def main(args):
print('---------------------ARGS---------------------')
print('--train_batch_size : %d' % args.train_batch_size)
print('--train_epoch : %d' % args.train_epoch)
print('--model_base_dir : %s' % args.model_base_dir)
print('--log_base_dir : %s' % args.log_base_dir)
print('--learning_rate : %f' % args.learning_rate)
print('--width : %d' % args.width)
print('--height : %d' % args.height)
print('--save_steps : %d' % args.save_steps)
print('--val_steps : %d' % args.val_steps)
print('--log_steps : %d' % args.log_steps)
print('--train_imgs_dir : %s' % args.train_imgs_dir)
print('--test_imgs_dir : %s' % args.test_imgs_dir)
print('---------------------END----------------------')
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir_today = os.path.join(os.path.expanduser(args.log_base_dir), subdir)
if not os.path.isdir(log_dir_today):
os.makedirs(log_dir_today)
model_dir = os.path.join(os.path.expanduser(args.model_base_dir), subdir)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
print('Model directory: %s' % model_dir)
print('Log directory : %s' % log_dir_today)
print('Load train and validation data...')
train_imgs_dir_list = [os.path.join(args.train_imgs_dir, l)
for l in os.listdir(os.path.expanduser(args.train_imgs_dir))]
test_imgs_dir_list = [os.path.join(args.test_imgs_dir, l)
for l in os.listdir(os.path.expanduser(args.test_imgs_dir))]
assert len(train_imgs_dir_list) != 0 or len(test_imgs_dir_list) != 0
train_x, train_y = utils.prepare_dataset(train_imgs_dir_list)
test_x, test_y = utils.prepare_dataset(test_imgs_dir_list)
train_ds = utils.create_inputs(train_x, train_y, batch_size=args.train_batch_size)
test_ds = utils.create_inputs(test_x, test_y, batch_size=args.train_batch_size, train_data=False)
print('Create model...')
model = create_model()
print('Callbacks for tensorboard...')
tb = tf.keras.callbacks.TensorBoard(log_dir=log_dir_today, histogram_freq=1, write_images=True)
st = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
print('Compile model...')
model.compile(optimizer='SGD', loss='sparse_categorical_crossentropy', metrics=['accuracy'],
learning_rate=args.learning_rate)
print('Train model with args...')
h = model.fit(train_ds, epochs=args.train_epoch, validation_data=test_ds, callbacks=[tb])
print('Done, save model to: %s' % os.path.join(model_dir, 'cafar10.h5'))
model.save(os.path.join(model_dir, 'cafar10.h5'))
def main(_):
with tf.Graph().as_default():
num_batches_per_epoch_train = int(60000 / cfg.batch_size)
num_batches_test = int(10000 / cfg.batch_size)
batch_x, batch_labels = create_inputs(is_train=False)
output = net.build_arch(batch_x, is_train=False)
batch_acc = net.test_accuracy(output, batch_labels)
saver = tf.train.Saver()
step = 0
summaries = []
summaries.append(tf.summary.scalar('accuracy', batch_acc))
summary_op = tf.summary.merge(summaries)
with tf.Session() as sess:
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(cfg.test_logdir,
graph=sess.graph)
for epoch in range(cfg.epoch):
ckpt = os.path.join(
cfg.logdir,
'model.ckpt-%d' % (num_batches_per_epoch_train * epoch))
saver.restore(sess, ckpt)
for i in range(num_batches_test):
summary_str = sess.run(summary_op)
print('%d batches are tested.' % step)
summary_writer.add_summary(summary_str, step)
step += 1
def main(_):
with tf.Graph().as_default(), tf.device('/cpu:0'):
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
num_batches_per_epoch = int(60000 / cfg.batch_size)
opt = tf.train.AdamOptimizer()
batch_x, batch_labels = create_inputs(is_train=True)
# batch_y = tf.one_hot(batch_labels, depth=10, axis=1, dtype=tf.float32)
with tf.device('/gpu:0'):
with slim.arg_scope([slim.variable], device='/cpu:0'):
output = net.build_arch(batch_x, is_train=True)
loss = net.cross_ent_loss(output, batch_labels)
grad = opt.compute_gradients(loss)
loss_name = 'cross_ent_loss'
summaries = []
summaries.append(tf.summary.scalar(loss_name, loss))
train_op = opt.apply_gradients(grad, global_step=global_step)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False))
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=cfg.epoch)
#read snapshot
# latest = os.path.join(cfg.logdir, 'model.ckpt-4680')
# saver.restore(sess, latest)
summary_op = tf.summary.merge(summaries)
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(cfg.logdir, graph=sess.graph)
for step in range(cfg.epoch * num_batches_per_epoch):
tic = time.time()
_, loss_value = sess.run([train_op, loss])
print('%d iteration is finished in ' % step + '%f second' %
(time.time() - tic))
# test1_v = sess.run(test2)
# if np.isnan(loss_value):
# print('bbb')
# assert not np.isnan(np.any(test2_v[0])), 'a is nan'
assert not np.isnan(loss_value), 'loss is nan'
if step % 10 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
if (step % num_batches_per_epoch) == 0:
ckpt_path = os.path.join(cfg.logdir, 'model.ckpt')
saver.save(sess, ckpt_path, global_step=step)