def test_adjust_weight(self): backpropagator = Backpropagator() output = ReceiveAllNeuron() hidden_1 = ReceiveAllNeuron() hidden_2 = ReceiveAllNeuron() hidden_1.connect_to(output) hidden_1.out_connections_list[0].weight = -0.75 hidden_2.connect_to(output) hidden_2.out_connections_list[0].weight = -0.25 hidden_1.receive_signal(0.434719) hidden_2.receive_signal(0.434719) output.error = 0.09754925 hidden_1.error = -0.01745285 hidden_2.error = -0.00581762 connection_1 = hidden_1.out_connections_list[0] backpropagator.adjust_weight(connection_1, 1.1) # -0.75 + (1.1 * 0.09754925 * -0.45525) self.assertAlmostEqual(connection_1.weight, -0.68486637) connection_2 = hidden_2.out_connections_list[0] backpropagator.adjust_weight(connection_2, 1.1) # -0.25 + (1.1 * 0.09754925 * -0.15175) self.assertAlmostEqual(connection_2.weight, -0.18486637)
def test_hiddenError(self): backpropagator = Backpropagator() output = ReceiveAllNeuron() hidden_1 = ReceiveAllNeuron() hidden_2 = ReceiveAllNeuron() hidden_1.connect_to(output) hidden_1.out_connections_list[0].weight = -0.75 hidden_2.connect_to(output) hidden_2.out_connections_list[0].weight = -0.25 # sigmoid(0.434719) == 0.6070000 hidden_1.receive_signal(0.434719) hidden_2.receive_signal(0.434719) # sigmoid(0.434719) * -0.75 + sigmoid(0.434719) * -0.25 == -0.607 # sigmoid(-0.607) == 0.352744 # 0.78 - 0.352744 == 0.3527438 output.error = backpropagator.output_error(output, 0.78) self.assertAlmostEqual(output.error, 0.09754925) hidden_1.error = backpropagator.hidden_error(hidden_1) hidden_2.error = backpropagator.hidden_error(hidden_2) # 0.6070000 * (1 - 0.6070000)*(-0.75 * 0.09754925) self.assertAlmostEqual(hidden_1.error, -0.01745285) # 0.6070000 * (1 - 0.6070000)*(-0.25 * 0.09754925) self.assertAlmostEqual(hidden_2.error, -0.00581762)