trainer = unet.Trainer(net, optimizer="momentum", opt_kwargs=dict(momentum=0.2)) path = trainer.train(dp, model_dir, training_iters=training_iters, epochs=epochs, dropout=0.5, display_step=1000000) else: test_files = sorted(glob.glob('../data/data/test/*.h5')) pred_dir = 'predictions/unet_' + str(features_root) + '/' rfc.ch_mkdir(pred_dir) res_file = 'results/unet_' + str(features_root) + '_' + str(threshold) rfc.ch_mkdir('results') times = [] pr_list = [] roc_list = [] auc_list = [] clrs = ['b', 'r', 'g', 'k'] trsh = np.linspace(1, 0, 300, endpoint=1) for fil in test_files: fname = fil.split('/')[-1] dp = rfc.DataProvider(a_min=0,
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) conv = rfc.ConvolutionalLayers(nx=nx,ny=ny,n_channel=1,n_class=nc, restore=os.path.exists(model_add), model_add=model_add, arch_file_name=arch) res_file = 'results/threeclass_'+arch+'_'+mode rfc.ch_mkdir('results') rfc.ch_mkdir('predictions') pred_dir = 'predictions/' times = [] cnf_matrix = [] for fil in test_files: fname = fil.split('/')[-1] dp = rfc.DataProvider(files=[fil],label_name='RFI', one_hot=1, thresholds=thresholds, th_labels=th_labels, a_min=0, a_max=200) data,mask = dp(1)
conv.train(data_provider=dp, training_epochs=10000000, n_s=n_rounds, learning_rate=learning_rate, dropout=0.7, time_limit=time_limit // n_rounds, verbose=1) learning_rate = learning_rate / 4. else: import pickle # pred_dir = 'predictions/threeclass_'+arch+'_'+mode+'/' # rfc.ch_mkdir(pred_dir) res_file = 'results/threeclass_' + arch + '_' + mode rfc.ch_mkdir('results') # weights = conv.get_filters() # with open(res_file+'_filters', 'w') as filehandler: # pickle.dump(weights, filehandler) # np.save(res_file+'_filters',weights) test_files = sorted( glob.glob('../../../data/hide_sims_test/calib_1month/*.fits')) times = [] pr_list = [] roc_list = [] auc_list = [] cnf_matrix = []
import matplotlib as mpl mpl.use('agg') import os import glob import numpy as np import pylab as plt from matplotlib.colors import LogNorm import rficnn as rfc dss = ['training', 'validation', 'test'] rfc.ch_mkdir('plots') for ds in dss: test_files = sorted(glob.glob('../../../data/kat7/dataset/' + ds + '/*.h5')) for fil in test_files: fname = fil.split('/')[-1] dp = rfc.DataProvider(a_min=0, a_max=100, files=[fil], label_name='mask') data, mask = dp(1) mask = mask[0, :, :, 0] fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))