feat: add test backward neuron

main.py

@@ -32,7 +32,7 @@ def main():
     network = NeuralNetwork([8, 16, 1])
 
     print("Start training...")
-    train_network(network, verbose=True, size_data=size, epochs=5_000)
+    train_network(network, verbose=True, size_data=size, epochs=5_000, max_val=max_val)
     print("End training...")
 
     while True:

nnetwork.ipynb

@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "id": "ee0fdb39",
    "metadata": {},
    "outputs": [],
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "f6de25ea",
    "metadata": {},
    "outputs": [],
@@ -272,7 +272,7 @@
     "        # dz/dw = x\n",
     "        dz_dw = x\n",
     "\n",
-    "        assert len(dz_dw) >= self.isize, \"too many value for input size\"\n",
+    "        assert len(dz_dw) >= self.isize, \"too many values for input size\"\n",
     "\n",
     "        # dz/db = 1\n",
     "        dz_db = 1\n",
@@ -293,15 +293,47 @@
    "id": "0c9baabf",
    "metadata": {},
    "source": [
-    "The `backward()` method train the neuron by adjusting its weights and bias using **the gradient descent**. This is based on erros and gradient of the activation function:\n",
+    "The `backward()` method train the neuron by adjusting its weights and bias using **the gradient descent**. This is based on erros between the neuron's prediction and the expected output, and gradient of the activation function:\n",
     "\n",
     "1. **derivates sigmoid, inputs, and lineear combination**:\n",
+    " \n",
     "    \n",
     "2. **adjust each input weight**:\n",
     "\n",
     "3. **adjust neuron bias**:\n",
     "\n",
-    "4. **return gradient vector**:"
+    "4. **return gradient vector**:\n",
+    "\n",
+    "#### Test - Backward Pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "e07b7881",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.279659636289641\n"
+     ]
+    }
+   ],
+   "source": [
+    "target = [0, 0, 1, 0, 1] # 5 \n",
+    "target_normalize = 5/31\n",
+    "epoch = 200\n",
+    "\n",
+    "neuron = Neuron(len(target))\n",
+    "\n",
+    "for i in range(epoch):\n",
+    "    output = neuron.forward(target)\n",
+    "    error = 2 * (output - target_normalize)\n",
+    "    neuron.backward(target, error, 0.1)\n",
+    "\n",
+    "print(neuron.forward(target)*31)"
    ]
   }
  ],
@@ -321,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.4"
   }
  },
  "nbformat": 4,