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))
