def test_save_and_load_weights(self):
# create a net with random weights
net = NeuralNet(model=self.model1)
w = net.layers[0].weights
b = net.layers[0].bias
# save weights
net.save_weights(self.weights_fname)
# create a new net with random weights
net = NeuralNet(model=self.model1)
# load saved weights
net.load_weights(self.weights_fname)
new_w = net.layers[0].weights
new_b = net.layers[0].bias
# check that weights are equal
self.assertTrue(np.alltrue(w == new_w),
msg="Saving and loading weights works")
self.assertTrue(np.alltrue(b == new_b),
msg="Saving and loading biases works")
# remove the temporarily saved weights
os.remove(self.weights_fname)
def test_save_and_load_weights_fails(self):
# create a net with random weights
net = NeuralNet(model=self.model1)
w = net.layers[0].weights
b = net.layers[0].bias
# save weights
net.save_weights(self.weights_fname)
# create a new net with random weights
net = NeuralNet(model=self.model2)
# load saved weights - this should fail
self.assertRaises(RuntimeError, net.load_weights, self.weights_fname)
# remove the temporarily saved weights
os.remove(self.weights_fname)
default='../data/saved_weights_linear.dat',
help=
'Path and filename for saving and loading the weights (../data/saved_weights_linear.dat)'
)
args = parser.parse_args()
weights_fname = args.weights_fname
# create the model
model = LinearModel()
net = NeuralNet(model=model)
if args.load_weights:
print('- Loading weights from:', weights_fname)
net.load_weights(weights_fname)
# create the optimizer
optimizer = SGD(net=net,
dataloader=RegressionDataLoader(),
batch_size=args.batch_size)
# fit the model
print('- Training model for', args.epochs, 'epoch, with learning rate',
args.lr)
optimizer.fit(n_epochs=args.epochs, learning_rate=args.lr)
if args.save_weights:
print("- Saving weights to:", weights_fname)
net.save_weights(weights_fname)