From 442b1797fd06c27d94b0866f69966478733d4e77 Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Sun, 27 Mar 2022 09:40:37 +0000
Subject: [PATCH] Minor Change

---
 ... - Object Detection and Segmentation.ipynb | 340 ++++++++++--------
 1 file changed, 194 insertions(+), 146 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 4e2da9a..a18828c 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	
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -151,7 +151,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -168,7 +168,7 @@
        "       0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -199,8 +199,7 @@
     "                                         classes=class_names,\n",
     "                                         batch_size=25, class_mode='sparse', shuffle=False)\n",
     "\n",
-    "plot_img(dir_iter[0][0][1,...])\n",
-    "dir_iter[0][1]"
+    "plot_img(dir_iter[0][0][1,...])"
    ]
   },
   {
@@ -256,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -298,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -311,7 +310,7 @@
      "output_type": "stream",
      "text": [
       "Found 480 images belonging to 8 classes.\n",
-      "Found 83 images belonging to 8 classes.\n"
+      "Found 80 images belonging to 8 classes.\n"
      ]
     }
    ],
@@ -346,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -360,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -387,45 +386,45 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "24/24 [==============================] - 30s 1s/step - loss: 2.1493 - accuracy: 0.1146 - val_loss: 2.0780 - val_accuracy: 0.1625\n",
+      "24/24 [==============================] - 33s 1s/step - loss: 2.1169 - accuracy: 0.1042 - val_loss: 2.0758 - val_accuracy: 0.1125\n",
       "Epoch 2/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 2.0772 - accuracy: 0.1437 - val_loss: 2.0687 - val_accuracy: 0.2250\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 2.0751 - accuracy: 0.1375 - val_loss: 2.0525 - val_accuracy: 0.1375\n",
       "Epoch 3/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 2.0690 - accuracy: 0.1667 - val_loss: 2.0434 - val_accuracy: 0.2125\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 2.0318 - accuracy: 0.1854 - val_loss: 1.9808 - val_accuracy: 0.2000\n",
       "Epoch 4/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 2.0251 - accuracy: 0.2229 - val_loss: 1.9642 - val_accuracy: 0.3500\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.9467 - accuracy: 0.2313 - val_loss: 1.9039 - val_accuracy: 0.2750\n",
       "Epoch 5/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.9674 - accuracy: 0.2438 - val_loss: 1.8757 - val_accuracy: 0.3000\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.8800 - accuracy: 0.2979 - val_loss: 1.7726 - val_accuracy: 0.3250\n",
       "Epoch 6/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.8926 - accuracy: 0.2750 - val_loss: 1.7863 - val_accuracy: 0.3625\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.7388 - accuracy: 0.3292 - val_loss: 1.7299 - val_accuracy: 0.4000\n",
       "Epoch 7/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.8776 - accuracy: 0.2979 - val_loss: 1.7791 - val_accuracy: 0.3125\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.6714 - accuracy: 0.3708 - val_loss: 1.6677 - val_accuracy: 0.4000\n",
       "Epoch 8/20\n",
-      "24/24 [==============================] - 35s 1s/step - loss: 1.7989 - accuracy: 0.3146 - val_loss: 1.7242 - val_accuracy: 0.3125\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.6361 - accuracy: 0.4021 - val_loss: 1.7391 - val_accuracy: 0.3875\n",
       "Epoch 9/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.7280 - accuracy: 0.3438 - val_loss: 1.6277 - val_accuracy: 0.3750\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.5418 - accuracy: 0.4146 - val_loss: 1.6107 - val_accuracy: 0.4625\n",
       "Epoch 10/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.6853 - accuracy: 0.3667 - val_loss: 1.6015 - val_accuracy: 0.4375\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.5107 - accuracy: 0.4396 - val_loss: 1.6142 - val_accuracy: 0.4000\n",
       "Epoch 11/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.5953 - accuracy: 0.3750 - val_loss: 1.5688 - val_accuracy: 0.3875\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.3988 - accuracy: 0.5104 - val_loss: 1.5144 - val_accuracy: 0.5125\n",
       "Epoch 12/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.5313 - accuracy: 0.4062 - val_loss: 1.5065 - val_accuracy: 0.5375\n",
+      "24/24 [==============================] - 27s 1s/step - loss: 1.3385 - accuracy: 0.5208 - val_loss: 1.5094 - val_accuracy: 0.5375\n",
       "Epoch 13/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.5017 - accuracy: 0.4271 - val_loss: 1.5153 - val_accuracy: 0.4625\n",
+      "24/24 [==============================] - 27s 1s/step - loss: 1.3107 - accuracy: 0.5250 - val_loss: 1.5407 - val_accuracy: 0.4875\n",
       "Epoch 14/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.4794 - accuracy: 0.4437 - val_loss: 1.5480 - val_accuracy: 0.4625\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.2837 - accuracy: 0.5437 - val_loss: 1.4935 - val_accuracy: 0.4375\n",
       "Epoch 15/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.4285 - accuracy: 0.4563 - val_loss: 1.4185 - val_accuracy: 0.4500\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.2692 - accuracy: 0.5396 - val_loss: 1.4885 - val_accuracy: 0.4875\n",
       "Epoch 16/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.4327 - accuracy: 0.4437 - val_loss: 1.4999 - val_accuracy: 0.4375\n",
+      "24/24 [==============================] - 29s 1s/step - loss: 1.1647 - accuracy: 0.5854 - val_loss: 1.6430 - val_accuracy: 0.4750\n",
       "Epoch 17/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.2907 - accuracy: 0.5083 - val_loss: 1.4782 - val_accuracy: 0.4125\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.1185 - accuracy: 0.5917 - val_loss: 1.5659 - val_accuracy: 0.4750\n",
       "Epoch 18/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.2766 - accuracy: 0.5292 - val_loss: 1.4866 - val_accuracy: 0.4500\n",
+      "24/24 [==============================] - 27s 1s/step - loss: 1.1576 - accuracy: 0.5688 - val_loss: 1.6319 - val_accuracy: 0.3875\n",
       "Epoch 19/20\n",
-      "24/24 [==============================] - 27s 1s/step - loss: 1.2222 - accuracy: 0.5375 - val_loss: 1.5021 - val_accuracy: 0.4500\n",
+      "24/24 [==============================] - 28s 1s/step - loss: 1.0591 - accuracy: 0.6042 - val_loss: 1.6289 - val_accuracy: 0.4125\n",
       "Epoch 20/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.2967 - accuracy: 0.4854 - val_loss: 1.4795 - val_accuracy: 0.4625\n"
+      "24/24 [==============================] - 30s 1s/step - loss: 1.0303 - accuracy: 0.6021 - val_loss: 1.6916 - val_accuracy: 0.4500\n"
      ]
     }
    ],
@@ -441,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -451,7 +450,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -463,7 +462,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -549,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -574,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +583,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -648,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 16,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -664,13 +663,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 17,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "eRes_n9BGhJ0"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+      "58892288/58889256 [==============================] - 1s 0us/step\n",
+      "58900480/58889256 [==============================] - 1s 0us/step\n"
+     ]
+    }
+   ],
    "source": [
     "conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\",\n",
     "                                           include_top=False,\n",
@@ -711,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 19,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -726,7 +735,7 @@
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_9 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
+      " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
       "                                                                 \n",
       " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
       "                                                                 \n",
@@ -824,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -832,7 +841,7 @@
      "output_type": "stream",
      "text": [
       "Found 480 files belonging to 8 classes.\n",
-      "Found 83 files belonging to 8 classes.\n"
+      "Found 80 files belonging to 8 classes.\n"
      ]
     }
    ],
@@ -856,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -882,12 +891,12 @@
     "Importantly, `predict()` only expects images, not labels, but our current dataset yields\n",
     "batches that contain both images and their labels. Moreover, the VGG16 model expects\n",
     "inputs that are preprocessed with the function `keras.applications.vgg16.preprocess_input`, which scales pixel values to an appropriate range.\n",
-    "The extracted features are currently of shape `(samples, 5, 5, 512)`:"
+    "The extracted features are currently of shape `(samples, 4, 4, 512)`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -896,7 +905,7 @@
        "(480, 4, 4, 512)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -914,7 +923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -923,7 +932,7 @@
        "(480, 8)"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -934,15 +943,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(83, 4, 4, 512)\n",
-      "(83, 8)\n"
+      "(80, 4, 4, 512)\n",
+      "(80, 8)\n"
      ]
     }
    ],
@@ -953,13 +962,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
     "inputs = keras.Input(shape=(4, 4, 512))\n",
-    "# Note the use of the Flatten\n",
-    "# layer before passing the\n",
+    "# Note the use of the Flatten layer before passing the\n",
     "# features to a Dense layer\n",
     "x = layers.Flatten()(inputs)\n",
     "x = layers.Dense(256)(x)\n",
@@ -970,26 +978,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"model_4\"\n",
+      "Model: \"model\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_10 (InputLayer)       [(None, 4, 4, 512)]       0         \n",
+      " input_2 (InputLayer)        [(None, 4, 4, 512)]       0         \n",
       "                                                                 \n",
-      " flatten_5 (Flatten)         (None, 8192)              0         \n",
+      " flatten_1 (Flatten)         (None, 8192)              0         \n",
       "                                                                 \n",
-      " dense_10 (Dense)            (None, 256)               2097408   \n",
+      " dense_2 (Dense)             (None, 256)               2097408   \n",
       "                                                                 \n",
-      " dropout_5 (Dropout)         (None, 256)               0         \n",
+      " dropout_1 (Dropout)         (None, 256)               0         \n",
       "                                                                 \n",
-      " dense_11 (Dense)            (None, 8)                 2056      \n",
+      " dense_3 (Dense)             (None, 8)                 2056      \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 2,099,464\n",
@@ -1005,7 +1013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1013,65 +1021,65 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/30\n",
-      "15/15 [==============================] - 2s 120ms/step - loss: 47.5901 - accuracy: 0.3042 - val_loss: 21.6888 - val_accuracy: 0.4217\n",
+      "15/15 [==============================] - 2s 137ms/step - loss: 37.5541 - accuracy: 0.3167 - val_loss: 18.9623 - val_accuracy: 0.4250\n",
       "Epoch 2/30\n",
-      "15/15 [==============================] - 1s 104ms/step - loss: 18.2980 - accuracy: 0.5708 - val_loss: 18.4650 - val_accuracy: 0.4337\n",
+      "15/15 [==============================] - 1s 93ms/step - loss: 16.9458 - accuracy: 0.5979 - val_loss: 17.9666 - val_accuracy: 0.4625\n",
       "Epoch 3/30\n",
-      "15/15 [==============================] - 1s 82ms/step - loss: 7.1367 - accuracy: 0.7500 - val_loss: 20.4785 - val_accuracy: 0.4458\n",
+      "15/15 [==============================] - 1s 90ms/step - loss: 10.3438 - accuracy: 0.7021 - val_loss: 15.7961 - val_accuracy: 0.4375\n",
       "Epoch 4/30\n",
-      "15/15 [==============================] - 1s 82ms/step - loss: 6.9484 - accuracy: 0.7771 - val_loss: 18.7156 - val_accuracy: 0.5060\n",
+      "15/15 [==============================] - 1s 98ms/step - loss: 7.4823 - accuracy: 0.7854 - val_loss: 21.9754 - val_accuracy: 0.4750\n",
       "Epoch 5/30\n",
-      "15/15 [==============================] - 1s 100ms/step - loss: 5.1403 - accuracy: 0.8208 - val_loss: 25.2956 - val_accuracy: 0.5181\n",
+      "15/15 [==============================] - 1s 96ms/step - loss: 5.9322 - accuracy: 0.8021 - val_loss: 22.0795 - val_accuracy: 0.4500\n",
       "Epoch 6/30\n",
-      "15/15 [==============================] - 1s 87ms/step - loss: 5.6793 - accuracy: 0.8167 - val_loss: 22.8389 - val_accuracy: 0.4940\n",
+      "15/15 [==============================] - 1s 91ms/step - loss: 4.1322 - accuracy: 0.8500 - val_loss: 21.3408 - val_accuracy: 0.4750\n",
       "Epoch 7/30\n",
-      "15/15 [==============================] - 1s 84ms/step - loss: 4.9009 - accuracy: 0.8500 - val_loss: 25.7880 - val_accuracy: 0.5663\n",
+      "15/15 [==============================] - 1s 93ms/step - loss: 5.8987 - accuracy: 0.8479 - val_loss: 21.4058 - val_accuracy: 0.5375\n",
       "Epoch 8/30\n",
-      "15/15 [==============================] - 1s 104ms/step - loss: 3.1247 - accuracy: 0.8854 - val_loss: 21.7678 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 92ms/step - loss: 3.0604 - accuracy: 0.9000 - val_loss: 22.8860 - val_accuracy: 0.5000\n",
       "Epoch 9/30\n",
-      "15/15 [==============================] - 1s 82ms/step - loss: 4.9890 - accuracy: 0.8729 - val_loss: 28.8869 - val_accuracy: 0.4940\n",
+      "15/15 [==============================] - 1s 88ms/step - loss: 3.6367 - accuracy: 0.8813 - val_loss: 28.5665 - val_accuracy: 0.4750\n",
       "Epoch 10/30\n",
-      "15/15 [==============================] - 1s 90ms/step - loss: 2.8775 - accuracy: 0.8958 - val_loss: 33.2878 - val_accuracy: 0.5060\n",
+      "15/15 [==============================] - 1s 89ms/step - loss: 1.9839 - accuracy: 0.9208 - val_loss: 25.9865 - val_accuracy: 0.4750\n",
       "Epoch 11/30\n",
-      "15/15 [==============================] - 1s 100ms/step - loss: 1.6595 - accuracy: 0.9354 - val_loss: 35.3880 - val_accuracy: 0.3976\n",
+      "15/15 [==============================] - 1s 105ms/step - loss: 2.9690 - accuracy: 0.9083 - val_loss: 23.8147 - val_accuracy: 0.5125\n",
       "Epoch 12/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 2.5381 - accuracy: 0.9125 - val_loss: 28.2466 - val_accuracy: 0.5181\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 2.1616 - accuracy: 0.9229 - val_loss: 26.9395 - val_accuracy: 0.4875\n",
       "Epoch 13/30\n",
-      "15/15 [==============================] - 1s 80ms/step - loss: 3.0611 - accuracy: 0.9250 - val_loss: 29.1160 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 92ms/step - loss: 2.9238 - accuracy: 0.9125 - val_loss: 25.8792 - val_accuracy: 0.5125\n",
       "Epoch 14/30\n",
-      "15/15 [==============================] - 1s 91ms/step - loss: 1.9787 - accuracy: 0.9104 - val_loss: 27.5122 - val_accuracy: 0.4699\n",
+      "15/15 [==============================] - 1s 94ms/step - loss: 1.6395 - accuracy: 0.9500 - val_loss: 30.7839 - val_accuracy: 0.5000\n",
       "Epoch 15/30\n",
-      "15/15 [==============================] - 1s 88ms/step - loss: 1.8686 - accuracy: 0.9417 - val_loss: 34.6468 - val_accuracy: 0.4337\n",
+      "15/15 [==============================] - 1s 86ms/step - loss: 2.6006 - accuracy: 0.9292 - val_loss: 28.3882 - val_accuracy: 0.5250\n",
       "Epoch 16/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 2.2055 - accuracy: 0.9187 - val_loss: 33.5793 - val_accuracy: 0.5181\n",
+      "15/15 [==============================] - 1s 92ms/step - loss: 1.5090 - accuracy: 0.9500 - val_loss: 31.0281 - val_accuracy: 0.4875\n",
       "Epoch 17/30\n",
-      "15/15 [==============================] - 1s 87ms/step - loss: 1.7554 - accuracy: 0.9417 - val_loss: 29.8020 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 1s 101ms/step - loss: 2.3228 - accuracy: 0.9208 - val_loss: 30.7241 - val_accuracy: 0.4875\n",
       "Epoch 18/30\n",
-      "15/15 [==============================] - 1s 102ms/step - loss: 1.1292 - accuracy: 0.9646 - val_loss: 29.8960 - val_accuracy: 0.4578\n",
+      "15/15 [==============================] - 1s 82ms/step - loss: 1.1543 - accuracy: 0.9521 - val_loss: 29.6124 - val_accuracy: 0.5125\n",
       "Epoch 19/30\n",
-      "15/15 [==============================] - 1s 86ms/step - loss: 2.0701 - accuracy: 0.9375 - val_loss: 36.3813 - val_accuracy: 0.4578\n",
+      "15/15 [==============================] - 1s 88ms/step - loss: 1.3184 - accuracy: 0.9458 - val_loss: 29.2623 - val_accuracy: 0.5500\n",
       "Epoch 20/30\n",
-      "15/15 [==============================] - 1s 81ms/step - loss: 1.9079 - accuracy: 0.9375 - val_loss: 33.0938 - val_accuracy: 0.5181\n",
+      "15/15 [==============================] - 1s 101ms/step - loss: 1.5346 - accuracy: 0.9500 - val_loss: 31.2510 - val_accuracy: 0.5000\n",
       "Epoch 21/30\n",
-      "15/15 [==============================] - 1s 97ms/step - loss: 2.1188 - accuracy: 0.9563 - val_loss: 33.5967 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 1.7351 - accuracy: 0.9271 - val_loss: 33.1795 - val_accuracy: 0.4500\n",
       "Epoch 22/30\n",
-      "15/15 [==============================] - 1s 79ms/step - loss: 1.8755 - accuracy: 0.9479 - val_loss: 33.4067 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 1.2074 - accuracy: 0.9563 - val_loss: 29.1916 - val_accuracy: 0.4875\n",
       "Epoch 23/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 1.4338 - accuracy: 0.9521 - val_loss: 37.3879 - val_accuracy: 0.4819\n",
+      "15/15 [==============================] - 1s 99ms/step - loss: 0.8211 - accuracy: 0.9688 - val_loss: 35.9889 - val_accuracy: 0.4875\n",
       "Epoch 24/30\n",
-      "15/15 [==============================] - 2s 112ms/step - loss: 1.5937 - accuracy: 0.9604 - val_loss: 36.4907 - val_accuracy: 0.4578\n",
+      "15/15 [==============================] - 1s 87ms/step - loss: 1.3403 - accuracy: 0.9583 - val_loss: 29.8561 - val_accuracy: 0.5000\n",
       "Epoch 25/30\n",
-      "15/15 [==============================] - 1s 83ms/step - loss: 0.7331 - accuracy: 0.9542 - val_loss: 36.2223 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 1.4869 - accuracy: 0.9375 - val_loss: 34.0957 - val_accuracy: 0.4625\n",
       "Epoch 26/30\n",
-      "15/15 [==============================] - 1s 86ms/step - loss: 1.1858 - accuracy: 0.9729 - val_loss: 34.4240 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 1s 99ms/step - loss: 1.2334 - accuracy: 0.9708 - val_loss: 33.3027 - val_accuracy: 0.5250\n",
       "Epoch 27/30\n",
-      "15/15 [==============================] - 1s 90ms/step - loss: 0.8428 - accuracy: 0.9667 - val_loss: 34.2543 - val_accuracy: 0.5542\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 0.6280 - accuracy: 0.9750 - val_loss: 34.0933 - val_accuracy: 0.4375\n",
       "Epoch 28/30\n",
-      "15/15 [==============================] - 1s 94ms/step - loss: 1.0834 - accuracy: 0.9563 - val_loss: 38.1041 - val_accuracy: 0.4940\n",
+      "15/15 [==============================] - 1s 93ms/step - loss: 0.6707 - accuracy: 0.9729 - val_loss: 32.8301 - val_accuracy: 0.4750\n",
       "Epoch 29/30\n",
-      "15/15 [==============================] - 1s 89ms/step - loss: 1.4204 - accuracy: 0.9479 - val_loss: 34.2720 - val_accuracy: 0.5663\n",
+      "15/15 [==============================] - 1s 94ms/step - loss: 0.6980 - accuracy: 0.9646 - val_loss: 33.7092 - val_accuracy: 0.4625\n",
       "Epoch 30/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 1.4186 - accuracy: 0.9646 - val_loss: 38.5918 - val_accuracy: 0.4699\n"
+      "15/15 [==============================] - 1s 87ms/step - loss: 1.2614 - accuracy: 0.9542 - val_loss: 33.9605 - val_accuracy: 0.4875\n"
      ]
     }
    ],
@@ -1121,12 +1129,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 28,
    "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 +1146,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1197,7 +1205,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We reach a validation accuracy of about 49% — much worse than we achieved in the\n",
+    "We reach a validation accuracy of about 46% — much worse than we achieved in the\n",
     "previous section with the small model trained from scratch. \n",
     "\n",
     "The learning curves indicate that we’re overfitting almost from the start—\n",
@@ -1263,7 +1271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1287,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1296,7 +1304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1313,7 +1321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1322,7 +1330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1359,12 +1367,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
     "data_augmentation = keras.Sequential(\n",
     "[\n",
+    "layers.Rescaling(1./255),\n",
     "layers.RandomFlip(\"horizontal\"),\n",
     "layers.RandomRotation(0.1),\n",
     "layers.RandomZoom(0.2),\n",
@@ -1374,7 +1383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1417,7 +1426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1432,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1440,62 +1449,101 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/50\n",
-      "15/15 [==============================] - 213s 14s/step - loss: 5.9087 - accuracy: 0.8000 - val_loss: 38.9280 - val_accuracy: 0.5060\n",
+      "15/15 [==============================] - 168s 11s/step - loss: 11.4518 - accuracy: 0.5458 - val_loss: 17.8862 - val_accuracy: 0.1250\n",
       "Epoch 2/50\n",
-      "15/15 [==============================] - 204s 14s/step - loss: 4.1850 - accuracy: 0.8521 - val_loss: 38.2262 - val_accuracy: 0.5422\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 10.9835 - accuracy: 0.3500 - val_loss: 13.7192 - val_accuracy: 0.1250\n",
       "Epoch 3/50\n",
-      "15/15 [==============================] - 199s 13s/step - loss: 3.1716 - accuracy: 0.8521 - val_loss: 35.7508 - val_accuracy: 0.5181\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 8.6858 - accuracy: 0.4062 - val_loss: 14.1361 - val_accuracy: 0.1250\n",
       "Epoch 4/50\n",
-      "15/15 [==============================] - 201s 13s/step - loss: 2.8206 - accuracy: 0.8854 - val_loss: 34.2308 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 7.9062 - accuracy: 0.4708 - val_loss: 12.8987 - val_accuracy: 0.1250\n",
       "Epoch 5/50\n",
-      "15/15 [==============================] - 201s 14s/step - loss: 2.8074 - accuracy: 0.9042 - val_loss: 35.6657 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 8.1438 - accuracy: 0.4271 - val_loss: 15.0843 - val_accuracy: 0.1250\n",
       "Epoch 6/50\n",
-      "15/15 [==============================] - 204s 14s/step - loss: 2.5794 - accuracy: 0.9000 - val_loss: 36.6189 - val_accuracy: 0.5181\n",
+      "15/15 [==============================] - 162s 11s/step - loss: 8.2263 - accuracy: 0.4250 - val_loss: 14.4411 - val_accuracy: 0.1250\n",
       "Epoch 7/50\n",
-      "15/15 [==============================] - 203s 14s/step - loss: 2.5663 - accuracy: 0.9000 - val_loss: 35.3423 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 164s 11s/step - loss: 7.2926 - accuracy: 0.4750 - val_loss: 13.8786 - val_accuracy: 0.1250\n",
       "Epoch 8/50\n",
-      "15/15 [==============================] - 206s 14s/step - loss: 2.8004 - accuracy: 0.8938 - val_loss: 35.5800 - val_accuracy: 0.5422\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 7.4656 - accuracy: 0.4688 - val_loss: 14.0992 - val_accuracy: 0.1250\n",
       "Epoch 9/50\n",
-      "15/15 [==============================] - 202s 14s/step - loss: 2.2963 - accuracy: 0.9208 - val_loss: 34.6054 - val_accuracy: 0.5422\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 8.5636 - accuracy: 0.4000 - val_loss: 12.5572 - val_accuracy: 0.1250\n",
       "Epoch 10/50\n",
-      "15/15 [==============================] - 204s 14s/step - loss: 1.4943 - accuracy: 0.9146 - val_loss: 34.1561 - val_accuracy: 0.5542\n",
+      "15/15 [==============================] - 163s 11s/step - loss: 7.1384 - accuracy: 0.4646 - val_loss: 13.2639 - val_accuracy: 0.1250\n",
       "Epoch 11/50\n",
-      "15/15 [==============================] - 205s 14s/step - loss: 1.2628 - accuracy: 0.9271 - val_loss: 33.4512 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 6.6628 - accuracy: 0.4917 - val_loss: 12.8577 - val_accuracy: 0.1250\n",
       "Epoch 12/50\n",
-      "15/15 [==============================] - 195s 13s/step - loss: 1.7920 - accuracy: 0.9125 - val_loss: 30.7742 - val_accuracy: 0.5542\n",
+      "15/15 [==============================] - 180s 12s/step - loss: 6.9991 - accuracy: 0.4812 - val_loss: 12.6606 - val_accuracy: 0.1250\n",
       "Epoch 13/50\n",
-      "15/15 [==============================] - 210s 14s/step - loss: 0.7292 - accuracy: 0.9583 - val_loss: 33.0453 - val_accuracy: 0.5542\n",
-      "15/15 [==============================] - 199s 13s/step - loss: 0.8244 - accuracy: 0.9438 - val_loss: 31.8265 - val_accuracy: 0.5663\n",
+      "15/15 [==============================] - 162s 11s/step - loss: 6.2083 - accuracy: 0.5208 - val_loss: 12.4139 - val_accuracy: 0.1250\n",
+      "Epoch 14/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 6.4968 - accuracy: 0.5188 - val_loss: 11.6879 - val_accuracy: 0.1250\n",
+      "Epoch 15/50\n",
+      "15/15 [==============================] - 159s 11s/step - loss: 6.8946 - accuracy: 0.4479 - val_loss: 14.3736 - val_accuracy: 0.1250\n",
+      "Epoch 16/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 6.0659 - accuracy: 0.5104 - val_loss: 12.9250 - val_accuracy: 0.1250\n",
+      "Epoch 17/50\n",
+      "15/15 [==============================] - 176s 12s/step - loss: 5.7141 - accuracy: 0.5417 - val_loss: 12.5595 - val_accuracy: 0.1250\n",
+      "Epoch 18/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 5.9447 - accuracy: 0.5063 - val_loss: 12.6128 - val_accuracy: 0.1250\n",
+      "Epoch 19/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 6.1720 - accuracy: 0.4896 - val_loss: 13.0455 - val_accuracy: 0.1250\n",
+      "Epoch 20/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 6.2603 - accuracy: 0.4667 - val_loss: 12.6287 - val_accuracy: 0.1250\n",
+      "Epoch 21/50\n",
+      "15/15 [==============================] - 156s 10s/step - loss: 5.7855 - accuracy: 0.5000 - val_loss: 13.2289 - val_accuracy: 0.1250\n",
+      "Epoch 22/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 5.6733 - accuracy: 0.5042 - val_loss: 13.3227 - val_accuracy: 0.1250\n",
+      "Epoch 23/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 5.3768 - accuracy: 0.5417 - val_loss: 12.7402 - val_accuracy: 0.1250\n",
+      "Epoch 25/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 5.1565 - accuracy: 0.5458 - val_loss: 12.4625 - val_accuracy: 0.1250\n",
+      "Epoch 26/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 5.7394 - accuracy: 0.4812 - val_loss: 13.7113 - val_accuracy: 0.1250\n",
+      "Epoch 27/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 4.9531 - accuracy: 0.5625 - val_loss: 12.8006 - val_accuracy: 0.1250\n",
+      "Epoch 28/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 5.7699 - accuracy: 0.4979 - val_loss: 12.4595 - val_accuracy: 0.1250\n",
       "Epoch 29/50\n",
-      "15/15 [==============================] - 199s 13s/step - loss: 1.3684 - accuracy: 0.9396 - val_loss: 30.7292 - val_accuracy: 0.5542\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 5.9169 - accuracy: 0.4625 - val_loss: 12.7894 - val_accuracy: 0.1250\n",
       "Epoch 30/50\n",
-      "15/15 [==============================] - 199s 13s/step - loss: 1.4007 - accuracy: 0.9458 - val_loss: 30.8636 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 5.2118 - accuracy: 0.5021 - val_loss: 11.7927 - val_accuracy: 0.1250\n",
       "Epoch 31/50\n",
-      "15/15 [==============================] - 198s 13s/step - loss: 0.8514 - accuracy: 0.9563 - val_loss: 33.0234 - val_accuracy: 0.5301\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 4.9629 - accuracy: 0.5271 - val_loss: 10.8219 - val_accuracy: 0.1250\n",
       "Epoch 32/50\n",
-      "15/15 [==============================] - 212s 14s/step - loss: 1.1778 - accuracy: 0.9479 - val_loss: 33.0162 - val_accuracy: 0.5542\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 4.6490 - accuracy: 0.5500 - val_loss: 11.7458 - val_accuracy: 0.1250\n",
+      "Epoch 33/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 4.8709 - accuracy: 0.5188 - val_loss: 12.5519 - val_accuracy: 0.1250\n",
       "Epoch 34/50\n",
-      " 2/15 [===>..........................] - ETA: 2:32 - loss: 0.8565 - accuracy: 0.9375"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-92-e759ae1d18a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m callbacks=callbacks)\n\u001b[0m",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1214\u001b[0m                 _r=1):\n\u001b[1;32m   1215\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1217\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    940\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3129\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3130\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3131\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3133\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1958\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1959\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1960\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1961\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1962\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    601\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 603\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    604\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m           outputs = execute.execute_with_cancellation(\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 59\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "15/15 [==============================] - 154s 10s/step - loss: 5.3610 - accuracy: 0.4667 - val_loss: 14.6036 - val_accuracy: 0.1250\n",
+      "Epoch 35/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 4.8914 - accuracy: 0.5271 - val_loss: 12.7487 - val_accuracy: 0.1250\n",
+      "Epoch 36/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 4.9687 - accuracy: 0.4917 - val_loss: 13.9772 - val_accuracy: 0.1250\n",
+      "Epoch 37/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 4.7699 - accuracy: 0.5333 - val_loss: 12.3055 - val_accuracy: 0.1250\n",
+      "Epoch 38/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 4.3138 - accuracy: 0.5667 - val_loss: 13.0587 - val_accuracy: 0.1250\n",
+      "Epoch 39/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 5.1554 - accuracy: 0.4812 - val_loss: 13.4789 - val_accuracy: 0.1250\n",
+      "Epoch 40/50\n",
+      "15/15 [==============================] - 176s 12s/step - loss: 4.3945 - accuracy: 0.5500 - val_loss: 12.5361 - val_accuracy: 0.1250\n",
+      "Epoch 41/50\n",
+      "15/15 [==============================] - 176s 12s/step - loss: 5.4641 - accuracy: 0.4604 - val_loss: 13.1655 - val_accuracy: 0.1250\n",
+      "Epoch 42/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 4.5458 - accuracy: 0.5354 - val_loss: 12.1975 - val_accuracy: 0.1250\n",
+      "Epoch 43/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 5.2304 - accuracy: 0.4708 - val_loss: 13.1451 - val_accuracy: 0.1250\n",
+      "Epoch 45/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 4.2872 - accuracy: 0.5271 - val_loss: 12.5154 - val_accuracy: 0.1250\n",
+      "Epoch 46/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 4.0149 - accuracy: 0.5354 - val_loss: 14.1210 - val_accuracy: 0.1250\n",
+      "Epoch 47/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 4.5010 - accuracy: 0.5083 - val_loss: 13.0028 - val_accuracy: 0.1250\n",
+      "Epoch 48/50\n",
+      "15/15 [==============================] - 155s 11s/step - loss: 4.3353 - accuracy: 0.5083 - val_loss: 14.1747 - val_accuracy: 0.1250\n",
+      "Epoch 49/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 4.6909 - accuracy: 0.5000 - val_loss: 13.3923 - val_accuracy: 0.1250\n",
+      "Epoch 50/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 3.8186 - accuracy: 0.5521 - val_loss: 13.2914 - val_accuracy: 0.1250\n"
      ]
     }
    ],
-- 
GitLab