def test_long_transform(): graph = ResNetGenerator(10, (28, 28, 1)).generate() graph.to_deeper_model(16, StubReLU()) graph.to_deeper_model(16, StubReLU()) graph.to_add_skip_model(13, 47) model = graph.produce_model() model(torch.Tensor(np.random.random((10, 1, 28, 28))))
def test_long_transform(): graph = ResNetGenerator(10, (28, 28, 1)).generate() graph.to_deeper_model(16, StubReLU()) graph.to_deeper_model(16, StubReLU()) graph.to_add_skip_model(13, 47) model = graph.produce_model() model(torch.Tensor(np.random.random((10, 1, 28, 28))))
def _init_generator(self, n_output_node, input_shape): return ResNetGenerator(n_output_node, input_shape)
def test_long_transform5(): graph = ResNetGenerator(10, (28, 28, 1)).generate() graph.to_concat_skip_model(19, 60) graph.to_wider_model(52, 256) model = graph.produce_model() model(torch.Tensor(np.random.random((10, 1, 28, 28))))
def _init_generator(self, n_output_node, input_shape): alex_gui = alexnet_gui() var = alex_gui.var return ResNetGenerator(n_output_node, input_shape)
def test_long_transform5(): graph = ResNetGenerator(10, (28, 28, 1)).generate() graph.to_concat_skip_model(19, 60) graph.to_wider_model(52, 256) model = graph.produce_model() model(torch.Tensor(np.random.random((10, 1, 28, 28))))