class Task(taskplan.Task):
def __init__(self, config, logger, metadata):
super(Task, self).__init__(config, logger, metadata)
self.sum = 0
self.model = Model(config.get_with_prefix("model"))
self.data = Data(config.get_with_prefix("data"))
self.trainer = Trainer(config.get_with_prefix("trainer"), self.model,
self.data)
self.best_val_acc = 0
self.number_worse_iterations = 0
def save(self, path):
self.model.save_weights(str(path / "model.h5py"))
pickle.dump(self.best_val_acc, open(str(path / "best_model.pkl"),
"wb"))
def step(self, tensorboard_writer, current_iteration):
with tensorboard_writer.as_default():
val_acc = self.trainer.step(current_iteration)
if val_acc is not None:
if val_acc > self.best_val_acc:
self.best_val_acc = val_acc
self.model.save_weights(str(self.task_dir / "model.h5py"))
self.number_worse_iterations = 0
else:
self.number_worse_iterations += 1
if self.number_worse_iterations > 5:
self.pause_computation = True
tf.summary.scalar('val/best_acc',
self.best_val_acc,
step=current_iteration)
def load(self, path):
self.model.load_weights(str(path / "model.h5py"))
self.best_val_acc = pickle.load(
open(str(path / "best_model.pkl"), "rb"))
from src.Data import Data
import tensorflow as tf
import argparse
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('task_path')
args = parser.parse_args()
api = taskplan.Api()
task = api.load_task(args.task_path)
config = task.config
path = task.build_save_dir()
model = Model(config.get_with_prefix("model"))
model.load_weights(str(path / Path("model.h5py")))
data = Data(config.get_with_prefix("data"))
acc = tf.keras.metrics.SparseCategoricalAccuracy()
for data in data.build_test_dataset():
images, labels = data
pred = model(images)
acc(labels, pred)
print("Acc: " + str(acc.result()))
tensorboard_writer = tf.summary.create_file_writer(str(path))
with tensorboard_writer.as_default():