def tet_backward_propagate(self): learning_rate = 0.8 structure = {'num_inputs': 2, 'num_outputs': 1, 'num_hidden': 1} candidate = NeuralNet(structure, learning_rate) cand_weights = candidate.get_weights() X = np.array([np.array([1, 0])]) Y = np.array([np.array([0])]) candidate.train(X, Y) cand_weights = candidate.get_weights() print(cand_weights) # You can do the math to see what the new weights should be # and assert them here. self.assertTrue(True)
def test_weight_shapes(self): learning_rate = 0.8 structure = {'num_inputs': 2, 'num_outputs': 1, 'num_hidden': 5} candidate = NeuralNet(structure, learning_rate) cand_weights = candidate.get_weights() self.assertEqual(cand_weights[0].shape, (3, 5)) self.assertEqual(cand_weights[1].shape, (5, 1))