run problem

[Hzither]

i set 100 epochs ,but only run one epoch

[LongxingTan]

@Hzither

Hi, can you copy more your training code?

from the screen shot, it seems fine. Epoch 1/100

[Hzither]

我是直接用的你那个代码，没有做任何修改，把你给的那个数据下载导入到raw后直接在nn.train开始运行，但是不管怎么修改epoch都这样 ---- Replied Message ---- | From | ***@***.***> | | Date | 09/19/2023 12:42 | | To | LongxingTan/Time-series-prediction ***@***.***> | | Cc | Hzither ***@***.***>, Mention ***@***.***> | | Subject | Re: [LongxingTan/Time-series-prediction] run problem (Issue #46) | @Hzither Hi, can you copy more your training code? from the screen shot, it seems fine. Epoch 1/100 — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***>

[Hzither]

this is code of nn.train """ train the time series model """ import sys sys.path.insert(0, '../Time-series-prediction') sys.path.insert(1, '../utils') sys.path.insert(2, './configs') sys.path.insert(3, './dataset') import argparse import warnings import functools import importlib from copy import copy import numpy as np import pandas as pd import tensorflow as tf from tensorflow.keras.layers import Input from tensorflow.keras.callbacks import ModelCheckpoint import matplotlib.pyplot as plt import joblib from tfts import build_tfts_model, KerasTrainer from read_data import DataReader, DataLoader from prepare_data import get_idx_from_days2 from util import set_seed, compress_submitted_zip from feature import * warnings.filterwarnings("ignore") np.random.seed(315) physical_devices = tf.config.list_physical_devices('GPU') for device in physical_devices: tf.config.experimental.set_memory_growth(device, True) # ----------------------------config----------------------------------- def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("-C", "--config", default='bert_v1', help="config filename") parser.add_argument("--seed", type=int, default=3150, required=False, help='seed') parser.add_argument("--debug", type=lambda x: (str(x).lower() == 'true'), default=False, required=False, help='debug or not') return parser.parse_args() # ----------------------------data----------------------------------- def build_data(cfg): """ prepare data pipe for model """ data = pd.read_csv(cfg.base_dir + 'raw/wtbdata_245days.csv') if args.debug: data = data.loc[data['TurbID'].isin([100])] print(data.shape, np.max(data['Day'])) data['start_time'] = data['Day'].astype(str) + ' ' + data['Tmstamp'].astype(str) # Flag of the first of 288 valid_df_raw = data.loc[data['Day'].isin(cfg.valid_days)] # for metrics valid_df_raw.to_pickle('../../data/user_data/valid_df_raw.pkl') data[['Wspd', 'Wdir', 'Patv']] = data.groupby(['TurbID'])[['Wspd', 'Wdir', 'Patv']].apply(lambda x: x.ffill().bfill()) data['Hour'] = pd.to_datetime(data['Tmstamp'], format='%H:%M').dt.hour data['Minute'] = pd.to_datetime(data['Tmstamp'], format='%H:%M').dt.minute data['MinuteofDay'] = data['Hour'] * 6 + data['Minute'] / 10 data['DayofWeek'] = data['Day'] // 7 data['is_valid'] = 1 # 1 means valid # if cfg.scaler: # data[['Wspd', 'Wdir', 'Prtv']] = cfg.scaler.fit_transform(data[['Wspd', 'Wdir', 'Prtv']]) # joblib.dump(cfg.scaler, '../../submit/checkpoints/feature_scaler.pkl') # if cfg.target_scaler: # data[['Patv']] = cfg.target_scaler.fit_transform(data[['Patv']]) # joblib.dump(cfg.target_scaler, '../../submit/checkpoints/target_scaler.pkl') feature_cols = [] data = get_lag_rolling_feature(data, roll_column='Wspd', period=6, lags=[6, 144], id_col='TurbID', agg_funs=['mean', 'max'], feature_cols=feature_cols) cfg.feature_column_short += feature_cols data = data.reset_index(drop=True) # if sampling, update the index target_column_idx = [data.columns.get_loc(c) for c in cfg.target_column] feature_column_idx_short = [data.columns.get_loc(c) for c in cfg.feature_column_short] feature_column_idx_long = [data.columns.get_loc(c) for c in cfg.feature_column_long] train_idx = get_idx_from_days2( data, cfg.train_days, day_columns='Day', mode='train', train_sequence_length=cfg.train_sequence_length, predict_sequence_length=cfg.predict_sequence_length, strides=cfg.train_strides, max_lags=288) train_data_reader = DataReader( data, cfg.train_sequence_length, cfg.predict_sequence_length, idx=train_idx, target_aggs=cfg.target_aggs, target_column_idx=target_column_idx, feature_column_idx_short=feature_column_idx_short, feature_column_idx_long=feature_column_idx_long) train_data_loader = DataLoader( train_data_reader, len(cfg.feature_column_short), len(cfg.feature_column_long))(batch_size=cfg.fit_params['batch_size'], shuffle=True, drop_remainder=True) valid_idx = get_idx_from_days2( data, cfg.valid_days, day_columns='Day', mode='valid', train_sequence_length=cfg.train_sequence_length, predict_sequence_length=cfg.predict_sequence_length, strides=cfg.valid_strides) valid_data_reader = DataReader( data, cfg.train_sequence_length, cfg.predict_sequence_length, idx=valid_idx, target_aggs=cfg.target_aggs, target_column_idx=target_column_idx, feature_column_idx_short=feature_column_idx_short, feature_column_idx_long=feature_column_idx_long) valid_data_loader = DataLoader( valid_data_reader, len(cfg.feature_column_short), len(cfg.feature_column_long))(batch_size=cfg.fit_params['batch_size'] * 2, shuffle=False) valid_df = data.iloc[valid_idx] return train_data_reader, train_data_loader, valid_data_reader, valid_data_loader, valid_df # ----------------------------model----------------------------------- class KDD(object): def __init__(self, train_sequence_length, predict_sequence_length) -> None: self.train_sequence_length = train_sequence_length self.predict_sequence_length = predict_sequence_length def __call__(self, x, **kwargs): _, raw, raw_long = x # feature is here raw, manual = tf.split(raw, [10, tf.shape(raw)[-1]-10], axis=-1) wind_speed, wind_dir, _, _, _, _, _, _, _, active_power = tf.split(raw, 10, axis=-1) manual = tf.where(tf.math.is_nan(manual), tf.zeros_like(manual), manual) day_of_week, hour_feature, minute_of_day = tf.split(raw_long, 3, axis=-1) hour_feature = hour_feature / 23 - 0.5 minute_of_day = minute_of_day / 143 - 0.5 day_of_week = day_of_week / 6 - 0.5 _, decoder_hour_feature = tf.split(hour_feature, [self.train_sequence_length, self.predict_sequence_length], axis=1) _, decoder_minute_feature = tf.split(minute_of_day, [self.train_sequence_length, self.predict_sequence_length], axis=1) _, decoder_day_feature = tf.split(day_of_week, [self.train_sequence_length, self.predict_sequence_length], axis=1) encoder_features = tf.concat([wind_speed, wind_dir], axis=-1) decoder_features = tf.concat([decoder_hour_feature, decoder_minute_feature, decoder_day_feature], axis=-1) decoder_features = tf.cast(decoder_features, tf.float32) print('Feature shape', encoder_features.shape, decoder_features.shape) return active_power, encoder_features, decoder_features def build_model(use_model, train_sequence_length, predict_sequence_length=288, target_aggs=1, short_feature_nums=10, long_feature_nums=1): inputs = ( Input([1]), Input([train_sequence_length, short_feature_nums]), # raw feature numbers Input([train_sequence_length+predict_sequence_length, long_feature_nums]) # long feature ) teacher_inputs = Input([predict_sequence_length//target_aggs, 1]) ts_inputs = KDD(train_sequence_length, predict_sequence_length)(inputs) outputs = build_tfts_model( use_model=use_model, predict_sequence_length=predict_sequence_length//target_aggs, custom_model_params=cfg.custom_model_params)(ts_inputs, teacher_inputs) model = tf.keras.Model(inputs={'inputs':inputs, 'teacher': teacher_inputs}, outputs=outputs) return model def custom_loss(y_true, y_pred): true, mask = tf.split(y_true, 2, axis=-1) mask = tf.cast(mask, dtype=tf.float32) true *= mask y_pred *= mask rmse_score = tf.math.sqrt(tf.reduce_mean(tf.square(true - y_pred)) + 1e-9) return rmse_score # ----------------------------train----------------------------------- def run_train(cfg): train_data_reader, train_data_loader, valid_data_reader, valid_data_loader, valid_df = build_data(cfg) print(len(train_data_reader), len(valid_data_reader), len(valid_df)) build_model_fn = functools.partial( build_model, cfg.use_model, train_sequence_length=cfg.train_sequence_length, predict_sequence_length=cfg.predict_sequence_length, target_aggs=cfg.target_aggs, short_feature_nums=len(cfg.feature_column_short), long_feature_nums=len(cfg.feature_column_long) ) optimizer = tf.keras.optimizers.Adam(learning_rate = cfg.fit_params['learning_rate']) loss_fn = custom_loss trainer = KerasTrainer(build_model_fn, loss_fn=loss_fn, optimizer=optimizer, strategy=None) trainer.train(train_data_loader, valid_dataset=valid_data_loader, **cfg.fit_params) trainer.save_model( model_dir=cfg.model_dir + '/checkpoints/{}_{}'.format(cfg.use_model, args.seed), checkpoint_dir=cfg.checkpoint_dir+'/nn_{}.h5'.format(cfg.use_model) ) compress_submitted_zip(res_dir='../inference', output_dir='../../weights/result') # save to submit if __name__ == '__main__': args = parse_args() cfg = copy(importlib.import_module(args.config).cfg) set_seed(args.seed) cfg.fit_params = { 'n_epochs': 100, 'batch_size': 128, 'learning_rate': 5e-3, 'verbose': 1, 'checkpoint': ModelCheckpoint( cfg.checkpoint_dir+'/nn_{}.h5'.format(cfg.use_model), monitor='val_loss', save_weights_only=True, save_best_only=False, verbose=1), } run_train(cfg) | | 周航 | | ***@***.*** | ---- Replied Message ---- | From | ***@***.***> | | Date | 9/19/2023 12:42 | | To | ***@***.***> | | Cc | ***@***.***> , ***@***.***> | | Subject | Re: [LongxingTan/Time-series-prediction] run problem (Issue #46) | @Hzither Hi, can you copy more your training code? from the screen shot, it seems fine. Epoch 1/100 — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***>

[LongxingTan]

是不是跑的时间比较长, 其实还在跑？我用了kaggle一份上传的数据，跑了三个epoch是没有问题的。不过上传的人改了一些代码导致后来会报错

https://www.kaggle.com/code/tanlongxing/kdd-cup-2022-wind-power-forecast

[Hzither]

抱歉抱歉，感谢你的回复，是我自己的问题，之前用的是torch，新装的tensorflow和cude的版本对不上，是版本的问题 | | 周航 | | ***@***.*** | ---- Replied Message ---- | From | ***@***.***> | | Date | 9/19/2023 17:30 | | To | ***@***.***> | | Cc | ***@***.***> , ***@***.***> | | Subject | Re: [LongxingTan/Time-series-prediction] run problem (Issue #46) | 是不是跑的时间比较长, 其实还在跑？我用了kaggle一份上传的数据，跑了三个epoch是没有问题的。不过上传的人改了一些代码导致后来会报错 https://www.kaggle.com/code/tanlongxing/kdd-cup-2022-wind-power-forecast — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***>

[Hzither]

抱歉，我比较新手，训练生成result.zip之后，我该怎么用测试集查看结果呢 | | 周航 | | ***@***.*** | ---- Replied Message ---- | From | ***@***.***> | | Date | 9/19/2023 17:30 | | To | ***@***.***> | | Cc | ***@***.***> , ***@***.***> | | Subject | Re: [LongxingTan/Time-series-prediction] run problem (Issue #46) | 是不是跑的时间比较长, 其实还在跑？我用了kaggle一份上传的数据，跑了三个epoch是没有问题的。不过上传的人改了一些代码导致后来会报错 https://www.kaggle.com/code/tanlongxing/kdd-cup-2022-wind-power-forecast — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***>

[LongxingTan]

当时是把这个压缩文件传到网上提交出分了。可以根据预测代码，自己从本地数据里给预测

[Hzither]

好的，感谢 | | 周航 | | ***@***.*** | ---- Replied Message ---- | From | ***@***.***> | | Date | 9/19/2023 20:36 | | To | ***@***.***> | | Cc | ***@***.***> , ***@***.***> | | Subject | Re: [LongxingTan/Time-series-prediction] run problem (Issue #46) | 当时是把这个压缩文件传到网上提交出分了。可以根据预测代码，自己从本地数据里给预测 — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***>