Python config.cfg.epoch() Examples

The following are 3 code examples of config.cfg.epoch(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module config.cfg , or try the search function .
Example #1
Source File: main.py    From DFN-tensorflow with Apache License 2.0 5 votes vote down vote up 
def main(_):
	
	# get dataset info
	result = create_image_lists(cfg.images)
	max_iters = len(result["train"]) * cfg.epoch // cfg.batch_size
	
	tf.logging.info('Loading Graph...')
	model = DFN(max_iters, batch_size=cfg.batch_size, init_lr=cfg.init_lr, power=cfg.power, momentum=cfg.momentum, stddev=cfg.stddev, regularization_scale=cfg.regularization_scale, alpha=cfg.alpha, gamma=cfg.gamma, fl_weight=cfg.fl_weight)
	tf.logging.info('Graph loaded.')
	
	if cfg.is_training:
		
		if not tf.gfile.Exists(cfg.logdir):
			
			tf.gfile.MakeDirs(cfg.logdir)
		
		if not tf.gfile.Exists(cfg.models):
			
			tf.gfile.MakeDirs(cfg.models)
		
		if os.path.exists(cfg.log):
			
			os.remove(cfg.log)
		
		fd = open(cfg.log, "a")
		tf.logging.info('Start training...')
		fd.write('Start training...\n')
		train(result, model, cfg.logdir, cfg.train_sum_freq, cfg.val_sum_freq, cfg.save_freq, cfg.models, fd)
		tf.logging.info('Training done.')
		fd.write('Training done.')
		fd.close()
	
	else:
		
		if not tf.gfile.Exists(cfg.test_outputs):
			
			tf.gfile.MakeDirs(cfg.test_outputs)
		
		tf.logging.info('Start testing...')
		test(result, model, cfg.models, cfg.test_outputs)
		tf.logging.info('Testing done.')
Example #2
Source File: main.py    From capsule-networks with MIT License 4 votes vote down vote up 
def train(model, supervisor, num_label):
    trX, trY, num_tr_batch, valX, valY, num_val_batch = load_data(cfg.dataset, cfg.batch_size, is_training=True)
    Y = valY[:num_val_batch * cfg.batch_size].reshape((-1, 1))

    fd_train_acc, fd_loss, fd_val_acc = save_to()
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    with supervisor.managed_session(config=config) as sess:
        print("\nNote: all of results will be saved to directory: " + cfg.results)
        for epoch in range(cfg.epoch):
            print('Training for epoch ' + str(epoch) + '/' + str(cfg.epoch) + ':')
            if supervisor.should_stop():
                print('supervisor stoped!')
                break
            for step in tqdm(range(num_tr_batch), total=num_tr_batch, ncols=70, leave=False, unit='b'):
                start = step * cfg.batch_size
                end = start + cfg.batch_size
                global_step = epoch * num_tr_batch + step

                if global_step % cfg.train_sum_freq == 0:
                    _, loss, train_acc, summary_str = sess.run([model.train_op, model.total_loss, model.accuracy, model.train_summary])
                    assert not np.isnan(loss), 'Something wrong! loss is nan...'
                    supervisor.summary_writer.add_summary(summary_str, global_step)

                    fd_loss.write(str(global_step) + ',' + str(loss) + "\n")
                    fd_loss.flush()
                    fd_train_acc.write(str(global_step) + ',' + str(train_acc / cfg.batch_size) + "\n")
                    fd_train_acc.flush()
                else:
                    sess.run(model.train_op)

                if cfg.val_sum_freq != 0 and (global_step) % cfg.val_sum_freq == 0:
                    val_acc = 0
                    for i in range(num_val_batch):
                        start = i * cfg.batch_size
                        end = start + cfg.batch_size
                        acc = sess.run(model.accuracy, {model.X: valX[start:end], model.labels: valY[start:end]})
                        val_acc += acc
                    val_acc = val_acc / (cfg.batch_size * num_val_batch)
                    fd_val_acc.write(str(global_step) + ',' + str(val_acc) + '\n')
                    fd_val_acc.flush()

            if (epoch + 1) % cfg.save_freq == 0:
                supervisor.saver.save(sess, cfg.logdir + '/model_epoch_%04d_step_%02d' % (epoch, global_step))

        fd_val_acc.close()
        fd_train_acc.close()
        fd_loss.close()
Example #3
Source File: main.py    From CapsNet-Tensorflow with Apache License 2.0 4 votes vote down vote up 
def train(model, supervisor, num_label):
    trX, trY, num_tr_batch, valX, valY, num_val_batch = load_data(cfg.dataset, cfg.batch_size, is_training=True)
    Y = valY[:num_val_batch * cfg.batch_size].reshape((-1, 1))

    fd_train_acc, fd_loss, fd_val_acc = save_to()
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    with supervisor.managed_session(config=config) as sess:
        print("\nNote: all of results will be saved to directory: " + cfg.results)
        for epoch in range(cfg.epoch):
            print("Training for epoch %d/%d:" % (epoch, cfg.epoch))
            if supervisor.should_stop():
                print('supervisor stoped!')
                break
            for step in tqdm(range(num_tr_batch), total=num_tr_batch, ncols=70, leave=False, unit='b'):
                start = step * cfg.batch_size
                end = start + cfg.batch_size
                global_step = epoch * num_tr_batch + step

                if global_step % cfg.train_sum_freq == 0:
                    _, loss, train_acc, summary_str = sess.run([model.train_op, model.total_loss, model.accuracy, model.train_summary])
                    assert not np.isnan(loss), 'Something wrong! loss is nan...'
                    supervisor.summary_writer.add_summary(summary_str, global_step)

                    fd_loss.write(str(global_step) + ',' + str(loss) + "\n")
                    fd_loss.flush()
                    fd_train_acc.write(str(global_step) + ',' + str(train_acc / cfg.batch_size) + "\n")
                    fd_train_acc.flush()
                else:
                    sess.run(model.train_op)

                if cfg.val_sum_freq != 0 and (global_step) % cfg.val_sum_freq == 0:
                    val_acc = 0
                    for i in range(num_val_batch):
                        start = i * cfg.batch_size
                        end = start + cfg.batch_size
                        acc = sess.run(model.accuracy, {model.X: valX[start:end], model.labels: valY[start:end]})
                        val_acc += acc
                    val_acc = val_acc / (cfg.batch_size * num_val_batch)
                    fd_val_acc.write(str(global_step) + ',' + str(val_acc) + '\n')
                    fd_val_acc.flush()

            if (epoch + 1) % cfg.save_freq == 0:
                supervisor.saver.save(sess, cfg.logdir + '/model_epoch_%04d_step_%02d' % (epoch, global_step))

        fd_val_acc.close()
        fd_train_acc.close()
        fd_loss.close()