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"]))
