def train():
train_dir='/home/daijiaming/Galaxy/data3/trainset/'
train_label_dir='/home/daijiaming/Galaxy/data3/train_label.csv'
test_dir='/home/daijiaming/Galaxy/data3/testset/'
test_label_dir='/home/daijiaming/Galaxy/data3/test_label.csv'
train_log_dir = '/home/daijiaming/Galaxy/Dieleman/logs/train/'
val_log_dir = '/home/daijiaming/Galaxy/Dieleman/logs//val/'
tra_image_batch, tra_label_batch,tra_galalxyid_batch = input_data.read_galaxy11(data_dir=train_dir,
label_dir=train_label_dir,
batch_size= BATCH_SIZE)
val_image_batch, val_label_batch,val_galalxyid_batch = input_data.read_galaxy11_test(data_dir=test_dir,
label_dir=test_label_dir,
batch_size= BATCH_SIZE)
x = tf.placeholder(tf.float32, [BATCH_SIZE, 64, 64, 3])
y_ = tf.placeholder(tf.float32, [BATCH_SIZE, N_CLASSES])
keep_prob=tf.placeholder(tf.float32)
logits,fc_output = model.inference(x, BATCH_SIZE, N_CLASSES,keep_prob)
loss = model.loss(logits, y_)
accuracy = model.accuracy(logits, y_)
my_global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = model.optimize(loss, learning_rate, my_global_step)
saver = tf.train.Saver(tf.global_variables())
summary_op = tf.summary.merge_all()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
tra_summary_writer = tf.summary.FileWriter(train_log_dir, sess.graph)
val_summary_writer = tf.summary.FileWriter(val_log_dir, sess.graph)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
tra_images,tra_labels = sess.run([tra_image_batch, tra_label_batch])
_, tra_loss,tra_acc,summary_str = sess.run([train_op,loss, accuracy,summary_op],feed_dict={x:tra_images, y_:tra_labels,keep_prob:0.5})
if step % 50 == 0 or (step + 1) == MAX_STEP:
print ('Step: %d, tra_loss: %.4f, tra_accuracy: %.2f%%' % (step, tra_loss, tra_acc))
# summary_str = sess.run(summary_op,feed_dict={x:tra_images, y_:tra_labels})
tra_summary_writer.add_summary(summary_str, step)
if step % 200 == 0 or (step + 1) == MAX_STEP:
val_images, val_labels = sess.run([val_image_batch, val_label_batch])
val_loss, val_acc, summary_str = sess.run([loss, accuracy,summary_op],feed_dict={x:val_images,y_:val_labels,keep_prob:1})
print('** Step %d, test_loss = %.4f, test_accuracy = %.2f%% **' %(step, val_loss, val_acc))
# summary_str = sess.run([summary_op],feed_dict={x:val_images,y_:val_labels})
val_summary_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(train_log_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def evaluate():
with tf.Graph().as_default():
log_dir = '/home/daijiaming/Galaxy/VGG16/logs/train/'
# test_dir='/home/daijiaming/Galaxy/data3/testset/'
# test_label_dir='/home/daijiaming/Galaxy/data3/test_label.csv'
# test_dir='/home/daijiaming/Galaxy/data3/test1000/'
# test_label_dir='/home/daijiaming/Galaxy/data3/test1000_label.csv'
test_dir = '/home/daijiaming/Galaxy/data3/train1000/'
test_label_dir = '/home/daijiaming/Galaxy/data3/train1000_label.csv'
# test_dir='/home/daijiaming/Galaxy/data3/trainset/'
# test_label_dir='/home/daijiaming/Galaxy/data3/train_label.csv'
n_test = 1000
val_image_batch, val_label_batch, val_galalxyid_batch = input_data.read_galaxy11_test(
data_dir=test_dir, label_dir=test_label_dir, batch_size=BATCH_SIZE)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, 64, 64, 3])
y_ = tf.placeholder(tf.int32, shape=[BATCH_SIZE, N_CLASSES])
keep_prob = tf.placeholder(tf.float32)
logits, fc_output = VGG.VGG16N(x, N_CLASSES, keep_prob, IS_PRETRAIN)
correct = tools.num_correct_prediction(logits, y_)
accuracy = tools.accuracy(logits, y_)
# top_k_op = tf.nn.in_top_k(predictions=logits,targets=y_, k=1)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(log_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
return
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
print('\nEvaluating......')
num_step = int(math.ceil(n_test / BATCH_SIZE))
num_sample = num_step * BATCH_SIZE
step = 0
total_correct = 0
totall_acc = 0.0
# true_count = 0
while step < num_step and not coord.should_stop():
test_images, test_labels = sess.run(
[val_image_batch, val_label_batch])
batch_correct, tes_acc, batch_logits = sess.run(
[correct, accuracy, logits],
feed_dict={
x: test_images,
y_: test_labels,
keep_prob: 1
})
# print('tes_acc = %.3f' % tes_acc)
total_correct += np.sum(batch_correct)
totall_acc = totall_acc + tes_acc
# print('totall_acc = %.3f' % totall_acc)
# true_count += np.sum(predictions)
if step == 0:
a = test_labels
b = batch_logits
if step >= 1:
a = np.concatenate((a, test_labels))
b = np.concatenate((b, batch_logits))
step += 1
# precision = true_count / num_sample
aver_acc = totall_acc / num_step
print('Aver acc = %.4f' % aver_acc)
print('Total testing samples: %d' % num_sample)
print('Total correct predictions: %d' % total_correct)
print('Average accuracy: %.4f%%' %
(100 * total_correct / num_sample))
np.savetxt('./labels1000.csv', a, delimiter=',')
np.savetxt('./logits1000.csv', b, delimiter=',')
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def evaluate():
with tf.Graph().as_default():
log_dir = DATA_DIR + 'logs/train/'
test_dir = DATA_DIR + 'testset/'
test_label_dir = DATA_DIR + 'test_label.csv'
# what is the n_test? change for image that we have
n_test = 2879
val_image_batch, val_label_batch = input_data.read_galaxy11_test(
data_dir=test_dir, label_dir=test_label_dir, batch_size=BATCH_SIZE)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, 64, 64, 3])
y_ = tf.placeholder(tf.int32, shape=[BATCH_SIZE, N_CLASSES])
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points, output0, output1 = resnet_v2.resnet_v2_26_2(
x, N_CLASSES, is_training=True)
correct = resnet_v2.num_correct_prediction(logits, y_)
accuracy = resnet_v2.accuracy(logits, y_)
# top_k_op = tf.nn.in_top_k(predictions=logits,targets=y_, k=1)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(log_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
return
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
print('\nEvaluating......')
num_step = int(math.ceil(n_test / BATCH_SIZE))
num_sample = num_step * BATCH_SIZE
step = 0
total_correct = 0
totall_acc = 0.0
# true_count = 0
while step < num_step and not coord.should_stop():
test_images, test_labels = sess.run(
[val_image_batch, val_label_batch])
batch_correct, tes_acc, batch_logits = sess.run(
[correct, accuracy, logits],
feed_dict={
x: test_images,
y_: test_labels
})
# print('tes_acc = %.3f' % tes_acc)
total_correct += np.sum(batch_correct)
totall_acc = totall_acc + tes_acc
# print('totall_acc = %.3f' % totall_acc)
# true_count += np.sum(predictions)
if step == 0:
a = test_labels
b = batch_logits
if step >= 1:
a = np.concatenate((a, test_labels))
b = np.concatenate((b, batch_logits))
step += 1
# precision = true_count / num_sample
aver_acc = totall_acc / num_step
print('Aver acc = %.4f' % aver_acc)
print('Total testing samples: %d' % num_sample)
print('Total correct predictions: %d' % total_correct)
print('Average accuracy: %.4f%%' %
(100 * total_correct / num_sample))
np.savetxt('labels2879.csv', a, delimiter=',')
np.savetxt('logits2879.csv', b, delimiter=',')
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def train():
train_dir = DATA_DIR + 'trainset/'
train_label_dir = DATA_DIR + 'train_label.csv'
test_dir = DATA_DIR + 'testset/'
test_label_dir = DATA_DIR + 'test_label.csv'
train_log_dir = DATA_DIR + 'logs/train/'
val_log_dir = DATA_DIR + 'logs/val/'
tra_image_batch, tra_label_batch = input_data.read_galaxy11(
data_dir=train_dir, label_dir=train_label_dir, batch_size=BATCH_SIZE)
val_image_batch, val_label_batch = input_data.read_galaxy11_test(
data_dir=test_dir, label_dir=test_label_dir, batch_size=BATCH_SIZE)
embed()
x = tf.placeholder(tf.float32, [BATCH_SIZE, 64, 64, 3])
y_ = tf.placeholder(tf.float32, [BATCH_SIZE, N_CLASSES])
# keep_prob=tf.placeholder(tf.float32)
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points, output0, output1 = resnet_v2.resnet_v2_26_2(
x, N_CLASSES, is_training=True)
loss = resnet_v2.loss(logits, y_)
# rmse=resnet_v2.compute_rmse(logits, y_)
accuracy = resnet_v2.accuracy(logits, y_)
my_global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = resnet_v2.optimize(loss, learning_rate, my_global_step)
saver = tf.train.Saver(tf.global_variables())
summary_op = tf.summary.merge_all()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
tra_summary_writer = tf.summary.FileWriter(train_log_dir, sess.graph)
val_summary_writer = tf.summary.FileWriter(val_log_dir, sess.graph)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
tra_images, tra_labels = sess.run(
[tra_image_batch, tra_label_batch])
_, tra_loss, tra_acc, summary_str = sess.run(
[train_op, loss, accuracy, summary_op],
feed_dict={
x: tra_images,
y_: tra_labels
})
if step % 50 == 0 or (step + 1) == MAX_STEP:
print('Step: %d, tra_loss: %.4f, tra_accuracy: %.2f%%' %
(step, tra_loss, tra_acc))
# summary_str = sess.run(summary_op,feed_dict={x:tra_images, y_:tra_labels})
tra_summary_writer.add_summary(summary_str, step)
if step % 200 == 0 or (step + 1) == MAX_STEP:
val_images, val_labels = sess.run(
[val_image_batch, val_label_batch])
val_loss, val_acc, summary_str = sess.run(
[loss, accuracy, summary_op],
feed_dict={
x: val_images,
y_: val_labels
})
print(
'** Step %d, test_loss = %.4f, test_accuracy = %.2f%% **'
% (step, val_loss, val_acc))
# summary_str = sess.run([summary_op],feed_dict={x:val_images,y_:val_labels})
val_summary_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(train_log_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
sess.close()