From f615da811333699786202c90f485e017e338d8a2 Mon Sep 17 00:00:00 2001
From: Banyc <36535895+Banyc@users.noreply.github.com>
Date: Tue, 26 Jul 2022 17:04:18 +0800
Subject: [PATCH 1/2] feat: test case

---
 neural_network/neural_network_test.py | 56 +++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)

diff --git a/neural_network/neural_network_test.py b/neural_network/neural_network_test.py
index 8bec17a..ed9f07d 100644
--- a/neural_network/neural_network_test.py
+++ b/neural_network/neural_network_test.py
@@ -253,3 +253,59 @@ def test_neural_network_backpropagation_step():
 
     # ∂E/∂w_i = [8, 16, -16], delta is [-4, -8, 8]
     assert_that(linear_node.weights).is_equal_to(new_weights)
+
+def test_neural_network_gradients2():
+    input_nodes = InputNode.make_input_nodes(1)
+    initial_weights_f = [0, 2]
+    linear_node_f = LinearNode(input_nodes, initial_weights=initial_weights_f)
+    initial_weights_h = [0, 3]
+    linear_node_h = LinearNode([linear_node_f], initial_weights=initial_weights_h)
+    error_node = L2ErrorNode(linear_node_h)
+    network = NeuralNetwork(linear_node_h, input_nodes, error_node=error_node)
+
+    example = [2]
+    label = 1
+    step_size = 0.5
+
+    '''
+    h(w_h, x): linear node
+    f(w_f, x): linear node
+    E(w_h, w_f, x, y): (h(w_h, f(w_h, x)) - y) ^ 2
+    '''
+
+    network.backpropagation_step(example, label, step_size=step_size)
+
+    # h(w, x) = 12
+    # E(w_h, w_f, x, y) = 121
+    # assert_that(network.evaluate(example)).is_equal_to(12)
+    # assert_that(network.compute_error(example, label)).is_equal_to(121)
+
+    # ∂E/∂E = 1, ∂E/∂h = 22
+    assert_that(error_node.global_gradient).is_equal_to(1)
+    assert_that(error_node.local_gradient).is_equal_to([22])
+
+    # w_h = -41
+    assert_that(linear_node_h.parameters[1]).is_equal_to(-41)
+    # ∂E/∂h = 22
+    assert_that(linear_node_h.global_gradient).is_equal_to(22)
+    # ∂h/∂w_h = 4
+    assert_that(linear_node_h.local_parameter_gradient[1]).is_equal_to(4)
+    # ∂E/∂w_h = 88
+    assert_that(linear_node_h.global_parameter_gradient[1]).is_equal_to(88)
+    # expected: ∂h/∂f = 3
+    # reality: ∂h/∂f = -41
+    assert_that(linear_node_h.local_gradient[1]).is_equal_to(3)
+
+    # w_f = -64
+    assert_that(linear_node_h.parameters[1]).is_equal_to(-64)
+    # expected: ∂E/∂f = 66
+    # reality: ∂E/∂f = -902
+    assert_that(linear_node_f.global_gradient).is_equal_to(66)
+    # ∂f/∂w_f = 2
+    assert_that(linear_node_f.local_parameter_gradient[1]).is_equal_to(2)
+    # expected: ∂E/∂w_f = 132
+    # reality: ∂E/∂w_f = -1804
+    assert_that(linear_node_f.global_parameter_gradient[1]).is_equal_to(132)
+    # expected: ∂f/∂x = 2
+    # reality: ∂f/∂x = 904
+    assert_that(linear_node_f.local_gradient[1]).is_equal_to(2)

From 99e81a51f73fb79244479931ed2878335fcf53ab Mon Sep 17 00:00:00 2001
From: Banyc <36535895+Banyc@users.noreply.github.com>
Date: Tue, 26 Jul 2022 17:45:09 +0800
Subject: [PATCH 2/2] fix: test case

---
 neural_network/neural_network_test.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/neural_network/neural_network_test.py b/neural_network/neural_network_test.py
index ed9f07d..e9523f9 100644
--- a/neural_network/neural_network_test.py
+++ b/neural_network/neural_network_test.py
@@ -256,8 +256,12 @@ def test_neural_network_backpropagation_step():
 
 def test_neural_network_gradients2():
     input_nodes = InputNode.make_input_nodes(1)
+    # bias: 0
+    # weights: [2]
     initial_weights_f = [0, 2]
     linear_node_f = LinearNode(input_nodes, initial_weights=initial_weights_f)
+    # bias: 0
+    # weights: [3]
     initial_weights_h = [0, 3]
     linear_node_h = LinearNode([linear_node_f], initial_weights=initial_weights_h)
     error_node = L2ErrorNode(linear_node_h)
@@ -296,8 +300,9 @@ def test_neural_network_gradients2():
     # reality: ∂h/∂f = -41
     assert_that(linear_node_h.local_gradient[1]).is_equal_to(3)
 
-    # w_f = -64
-    assert_that(linear_node_h.parameters[1]).is_equal_to(-64)
+    # expected: w_f = -64
+    # reality: w_f = 904
+    assert_that(linear_node_f.parameters[1]).is_equal_to(-64)
     # expected: ∂E/∂f = 66
     # reality: ∂E/∂f = -902
     assert_that(linear_node_f.global_gradient).is_equal_to(66)