Model:
  batch_size: 64
  dropout_rate: 0.2
  epoch: 50
  hidden_size: 64
  learning_rate: 0.001
  lstm_layers: 2
  patience: 5
  random_seed: 42
  time_step: 16
add_train: false
cap: 50
continue_flag: 'xiushui'
data_format:
  cdq: cdq.csv
  dq: dq.csv
  test: xiushui_test.csv
  envir: envir.xlsx
  nwp: xiushui_train.csv
  rp: rp.xlsx
  rp1: power.csv
debug_model: false
debug_num: 500
do_continue_train: false
do_figure_save: false
do_log_print_to_screen: true
do_log_save_to_file: true
do_predict: true
do_train: true
do_train_visualized: true
electricType: E1
envir_columns: 16
excel_data_path: ./data/xiushui/xiushui15/
figure_save_path: ./figure/
formulaType: DAY_SHORT_ACCURACY
is_continuous_predict: true
is_photovoltaic: true
log_save_path: ./log/

model_postfix:
  keras: .h5
  pytorch: .pthc
  tensorflow: .ckpt
predict_point: 0
province: E13
save_frame: lstm
shuffle_train_data: false
stationCode: J00301

train_data_path: ./data/
train_data_rate: 0.9
use_cuda: false
used_frame: keras
valid_data_rate: 0.15