diff --git a/PaddleRec/multi-task/MMoE/README.md b/PaddleRec/multi-task/MMoE/README.md index 586cda780e8833916bee93c1f65f74a3ff8ef1e7..67da6cd53dfddd6f3e432e1f6e3c5fd590752b9d 100644 --- a/PaddleRec/multi-task/MMoE/README.md +++ b/PaddleRec/multi-task/MMoE/README.md @@ -121,19 +121,19 @@ python train_mmoe.py --use_gpu 0 \ #使用cpu训练 ## 预测 -本模型训练和预测交替进行，运行train_mmoe.py 即可得到预测结果 +本模型训练和预测交替进行，训练的同时可得到预测结果 ## 模型效果 epoch设置为100的训练和测试效果如下： ```text -batch_size:[32],feature_size:[499],expert_num:[8],gate_num[2],expert_size[16],tower_size[8],epochs:[100] -2020-04-16 11:28:06,- INFO - epoch_id: 0,epoch_time: 129.17434 s,loss: 0.62215,train_auc_income: 0.86302,train_auc_marital: 0.92316,test_auc_income: 0.84525,test_auc_marital: 0.98269 -2020-04-16 11:30:36,- INFO - epoch_id: 1,epoch_time: 149.79017 s,loss: 0.42484,train_auc_income: 0.90634,train_auc_marital: 0.98418,test_auc_income: +2020-04-21 12:39:08,-INFO: batch_size:32,feature_size:499,expert_num:8,gate_num:2,expert_size:16,tower_size:8,epochs:2 +2020-04-16 11:28:06,-INFO: epoch_id: 0,epoch_time: 129.17434 s,loss: 0.62215,train_auc_income: 0.86302,train_auc_marital: 0.92316,test_auc_income: 0.84525,test_auc_marital: 0.98269 +2020-04-16 11:30:36,-INFO: epoch_id: 1,epoch_time: 149.79017 s,loss: 0.42484,train_auc_income: 0.90634,train_auc_marital: 0.98418,test_auc_income: ...... -2020-04-16 15:31:23,- INFO - epoch_id: 97,epoch_time: 147.07304 s,loss: 0.30267,train_auc_income: 0.94743,train_auc_marital: 0.99430,test_auc_income: 0.94905,test_auc_marital: 0.99414 -2020-04-16 15:33:51,- INFO - epoch_id: 98,epoch_time: 148.34412 s,loss: 0.29688,train_auc_income: 0.94736,train_auc_marital: 0.99433,test_auc_income: 0.94846,test_auc_marital: 0.99409 -2020-04-16 15:36:21,- INFO - epoch_id: 99,epoch_time: 149.91047 s,loss: 0.31330,train_auc_income: 0.94732,train_auc_marital: 0.99403,test_auc_income: 0.94881,test_auc_marital: 0.99386 -2020-04-16 15:36:21,- INFO - mean_mmoe_test_auc_income: 0.94465,mean_mmoe_test_auc_marital 0.99324,max_mmoe_test_auc_income: 0.94937,max_mmoe_test_auc_marital 0.99419 +2020-04-16 15:31:23,-INFO: epoch_id: 97,epoch_time: 147.07304 s,loss: 0.30267,train_auc_income: 0.94743,train_auc_marital: 0.99430,test_auc_income: 0.94905,test_auc_marital: 0.99414 +2020-04-16 15:33:51,-INFO: epoch_id: 98,epoch_time: 148.34412 s,loss: 0.29688,train_auc_income: 0.94736,train_auc_marital: 0.99433,test_auc_income: 0.94846,test_auc_marital: 0.99409 +2020-04-16 15:36:21,-INFO: epoch_id: 99,epoch_time: 149.91047 s,loss: 0.31330,train_auc_income: 0.94732,train_auc_marital: 0.99403,test_auc_income: 0.94881,test_auc_marital: 0.99386 +2020-04-16 15:36:21,-INFO: mean_mmoe_test_auc_income: 0.94465,mean_mmoe_test_auc_marital 0.99324,max_mmoe_test_auc_income: 0.94937,max_mmoe_test_auc_marital 0.99419 ``` diff --git a/PaddleRec/multi-task/MMoE/args.py b/PaddleRec/multi-task/MMoE/args.py index d20eca4acf67822b51442c163217f5f30178af37..8b259bcd2d02716c90e587361f795e8f56983bc3 100644 --- a/PaddleRec/multi-task/MMoE/args.py +++ b/PaddleRec/multi-task/MMoE/args.py @@ -27,7 +27,7 @@ def parse_args(): parser.add_argument("--tower_size", type=int, default=8, help="tower_size") parser.add_argument("--expert_num", type=int, default=8, help="expert_num") parser.add_argument("--gate_num", type=int, default=2, help="gate_num") - parser.add_argument("--epochs", type=int, default=400, help="epochs") + parser.add_argument("--epochs", type=int, default=100, help="epochs") parser.add_argument("--batch_size", type=int, default=32, help="batch_size") parser.add_argument('--use_gpu', type=int, default=0, help='whether using gpu') parser.add_argument('--model_dir',type=str, default='model_dir', help="model_dir") @@ -38,10 +38,9 @@ def parse_args(): def data_preparation_args(): parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--train_path", type=str, default='', help="train_path") - parser.add_argument("--test_path", type=str, default='', help="test_path") - - parser.add_argument('--train_data_path',type=str, default='', help="train_data_path") - parser.add_argument('--test_data_path',type=str, default='', help="test_data_path") + parser.add_argument("--train_path", type=str, default='data/census-income.data', help="train_path") + parser.add_argument("--test_path", type=str, default='data/census-income.test', help="test_path") + parser.add_argument('--train_data_path',type=str, default='train_data/', help="train_data_path") + parser.add_argument('--test_data_path',type=str, default='test_data/', help="test_data_path") args = parser.parse_args() return args diff --git a/PaddleRec/multi-task/MMoE/mmoe_train.py b/PaddleRec/multi-task/MMoE/mmoe_train.py index c3a1d00b8d06525199acb21bfd0d418c6a0de0da..abdb6e283f6229b3042d9cb2942ded616cbf2a08 100644 --- a/PaddleRec/multi-task/MMoE/mmoe_train.py +++ b/PaddleRec/multi-task/MMoE/mmoe_train.py @@ -7,6 +7,11 @@ import datetime import os import utils from args import * +import logging + +logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger("fluid") +logger.setLevel(logging.INFO) def set_zero(var_name,scope=fluid.global_scope(),place=fluid.CPUPlace(),param_type="int64"): """ @@ -62,16 +67,16 @@ def MMOE(feature_size=499,expert_num=8, gate_num=2, expert_size=16, tower_size=8 output_layers.append(out) - cost_income = paddle.fluid.layers.cross_entropy(input=output_layers[0], label=label_income,soft_label = True) - cost_marital = paddle.fluid.layers.cross_entropy(input=output_layers[1], label=label_marital,soft_label = True) + pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15) + pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15) + + cost_income = paddle.fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True) + cost_marital = paddle.fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True) label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2]) label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2]) - pred_income = fluid.layers.clip(output_layers[0], min=1e-10, max=1.0 - 1e-10) - pred_marital = fluid.layers.clip(output_layers[1], min=1e-10, max=1.0 - 1e-10) - auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64')) auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64')) @@ -95,8 +100,8 @@ expert_num = args.expert_num epochs = args.epochs gate_num = args.gate_num -print("batch_size:[%d],feature_size:[%d],expert_num:[%d],gate_num[%d],expert_size[%d],tower_size[%d],epochs:[%d]"%(batch_size,feature_size,expert_num, - gate_num,expert_size,tower_size,epochs)) +logger.info("batch_size:{} ,feature_size:{} ,expert_num:{} ,gate_num:{} ,expert_size:{} ,tower_size:{} ,epochs:{} ".format( + batch_size,feature_size,expert_num,gate_num,expert_size,tower_size,epochs)) train_reader = utils.prepare_reader(train_path,batch_size) test_reader = utils.prepare_reader(test_path,batch_size) @@ -156,14 +161,12 @@ for epoch in range(epochs): auc_income_list.append(test_auc_1_p) auc_marital_list.append(test_auc_2_p) end = time.time() - time_stamp = datetime.datetime.now() - print("%s,- INFO - epoch_id: %d,epoch_time: %.5f s,loss: %.5f,train_auc_income: %.5f,train_auc_marital: %.5f,test_auc_income: %.5f,test_auc_marital: %.5f"% - (time_stamp.strftime('%Y-%m-%d %H:%M:%S'),epoch,end - begin,loss_data,auc_1_p,auc_2_p,test_auc_1_p,test_auc_2_p)) - -time_stamp = datetime.datetime.now() -print("%s,- INFO - mean_mmoe_test_auc_income: %.5f,mean_mmoe_test_auc_marital %.5f,max_mmoe_test_auc_income: %.5f,max_mmoe_test_auc_marital %.5f"%( - time_stamp.strftime('%Y-%m-%d %H:%M:%S'),np.mean(auc_income_list),np.mean(auc_marital_list),np.max(auc_income_list),np.max(auc_marital_list))) - + + logger.info("epoch_id:{},epoch_time:{} s,loss:{},train_auc_income:{},train_auc_marital:{},test_auc_income:{},test_auc_marital:{}".format( + epoch,end - begin,loss_data,auc_1_p,auc_2_p,test_auc_1_p,test_auc_2_p)) + +logger.info("mean_sb_test_auc_income:{},mean_sb_test_auc_marital:{},max_sb_test_auc_income:{},max_sb_test_auc_marital:{}".format( + np.mean(auc_income_list),np.mean(auc_marital_list),np.max(auc_income_list),np.max(auc_marital_list))) diff --git a/PaddleRec/multi-task/Share_bottom/README.md b/PaddleRec/multi-task/Share_bottom/README.md index a996144da1f92fb9ff9bb120965882d088043598..c3c27a8a6df8be299dfa98cdb5cbd46c3799de3a 100644 --- a/PaddleRec/multi-task/Share_bottom/README.md +++ b/PaddleRec/multi-task/Share_bottom/README.md @@ -119,14 +119,14 @@ python share_bottom.py --use_gpu 0\ #使用cpu训练 ## 预测 -本模型训练和预测交替进行，运行share_bottom.py即可得到预测结果 +本模型训练和预测交替进行，训练的同时可得到预测结果。 ## 模型效果 epoch设置为100的训练和测试效果如下： ```text -batch_size:[32],epochs:[100],feature_size:[499],bottom_size:[117],tower_nums:[2],tower_size:[8] +2020-04-16 16:01:04,-INFO: batch_size:[32],epochs:[100],feature_size:[499],bottom_size:[117],tower_nums:[2],tower_size:[8] 2020-04-16 16:01:04,- INFO - epoch_id: 0,epoch_time: 77.17624 s,loss: 0.62643,train_auc_income: 0.49442,train_auc_marital: 0.93509,test_auc_income: 0.50000,test_auc_marital: 0.93920 2020-04-16 16:02:23,- INFO - epoch_id: 1,epoch_time: 78.84795 s,loss: 0.47955,train_auc_income: 0.49721,train_auc_marital: 0.98118,test_auc_income: 0.50000,test_auc_marital: 0.98804 2020-04-16 16:03:43,- INFO - epoch_id: 2,epoch_time: 79.67485 s,loss: diff --git a/PaddleRec/multi-task/Share_bottom/args.py b/PaddleRec/multi-task/Share_bottom/args.py index 5ea0698dcf29a14d39e7ccd4cdd97f947a9ac3c2..93cf9240556a367edd7c0a95d8e447ff968909a8 100644 --- a/PaddleRec/multi-task/Share_bottom/args.py +++ b/PaddleRec/multi-task/Share_bottom/args.py @@ -26,7 +26,7 @@ def parse_args(): parser.add_argument("--bottom_size", type=int, default=117, help="bottom_size") parser.add_argument("--tower_nums", type=int, default=2, help="tower_nums") parser.add_argument("--tower_size", type=int, default=8, help="tower_size") - parser.add_argument("--epochs", type=int, default=400, help="epochs") + parser.add_argument("--epochs", type=int, default=100, help="epochs") parser.add_argument("--batch_size", type=int, default=32, help="batch_size") parser.add_argument('--use_gpu', type=int, default=0, help='whether using gpu') parser.add_argument('--train_data_path',type=str,default='train_data',help="train_data_path") @@ -38,9 +38,9 @@ def parse_args(): def data_preparation_args(): parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--train_path", type=str, default='', help="train_path") - parser.add_argument("--test_path", type=str, default='', help="test_path") - parser.add_argument('--train_data_path', type=str, default='train_data', help="train_data_path") - parser.add_argument('--test_data_path', type=str, default='test_data', help="test_data_path") + parser.add_argument("--train_path", type=str, default='data/census-income.data', help="train_path") + parser.add_argument("--test_path", type=str, default='data/census-income.test', help="test_path") + parser.add_argument('--train_data_path', type=str, default='train_data/', help="train_data_path") + parser.add_argument('--test_data_path', type=str, default='test_data/', help="test_data_path") args = parser.parse_args() return args diff --git a/PaddleRec/multi-task/Share_bottom/share_bottom.py b/PaddleRec/multi-task/Share_bottom/share_bottom.py index e0dcafbf94dba0b792cbbad613f901ece753de4e..f1df284a94e5e4de0fbcfbf4e9352923e94e2015 100644 --- a/PaddleRec/multi-task/Share_bottom/share_bottom.py +++ b/PaddleRec/multi-task/Share_bottom/share_bottom.py @@ -5,8 +5,13 @@ import os import time import datetime import utils +import logging from args import * +logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger("fluid") +logger.setLevel(logging.INFO) + def set_zero(var_name,scope=fluid.global_scope(),place=fluid.CPUPlace(),param_type="int64"): """ Set tensor of a Variable to zero. @@ -49,25 +54,25 @@ def share_bottom(feature_size=499,bottom_size=117,tower_nums=2,tower_size=8): name='output_layer_' + str(index)) output_layers.append(output_layer) - cost_income = paddle.fluid.layers.cross_entropy(input=output_layers[0], label=label_income,soft_label = True) - cost_marital = paddle.fluid.layers.cross_entropy(input=output_layers[1], label=label_marital,soft_label = True) + + pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15) + pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15) + + cost_income = paddle.fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True) + cost_marital = paddle.fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True) label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2]) label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2]) - pred_income = fluid.layers.clip(output_layers[0], min=1e-10, max=1.0 - 1e-10) - pred_marital = fluid.layers.clip(output_layers[1], min=1e-10, max=1.0 - 1e-10) - auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64')) auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64')) + + cost = fluid.layers.elementwise_add(cost_income, cost_marital, axis=1) - avg_cost_income = fluid.layers.mean(x=cost_income) - avg_cost_marital = fluid.layers.mean(x=cost_marital) - - cost = avg_cost_income + avg_cost_marital + avg_cost = fluid.layers.mean(x=cost) - return [a_data,label_income,label_marital],cost,output_layers[0],output_layers[1],label_income,label_marital,auc_income,auc_marital,auc_states_1,auc_states_2 + return [a_data,label_income,label_marital],avg_cost,output_layers[0],output_layers[1],label_income,label_marital,auc_income,auc_marital,auc_states_1,auc_states_2 @@ -81,7 +86,8 @@ tower_nums = args.tower_nums tower_size = args.tower_size epochs = args.epochs -print("batch_size:[%d],epochs:[%d],feature_size:[%d],bottom_size:[%d],tower_nums:[%d],tower_size:[%d]"%(batch_size,epochs,feature_size,bottom_size,tower_nums,tower_size)) +logger.info("batch_size:{} ,epochs:{} ,feature_size:{} ,bottom_size:{} ,tower_nums:{} ,tower_size:{} ".format( + batch_size,epochs,feature_size,bottom_size,tower_nums,tower_size)) train_reader = utils.prepare_reader(train_path,batch_size) test_reader = utils.prepare_reader(test_path,batch_size) @@ -142,14 +148,12 @@ for epoch in range(epochs): auc_income_list.append(test_auc_1_p) auc_marital_list.append(test_auc_2_p) end = time.time() - time_stamp = datetime.datetime.now() - print("%s,- INFO - epoch_id: %d,epoch_time: %.5f s,loss: %.5f,train_auc_income: %.5f,train_auc_marital: %.5f,test_auc_income: %.5f,test_auc_marital: %.5f"% - (time_stamp.strftime('%Y-%m-%d %H:%M:%S'),epoch,end - begin,loss_data,auc_1_p,auc_2_p,test_auc_1_p,test_auc_2_p)) - -time_stamp = datetime.datetime.now() -print("%s,- INFO - mean_sb_test_auc_income: %.5f,mean_sb_test_auc_marital %.5f,max_sb_test_auc_income: %.5f,max_sb_test_auc_marital %.5f"%( - time_stamp.strftime('%Y-%m-%d %H:%M:%S'),np.mean(auc_income_list),np.mean(auc_marital_list),np.max(auc_income_list),np.max(auc_marital_list))) + + logger.info("epoch_id:{},epoch_time:{} s,loss:{},train_auc_income:{},train_auc_marital:{},test_auc_income:{},test_auc_marital:{}".format( + epoch,end - begin,loss_data,auc_1_p,auc_2_p,test_auc_1_p,test_auc_2_p)) +logger.info("mean_sb_test_auc_income:{},mean_sb_test_auc_marital:{},max_sb_test_auc_income:{},max_sb_test_auc_marital:{}".format( + np.mean(auc_income_list),np.mean(auc_marital_list),np.max(auc_income_list),np.max(auc_marital_list))) diff --git a/PaddleRec/multi-task/Share_bottom/train_gpu.sh b/PaddleRec/multi-task/Share_bottom/train_gpu.sh index a8c6cb3dcabaea3235828185f29548194e6edb03..82c5f893873b731ff83fdf4497aaed621037f996 100644 --- a/PaddleRec/multi-task/Share_bottom/train_gpu.sh +++ b/PaddleRec/multi-task/Share_bottom/train_gpu.sh @@ -2,8 +2,6 @@ CUDA_VISIBLE_DEVICES=0 python share_bottom.py --use_gpu 1 \ --epochs 100 \ --train_data_path 'train_data' \ --test_data_path 'test_data' \ - --train_data_path '.train_data' \ - --test_data_path '.test_data' \ --model_dir 'model_dir' \ --batch_size 32 \ --feature_size 499 \