def test_backpropagation(self): weight1 = np.array([ [0.22667075, 0.38116981, 0.62686969], [1.13062085, 0.40836474, -0.50492125], [-0.22645265, 1.13541005, -2.7876409] ]) weight2 = np.array([ [0.63547163], [0.63347214], [-1.3669957], [-0.42770718] ]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) output = StepOutputLayer(1, output_bounds=(-1, 1)) network = Backpropagation( input_layer > hidden_layer > output, step=0.3, zero_weight=20, optimizations=[WeightElimination] ) network.train(xor_input_train, xor_target_train, epochs=350) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_backpropagation(self): output = StepOutputLayer(1, output_bounds=(-1, 1)) weight1 = np.array([ [0.31319847, -1.17858149, 0.71556407], [1.60798015, 0.16304449, -0.22483005], [-0.90144173, 0.58500625, -0.01724167] ]) weight2 = np.array([ [-1.34351428], [0.45506056], [0.24790366], [-0.74360389] ]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) network = Backpropagation( (input_layer > hidden_layer > output), step=0.3, verbose=False ) network.train(xor_input_train, xor_target_train, epochs=1000) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_first_step_updates(self): def square_error_deriv(output_train, target_train): return output_train - target_train @with_derivative(square_error_deriv) def square_error(output_train, target_train): return np.sum((target_train - output_train) ** 2) / 2 weight1 = np.array([[0.1, 0.2], [0.5, 0.5], [0.5, 0.5]]) weight2 = np.array([[0.3, 0.5, 0.5]]).T input_layer = SigmoidLayer(2, weight=weight1) hidden_layer = SigmoidLayer(2, weight=weight2) output = OutputLayer(1) network = Backpropagation( (input_layer > hidden_layer > output), error=square_error, step=1, verbose=False ) test_input = np.array([[1, 1]]) test_target = np.array([[1]]) network.train(test_input, test_target, epochs=1) np.testing.assert_array_almost_equal( network.train_layers[0].weight_without_bias, np.array([[0.50461013, 0.50437699], [0.50461013, 0.50437699]]), ) np.testing.assert_array_almost_equal( network.train_layers[1].weight_without_bias, np.array([[0.53691945, 0.53781823]]).T, )
def test_backpropagation(self): weight1 = np.array([ [-0.53980522, -0.64724144, -0.92496063], [-0.04144865, -0.60458235, 0.25735483], [0.08818209, -0.10212516, -1.46030816] ]) weight2 = np.array([ [0.54230442], [0.1393251], [1.59479241], [0.1479949] ]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) output = StepOutputLayer(1, output_bounds=(-1, 1)) network = Backpropagation( input_layer > hidden_layer > output, step=0.3, decay_rate=0.0001, optimizations=[WeightDecay] ) network.train(xor_input_train, xor_target_train, epochs=500) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_backpropagation(self): weight1 = np.array([[-0.53980522, -0.64724144, -0.92496063], [-0.04144865, -0.60458235, 0.25735483], [0.08818209, -0.10212516, -1.46030816]]) weight2 = np.array([[0.54230442], [0.1393251], [1.59479241], [0.1479949]]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) output = StepOutputLayer(1, output_bounds=(-1, 1)) network = Backpropagation(input_layer > hidden_layer > output, step=0.3, decay_rate=0.0001, optimizations=[WeightDecay]) network.train(xor_input_train, xor_target_train, epochs=500) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_backpropagation(self): weight1 = np.array([[0.22667075, 0.38116981, 0.62686969], [1.13062085, 0.40836474, -0.50492125], [-0.22645265, 1.13541005, -2.7876409]]) weight2 = np.array([[0.63547163], [0.63347214], [-1.3669957], [-0.42770718]]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) output = StepOutputLayer(1, output_bounds=(-1, 1)) network = Backpropagation(input_layer > hidden_layer > output, step=0.3, zero_weight=20, optimizations=[WeightElimination]) network.train(xor_input_train, xor_target_train, epochs=350) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_train_state(self): global triggered_times triggered_times = 0 epochs = 4 def print_message(network): global triggered_times triggered_times += 1 def print_message2(network): global triggered_times triggered_times += 1 network = Backpropagation( connection=(2, 2, 1), train_epoch_end_signal=print_message, train_end_signal=print_message2, ) network.train(xor_input_train, xor_target_train, epochs=epochs) self.assertEqual(triggered_times, epochs + 1)
def test_first_step_updates(self): def square_error_deriv(output_train, target_train): return output_train - target_train @with_derivative(square_error_deriv) def square_error(output_train, target_train): return np.sum((target_train - output_train) ** 2) / 2 weight1 = np.array([[0.1, 0.2], [0.5, 0.5], [0.5, 0.5]]) weight2 = np.array([[0.3, 0.5, 0.5]]).T weight1_new = np.array([[0.50461013, 0.50437699], [0.50461013, 0.50437699]]) weight2_new = np.array([[0.53691945, 0.53781823]]).T input_layer = SigmoidLayer(2, weight=weight1) hidden_layer = SigmoidLayer(2, weight=weight2) output = OutputLayer(1) network = Backpropagation( (input_layer > hidden_layer > output), error=square_error, step=1, ) network.train(np.array([[1, 1]]), np.array([[1]]), epochs=1) trained_weight1 = network.train_layers[0].weight_without_bias trained_weight2 = network.train_layers[1].weight_without_bias self.assertTrue(np.all( np.round(trained_weight1, 8) == np.round(weight1_new, 8)) ) self.assertTrue(np.all( np.round(trained_weight2, 8) == np.round(weight2_new, 8)) )