diff --git a/notebooks/Block_2/Exercises Block 2 - Neural Networks.ipynb b/notebooks/Block_2/Exercises Block 2 - Neural Networks.ipynb
index 683552c6214c49004d8f9ef8c0855590e2dd6a5c..f0aacd717a9f3992056d2cd5d8fce9ac37447acd 100644
--- a/notebooks/Block_2/Exercises Block 2 - Neural Networks.ipynb	
+++ b/notebooks/Block_2/Exercises Block 2 - Neural Networks.ipynb	
@@ -700,6 +700,50 @@
     "plt.plot(x_pred, y_pred)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(model.get_weights())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_val = model.get_weights()[0]\n",
+    "b_val = model.get_weights()[1]\n",
+    "plt.plot(data[:,0], data[:,1], 'o')\n",
+    "plt.axis([40, 85, -0.1, 1.2])\n",
+    "x_pred = np.linspace(40,85)\n",
+    "x_pred = np.resize(x_pred,[len(x_pred),1])\n",
+    "y_pred = 1 / (1 + np.exp(-x_pred*w_val - b_val))\n",
+    "plt.plot(x_pred, y_pred)\n",
+    "\n",
+    "# predicted probabilities\n",
+    "p_1 = 1 / (1 + np.exp(-x*w_val - b_val))\n",
+    "\n",
+    "# cross-entropy loss function\n",
+    "cross_entropy = -np.mean(y * np.log(p_1) + (1-y) * np.log(1-p_1))\n",
+    "print(\"Cross-entropy: \", cross_entropy)\n",
+    "y_pred = np.round(p_1, decimals=0).astype('int')\n",
+    "accuracy = np.mean(y==y_pred)\n",
+    "print(\"Accuracy: \", accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.evaluate(x, y)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb
index 1d12dd3bbf23e1b851d90914ee211e8c45d14edd..cac74e59aa3ec5d767da031d7792cb8c30e5725b 100644
--- a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
+++ b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
@@ -792,20 +792,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<keras.callbacks.History at 0x7ff9e419c450>"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "l0 = tf.keras.layers.Dense(units=1, activation = tf.nn.sigmoid, input_shape=[1])\n",
     "model = tf.keras.Sequential([l0])\n",