class TestSimpleModel(unittest.TestCase):
def setUp(self):
self.model = SimpleModel()
inp = tf.random.normal(shape=(5, 256 * 256 * 3))
self.out = self.model.forward(inp)
def test_forward(self):
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
print(sess.run(self.out))
from data.tf_datasets import OmniglotDataset
from models import SimpleModel
model_address = './saved_models/simple_model-1000'
model = SimpleModel()
omniglot_dataset = OmniglotDataset()
test_dataset = omniglot_dataset.get_test_dataset()
train_task, val_task, train_labels, val_labels = test_dataset.get_supervised_meta_learning_tasks(
meta_batch_size=1, n=6, k=2)
tf.summary.image('task',
tf.reshape(train_task, (-1, 28, 28, 1)),
max_outputs=12)
model.forward(train_task)
model.define_update_op(train_labels, with_batch_norm_dependency=True)
for item in tf.global_variables():
tf.summary.histogram(item.name, item)
merged_summary = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('./adaptaion_summary/train',
tf.get_default_graph())
test_writer = tf.summary.FileWriter('./adaptaion_summary/test')
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_task_np, val_task_np, train_labels_np, val_labels_np = sess.run(
(train_task, val_task, train_labels, val_labels))
