From 70ec88697134d5954c37710de759794caabf3084 Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Thu, 24 Mar 2022 09:40:54 +0000
Subject: [PATCH] Corrected class names for actor dataset

---
 ... - Object Detection and Segmentation.ipynb | 606 +++++++++++++-----
 1 file changed, 460 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 41ac672..e5ef704 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": 17,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 18,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -146,12 +146,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 480 images belonging to 8 classes.\n"
+      "Found 420 images belonging to 8 classes.\n"
      ]
     },
     {
      "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": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -178,6 +178,11 @@
     "# the constructor takes arguments defining the different image transformations\n",
     "# for augmentation purposes (rotation, x-/y-shift, intensity scaling - here 1./255 \n",
     "# to scale range to [0, 1], shear, zoom, flip, ... )\n",
+    "\n",
+    "class_names = [\"angelina jolie\", \"brad pitt\",\"catherine deneuve\" , \"johnny depp\",\"leonardo dicaprio\", \"marion cotillard\", \"robert de niro\",\"sandra bullock\"]\n",
+    "\n",
+    "\n",
+    "\n",
     "train_datagen = ImageDataGenerator(\n",
     "        rotation_range=10,\n",
     "        width_shift_range=0.2,\n",
@@ -251,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -293,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -305,8 +310,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 480 images belonging to 8 classes.\n",
-      "Found 80 images belonging to 8 classes.\n"
+      "Found 420 images belonging to 8 classes.\n",
+      "Found 70 images belonging to 8 classes.\n"
      ]
     }
    ],
@@ -341,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -355,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -368,7 +373,7 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "16/24 [===================>..........] - ETA: 9s - loss: 2.1282 - accuracy: 0.1250 "
+      " 6/24 [======>.......................] - ETA: 21s - loss: 2.0385 - accuracy: 0.1833"
      ]
     },
     {
@@ -383,45 +388,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "24/24 [==============================] - 33s 1s/step - loss: 2.1121 - accuracy: 0.1292 - val_loss: 2.0675 - val_accuracy: 0.2250\n",
-      "Epoch 2/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 2.0553 - accuracy: 0.1562 - val_loss: 2.0237 - val_accuracy: 0.1750\n",
-      "Epoch 3/20\n",
-      "24/24 [==============================] - 30s 1s/step - loss: 2.0001 - accuracy: 0.1937 - val_loss: 1.8520 - val_accuracy: 0.3000\n",
-      "Epoch 4/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.8642 - accuracy: 0.2937 - val_loss: 1.7641 - val_accuracy: 0.3125\n",
-      "Epoch 5/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.6895 - accuracy: 0.3562 - val_loss: 1.7250 - val_accuracy: 0.3125\n",
-      "Epoch 6/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.7312 - accuracy: 0.3271 - val_loss: 1.5483 - val_accuracy: 0.4750\n",
-      "Epoch 7/20\n",
-      "24/24 [==============================] - 27s 1s/step - loss: 1.6565 - accuracy: 0.3812 - val_loss: 1.5731 - val_accuracy: 0.4625\n",
-      "Epoch 8/20\n",
-      "24/24 [==============================] - 34s 1s/step - loss: 1.5992 - accuracy: 0.4083 - val_loss: 1.5869 - val_accuracy: 0.4125\n",
-      "Epoch 9/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.4686 - accuracy: 0.4583 - val_loss: 1.5046 - val_accuracy: 0.4375\n",
-      "Epoch 10/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.5162 - accuracy: 0.4292 - val_loss: 1.4340 - val_accuracy: 0.5250\n",
-      "Epoch 11/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.4071 - accuracy: 0.4771 - val_loss: 1.4478 - val_accuracy: 0.5000\n",
-      "Epoch 12/20\n",
-      "24/24 [==============================] - 27s 1s/step - loss: 1.3803 - accuracy: 0.4417 - val_loss: 1.5040 - val_accuracy: 0.4375\n",
-      "Epoch 13/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.3294 - accuracy: 0.5063 - val_loss: 1.4049 - val_accuracy: 0.5375\n",
-      "Epoch 14/20\n",
-      "24/24 [==============================] - 29s 1s/step - loss: 1.2694 - accuracy: 0.5188 - val_loss: 1.4299 - val_accuracy: 0.4875\n",
-      "Epoch 15/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.2913 - accuracy: 0.4958 - val_loss: 1.4126 - val_accuracy: 0.5500\n",
-      "Epoch 16/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.3212 - accuracy: 0.4875 - val_loss: 1.4329 - val_accuracy: 0.5750\n",
-      "Epoch 17/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.2693 - accuracy: 0.5458 - val_loss: 1.4232 - val_accuracy: 0.5500\n",
-      "Epoch 18/20\n",
-      "24/24 [==============================] - 28s 1s/step - loss: 1.2044 - accuracy: 0.5229 - val_loss: 1.4952 - val_accuracy: 0.5250\n",
-      "Epoch 19/20\n",
-      "24/24 [==============================] - 27s 1s/step - loss: 1.2422 - accuracy: 0.5458 - val_loss: 1.4029 - val_accuracy: 0.4625\n",
-      "Epoch 20/20\n",
-      "24/24 [==============================] - 27s 1s/step - loss: 1.0991 - accuracy: 0.5813 - val_loss: 1.6313 - val_accuracy: 0.4250\n"
+      "21/24 [=========================>....] - ETA: 3s - loss: 2.0452 - accuracy: 0.1619WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 480 batches). You may need to use the repeat() function when building your dataset.\n",
+      "24/24 [==============================] - 27s 1s/step - loss: 2.0452 - accuracy: 0.1619 - val_loss: 2.0012 - val_accuracy: 0.1571\n"
      ]
     }
    ],
@@ -545,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -580,7 +548,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -644,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 28,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -660,23 +628,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 29,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "eRes_n9BGhJ0"
    },
-   "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 [==============================] - 0s 0us/step\n",
-      "58900480/58889256 [==============================] - 0s 0us/step\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\",\n",
     "                                           include_top=False,\n",
@@ -717,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -732,7 +690,7 @@
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
+      " input_2 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
       "                                                                 \n",
       " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
       "                                                                 \n",
@@ -830,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -862,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -893,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -902,7 +860,7 @@
        "(480, 4, 4, 512)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -920,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -929,7 +887,7 @@
        "(480, 8)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -940,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -959,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -976,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -987,7 +945,7 @@
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " input_2 (InputLayer)        [(None, 4, 4, 512)]       0         \n",
+      " input_3 (InputLayer)        [(None, 4, 4, 512)]       0         \n",
       "                                                                 \n",
       " flatten_1 (Flatten)         (None, 8192)              0         \n",
       "                                                                 \n",
@@ -1011,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1019,65 +977,65 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/30\n",
-      "15/15 [==============================] - 2s 113ms/step - loss: 48.5603 - accuracy: 0.2917 - val_loss: 17.6055 - val_accuracy: 0.4625\n",
+      "15/15 [==============================] - 2s 104ms/step - loss: 45.7096 - accuracy: 0.3167 - val_loss: 27.5370 - val_accuracy: 0.4250\n",
       "Epoch 2/30\n",
-      "15/15 [==============================] - 1s 98ms/step - loss: 17.1425 - accuracy: 0.6062 - val_loss: 16.6017 - val_accuracy: 0.4625\n",
+      "15/15 [==============================] - 1s 82ms/step - loss: 17.4056 - accuracy: 0.6062 - val_loss: 23.3888 - val_accuracy: 0.4250\n",
       "Epoch 3/30\n",
-      "15/15 [==============================] - 1s 90ms/step - loss: 9.7542 - accuracy: 0.7271 - val_loss: 20.5582 - val_accuracy: 0.4500\n",
+      "15/15 [==============================] - 1s 97ms/step - loss: 10.0907 - accuracy: 0.7000 - val_loss: 27.9630 - val_accuracy: 0.4000\n",
       "Epoch 4/30\n",
-      "15/15 [==============================] - 1s 96ms/step - loss: 7.9549 - accuracy: 0.7729 - val_loss: 25.4890 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 88ms/step - loss: 8.1440 - accuracy: 0.7729 - val_loss: 22.5127 - val_accuracy: 0.4625\n",
       "Epoch 5/30\n",
-      "15/15 [==============================] - 1s 96ms/step - loss: 5.6834 - accuracy: 0.8167 - val_loss: 16.6779 - val_accuracy: 0.5625\n",
+      "15/15 [==============================] - 1s 79ms/step - loss: 5.6496 - accuracy: 0.8271 - val_loss: 29.9891 - val_accuracy: 0.4625\n",
       "Epoch 6/30\n",
-      "15/15 [==============================] - 1s 84ms/step - loss: 4.8402 - accuracy: 0.8333 - val_loss: 17.9758 - val_accuracy: 0.5625\n",
+      "15/15 [==============================] - 1s 97ms/step - loss: 4.4128 - accuracy: 0.8562 - val_loss: 23.5226 - val_accuracy: 0.4625\n",
       "Epoch 7/30\n",
-      "15/15 [==============================] - 1s 90ms/step - loss: 3.3293 - accuracy: 0.8833 - val_loss: 20.8828 - val_accuracy: 0.5000\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 4.9496 - accuracy: 0.8729 - val_loss: 23.6873 - val_accuracy: 0.5000\n",
       "Epoch 8/30\n",
-      "15/15 [==============================] - 1s 97ms/step - loss: 4.5972 - accuracy: 0.8562 - val_loss: 30.2963 - val_accuracy: 0.5000\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 3.2162 - accuracy: 0.8667 - val_loss: 29.5564 - val_accuracy: 0.5250\n",
       "Epoch 9/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 3.5146 - accuracy: 0.8729 - val_loss: 23.9580 - val_accuracy: 0.5375\n",
+      "15/15 [==============================] - 1s 96ms/step - loss: 2.5075 - accuracy: 0.9146 - val_loss: 26.9293 - val_accuracy: 0.4500\n",
       "Epoch 10/30\n",
-      "15/15 [==============================] - 1s 92ms/step - loss: 3.1607 - accuracy: 0.8958 - val_loss: 28.4524 - val_accuracy: 0.5250\n",
+      "15/15 [==============================] - 1s 84ms/step - loss: 3.2294 - accuracy: 0.8917 - val_loss: 29.0235 - val_accuracy: 0.5375\n",
       "Epoch 11/30\n",
-      "15/15 [==============================] - 1s 97ms/step - loss: 3.7148 - accuracy: 0.8917 - val_loss: 25.1327 - val_accuracy: 0.5375\n",
+      "15/15 [==============================] - 1s 82ms/step - loss: 2.2690 - accuracy: 0.9125 - val_loss: 28.3215 - val_accuracy: 0.4375\n",
       "Epoch 12/30\n",
-      "15/15 [==============================] - 1s 88ms/step - loss: 1.5648 - accuracy: 0.9312 - val_loss: 28.7889 - val_accuracy: 0.4250\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 3.3047 - accuracy: 0.8979 - val_loss: 25.3148 - val_accuracy: 0.4875\n",
       "Epoch 13/30\n",
-      "15/15 [==============================] - 1s 91ms/step - loss: 1.9269 - accuracy: 0.9271 - val_loss: 30.9736 - val_accuracy: 0.5000\n",
+      "15/15 [==============================] - 1s 98ms/step - loss: 1.9621 - accuracy: 0.9312 - val_loss: 26.1054 - val_accuracy: 0.5250\n",
       "Epoch 14/30\n",
-      "15/15 [==============================] - 1s 99ms/step - loss: 2.8450 - accuracy: 0.9125 - val_loss: 27.4909 - val_accuracy: 0.5625\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 3.0970 - accuracy: 0.9146 - val_loss: 35.0209 - val_accuracy: 0.4625\n",
       "Epoch 15/30\n",
-      "15/15 [==============================] - 1s 82ms/step - loss: 2.2349 - accuracy: 0.9333 - val_loss: 35.6591 - val_accuracy: 0.4750\n",
+      "15/15 [==============================] - 1s 78ms/step - loss: 1.3575 - accuracy: 0.9458 - val_loss: 29.6890 - val_accuracy: 0.5000\n",
       "Epoch 16/30\n",
-      "15/15 [==============================] - 1s 87ms/step - loss: 1.3261 - accuracy: 0.9479 - val_loss: 29.8620 - val_accuracy: 0.4500\n",
+      "15/15 [==============================] - 1s 97ms/step - loss: 1.2133 - accuracy: 0.9479 - val_loss: 30.5464 - val_accuracy: 0.5125\n",
       "Epoch 17/30\n",
-      "15/15 [==============================] - 2s 108ms/step - loss: 1.5269 - accuracy: 0.9438 - val_loss: 26.1654 - val_accuracy: 0.5875\n",
+      "15/15 [==============================] - 1s 79ms/step - loss: 1.8159 - accuracy: 0.9396 - val_loss: 33.3255 - val_accuracy: 0.4750\n",
       "Epoch 18/30\n",
-      "15/15 [==============================] - 1s 81ms/step - loss: 2.6730 - accuracy: 0.9250 - val_loss: 26.6652 - val_accuracy: 0.4875\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 1.4012 - accuracy: 0.9396 - val_loss: 30.1178 - val_accuracy: 0.5125\n",
       "Epoch 19/30\n",
-      "15/15 [==============================] - 1s 94ms/step - loss: 0.8446 - accuracy: 0.9625 - val_loss: 25.5028 - val_accuracy: 0.5500\n",
+      "15/15 [==============================] - 1s 100ms/step - loss: 2.6479 - accuracy: 0.9438 - val_loss: 32.6207 - val_accuracy: 0.5375\n",
       "Epoch 20/30\n",
-      "15/15 [==============================] - 1s 104ms/step - loss: 0.7206 - accuracy: 0.9521 - val_loss: 28.1912 - val_accuracy: 0.5625\n",
+      "15/15 [==============================] - 1s 80ms/step - loss: 1.1343 - accuracy: 0.9521 - val_loss: 33.7492 - val_accuracy: 0.5125\n",
       "Epoch 21/30\n",
-      "15/15 [==============================] - 1s 87ms/step - loss: 1.2045 - accuracy: 0.9479 - val_loss: 28.3712 - val_accuracy: 0.5375\n",
+      "15/15 [==============================] - 1s 80ms/step - loss: 1.4898 - accuracy: 0.9542 - val_loss: 32.9078 - val_accuracy: 0.5125\n",
       "Epoch 22/30\n",
-      "15/15 [==============================] - 1s 87ms/step - loss: 1.4085 - accuracy: 0.9542 - val_loss: 30.7843 - val_accuracy: 0.5375\n",
+      "15/15 [==============================] - 1s 88ms/step - loss: 1.4119 - accuracy: 0.9583 - val_loss: 35.3497 - val_accuracy: 0.5125\n",
       "Epoch 23/30\n",
-      "15/15 [==============================] - 1s 90ms/step - loss: 2.0227 - accuracy: 0.9500 - val_loss: 29.8337 - val_accuracy: 0.5250\n",
+      "15/15 [==============================] - 1s 97ms/step - loss: 2.7630 - accuracy: 0.9125 - val_loss: 33.3531 - val_accuracy: 0.5250\n",
       "Epoch 24/30\n",
-      "15/15 [==============================] - 1s 104ms/step - loss: 1.3282 - accuracy: 0.9604 - val_loss: 30.5257 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 89ms/step - loss: 1.1729 - accuracy: 0.9625 - val_loss: 37.2347 - val_accuracy: 0.4750\n",
       "Epoch 25/30\n",
-      "15/15 [==============================] - 1s 89ms/step - loss: 1.6409 - accuracy: 0.9542 - val_loss: 32.1752 - val_accuracy: 0.5250\n",
+      "15/15 [==============================] - 1s 85ms/step - loss: 1.0788 - accuracy: 0.9500 - val_loss: 36.1926 - val_accuracy: 0.5250\n",
       "Epoch 26/30\n",
-      "15/15 [==============================] - 1s 90ms/step - loss: 1.1011 - accuracy: 0.9708 - val_loss: 31.8869 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 101ms/step - loss: 0.8924 - accuracy: 0.9604 - val_loss: 33.2011 - val_accuracy: 0.5125\n",
       "Epoch 27/30\n",
-      "15/15 [==============================] - 1s 100ms/step - loss: 0.6049 - accuracy: 0.9604 - val_loss: 33.1560 - val_accuracy: 0.5000\n",
+      "15/15 [==============================] - 1s 83ms/step - loss: 1.4114 - accuracy: 0.9563 - val_loss: 32.9735 - val_accuracy: 0.5500\n",
       "Epoch 28/30\n",
-      "15/15 [==============================] - 1s 83ms/step - loss: 0.7594 - accuracy: 0.9667 - val_loss: 35.5192 - val_accuracy: 0.4875\n",
+      "15/15 [==============================] - 1s 81ms/step - loss: 1.0081 - accuracy: 0.9583 - val_loss: 35.2063 - val_accuracy: 0.5125\n",
       "Epoch 29/30\n",
-      "15/15 [==============================] - 1s 85ms/step - loss: 0.7181 - accuracy: 0.9688 - val_loss: 32.3942 - val_accuracy: 0.5125\n",
+      "15/15 [==============================] - 1s 93ms/step - loss: 1.0290 - accuracy: 0.9583 - val_loss: 35.6483 - val_accuracy: 0.4875\n",
       "Epoch 30/30\n",
-      "15/15 [==============================] - 2s 107ms/step - loss: 1.0921 - accuracy: 0.9625 - val_loss: 33.1947 - val_accuracy: 0.4625\n"
+      "15/15 [==============================] - 1s 84ms/step - loss: 0.6959 - accuracy: 0.9667 - val_loss: 35.5286 - val_accuracy: 0.5000\n"
      ]
     }
    ],
@@ -1127,12 +1085,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1144,7 +1102,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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Y1ZXwyR0Q3xOO/71n2xceAxe9AcYFMy6zdxiekrveXvxL8+CKD2zaqR0KqACQGBPGvsoayiq1HlDAy06HfXkw4V5I6gc//M+OYFHuWTcH3rnS5tgvfddebA/W/1S49ksbGF4+w6aKDrbwSZsqOuNRCPVCZ2diHzh/uv28Z//WM5/xzuXw8kSoqYSrPrZDUNupgAoAybWTwTQNpDIX2N99T4RxN8LOZbB1oU+b1G5smAvvXG5z65fOtGmfhnQcBL/6CrqOsXcLXz1o774ACjbDN4/AkHNhwGnea2//U+DkP0P6TBvoW2LL9/DK2Xb28dVzoPMIz7TRRwIqACTp4vCqVuZ8mx+O6wIjL4bIRPjhKV+3qnXkbbAjdh7pZ7/FZ3wM1W5+Kdo0H2ZcYnP1l70HkfGH3yc6Ca74EEZdBt/8C9690o7Aqi32NvHhFp2OW479nQ00X/7FnkNzrP8CXp8CMalwzRxI7ufZNvpAQI0Cqi0Ip7OBA1xVuf22P+Zq+3dYFBxxLXzzqJ28k9S3ddpRUwVI4+UOPGnnMvj2MVtoLSTCfjPe8p3t/4joYC+Qw6dCz/EQFHzo/pu/hbcutimzK2ZDVKL77x0SBuf8D1IG2xE/O5fD3m1w+kM2CHubCJz7tM3dz7zGzhRO6On+/umzbGd36lAb+KKTvdbU1hRQASDZKQmdp/WAAtv2H6G6HPpM+GXbEb+G75+wdwFnPeb9NuwrsJ2IkfFw5cf2AukNxthRNt89ZtNe4R3guDvgqOttUbWaati8AFa+a4fFLn0VYjvbES3DL4DOafbiufUHeHOavWhe8WHTLv61ROCYm20AmXWtrd1z5HUePuFG1HYKv3CiHbc//AL7bT6mE8R0hNhOEJV8aEBe/DJ8fDv0ONqO9ono0Hpt9rKACgBJWhFUgU3/BIVAr/G/bItNhREXwvI34aQ/Ne8C567qCluqoGATuKrtXISJ//Dse7hcdhjmd4/BjiUQ3RFO+SuMvebAnH1wiB3B0u8UO0pm/RzbWfvjczZfntQPBp4Bi6fbb+pXzLaBoyUGToRblkFwWOvd/dRK6mvH679/A3z1QD0vEPvtvjYohEbC2o+h/2kw9RW/mzAYUAEgKiyEiNAgTQEFuk3z7dDF8NgDt4+76ZfFRo6/0zvvbYwdjbL1ezj/JXs3sugp6HUsDDqj5cevqbIX8O//YycnxfeEMx+DtEshNKLxfcOiYNgU+1O2B9bMhlXvwsL/2sJqV35kA6UnxHT0zHGao98pcOcGW5CtJMf5yYaS3b88Ls62v3PXwegr4Ix/e+8uzYcCKgCALQutKaAAtq8Adq2wwz8PljoE+p4MP70Ax9zinen7X/8TVr4NJ/7JpiAGn22DwAc3wPXfQXz35h+7rBBePRd2LYeOQ2HKizB0cvO+ZUcmwJgr7U9xtv0m3Nhon/YoNNKmtJrSF+BnAmoUENg0kN4BBLDNXwPGDv+szzE3229+q971/HuveBsWPAQjL/ll0lNIOFzwsq0eOfMap2O4GSqK4Y0LIHu1vbO44XsYMdUzKZbYVP+7+CsgEAOAzgYObJkLbMGyLqPrf77Pifbb8w9PeXZi2Jbv7dDLXsfZUgl1SwYk9YVznoCsn+Crvzf92FVldnTOjqW2dPDwC9pdSQLlG4EXAGLCdVWwQLZpvr0IN/TNWASOvgly1nhuZam8DXbsfGJvmPZa/bnkYefbYanfPwEbvnT/2NWV8Pbldjjn5OdsSkkpNwVeAIgOI7+0EqPT/gNPQSYUbm04/VOrdnhgS2eNgq0V88YFdtTRJe80Xud+4kO2hPD7v4GinYc/dk21HU658Ut7VzFiasvbqwJK4AWAmDAqql2Uaj2gwFNb/qHu+P/6hITb8embvrI59eaqKrff/It320qZh1uiMDQSpv6f3W/mtY1XKHW54MMbIWO2nUk75srmt1MFrMALALo4fODaNB/iutmx7Ycz9hpb7+WHp5v3Xi6XHdmz/UeY/Kz769sm97eLoW9baDuM62OMXTpx5dt20ZVxNzSvjSrgBVwASNTJYIHJVWNnxPaZ4F4HaVQipF0Cq96xwyCbav6DsPo9OOV+OxSzKUZOs3Vzvv33of0QxsDnf4QlL9sZvZ4uoawCSsAFgP0VQXUkUGDZtRzKCw+f/69r3I12WOZPzzftvZa9Dt8+aicQjb+tafvWmvSILVb33nU2hVRr/j/sxLGjrrff/pVqAa8FABGJEJGfRGSFiKwWkb8623uLyI8islFE3haRVp1eV3sHoCuDBZjaCpC9T3B/n6S+MOhMOzO4srTx17pqbB/DBzfCR7faO40zH2v+cMywKNsfUFECs35lj//d47aa5ugrbN5fh3qqFvLmHUAFcJIxZiSQBkwUkXHAP4HHjTH9gD3AtV5swyGSnIqgeXoHEFgyF0Dq8KbXsTn6ZlsWYcVbhz5nDOxeZatbPj7UzsJdM9uWl77wVQgObVmbOw6CMx+FLd/aY8+931brPOs/evFXHuG1UhDGjrMscf4MdX4McBJwibP9FeB+4BlvteNgEaHBxISHtK0U0K4VdsRHtzG+bol/qtxnO2OP+k3T9+0xzk4a++FpGHMNBAXZNW5XvQsr37HzBYJCbLGw4f+AgZM8u7JV2qW2DPPKGTDoLDjvmfpLNSvVDF6tBSQiwcASoB/wFLAJKDTG1I5vywK6NrDvdcB1AD169PBouxLb2uLws34NFUVw+2r9x+0NWxfa5fv6NCH/X6u2hPHMa+Dze2F3Omz9zj7X/Sg4898wZLJd9MQbROyooP6n2kleLb2rUKoOrwYAY0wNkCYi8cD7wKAm7Ps88DzA2LFjPTprKykmrO2MAspdB3nr7OPMBdDv5NZ9/8p9sOT/YORF3i2B7EuZ823p4R5HN2//wedCfA/48VlI6v9LIbfDjev3lLAo+35KeVirVAM1xhSKyHzgaCBeREKcu4BuwI7WaENdSdFh7Cwsb+23rV/GR/Z3WIzNM7d2AFj6iv1mu/o9u9BHWHTrvn9ryFxgUznNreUeHGL/25QXQeeRmn9XfsObo4BSnG/+iEgkcCqQAcwHar/OXAl86K02NCQpOrztLAyf8ZGtTT/iQrs2a3lR6723MXahj9gudtGQd65sfjXKg49bsNnWqfG1khzITm9e+qeuxD7QJU0v/sqveHMUUGdgvoisBH4GvjTGfAzcDfxORDYCScBLXmxDvWpLQvu8HlDhNjs+fdBZtkRwdRmsacV4uPV7yFtvV8A663FbU+bDm+0s1uaqqYKPboEn0+BffezKV0tfa95kKk/I/Nr+Plz5B6UCkDdHAa0ERtWzPRM40lvv647E6DCqagxF5dV0iPRhp1rGx/b34LPtN8zEvrBiBoy+vHXe/+eX7PqmQyfb9EhJLsx/wC6Jd/qDTT9eRTG8exVsnGtr6dRUwYYvfklzdU6DAafbn86j7Igab8ucbwuwdR7p/fdSqp0JuBXB4JfF4fNLKnwbANZ+bGvPJ/W1f6ddbNcp3bMFEnp5971LcuyF+chf/5IbP/73UJpjq2DGdITxt7p/vKJd8OZUyF5jK1OOucpuN8amYNZ/boPBN4/YVbGiO9qRLf1Ps7+90fdgjM3/9z5eR1cpVY+AKwUBbWRx+JIcOzyxbv32ERfZ3yve9v77L3sdXFW2Bn0tEZj4Txg6Bb78i10g3R05GfDSqZCfCZe8/cvFv/aYnYbb4HLtF3DnJpjygr0or/0Y3r0SXjzVznj1tLwNULSj5fl/pfxUQAaARGc2cLMmg236CtZ91vJGrPsUMAcGgPjudrGSFW95djWqg7lctphYr+MgZcCBzwUF2eqVfSbY/oB1cxo/1uZv4KXT7Tj7qz+13+YbE5VoO7wveAnuzLTlDnIz7GpZnj7nTKf8Q1Pq/ygVQAIyAOxPATV1JFD5Xnj3ajtxq6WjdTI+goTekDr0wO1pl8CezXbmqrdsmmc7oMdeXf/zIeEw7XX7zf3dq2BbA21Z+S68NgXiOsOv5tpRMk0RHGL7H075K6z5wK6G5UmZC2wqzdvpNKXaKf8OAF//C+bce8jmhCinIFxT7wAW/s9WlKwsdj89Up/yvXZ0yuCzDh1WOPgcW4e+Jcc/nMXTIToFBjWyfGB4LFw6E+K6wJsX2jRPLWNsqeL3fmXH118zx06Uaq5jfmvTTvP+6rllGGuqbAkFTf8o1SD/DgD7Cuzszd2rDtgcFhJEXERI0/oASnLtQuFDzoXu4+xxXc1cVWz9Fzb/PvicQ58Lj7HbV39gF/v2tL1ZsH4OjLq8/rVp64pJgcvfs3cEr02Bwu22ZtHHt8O8v9nCZJfNanyZQ3eIwLn/g5TBtuTCni0tOx7YeQ2VxZr+UaoR/h0AJtwNEfH2LuCg/HJyTDh5TVkV7LvH7Tj9E/9oV2Das9leSJsjYzbEdIKuY+t/Pu1iqNjr9BN42NJX7X8Ld5cQTOgFl71nyyG/NhlmXGz7D479HUx+3gYHTwiLhoteB+Oycwcq97XseJkLALH9HEqpevl3AIhMgJP+aMvpZsw+4ClbEM7NO4C9O+DnF22Z35SBduJWh+6wqBlFTCv32XHyg89qeBx8r+MgrqudE+BJNVWw5BXod0rT8uKdhsHFb9l+g41z7aSxU+7z/Dj+xD5w/ku24NpHt7asU3jTfOgyyn/rGynlAf4dAABGXwUdh9ia7VW/1P9JiglzfxTQN/+y30xPuNv+HRxiJzpt+RZ2rWxaezZ9BVX7Dhz9c7CgYBgxDTbO8+wM2nWfQcluOKIZSzD0Gm9H+Vw9x66X6y39T7VBe9U7zQuwYDvos37W2b9KHYb/B4DgEJj4kP32+sP/9m9OjA53rw8gf5MtZTD2akjo+cv20VdAaLTtC2iKjI/snUnP8Y2/buTFYGrshdBTFk+3i6L3P615+3cbCz2O8lx7GnLsHfYu64s/2Y7cptr6vf1vp/l/pRrl/wEA7DfBQWfBt4/ZGatAcoxdE8DlOkyaYcFDtpTwcQctvh0ZD6MutQuDuPstvaYK1n8GAyYdvq57ygDoOsZzaaD8TXZc/Jgr2/6s2KAgu/BJUl87DHVvlvv7VlfYekohkbZev1KqQYERAABO+7sdeTPvr4AtCe0yUFjWSPXL7NWwaqZdSSo29dDnj7reToBaPN29Nmz51g4BbSz9U9fIi20Zhaammeqz5GWQYDv6pz2IiIOL3rQX9LcvOyB9d4iqclj7qV1A/ZF+diLd4LM810GtlJ8KnACQ2AfG3WgvDllLSHQmgzW6MthXD9rx8A3VxEnqCwMm2kXDG7tA1cr4yKaN3E1NDDsfgkLrX4+2KarKYdkbdoHzuM4tO1ZrSu4PU56DncvgkzsO7BSuKrPF9Gb9yl70Z1xs6w0NOcfOXziv1VYZVardCqxicMf/3l5M59xN8gn2oppXUkm/jvW8NmsxrPvErv7U2EiScTfCq+dA+iybEmqIq8ZesPqf6v6asVGJMHCiTTOd+rfmLweYMRvKCrzbeestg86E4++yHfGpQ6FDNztreP3nUFkCkYkwbLKdn9H7BF0yUakmCKwAEB4LJ/8FPryJXrs+BZIbHgn01d8hKhnGXd/4MXsfbyt6LnralnFoaMGQrJ9tpU130z+1Rl5i7xw2zrPBoDkWT7d3QL1PaN7+vjbhXrtuwufOrO6oJLtE4pDzoNexetFXqpkCJwVUa+Ql0DmN1J8eIpLy+lNAmV/biUTH/c4GjcaI2Ilh2ek2x9+QjI9sZ3JTR+D0P9Ve8JqbBspeDdt+sFU/W6P+vjcEBcH5L9q7sStmwx3rbcnpvifqxV+pFminV4QWCAqCSf8kuGQXN4R8RN7BdwDG2G//cV1hrJvj5YdPtXcLDY1bN8amYfpMsJ2bTREcao+/7lMo29O0fQEWvwzB4ZDWSHqqPYjoACfcCX1OsEN7lVItFngBAGwBs2Hnc13Ix9Ts2Xbgc+vn2HTNCXdBaIR7xwuNsJOr1n1mh1sebPcqOw+hqemfWiMvtqON0t9r2n4VJXYY6dDzIDqpee+tlPJbbgUAEblVROLEeklElopIM2cTtRGn/BUQTtz231+2uVx2Ra7EPk3/xjz2WggKgR+fO/S5jI9AgmDgGc1ra+eRdjZzU+cEpM+0BdHaY+evUsrr3L0DuMYYUwScBiQAlwMPe61VrSG+Ox/FTGVMyQLY8r3dtvo9m8s/8Y9Nzy3HptqOyWWvQ1nhgc9lfGRn/kYnN6+tIjDyIsj6CfI2urePMXbN345DdEKUUqpe7gaA2qEtZwCvGWNW19nWbn2Xeik5kgxz7rETjub/w47oGTqleQccdwNUldogUCtvg13xqrnpn1rDL7R3Ee52Bu9cCrtX2m//DY1MUkoFNHd705aIyBdAb+BeEYkFXN5rVuuIi+3A41zKQ7ufsIueFGyCi95q/miZziOh57E2DXTU9bazMuMj+9ygM1vY2M52cZOVb9u7gbJCuzhN2R7nx3lcuy0nw046GzGtZe+rlPJb7gaAa4E0INMYs09EEoEG1hNsPxKjw3ii7Ej+0f8oJHOBrc8/cFLLDjruBnj7UjuJbMi5NgB0GW0nMLVU2iUw61r4XwPrCITF2kJzkfG2cN34W5s+6kgpFTDcDQBHA8uNMaUichkwGvDwAq6tLzkmDBAKT3iQhE9+Bac90PJ0ycBJttb+omdsMbedS+Hk+zzRXLt+bk2VLeYWEe9c7J0LfkQHHROvlGoSdwPAM8BIERkJ3AG8CLwKtNOppVaSUw8oO2YgCbcs90yuPCjYpn/m3GNHFEH9Sz8299hpF3vmWEqpgOdusrvaGGOAc4H/GWOeAg4zRbbtS4yuszi8JztK0y616ZgVb9l1bpP7ee7YSinlIe4GgGIRuRc7/PMTEQkC2n2+waaAIK8pi8O7IyIORjtll1s6+kcppbzE3QAwDajAzgfYDXQDHvFaq1pJUrRNAeU3ZXF4d427EXocoykbpVSb5VYAcC76bwAdROQsoNwY86pXW9YKOkSGEhwk7i8O3xTx3eGaz+ysYqWUaoPcLQVxIfATMBW4EPhRRC7wZsNaQ1CQkBAVdmhBOKWUCgDujgL6I3CEMSYHQERSgLnATG81rLUkRYc1viqYUkr5KXf7AIJqL/6O/Cbs26YlxYQ1vCiMUkr5MXfvAOaIyOdAbSGaacCn3mlS60qKCSd9x15fN0MppVqdWwHAGHOniJwPjHc2PW+Med97zWo9SdFh3hkFpJRSbZzbSysZY2YBs7zYFp9Iig6jqLyaymoXYSF+kdVSSim3NHrFE5FiESmq56dYRIoOs293EZkvImtEZLWI3OpsTxSRL0Vkg/M7wZMn1FSJzmQwrwwFVUqpNqzRAGCMiTXGxNXzE2uMOVyZyWrgDmPMEGAccJOIDAHuAeYZY/oD85y/fWb/ZDAdCaSUCjBey3kYY3YZY5Y6j4uBDKArtp7QK87LXgHO81Yb3FFbDkJHAimlAk2rJL1FpBcwCvgRSDXG7HKe2g2kNrDPdSKyWEQW5+bmeq1t+wvCaQpIKRVgvB4ARCQG23l8m7Ou8H5OhVFT337GmOeNMWONMWNTUlK81r7aktB5OhJIKRVgvBoARCQUe/F/wxjznrM5W0Q6O893BnIa2r81xEWEEBos5OsdgFIqwHgtAIiIAC8BGcaYx+o8NRu40nl8JfCht9rgDhEhMTrMrgmglFIBxO15AM0wHrt+wCoRWe5s+wPwMPCOiFwLbMUWl/OppOhwHQWklAo4XgsAxpjvgIaW2TrZW+/bHEkxYZoCUkoFHJ36Sm05CA0ASqnAogEAOxJI6wEppQKNBgDsXIDSyhrKq2p83RSllGo1GgCoMxtY+wGUUgFEAwCQ6M3F4ZVSqo3SAAB0jLUBYHtBmY9bopRSrUcDADCkSxzxUaF8uWa3r5uilFKtRgMAEBocxGlDUpmXkUNFtXYEK6UCgwYAx6ThnSmuqOb7jXm+bopSSrUKDQCO8X2TiY0I4dNVmgZSSgUGDQCOsJAgTh2cypdrsqmqcfm6OUop5XUaAOqYNLwze8uq+GFTvq+bopRSXqcBoI7j+icTHRbMZ+m7Dv9ipZRq5zQA1BERGsxJg1P5YnU21ZoGUkr5OQ0ABzljWCfySyv5aUuBr5uilFJepQHgICcMTCEiNIjPdDSQUsrPaQA4SFRYCCcO7Mic1btxuepdr14ppfyCBoB6TBzWidziCpZs2+PrpiillNdoAKjHSYM6EhYSxKerdDSQUsp/aQCoR2xEKMf3T2FOuqaBlFL+SwNAAyYN68SuveWsyCr0dVOUUsorNAA04JTBqYQGC5+l62ggpZR/0gDQgA5RoYzvl8xn6bswRtNASin/owGgEZOGdWJ7QRmrdxb5uilKKeVxGgAaceqQTgQHiY4GUkr5JQ0AjUiMDuPoPkl8lr5b00BKKb+jAeAwJg7rxOa8UtZlF/u6KUop5VEaAA7j9KGdEEFXClNK+R0NAIeREhvOkb0SmaNrBCil/IwGADdMGtaJ9dklbMzRNJBSyn9oAHDDxGGdAbREtFLKr2gAcEOnDhGM7hGvs4KVUn5FA4CbzhjemTW7itiSV+rrpiillEdoAHDTxGGdAPQuQCnlNzQAuKlbQhQjunXQ0UBKKb/htQAgItNFJEdE0utsSxSRL0Vkg/M7wVvv7w2ThnVmRdZesvbs83VTlFKqxbx5B/B/wMSDtt0DzDPG9AfmOX+3G5OcNNAcTQMppfyA1wKAMeYboOCgzecCrziPXwHO89b7e0Ov5GiGdI5j+nebWa+lIZRS7Vxr9wGkGmNqk+i7gdSGXigi14nIYhFZnJub2zqtc8NDU4ZTWWOY/NT3fLFa7wSUUu2XzzqBjS2v2WCJTWPM88aYscaYsSkpKa3YssaN7B7PR78dT9+OMVz32hL+O2+DVgpVSrVLrR0AskWkM4DzO6eV398jOneI5J3fHM15aV3495fruenNpeyrrPZ1s5RSqklaOwDMBq50Hl8JfNjK7+8xEaHBPD4tjT+cMYg56buZ8vRCthfo6CClVPvhzWGgbwE/AANFJEtErgUeBk4VkQ3AKc7f7ZaIcN3xfZl+1RHsKCzj3Ke+Z1Fmvq+bpZRSbpH2kL8eO3asWbx4sa+b0ajM3BJ+9epituXv475zhnLZUT0QEV83SykVwERkiTFmbEPP60xgD+mTEsMHN43n+AEp/PmDdP7wfjqV1S5fN0sppRqkAcCD4iJCeeGKsdw4oS9v/bSNS19cROG+Sl83Syml6qUBwMOCg4S7Jg7iyYtHsSJrL1dM/4mi8ipfN0sppQ6hAcBLzhnZhWcuHc2anUVc8/LPOkxUKdXmaADwopMHp/LERaNYum0P1726hPKqGl83SSml9tMA4GVnjujMIxeM5LuNedz0xlLtGFZKtRkaAFrB+WO68cB5w5i3Nofb315OdY0GAaWU74X4ugGB4rJxPSmvquGBTzIIDwni0akjCQrSeQJKKd/RANCKfnVcH8oqa/j3l+uJDAvmgfOG6WQxpZTPaABoZTef1I+yqhqeXrCJiNBg/nTmYA0CSimf0ADQykSEO08fyL7KGl76bjNRYcHccdpAXzdLKRWANAD4gIhw39lDKK+q4b9fbSQyLJgbJ/TzdbOUUgFGA4CPiAgPTh5OeVUN/5qzjooqFzdM6EtEaLCvm6aUChAaAHwoOEh4dOpIXAaemLeBt3/ezm9P7sfUMd0JC9ERukop79KrjI+FBAfx5MWjePPXR9ElPoI/vp/OyY8tYNaSLGpcbb9Ut1Kq/dL1ANoQYwwL1uXy6BfrWL2ziL4p0fzu1IFMGtbJ7TkD1TUu1u4uZum2PXRPiOLEQR293GqlVFt1uPUANAC0QcYY5qTv5rEv17Mhp4QhneO447QBnDSo4yFDRgtKK1m2bQ9Ltu5h6bY9rNi+l7I6NYemjunG/ecMJTpcs31KBRoNAO1Yjcswe8UO/jN3A1vz95HWPZ7rT+hLQWklS7buYdm2PWTmlQIQEiQM6RLH6B4JjO6ZQFq3eN5ZvJ2nFmykd1I0T148imFdO/j4jJRSrUkDgB+oqnExc0kWT87bwK695QAkRYcxqkcCY3omMLpHPCO6xRMZdugIooWb8rj97eUUlFZy98RBXDO+t5agUCpAaADwI+VVNSzKzKdXUjQ9k6LcnkFcUFrJXTNXMjcjmwkDU3h06kiSY8K93FqllK/pmsB+JCI0mAkDO9IrObpJ5SMSo8N44Yox/O3coSzclM/E/3zLN+tzvdhSpVR7oAEgQIgIVxzdiw9vGk9CVChXTP+Jhz7N0PUJlApgGgACzODOccy++VguOaoHz32TyQXPLmSz05HcllVU17Aqa6+upaCUB2kfQACbk76Lu2aupKrGcNrQVE4a1JEJAzrSISq0WcfLKS7nq4wc5mbksDKrkOMHpHDJUT0Y1T2+2RVPc4rLefPHbby+aBt5JRUMTI3lj2cO5vgBKc06Xl1F5VWs3L6XI3sn6sxr5Ze0E1g1akdhGY9/uZ6v1uZQUFpJcJAwpmcCJw/qyMmDO9I3JabBi7cxhoxdxczLyGbu2hxWbC8EoGt8JMO7duCbDbnsq6xhUKdYLj2qB+eO6kpchHvBZVXWXl7+fjMfr9xFZY2LEwemcMKAFKZ/v4VtBfs4YUAKfzxzMANSY5t8znklFbz8/WZeXbiV4opqRnTrwJMXjaJXcnSTj6VUW6YBQLmlxmVYkVXIVxk5zFubQ8auIgB6JEZxkhMMjuydCMCizALmZWQzLyOHHYVlAKR1j+eUwR05eXAqgzrFIiIUl1cxe8VO3vxxG6t3FhEZGsw5I7twyVE9GNGtwyGBpbrGxeers3n5+80s3rqH6LBgLhjTjSuP6UWflBjApoJeXbiVJ7/aQGlFNRcf2YPbTx3g1qimHYVlvPBNJjN+3kZFtYtJwzpxdN9kHv18HdU1Lh6YPIzJo7p58j9rqzLGsHRbIdHhwQzqFOfr5jRo2bY9/HPOWhKiwvjrOUPpGBfh6yb5LQ0Aqll2Fpbx1docvlqbw/cb86iodhHtzDMorawhIjSI4/qncMrgjpw4qCMdYxv+R2yMYWXWXt78cRuzV+ykrKqGoV3iuOSoHpyb1pXqGhdv/bSd137Yws695XRPjOTKo3tx4RHdG7xjKCit5Ml5G3ht0VYiQ4O56cR+XD2+V73VVDfmlPDs15v4YNkOACaP6spvTuhLv442qOwoLOP2Gcv5aUsBU0Z35W/nDiOmnc2cXrK1gEc+X8eizAJE4Mqje/H70we2qfPYUVjGPz9by+wVO0mOCae4vIqI0GDuP2cI56V11YWRvEADgGqxssoafsjM46u1ORgDJw/uyDF9k5tVurqovIoPl+3gjR+3sXZ3MVFhwbiMobzKxTF9k7h6fG9OGtSRYDcnq23MKeHhzzKYm5FD1/hI7pk0iLNGdEZEWJW1l6cXbGTO6t2EhwRx0RE9+PXxfegaH3nIcaprXPxv/kaenLeBHolR/Pfi0Qzv1vZnTq/euZd/f2FTeMkx4dx0Yl+25JXy6qKtdIqL4O/nDuOUIak+bWNpRTXPLNjEC99mAnDd8X24/oS+7C4q566ZK1mydQ+nDE7lH5OH6d2Ah2kAUG2SMYbl2wt5Z/F2gkS4/OieLUpbLNyYxwOfZLBmVxGjesQTEx7CtxvyiI0I4Yqje3L1+N5upYl+2lzArTOWkVdSwV2nD+LaY9vmzOlNuSU89uV6Plm5i7iIEK6f0JerjulFVJj9xr902x7unbWKddnFnDG8E/ef3fqplhqXYeaS7Tz6xXpyiys4N60Ld00cdEAArnEZXv5+M498vo7wkCDuP2cok0fp3YCnaABQAaPGZZi1NItHP1+HyxiuObY3l43r6XbHc63CfZXcPWsln6/O5oQBduZ0SqxnZk4bY9iUW8oPmfn8mJlPjcswIDWWgZ3sT8/EKEKCGx6RlLVnH0/M3cCspVlEhAZz7bG9+dVxfegQeeg5VtW4eP6bTJ6Yt4HwkCDumTSIi4/o0SoBbeGmPB742Abk0T3i+fNZQxjVI6HB12fmlnCnczdw8qCO/GPKcFL1bqDFNACogONyGQy4nUaqjzGGN37cxt8/XkNsRCiPTxvJcf2bPvTUGMPmPHvBX5RZwKLMfHKLKwDoFBdBZFgwW/JLqf1nGBYSRL+UGAZ1imWAExQGpsYSEiQ8NX8jb/60DRHh8nE9uWFCX7fuajbnlfLH91excFM+Y3sm8NCU4fR3Y/SUy2XYVVTO5txSyqpqCA8JIjwkiIjQYMJDg4gIsb/DQ4KJcH5vzS/lH5+uZW5G9iEpucM5+G7gvrOHMmW03g20hAYApVpg3e5ibn5zKRtySji2XzIJ0WHERYQQGxFKXKTzOyKEuIhQYp3tIrBk6x4WZeazKDOf7CJ7we8YG87RfZMY1yeJo/sk7a/nVFZZw8acEtZlF7NudxHrsktYv7uY3UXlB7QlOEi4cGw3fntSf7rU04/RGGMMM5dk8eCnGZRWVHPDhH7c6CxBuqe0ksy8UjbnlbI5r4TNeaVk5payJb+U8qqmT7yLCQ/hxhP7cs343s3qJ9qcV8qd765gsQ/uBkoqqlmZVcjy7YUs21bIiu2FBInQPzWGAamxDEiNoX9qLP07xhDbxDtLX9AAoFQLlVXW8O8v1vHTlgKKy6spKquiqLyKqprG/+0kx9Re8BM5uk8SvZtYw6lwXyXrs0tYt7uI3OIKpozu1uK5CnklFTzw8Ro+WL6TlNhwqmpcFO6r2v98SJDQIzGK3snR9iclmt5J0cRGhFJRXUN5lYuK6hoqql2UV9nfFVU1lFe7qKhyERIsXDi2e4tTZjUuwysLt/Cvz9fiMrb6bWRoMBGhwUSGBR/0OMj+HRZMXEQoCVFhJEbX/g4jITqM+MjQQ1JrLpdhY24Jy7cVsmz7HpZtK2R9djG1C/H1To4mrXs8AqzPKWZDdgkVdUqndI2P3B8Y+neMoXdyNEFBgjEGl7HHrzEGY8BVZ5vLGOKjwuiZFEVSdJhX73A0ACjlBcYYKqpdFJVXUVRWTXF5FUXl9ndltYsR3eLpm9K0C35r+np9Lq8v2kpKbDh9ai/2ydF0T4witJE+iNa2Ja+U1xdtpai8irIqF2WVNZRX1VBWVXPA432V9u/KRkqFdIgMtQEhygaDjJ1FFFdUAxAXEUJajwRGdY8nrUc8ad3iSYgOO2D/Gpdhe8E+1mcXsyGnhPXZxazPLmFTTkmj79uYmPAQeiZFOT/R9EqKokdiNL2So0iNjWhxf40GAKVUwCivqmHPvkoKSivZU1pFwb5KCvf/XUnBvir2lFZSVlXD4M6xjOqeQFqPeHonRTf7Yltd42JrwT62FewDIEiEIIFgEaT2cdAvj4NEKCitZEt+KVvz97HV+b19z74D7irDQ4LokRjFs5ePoa8zEbKpDhcAfDJLREQmAk8AwcCLxpiHfdEOpZR/iQgNpnOHSDp3aFofSUuEBAfRNyWm2RfpWtU1LnbtLWdr/j4nONjAkBAVdvidm6nVA4CIBANPAacCWcDPIjLbGLOmtduilFJtRUhwEN0To+ieGMWx/ZNb5T19kew7EthojMk0xlQCM4BzfdAOpZQKaL4IAF2B7XX+znK2HUBErhORxSKyODdXV69SSilPazvd/QcxxjxvjBlrjBmbktLy2u9KKaUO5IsAsAPoXufvbs42pZRSrcgXAeBnoL+I9BaRMOAiYLYP2qGUUgGt1UcBGWOqReRm4HPsMNDpxpjVrd0OpZQKdD6ZB2CM+RT41BfvrZRSymqzncBKKaW8q12UghCRXGBrM3dPBvI82Jy2wN/OSc+n7fO3c/K384H6z6mnMabBYZTtIgC0hIgsbqwWRnvkb+ek59P2+ds5+dv5QPPOSVNASikVoDQAKKVUgAqEAPC8rxvgBf52Tno+bZ+/nZO/nQ8045z8vg9AKaVU/QLhDkAppVQ9NAAopVSA8usAICITRWSdiGwUkXt83Z6WEpEtIrJKRJaLSLtcI1NEpotIjoik19mWKCJfisgG53eCL9vYFA2cz/0issP5nJaLyBm+bGNTiEh3EZkvImtEZLWI3Opsb8+fUUPn1C4/JxGJEJGfRGSFcz5/dbb3FpEfnevd206ttcaP5a99AM7KY+ups/IYcHF7XnlMRLYAY40x7XYCi4gcD5QArxpjhjnb/gUUGGMedgJ1gjHmbl+2010NnM/9QIkx5lFftq05RKQz0NkYs1REYoElwHnAVbTfz6ihc7qQdvg5iYgA0caYEhEJBb4DbgV+B7xnjJkhIs8CK4wxzzR2LH++A9CVx9ogY8w3QMFBm88FXnEev4L9x9kuNHA+7ZYxZpcxZqnzuBjIwC7Y1J4/o4bOqV0yVonzZ6jzY4CTgJnOdrc+I38OAG6tPNbOGOALEVkiItf5ujEelGqM2eU83g2k+rIxHnKziKx0UkTtJl1Sl4j0AkYBP+Inn9FB5wTt9HMSkWARWQ7kAF8Cm4BCY0y18xK3rnf+HAD80bHGmNHAJOAmJ/3gV4zNSbb3vOQzQF8gDdgF/NunrWkGEYkBZgG3GWOK6j7XXj+jes6p3X5OxpgaY0wadkGtI4FBzTmOPwcAv1t5zBizw/mdA7yP/eD9QbaTp63N1+b4uD0tYozJdv6BuoAXaGefk5NXngW8YYx5z9ncrj+j+s6pvX9OAMaYQmA+cDQQLyK1Jf7dut75cwDwq5XHRCTa6cBCRKKB04D0xvdqN2YDVzqPrwQ+9GFbWqz2QumYTDv6nJwOxpeADGPMY3WearefUUPn1F4/JxFJEZF453EkdqBLBjYQXOC8zK3PyG9HAQE4w7r+wy8rjz3o2xY1n4j0wX7rB7uQz5vt8XxE5C1gArZ0bTZwH/AB8A7QA1v2+0JjTLvoWG3gfCZg0woG2AL8pk7+vE0TkWOBb4FVgMvZ/Adszry9fkYNndPFtMPPSURGYDt5g7Ff4t8xxvzNuUbMABKBZcBlxpiKRo/lzwFAKaVUw/w5BaSUUqoRGgCUUipAaQBQSqkApQFAKaUClAYApZQKUBoAlPIyEZkgIh/7uh1KHUwDgFJKBSgNAEo5ROQyp876chF5zim4VSIijzt11+eJSIrz2jQRWeQUEnu/tpCYiPQTkblOrfalItLXOXyMiMwUkbUi8oYzO1Upn9IAoBQgIoOBacB4p8hWDXApEA0sNsYMBb7GzvQFeBW42xgzAjvDtHb7G8BTxpiRwDHYImNgK1DeBgwB+gDjvXxKSh1WyOFfolRAOBkYA/zsfDmPxBY8cwFvO695HXhPRDoA8caYr53trwDvOrWauhpj3gcwxpQDOMf7yRiT5fy9HOiFXchDKZ/RAKCUJcArxph7D9go8ueDXtfc2il1a7LUoP/2VBugKSClrHnABSLSEfavgdsT+2+ktsLiJcB3xpi9wB4ROc7ZfjnwtbPaVJaInOccI1xEolrzJJRqCv0WohRgjFkjIn/CrrgWBFQBNwGlwJHOcznYfgKw5XafdS7wmcDVzvbLgedE5G/OMaa24mko1SRaDVSpRohIiTEmxtftUMobNAWklFIBSu8AlFIqQOkdgFJKBSgNAEopFaA0ACilVIDSAKCUUgFKA4BSSgWo/weXxu33JIap0QAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1174,23 +1132,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3/3 [==============================] - 0s 7ms/step - loss: 33.1947 - accuracy: 0.4625\n"
+      "3/3 [==============================] - 0s 9ms/step - loss: 35.5286 - accuracy: 0.5000\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[33.194679260253906, 0.4625000059604645]"
+       "[35.52861785888672, 0.5]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1438,7 +1396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1450,7 +1408,97 @@
       "Epoch 2/50\n",
       "15/15 [==============================] - 161s 11s/step - loss: 34.7940 - accuracy: 0.5771 - val_loss: 117.0612 - val_accuracy: 0.2000\n",
       "Epoch 3/50\n",
-      "15/15 [==============================] - ETA: 0s - loss: 32.3914 - accuracy: 0.5688"
+      "15/15 [==============================] - 164s 11s/step - loss: 32.3914 - accuracy: 0.5688 - val_loss: 100.1375 - val_accuracy: 0.2250\n",
+      "Epoch 4/50\n",
+      "15/15 [==============================] - 162s 11s/step - loss: 24.2779 - accuracy: 0.6146 - val_loss: 98.1699 - val_accuracy: 0.2125\n",
+      "Epoch 5/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 21.5480 - accuracy: 0.6646 - val_loss: 86.7904 - val_accuracy: 0.2500\n",
+      "Epoch 6/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 19.2203 - accuracy: 0.6625 - val_loss: 97.0842 - val_accuracy: 0.2500\n",
+      "Epoch 7/50\n",
+      "15/15 [==============================] - 158s 11s/step - loss: 19.1349 - accuracy: 0.6833 - val_loss: 92.7148 - val_accuracy: 0.2875\n",
+      "Epoch 8/50\n",
+      "15/15 [==============================] - 153s 10s/step - loss: 18.4256 - accuracy: 0.6917 - val_loss: 89.8846 - val_accuracy: 0.3000\n",
+      "Epoch 9/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 15.9491 - accuracy: 0.7312 - val_loss: 81.9328 - val_accuracy: 0.3375\n",
+      "Epoch 10/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 15.8089 - accuracy: 0.7000 - val_loss: 91.3705 - val_accuracy: 0.3000\n",
+      "Epoch 11/50\n",
+      "15/15 [==============================] - 161s 11s/step - loss: 14.6212 - accuracy: 0.7104 - val_loss: 85.8852 - val_accuracy: 0.3375\n",
+      "Epoch 12/50\n",
+      "15/15 [==============================] - 165s 11s/step - loss: 14.2607 - accuracy: 0.7375 - val_loss: 79.6010 - val_accuracy: 0.3625\n",
+      "Epoch 13/50\n",
+      "15/15 [==============================] - 166s 11s/step - loss: 14.5002 - accuracy: 0.7229 - val_loss: 81.6204 - val_accuracy: 0.3750\n",
+      "Epoch 14/50\n",
+      "15/15 [==============================] - 160s 11s/step - loss: 11.8944 - accuracy: 0.7167 - val_loss: 78.6013 - val_accuracy: 0.3625\n",
+      "Epoch 15/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 11.1978 - accuracy: 0.7563 - val_loss: 97.3765 - val_accuracy: 0.3250\n",
+      "Epoch 16/50\n",
+      "15/15 [==============================] - 156s 10s/step - loss: 17.9992 - accuracy: 0.6958 - val_loss: 79.1425 - val_accuracy: 0.3500\n",
+      "Epoch 17/50\n",
+      "15/15 [==============================] - 154s 10s/step - loss: 14.9720 - accuracy: 0.7104 - val_loss: 71.0003 - val_accuracy: 0.3750\n",
+      "Epoch 18/50\n",
+      "15/15 [==============================] - 153s 10s/step - loss: 10.8481 - accuracy: 0.7521 - val_loss: 93.2622 - val_accuracy: 0.3500\n",
+      "Epoch 19/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 11.0923 - accuracy: 0.7625 - val_loss: 63.9837 - val_accuracy: 0.4250\n",
+      "Epoch 20/50\n",
+      "15/15 [==============================] - 152s 10s/step - loss: 11.5035 - accuracy: 0.7500 - val_loss: 70.1764 - val_accuracy: 0.4000\n",
+      "Epoch 21/50\n",
+      "15/15 [==============================] - 152s 10s/step - loss: 9.8748 - accuracy: 0.7958 - val_loss: 67.4912 - val_accuracy: 0.4125\n",
+      "Epoch 22/50\n",
+      "15/15 [==============================] - 155s 10s/step - loss: 9.0496 - accuracy: 0.7854 - val_loss: 68.4334 - val_accuracy: 0.4000\n",
+      "Epoch 24/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 10.7076 - accuracy: 0.7563 - val_loss: 76.1538 - val_accuracy: 0.3750\n",
+      "Epoch 25/50\n",
+      "15/15 [==============================] - 158s 11s/step - loss: 9.3391 - accuracy: 0.7667 - val_loss: 73.5574 - val_accuracy: 0.4000\n",
+      "Epoch 26/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 11.0616 - accuracy: 0.7646 - val_loss: 65.0781 - val_accuracy: 0.4375\n",
+      "Epoch 27/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 10.4314 - accuracy: 0.7875 - val_loss: 73.0751 - val_accuracy: 0.4375\n",
+      "Epoch 28/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 8.8000 - accuracy: 0.7937 - val_loss: 56.8248 - val_accuracy: 0.4125\n",
+      "Epoch 29/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 6.1402 - accuracy: 0.8208 - val_loss: 61.1947 - val_accuracy: 0.4250\n",
+      "Epoch 31/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 6.7533 - accuracy: 0.8125 - val_loss: 72.0019 - val_accuracy: 0.4000\n",
+      "Epoch 32/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 7.7121 - accuracy: 0.8250 - val_loss: 59.1955 - val_accuracy: 0.4625\n",
+      "Epoch 33/50\n",
+      "15/15 [==============================] - 156s 11s/step - loss: 8.7896 - accuracy: 0.7771 - val_loss: 66.8333 - val_accuracy: 0.4250\n",
+      "Epoch 34/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 7.4364 - accuracy: 0.8167 - val_loss: 66.7417 - val_accuracy: 0.4000\n",
+      "Epoch 35/50\n",
+      "15/15 [==============================] - 158s 11s/step - loss: 6.2558 - accuracy: 0.8042 - val_loss: 65.5794 - val_accuracy: 0.4250\n",
+      "Epoch 36/50\n",
+      "15/15 [==============================] - 158s 11s/step - loss: 6.1949 - accuracy: 0.8354 - val_loss: 54.5587 - val_accuracy: 0.4375\n",
+      "Epoch 37/50\n",
+      "15/15 [==============================] - 157s 11s/step - loss: 6.6892 - accuracy: 0.8125 - val_loss: 63.7705 - val_accuracy: 0.4375\n",
+      "Epoch 38/50\n",
+      "15/15 [==============================] - 158s 11s/step - loss: 5.5650 - accuracy: 0.8208 - val_loss: 64.0116 - val_accuracy: 0.4750\n",
+      "Epoch 39/50\n",
+      "15/15 [==============================] - 159s 11s/step - loss: 6.0108 - accuracy: 0.8271 - val_loss: 70.6677 - val_accuracy: 0.4250\n",
+      "Epoch 40/50\n",
+      "15/15 [==============================] - 159s 11s/step - loss: 6.9199 - accuracy: 0.8417 - val_loss: 56.8354 - val_accuracy: 0.4500\n",
+      "Epoch 41/50\n",
+      "15/15 [==============================] - 158s 11s/step - loss: 4.1278 - accuracy: 0.8583 - val_loss: 58.6842 - val_accuracy: 0.4625\n",
+      "Epoch 42/50\n",
+      "15/15 [==============================] - 158s 11s/step - loss: 7.9539 - accuracy: 0.8125 - val_loss: 73.3030 - val_accuracy: 0.4625\n",
+      "Epoch 43/50\n",
+      "15/15 [==============================] - 160s 11s/step - loss: 7.7179 - accuracy: 0.8062 - val_loss: 58.1898 - val_accuracy: 0.4500\n",
+      "Epoch 44/50\n",
+      "15/15 [==============================] - 180s 12s/step - loss: 5.4911 - accuracy: 0.8417 - val_loss: 63.1966 - val_accuracy: 0.4500\n",
+      "Epoch 45/50\n",
+      "15/15 [==============================] - 161s 11s/step - loss: 6.8619 - accuracy: 0.8354 - val_loss: 53.5746 - val_accuracy: 0.4625\n",
+      "Epoch 46/50\n",
+      "15/15 [==============================] - 159s 11s/step - loss: 6.8473 - accuracy: 0.8083 - val_loss: 55.5791 - val_accuracy: 0.4625\n",
+      "Epoch 47/50\n",
+      "15/15 [==============================] - 159s 11s/step - loss: 5.0699 - accuracy: 0.8458 - val_loss: 63.1028 - val_accuracy: 0.4500\n",
+      "Epoch 48/50\n",
+      "15/15 [==============================] - 160s 11s/step - loss: 4.7313 - accuracy: 0.8646 - val_loss: 50.6113 - val_accuracy: 0.4875\n",
+      "Epoch 49/50\n",
+      "15/15 [==============================] - 160s 11s/step - loss: 6.3342 - accuracy: 0.8188 - val_loss: 61.1096 - val_accuracy: 0.4500\n",
+      "Epoch 50/50\n",
+      "15/15 [==============================] - 160s 11s/step - loss: 5.7529 - accuracy: 0.8396 - val_loss: 60.3737 - val_accuracy: 0.4375\n"
      ]
     }
    ],
@@ -1471,9 +1519,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": [
     "plt.plot(history.history['accuracy'])\n",
     "plt.plot(history.history['val_accuracy'])\n",
@@ -1493,9 +1566,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 21s 6s/step - loss: 60.3737 - accuracy: 0.4375\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[60.3736572265625, 0.4375]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.evaluate(validation_dataset)"
    ]
@@ -1504,7 +1595,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see, we reach a validation accuracy of over 56%. "
+    "As you can see, we reach a validation accuracy of over 43%. "
    ]
   },
   {
@@ -1532,7 +1623,7 @@
     "id": "FZYRLtbkGhLV"
    },
    "source": [
-    "## Fine Tuning\n",
+    "## 2.2 Fine Tuning\n",
     "\n",
     "Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
     "Fine-tuning consists of unfreezing a few of the top layers of a frozen model base used\n",
@@ -1563,14 +1654,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "cnObzTupGhLV",
     "outputId": "3754b2b3-8885-44b3-cb87-82612d223ec3"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"vgg16\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
+      "                                                                 \n",
+      " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
+      "                                                                 \n",
+      " block1_conv2 (Conv2D)       (None, 150, 150, 64)      36928     \n",
+      "                                                                 \n",
+      " block1_pool (MaxPooling2D)  (None, 75, 75, 64)        0         \n",
+      "                                                                 \n",
+      " block2_conv1 (Conv2D)       (None, 75, 75, 128)       73856     \n",
+      "                                                                 \n",
+      " block2_conv2 (Conv2D)       (None, 75, 75, 128)       147584    \n",
+      "                                                                 \n",
+      " block2_pool (MaxPooling2D)  (None, 37, 37, 128)       0         \n",
+      "                                                                 \n",
+      " block3_conv1 (Conv2D)       (None, 37, 37, 256)       295168    \n",
+      "                                                                 \n",
+      " block3_conv2 (Conv2D)       (None, 37, 37, 256)       590080    \n",
+      "                                                                 \n",
+      " block3_conv3 (Conv2D)       (None, 37, 37, 256)       590080    \n",
+      "                                                                 \n",
+      " block3_pool (MaxPooling2D)  (None, 18, 18, 256)       0         \n",
+      "                                                                 \n",
+      " block4_conv1 (Conv2D)       (None, 18, 18, 512)       1180160   \n",
+      "                                                                 \n",
+      " block4_conv2 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
+      "                                                                 \n",
+      " block4_conv3 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
+      "                                                                 \n",
+      " block4_pool (MaxPooling2D)  (None, 9, 9, 512)         0         \n",
+      "                                                                 \n",
+      " block5_conv1 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
+      "                                                                 \n",
+      " block5_conv2 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
+      "                                                                 \n",
+      " block5_conv3 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
+      "                                                                 \n",
+      " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 14,714,688\n",
+      "Trainable params: 7,079,424\n",
+      "Non-trainable params: 7,635,264\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
    "source": [
     "conv_base.summary()"
    ]
@@ -1603,18 +1748,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "tBXYN1t2GhLc",
     "outputId": "b33ae8d1-925b-4e8a-f15d-a62356070896"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "layer name = input_2, shape = [(None, 150, 150, 3)], trainable = False\n",
+      "layer name = block1_conv1, shape = (None, 150, 150, 64), trainable = False\n",
+      "layer name = block1_conv2, shape = (None, 150, 150, 64), trainable = False\n",
+      "layer name = block1_pool, shape = (None, 75, 75, 64), trainable = False\n",
+      "layer name = block2_conv1, shape = (None, 75, 75, 128), trainable = False\n",
+      "layer name = block2_conv2, shape = (None, 75, 75, 128), trainable = False\n",
+      "layer name = block2_pool, shape = (None, 37, 37, 128), trainable = False\n",
+      "layer name = block3_conv1, shape = (None, 37, 37, 256), trainable = False\n",
+      "layer name = block3_conv2, shape = (None, 37, 37, 256), trainable = False\n",
+      "layer name = block3_conv3, shape = (None, 37, 37, 256), trainable = False\n",
+      "layer name = block3_pool, shape = (None, 18, 18, 256), trainable = False\n",
+      "layer name = block4_conv1, shape = (None, 18, 18, 512), trainable = False\n",
+      "layer name = block4_conv2, shape = (None, 18, 18, 512), trainable = False\n",
+      "layer name = block4_conv3, shape = (None, 18, 18, 512), trainable = False\n",
+      "layer name = block4_pool, shape = (None, 9, 9, 512), trainable = False\n",
+      "layer name = block5_conv1, shape = (None, 9, 9, 512), trainable = True\n",
+      "layer name = block5_conv2, shape = (None, 9, 9, 512), trainable = True\n",
+      "layer name = block5_conv3, shape = (None, 9, 9, 512), trainable = True\n",
+      "layer name = block5_pool, shape = (None, 4, 4, 512), trainable = True\n"
+     ]
+    }
+   ],
    "source": [
     "conv_base.trainable = True\n",
     "for layer in conv_base.layers[:-4]:\n",
-    "    layer.trainable = False"
+    "    layer.trainable = False\n",
+    "    \n",
+    "for layer in conv_base.layers[0:]:\n",
+    "    print('layer name = ' + layer.name + ', shape = ' + repr(layer.output_shape)\n",
+    "            + ', trainable = ' + repr(layer.trainable))        \n",
+    " \n",
+    "    "
    ]
   },
   {
@@ -1637,19 +1814,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "4YBjFhSVGhLh",
     "outputId": "c688820a-0f28-4aa0-b247-15a9684fa08f"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "layer name = input_4, shape = [(None, 150, 150, 3)], trainable = False\n",
+      "layer name = sequential_1, shape = (None, 150, 150, 3), trainable = False\n",
+      "layer name = tf.__operators__.getitem, shape = (None, 150, 150, 3), trainable = False\n",
+      "layer name = tf.nn.bias_add, shape = (None, 150, 150, 3), trainable = False\n",
+      "layer name = vgg16, shape = (None, None, None, 512), trainable = False\n",
+      "layer name = flatten_2, shape = (None, 8192), trainable = False\n",
+      "layer name = dense_4, shape = (None, 256), trainable = False\n",
+      "layer name = dropout_2, shape = (None, 256), trainable = False\n",
+      "layer name = dense_5, shape = (None, 8), trainable = False\n"
+     ]
+    }
+   ],
    "source": [
-    "model.compile(loss=\"binary_crossentropy\",\n",
-    "        optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
-    "        metrics=[\"accuracy\"])\n",
+    "model.compile(loss=\"categorical_crossentropy\",\n",
+    "    optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
+    "    metrics=[\"accuracy\"])\n",
     "\n",
+    "for layer in model.layers[0:]:\n",
+    "    print('layer name = ' + layer.name + ', shape = ' + repr(layer.output_shape)\n",
+    "            + ', trainable = ' + repr(layer.trainable))        \n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "logdir = os.path.join(\"logs_fine_tuning\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
     "\n",
     "\n",
@@ -1658,7 +1863,7 @@
     "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
     "]\n",
     "\n",
-    "history = model.fit(train_dataset,\n",
+    "history = conv_base.fit(train_dataset,\n",
     "                    epochs=30,\n",
     "                    validation_data=validation_dataset,\n",
     "                    callbacks=callbacks)"
@@ -1718,7 +1923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1727,15 +1932,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "WoDOi_F8GhL5",
     "outputId": "17c21c92-2a5d-4e21-c367-57e818046762"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  2   0   0   0   0   2   0   6 ], angelina jolie\n",
+      "[  0   2   0   1   0   0   7   0 ], brad pitt\n",
+      "[  0   0   5   0   0   2   0   3 ], catherine deneuve\n",
+      "[  0   0   0   5   2   1   2   0 ], johnny depp\n",
+      "[  0   1   0   0   5   0   4   0 ], leonardo dicaprio\n",
+      "[  1   0   0   0   0   2   0   7 ], marion cotillard\n",
+      "[  0   1   0   0   0   0   9   0 ], robert de niro\n",
+      "[  0   0   0   0   0   1   0   9 ], sandra bullock\n"
+     ]
+    }
+   ],
    "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "import sys\n",
+    "class_names = [\"angelina jolie\", \"brad pitt\",\"catherine deneuve\" , \"johnny depp\",\"leonardo dicaprio\", \"marion cotillard\", \"robert de niro\",\"sandra bullock\"]\n",
+    "\n",
+    "\n",
     "Y_valid = np.zeros((num_valid_images,1),dtype=int)\n",
     "\n",
     "step = num_valid_images // num_classes\n",
@@ -1756,14 +1981,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "nNp0qChLGhL-",
     "outputId": "f22e9bfe-e5da-4d57-fbdc-2ea55d6681e7"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrong classification for: sandra bullock\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "matched to: marion cotillard\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": [
     "# Choose the class label you want to check\n",
     "clbl = 7\n",
@@ -1772,7 +2036,7 @@
     "wrong_labels = np.transpose(np.nonzero(pred_labels != clbl))\n",
     "\n",
     "\n",
-    "# Get the validation dataset as numpy array\n",
+    "# Get the validation images as numpy arrays\n",
     "\n",
     "import numpy as np\n",
     "def get_images_and_labels(dataset):\n",
@@ -1983,10 +2247,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "raw",
    "metadata": {},
-   "outputs": [],
    "source": [
     "from tensorflow import keras\n",
     "from keras import layers\n",
@@ -2061,6 +2323,48 @@
     "model.summary()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow import keras\n",
+    "from keras import layers\n",
+    "\n",
+    "def get_model(img_size, num_classes):\n",
+    "  inputs = keras.Input(shape=img_size + (3,))\n",
+    "  x = layers.Rescaling(1./255)(inputs)  \n",
+    "  x = layers.Conv2D(64, 3, strides=2, activation=\"relu\", padding=\"same\")(x)\n",
+    "  x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
+    "  x = layers.Conv2D(128, 3, strides=2, activation=\"relu\", padding=\"same\")(x)\n",
+    "  x = layers.Conv2D(128, 3, activation=\"relu\", padding=\"same\")(x)\n",
+    "  x = layers.Conv2D(256, 3, strides=2, padding=\"same\", activation=\"relu\")(x)\n",
+    "  x = layers.Conv2D(256, 3, activation=\"relu\", padding=\"same\")(x)\n",
+    "  x = layers.Conv2DTranspose(256, 3, activation=\"relu\", padding=\"same\")(x)\n",
+    "  x = layers.Conv2DTranspose(256, 3, activation=\"relu\", padding=\"same\", strides=2)(x)\n",
+    "  x = layers.Conv2DTranspose(128, 3, activation=\"relu\", padding=\"same\")(x)\n",
+    "  x = layers.Conv2DTranspose(128, 3, activation=\"relu\", padding=\"same\", strides=2)(x)\n",
+    "  x = layers.Conv2DTranspose(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
+    "  x = layers.Conv2DTranspose(64, 3, activation=\"relu\", padding=\"same\", strides=2)(x)\n",
+    "  outputs = layers.Conv2D(num_classes, 3, activation=\"softmax\", padding=\"same\")(x)\n",
+    "  model = keras.Model(inputs, outputs)\n",
+    "  return model\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Free up RAM in case the model definition cells were run multiple times\n",
+    "keras.backend.clear_session()\n",
+    "\n",
+    "# Build model\n",
+    "model = get_model(img_size, num_classes=3)\n",
+    "model.summary()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2199,7 +2503,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Part IV : Object Detection with Yolo"
+    "# Part IV : Object Detection with Yolo and Retinanet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please consult the following sources:\n",
+    "\n",
+    "1. [Retinanet](https://keras.io/examples/vision/retinanet/)\n",
+    "2. [Yolo](https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/)"
    ]
   },
   {
-- 
GitLab