From 091d565a2a19f1c5a2f3d9cf11c8cce55c3cef69 Mon Sep 17 00:00:00 2001 From: Jia Guo Date: Sun, 24 Dec 2017 16:38:40 +0800 Subject: [PATCH] save if lfw>=998 --- src/train_softmax.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/src/train_softmax.py b/src/train_softmax.py index 3823fd4..5d5f30c 100644 --- a/src/train_softmax.py +++ b/src/train_softmax.py @@ -577,7 +577,7 @@ def train_net(args): print('VACC: %f'%(acc_value)) - highest_acc = [0.0] + highest_acc = [0.0, 0.0] #lfw and target #for i in xrange(len(ver_list)): # highest_acc.append(0.0) global_step = [0] @@ -612,8 +612,12 @@ def train_net(args): msave = save_step[0] do_save = False lfw_score = acc_list[0] - if acc_list[-1]>=highest_acc[0]: - highest_acc[0] = acc_list[-1] + if lfw_score>highest_acc[0]: + highest_acc[0] = lfw_score + if lfw_score>=0.998: + do_save = True + if acc_list[-1]>=highest_acc[-1]: + highest_acc[-1] = acc_list[-1] if lfw_score>=0.99: do_save = True #for i in xrange(len(acc_list)): @@ -635,7 +639,7 @@ def train_net(args): # X = np.concatenate(embeddings_list, axis=0) # print('saving lfw npy', X.shape) # np.save(lfw_npy, X) - print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[0])) + print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1])) if mbatch<=args.beta_freeze: _beta = args.beta else: