def _test_save_load(model, X, optimizer_name, optimizer):
saved_model_preds = model.predict(X[0])
saved_model_weights = K.batch_get_value(model.trainable_weights)
saved_optim_weights = K.batch_get_value(model.optimizer.weights)
test_name = 'test__%f{}.h5'.format(np.random.random())
modelpath = os.path.join(tempfile.gettempdir(), test_name)
model.save(modelpath)
del model
if TF_2 and not TF_EAGER and not TF_KERAS:
tf.compat.v1.experimental.output_all_intermediates(True) # bug fix
model = load_model(modelpath, custom_objects={optimizer_name: optimizer})
loaded_model_preds = model.predict(X[0])
loaded_model_weights = K.batch_get_value(model.trainable_weights)
loaded_optim_weights = K.batch_get_value(model.optimizer.weights)
assert np.allclose(saved_model_preds,
loaded_model_preds,
rtol=0,
atol=1e-8)
for smw, lmw in zip(saved_model_weights, loaded_model_weights):
assert np.allclose(smw, lmw, rtol=0, atol=1e-8)
for sow, low in zip(saved_optim_weights, loaded_optim_weights):
assert np.allclose(sow, low, rtol=0, atol=1e-8)
def _validate_save_load(tg, C):
def _get_load_path(tg, logdir):
for postfix in ('weights', 'model', 'model_noopt'):
postfix += '.h5'
path = [str(p) for p in Path(logdir).iterdir()
if p.name.endswith(postfix)]
if path:
return path[0]
raise Exception(f"no model save file found in {logdir}")
# get behavior before saving, to ensure no changes presave-to-postload
data = np.random.randn(*tg.model.input_shape)
Wm_save = tg.model.get_weights()
Wo_save = K.batch_get_value(tg.model.optimizer.weights)
preds_save = tg.model.predict(data, batch_size=len(data))
tg.checkpoint()
logdir = tg.logdir
tg.destroy(confirm=True)
C['traingen']['logdir'] = logdir
path = _get_load_path(tg, logdir)
if path.endswith('weights.h5'):
model = make_classifier(**C['model'])
model.load_weights(path)
else:
model = load_model(path)
tg = init_session(C, model=model)
tg.load()
Wm_load = tg.model.get_weights()
Wo_load = K.batch_get_value(tg.model.optimizer.weights)
preds_load = tg.model.predict(data, batch_size=len(data))
for s, l in zip(Wm_save, Wm_load):
assert np.allclose(s, l), "max absdiff: %s" % np.max(np.abs(s - l))
for s, l in zip(Wo_save, Wo_load):
assert np.allclose(s, l), "max absdiff: %s" % np.max(np.abs(s - l))
assert np.allclose(preds_save, preds_load), (
"max absdiff: %s" % np.max(np.abs(preds_save - preds_load)))