From aca8ab8e7d30f1f79829d57897238469df5468b0 Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Mon, 19 Jun 2017 08:33:11 +0200 Subject: [PATCH] Added --nb_validation_samples and --validation_error_threshold to terminate learning when the validation error gets under a threshold. --- cnn-svrt.py | 63 +++++++++++++++++++++++++++++++++++++---------------- 1 file changed, 44 insertions(+), 19 deletions(-) diff --git a/cnn-svrt.py b/cnn-svrt.py index e5ecf76..f3d350e 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -56,6 +56,13 @@ parser.add_argument('--nb_train_samples', parser.add_argument('--nb_test_samples', type = int, default = 10000) +parser.add_argument('--nb_validation_samples', + type = int, default = 10000) + +parser.add_argument('--validation_error_threshold', + type = float, default = 0.0, + help = 'Early training termination criterion') + parser.add_argument('--nb_epochs', type = int, default = 50) @@ -194,7 +201,22 @@ class AfrozeDeepNet(nn.Module): ###################################################################### -def train_model(model, train_set): +def nb_errors(model, data_set): + ne = 0 + for b in range(0, data_set.nb_batches): + input, target = data_set.get_batch(b) + output = model.forward(Variable(input)) + wta_prediction = output.data.max(1)[1].view(-1) + + for i in range(0, data_set.batch_size): + if wta_prediction[i] != target[i]: + ne = ne + 1 + + return ne + +###################################################################### + +def train_model(model, train_set, validation_set): batch_size = args.batch_size criterion = nn.CrossEntropyLoss() @@ -216,25 +238,24 @@ def train_model(model, train_set): loss.backward() optimizer.step() dt = (time.time() - start_t) / (e + 1) + log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss), ' [ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + ']') - return model + if validation_set is not None: + nb_validation_errors = nb_errors(model, validation_set) -###################################################################### + log_string('validation_error {:.02f}% {:d} {:d}'.format( + 100 * nb_validation_errors / validation_set.nb_samples, + nb_validation_errors, + validation_set.nb_samples) + ) -def nb_errors(model, data_set): - ne = 0 - for b in range(0, data_set.nb_batches): - input, target = data_set.get_batch(b) - output = model.forward(Variable(input)) - wta_prediction = output.data.max(1)[1].view(-1) + if nb_validation_errors / validation_set.nb_samples <= args.validation_error_threshold: + log_string('below validation_error_threshold') + break - for i in range(0, data_set.batch_size): - if wta_prediction[i] != target[i]: - ne = ne + 1 - - return ne + return model ###################################################################### @@ -329,7 +350,15 @@ for problem_number in map(int, args.problems.split(',')): train_set.nb_samples / (time.time() - t)) ) - train_model(model, train_set) + if args.validation_error_threshold > 0.0: + validation_set = VignetteSet(problem_number, + args.nb_validation_samples, args.batch_size, + cuda = torch.cuda.is_available(), + logger = vignette_logger()) + else: + validation_set = None + + train_model(model, train_set, validation_set) torch.save(model.state_dict(), model_filename) log_string('saved_model ' + model_filename) @@ -353,10 +382,6 @@ for problem_number in map(int, args.problems.split(',')): args.nb_test_samples, args.batch_size, cuda = torch.cuda.is_available()) - log_string('data_generation {:0.2f} samples / s'.format( - test_set.nb_samples / (time.time() - t)) - ) - nb_test_errors = nb_errors(model, test_set) log_string('test_error {:d} {:.02f}% {:d} {:d}'.format( -- 2.39.5