diff --git a/11_training_deep_neural_networks.ipynb b/11_training_deep_neural_networks.ipynb
index b0ff4aa..1976b6f 100644
--- a/11_training_deep_neural_networks.ipynb
+++ b/11_training_deep_neural_networks.ipynb
@@ -3582,7 +3582,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(X_train_full, y_train_full), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
+    "cifar10 = tf.keras.datasets.cifar10.load_data()\n",
+    "(X_train_full, y_train_full), (X_test, y_test) = cifar10\n",
     "\n",
     "X_train = X_train_full[5000:]\n",
     "y_train = y_train_full[5000:]\n",
@@ -3601,17 +3602,7 @@
    "cell_type": "code",
    "execution_count": 121,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-12-15 16:33:42.099263: I tensorflow/core/profiler/lib/profiler_session.cc:131] Profiler session initializing.\n",
-      "2021-12-15 16:33:42.099279: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.\n",
-      "2021-12-15 16:33:42.099858: I tensorflow/core/profiler/lib/profiler_session.cc:164] Profiler session tear down.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "early_stopping_cb = tf.keras.callbacks.EarlyStopping(patience=20,\n",
     "                                                     restore_best_weights=True)\n",
@@ -3629,14 +3620,34 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "UsageError: Line magic function `%tensorboard` not found.\n"
-     ]
+     "data": {
+      "text/html": [
+       "\n",
+       "      <iframe id=\"tensorboard-frame-d05c16b556c70d97\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
+       "      </iframe>\n",
+       "      <script>\n",
+       "        (function() {\n",
+       "          const frame = document.getElementById(\"tensorboard-frame-d05c16b556c70d97\");\n",
+       "          const url = new URL(\"/\", window.location);\n",
+       "          const port = 6006;\n",
+       "          if (port) {\n",
+       "            url.port = port;\n",
+       "          }\n",
+       "          frame.src = url;\n",
+       "        })();\n",
+       "      </script>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
+    "%load_ext tensorboard\n",
     "%tensorboard --logdir=./my_cifar10_logs"
    ]
   },
@@ -3644,7 +3655,95 @@
    "cell_type": "code",
    "execution_count": 123,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1404/1407 [============================>.] - ETA: 0s - loss: 4.0493 - accuracy: 0.1598INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 17s 10ms/step - loss: 4.0462 - accuracy: 0.1597 - val_loss: 2.1441 - val_accuracy: 0.2036\n",
+      "Epoch 2/100\n",
+      "1407/1407 [==============================] - ETA: 0s - loss: 2.0667 - accuracy: 0.2320INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 12s 9ms/step - loss: 2.0667 - accuracy: 0.2320 - val_loss: 2.0134 - val_accuracy: 0.2472\n",
+      "Epoch 3/100\n",
+      "1407/1407 [==============================] - ETA: 0s - loss: 1.9472 - accuracy: 0.2819INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.9472 - accuracy: 0.2819 - val_loss: 1.9427 - val_accuracy: 0.2796\n",
+      "Epoch 4/100\n",
+      "1405/1407 [============================>.] - ETA: 0s - loss: 1.8636 - accuracy: 0.3182INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.8637 - accuracy: 0.3182 - val_loss: 1.8934 - val_accuracy: 0.3222\n",
+      "Epoch 5/100\n",
+      "1402/1407 [============================>.] - ETA: 0s - loss: 1.7975 - accuracy: 0.3464INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.7974 - accuracy: 0.3465 - val_loss: 1.8389 - val_accuracy: 0.3284\n",
+      "Epoch 6/100\n",
+      "1407/1407 [==============================] - 9s 7ms/step - loss: 1.7446 - accuracy: 0.3664 - val_loss: 2.0006 - val_accuracy: 0.3030\n",
+      "Epoch 7/100\n",
+      "1407/1407 [==============================] - ETA: 0s - loss: 1.6974 - accuracy: 0.3852INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 1.6974 - accuracy: 0.3852 - val_loss: 1.7075 - val_accuracy: 0.3738\n",
+      "Epoch 8/100\n",
+      "1405/1407 [============================>.] - ETA: 0s - loss: 1.6605 - accuracy: 0.3984INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 1.6604 - accuracy: 0.3984 - val_loss: 1.6788 - val_accuracy: 0.3836\n",
+      "Epoch 9/100\n",
+      "1405/1407 [============================>.] - ETA: 0s - loss: 1.6322 - accuracy: 0.4114INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.6321 - accuracy: 0.4114 - val_loss: 1.6477 - val_accuracy: 0.4014\n",
+      "Epoch 10/100\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 1.6065 - accuracy: 0.4205 - val_loss: 1.6623 - val_accuracy: 0.3980\n",
+      "Epoch 11/100\n",
+      "1401/1407 [============================>.] - ETA: 0s - loss: 1.5843 - accuracy: 0.4287INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.5845 - accuracy: 0.4285 - val_loss: 1.6032 - val_accuracy: 0.4198\n",
+      "Epoch 12/100\n",
+      "1407/1407 [==============================] - 9s 6ms/step - loss: 1.5634 - accuracy: 0.4367 - val_loss: 1.6063 - val_accuracy: 0.4258\n",
+      "Epoch 13/100\n",
+      "1401/1407 [============================>.] - ETA: 0s - loss: 1.5443 - accuracy: 0.4420INFO:tensorflow:Assets written to: my_cifar10_model/assets\n",
+      "<<47 more lines>>\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 1.3247 - accuracy: 0.5256 - val_loss: 1.5130 - val_accuracy: 0.4616\n",
+      "Epoch 33/100\n",
+      "1407/1407 [==============================] - 13s 9ms/step - loss: 1.3164 - accuracy: 0.5286 - val_loss: 1.5284 - val_accuracy: 0.4686\n",
+      "Epoch 34/100\n",
+      "1407/1407 [==============================] - 12s 9ms/step - loss: 1.3091 - accuracy: 0.5303 - val_loss: 1.5208 - val_accuracy: 0.4682\n",
+      "Epoch 35/100\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 1.3026 - accuracy: 0.5319 - val_loss: 1.5479 - val_accuracy: 0.4604\n",
+      "Epoch 36/100\n",
+      "1407/1407 [==============================] - 11s 8ms/step - loss: 1.2930 - accuracy: 0.5378 - val_loss: 1.5443 - val_accuracy: 0.4580\n",
+      "Epoch 37/100\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 1.2833 - accuracy: 0.5406 - val_loss: 1.5165 - val_accuracy: 0.4710\n",
+      "Epoch 38/100\n",
+      "1407/1407 [==============================] - 11s 8ms/step - loss: 1.2763 - accuracy: 0.5433 - val_loss: 1.5345 - val_accuracy: 0.4672\n",
+      "Epoch 39/100\n",
+      "1407/1407 [==============================] - 12s 9ms/step - loss: 1.2687 - accuracy: 0.5437 - val_loss: 1.5162 - val_accuracy: 0.4712\n",
+      "Epoch 40/100\n",
+      "1407/1407 [==============================] - 11s 7ms/step - loss: 1.2623 - accuracy: 0.5490 - val_loss: 1.5717 - val_accuracy: 0.4566\n",
+      "Epoch 41/100\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.2580 - accuracy: 0.5467 - val_loss: 1.5296 - val_accuracy: 0.4738\n",
+      "Epoch 42/100\n",
+      "1407/1407 [==============================] - 13s 9ms/step - loss: 1.2469 - accuracy: 0.5532 - val_loss: 1.5179 - val_accuracy: 0.4690\n",
+      "Epoch 43/100\n",
+      "1407/1407 [==============================] - 11s 8ms/step - loss: 1.2404 - accuracy: 0.5542 - val_loss: 1.5542 - val_accuracy: 0.4566\n",
+      "Epoch 44/100\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 1.2292 - accuracy: 0.5605 - val_loss: 1.5536 - val_accuracy: 0.4608\n",
+      "Epoch 45/100\n",
+      "1407/1407 [==============================] - 12s 9ms/step - loss: 1.2276 - accuracy: 0.5606 - val_loss: 1.5522 - val_accuracy: 0.4624\n",
+      "Epoch 46/100\n",
+      "1407/1407 [==============================] - 13s 9ms/step - loss: 1.2200 - accuracy: 0.5637 - val_loss: 1.5339 - val_accuracy: 0.4794\n",
+      "Epoch 47/100\n",
+      "1407/1407 [==============================] - 13s 9ms/step - loss: 1.2080 - accuracy: 0.5677 - val_loss: 1.5451 - val_accuracy: 0.4688\n",
+      "Epoch 48/100\n",
+      "1407/1407 [==============================] - 15s 10ms/step - loss: 1.2050 - accuracy: 0.5675 - val_loss: 1.5209 - val_accuracy: 0.4770\n",
+      "Epoch 49/100\n",
+      "1407/1407 [==============================] - 10s 7ms/step - loss: 1.1947 - accuracy: 0.5718 - val_loss: 1.5435 - val_accuracy: 0.4736\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7fb9f02fc070>"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.fit(X_train, y_train, epochs=100,\n",
     "          validation_data=(X_valid, y_valid),\n",
@@ -3655,7 +3754,25 @@
    "cell_type": "code",
    "execution_count": 124,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "157/157 [==============================] - 0s 2ms/step - loss: 1.5062 - accuracy: 0.4676\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[1.5061508417129517, 0.4675999879837036]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.evaluate(X_valid, y_valid)"
    ]
@@ -3664,7 +3781,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The model with the lowest validation loss gets about 46.7% accuracy on the validation set. It took 29 epochs to reach the lowest validation loss, with roughly 10 seconds per epoch on my laptop (without a GPU). Let's see if we can improve the model using Batch Normalization."
+    "The model with the lowest validation loss gets about 46.8% accuracy on the validation set. It took 29 epochs to reach the lowest validation loss, with roughly 10 seconds per epoch on my laptop (without a GPU). Let's see if we can improve the model using Batch Normalization."
    ]
   },
   {
@@ -3690,7 +3807,95 @@
    "cell_type": "code",
    "execution_count": 125,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1403/1407 [============================>.] - ETA: 0s - loss: 2.0377 - accuracy: 0.2523INFO:tensorflow:Assets written to: my_cifar10_bn_model/assets\n",
+      "1407/1407 [==============================] - 32s 18ms/step - loss: 2.0374 - accuracy: 0.2525 - val_loss: 1.8766 - val_accuracy: 0.3154\n",
+      "Epoch 2/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.7874 - accuracy: 0.3542 - val_loss: 1.8784 - val_accuracy: 0.3268\n",
+      "Epoch 3/100\n",
+      "1407/1407 [==============================] - 20s 15ms/step - loss: 1.6806 - accuracy: 0.3969 - val_loss: 1.9764 - val_accuracy: 0.3252\n",
+      "Epoch 4/100\n",
+      "1403/1407 [============================>.] - ETA: 0s - loss: 1.6111 - accuracy: 0.4229INFO:tensorflow:Assets written to: my_cifar10_bn_model/assets\n",
+      "1407/1407 [==============================] - 24s 17ms/step - loss: 1.6112 - accuracy: 0.4228 - val_loss: 1.7087 - val_accuracy: 0.3750\n",
+      "Epoch 5/100\n",
+      "1402/1407 [============================>.] - ETA: 0s - loss: 1.5520 - accuracy: 0.4478INFO:tensorflow:Assets written to: my_cifar10_bn_model/assets\n",
+      "1407/1407 [==============================] - 21s 15ms/step - loss: 1.5521 - accuracy: 0.4476 - val_loss: 1.6272 - val_accuracy: 0.4176\n",
+      "Epoch 6/100\n",
+      "1406/1407 [============================>.] - ETA: 0s - loss: 1.5030 - accuracy: 0.4659INFO:tensorflow:Assets written to: my_cifar10_bn_model/assets\n",
+      "1407/1407 [==============================] - 23s 16ms/step - loss: 1.5030 - accuracy: 0.4660 - val_loss: 1.5401 - val_accuracy: 0.4452\n",
+      "Epoch 7/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.4559 - accuracy: 0.4812 - val_loss: 1.6990 - val_accuracy: 0.3952\n",
+      "Epoch 8/100\n",
+      "1403/1407 [============================>.] - ETA: 0s - loss: 1.4169 - accuracy: 0.4987INFO:tensorflow:Assets written to: my_cifar10_bn_model/assets\n",
+      "1407/1407 [==============================] - 21s 15ms/step - loss: 1.4168 - accuracy: 0.4987 - val_loss: 1.5078 - val_accuracy: 0.4652\n",
+      "Epoch 9/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.3863 - accuracy: 0.5123 - val_loss: 1.5513 - val_accuracy: 0.4470\n",
+      "Epoch 10/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.3514 - accuracy: 0.5216 - val_loss: 1.5208 - val_accuracy: 0.4562\n",
+      "Epoch 11/100\n",
+      "1407/1407 [==============================] - 16s 12ms/step - loss: 1.3220 - accuracy: 0.5314 - val_loss: 1.7301 - val_accuracy: 0.4206\n",
+      "Epoch 12/100\n",
+      "1404/1407 [============================>.] - ETA: 0s - loss: 1.2933 - accuracy: 0.5410INFO:tensorflow:Assets written to: my_cifar10_bn_model/assets\n",
+      "1407/1407 [==============================] - 25s 18ms/step - loss: 1.2931 - accuracy: 0.5410 - val_loss: 1.4909 - val_accuracy: 0.4734\n",
+      "Epoch 13/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.2702 - accuracy: 0.5490 - val_loss: 1.5256 - val_accuracy: 0.4636\n",
+      "Epoch 14/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.2424 - accuracy: 0.5591 - val_loss: 1.5569 - val_accuracy: 0.4624\n",
+      "Epoch 15/100\n",
+      "<<12 more lines>>\n",
+      "Epoch 21/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.1174 - accuracy: 0.6066 - val_loss: 1.5241 - val_accuracy: 0.4828\n",
+      "Epoch 22/100\n",
+      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0978 - accuracy: 0.6128 - val_loss: 1.5313 - val_accuracy: 0.4772\n",
+      "Epoch 23/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.0844 - accuracy: 0.6198 - val_loss: 1.4993 - val_accuracy: 0.4924\n",
+      "Epoch 24/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.0677 - accuracy: 0.6244 - val_loss: 1.4622 - val_accuracy: 0.5078\n",
+      "Epoch 25/100\n",
+      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0571 - accuracy: 0.6297 - val_loss: 1.4917 - val_accuracy: 0.4990\n",
+      "Epoch 26/100\n",
+      "1407/1407 [==============================] - 19s 14ms/step - loss: 1.0395 - accuracy: 0.6327 - val_loss: 1.4888 - val_accuracy: 0.4896\n",
+      "Epoch 27/100\n",
+      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0298 - accuracy: 0.6370 - val_loss: 1.5358 - val_accuracy: 0.5024\n",
+      "Epoch 28/100\n",
+      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0150 - accuracy: 0.6444 - val_loss: 1.5219 - val_accuracy: 0.5030\n",
+      "Epoch 29/100\n",
+      "1407/1407 [==============================] - 16s 12ms/step - loss: 1.0100 - accuracy: 0.6456 - val_loss: 1.4933 - val_accuracy: 0.5098\n",
+      "Epoch 30/100\n",
+      "1407/1407 [==============================] - 20s 14ms/step - loss: 0.9956 - accuracy: 0.6492 - val_loss: 1.4756 - val_accuracy: 0.5012\n",
+      "Epoch 31/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 0.9787 - accuracy: 0.6576 - val_loss: 1.5181 - val_accuracy: 0.4936\n",
+      "Epoch 32/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 0.9710 - accuracy: 0.6565 - val_loss: 1.7510 - val_accuracy: 0.4568\n",
+      "Epoch 33/100\n",
+      "1407/1407 [==============================] - 20s 14ms/step - loss: 0.9613 - accuracy: 0.6628 - val_loss: 1.5576 - val_accuracy: 0.4910\n",
+      "Epoch 34/100\n",
+      "1407/1407 [==============================] - 19s 14ms/step - loss: 0.9530 - accuracy: 0.6651 - val_loss: 1.5087 - val_accuracy: 0.5046\n",
+      "Epoch 35/100\n",
+      "1407/1407 [==============================] - 19s 13ms/step - loss: 0.9388 - accuracy: 0.6701 - val_loss: 1.5534 - val_accuracy: 0.4950\n",
+      "Epoch 36/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 0.9331 - accuracy: 0.6743 - val_loss: 1.5033 - val_accuracy: 0.5046\n",
+      "Epoch 37/100\n",
+      "1407/1407 [==============================] - 19s 14ms/step - loss: 0.9144 - accuracy: 0.6808 - val_loss: 1.5679 - val_accuracy: 0.5028\n",
+      "157/157 [==============================] - 0s 2ms/step - loss: 1.4236 - accuracy: 0.5074\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[1.4236289262771606, 0.5073999762535095]"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tf.random.set_seed(42)\n",
     "\n",
@@ -3700,6 +3905,7 @@
     "    model.add(tf.keras.layers.Dense(100, kernel_initializer=\"he_normal\"))\n",
     "    model.add(tf.keras.layers.BatchNormalization())\n",
     "    model.add(tf.keras.layers.Activation(\"swish\"))\n",
+    "\n",
     "model.add(tf.keras.layers.Dense(10, activation=\"softmax\"))\n",
     "\n",
     "optimizer = tf.keras.optimizers.Nadam(learning_rate=5e-4)\n",
@@ -3746,7 +3952,101 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1403/1407 [============================>.] - ETA: 0s - loss: 1.9386 - accuracy: 0.3045INFO:tensorflow:Assets written to: my_cifar10_selu_model/assets\n",
+      "1407/1407 [==============================] - 20s 13ms/step - loss: 1.9385 - accuracy: 0.3046 - val_loss: 1.8175 - val_accuracy: 0.3510\n",
+      "Epoch 2/100\n",
+      "1405/1407 [============================>.] - ETA: 0s - loss: 1.7241 - accuracy: 0.3869INFO:tensorflow:Assets written to: my_cifar10_selu_model/assets\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.7241 - accuracy: 0.3869 - val_loss: 1.7677 - val_accuracy: 0.3614\n",
+      "Epoch 3/100\n",
+      "1407/1407 [==============================] - ETA: 0s - loss: 1.6272 - accuracy: 0.4263INFO:tensorflow:Assets written to: my_cifar10_selu_model/assets\n",
+      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.6272 - accuracy: 0.4263 - val_loss: 1.6878 - val_accuracy: 0.4054\n",
+      "Epoch 4/100\n",
+      "1406/1407 [============================>.] - ETA: 0s - loss: 1.5644 - accuracy: 0.4492INFO:tensorflow:Assets written to: my_cifar10_selu_model/assets\n",
+      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5643 - accuracy: 0.4492 - val_loss: 1.6589 - val_accuracy: 0.4304\n",
+      "Epoch 5/100\n",
+      "1404/1407 [============================>.] - ETA: 0s - loss: 1.5080 - accuracy: 0.4712INFO:tensorflow:Assets written to: my_cifar10_selu_model/assets\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.5080 - accuracy: 0.4712 - val_loss: 1.5651 - val_accuracy: 0.4538\n",
+      "Epoch 6/100\n",
+      "1404/1407 [============================>.] - ETA: 0s - loss: 1.4611 - accuracy: 0.4873INFO:tensorflow:Assets written to: my_cifar10_selu_model/assets\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.4613 - accuracy: 0.4872 - val_loss: 1.5305 - val_accuracy: 0.4678\n",
+      "Epoch 7/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.4174 - accuracy: 0.5077 - val_loss: 1.5346 - val_accuracy: 0.4558\n",
+      "Epoch 8/100\n",
+      "1406/1407 [============================>.] - ETA: 0s - loss: 1.3781 - accuracy: 0.5175INFO:tensorflow:Assets written to: my_cifar10_selu_model/assets\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.3781 - accuracy: 0.5175 - val_loss: 1.4773 - val_accuracy: 0.4882\n",
+      "Epoch 9/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.3413 - accuracy: 0.5345 - val_loss: 1.5021 - val_accuracy: 0.4764\n",
+      "Epoch 10/100\n",
+      "1407/1407 [==============================] - 15s 10ms/step - loss: 1.3182 - accuracy: 0.5422 - val_loss: 1.5709 - val_accuracy: 0.4762\n",
+      "Epoch 11/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.2832 - accuracy: 0.5571 - val_loss: 1.5345 - val_accuracy: 0.4868\n",
+      "Epoch 12/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.2557 - accuracy: 0.5667 - val_loss: 1.5024 - val_accuracy: 0.4900\n",
+      "Epoch 13/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.2373 - accuracy: 0.5710 - val_loss: 1.5114 - val_accuracy: 0.5028\n",
+      "Epoch 14/100\n",
+      "1404/1407 [============================>.] - ETA: 0s - loss: 1.2071 - accuracy: 0.5846INFO:tensorflow:Assets written to: my_cifar10_selu_model/assets\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.2073 - accuracy: 0.5847 - val_loss: 1.4608 - val_accuracy: 0.5026\n",
+      "Epoch 15/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.1843 - accuracy: 0.5940 - val_loss: 1.4962 - val_accuracy: 0.5038\n",
+      "Epoch 16/100\n",
+      "1407/1407 [==============================] - 16s 12ms/step - loss: 1.1617 - accuracy: 0.6026 - val_loss: 1.5255 - val_accuracy: 0.5062\n",
+      "Epoch 17/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.1452 - accuracy: 0.6084 - val_loss: 1.5057 - val_accuracy: 0.5036\n",
+      "Epoch 18/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.1297 - accuracy: 0.6145 - val_loss: 1.5097 - val_accuracy: 0.5010\n",
+      "Epoch 19/100\n",
+      "1407/1407 [==============================] - 16s 12ms/step - loss: 1.1004 - accuracy: 0.6245 - val_loss: 1.5218 - val_accuracy: 0.5014\n",
+      "Epoch 20/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.0971 - accuracy: 0.6304 - val_loss: 1.5253 - val_accuracy: 0.5090\n",
+      "Epoch 21/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.0670 - accuracy: 0.6345 - val_loss: 1.5006 - val_accuracy: 0.5034\n",
+      "Epoch 22/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.0544 - accuracy: 0.6407 - val_loss: 1.5244 - val_accuracy: 0.5010\n",
+      "Epoch 23/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.0338 - accuracy: 0.6502 - val_loss: 1.5355 - val_accuracy: 0.5096\n",
+      "Epoch 24/100\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.0281 - accuracy: 0.6514 - val_loss: 1.5257 - val_accuracy: 0.5164\n",
+      "Epoch 25/100\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.4097 - accuracy: 0.6478 - val_loss: 1.8203 - val_accuracy: 0.3514\n",
+      "Epoch 26/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.3733 - accuracy: 0.5157 - val_loss: 1.5600 - val_accuracy: 0.4664\n",
+      "Epoch 27/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.2032 - accuracy: 0.5814 - val_loss: 1.5367 - val_accuracy: 0.4944\n",
+      "Epoch 28/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.1291 - accuracy: 0.6121 - val_loss: 1.5333 - val_accuracy: 0.4852\n",
+      "Epoch 29/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.0734 - accuracy: 0.6317 - val_loss: 1.5475 - val_accuracy: 0.5032\n",
+      "Epoch 30/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.0294 - accuracy: 0.6469 - val_loss: 1.5400 - val_accuracy: 0.5052\n",
+      "Epoch 31/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.0081 - accuracy: 0.6605 - val_loss: 1.5617 - val_accuracy: 0.4856\n",
+      "Epoch 32/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.0109 - accuracy: 0.6603 - val_loss: 1.5727 - val_accuracy: 0.5124\n",
+      "Epoch 33/100\n",
+      "1407/1407 [==============================] - 17s 12ms/step - loss: 0.9646 - accuracy: 0.6762 - val_loss: 1.5333 - val_accuracy: 0.5174\n",
+      "Epoch 34/100\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 0.9597 - accuracy: 0.6789 - val_loss: 1.5601 - val_accuracy: 0.5016\n",
+      "157/157 [==============================] - 0s 1ms/step - loss: 1.4608 - accuracy: 0.5026\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[1.4607702493667603, 0.5026000142097473]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tf.random.set_seed(42)\n",
     "\n",
@@ -3756,6 +4056,7 @@
     "    model.add(tf.keras.layers.Dense(100,\n",
     "                                    kernel_initializer=\"lecun_normal\",\n",
     "                                    activation=\"selu\"))\n",
+    "\n",
     "model.add(tf.keras.layers.Dense(10, activation=\"softmax\"))\n",
     "\n",
     "optimizer = tf.keras.optimizers.Nadam(learning_rate=7e-4)\n",
@@ -3804,7 +4105,89 @@
    "cell_type": "code",
    "execution_count": 127,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "1405/1407 [============================>.] - ETA: 0s - loss: 1.8953 - accuracy: 0.3240INFO:tensorflow:Assets written to: my_cifar10_alpha_dropout_model/assets\n",
+      "1407/1407 [==============================] - 18s 11ms/step - loss: 1.8950 - accuracy: 0.3239 - val_loss: 1.7556 - val_accuracy: 0.3812\n",
+      "Epoch 2/100\n",
+      "1403/1407 [============================>.] - ETA: 0s - loss: 1.6618 - accuracy: 0.4129INFO:tensorflow:Assets written to: my_cifar10_alpha_dropout_model/assets\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.6618 - accuracy: 0.4130 - val_loss: 1.6563 - val_accuracy: 0.4114\n",
+      "Epoch 3/100\n",
+      "1402/1407 [============================>.] - ETA: 0s - loss: 1.5772 - accuracy: 0.4431INFO:tensorflow:Assets written to: my_cifar10_alpha_dropout_model/assets\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.5770 - accuracy: 0.4432 - val_loss: 1.6507 - val_accuracy: 0.4232\n",
+      "Epoch 4/100\n",
+      "1406/1407 [============================>.] - ETA: 0s - loss: 1.5081 - accuracy: 0.4673INFO:tensorflow:Assets written to: my_cifar10_alpha_dropout_model/assets\n",
+      "1407/1407 [==============================] - 15s 10ms/step - loss: 1.5081 - accuracy: 0.4672 - val_loss: 1.5892 - val_accuracy: 0.4566\n",
+      "Epoch 5/100\n",
+      "1403/1407 [============================>.] - ETA: 0s - loss: 1.4560 - accuracy: 0.4902INFO:tensorflow:Assets written to: my_cifar10_alpha_dropout_model/assets\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.4561 - accuracy: 0.4902 - val_loss: 1.5382 - val_accuracy: 0.4696\n",
+      "Epoch 6/100\n",
+      "1401/1407 [============================>.] - ETA: 0s - loss: 1.4095 - accuracy: 0.5050INFO:tensorflow:Assets written to: my_cifar10_alpha_dropout_model/assets\n",
+      "1407/1407 [==============================] - 16s 11ms/step - loss: 1.4094 - accuracy: 0.5050 - val_loss: 1.5236 - val_accuracy: 0.4818\n",
+      "Epoch 7/100\n",
+      "1401/1407 [============================>.] - ETA: 0s - loss: 1.3634 - accuracy: 0.5234INFO:tensorflow:Assets written to: my_cifar10_alpha_dropout_model/assets\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.3636 - accuracy: 0.5232 - val_loss: 1.5139 - val_accuracy: 0.4840\n",
+      "Epoch 8/100\n",
+      "1405/1407 [============================>.] - ETA: 0s - loss: 1.3297 - accuracy: 0.5377INFO:tensorflow:Assets written to: my_cifar10_alpha_dropout_model/assets\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.3296 - accuracy: 0.5378 - val_loss: 1.4780 - val_accuracy: 0.4982\n",
+      "Epoch 9/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.2907 - accuracy: 0.5485 - val_loss: 1.5151 - val_accuracy: 0.4854\n",
+      "Epoch 10/100\n",
+      "1407/1407 [==============================] - 13s 10ms/step - loss: 1.2559 - accuracy: 0.5646 - val_loss: 1.4980 - val_accuracy: 0.4976\n",
+      "Epoch 11/100\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.2221 - accuracy: 0.5767 - val_loss: 1.5199 - val_accuracy: 0.4990\n",
+      "Epoch 12/100\n",
+      "1407/1407 [==============================] - 13s 9ms/step - loss: 1.1960 - accuracy: 0.5870 - val_loss: 1.5167 - val_accuracy: 0.5030\n",
+      "Epoch 13/100\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.1684 - accuracy: 0.5955 - val_loss: 1.5815 - val_accuracy: 0.5014\n",
+      "Epoch 14/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.1463 - accuracy: 0.6025 - val_loss: 1.5427 - val_accuracy: 0.5112\n",
+      "Epoch 15/100\n",
+      "1407/1407 [==============================] - 13s 9ms/step - loss: 1.1125 - accuracy: 0.6169 - val_loss: 1.5868 - val_accuracy: 0.5212\n",
+      "Epoch 16/100\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 1.0854 - accuracy: 0.6243 - val_loss: 1.6234 - val_accuracy: 0.5090\n",
+      "Epoch 17/100\n",
+      "1407/1407 [==============================] - 15s 11ms/step - loss: 1.0668 - accuracy: 0.6328 - val_loss: 1.6162 - val_accuracy: 0.5072\n",
+      "Epoch 18/100\n",
+      "1407/1407 [==============================] - 15s 10ms/step - loss: 1.0440 - accuracy: 0.6442 - val_loss: 1.5748 - val_accuracy: 0.5162\n",
+      "Epoch 19/100\n",
+      "1407/1407 [==============================] - 12s 9ms/step - loss: 1.0272 - accuracy: 0.6477 - val_loss: 1.6518 - val_accuracy: 0.5200\n",
+      "Epoch 20/100\n",
+      "1407/1407 [==============================] - 13s 10ms/step - loss: 1.0007 - accuracy: 0.6594 - val_loss: 1.6224 - val_accuracy: 0.5186\n",
+      "Epoch 21/100\n",
+      "1407/1407 [==============================] - 15s 10ms/step - loss: 0.9824 - accuracy: 0.6639 - val_loss: 1.6972 - val_accuracy: 0.5136\n",
+      "Epoch 22/100\n",
+      "1407/1407 [==============================] - 12s 9ms/step - loss: 0.9660 - accuracy: 0.6714 - val_loss: 1.7210 - val_accuracy: 0.5278\n",
+      "Epoch 23/100\n",
+      "1407/1407 [==============================] - 13s 10ms/step - loss: 0.9472 - accuracy: 0.6780 - val_loss: 1.6436 - val_accuracy: 0.5006\n",
+      "Epoch 24/100\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 0.9314 - accuracy: 0.6819 - val_loss: 1.7059 - val_accuracy: 0.5160\n",
+      "Epoch 25/100\n",
+      "1407/1407 [==============================] - 13s 9ms/step - loss: 0.9172 - accuracy: 0.6888 - val_loss: 1.6926 - val_accuracy: 0.5200\n",
+      "Epoch 26/100\n",
+      "1407/1407 [==============================] - 14s 10ms/step - loss: 0.8990 - accuracy: 0.6947 - val_loss: 1.7705 - val_accuracy: 0.5148\n",
+      "Epoch 27/100\n",
+      "1407/1407 [==============================] - 13s 9ms/step - loss: 0.8758 - accuracy: 0.7028 - val_loss: 1.7023 - val_accuracy: 0.5198\n",
+      "Epoch 28/100\n",
+      "1407/1407 [==============================] - 12s 8ms/step - loss: 0.8622 - accuracy: 0.7090 - val_loss: 1.7567 - val_accuracy: 0.5184\n",
+      "157/157 [==============================] - 0s 1ms/step - loss: 1.4780 - accuracy: 0.4982\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[1.4779616594314575, 0.498199999332428]"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tf.random.set_seed(42)\n",
     "\n",
@@ -3926,7 +4309,18 @@
    "cell_type": "code",
    "execution_count": 131,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4984"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tf.random.set_seed(42)\n",
     "\n",
@@ -3939,7 +4333,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We get back to the accuracy of the model without dropout in this case (about 50.3% accuracy).\n",
+    "We get back to roughly the accuracy of the model without dropout in this case (about 50.3% accuracy).\n",
     "\n",
     "So the best model we got in this exercise is the Batch Normalization model."
    ]
@@ -3980,7 +4374,27 @@
    "cell_type": "code",
    "execution_count": 133,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "352/352 [==============================] - 3s 8ms/step - loss: nan - accuracy: 0.1706\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "batch_size = 128\n",
     "rates, losses = find_learning_rate(model, X_train_scaled, y_train, epochs=1,\n",
@@ -4016,10 +4430,48 @@
    "cell_type": "code",
    "execution_count": 135,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 2.0559 - accuracy: 0.2839 - val_loss: 1.7917 - val_accuracy: 0.3768\n",
+      "Epoch 2/15\n",
+      "352/352 [==============================] - 3s 8ms/step - loss: 1.7596 - accuracy: 0.3797 - val_loss: 1.6566 - val_accuracy: 0.4258\n",
+      "Epoch 3/15\n",
+      "352/352 [==============================] - 3s 8ms/step - loss: 1.6199 - accuracy: 0.4247 - val_loss: 1.6395 - val_accuracy: 0.4260\n",
+      "Epoch 4/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 1.5451 - accuracy: 0.4524 - val_loss: 1.6202 - val_accuracy: 0.4408\n",
+      "Epoch 5/15\n",
+      "352/352 [==============================] - 3s 8ms/step - loss: 1.4952 - accuracy: 0.4691 - val_loss: 1.5981 - val_accuracy: 0.4488\n",
+      "Epoch 6/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 1.4541 - accuracy: 0.4842 - val_loss: 1.5720 - val_accuracy: 0.4490\n",
+      "Epoch 7/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 1.4171 - accuracy: 0.4967 - val_loss: 1.6035 - val_accuracy: 0.4470\n",
+      "Epoch 8/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 1.3497 - accuracy: 0.5194 - val_loss: 1.4918 - val_accuracy: 0.4864\n",
+      "Epoch 9/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 1.2788 - accuracy: 0.5459 - val_loss: 1.5597 - val_accuracy: 0.4672\n",
+      "Epoch 10/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 1.2070 - accuracy: 0.5707 - val_loss: 1.5845 - val_accuracy: 0.4864\n",
+      "Epoch 11/15\n",
+      "352/352 [==============================] - 3s 10ms/step - loss: 1.1433 - accuracy: 0.5926 - val_loss: 1.5293 - val_accuracy: 0.4998\n",
+      "Epoch 12/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 1.0745 - accuracy: 0.6182 - val_loss: 1.5118 - val_accuracy: 0.5072\n",
+      "Epoch 13/15\n",
+      "352/352 [==============================] - 3s 10ms/step - loss: 1.0030 - accuracy: 0.6413 - val_loss: 1.5388 - val_accuracy: 0.5204\n",
+      "Epoch 14/15\n",
+      "352/352 [==============================] - 3s 10ms/step - loss: 0.9388 - accuracy: 0.6654 - val_loss: 1.5547 - val_accuracy: 0.5210\n",
+      "Epoch 15/15\n",
+      "352/352 [==============================] - 3s 9ms/step - loss: 0.8989 - accuracy: 0.6805 - val_loss: 1.5835 - val_accuracy: 0.5242\n"
+     ]
+    }
+   ],
    "source": [
     "n_epochs = 15\n",
-    "onecycle = OneCycleScheduler(math.ceil(len(X_train_scaled) / batch_size) * n_epochs, max_rate=0.05)\n",
+    "n_iterations = math.ceil(len(X_train_scaled) / batch_size) * n_epochs\n",
+    "onecycle = OneCycleScheduler(n_iterations, max_lr=0.05)\n",
     "history = model.fit(X_train_scaled, y_train, epochs=n_epochs, batch_size=batch_size,\n",
     "                    validation_data=(X_valid_scaled, y_valid),\n",
     "                    callbacks=[onecycle])"
@@ -4031,32 +4483,6 @@
    "source": [
     "One cycle allowed us to train the model in just 15 epochs, each taking only 2 seconds (thanks to the larger batch size). This is several times faster than the fastest model we trained so far. Moreover, we improved the model's performance (from 50.7% to 52.0%)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "time.time()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!date"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {