week3_task2_fine_tuning_clean.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.5"},"toc":{"colors":{"hover_highlight":"#DAA520","navigate_num":"#000000","navigate_text":"#333333","running_highlight":"#FF0000","selected_highlight":"#FFD700","sidebar_border":"#EEEEEE","wrapper_background":"#FFFFFF"},"moveMenuLeft":true,"nav_menu":{"height":"120px","width":"252px"},"navigate_menu":true,"number_sections":true,"sideBar":true,"threshold":4,"toc_cell":false,"toc_section_display":"block","toc_window_display":false,"widenNotebook":false},"colab":{"name":"week3_task2_fine_tuning_clean.ipynb","provenance":[{"file_id":"https://github.com/hse-aml/intro-to-dl/blob/master/week3/week3_task2_fine_tuning_clean.ipynb","timestamp":1589701800472}],"collapsed_sections":["V1jsRxGbg8ap","5EqDsi5Dg8bc","tfPduGFAg8dF"]},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"4i9WY8CMhV2D","colab_type":"code","outputId":"7683daeb-e428-465d-b298-ccde20ba11f4","executionInfo":{"status":"ok","timestamp":1589700332751,"user_tz":-330,"elapsed":14299,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":221}},"source":["! shred -u setup_google_colab.py\n","! wget https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py -O setup_google_colab.py\n","import setup_google_colab"],"execution_count":0,"outputs":[{"output_type":"stream","text":["shred: setup_google_colab.py: failed to open for writing: No such file or directory\n","--2020-05-17 07:25:27--  https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 3636 (3.6K) [text/plain]\n","Saving to: ‘setup_google_colab.py’\n","\n","setup_google_colab. 100%[===================>]   3.55K  --.-KB/s    in 0s      \n","\n","2020-05-17 07:25:27 (31.0 MB/s) - ‘setup_google_colab.py’ saved [3636/3636]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JG3B8eU4hibi","colab_type":"code","outputId":"4f6f34e6-57d3-4618-edfe-5fac51aa3ecd","executionInfo":{"status":"ok","timestamp":1589700375416,"user_tz":-330,"elapsed":33857,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["setup_google_colab.setup_week3()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**************************************************\n","102flowers.tgz\n","**************************************************\n","imagelabels.mat\n","**************************************************\n","inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n","**************************************************\n","cifar-10-batches-py.tar.gz\n","**************************************************\n","mnist.npz\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"962U9lT-g8ZN","colab_type":"code","outputId":"ee32dd8f-895e-49de-8d8c-2279b8dd61d5","executionInfo":{"status":"ok","timestamp":1589700332754,"user_tz":-330,"elapsed":14289,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# set tf 1.x for colab\n","%tensorflow_version 1.x"],"execution_count":0,"outputs":[{"output_type":"stream","text":["TensorFlow 1.x selected.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"PezIMTGJg8ZZ","colab_type":"text"},"source":["# Fine-tuning InceptionV3 for flowers classification\n","\n","In this task you will fine-tune InceptionV3 architecture for flowers classification task.\n","\n","InceptionV3 architecture (https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html):\n","<img src=\"https://github.com/hse-aml/intro-to-dl/blob/master/week3/images/inceptionv3.png?raw=1\" style=\"width:70%\">\n","\n","Flowers classification dataset (http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) consists of 102 flower categories commonly occurring in the United Kingdom. Each class contains between 40 and 258 images:\n","<img src=\"https://github.com/hse-aml/intro-to-dl/blob/master/week3/images/flowers.jpg?raw=1\" style=\"width:70%\">"]},{"cell_type":"markdown","metadata":{"id":"JphKb1Jrg8Za","colab_type":"text"},"source":["# Import stuff"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"HykKGJlsg8Zb","colab_type":"code","colab":{}},"source":["import sys\n","sys.path.append(\"..\")\n","import grading\n","import download_utils"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"eOJT0oYNg8Zl","colab_type":"code","colab":{}},"source":["# !!! remember to clear session/graph if you rebuild your graph to avoid out-of-memory errors !!!"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":false,"id":"09CND7ezg8Zt","colab_type":"code","colab":{}},"source":["download_utils.link_all_keras_resources()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:39.210959Z","start_time":"2017-09-03T13:00:39.057800Z"},"scrolled":true,"id":"HPbbTHbSg8Z3","colab_type":"code","outputId":"5566a304-992f-4adf-9fa0-bd841fcd4902","executionInfo":{"status":"ok","timestamp":1589700385990,"user_tz":-330,"elapsed":10563,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["import tensorflow as tf\n","import keras\n","from keras import backend as K\n","import numpy as np\n","%matplotlib inline\n","import matplotlib.pyplot as plt\n","print(tf.__version__)\n","print(keras.__version__)\n","import cv2  # for image processing\n","from sklearn.model_selection import train_test_split\n","import scipy.io\n","import os\n","import tarfile\n","import keras_utils\n","from keras_utils import reset_tf_session "],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"stream","text":["1.15.2\n","2.0.6\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ChI3PuS0g8aA","colab_type":"text"},"source":["# Fill in your Coursera token and email\n","To successfully submit your answers to our grader, please fill in your Coursera submission token and email"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"KXwtjGPlg8aD","colab_type":"code","colab":{}},"source":["grader = grading.Grader(assignment_key=\"2v-uxpD7EeeMxQ6FWsz5LA\", \n","                        all_parts=[\"wuwwC\", \"a4FK1\", \"qRsZ1\"])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":false,"id":"gk3tdX87g8aQ","colab_type":"code","colab":{}},"source":["# token expires every 30 min\n","COURSERA_TOKEN = \"gsqLznyt0zDVrZrJ\"\n","COURSERA_EMAIL = \"chiragsaini7777@gmail.com\""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iRU_Q7Awg8aY","colab_type":"text"},"source":["# Load dataset"]},{"cell_type":"markdown","metadata":{"id":"ifO41SDig8aa","colab_type":"text"},"source":["Dataset was downloaded for you, it takes 12 min and 400mb.\n","Relevant links (just in case):\n","- http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html\n","- http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz\n","- http://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"CQoPug6Ag8ac","colab_type":"code","colab":{}},"source":["# we downloaded them for you, just link them here\n","download_utils.link_week_3_resources()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"V1jsRxGbg8ap","colab_type":"text"},"source":["# Prepare images for model"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:39.214524Z","start_time":"2017-09-03T13:00:39.212453Z"},"scrolled":false,"id":"1eSNocsRg8aq","colab_type":"code","colab":{}},"source":["# we will crop and resize input images to IMG_SIZE x IMG_SIZE\n","IMG_SIZE = 250"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-10T12:46:09.790818Z","start_time":"2017-09-10T12:46:09.777771Z"},"scrolled":false,"id":"A3QLnp37g8av","colab_type":"code","colab":{}},"source":["def decode_image_from_raw_bytes(raw_bytes):\n","    img = cv2.imdecode(np.asarray(bytearray(raw_bytes), dtype=np.uint8), 1)\n","    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n","    return img"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kMpYgE9-g8a0","colab_type":"text"},"source":["We will take a center crop from each image like this:\n","<img src=\"https://github.com/hse-aml/intro-to-dl/blob/master/week3/images/center_crop.jpg?raw=1\" style=\"width:50%\">"]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:39.393675Z","start_time":"2017-09-03T13:00:39.302130Z"},"scrolled":false,"id":"UgqT7t4Ag8a1","colab_type":"code","colab":{}},"source":["def image_center_crop(img):\n","    \"\"\"\n","    Makes a square center crop of an img, which is a [h, w, 3] numpy array.\n","    Returns [min(h, w), min(h, w), 3] output with same width and height.\n","    For cropping use numpy slicing.\n","    \"\"\"\n","    h,w,c = img.shape\n","    a = min(h,w)\n","    cropped_img = img[(h-a)//2:h-(h-a)//2, (w-a)//2:w-(w-a)//2,  :]\n","    # print(cropped_img.shape)\n","    # print((min(h, w), min(h, w), c))\n","    # checks for errors\n","    # h, w, c = img.shape\n","    # assert cropped_img.shape == (min(h, w), min(h, w), c), \"error in image_center_crop!\"\n","    \n","    return cropped_img"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:39.506473Z","start_time":"2017-09-03T13:00:39.395470Z"},"scrolled":false,"id":"jVGGTyebg8a6","colab_type":"code","colab":{}},"source":["def prepare_raw_bytes_for_model(raw_bytes, normalize_for_model=True):\n","    img = decode_image_from_raw_bytes(raw_bytes)  # decode image raw bytes to matrix\n","    img = image_center_crop(img)  # take squared center crop\n","    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))  # resize for our model\n","    if normalize_for_model:\n","        img = img.astype(\"float32\")  # prepare for normalization\n","        img = keras.applications.inception_v3.preprocess_input(img)  # normalize for model\n","    return img"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":false,"id":"Q1x4haYeg8a_","colab_type":"code","colab":{}},"source":["# reads bytes directly from tar by filename (slow, but ok for testing, takes ~6 sec)\n","def read_raw_from_tar(tar_fn, fn):\n","    with tarfile.open(tar_fn) as f:\n","        m = f.getmember(fn)\n","        return f.extractfile(m).read()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:39.961301Z","start_time":"2017-09-03T13:00:39.508004Z"},"scrolled":false,"id":"TKZY4wFlg8bH","colab_type":"code","outputId":"8b056a34-0f27-433b-c20f-ee1702ca94bd","executionInfo":{"status":"ok","timestamp":1589701007290,"user_tz":-330,"elapsed":6280,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":589}},"source":["# test cropping\n","raw_bytes = read_raw_from_tar(\"102flowers.tgz\", \"jpg/image_00001.jpg\")\n","\n","img = decode_image_from_raw_bytes(raw_bytes)\n","print(img.shape)\n","plt.imshow(img)\n","plt.show()\n","\n","img = prepare_raw_bytes_for_model(raw_bytes, normalize_for_model=False)\n","print(img.shape)\n","plt.imshow(img)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(500, 591, 3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["(500, 501, 3)\n","(500, 500, 3)\n","(250, 250, 3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"scrolled":false,"id":"AysPKfC1g8bN","colab_type":"code","outputId":"10558f6e-623e-44f3-a274-eca32ed5f81f","executionInfo":{"status":"ok","timestamp":1589701021585,"user_tz":-330,"elapsed":5021,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["## GRADED PART, DO NOT CHANGE!\n","# Test image preparation for model\n","prepared_img = prepare_raw_bytes_for_model(read_raw_from_tar(\"102flowers.tgz\", \"jpg/image_00001.jpg\"))\n","grader.set_answer(\"qRsZ1\", list(prepared_img.shape) + [np.mean(prepared_img), np.std(prepared_img)])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(500, 501, 3)\n","(500, 500, 3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"scrolled":false,"id":"00Hvndivg8bX","colab_type":"code","outputId":"50038bfa-42f1-4c8f-861d-23dd6edd51c5","executionInfo":{"status":"ok","timestamp":1589701022406,"user_tz":-330,"elapsed":4995,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# you can make submission with answers so far to check yourself at this stage\n","grader.submit(COURSERA_EMAIL, COURSERA_TOKEN)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Submitted to Coursera platform. See results on assignment page!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5EqDsi5Dg8bc","colab_type":"text"},"source":["# Prepare for training"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"03erE-_5g8bd","colab_type":"code","outputId":"95a8d3ae-a866-40d2-89a6-2692d26aaa18","executionInfo":{"status":"ok","timestamp":1589701057809,"user_tz":-330,"elapsed":4481,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# read all filenames and labels for them\n","\n","# read filenames firectly from tar\n","def get_all_filenames(tar_fn):\n","    with tarfile.open(tar_fn) as f:\n","        return [m.name for m in f.getmembers() if m.isfile()]\n","\n","all_files = sorted(get_all_filenames(\"102flowers.tgz\"))  # list all files in tar sorted by name\n","all_labels = scipy.io.loadmat('imagelabels.mat')['labels'][0] - 1  # read class labels (0, 1, 2, ...)\n","# all_files and all_labels are aligned now\n","N_CLASSES = len(np.unique(all_labels))\n","print(N_CLASSES)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["102\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:40.185940Z","start_time":"2017-09-03T13:00:40.175758Z"},"scrolled":false,"id":"Wa5nANSyg8br","colab_type":"code","colab":{}},"source":["# split into train/test\n","tr_files, te_files, tr_labels, te_labels = \\\n","    train_test_split(all_files, all_labels, test_size=0.2, random_state=42, stratify=all_labels)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":false,"id":"0PmHZGx2g8bw","colab_type":"code","colab":{}},"source":["# will yield raw image bytes from tar with corresponding label\n","def raw_generator_with_label_from_tar(tar_fn, files, labels):\n","    label_by_fn = dict(zip(files, labels))\n","    with tarfile.open(tar_fn) as f:\n","        while True:\n","            m = f.next()\n","            if m is None:\n","                break\n","            if m.name in label_by_fn:\n","                yield f.extractfile(m).read(), label_by_fn[m.name]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:40.529088Z","start_time":"2017-09-03T13:00:40.423114Z"},"scrolled":false,"id":"ymOSC8i2g8b8","colab_type":"code","colab":{}},"source":["# batch generator\n","BATCH_SIZE = 32\n","\n","def batch_generator(items, batch_size):\n","    \"\"\"\n","    Implement batch generator that yields items in batches of size batch_size.\n","    There's no need to shuffle input items, just chop them into batches.\n","    Remember about the last batch that can be smaller than batch_size!\n","    Input: any iterable (list, generator, ...). You should do `for item in items: ...`\n","        In case of generator you can pass through your items only once!\n","    Output: In output yield each batch as a list of items.\n","    \"\"\"\n","    \n","    ### YOUR CODE HERE\n","    batch = [None] * batch_size\n","    for i, item in enumerate(items):\n","        batch[i%batch_size] = item\n","        if i%batch_size == batch_size-1:\n","            yield batch\n","            batch = [None] * batch_size\n","    if batch[0]:\n","        yield [item for item in batch if item]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":false,"id":"O_hwfFrBg8cd","colab_type":"code","colab":{}},"source":["## GRADED PART, DO NOT CHANGE!\n","# Test batch generator\n","def _test_items_generator():\n","    for i in range(10):\n","        yield i\n","\n","grader.set_answer(\"a4FK1\", list(map(lambda x: len(x), batch_generator(_test_items_generator(), 3))))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":false,"id":"dd92NX84g8ch","colab_type":"code","outputId":"7318a34e-71f8-4891-fd42-8770510d7455","executionInfo":{"status":"ok","timestamp":1589701205045,"user_tz":-330,"elapsed":3906,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# you can make submission with answers so far to check yourself at this stage\n","grader.submit(COURSERA_EMAIL, COURSERA_TOKEN)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Submitted to Coursera platform. See results on assignment page!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:40.637615Z","start_time":"2017-09-03T13:00:40.530642Z"},"scrolled":false,"id":"sxDdoAA-g8c3","colab_type":"code","colab":{}},"source":["def train_generator(files, labels):\n","    while True:  # so that Keras can loop through this as long as it wants\n","        for batch in batch_generator(raw_generator_with_label_from_tar(\n","                \"102flowers.tgz\", files, labels), BATCH_SIZE):\n","            # prepare batch images\n","            batch_imgs = []\n","            batch_targets = []\n","            for raw, label in batch:\n","                img = prepare_raw_bytes_for_model(raw)\n","                batch_imgs.append(img)\n","                batch_targets.append(label)\n","            # stack images into 4D tensor [batch_size, img_size, img_size, 3]\n","            batch_imgs = np.stack(batch_imgs, axis=0)\n","            # convert targets into 2D tensor [batch_size, num_classes]\n","            batch_targets = keras.utils.np_utils.to_categorical(batch_targets, N_CLASSES)\n","            yield batch_imgs, batch_targets"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:41.092659Z","start_time":"2017-09-03T13:00:40.639132Z"},"scrolled":false,"id":"l6DeKn_ig8c-","colab_type":"code","outputId":"74b5e156-e9ad-4bfd-89e6-770457dcc64a","executionInfo":{"status":"ok","timestamp":1589701240281,"user_tz":-330,"elapsed":3075,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# test training generator\n","for _ in train_generator(tr_files, tr_labels):\n","    print(_[0].shape, _[1].shape)\n","    plt.imshow(np.clip(_[0][0] / 2. + 0.5, 0, 1))\n","    break"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(500, 500, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(501, 500, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(501, 500, 3)\n","(500, 500, 3)\n","(501, 501, 3)\n","(501, 501, 3)\n","(501, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(501, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(500, 501, 3)\n","(500, 500, 3)\n","(32, 250, 250, 3) (32, 102)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"tfPduGFAg8dF","colab_type":"text"},"source":["# Training"]},{"cell_type":"markdown","metadata":{"ExecuteTime":{"end_time":"2017-09-10T13:16:35.109044Z","start_time":"2017-09-10T13:16:35.105127Z"},"id":"xDTg32yRg8dH","colab_type":"text"},"source":["You cannot train such a huge architecture from scratch with such a small dataset.\n","\n","But using fine-tuning of last layers of pre-trained network you can get a pretty good classifier very quickly."]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:41.097588Z","start_time":"2017-09-03T13:00:41.094216Z"},"scrolled":false,"id":"4_fvMNnCg8dI","colab_type":"code","colab":{}},"source":["# remember to clear session if you start building graph from scratch!\n","s = reset_tf_session()\n","# don't call K.set_learning_phase() !!! (otherwise will enable dropout in train/test simultaneously)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:41.222209Z","start_time":"2017-09-03T13:00:41.098974Z"},"scrolled":false,"id":"BT9Mwtpmg8dX","colab_type":"code","colab":{}},"source":["def inception(use_imagenet=True):\n","    # load pre-trained model graph, don't add final layer\n","    model = keras.applications.InceptionV3(include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3),\n","                                          weights='imagenet' if use_imagenet else None)\n","    # add global pooling just like in InceptionV3\n","    new_output = keras.layers.GlobalAveragePooling2D()(model.output)\n","    # add new dense layer for our labels\n","    new_output = keras.layers.Dense(N_CLASSES, activation='softmax')(new_output)\n","    model = keras.engine.training.Model(model.inputs, new_output)\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:45.150429Z","start_time":"2017-09-03T13:00:41.223777Z"},"scrolled":false,"id":"aa5FuuLkg8df","colab_type":"code","colab":{}},"source":["model = inception()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:45.252883Z","start_time":"2017-09-03T13:00:45.152062Z"},"scrolled":false,"id":"Lg96XEqvg8dl","colab_type":"code","outputId":"3b14bea6-2c1b-4599-84f5-a924d61c6db3","executionInfo":{"status":"ok","timestamp":1589701507677,"user_tz":-330,"elapsed":7672,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["____________________________________________________________________________________________________\n","Layer (type)                     Output Shape          Param #     Connected to                     \n","====================================================================================================\n","input_1 (InputLayer)             (None, 250, 250, 3)   0                                            \n","____________________________________________________________________________________________________\n","conv2d_1 (Conv2D)                (None, 124, 124, 32)  864         input_1[0][0]                    \n","____________________________________________________________________________________________________\n","batch_normalization_1 (BatchNorm (None, 124, 124, 32)  96          conv2d_1[0][0]                   \n","____________________________________________________________________________________________________\n","activation_1 (Activation)        (None, 124, 124, 32)  0           batch_normalization_1[0][0]      \n","____________________________________________________________________________________________________\n","conv2d_2 (Conv2D)                (None, 122, 122, 32)  9216        activation_1[0][0]               \n","____________________________________________________________________________________________________\n","batch_normalization_2 (BatchNorm (None, 122, 122, 32)  96          conv2d_2[0][0]                   \n","____________________________________________________________________________________________________\n","activation_2 (Activation)        (None, 122, 122, 32)  0           batch_normalization_2[0][0]      \n","____________________________________________________________________________________________________\n","conv2d_3 (Conv2D)                (None, 122, 122, 64)  18432       activation_2[0][0]               \n","____________________________________________________________________________________________________\n","batch_normalization_3 (BatchNorm (None, 122, 122, 64)  192         conv2d_3[0][0]                   \n","____________________________________________________________________________________________________\n","activation_3 (Activation)        (None, 122, 122, 64)  0           batch_normalization_3[0][0]      \n","____________________________________________________________________________________________________\n","max_pooling2d_1 (MaxPooling2D)   (None, 60, 60, 64)    0           activation_3[0][0]               \n","____________________________________________________________________________________________________\n","conv2d_4 (Conv2D)                (None, 60, 60, 80)    5120        max_pooling2d_1[0][0]            \n","____________________________________________________________________________________________________\n","batch_normalization_4 (BatchNorm (None, 60, 60, 80)    240         conv2d_4[0][0]                   \n","____________________________________________________________________________________________________\n","activation_4 (Activation)        (None, 60, 60, 80)    0           batch_normalization_4[0][0]      \n","____________________________________________________________________________________________________\n","conv2d_5 (Conv2D)                (None, 58, 58, 192)   138240      activation_4[0][0]               \n","____________________________________________________________________________________________________\n","batch_normalization_5 (BatchNorm (None, 58, 58, 192)   576         conv2d_5[0][0]                   \n","____________________________________________________________________________________________________\n","activation_5 (Activation)        (None, 58, 58, 192)   0           batch_normalization_5[0][0]      \n","____________________________________________________________________________________________________\n","max_pooling2d_2 (MaxPooling2D)   (None, 28, 28, 192)   0           activation_5[0][0]               \n","____________________________________________________________________________________________________\n","conv2d_9 (Conv2D)                (None, 28, 28, 64)    12288       max_pooling2d_2[0][0]            \n","____________________________________________________________________________________________________\n","batch_normalization_9 (BatchNorm (None, 28, 28, 64)    192         conv2d_9[0][0]                   \n","____________________________________________________________________________________________________\n","activation_9 (Activation)        (None, 28, 28, 64)    0           batch_normalization_9[0][0]      \n","____________________________________________________________________________________________________\n","conv2d_7 (Conv2D)                (None, 28, 28, 48)    9216        max_pooling2d_2[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_10 (Conv2D)               (None, 28, 28, 96)    55296       activation_9[0][0]               \n","____________________________________________________________________________________________________\n","batch_normalization_7 (BatchNorm (None, 28, 28, 48)    144         conv2d_7[0][0]                   \n","____________________________________________________________________________________________________\n","batch_normalization_10 (BatchNor (None, 28, 28, 96)    288         conv2d_10[0][0]                  \n","____________________________________________________________________________________________________\n","activation_7 (Activation)        (None, 28, 28, 48)    0           batch_normalization_7[0][0]      \n","____________________________________________________________________________________________________\n","activation_10 (Activation)       (None, 28, 28, 96)    0           batch_normalization_10[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_1 (AveragePool (None, 28, 28, 192)   0           max_pooling2d_2[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_6 (Conv2D)                (None, 28, 28, 64)    12288       max_pooling2d_2[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_8 (Conv2D)                (None, 28, 28, 64)    76800       activation_7[0][0]               \n","____________________________________________________________________________________________________\n","conv2d_11 (Conv2D)               (None, 28, 28, 96)    82944       activation_10[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_12 (Conv2D)               (None, 28, 28, 32)    6144        average_pooling2d_1[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_6 (BatchNorm (None, 28, 28, 64)    192         conv2d_6[0][0]                   \n","____________________________________________________________________________________________________\n","batch_normalization_8 (BatchNorm (None, 28, 28, 64)    192         conv2d_8[0][0]                   \n","____________________________________________________________________________________________________\n","batch_normalization_11 (BatchNor (None, 28, 28, 96)    288         conv2d_11[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_12 (BatchNor (None, 28, 28, 32)    96          conv2d_12[0][0]                  \n","____________________________________________________________________________________________________\n","activation_6 (Activation)        (None, 28, 28, 64)    0           batch_normalization_6[0][0]      \n","____________________________________________________________________________________________________\n","activation_8 (Activation)        (None, 28, 28, 64)    0           batch_normalization_8[0][0]      \n","____________________________________________________________________________________________________\n","activation_11 (Activation)       (None, 28, 28, 96)    0           batch_normalization_11[0][0]     \n","____________________________________________________________________________________________________\n","activation_12 (Activation)       (None, 28, 28, 32)    0           batch_normalization_12[0][0]     \n","____________________________________________________________________________________________________\n","mixed0 (Concatenate)             (None, 28, 28, 256)   0           activation_6[0][0]               \n","                                                                   activation_8[0][0]               \n","                                                                   activation_11[0][0]              \n","                                                                   activation_12[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_16 (Conv2D)               (None, 28, 28, 64)    16384       mixed0[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_16 (BatchNor (None, 28, 28, 64)    192         conv2d_16[0][0]                  \n","____________________________________________________________________________________________________\n","activation_16 (Activation)       (None, 28, 28, 64)    0           batch_normalization_16[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_14 (Conv2D)               (None, 28, 28, 48)    12288       mixed0[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_17 (Conv2D)               (None, 28, 28, 96)    55296       activation_16[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_14 (BatchNor (None, 28, 28, 48)    144         conv2d_14[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_17 (BatchNor (None, 28, 28, 96)    288         conv2d_17[0][0]                  \n","____________________________________________________________________________________________________\n","activation_14 (Activation)       (None, 28, 28, 48)    0           batch_normalization_14[0][0]     \n","____________________________________________________________________________________________________\n","activation_17 (Activation)       (None, 28, 28, 96)    0           batch_normalization_17[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_2 (AveragePool (None, 28, 28, 256)   0           mixed0[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_13 (Conv2D)               (None, 28, 28, 64)    16384       mixed0[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_15 (Conv2D)               (None, 28, 28, 64)    76800       activation_14[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_18 (Conv2D)               (None, 28, 28, 96)    82944       activation_17[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_19 (Conv2D)               (None, 28, 28, 64)    16384       average_pooling2d_2[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_13 (BatchNor (None, 28, 28, 64)    192         conv2d_13[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_15 (BatchNor (None, 28, 28, 64)    192         conv2d_15[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_18 (BatchNor (None, 28, 28, 96)    288         conv2d_18[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_19 (BatchNor (None, 28, 28, 64)    192         conv2d_19[0][0]                  \n","____________________________________________________________________________________________________\n","activation_13 (Activation)       (None, 28, 28, 64)    0           batch_normalization_13[0][0]     \n","____________________________________________________________________________________________________\n","activation_15 (Activation)       (None, 28, 28, 64)    0           batch_normalization_15[0][0]     \n","____________________________________________________________________________________________________\n","activation_18 (Activation)       (None, 28, 28, 96)    0           batch_normalization_18[0][0]     \n","____________________________________________________________________________________________________\n","activation_19 (Activation)       (None, 28, 28, 64)    0           batch_normalization_19[0][0]     \n","____________________________________________________________________________________________________\n","mixed1 (Concatenate)             (None, 28, 28, 288)   0           activation_13[0][0]              \n","                                                                   activation_15[0][0]              \n","                                                                   activation_18[0][0]              \n","                                                                   activation_19[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_23 (Conv2D)               (None, 28, 28, 64)    18432       mixed1[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_23 (BatchNor (None, 28, 28, 64)    192         conv2d_23[0][0]                  \n","____________________________________________________________________________________________________\n","activation_23 (Activation)       (None, 28, 28, 64)    0           batch_normalization_23[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_21 (Conv2D)               (None, 28, 28, 48)    13824       mixed1[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_24 (Conv2D)               (None, 28, 28, 96)    55296       activation_23[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_21 (BatchNor (None, 28, 28, 48)    144         conv2d_21[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_24 (BatchNor (None, 28, 28, 96)    288         conv2d_24[0][0]                  \n","____________________________________________________________________________________________________\n","activation_21 (Activation)       (None, 28, 28, 48)    0           batch_normalization_21[0][0]     \n","____________________________________________________________________________________________________\n","activation_24 (Activation)       (None, 28, 28, 96)    0           batch_normalization_24[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_3 (AveragePool (None, 28, 28, 288)   0           mixed1[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_20 (Conv2D)               (None, 28, 28, 64)    18432       mixed1[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_22 (Conv2D)               (None, 28, 28, 64)    76800       activation_21[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_25 (Conv2D)               (None, 28, 28, 96)    82944       activation_24[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_26 (Conv2D)               (None, 28, 28, 64)    18432       average_pooling2d_3[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_20 (BatchNor (None, 28, 28, 64)    192         conv2d_20[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_22 (BatchNor (None, 28, 28, 64)    192         conv2d_22[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_25 (BatchNor (None, 28, 28, 96)    288         conv2d_25[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_26 (BatchNor (None, 28, 28, 64)    192         conv2d_26[0][0]                  \n","____________________________________________________________________________________________________\n","activation_20 (Activation)       (None, 28, 28, 64)    0           batch_normalization_20[0][0]     \n","____________________________________________________________________________________________________\n","activation_22 (Activation)       (None, 28, 28, 64)    0           batch_normalization_22[0][0]     \n","____________________________________________________________________________________________________\n","activation_25 (Activation)       (None, 28, 28, 96)    0           batch_normalization_25[0][0]     \n","____________________________________________________________________________________________________\n","activation_26 (Activation)       (None, 28, 28, 64)    0           batch_normalization_26[0][0]     \n","____________________________________________________________________________________________________\n","mixed2 (Concatenate)             (None, 28, 28, 288)   0           activation_20[0][0]              \n","                                                                   activation_22[0][0]              \n","                                                                   activation_25[0][0]              \n","                                                                   activation_26[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_28 (Conv2D)               (None, 28, 28, 64)    18432       mixed2[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_28 (BatchNor (None, 28, 28, 64)    192         conv2d_28[0][0]                  \n","____________________________________________________________________________________________________\n","activation_28 (Activation)       (None, 28, 28, 64)    0           batch_normalization_28[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_29 (Conv2D)               (None, 28, 28, 96)    55296       activation_28[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_29 (BatchNor (None, 28, 28, 96)    288         conv2d_29[0][0]                  \n","____________________________________________________________________________________________________\n","activation_29 (Activation)       (None, 28, 28, 96)    0           batch_normalization_29[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_27 (Conv2D)               (None, 13, 13, 384)   995328      mixed2[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_30 (Conv2D)               (None, 13, 13, 96)    82944       activation_29[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_27 (BatchNor (None, 13, 13, 384)   1152        conv2d_27[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_30 (BatchNor (None, 13, 13, 96)    288         conv2d_30[0][0]                  \n","____________________________________________________________________________________________________\n","activation_27 (Activation)       (None, 13, 13, 384)   0           batch_normalization_27[0][0]     \n","____________________________________________________________________________________________________\n","activation_30 (Activation)       (None, 13, 13, 96)    0           batch_normalization_30[0][0]     \n","____________________________________________________________________________________________________\n","max_pooling2d_3 (MaxPooling2D)   (None, 13, 13, 288)   0           mixed2[0][0]                     \n","____________________________________________________________________________________________________\n","mixed3 (Concatenate)             (None, 13, 13, 768)   0           activation_27[0][0]              \n","                                                                   activation_30[0][0]              \n","                                                                   max_pooling2d_3[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_35 (Conv2D)               (None, 13, 13, 128)   98304       mixed3[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_35 (BatchNor (None, 13, 13, 128)   384         conv2d_35[0][0]                  \n","____________________________________________________________________________________________________\n","activation_35 (Activation)       (None, 13, 13, 128)   0           batch_normalization_35[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_36 (Conv2D)               (None, 13, 13, 128)   114688      activation_35[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_36 (BatchNor (None, 13, 13, 128)   384         conv2d_36[0][0]                  \n","____________________________________________________________________________________________________\n","activation_36 (Activation)       (None, 13, 13, 128)   0           batch_normalization_36[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_32 (Conv2D)               (None, 13, 13, 128)   98304       mixed3[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_37 (Conv2D)               (None, 13, 13, 128)   114688      activation_36[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_32 (BatchNor (None, 13, 13, 128)   384         conv2d_32[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_37 (BatchNor (None, 13, 13, 128)   384         conv2d_37[0][0]                  \n","____________________________________________________________________________________________________\n","activation_32 (Activation)       (None, 13, 13, 128)   0           batch_normalization_32[0][0]     \n","____________________________________________________________________________________________________\n","activation_37 (Activation)       (None, 13, 13, 128)   0           batch_normalization_37[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_33 (Conv2D)               (None, 13, 13, 128)   114688      activation_32[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_38 (Conv2D)               (None, 13, 13, 128)   114688      activation_37[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_33 (BatchNor (None, 13, 13, 128)   384         conv2d_33[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_38 (BatchNor (None, 13, 13, 128)   384         conv2d_38[0][0]                  \n","____________________________________________________________________________________________________\n","activation_33 (Activation)       (None, 13, 13, 128)   0           batch_normalization_33[0][0]     \n","____________________________________________________________________________________________________\n","activation_38 (Activation)       (None, 13, 13, 128)   0           batch_normalization_38[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_4 (AveragePool (None, 13, 13, 768)   0           mixed3[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_31 (Conv2D)               (None, 13, 13, 192)   147456      mixed3[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_34 (Conv2D)               (None, 13, 13, 192)   172032      activation_33[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_39 (Conv2D)               (None, 13, 13, 192)   172032      activation_38[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_40 (Conv2D)               (None, 13, 13, 192)   147456      average_pooling2d_4[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_31 (BatchNor (None, 13, 13, 192)   576         conv2d_31[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_34 (BatchNor (None, 13, 13, 192)   576         conv2d_34[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_39 (BatchNor (None, 13, 13, 192)   576         conv2d_39[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_40 (BatchNor (None, 13, 13, 192)   576         conv2d_40[0][0]                  \n","____________________________________________________________________________________________________\n","activation_31 (Activation)       (None, 13, 13, 192)   0           batch_normalization_31[0][0]     \n","____________________________________________________________________________________________________\n","activation_34 (Activation)       (None, 13, 13, 192)   0           batch_normalization_34[0][0]     \n","____________________________________________________________________________________________________\n","activation_39 (Activation)       (None, 13, 13, 192)   0           batch_normalization_39[0][0]     \n","____________________________________________________________________________________________________\n","activation_40 (Activation)       (None, 13, 13, 192)   0           batch_normalization_40[0][0]     \n","____________________________________________________________________________________________________\n","mixed4 (Concatenate)             (None, 13, 13, 768)   0           activation_31[0][0]              \n","                                                                   activation_34[0][0]              \n","                                                                   activation_39[0][0]              \n","                                                                   activation_40[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_45 (Conv2D)               (None, 13, 13, 160)   122880      mixed4[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_45 (BatchNor (None, 13, 13, 160)   480         conv2d_45[0][0]                  \n","____________________________________________________________________________________________________\n","activation_45 (Activation)       (None, 13, 13, 160)   0           batch_normalization_45[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_46 (Conv2D)               (None, 13, 13, 160)   179200      activation_45[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_46 (BatchNor (None, 13, 13, 160)   480         conv2d_46[0][0]                  \n","____________________________________________________________________________________________________\n","activation_46 (Activation)       (None, 13, 13, 160)   0           batch_normalization_46[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_42 (Conv2D)               (None, 13, 13, 160)   122880      mixed4[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_47 (Conv2D)               (None, 13, 13, 160)   179200      activation_46[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_42 (BatchNor (None, 13, 13, 160)   480         conv2d_42[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_47 (BatchNor (None, 13, 13, 160)   480         conv2d_47[0][0]                  \n","____________________________________________________________________________________________________\n","activation_42 (Activation)       (None, 13, 13, 160)   0           batch_normalization_42[0][0]     \n","____________________________________________________________________________________________________\n","activation_47 (Activation)       (None, 13, 13, 160)   0           batch_normalization_47[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_43 (Conv2D)               (None, 13, 13, 160)   179200      activation_42[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_48 (Conv2D)               (None, 13, 13, 160)   179200      activation_47[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_43 (BatchNor (None, 13, 13, 160)   480         conv2d_43[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_48 (BatchNor (None, 13, 13, 160)   480         conv2d_48[0][0]                  \n","____________________________________________________________________________________________________\n","activation_43 (Activation)       (None, 13, 13, 160)   0           batch_normalization_43[0][0]     \n","____________________________________________________________________________________________________\n","activation_48 (Activation)       (None, 13, 13, 160)   0           batch_normalization_48[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_5 (AveragePool (None, 13, 13, 768)   0           mixed4[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_41 (Conv2D)               (None, 13, 13, 192)   147456      mixed4[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_44 (Conv2D)               (None, 13, 13, 192)   215040      activation_43[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_49 (Conv2D)               (None, 13, 13, 192)   215040      activation_48[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_50 (Conv2D)               (None, 13, 13, 192)   147456      average_pooling2d_5[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_41 (BatchNor (None, 13, 13, 192)   576         conv2d_41[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_44 (BatchNor (None, 13, 13, 192)   576         conv2d_44[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_49 (BatchNor (None, 13, 13, 192)   576         conv2d_49[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_50 (BatchNor (None, 13, 13, 192)   576         conv2d_50[0][0]                  \n","____________________________________________________________________________________________________\n","activation_41 (Activation)       (None, 13, 13, 192)   0           batch_normalization_41[0][0]     \n","____________________________________________________________________________________________________\n","activation_44 (Activation)       (None, 13, 13, 192)   0           batch_normalization_44[0][0]     \n","____________________________________________________________________________________________________\n","activation_49 (Activation)       (None, 13, 13, 192)   0           batch_normalization_49[0][0]     \n","____________________________________________________________________________________________________\n","activation_50 (Activation)       (None, 13, 13, 192)   0           batch_normalization_50[0][0]     \n","____________________________________________________________________________________________________\n","mixed5 (Concatenate)             (None, 13, 13, 768)   0           activation_41[0][0]              \n","                                                                   activation_44[0][0]              \n","                                                                   activation_49[0][0]              \n","                                                                   activation_50[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_55 (Conv2D)               (None, 13, 13, 160)   122880      mixed5[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_55 (BatchNor (None, 13, 13, 160)   480         conv2d_55[0][0]                  \n","____________________________________________________________________________________________________\n","activation_55 (Activation)       (None, 13, 13, 160)   0           batch_normalization_55[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_56 (Conv2D)               (None, 13, 13, 160)   179200      activation_55[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_56 (BatchNor (None, 13, 13, 160)   480         conv2d_56[0][0]                  \n","____________________________________________________________________________________________________\n","activation_56 (Activation)       (None, 13, 13, 160)   0           batch_normalization_56[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_52 (Conv2D)               (None, 13, 13, 160)   122880      mixed5[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_57 (Conv2D)               (None, 13, 13, 160)   179200      activation_56[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_52 (BatchNor (None, 13, 13, 160)   480         conv2d_52[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_57 (BatchNor (None, 13, 13, 160)   480         conv2d_57[0][0]                  \n","____________________________________________________________________________________________________\n","activation_52 (Activation)       (None, 13, 13, 160)   0           batch_normalization_52[0][0]     \n","____________________________________________________________________________________________________\n","activation_57 (Activation)       (None, 13, 13, 160)   0           batch_normalization_57[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_53 (Conv2D)               (None, 13, 13, 160)   179200      activation_52[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_58 (Conv2D)               (None, 13, 13, 160)   179200      activation_57[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_53 (BatchNor (None, 13, 13, 160)   480         conv2d_53[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_58 (BatchNor (None, 13, 13, 160)   480         conv2d_58[0][0]                  \n","____________________________________________________________________________________________________\n","activation_53 (Activation)       (None, 13, 13, 160)   0           batch_normalization_53[0][0]     \n","____________________________________________________________________________________________________\n","activation_58 (Activation)       (None, 13, 13, 160)   0           batch_normalization_58[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_6 (AveragePool (None, 13, 13, 768)   0           mixed5[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_51 (Conv2D)               (None, 13, 13, 192)   147456      mixed5[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_54 (Conv2D)               (None, 13, 13, 192)   215040      activation_53[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_59 (Conv2D)               (None, 13, 13, 192)   215040      activation_58[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_60 (Conv2D)               (None, 13, 13, 192)   147456      average_pooling2d_6[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_51 (BatchNor (None, 13, 13, 192)   576         conv2d_51[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_54 (BatchNor (None, 13, 13, 192)   576         conv2d_54[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_59 (BatchNor (None, 13, 13, 192)   576         conv2d_59[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_60 (BatchNor (None, 13, 13, 192)   576         conv2d_60[0][0]                  \n","____________________________________________________________________________________________________\n","activation_51 (Activation)       (None, 13, 13, 192)   0           batch_normalization_51[0][0]     \n","____________________________________________________________________________________________________\n","activation_54 (Activation)       (None, 13, 13, 192)   0           batch_normalization_54[0][0]     \n","____________________________________________________________________________________________________\n","activation_59 (Activation)       (None, 13, 13, 192)   0           batch_normalization_59[0][0]     \n","____________________________________________________________________________________________________\n","activation_60 (Activation)       (None, 13, 13, 192)   0           batch_normalization_60[0][0]     \n","____________________________________________________________________________________________________\n","mixed6 (Concatenate)             (None, 13, 13, 768)   0           activation_51[0][0]              \n","                                                                   activation_54[0][0]              \n","                                                                   activation_59[0][0]              \n","                                                                   activation_60[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_65 (Conv2D)               (None, 13, 13, 192)   147456      mixed6[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_65 (BatchNor (None, 13, 13, 192)   576         conv2d_65[0][0]                  \n","____________________________________________________________________________________________________\n","activation_65 (Activation)       (None, 13, 13, 192)   0           batch_normalization_65[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_66 (Conv2D)               (None, 13, 13, 192)   258048      activation_65[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_66 (BatchNor (None, 13, 13, 192)   576         conv2d_66[0][0]                  \n","____________________________________________________________________________________________________\n","activation_66 (Activation)       (None, 13, 13, 192)   0           batch_normalization_66[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_62 (Conv2D)               (None, 13, 13, 192)   147456      mixed6[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_67 (Conv2D)               (None, 13, 13, 192)   258048      activation_66[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_62 (BatchNor (None, 13, 13, 192)   576         conv2d_62[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_67 (BatchNor (None, 13, 13, 192)   576         conv2d_67[0][0]                  \n","____________________________________________________________________________________________________\n","activation_62 (Activation)       (None, 13, 13, 192)   0           batch_normalization_62[0][0]     \n","____________________________________________________________________________________________________\n","activation_67 (Activation)       (None, 13, 13, 192)   0           batch_normalization_67[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_63 (Conv2D)               (None, 13, 13, 192)   258048      activation_62[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_68 (Conv2D)               (None, 13, 13, 192)   258048      activation_67[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_63 (BatchNor (None, 13, 13, 192)   576         conv2d_63[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_68 (BatchNor (None, 13, 13, 192)   576         conv2d_68[0][0]                  \n","____________________________________________________________________________________________________\n","activation_63 (Activation)       (None, 13, 13, 192)   0           batch_normalization_63[0][0]     \n","____________________________________________________________________________________________________\n","activation_68 (Activation)       (None, 13, 13, 192)   0           batch_normalization_68[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_7 (AveragePool (None, 13, 13, 768)   0           mixed6[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_61 (Conv2D)               (None, 13, 13, 192)   147456      mixed6[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_64 (Conv2D)               (None, 13, 13, 192)   258048      activation_63[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_69 (Conv2D)               (None, 13, 13, 192)   258048      activation_68[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_70 (Conv2D)               (None, 13, 13, 192)   147456      average_pooling2d_7[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_61 (BatchNor (None, 13, 13, 192)   576         conv2d_61[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_64 (BatchNor (None, 13, 13, 192)   576         conv2d_64[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_69 (BatchNor (None, 13, 13, 192)   576         conv2d_69[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_70 (BatchNor (None, 13, 13, 192)   576         conv2d_70[0][0]                  \n","____________________________________________________________________________________________________\n","activation_61 (Activation)       (None, 13, 13, 192)   0           batch_normalization_61[0][0]     \n","____________________________________________________________________________________________________\n","activation_64 (Activation)       (None, 13, 13, 192)   0           batch_normalization_64[0][0]     \n","____________________________________________________________________________________________________\n","activation_69 (Activation)       (None, 13, 13, 192)   0           batch_normalization_69[0][0]     \n","____________________________________________________________________________________________________\n","activation_70 (Activation)       (None, 13, 13, 192)   0           batch_normalization_70[0][0]     \n","____________________________________________________________________________________________________\n","mixed7 (Concatenate)             (None, 13, 13, 768)   0           activation_61[0][0]              \n","                                                                   activation_64[0][0]              \n","                                                                   activation_69[0][0]              \n","                                                                   activation_70[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_73 (Conv2D)               (None, 13, 13, 192)   147456      mixed7[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_73 (BatchNor (None, 13, 13, 192)   576         conv2d_73[0][0]                  \n","____________________________________________________________________________________________________\n","activation_73 (Activation)       (None, 13, 13, 192)   0           batch_normalization_73[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_74 (Conv2D)               (None, 13, 13, 192)   258048      activation_73[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_74 (BatchNor (None, 13, 13, 192)   576         conv2d_74[0][0]                  \n","____________________________________________________________________________________________________\n","activation_74 (Activation)       (None, 13, 13, 192)   0           batch_normalization_74[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_71 (Conv2D)               (None, 13, 13, 192)   147456      mixed7[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_75 (Conv2D)               (None, 13, 13, 192)   258048      activation_74[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_71 (BatchNor (None, 13, 13, 192)   576         conv2d_71[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_75 (BatchNor (None, 13, 13, 192)   576         conv2d_75[0][0]                  \n","____________________________________________________________________________________________________\n","activation_71 (Activation)       (None, 13, 13, 192)   0           batch_normalization_71[0][0]     \n","____________________________________________________________________________________________________\n","activation_75 (Activation)       (None, 13, 13, 192)   0           batch_normalization_75[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_72 (Conv2D)               (None, 6, 6, 320)     552960      activation_71[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_76 (Conv2D)               (None, 6, 6, 192)     331776      activation_75[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_72 (BatchNor (None, 6, 6, 320)     960         conv2d_72[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_76 (BatchNor (None, 6, 6, 192)     576         conv2d_76[0][0]                  \n","____________________________________________________________________________________________________\n","activation_72 (Activation)       (None, 6, 6, 320)     0           batch_normalization_72[0][0]     \n","____________________________________________________________________________________________________\n","activation_76 (Activation)       (None, 6, 6, 192)     0           batch_normalization_76[0][0]     \n","____________________________________________________________________________________________________\n","max_pooling2d_4 (MaxPooling2D)   (None, 6, 6, 768)     0           mixed7[0][0]                     \n","____________________________________________________________________________________________________\n","mixed8 (Concatenate)             (None, 6, 6, 1280)    0           activation_72[0][0]              \n","                                                                   activation_76[0][0]              \n","                                                                   max_pooling2d_4[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_81 (Conv2D)               (None, 6, 6, 448)     573440      mixed8[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_81 (BatchNor (None, 6, 6, 448)     1344        conv2d_81[0][0]                  \n","____________________________________________________________________________________________________\n","activation_81 (Activation)       (None, 6, 6, 448)     0           batch_normalization_81[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_78 (Conv2D)               (None, 6, 6, 384)     491520      mixed8[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_82 (Conv2D)               (None, 6, 6, 384)     1548288     activation_81[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_78 (BatchNor (None, 6, 6, 384)     1152        conv2d_78[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_82 (BatchNor (None, 6, 6, 384)     1152        conv2d_82[0][0]                  \n","____________________________________________________________________________________________________\n","activation_78 (Activation)       (None, 6, 6, 384)     0           batch_normalization_78[0][0]     \n","____________________________________________________________________________________________________\n","activation_82 (Activation)       (None, 6, 6, 384)     0           batch_normalization_82[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_79 (Conv2D)               (None, 6, 6, 384)     442368      activation_78[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_80 (Conv2D)               (None, 6, 6, 384)     442368      activation_78[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_83 (Conv2D)               (None, 6, 6, 384)     442368      activation_82[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_84 (Conv2D)               (None, 6, 6, 384)     442368      activation_82[0][0]              \n","____________________________________________________________________________________________________\n","average_pooling2d_8 (AveragePool (None, 6, 6, 1280)    0           mixed8[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_77 (Conv2D)               (None, 6, 6, 320)     409600      mixed8[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_79 (BatchNor (None, 6, 6, 384)     1152        conv2d_79[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_80 (BatchNor (None, 6, 6, 384)     1152        conv2d_80[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_83 (BatchNor (None, 6, 6, 384)     1152        conv2d_83[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_84 (BatchNor (None, 6, 6, 384)     1152        conv2d_84[0][0]                  \n","____________________________________________________________________________________________________\n","conv2d_85 (Conv2D)               (None, 6, 6, 192)     245760      average_pooling2d_8[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_77 (BatchNor (None, 6, 6, 320)     960         conv2d_77[0][0]                  \n","____________________________________________________________________________________________________\n","activation_79 (Activation)       (None, 6, 6, 384)     0           batch_normalization_79[0][0]     \n","____________________________________________________________________________________________________\n","activation_80 (Activation)       (None, 6, 6, 384)     0           batch_normalization_80[0][0]     \n","____________________________________________________________________________________________________\n","activation_83 (Activation)       (None, 6, 6, 384)     0           batch_normalization_83[0][0]     \n","____________________________________________________________________________________________________\n","activation_84 (Activation)       (None, 6, 6, 384)     0           batch_normalization_84[0][0]     \n","____________________________________________________________________________________________________\n","batch_normalization_85 (BatchNor (None, 6, 6, 192)     576         conv2d_85[0][0]                  \n","____________________________________________________________________________________________________\n","activation_77 (Activation)       (None, 6, 6, 320)     0           batch_normalization_77[0][0]     \n","____________________________________________________________________________________________________\n","mixed9_0 (Concatenate)           (None, 6, 6, 768)     0           activation_79[0][0]              \n","                                                                   activation_80[0][0]              \n","____________________________________________________________________________________________________\n","concatenate_1 (Concatenate)      (None, 6, 6, 768)     0           activation_83[0][0]              \n","                                                                   activation_84[0][0]              \n","____________________________________________________________________________________________________\n","activation_85 (Activation)       (None, 6, 6, 192)     0           batch_normalization_85[0][0]     \n","____________________________________________________________________________________________________\n","mixed9 (Concatenate)             (None, 6, 6, 2048)    0           activation_77[0][0]              \n","                                                                   mixed9_0[0][0]                   \n","                                                                   concatenate_1[0][0]              \n","                                                                   activation_85[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_90 (Conv2D)               (None, 6, 6, 448)     917504      mixed9[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_90 (BatchNor (None, 6, 6, 448)     1344        conv2d_90[0][0]                  \n","____________________________________________________________________________________________________\n","activation_90 (Activation)       (None, 6, 6, 448)     0           batch_normalization_90[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_87 (Conv2D)               (None, 6, 6, 384)     786432      mixed9[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_91 (Conv2D)               (None, 6, 6, 384)     1548288     activation_90[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_87 (BatchNor (None, 6, 6, 384)     1152        conv2d_87[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_91 (BatchNor (None, 6, 6, 384)     1152        conv2d_91[0][0]                  \n","____________________________________________________________________________________________________\n","activation_87 (Activation)       (None, 6, 6, 384)     0           batch_normalization_87[0][0]     \n","____________________________________________________________________________________________________\n","activation_91 (Activation)       (None, 6, 6, 384)     0           batch_normalization_91[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_88 (Conv2D)               (None, 6, 6, 384)     442368      activation_87[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_89 (Conv2D)               (None, 6, 6, 384)     442368      activation_87[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_92 (Conv2D)               (None, 6, 6, 384)     442368      activation_91[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_93 (Conv2D)               (None, 6, 6, 384)     442368      activation_91[0][0]              \n","____________________________________________________________________________________________________\n","average_pooling2d_9 (AveragePool (None, 6, 6, 2048)    0           mixed9[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_86 (Conv2D)               (None, 6, 6, 320)     655360      mixed9[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_88 (BatchNor (None, 6, 6, 384)     1152        conv2d_88[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_89 (BatchNor (None, 6, 6, 384)     1152        conv2d_89[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_92 (BatchNor (None, 6, 6, 384)     1152        conv2d_92[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_93 (BatchNor (None, 6, 6, 384)     1152        conv2d_93[0][0]                  \n","____________________________________________________________________________________________________\n","conv2d_94 (Conv2D)               (None, 6, 6, 192)     393216      average_pooling2d_9[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_86 (BatchNor (None, 6, 6, 320)     960         conv2d_86[0][0]                  \n","____________________________________________________________________________________________________\n","activation_88 (Activation)       (None, 6, 6, 384)     0           batch_normalization_88[0][0]     \n","____________________________________________________________________________________________________\n","activation_89 (Activation)       (None, 6, 6, 384)     0           batch_normalization_89[0][0]     \n","____________________________________________________________________________________________________\n","activation_92 (Activation)       (None, 6, 6, 384)     0           batch_normalization_92[0][0]     \n","____________________________________________________________________________________________________\n","activation_93 (Activation)       (None, 6, 6, 384)     0           batch_normalization_93[0][0]     \n","____________________________________________________________________________________________________\n","batch_normalization_94 (BatchNor (None, 6, 6, 192)     576         conv2d_94[0][0]                  \n","____________________________________________________________________________________________________\n","activation_86 (Activation)       (None, 6, 6, 320)     0           batch_normalization_86[0][0]     \n","____________________________________________________________________________________________________\n","mixed9_1 (Concatenate)           (None, 6, 6, 768)     0           activation_88[0][0]              \n","                                                                   activation_89[0][0]              \n","____________________________________________________________________________________________________\n","concatenate_2 (Concatenate)      (None, 6, 6, 768)     0           activation_92[0][0]              \n","                                                                   activation_93[0][0]              \n","____________________________________________________________________________________________________\n","activation_94 (Activation)       (None, 6, 6, 192)     0           batch_normalization_94[0][0]     \n","____________________________________________________________________________________________________\n","mixed10 (Concatenate)            (None, 6, 6, 2048)    0           activation_86[0][0]              \n","                                                                   mixed9_1[0][0]                   \n","                                                                   concatenate_2[0][0]              \n","                                                                   activation_94[0][0]              \n","____________________________________________________________________________________________________\n","global_average_pooling2d_1 (Glob (None, 2048)          0           mixed10[0][0]                    \n","____________________________________________________________________________________________________\n","dense_1 (Dense)                  (None, 102)           208998      global_average_pooling2d_1[0][0] \n","====================================================================================================\n","Total params: 22,011,782\n","Trainable params: 21,977,350\n","Non-trainable params: 34,432\n","____________________________________________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:45.273005Z","start_time":"2017-09-03T13:00:45.254250Z"},"scrolled":false,"id":"rdc0e36cg8dp","colab_type":"code","outputId":"461ae20a-a884-4bbd-f510-8b4367350d2a","executionInfo":{"status":"ok","timestamp":1589701507714,"user_tz":-330,"elapsed":6556,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# how many layers our model has\n","print(len(model.layers))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["313\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:45.370171Z","start_time":"2017-09-03T13:00:45.274354Z"},"scrolled":false,"id":"rTzfs9Omg8dv","colab_type":"code","colab":{}},"source":["# set all layers trainable by default\n","for layer in model.layers:\n","    layer.trainable = True\n","    if isinstance(layer, keras.layers.BatchNormalization):\n","        # we do aggressive exponential smoothing of batch norm\n","        # parameters to faster adjust to our new dataset\n","        layer.momentum = 0.9\n","    \n","# fix deep layers (fine-tuning only last 50)\n","for layer in model.layers[:-50]:\n","    # fix all but batch norm layers, because we neeed to update moving averages for a new dataset!\n","    if not isinstance(layer, keras.layers.BatchNormalization):\n","        layer.trainable = False"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T13:00:45.494833Z","start_time":"2017-09-03T13:00:45.371512Z"},"scrolled":false,"id":"9Y54EtFqg8d1","colab_type":"code","colab":{}},"source":["# compile new model\n","model.compile(\n","    loss='categorical_crossentropy',  # we train 102-way classification\n","    optimizer=keras.optimizers.adamax(lr=1e-2),  # we can take big lr here because we fixed first layers\n","    metrics=['accuracy']  # report accuracy during training\n",")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"mdblt44alh4x","colab_type":"code","outputId":"9868e5c0-422e-4d80-aea1-99892a19f705","executionInfo":{"status":"ok","timestamp":1589701507717,"user_tz":-330,"elapsed":3696,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["____________________________________________________________________________________________________\n","Layer (type)                     Output Shape          Param #     Connected to                     \n","====================================================================================================\n","input_1 (InputLayer)             (None, 250, 250, 3)   0                                            \n","____________________________________________________________________________________________________\n","conv2d_1 (Conv2D)                (None, 124, 124, 32)  864         input_1[0][0]                    \n","____________________________________________________________________________________________________\n","batch_normalization_1 (BatchNorm (None, 124, 124, 32)  96          conv2d_1[0][0]                   \n","____________________________________________________________________________________________________\n","activation_1 (Activation)        (None, 124, 124, 32)  0           batch_normalization_1[0][0]      \n","____________________________________________________________________________________________________\n","conv2d_2 (Conv2D)                (None, 122, 122, 32)  9216        activation_1[0][0]               \n","____________________________________________________________________________________________________\n","batch_normalization_2 (BatchNorm (None, 122, 122, 32)  96          conv2d_2[0][0]                   \n","____________________________________________________________________________________________________\n","activation_2 (Activation)        (None, 122, 122, 32)  0           batch_normalization_2[0][0]      \n","____________________________________________________________________________________________________\n","conv2d_3 (Conv2D)                (None, 122, 122, 64)  18432       activation_2[0][0]               \n","____________________________________________________________________________________________________\n","batch_normalization_3 (BatchNorm (None, 122, 122, 64)  192         conv2d_3[0][0]                   \n","____________________________________________________________________________________________________\n","activation_3 (Activation)        (None, 122, 122, 64)  0           batch_normalization_3[0][0]      \n","____________________________________________________________________________________________________\n","max_pooling2d_1 (MaxPooling2D)   (None, 60, 60, 64)    0           activation_3[0][0]               \n","____________________________________________________________________________________________________\n","conv2d_4 (Conv2D)                (None, 60, 60, 80)    5120        max_pooling2d_1[0][0]            \n","____________________________________________________________________________________________________\n","batch_normalization_4 (BatchNorm (None, 60, 60, 80)    240         conv2d_4[0][0]                   \n","____________________________________________________________________________________________________\n","activation_4 (Activation)        (None, 60, 60, 80)    0           batch_normalization_4[0][0]      \n","____________________________________________________________________________________________________\n","conv2d_5 (Conv2D)                (None, 58, 58, 192)   138240      activation_4[0][0]               \n","____________________________________________________________________________________________________\n","batch_normalization_5 (BatchNorm (None, 58, 58, 192)   576         conv2d_5[0][0]                   \n","____________________________________________________________________________________________________\n","activation_5 (Activation)        (None, 58, 58, 192)   0           batch_normalization_5[0][0]      \n","____________________________________________________________________________________________________\n","max_pooling2d_2 (MaxPooling2D)   (None, 28, 28, 192)   0           activation_5[0][0]               \n","____________________________________________________________________________________________________\n","conv2d_9 (Conv2D)                (None, 28, 28, 64)    12288       max_pooling2d_2[0][0]            \n","____________________________________________________________________________________________________\n","batch_normalization_9 (BatchNorm (None, 28, 28, 64)    192         conv2d_9[0][0]                   \n","____________________________________________________________________________________________________\n","activation_9 (Activation)        (None, 28, 28, 64)    0           batch_normalization_9[0][0]      \n","____________________________________________________________________________________________________\n","conv2d_7 (Conv2D)                (None, 28, 28, 48)    9216        max_pooling2d_2[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_10 (Conv2D)               (None, 28, 28, 96)    55296       activation_9[0][0]               \n","____________________________________________________________________________________________________\n","batch_normalization_7 (BatchNorm (None, 28, 28, 48)    144         conv2d_7[0][0]                   \n","____________________________________________________________________________________________________\n","batch_normalization_10 (BatchNor (None, 28, 28, 96)    288         conv2d_10[0][0]                  \n","____________________________________________________________________________________________________\n","activation_7 (Activation)        (None, 28, 28, 48)    0           batch_normalization_7[0][0]      \n","____________________________________________________________________________________________________\n","activation_10 (Activation)       (None, 28, 28, 96)    0           batch_normalization_10[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_1 (AveragePool (None, 28, 28, 192)   0           max_pooling2d_2[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_6 (Conv2D)                (None, 28, 28, 64)    12288       max_pooling2d_2[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_8 (Conv2D)                (None, 28, 28, 64)    76800       activation_7[0][0]               \n","____________________________________________________________________________________________________\n","conv2d_11 (Conv2D)               (None, 28, 28, 96)    82944       activation_10[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_12 (Conv2D)               (None, 28, 28, 32)    6144        average_pooling2d_1[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_6 (BatchNorm (None, 28, 28, 64)    192         conv2d_6[0][0]                   \n","____________________________________________________________________________________________________\n","batch_normalization_8 (BatchNorm (None, 28, 28, 64)    192         conv2d_8[0][0]                   \n","____________________________________________________________________________________________________\n","batch_normalization_11 (BatchNor (None, 28, 28, 96)    288         conv2d_11[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_12 (BatchNor (None, 28, 28, 32)    96          conv2d_12[0][0]                  \n","____________________________________________________________________________________________________\n","activation_6 (Activation)        (None, 28, 28, 64)    0           batch_normalization_6[0][0]      \n","____________________________________________________________________________________________________\n","activation_8 (Activation)        (None, 28, 28, 64)    0           batch_normalization_8[0][0]      \n","____________________________________________________________________________________________________\n","activation_11 (Activation)       (None, 28, 28, 96)    0           batch_normalization_11[0][0]     \n","____________________________________________________________________________________________________\n","activation_12 (Activation)       (None, 28, 28, 32)    0           batch_normalization_12[0][0]     \n","____________________________________________________________________________________________________\n","mixed0 (Concatenate)             (None, 28, 28, 256)   0           activation_6[0][0]               \n","                                                                   activation_8[0][0]               \n","                                                                   activation_11[0][0]              \n","                                                                   activation_12[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_16 (Conv2D)               (None, 28, 28, 64)    16384       mixed0[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_16 (BatchNor (None, 28, 28, 64)    192         conv2d_16[0][0]                  \n","____________________________________________________________________________________________________\n","activation_16 (Activation)       (None, 28, 28, 64)    0           batch_normalization_16[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_14 (Conv2D)               (None, 28, 28, 48)    12288       mixed0[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_17 (Conv2D)               (None, 28, 28, 96)    55296       activation_16[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_14 (BatchNor (None, 28, 28, 48)    144         conv2d_14[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_17 (BatchNor (None, 28, 28, 96)    288         conv2d_17[0][0]                  \n","____________________________________________________________________________________________________\n","activation_14 (Activation)       (None, 28, 28, 48)    0           batch_normalization_14[0][0]     \n","____________________________________________________________________________________________________\n","activation_17 (Activation)       (None, 28, 28, 96)    0           batch_normalization_17[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_2 (AveragePool (None, 28, 28, 256)   0           mixed0[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_13 (Conv2D)               (None, 28, 28, 64)    16384       mixed0[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_15 (Conv2D)               (None, 28, 28, 64)    76800       activation_14[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_18 (Conv2D)               (None, 28, 28, 96)    82944       activation_17[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_19 (Conv2D)               (None, 28, 28, 64)    16384       average_pooling2d_2[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_13 (BatchNor (None, 28, 28, 64)    192         conv2d_13[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_15 (BatchNor (None, 28, 28, 64)    192         conv2d_15[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_18 (BatchNor (None, 28, 28, 96)    288         conv2d_18[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_19 (BatchNor (None, 28, 28, 64)    192         conv2d_19[0][0]                  \n","____________________________________________________________________________________________________\n","activation_13 (Activation)       (None, 28, 28, 64)    0           batch_normalization_13[0][0]     \n","____________________________________________________________________________________________________\n","activation_15 (Activation)       (None, 28, 28, 64)    0           batch_normalization_15[0][0]     \n","____________________________________________________________________________________________________\n","activation_18 (Activation)       (None, 28, 28, 96)    0           batch_normalization_18[0][0]     \n","____________________________________________________________________________________________________\n","activation_19 (Activation)       (None, 28, 28, 64)    0           batch_normalization_19[0][0]     \n","____________________________________________________________________________________________________\n","mixed1 (Concatenate)             (None, 28, 28, 288)   0           activation_13[0][0]              \n","                                                                   activation_15[0][0]              \n","                                                                   activation_18[0][0]              \n","                                                                   activation_19[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_23 (Conv2D)               (None, 28, 28, 64)    18432       mixed1[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_23 (BatchNor (None, 28, 28, 64)    192         conv2d_23[0][0]                  \n","____________________________________________________________________________________________________\n","activation_23 (Activation)       (None, 28, 28, 64)    0           batch_normalization_23[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_21 (Conv2D)               (None, 28, 28, 48)    13824       mixed1[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_24 (Conv2D)               (None, 28, 28, 96)    55296       activation_23[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_21 (BatchNor (None, 28, 28, 48)    144         conv2d_21[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_24 (BatchNor (None, 28, 28, 96)    288         conv2d_24[0][0]                  \n","____________________________________________________________________________________________________\n","activation_21 (Activation)       (None, 28, 28, 48)    0           batch_normalization_21[0][0]     \n","____________________________________________________________________________________________________\n","activation_24 (Activation)       (None, 28, 28, 96)    0           batch_normalization_24[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_3 (AveragePool (None, 28, 28, 288)   0           mixed1[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_20 (Conv2D)               (None, 28, 28, 64)    18432       mixed1[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_22 (Conv2D)               (None, 28, 28, 64)    76800       activation_21[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_25 (Conv2D)               (None, 28, 28, 96)    82944       activation_24[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_26 (Conv2D)               (None, 28, 28, 64)    18432       average_pooling2d_3[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_20 (BatchNor (None, 28, 28, 64)    192         conv2d_20[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_22 (BatchNor (None, 28, 28, 64)    192         conv2d_22[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_25 (BatchNor (None, 28, 28, 96)    288         conv2d_25[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_26 (BatchNor (None, 28, 28, 64)    192         conv2d_26[0][0]                  \n","____________________________________________________________________________________________________\n","activation_20 (Activation)       (None, 28, 28, 64)    0           batch_normalization_20[0][0]     \n","____________________________________________________________________________________________________\n","activation_22 (Activation)       (None, 28, 28, 64)    0           batch_normalization_22[0][0]     \n","____________________________________________________________________________________________________\n","activation_25 (Activation)       (None, 28, 28, 96)    0           batch_normalization_25[0][0]     \n","____________________________________________________________________________________________________\n","activation_26 (Activation)       (None, 28, 28, 64)    0           batch_normalization_26[0][0]     \n","____________________________________________________________________________________________________\n","mixed2 (Concatenate)             (None, 28, 28, 288)   0           activation_20[0][0]              \n","                                                                   activation_22[0][0]              \n","                                                                   activation_25[0][0]              \n","                                                                   activation_26[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_28 (Conv2D)               (None, 28, 28, 64)    18432       mixed2[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_28 (BatchNor (None, 28, 28, 64)    192         conv2d_28[0][0]                  \n","____________________________________________________________________________________________________\n","activation_28 (Activation)       (None, 28, 28, 64)    0           batch_normalization_28[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_29 (Conv2D)               (None, 28, 28, 96)    55296       activation_28[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_29 (BatchNor (None, 28, 28, 96)    288         conv2d_29[0][0]                  \n","____________________________________________________________________________________________________\n","activation_29 (Activation)       (None, 28, 28, 96)    0           batch_normalization_29[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_27 (Conv2D)               (None, 13, 13, 384)   995328      mixed2[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_30 (Conv2D)               (None, 13, 13, 96)    82944       activation_29[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_27 (BatchNor (None, 13, 13, 384)   1152        conv2d_27[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_30 (BatchNor (None, 13, 13, 96)    288         conv2d_30[0][0]                  \n","____________________________________________________________________________________________________\n","activation_27 (Activation)       (None, 13, 13, 384)   0           batch_normalization_27[0][0]     \n","____________________________________________________________________________________________________\n","activation_30 (Activation)       (None, 13, 13, 96)    0           batch_normalization_30[0][0]     \n","____________________________________________________________________________________________________\n","max_pooling2d_3 (MaxPooling2D)   (None, 13, 13, 288)   0           mixed2[0][0]                     \n","____________________________________________________________________________________________________\n","mixed3 (Concatenate)             (None, 13, 13, 768)   0           activation_27[0][0]              \n","                                                                   activation_30[0][0]              \n","                                                                   max_pooling2d_3[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_35 (Conv2D)               (None, 13, 13, 128)   98304       mixed3[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_35 (BatchNor (None, 13, 13, 128)   384         conv2d_35[0][0]                  \n","____________________________________________________________________________________________________\n","activation_35 (Activation)       (None, 13, 13, 128)   0           batch_normalization_35[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_36 (Conv2D)               (None, 13, 13, 128)   114688      activation_35[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_36 (BatchNor (None, 13, 13, 128)   384         conv2d_36[0][0]                  \n","____________________________________________________________________________________________________\n","activation_36 (Activation)       (None, 13, 13, 128)   0           batch_normalization_36[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_32 (Conv2D)               (None, 13, 13, 128)   98304       mixed3[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_37 (Conv2D)               (None, 13, 13, 128)   114688      activation_36[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_32 (BatchNor (None, 13, 13, 128)   384         conv2d_32[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_37 (BatchNor (None, 13, 13, 128)   384         conv2d_37[0][0]                  \n","____________________________________________________________________________________________________\n","activation_32 (Activation)       (None, 13, 13, 128)   0           batch_normalization_32[0][0]     \n","____________________________________________________________________________________________________\n","activation_37 (Activation)       (None, 13, 13, 128)   0           batch_normalization_37[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_33 (Conv2D)               (None, 13, 13, 128)   114688      activation_32[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_38 (Conv2D)               (None, 13, 13, 128)   114688      activation_37[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_33 (BatchNor (None, 13, 13, 128)   384         conv2d_33[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_38 (BatchNor (None, 13, 13, 128)   384         conv2d_38[0][0]                  \n","____________________________________________________________________________________________________\n","activation_33 (Activation)       (None, 13, 13, 128)   0           batch_normalization_33[0][0]     \n","____________________________________________________________________________________________________\n","activation_38 (Activation)       (None, 13, 13, 128)   0           batch_normalization_38[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_4 (AveragePool (None, 13, 13, 768)   0           mixed3[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_31 (Conv2D)               (None, 13, 13, 192)   147456      mixed3[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_34 (Conv2D)               (None, 13, 13, 192)   172032      activation_33[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_39 (Conv2D)               (None, 13, 13, 192)   172032      activation_38[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_40 (Conv2D)               (None, 13, 13, 192)   147456      average_pooling2d_4[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_31 (BatchNor (None, 13, 13, 192)   576         conv2d_31[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_34 (BatchNor (None, 13, 13, 192)   576         conv2d_34[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_39 (BatchNor (None, 13, 13, 192)   576         conv2d_39[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_40 (BatchNor (None, 13, 13, 192)   576         conv2d_40[0][0]                  \n","____________________________________________________________________________________________________\n","activation_31 (Activation)       (None, 13, 13, 192)   0           batch_normalization_31[0][0]     \n","____________________________________________________________________________________________________\n","activation_34 (Activation)       (None, 13, 13, 192)   0           batch_normalization_34[0][0]     \n","____________________________________________________________________________________________________\n","activation_39 (Activation)       (None, 13, 13, 192)   0           batch_normalization_39[0][0]     \n","____________________________________________________________________________________________________\n","activation_40 (Activation)       (None, 13, 13, 192)   0           batch_normalization_40[0][0]     \n","____________________________________________________________________________________________________\n","mixed4 (Concatenate)             (None, 13, 13, 768)   0           activation_31[0][0]              \n","                                                                   activation_34[0][0]              \n","                                                                   activation_39[0][0]              \n","                                                                   activation_40[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_45 (Conv2D)               (None, 13, 13, 160)   122880      mixed4[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_45 (BatchNor (None, 13, 13, 160)   480         conv2d_45[0][0]                  \n","____________________________________________________________________________________________________\n","activation_45 (Activation)       (None, 13, 13, 160)   0           batch_normalization_45[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_46 (Conv2D)               (None, 13, 13, 160)   179200      activation_45[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_46 (BatchNor (None, 13, 13, 160)   480         conv2d_46[0][0]                  \n","____________________________________________________________________________________________________\n","activation_46 (Activation)       (None, 13, 13, 160)   0           batch_normalization_46[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_42 (Conv2D)               (None, 13, 13, 160)   122880      mixed4[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_47 (Conv2D)               (None, 13, 13, 160)   179200      activation_46[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_42 (BatchNor (None, 13, 13, 160)   480         conv2d_42[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_47 (BatchNor (None, 13, 13, 160)   480         conv2d_47[0][0]                  \n","____________________________________________________________________________________________________\n","activation_42 (Activation)       (None, 13, 13, 160)   0           batch_normalization_42[0][0]     \n","____________________________________________________________________________________________________\n","activation_47 (Activation)       (None, 13, 13, 160)   0           batch_normalization_47[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_43 (Conv2D)               (None, 13, 13, 160)   179200      activation_42[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_48 (Conv2D)               (None, 13, 13, 160)   179200      activation_47[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_43 (BatchNor (None, 13, 13, 160)   480         conv2d_43[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_48 (BatchNor (None, 13, 13, 160)   480         conv2d_48[0][0]                  \n","____________________________________________________________________________________________________\n","activation_43 (Activation)       (None, 13, 13, 160)   0           batch_normalization_43[0][0]     \n","____________________________________________________________________________________________________\n","activation_48 (Activation)       (None, 13, 13, 160)   0           batch_normalization_48[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_5 (AveragePool (None, 13, 13, 768)   0           mixed4[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_41 (Conv2D)               (None, 13, 13, 192)   147456      mixed4[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_44 (Conv2D)               (None, 13, 13, 192)   215040      activation_43[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_49 (Conv2D)               (None, 13, 13, 192)   215040      activation_48[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_50 (Conv2D)               (None, 13, 13, 192)   147456      average_pooling2d_5[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_41 (BatchNor (None, 13, 13, 192)   576         conv2d_41[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_44 (BatchNor (None, 13, 13, 192)   576         conv2d_44[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_49 (BatchNor (None, 13, 13, 192)   576         conv2d_49[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_50 (BatchNor (None, 13, 13, 192)   576         conv2d_50[0][0]                  \n","____________________________________________________________________________________________________\n","activation_41 (Activation)       (None, 13, 13, 192)   0           batch_normalization_41[0][0]     \n","____________________________________________________________________________________________________\n","activation_44 (Activation)       (None, 13, 13, 192)   0           batch_normalization_44[0][0]     \n","____________________________________________________________________________________________________\n","activation_49 (Activation)       (None, 13, 13, 192)   0           batch_normalization_49[0][0]     \n","____________________________________________________________________________________________________\n","activation_50 (Activation)       (None, 13, 13, 192)   0           batch_normalization_50[0][0]     \n","____________________________________________________________________________________________________\n","mixed5 (Concatenate)             (None, 13, 13, 768)   0           activation_41[0][0]              \n","                                                                   activation_44[0][0]              \n","                                                                   activation_49[0][0]              \n","                                                                   activation_50[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_55 (Conv2D)               (None, 13, 13, 160)   122880      mixed5[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_55 (BatchNor (None, 13, 13, 160)   480         conv2d_55[0][0]                  \n","____________________________________________________________________________________________________\n","activation_55 (Activation)       (None, 13, 13, 160)   0           batch_normalization_55[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_56 (Conv2D)               (None, 13, 13, 160)   179200      activation_55[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_56 (BatchNor (None, 13, 13, 160)   480         conv2d_56[0][0]                  \n","____________________________________________________________________________________________________\n","activation_56 (Activation)       (None, 13, 13, 160)   0           batch_normalization_56[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_52 (Conv2D)               (None, 13, 13, 160)   122880      mixed5[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_57 (Conv2D)               (None, 13, 13, 160)   179200      activation_56[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_52 (BatchNor (None, 13, 13, 160)   480         conv2d_52[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_57 (BatchNor (None, 13, 13, 160)   480         conv2d_57[0][0]                  \n","____________________________________________________________________________________________________\n","activation_52 (Activation)       (None, 13, 13, 160)   0           batch_normalization_52[0][0]     \n","____________________________________________________________________________________________________\n","activation_57 (Activation)       (None, 13, 13, 160)   0           batch_normalization_57[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_53 (Conv2D)               (None, 13, 13, 160)   179200      activation_52[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_58 (Conv2D)               (None, 13, 13, 160)   179200      activation_57[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_53 (BatchNor (None, 13, 13, 160)   480         conv2d_53[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_58 (BatchNor (None, 13, 13, 160)   480         conv2d_58[0][0]                  \n","____________________________________________________________________________________________________\n","activation_53 (Activation)       (None, 13, 13, 160)   0           batch_normalization_53[0][0]     \n","____________________________________________________________________________________________________\n","activation_58 (Activation)       (None, 13, 13, 160)   0           batch_normalization_58[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_6 (AveragePool (None, 13, 13, 768)   0           mixed5[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_51 (Conv2D)               (None, 13, 13, 192)   147456      mixed5[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_54 (Conv2D)               (None, 13, 13, 192)   215040      activation_53[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_59 (Conv2D)               (None, 13, 13, 192)   215040      activation_58[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_60 (Conv2D)               (None, 13, 13, 192)   147456      average_pooling2d_6[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_51 (BatchNor (None, 13, 13, 192)   576         conv2d_51[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_54 (BatchNor (None, 13, 13, 192)   576         conv2d_54[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_59 (BatchNor (None, 13, 13, 192)   576         conv2d_59[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_60 (BatchNor (None, 13, 13, 192)   576         conv2d_60[0][0]                  \n","____________________________________________________________________________________________________\n","activation_51 (Activation)       (None, 13, 13, 192)   0           batch_normalization_51[0][0]     \n","____________________________________________________________________________________________________\n","activation_54 (Activation)       (None, 13, 13, 192)   0           batch_normalization_54[0][0]     \n","____________________________________________________________________________________________________\n","activation_59 (Activation)       (None, 13, 13, 192)   0           batch_normalization_59[0][0]     \n","____________________________________________________________________________________________________\n","activation_60 (Activation)       (None, 13, 13, 192)   0           batch_normalization_60[0][0]     \n","____________________________________________________________________________________________________\n","mixed6 (Concatenate)             (None, 13, 13, 768)   0           activation_51[0][0]              \n","                                                                   activation_54[0][0]              \n","                                                                   activation_59[0][0]              \n","                                                                   activation_60[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_65 (Conv2D)               (None, 13, 13, 192)   147456      mixed6[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_65 (BatchNor (None, 13, 13, 192)   576         conv2d_65[0][0]                  \n","____________________________________________________________________________________________________\n","activation_65 (Activation)       (None, 13, 13, 192)   0           batch_normalization_65[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_66 (Conv2D)               (None, 13, 13, 192)   258048      activation_65[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_66 (BatchNor (None, 13, 13, 192)   576         conv2d_66[0][0]                  \n","____________________________________________________________________________________________________\n","activation_66 (Activation)       (None, 13, 13, 192)   0           batch_normalization_66[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_62 (Conv2D)               (None, 13, 13, 192)   147456      mixed6[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_67 (Conv2D)               (None, 13, 13, 192)   258048      activation_66[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_62 (BatchNor (None, 13, 13, 192)   576         conv2d_62[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_67 (BatchNor (None, 13, 13, 192)   576         conv2d_67[0][0]                  \n","____________________________________________________________________________________________________\n","activation_62 (Activation)       (None, 13, 13, 192)   0           batch_normalization_62[0][0]     \n","____________________________________________________________________________________________________\n","activation_67 (Activation)       (None, 13, 13, 192)   0           batch_normalization_67[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_63 (Conv2D)               (None, 13, 13, 192)   258048      activation_62[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_68 (Conv2D)               (None, 13, 13, 192)   258048      activation_67[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_63 (BatchNor (None, 13, 13, 192)   576         conv2d_63[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_68 (BatchNor (None, 13, 13, 192)   576         conv2d_68[0][0]                  \n","____________________________________________________________________________________________________\n","activation_63 (Activation)       (None, 13, 13, 192)   0           batch_normalization_63[0][0]     \n","____________________________________________________________________________________________________\n","activation_68 (Activation)       (None, 13, 13, 192)   0           batch_normalization_68[0][0]     \n","____________________________________________________________________________________________________\n","average_pooling2d_7 (AveragePool (None, 13, 13, 768)   0           mixed6[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_61 (Conv2D)               (None, 13, 13, 192)   147456      mixed6[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_64 (Conv2D)               (None, 13, 13, 192)   258048      activation_63[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_69 (Conv2D)               (None, 13, 13, 192)   258048      activation_68[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_70 (Conv2D)               (None, 13, 13, 192)   147456      average_pooling2d_7[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_61 (BatchNor (None, 13, 13, 192)   576         conv2d_61[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_64 (BatchNor (None, 13, 13, 192)   576         conv2d_64[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_69 (BatchNor (None, 13, 13, 192)   576         conv2d_69[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_70 (BatchNor (None, 13, 13, 192)   576         conv2d_70[0][0]                  \n","____________________________________________________________________________________________________\n","activation_61 (Activation)       (None, 13, 13, 192)   0           batch_normalization_61[0][0]     \n","____________________________________________________________________________________________________\n","activation_64 (Activation)       (None, 13, 13, 192)   0           batch_normalization_64[0][0]     \n","____________________________________________________________________________________________________\n","activation_69 (Activation)       (None, 13, 13, 192)   0           batch_normalization_69[0][0]     \n","____________________________________________________________________________________________________\n","activation_70 (Activation)       (None, 13, 13, 192)   0           batch_normalization_70[0][0]     \n","____________________________________________________________________________________________________\n","mixed7 (Concatenate)             (None, 13, 13, 768)   0           activation_61[0][0]              \n","                                                                   activation_64[0][0]              \n","                                                                   activation_69[0][0]              \n","                                                                   activation_70[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_73 (Conv2D)               (None, 13, 13, 192)   147456      mixed7[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_73 (BatchNor (None, 13, 13, 192)   576         conv2d_73[0][0]                  \n","____________________________________________________________________________________________________\n","activation_73 (Activation)       (None, 13, 13, 192)   0           batch_normalization_73[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_74 (Conv2D)               (None, 13, 13, 192)   258048      activation_73[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_74 (BatchNor (None, 13, 13, 192)   576         conv2d_74[0][0]                  \n","____________________________________________________________________________________________________\n","activation_74 (Activation)       (None, 13, 13, 192)   0           batch_normalization_74[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_71 (Conv2D)               (None, 13, 13, 192)   147456      mixed7[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_75 (Conv2D)               (None, 13, 13, 192)   258048      activation_74[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_71 (BatchNor (None, 13, 13, 192)   576         conv2d_71[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_75 (BatchNor (None, 13, 13, 192)   576         conv2d_75[0][0]                  \n","____________________________________________________________________________________________________\n","activation_71 (Activation)       (None, 13, 13, 192)   0           batch_normalization_71[0][0]     \n","____________________________________________________________________________________________________\n","activation_75 (Activation)       (None, 13, 13, 192)   0           batch_normalization_75[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_72 (Conv2D)               (None, 6, 6, 320)     552960      activation_71[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_76 (Conv2D)               (None, 6, 6, 192)     331776      activation_75[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_72 (BatchNor (None, 6, 6, 320)     960         conv2d_72[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_76 (BatchNor (None, 6, 6, 192)     576         conv2d_76[0][0]                  \n","____________________________________________________________________________________________________\n","activation_72 (Activation)       (None, 6, 6, 320)     0           batch_normalization_72[0][0]     \n","____________________________________________________________________________________________________\n","activation_76 (Activation)       (None, 6, 6, 192)     0           batch_normalization_76[0][0]     \n","____________________________________________________________________________________________________\n","max_pooling2d_4 (MaxPooling2D)   (None, 6, 6, 768)     0           mixed7[0][0]                     \n","____________________________________________________________________________________________________\n","mixed8 (Concatenate)             (None, 6, 6, 1280)    0           activation_72[0][0]              \n","                                                                   activation_76[0][0]              \n","                                                                   max_pooling2d_4[0][0]            \n","____________________________________________________________________________________________________\n","conv2d_81 (Conv2D)               (None, 6, 6, 448)     573440      mixed8[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_81 (BatchNor (None, 6, 6, 448)     1344        conv2d_81[0][0]                  \n","____________________________________________________________________________________________________\n","activation_81 (Activation)       (None, 6, 6, 448)     0           batch_normalization_81[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_78 (Conv2D)               (None, 6, 6, 384)     491520      mixed8[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_82 (Conv2D)               (None, 6, 6, 384)     1548288     activation_81[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_78 (BatchNor (None, 6, 6, 384)     1152        conv2d_78[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_82 (BatchNor (None, 6, 6, 384)     1152        conv2d_82[0][0]                  \n","____________________________________________________________________________________________________\n","activation_78 (Activation)       (None, 6, 6, 384)     0           batch_normalization_78[0][0]     \n","____________________________________________________________________________________________________\n","activation_82 (Activation)       (None, 6, 6, 384)     0           batch_normalization_82[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_79 (Conv2D)               (None, 6, 6, 384)     442368      activation_78[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_80 (Conv2D)               (None, 6, 6, 384)     442368      activation_78[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_83 (Conv2D)               (None, 6, 6, 384)     442368      activation_82[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_84 (Conv2D)               (None, 6, 6, 384)     442368      activation_82[0][0]              \n","____________________________________________________________________________________________________\n","average_pooling2d_8 (AveragePool (None, 6, 6, 1280)    0           mixed8[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_77 (Conv2D)               (None, 6, 6, 320)     409600      mixed8[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_79 (BatchNor (None, 6, 6, 384)     1152        conv2d_79[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_80 (BatchNor (None, 6, 6, 384)     1152        conv2d_80[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_83 (BatchNor (None, 6, 6, 384)     1152        conv2d_83[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_84 (BatchNor (None, 6, 6, 384)     1152        conv2d_84[0][0]                  \n","____________________________________________________________________________________________________\n","conv2d_85 (Conv2D)               (None, 6, 6, 192)     245760      average_pooling2d_8[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_77 (BatchNor (None, 6, 6, 320)     960         conv2d_77[0][0]                  \n","____________________________________________________________________________________________________\n","activation_79 (Activation)       (None, 6, 6, 384)     0           batch_normalization_79[0][0]     \n","____________________________________________________________________________________________________\n","activation_80 (Activation)       (None, 6, 6, 384)     0           batch_normalization_80[0][0]     \n","____________________________________________________________________________________________________\n","activation_83 (Activation)       (None, 6, 6, 384)     0           batch_normalization_83[0][0]     \n","____________________________________________________________________________________________________\n","activation_84 (Activation)       (None, 6, 6, 384)     0           batch_normalization_84[0][0]     \n","____________________________________________________________________________________________________\n","batch_normalization_85 (BatchNor (None, 6, 6, 192)     576         conv2d_85[0][0]                  \n","____________________________________________________________________________________________________\n","activation_77 (Activation)       (None, 6, 6, 320)     0           batch_normalization_77[0][0]     \n","____________________________________________________________________________________________________\n","mixed9_0 (Concatenate)           (None, 6, 6, 768)     0           activation_79[0][0]              \n","                                                                   activation_80[0][0]              \n","____________________________________________________________________________________________________\n","concatenate_1 (Concatenate)      (None, 6, 6, 768)     0           activation_83[0][0]              \n","                                                                   activation_84[0][0]              \n","____________________________________________________________________________________________________\n","activation_85 (Activation)       (None, 6, 6, 192)     0           batch_normalization_85[0][0]     \n","____________________________________________________________________________________________________\n","mixed9 (Concatenate)             (None, 6, 6, 2048)    0           activation_77[0][0]              \n","                                                                   mixed9_0[0][0]                   \n","                                                                   concatenate_1[0][0]              \n","                                                                   activation_85[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_90 (Conv2D)               (None, 6, 6, 448)     917504      mixed9[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_90 (BatchNor (None, 6, 6, 448)     1344        conv2d_90[0][0]                  \n","____________________________________________________________________________________________________\n","activation_90 (Activation)       (None, 6, 6, 448)     0           batch_normalization_90[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_87 (Conv2D)               (None, 6, 6, 384)     786432      mixed9[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_91 (Conv2D)               (None, 6, 6, 384)     1548288     activation_90[0][0]              \n","____________________________________________________________________________________________________\n","batch_normalization_87 (BatchNor (None, 6, 6, 384)     1152        conv2d_87[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_91 (BatchNor (None, 6, 6, 384)     1152        conv2d_91[0][0]                  \n","____________________________________________________________________________________________________\n","activation_87 (Activation)       (None, 6, 6, 384)     0           batch_normalization_87[0][0]     \n","____________________________________________________________________________________________________\n","activation_91 (Activation)       (None, 6, 6, 384)     0           batch_normalization_91[0][0]     \n","____________________________________________________________________________________________________\n","conv2d_88 (Conv2D)               (None, 6, 6, 384)     442368      activation_87[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_89 (Conv2D)               (None, 6, 6, 384)     442368      activation_87[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_92 (Conv2D)               (None, 6, 6, 384)     442368      activation_91[0][0]              \n","____________________________________________________________________________________________________\n","conv2d_93 (Conv2D)               (None, 6, 6, 384)     442368      activation_91[0][0]              \n","____________________________________________________________________________________________________\n","average_pooling2d_9 (AveragePool (None, 6, 6, 2048)    0           mixed9[0][0]                     \n","____________________________________________________________________________________________________\n","conv2d_86 (Conv2D)               (None, 6, 6, 320)     655360      mixed9[0][0]                     \n","____________________________________________________________________________________________________\n","batch_normalization_88 (BatchNor (None, 6, 6, 384)     1152        conv2d_88[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_89 (BatchNor (None, 6, 6, 384)     1152        conv2d_89[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_92 (BatchNor (None, 6, 6, 384)     1152        conv2d_92[0][0]                  \n","____________________________________________________________________________________________________\n","batch_normalization_93 (BatchNor (None, 6, 6, 384)     1152        conv2d_93[0][0]                  \n","____________________________________________________________________________________________________\n","conv2d_94 (Conv2D)               (None, 6, 6, 192)     393216      average_pooling2d_9[0][0]        \n","____________________________________________________________________________________________________\n","batch_normalization_86 (BatchNor (None, 6, 6, 320)     960         conv2d_86[0][0]                  \n","____________________________________________________________________________________________________\n","activation_88 (Activation)       (None, 6, 6, 384)     0           batch_normalization_88[0][0]     \n","____________________________________________________________________________________________________\n","activation_89 (Activation)       (None, 6, 6, 384)     0           batch_normalization_89[0][0]     \n","____________________________________________________________________________________________________\n","activation_92 (Activation)       (None, 6, 6, 384)     0           batch_normalization_92[0][0]     \n","____________________________________________________________________________________________________\n","activation_93 (Activation)       (None, 6, 6, 384)     0           batch_normalization_93[0][0]     \n","____________________________________________________________________________________________________\n","batch_normalization_94 (BatchNor (None, 6, 6, 192)     576         conv2d_94[0][0]                  \n","____________________________________________________________________________________________________\n","activation_86 (Activation)       (None, 6, 6, 320)     0           batch_normalization_86[0][0]     \n","____________________________________________________________________________________________________\n","mixed9_1 (Concatenate)           (None, 6, 6, 768)     0           activation_88[0][0]              \n","                                                                   activation_89[0][0]              \n","____________________________________________________________________________________________________\n","concatenate_2 (Concatenate)      (None, 6, 6, 768)     0           activation_92[0][0]              \n","                                                                   activation_93[0][0]              \n","____________________________________________________________________________________________________\n","activation_94 (Activation)       (None, 6, 6, 192)     0           batch_normalization_94[0][0]     \n","____________________________________________________________________________________________________\n","mixed10 (Concatenate)            (None, 6, 6, 2048)    0           activation_86[0][0]              \n","                                                                   mixed9_1[0][0]                   \n","                                                                   concatenate_2[0][0]              \n","                                                                   activation_94[0][0]              \n","____________________________________________________________________________________________________\n","global_average_pooling2d_1 (Glob (None, 2048)          0           mixed10[0][0]                    \n","____________________________________________________________________________________________________\n","dense_1 (Dense)                  (None, 102)           208998      global_average_pooling2d_1[0][0] \n","====================================================================================================\n","Total params: 22,011,782\n","Trainable params: 6,951,846\n","Non-trainable params: 15,059,936\n","____________________________________________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8y_vo0ipg8eB","colab_type":"code","colab":{}},"source":["# we will save model checkpoints to continue training in case of kernel death\n","model_filename = 'flowers.{0:03d}.hdf5'\n","last_finished_epoch = None\n","\n","#### uncomment below to continue training from model checkpoint\n","#### fill `last_finished_epoch` with your latest finished epoch\n","# from keras.models import load_model\n","# s = reset_tf_session()\n","# last_finished_epoch = 10\n","# model = load_model(model_filename.format(last_finished_epoch))"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wvbxVY3-g8eP","colab_type":"text"},"source":["Training takes **2 hours**. You're aiming for ~0.93 validation accuracy."]},{"cell_type":"code","metadata":{"ExecuteTime":{"end_time":"2017-09-03T14:23:36.792701Z","start_time":"2017-09-03T13:00:45.496194Z"},"scrolled":false,"id":"nDflQfHFg8eP","colab_type":"code","outputId":"19aa8b39-6697-496e-894e-e7e8527bd1ab","executionInfo":{"status":"ok","timestamp":1589701779283,"user_tz":-330,"elapsed":261223,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# fine tune for 2 epochs (full passes through all training data)\n","# we make 2*8 epochs, where epoch is 1/8 of our training data to see progress more often\n","model.fit_generator(\n","    train_generator(tr_files, tr_labels), \n","    steps_per_epoch=len(tr_files) // BATCH_SIZE // 8,\n","    epochs=2 * 8,\n","    validation_data=train_generator(te_files, te_labels), \n","    validation_steps=len(te_files) // BATCH_SIZE // 4,\n","    callbacks=[keras_utils.TqdmProgressCallback(), \n","               keras_utils.ModelSaveCallback(model_filename)],\n","    verbose=1,\n","    initial_epoch=last_finished_epoch or 0\n",")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\n","Epoch 1/16\n","Epoch 1/16\n","24/25 [===========================>..] - ETA: 0s - loss: 4.0134 - acc: 0.2826**\n","loss: 3.9517; acc: 0.2938; val_loss: 14.5041; val_acc: 0.0651\n","Model saved in flowers.000.hdf5\n","25/25 [==============================] - 55s - loss: 3.9517 - acc: 0.2938 - val_loss: 14.5041 - val_acc: 0.0651\n","\n","Epoch 2/16\n","Epoch 2/16\n","24/25 [===========================>..] - ETA: 0s - loss: 2.1254 - acc: 0.5221**\n","loss: 2.0998; acc: 0.5300; val_loss: 7.9688; val_acc: 0.3047\n","Model saved in flowers.001.hdf5\n","25/25 [==============================] - 12s - loss: 2.0998 - acc: 0.5300 - val_loss: 7.9688 - val_acc: 0.3047\n","\n","Epoch 3/16\n","Epoch 3/16\n","24/25 [===========================>..] - ETA: 0s - loss: 1.1424 - acc: 0.7357**\n","loss: 1.1179; acc: 0.7413; val_loss: 3.8845; val_acc: 0.4688\n","Model saved in flowers.002.hdf5\n","25/25 [==============================] - 13s - loss: 1.1179 - acc: 0.7413 - val_loss: 3.8845 - val_acc: 0.4688\n","\n","Epoch 4/16\n","Epoch 4/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.8486 - acc: 0.7943**\n","loss: 0.8333; acc: 0.8000; val_loss: 1.7893; val_acc: 0.6458\n","Model saved in flowers.003.hdf5\n","25/25 [==============================] - 12s - loss: 0.8333 - acc: 0.8000 - val_loss: 1.7893 - val_acc: 0.6458\n","\n","Epoch 5/16\n","Epoch 5/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.6115 - acc: 0.8490**\n","loss: 0.6172; acc: 0.8513; val_loss: 0.7012; val_acc: 0.8229\n","Model saved in flowers.004.hdf5\n","25/25 [==============================] - 12s - loss: 0.6172 - acc: 0.8513 - val_loss: 0.7012 - val_acc: 0.8229\n","\n","Epoch 6/16\n","Epoch 6/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.5805 - acc: 0.8581**\n","loss: 0.5686; acc: 0.8612; val_loss: 0.4162; val_acc: 0.8750\n","Model saved in flowers.005.hdf5\n","25/25 [==============================] - 12s - loss: 0.5686 - acc: 0.8612 - val_loss: 0.4162 - val_acc: 0.8750\n","\n","Epoch 7/16\n","Epoch 7/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.4594 - acc: 0.8776**\n","loss: 0.4639; acc: 0.8762; val_loss: 0.3383; val_acc: 0.9141\n","Model saved in flowers.006.hdf5\n","25/25 [==============================] - 12s - loss: 0.4639 - acc: 0.8762 - val_loss: 0.3383 - val_acc: 0.9141\n","\n","Epoch 8/16\n","Epoch 8/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.3970 - acc: 0.8919**\n","loss: 0.3930; acc: 0.8925; val_loss: 0.3581; val_acc: 0.9078\n","Model saved in flowers.007.hdf5\n","25/25 [==============================] - 12s - loss: 0.3930 - acc: 0.8925 - val_loss: 0.3581 - val_acc: 0.9078\n","\n","Epoch 9/16\n","Epoch 9/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.2269 - acc: 0.9404**\n","loss: 0.2254; acc: 0.9403; val_loss: 0.2670; val_acc: 0.9297\n","Model saved in flowers.008.hdf5\n","25/25 [==============================] - 13s - loss: 0.2254 - acc: 0.9403 - val_loss: 0.2670 - val_acc: 0.9297\n","\n","Epoch 10/16\n","Epoch 10/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.1466 - acc: 0.9674**\n","loss: 0.1469; acc: 0.9675; val_loss: 0.3355; val_acc: 0.9010\n","Model saved in flowers.009.hdf5\n","25/25 [==============================] - 12s - loss: 0.1469 - acc: 0.9675 - val_loss: 0.3355 - val_acc: 0.9010\n","\n","Epoch 11/16\n","Epoch 11/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.1052 - acc: 0.9766**\n","loss: 0.1044; acc: 0.9763; val_loss: 0.2545; val_acc: 0.9297\n","Model saved in flowers.010.hdf5\n","25/25 [==============================] - 12s - loss: 0.1044 - acc: 0.9763 - val_loss: 0.2545 - val_acc: 0.9297\n","\n","Epoch 12/16\n","Epoch 12/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.0917 - acc: 0.9792**\n","loss: 0.0947; acc: 0.9788; val_loss: 0.2293; val_acc: 0.9078\n","Model saved in flowers.011.hdf5\n","25/25 [==============================] - 12s - loss: 0.0947 - acc: 0.9788 - val_loss: 0.2293 - val_acc: 0.9078\n","\n","Epoch 13/16\n","Epoch 13/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.0554 - acc: 0.9909**\n","loss: 0.0535; acc: 0.9913; val_loss: 0.1924; val_acc: 0.9557\n","Model saved in flowers.012.hdf5\n","25/25 [==============================] - 12s - loss: 0.0535 - acc: 0.9913 - val_loss: 0.1924 - val_acc: 0.9557\n","\n","Epoch 14/16\n","Epoch 14/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.0527 - acc: 0.9909**\n","loss: 0.0514; acc: 0.9913; val_loss: 0.1914; val_acc: 0.9505\n","Model saved in flowers.013.hdf5\n","25/25 [==============================] - 12s - loss: 0.0514 - acc: 0.9913 - val_loss: 0.1914 - val_acc: 0.9505\n","\n","Epoch 15/16\n","Epoch 15/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.0353 - acc: 0.9935**\n","loss: 0.0359; acc: 0.9937; val_loss: 0.1876; val_acc: 0.9479\n","Model saved in flowers.014.hdf5\n","25/25 [==============================] - 12s - loss: 0.0359 - acc: 0.9937 - val_loss: 0.1876 - val_acc: 0.9479\n","\n","Epoch 16/16\n","Epoch 16/16\n","24/25 [===========================>..] - ETA: 0s - loss: 0.0251 - acc: 0.9974**\n","loss: 0.0255; acc: 0.9975; val_loss: 0.2312; val_acc: 0.9218\n","Model saved in flowers.015.hdf5\n","25/25 [==============================] - 12s - loss: 0.0255 - acc: 0.9975 - val_loss: 0.2312 - val_acc: 0.9218\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f69e57f7710>"]},"metadata":{"tags":[]},"execution_count":53}]},{"cell_type":"code","metadata":{"scrolled":false,"id":"OCF7JDw2g8eY","colab_type":"code","outputId":"89a315fc-9d0e-448d-b83f-a6e01ce728c5","executionInfo":{"status":"ok","timestamp":1589701787614,"user_tz":-330,"elapsed":8264,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["## GRADED PART, DO NOT CHANGE!\n","# Accuracy on validation set\n","test_accuracy = model.evaluate_generator(\n","    train_generator(te_files, te_labels), \n","    len(te_files) // BATCH_SIZE // 2\n",")[1]\n","grader.set_answer(\"wuwwC\", test_accuracy)\n","print(test_accuracy)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.94625\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"scrolled":false,"id":"RviZOdBHg8eb","colab_type":"code","outputId":"e35fb0d5-5d4e-4913-9940-3b5722bca519","executionInfo":{"status":"ok","timestamp":1589701787617,"user_tz":-330,"elapsed":8243,"user":{"displayName":"","photoUrl":"","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# you can make submission with answers so far to check yourself at this stage\n","grader.submit(COURSERA_EMAIL, COURSERA_TOKEN)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Submitted to Coursera platform. See results on assignment page!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"nwP7nyADg8e1","colab_type":"text"},"source":["That's it! Congratulations!\n","\n","What you've done:\n","- prepared images for the model\n","- implemented your own batch generator\n","- fine-tuned the pre-trained model"]}]}