def test_subclass_save_model(self):
num_classes = 10
# Input size, e.g. image
batch_size = None
input_shape = (32, 32, 3)
model = model_util.SimpleConvTestModel(num_classes)
self.assertFalse(model.built, 'Model should not have been built')
self.assertFalse(model.weights,
('Model should have no weights since it '
'has not been built.'))
model.build(input_shape=tensor_shape.TensorShape((batch_size, ) +
input_shape))
self.assertTrue(model.weights,
('Model should have weights now that it '
'has been properly built.'))
self.assertTrue(model.built,
'Model should be built after calling `build`.')
weights = model.get_weights()
tf_format_name = os.path.join(self.get_temp_dir(), 'ckpt')
model.save_weights(tf_format_name)
if h5py is not None:
hdf5_format_name = os.path.join(self.get_temp_dir(), 'weights.h5')
model.save_weights(hdf5_format_name)
model = model_util.SimpleConvTestModel(num_classes)
model.build(input_shape=tensor_shape.TensorShape((batch_size, ) +
input_shape))
if h5py is not None:
model.load_weights(hdf5_format_name)
self.assertAllClose(weights, model.get_weights())
model.load_weights(tf_format_name)
self.assertAllClose(weights, model.get_weights())
def test_tensorshape_io_subclass_build(self):
num_classes = 10
# Input size, e.g. image
batch_size = None
input_shape = (32, 32, 3)
model = model_util.SimpleConvTestModel(num_classes)
self.assertFalse(model.built, 'Model should not have been built')
self.assertFalse(model.weights, ('Model should have no weights since it '
'has not been built.'))
model.build(
input_shape=tensor_shape.TensorShape((batch_size,) + input_shape))
self.assertTrue(model.weights, ('Model should have weights now that it '
'has been properly built.'))
self.assertTrue(model.built, 'Model should be built after calling `build`.')
model(array_ops.ones((32,) + input_shape))