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x*tf.atan(x)\n\nbigquant_run=swish","type":"Literal","bound_global_parameter":null},{"name":"use_bias","value":"True","type":"Literal","bound_global_parameter":null},{"name":"kernel_initializer","value":"glorot_uniform","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_initializer","value":"Zeros","type":"Literal","bound_global_parameter":null},{"name":"user_bias_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_kernel_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_bias_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_activity_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"kernel_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_bias_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"inputs","node_id":"-196"}],"output_ports":[{"name":"data","node_id":"-196"}],"cacheable":false,"seq_num":20,"comment":"","comment_collapsed":true},{"node_id":"-224","module_id":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","parameters":[{"name":"rate","value":"0.1","type":"Literal","bound_global_parameter":null},{"name":"noise_shape","value":"","type":"Literal","bound_global_parameter":null},{"name":"seed","value":"","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"inputs","node_id":"-224"}],"output_ports":[{"name":"data","node_id":"-224"}],"cacheable":false,"seq_num":21,"comment":"","comment_collapsed":true},{"node_id":"-231","module_id":"BigQuantSpace.dl_layer_dropout.dl_layer_dropout-v1","parameters":[{"name":"rate","value":"0.1","type":"Literal","bound_global_parameter":null},{"name":"noise_shape","value":"","type":"Literal","bound_global_parameter":null},{"name":"seed","value":"","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"inputs","node_id":"-231"}],"output_ports":[{"name":"data","node_id":"-231"}],"cacheable":false,"seq_num":22,"comment":"","comment_collapsed":true},{"node_id":"-238","module_id":"BigQuantSpace.dl_layer_dense.dl_layer_dense-v1","parameters":[{"name":"units","value":"1","type":"Literal","bound_global_parameter":null},{"name":"activation","value":"linear","type":"Literal","bound_global_parameter":null},{"name":"user_activation","value":"","type":"Literal","bound_global_parameter":null},{"name":"use_bias","value":"True","type":"Literal","bound_global_parameter":null},{"name":"kernel_initializer","value":"glorot_uniform","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_initializer","value":"Zeros","type":"Literal","bound_global_parameter":null},{"name":"user_bias_initializer","value":"","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"kernel_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_kernel_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"bias_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_bias_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer","value":"None","type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer_l1","value":0,"type":"Literal","bound_global_parameter":null},{"name":"activity_regularizer_l2","value":0,"type":"Literal","bound_global_parameter":null},{"name":"user_activity_regularizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"kernel_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_kernel_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"bias_constraint","value":"None","type":"Literal","bound_global_parameter":null},{"name":"user_bias_constraint","value":"","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"inputs","node_id":"-238"}],"output_ports":[{"name":"data","node_id":"-238"}],"cacheable":false,"seq_num":23,"comment":"","comment_collapsed":true},{"node_id":"-682","module_id":"BigQuantSpace.dl_model_init.dl_model_init-v1","parameters":[],"input_ports":[{"name":"inputs","node_id":"-682"},{"name":"outputs","node_id":"-682"}],"output_ports":[{"name":"data","node_id":"-682"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-1098","module_id":"BigQuantSpace.dl_model_train.dl_model_train-v1","parameters":[{"name":"optimizer","value":"Adam","type":"Literal","bound_global_parameter":null},{"name":"user_optimizer","value":"","type":"Literal","bound_global_parameter":null},{"name":"loss","value":"自定义","type":"Literal","bound_global_parameter":null},{"name":"user_loss","value":"tf.keras.losses.Huber(\n delta=1,\n reduction=tf.keras.losses.Reduction.SUM,\n name='huber_loss'\n )","type":"Literal","bound_global_parameter":null},{"name":"metrics","value":"mse","type":"Literal","bound_global_parameter":null},{"name":"batch_size","value":"1024","type":"Literal","bound_global_parameter":null},{"name":"epochs","value":"30","type":"Literal","bound_global_parameter":null},{"name":"earlystop","value":"from tensorflow.keras.callbacks import EarlyStopping\nbigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.0001, patience=5)","type":"Literal","bound_global_parameter":null},{"name":"custom_objects","value":"# 用户的自定义层需要写到字典中,比如\n# {\n# \"MyLayer\": MyLayer\n# }\nbigquant_run = {'swish':swish\n \n}\n","type":"Literal","bound_global_parameter":null},{"name":"n_gpus","value":"0","type":"Literal","bound_global_parameter":null},{"name":"verbose","value":"2:每个epoch输出一行记录","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_model","node_id":"-1098"},{"name":"training_data","node_id":"-1098"},{"name":"validation_data","node_id":"-1098"}],"output_ports":[{"name":"data","node_id":"-1098"}],"cacheable":false,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-1540","module_id":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","parameters":[{"name":"batch_size","value":"1024","type":"Literal","bound_global_parameter":null},{"name":"n_gpus","value":0,"type":"Literal","bound_global_parameter":null},{"name":"verbose","value":"2:每个epoch输出一行记录","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"trained_model","node_id":"-1540"},{"name":"input_data","node_id":"-1540"}],"output_ports":[{"name":"data","node_id":"-1540"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true},{"node_id":"-2431","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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","type":"Literal","bound_global_parameter":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","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-2431"},{"name":"input_2","node_id":"-2431"},{"name":"input_3","node_id":"-2431"}],"output_ports":[{"name":"data_1","node_id":"-2431"},{"name":"data_2","node_id":"-2431"},{"name":"data_3","node_id":"-2431"}],"cacheable":true,"seq_num":24,"comment":"","comment_collapsed":true},{"node_id":"-243","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":1,"type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"3","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"True","type":"Literal","bound_global_parameter":null},{"name":"window_along_col","value":"instrument","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-243"},{"name":"features","node_id":"-243"}],"output_ports":[{"name":"data","node_id":"-243"}],"cacheable":true,"seq_num":26,"comment":"","comment_collapsed":true},{"node_id":"-251","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":1,"type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"3","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"True","type":"Literal","bound_global_parameter":null},{"name":"window_along_col","value":"instrument","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-251"},{"name":"features","node_id":"-251"}],"output_ports":[{"name":"data","node_id":"-251"}],"cacheable":true,"seq_num":27,"comment":"","comment_collapsed":true},{"node_id":"-436","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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 from sklearn.model_selection import train_test_split\n data = input_1.read()\n x_train, x_val, y_train, y_val = train_test_split(data[\"x\"], data['y'])\n data_1 = DataSource.write_pickle({'x': x_train, 'y': y_train})\n data_2 = DataSource.write_pickle({'x': x_val, 'y': y_val})\n return Outputs(data_1=data_1, data_2=data_2, data_3=None)\n","type":"Literal","bound_global_parameter":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 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[2022-06-30 14:43:09.277479] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.020576s].
[2022-06-30 14:43:09.298389] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.012357s].
[2022-06-30 14:43:09.345028] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.004233s].
[2022-06-30 14:43:09.365720] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.012464s].
[2022-06-30 14:43:09.412721] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.006568s].
[2022-06-30 14:43:09.435845] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.011705s].
[2022-06-30 14:43:09.471030] INFO: moduleinvoker: cached.v3 开始运行..
[2022-06-30 14:43:09.500119] INFO: moduleinvoker: cached.v3 运行完成[0.029106s].
[2022-06-30 14:43:09.502994] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.061211s].
[2022-06-30 14:43:09.507759] INFO: moduleinvoker: dl_model_train.v1 开始运行..
[2022-06-30 14:43:13.875304] INFO: dl_model_train: 准备训练,训练样本个数:320874,迭代次数:30
[2022-06-30 14:43:40.704441] INFO: dl_model_train: 训练结束,耗时:26.83s
[2022-06-30 14:43:40.796994] INFO: moduleinvoker: dl_model_train.v1 运行完成[31.289214s].
[2022-06-30 14:43:40.832180] INFO: moduleinvoker: dl_model_predict.v1 开始运行..
[2022-06-30 14:44:37.149842] INFO: moduleinvoker: dl_model_predict.v1 运行完成[56.317677s].
Epoch 1/30
314/314 - 5s - loss: 385.3247 - mse: 1.0073 - val_loss: 382.1423 - val_mse: 0.9960
Epoch 2/30
314/314 - 3s - loss: 383.0572 - mse: 1.0001 - val_loss: 381.8679 - val_mse: 0.9946
Epoch 3/30
314/314 - 3s - loss: 382.5898 - mse: 0.9985 - val_loss: 381.3745 - val_mse: 0.9947
Epoch 4/30
314/314 - 1s - loss: 382.3132 - mse: 0.9981 - val_loss: 381.4762 - val_mse: 0.9971
Epoch 5/30
314/314 - 1s - loss: 382.0121 - mse: 0.9969 - val_loss: 380.9921 - val_mse: 0.9939
Epoch 6/30
314/314 - 1s - loss: 381.7484 - mse: 0.9965 - val_loss: 381.3236 - val_mse: 0.9896
Epoch 7/30
314/314 - 1s - loss: 381.4542 - mse: 0.9958 - val_loss: 381.4939 - val_mse: 0.9942
Epoch 8/30
314/314 - 1s - loss: 381.5289 - mse: 0.9961 - val_loss: 380.4246 - val_mse: 0.9916
Epoch 9/30
314/314 - 1s - loss: 381.0350 - mse: 0.9950 - val_loss: 381.0897 - val_mse: 1.0026
Epoch 10/30
314/314 - 1s - loss: 380.9453 - mse: 0.9949 - val_loss: 381.2305 - val_mse: 0.9898
Epoch 11/30
314/314 - 1s - loss: 380.8914 - mse: 0.9944 - val_loss: 380.4555 - val_mse: 0.9942
3214/3214 - 2s
DataSource(44512aa76e514f06aef6d6a84383a69eT)