def setUpClass(cls): cls.tf_data = tf.Variable([1.0, 2.0, 4.0]) cls.tf_output = [tf.constant([2, 4, 8])] cls.torch_data = torch.tensor([1.0, 2.0, 4.0], requires_grad=True) cls.torch_output = [torch.tensor([2, 4, 8], dtype=torch.float32)] cls.tf_model = one_layer_tf_model() cls.torch_model = OneLayerTorchModel()
def instantiate_system(): system = sample_system_object() submodel = one_layer_tf_model() model = fe.build(model_fn=lambda: test_model(submodel), optimizer_fn='adam', model_name='tf') model2 = fe.build(model_fn=lambda: test_model(submodel), optimizer_fn='adam', model_name='tf2') system.network = fe.Network(ops=[ ModelOp(model=model, inputs="x_out", outputs="y_pred"), ModelOp(model=model2, inputs="x_out", outputs="y_pred2"), ]) return system
def setUpClass(cls): cls.tf_model = one_layer_tf_model() cls.torch_model = OneLayerTorchModel()
def test_feed_forward_tf(self): model = one_layer_tf_model() x = tf.constant([[1.0, 1.0, 1.0], [1.0, -1.0, -0.5]]) obj1 = fe.backend.feed_forward(model, x) obj2 = tf.constant([[6.0], [-2.5]]) self.assertTrue(is_equal(obj1, obj2))