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Milestone Project 1 : Food vision big \n","\n","This notebook, we'll train a feature extraction using base model EfficientNetB4"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Yv94bS-iRHqL","executionInfo":{"status":"ok","timestamp":1628820540457,"user_tz":-420,"elapsed":356,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"c6556cb6-cf74-4aad-b592-392e9511c5c6"},"source":["# check GPU\n","!nvidia-smi -L"],"execution_count":1,"outputs":[{"output_type":"stream","text":["GPU 0: Tesla T4 (UUID: GPU-f93faba9-5be9-b630-5541-7d8728dc4131)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZzXxSi1YRJ3G","executionInfo":{"status":"ok","timestamp":1628820624132,"user_tz":-420,"elapsed":77022,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"9f6f80a1-188a-46a1-c1f5-7093e76ce322"},"source":["# downgrade the tensorflow version because the EfficientNetB0 is running error when using mixed precision\n","!pip install tensorflow==2.4.1  "],"execution_count":2,"outputs":[{"output_type":"stream","text":["Collecting tensorflow==2.4.1\n","  Downloading tensorflow-2.4.1-cp37-cp37m-manylinux2010_x86_64.whl (394.3 MB)\n","\u001b[K     |████████████████████████████████| 394.3 MB 14 kB/s \n","\u001b[?25hRequirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (1.12.1)\n","Collecting grpcio~=1.32.0\n","  Downloading grpcio-1.32.0-cp37-cp37m-manylinux2014_x86_64.whl (3.8 MB)\n","\u001b[K     |████████████████████████████████| 3.8 MB 48.0 MB/s \n","\u001b[?25hRequirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (0.36.2)\n","Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (3.17.3)\n","Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (1.12)\n","Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (0.2.0)\n","Requirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (1.1.0)\n","Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (1.6.3)\n","Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (1.1.2)\n","Requirement already satisfied: six~=1.15.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (1.15.0)\n","Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (3.7.4.3)\n","Collecting h5py~=2.10.0\n","  Downloading h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB)\n","\u001b[K     |████████████████████████████████| 2.9 MB 67.4 MB/s \n","\u001b[?25hRequirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (0.12.0)\n","Requirement already satisfied: tensorboard~=2.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (2.5.0)\n","Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (3.3.0)\n","Collecting gast==0.3.3\n","  Downloading gast-0.3.3-py2.py3-none-any.whl (9.7 kB)\n","Requirement already satisfied: numpy~=1.19.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.4.1) (1.19.5)\n","Collecting tensorflow-estimator<2.5.0,>=2.4.0\n","  Downloading tensorflow_estimator-2.4.0-py2.py3-none-any.whl (462 kB)\n","\u001b[K     |████████████████████████████████| 462 kB 66.9 MB/s \n","\u001b[?25hRequirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.1) (0.6.1)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.1) (1.0.1)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.1) (2.23.0)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.1) (57.2.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.1) (0.4.4)\n","Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.1) (1.8.0)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.1) (1.32.1)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow==2.4.1) (3.3.4)\n","Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow==2.4.1) (0.2.8)\n","Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow==2.4.1) (4.2.2)\n","Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow==2.4.1) (4.7.2)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow==2.4.1) (1.3.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.4->tensorflow==2.4.1) (4.6.1)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow==2.4.1) (0.4.8)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow==2.4.1) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow==2.4.1) (2021.5.30)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow==2.4.1) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow==2.4.1) (1.24.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow==2.4.1) (3.1.1)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.4->tensorflow==2.4.1) (3.5.0)\n","Installing collected packages: grpcio, tensorflow-estimator, h5py, gast, tensorflow\n","  Attempting uninstall: grpcio\n","    Found existing installation: grpcio 1.34.1\n","    Uninstalling grpcio-1.34.1:\n","      Successfully uninstalled grpcio-1.34.1\n","  Attempting uninstall: tensorflow-estimator\n","    Found existing installation: tensorflow-estimator 2.5.0\n","    Uninstalling tensorflow-estimator-2.5.0:\n","      Successfully uninstalled tensorflow-estimator-2.5.0\n","  Attempting uninstall: h5py\n","    Found existing installation: h5py 3.1.0\n","    Uninstalling h5py-3.1.0:\n","      Successfully uninstalled h5py-3.1.0\n","  Attempting uninstall: gast\n","    Found existing installation: gast 0.4.0\n","    Uninstalling gast-0.4.0:\n","      Successfully uninstalled gast-0.4.0\n","  Attempting uninstall: tensorflow\n","    Found existing installation: tensorflow 2.5.0\n","    Uninstalling tensorflow-2.5.0:\n","      Successfully uninstalled tensorflow-2.5.0\n","Successfully installed gast-0.3.3 grpcio-1.32.0 h5py-2.10.0 tensorflow-2.4.1 tensorflow-estimator-2.4.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"EscnUPYKRSQP"},"source":["## Get helper functions"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PogRAKBwRWN3","executionInfo":{"status":"ok","timestamp":1628820625582,"user_tz":-420,"elapsed":7,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"dc0aec84-0e12-4d26-ebfc-c2fba651a10c"},"source":["!wget https://raw.githubusercontent.com/BaoLocPham/Tensorflow_Deep_Learning/main/Utils/helper_functions.py"],"execution_count":3,"outputs":[{"output_type":"stream","text":["--2021-08-13 02:10:25--  https://raw.githubusercontent.com/BaoLocPham/Tensorflow_Deep_Learning/main/Utils/helper_functions.py\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 11240 (11K) [text/plain]\n","Saving to: ‘helper_functions.py’\n","\n","\rhelper_functions.py   0%[                    ]       0  --.-KB/s               \rhelper_functions.py 100%[===================>]  10.98K  --.-KB/s    in 0s      \n","\n","2021-08-13 02:10:25 (57.3 MB/s) - ‘helper_functions.py’ saved [11240/11240]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MRwwguXmRYRs","executionInfo":{"status":"ok","timestamp":1628820628507,"user_tz":-420,"elapsed":3,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["from helper_functions import create_tensorboard_callback, plot_loss_curves, compare_historys, make_confusion_matrix"],"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3XsHI8LPRl2K"},"source":["## Use TensorFlow Dataset to download data"]},{"cell_type":"code","metadata":{"id":"08-Zqx_ARsue","executionInfo":{"status":"ok","timestamp":1628820679255,"user_tz":-420,"elapsed":894,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["import tensorflow_datasets as tfds"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3lAHnbV1RyS1","executionInfo":{"status":"ok","timestamp":1628820828435,"user_tz":-420,"elapsed":329,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"b43a8024-8a41-4396-84cd-de2485f1dd5f"},"source":["dataset_list = tfds.list_builders()\n","print(\"food101\" in dataset_list)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["True\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":355,"referenced_widgets":["bdb8b8e8aac845e5b77fa60194b248dc","65af92743fec451e9d33513fd27a26d8","8bb828b095d8412a87e3298a9edd7079","b46adc511c22490e997fab0f9cb7e3c8","53b6be1fa5674bc2a375d79dcfd753c9","b614847bd5ca4895bcf72247dc51a81a","cf9d62396882481199b6d64a824fd433","5359d4b3213d49a19b241947f8870044","4fba26c2092d45fe8a4e8018101c79e4","e62c309d3a05412c9d02bffb12bbe794","047383028d6b40f19989750241949730","88df959066a24757bdf7db9fd450464f","8443bc10376846faa76aed52164d2625","15b5e4400205475fb7f9580d76fd66f8","df54004446a942fd831357291499610c","2cb6313603a344d393c4716f92c23b69","55c106499292419abacac7d8202c4fce","1e113a5db18a4cb89c8582ff17697a56","23558a3a491243958733599365ee7ab3","692fe93cbf0d4170b2e3826968682169","92f987462ab3432bade8a8837e769def","6cf9350492354c23b4896ccb79d2820a","aa2d1ce727214e92a8c8882f7f075feb","74144d58e44945f39984379e1dd324e7","9f9e3ac9035949f88560598bfb2e2747","7be2681e06f646da8853ef54d63fe86d","5d89201078a14f49ad319dee40559525","ecb1d587317948f18a5dafa9a14cde69","eb99ceaae3b843acb689fab3ed5b1394","37e8a3cda06045eea45d488bbf082945","da65eea78a9c4f25bcb1f6b19d7cd71a","05a1952452964ca2914115fbe49fcaaa","994d7537e2b541aca86e3e61bc2d3d48","bb3ed326b8a8411d980d70553bf2d722","8817136994e846958e34008a5947f43b","564a41314e5742ad991a4419031b7963","1817a21787c249c88a98790d65a43f51","e4ec3859c154480084277ea82258f04c","6264fd44bcd24e0a8769cefad6a5f7d9","acf9d05fe3374592bf1fcb5e57a23dba","e78a82c48bf14694806ca7ab14ec64bb","31f6a574e04d489aaa1bafcead384e2a","9a993929bd274e31990f78313a4484cb","35758e520c574a1b97770ff26ba62410","6ef017b2a39a4cc5a3270a87a68acc25","c2691f7235554563bb99f8fe025875f7","6e11419b935648dcba8e19ceb50ef478","73fcb5ab28214ce79ead91b94210ace6","0e7a326a7c8549eeafcbfc3433d02e23","4fbb7e2e18064e2eb1df8ea06e3e46e5","812192f6dda94e9b9ee56a7269bc21a9","9fed1b475ba3428e960b0503317cfcbc","2c5013719bdd4c88861b2cc30f46950f","a01692a608e94f0a8a74ada13acb69e4","7f6723534f8449489ebb876e41f0042a","0ef32f8457a948c894a120d322cfe285"]},"id":"WVRnGv1NSVXX","executionInfo":{"status":"ok","timestamp":1628821620739,"user_tz":-420,"elapsed":682526,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"b43fc9a0-6b89-4efc-a0df-e67775be17c9"},"source":["(train_data, test_data), ds_info = tfds.load(name=\"food101\",\n","                                             split=[\"train\", \"validation\"],\n","                                             shuffle_files=False,\n","                                             as_supervised=True,\n","                                             with_info=True)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["\u001b[1mDownloading and preparing dataset food101/2.0.0 (download: 4.65 GiB, generated: Unknown size, total: 4.65 GiB) to /root/tensorflow_datasets/food101/2.0.0...\u001b[0m\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bdb8b8e8aac845e5b77fa60194b248dc","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4fba26c2092d45fe8a4e8018101c79e4","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"55c106499292419abacac7d8202c4fce","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","\n","\n","\n","\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9f9e3ac9035949f88560598bfb2e2747","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\rShuffling and writing examples to /root/tensorflow_datasets/food101/2.0.0.incompleteLIQRYZ/food101-train.tfrecord\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"994d7537e2b541aca86e3e61bc2d3d48","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=75750.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e78a82c48bf14694806ca7ab14ec64bb","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Shuffling and writing examples to /root/tensorflow_datasets/food101/2.0.0.incompleteLIQRYZ/food101-validation.tfrecord\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0e7a326a7c8549eeafcbfc3433d02e23","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, max=25250.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\u001b[1mDataset food101 downloaded and prepared to /root/tensorflow_datasets/food101/2.0.0. Subsequent calls will reuse this data.\u001b[0m\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wtlno5HPVcqq","executionInfo":{"status":"ok","timestamp":1628821702854,"user_tz":-420,"elapsed":359,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"b75d3621-7a7d-42b2-882b-491cdf147fed"},"source":["# Get the class names\n","class_names = ds_info.features[\"label\"].names\n","class_names[:10]"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['apple_pie',\n"," 'baby_back_ribs',\n"," 'baklava',\n"," 'beef_carpaccio',\n"," 'beef_tartare',\n"," 'beet_salad',\n"," 'beignets',\n"," 'bibimbap',\n"," 'bread_pudding',\n"," 'breakfast_burrito']"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n7LEg12MTK64","executionInfo":{"status":"ok","timestamp":1628821705985,"user_tz":-420,"elapsed":846,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"823e1dc5-a776-4327-d550-d9675575bf9b"},"source":["# Take one sample of the train_data\n","train_one_sample = train_data.take(1) # samples are in format (image_tensor, label)\n","\n","# output info about our training sample\n","for image, label in train_one_sample:\n","  print(f\"\"\"\n","    Image shape: {image.shape},\n","    Image datatype: {image.dtype},\n","    Target class from Food101 (tensor form): {label}\n","    Class name (str form): {class_names[label.numpy()]}\n","  \"\"\")"],"execution_count":14,"outputs":[{"output_type":"stream","text":["\n","    Image shape: (512, 512, 3),\n","    Image datatype: <dtype: 'uint8'>,\n","    Target class from Food101 (tensor form): 56\n","    Class name (str form): huevos_rancheros\n","  \n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gpA9nhRtTczM","executionInfo":{"status":"ok","timestamp":1628821708654,"user_tz":-420,"elapsed":339,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"98752ff0-dba0-4be4-face-fba2daa08937"},"source":["# What are the min and the max value in the image tensor\n","import tensorflow as tf\n","\n","tf.reduce_max(image), tf.reduce_min(image)"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(<tf.Tensor: shape=(), dtype=uint8, numpy=255>,\n"," <tf.Tensor: shape=(), dtype=uint8, numpy=0>)"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":535},"id":"9vXuo8Z3ThDw","executionInfo":{"status":"ok","timestamp":1628821712645,"user_tz":-420,"elapsed":2356,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"984dddfa-a8cb-4e53-ae44-358180aa3632"},"source":["import matplotlib.pyplot as plt\n","\n","fig = tfds.show_examples(train_data, ds_info)"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 9 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"rjLs9RlXSxup"},"source":["## Create preprocessing functions for our data\n","\n","Neural network perform best when data in a certain way (e.g batched, normalized, etc).\n","\n","However, not all data, including data come tfds comes like this.\n","\n","So in order to get it ready for ANN, you'll often have to write preprocessing functions and map it to your data.\n","\n","What we know about data:\n","* In `uint8` datatype.\n","* Comprised of all different size tensors (different size images).\n","* Not scaled (value still in between 0 to 255).\n","\n","What we know model likes:\n","* Data in `float32` dtype (or mixed precision `float16` and `float32`).\n","* For batches, tensorflow likes all of the tensor within a batch to be of the same size.\n","* Scaled (value between 0 and 1) also called normalized tensor generally perform better.\n","\n","With these points in mind, we got a few thing we can tackle with a preprocessing function.\n","\n","Since we're going to be using EfficientNetBx pretrained model from `tf.keras.applications` we don't need to rescale our data (these architecture have rescaling built-in).\n","\n","This means our function needs to:\n","1. Reshape our images to all the same size.\n","2. Convert the dtype of our images from `unint8` to `float32`."]},{"cell_type":"code","metadata":{"id":"UA4RnEoTTj-q","executionInfo":{"status":"ok","timestamp":1628821714575,"user_tz":-420,"elapsed":2,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["import tensorflow as tf\n","\n","def preprocess_image(img, label, rescale=False, img_size=224):\n","  \"\"\"\n","  Reshape img to (img_size, img_size, 3) and Rescaling if true\n","  \"\"\"\n","  img = tf.image.resize(img, size=[img_size, img_size])\n","  if rescale:\n","    img = img/255.\n","  return tf.cast(img, dtype=tf.float32), label"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hZz5m3n7URqK","executionInfo":{"status":"ok","timestamp":1628821716075,"user_tz":-420,"elapsed":3,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"0bcf8eaa-ba82-4fb2-f783-1c027df8eb77"},"source":["preprocessed_img = preprocess_image(image, label)[0]\n","print(f\"Image before processing:\\n {image[:2]}..., \\n shape: {image.shape}, dtype {image.dtype}\")\n","print(f\"Image after  processing:\\n {preprocessed_img[:2]}..., \\n shape: {preprocessed_img.shape}, dtype {preprocessed_img.dtype}\")"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Image before processing:\n"," [[[233 253 251]\n","  [231 253 250]\n","  [228 254 251]\n","  ...\n","  [ 85  82  89]\n","  [ 68  67  75]\n","  [ 57  57  67]]\n","\n"," [[232 254 252]\n","  [229 254 251]\n","  [226 255 251]\n","  ...\n","  [121 116 120]\n","  [100  99 104]\n","  [ 86  85  91]]]..., \n"," shape: (512, 512, 3), dtype <dtype: 'uint8'>\n","Image after  processing:\n"," [[[230.65816 253.64285 251.     ]\n","  [222.99998 254.97449 252.19388]\n","  [207.06633 255.      250.36734]\n","  ...\n","  [140.66287 129.52519 121.22428]\n","  [121.14268 115.26515 116.95397]\n","  [ 83.95363  83.08119  89.63737]]\n","\n"," [[221.47449 254.37755 253.33163]\n","  [214.5102  255.      253.92348]\n","  [198.41327 254.58673 251.96939]\n","  ...\n","  [208.66318 195.82143 173.40823]\n","  [197.03056 190.1071  174.83162]\n","  [175.54036 171.6169  161.21384]]]..., \n"," shape: (224, 224, 3), dtype <dtype: 'float32'>\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"LrVHH38RUyQ9"},"source":["## Batch and prepare dataset\n","\n","We're now going to make our data input pipeline run really fast.\n","\n","Read the document here: https://www.tensorflow.org/guide/data_performance"]},{"cell_type":"code","metadata":{"id":"D6_1Vv5SU5mD","executionInfo":{"status":"ok","timestamp":1628822029947,"user_tz":-420,"elapsed":323,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# Map preprocess function to train_data and run parallel\n","train_data_optimized = train_data.map(preprocess_image, num_parallel_calls=tf.data.AUTOTUNE)\n","# Shuffle the train data and turn into batches and prefetch it\n","train_data_optimized = train_data_optimized.shuffle(buffer_size=1000).batch(batch_size=32).prefetch(buffer_size=tf.data.AUTOTUNE)\n","\n","# Map preprocess function to test data and run parallel\n","test_data_optimized = test_data.map(preprocess_image, num_parallel_calls=tf.data.AUTOTUNE)\n","# turn test data into batches and prefetch it -> not shuffle for validation\n","test_data_optimized = test_data_optimized.batch(batch_size=32).prefetch(buffer_size=tf.data.AUTOTUNE)"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"us5kJYMYW5IT","executionInfo":{"status":"ok","timestamp":1628822036767,"user_tz":-420,"elapsed":525,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"057eade8-9340-4fdf-b6e3-78fb3c932e13"},"source":["train_data_optimized, test_data_optimized"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(<PrefetchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int64)>,\n"," <PrefetchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int64)>)"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"W5kDjeWkW90R"},"source":["## Set the mixed precision "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fVNipy4YXGPY","executionInfo":{"status":"ok","timestamp":1628822127297,"user_tz":-420,"elapsed":520,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"5840e062-8cfc-4765-a14b-63455e05eca4"},"source":["tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n","\n","tf.keras.mixed_precision.global_policy()"],"execution_count":23,"outputs":[{"output_type":"stream","text":["INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK\n","Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: Tesla T4, compute capability 7.5\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK\n","Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: Tesla T4, compute capability 7.5\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<Policy \"mixed_float16\">"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"kgoJjyCZXP8S"},"source":["## Build a feature extraction model\n","\n","this time we'll using EfficientNetB4"]},{"cell_type":"code","metadata":{"id":"cqn7fR_XXjam","executionInfo":{"status":"ok","timestamp":1628822298884,"user_tz":-420,"elapsed":315,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["from tensorflow.keras import layers\n","from tensorflow.keras.layers.experimental import preprocessing\n","from tensorflow.keras import Model"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"id":"vQViit_hYE6M","executionInfo":{"status":"ok","timestamp":1628822340412,"user_tz":-420,"elapsed":327,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["input_shape = (224,224,3)"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"id":"NMEpJSdEXfDg","executionInfo":{"status":"ok","timestamp":1628822723328,"user_tz":-420,"elapsed":4561,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# Create the inputs layer\n","inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n","\n","# Create preprocess layers\n","# x = preprocessing.Rescaling(1./255)(inputs)\n","\n","# Create base model\n","base_model = tf.keras.applications.EfficientNetB4(include_top=False)\n","base_model.trainable = False # Freeze all layers\n","\n","# Stack the base model\n","x = base_model(inputs, training=False)\n","\n","# Create GlobalAveragePool2D layer\n","x = layers.GlobalAveragePooling2D(name=\"global_average_pool_2D_layer\")(x)\n","\n","# Create Fully Connectected layer\n","x = layers.Dense(len(class_names))(x)\n","# Create output layer\n","outputs = layers.Activation(activation=\"softmax\",\n","                            dtype=tf.float32, # we should store number in float32 for stable\n","                            name=\"output_layer\")(x)\n","\n","# Create the model\n","model = Model(inputs, outputs)"],"execution_count":28,"outputs":[]},{"cell_type":"code","metadata":{"id":"b5DSyRWQZlvQ","executionInfo":{"status":"ok","timestamp":1628822773459,"user_tz":-420,"elapsed":383,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# Compile the model\n","model.compile(\n","    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(),\n","    metrics=[\"accuracy\"]\n",")"],"execution_count":29,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GBCUsU1LZbMH","executionInfo":{"status":"ok","timestamp":1628822816247,"user_tz":-420,"elapsed":3,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"f65e63cb-746b-45b8-e9fb-035568ae3c5e"},"source":["for layer in model.layers:\n","  print(layer.name, layer.trainable, layer.dtype_policy)"],"execution_count":31,"outputs":[{"output_type":"stream","text":["input_layer True <Policy \"float32\">\n","efficientnetb4 False <Policy \"mixed_float16\">\n","global_average_pool_2D_layer True <Policy \"mixed_float16\">\n","dense_1 True <Policy \"mixed_float16\">\n","output_layer True <Policy \"float32\">\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vwOGxfiYZ6QV","executionInfo":{"status":"ok","timestamp":1628822875305,"user_tz":-420,"elapsed":444,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"c55ee60b-86af-4994-8d7b-db5016bca610"},"source":["for layer in model.layers[1].layers:\n","  print(layer.name, layer.trainable, layer.dtype_policy)"],"execution_count":32,"outputs":[{"output_type":"stream","text":["input_3 False <Policy \"float32\">\n","rescaling_2 False <Policy \"mixed_float16\">\n","normalization_2 False <Policy \"float32\">\n","stem_conv_pad False <Policy \"mixed_float16\">\n","stem_conv False <Policy \"mixed_float16\">\n","stem_bn False <Policy \"mixed_float16\">\n","stem_activation False <Policy \"mixed_float16\">\n","block1a_dwconv False <Policy \"mixed_float16\">\n","block1a_bn False <Policy \"mixed_float16\">\n","block1a_activation False <Policy \"mixed_float16\">\n","block1a_se_squeeze False <Policy \"mixed_float16\">\n","block1a_se_reshape False <Policy \"mixed_float16\">\n","block1a_se_reduce False <Policy \"mixed_float16\">\n","block1a_se_expand False <Policy \"mixed_float16\">\n","block1a_se_excite False <Policy \"mixed_float16\">\n","block1a_project_conv False <Policy \"mixed_float16\">\n","block1a_project_bn False <Policy \"mixed_float16\">\n","block1b_dwconv False <Policy \"mixed_float16\">\n","block1b_bn False <Policy \"mixed_float16\">\n","block1b_activation False <Policy \"mixed_float16\">\n","block1b_se_squeeze False <Policy \"mixed_float16\">\n","block1b_se_reshape False <Policy \"mixed_float16\">\n","block1b_se_reduce False <Policy \"mixed_float16\">\n","block1b_se_expand False <Policy \"mixed_float16\">\n","block1b_se_excite False <Policy \"mixed_float16\">\n","block1b_project_conv False <Policy \"mixed_float16\">\n","block1b_project_bn False <Policy \"mixed_float16\">\n","block1b_drop False <Policy \"mixed_float16\">\n","block1b_add False <Policy \"mixed_float16\">\n","block2a_expand_conv False <Policy \"mixed_float16\">\n","block2a_expand_bn False <Policy \"mixed_float16\">\n","block2a_expand_activation False <Policy \"mixed_float16\">\n","block2a_dwconv_pad False <Policy \"mixed_float16\">\n","block2a_dwconv False <Policy \"mixed_float16\">\n","block2a_bn False <Policy \"mixed_float16\">\n","block2a_activation False <Policy \"mixed_float16\">\n","block2a_se_squeeze False <Policy \"mixed_float16\">\n","block2a_se_reshape False <Policy \"mixed_float16\">\n","block2a_se_reduce False <Policy \"mixed_float16\">\n","block2a_se_expand False <Policy \"mixed_float16\">\n","block2a_se_excite False <Policy \"mixed_float16\">\n","block2a_project_conv False <Policy \"mixed_float16\">\n","block2a_project_bn False <Policy \"mixed_float16\">\n","block2b_expand_conv False <Policy \"mixed_float16\">\n","block2b_expand_bn False <Policy \"mixed_float16\">\n","block2b_expand_activation False <Policy \"mixed_float16\">\n","block2b_dwconv False <Policy \"mixed_float16\">\n","block2b_bn False <Policy \"mixed_float16\">\n","block2b_activation False <Policy \"mixed_float16\">\n","block2b_se_squeeze False <Policy \"mixed_float16\">\n","block2b_se_reshape False <Policy \"mixed_float16\">\n","block2b_se_reduce False <Policy \"mixed_float16\">\n","block2b_se_expand False <Policy \"mixed_float16\">\n","block2b_se_excite False <Policy \"mixed_float16\">\n","block2b_project_conv False <Policy \"mixed_float16\">\n","block2b_project_bn False <Policy \"mixed_float16\">\n","block2b_drop False <Policy \"mixed_float16\">\n","block2b_add False <Policy \"mixed_float16\">\n","block2c_expand_conv False <Policy \"mixed_float16\">\n","block2c_expand_bn False <Policy \"mixed_float16\">\n","block2c_expand_activation False <Policy \"mixed_float16\">\n","block2c_dwconv False <Policy \"mixed_float16\">\n","block2c_bn False <Policy \"mixed_float16\">\n","block2c_activation False <Policy \"mixed_float16\">\n","block2c_se_squeeze False <Policy \"mixed_float16\">\n","block2c_se_reshape False <Policy \"mixed_float16\">\n","block2c_se_reduce False <Policy \"mixed_float16\">\n","block2c_se_expand False <Policy \"mixed_float16\">\n","block2c_se_excite False <Policy \"mixed_float16\">\n","block2c_project_conv False <Policy \"mixed_float16\">\n","block2c_project_bn False <Policy \"mixed_float16\">\n","block2c_drop False <Policy \"mixed_float16\">\n","block2c_add False <Policy \"mixed_float16\">\n","block2d_expand_conv False <Policy \"mixed_float16\">\n","block2d_expand_bn False <Policy \"mixed_float16\">\n","block2d_expand_activation False <Policy \"mixed_float16\">\n","block2d_dwconv False <Policy \"mixed_float16\">\n","block2d_bn False <Policy \"mixed_float16\">\n","block2d_activation False <Policy \"mixed_float16\">\n","block2d_se_squeeze False <Policy \"mixed_float16\">\n","block2d_se_reshape False <Policy \"mixed_float16\">\n","block2d_se_reduce False <Policy \"mixed_float16\">\n","block2d_se_expand False <Policy \"mixed_float16\">\n","block2d_se_excite False <Policy \"mixed_float16\">\n","block2d_project_conv False <Policy \"mixed_float16\">\n","block2d_project_bn False <Policy \"mixed_float16\">\n","block2d_drop False <Policy \"mixed_float16\">\n","block2d_add False <Policy \"mixed_float16\">\n","block3a_expand_conv False <Policy \"mixed_float16\">\n","block3a_expand_bn False <Policy \"mixed_float16\">\n","block3a_expand_activation False <Policy \"mixed_float16\">\n","block3a_dwconv_pad False <Policy \"mixed_float16\">\n","block3a_dwconv False <Policy \"mixed_float16\">\n","block3a_bn False <Policy \"mixed_float16\">\n","block3a_activation False <Policy \"mixed_float16\">\n","block3a_se_squeeze False <Policy \"mixed_float16\">\n","block3a_se_reshape False <Policy \"mixed_float16\">\n","block3a_se_reduce False <Policy \"mixed_float16\">\n","block3a_se_expand False <Policy \"mixed_float16\">\n","block3a_se_excite False <Policy \"mixed_float16\">\n","block3a_project_conv False <Policy \"mixed_float16\">\n","block3a_project_bn False <Policy \"mixed_float16\">\n","block3b_expand_conv False <Policy \"mixed_float16\">\n","block3b_expand_bn False <Policy \"mixed_float16\">\n","block3b_expand_activation False <Policy \"mixed_float16\">\n","block3b_dwconv False <Policy \"mixed_float16\">\n","block3b_bn False <Policy \"mixed_float16\">\n","block3b_activation False <Policy \"mixed_float16\">\n","block3b_se_squeeze False <Policy \"mixed_float16\">\n","block3b_se_reshape False <Policy \"mixed_float16\">\n","block3b_se_reduce False <Policy \"mixed_float16\">\n","block3b_se_expand False <Policy \"mixed_float16\">\n","block3b_se_excite False <Policy \"mixed_float16\">\n","block3b_project_conv False <Policy \"mixed_float16\">\n","block3b_project_bn False <Policy \"mixed_float16\">\n","block3b_drop False <Policy \"mixed_float16\">\n","block3b_add False <Policy \"mixed_float16\">\n","block3c_expand_conv False <Policy \"mixed_float16\">\n","block3c_expand_bn False <Policy \"mixed_float16\">\n","block3c_expand_activation False <Policy \"mixed_float16\">\n","block3c_dwconv False <Policy \"mixed_float16\">\n","block3c_bn False <Policy \"mixed_float16\">\n","block3c_activation False <Policy \"mixed_float16\">\n","block3c_se_squeeze False <Policy \"mixed_float16\">\n","block3c_se_reshape False <Policy \"mixed_float16\">\n","block3c_se_reduce False <Policy \"mixed_float16\">\n","block3c_se_expand False <Policy \"mixed_float16\">\n","block3c_se_excite False <Policy \"mixed_float16\">\n","block3c_project_conv False <Policy \"mixed_float16\">\n","block3c_project_bn False <Policy \"mixed_float16\">\n","block3c_drop False <Policy \"mixed_float16\">\n","block3c_add False <Policy \"mixed_float16\">\n","block3d_expand_conv False <Policy \"mixed_float16\">\n","block3d_expand_bn False <Policy \"mixed_float16\">\n","block3d_expand_activation False <Policy \"mixed_float16\">\n","block3d_dwconv False <Policy \"mixed_float16\">\n","block3d_bn False <Policy \"mixed_float16\">\n","block3d_activation False <Policy \"mixed_float16\">\n","block3d_se_squeeze False <Policy \"mixed_float16\">\n","block3d_se_reshape False <Policy \"mixed_float16\">\n","block3d_se_reduce False <Policy \"mixed_float16\">\n","block3d_se_expand False <Policy \"mixed_float16\">\n","block3d_se_excite False <Policy \"mixed_float16\">\n","block3d_project_conv False <Policy \"mixed_float16\">\n","block3d_project_bn False <Policy \"mixed_float16\">\n","block3d_drop False <Policy \"mixed_float16\">\n","block3d_add False <Policy \"mixed_float16\">\n","block4a_expand_conv False <Policy \"mixed_float16\">\n","block4a_expand_bn False <Policy \"mixed_float16\">\n","block4a_expand_activation False <Policy \"mixed_float16\">\n","block4a_dwconv_pad False <Policy \"mixed_float16\">\n","block4a_dwconv False <Policy \"mixed_float16\">\n","block4a_bn False <Policy \"mixed_float16\">\n","block4a_activation False <Policy \"mixed_float16\">\n","block4a_se_squeeze False <Policy \"mixed_float16\">\n","block4a_se_reshape False <Policy \"mixed_float16\">\n","block4a_se_reduce False <Policy \"mixed_float16\">\n","block4a_se_expand False <Policy \"mixed_float16\">\n","block4a_se_excite False <Policy \"mixed_float16\">\n","block4a_project_conv False <Policy \"mixed_float16\">\n","block4a_project_bn False <Policy \"mixed_float16\">\n","block4b_expand_conv False <Policy \"mixed_float16\">\n","block4b_expand_bn False <Policy \"mixed_float16\">\n","block4b_expand_activation False <Policy \"mixed_float16\">\n","block4b_dwconv False <Policy \"mixed_float16\">\n","block4b_bn False <Policy \"mixed_float16\">\n","block4b_activation False <Policy \"mixed_float16\">\n","block4b_se_squeeze False <Policy \"mixed_float16\">\n","block4b_se_reshape False <Policy \"mixed_float16\">\n","block4b_se_reduce False <Policy \"mixed_float16\">\n","block4b_se_expand False <Policy \"mixed_float16\">\n","block4b_se_excite False <Policy \"mixed_float16\">\n","block4b_project_conv False <Policy \"mixed_float16\">\n","block4b_project_bn False <Policy \"mixed_float16\">\n","block4b_drop False <Policy \"mixed_float16\">\n","block4b_add False <Policy \"mixed_float16\">\n","block4c_expand_conv False <Policy \"mixed_float16\">\n","block4c_expand_bn False <Policy \"mixed_float16\">\n","block4c_expand_activation False <Policy \"mixed_float16\">\n","block4c_dwconv False <Policy \"mixed_float16\">\n","block4c_bn False <Policy \"mixed_float16\">\n","block4c_activation False <Policy \"mixed_float16\">\n","block4c_se_squeeze False <Policy \"mixed_float16\">\n","block4c_se_reshape False <Policy \"mixed_float16\">\n","block4c_se_reduce False <Policy \"mixed_float16\">\n","block4c_se_expand False <Policy \"mixed_float16\">\n","block4c_se_excite False <Policy \"mixed_float16\">\n","block4c_project_conv False <Policy \"mixed_float16\">\n","block4c_project_bn False <Policy \"mixed_float16\">\n","block4c_drop False <Policy \"mixed_float16\">\n","block4c_add False <Policy \"mixed_float16\">\n","block4d_expand_conv False <Policy \"mixed_float16\">\n","block4d_expand_bn False <Policy \"mixed_float16\">\n","block4d_expand_activation False <Policy \"mixed_float16\">\n","block4d_dwconv False <Policy \"mixed_float16\">\n","block4d_bn False <Policy \"mixed_float16\">\n","block4d_activation False <Policy \"mixed_float16\">\n","block4d_se_squeeze False <Policy \"mixed_float16\">\n","block4d_se_reshape False <Policy \"mixed_float16\">\n","block4d_se_reduce False <Policy \"mixed_float16\">\n","block4d_se_expand False <Policy \"mixed_float16\">\n","block4d_se_excite False <Policy \"mixed_float16\">\n","block4d_project_conv False <Policy \"mixed_float16\">\n","block4d_project_bn False <Policy \"mixed_float16\">\n","block4d_drop False <Policy \"mixed_float16\">\n","block4d_add False <Policy \"mixed_float16\">\n","block4e_expand_conv False <Policy \"mixed_float16\">\n","block4e_expand_bn False <Policy \"mixed_float16\">\n","block4e_expand_activation False <Policy \"mixed_float16\">\n","block4e_dwconv False <Policy \"mixed_float16\">\n","block4e_bn False <Policy \"mixed_float16\">\n","block4e_activation False <Policy \"mixed_float16\">\n","block4e_se_squeeze False <Policy \"mixed_float16\">\n","block4e_se_reshape False <Policy \"mixed_float16\">\n","block4e_se_reduce False <Policy \"mixed_float16\">\n","block4e_se_expand False <Policy \"mixed_float16\">\n","block4e_se_excite False <Policy \"mixed_float16\">\n","block4e_project_conv False <Policy \"mixed_float16\">\n","block4e_project_bn False <Policy \"mixed_float16\">\n","block4e_drop False <Policy \"mixed_float16\">\n","block4e_add False <Policy \"mixed_float16\">\n","block4f_expand_conv False <Policy \"mixed_float16\">\n","block4f_expand_bn False <Policy \"mixed_float16\">\n","block4f_expand_activation False <Policy \"mixed_float16\">\n","block4f_dwconv False <Policy \"mixed_float16\">\n","block4f_bn False <Policy \"mixed_float16\">\n","block4f_activation False <Policy \"mixed_float16\">\n","block4f_se_squeeze False <Policy \"mixed_float16\">\n","block4f_se_reshape False <Policy \"mixed_float16\">\n","block4f_se_reduce False <Policy \"mixed_float16\">\n","block4f_se_expand False <Policy \"mixed_float16\">\n","block4f_se_excite False <Policy \"mixed_float16\">\n","block4f_project_conv False <Policy \"mixed_float16\">\n","block4f_project_bn False <Policy 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\"mixed_float16\">\n","block5d_se_expand False <Policy \"mixed_float16\">\n","block5d_se_excite False <Policy \"mixed_float16\">\n","block5d_project_conv False <Policy \"mixed_float16\">\n","block5d_project_bn False <Policy \"mixed_float16\">\n","block5d_drop False <Policy \"mixed_float16\">\n","block5d_add False <Policy \"mixed_float16\">\n","block5e_expand_conv False <Policy \"mixed_float16\">\n","block5e_expand_bn False <Policy \"mixed_float16\">\n","block5e_expand_activation False <Policy \"mixed_float16\">\n","block5e_dwconv False <Policy \"mixed_float16\">\n","block5e_bn False <Policy \"mixed_float16\">\n","block5e_activation False <Policy \"mixed_float16\">\n","block5e_se_squeeze False <Policy \"mixed_float16\">\n","block5e_se_reshape False <Policy \"mixed_float16\">\n","block5e_se_reduce False <Policy \"mixed_float16\">\n","block5e_se_expand False <Policy \"mixed_float16\">\n","block5e_se_excite False <Policy \"mixed_float16\">\n","block5e_project_conv False <Policy \"mixed_float16\">\n","block5e_project_bn False <Policy \"mixed_float16\">\n","block5e_drop False <Policy \"mixed_float16\">\n","block5e_add False <Policy \"mixed_float16\">\n","block5f_expand_conv False <Policy \"mixed_float16\">\n","block5f_expand_bn False <Policy \"mixed_float16\">\n","block5f_expand_activation False <Policy \"mixed_float16\">\n","block5f_dwconv False <Policy \"mixed_float16\">\n","block5f_bn False <Policy \"mixed_float16\">\n","block5f_activation False <Policy \"mixed_float16\">\n","block5f_se_squeeze False <Policy \"mixed_float16\">\n","block5f_se_reshape False <Policy \"mixed_float16\">\n","block5f_se_reduce False <Policy \"mixed_float16\">\n","block5f_se_expand False <Policy \"mixed_float16\">\n","block5f_se_excite False <Policy \"mixed_float16\">\n","block5f_project_conv False <Policy \"mixed_float16\">\n","block5f_project_bn False <Policy \"mixed_float16\">\n","block5f_drop False <Policy \"mixed_float16\">\n","block5f_add False <Policy \"mixed_float16\">\n","block6a_expand_conv False <Policy \"mixed_float16\">\n","block6a_expand_bn False <Policy \"mixed_float16\">\n","block6a_expand_activation False <Policy \"mixed_float16\">\n","block6a_dwconv_pad False <Policy \"mixed_float16\">\n","block6a_dwconv False <Policy \"mixed_float16\">\n","block6a_bn False <Policy \"mixed_float16\">\n","block6a_activation False <Policy \"mixed_float16\">\n","block6a_se_squeeze False <Policy \"mixed_float16\">\n","block6a_se_reshape False <Policy \"mixed_float16\">\n","block6a_se_reduce False <Policy \"mixed_float16\">\n","block6a_se_expand False <Policy \"mixed_float16\">\n","block6a_se_excite False <Policy \"mixed_float16\">\n","block6a_project_conv False <Policy \"mixed_float16\">\n","block6a_project_bn False <Policy \"mixed_float16\">\n","block6b_expand_conv False <Policy \"mixed_float16\">\n","block6b_expand_bn False <Policy \"mixed_float16\">\n","block6b_expand_activation False <Policy \"mixed_float16\">\n","block6b_dwconv False <Policy \"mixed_float16\">\n","block6b_bn False <Policy \"mixed_float16\">\n","block6b_activation False <Policy \"mixed_float16\">\n","block6b_se_squeeze False <Policy \"mixed_float16\">\n","block6b_se_reshape False <Policy \"mixed_float16\">\n","block6b_se_reduce False <Policy \"mixed_float16\">\n","block6b_se_expand False <Policy \"mixed_float16\">\n","block6b_se_excite False <Policy \"mixed_float16\">\n","block6b_project_conv False <Policy \"mixed_float16\">\n","block6b_project_bn False <Policy \"mixed_float16\">\n","block6b_drop False <Policy \"mixed_float16\">\n","block6b_add False <Policy \"mixed_float16\">\n","block6c_expand_conv False <Policy \"mixed_float16\">\n","block6c_expand_bn False <Policy \"mixed_float16\">\n","block6c_expand_activation False <Policy \"mixed_float16\">\n","block6c_dwconv False <Policy \"mixed_float16\">\n","block6c_bn False <Policy \"mixed_float16\">\n","block6c_activation False <Policy \"mixed_float16\">\n","block6c_se_squeeze False <Policy \"mixed_float16\">\n","block6c_se_reshape False <Policy \"mixed_float16\">\n","block6c_se_reduce False <Policy \"mixed_float16\">\n","block6c_se_expand False <Policy \"mixed_float16\">\n","block6c_se_excite False <Policy \"mixed_float16\">\n","block6c_project_conv False <Policy \"mixed_float16\">\n","block6c_project_bn False <Policy \"mixed_float16\">\n","block6c_drop False <Policy \"mixed_float16\">\n","block6c_add False <Policy \"mixed_float16\">\n","block6d_expand_conv False <Policy \"mixed_float16\">\n","block6d_expand_bn False <Policy \"mixed_float16\">\n","block6d_expand_activation False <Policy \"mixed_float16\">\n","block6d_dwconv False <Policy \"mixed_float16\">\n","block6d_bn False <Policy \"mixed_float16\">\n","block6d_activation False <Policy \"mixed_float16\">\n","block6d_se_squeeze False <Policy \"mixed_float16\">\n","block6d_se_reshape False <Policy \"mixed_float16\">\n","block6d_se_reduce False <Policy \"mixed_float16\">\n","block6d_se_expand False <Policy \"mixed_float16\">\n","block6d_se_excite False <Policy \"mixed_float16\">\n","block6d_project_conv False <Policy \"mixed_float16\">\n","block6d_project_bn False <Policy \"mixed_float16\">\n","block6d_drop False <Policy \"mixed_float16\">\n","block6d_add False <Policy \"mixed_float16\">\n","block6e_expand_conv False <Policy \"mixed_float16\">\n","block6e_expand_bn False <Policy \"mixed_float16\">\n","block6e_expand_activation False <Policy \"mixed_float16\">\n","block6e_dwconv False <Policy \"mixed_float16\">\n","block6e_bn False <Policy \"mixed_float16\">\n","block6e_activation False <Policy \"mixed_float16\">\n","block6e_se_squeeze False <Policy \"mixed_float16\">\n","block6e_se_reshape False <Policy \"mixed_float16\">\n","block6e_se_reduce False <Policy \"mixed_float16\">\n","block6e_se_expand False <Policy \"mixed_float16\">\n","block6e_se_excite False <Policy \"mixed_float16\">\n","block6e_project_conv False <Policy \"mixed_float16\">\n","block6e_project_bn False <Policy \"mixed_float16\">\n","block6e_drop False <Policy \"mixed_float16\">\n","block6e_add False <Policy \"mixed_float16\">\n","block6f_expand_conv False <Policy \"mixed_float16\">\n","block6f_expand_bn False <Policy \"mixed_float16\">\n","block6f_expand_activation False <Policy \"mixed_float16\">\n","block6f_dwconv False <Policy \"mixed_float16\">\n","block6f_bn False <Policy \"mixed_float16\">\n","block6f_activation False <Policy \"mixed_float16\">\n","block6f_se_squeeze False <Policy \"mixed_float16\">\n","block6f_se_reshape False <Policy \"mixed_float16\">\n","block6f_se_reduce False <Policy \"mixed_float16\">\n","block6f_se_expand False <Policy \"mixed_float16\">\n","block6f_se_excite False <Policy \"mixed_float16\">\n","block6f_project_conv False <Policy \"mixed_float16\">\n","block6f_project_bn False <Policy \"mixed_float16\">\n","block6f_drop False <Policy \"mixed_float16\">\n","block6f_add False <Policy \"mixed_float16\">\n","block6g_expand_conv False <Policy \"mixed_float16\">\n","block6g_expand_bn False <Policy \"mixed_float16\">\n","block6g_expand_activation False <Policy \"mixed_float16\">\n","block6g_dwconv False <Policy \"mixed_float16\">\n","block6g_bn False <Policy \"mixed_float16\">\n","block6g_activation False <Policy \"mixed_float16\">\n","block6g_se_squeeze False <Policy \"mixed_float16\">\n","block6g_se_reshape False <Policy \"mixed_float16\">\n","block6g_se_reduce False <Policy \"mixed_float16\">\n","block6g_se_expand False <Policy \"mixed_float16\">\n","block6g_se_excite False <Policy \"mixed_float16\">\n","block6g_project_conv False <Policy \"mixed_float16\">\n","block6g_project_bn False <Policy \"mixed_float16\">\n","block6g_drop False <Policy \"mixed_float16\">\n","block6g_add False <Policy \"mixed_float16\">\n","block6h_expand_conv False <Policy \"mixed_float16\">\n","block6h_expand_bn False <Policy \"mixed_float16\">\n","block6h_expand_activation False <Policy \"mixed_float16\">\n","block6h_dwconv False <Policy \"mixed_float16\">\n","block6h_bn False <Policy \"mixed_float16\">\n","block6h_activation False <Policy \"mixed_float16\">\n","block6h_se_squeeze False <Policy \"mixed_float16\">\n","block6h_se_reshape False <Policy \"mixed_float16\">\n","block6h_se_reduce False <Policy \"mixed_float16\">\n","block6h_se_expand False <Policy \"mixed_float16\">\n","block6h_se_excite False <Policy \"mixed_float16\">\n","block6h_project_conv False <Policy \"mixed_float16\">\n","block6h_project_bn False <Policy \"mixed_float16\">\n","block6h_drop False <Policy \"mixed_float16\">\n","block6h_add False <Policy \"mixed_float16\">\n","block7a_expand_conv False <Policy \"mixed_float16\">\n","block7a_expand_bn False <Policy \"mixed_float16\">\n","block7a_expand_activation False <Policy \"mixed_float16\">\n","block7a_dwconv False <Policy \"mixed_float16\">\n","block7a_bn False <Policy \"mixed_float16\">\n","block7a_activation False <Policy \"mixed_float16\">\n","block7a_se_squeeze False <Policy \"mixed_float16\">\n","block7a_se_reshape False <Policy \"mixed_float16\">\n","block7a_se_reduce False <Policy \"mixed_float16\">\n","block7a_se_expand False <Policy \"mixed_float16\">\n","block7a_se_excite False <Policy \"mixed_float16\">\n","block7a_project_conv False <Policy \"mixed_float16\">\n","block7a_project_bn False <Policy \"mixed_float16\">\n","block7b_expand_conv False <Policy \"mixed_float16\">\n","block7b_expand_bn False <Policy \"mixed_float16\">\n","block7b_expand_activation False <Policy \"mixed_float16\">\n","block7b_dwconv False <Policy \"mixed_float16\">\n","block7b_bn False <Policy \"mixed_float16\">\n","block7b_activation False <Policy \"mixed_float16\">\n","block7b_se_squeeze False <Policy \"mixed_float16\">\n","block7b_se_reshape False <Policy \"mixed_float16\">\n","block7b_se_reduce False <Policy \"mixed_float16\">\n","block7b_se_expand False <Policy \"mixed_float16\">\n","block7b_se_excite False <Policy \"mixed_float16\">\n","block7b_project_conv False <Policy \"mixed_float16\">\n","block7b_project_bn False <Policy \"mixed_float16\">\n","block7b_drop False <Policy \"mixed_float16\">\n","block7b_add False <Policy \"mixed_float16\">\n","top_conv False <Policy \"mixed_float16\">\n","top_bn False <Policy \"mixed_float16\">\n","top_activation False <Policy \"mixed_float16\">\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Pjh0YWndfeJ","executionInfo":{"status":"ok","timestamp":1628823802816,"user_tz":-420,"elapsed":9,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"3a1251c1-36fa-499c-fbb3-44f63120b045"},"source":["model.summary()"],"execution_count":36,"outputs":[{"output_type":"stream","text":["Model: \"model_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_layer (InputLayer)     [(None, 224, 224, 3)]     0         \n","_________________________________________________________________\n","efficientnetb4 (Functional)  (None, None, None, 1792)  17673823  \n","_________________________________________________________________\n","global_average_pool_2D_layer (None, 1792)              0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 101)               181093    \n","_________________________________________________________________\n","output_layer (Activation)    (None, 101)               0         \n","=================================================================\n","Total params: 17,854,916\n","Trainable params: 181,093\n","Non-trainable params: 17,673,823\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lSnNmvgCaKi6"},"source":["## Fit the feature extraction model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fGb307WHaTZs","executionInfo":{"status":"ok","timestamp":1628823092674,"user_tz":-420,"elapsed":328,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"c6372b79-7d92-48af-bedd-4df0dfea9cd5"},"source":["# create callback \n","checkpoint_path = \"Food101_feature_extract_efficientnetb4/checkpoint.ckpt\"\n","\n","checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n","                                                         monitor=\"val_accuracy\",\n","                                                         save_best_only=True,\n","                                                         save_weights_only=True)\n","\n","tensorboard_callback = create_tensorboard_callback(dir_name=\"training_logs\",\n","                                                   experiment_name=\"efficientnetb4_101_classes_all_data_feature_extract\")"],"execution_count":34,"outputs":[{"output_type":"stream","text":["Saving TensorBoard log files to: training_logs/efficientnetb4_101_classes_all_data_feature_extract/20210813-025131\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hRCM73nfay3G","executionInfo":{"status":"ok","timestamp":1628823801733,"user_tz":-420,"elapsed":706138,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"6dedcc5a-81a7-4ed6-b706-277f88d93a9a"},"source":["# Fit the feature extraction model with mixed precision \n","history_efficientnetb4_feature_extraction = model.fit(train_data_optimized,\n","                                                        epochs=3,\n","                                                        steps_per_epoch=len(train_data_optimized),\n","                                                        validation_data=test_data_optimized,\n","                                                        validation_steps=int(0.15 * len(test_data_optimized)),\n","                                                        callbacks=[tensorboard_callback,\n","                                                                   checkpoint_callback])"],"execution_count":35,"outputs":[{"output_type":"stream","text":["Epoch 1/3\n","2368/2368 [==============================] - 270s 96ms/step - loss: 2.3273 - accuracy: 0.4542 - val_loss: 1.3319 - val_accuracy: 0.6552\n","Epoch 2/3\n","2368/2368 [==============================] - 224s 94ms/step - loss: 1.4277 - accuracy: 0.6306 - val_loss: 1.2413 - val_accuracy: 0.6727\n","Epoch 3/3\n","2368/2368 [==============================] - 211s 88ms/step - loss: 1.2679 - accuracy: 0.6667 - val_loss: 1.2092 - val_accuracy: 0.6772\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NZMHq9sZbAcN","executionInfo":{"status":"ok","timestamp":1628823899193,"user_tz":-420,"elapsed":68441,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"2919d13d-de3c-405c-82a2-12eda773383d"},"source":["results_feature_extract = model.evaluate(test_data_optimized)\n","results_feature_extract"],"execution_count":37,"outputs":[{"output_type":"stream","text":["790/790 [==============================] - 68s 86ms/step - loss: 1.2015 - accuracy: 0.6779\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[1.2014997005462646, 0.6779009699821472]"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"fSbG7n7pqgaD","executionInfo":{"status":"ok","timestamp":1628827195028,"user_tz":-420,"elapsed":910,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"acaba8e3-2e49-4905-cd58-85027d1a54fb"},"source":["plot_loss_curves(history_efficientnetb4_feature_extraction)"],"execution_count":62,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtAAAAF2CAYAAABH+q/yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXhU9fn+8feTPZAAIYGwkwBhTQDLIogsgiKyLxbc6tLWBa3Vai2LCoobVmtbv25VS239VVstiyAIooKIogIKJOw7hH1fhECWz++PGSQiS8BMTmZyv65rLjJnzszcA+HwcPI5z2POOUREREREpGjCvA4gIiIiIhJMVECLiIiIiJwHFdAiIiIiIudBBbSIiIiIyHlQAS0iIiIich5UQIuIiIiInIeAFdBmNs7MdppZ1hkeTzCziWa2xMy+NrP0QGURERERESkugTwD/QbQ4yyPjwQWOeeaAzcCfw1gFhERERGRYhGwAto5NwfYe5ZdmgKf+PddAaSYWXKg8oiIiIiIFIcID997MTAQ+MzM2gJ1gVrAjrM9KSkpyaWkpAQ+nYhIACxcuHC3c66K1zlKio7ZIhLMznTM9rKAHgv81cwWAZnAt0D+6XY0s9uA2wDq1KnDggULSiykiEhxMrONXmcoSSkpKTpmi0jQOtMx27MC2jl3ELgFwMwMWA+sO8O+rwKvArRu3dqVVEYRERERkVN51sbOzCqZWZT/7q+BOf6iWkRERESk1ArYGWgzexvoAiSZWTYwGogEcM69AjQB/mlmDlgK/CpQWUREREREikvACmjn3LXneHwe0DBQ7y8SDHJzc8nOziYnJ8frKFLMYmJiqFWrFpGRkV5H+REz64GvdWg48Lpzbuxp9hkMPAI4YLFz7jr/9j8CvfD9BHMmcI9zTkvrRKRM8fIiQpEyLzs7m/j4eFJSUvBdCiChwDnHnj17yM7OJjU11es4P2Bm4cCLwBVANjDfzCY755YV2icNGAF0cM7tM7Oq/u2XAB2A5v5d5wKdgdkl9wlERLynUd4iHsrJySExMVHFc4gxMxITE0vrTxbaAmucc+ucc8eB/wD9TtnnVuBF59w+AOfcTv92B8QAUUA0vmV5Z209KiISilRAi3hMxXNoKsV/rjWBzYXuZ/u3FdYQaGhmn5vZl/4lHyeW3s0CtvlvM5xzy099AzO7zcwWmNmCXbt2BeRDiIh4SQW0iIicKgJIw3ch+LXAa/7OSQ3wXQBeC1/R3dXMOp76ZOfcq8651s651lWqlJmZMSJShqiAFinj9u/fz0svvXTez+vZsyf79+8/6z6jRo3io48+utBopxUXF1esr1cGbQFqF7pfy7+tsGxgsnMu1zm3HliFr6AeAHzpnDvsnDsMfAC0L4HMIiKligpokTLuTAV0Xl7eWZ83bdo0KlWqdNZ9xowZw+WXX/6T8kmxmw+kmVmqvxf/NcDkU/aZhO/sM2aWhG9JxzpgE9DZzCLMLBLfBYQ/WsIhIhLq1IVDpJR4dMpSlm0t3llCTWtUYHSfZmfdZ/jw4axdu5aWLVsSGRlJTEwMCQkJrFixglWrVtG/f382b95MTk4O99xzD7fddhtwckTz4cOHueqqq7j00kv54osvqFmzJu+99x6xsbHcfPPN9O7dm6uvvpqUlBRuuukmpkyZQm5uLu+++y6NGzdm165dXHfddWzdupX27dszc+ZMFi5cSFJS0llzO+f4wx/+wAcffICZ8dBDDzFkyBC2bdvGkCFDOHjwIHl5ebz88stccskl/OpXv2LBggWYGb/85S/53e9+V2y/z8HEOZdnZr8BZuBrYzfOObfUzMYAC5xzk/2PdTezZUA+8IBzbo+Z/Q/oCmTiu6BwunNuijefRETEO2WigN689wjbD+bQJqWy11FESp2xY8eSlZXFokWLmD17Nr169SIrK+v79mvjxo2jcuXKHD16lDZt2jBo0CASExN/8BqrV6/m7bff5rXXXmPw4MGMHz+eG2644UfvlZSUxDfffMNLL73Es88+y+uvv86jjz5K165dGTFiBNOnT+fvf/97kXJPmDCBRYsWsXjxYnbv3k2bNm3o1KkTb731FldeeSUPPvgg+fn5HDlyhEWLFrFlyxaysrIAzrn0JNQ556YB007ZNqrQ1w64z38rvE8+cHtJZBQRuWDOwfHDcHQfHN3v+zWqPNRqXWxvUSYK6Hv+8y1b9h9l5n2dqRBT+oYaiADnPFNcUtq2bfuD3sXPP/88EydOBGDz5s2sXr36RwV0amoqLVu2BKBVq1Zs2LDhtK89cODA7/eZMGECAHPnzv3+9Xv06EFCQkKRcs6dO5drr72W8PBwkpOT6dy5M/Pnz6dNmzb88pe/JDc3l/79+9OyZUvq1avHunXruPvuu+nVqxfdu3cv+m+IiIh4Iz8Pcg74C2H/LWf/D+8f3X/6xwtOWYZYvxv8YkKxRSsTBfToPs0Y8NLnPP3BCp4YkOF1HJFSrXz58t9/PXv2bD766CPmzZtHuXLl6NKly2l7G0dHR3//dXh4OEePHj3ta5/YLzw8/JxrrC9Up06dmDNnDlOnTuXmm2/mvvvu48Ybb2Tx4sXMmDGDV155hXfeeYdx48YF5P1FRKQQ5yD36FmK4DMUwEf3w7FzLGuMrgCxlSA2wXerUOPk17EJP3wsvnqxfqwyUUC3qF2JX3ZI5fW56+nXsiZtU7WUQ+SE+Ph4Dh06dNrHDhw4QEJCAuXKlWPFihV8+eWXxf7+HTp04J133mHYsGF8+OGH7Nu3r0jP69ixI3/729+46aab2Lt3L3PmzOGZZ55h48aN1KpVi1tvvZVjx47xzTff0LNnT6Kiohg0aBCNGjU67fISERE5i4L8k2eDCxe5Zy2C/bf842d+3bCIHxa9cdWgSpPTF8GxCRDjvx9TEcK9K2PLRAENcF/3hkxfup3hE5Yw7bcdiYkM9zqSSKmQmJhIhw4dSE9PJzY2luTk5O8f69GjB6+88gpNmjShUaNGtGvXrtjff/To0Vx77bW8+eabtG/fnmrVqhEfH3/O5w0YMIB58+bRokULzIw//vGPVKtWjX/+858888wzREZGEhcXx7/+9S+2bNnCLbfcQkFBAQBPPfVUsX8OEZGgkJtT9GUQhR/POYDv2uEziIorVOBWgqSGZy+AT9yiykPpHTx1Rua7ViR4tG7d2i1YsOCCnjtn1S5uHPc1d3dtwP3dGxVzMpHzt3z5cpo0aeJ1DE8dO3aM8PBwIiIimDdvHkOHDmXRokVexyoWp/vzNbOFzrniu5KllPspx2wROQPnfMsbzlkEn+YMcd7pl9gBYGE/LnDPVQDHVvJti4gquc9fgs50zC4zZ6ABOjWswsCf1eTl2Wvp1bw6jatV8DqSSJm3adMmBg8eTEFBAVFRUbz22mteRxIRKRl5x0+zHKIIF8vl7AdXcObXjYj9YZFbORViLzpDEVzo66h4CNOIkKIoUwU0wMO9mvLpyl0MG5/JhKGXEB4WfD82EAklaWlpfPvttz/YtmfPHrp16/ajfT/++OMfdQAREfHU6VqmnbUA3n/y8eOHz/LC5lvnW7jITah75rPAhYvjyJgS+/ilXUGB4/3MbYQZ9G5eo9het8wV0Anloxjdtxm/fftb3vhiA7+6NPXcTxKREpWYmBgyyzhEJEgUZ8u0wsKjfljsVqoN1TJOU/yeUgTHVIQwXa91oZxzzF61i2emr2TZtoN0TEtSAf1T9WlenUnfbuHZGSvp3jSZ2pXLeR1JREREfqpTW6YV+WK582yZFlPplJZpZ1knHBkblBfJBbOFG/fy9PSVfL1+L7Urx/KXIS3p06L4imcoowW0mfFY/3S6P/cpD07K4p+3tMH0zS0iIlL6OQe7V8G62bDxczi884cFcv6xMz+3cMu0mEqntEyrdPoCuBS0TJOiWbH9IM/OWMlHy3eSFBfNY/2aMaRNHaIiin9dd5n9bqhZKZY/9GjM6MlLmbRoCwMuquV1JBERETmdw7t8BfO6Wb5fD27xba9UFyrV8bdMK0K3iKg4nQ0OQZv2HOHPH61i0qItxEVH8MCVjbilQwrlogJX5pbZAhrghnZ1eW/RFsZMWUantCokxkWf+0kiIiISWLlHYeMXvoJ57WzYkenbHlMJ6nWGeg9A/csgIcXLlOKxnYdyeOGTNbz99SbCzLi9U33u6FyPSuUC31KvTBfQ4WHG04Oa0/P5zxjz/jL+es1FXkcSKXH79+/nrbfe4s477zyv5/Xs2ZO33nqLSpUqnXGfUaNG0alTJy6//PKfGvOCxcXFcfjw6a9037BhA7179yYrK6uEU4nIDxQUwPbFsHaWr2je9JVvKUZYJNRpB10f9hXM1VvqwjrhwNFcXp2zlnFzN3A8v4Br2tTmt93SSK5Qct1HynQBDZCWHM9dlzXgLx+tpn/LmlzWuKrXkURK1P79+3nppZd+VEDn5eUREXHmQ8S0adPO+dpjxoz5yflEJETt33SyYF73KRzd69tetRm0vRXqXQZ12/sm1YkAR4/n8895G3h59loOHM2lb4sa3HdFQ1KSSv57pMwX0ABDu9Rn6pJtPDgxkw/v60xctH5bxAMfDIftmcX7mtUy4KqxZ91l+PDhrF27lpYtWxIZGUlMTAwJCQmsWLGCVatW0b9/fzZv3kxOTg733HMPt912GwApKSksWLCAw4cPc9VVV3HppZfyxRdfULNmTd577z1iY2O5+eab6d27N1dffTUpKSncdNNNTJkyhdzcXN59910aN27Mrl27uO6669i6dSvt27dn5syZLFy4kKSkpNNmrV27NnfddRcAjzzyCHFxcdxxxx3069ePffv2kZuby+OPP06/fv3O67cqJyeHoUOHsmDBAiIiInjuuee47LLLWLp0KbfccgvHjx+noKCA8ePHU6NGDQYPHkx2djb5+fk8/PDDDBky5LzeT6TMObofNnzmL5pnw961vu1x1aDhlb6CuV4XiE/2MKSURrn5BbyzYDPPf7yaHQePcVmjKvz+ykY0q1HRs0yqFIHoiHDGDmrO1a98wbMzVvJI32ZeRxIpMWPHjiUrK4tFixYxe/ZsevXqRVZWFqmpvh7p48aNo3Llyhw9epQ2bdowaNCgHw0zWb16NW+//TavvfYagwcPZvz48dxwww0/eq+kpCS++eYbXnrpJZ599llef/11Hn30Ubp27cqIESOYPn06f//738+YdciQIdx7773fF9DvvPMOM2bMICYmhokTJ1KhQgV2795Nu3bt6Nu373l113nxxRcxMzIzM1mxYgXdu3dn1apVvPLKK9xzzz1cf/31HD9+nPz8fKZNm0aNGjWYOnUqAAcOHCjy+4iUGfm5kD3/5FnmLQt90/Miy0PKpdDm175lGVUa68I+Oa0TQ1Ce+3AlG/YcoVXdBJ6/5iIuruf9QC0V0H6t6iZwY7u6/HPeBvq2rMHP6iR4HUnKmnOcKS4pbdu2/b54Bnj++eeZOHEiAJs3b2b16tU/KqBTU1Np2bIlAK1atWLDhg2nfe2BAwd+v8+ECRMAmDt37vev36NHDxISzvx376KLLmLnzp1s3bqVXbt2kZCQQO3atcnNzWXkyJHMmTOHsLAwtmzZwo4dO6hWrVqRP/fcuXO5++67AWjcuDF169Zl1apVtG/fnieeeILs7GwGDhxIWloaGRkZ3H///QwbNozevXvTsWPHIr+PSMhyDnatPNkpY8Nc36Q9C4MaP4OO9/vOMtdqAxGBv8hLgtepQ1AaV4vn7ze1pmvjqqWm7bAK6EIe6NGYmct2MHz8Et6/u2NA+gaKlHbly59cSzZ79mw++ugj5s2bR7ly5ejSpQs5OTk/ek509MkONuHh4Rw9evS0r31iv/DwcPLyzjK56yx+/vOf87///Y/t27d/v2zi3//+N7t27WLhwoVERkaSkpJy2pwX4rrrruPiiy9m6tSp9OzZk7/97W907dqVb775hmnTpvHQQw/RrVs3Ro0aVSzvJxJUDu/0FcsnlmUc2urbnpAKzQf7CubUjr42ciJFcKYhKOFhpaNwPkEFdCFx0RE8PiCdX76xgJdnr+Wey9O8jiQScPHx8Rw6dOi0jx04cICEhATKlSvHihUr+PLLL4v9/Tt06MA777zDsGHD+PDDD9m3b99Z9x8yZAi33noru3fv5tNPP/0+Z9WqVYmMjGTWrFls3LjxvHN07NiRf//733Tt2pVVq1axadMmGjVqxLp166hXrx6//e1v2bRpE0uWLKFx48ZUrlyZG264gUqVKvH6669f0GcXCTrHj8CmL04WzDv8HWxiEyC1s29JRr0uai8n560kh6AUBxXQp+jaOJm+LWrwwqzV9MyoRlpyvNeRRAIqMTGRDh06kJ6eTmxsLMnJJy/g6dGjB6+88gpNmjShUaNGtGvXrtjff/To0Vx77bW8+eabtG/fnmrVqhEff+a/d82aNePQoUPUrFmT6tWrA3D99dfTp08fMjIyaN26NY0bNz7vHHfeeSdDhw4lIyODiIgI3njjDaKjo3nnnXd48803iYyMpFq1aowcOZL58+fzwAMPEBYWRmRkJC+//PIFf36RUq0gH7Yt9vdjngWbv4L84xAeBbUvhm6jfGeZq7dQezm5IF4MQSkO5pzzOsN5ad26tVuwYEFA32P34WNc/tyn1K8Sx7u3tyeslP3YQELH8uXLadKkidcxPHXs2DHCw8OJiIhg3rx5DB06lEWLFnkdq1ic7s/XzBY651p7FKnElcQxW4rZvo0nC+b1n/pGZAMkp/vOLte/DOpcAlHlvEwpQe7UISi3dEgtsSEo5+NMx+zSXd57JCkumod7NeX+dxfz76828ov2KV5HEglZmzZtYvDgwRQUFBAVFcVrr73mdSSRsuXoflg/52TRvG+9b3t8dWh41cllGXGakyA/3alDUIa0qc1vu6ZRrWLJDUEpDiqgz2Dgz2oyadEWnp6+km5NkqlRKdbrSCIhKS0tjW+//fYH2/bs2UO3bt1+tO/HH3/8ow4gRZGZmckvfvGLH2yLjo7mq6++Ou/XEgl6ecd97eVOFMxbv/lhe7mLb/cty6jSSO3lpNicOgSlj38ISqoHQ1CKgwroMzAznhyQQfc/z+HhSVm8flPrUtM6RUKLc07fW6dITEws1mUcGRkZJb4sJNiWx0kIcw52rTjZLWPDXMj9ztdermYr6Ph731nmmq3VXk6K3alDULo0qsLvuzcivaZ3Q1CKgwros6hduRz3d2/I41OX8/6SbfRpUcPrSBJiYmJi2LNnD4mJiSqiQ4hzjj179hATE1w/kpQQcmiHr2A+0ZP50Dbf9sr1oMU1voI5pSPEVvIypYSw0jwEpTiogD6HWzqkMmXxVh6ZvJRLGySRUF7/O5fiU6tWLbKzs9m1a5fXUaSYxcTEUKtWLa9jSFlx/DvYOO/ksoydS33bYytDvc4nx2Qn1PUypZQBwTAEpTiogD6H8DDjqYHN6fvCXJ6Ytpxnf97C60gSQiIjI38w9U9EpEgK8mHbopP9mAu3l6vTDrqN9p1lrtYCwkpnH10JPcEyBKU4qIAugqY1KnB753q8OGst/VvW5NK0JK8jiYhIWbNvg79gnuXrmvF9e7mMkxf+1Wmv9nJS4oJtCEpxCFgBbWbjgN7ATudc+mkerwj8P6COP8ezzrl/BCrPT3V31zQ+yNzOiIlLmHFvp1Lf4FtERILc0X2+QvnEWebv28vVgEY9/csyOqu9nHgmWIegFIdAfsI3gBeAf53h8buAZc65PmZWBVhpZv92zh0PYKYLFhMZzlMDMxjy6pf8eeYqHuzV1OtIIiISSvKOQ/bXJ88yb/3W114uKs53wd/Fd/iWZSQ1VHs58dSpQ1Bu71S/VA5BCaSAFdDOuTlmlnK2XYB4860ojwP2AnmBylMcLq6XyHUX1+Hvc9fTp0UNmtfS1csiInKBTrSXO1Ewb/jc314u3NdertMDvrPMtVpDeKTXaUVCZghKcfDyHPsLwGRgKxAPDHHOFXiYp0iGX9WYj5btYNj4TCb/pgOR4aG7vkdERIrZoe0n+zGvmw2Ht/u2JzaAltf6CubUjhAT3D1yJbSE2hCU4uBlAX0lsAjoCtQHZprZZ865g6fuaGa3AbcB1KlTp0RDnqpCTCSP9U/n9jcX8tpn67izSwNP84iISCl2/DvY+MXJs8w7l/m2x1b2tZU7MSa7krf/tomcTqgOQSkOXhbQtwBjnW9c1xozWw80Br4+dUfn3KvAqwCtW7f2fLzXlc2qcVV6Nf7y0WquSq9epv8HJiLBx8x6AH8FwoHXnXNjT7PPYOARfMvtFjvnrvNvrwO8DtT2P9bTObehZJIHgYJ82LoI1n0Ca2f72ssV5EJ4tK+93OWP+M4yV2uu9nJSaoX6EJTi4GUBvQnoBnxmZslAI2Cdh3nOy6P9mvH5mt0MH7+Et29tR1gI9jgUkdBjZuHAi8AVQDYw38wmO+eWFdonDRgBdHDO7TOzwm0e/gU84ZybaWZxQKlfehdwe9efHGCyfg7k7Pdtr5YB7Yb6zjLXaQ+Rsd7mFDmHsjIEpTgEso3d20AXIMnMsoHRQCSAc+4V4DHgDTPLBAwY5pzbHag8xa1qfAwP9mrCsPGZ/HfBZq5tqx+/iUhQaAuscc6tAzCz/wD9gGWF9rkVeNE5tw/AObfTv29TIMI5N9O//XBJBi81juz1FconxmTv2+DbXqEmNO7tK5hTO0NcFS9TipyXsjQEpTgEsgvHted4fCvQPVDvXxIGt67NpG+38uS05XRtXJXkCmXvKlQRCTo1gc2F7mcDF5+yT0MAM/sc3zKPR5xz0/3b95vZBCAV+AgY7pzLD3hqL+Udg81fnzzLvG2Rv71cvO+Cv3Z3+pZlJKWpvZwEnVOHoIzp14xrQnwISnEI/U7XAWRmPDkwgx5/mcPo95byyi9aeR1JRKQ4RABp+H6KWAuYY2YZ/u0dgYvwLcP7L3Az8PfCTy5NF35fEOd8F/ud6Jax8XPIPeJrL1erNXT6g+8sc81Wai8nQassD0EpDvpd+olSk8pz7+UNeXr6CqZnbaNHenWvI4mInM0WfBcAnlDLv62wbOAr51wusN7MVuErqLOBRYWWf0wC2nFKAV3aLvwukoPbfAXziWUZh3f4tiemQcvrfQVzyqVqLydBT0NQiocK6GLw646pTFm8lVHvLaV9/SQqxuqMhIiUWvOBNDNLxVc4XwNcd8o+k4BrgX+YWRK+pRvrgP1AJTOr4pzbha8N6YISS16cjh32tZc7sSxj13Lf9nKJvrZy9U60l6t95tcQCSIaglK8VEAXg8jwMP54dXP6vfg5Yz9YzlMDm3sdSUTktJxzeWb2G2AGvvXN45xzS81sDLDAOTfZ/1h3M1sG5AMPOOf2AJjZ74GP/VNkFwKvefJBzldBvm809ol+zJu/Ptlerm57aHGN7yxzcobay0lI0RCUwFABXUzSa1bk15em8rc56+jboibt66tXooiUTs65acC0U7aNKvS1A+7z30597kwgOM4S7F13smBePwdyDvi2V2sO7f0X/tVpp/ZyEpI0BCWwVEAXo3svb8gHWdsZOTGTD+7pSExkuNeRRETKjiN7Yf2nJy/+27/Rt71CLWjS5+SyjPJJHoYUCSwNQSkZKqCLUWxUOE8NzOD617/i+Y9X84cejb2OJCISuvKO+Sb9nTjLvHUR4E62l2v/G9+yjMQGai8nIU9DUEqWCuhi1qFBEj9vVYu/zVlH7+Y1aFqjgteRRERCg3OwY+nJbhkbvyjUXq4NdBnuO8tcsxWE6583KTs0BKXk6QgTAA/2asKslbsYNn4JE++8hIhwXZAiInJBDm472Slj3Wz4bqdve2IaXHSDr2BOuRRidLJCyh4NQfGOCugAqFQuikf7NuOut77hH59v4NZO9byOJCISPLIXQua7vsJ51wrftnJJvvXL9f3rmCvW8i6fiMc0BMV7+p0OkJ4Z1bi8STJ/mrmSK5tVo05iOa8jiYgEh01fwMJ/QJ320PI631nm5HS1l5My79QhKLd1qsfQzvU1BMUDKqADxMx4rH8zrnhuDiMnZvLmr9pqEb+ISFG0uhna3AqRGvAgAhqCUhqpgA6g6hVjGXZVYx6elMX4b7ZwdSv9yFFE5Jyi471OIFIqaAhK6aUCOsCub1uHyYu28Nj7y+jcsApV4qO9jiQiIiKlmIaglH5aUBZgYWHGUwObc/R4Po9OWep1HBERESmlCgockxdv5YrnPuXBiVnUSijHf29rxxu3tFXxXMroDHQJaFA1jru7NuBPM1fRv+UOLm+a7HUkERERKSU0BCX4qIAuIbd3rs/7S7bx8HtZXFyvMvExkV5HEhEREY9pCEpw0hKOEhIVEcbYQRlsP5jDMzNWeh1HREREPLRi+0F+/c/5DHp5Hut2fceYfs34+L4u9L+opornIKAz0CXoojoJ3HxJCm98sYG+LWrQOqWy15FERESkBGkISmjQn1YJ+333Rny4dAfDxi9h2j0diY4I9zqSiIiIBJiGoIQWFdAlrHx0BE8MSOfmf8znxVlrue+Khl5HEhERkQDREJTQpALaA10aVWXARTV5efYaemVUp1E1DQ0QEREJJRqCEtpUQHvk4d5N+XTVLoaNX8L4oZfoggEREZEQoCEoZYMKaI9ULh/FqN5Nufe/i3hz3gZu7pDqdSQRERG5QAUFjvczt/HchyvZsOcIreom8Pw1F3FxvUSvo0kAqID2UL+WNZi0aAt/nLGSy5smUyuhnNeRRERE5DycOgSlUXI8r9/Ymm5NNAQllKkPtIfMjMf7pwPw0KQsnHMeJxIREZGiWrhxL0Ne/ZJb/jGfQ8dy+fOQFky7pyOXN01W8RzidAbaY7USyvHAlY14dMoyJi/eSr+WNb2OJCIiImexYvtBnp2xko+W7yQpLpox/ZpxTZs6REXovGRZoQK6FLixfQrvLdrKo1OW0TGtCpXLqyekiIhIaaMhKHKC/sRLgfAw4+lBzen1/Gc8/v4ynhvS0utIIiIi4qchKHIqFdClRKNq8dzZpT7Pf7KGvi1r0KVRVa8jiYiIlGkagiJnogK6FLmrawOmZm7jwYlZfPi7TpSP1h+PiIhISdMQFDkXVWilSHREOE8Pas7Vr8zjT3wldIYAACAASURBVB+uYlSfpl5HEhERKTM0BEWKSgV0KdM6pTK/aFeXf3yxnj4tqnNRnQSvI4mIiIQ0DUGR86UCuhT6Q49GzFy2gxETMpn8m0vVFkdERCQANARFLpQqs1IoPiaSx/qns2L7IV6ds9brOCIiIiFHQ1Dkp9AZ6FLqiqbJ9Gpenec/XkOP9Oo0qBrndSQREZGgpyEoUhwCVkCb2TigN7DTOZd+mscfAK4vlKMJUMU5tzdQmYLNI32aMXf1bkZMWMJ/b2tPWJj+RywiInIhNARFilMgv2veAF4A/nW6B51zzwDPAJhZH+B3Kp5/qEp8NA/1asID/1vCW19v4oZ2db2OJCIiElQ0BEUCIWAFtHNujpmlFHH3a4G3A5UlmF3dqhaTFm1h7AcruLxJspq3i4iIFIGGoEggeb7gx8zKAT2A8V5nKY3MjCcHZJBXUMBDk7JwznkdSUSCnJn1MLOVZrbGzIafYZ/BZrbMzJaa2VunPFbBzLLN7IWSSSxSdEeP5/PKp2vp9MdZvDhrLZc3Teaj+zrz5IAMFc9SbErDwp8+wOdnW75hZrcBtwHUqVOnpHKVGnUTy3PfFQ15ctoKpmVup1fz6l5HEpEgZWbhwIvAFUA2MN/MJjvnlhXaJw0YAXRwzu0zs6qnvMxjwJySyixSFBqCIiWpNBTQ13CO5RvOuVeBVwFat25dJk/B/rJDKlMWb2P05Cw6NEjU2i0RuVBtgTXOuXUAZvYfoB+wrNA+twIvOuf2ATjndp54wMxaAcnAdKB1SYUWORMNQREveLqEw8wqAp2B97zMEQwiwsMYOyiDfUdyeXLacq/jiEjwqglsLnQ/27+tsIZAQzP73My+NLMeAGYWBvwJ+H2JJBU5hzmrdtH7/+by27e/JToinNdvbM3/7miv4lkCLpBt7N4GugBJZpYNjAYiAZxzr/h3GwB86Jz7LlA5QkmzGhW5rVM9Xp69ln4ta9KhQZLXkUQkNEUAafiO4bWAOWaWAdwATHPOZZ9t0ERZX3YnJWPOql3cOO5raleO5c9DWtC3RU3C1e5VSkggu3BcW4R93sDX7k6K6J5uaXyQuY2REzOZfk8nYqPCvY4kIsFlC1C70P1a/m2FZQNfOedygfVmtgpfQd0e6GhmdwJxQJSZHXbO/eBCRC27k0A7cjyPkRMzqZdUnmn3dCQmUv8WSsnyvAuHnJ+YyHCeHJjBxj1H+MtHq7yOIyLBZz6QZmapZhaF7zqUyafsMwnf2WfMLAnfko51zrnrnXN1nHMp+JZx/OvU4lmkJDz34Sqy9x3lqYEZKp7FEyqgg9Al9ZO4pk1tXvtsHVlbDngdR0SCiHMuD/gNMANYDrzjnFtqZmPMrK9/txnAHjNbBswCHnDO7fEmscgPLd68n3Gfr+e6i+torbN4xoKtr3Dr1q3dggULvI7huQNHc7n8uU+pEhfNe7/pQGS4/i8kEgzMbKFzrsx0r9AxW4pTbn4BfV/4nD2Hj/HR/Z2pEBPpdSQJcWc6ZqvqClIVYyN5rF8zlm07yN/nrvc6joiISMC99tk6lm87yJh+6SqexVMqoINYj/TqXNksmT/PXMWG3WpkIiIioWv97u/4y0erubJZMj3Sq3kdR8o4FdBBbky/dKLCwxgxIVNjvkVEJCQ55xgxYQnR4WGM6ZfudRwRFdDBLrlCDCN6NmHeuj28s2DzuZ8gIiISZN5ZsJkv1+1lRM8mJFeI8TqOiAroUHBNm9q0Ta3ME1OXs/NgjtdxREREis3OQzk8MXU5bVMqc02b2ud+gkgJUAEdAsLCjLEDM8jJK+CRKUu9jiMiIlJsHp28jJzcAp4alEGYJg1KKaECOkTUqxLHPd3SmJa5nRlLt3sdR0RE5CebuWwHUzO3cXfXBtSvEud1HJHvqYAOIbd1qkfjavGMei+Lgzm5XscRERG5YIdycnl4UhaNkuO5vXN9r+OI/IAK6BASGR7G04Oas+vQMcZ+sMLrOCIiIhfsj9NXsuNQDmMHZRAVoXJFShd9R4aYFrUr8csOqbz11Sa+WqfJuyIiEnwWbNjL//tqIze1T+GiOglexxH5ERXQIei+7g2pXTmWERMyycnN9zqOiIhIkR3Ly2f4hExqVIzl91c28jqOyGmpgA5B5aIieHJABut2f8cLn6zxOo6IiEiRvTRrLWt2HubxAenERUd4HUfktFRAh6iOaVUY9LNavPLpWpZvO+h1HBERkXNaveMQL81eQ98WNbisUVWv44ickQroEPZQryZUjI1k+Pgl5BdozLeIiJReBQWO4RMyKR8dwag+Tb2OI3JWKqBDWEL5KEb3bcbi7AP84/P1XscRERE5o39/tZGFG/fxUK+mJMVFex1H5KxUQIe4Ps2r07VxVf704So27z3idRwREZEf2br/KE9PX8mlDZIY9LOaXscROScV0CHOzHi8fzphBiMnZuKclnKIiEjp4Zxj1HtZ5BUU8OSADMw0rltKPxXQZUCNSrEMu6oxn63ezcRvt3gdR0RE5HvTMrfz0fKd3HdFQ+oklvM6jkiRqIAuI264uC4/q1OJMe8vY/fhY17HERERYf+R44yenEV6zQr8skOq13FEikwFdBkRFmY8Pag53x3LY8yUZV7HERER4clpy9l3JJexA5sTEa6SRIKHvlvLkLTkeO66rAGTF2/lkxU7vI4jIiJl2BdrdvPOgmx+3TGV9JoVvY4jcl5UQJcxQ7vUJ61qHA9NzOLwsTyv44iISBmUk5vPiImZ1E0sx73dGnodR+S8qYAuY6Ijwhk7qDnbDubw7IyVXscREZEy6K8fr2bjniM8OSCD2Khwr+OInDcV0GVQq7oJ3NQ+hX/O28DCjfu8jiMiImXI0q0HeHXOOn7eqhYdGiR5HUfkgqiALqN+f2UjqleIYfj4JRzLy/c6joiIlAF5+QUMH59JQrlIHuzVxOs4IhdMBXQZFRcdweMD0lm98zAvz17rdRwRESkD3vhiA5lbDjC6TzMqlYvyOo7IBVMBXYZ1bZxM3xY1eHHWGlbvOOR1HBERCWGb9x7hTx+uolvjqvRuXt3rOCI/iQroMm50n6bERUcwbPwSCgo05ltERIqfc46REzMJM3isf7rGdUvQUwFdxiXGRfNw76Z8s2k//++rjV7HERGREDTx2y18tno3f+jRmBqVYr2OI/KTqYAWBlxUk45pSTz9wQq27j/qdRwREQkhew4f47H3l/GzOpW4oV1dr+OIFAsV0IKZ8eSADAocPDQpC+e0lENERIrHY+8v4/CxPMYOak54mJZuSGhQAS0A1K5cjvu7N+STFTuZsmSb13FERCQEzFq5k0mLtjK0SwMaJsd7HUek2KiAlu/d0iGVFrUq8ujkpez77rjXcUREJIh9dyyPhyZmUb9Kee66rL7XcUSKVcAKaDMbZ2Y7zSzrLPt0MbNFZrbUzD4NVBYpmvAwY+yg5hw4msvjU5d7HUdEAsTMepjZSjNbY2bDz7DPYDNb5j8+v+Xf1tLM5vm3LTGzISWbXILJnz5cxZb9Rxk7qDnRERrXLaElkGeg3wB6nOlBM6sEvAT0dc41A34ewCxSRE2qV+COzvUZ/002n63e5XUcESlmZhYOvAhcBTQFrjWzpqfskwaMADr4j8/3+h86Atzo39YD+Iv/WC7yA4s27+cfX6znhnZ1aJNS2es4IsUuYAW0c24OsPcsu1wHTHDObfLvvzNQWeT8/KZrA+ollWfEhEyOHM/zOo6IFK+2wBrn3Drn3HHgP0C/U/a5FXjRObcPTh6fnXOrnHOr/V9vBXYCVUosuQSF3PwCho9fQtX4aP7Qo7HXcUQCwss10A2BBDObbWYLzexGD7NIITGR4Tw1MIPsfUd57sNVXscRkeJVE9hc6H62f1thDYGGZva5mX1pZj/6aaKZtQWigLUBSypB6dU561ix/RCP9UunQkyk13FEAsLLAjoCaAX0Aq4EHjazhqfb0cxuM7MFZrZg1y4tKygJF9dL5LqL6zDu8/Us3rzf6zgiUrIigDSgC3At8FrhpRpmVh14E7jFOVdw6pN1zC671u06zF8/Xs1V6dXo3qya13FEAsbLAjobmOGc+845txuYA7Q43Y7OuVedc62dc62rVNFPC0vK8KsaUyU+mmHjl5Cb/6N/I0UkOG0Bahe6X8u/rbBsYLJzLtc5tx5Yha+gxswqAFOBB51zX57uDXTMLpsKChwjJmQSHRHGo32beR1HJKC8LKDfAy41swgzKwdcDKj1QylSISaSx/qls2L7IV6ds87rOCJSPOYDaWaWamZRwDXA5FP2mYTv7DNmloRvScc6//4TgX855/5XcpElGPx3wWa+Wr+XB3s2oWqFGK/jiARUINvYvQ3MAxqZWbaZ/crM7jCzOwCcc8uB6cAS4GvgdefcGVveiTe6N6tGz4xq/PXj1azdddjrOCLyEznn8oDfADPwnbR4xzm31MzGmFlf/24zgD1mtgyYBTzgnNsDDAY6ATf7W5AuMrOWHnwMKWV2HszhyWnLuTi1MkPa1D73E0SCnAXb2ObWrVu7BQsWeB2jTNl5KIfL//QpjatX4D+3tiNMo1hFLpiZLXTOtfY6R0nRMbtsGPr/FvLxip1Mv6cj9arEeR1HpNic6ZitSYRyTlXjY3iwVxO+Xr+X/8zffO4niIhImTFj6XY+yNrOPd3SVDxLmaECWopkcOvatK+XyFPTlrPjYI7XcUTKPDPrY2Y6hounDubkMuq9LBpXi+e2TvW8jiNSYnTwlSIxM54amMHx/AJGvael6iKlwBBgtZn90cw0rUI88fQHK9h16BhjBzUnMlwlhZQd+m6XIktJKs/vrmjIjKU7mJ61zes4ImWac+4G4CJ8g0zeMLN5/v7L8R5HkzJi/oa9/PurTdx8SSota2uiu5QtKqDlvPz60lSa1ajAw+8t5cCRXK/jiJRpzrmDwP/wjeOuDgwAvjGzuz0NJiEvJzef4eOXULNSLPd3P+0MNJGQpgJazktEeBhPD2rO3u+O89QHatst4hUz62tmE4HZQCTQ1jl3Fb6BVPd7mU1C30uz1rB213c8MSCd8tERXscRKXEqoOW8pdesyK8vTeU/8zfzxdrdXscRKasGAX92zmU4555xzu0EcM4dAX7lbTQJZSu3H+LlT9fSv2UNujSq6nUcEU+ogJYLcu/lDambWI6REzLJyc33Oo5IWfQIviFUAJhZrJmlADjnPvYmkoS6/ALH8AlLiIuO4OHeTb2OI+IZFdByQWKjwnlqQAYb9hzhrx+v9jqOSFn0LlBQ6H6+f5tIwPy/Lzfy7ab9PNy7KYlx0V7HEfGMCmi5YJc0SGJw61q8OmcdWVsOeB1HpKyJcM4dP3HH/3WUh3kkxG3Zf5Q/Tl9Bx7QkBlxU0+s4Ip5SAS0/ycieTUgoF8XwCUvIyy849xNEpLjsMrO+J+6YWT9AFyVIQDjneHhSFgUOnhyQgZl5HUnEUyqg5SepVC6KR/s2I2vLQcZ9vt7rOCJlyR3ASDPbZGabgWHA7R5nkhD1/pJtfLJiJ/d3b0jtyuW8jiPiOfWekZ+sZ0Y1Lm+SzHMzV3Fls2rUTSzvdSSRkOecWwu0M7M4//3DHkeSELX/yHEenbKU5rUqcvMlKV7HESkVinQG2szKm1mY/+uG/v6jkYGNJsHCzHi8fzqRYWGMnJiJc87rSCJlgpn1Au4E7jOzUWY2yutMEnqemLqcfUdyGTuwOREa1y0CFH0JxxwgxsxqAh8CvwDeCFQoCT7VKsYw7KrGfL5mD/9bmO11HJGQZ2avAEOAuwEDfg7U9TSUhJy5q3fz7sJsbutUj6Y1KngdR6TUKGoBbf7m/AOBl5xzPweaBS6WBKPr2tahTUoCj09dzq5Dx7yOIxLqLnHO3Qjsc849CrQHNFNZis3R4/mMnJhJSmI57umW5nUckVKlyAW0mbUHrgem+reFByaSBKuwMOOpgc05ejyfR6Ys9TqOSKjL8f96xMxqALlAdQ/zSIj5y8er2LT3CE8OzCAmUv/kixRW1AL6XmAEMNE5t9TM6gGzAhdLglWDqnHc3bUBU5dsY+ayHV7HEQllU8ysEvAM8A2wAXjL00QSMrK2HOD1z9YzpHVtLqmf5HUckVKnSF04nHOfAp8C+C8m3O2c+20gg0nwur1zfaZmbuPhSVm0q1eZ+BhdbypSnPzH4Y+dc/uB8Wb2PhDjnNNEI/nJ8vILGDZ+CQnlohjZs4nXcURKpaJ24XjLzCqYWXkgC1hmZg8ENpoEq6iIMMYOas6OQzn8cfpKr+OIhBznXAHwYqH7x1Q8S3EZ9/l6lm49yKN9m1GxnE6AiJxOUZdwNHXOHQT6Ax8Aqfg6cYicVsvalbjlklTe/HIj8zfs9TqOSCj62MwGmUbCSTHatOcIz81cxeVNkumZUc3rOCKlVlEL6Eh/3+f+wGTnXC6gZr9yVvd3b0jNSrEMH7+EnNx8r+OIhJrbgXeBY2Z20MwOmdlBr0NJ8HLOMXJiJhFhYTzWv5nGdYucRVEL6L/hu0ClPDDHzOoCOlDLWZWPjuCJAems3fUdL81a43UckZDinIt3zoU556KccxX899WoVy7Y+G+2MHfNbob1aET1irFexxEp1Yp6EeHzwPOFNm00s8sCE0lCSZdGVRlwUU1emr2Wns2r07ia/n0XKQ5m1ul0251zc0o6iwS/3YeP8fjUZbSqm8D1F2sej8i5FKmANrOKwGjgxAH7U2AMoItW5Jwe7t2UT1ftYvj4TMYPvYTwMP1YUKQYFL6QOwZoCywEunoTR4LZmCnL+O5YHmMHZhCmY7TIORV1Ccc44BAw2H87CPwjUKEktFQuH8XoPk1ZtHk///xig9dxREKCc65PodsVQDqwz+tcEnxmrdjJ5MVbueuyBqQlx3sdRyQoFLWAru+cG+2cW+e/PQrUC2QwCS19W9SgS6MqPPvhSjbvPeJ1HJFQlA2oaa+cl8PH8nhwYiYNqsYxtEt9r+OIBI2iFtBHzezSE3fMrANwNDCRJBSZGY/3TwfgwUlZOKcmLiI/hZn9n5k977+9AHyGbyKhSJE9O2Ml2w7m8PSgDKIjNK5bpKiKtAYauAP4l38tNPh+THhTYCJJqKqVUI4HrmzEo1OW8d6irfS/qKbXkUSC2YJCX+cBbzvnPvcqjASfbzbt45/zNvCLdnVpVbey13FEgkpRu3AsBlqYWQX//YNmdi+wJJDhJPTc2D6F9xZt5dEpS+mYlkRiXLTXkUSC1f+AHOdcPoCZhZtZOeec1kjJOR3PK2DE+EyS42N44MpGXscRCTpFXcIB+Apn/0RCgPsCkEdCXHiY8cerm3P4WB6PT13udRyRYPYxULhZbyzwkUdZJMj87dO1rNxxiMf6pxMfo3HdIufrvAroU6jPjVyQhsnxDO3SgInfbmH2yp1exxEJVjHOucMn7vi/LudhHgkSa3Ye5v8+WUOvjOpc0TTZ6zgiQemnFNC6Ckwu2F2X1ad+lfI8ODGL747leR1HJBh9Z2Y/O3HHzFqhi7vlHAoKHCMnZBITGcbovk29jiMStM5aQJvZITM7eJrbIaBGCWWUEBQdEc7Tg5qzZf9Rnv1wpddxRILRvcC7ZvaZmc0F/gv8xuNMUsq9PX8TX2/Yy0O9mlI1PsbrOCJB66wFtHMu3jlX4TS3eOdcUTt4iJxW65TK/KJdXd74YgPfbtL8B5Hz4ZybDzQGhuLrlNTEObewKM81sx5mttLM1pjZ8DPsM9jMlpnZUjN7q9D2m8xstf+mbkxBZMfBHMZOW0H7eon8vHUtr+OIBLWfsoRD5Cf7Q49GVKsQw/DxmRzPK/A6jkjQMLO7gPLOuSznXBYQZ2Z3FuF54cCLwFVAU+BaM2t6yj5pwAigg3OuGb6z3ZhZZWA0cDG+0eGjzSyhGD+WBNCo97I4nl/AkwMzMNNlTCI/RcAKaDMbZ2Y7zSzrDI93MbMDZrbIfxsVqCxSesXHRPJ4/3RW7jjEK5+u9TqOSDC51Tm3/8Qd59w+4NYiPK8tsMY/VfY48B+g36mvDbzof02ccyeu9r0SmOmc2+t/bCbQ4yd+DikB07O2MWPpDu69vCGpSeW9jiMS9AJ5BvoNzn1g/cw519J/GxPALFKKdWuSTO/m1XnhkzWs2XnI6zgiwSLcCp1G9J9ZjirC82oCmwvdz/ZvK6wh0NDMPjezL82sx3k8V0qZA0dzGfXeUppUr8CvO6Z6HUckJASsgHbOzQH2Bur1JbSM7tOM2Khwho/PpKBADV5EimA68F8z62Zm3YC3gQ+K6bUjgDSgC3At8JqZVSrqk83sNjNbYGYLdu3aVUyR5EKN/WAFuw8f4+lBGUSGa+WmSHHw+m9SezNbbGYfmFkzj7OIh6rER/NQryYs2LiPf3+9yes4IsFgGPAJvgsI7wAy+eFglTPZAtQudL+Wf1th2cBk51yuc249sApfQV2U5+Kce9U519o517pKlSpF/DgSCF+t28PbX2/ilx1SaV6ryP8HEpFz8LKA/gao65xrAfwfMOlMO+psRtlwdataXNogiac/WMG2A2pnK3I2zrkC4CtgA751zV2Booz3nA+kmVmqmUUB1wCTT9lnEr6zz5hZEr4lHeuAGUB3M0vwXzzY3b9NSqGc3HxGTMikVkIs93Vv6HUckZDiWQHtHwt+2P/1NCDSf6A+3b46m1EGmBlPDsggr6CAhydl4ZyWcoicyswamtloM1uB7+TDJgDn3GXOuRfO9XznXB6+ftEz8BXc7zjnlprZGDPr699tBrDHzJYBs4AHnHN7nHN7gcfwFeHzgTH+bVIKvfDJGtbt/o4nB2RQLkqdZ0WKk2d/o8ysGrDDOefMrC2+Yn6PV3mkdKiTWI77r2jEE9OWMzVzG72ba16PyClWAJ8BvZ1zawDM7Hfn8wL+kxbTTtk2qtDXDrjPfzv1ueOAcecfW0rSiu0HeeXTtQy8qCadGurEk0hxC2Qbu7eBeUAjM8s2s1+Z2R1mdod/l6uBLDNbDDwPXON0ylGAWzqkkFGzIo9MXsr+I8e9jiNS2gwEtgGzzOw1/wWEauor38svcAwbn0mF2Ege6q1x3SKBEMguHNc656o75yKdc7Wcc393zr3inHvF//gLzrlmzrkWzrl2zrkvApVFgktEeBhjB2Ww70guT0wtypJOkbLDOTfJOXcNvimEs/ANOalqZi+bWXdv00lp8K95G1i8eT+jejelcvmidDYUkfPldRcOkdNqVqMit3Wqx7sLs5m7erfXcURKHefcd865t5xzffB1w/gWX2cOKcOy9x3hmRkr6dywCv1aagmcSKCogJZS655uaaQmlWfkxEyOHs/3Oo5IqeWc2+e/2Lqb11nEO845HprkG/77xIB0jesWCSAV0FJqxUSG89TADDbtPcKfP1rldRwRkVJt8uKtzF65i/u7N6JWQjmv44iENBXQUqq1q5fItW1r8/pn68jMPuB1HBGRUmnfd8cZM2UZLWpV5OZLUryOIxLyVEBLqTf8qiYkxkUzbPwScvMLvI4jIlLqPD51OQeO5jJ2UHPCw7R0QyTQVEBLqVcxNpLH+jVj2baDvP7Zeq/jiIiUKp+t3sX4b7K5vXM9mlSv4HUckTJBBbQEhR7p1bmyWTJ/+WgV63d/53UcEZFS4cjxPEZOzKReUnnu7prmdRyRMkMFtASNMf3SiYoIY8SEJRrzLSIC/OWj1Wzee5QnB2YQExnudRyRMkMFtASN5AoxjOzZhC/X7eW/8zd7HUdExFOZ2Qd4/bN1XNu2Nu3qJXodR6RMUQEtQWVI69pcnFqZJ6YtZ+fBHK/jiIh4Ije/gGHjl5AYF83wq5p4HUekzFEBLUElLMx4amAGx/IKGD15qddxREQ88fe561m27SBj+jajYmyk13FEyhwV0BJ06lWJ455uaXyQtZ3pWdu9jiMiUqI27P6OP89cRfemyfRIr+Z1HJEySQW0BKXbOvnaNY16L4sDR3O9jiMiUiKcc4ycmElUeBhj+mlct4hXVEBLUIoMD+PpQRnsPnyMsR+s8DqOiEiJeHdhNl+s3cOwqxpTrWKM13FEyiwV0BK0mteqxK8uTeXtrzfx5bo9XscREQmoXYeO8cTU5bRJSeC6tnW8jiNSpqmAlqD2uysaUrtyLCMmZJKTm+91HBGRgHl0ylKOHs/nqYHNCdO4bhFPqYCWoFYuKoInB2Swfvd3/N8nq72OIyISEB8v38H7S7bxm64NaFA1zus4ImWeCmgJeh3TqjDoZ7X426frWLb1oNdxRESK1eFjeTw0KYuGyXHc0bm+13FEBBXQEiIe6tWESuUiGT5hCXn5BV7HEREpNs9MX8H2gzk8NbA5URH6Z1ukNNDfRAkJCeWjGN2nGUuyD/DGFxu8jiMiUiwWbtzHv77cyE3tU2hVN8HrOCLipwJaQkbv5tXp1rgqz364kk17jngdR0TkJzmeV8Dw8UuoXiGG31/ZyOs4IlKICmgJGWbGY/3TCTfjwUmZOOe8jiQicsFenr2W1TsP81j/dOKiI7yOIyKFqICWkFKjUizDrmrMZ6t3M+GbLV7HERG5IGt2HuLFWWvo06IG3Zokex1HRE6hAlpCzg0X16VV3QQem7qM3YePeR1HROS8FBQ4ho/PJDYqnFG9m3odR0ROQwW0hJywMGPswAyOHMvn0SnLvI4jInJe/v31JhZs3MdDvZpQJT7a6zgichoqoCUkpSXHc9dlDZiyeCsfL9/hdRwRkSLZfiCHpz9YQYcGiVzdqpbXcUTkDFRAS8ga2qU+DZPjeGhSFoeP5XkdR0TkrJxzPPxeFrn5BTw5IAMzjesWKa1UQEvIiooI46mBzdl+MIdnpq/wOo6IyFlNz9rOzGU7uO+KhtRNLO91HBE5CxXQEtJa1U3gpvYp/OvLjSzcuNfrOCL/v737jqu63h84/vqwQRBZLhwguHLnHimYlqZladq+aes62veWNm42vPd6W7f8FZaa2dTKUVld9ktZxQAAIABJREFUN7itcIKLJQpORFFRQcbn98f3KEdky+F7gPfz8fg+/J7vfJ8vxw9vPuczhCjSmQs5vPbzbto1rsuj/YLNDkcIUQpJoEWN9/dbW9PY253Ji2LIzs0zOxwhhLjGv/+3l1PnL/GfUR1xcpRfzULYO/lfKmo8T1cnpt3VnoQTmUREJpodjhBCXGVzYjoL/kzh0X7BtA/0NjscIUQZSAItaoXw1vUZ0bkxEVEJxB0/Z3Y4QggBQFZOHi8viaGZrwfPDWpldjhCiDKSBFrUGq8NvwFPVycmL9pFXr5M8y1qL6XUEKXUfqVUglJqShH7xyql0pRSOyzLY1b73lZK7VZK7VVKzVAyVMR1mbE6ngMnz/Ovuzrg7uJodjhCiDKSBFrUGn6ervxj+A1sP5TB11sOmh2OEKZQSjkCHwNDgRuA+5RSRU13953WurNlmWM5tw/QF+gItAe6AwOqJvKaZ8+Rs8xal8SoG5vQr6W/2eEIIcpBEmhRq9zVJZCbWvrz9rJ9HM64aHY4QpihB5CgtU7SWl8CFgAjyniuBtwAF8AVcAZkpqIKyMvXvLR4F97uzrw6rK3Z4QghykkSaFGrKKX4110dyNfw6pIYtJamHKLWCQRSrF6nWrYVNkoptUsptVAp1RRAa70ZiASOWpblWuu9hU9USj2hlIpWSkWnpaVV/juoAeZtSmZn6hleu/0GfOq4mB2OEKKcbJZAK6XmKqVOKKViSzmuu1IqVyl1t61iEcJaU18P/n5rayL3p/HzziNmhyOEPVoKBGmtOwIrgS8AlFKhQFugCUbSPVApdVPhk7XWs7TW3bTW3QICAqow7Ooh5dQF3l2+n/DWAdzRqbHZ4QghKsCWNdDzgCElHWBpi/cfYIUN4xDiGmP7BNGpiTdvLN3DqfOXzA5HiKp0GGhq9bqJZdsVWut0rXW25eUcoKtl/S5gi9Y6U2udCfwP6G3jeGsUrTWv/BiLUjBNpusWotqyWQKttV4HlDb121PAIuCEreIQoiiODorpozpy9mIO037dY3Y4QlSlP4GWSqlgpZQLcC/ws/UBSqlGVi/vAC430zgEDFBKOSmlnDE6EF7ThEMU76cdR1gXl8YLt7YmsJ672eEIISrItDbQSqlAjNqMmWU4VtrTiUrXtlFdxg8IYfG2w6yLk8+VqB201rnAk8ByjOT3e631bqXUm0qpOyyHPW0Zqm4n8DQw1rJ9IZAIxAA7gZ1a66VV+gaqsVPnL/HmL3vo3LQef+kdZHY4Qojr4GTivT8AJmut80v7CktrPQuYBdCtWzfp9SUqzZMDQ/kt9igvL4lh+bP9qeNq5n8JIaqG1vo34LdC216zWn8JeKmI8/KAv9o8wBpq2i97OHsxh/+M6oijgzTdEKI6M3MUjm7AAqVUMnA3EKGUutPEeEQt5ObsyPSRHUk9fZH3V8aZHY4QooZaG5fG4u2HmRAWQuuGXmaHI4S4TqYl0FrrYK11kNY6CONrwYla6x/NikfUXj2CfXmgZzM+33iAHSkZZocjhKhhLlzK5ZUlMbQIqMOk8FCzwxFCVAJbDmM3H9gMtFZKpSqlHlVKjVdKjbfVPYWoqMlD2xDg5crkhbs4mH7e7HCEEDXI+yviSD19kekjO+LmLNN1C1ET2KzBp9b6vnIcO9ZWcQhRFnXdnJk+qiN//Wor4e9GcXunxkwIC6FNw7pmhyaEqMZ2pmQwd+MB7u/ZjB7BvmaHI4SoJDIToRAW4a3rs+HFcB67qQUr9xxnyAfreeyLaLYfOm12aEKIaignL58pi2Pw93RlytA2ZocjhKhEkkALYaV+XTdevq0tm6YM5NlBLfkz+RR3RWzigTlb2JRwUqb+FkKU2ez1Sew9epY3R7Snrpuz2eEIISqRJNBCFKGehwvPDmrFxikDeeW2tsQfz+T+Ob9zV8QmVu45Tn6+JNJCiOIdOHmeD1bFM6RdQ4a0b2h2OEKISiYJtBAl8HR14vH+LVj3Yjj/vKs96eezefzLaIZ+uJ6fdhwmNy/f7BCFEHZGa81Li3fh6uTAGyPamR2OEMIGJIEWogzcnB15oGdzIv8Wxgf3dEajeWbBDga+t5Zvfz9Edm6e2SEKIezE99EpbEk6xUtD29KgrpvZ4QghbEASaCHKwcnRgTu7BLLsmf7MeqgrPh7OvLwkhv5vRzJnfRLns3PNDlEIYaIT57L456976RHsy73dm5odjhDCRiSBFqICHBwUt7RryI+T+vLNYz0JCfBk2q976fufNXy4Kp6MC5fMDlEIYYI3ft5DVm4+/x7ZAQeZrluIGstm40ALURsopegb6k/fUH+2HTpNRGQC/10Vx6x1iTzYqzmP9gumvnyFK0StsHLPcX6NOcrfb2lFSICn2eEIIWxIEmghKsmNzXyY83B39h49y8yoRGavT+LzTcmM6daEv/YPoamvh9khCiFs5FxWDv/4MZbWDbx4on+I2eEIIWxMmnAIUcnaNqrLjPu6sOZvYYy6MZDv/kwh7N0onv9uB/HHz5kdnhDCBt5etp/j57KYPqoDLk7yq1WImk7+lwthI0H+dfj3yI6sezGcsX2C+F/sMQb/dx1//SqaXakZZocnhKgk0cmn+GrLQcb2CaJLMx+zwxFCVAFpwiGEjTXyducfw29gUngo8zYeYN6mZJbvPs5NLf2ZFB5Kz2BflJLORkJUR9m5eUxZHENgPXf+fktrs8MRQlQRqYEWoor41nHh+Vtas3HKQKYMbcPeo+e4d9YW7v5kM2v2HZdpwoWohiIiE0k4kcm0u9pTx1XqpISoLSSBFqKKebk5M35ACBsmh/PWiHYcO5PFI/OiuW3GBpbuPEKeTBMuRLUQf/wcEVEJjOjcmPDW9c0ORwhRhSSBFsIkbs6OPNQ7iKgXwnhvdCcu5ebx1PztDHp/Ld/9eYhLuTJNuBD2Kj9fM3nRLuq4OvGP4TeYHY4QoopJAi2EyZwdHRjVtQkrnxvAJw/eSB1XRyYvimHAO5HM3XCAC5dkdkMh7M3Xvx9k26EM/jHsBvw9Xc0ORwhRxSSBFsJOODgohrRvxNIn+/HFIz1o6uvBm7/sod9/IvloTTxnLuaYHaIQAjiScZG3l+3nppb+jLwx0OxwhBAmkB4PQtgZpRQDWgUwoFUAfyafIiIygXdXxPHp2iQe6t2cR/oFS42XECbRWvPaT7Hk5ufzzzs7yAg6QtRSUgMthB3rHuTL5+N68OvT/ejfOoCZaxPpO30Nr/+8m8MZF80OT4ha57eYY6zae4K/DW5NMz+ZXVSI2kpqoIWoBto19ubj+28kKS2TT9Ym8vWWg3y95SB3dQlkfFgIIQGeZocoRI2XceESU3+OpUOgN+P6BpkdjhDCRFIDLUQ10iLAk7fv7sTaF8N5sFdzlu46wqD31zLpm23EHj5jdnhC1Gj/+m0vpy/k8O+RHXBylF+fQtRmUgIIUQ0F1nPn9TvasWHyQCaGhbAuLo3h/7eBsZ//wZ/Jp8wOT4gaZ1PCSb6PTuWxm4JpH+htdjhCCJNJAi1ENebv6coLt7Zhw5SBvHBra3alnmH0J5sZ88lmovafkNkNhagEWTl5vLQkhuZ+Hjx7cyuzwxFC2AFJoIWoAbzdnZkUHsrGyQOZevsNpJy+wNjP/+T2jzbwv5ij5MvshkJU2Ier4zmYfoF/39UBdxdHs8MRQtiB2tGJ8Ne/QcYhaHUrtLwV6jU1OyIhbMLdxZFxfYN5oGdzftx+mJlrE5nwzTZCAuowISyUEZ0b4yxtN4Uos91HzjBrXRKjuzahT6i/2eEIIexE7fhNWicATsYZifQH7SGiN6ycCgc3QZ7M8iZqHhcnB8Z0b8qq5wfw0f1dcHFy5O8/7CTsnSi+3JxMVk6e2SEKYfdy8/KZsigGHw9nXhnW1uxwhBB2RFW3NpLdunXT0dHR5T9RazgZD/HLIW45HNoM+bngVg9CBxm106GDwMO38oMWwmRaa6L2p/FRZAJbD57G39OFR/u14MFezfByczY7vFpFKbVVa93N7DiqSoXLbDswZ30S037dy0f3d2F4x8ZmhyOEMEFxZXbtaMIBoBQEtDKWPk9B1hlIXANxKyB+BcQuBOUATbpDy1ug1RBo0M44T4hqTilFeJv6hLUO4I8Dp/g4KpH/LNtHRFQCY/sEMa5vML51XMwOUwi7cSj9Au+u2M/NbeozrEMjs8MRQtiZ2lMDXZL8fDiyzaiZjl8OR3ca2+sGWpLpWyF4ALjIrFOi5ohJPUNEVALLdh/DzcmR+3o04/H+wTTydjc7tBpNaqDtn9aav8z9g20HT7Py+QE0rif/J4SoraQGuiQODtCkm7EMfAXOHoWElUZCHfMDbP0cHF0huL+lI+It4NPc7KiFuC4dmngz88GuJJw4x8yoJL7YnMxXW5IZdWMTxg8IIci/jtkhChtRSg0BPgQcgTla6+mF9o8F3gEOWzZ9pLWeY9nXDJgDNAU0cJvWOrlqIq8aS7YfZn38Sd64o50kz0KIIkkNdGlys+HgRktTj+VwKsnYHtCmoHa6aU9wlHakonpLOXWB2euTWPBnCrl5+Qzr2JiJYSG0bVTX7NBqFLNroJVSjkAcMBhIBf4E7tNa77E6ZizQTWv9ZBHnRwH/1FqvVEp5Avla6wvF3a+61UCnZ2Yz6P21BPvX4YfxfXB0kGZ8QtRmUgNdUU6uEDLQWIZOh5MJELfMSKa3RMCmGeDmDSE3WzoiDoY6fmZHLUS5NfX14M0R7XlyYChzNyTz9ZaDLN15hJvb1GdieChdm/uYHaKoHD2ABK11EoBSagEwAthT4lnGsTcATlrrlQBa60xbBmqGt37ZQ2Z2LtNHdZTkWQhRLEmgy8s/FPyfhD5PQtZZSIos6Ii4ezGgjI6IrW4xxpxu2EE6Iopqpb6XG1OGtmHCgBC+3JzM3I0HGDVzE71a+DIpPJR+of4o+UxXZ4FAitXrVKBnEceNUkr1x6itfk5rnQK0AjKUUouBYGAVMEVrXSPGRYzcf4Ifdxzh6Ztb0qqBl9nhCCHsmDThqCz5+XB0R0FHxCPbje1ejaHlYKN2ukUYuEi7UlG9nM/OZf4fh5i9PonjZ7Pp2MSbiWGh3HJDAxykhq7c7KAJx93AEK31Y5bXDwE9rZtrKKX8gEytdbZS6q/APVrrgZZzPwO6AIeA74DftNafFbrHE8ATAM2aNet68ODBqnhr1+V8di63/Hcdbs4O/PbMTbg6yYyDQghpwmF7Dg4QeKOxhL8E545bOiIug9jFsO0LcHSBoJsKOiL6BpsdtRClquPqxGM3teCh3s1ZvO0wM6MSGf/1VlrW92RieAi3d2yMk8xuWJ0cxugAeFkTCjoLAqC1Trd6OQd427KeCuywav7xI9ALI6m2Pn8WMAuMSo/KDN5W3lsRx+GMi/wwvrckz0KIUtnst55Saq5S6oRSKraY/SOUUruUUjuUUtFKqX62isUUXg2gy4Nwz9fwYhL85Sfo/jhkHIT/vQgzOsNH3WHFq3BgPeTlmB2xECVytQx1t+ZvA/jw3s44KMVz3+0k/L0ovt5yUGY3rD7+BFoqpYKVUi7AvcDP1gcopawHPr4D2Gt1bj2lVIDl9UDK0Hba3u1IyeDzTQd4sFczugfJZFpCiNLZrAmHpe1cJvCl1rp9Efs9gfNaa62U6gh8r7VuU9p17bYJR3mkJxY09UjeCPk54OoNIeEFHRE9A0q/jhAmys/XrNl3go8iE9iRkkGAlyuP3xTM/T2b4+kqX24Vx+wmHJYYbgM+wBjGbq7W+p9KqTeBaK31z0qpf2MkzrnAKWCC1nqf5dzBwHuAArYCT2itLxV3L3svs3Py8rn9/zaQcSGHFc/3p67MzCmEsFJcmW3TNtBKqSDgl6IS6ELH9cYoxNuWdk17L4zLLfscJEVZEuoVkHkcUBDYtaCpR6NO0hFR2C2tNZuT0omITGRDwkm83Z0Z2yeIsX2C8JHZDa9hDwl0VbL3MvvjyATeWb6fWQ915ZZ2Dc0ORwhhZ+yyDbRS6i7g30B9YJiZsZjG1Qva3m4s+flwbGfBmNOR/4LIf4JnQ0tHxCFGR0RXT7OjFuIKpRR9QvzpE+LPjpQMIiIT+HB1PLPXJ/FAz2Y8dlMLGtR1MztMIa6RlJbJh6vjua1DQ0mehRDlYi810P2B17TWg4rZX+16dFeKzBMQv9JIphMjIfus0RGxed+C2mm/ELOjFOIa+4+dY2ZUAj/vPIKTgwN3d2vC+P4hNPPzMDs000kNtH3Iz9fcN3sLe46eZfXzA6gvf+QJIYpg1004LMcmAT201idLOs5eC2Oby8uBQ5sLmnqcjDO2+4UaNdMtb4FmvcFJvjIX9uNQ+gU+WZfIwuhU8rTm9o6NmBAWSuuGtXeMXUmg7cP8Pw7x0uIYpo/swL09mpkdjhDCTtldAq2UCgUSLZ0IbwSWAk10KQHZa2Fc5U4lFTT1SN4AeZfAxaugI2LLW8CzvtlRCgHA8bNZzFmfxDe/H+LCpTxuuaEBE8ND6dy0ntmhVTlJoM134mwWN7+/lnaN6zL/8V4yMZAQolhV3gZaKTUfCAP8lVKpwFTAGUBr/QkwCviLUioHuIgxUH+1GC/ULvi2gF7jjSU7Ew6stUwxvhL2WkakanyjVUfEzsZY1UKYoEFdN14ZdgMTw0KZtymZeZuSWbFnI/1C/ZkYHkLvFn6SxIgqM/Xn3WTn5vPvkR3lcyeEqBCZibCm0RqO7SqonU6NBjR4NjA6Ira81aildq29X6EL82Vm5/Lt7weZvf4Aaeey6dKsHpPCQhnYpn6Nn91QaqDNtXz3Mf761VZeuLU1k8JDzQ5HCGHnTGnCYQv2VhjbvfMnCzoiJqyB7DPg4AzN+1hqp28Ff/klIsyRlZPHwq2pfLI2kdTTF2nT0IsJYSEM69Coxs5uKAm0ec5m5TD4/bX4eLiw9Kl+ONfQz5gQovJIAi2MjogpvxsdEeOWw8n9xnbfkIKmHs37SkdEUeVy8/JZuusIEZGJxJ/IpLmfB+MHhDDyxsAaN62yJNDmeWVJDPP/OMSSiX3pVAvb3wshyk8SaHGt08kFTT0OrIe8bHDxNMaavjyyh1cDk4MUtUl+vmbl3uN8HJnArtQzNKjryuM3teD+ns3wcKkZsxtKAm2OPw6cYsynm3m0XzD/GH6D2eEIIaoJSaBFyS6dhwPrjI6IcSvg3BFje6POBU09GneRjoiiSmit2ZBwko8jE9iSdAofD2ce6RvMX3oH4e1RvadalgS66mXl5DFsxnqycvJZ8Vx/6shU80KIMrLLmQiFHXGpA62HGovWcDy2oKnH2rdh7X+gToBRK93yFggZCG51zY5a1FBKKW5qGcBNLQPYevAUEZGJvLcyjk/XJfFgr+Y82i+YAC9Xs8MU1UREZAKJaeeZN667JM9CiEohJYm4llLQsIOx9P87nE+HhFVGU499v8COb8DByZi4pdWtRnMPv1DjPCEqWdfmvnw21pc9R84yc20is9Yl8vnGA9zTvSlP9G9BEx+Z3VAUb/+xc8xcm8hdXQIJay1j4wshKoc04RDlk5cLqX8UNPVI22ts9wku6IgY1A+cpHZQ2MaBk+f5dG0ii7alojWM6BzIhLAWhNavHkMzShOOqpOXrxk1cxMH08+z6vkB+HlKuSSEKB9pAy1s4/RBY2rx+BVGG+rcLHCuY4w1fbm5R91GZkcpaqCjZy4ye90Bvv3jINm5+Qxp15CJYaF0aOJtdmglkgS66szbeIDXl+7hv/d04q4uTUyJQQhRvUkCLWzv0gUjiY5fbtROn001tjfsWNARMfBGcKhZw5IJc6VnZl+Z3fBcVi79WwUwKSyEHsG+djnLnCTQVeNwxkVueX8tXYN8+WJcd7v8LAgh7J8k0KJqaQ0n9hQ09Uj9A3Q+ePhbZkS0dER0l7FYReU4l5XD11sO8dmGJE5mXqJbcx8mhYcS1jrArpInSaBtT2vNo19EszkxnRXP9aepr7STF0JUjIzCIaqWUtCgnbHc9De4cAoSVltqp5fBzvkFHRFb3mLUUPu3ko6IosK83JyZEBbCuL5BfB+dwqdrkxg370/aNqrLpPAQhrZvhGMNnyZcGH7ZdZQ1+07w6rC2kjwLIWxCaqBF1cvLhdQ/C5p6nNhtbK/XvKCpR1A/cHYzN05RreXk5fPTjiNERCWQlHaeYP86TBgQwp1dAnFxMm88c6mBtq3T5y8x6P21BPq4s2RiX/mjqRbJyckhNTWVrKwss0MR1ZCbmxtNmjTB2fnquQakCYewXxkpRifEuOWWjogXwdnDmBHxcu103cZmRymqqbx8zYrdx/goMoHdR87SyNuNJ/q34N7uzXB3qfr2+JJA29bff9jJku2HWfpkP25oLGPV1yYHDhzAy8sLPz8/u2q2Jeyf1pr09HTOnTtHcHDwVfukCYewX/WaQvdHjSXnojGt+OXa6f2/Gcc06GAZc/pWCOwqHRFFmTk6KIZ2aMSQ9g1ZG5dGRGQibyzdw0drEnikXzAP9W5OXbfqPbuhMGyIP8nCralMDAuR5LkWysrKIigoSJJnUW5KKfz8/EhLSyvzOZJAC/vi7A6tbjGW2zSk7SvoiLjhv7D+XfDwg9BBRu106M3g7mN21KIaUEoR1ro+Ya3r88eBU0REJfDO8v18EpXIX/o0Z1zfYPxlnOBq6+KlPF5eEkOQnwdP39zS7HCESSR5FhVV3s+OJNDCfikF9dsaS7/n4OJpS0fEFRC/EnZ9B8oRmvUqaOoR0EY6IopS9Qj2pUdwD2IPn2FmVCIRUYl8tuEA93ZvxhP9W9C4nrvZIYpy+mB1HIdOXeDbx3vi5izfUAkhbMu8njRClJe7D3S4G0bOghcS4JEV0O9ZyDoLq6ZCRC/4oCP8+jcjwc65aHbEws61D/Tm4wduZNXzA7i9Y2O+3nKQAe9E8uLCnSSlZZodniij2MNnmLP+APd0a0qfEH+zwxG1VEZGBhEREeU+77bbbiMjI6PEY1577TVWrVpV0dCEDUgnQlEznDls1RFxLeRcACd3aDGgoHbaW2YiEyU7nHGR2euSmP/HIS7l5XNbh0ZMDAuhXePKm91QOhFWrty8fEZ8vJHjZ7NZ/fwAvD2kPXtttXfvXtq2bWva/ZOTkxk+fDixsbFXbc/NzcXJqfZ+4V+d3n9RnyHpRChqNu9A6DbOWHKyIHmDpSOiZdzpX4H67Qo6IjbpLh0RxTUC67nz+h3tmBQeyucbD/DV5oP8uuso4a0DmBQeSrcgX7NDFIXM3XiA3UfOEvHAjZI8iyveWLqbPUfOVuo1b2hcl6m3tyt2/5QpU0hMTKRz5844Ozvj5uaGj48P+/btIy4ujjvvvJOUlBSysrJ45plneOKJJwAICgoiOjqazMxMhg4dSr9+/di0aROBgYH89NNPuLu7M3bsWIYPH87dd99NUFAQDz/8MEuXLiUnJ4cffviBNm3akJaWxv3338+RI0fo3bs3K1euZOvWrfj7F/2tTHHxLFu2jJdffpm8vDz8/f1ZvXo1mZmZPPXUU0RHR6OUYurUqYwaNQpPT08yM41v6xYuXMgvv/zCvHnzGDt2LG5ubmzfvp2+ffty77338swzz5CVlYW7uzuff/45rVu3Ji8vj8mTJ7Ns2TIcHBx4/PHHadeuHTNmzODHH38EYOXKlURERLBkyZLK/HFeN0mgRc3j7AYtBxnL0LchbX/BqB4bP4QN7xvNQUIHGWNOh94MHpIYiQIBXq68OKQNfx0Qwlebk5m7MZm7P9lMj2BfJoWH0r+lv3RWsgMH08/z/so4BrVtwND2Dc0OR9Ry06dPJzY2lh07dhAVFcWwYcOIjY29Miza3Llz8fX15eLFi3Tv3p1Ro0bh5+d31TXi4+OZP38+s2fPZsyYMSxatIgHH3zwmnv5+/uzbds2IiIiePfdd5kzZw5vvPEGAwcO5KWXXmLZsmV89tlnJcZbVDz5+fk8/vjjrFu3juDgYE6dOgXAW2+9hbe3NzExMQCcPn261OeRmprKpk2bcHR05OzZs6xfvx4nJydWrVrFyy+/zKJFi5g1axbJycns2LEDJycnTp06hY+PDxMnTiQtLY2AgAA+//xzHnnkkTL9DKqSJNCiZlMK6rcxlr7PwMUMSFxtJNMJKyHmB1AO0LRnwfTiPs3BrZ50RhR4uzvz5MCWPNIvmAV/pDBrXRIPz/2D9oF1efHWNvRvFWB2iLWW1pqXl8Tg5ODAW3e2kz9oxFVKqimuKj169LhqTOEZM2ZcqUVNSUkhPj7+mgQ6ODiYzp07A9C1a1eSk5OLvPbIkSOvHLN48WIANmzYcOX6Q4YMwcen5BGqioonLS2N/v37X4nb19eoXFq1ahULFiy4cm5p1wYYPXo0jo7GN71nzpzh4YcfJj4+HqUUOTk5V647fvz4K008Lt/voYce4uuvv2bcuHFs3ryZL7/8stT7VTVJoEXt4l4P2o8ylvw8OLytoKnH6jeMBYz2014NjQlcvBoVWm8EdS3/OsmwZ7WBh4sTj/QL5sFezVmyPZWZUYkcyZBOqmZatO0wGxPSeWtEOxp5y6gpwv7UqVPnynpUVBSrVq1i8+bNeHh4EBYWVuSMia6uBb9THB0duXix6HLm8nGOjo7k5uaWO7ayxlMa6z9cC59v/f7/8Y9/EB4ezpIlS0hOTiYsLKzE644bN47bb78dNzc3Ro8ebZdtqO0vIiGqioMjNO1uLANfhbNH4NAW499zR43l7FE4vNVYzy2icHH3tSTWDS2J9eX1xgVJtoc/OMiANzWBi5MD93Rvxt1dm1LdOmDXJCczs5n26x66NvfhgZ7NzQ5HCAC8vLw4d+5ckfvOnDmDj48PHh4e7Nu3jy1btlT6/fv27cv333/P5MmTWbFiRYnNLIqLp1evXkycOJEDBw5cacLh6+vL4MGD+fjjj/nggw8AowmHj48PDRo0YO/evbRu3ZolS5bg5eVV7P0CAwNtJSleAAAXQklEQVQBmDdv3pXtgwcP5tNPPyU8PPxKEw5fX18aN25M48aNmTZtmt2OPiIJtBCX1W0M7UcWvU9rYxzqc8fg3BEjsb68fu6YkXQfi4HME0ChxMrB2ZJUl5BkezUCV0+bv0VRORwdFCBNBszy5tI9XMjOY/rIDjg4yM9B2Ac/Pz/69u1L+/btcXd3p0GDBlf2DRkyhE8++YS2bdvSunVrevXqVen3nzp1Kvfddx9fffUVvXv3pmHDhsUmtMXFExAQwKxZsxg5ciT5+fnUr1+flStX8uqrrzJp0iTat2+Po6MjU6dOZeTIkUyfPp3hw4cTEBBAt27drnQoLOzFF1/k4YcfZtq0aQwbNuzK9scee4y4uDg6duyIs7Mzjz/+OE8++SQADzzwAGlpaaaOrFISGcZOiMqUlwuZx6+uwbZOss9ZEu/sInqHu3hdnVDXbWQk2dbNRzzrg6OMNFCdyTB212fNvuM8Mi+aZwe15NlBrSrtuqL6M3sYO7NlZ2fj6OiIk5MTmzdvZsKECezYscPssCrsySefpEuXLjz66KNVdk8Zxk4Iszg6GUPqeQeWfFx2ZqHa7MsJtyXZPrjReJ1fuG2bMpLowm2xC6+7+0gnSFHjZGbn8uqSWFrW92RCWIjZ4QhhVw4dOsSYMWPIz8/HxcWF2bNnmx1ShXXt2pU6derw3nvvmR1KsSSBFsIMrp7gGgr+ocUfk58PF9KLT7LPpEDqH8YxhTm5Fd1MpHBnSGc3271HISrZu8v3c/RsFgvH98bVScZxF8Jay5Yt2b59+1Xb0tPTufnmm685dvXq1deMAGJPtm7danYIpZIEWgh75eAAngHG0qhT8cflZhc0DSncAfLcUTiyHc7+BrlF9OZ297FqJlJEkxGvRlAnQDpBCtNtO3SaLzYn81Cv5nRtLuO2C1EWfn5+1boZhz2TBFqI6s7JFXyCjKU4WkPWmatrsAt3hjy+G86fAJ1/9bkOTuDZoIgmI4U6Q7oW3VlF2B+l1BDgQ8ARmKO1nl5o/1jgHeCwZdNHWus5VvvrAnuAH7XWT9o63ku5+by0KIaGdd144dbWtr6dEEKUShJoIWoDpYwxsN3rQf0SOtnk5RpJtHWTEevOkCfjIWkdZJ+59lwXz5KT7MsjkUgnSFMppRyBj4HBQCrwp1LqZ631nkKHfldCcvwWsM6GYV7l07WJ7D9+jjl/6YaXm3x+hBDmkwRaCFHA0clIeus2Lvm4S+eLaTJyuRPkZksnyJxCJyqjSUiRE9NYNR+RTpC21ANI0FonASilFgAjMGqUS6WU6go0AJYBNh9NJOFEJv+3JoFhHRsx6IYGpZ8ghBBVQBJoIUT5udQBvxBjKU5+Plw8VUSTEcty5jCkRsOFk9ee6+hqlWQX0Rny8rqzzEBXAYFAitXrVKBnEceNUkr1B+KA57TWKUopB+A94EFgkK0Dzc/XvLw4BncXR163g6mZhRDiMkmghRC24eAAdfyNpVHH4o/LzbYk2EUk2eeOwdFdxlTrOReuPdetXhFJdsOra7PrBBizToryWArM11pnK6X+CnwBDAQmAr9prVNVCd8QKKWeAJ4AaNasWYWDmP/nIf5IPsXbozoS4OVa+glCmCgjI4Nvv/2WiRMnluu82267jW+//ZZ69eoVe8xrr71G//79GTTI5n+3FsvT07PYiVJqI0mghRDmcnIFn+bGUhytjclnipuY5uwROLHXmMSmcCdI5Wh0gixtkhpXr9rSbOQw0NTqdRMKOgsCoLW2HhtxDvC2Zb03cJNSaiLgCbgopTK11lMKnT8LmAXGRCoVCfL42Sym/7aPPiF+jO7WpCKXEKJKZWRkEBERcU0CnZubi5NT8enWb7/9Vuq133zzzeuOr6Yo7XlWFfMjEEKI0igFbt7GUr9N8cfl5xnTqRceM/vyenoiJK83RiQpzLlO8RPTXK7l9mwITi62e59V40+gpVIqGCNxvhe43/oApVQjrfVRy8s7gL0AWusHrI4ZC3QrnDxXltd+iuVSXj7/uqsDJdV2C1Gk/02BYzGVe82GHWDo9GJ3T5kyhcTERDp37oyzszNubm74+Piwb98+4uLiuPPOO0lJSSErK4tnnnmGJ554AoCgoCCio6PJzMxk6NCh9OvXj02bNhEYGMhPP/2Eu7s7Y8eOZfjw4dx9990EBQXx8MMPs3TpUnJycvjhhx9o06YNaWlp3H///Rw5coTevXuzcuVKtm7dir+/f5GxNm3alEmTJgHw+uuv4+npyfjx4xkxYgSnT58mJyeHadOmMWLEiFIfTWZmZrHnffnll7z77rsopejYsSNfffUVx48fZ/z48SQlJQEwc+ZMGjduzPDhw4mNjQXg3XffJTMzk9dff52wsDA6d+7Mhg0buO+++2jVqhXTpk3j0qVL+Pn58c0339CgQQMyMzN56qmniI6ORinF1KlTOXPmDLt27eKDDz4AYPbs2ezZs4f//ve/5fjhX0sSaCFEzeHgaCS+dRuVfNylC9eOl22dcKdsMf7Nu3TtuZc7QfacAF0euHa/ndNa5yqlngSWYwxjN1drvVsp9SYQrbX+GXhaKXUHkAucAsZWZYzLYo+yfPdxJg9pQ5B/naq8tRAVNn36dGJjY9mxYwdRUVEMGzaM2NhYgoODAZg7dy6+vr5cvHiR7t27M2rUqGsmM4mPj2f+/PnMnj2bMWPGsGjRIh588MFr7uXv78+2bduIiIjg3XffZc6cObzxxhsMHDiQl156iWXLlvHZZ58VG+s999zDs88+eyWB/v7771m+fDlubm4sWbKEunXrcvLkSXr16sUdd9xR6h+xxZ23Z88epk2bxqZNm/D39+fUqVMAPP300wwYMIAlS5aQl5dHZmYmp0+fLvEely5dIjo6GoDTp0+zZcsWlFLMmTOHt99+m/fee4+33noLb29vYmJirhzn7OzMP//5T9555x2cnZ35/PPP+fTTT0u8V1nYLIFWSs0FhgMntNbti9j/ADAZUMA5YILWeqet4hFCiCtcPErvBKk1XDhVRJMRS9LtVH3b5GqtfwN+K7TtNav1l4CXSrnGPGCeDcIjLx/6hvrx2E3Btri8qA1KqCmuKj169LiSPAPMmDGDJUuWAJCSkkJ8fPw1CXRwcDCdO3cGjOmsk5OTi7z2yJEjrxyzePFiADZs2HDl+kOGDMHHx6fY2Lp06cKJEyc4cuQIaWlp+Pj40LRpU3Jycnj55ZdZt24dDg4OHD58mOPHj9OwYcMS36vWusjz1qxZw+jRo6/Ugvv6GpMgrVmzhi+//BIAR0dHvL29S02g77nnnivrqamp3HPPPRw9epRLly5dec6rVq1iwYIFV467/AwGDhzIL7/8Qtu2bcnJyaFDhw4l3qssbFkDPQ/4CPiymP0HgAFa69NKqaEY7eWK6gkuhBBVTymo42csDa+/sBVlN6xjI27r0FCabohqrU6dgm9PoqKiWLVqFZs3b8bDw4OwsDCysrKuOcfVteAPc0dHRy5eLGIGWavjHB0dyc3NrVB8o0ePZuHChRw7duxKcvrNN9+QlpbG1q1bcXZ2JigoqMg4C6voedacnJzIzy/ow1L4fOvn+dRTT/H8889zxx13EBUVxeuvv17itR977DH+9a9/0aZNG8aNG1euuIpjs/l5tdbrML76K27/Jq315T83tmB0ZBFCCCEkeRbVjpeXF+fOnSty35kzZ/Dx8cHDw4N9+/axZcuWSr9/3759+f777wFYsWJFmWp0FyxYwMKFCxk9evSVOOvXr4+zszORkZEcPHiwTPcu7ryBAwfyww8/kJ5u9Eu+3ITj5ptvZubMmQDk5eVx5swZGjRowIkTJ0hPTyc7O5tffvmlxPsFBgYC8MUXX1zZPnjwYD7++OMrry8/g549e5KSksK3337LfffdV6b3VBqbJdDl9CjwP7ODEEIIIYSoCD8/P/r27Uv79u154YUXrto3ZMgQcnNzadu2LVOmTKFXr16Vfv+pU6eyYsUK2rdvzw8//EDDhg3x8vIq9vh27dpx7tw5AgMDadTI6DfywAMPEB0dTYcOHfjyyy9p06aETttWijuvXbt2vPLKKwwYMIBOnTrx/PPPA/Dhhx8SGRlJhw4d6Nq1K3v27MHZ2ZnXXnuNHj16MHjw4BLv/frrrzN69Gi6du16VSfJV199ldOnT9O+fXs6depEZGTklX1jxoyhb9++JTZtKQ+ldYVGGCrbxZUKAn4pqg201THhQATQr9DQSdbHWI8p2rWsfxEJIYS9UUpt1VrbfAY/e9GtWzd9ueOPELa0d+9e2rZta3YYpsnOzsbR0REnJyc2b97MhAkT2LFjh9lh2Y3hw4fz3HPPcfPNNxd7TFGfoeLKbFNH4VBKdcQYY3RocckzVM6YokIIIYQQNdWhQ4cYM2YM+fn5uLi4MHv2bLNDsgsZGRn06NGDTp06lZg8l5dpCbRSqhmwGHhIax1nVhxCCCGEENVdy5Yt2b59+1Xb0tPTi0waV69efc0IIGURExPDQw89dNU2V1dXfv/993Jfq6rUq1ePuLjKTzNtOYzdfCAM8FdKpQJTAWcArfUnwGuAHxBh6SySW5u+1hRCCCGEsCU/P79KbcbRoUMHaRZiYbMEWmtdYjdHrfVjwGO2ur8QQgghahettYzgIiqkvH0C7WUUDiGEEEKICnNzcyM9Pb3ciZAQWmvS09Nxc3Mr8zkylbcQQgghqr0mTZqQmppKWlqa2aGIasjNzY0mTco+JYkk0EIIIYSo9pydna+aOlsIW5ImHEIIIYQQQpSDJNBCCCGEEEKUgyTQQgghhBBClINNp/K2BaVUGlCRubz9gZOVHE5F2Uss9hIH2E8s9hIHSCxFsZc4oOKxNNdaB1R2MPZKyuxKZS9xgMRSFHuJA+wnFnuJAyq5zK52CXRFKaWi7WWiFnuJxV7iAPuJxV7iAInFnuMA+4qlJrKn52svsdhLHCCx2HMcYD+x2EscUPmxSBMOIYQQQgghykESaCGEEEIIIcqhNiXQs8wOwIq9xGIvcYD9xGIvcYDEUhR7iQPsK5aayJ6er73EYi9xgMRSFHuJA+wnFnuJAyo5llrTBloIIYQQQojKUJtqoIUQQgghhLhuNSKBVkoNUUrtV0olKKWmFLHfVSn1nWX/70qpIKt9L1m271dK3WrjOJ5XSu1RSu1SSq1WSjW32penlNphWX6+njjKGMtYpVSa1T0fs9r3sFIq3rI8bOM4/msVQ5xSKsNqX6U9E6XUXKXUCaVUbDH7lVJqhiXOXUqpG632VdrzKGMsD1hiiFFKbVJKdbLal2zZvkMpFV0FsYQppc5Y/Rxes9pX4s+2kuN4wSqGWMtnw9eyr9KeiVKqqVIq0vL/dLdS6pkijqmyz0pNJWV2hWKpkjK7jLHUqnJbyuwKxVElZbbleuaU21rrar0AjkAi0AJwAXYCNxQ6ZiLwiWX9XuA7y/oNluNdgWDLdRxtGEc44GFZn3A5DsvrzCp+JmOBj4o41xdIsvzrY1n3sVUchY5/Cphro2fSH7gRiC1m/23A/wAF9AJ+r+znUY5Y+ly+BzD0ciyW18mAfxU+lzDgl+v92V5vHIWOvR1YY4tnAjQCbrSsewFxRfzfqbLPSk1cylg+SZltQpld1lgKHV/jy+0yxCFldsnH2qzMtlzPlHK7JtRA9wAStNZJWutLwAJgRKFjRgBfWNYXAjcrpZRl+wKtdbbW+gCQYLmeTeLQWkdqrS9YXm4BmlTwXtcdSwluBVZqrU9prU8DK4EhVRTHfcD8Ct6rRFrrdcCpEg4ZAXypDVuAekqpRlTu8yhTLFrrTZZ7gW0/J2V5LsW5ns/Y9cZhy8/JUa31Nsv6OWAvEFjosCr7rNRQUmZXIJYSVPbnTsrtcsYhZXapbPYZscRiSrldExLoQCDF6nUq1z64K8dorXOBM4BfGc+tzDisPYrx19BlbkqpaKXUFqXUnRWMobyxjLJ8lbFQKdW0nOdWZhxYvhoNBtZYba7MZ1Ka4mKtzOdREYU/JxpYoZTaqpR6oopi6K2U2qmU+p9Sqp1lmynPRSnlgVG4LbLabJNnooxmA12A3wvtstfPSnUhZXbFY7F1mV2u60m5XSQps61UZZltuV8QVVRuO1U0SFFxSqkHgW7AAKvNzbXWh5VSLYA1SqkYrXWiDcNYCszXWmcrpf6KUdsz0Ib3K829wEKtdZ7Vtqp+JnZFKRWOURj3s9rcz/JM6gMrlVL7LDUBtrIN4+eQqZS6DfgRaGnD+5XmdmCj1tq65qPSn4lSyhOjwH9Wa332eq4lqj8ps4sl5bYVKbOLVCVlNlR9uV0TaqAPA02tXjexbCvyGKWUE+ANpJfx3MqMA6XUIOAV4A6tdfbl7Vrrw5Z/k4AojL+gKqrUWLTW6Vb3nwN0Lc/7qKw4rNxLoa94KvmZlKa4WCvzeZSZUqojxs9lhNY6/fJ2q2dyAlhCxb++LhOt9VmtdaZl/TfAWSnlj0nPhZI/J5XyTJRSzhiF8Dda68VFHGJXn5VqSMrsCsRSRWV2ea8n5baFlNnFsnmZDSaV27qSGnGbtWDUoidhfI10uWF8u0LHTOLqDinfW9bbcXWHlCQq3iGlLHF0wWjE37LQdh/A1bLuD8RzfY37yxJLI6v1u4AtuqBB/QFLTD6WdV9bxWE5rg1GpwJlq2diuU4QxXe8GMbVHQz+qOznUY5YmmG07exTaHsdwMtqfRMwxMaxNLz8c8Eo5A5ZnlGZfraVFYdlvzdGm7s6tnomlvf2JfBBCcdU6Welpi1lLJ+kzDahzC5rLJbjalW5XUocUmYXvd/mZbblOqaU29f1Q7SXBaN3ZRxGQfeKZdubGDUGAG7AD5YP+B9AC6tzX7Gctx8YauM4VgHHgR2W5WfL9j5AjOUDHQM8WgXP5N/Abss9I4E2Vuc+YnlWCcA4W8Zhef06ML3QeZX6TDD+Aj4K5GC0cXoUGA+Mt+xXwMeWOGOAbrZ4HmWMZQ5w2upzEm3Z3sLyPHZafnavVEEsT1p9TrZg9QuiqJ+treKwHDMWowOZ9XmV+kwwvnrVwC6r53+bWZ+VmrqUVi4gZbZpZXZZYrG8fp1aUm6XIQ4ps00qsy3XNKXclpkIhRBCCCGEKIea0AZaCCGEEEKIKiMJtBBCCCGEEOUgCbQQQgghhBDlIAm0EEIIIYQQ5SAJtBBCCCGEEOUgCbSoUZRSeUqpHVbLlEq8dpBSKrayrieEELWdlNmiupKpvEVNc1Fr3dnsIIQQQpSJlNmiWpIaaFErKKWSlVJvK6VilFJ/KKVCLduDlFJrlFK7lFKrlVLNLNsbKKWWKKV2WpY+lks5KqVmK6V2K6VWKKXcLcc/rZTaY7nOApPephBC1AhSZgt7Jwm0qGncC30deI/VvjNa6w7AR8AHlm3/B3yhte4IfAPMsGyfAazVWncCbsSYMQmgJfCx1rodkAGMsmyfAnSxXGe8rd6cEELUMFJmi2pJZiIUNYpSKlNr7VnE9mRgoNY6SSnlDBzTWvsppU4CjbTWOZbtR7XW/kqpNKCJ1jrb6hpBwEqtdUvL68mAs9Z6mlJqGZAJ/Aj8qLXOtPFbFUKIak/KbFFdSQ20qE10MevlkW21nkdBP4JhwMcYNR9/KqWkf4EQQlwfKbOF3ZIEWtQm91j9u9myvgm417L+ALDesr4amACglHJUSnkXd1GllAPQVGsdCUwGvIFralSEEEKUi5TZwm7JX1yipnFXSu2wer1Ma315WCQfpdQujBqJ+yzbngI+V0q9AKQB4yzbnwFmKaUexai1mAAcLeaejsDXlgJbATO01hmV9o6EEKLmkjJbVEvSBlrUCpb2dN201ifNjkUIIUTJpMwW9k6acAghhBBCCFEOUgMthBBCCCFEOUgNtBBCCCGEEOUgCbQQQgghhBDlIAm0EEIIIYQQ5SAJtBBCCCGEEOUgCbQQQgghhBDlIAm0EEIIIYQQ5fD/2IPwwQAO/qAAAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x432 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"mA16moUOdz7P"},"source":["## Fine tuning last 10 layers"]},{"cell_type":"code","metadata":{"id":"FR5YqSBeeLbc","executionInfo":{"status":"ok","timestamp":1628824013644,"user_tz":-420,"elapsed":342,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# unfreeze all layers in the base_model\n","base_model.trainable = True\n","\n","# Freeze all layers except last 10 layers \n","for layer in base_model.layers[:-10]:\n","  layer.trainable = False"],"execution_count":38,"outputs":[]},{"cell_type":"code","metadata":{"id":"2CpQIUEJegeu","executionInfo":{"status":"ok","timestamp":1628824054648,"user_tz":-420,"elapsed":1131,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# Compile the model again\n","model.compile(\n","    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n","    metrics=[\"accuracy\"]\n",")"],"execution_count":40,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y_uo4gFaempH","executionInfo":{"status":"ok","timestamp":1628824066536,"user_tz":-420,"elapsed":319,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"1bf65d69-4706-4bc1-b291-6c6dd4415d3e"},"source":["for layer in model.layers:\n","  print(layer.name, layer.trainable, layer.dtype_policy)"],"execution_count":41,"outputs":[{"output_type":"stream","text":["input_layer True <Policy \"float32\">\n","efficientnetb4 True <Policy \"mixed_float16\">\n","global_average_pool_2D_layer True <Policy \"mixed_float16\">\n","dense_1 True <Policy \"mixed_float16\">\n","output_layer True <Policy \"float32\">\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EkpW000netOT","executionInfo":{"status":"ok","timestamp":1628824095324,"user_tz":-420,"elapsed":338,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"2f4d1476-71b3-4ad6-b7a7-5fa3f0742a78"},"source":["for i, layer in enumerate(model.layers[1].layers):\n","  print(i, layer.name, layer.trainable, layer.dtype_policy)"],"execution_count":43,"outputs":[{"output_type":"stream","text":["0 input_3 False <Policy \"float32\">\n","1 rescaling_2 False <Policy \"mixed_float16\">\n","2 normalization_2 False <Policy \"float32\">\n","3 stem_conv_pad False <Policy \"mixed_float16\">\n","4 stem_conv False <Policy \"mixed_float16\">\n","5 stem_bn False <Policy \"mixed_float16\">\n","6 stem_activation False <Policy \"mixed_float16\">\n","7 block1a_dwconv False <Policy \"mixed_float16\">\n","8 block1a_bn False <Policy \"mixed_float16\">\n","9 block1a_activation False <Policy \"mixed_float16\">\n","10 block1a_se_squeeze False <Policy \"mixed_float16\">\n","11 block1a_se_reshape False <Policy \"mixed_float16\">\n","12 block1a_se_reduce False <Policy \"mixed_float16\">\n","13 block1a_se_expand False <Policy \"mixed_float16\">\n","14 block1a_se_excite False <Policy \"mixed_float16\">\n","15 block1a_project_conv False <Policy \"mixed_float16\">\n","16 block1a_project_bn False <Policy \"mixed_float16\">\n","17 block1b_dwconv False <Policy \"mixed_float16\">\n","18 block1b_bn False <Policy \"mixed_float16\">\n","19 block1b_activation False <Policy \"mixed_float16\">\n","20 block1b_se_squeeze False <Policy \"mixed_float16\">\n","21 block1b_se_reshape False <Policy \"mixed_float16\">\n","22 block1b_se_reduce False <Policy \"mixed_float16\">\n","23 block1b_se_expand False <Policy \"mixed_float16\">\n","24 block1b_se_excite False <Policy \"mixed_float16\">\n","25 block1b_project_conv False <Policy \"mixed_float16\">\n","26 block1b_project_bn False <Policy \"mixed_float16\">\n","27 block1b_drop False <Policy \"mixed_float16\">\n","28 block1b_add False <Policy \"mixed_float16\">\n","29 block2a_expand_conv False <Policy \"mixed_float16\">\n","30 block2a_expand_bn False <Policy \"mixed_float16\">\n","31 block2a_expand_activation False <Policy \"mixed_float16\">\n","32 block2a_dwconv_pad False <Policy \"mixed_float16\">\n","33 block2a_dwconv False <Policy \"mixed_float16\">\n","34 block2a_bn False <Policy \"mixed_float16\">\n","35 block2a_activation False <Policy \"mixed_float16\">\n","36 block2a_se_squeeze False <Policy \"mixed_float16\">\n","37 block2a_se_reshape False <Policy \"mixed_float16\">\n","38 block2a_se_reduce False <Policy \"mixed_float16\">\n","39 block2a_se_expand False <Policy \"mixed_float16\">\n","40 block2a_se_excite False <Policy \"mixed_float16\">\n","41 block2a_project_conv False <Policy \"mixed_float16\">\n","42 block2a_project_bn False <Policy \"mixed_float16\">\n","43 block2b_expand_conv False <Policy \"mixed_float16\">\n","44 block2b_expand_bn False <Policy \"mixed_float16\">\n","45 block2b_expand_activation False <Policy \"mixed_float16\">\n","46 block2b_dwconv False <Policy \"mixed_float16\">\n","47 block2b_bn False <Policy \"mixed_float16\">\n","48 block2b_activation False <Policy \"mixed_float16\">\n","49 block2b_se_squeeze False <Policy \"mixed_float16\">\n","50 block2b_se_reshape False <Policy \"mixed_float16\">\n","51 block2b_se_reduce False <Policy \"mixed_float16\">\n","52 block2b_se_expand False <Policy \"mixed_float16\">\n","53 block2b_se_excite False <Policy \"mixed_float16\">\n","54 block2b_project_conv False <Policy \"mixed_float16\">\n","55 block2b_project_bn False <Policy \"mixed_float16\">\n","56 block2b_drop False <Policy \"mixed_float16\">\n","57 block2b_add False <Policy \"mixed_float16\">\n","58 block2c_expand_conv False <Policy \"mixed_float16\">\n","59 block2c_expand_bn False <Policy \"mixed_float16\">\n","60 block2c_expand_activation False <Policy \"mixed_float16\">\n","61 block2c_dwconv False <Policy \"mixed_float16\">\n","62 block2c_bn False <Policy \"mixed_float16\">\n","63 block2c_activation False <Policy \"mixed_float16\">\n","64 block2c_se_squeeze False <Policy \"mixed_float16\">\n","65 block2c_se_reshape False <Policy \"mixed_float16\">\n","66 block2c_se_reduce False <Policy \"mixed_float16\">\n","67 block2c_se_expand False <Policy \"mixed_float16\">\n","68 block2c_se_excite False <Policy \"mixed_float16\">\n","69 block2c_project_conv False <Policy \"mixed_float16\">\n","70 block2c_project_bn False <Policy \"mixed_float16\">\n","71 block2c_drop False <Policy \"mixed_float16\">\n","72 block2c_add False <Policy \"mixed_float16\">\n","73 block2d_expand_conv False <Policy \"mixed_float16\">\n","74 block2d_expand_bn False <Policy \"mixed_float16\">\n","75 block2d_expand_activation False <Policy \"mixed_float16\">\n","76 block2d_dwconv False <Policy \"mixed_float16\">\n","77 block2d_bn False <Policy \"mixed_float16\">\n","78 block2d_activation False <Policy \"mixed_float16\">\n","79 block2d_se_squeeze False <Policy \"mixed_float16\">\n","80 block2d_se_reshape False <Policy \"mixed_float16\">\n","81 block2d_se_reduce False <Policy \"mixed_float16\">\n","82 block2d_se_expand False <Policy \"mixed_float16\">\n","83 block2d_se_excite False <Policy \"mixed_float16\">\n","84 block2d_project_conv False <Policy \"mixed_float16\">\n","85 block2d_project_bn False <Policy \"mixed_float16\">\n","86 block2d_drop False <Policy \"mixed_float16\">\n","87 block2d_add False <Policy \"mixed_float16\">\n","88 block3a_expand_conv False <Policy \"mixed_float16\">\n","89 block3a_expand_bn False <Policy \"mixed_float16\">\n","90 block3a_expand_activation False <Policy \"mixed_float16\">\n","91 block3a_dwconv_pad False <Policy \"mixed_float16\">\n","92 block3a_dwconv False <Policy \"mixed_float16\">\n","93 block3a_bn False <Policy \"mixed_float16\">\n","94 block3a_activation False <Policy \"mixed_float16\">\n","95 block3a_se_squeeze False <Policy \"mixed_float16\">\n","96 block3a_se_reshape False <Policy \"mixed_float16\">\n","97 block3a_se_reduce False <Policy \"mixed_float16\">\n","98 block3a_se_expand False <Policy \"mixed_float16\">\n","99 block3a_se_excite False <Policy \"mixed_float16\">\n","100 block3a_project_conv False <Policy \"mixed_float16\">\n","101 block3a_project_bn False <Policy \"mixed_float16\">\n","102 block3b_expand_conv False <Policy \"mixed_float16\">\n","103 block3b_expand_bn False <Policy \"mixed_float16\">\n","104 block3b_expand_activation False <Policy \"mixed_float16\">\n","105 block3b_dwconv False <Policy \"mixed_float16\">\n","106 block3b_bn False <Policy \"mixed_float16\">\n","107 block3b_activation False <Policy \"mixed_float16\">\n","108 block3b_se_squeeze False <Policy \"mixed_float16\">\n","109 block3b_se_reshape False <Policy \"mixed_float16\">\n","110 block3b_se_reduce False <Policy \"mixed_float16\">\n","111 block3b_se_expand False <Policy \"mixed_float16\">\n","112 block3b_se_excite False <Policy \"mixed_float16\">\n","113 block3b_project_conv False <Policy \"mixed_float16\">\n","114 block3b_project_bn False <Policy \"mixed_float16\">\n","115 block3b_drop False <Policy \"mixed_float16\">\n","116 block3b_add False <Policy \"mixed_float16\">\n","117 block3c_expand_conv False <Policy \"mixed_float16\">\n","118 block3c_expand_bn False <Policy \"mixed_float16\">\n","119 block3c_expand_activation False <Policy \"mixed_float16\">\n","120 block3c_dwconv False <Policy \"mixed_float16\">\n","121 block3c_bn False <Policy \"mixed_float16\">\n","122 block3c_activation False <Policy \"mixed_float16\">\n","123 block3c_se_squeeze False <Policy \"mixed_float16\">\n","124 block3c_se_reshape False <Policy \"mixed_float16\">\n","125 block3c_se_reduce False <Policy \"mixed_float16\">\n","126 block3c_se_expand False <Policy \"mixed_float16\">\n","127 block3c_se_excite False <Policy \"mixed_float16\">\n","128 block3c_project_conv False <Policy \"mixed_float16\">\n","129 block3c_project_bn False <Policy \"mixed_float16\">\n","130 block3c_drop False <Policy \"mixed_float16\">\n","131 block3c_add False <Policy \"mixed_float16\">\n","132 block3d_expand_conv False <Policy \"mixed_float16\">\n","133 block3d_expand_bn False <Policy \"mixed_float16\">\n","134 block3d_expand_activation False <Policy \"mixed_float16\">\n","135 block3d_dwconv False <Policy \"mixed_float16\">\n","136 block3d_bn False <Policy \"mixed_float16\">\n","137 block3d_activation False <Policy \"mixed_float16\">\n","138 block3d_se_squeeze False <Policy \"mixed_float16\">\n","139 block3d_se_reshape False <Policy \"mixed_float16\">\n","140 block3d_se_reduce False <Policy \"mixed_float16\">\n","141 block3d_se_expand False <Policy \"mixed_float16\">\n","142 block3d_se_excite False <Policy \"mixed_float16\">\n","143 block3d_project_conv False <Policy \"mixed_float16\">\n","144 block3d_project_bn False <Policy \"mixed_float16\">\n","145 block3d_drop False <Policy \"mixed_float16\">\n","146 block3d_add False <Policy \"mixed_float16\">\n","147 block4a_expand_conv False <Policy \"mixed_float16\">\n","148 block4a_expand_bn False <Policy \"mixed_float16\">\n","149 block4a_expand_activation False <Policy \"mixed_float16\">\n","150 block4a_dwconv_pad False <Policy \"mixed_float16\">\n","151 block4a_dwconv False <Policy \"mixed_float16\">\n","152 block4a_bn False <Policy \"mixed_float16\">\n","153 block4a_activation False <Policy \"mixed_float16\">\n","154 block4a_se_squeeze False <Policy \"mixed_float16\">\n","155 block4a_se_reshape False <Policy \"mixed_float16\">\n","156 block4a_se_reduce False <Policy \"mixed_float16\">\n","157 block4a_se_expand False <Policy \"mixed_float16\">\n","158 block4a_se_excite False <Policy \"mixed_float16\">\n","159 block4a_project_conv False <Policy \"mixed_float16\">\n","160 block4a_project_bn False <Policy \"mixed_float16\">\n","161 block4b_expand_conv False <Policy \"mixed_float16\">\n","162 block4b_expand_bn False <Policy \"mixed_float16\">\n","163 block4b_expand_activation False <Policy \"mixed_float16\">\n","164 block4b_dwconv False <Policy \"mixed_float16\">\n","165 block4b_bn False <Policy \"mixed_float16\">\n","166 block4b_activation False <Policy \"mixed_float16\">\n","167 block4b_se_squeeze False <Policy \"mixed_float16\">\n","168 block4b_se_reshape False <Policy \"mixed_float16\">\n","169 block4b_se_reduce False <Policy \"mixed_float16\">\n","170 block4b_se_expand False <Policy \"mixed_float16\">\n","171 block4b_se_excite False <Policy \"mixed_float16\">\n","172 block4b_project_conv False <Policy \"mixed_float16\">\n","173 block4b_project_bn False <Policy \"mixed_float16\">\n","174 block4b_drop False <Policy \"mixed_float16\">\n","175 block4b_add False <Policy \"mixed_float16\">\n","176 block4c_expand_conv False <Policy \"mixed_float16\">\n","177 block4c_expand_bn False <Policy \"mixed_float16\">\n","178 block4c_expand_activation False <Policy \"mixed_float16\">\n","179 block4c_dwconv False <Policy \"mixed_float16\">\n","180 block4c_bn False <Policy \"mixed_float16\">\n","181 block4c_activation False <Policy \"mixed_float16\">\n","182 block4c_se_squeeze False <Policy \"mixed_float16\">\n","183 block4c_se_reshape False <Policy \"mixed_float16\">\n","184 block4c_se_reduce False <Policy \"mixed_float16\">\n","185 block4c_se_expand False <Policy \"mixed_float16\">\n","186 block4c_se_excite False <Policy \"mixed_float16\">\n","187 block4c_project_conv False <Policy \"mixed_float16\">\n","188 block4c_project_bn False <Policy \"mixed_float16\">\n","189 block4c_drop False <Policy \"mixed_float16\">\n","190 block4c_add False <Policy \"mixed_float16\">\n","191 block4d_expand_conv False <Policy \"mixed_float16\">\n","192 block4d_expand_bn False <Policy \"mixed_float16\">\n","193 block4d_expand_activation False <Policy \"mixed_float16\">\n","194 block4d_dwconv False <Policy \"mixed_float16\">\n","195 block4d_bn False <Policy \"mixed_float16\">\n","196 block4d_activation False <Policy \"mixed_float16\">\n","197 block4d_se_squeeze False <Policy \"mixed_float16\">\n","198 block4d_se_reshape False <Policy \"mixed_float16\">\n","199 block4d_se_reduce False <Policy \"mixed_float16\">\n","200 block4d_se_expand False <Policy \"mixed_float16\">\n","201 block4d_se_excite False <Policy \"mixed_float16\">\n","202 block4d_project_conv False <Policy \"mixed_float16\">\n","203 block4d_project_bn False <Policy \"mixed_float16\">\n","204 block4d_drop False <Policy \"mixed_float16\">\n","205 block4d_add False <Policy \"mixed_float16\">\n","206 block4e_expand_conv False <Policy \"mixed_float16\">\n","207 block4e_expand_bn False <Policy \"mixed_float16\">\n","208 block4e_expand_activation False <Policy \"mixed_float16\">\n","209 block4e_dwconv False <Policy \"mixed_float16\">\n","210 block4e_bn False <Policy \"mixed_float16\">\n","211 block4e_activation False <Policy \"mixed_float16\">\n","212 block4e_se_squeeze False <Policy \"mixed_float16\">\n","213 block4e_se_reshape False <Policy \"mixed_float16\">\n","214 block4e_se_reduce False <Policy \"mixed_float16\">\n","215 block4e_se_expand False <Policy \"mixed_float16\">\n","216 block4e_se_excite False <Policy \"mixed_float16\">\n","217 block4e_project_conv False <Policy \"mixed_float16\">\n","218 block4e_project_bn False <Policy \"mixed_float16\">\n","219 block4e_drop False <Policy \"mixed_float16\">\n","220 block4e_add False <Policy \"mixed_float16\">\n","221 block4f_expand_conv False <Policy \"mixed_float16\">\n","222 block4f_expand_bn False <Policy \"mixed_float16\">\n","223 block4f_expand_activation False <Policy \"mixed_float16\">\n","224 block4f_dwconv False <Policy \"mixed_float16\">\n","225 block4f_bn False <Policy \"mixed_float16\">\n","226 block4f_activation False <Policy \"mixed_float16\">\n","227 block4f_se_squeeze False <Policy \"mixed_float16\">\n","228 block4f_se_reshape False <Policy \"mixed_float16\">\n","229 block4f_se_reduce False <Policy \"mixed_float16\">\n","230 block4f_se_expand False <Policy \"mixed_float16\">\n","231 block4f_se_excite False <Policy \"mixed_float16\">\n","232 block4f_project_conv False <Policy \"mixed_float16\">\n","233 block4f_project_bn False <Policy \"mixed_float16\">\n","234 block4f_drop False <Policy \"mixed_float16\">\n","235 block4f_add False <Policy \"mixed_float16\">\n","236 block5a_expand_conv False <Policy \"mixed_float16\">\n","237 block5a_expand_bn False <Policy \"mixed_float16\">\n","238 block5a_expand_activation False <Policy \"mixed_float16\">\n","239 block5a_dwconv False <Policy \"mixed_float16\">\n","240 block5a_bn False <Policy \"mixed_float16\">\n","241 block5a_activation False <Policy \"mixed_float16\">\n","242 block5a_se_squeeze False <Policy \"mixed_float16\">\n","243 block5a_se_reshape False <Policy \"mixed_float16\">\n","244 block5a_se_reduce False <Policy \"mixed_float16\">\n","245 block5a_se_expand False <Policy \"mixed_float16\">\n","246 block5a_se_excite False <Policy \"mixed_float16\">\n","247 block5a_project_conv False <Policy \"mixed_float16\">\n","248 block5a_project_bn False <Policy \"mixed_float16\">\n","249 block5b_expand_conv False <Policy \"mixed_float16\">\n","250 block5b_expand_bn False <Policy \"mixed_float16\">\n","251 block5b_expand_activation False <Policy \"mixed_float16\">\n","252 block5b_dwconv False <Policy \"mixed_float16\">\n","253 block5b_bn False <Policy \"mixed_float16\">\n","254 block5b_activation False <Policy \"mixed_float16\">\n","255 block5b_se_squeeze False <Policy \"mixed_float16\">\n","256 block5b_se_reshape False <Policy \"mixed_float16\">\n","257 block5b_se_reduce False <Policy \"mixed_float16\">\n","258 block5b_se_expand False <Policy \"mixed_float16\">\n","259 block5b_se_excite False <Policy \"mixed_float16\">\n","260 block5b_project_conv False <Policy \"mixed_float16\">\n","261 block5b_project_bn False <Policy \"mixed_float16\">\n","262 block5b_drop False <Policy \"mixed_float16\">\n","263 block5b_add False <Policy \"mixed_float16\">\n","264 block5c_expand_conv False <Policy \"mixed_float16\">\n","265 block5c_expand_bn False <Policy \"mixed_float16\">\n","266 block5c_expand_activation False <Policy \"mixed_float16\">\n","267 block5c_dwconv False <Policy \"mixed_float16\">\n","268 block5c_bn False <Policy \"mixed_float16\">\n","269 block5c_activation False <Policy \"mixed_float16\">\n","270 block5c_se_squeeze False <Policy \"mixed_float16\">\n","271 block5c_se_reshape False <Policy \"mixed_float16\">\n","272 block5c_se_reduce False <Policy \"mixed_float16\">\n","273 block5c_se_expand False <Policy \"mixed_float16\">\n","274 block5c_se_excite False <Policy \"mixed_float16\">\n","275 block5c_project_conv False <Policy \"mixed_float16\">\n","276 block5c_project_bn False <Policy \"mixed_float16\">\n","277 block5c_drop False <Policy \"mixed_float16\">\n","278 block5c_add False <Policy \"mixed_float16\">\n","279 block5d_expand_conv False <Policy \"mixed_float16\">\n","280 block5d_expand_bn False <Policy \"mixed_float16\">\n","281 block5d_expand_activation False <Policy \"mixed_float16\">\n","282 block5d_dwconv False <Policy \"mixed_float16\">\n","283 block5d_bn False <Policy \"mixed_float16\">\n","284 block5d_activation False <Policy \"mixed_float16\">\n","285 block5d_se_squeeze False <Policy \"mixed_float16\">\n","286 block5d_se_reshape False <Policy \"mixed_float16\">\n","287 block5d_se_reduce False <Policy \"mixed_float16\">\n","288 block5d_se_expand False <Policy \"mixed_float16\">\n","289 block5d_se_excite False <Policy \"mixed_float16\">\n","290 block5d_project_conv False <Policy \"mixed_float16\">\n","291 block5d_project_bn False <Policy \"mixed_float16\">\n","292 block5d_drop False <Policy \"mixed_float16\">\n","293 block5d_add False <Policy \"mixed_float16\">\n","294 block5e_expand_conv False <Policy \"mixed_float16\">\n","295 block5e_expand_bn False <Policy \"mixed_float16\">\n","296 block5e_expand_activation False <Policy \"mixed_float16\">\n","297 block5e_dwconv False <Policy \"mixed_float16\">\n","298 block5e_bn False <Policy \"mixed_float16\">\n","299 block5e_activation False <Policy \"mixed_float16\">\n","300 block5e_se_squeeze False <Policy \"mixed_float16\">\n","301 block5e_se_reshape False <Policy \"mixed_float16\">\n","302 block5e_se_reduce False <Policy \"mixed_float16\">\n","303 block5e_se_expand False <Policy \"mixed_float16\">\n","304 block5e_se_excite False <Policy \"mixed_float16\">\n","305 block5e_project_conv False <Policy \"mixed_float16\">\n","306 block5e_project_bn False <Policy \"mixed_float16\">\n","307 block5e_drop False <Policy \"mixed_float16\">\n","308 block5e_add False <Policy \"mixed_float16\">\n","309 block5f_expand_conv False <Policy \"mixed_float16\">\n","310 block5f_expand_bn False <Policy \"mixed_float16\">\n","311 block5f_expand_activation False <Policy \"mixed_float16\">\n","312 block5f_dwconv False <Policy \"mixed_float16\">\n","313 block5f_bn False <Policy \"mixed_float16\">\n","314 block5f_activation False <Policy \"mixed_float16\">\n","315 block5f_se_squeeze False <Policy \"mixed_float16\">\n","316 block5f_se_reshape False <Policy \"mixed_float16\">\n","317 block5f_se_reduce False <Policy \"mixed_float16\">\n","318 block5f_se_expand False <Policy \"mixed_float16\">\n","319 block5f_se_excite False <Policy \"mixed_float16\">\n","320 block5f_project_conv False <Policy \"mixed_float16\">\n","321 block5f_project_bn False <Policy \"mixed_float16\">\n","322 block5f_drop False <Policy \"mixed_float16\">\n","323 block5f_add False <Policy \"mixed_float16\">\n","324 block6a_expand_conv False <Policy \"mixed_float16\">\n","325 block6a_expand_bn False <Policy \"mixed_float16\">\n","326 block6a_expand_activation False <Policy \"mixed_float16\">\n","327 block6a_dwconv_pad False <Policy \"mixed_float16\">\n","328 block6a_dwconv False <Policy \"mixed_float16\">\n","329 block6a_bn False <Policy \"mixed_float16\">\n","330 block6a_activation False <Policy \"mixed_float16\">\n","331 block6a_se_squeeze False <Policy \"mixed_float16\">\n","332 block6a_se_reshape False <Policy \"mixed_float16\">\n","333 block6a_se_reduce False <Policy \"mixed_float16\">\n","334 block6a_se_expand False <Policy \"mixed_float16\">\n","335 block6a_se_excite False <Policy \"mixed_float16\">\n","336 block6a_project_conv False <Policy \"mixed_float16\">\n","337 block6a_project_bn False <Policy \"mixed_float16\">\n","338 block6b_expand_conv False <Policy \"mixed_float16\">\n","339 block6b_expand_bn False <Policy \"mixed_float16\">\n","340 block6b_expand_activation False <Policy \"mixed_float16\">\n","341 block6b_dwconv False <Policy \"mixed_float16\">\n","342 block6b_bn False <Policy \"mixed_float16\">\n","343 block6b_activation False <Policy \"mixed_float16\">\n","344 block6b_se_squeeze False <Policy \"mixed_float16\">\n","345 block6b_se_reshape False <Policy \"mixed_float16\">\n","346 block6b_se_reduce False <Policy \"mixed_float16\">\n","347 block6b_se_expand False <Policy \"mixed_float16\">\n","348 block6b_se_excite False <Policy \"mixed_float16\">\n","349 block6b_project_conv False <Policy \"mixed_float16\">\n","350 block6b_project_bn False <Policy \"mixed_float16\">\n","351 block6b_drop False <Policy \"mixed_float16\">\n","352 block6b_add False <Policy \"mixed_float16\">\n","353 block6c_expand_conv False <Policy \"mixed_float16\">\n","354 block6c_expand_bn False <Policy \"mixed_float16\">\n","355 block6c_expand_activation False <Policy \"mixed_float16\">\n","356 block6c_dwconv False <Policy \"mixed_float16\">\n","357 block6c_bn False <Policy \"mixed_float16\">\n","358 block6c_activation False <Policy \"mixed_float16\">\n","359 block6c_se_squeeze False <Policy \"mixed_float16\">\n","360 block6c_se_reshape False <Policy \"mixed_float16\">\n","361 block6c_se_reduce False <Policy \"mixed_float16\">\n","362 block6c_se_expand False <Policy \"mixed_float16\">\n","363 block6c_se_excite False <Policy \"mixed_float16\">\n","364 block6c_project_conv False <Policy \"mixed_float16\">\n","365 block6c_project_bn False <Policy \"mixed_float16\">\n","366 block6c_drop False <Policy \"mixed_float16\">\n","367 block6c_add False <Policy \"mixed_float16\">\n","368 block6d_expand_conv False <Policy \"mixed_float16\">\n","369 block6d_expand_bn False <Policy \"mixed_float16\">\n","370 block6d_expand_activation False <Policy \"mixed_float16\">\n","371 block6d_dwconv False <Policy \"mixed_float16\">\n","372 block6d_bn False <Policy \"mixed_float16\">\n","373 block6d_activation False <Policy \"mixed_float16\">\n","374 block6d_se_squeeze False <Policy \"mixed_float16\">\n","375 block6d_se_reshape False <Policy \"mixed_float16\">\n","376 block6d_se_reduce False <Policy \"mixed_float16\">\n","377 block6d_se_expand False <Policy \"mixed_float16\">\n","378 block6d_se_excite False <Policy \"mixed_float16\">\n","379 block6d_project_conv False <Policy \"mixed_float16\">\n","380 block6d_project_bn False <Policy \"mixed_float16\">\n","381 block6d_drop False <Policy \"mixed_float16\">\n","382 block6d_add False <Policy \"mixed_float16\">\n","383 block6e_expand_conv False <Policy \"mixed_float16\">\n","384 block6e_expand_bn False <Policy \"mixed_float16\">\n","385 block6e_expand_activation False <Policy \"mixed_float16\">\n","386 block6e_dwconv False <Policy \"mixed_float16\">\n","387 block6e_bn False <Policy \"mixed_float16\">\n","388 block6e_activation False <Policy \"mixed_float16\">\n","389 block6e_se_squeeze False <Policy \"mixed_float16\">\n","390 block6e_se_reshape False <Policy \"mixed_float16\">\n","391 block6e_se_reduce False <Policy \"mixed_float16\">\n","392 block6e_se_expand False <Policy \"mixed_float16\">\n","393 block6e_se_excite False <Policy \"mixed_float16\">\n","394 block6e_project_conv False <Policy \"mixed_float16\">\n","395 block6e_project_bn False <Policy \"mixed_float16\">\n","396 block6e_drop False <Policy \"mixed_float16\">\n","397 block6e_add False <Policy \"mixed_float16\">\n","398 block6f_expand_conv False <Policy \"mixed_float16\">\n","399 block6f_expand_bn False <Policy \"mixed_float16\">\n","400 block6f_expand_activation False <Policy \"mixed_float16\">\n","401 block6f_dwconv False <Policy \"mixed_float16\">\n","402 block6f_bn False <Policy \"mixed_float16\">\n","403 block6f_activation False <Policy \"mixed_float16\">\n","404 block6f_se_squeeze False <Policy \"mixed_float16\">\n","405 block6f_se_reshape False <Policy \"mixed_float16\">\n","406 block6f_se_reduce False <Policy \"mixed_float16\">\n","407 block6f_se_expand False <Policy \"mixed_float16\">\n","408 block6f_se_excite False <Policy \"mixed_float16\">\n","409 block6f_project_conv False <Policy \"mixed_float16\">\n","410 block6f_project_bn False <Policy \"mixed_float16\">\n","411 block6f_drop False <Policy \"mixed_float16\">\n","412 block6f_add False <Policy \"mixed_float16\">\n","413 block6g_expand_conv False <Policy \"mixed_float16\">\n","414 block6g_expand_bn False <Policy \"mixed_float16\">\n","415 block6g_expand_activation False <Policy \"mixed_float16\">\n","416 block6g_dwconv False <Policy \"mixed_float16\">\n","417 block6g_bn False <Policy \"mixed_float16\">\n","418 block6g_activation False <Policy \"mixed_float16\">\n","419 block6g_se_squeeze False <Policy \"mixed_float16\">\n","420 block6g_se_reshape False <Policy \"mixed_float16\">\n","421 block6g_se_reduce False <Policy \"mixed_float16\">\n","422 block6g_se_expand False <Policy \"mixed_float16\">\n","423 block6g_se_excite False <Policy \"mixed_float16\">\n","424 block6g_project_conv False <Policy \"mixed_float16\">\n","425 block6g_project_bn False <Policy \"mixed_float16\">\n","426 block6g_drop False <Policy \"mixed_float16\">\n","427 block6g_add False <Policy \"mixed_float16\">\n","428 block6h_expand_conv False <Policy \"mixed_float16\">\n","429 block6h_expand_bn False <Policy \"mixed_float16\">\n","430 block6h_expand_activation False <Policy \"mixed_float16\">\n","431 block6h_dwconv False <Policy \"mixed_float16\">\n","432 block6h_bn False <Policy \"mixed_float16\">\n","433 block6h_activation False <Policy \"mixed_float16\">\n","434 block6h_se_squeeze False <Policy \"mixed_float16\">\n","435 block6h_se_reshape False <Policy \"mixed_float16\">\n","436 block6h_se_reduce False <Policy \"mixed_float16\">\n","437 block6h_se_expand False <Policy \"mixed_float16\">\n","438 block6h_se_excite False <Policy \"mixed_float16\">\n","439 block6h_project_conv False <Policy \"mixed_float16\">\n","440 block6h_project_bn False <Policy \"mixed_float16\">\n","441 block6h_drop False <Policy \"mixed_float16\">\n","442 block6h_add False <Policy \"mixed_float16\">\n","443 block7a_expand_conv False <Policy \"mixed_float16\">\n","444 block7a_expand_bn False <Policy \"mixed_float16\">\n","445 block7a_expand_activation False <Policy \"mixed_float16\">\n","446 block7a_dwconv False <Policy \"mixed_float16\">\n","447 block7a_bn False <Policy \"mixed_float16\">\n","448 block7a_activation False <Policy \"mixed_float16\">\n","449 block7a_se_squeeze False <Policy \"mixed_float16\">\n","450 block7a_se_reshape False <Policy \"mixed_float16\">\n","451 block7a_se_reduce False <Policy \"mixed_float16\">\n","452 block7a_se_expand False <Policy \"mixed_float16\">\n","453 block7a_se_excite False <Policy \"mixed_float16\">\n","454 block7a_project_conv False <Policy \"mixed_float16\">\n","455 block7a_project_bn False <Policy \"mixed_float16\">\n","456 block7b_expand_conv False <Policy \"mixed_float16\">\n","457 block7b_expand_bn False <Policy \"mixed_float16\">\n","458 block7b_expand_activation False <Policy \"mixed_float16\">\n","459 block7b_dwconv False <Policy \"mixed_float16\">\n","460 block7b_bn False <Policy \"mixed_float16\">\n","461 block7b_activation False <Policy \"mixed_float16\">\n","462 block7b_se_squeeze False <Policy \"mixed_float16\">\n","463 block7b_se_reshape False <Policy \"mixed_float16\">\n","464 block7b_se_reduce True <Policy \"mixed_float16\">\n","465 block7b_se_expand True <Policy \"mixed_float16\">\n","466 block7b_se_excite True <Policy \"mixed_float16\">\n","467 block7b_project_conv True <Policy \"mixed_float16\">\n","468 block7b_project_bn True <Policy \"mixed_float16\">\n","469 block7b_drop True <Policy \"mixed_float16\">\n","470 block7b_add True <Policy \"mixed_float16\">\n","471 top_conv True <Policy \"mixed_float16\">\n","472 top_bn True <Policy \"mixed_float16\">\n","473 top_activation True <Policy \"mixed_float16\">\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y1MbQM1xevaN","executionInfo":{"status":"ok","timestamp":1628824205043,"user_tz":-420,"elapsed":357,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"9a033763-509e-4bef-9de5-e0dc83c946d1"},"source":["# create callback \n","checkpoint_path = \"Food101_fine_tune_10_layers_efficientnetb4/checkpoint.ckpt\"\n","\n","checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n","                                                         monitor=\"val_accuracy\",\n","                                                         save_best_only=True,\n","                                                         save_weights_only=True)\n","\n","tensorboard_callback = create_tensorboard_callback(dir_name=\"training_logs\",\n","                                                   experiment_name=\"efficientnetb4_101_classes_all_data_fine_tune_10_layers\")\n","\n","earlystopping_callback = tf.keras.callbacks.EarlyStopping(monitor=\"val_accuracy\",\n","                                                          patience=3)"],"execution_count":45,"outputs":[{"output_type":"stream","text":["Saving TensorBoard log files to: training_logs/efficientnetb4_101_classes_all_data_fine_tune_10_layers/20210813-031004\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"toYm3giCfPt2","executionInfo":{"status":"ok","timestamp":1628824243440,"user_tz":-420,"elapsed":325,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["initial_epochs = 3\n","\n","fine_tune_epochs = initial_epochs + 10"],"execution_count":47,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ij0CCIQRe_8I","executionInfo":{"status":"ok","timestamp":1628825377616,"user_tz":-420,"elapsed":1103038,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"74569c02-d095-433f-ab3b-144debc9676e"},"source":["# Fit the feature extraction model with mixed precision \n","history_efficientnetb4_fine_tune_10_layer = model.fit(train_data_optimized,\n","                                                        epochs=fine_tune_epochs,\n","                                                        initial_epoch=inital_epochs,\n","                                                        steps_per_epoch=len(train_data_optimized),\n","                                                        validation_data=test_data_optimized,\n","                                                        validation_steps=int(0.15 * len(test_data_optimized)),\n","                                                        callbacks=[tensorboard_callback,\n","                                                                   earlystopping_callback,\n","                                                                   checkpoint_callback])"],"execution_count":48,"outputs":[{"output_type":"stream","text":["Epoch 4/13\n","2368/2368 [==============================] - 236s 92ms/step - loss: 1.1104 - accuracy: 0.7091 - val_loss: 1.0808 - val_accuracy: 0.7100\n","Epoch 5/13\n","2368/2368 [==============================] - 216s 91ms/step - loss: 0.8999 - accuracy: 0.7594 - val_loss: 1.0660 - val_accuracy: 0.7150\n","Epoch 6/13\n","2368/2368 [==============================] - 216s 90ms/step - loss: 0.7184 - accuracy: 0.8084 - val_loss: 1.0840 - val_accuracy: 0.7142\n","Epoch 7/13\n","2368/2368 [==============================] - 216s 90ms/step - loss: 0.5610 - accuracy: 0.8537 - val_loss: 1.1203 - val_accuracy: 0.7082\n","Epoch 8/13\n","2368/2368 [==============================] - 216s 91ms/step - loss: 0.4270 - accuracy: 0.8956 - val_loss: 1.1892 - val_accuracy: 0.6999\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QZicSgbQfgR1","executionInfo":{"status":"ok","timestamp":1628825477036,"user_tz":-420,"elapsed":67050,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"fa0adc6f-26ed-4c1f-c5e9-549839191b50"},"source":["results_fine_tune_10_layers = model.evaluate(test_data_optimized)\n","results_fine_tune_10_layers"],"execution_count":49,"outputs":[{"output_type":"stream","text":["790/790 [==============================] - 67s 84ms/step - loss: 1.1633 - accuracy: 0.7027\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[1.1632651090621948, 0.7026534676551819]"]},"metadata":{"tags":[]},"execution_count":49}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"yhtoTUQakJhz","executionInfo":{"status":"ok","timestamp":1628825497194,"user_tz":-420,"elapsed":345,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"02de0c15-26fd-4a2a-f398-dab3643d09fc"},"source":["checkpoint_path"],"execution_count":50,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'Food101_fine_tune_10_layers_efficientnetb4/checkpoint.ckpt'"]},"metadata":{"tags":[]},"execution_count":50}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GNSIjOI8j1e1","executionInfo":{"status":"ok","timestamp":1628825594758,"user_tz":-420,"elapsed":65749,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"031f80cd-d7a0-47cf-e8e2-c8469b8552b6"},"source":["model.load_weights(checkpoint_path)\n","results_fine_tune_10_layers = model.evaluate(test_data_optimized)\n","results_fine_tune_10_layers"],"execution_count":51,"outputs":[{"output_type":"stream","text":["790/790 [==============================] - 63s 79ms/step - loss: 1.0524 - accuracy: 0.7114\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[1.0524293184280396, 0.7113663554191589]"]},"metadata":{"tags":[]},"execution_count":51}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":513},"id":"JmC6XKlwlGGF","executionInfo":{"status":"ok","timestamp":1628825755109,"user_tz":-420,"elapsed":1111,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"7eae623c-ac92-4e3e-8d80-17a872516a70"},"source":["compare_historys(history_efficientnetb4_feature_extraction, history_efficientnetb4_fine_tune_10_layer, 3)"],"execution_count":55,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Pmpa9FTZkSf7"},"source":["## Fine tune more 20 layers (last 30 layers)"]},{"cell_type":"code","metadata":{"id":"NtGF0O9DkmPQ","executionInfo":{"status":"ok","timestamp":1628825629709,"user_tz":-420,"elapsed":539,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# unfreeze all layers in the base_model\n","base_model.trainable = True\n","\n","# Freeze all layers except last 10 layers \n","for layer in base_model.layers[:-30]:\n","  layer.trainable = False"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cuuo59tkkq_c","executionInfo":{"status":"ok","timestamp":1628825639047,"user_tz":-420,"elapsed":371,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"88e07695-5dbd-44aa-f069-f215a3b747a9"},"source":["for i, layer in enumerate(model.layers[1].layers):\n","  print(i, layer.name, layer.trainable, layer.dtype_policy)"],"execution_count":53,"outputs":[{"output_type":"stream","text":["0 input_3 False <Policy \"float32\">\n","1 rescaling_2 False <Policy \"mixed_float16\">\n","2 normalization_2 False <Policy \"float32\">\n","3 stem_conv_pad False <Policy \"mixed_float16\">\n","4 stem_conv False <Policy \"mixed_float16\">\n","5 stem_bn False <Policy \"mixed_float16\">\n","6 stem_activation False <Policy \"mixed_float16\">\n","7 block1a_dwconv False <Policy \"mixed_float16\">\n","8 block1a_bn False <Policy \"mixed_float16\">\n","9 block1a_activation False <Policy \"mixed_float16\">\n","10 block1a_se_squeeze False <Policy \"mixed_float16\">\n","11 block1a_se_reshape False <Policy \"mixed_float16\">\n","12 block1a_se_reduce False <Policy \"mixed_float16\">\n","13 block1a_se_expand False <Policy \"mixed_float16\">\n","14 block1a_se_excite False <Policy \"mixed_float16\">\n","15 block1a_project_conv False <Policy \"mixed_float16\">\n","16 block1a_project_bn False <Policy \"mixed_float16\">\n","17 block1b_dwconv False <Policy \"mixed_float16\">\n","18 block1b_bn False <Policy \"mixed_float16\">\n","19 block1b_activation False <Policy \"mixed_float16\">\n","20 block1b_se_squeeze False <Policy \"mixed_float16\">\n","21 block1b_se_reshape False <Policy \"mixed_float16\">\n","22 block1b_se_reduce False <Policy \"mixed_float16\">\n","23 block1b_se_expand False <Policy \"mixed_float16\">\n","24 block1b_se_excite False <Policy \"mixed_float16\">\n","25 block1b_project_conv False <Policy \"mixed_float16\">\n","26 block1b_project_bn False <Policy \"mixed_float16\">\n","27 block1b_drop False <Policy \"mixed_float16\">\n","28 block1b_add False <Policy \"mixed_float16\">\n","29 block2a_expand_conv False <Policy \"mixed_float16\">\n","30 block2a_expand_bn False <Policy \"mixed_float16\">\n","31 block2a_expand_activation False <Policy \"mixed_float16\">\n","32 block2a_dwconv_pad False <Policy \"mixed_float16\">\n","33 block2a_dwconv False <Policy \"mixed_float16\">\n","34 block2a_bn False <Policy \"mixed_float16\">\n","35 block2a_activation False <Policy \"mixed_float16\">\n","36 block2a_se_squeeze False <Policy \"mixed_float16\">\n","37 block2a_se_reshape False <Policy \"mixed_float16\">\n","38 block2a_se_reduce False <Policy \"mixed_float16\">\n","39 block2a_se_expand False <Policy \"mixed_float16\">\n","40 block2a_se_excite False <Policy \"mixed_float16\">\n","41 block2a_project_conv False <Policy \"mixed_float16\">\n","42 block2a_project_bn False <Policy \"mixed_float16\">\n","43 block2b_expand_conv False <Policy \"mixed_float16\">\n","44 block2b_expand_bn False <Policy \"mixed_float16\">\n","45 block2b_expand_activation False <Policy \"mixed_float16\">\n","46 block2b_dwconv False <Policy \"mixed_float16\">\n","47 block2b_bn False <Policy \"mixed_float16\">\n","48 block2b_activation False <Policy \"mixed_float16\">\n","49 block2b_se_squeeze False <Policy \"mixed_float16\">\n","50 block2b_se_reshape False <Policy \"mixed_float16\">\n","51 block2b_se_reduce False <Policy \"mixed_float16\">\n","52 block2b_se_expand False <Policy \"mixed_float16\">\n","53 block2b_se_excite False <Policy \"mixed_float16\">\n","54 block2b_project_conv False <Policy \"mixed_float16\">\n","55 block2b_project_bn False <Policy \"mixed_float16\">\n","56 block2b_drop False <Policy \"mixed_float16\">\n","57 block2b_add False <Policy \"mixed_float16\">\n","58 block2c_expand_conv False <Policy \"mixed_float16\">\n","59 block2c_expand_bn False <Policy \"mixed_float16\">\n","60 block2c_expand_activation False <Policy \"mixed_float16\">\n","61 block2c_dwconv False <Policy \"mixed_float16\">\n","62 block2c_bn False <Policy \"mixed_float16\">\n","63 block2c_activation False <Policy \"mixed_float16\">\n","64 block2c_se_squeeze False <Policy \"mixed_float16\">\n","65 block2c_se_reshape False <Policy \"mixed_float16\">\n","66 block2c_se_reduce False <Policy \"mixed_float16\">\n","67 block2c_se_expand False <Policy \"mixed_float16\">\n","68 block2c_se_excite False <Policy \"mixed_float16\">\n","69 block2c_project_conv False <Policy \"mixed_float16\">\n","70 block2c_project_bn False <Policy \"mixed_float16\">\n","71 block2c_drop False <Policy \"mixed_float16\">\n","72 block2c_add False <Policy \"mixed_float16\">\n","73 block2d_expand_conv False <Policy \"mixed_float16\">\n","74 block2d_expand_bn False <Policy \"mixed_float16\">\n","75 block2d_expand_activation False <Policy \"mixed_float16\">\n","76 block2d_dwconv False <Policy \"mixed_float16\">\n","77 block2d_bn False <Policy \"mixed_float16\">\n","78 block2d_activation False <Policy \"mixed_float16\">\n","79 block2d_se_squeeze False <Policy \"mixed_float16\">\n","80 block2d_se_reshape False <Policy \"mixed_float16\">\n","81 block2d_se_reduce False <Policy \"mixed_float16\">\n","82 block2d_se_expand False <Policy \"mixed_float16\">\n","83 block2d_se_excite False <Policy \"mixed_float16\">\n","84 block2d_project_conv False <Policy \"mixed_float16\">\n","85 block2d_project_bn False <Policy \"mixed_float16\">\n","86 block2d_drop False <Policy \"mixed_float16\">\n","87 block2d_add False <Policy \"mixed_float16\">\n","88 block3a_expand_conv False <Policy \"mixed_float16\">\n","89 block3a_expand_bn False <Policy \"mixed_float16\">\n","90 block3a_expand_activation False <Policy \"mixed_float16\">\n","91 block3a_dwconv_pad False <Policy \"mixed_float16\">\n","92 block3a_dwconv False <Policy \"mixed_float16\">\n","93 block3a_bn False <Policy \"mixed_float16\">\n","94 block3a_activation False <Policy \"mixed_float16\">\n","95 block3a_se_squeeze False <Policy \"mixed_float16\">\n","96 block3a_se_reshape False <Policy \"mixed_float16\">\n","97 block3a_se_reduce False <Policy \"mixed_float16\">\n","98 block3a_se_expand False <Policy \"mixed_float16\">\n","99 block3a_se_excite False <Policy \"mixed_float16\">\n","100 block3a_project_conv False <Policy \"mixed_float16\">\n","101 block3a_project_bn False <Policy \"mixed_float16\">\n","102 block3b_expand_conv False <Policy \"mixed_float16\">\n","103 block3b_expand_bn False <Policy \"mixed_float16\">\n","104 block3b_expand_activation False <Policy \"mixed_float16\">\n","105 block3b_dwconv False <Policy \"mixed_float16\">\n","106 block3b_bn False <Policy \"mixed_float16\">\n","107 block3b_activation False <Policy \"mixed_float16\">\n","108 block3b_se_squeeze False <Policy \"mixed_float16\">\n","109 block3b_se_reshape False <Policy \"mixed_float16\">\n","110 block3b_se_reduce False <Policy \"mixed_float16\">\n","111 block3b_se_expand False <Policy \"mixed_float16\">\n","112 block3b_se_excite False <Policy \"mixed_float16\">\n","113 block3b_project_conv False <Policy \"mixed_float16\">\n","114 block3b_project_bn False <Policy \"mixed_float16\">\n","115 block3b_drop False <Policy \"mixed_float16\">\n","116 block3b_add False <Policy \"mixed_float16\">\n","117 block3c_expand_conv False <Policy \"mixed_float16\">\n","118 block3c_expand_bn False <Policy \"mixed_float16\">\n","119 block3c_expand_activation False <Policy \"mixed_float16\">\n","120 block3c_dwconv False <Policy \"mixed_float16\">\n","121 block3c_bn False <Policy \"mixed_float16\">\n","122 block3c_activation False <Policy \"mixed_float16\">\n","123 block3c_se_squeeze False <Policy \"mixed_float16\">\n","124 block3c_se_reshape False <Policy \"mixed_float16\">\n","125 block3c_se_reduce False <Policy \"mixed_float16\">\n","126 block3c_se_expand False <Policy \"mixed_float16\">\n","127 block3c_se_excite False <Policy \"mixed_float16\">\n","128 block3c_project_conv False <Policy \"mixed_float16\">\n","129 block3c_project_bn False <Policy \"mixed_float16\">\n","130 block3c_drop False <Policy \"mixed_float16\">\n","131 block3c_add False <Policy \"mixed_float16\">\n","132 block3d_expand_conv False <Policy \"mixed_float16\">\n","133 block3d_expand_bn False <Policy \"mixed_float16\">\n","134 block3d_expand_activation False <Policy \"mixed_float16\">\n","135 block3d_dwconv False <Policy \"mixed_float16\">\n","136 block3d_bn False <Policy \"mixed_float16\">\n","137 block3d_activation False <Policy \"mixed_float16\">\n","138 block3d_se_squeeze False <Policy \"mixed_float16\">\n","139 block3d_se_reshape False <Policy \"mixed_float16\">\n","140 block3d_se_reduce False <Policy \"mixed_float16\">\n","141 block3d_se_expand False <Policy \"mixed_float16\">\n","142 block3d_se_excite False <Policy \"mixed_float16\">\n","143 block3d_project_conv False <Policy \"mixed_float16\">\n","144 block3d_project_bn False <Policy \"mixed_float16\">\n","145 block3d_drop False <Policy \"mixed_float16\">\n","146 block3d_add False <Policy \"mixed_float16\">\n","147 block4a_expand_conv False <Policy \"mixed_float16\">\n","148 block4a_expand_bn False <Policy \"mixed_float16\">\n","149 block4a_expand_activation False <Policy \"mixed_float16\">\n","150 block4a_dwconv_pad False <Policy \"mixed_float16\">\n","151 block4a_dwconv False <Policy \"mixed_float16\">\n","152 block4a_bn False <Policy \"mixed_float16\">\n","153 block4a_activation False <Policy \"mixed_float16\">\n","154 block4a_se_squeeze False <Policy \"mixed_float16\">\n","155 block4a_se_reshape False <Policy \"mixed_float16\">\n","156 block4a_se_reduce False <Policy \"mixed_float16\">\n","157 block4a_se_expand False <Policy \"mixed_float16\">\n","158 block4a_se_excite False <Policy \"mixed_float16\">\n","159 block4a_project_conv False <Policy \"mixed_float16\">\n","160 block4a_project_bn False <Policy \"mixed_float16\">\n","161 block4b_expand_conv False <Policy \"mixed_float16\">\n","162 block4b_expand_bn False <Policy \"mixed_float16\">\n","163 block4b_expand_activation False <Policy \"mixed_float16\">\n","164 block4b_dwconv False <Policy \"mixed_float16\">\n","165 block4b_bn False <Policy \"mixed_float16\">\n","166 block4b_activation False <Policy \"mixed_float16\">\n","167 block4b_se_squeeze False <Policy \"mixed_float16\">\n","168 block4b_se_reshape False <Policy \"mixed_float16\">\n","169 block4b_se_reduce False <Policy \"mixed_float16\">\n","170 block4b_se_expand False <Policy \"mixed_float16\">\n","171 block4b_se_excite False <Policy \"mixed_float16\">\n","172 block4b_project_conv False <Policy \"mixed_float16\">\n","173 block4b_project_bn False <Policy \"mixed_float16\">\n","174 block4b_drop False <Policy \"mixed_float16\">\n","175 block4b_add False <Policy \"mixed_float16\">\n","176 block4c_expand_conv False <Policy \"mixed_float16\">\n","177 block4c_expand_bn False <Policy \"mixed_float16\">\n","178 block4c_expand_activation False <Policy \"mixed_float16\">\n","179 block4c_dwconv False <Policy \"mixed_float16\">\n","180 block4c_bn False <Policy \"mixed_float16\">\n","181 block4c_activation False <Policy \"mixed_float16\">\n","182 block4c_se_squeeze False <Policy \"mixed_float16\">\n","183 block4c_se_reshape False <Policy \"mixed_float16\">\n","184 block4c_se_reduce False <Policy \"mixed_float16\">\n","185 block4c_se_expand False <Policy \"mixed_float16\">\n","186 block4c_se_excite False <Policy \"mixed_float16\">\n","187 block4c_project_conv False <Policy \"mixed_float16\">\n","188 block4c_project_bn False <Policy \"mixed_float16\">\n","189 block4c_drop False <Policy \"mixed_float16\">\n","190 block4c_add False <Policy \"mixed_float16\">\n","191 block4d_expand_conv False <Policy \"mixed_float16\">\n","192 block4d_expand_bn False <Policy \"mixed_float16\">\n","193 block4d_expand_activation False <Policy \"mixed_float16\">\n","194 block4d_dwconv False <Policy \"mixed_float16\">\n","195 block4d_bn False <Policy \"mixed_float16\">\n","196 block4d_activation False <Policy \"mixed_float16\">\n","197 block4d_se_squeeze False <Policy \"mixed_float16\">\n","198 block4d_se_reshape False <Policy \"mixed_float16\">\n","199 block4d_se_reduce False <Policy \"mixed_float16\">\n","200 block4d_se_expand False <Policy \"mixed_float16\">\n","201 block4d_se_excite False <Policy \"mixed_float16\">\n","202 block4d_project_conv False <Policy \"mixed_float16\">\n","203 block4d_project_bn False <Policy \"mixed_float16\">\n","204 block4d_drop False <Policy \"mixed_float16\">\n","205 block4d_add False <Policy \"mixed_float16\">\n","206 block4e_expand_conv False <Policy \"mixed_float16\">\n","207 block4e_expand_bn False <Policy \"mixed_float16\">\n","208 block4e_expand_activation False <Policy \"mixed_float16\">\n","209 block4e_dwconv False <Policy \"mixed_float16\">\n","210 block4e_bn False <Policy \"mixed_float16\">\n","211 block4e_activation False <Policy \"mixed_float16\">\n","212 block4e_se_squeeze False <Policy \"mixed_float16\">\n","213 block4e_se_reshape False <Policy \"mixed_float16\">\n","214 block4e_se_reduce False <Policy \"mixed_float16\">\n","215 block4e_se_expand False <Policy \"mixed_float16\">\n","216 block4e_se_excite False <Policy \"mixed_float16\">\n","217 block4e_project_conv False <Policy \"mixed_float16\">\n","218 block4e_project_bn False <Policy \"mixed_float16\">\n","219 block4e_drop False <Policy \"mixed_float16\">\n","220 block4e_add False <Policy \"mixed_float16\">\n","221 block4f_expand_conv False <Policy \"mixed_float16\">\n","222 block4f_expand_bn False <Policy \"mixed_float16\">\n","223 block4f_expand_activation False <Policy \"mixed_float16\">\n","224 block4f_dwconv False <Policy \"mixed_float16\">\n","225 block4f_bn False <Policy \"mixed_float16\">\n","226 block4f_activation False <Policy \"mixed_float16\">\n","227 block4f_se_squeeze False <Policy \"mixed_float16\">\n","228 block4f_se_reshape False <Policy \"mixed_float16\">\n","229 block4f_se_reduce False <Policy \"mixed_float16\">\n","230 block4f_se_expand False <Policy \"mixed_float16\">\n","231 block4f_se_excite False <Policy \"mixed_float16\">\n","232 block4f_project_conv False <Policy \"mixed_float16\">\n","233 block4f_project_bn False <Policy \"mixed_float16\">\n","234 block4f_drop False <Policy \"mixed_float16\">\n","235 block4f_add False <Policy \"mixed_float16\">\n","236 block5a_expand_conv False <Policy \"mixed_float16\">\n","237 block5a_expand_bn False <Policy \"mixed_float16\">\n","238 block5a_expand_activation False <Policy \"mixed_float16\">\n","239 block5a_dwconv False <Policy \"mixed_float16\">\n","240 block5a_bn False <Policy \"mixed_float16\">\n","241 block5a_activation False <Policy \"mixed_float16\">\n","242 block5a_se_squeeze False <Policy \"mixed_float16\">\n","243 block5a_se_reshape False <Policy \"mixed_float16\">\n","244 block5a_se_reduce False <Policy \"mixed_float16\">\n","245 block5a_se_expand False <Policy \"mixed_float16\">\n","246 block5a_se_excite False <Policy \"mixed_float16\">\n","247 block5a_project_conv False <Policy \"mixed_float16\">\n","248 block5a_project_bn False <Policy \"mixed_float16\">\n","249 block5b_expand_conv False <Policy \"mixed_float16\">\n","250 block5b_expand_bn False <Policy \"mixed_float16\">\n","251 block5b_expand_activation False <Policy \"mixed_float16\">\n","252 block5b_dwconv False <Policy \"mixed_float16\">\n","253 block5b_bn False <Policy \"mixed_float16\">\n","254 block5b_activation False <Policy \"mixed_float16\">\n","255 block5b_se_squeeze False <Policy \"mixed_float16\">\n","256 block5b_se_reshape False <Policy \"mixed_float16\">\n","257 block5b_se_reduce False <Policy \"mixed_float16\">\n","258 block5b_se_expand False <Policy \"mixed_float16\">\n","259 block5b_se_excite False <Policy \"mixed_float16\">\n","260 block5b_project_conv False <Policy \"mixed_float16\">\n","261 block5b_project_bn False <Policy \"mixed_float16\">\n","262 block5b_drop False <Policy \"mixed_float16\">\n","263 block5b_add False <Policy \"mixed_float16\">\n","264 block5c_expand_conv False <Policy \"mixed_float16\">\n","265 block5c_expand_bn False <Policy \"mixed_float16\">\n","266 block5c_expand_activation False <Policy \"mixed_float16\">\n","267 block5c_dwconv False <Policy \"mixed_float16\">\n","268 block5c_bn False <Policy \"mixed_float16\">\n","269 block5c_activation False <Policy \"mixed_float16\">\n","270 block5c_se_squeeze False <Policy \"mixed_float16\">\n","271 block5c_se_reshape False <Policy \"mixed_float16\">\n","272 block5c_se_reduce False <Policy \"mixed_float16\">\n","273 block5c_se_expand False <Policy \"mixed_float16\">\n","274 block5c_se_excite False <Policy \"mixed_float16\">\n","275 block5c_project_conv False <Policy \"mixed_float16\">\n","276 block5c_project_bn False <Policy \"mixed_float16\">\n","277 block5c_drop False <Policy \"mixed_float16\">\n","278 block5c_add False <Policy \"mixed_float16\">\n","279 block5d_expand_conv False <Policy \"mixed_float16\">\n","280 block5d_expand_bn False <Policy \"mixed_float16\">\n","281 block5d_expand_activation False <Policy \"mixed_float16\">\n","282 block5d_dwconv False <Policy \"mixed_float16\">\n","283 block5d_bn False <Policy \"mixed_float16\">\n","284 block5d_activation False <Policy \"mixed_float16\">\n","285 block5d_se_squeeze False <Policy \"mixed_float16\">\n","286 block5d_se_reshape False <Policy \"mixed_float16\">\n","287 block5d_se_reduce False <Policy \"mixed_float16\">\n","288 block5d_se_expand False <Policy \"mixed_float16\">\n","289 block5d_se_excite False <Policy \"mixed_float16\">\n","290 block5d_project_conv False <Policy \"mixed_float16\">\n","291 block5d_project_bn False <Policy \"mixed_float16\">\n","292 block5d_drop False <Policy \"mixed_float16\">\n","293 block5d_add False <Policy \"mixed_float16\">\n","294 block5e_expand_conv False <Policy \"mixed_float16\">\n","295 block5e_expand_bn False <Policy \"mixed_float16\">\n","296 block5e_expand_activation False <Policy \"mixed_float16\">\n","297 block5e_dwconv False <Policy \"mixed_float16\">\n","298 block5e_bn False <Policy \"mixed_float16\">\n","299 block5e_activation False <Policy \"mixed_float16\">\n","300 block5e_se_squeeze False <Policy \"mixed_float16\">\n","301 block5e_se_reshape False <Policy \"mixed_float16\">\n","302 block5e_se_reduce False <Policy \"mixed_float16\">\n","303 block5e_se_expand False <Policy \"mixed_float16\">\n","304 block5e_se_excite False <Policy \"mixed_float16\">\n","305 block5e_project_conv False <Policy \"mixed_float16\">\n","306 block5e_project_bn False <Policy \"mixed_float16\">\n","307 block5e_drop False <Policy \"mixed_float16\">\n","308 block5e_add False <Policy \"mixed_float16\">\n","309 block5f_expand_conv False <Policy \"mixed_float16\">\n","310 block5f_expand_bn False <Policy \"mixed_float16\">\n","311 block5f_expand_activation False <Policy \"mixed_float16\">\n","312 block5f_dwconv False <Policy \"mixed_float16\">\n","313 block5f_bn False <Policy \"mixed_float16\">\n","314 block5f_activation False <Policy \"mixed_float16\">\n","315 block5f_se_squeeze False <Policy \"mixed_float16\">\n","316 block5f_se_reshape False <Policy \"mixed_float16\">\n","317 block5f_se_reduce False <Policy \"mixed_float16\">\n","318 block5f_se_expand False <Policy \"mixed_float16\">\n","319 block5f_se_excite False <Policy \"mixed_float16\">\n","320 block5f_project_conv False <Policy \"mixed_float16\">\n","321 block5f_project_bn False <Policy \"mixed_float16\">\n","322 block5f_drop False <Policy \"mixed_float16\">\n","323 block5f_add False <Policy \"mixed_float16\">\n","324 block6a_expand_conv False <Policy \"mixed_float16\">\n","325 block6a_expand_bn False <Policy \"mixed_float16\">\n","326 block6a_expand_activation False <Policy \"mixed_float16\">\n","327 block6a_dwconv_pad False <Policy \"mixed_float16\">\n","328 block6a_dwconv False <Policy \"mixed_float16\">\n","329 block6a_bn False <Policy \"mixed_float16\">\n","330 block6a_activation False <Policy \"mixed_float16\">\n","331 block6a_se_squeeze False <Policy \"mixed_float16\">\n","332 block6a_se_reshape False <Policy \"mixed_float16\">\n","333 block6a_se_reduce False <Policy \"mixed_float16\">\n","334 block6a_se_expand False <Policy \"mixed_float16\">\n","335 block6a_se_excite False <Policy \"mixed_float16\">\n","336 block6a_project_conv False <Policy \"mixed_float16\">\n","337 block6a_project_bn False <Policy \"mixed_float16\">\n","338 block6b_expand_conv False <Policy \"mixed_float16\">\n","339 block6b_expand_bn False <Policy \"mixed_float16\">\n","340 block6b_expand_activation False <Policy \"mixed_float16\">\n","341 block6b_dwconv False <Policy \"mixed_float16\">\n","342 block6b_bn False <Policy \"mixed_float16\">\n","343 block6b_activation False <Policy \"mixed_float16\">\n","344 block6b_se_squeeze False <Policy \"mixed_float16\">\n","345 block6b_se_reshape False <Policy \"mixed_float16\">\n","346 block6b_se_reduce False <Policy \"mixed_float16\">\n","347 block6b_se_expand False <Policy \"mixed_float16\">\n","348 block6b_se_excite False <Policy \"mixed_float16\">\n","349 block6b_project_conv False <Policy \"mixed_float16\">\n","350 block6b_project_bn False <Policy \"mixed_float16\">\n","351 block6b_drop False <Policy \"mixed_float16\">\n","352 block6b_add False <Policy \"mixed_float16\">\n","353 block6c_expand_conv False <Policy \"mixed_float16\">\n","354 block6c_expand_bn False <Policy \"mixed_float16\">\n","355 block6c_expand_activation False <Policy \"mixed_float16\">\n","356 block6c_dwconv False <Policy \"mixed_float16\">\n","357 block6c_bn False <Policy \"mixed_float16\">\n","358 block6c_activation False <Policy \"mixed_float16\">\n","359 block6c_se_squeeze False <Policy \"mixed_float16\">\n","360 block6c_se_reshape False <Policy \"mixed_float16\">\n","361 block6c_se_reduce False <Policy \"mixed_float16\">\n","362 block6c_se_expand False <Policy \"mixed_float16\">\n","363 block6c_se_excite False <Policy \"mixed_float16\">\n","364 block6c_project_conv False <Policy \"mixed_float16\">\n","365 block6c_project_bn False <Policy \"mixed_float16\">\n","366 block6c_drop False <Policy \"mixed_float16\">\n","367 block6c_add False <Policy \"mixed_float16\">\n","368 block6d_expand_conv False <Policy \"mixed_float16\">\n","369 block6d_expand_bn False <Policy \"mixed_float16\">\n","370 block6d_expand_activation False <Policy \"mixed_float16\">\n","371 block6d_dwconv False <Policy \"mixed_float16\">\n","372 block6d_bn False <Policy \"mixed_float16\">\n","373 block6d_activation False <Policy \"mixed_float16\">\n","374 block6d_se_squeeze False <Policy \"mixed_float16\">\n","375 block6d_se_reshape False <Policy \"mixed_float16\">\n","376 block6d_se_reduce False <Policy \"mixed_float16\">\n","377 block6d_se_expand False <Policy \"mixed_float16\">\n","378 block6d_se_excite False <Policy \"mixed_float16\">\n","379 block6d_project_conv False <Policy \"mixed_float16\">\n","380 block6d_project_bn False <Policy \"mixed_float16\">\n","381 block6d_drop False <Policy \"mixed_float16\">\n","382 block6d_add False <Policy \"mixed_float16\">\n","383 block6e_expand_conv False <Policy \"mixed_float16\">\n","384 block6e_expand_bn False <Policy \"mixed_float16\">\n","385 block6e_expand_activation False <Policy \"mixed_float16\">\n","386 block6e_dwconv False <Policy \"mixed_float16\">\n","387 block6e_bn False <Policy \"mixed_float16\">\n","388 block6e_activation False <Policy \"mixed_float16\">\n","389 block6e_se_squeeze False <Policy \"mixed_float16\">\n","390 block6e_se_reshape False <Policy \"mixed_float16\">\n","391 block6e_se_reduce False <Policy \"mixed_float16\">\n","392 block6e_se_expand False <Policy \"mixed_float16\">\n","393 block6e_se_excite False <Policy \"mixed_float16\">\n","394 block6e_project_conv False <Policy \"mixed_float16\">\n","395 block6e_project_bn False <Policy \"mixed_float16\">\n","396 block6e_drop False <Policy \"mixed_float16\">\n","397 block6e_add False <Policy \"mixed_float16\">\n","398 block6f_expand_conv False <Policy \"mixed_float16\">\n","399 block6f_expand_bn False <Policy \"mixed_float16\">\n","400 block6f_expand_activation False <Policy \"mixed_float16\">\n","401 block6f_dwconv False <Policy \"mixed_float16\">\n","402 block6f_bn False <Policy \"mixed_float16\">\n","403 block6f_activation False <Policy \"mixed_float16\">\n","404 block6f_se_squeeze False <Policy \"mixed_float16\">\n","405 block6f_se_reshape False <Policy \"mixed_float16\">\n","406 block6f_se_reduce False <Policy \"mixed_float16\">\n","407 block6f_se_expand False <Policy \"mixed_float16\">\n","408 block6f_se_excite False <Policy \"mixed_float16\">\n","409 block6f_project_conv False <Policy \"mixed_float16\">\n","410 block6f_project_bn False <Policy \"mixed_float16\">\n","411 block6f_drop False <Policy \"mixed_float16\">\n","412 block6f_add False <Policy \"mixed_float16\">\n","413 block6g_expand_conv False <Policy \"mixed_float16\">\n","414 block6g_expand_bn False <Policy \"mixed_float16\">\n","415 block6g_expand_activation False <Policy \"mixed_float16\">\n","416 block6g_dwconv False <Policy \"mixed_float16\">\n","417 block6g_bn False <Policy \"mixed_float16\">\n","418 block6g_activation False <Policy \"mixed_float16\">\n","419 block6g_se_squeeze False <Policy \"mixed_float16\">\n","420 block6g_se_reshape False <Policy \"mixed_float16\">\n","421 block6g_se_reduce False <Policy \"mixed_float16\">\n","422 block6g_se_expand False <Policy \"mixed_float16\">\n","423 block6g_se_excite False <Policy \"mixed_float16\">\n","424 block6g_project_conv False <Policy \"mixed_float16\">\n","425 block6g_project_bn False <Policy \"mixed_float16\">\n","426 block6g_drop False <Policy \"mixed_float16\">\n","427 block6g_add False <Policy \"mixed_float16\">\n","428 block6h_expand_conv False <Policy \"mixed_float16\">\n","429 block6h_expand_bn False <Policy \"mixed_float16\">\n","430 block6h_expand_activation False <Policy \"mixed_float16\">\n","431 block6h_dwconv False <Policy \"mixed_float16\">\n","432 block6h_bn False <Policy \"mixed_float16\">\n","433 block6h_activation False <Policy \"mixed_float16\">\n","434 block6h_se_squeeze False <Policy \"mixed_float16\">\n","435 block6h_se_reshape False <Policy \"mixed_float16\">\n","436 block6h_se_reduce False <Policy \"mixed_float16\">\n","437 block6h_se_expand False <Policy \"mixed_float16\">\n","438 block6h_se_excite False <Policy \"mixed_float16\">\n","439 block6h_project_conv False <Policy \"mixed_float16\">\n","440 block6h_project_bn False <Policy \"mixed_float16\">\n","441 block6h_drop False <Policy \"mixed_float16\">\n","442 block6h_add False <Policy \"mixed_float16\">\n","443 block7a_expand_conv False <Policy \"mixed_float16\">\n","444 block7a_expand_bn True <Policy \"mixed_float16\">\n","445 block7a_expand_activation True <Policy \"mixed_float16\">\n","446 block7a_dwconv True <Policy \"mixed_float16\">\n","447 block7a_bn True <Policy \"mixed_float16\">\n","448 block7a_activation True <Policy \"mixed_float16\">\n","449 block7a_se_squeeze True <Policy \"mixed_float16\">\n","450 block7a_se_reshape True <Policy \"mixed_float16\">\n","451 block7a_se_reduce True <Policy \"mixed_float16\">\n","452 block7a_se_expand True <Policy \"mixed_float16\">\n","453 block7a_se_excite True <Policy \"mixed_float16\">\n","454 block7a_project_conv True <Policy \"mixed_float16\">\n","455 block7a_project_bn True <Policy \"mixed_float16\">\n","456 block7b_expand_conv True <Policy \"mixed_float16\">\n","457 block7b_expand_bn True <Policy \"mixed_float16\">\n","458 block7b_expand_activation True <Policy \"mixed_float16\">\n","459 block7b_dwconv True <Policy \"mixed_float16\">\n","460 block7b_bn True <Policy \"mixed_float16\">\n","461 block7b_activation True <Policy \"mixed_float16\">\n","462 block7b_se_squeeze True <Policy \"mixed_float16\">\n","463 block7b_se_reshape True <Policy \"mixed_float16\">\n","464 block7b_se_reduce True <Policy \"mixed_float16\">\n","465 block7b_se_expand True <Policy \"mixed_float16\">\n","466 block7b_se_excite True <Policy \"mixed_float16\">\n","467 block7b_project_conv True <Policy \"mixed_float16\">\n","468 block7b_project_bn True <Policy \"mixed_float16\">\n","469 block7b_drop True <Policy \"mixed_float16\">\n","470 block7b_add True <Policy \"mixed_float16\">\n","471 top_conv True <Policy \"mixed_float16\">\n","472 top_bn True <Policy \"mixed_float16\">\n","473 top_activation True <Policy \"mixed_float16\">\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vmMi7ICzktTs","executionInfo":{"status":"ok","timestamp":1628825677681,"user_tz":-420,"elapsed":893,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"8ca269ec-0b08-4d72-ca38-6dda9a7cdec1"},"source":["# create callback \n","checkpoint_path = \"Food101_fine_tune_30_layers_efficientnetb4/checkpoint.ckpt\"\n","\n","checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n","                                                         monitor=\"val_accuracy\",\n","                                                         save_best_only=True,\n","                                                         save_weights_only=True)\n","\n","tensorboard_callback = create_tensorboard_callback(dir_name=\"training_logs\",\n","                                                   experiment_name=\"efficientnetb4_101_classes_all_data_fine_tune_30_layers\")\n","\n","earlystopping_callback = tf.keras.callbacks.EarlyStopping(monitor=\"val_accuracy\",\n","                                                          patience=3)"],"execution_count":54,"outputs":[{"output_type":"stream","text":["Saving TensorBoard log files to: training_logs/efficientnetb4_101_classes_all_data_fine_tune_30_layers/20210813-033436\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"X4h4c1aGlPvJ","executionInfo":{"status":"ok","timestamp":1628825813585,"user_tz":-420,"elapsed":510,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["model.compile(\n","    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(learning_rate=0.00001), # 100x the default\n","    metrics=[\"accuracy\"]\n",")"],"execution_count":56,"outputs":[]},{"cell_type":"code","metadata":{"id":"VDqIbeGslCv8","executionInfo":{"status":"ok","timestamp":1628825815216,"user_tz":-420,"elapsed":2,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["initial_epochs = 5\n","\n","fine_tune_epochs = initial_epochs + 10"],"execution_count":57,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qvScz-syk2nE","executionInfo":{"status":"ok","timestamp":1628826984203,"user_tz":-420,"elapsed":1166655,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"41786cb4-7f4c-4d5c-f0f3-623f4432aed1"},"source":["# Fit the feature extraction model with mixed precision \n","history_efficientnetb4_fine_tune_30_layer = model.fit(train_data_optimized,\n","                                                        epochs=fine_tune_epochs,\n","                                                        initial_epoch=inital_epochs,\n","                                                        steps_per_epoch=len(train_data_optimized),\n","                                                        validation_data=test_data_optimized,\n","                                                        validation_steps=int(0.15 * len(test_data_optimized)),\n","                                                        callbacks=[tensorboard_callback,\n","                                                                   earlystopping_callback,\n","                                                                   checkpoint_callback])"],"execution_count":58,"outputs":[{"output_type":"stream","text":["Epoch 4/15\n","2368/2368 [==============================] - 245s 98ms/step - loss: 0.6636 - accuracy: 0.8239 - val_loss: 1.0996 - val_accuracy: 0.7142\n","Epoch 5/15\n","2368/2368 [==============================] - 231s 97ms/step - loss: 0.5672 - accuracy: 0.8517 - val_loss: 1.1181 - val_accuracy: 0.7164\n","Epoch 6/15\n","2368/2368 [==============================] - 230s 96ms/step - loss: 0.4998 - accuracy: 0.8711 - val_loss: 1.1477 - val_accuracy: 0.7140\n","Epoch 7/15\n","2368/2368 [==============================] - 230s 96ms/step - loss: 0.4393 - accuracy: 0.8894 - val_loss: 1.1797 - val_accuracy: 0.7076\n","Epoch 8/15\n","2368/2368 [==============================] - 229s 96ms/step - loss: 0.3833 - accuracy: 0.9066 - val_loss: 1.2196 - val_accuracy: 0.7071\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Zx7NLnlp4ta","executionInfo":{"status":"ok","timestamp":1628827096434,"user_tz":-420,"elapsed":67671,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"17290d46-6126-4b68-9f14-a888f49fe22b"},"source":["results_fine_tune_30_layers = model.evaluate(test_data_optimized)\n","results_fine_tune_30_layers"],"execution_count":59,"outputs":[{"output_type":"stream","text":["790/790 [==============================] - 66s 84ms/step - loss: 1.1955 - accuracy: 0.7093\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[1.1954814195632935, 0.7092673182487488]"]},"metadata":{"tags":[]},"execution_count":59}]},{"cell_type":"markdown","metadata":{"id":"eXyEFjZMlY8R"},"source":["## Fine tune the whole base model layers\n","\n","this time we load the feature extract checkpoint and then fine tune the whole model"]},{"cell_type":"code","metadata":{"id":"n-GI21K5ohp-","executionInfo":{"status":"ok","timestamp":1628827121909,"user_tz":-420,"elapsed":624,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# create callback \n","checkpoint_path = \"Food101_feature_extract_efficientnetb4/checkpoint.ckpt\""],"execution_count":60,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6TWmb1TwonGi","executionInfo":{"status":"ok","timestamp":1628827191267,"user_tz":-420,"elapsed":67638,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"23698f97-a4ed-4bce-f894-c8eaa429f093"},"source":["model.load_weights(checkpoint_path)\n","model.evaluate(test_data_optimized)"],"execution_count":61,"outputs":[{"output_type":"stream","text":["790/790 [==============================] - 63s 80ms/step - loss: 1.2015 - accuracy: 0.6779\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[1.2014997005462646, 0.6779009699821472]"]},"metadata":{"tags":[]},"execution_count":61}]},{"cell_type":"code","metadata":{"id":"d7T41QO0ou_h","executionInfo":{"status":"ok","timestamp":1628827210296,"user_tz":-420,"elapsed":492,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# Unfreeze all the layers in the base model\n","base_model.trainable = True"],"execution_count":63,"outputs":[]},{"cell_type":"code","metadata":{"id":"oyI-XATco1OA","executionInfo":{"status":"ok","timestamp":1628827214274,"user_tz":-420,"elapsed":3,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["# Compile the model again\n","model.compile(\n","    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), # 10x the default lr\n","    metrics=[\"accuracy\"]\n",")"],"execution_count":64,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SHjPLxyzovP_","executionInfo":{"status":"ok","timestamp":1628827243326,"user_tz":-420,"elapsed":801,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"0fec37b1-ecfd-42a8-e852-8da11e1830f2"},"source":["# create callback \n","checkpoint_path = \"Food101_fine_tune_all_layers_efficientnetb4/checkpoint.ckpt\"\n","\n","checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n","                                                         monitor=\"val_accuracy\",\n","                                                         save_best_only=True,\n","                                                         save_weights_only=True)\n","\n","tensorboard_callback = create_tensorboard_callback(dir_name=\"training_logs\",\n","                                                   experiment_name=\"efficientnetb4_101_classes_all_data_fine_tune_all_layers\")\n","\n","earlystopping_callback = tf.keras.callbacks.EarlyStopping(monitor=\"val_accuracy\",\n","                                                          patience=5)"],"execution_count":68,"outputs":[{"output_type":"stream","text":["Saving TensorBoard log files to: training_logs/efficientnetb4_101_classes_all_data_fine_tune_all_layers/20210813-040042\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"lKmjenCLpwuE","executionInfo":{"status":"ok","timestamp":1628827245285,"user_tz":-420,"elapsed":2,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["initial_epochs = 3\n","\n","fine_tune_epochs = initial_epochs + 20"],"execution_count":69,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":530},"id":"rgR9bBMspOaF","executionInfo":{"status":"error","timestamp":1628829902965,"user_tz":-420,"elapsed":2651816,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"817aa78e-a278-4ec0-8759-6fc606db58b3"},"source":["# Fit the feature extraction model with mixed precision \n","history_efficientnetb4_fine_tune_all_layer = model.fit(train_data_optimized,\n","                                                        epochs=fine_tune_epochs,\n","                                                        initial_epoch=inital_epochs,\n","                                                        steps_per_epoch=len(train_data_optimized),\n","                                                        validation_data=test_data_optimized,\n","                                                        validation_steps=int(0.15 * len(test_data_optimized)),\n","                                                        callbacks=[tensorboard_callback,\n","                                                                   earlystopping_callback,\n","                                                                   checkpoint_callback])"],"execution_count":70,"outputs":[{"output_type":"stream","text":["Epoch 4/23\n","2368/2368 [==============================] - 684s 279ms/step - loss: 1.0390 - accuracy: 0.7225 - val_loss: 0.6910 - val_accuracy: 0.8096\n","Epoch 5/23\n","2368/2368 [==============================] - 652s 275ms/step - loss: 0.4193 - accuracy: 0.8828 - val_loss: 0.8002 - val_accuracy: 0.7940\n","Epoch 6/23\n","2368/2368 [==============================] - 657s 274ms/step - loss: 0.1437 - accuracy: 0.9566 - val_loss: 0.9199 - val_accuracy: 0.7953\n","Epoch 7/23\n","2368/2368 [==============================] - 650s 274ms/step - loss: 0.0906 - accuracy: 0.9722 - val_loss: 0.9634 - val_accuracy: 0.8027\n","Epoch 8/23\n","  17/2368 [..............................] - ETA: 10:36 - loss: 0.0590 - accuracy: 0.9827"],"name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-70-3bef296ba972>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m                                                         callbacks=[tensorboard_callback,\n\u001b[1;32m      9\u001b[0m                                                                    \u001b[0mearlystopping_callback\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m                                                                    checkpoint_callback])\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/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   1098\u001b[0m                 _r=1):\n\u001b[1;32m   1099\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-> 1100\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   1101\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   1102\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/usr/local/lib/python3.7/dist-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    826\u001b[0m     \u001b[0mtracing_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[1;32m    827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\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    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\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/usr/local/lib/python3.7/dist-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    853\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    854\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--> 855\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 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graph_function._call_flat(\n\u001b[0;32m-> 2943\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2945\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/usr/local/lib/python3.7/dist-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   1917\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   1918\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1919\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1921\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/usr/local/lib/python3.7/dist-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    558\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    559\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--> 560\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    561\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    562\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/usr/local/lib/python3.7/dist-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     58\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     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\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     62\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: "]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9aqHid1UuRh9","executionInfo":{"status":"ok","timestamp":1628829973619,"user_tz":-420,"elapsed":67710,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"daa49533-ad8e-4b79-8876-a92ad6fdfea6"},"source":["results_fine_tune_all_layers = model.evaluate(test_data_optimized)\n","\n","results_fine_tune_all_layers"],"execution_count":71,"outputs":[{"output_type":"stream","text":["790/790 [==============================] - 67s 85ms/step - loss: 1.0384 - accuracy: 0.7851\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[1.0383784770965576, 0.7850692868232727]"]},"metadata":{"tags":[]},"execution_count":71}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iy95ujS21QsO","executionInfo":{"status":"ok","timestamp":1628830095626,"user_tz":-420,"elapsed":76685,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"c868781c-64de-4870-af7a-7db72e0967f1"},"source":["checkpoint_path = \"Food101_fine_tune_all_layers_efficientnetb4/checkpoint.ckpt\"\n","\n","model.load_weights(checkpoint_path)\n","\n","model.evaluate(test_data_optimized)"],"execution_count":72,"outputs":[{"output_type":"stream","text":["790/790 [==============================] - 67s 85ms/step - loss: 0.7009 - accuracy: 0.8021\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[0.7009186744689941, 0.80213862657547]"]},"metadata":{"tags":[]},"execution_count":72}]},{"cell_type":"markdown","metadata":{"id":"bB-o98Nlq28x"},"source":["Wow, the valid accuracy is over 80%, beat [the paper](https://arxiv.org/ftp/arxiv/papers/1606/1606.05675.pdf)'s accuracy (77.4%) already 😎😎😎"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HnGKkvNTuOox","executionInfo":{"status":"ok","timestamp":1628830122001,"user_tz":-420,"elapsed":22360,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"0394720f-711b-4236-b19a-762be5b75beb"},"source":["# Save the model to drive\n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":73,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DSmfUXEOuidU","executionInfo":{"status":"ok","timestamp":1628830128850,"user_tz":-420,"elapsed":2981,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["model.save(\"/content/drive/MyDrive/Tensorflow Certificate Exam/Weights/101_classes_efficientnetb4_fine_tune_all_layers.h5\")"],"execution_count":74,"outputs":[]},{"cell_type":"code","metadata":{"id":"mDNPpogeu9ay","executionInfo":{"status":"ok","timestamp":1628830168150,"user_tz":-420,"elapsed":12213,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}}},"source":["loaded_model = tf.keras.models.load_model(\"/content/drive/MyDrive/Tensorflow Certificate Exam/Weights/101_classes_efficientnetb4_fine_tune_all_layers.h5\")"],"execution_count":75,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AZyE27vh18KZ","executionInfo":{"status":"ok","timestamp":1628830237969,"user_tz":-420,"elapsed":67935,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"29752a36-2746-4926-d9f1-970203a5399b"},"source":["loaded_model.evaluate(test_data_optimized)"],"execution_count":76,"outputs":[{"output_type":"stream","text":["790/790 [==============================] - 68s 81ms/step - loss: 0.7009 - accuracy: 0.8021\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[0.7009186744689941, 0.80213862657547]"]},"metadata":{"tags":[]},"execution_count":76}]},{"cell_type":"markdown","metadata":{"id":"0mzlBpIb20ID"},"source":["### summary"]},{"cell_type":"markdown","metadata":{"id":"Lnj6S5Jm1_aC"},"source":[">🔑**Summary :** Compare to EfficientNetB0, the EfficientNetB4 is when performce fine tuning is results more accuracy, but train longer."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":186},"id":"tTRAn6en2xZC","executionInfo":{"status":"error","timestamp":1628830478505,"user_tz":-420,"elapsed":321,"user":{"displayName":"Lộc Bảo","photoUrl":"","userId":"01406170824357259025"}},"outputId":"39d608cb-e49e-4358-9ed1-e013a579274e"},"source":["compare_historys(history_efficientnetb4_feature_extraction, history_efficientnetb4_fine_tune_all_layer, 3)"],"execution_count":78,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-78-33fce14432a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcompare_historys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory_efficientnetb4_feature_extraction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory_efficientnetb4_fine_tune_all_layer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'history_efficientnetb4_fine_tune_all_layer' is not defined"]}]},{"cell_type":"code","metadata":{"id":"8ertG27A3FeH"},"source":[""],"execution_count":null,"outputs":[]}]}