From 8441e14c5a22524c32683ae60e34c14e8214ae9c Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Wed, 23 Mar 2022 14:56:40 +0000
Subject: [PATCH] completed fine-tuning

---
 ... - Object Detection and Segmentation.ipynb | 1199 +++--------------
 1 file changed, 162 insertions(+), 1037 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 6c81d53..03f9606 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	
@@ -34,7 +34,20 @@
       "Found 2 image links\n",
       "Saved 2 images\n",
       "Found 2 image links\n",
-      "Saved 2 images\n"
+      "Saved 2 images\n",
+      "Found 2 image links\n",
+      "Saved 2 images\n",
+      "Found 2 image links\n",
+      "Saved 2 images\n",
+      "Found 2 image links\n",
+      "Saved 2 images\n",
+      "Found 2 image links\n",
+      "Saved 2 images\n",
+      "Found 2 image links\n",
+      "Saved 2 images\n",
+      "Found 2 image links\n",
+      "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/5/59/Marion_Cotillard_at_2019_Cannes.jpg - cannot identify image file <_io.BytesIO object at 0x7f1a0b4d6d70>\n",
+      "Saved 1 images\n"
      ]
     }
    ],
@@ -84,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -99,6 +112,7 @@
     ")\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import os, datetime\n",
     "\n",
     "# Shortcuts to keras if (however from tensorflow)\n",
     "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
@@ -120,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -137,7 +151,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -154,7 +168,7 @@
        "       0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -242,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -284,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -332,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -340,15 +354,13 @@
    },
    "outputs": [],
    "source": [
-    "name = 'cnn_face_1'\n",
-    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir ='./tensorboard/' + name + '/', \n",
-    "                                                      histogram_freq=1, \n",
-    "                                                      write_graph=True)"
+    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
+    "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -361,7 +373,7 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "10/24 [===========>..................] - ETA: 26s - loss: 2.1636 - accuracy: 0.1200"
+      " 2/24 [=>............................] - ETA: 34s - loss: 2.1133 - accuracy: 0.0500    "
      ]
     },
     {
@@ -376,45 +388,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "24/24 [==============================] - 45s 2s/step - loss: 2.1158 - accuracy: 0.0979 - val_loss: 2.0759 - val_accuracy: 0.1625\n",
+      "24/24 [==============================] - 33s 1s/step - loss: 2.0931 - accuracy: 0.1250 - val_loss: 2.0772 - val_accuracy: 0.1500\n",
       "Epoch 2/20\n",
-      "24/24 [==============================] - 38s 2s/step - loss: 2.0772 - accuracy: 0.1667 - val_loss: 2.0598 - val_accuracy: 0.3875\n",
-      "Epoch 3/20\n",
-      "24/24 [==============================] - 38s 2s/step - loss: 2.0419 - accuracy: 0.2062 - val_loss: 1.9549 - val_accuracy: 0.3250\n",
-      "Epoch 4/20\n",
-      "24/24 [==============================] - 38s 2s/step - loss: 1.9731 - accuracy: 0.2542 - val_loss: 1.8280 - val_accuracy: 0.3625\n",
-      "Epoch 5/20\n",
-      "24/24 [==============================] - 36s 1s/step - loss: 1.8939 - accuracy: 0.2458 - val_loss: 1.7558 - val_accuracy: 0.4250\n",
-      "Epoch 6/20\n",
-      "24/24 [==============================] - 36s 1s/step - loss: 1.8929 - accuracy: 0.2625 - val_loss: 1.6907 - val_accuracy: 0.3750\n",
-      "Epoch 7/20\n",
-      "24/24 [==============================] - 36s 1s/step - loss: 1.7998 - accuracy: 0.3042 - val_loss: 1.6398 - val_accuracy: 0.4250\n",
-      "Epoch 8/20\n",
-      "24/24 [==============================] - 35s 1s/step - loss: 1.7445 - accuracy: 0.3458 - val_loss: 1.6191 - val_accuracy: 0.4125\n",
-      "Epoch 9/20\n",
-      "24/24 [==============================] - 32s 1s/step - loss: 1.6618 - accuracy: 0.3688 - val_loss: 1.5744 - val_accuracy: 0.4000\n",
-      "Epoch 10/20\n",
-      "24/24 [==============================] - 34s 1s/step - loss: 1.6509 - accuracy: 0.3562 - val_loss: 1.6113 - val_accuracy: 0.3750\n",
-      "Epoch 11/20\n",
-      "24/24 [==============================] - 34s 1s/step - loss: 1.5888 - accuracy: 0.3854 - val_loss: 1.4971 - val_accuracy: 0.4750\n",
-      "Epoch 12/20\n",
-      "24/24 [==============================] - 34s 1s/step - loss: 1.5139 - accuracy: 0.4333 - val_loss: 1.4896 - val_accuracy: 0.5125\n",
-      "Epoch 13/20\n",
-      "24/24 [==============================] - 36s 1s/step - loss: 1.4464 - accuracy: 0.4646 - val_loss: 1.4945 - val_accuracy: 0.5250\n",
-      "Epoch 14/20\n",
-      "24/24 [==============================] - 34s 1s/step - loss: 1.4098 - accuracy: 0.5063 - val_loss: 1.4352 - val_accuracy: 0.5000\n",
-      "Epoch 15/20\n",
-      "24/24 [==============================] - 36s 1s/step - loss: 1.3459 - accuracy: 0.4938 - val_loss: 1.4270 - val_accuracy: 0.5000\n",
-      "Epoch 16/20\n",
-      "24/24 [==============================] - 35s 1s/step - loss: 1.3225 - accuracy: 0.5271 - val_loss: 1.4735 - val_accuracy: 0.4375\n",
-      "Epoch 17/20\n",
-      "24/24 [==============================] - 35s 1s/step - loss: 1.1904 - accuracy: 0.5771 - val_loss: 1.7510 - val_accuracy: 0.4625\n",
-      "Epoch 18/20\n",
-      "24/24 [==============================] - 34s 1s/step - loss: 1.4096 - accuracy: 0.4958 - val_loss: 1.4725 - val_accuracy: 0.4875\n",
-      "Epoch 19/20\n",
-      "24/24 [==============================] - 34s 1s/step - loss: 1.1918 - accuracy: 0.5146 - val_loss: 1.5643 - val_accuracy: 0.4375\n",
-      "Epoch 20/20\n",
-      "24/24 [==============================] - 35s 1s/step - loss: 1.1331 - accuracy: 0.5938 - val_loss: 1.5486 - val_accuracy: 0.4375\n"
+      "18/24 [=====================>........] - ETA: 6s - loss: 2.0769 - accuracy: 0.1333"
      ]
     }
    ],
@@ -430,39 +406,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "wt_ONw5PGhJm",
     "outputId": "e75d8a73-da49-4dbe-ffcf-7cb316be39a2"
    },
-   "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(history.history['accuracy'])\n",
     "plt.plot(history.history['val_accuracy'])\n",
@@ -489,18 +440,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "can't assign to operator (<ipython-input-23-a8183cac7339>, line 4)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-23-a8183cac7339>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    tensorboard --logdir=\"./tensorboard/log\" --port=8006\u001b[0m\n\u001b[0m                                                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Load the TensorBoard notebook extension\n",
     "%load_ext tensorboard\n",
@@ -547,21 +489,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from IPython.display import Image\n",
     "Image(\"./Images/feature_extraction.png\")"
@@ -621,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -661,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -708,67 +638,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "M7Bk7t1MGhJ6"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"vgg16\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " input_4 (InputLayer)        [(None, 180, 180, 3)]     0         \n",
-      "                                                                 \n",
-      " block1_conv1 (Conv2D)       (None, 180, 180, 64)      1792      \n",
-      "                                                                 \n",
-      " block1_conv2 (Conv2D)       (None, 180, 180, 64)      36928     \n",
-      "                                                                 \n",
-      " block1_pool (MaxPooling2D)  (None, 90, 90, 64)        0         \n",
-      "                                                                 \n",
-      " block2_conv1 (Conv2D)       (None, 90, 90, 128)       73856     \n",
-      "                                                                 \n",
-      " block2_conv2 (Conv2D)       (None, 90, 90, 128)       147584    \n",
-      "                                                                 \n",
-      " block2_pool (MaxPooling2D)  (None, 45, 45, 128)       0         \n",
-      "                                                                 \n",
-      " block3_conv1 (Conv2D)       (None, 45, 45, 256)       295168    \n",
-      "                                                                 \n",
-      " block3_conv2 (Conv2D)       (None, 45, 45, 256)       590080    \n",
-      "                                                                 \n",
-      " block3_conv3 (Conv2D)       (None, 45, 45, 256)       590080    \n",
-      "                                                                 \n",
-      " block3_pool (MaxPooling2D)  (None, 22, 22, 256)       0         \n",
-      "                                                                 \n",
-      " block4_conv1 (Conv2D)       (None, 22, 22, 512)       1180160   \n",
-      "                                                                 \n",
-      " block4_conv2 (Conv2D)       (None, 22, 22, 512)       2359808   \n",
-      "                                                                 \n",
-      " block4_conv3 (Conv2D)       (None, 22, 22, 512)       2359808   \n",
-      "                                                                 \n",
-      " block4_pool (MaxPooling2D)  (None, 11, 11, 512)       0         \n",
-      "                                                                 \n",
-      " block5_conv1 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
-      "                                                                 \n",
-      " block5_conv2 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
-      "                                                                 \n",
-      " block5_conv3 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
-      "                                                                 \n",
-      " block5_pool (MaxPooling2D)  (None, 5, 5, 512)         0         \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 14,714,688\n",
-      "Trainable params: 14,714,688\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "conv_base.summary()"
    ]
@@ -821,37 +697,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 480 files belonging to 8 classes.\n",
-      "Found 80 files belonging to 8 classes.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tensorflow.keras.utils import image_dataset_from_directory\n",
     "\n",
     "train_dataset = image_dataset_from_directory(\n",
     "    './train',\n",
-    "    image_size=(180, 180),\n",
+    "    image_size=(150, 150),\n",
     "    batch_size=32,\n",
     "    label_mode=\"categorical\")\n",
     "\n",
     "validation_dataset = image_dataset_from_directory(\n",
     "    './validation',\n",
-    "    image_size=(180, 180),\n",
+    "    image_size=(150, 150),\n",
     "    batch_size=32,\n",
     "    label_mode=\"categorical\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -882,20 +749,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(480, 5, 5, 512)"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_features.shape"
    ]
@@ -909,38 +765,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(480, 8)"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_labels.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(80, 5, 5, 512)\n",
-      "(80, 8)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(val_features.shape)\n",
     "print(val_labels.shape)"
@@ -948,11 +784,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "inputs = keras.Input(shape=(5, 5, 512))\n",
+    "inputs = keras.Input(shape=(4, 4, 512))\n",
     "# Note the use of the Flatten\n",
     "# layer before passing the\n",
     "# features to a Dense layer\n",
@@ -965,121 +801,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"model_8\"\n",
-      "_________________________________________________________________\n",
-      " Layer (type)                Output Shape              Param #   \n",
-      "=================================================================\n",
-      " input_11 (InputLayer)       [(None, 5, 5, 512)]       0         \n",
-      "                                                                 \n",
-      " flatten_8 (Flatten)         (None, 12800)             0         \n",
-      "                                                                 \n",
-      " dense_16 (Dense)            (None, 256)               3277056   \n",
-      "                                                                 \n",
-      " dropout_8 (Dropout)         (None, 256)               0         \n",
-      "                                                                 \n",
-      " dense_17 (Dense)            (None, 8)                 2056      \n",
-      "                                                                 \n",
-      "=================================================================\n",
-      "Total params: 3,279,112\n",
-      "Trainable params: 3,279,112\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/30\n",
-      "15/15 [==============================] - 4s 248ms/step - loss: 65.2728 - accuracy: 0.3146 - val_loss: 24.8148 - val_accuracy: 0.5125\n",
-      "Epoch 2/30\n",
-      "15/15 [==============================] - 2s 161ms/step - loss: 18.1388 - accuracy: 0.6542 - val_loss: 25.5910 - val_accuracy: 0.5500\n",
-      "Epoch 3/30\n",
-      "15/15 [==============================] - 3s 180ms/step - loss: 10.0579 - accuracy: 0.7500 - val_loss: 23.1626 - val_accuracy: 0.5625\n",
-      "Epoch 4/30\n",
-      "15/15 [==============================] - 2s 164ms/step - loss: 5.0208 - accuracy: 0.8292 - val_loss: 21.7820 - val_accuracy: 0.6000\n",
-      "Epoch 5/30\n",
-      "15/15 [==============================] - 3s 181ms/step - loss: 6.4743 - accuracy: 0.8542 - val_loss: 25.6429 - val_accuracy: 0.5625\n",
-      "Epoch 6/30\n",
-      "15/15 [==============================] - 3s 180ms/step - loss: 5.3762 - accuracy: 0.8646 - val_loss: 27.3599 - val_accuracy: 0.5750\n",
-      "Epoch 7/30\n",
-      "15/15 [==============================] - 3s 178ms/step - loss: 4.8907 - accuracy: 0.8938 - val_loss: 37.2820 - val_accuracy: 0.5000\n",
-      "Epoch 8/30\n",
-      "15/15 [==============================] - 3s 180ms/step - loss: 5.0473 - accuracy: 0.9021 - val_loss: 27.6422 - val_accuracy: 0.5875\n",
-      "Epoch 9/30\n",
-      "15/15 [==============================] - 2s 167ms/step - loss: 3.4218 - accuracy: 0.8917 - val_loss: 29.6933 - val_accuracy: 0.5125\n",
-      "Epoch 10/30\n",
-      "15/15 [==============================] - 2s 160ms/step - loss: 4.9656 - accuracy: 0.8958 - val_loss: 32.6171 - val_accuracy: 0.5625\n",
-      "Epoch 11/30\n",
-      "15/15 [==============================] - 2s 152ms/step - loss: 2.0922 - accuracy: 0.9479 - val_loss: 30.1656 - val_accuracy: 0.6000\n",
-      "Epoch 12/30\n",
-      "15/15 [==============================] - 3s 203ms/step - loss: 3.6218 - accuracy: 0.9083 - val_loss: 32.6290 - val_accuracy: 0.6250\n",
-      "Epoch 13/30\n",
-      "15/15 [==============================] - 2s 169ms/step - loss: 0.9043 - accuracy: 0.9625 - val_loss: 29.7618 - val_accuracy: 0.6000\n",
-      "Epoch 14/30\n",
-      "15/15 [==============================] - 2s 167ms/step - loss: 2.2434 - accuracy: 0.9417 - val_loss: 36.3292 - val_accuracy: 0.5625\n",
-      "Epoch 15/30\n",
-      "15/15 [==============================] - 2s 168ms/step - loss: 3.4037 - accuracy: 0.9312 - val_loss: 40.2522 - val_accuracy: 0.5750\n",
-      "Epoch 16/30\n",
-      "15/15 [==============================] - 2s 172ms/step - loss: 1.1601 - accuracy: 0.9604 - val_loss: 35.9677 - val_accuracy: 0.5625\n",
-      "Epoch 17/30\n",
-      "15/15 [==============================] - 2s 177ms/step - loss: 2.7122 - accuracy: 0.9438 - val_loss: 28.7434 - val_accuracy: 0.6000\n",
-      "Epoch 18/30\n",
-      "15/15 [==============================] - 2s 156ms/step - loss: 1.9050 - accuracy: 0.9563 - val_loss: 29.7894 - val_accuracy: 0.6125\n",
-      "Epoch 19/30\n",
-      "15/15 [==============================] - 2s 154ms/step - loss: 0.6631 - accuracy: 0.9708 - val_loss: 29.0170 - val_accuracy: 0.6500\n",
-      "Epoch 20/30\n",
-      "15/15 [==============================] - 3s 178ms/step - loss: 2.7721 - accuracy: 0.9521 - val_loss: 37.8384 - val_accuracy: 0.5375\n",
-      "Epoch 21/30\n",
-      "15/15 [==============================] - 2s 174ms/step - loss: 1.5186 - accuracy: 0.9521 - val_loss: 31.6248 - val_accuracy: 0.6000\n",
-      "Epoch 22/30\n",
-      "15/15 [==============================] - 3s 185ms/step - loss: 0.7839 - accuracy: 0.9667 - val_loss: 32.6029 - val_accuracy: 0.6000\n",
-      "Epoch 23/30\n",
-      "15/15 [==============================] - 2s 162ms/step - loss: 1.2231 - accuracy: 0.9667 - val_loss: 34.5317 - val_accuracy: 0.5625\n",
-      "Epoch 24/30\n",
-      "15/15 [==============================] - 3s 179ms/step - loss: 1.7793 - accuracy: 0.9563 - val_loss: 40.0067 - val_accuracy: 0.5875\n",
-      "Epoch 25/30\n",
-      "15/15 [==============================] - 2s 165ms/step - loss: 1.8603 - accuracy: 0.9604 - val_loss: 37.0658 - val_accuracy: 0.6250\n",
-      "Epoch 26/30\n",
-      "15/15 [==============================] - 2s 166ms/step - loss: 0.7310 - accuracy: 0.9688 - val_loss: 33.0265 - val_accuracy: 0.5875\n",
-      "Epoch 27/30\n",
-      "15/15 [==============================] - 2s 173ms/step - loss: 1.1499 - accuracy: 0.9646 - val_loss: 36.2242 - val_accuracy: 0.6000\n",
-      "Epoch 28/30\n",
-      "15/15 [==============================] - 2s 162ms/step - loss: 1.6870 - accuracy: 0.9625 - val_loss: 37.4465 - val_accuracy: 0.6250\n",
-      "Epoch 29/30\n",
-      "15/15 [==============================] - 3s 176ms/step - loss: 0.7370 - accuracy: 0.9812 - val_loss: 33.2157 - val_accuracy: 0.5750\n",
-      "Epoch 30/30\n",
-      "15/15 [==============================] - 2s 170ms/step - loss: 0.8399 - accuracy: 0.9729 - val_loss: 32.1743 - val_accuracy: 0.6125\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.compile(loss=\"categorical_crossentropy\",\n",
     "    optimizer=\"rmsprop\",\n",
     "    metrics=[\"accuracy\"])\n",
     "\n",
-    "name = 'feature_extractor'\n",
+    "\n",
+    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
+    "\n",
     "\n",
     "callbacks = [\n",
     "    keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
-    "    tf.keras.callbacks.TensorBoard(log_dir ='./tensorboard/' + name + '/', histogram_freq=1, write_graph=True)\n",
+    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
     "]\n",
     "\n",
     "history = model.fit(\n",
@@ -1114,34 +859,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
-   "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(history.history['accuracy'])\n",
     "plt.plot(history.history['val_accuracy'])\n",
@@ -1190,9 +910,6 @@
     "# Load the TensorBoard notebook extension\n",
     "%load_ext tensorboard\n",
     "\n",
-    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
-    "\n",
-    "os.makedirs(logdir, exist_ok=True)\n",
     "%tensorboard --logdir logs"
    ]
   },
@@ -1234,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1258,7 +975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1267,24 +984,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This is the number of trainable weights before freezing the conv base: 26\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"This is the number of trainable weights before freezing the conv base:\", len(conv_base.trainable_weights))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1293,17 +1002,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This is the number of trainable weights after freezing the conv base: 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"This is the number of trainable weights after freezing the conv base:\", len(conv_base.trainable_weights))"
    ]
@@ -1345,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1365,13 +1066,6 @@
     "    metrics=[\"accuracy\"])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1399,51 +1093,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
+    "\n",
+    "\n",
     "callbacks = [\n",
-    "keras.callbacks.ModelCheckpoint(\n",
-    "    filepath=\"feature_extraction_with_data_augmentation.keras\",\n",
-    "    save_best_only=True,\n",
-    "    monitor=\"val_loss\"),\n",
-    "  tf.keras.callbacks.TensorBoard(log_dir ='./tensorboard/' + 'feature_extraction_with_data_augmentation' + '/', histogram_freq=1, write_graph=True)\n",
-    "]"
+    "    keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction_with_augmentation.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
+    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
+    "]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/50\n",
-      "15/15 [==============================] - 336s 22s/step - loss: -814.3186 - accuracy: 0.1271 - val_loss: -1698.7024 - val_accuracy: 0.1250\n",
-      "Epoch 2/50\n",
-      "10/15 [===================>..........] - ETA: 1:31 - loss: -4186.0371 - accuracy: 0.1344"
-     ]
-    },
-    {
-     "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-74-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: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "history = model.fit(\n",
     "train_dataset,\n",
@@ -1490,49 +1153,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "kR283kCnGhK2"
-   },
-   "source": [
-    "This is what the model looks like now:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "BZlIimqeGhK3",
-    "outputId": "300b3de7-51bc-4cab-c408-67356e39fdf2"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_4\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "vgg16 (Model)                (None, 4, 4, 512)         14714688  \n",
-      "_________________________________________________________________\n",
-      "flatten_4 (Flatten)          (None, 8192)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_8 (Dense)              (None, 256)               2097408   \n",
-      "_________________________________________________________________\n",
-      "dense_9 (Dense)              (None, 8)                 2056      \n",
-      "=================================================================\n",
-      "Total params: 16,814,152\n",
-      "Trainable params: 16,814,152\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
+   "metadata": {},
    "source": [
-    "model_freeze_conv.summary()"
+    "#### Tensorboard"
    ]
   },
   {
@@ -1541,11 +1164,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import datetime, os\n",
     "# Load the TensorBoard notebook extension\n",
     "%load_ext tensorboard\n",
-    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
-    "os.makedirs(logdir, exist_ok=True)\n",
     "%tensorboard --logdir logs"
    ]
   },
@@ -1553,441 +1173,92 @@
    "cell_type": "markdown",
    "metadata": {
     "colab_type": "text",
-    "id": "8pEh6axEGhK7"
-   },
-   "source": [
-    "As you can see, the convolutional base of VGG16 has 14'714'688 parameters, which is very large. The classifier we are adding on top has \n",
-    "2 million parameters.\n",
-    "\n",
-    "Before we compile a layer and train the model, it is very important to __freeze__  the convolutional base. _Freezing_ a layer or a set of layers means preventing their weights from being updated during training. If you don't do this, then the representations that were previously learned by the convolutional base will be modified during training. Because the `Dense` layers on top are randomly initialized, very large weight updates would be propagated through the network, effectively destroying the representations previously learned.\n",
-    "\n",
-    "In Keras, you freeze  a network by setting its `trainable` attribute to `False`: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "H-oqHrUlGhK8",
-    "outputId": "3bb9a0db-7151-4f17-f8ae-91537bc2b776"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This is the number of trainable weights before freezing the conv base: 30\n",
-      "This is the number of trainable weights after freezing the conv base: 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('This is the number of trainable weights '\n",
-    "     'before freezing the conv base:', len(model_freeze_conv.trainable_weights))\n",
-    "\n",
-    "vgg16.trainable = False\n",
-    "\n",
-    "print('This is the number of trainable weights '\n",
-    "     'after freezing the conv base:', len(model_freeze_conv.trainable_weights))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ixxFt45kGhLC"
+    "id": "FZYRLtbkGhLV"
    },
    "source": [
-    "With this setup, only the weights from the two `Dense` layers that we added will be trained. That's a total of four weight tensors: two per layer (the main weight matrix and the bias vector). Note that in order for these changes to take effect, we must first compile the model. If we ever modify weight trainability after compilation, we should then recompile the model, or these changes will be ignored.\n",
-    "\n",
-    "Now, we can start training our model, with the same data-augmentation configuration that we used in the previous example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "dKp7Nj86GhLE",
-    "outputId": "33676409-3316-4386-c177-4fc3589d4db7"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 480 images belonging to 8 classes.\n",
-      "Found 80 images belonging to 8 classes.\n",
-      "Model: \"sequential_4\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "vgg16 (Model)                (None, 4, 4, 512)         14714688  \n",
-      "_________________________________________________________________\n",
-      "flatten_4 (Flatten)          (None, 8192)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_8 (Dense)              (None, 256)               2097408   \n",
-      "_________________________________________________________________\n",
-      "dense_9 (Dense)              (None, 8)                 2056      \n",
-      "=================================================================\n",
-      "Total params: 16,814,152\n",
-      "Trainable params: 2,099,464\n",
-      "Non-trainable params: 14,714,688\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "image_size = 150\n",
-    "batch_size = 20\n",
-    "num_train_images = 480\n",
-    "num_valid_images = 80\n",
-    "num_classes = 8\n",
-    "\n",
-    "class_names = [\"brad pitt\", \"johnny deep\", \"leonardo dicaprio\", \"robert de niro\",\n",
-    "           \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
-    "\n",
+    "## Fine Tuning\n",
     "\n",
-    "# prepare data augmentation configuration\n",
-    "train_datagen = ImageDataGenerator(\n",
-    "        rescale=1./255,\n",
-    "        shear_range=0.2,\n",
-    "        zoom_range=0.2,\n",
-    "        horizontal_flip=True)\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",
+    "for feature extraction, and jointly training both the newly added part of the model (in this case, the\n",
+    "fully connected classifier) and these top layers. This is called _fine-tuning_ because it slightly \n",
+    "adjusts the more abstract representations of the model being reused in order to make them more relevant for the problem at hand.\n",
+    "\n",
+    "I stated earlier that it’s necessary to freeze the convolution base of VGG16 in order to be able to\n",
+    "train a randomly initialized classifier on top. For the same reason, it’s only possible to fine-tune the top\n",
+    "layers of the convolutional base once the classifier on top has already been trained. If the classifier isn’t\n",
+    "already trained, the error signal propagating through the network during training will be too\n",
+    "large, and the representations previously learned by the layers being fine-tuned will be destroyed. Thus\n",
+    "the steps for fine-tuning a network are as follows:\n",
     "\n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "The steps for fine-tuning are as follows:\n",
     "\n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        './train',\n",
-    "        target_size=(image_size, image_size),\n",
-    "        classes=class_names,\n",
-    "        batch_size=batch_size)\n",
+    "1. Add our custom network on top of an already-trained base network.\n",
+    "2. Freeze the base network.\n",
+    "3. Train the part we added.\n",
+    "4. Unfreeze some layers in the base network. (Note that you should not unfreeze “batch normalization” layers, which are not relevant here since there are no such layers in VGG16. )\n",
+    "5. Jointly train both these layers and the part we added.\n",
     "\n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        './validation',\n",
-    "        target_size=(image_size, image_size),\n",
-    "        classes=class_names,\n",
-    "        batch_size=batch_size)\n",
+    "We already completed the first three steps when doing feature extraction. Let’s proceed with step 4:\n",
+    "we’ll unfreeze our `conv_base` and then freeze individual layers inside it.\n",
     "\n",
-    "model_freeze_conv.compile(optimizer='adam',\n",
-    "              loss='categorical_crossentropy',\n",
-    "              metrics=['accuracy'])\n",
-    "model_freeze_conv.summary()"
+    "As a reminder, this is what our convolutional base looks like:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
-    "id": "K7y1YStkGhLH"
+    "id": "cnObzTupGhLV",
+    "outputId": "3754b2b3-8885-44b3-cb87-82612d223ec3"
    },
    "outputs": [],
    "source": [
-    "name = 'cnn_face_2'\n",
-    "\n",
-    "tensorboard_2 = TensorBoard(\n",
-    "        log_dir='./tensorboard/' + name + '/', \n",
-    "        write_graph=True,\n",
-    "        histogram_freq=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "vdv2sfynGhLM",
-    "outputId": "070059cb-2d72-4957-8355-2a1bda385bbc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From <ipython-input-43-cdb855471355>:9: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use Model.fit, which supports generators.\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train for 24 steps, validate for 4 steps\n",
-      "Epoch 1/15\n",
-      " 9/24 [==========>...................] - ETA: 10s - loss: 2.7824 - accuracy: 0.2000"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.7/site-packages/PIL/Image.py:952: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
-      "  \"Palette images with Transparency expressed in bytes should be \"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "24/24 [==============================] - 16s 683ms/step - loss: 2.1956 - accuracy: 0.3187 - val_loss: 1.4464 - val_accuracy: 0.4125\n",
-      "Epoch 2/15\n",
-      "24/24 [==============================] - 14s 579ms/step - loss: 1.0362 - accuracy: 0.6292 - val_loss: 1.1068 - val_accuracy: 0.6000\n",
-      "Epoch 3/15\n",
-      "24/24 [==============================] - 15s 631ms/step - loss: 0.7250 - accuracy: 0.7688 - val_loss: 0.9805 - val_accuracy: 0.6875\n",
-      "Epoch 4/15\n",
-      "24/24 [==============================] - 14s 582ms/step - loss: 0.5073 - accuracy: 0.8542 - val_loss: 0.9652 - val_accuracy: 0.6125\n",
-      "Epoch 5/15\n",
-      "24/24 [==============================] - 15s 634ms/step - loss: 0.3990 - accuracy: 0.8896 - val_loss: 0.9173 - val_accuracy: 0.6500\n",
-      "Epoch 6/15\n",
-      "24/24 [==============================] - 15s 641ms/step - loss: 0.3288 - accuracy: 0.9083 - val_loss: 0.8168 - val_accuracy: 0.6625\n",
-      "Epoch 7/15\n",
-      "24/24 [==============================] - 15s 640ms/step - loss: 0.2804 - accuracy: 0.9396 - val_loss: 0.7954 - val_accuracy: 0.6875\n",
-      "Epoch 8/15\n",
-      "24/24 [==============================] - 15s 627ms/step - loss: 0.1817 - accuracy: 0.9708 - val_loss: 0.7999 - val_accuracy: 0.7375\n",
-      "Epoch 9/15\n",
-      "24/24 [==============================] - 15s 619ms/step - loss: 0.1650 - accuracy: 0.9708 - val_loss: 0.9104 - val_accuracy: 0.6625\n",
-      "Epoch 10/15\n",
-      "24/24 [==============================] - 15s 635ms/step - loss: 0.1152 - accuracy: 0.9896 - val_loss: 0.8283 - val_accuracy: 0.6875\n",
-      "Epoch 11/15\n",
-      "24/24 [==============================] - 16s 668ms/step - loss: 0.1054 - accuracy: 0.9917 - val_loss: 0.7761 - val_accuracy: 0.7250\n",
-      "Epoch 12/15\n",
-      "24/24 [==============================] - 18s 735ms/step - loss: 0.0865 - accuracy: 0.9979 - val_loss: 0.7828 - val_accuracy: 0.7500\n",
-      "Epoch 13/15\n",
-      "24/24 [==============================] - 17s 716ms/step - loss: 0.0758 - accuracy: 0.9937 - val_loss: 0.8021 - val_accuracy: 0.7250\n",
-      "Epoch 14/15\n",
-      "24/24 [==============================] - 17s 701ms/step - loss: 0.0634 - accuracy: 0.9979 - val_loss: 0.7884 - val_accuracy: 0.7375\n",
-      "Epoch 15/15\n",
-      "24/24 [==============================] - 17s 715ms/step - loss: 0.0657 - accuracy: 0.9937 - val_loss: 0.7899 - val_accuracy: 0.7500\n"
-     ]
-    }
-   ],
-   "source": [
-    "epochs = 15\n",
-    "\n",
-    "history=model_freeze_conv.fit_generator(\n",
-    "        train_generator,\n",
-    "        steps_per_epoch=num_train_images // batch_size,\n",
-    "        epochs=epochs,\n",
-    "        validation_data=validation_generator,\n",
-    "        validation_steps=num_valid_images // batch_size,\n",
-    "        callbacks=[tensorboard_2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gMn5uImSGhLQ",
-    "outputId": "43db6d70-7b50-4c94-d854-931886a48502"
-   },
-   "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",
-    "plt.title('model accuracy')\n",
-    "plt.ylabel('accuracy')\n",
-    "plt.xlabel('epoch')\n",
-    "plt.legend(['train', 'valid'], loc='lower right')\n",
-    "plt.show()\n",
-    "plt.plot(history.history['loss'])\n",
-    "plt.plot(history.history['val_loss'])\n",
-    "plt.title('model loss')\n",
-    "plt.ylabel('loss')\n",
-    "plt.xlabel('epoch')\n",
-    "plt.legend(['train', 'valid'], loc='upper right')\n",
-    "plt.show()"
+    "conv_base.summary()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "colab_type": "text",
-    "id": "FZYRLtbkGhLV"
+    "id": "aDtcl5X2GhLa"
    },
    "source": [
-    "## Fine Tuning\n",
+    "We will fine-tune the last three convolutional layers, which means all layers up to `block4_pool` should be frozen, and the layers `block5_conv1`, `block5_conv2`, and `block5_conv3` should be trainable.\n",
     "\n",
-    "Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
-    "\n",
-    "Fine-tuning consists of unfreezing a few of the top layers of a frozen model base used for feature extraction, and jointly training both the newly added part of the model (in this case, the fully connected classifier) and these top layers. This is called _fine-tuning_ because it slightly adjusts the more abstract representations of the model being reused, in order to make them more relevant at hand.\n",
+    "Why not fine-tune more layers? Why not fine-tune the entire convolutional base?\n",
+    "You could. But you need to consider the following:\n",
     "\n",
-    "The steps for fine-tuning are as follows:\n",
+    "- Earlier layers in the convolutional base encode more generic, reusable features, whereas layers higher up encode more specialized features. It’s more useful to fine-tune the more specialized features, because these are the ones that need to be repurposed on your new problem. There would be fast-decreasing returns in fine-tuning lower layers.\n",
     "\n",
-    "1. Add your custom network on top of an already-trained base network\n",
-    "2. Freeze the base network\n",
-    "3. Train the part you added\n",
-    "4. Unfreeze some layers in the base network\n",
-    "5. Jointly train both these layers and the part you added.\n",
+    "- The more parameters you’re training, the more you’re at risk of overfitting. The convolutional base has 15 million parameters, so it would be risky to attempt to train it on your small dataset. \n",
     "\n",
-    "We already completed the first three steps in the previous example. As a remainder, this is what our convolutional base looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "cnObzTupGhLV",
-    "outputId": "3754b2b3-8885-44b3-cb87-82612d223ec3"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"vgg16\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "input_2 (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",
-      "Total params: 14,714,688\n",
-      "Trainable params: 0\n",
-      "Non-trainable params: 14,714,688\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "vgg16.summary()"
+    "Thus, in this situation, it’s a good strategy to fine-tune only the top two or three layers in the convolutional base. Let’s set this up, starting from where we left off in the previous example."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "aDtcl5X2GhLa"
-   },
+   "metadata": {},
    "source": [
-    "We will fine-tune the last three convolutional layers, which means all layers up to `block4_pool` should be frozen, and the layers `block5_conv1`, `block5_conv2`, and `block5_conv3` should be trainable."
+    "#### Freezing all layers until the fourth from the last"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "tBXYN1t2GhLc",
     "outputId": "b33ae8d1-925b-4e8a-f15d-a62356070896"
    },
-   "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "vgg16.trainable = True\n",
-    "\n",
-    "set_trainable = False\n",
-    "\n",
-    "for layer in vgg16.layers:\n",
-    "    if layer.name == 'block5_conv1':\n",
-    "        set_trainable = True\n",
-    "    if set_trainable:\n",
-    "        layer.trainable = True\n",
-    "    else:\n",
-    "        layer.trainable = False\n",
-    "\n",
-    "for layer in vgg16.layers[0:]:\n",
-    "    print('layer name = ' + layer.name + ', shape = ' + repr(layer.output_shape)\n",
-    "            + ', trainable = ' + repr(layer.trainable))        \n",
-    "    "
+    "conv_base.trainable = True\n",
+    "for layer in conv_base.layers[:-4]:\n",
+    "    layer.trainable = False"
    ]
   },
   {
@@ -1997,195 +1268,44 @@
     "id": "XWw1mYfUGhLg"
    },
    "source": [
-    "Now we can begin fine-tuning the network. First we join the top_model layer on top of the vgg16 model with some top layers unfrozen:"
+    "Now we can begin fine-tuning the model. We’ll do this with the `RMSprop` optimizer, using a very low learning rate. The reason for using a low learning rate is that we want to limit the magnitude of the modifications we make to the representations of the three\n",
+    "layers we’re fine-tuning. Updates that are too large may harm these representations."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4YBjFhSVGhLh",
-    "outputId": "c688820a-0f28-4aa0-b247-15a9684fa08f"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_5\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "vgg16 (Model)                (None, 4, 4, 512)         14714688  \n",
-      "_________________________________________________________________\n",
-      "flatten_3 (Flatten)          (None, 8192)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_6 (Dense)              (None, 256)               2097408   \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 256)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_7 (Dense)              (None, 8)                 2056      \n",
-      "=================================================================\n",
-      "Total params: 16,814,152\n",
-      "Trainable params: 9,178,888\n",
-      "Non-trainable params: 7,635,264\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "model_fine_tuned = models.Sequential()\n",
-    "model_fine_tuned.add(vgg16)\n",
-    "from tensorflow.keras import optimizers\n",
-    "\n",
-    "for layer in top_model.layers[0:]:\n",
-    "    layer.trainable = True\n",
-    "    model_fine_tuned.add(layer)  \n",
-    "\n",
-    "\n",
-    "model_fine_tuned.compile(optimizer=optimizers.RMSprop(lr=1e-5),\n",
-    "              loss='categorical_crossentropy',\n",
-    "              metrics=['accuracy'])  \n",
-    "\n",
-    "model_fine_tuned.summary()"
+    "#### Fine-tuning the model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "o55QhMGMGhLl",
-    "outputId": "e7a40f35-3115-454d-9dc4-c0ae322e90f3"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 480 images belonging to 8 classes.\n",
-      "Found 80 images belonging to 8 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Prepare data augmentation configuration\n",
-    "train_datagen = ImageDataGenerator(\n",
-    "        rescale=1./255,\n",
-    "        shear_range=0.2,\n",
-    "        zoom_range=0.2,\n",
-    "        horizontal_flip=True)\n",
-    "\n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
-    "\n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        './train',\n",
-    "        target_size=(image_size, image_size),\n",
-    "        classes=class_names,\n",
-    "        batch_size=batch_size)\n",
-    "\n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        './validation',\n",
-    "        target_size=(image_size, image_size),\n",
-    "        classes=class_names,\n",
-    "        batch_size=batch_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
-    "id": "djMkvkOCGhLp"
+    "id": "4YBjFhSVGhLh",
+    "outputId": "c688820a-0f28-4aa0-b247-15a9684fa08f"
    },
    "outputs": [],
    "source": [
-    "name = 'vgg16_face_fine_tuned'\n",
+    "model.compile(loss=\"binary_crossentropy\",\n",
+    "        optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
+    "        metrics=[\"accuracy\"])\n",
     "\n",
-    "tensorboard_3 = TensorBoard(\n",
-    "        log_dir='./tensorboard/' + name + '/', \n",
-    "        write_graph=True,\n",
-    "        histogram_freq=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "40ng4ObKGhLs",
-    "outputId": "5f7bb4f3-aa7f-4e7e-b0fd-9222e83055b6"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train for 24 steps, validate for 4 steps\n",
-      "Epoch 1/10\n",
-      " 7/24 [=======>......................] - ETA: 16s - loss: 0.5607 - accuracy: 0.8000"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.7/site-packages/PIL/Image.py:952: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
-      "  \"Palette images with Transparency expressed in bytes should be \"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "24/24 [==============================] - 23s 974ms/step - loss: 0.5908 - accuracy: 0.8125 - val_loss: 0.8079 - val_accuracy: 0.6750\n",
-      "Epoch 2/10\n",
-      "24/24 [==============================] - 22s 917ms/step - loss: 0.5403 - accuracy: 0.7958 - val_loss: 0.8024 - val_accuracy: 0.7000\n",
-      "Epoch 3/10\n",
-      "24/24 [==============================] - 22s 917ms/step - loss: 0.4399 - accuracy: 0.8479 - val_loss: 0.7531 - val_accuracy: 0.7500\n",
-      "Epoch 4/10\n",
-      "24/24 [==============================] - 22s 933ms/step - loss: 0.4406 - accuracy: 0.8458 - val_loss: 0.7849 - val_accuracy: 0.7375\n",
-      "Epoch 5/10\n",
-      "24/24 [==============================] - 21s 889ms/step - loss: 0.4305 - accuracy: 0.8604 - val_loss: 0.7511 - val_accuracy: 0.7500\n",
-      "Epoch 6/10\n",
-      "24/24 [==============================] - 22s 896ms/step - loss: 0.3771 - accuracy: 0.8604 - val_loss: 0.7256 - val_accuracy: 0.7375\n",
-      "Epoch 7/10\n",
-      "24/24 [==============================] - 22s 910ms/step - loss: 0.3223 - accuracy: 0.9083 - val_loss: 0.7400 - val_accuracy: 0.7500\n",
-      "Epoch 8/10\n",
-      "24/24 [==============================] - 21s 877ms/step - loss: 0.2901 - accuracy: 0.9125 - val_loss: 0.7399 - val_accuracy: 0.7750\n",
-      "Epoch 9/10\n",
-      "24/24 [==============================] - 22s 912ms/step - loss: 0.2560 - accuracy: 0.9083 - val_loss: 0.7327 - val_accuracy: 0.7250\n",
-      "Epoch 10/10\n",
-      "24/24 [==============================] - 22s 921ms/step - loss: 0.2518 - accuracy: 0.9292 - val_loss: 0.7085 - val_accuracy: 0.7500\n"
-     ]
-    }
-   ],
-   "source": [
-    "# fine-tune the model\n",
-    "epochs = 10\n",
+    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
     "\n",
-    "history=model_fine_tuned.fit_generator(\n",
-    "        train_generator,\n",
-    "        steps_per_epoch=num_train_images // batch_size,\n",
-    "        epochs=epochs,\n",
-    "        validation_data=validation_generator,\n",
-    "        validation_steps=num_valid_images // batch_size,\n",
-    "        callbacks=[tensorboard_3])\n",
     "\n",
-    "model_fine_tuned.save_weights('./models/model_fined_tuned.h5')"
+    "callbacks = [\n",
+    "    keras.callbacks.ModelCheckpoint(filepath=\"fine_tuning.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
+    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
+    "]\n",
+    "\n",
+    "history = model.fit(train_dataset,\n",
+    "                    epochs=30,\n",
+    "                    validation_data=validation_dataset,\n",
+    "                    callbacks=callbacks)"
    ]
   },
   {
@@ -2240,24 +1360,29 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tensorboard"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "ACVGYteTGhL2",
-    "outputId": "9b5b67be-0639-4bc5-a738-2f295fd0bc57"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 80 images belonging to 8 classes.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the TensorBoard notebook extension\n",
+    "%load_ext tensorboard\n",
+    "%tensorboard --logdir logs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "validation_generator_no_shuffle = validation_datagen.flow_from_directory(\n",
     "        './validation',\n",
-- 
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