def guild_list():
guild_name = request.args.get("guild").title()
guild_server = request.args.get("server").title()
guild_region = request.args.get("region").upper()
guild_id_string = guild_name+"_"+guild_server+"_"+guild_region
redis_guild = r.hgetall(guild_id_string)
if model.is_empty(redis_guild) == True:
guild = model.logs_new_guild(guild_name, guild_server, guild_region)
else:
# print redis_guild
guild = {}
guild["guild_name"] = redis_guild["guild_name"]
guild["guild_server"] = redis_guild["guild_server"]
guild["guild_region"] = redis_guild["guild_region"]
guild["logs"] = eval(redis_guild["logs"])
return render_template("guild_list.html", guild=guild)
def guild_list():
guild_name = request.args.get("guild").title()
guild_server = request.args.get("server").title()
guild_region = request.args.get("region").upper()
guild_id_string = guild_name + "_" + guild_server + "_" + guild_region
redis_guild = r.hgetall(guild_id_string)
if model.is_empty(redis_guild) == True:
guild = model.logs_new_guild(guild_name, guild_server, guild_region)
else:
# print redis_guild
guild = {}
guild["guild_name"] = redis_guild["guild_name"]
guild["guild_server"] = redis_guild["guild_server"]
guild["guild_region"] = redis_guild["guild_region"]
guild["logs"] = eval(redis_guild["logs"])
return render_template("guild_list.html", guild=guild)
def test_most_popular_empty(self): popular = model.most_popular() self.assertFalse(model.is_empty(popular[1]["url"]))
def test_recently_shortened(self): recent = model.recently_shortened() self.assertFalse(model.is_empty(recent))
def test_empty(self): self.assertTrue(model.is_empty(self.empty)) self.assertFalse(model.is_empty(self.code))
def main():
training_images, training_labels, test_images, test_labels = md.Load_Data(
mat)
if REMOVE_EMPTY:
empty_idx = md.is_empty(training_images, training_labels)
images_transform = np.delete(training_images, empty_idx, 0)
labels_transform = np.delete(training_labels, empty_idx, 0)
training_data = md.GetData(images_transform, labels_transform)
else:
training_data = md.GetData(training_images, training_labels)
test_data = md.GetData(test_images, test_labels)
g = tf.Graph()
with g.as_default():
images = tf.placeholder(tf.float32, [BATCH_SIZE, 256, 256, 1])
labels = tf.placeholder(tf.int64, [BATCH_SIZE, 256, 256])
is_training = tf.placeholder(tf.bool)
if AUGMENT_IMAGE:
images, labels = md.image_augmentation(images, labels)
logits = md.inference(images, is_training)
loss = md.loss_calc(logits=logits, labels=labels)
train_op, global_step = md.training(loss=loss, learning_rate=1e-04)
accuracy = md.evaluation(logits=logits, labels=labels)
dice = md.get_dice(logits=logits, labels=labels)
summary = tf.summary.merge_all()
init = tf.global_variables_initializer()
saver = tf.train.Saver(
[x for x in tf.global_variables() if 'Adam' not in x.name])
sm = tf.train.SessionManager()
with sm.prepare_session("",
init_op=init,
saver=saver,
checkpoint_dir=LOG_DIR) as sess:
sess.run(
tf.variables_initializer(
[x for x in tf.global_variables() if 'Adam' in x.name]))
train_writer = tf.summary.FileWriter(LOG_DIR + "/Train",
sess.graph)
test_writer = tf.summary.FileWriter(LOG_DIR + "/Test")
global_step_value, = sess.run([global_step])
print("Last trained iteration was: ", global_step_value)
for step in range(global_step_value + 1,
global_step_value + MAX_STEPS + 1):
print("Iteration: ", step)
images_batch, labels_batch = training_data.next_batch(
BATCH_SIZE)
train_feed_dict = {
images: images_batch,
labels: labels_batch,
is_training: True
}
train_dice_value, _, train_loss_value, train_accuracy_value, train_summary_str = sess.run(
[dice, train_op, loss, accuracy, summary],
feed_dict=train_feed_dict)
if step % SAVE_INTERVAL == 0:
print("Train Loss: ", train_loss_value)
print("Train accuracy: ", train_accuracy_value)
print("Train dice: ", train_dice_value)
train_writer.add_summary(train_summary_str, step)
train_writer.flush()
images_batch, labels_batch = test_data.next_batch(
BATCH_SIZE)
test_feed_dict = {
images: images_batch,
labels: labels_batch,
is_training: False
}
test_dice_value, test_loss_value, test_accuracy_value, test_summary_str = sess.run(
[dice, loss, accuracy, summary],
feed_dict=test_feed_dict)
print("Test Loss: ", test_loss_value)
print("Test accuracy: ", test_accuracy_value)
print("Test dice: ", test_dice_value)
test_writer.add_summary(test_summary_str, step)
test_writer.flush()
saver.save(sess, CHECKPOINT_FL, global_step=step)
print("Session Saved")
print("================")