ES LEBT

tasks/04 - neuron.ipynb

@@ -21,9 +21,11 @@
     "def sigmoid(z):\n",
     "    return 1 / (1 + e ** (-z))\n",
     "\n",
+    "\n",
     "def ht(weights, x):\n",
     "    return g(weights.T @ x)\n",
     "\n",
+    "\n",
     "def g(x):\n",
     "    return sigmoid(x)"
    ]
@@ -144,6 +146,45 @@
     "print(layer(np.array([1, 0, 0]), weights))\n",
     "print(layer(np.array([1, 1, 0]), weights))\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9999545869652744\n",
+      "4.5622486386054965e-05\n",
+      "4.5622486386054965e-05\n",
+      "0.9999545869652744\n"
+     ]
+    }
+   ],
+   "source": [
+    "# xnor gate, needs 3 layers\n",
+    "weights = [\n",
+    "    np.array(\n",
+    "        [\n",
+    "            [1, -30, 20, 20],\n",
+    "            [1, 10, -20, -20],\n",
+    "        ]\n",
+    "    ),\n",
+    "    np.array([-10, 20, 20]),\n",
+    "]\n",
+    "\n",
+    "def l2(x: array, w1: array, w2: array) -> array:\n",
+    "    z = layer(x, w1)\n",
+    "    z2 = layer(z, w2)\n",
+    "    return z2\n",
+    "\n",
+    "print(l2(np.array([1,0,0]), weights[0], weights[1]))\n",
+    "print(l2(np.array([1,1,0]), weights[0], weights[1]))\n",
+    "print(l2(np.array([1,0,1]), weights[0], weights[1]))\n",
+    "print(l2(np.array([1,1,1]), weights[0], weights[1]))"
+   ]
   }
  ],
  "metadata": {