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))