From 0ab5cb8bc184ff8cb190919ef5ab6d4334912f14 Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Thu, 24 Mar 2022 18:36:43 +0000
Subject: [PATCH] with output

---
 ... - Object Detection and Segmentation.ipynb | 206 +++++++-----------
 1 file changed, 76 insertions(+), 130 deletions(-)

diff --git a/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb b/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb
index a7a8616..5e433fe 100644
--- a/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb	
+++ b/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb	
@@ -504,7 +504,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the TensorBoard notebook extension\n",
+    "# Load the TensorBoard notebook extension on google colab\n",
     "%load_ext tensorboard\n",
     "\n",
     "os.makedirs(logdir, exist_ok=True)\n",
@@ -549,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -574,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +584,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -648,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 62,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -664,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 63,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -711,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 64,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -726,7 +726,7 @@
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_5 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
+      " input_9 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
       "                                                                 \n",
       " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
       "                                                                 \n",
@@ -824,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -856,7 +856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -887,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -896,7 +896,7 @@
        "(480, 4, 4, 512)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -914,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -923,7 +923,7 @@
        "(480, 8)"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -934,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -953,7 +953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -970,26 +970,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"model_2\"\n",
+      "Model: \"model_4\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_6 (InputLayer)        [(None, 4, 4, 512)]       0         \n",
+      " input_10 (InputLayer)       [(None, 4, 4, 512)]       0         \n",
       "                                                                 \n",
-      " flatten_3 (Flatten)         (None, 8192)              0         \n",
+      " flatten_5 (Flatten)         (None, 8192)              0         \n",
       "                                                                 \n",
-      " dense_6 (Dense)             (None, 256)               2097408   \n",
+      " dense_10 (Dense)            (None, 256)               2097408   \n",
       "                                                                 \n",
-      " dropout_3 (Dropout)         (None, 256)               0         \n",
+      " dropout_5 (Dropout)         (None, 256)               0         \n",
       "                                                                 \n",
-      " dense_7 (Dense)             (None, 8)                 2056      \n",
+      " dense_11 (Dense)            (None, 8)                 2056      \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 2,099,464\n",
@@ -1005,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1013,65 +1013,65 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/30\n",
-      "15/15 [==============================] - 2s 102ms/step - loss: 44.8126 - accuracy: 0.2958 - val_loss: 25.7535 - val_accuracy: 0.4096\n",
+      "15/15 [==============================] - 2s 120ms/step - loss: 47.5901 - accuracy: 0.3042 - val_loss: 21.6888 - val_accuracy: 0.4217\n",
       "Epoch 2/30\n",
-      "15/15 [==============================] - 1s 90ms/step - loss: 13.9936 - accuracy: 0.6562 - val_loss: 17.8460 - val_accuracy: 0.4699\n",
+      "15/15 [==============================] - 1s 104ms/step - loss: 18.2980 - accuracy: 0.5708 - val_loss: 18.4650 - val_accuracy: 0.4337\n",
       "Epoch 3/30\n",
-      "15/15 [==============================] - 1s 95ms/step - loss: 11.5003 - accuracy: 0.7000 - val_loss: 25.4290 - val_accuracy: 0.4578\n",
+      "15/15 [==============================] - 1s 82ms/step - loss: 7.1367 - accuracy: 0.7500 - val_loss: 20.4785 - val_accuracy: 0.4458\n",
       "Epoch 4/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 7.5189 - accuracy: 0.7500 - val_loss: 21.8036 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 82ms/step - loss: 6.9484 - accuracy: 0.7771 - val_loss: 18.7156 - val_accuracy: 0.5060\n",
       "Epoch 5/30\n",
-      "15/15 [==============================] - 1s 80ms/step - loss: 5.2865 - accuracy: 0.8313 - val_loss: 20.3280 - val_accuracy: 0.4699\n",
+      "15/15 [==============================] - 1s 100ms/step - loss: 5.1403 - accuracy: 0.8208 - val_loss: 25.2956 - val_accuracy: 0.5181\n",
       "Epoch 6/30\n",
-      "15/15 [==============================] - 1s 88ms/step - loss: 4.7574 - accuracy: 0.8417 - val_loss: 30.6355 - val_accuracy: 0.4699\n",
+      "15/15 [==============================] - 1s 87ms/step - loss: 5.6793 - accuracy: 0.8167 - val_loss: 22.8389 - val_accuracy: 0.4940\n",
       "Epoch 7/30\n",
-      "15/15 [==============================] - 1s 80ms/step - loss: 4.5704 - accuracy: 0.8521 - val_loss: 25.3694 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 84ms/step - loss: 4.9009 - accuracy: 0.8500 - val_loss: 25.7880 - val_accuracy: 0.5663\n",
       "Epoch 8/30\n",
-      "15/15 [==============================] - 1s 78ms/step - loss: 3.4702 - accuracy: 0.8771 - val_loss: 22.3616 - val_accuracy: 0.5783\n",
+      "15/15 [==============================] - 1s 104ms/step - loss: 3.1247 - accuracy: 0.8854 - val_loss: 21.7678 - val_accuracy: 0.4819\n",
       "Epoch 9/30\n",
-      "15/15 [==============================] - 1s 78ms/step - loss: 2.9711 - accuracy: 0.8875 - val_loss: 27.0335 - val_accuracy: 0.5060\n",
+      "15/15 [==============================] - 1s 82ms/step - loss: 4.9890 - accuracy: 0.8729 - val_loss: 28.8869 - val_accuracy: 0.4940\n",
       "Epoch 10/30\n",
-      "15/15 [==============================] - 1s 86ms/step - loss: 2.8419 - accuracy: 0.9125 - val_loss: 33.1303 - val_accuracy: 0.4578\n",
+      "15/15 [==============================] - 1s 90ms/step - loss: 2.8775 - accuracy: 0.8958 - val_loss: 33.2878 - val_accuracy: 0.5060\n",
       "Epoch 11/30\n",
-      "15/15 [==============================] - 1s 84ms/step - loss: 3.0754 - accuracy: 0.8917 - val_loss: 22.9139 - val_accuracy: 0.5422\n",
+      "15/15 [==============================] - 1s 100ms/step - loss: 1.6595 - accuracy: 0.9354 - val_loss: 35.3880 - val_accuracy: 0.3976\n",
       "Epoch 12/30\n",
-      "15/15 [==============================] - 1s 88ms/step - loss: 1.9240 - accuracy: 0.9354 - val_loss: 27.3888 - val_accuracy: 0.5060\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 2.5381 - accuracy: 0.9125 - val_loss: 28.2466 - val_accuracy: 0.5181\n",
       "Epoch 13/30\n",
-      "15/15 [==============================] - 1s 96ms/step - loss: 2.5214 - accuracy: 0.9125 - val_loss: 26.3481 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 1s 80ms/step - loss: 3.0611 - accuracy: 0.9250 - val_loss: 29.1160 - val_accuracy: 0.4819\n",
       "Epoch 14/30\n",
-      "15/15 [==============================] - 1s 77ms/step - loss: 1.1142 - accuracy: 0.9542 - val_loss: 31.6295 - val_accuracy: 0.4458\n",
+      "15/15 [==============================] - 1s 91ms/step - loss: 1.9787 - accuracy: 0.9104 - val_loss: 27.5122 - val_accuracy: 0.4699\n",
       "Epoch 15/30\n",
-      "15/15 [==============================] - 1s 77ms/step - loss: 2.7297 - accuracy: 0.9250 - val_loss: 27.2849 - val_accuracy: 0.5542\n",
+      "15/15 [==============================] - 1s 88ms/step - loss: 1.8686 - accuracy: 0.9417 - val_loss: 34.6468 - val_accuracy: 0.4337\n",
       "Epoch 16/30\n",
-      "15/15 [==============================] - 1s 98ms/step - loss: 2.8289 - accuracy: 0.9312 - val_loss: 37.3016 - val_accuracy: 0.4940\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 2.2055 - accuracy: 0.9187 - val_loss: 33.5793 - val_accuracy: 0.5181\n",
       "Epoch 17/30\n",
-      "15/15 [==============================] - 1s 82ms/step - loss: 1.7244 - accuracy: 0.9333 - val_loss: 32.6699 - val_accuracy: 0.5181\n",
+      "15/15 [==============================] - 1s 87ms/step - loss: 1.7554 - accuracy: 0.9417 - val_loss: 29.8020 - val_accuracy: 0.5301\n",
       "Epoch 18/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 1.6433 - accuracy: 0.9625 - val_loss: 34.0047 - val_accuracy: 0.4578\n",
+      "15/15 [==============================] - 1s 102ms/step - loss: 1.1292 - accuracy: 0.9646 - val_loss: 29.8960 - val_accuracy: 0.4578\n",
       "Epoch 19/30\n",
-      "15/15 [==============================] - 1s 83ms/step - loss: 2.1252 - accuracy: 0.9208 - val_loss: 32.4775 - val_accuracy: 0.4940\n",
+      "15/15 [==============================] - 1s 86ms/step - loss: 2.0701 - accuracy: 0.9375 - val_loss: 36.3813 - val_accuracy: 0.4578\n",
       "Epoch 20/30\n",
-      "15/15 [==============================] - 1s 93ms/step - loss: 1.9605 - accuracy: 0.9417 - val_loss: 38.5654 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 81ms/step - loss: 1.9079 - accuracy: 0.9375 - val_loss: 33.0938 - val_accuracy: 0.5181\n",
       "Epoch 21/30\n",
-      "15/15 [==============================] - 1s 82ms/step - loss: 1.2957 - accuracy: 0.9521 - val_loss: 34.1380 - val_accuracy: 0.5060\n",
+      "15/15 [==============================] - 1s 97ms/step - loss: 2.1188 - accuracy: 0.9563 - val_loss: 33.5967 - val_accuracy: 0.5301\n",
       "Epoch 22/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 0.9360 - accuracy: 0.9688 - val_loss: 35.2782 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 79ms/step - loss: 1.8755 - accuracy: 0.9479 - val_loss: 33.4067 - val_accuracy: 0.5301\n",
       "Epoch 23/30\n",
-      "15/15 [==============================] - 1s 90ms/step - loss: 1.6199 - accuracy: 0.9521 - val_loss: 42.6388 - val_accuracy: 0.4096\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 1.4338 - accuracy: 0.9521 - val_loss: 37.3879 - val_accuracy: 0.4819\n",
       "Epoch 24/30\n",
-      "15/15 [==============================] - 1s 81ms/step - loss: 1.3869 - accuracy: 0.9563 - val_loss: 36.0665 - val_accuracy: 0.5181\n",
+      "15/15 [==============================] - 2s 112ms/step - loss: 1.5937 - accuracy: 0.9604 - val_loss: 36.4907 - val_accuracy: 0.4578\n",
       "Epoch 25/30\n",
-      "15/15 [==============================] - 1s 68ms/step - loss: 0.9746 - accuracy: 0.9646 - val_loss: 33.6369 - val_accuracy: 0.4940\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 0.7331 - accuracy: 0.9542 - val_loss: 36.2223 - val_accuracy: 0.5301\n",
       "Epoch 26/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 0.9503 - accuracy: 0.9708 - val_loss: 35.7475 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 86ms/step - loss: 1.1858 - accuracy: 0.9729 - val_loss: 34.4240 - val_accuracy: 0.5301\n",
       "Epoch 27/30\n",
-      "15/15 [==============================] - 1s 97ms/step - loss: 0.5366 - accuracy: 0.9771 - val_loss: 32.7837 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 90ms/step - loss: 0.8428 - accuracy: 0.9667 - val_loss: 34.2543 - val_accuracy: 0.5542\n",
       "Epoch 28/30\n",
-      "15/15 [==============================] - 1s 83ms/step - loss: 1.1540 - accuracy: 0.9583 - val_loss: 32.0780 - val_accuracy: 0.5060\n",
+      "15/15 [==============================] - 1s 94ms/step - loss: 1.0834 - accuracy: 0.9563 - val_loss: 38.1041 - val_accuracy: 0.4940\n",
       "Epoch 29/30\n",
-      "15/15 [==============================] - 1s 84ms/step - loss: 1.2960 - accuracy: 0.9542 - val_loss: 39.2627 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 89ms/step - loss: 1.4204 - accuracy: 0.9479 - val_loss: 34.2720 - val_accuracy: 0.5663\n",
       "Epoch 30/30\n",
-      "15/15 [==============================] - 1s 92ms/step - loss: 0.9093 - accuracy: 0.9771 - val_loss: 36.2351 - val_accuracy: 0.4940\n"
+      "15/15 [==============================] - 1s 85ms/step - loss: 1.4186 - accuracy: 0.9646 - val_loss: 38.5918 - val_accuracy: 0.4699\n"
      ]
     }
    ],
@@ -1121,12 +1121,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1138,7 +1138,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1168,23 +1168,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3/3 [==============================] - 0s 7ms/step - loss: 36.2351 - accuracy: 0.4940\n"
+      "3/3 [==============================] - 0s 5ms/step - loss: 38.5918 - accuracy: 0.4699\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[36.235076904296875, 0.4939759075641632]"
+       "[38.59177780151367, 0.46987950801849365]"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1219,7 +1219,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the TensorBoard notebook extension\n",
+    "# Load the TensorBoard notebook extension on google colab\n",
     "%load_ext tensorboard\n",
     "\n",
     "%tensorboard --logdir logs"
@@ -1263,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1287,7 +1287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1296,7 +1296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -1313,7 +1313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1322,7 +1322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -1359,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1374,7 +1374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1417,7 +1417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1440,63 +1440,8 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/50\n",
-      "15/15 [==============================] - 166s 11s/step - loss: 91.1297 - accuracy: 0.4229 - val_loss: 82.8456 - val_accuracy: 0.1205\n",
-      "Epoch 2/50\n",
-      "15/15 [==============================] - 182s 12s/step - loss: 34.8701 - accuracy: 0.5875 - val_loss: 109.8627 - val_accuracy: 0.2169\n",
-      "Epoch 3/50\n",
-      "15/15 [==============================] - 162s 11s/step - loss: 36.2388 - accuracy: 0.5521 - val_loss: 94.7938 - val_accuracy: 0.2410\n",
-      "Epoch 4/50\n",
-      "15/15 [==============================] - 156s 11s/step - loss: 28.6837 - accuracy: 0.6146 - val_loss: 99.4623 - val_accuracy: 0.2410\n",
-      "Epoch 5/50\n",
-      "15/15 [==============================] - 155s 10s/step - loss: 23.9077 - accuracy: 0.6229 - val_loss: 89.0602 - val_accuracy: 0.2771\n",
-      "Epoch 6/50\n",
-      "15/15 [==============================] - 154s 10s/step - loss: 18.6794 - accuracy: 0.6583 - val_loss: 91.0110 - val_accuracy: 0.2651\n",
-      "Epoch 7/50\n",
-      "15/15 [==============================] - 153s 10s/step - loss: 19.0228 - accuracy: 0.6854 - val_loss: 83.4722 - val_accuracy: 0.3012\n",
-      "Epoch 8/50\n",
-      "15/15 [==============================] - 152s 10s/step - loss: 18.7669 - accuracy: 0.6729 - val_loss: 81.9506 - val_accuracy: 0.2651\n",
-      "Epoch 9/50\n",
-      "15/15 [==============================] - 154s 10s/step - loss: 16.7555 - accuracy: 0.6896 - val_loss: 73.5456 - val_accuracy: 0.2892\n",
-      "Epoch 10/50\n",
-      "15/15 [==============================] - 155s 10s/step - loss: 14.7009 - accuracy: 0.7083 - val_loss: 91.4017 - val_accuracy: 0.3253\n",
-      "Epoch 11/50\n",
-      "15/15 [==============================] - 155s 10s/step - loss: 14.5194 - accuracy: 0.6958 - val_loss: 86.6846 - val_accuracy: 0.3494\n",
-      "Epoch 12/50\n",
-      "15/15 [==============================] - 155s 10s/step - loss: 14.1941 - accuracy: 0.7229 - val_loss: 79.6319 - val_accuracy: 0.3494\n",
-      "Epoch 13/50\n",
-      "15/15 [==============================] - 156s 11s/step - loss: 14.7247 - accuracy: 0.7250 - val_loss: 69.4316 - val_accuracy: 0.4217\n",
-      "Epoch 14/50\n",
-      "15/15 [==============================] - 160s 11s/step - loss: 11.2260 - accuracy: 0.7458 - val_loss: 80.7598 - val_accuracy: 0.3133\n",
-      "Epoch 15/50\n",
-      "15/15 [==============================] - 181s 12s/step - loss: 10.3544 - accuracy: 0.7583 - val_loss: 76.6999 - val_accuracy: 0.4096\n",
-      "Epoch 16/50\n",
-      "15/15 [==============================] - 163s 11s/step - loss: 12.8780 - accuracy: 0.6958 - val_loss: 60.4076 - val_accuracy: 0.4096\n",
-      "Epoch 17/50\n",
-      "15/15 [==============================] - 163s 11s/step - loss: 13.8035 - accuracy: 0.7542 - val_loss: 78.4311 - val_accuracy: 0.3855\n",
-      "Epoch 18/50\n",
-      "15/15 [==============================] - 163s 11s/step - loss: 14.4556 - accuracy: 0.7271 - val_loss: 62.9922 - val_accuracy: 0.4337\n",
-      "Epoch 19/50\n",
-      "15/15 [==============================] - 163s 11s/step - loss: 13.1251 - accuracy: 0.7563 - val_loss: 63.2732 - val_accuracy: 0.4458\n",
-      "Epoch 20/50\n",
-      "15/15 [==============================] - 162s 11s/step - loss: 10.3900 - accuracy: 0.7708 - val_loss: 72.5239 - val_accuracy: 0.3855\n",
-      "Epoch 21/50\n",
-      "15/15 [==============================] - 161s 11s/step - loss: 8.2427 - accuracy: 0.8021 - val_loss: 76.2614 - val_accuracy: 0.3494\n",
-      "Epoch 22/50\n",
-      "15/15 [==============================] - 157s 11s/step - loss: 10.2765 - accuracy: 0.7875 - val_loss: 61.1442 - val_accuracy: 0.4096\n",
-      "Epoch 23/50\n",
-      "15/15 [==============================] - 157s 11s/step - loss: 8.8797 - accuracy: 0.7750 - val_loss: 66.7209 - val_accuracy: 0.3614\n",
-      "Epoch 24/50\n",
-      "15/15 [==============================] - 159s 11s/step - loss: 8.3769 - accuracy: 0.7896 - val_loss: 55.9643 - val_accuracy: 0.4458\n",
-      "Epoch 25/50\n",
-      "15/15 [==============================] - 158s 11s/step - loss: 6.8533 - accuracy: 0.8083 - val_loss: 65.5897 - val_accuracy: 0.4217\n",
-      "Epoch 26/50\n",
-      "15/15 [==============================] - 160s 11s/step - loss: 11.2915 - accuracy: 0.7563 - val_loss: 70.5098 - val_accuracy: 0.4217\n",
-      "Epoch 27/50\n",
-      "15/15 [==============================] - 180s 12s/step - loss: 7.8575 - accuracy: 0.7812 - val_loss: 85.2074 - val_accuracy: 0.3976\n",
-      "Epoch 28/50\n",
-      "15/15 [==============================] - 164s 11s/step - loss: 9.7024 - accuracy: 0.7583 - val_loss: 60.2458 - val_accuracy: 0.4458\n",
-      "Epoch 29/50\n",
-      " 1/15 [=>............................] - ETA: 2:05 - loss: 4.9456 - accuracy: 0.8438"
+      "15/15 [==============================] - 213s 14s/step - loss: 5.9087 - accuracy: 0.8000 - val_loss: 38.9280 - val_accuracy: 0.5060\n",
+      "Epoch 2/50\n"
      ]
     }
    ],
@@ -1566,7 +1511,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the TensorBoard notebook extension\n",
+    "# Load the TensorBoard notebook extension on google colab\n",
     "%load_ext tensorboard\n",
     "%tensorboard --logdir logs"
    ]
@@ -1768,7 +1713,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the TensorBoard notebook extension\n",
+    "# Load the TensorBoard notebook extension on google colab\n",
     "%load_ext tensorboard\n",
     "%tensorboard --logdir logs"
    ]
@@ -2219,7 +2164,8 @@
     "import os, datetime\n",
     "import tensorflow as tf\n",
     "\n",
-    "model.compile(optimizer=\"rmsprop\", loss=\"sparse_categorical_crossentropy\")\n",
+    "model.compile(optimizer=\"rmsprop\", loss=\"sparse_categorical_crossentropy\",\n",
+    "    metrics=[\"accuracy\"])\n",
     "\n",
     "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
     "callbacks = [\n",
-- 
GitLab