def test_forward_pass(self):
network = NeuralNetwork(3, 2, 1, 0.5)
final_out, hidden_out = network.forward_pass_train(inputs)
self.assertIsNotNone(final_out)
self.assertIsNotNone(hidden_out)
self.assertEqual((1,2),np.shape(hidden_out))
self.assertEqual((1,1),np.shape(final_out))
def test_backprop(self):
network = NeuralNetwork(3, 4, 1, 0.5)
final_out, hidden_out = network.forward_pass_train(inputs)
deltawOut = np.zeros(np.shape(network.weights_hidden_to_output))
#self.assertEqual(np.shape(deltawOut),np.shape(test_w_h_o))
deltawHidden = np.zeros(np.shape(network.weights_input_to_hidden))
#self.assertEqual(np.shape(deltawHidden), np.shape(test_w_i_h))
deltaWIH, deltaWHO = network.backpropagation(final_out, hidden_out, inputs, targets, deltawHidden,deltawOut)
self.assertIsNotNone(deltaWIH)
self.assertIsNotNone(deltaWHO)
