训练集+测试集（图像识别）.7z

from paddle.trainer.PyDataProvider2 import * import sys import numpy as np TERM_NUM = 24 FORECASTING_NUM = 24 LABEL_VALUE_NUM = 4 def initHook(settings, file_list, **kwargs): """ Init hook is invoked before process data. It will set obj.slots and store data meta. :param settings: global object. It will passed to process routine. :type obj: object :param file_list: the meta file object, which passed from trainer_config.py,but unused in this function. :param kwargs: unused other arguments. """ del kwargs #unused settings.pool_size = sys.maxint #Use a time seires of the past as feature. #Dense_vector's expression form is [float,float,...,float] settings.input_types = [dense_vector(TERM_NUM)] #There are next FORECASTING_NUM fragments you need predict. #Every predicted condition at time point has four states. for i in range(FORECASTING_NUM): settings.input_types.append(integer_value(LABEL_VALUE_NUM)) @provider( init_hook=initHook, cache=CacheType.CACHE_PASS_IN_MEM, should_shuffle=True) def process(settings, file_name): with open(file_name) as f: #abandon fields name f.next() for row_num, line in enumerate(f): speeds = map(int, line.rstrip('\r\n').split(",")[1:]) # Get the max index. end_time = len(speeds) # Scanning and generating samples for i in range(TERM_NUM, end_time - FORECASTING_NUM): # For dense slot pre_spd = map(float, speeds[i - TERM_NUM:i]) # Integer value need predicting, values start from 0, so every one minus 1. fol_spd = [j - 1 for j in speeds[i:i + FORECASTING_NUM]] # Predicting label is missing, abandon the sample. if -1 in fol_spd: continue yield [pre_spd] + fol_spd def predict_initHook(settings, file_list, **kwargs): settings.pool_size = sys.maxint settings.input_types = [dense_vector(TERM_NUM)] @provider(init_hook=predict_initHook, should_shuffle=False) def process_predict(settings, file_name): with open(file_name) as f: #abandon fields name f.next() for row_num, line in enumerate(f): speeds = map(int, line.rstrip('\r\n').split(",")) end_time = len(speeds) pre_spd = map(float, speeds[end_time - TERM_NUM:end_time]) yield pre_spd