def test_reuse_model(self, rank, is_training, special_type):
batch_size = 5
num_classes = 10
num_channels = 3
images = _construct_images(batch_size)
config = simple_model_config.get_config()
config.num_classes = num_classes
config.num_channels = num_channels
config.batch_norm = False
config.kernel_size_list = [3, 3, 3]
config.num_filters_list = [64, 64, 64]
config.strides_list = [2, 2, 1]
config.layer_types = ['conv2d', special_type, 'conv2d']
config.rank = rank
model = simple_model.SimpleNetwork(config)
# Build once.
logits1, _ = model(images, is_training)
num_params = len(tf.all_variables())
# Build twice.
logits2, _ = model(images, is_training)
# Ensure variables are reused.
self.assertLen(tf.all_variables(), num_params)
init = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init)
# Ensure operations are the same after reuse.
err_logits = (np.abs(sess.run(logits1 - logits2))).sum()
self.assertAlmostEqual(err_logits, 0, 9)
def test_build_model(self, rank, is_training, special_type):
batch_size = 5
num_classes = 10
num_channels = 3
images = _construct_images(batch_size)
config = simple_model_config.get_config()
config.num_classes = num_classes
config.num_channels = num_channels
config.rank = rank
config.batch_norm = True
config.kernel_size_list = [3, 3, 3]
config.num_filters_list = [64, 64, 64]
config.strides_list = [2, 2, 1]
config.layer_types = ['conv2d', special_type, 'conv2d']
model = simple_model.SimpleNetwork(config)
logits, _ = model(images, is_training)
final_shape = (batch_size, num_classes)
init = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init)
self.assertEqual(final_shape, sess.run(logits).shape)