parser.add_argument('--time_limit', action="store", type=int, default=60)
parser.add_argument('--learning_rate',
action="store",
type=float,
default=0.001)
parser.add_argument('--train', action="store_true", default=False)
args = parser.parse_args()
arch = args.arch
nx = args.nx
ny = args.ny
time_limit = args.time_limit
learning_rate = args.learning_rate
rfc.the_print('Chosen architecture is: ' + args.arch + '; learning rate = ' +
str(learning_rate),
bgc='green')
name = arch + '_' + str(nx) + 'x' + str(ny)
model_add = './models/kat7_model_' + name
files_list = sorted(glob.glob('../../data/kat7/dataset2/training*.h5'))
rfc.the_print('number of files: ' + str(len(files_list)))
dpt = rfc.DataProvider(nx=nx,
ny=ny,
a_min=0,
a_max=200,
files=files_list,
label_name='mask')
from time import time
from sklearn.metrics import confusion_matrix
parser = argparse.ArgumentParser()
parser.add_argument('--arch', required=False, help='choose architecture', type=str, default='0')
#parser.add_argument('--trsh', required=False, help='choose threshold', type=float, default=0.1)
args = parser.parse_args()
arch = 'arch_'+args.arch+'_3class'
mode = 'one_hot'
thresholds = [1e-10, 0.1]
th_labels = [0,1,2]
rfc.the_print('Chosen architecture is: '+args.arch,bgc='green')
model_add = './models/multiclass_model_'+arch+'_'+mode
test_files = sorted(glob.glob('/home/anke/HIDE_simulations/hide_sims_test/calib_1month/*.fits'))
rfc.the_print('number of files: '+str(len(test_files)))
ws = 400
dp = rfc.DataProvider(files=test_files,label_name='RFI',
ny=ws,
one_hot=1,
thresholds=thresholds,
th_labels=th_labels,
a_min=0, a_max=200)
_,nx,ny,nc = dp(1)[1].shape
print(dp(1)[1].shape)