import numpy as np sys.path.append( "/home/zebo/git/myRep/Kaggle/Kaggle-DataScience-Bowl/pkugoodspeed/models") sys.path.append("/home/zebo/git/myRep/Kaggle/Kaggle-DataScience-Bowl/utils") from process import ImagePrec from resnet import ResNet from unet import UNet from uresnet import UResNet from opts_parser import getopts TRAIN_PATH = "/home/zebo/git/myRep/Kaggle/Kaggle-DataScience-Bowl/data/train" TEST_PATH = "/home/zebo/git/myRep/Kaggle/Kaggle-DataScience-Bowl/data/test" if __name__ == '__main__': C = getopts() ip = ImagePrec(path=TRAIN_PATH, size=C['proc']['size'], channel=3, normalize=C['proc']['normalize']) n_img = ip.get_num() train_x, train_y = ip.get_batch_resized( train_idx=[i for i in range(n_img)]) if C['augment']: train_x, train_y = ip.augment(train_x, train_y) # resn = ResNet(input_shape=(C['proc']['size'], C['proc']['size'], 3)) # resn = UNet(input_shape=(C['proc']['size'], C['proc']['size'], 3)) resn = UResNet(input_shape=(C['proc']['size'], C['proc']['size'], 3)) resn.build_model(**C['model_kargs']) resn.fit(x=train_x, y=train_y, **C['fit_kargs']) model = resn.get_model()
import pandas as pd import json from models import KerasModel import opts_parser from features import from sampler import sample_market_data if __name__ == '__main__': train_data_file, test_data_file, config_file = opts_parser.getopts() train = pd.read_csv(train_data_file) test = pd.read_csv(test_data_file) ## Read From Config file cfg = json.load(open(config_file)) print cfg preprc_kargs = cfg["preprc_kargs"] train_x, train_y, valid_x, valid_y, test = preprocess.embProcess(train, test, **preprc_kargs) print train_x.shape print train_y.shape print valid_x.shape print valid_y.shape keras_model = KerasModel(input_shape=train_x[0].shape, output_dim=len(train_y[0])) model_kargs = cfg["model_kargs"] model = keras_model.getModel(model_kargs["model_type"], **model_kargs["kargs"]) model.summary() history = keras_model.train(train_x, train_y, valid_x, valid_y, **cfg["train_kargs"]) output_file="{0}_{1}_convergence.png".format(cfg['model_name'], '.'.join(cfg["preprc_kargs"]["target_list"]))