch 7/10
     - 81s - loss: 0.9847 - mean_absolute_error: 0.7199 - val_loss: 0.9913 - val_mean_absolute_error: 0.7402
    Epoch 8/10
     - 79s - loss: 0.9846 - mean_absolute_error: 0.7199 - val_loss: 0.9933 - val_mean_absolute_error: 0.7441
    Epoch 9/10
     - 86s - loss: 0.9847 - mean_absolute_error: 0.7199 - val_loss: 0.9913 - val_mean_absolute_error: 0.7403
    Epoch 10/10
     - 83s - loss: 0.9848 - mean_absolute_error: 0.7200 - val_loss: 0.9993 - val_mean_absolute_error: 0.7495
    
    <Figure size 800x550 with 1 Axes>
    <Figure size 800x550 with 1 Axes>