def test(test_images,test_labels):
tf.reset_default_graph()
sess=tf.Session()
new_saver = tf.train.import_meta_graph(os.path.join(os.getcwd(),FLAGS.save_name,'var.ckpt.meta'))
new_saver.restore(sess,os.path.join(os.getcwd(),FLAGS.save_name,'var.ckpt'))
print('graph restored')
ops=sess.graph.get_operations()
for x in ops:
print(x.name)
try:
if isinstance(test_images,np.ndarray):
x=0
except NameError:
x=1
if x==1:
_,_,test_images,test_labels,_,_=input_data(num_classes=2,num_images=10,val_num=FLAGS.val_size,test_num=FLAGS.test_size)
test_size=test_images.shape[0]
sess_input=sess.graph.get_tensor_by_name('input/image:0')
sess_logits=sess.graph.get_tensor_by_name('final_layer/BiasAdd:0')
sess_keep_prob=sess.graph.get_tensor_by_name('hparams/keep_prob:0')
sess_training=sess.graph.get_tensor_by_name('is_train:0')
logits_out=sess.run(sess_logits,feed_dict={sess_input:test_images,sess_keep_prob:1.0,sess_training:False})
out=np.argmax(logits_out,1)
acc=np.sum(out==test_labels)/test_size
print('accuracy :: {}'.format(acc))
sess.close()
def train():
# take input data from take_input
train_images, train_labels, test_images, test_labels, val_images, val_labels = input_data(
num_classes=2,
num_images=10,
val_num=FLAGS.val_size,
test_num=FLAGS.test_size)
print(test_images.shape[0])
print(val_images.shape[0])
img_size = train_images.shape[2]
img_channel = train_images.shape[3]
label_cnt = train_labels.shape[1]
# let's have the input placeholders
X, y, learning_rate, dropout_keep_prob = model.placeholders(
img_size, img_channel, label_cnt) ####
logits, out_probs = model.network(X,
dropout_keep_prob,
label_cnt,
type_=FLAGS.type_) ####
loss = model.loss(logits, y)
optimizer = model.optimizer(loss, learning_rate)
init = tf.global_variables_initializer()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False))
sess.run(init)
merged = tf.summary.merge_all()
writer_train_addr = './summary/train'
writer_val_addr = './summary/val'
train_writer = tf.summary.FileWriter(writer_train_addr, sess.graph)
val_writer = tf.summary.FileWriter(writer_val_addr)
saver = tf.train.Saver()
saver_addr = os.path.join(os.getcwd(), FLAGS.save_name, 'var.ckpt')
if not os.path.isdir(os.path.join(os.getcwd(), FLAGS.save_name)):
os.mkdir(FLAGS.save_name)
if os.path.isfile(saver_addr):
saver.restore(sess, saver_addr)
train_num = train_images.shape[0]
batch_size = 128
num_batches = int(np.ceil(train_num / batch_size))
lr = FLAGS.learning_rate_
kp = FLAGS.keep_prob
epochs = FLAGS.epoch
train_size = train_images.shape[0]
is_train = FLAGS.train
#start_time=time.time()
for epoch in range(epochs):
#epoch_start_time=time.time()
if epoch % 3 == 0 and epoch > 0:
lr /= 2
i = 0
epoch_loss = 0
for batch in range(num_batches):
train_x, train_y, start, end = return_batches(
train_images,
train_labels,
batch_size=batch_size,
batch_num=batch)
print(train_x.shape)
print(train_y.shape)
if i % 20 == 0:
summary, _, batch_loss = sess.run(
[merged, optimizer, loss],
feed_dict={
X: train_x,
y: train_y,
learning_rate: lr,
dropout_keep_prob: kp
}) #########
train_writer.add_summary(summary, i / 20)
else:
_, _, batch_loss = sess.run(
[optimizer, loss],
feed_dict={
X: train_x,
y: train_y,
learning_rate: lr,
dropout_keep_prob: kp
}) ##########
epoch_loss += batch_loss
print(
'>> training loss computed :: {} on {} images from {} to {} out of {} with learning_rate {}'
.format(batch_loss, train_x.shape[0], start, end, train_size,
lr))
if i % FLAGS.validation_interval == 0 and i > 0:
#validation_start_time=time.time()
accuracy_batch, batch_loss, summary = sess.run(
[accuracy, loss, summary],
feed_dict={
X: val_images,
y: val_labels,
dropout_keep_prob: 1.0
}) ########
val_writer.add_summary(summary, i / FLAGS.validation_interval)
print(
'>> validation loss computed :: {} and validation accuracy :: {} on {} images'
.format(batch_loss, accuracy_batch, val_images.shape[0]))
print('>> epoch loss computed :: {} '.format(epoch_loss / num_batches))
saver.save(sess, saver_addr)
train_writer.close()
val_writer.close()
sess.close()
test(test_images, test_labels)