saba.hashemi
/
Domain-Adaptation


			
				
					
						
						
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							{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test adapt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from adapt.instance_based import KMM\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "X_source = np.random.randn(1000, 1) * 2 - 1\n",
    "Y_source = (X_source[:, 0] > -1) & (X_source[:, 0] < 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_target = np.random.randn(1000, 1) * 2 + 1\n",
    "Y_target = (X_target[:, 0] > -1.) & (X_target[:, 0] < 1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.209"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_only = LogisticRegression(penalty=None)\n",
    "src_only.fit(X_source, Y_source)\n",
    "\n",
    "src_only.score(X_target, Y_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "adapt_model = KMM(\n",
    "    estimator=LogisticRegression(penalty=\"none\"), \n",
    "    Xt=X_target,\n",
    "    kernel=\"rbf\",  # Gaussian kernel\n",
    "    gamma=1.,     # Bandwidth of the kernel\n",
    "    verbose=0,\n",
    "    random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.574"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adapt_model.fit(X_source, Y_source)\n",
    "adapt_model.score(X_target, Y_target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'datasets/office31_labeled/amazon/labels.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 27\u001b[0m\n\u001b[1;32m     23\u001b[0m                     \u001b[39mprint\u001b[39m(j, image)\n\u001b[1;32m     24\u001b[0m                     \u001b[39mbreak\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m create_dataset_labels()\n",
      "Cell \u001b[0;32mIn[17], line 19\u001b[0m, in \u001b[0;36mcreate_dataset_labels\u001b[0;34m(base_path, out_path)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[39mfor\u001b[39;00m domain \u001b[39min\u001b[39;00m DOMAINS:\n\u001b[1;32m     17\u001b[0m     os\u001b[39m.\u001b[39mmakedirs(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(out_path, domain), exist_ok\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)  \n\u001b[0;32m---> 19\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(out_path, domain, \u001b[39m\"\u001b[39;49m\u001b[39mlabels.txt\u001b[39;49m\u001b[39m\"\u001b[39;49m)) \u001b[39mas\u001b[39;00m f:\n\u001b[1;32m     20\u001b[0m         \u001b[39mfor\u001b[39;00m i, class_name \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(os\u001b[39m.\u001b[39mlistdir(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(base_path, domain))):\n\u001b[1;32m     21\u001b[0m             \u001b[39mprint\u001b[39m(i, class_name)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.8/site-packages/IPython/core/interactiveshell.py:282\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[39mif\u001b[39;00m file \u001b[39min\u001b[39;00m {\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m}:\n\u001b[1;32m    276\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    277\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIPython won\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt let you open fd=\u001b[39m\u001b[39m{\u001b[39;00mfile\u001b[39m}\u001b[39;00m\u001b[39m by default \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    278\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    279\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39myou can use builtins\u001b[39m\u001b[39m'\u001b[39m\u001b[39m open.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m     )\n\u001b[0;32m--> 282\u001b[0m \u001b[39mreturn\u001b[39;00m io_open(file, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'datasets/office31_labeled/amazon/labels.txt'"
     ]
    }
   ],
   "source": [
    "import os \n",
    "\n",
    "DOMAINS = [\"amazon\", \"dslr\", \"webcam\"]\n",
    "\n",
    "def create_dataset_labels(base_path=\"datasets/office31\", out_path=\"datasets/office31_labeled\"):\n",
    "    \"\"\"\n",
    "    source_domains: List of domain(s) used for training and evaluating\n",
    "    target_domain: List of domain(s) used for testing\n",
    "    base_path: Base directory of office31 dataset. The structure of this directory should be as follows: \n",
    "                {domain_name}/{class_name}/{image_name}.jpg\n",
    "                e.g. amazon/bike_helmet/frame_0003.jpg\n",
    "    out_path: The path to store splitted data\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    for domain in DOMAINS:\n",
    "        os.makedirs(os.path.join(out_path, domain), exist_ok=True)  \n",
    "\n",
    "        with open(os.path.join(out_path, domain, \"labels.txt\")) as f:\n",
    "            for i, class_name in enumerate(os.listdir(os.path.join(base_path, domain))):\n",
    "                print(i, class_name)\n",
    "                for j, image in enumerate(os.listdir(os.path.join(base_path, domain, class_name))):\n",
    "                    print(j, image)\n",
    "                    break\n",
    "\n",
    "\n",
    "create_dataset_labels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2817 files belonging to 31 classes.\n",
      "Using 2254 files for training.\n",
      "Using 563 files for validation.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "train_ds, val_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "  \"datasets/office31/amazon\",\n",
    "  validation_split=0.2,\n",
    "  subset=\"both\",\n",
    "  seed=123,\n",
    "  image_size=(150, 150),\n",
    "  batch_size=32)\n",
    "\n",
    "class_names = train_ds.class_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "for images, labels in train_ds.take(1):\n",
    "  for i in range(9):\n",
    "    ax = plt.subplot(3, 3, i + 1)\n",
    "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
    "    plt.title(class_names[labels[i]])\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 mug\n",
      "0 frame_0026.jpg\n",
      "1 phone\n",
      "0 frame_0012.jpg\n",
      "2 monitor\n",
      "0 frame_0026.jpg\n",
      "3 mobile_phone\n",
      "0 frame_0026.jpg\n",
      "4 paper_notebook\n",
      "0 frame_0026.jpg\n",
      "5 keyboard\n",
      "0 frame_0026.jpg\n",
      "6 bottle\n",
      "0 frame_0012.jpg\n",
      "7 stapler\n",
      "0 frame_0018.jpg\n",
      "8 scissors\n",
      "0 frame_0018.jpg\n",
      "9 punchers\n",
      "0 frame_0026.jpg\n",
      "10 laptop_computer\n",
      "0 frame_0026.jpg\n",
      "11 bookcase\n",
      "0 frame_0012.jpg\n",
      "12 printer\n",
      "0 frame_0018.jpg\n",
      "13 desktop_computer\n",
      "0 frame_0018.jpg\n",
      "14 letter_tray\n",
      "0 frame_0018.jpg\n",
      "15 ring_binder\n",
      "0 frame_0026.jpg\n",
      "16 back_pack\n",
      "0 frame_0026.jpg\n",
      "17 file_cabinet\n",
      "0 frame_0018.jpg\n",
      "18 headphones\n",
      "0 frame_0026.jpg\n",
      "19 desk_chair\n",
      "0 frame_0026.jpg\n",
      "20 speaker\n",
      "0 frame_0026.jpg\n",
      "21 projector\n",
      "0 frame_0026.jpg\n",
      "22 pen\n",
      "0 frame_0026.jpg\n",
      "23 trash_can\n",
      "0 frame_0018.jpg\n",
      "24 bike_helmet\n",
      "0 frame_0026.jpg\n",
      "25 bike\n",
      "0 frame_0018.jpg\n",
      "26 mouse\n",
      "0 frame_0026.jpg\n",
      "27 calculator\n",
      "0 frame_0026.jpg\n",
      "28 desk_lamp\n",
      "0 frame_0018.jpg\n",
      "29 ruler\n",
      "0 frame_0003.jpg\n",
      "30 tape_dispenser\n",
      "0 frame_0018.jpg\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "\n",
    "DOMAINS = [\"amazon\", \"dslr\", \"webcam\"]\n",
    "\n",
    "def split_dataset(source_domains=[], target_domains=[], base_path=\"datasets/office31\", out_path=\"datasets/office31_splitted\"):\n",
    "    \"\"\"\n",
    "    source_domains: List of domain(s) used for training and evaluating\n",
    "    target_domain: List of domain(s) used for testing\n",
    "    base_path: Base directory of office31 dataset. The structure of this directory should be as follows: \n",
    "                {domain_name}/{class_name}/{image_name}.jpg\n",
    "                e.g. amazon/bike_helmet/frame_0003.jpg\n",
    "    out_path: The path to store splitted data\n",
    "    \"\"\"\n",
    "\n",
    "    os.makedirs(os.path.join(out_path, \"source\"), exist_ok=True)\n",
    "    os.makedirs(os.path.join(out_path, \"target\"), exist_ok=True)\n",
    "\n",
    "    for domain in target_domains:\n",
    "        for i, class_name in enumerate(os.listdir(os.path.join(base_path, domain))):\n",
    "            print(i, class_name)\n",
    "            for j, image in enumerate(os.listdir(os.path.join(base_path, domain, class_name))):\n",
    "                print(j, image)\n",
    "                break\n",
    "\n",
    "\n",
    "split_dataset([\"amazon\"], [\"webcam\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for domain in [\"amazon\", \"dslr\", \"webcam\"]:\n",
    "    os.rename(\"office_dataset/{}/images\".format(domain), \"office31/train/{}\".format(domain))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for domain in [\"webcam\", \"dslr\"]:\n",
    "    os.makedirs(\"office31/val/{}\".format(domain))\n",
    "    os.makedirs(\"office31/test/{}\".format(domain))\n",
    "\n",
    "# Move a subset of the images to the validation and test sets\n",
    "for domain in [\"webcam\", \"dslr\"]:\n",
    "    for i, file in enumerate(os.listdir(\"office31/train/{}\".format(domain))):\n",
    "        if i % 10 == 0:\n",
    "            os.rename(\"office31/train/{}/{}\".format(domain, file), \"office31/val/{}/{}\".format(domain, file))\n",
    "        elif i % 10 == 1:\n",
    "            os.rename(\"office31/train/{}/{}\".format(domain, file), \"office31/test/{}/{}\".format(domain, file))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test adapt on office"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-05-14 11:01:16.482176: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data Saba's way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2817 files belonging to 31 classes.\n",
      "Using 2254 files for training.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-05-14 11:01:22.530751: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\n",
      "2021-05-14 11:01:22.537354: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.92GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-14 11:01:22.538192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties: \n",
      "pciBusID: 0000:0a:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.93GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-14 11:01:22.538216: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
      "2021-05-14 11:01:22.539810: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
      "2021-05-14 11:01:22.541316: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
      "2021-05-14 11:01:22.541638: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
      "2021-05-14 11:01:22.543283: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
      "2021-05-14 11:01:22.544128: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
      "2021-05-14 11:01:22.547357: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
      "2021-05-14 11:01:22.550681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1\n",
      "2021-05-14 11:01:22.551052: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-05-14 11:01:22.557188: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 3298130000 Hz\n",
      "2021-05-14 11:01:22.557609: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x64e5730 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "2021-05-14 11:01:22.557623: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
      "2021-05-14 11:01:22.648630: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x6551b60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2021-05-14 11:01:22.648661: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX TITAN X, Compute Capability 5.2\n",
      "2021-05-14 11:01:22.648668: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): GeForce GTX TITAN X, Compute Capability 5.2\n",
      "2021-05-14 11:01:22.650144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.92GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-14 11:01:22.650808: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties: \n",
      "pciBusID: 0000:0a:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.93GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-14 11:01:22.650842: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
      "2021-05-14 11:01:22.650867: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
      "2021-05-14 11:01:22.650881: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
      "2021-05-14 11:01:22.650894: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
      "2021-05-14 11:01:22.650908: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
      "2021-05-14 11:01:22.650921: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
      "2021-05-14 11:01:22.650934: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
      "2021-05-14 11:01:22.653499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1\n",
      "2021-05-14 11:01:22.653538: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
      "2021-05-14 11:01:24.149550: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-05-14 11:01:24.149578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 1 \n",
      "2021-05-14 11:01:24.149584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N Y \n",
      "2021-05-14 11:01:24.149588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 1:   Y N \n",
      "2021-05-14 11:01:24.151857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11021 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0, compute capability: 5.2)\n",
      "2021-05-14 11:01:24.152814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 4063 MB memory) -> physical GPU (device: 1, name: GeForce GTX TITAN X, pci bus id: 0000:0a:00.0, compute capability: 5.2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2817 files belonging to 31 classes.\n",
      "Using 563 files for validation.\n",
      "Found 795 files belonging to 31 classes.\n"
     ]
    }
   ],
   "source": [
    "from data_loader import load_data, get_input_and_labels_from_batch_ds\n",
    "\n",
    "train_ds, val_ds, target_ds = load_data(\"amazon\", \"webcam\", image_size=(224,224))\n",
    "Xs, ys = get_input_and_labels_from_batch_ds(train_ds)\n",
    "Xv, yv = get_input_and_labels_from_batch_ds(val_ds)\n",
    "Xt, yt = get_input_and_labels_from_batch_ds(target_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: (2254, 224, 224, 3) (2254,)\n",
      "val: (563, 224, 224, 3) (563,)\n",
      "target: (795, 224, 224, 3) (795,)\n"
     ]
    }
   ],
   "source": [
    "print(\"train:\", Xs.shape, ys.shape)\n",
    "print(\"val:\", Xv.shape, yv.shape)\n",
    "print(\"target:\", Xt.shape, yt.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data (official doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = \"datasets/office31/\" # path to downloaded Office dataset\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(4, 2))\n",
    "ax1.imshow(plt.imread(path+\"amazon/back_pack/frame_0001.jpg\"))\n",
    "ax2.imshow(plt.imread(path+\"amazon/bike/frame_0001.jpg\"))\n",
    "ax1.set_title(\"label = back-pack\"); ax2.set_title(\"label = bike\");\n",
    "plt.suptitle(\"Domain = Amazon\"); plt.show()\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(4, 2))\n",
    "ax1.imshow(plt.imread(path+\"webcam/back_pack/frame_0001.jpg\"))\n",
    "ax2.imshow(plt.imread(path+\"webcam/bike/frame_0001.jpg\"))\n",
    "ax1.set_title(\"label = back-pack\"); ax2.set_title(\"label = bike\");\n",
    "plt.suptitle(\"Domain = Webcam\"); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12245/3952787103.py:15: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.\n",
      "  image = image.resize((224, 224), Image.ANTIALIAS)\n"
     ]
    }
   ],
   "source": [
    "def get_Xy(domain, path_to_folder=path):\n",
    "\n",
    "    path = path_to_folder + domain \n",
    "    X = []\n",
    "    y = []\n",
    "\n",
    "    for r, d, f in os.walk(path):\n",
    "        for direct in d:\n",
    "            if not \".ipynb_checkpoints\" in direct:\n",
    "                for r, d, f in os.walk(os.path.join(path , direct)):\n",
    "                    for file in f:\n",
    "                        path_to_image = os.path.join(r, file)\n",
    "                        if not \".ipynb_checkpoints\" in path_to_image:\n",
    "                            image = Image.open(path_to_image)\n",
    "                            image = image.resize((224, 224), Image.ANTIALIAS)\n",
    "                            image = np.array(image, dtype=int)\n",
    "                            X.append(image)\n",
    "                            y.append(direct)\n",
    "    return X, y\n",
    "\n",
    "Xs, ys = get_Xy(\"amazon\")\n",
    "Xt, yt = get_Xy(\"webcam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2817 2817\n",
      "(224, 224, 3)\n",
      "mug\n"
     ]
    }
   ],
   "source": [
    "print(len(Xs), len(ys))\n",
    "print(Xs[0].shape)\n",
    "print(ys[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]\n",
      "\n",
      " [[255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]\n",
      "\n",
      " [[255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]\n",
      "\n",
      " [[255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]\n",
      "\n",
      " [[255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]] mug\n"
     ]
    }
   ],
   "source": [
    "print(Xs[0], ys[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  ...\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]]\n",
      "\n",
      " [[131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  ...\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]]\n",
      "\n",
      " [[131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  ...\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  ...\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]]\n",
      "\n",
      " [[131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  ...\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]]\n",
      "\n",
      " [[131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  ...\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]\n",
      "  [131.32    138.22101 151.061  ]]] 0\n"
     ]
    }
   ],
   "source": [
    "print(Xs_[0], Ys_[0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, ResNet50\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input\n",
    "\n",
    "# if you want to download weights, remove weights param in ResNet40 and remove this line\n",
    "WEIGHTS_PATH = 'model-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "\n",
    "resnet50 = ResNet50(include_top=False, input_shape=(224, 224, 3), pooling=\"avg\", weights=WEIGHTS_PATH, classes=31)\n",
    "\n",
    "first_layer = resnet50.get_layer('conv5_block2_out')\n",
    "inputs = Input(first_layer.output_shape[1:])\n",
    "\n",
    "for layer in resnet50.layers[resnet50.layers.index(first_layer)+1:]:\n",
    "    if layer.name == \"conv5_block3_1_conv\":\n",
    "        x = layer(inputs)\n",
    "    elif layer.name == \"conv5_block3_add\":\n",
    "        x = layer([inputs, x])\n",
    "    else:\n",
    "        x = layer(x)\n",
    "\n",
    "first_blocks = Model(resnet50.input, first_layer.output)\n",
    "last_block = Model(inputs, x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 7, 7, 2048)] 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[1][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[1][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[1][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[1][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[1][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[1][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[1][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           input_2[0][0]                    \n",
      "                                                                 conv5_block3_3_bn[1][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[1][0]           \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[1][0]           \n",
      "==================================================================================================\n",
      "Total params: 4,471,808\n",
      "Trainable params: 4,465,664\n",
      "Non-trainable params: 6,144\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def load_resnet50(path=\"resnet50_last_block.hdf5\"):\n",
    "    model = load_model(path)\n",
    "    for i in range(len(model.layers)):\n",
    "        if model.layers[i].__class__.__name__ == \"BatchNormalization\":\n",
    "            model.layers[i].trainable = False\n",
    "    return model\n",
    "\n",
    "last_block.summary()\n",
    "last_block.save(\"resnet50_last_block.hdf5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.constraints import MaxNorm\n",
    "\n",
    "def get_task(dropout=0.5, max_norm=0.5):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(1024, activation=\"relu\",\n",
    "                   kernel_constraint=MaxNorm(max_norm),\n",
    "                   bias_constraint=MaxNorm(max_norm)))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(1024, activation=\"relu\",\n",
    "                   kernel_constraint=MaxNorm(max_norm),\n",
    "                   bias_constraint=MaxNorm(max_norm)))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(31, activation=\"softmax\",\n",
    "                   kernel_constraint=MaxNorm(max_norm),\n",
    "                   bias_constraint=MaxNorm(max_norm)))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers.schedules import LearningRateSchedule\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "\n",
    "class MyDecay(LearningRateSchedule):\n",
    "\n",
    "    def __init__(self, max_steps=1000, mu_0=0.01, alpha=10, beta=0.75):\n",
    "        self.mu_0 = mu_0\n",
    "        self.alpha = alpha\n",
    "        self.beta = beta\n",
    "        self.max_steps = float(max_steps)\n",
    "\n",
    "    def __call__(self, step):\n",
    "        p = step / self.max_steps\n",
    "        return self.mu_0 / (1+self.alpha * p)**self.beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.04\n",
    "momentum = 0.9\n",
    "alpha = 0.0002\n",
    "\n",
    "optimizer_task = SGD(learning_rate=MyDecay(mu_0=lr, alpha=alpha),\n",
    "                     momentum=momentum, nesterov=True)\n",
    "optimizer_enc = SGD(learning_rate=MyDecay(mu_0=lr/10., alpha=alpha),\n",
    "                    momentum=momentum, nesterov=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normal Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "from adapt.parameter_based import FineTuning\n",
    "\n",
    "finetunig = FineTuning(encoder=load_resnet50(),\n",
    "                         task=get_task(),\n",
    "                         optimizer=optimizer_task,\n",
    "                         optimizer_enc=optimizer_enc,\n",
    "                         loss=\"categorical_crossentropy\",\n",
    "                         metrics=[\"acc\"],\n",
    "                         copy=False,\n",
    "                         pretrain=True,\n",
    "                         pretrain__epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-05-14 11:01:58.742681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.92GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-14 11:01:58.743821: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties: \n",
      "pciBusID: 0000:0a:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.93GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-14 11:01:58.743876: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
      "2021-05-14 11:01:58.743924: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
      "2021-05-14 11:01:58.743957: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
      "2021-05-14 11:01:58.743989: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
      "2021-05-14 11:01:58.744021: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
      "2021-05-14 11:01:58.744052: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
      "2021-05-14 11:01:58.744084: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
      "2021-05-14 11:01:58.748609: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1\n",
      "2021-05-14 11:01:58.748692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-05-14 11:01:58.748706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 1 \n",
      "2021-05-14 11:01:58.748714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N Y \n",
      "2021-05-14 11:01:58.748720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 1:   Y N \n",
      "2021-05-14 11:01:58.750999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11021 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0, compute capability: 5.2)\n",
      "2021-05-14 11:01:58.752017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 4063 MB memory) -> physical GPU (device: 1, name: GeForce GTX TITAN X, pci bus id: 0000:0a:00.0, compute capability: 5.2)\n",
      "2021-05-14 11:02:01.088707: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
      "2021-05-14 11:02:01.991597: W tensorflow/stream_executor/gpu/asm_compiler.cc:116] *** WARNING *** You are using ptxas 9.0.176, which is older than 9.2.88. ptxas 9.x before 9.2.88 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.\n",
      "\n",
      "You do not need to update to CUDA 9.2.88; cherry-picking the ptxas binary is sufficient.\n",
      "2021-05-14 11:02:02.134470: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X source shape: (2254, 7, 7, 2048)\n",
      "X target shape: (795, 7, 7, 2048)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shashemi/miniconda3/envs/tf/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import tensorflow as tf \n",
    "\n",
    "gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)\n",
    "session = tf.compat.v1.InteractiveSession(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))\n",
    "\n",
    "X_source = first_blocks.predict(preprocess_input(Xs))\n",
    "X_target = first_blocks.predict(preprocess_input(Xt))\n",
    "\n",
    "one = OneHotEncoder(sparse=False)\n",
    "one.fit(np.array(ys).reshape(-1, 1))\n",
    "\n",
    "y_source = one.transform(np.array(ys).reshape(-1, 1))\n",
    "y_target = one.transform(np.array(yt).reshape(-1, 1))\n",
    "\n",
    "print(\"X source shape: %s\"%str(X_source.shape))\n",
    "print(\"X target shape: %s\"%str(X_target.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shashemi/miniconda3/envs/tf/lib/python3.8/site-packages/adapt/base.py:1108: UserWarning: The model has already been compiled. To perform pretraining, the model will be compiled again. Please make sure to pass the compile parameters in __init__ to avoid errors.\n",
      "  warnings.warn(\"The model has already been compiled. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "71/71 [==============================] - 2s 31ms/step - loss: 1.8341 - acc: 0.4855 - val_loss: 2.0844 - val_acc: 0.4415\n",
      "Epoch 2/5\n",
      "71/71 [==============================] - 2s 25ms/step - loss: 1.6973 - acc: 0.5189 - val_loss: 2.1329 - val_acc: 0.4591\n",
      "Epoch 3/5\n",
      "71/71 [==============================] - 2s 23ms/step - loss: 1.6789 - acc: 0.5396 - val_loss: 2.0786 - val_acc: 0.3962\n",
      "Epoch 4/5\n",
      "71/71 [==============================] - 2s 25ms/step - loss: 1.6784 - acc: 0.5326 - val_loss: 2.1376 - val_acc: 0.4453\n",
      "Epoch 5/5\n",
      "71/71 [==============================] - 2s 25ms/step - loss: 1.7345 - acc: 0.5132 - val_loss: 2.0120 - val_acc: 0.4667\n",
      "Epoch 1/50\n",
      "71/71 [==============================] - 3s 38ms/step - loss: 1.5667 - acc: 0.5704 - val_loss: 2.1505 - val_acc: 0.4893\n",
      "Epoch 2/50\n",
      "71/71 [==============================] - 2s 33ms/step - loss: 1.2383 - acc: 0.6730 - val_loss: 1.8533 - val_acc: 0.5371\n",
      "Epoch 3/50\n",
      "71/71 [==============================] - 2s 32ms/step - loss: 0.9763 - acc: 0.7416 - val_loss: 1.9650 - val_acc: 0.5182\n",
      "Epoch 4/50\n",
      "71/71 [==============================] - 3s 38ms/step - loss: 0.8807 - acc: 0.7619 - val_loss: 2.0676 - val_acc: 0.5296\n",
      "Epoch 5/50\n",
      "71/71 [==============================] - 3s 42ms/step - loss: 0.8386 - acc: 0.7835 - val_loss: 1.9108 - val_acc: 0.5157\n",
      "Epoch 6/50\n",
      "71/71 [==============================] - 2s 35ms/step - loss: 0.7369 - acc: 0.8112 - val_loss: 1.9921 - val_acc: 0.5459\n",
      "Epoch 7/50\n",
      "71/71 [==============================] - 3s 38ms/step - loss: 0.5365 - acc: 0.8614 - val_loss: 2.2441 - val_acc: 0.5082\n",
      "Epoch 8/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.5106 - acc: 0.8666 - val_loss: 2.1089 - val_acc: 0.5220\n",
      "Epoch 9/50\n",
      "71/71 [==============================] - 3s 36ms/step - loss: 0.4369 - acc: 0.8869 - val_loss: 2.1065 - val_acc: 0.5459\n",
      "Epoch 10/50\n",
      "71/71 [==============================] - 3s 36ms/step - loss: 0.4204 - acc: 0.8988 - val_loss: 2.4316 - val_acc: 0.4478\n",
      "Epoch 11/50\n",
      "71/71 [==============================] - 2s 34ms/step - loss: 0.3477 - acc: 0.9098 - val_loss: 2.1849 - val_acc: 0.5358\n",
      "Epoch 12/50\n",
      "71/71 [==============================] - 3s 40ms/step - loss: 0.2530 - acc: 0.9397 - val_loss: 2.4108 - val_acc: 0.5270\n",
      "Epoch 13/50\n",
      "71/71 [==============================] - 3s 37ms/step - loss: 0.2921 - acc: 0.9265 - val_loss: 2.3467 - val_acc: 0.5358\n",
      "Epoch 14/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.3175 - acc: 0.9217 - val_loss: 2.4255 - val_acc: 0.5270\n",
      "Epoch 15/50\n",
      "71/71 [==============================] - 3s 38ms/step - loss: 0.2820 - acc: 0.9340 - val_loss: 2.3847 - val_acc: 0.4918\n",
      "Epoch 16/50\n",
      "71/71 [==============================] - 3s 45ms/step - loss: 0.2609 - acc: 0.9327 - val_loss: 2.6925 - val_acc: 0.5069\n",
      "Epoch 17/50\n",
      "71/71 [==============================] - 3s 40ms/step - loss: 0.3331 - acc: 0.9278 - val_loss: 2.4056 - val_acc: 0.5031\n",
      "Epoch 18/50\n",
      "71/71 [==============================] - 3s 41ms/step - loss: 0.3030 - acc: 0.9366 - val_loss: 2.3084 - val_acc: 0.5233\n",
      "Epoch 19/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.1907 - acc: 0.9551 - val_loss: 2.2059 - val_acc: 0.5346\n",
      "Epoch 20/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.1933 - acc: 0.9569 - val_loss: 2.4568 - val_acc: 0.5346\n",
      "Epoch 21/50\n",
      "71/71 [==============================] - 3s 40ms/step - loss: 0.1933 - acc: 0.9560 - val_loss: 2.7118 - val_acc: 0.4881\n",
      "Epoch 22/50\n",
      "71/71 [==============================] - 3s 36ms/step - loss: 0.1009 - acc: 0.9740 - val_loss: 2.8982 - val_acc: 0.5283\n",
      "Epoch 23/50\n",
      "71/71 [==============================] - 3s 38ms/step - loss: 0.0973 - acc: 0.9798 - val_loss: 3.1799 - val_acc: 0.5019\n",
      "Epoch 24/50\n",
      "71/71 [==============================] - 3s 42ms/step - loss: 0.1490 - acc: 0.9639 - val_loss: 2.9741 - val_acc: 0.4692\n",
      "Epoch 25/50\n",
      "71/71 [==============================] - 3s 40ms/step - loss: 0.3151 - acc: 0.9459 - val_loss: 2.8652 - val_acc: 0.4843\n",
      "Epoch 26/50\n",
      "71/71 [==============================] - 3s 40ms/step - loss: 0.2561 - acc: 0.9340 - val_loss: 2.8930 - val_acc: 0.5182\n",
      "Epoch 27/50\n",
      "71/71 [==============================] - 2s 34ms/step - loss: 0.1205 - acc: 0.9718 - val_loss: 2.6765 - val_acc: 0.5447\n",
      "Epoch 28/50\n",
      "71/71 [==============================] - 2s 34ms/step - loss: 0.1000 - acc: 0.9767 - val_loss: 3.0513 - val_acc: 0.5447\n",
      "Epoch 29/50\n",
      "71/71 [==============================] - 3s 37ms/step - loss: 0.1284 - acc: 0.9710 - val_loss: 3.1030 - val_acc: 0.4868\n",
      "Epoch 30/50\n",
      "71/71 [==============================] - 3s 41ms/step - loss: 0.0810 - acc: 0.9824 - val_loss: 2.7055 - val_acc: 0.5396\n",
      "Epoch 31/50\n",
      "71/71 [==============================] - 3s 36ms/step - loss: 0.0400 - acc: 0.9912 - val_loss: 3.1704 - val_acc: 0.5258\n",
      "Epoch 32/50\n",
      "71/71 [==============================] - 2s 34ms/step - loss: 0.0435 - acc: 0.9886 - val_loss: 2.7496 - val_acc: 0.5635\n",
      "Epoch 33/50\n",
      "71/71 [==============================] - 3s 40ms/step - loss: 0.1582 - acc: 0.9696 - val_loss: 2.6698 - val_acc: 0.5082\n",
      "Epoch 34/50\n",
      "71/71 [==============================] - 3s 42ms/step - loss: 0.1580 - acc: 0.9599 - val_loss: 2.6486 - val_acc: 0.5082\n",
      "Epoch 35/50\n",
      "71/71 [==============================] - 3s 36ms/step - loss: 0.0934 - acc: 0.9802 - val_loss: 3.4288 - val_acc: 0.5031\n",
      "Epoch 36/50\n",
      "71/71 [==============================] - 3s 38ms/step - loss: 0.2083 - acc: 0.9582 - val_loss: 2.6580 - val_acc: 0.5358\n",
      "Epoch 37/50\n",
      "71/71 [==============================] - 3s 38ms/step - loss: 0.2549 - acc: 0.9472 - val_loss: 3.1065 - val_acc: 0.5358\n",
      "Epoch 38/50\n",
      "71/71 [==============================] - 3s 37ms/step - loss: 0.1472 - acc: 0.9679 - val_loss: 2.4708 - val_acc: 0.5472\n",
      "Epoch 39/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.0755 - acc: 0.9859 - val_loss: 3.0871 - val_acc: 0.5308\n",
      "Epoch 40/50\n",
      "71/71 [==============================] - 2s 35ms/step - loss: 0.1422 - acc: 0.9740 - val_loss: 2.5991 - val_acc: 0.5321\n",
      "Epoch 41/50\n",
      "71/71 [==============================] - 2s 32ms/step - loss: 0.0768 - acc: 0.9837 - val_loss: 3.0134 - val_acc: 0.5270\n",
      "Epoch 42/50\n",
      "71/71 [==============================] - 3s 37ms/step - loss: 0.1279 - acc: 0.9754 - val_loss: 2.6041 - val_acc: 0.5321\n",
      "Epoch 43/50\n",
      "71/71 [==============================] - 3s 38ms/step - loss: 0.1088 - acc: 0.9754 - val_loss: 3.0312 - val_acc: 0.5145\n",
      "Epoch 44/50\n",
      "71/71 [==============================] - 2s 35ms/step - loss: 0.0512 - acc: 0.9894 - val_loss: 2.7629 - val_acc: 0.5447\n",
      "Epoch 45/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.0280 - acc: 0.9947 - val_loss: 4.0369 - val_acc: 0.5258\n",
      "Epoch 46/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.0386 - acc: 0.9934 - val_loss: 3.1922 - val_acc: 0.5308\n",
      "Epoch 47/50\n",
      "71/71 [==============================] - 2s 35ms/step - loss: 0.1014 - acc: 0.9828 - val_loss: 3.1563 - val_acc: 0.5321\n",
      "Epoch 48/50\n",
      "71/71 [==============================] - 2s 35ms/step - loss: 0.1209 - acc: 0.9758 - val_loss: 3.2513 - val_acc: 0.4579\n",
      "Epoch 49/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.0677 - acc: 0.9850 - val_loss: 3.0336 - val_acc: 0.5019\n",
      "Epoch 50/50\n",
      "71/71 [==============================] - 3s 39ms/step - loss: 0.0899 - acc: 0.9833 - val_loss: 3.0402 - val_acc: 0.5296\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<adapt.parameter_based._finetuning.FineTuning at 0x7ff7643edd60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finetunig.fit(X_source, y_source, epochs=50, batch_size=32, validation_data=(X_target, y_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "\n",
    "acc = finetunig.history.history[\"acc\"]\n",
    "val_acc = finetunig.history.history[\"val_acc\"]\n",
    "\n",
    "plt.plot(acc, label=\"Train acc - final value: %.3f\"%acc[-1])\n",
    "plt.plot(val_acc, label=\"Test acc - final value: %.3f\"%val_acc[-1])\n",
    "plt.legend(); plt.xlabel(\"Epochs\"); plt.ylabel(\"Acc\"); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "Xs_enc = finetunig.transform(X_source)\n",
    "Xt_enc = finetunig.transform(X_target)\n",
    "\n",
    "np.random.seed(0)\n",
    "X_ = np.concatenate((Xs_enc, Xt_enc))\n",
    "X_tsne = TSNE(2).fit_transform(X_)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(X_tsne[:len(X_source), 0], X_tsne[:len(X_source), 1], '.', label=\"source\")\n",
    "plt.plot(X_tsne[len(X_source):, 0], X_tsne[len(X_source):, 1], '.', label=\"target\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.title(\"Encoded Space tSNE for the Source Only model\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adapt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We shuffle the data, it may not be needed but we want to avoid classes being not represented\n",
    "# in some batch due to the fact that the datasets are sorted by classes.\n",
    "# np.random.seed(0)\n",
    "# shuffle_src = np.random.choice(len(Xs), len(Xs), replace=False)\n",
    "# shuffle_tgt = np.random.choice(len(Xt), len(Xt), replace=False)\n",
    "\n",
    "# Xs = Xs[shuffle_src]\n",
    "# ys_lab = ys_lab[shuffle_src]\n",
    "# Xt = Xt[shuffle_tgt]\n",
    "# yt_lab = yt_lab[shuffle_tgt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "from adapt.feature_based import MDD\n",
    "from adapt.utils import UpdateLambda\n",
    "import tensorflow as tf \n",
    "\n",
    "np.random.seed(123)\n",
    "tf.random.set_seed(123)\n",
    "\n",
    "lr = 0.04\n",
    "momentum = 0.9\n",
    "alpha = 0.0002\n",
    "\n",
    "encoder = load_resnet50()\n",
    "task = get_task()\n",
    "\n",
    "optimizer_task = SGD(learning_rate=MyDecay(mu_0=lr, alpha=alpha),\n",
    "                     momentum=momentum, nesterov=True)\n",
    "optimizer_enc = SGD(learning_rate=MyDecay(mu_0=lr/10., alpha=alpha),\n",
    "                    momentum=momentum, nesterov=True)\n",
    "optimizer_disc = SGD(learning_rate=MyDecay(mu_0=lr/10., alpha=alpha))\n",
    "\n",
    "\n",
    "mdd = MDD(encoder, task,\n",
    "            loss=\"categorical_crossentropy\",\n",
    "            metrics=[\"acc\"],\n",
    "            copy=False,\n",
    "            lambda_=tf.Variable(0.),\n",
    "            gamma=2.,\n",
    "            optimizer=optimizer_task,\n",
    "            optimizer_enc=optimizer_enc,\n",
    "            optimizer_disc=optimizer_disc,\n",
    "            callbacks=[UpdateLambda(lambda_max=0.1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer mdd_1 is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because its dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "Epoch 1/50\n",
      "71/71 [==============================] - 4s 54ms/step - loss: 2.8755 - acc: 0.2108 - disc_loss: 6.8939 - val_loss: 2.3044 - val_acc: 0.3686\n",
      "Epoch 2/50\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 1.6812 - acc: 0.5260 - disc_loss: 6.6367 - val_loss: 1.8391 - val_acc: 0.5208\n",
      "Epoch 3/50\n",
      "71/71 [==============================] - 4s 58ms/step - loss: 1.2456 - acc: 0.6567 - disc_loss: 6.2507 - val_loss: 2.0318 - val_acc: 0.5208\n",
      "Epoch 4/50\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 1.0195 - acc: 0.7205 - disc_loss: 5.6312 - val_loss: 2.1595 - val_acc: 0.4818\n",
      "Epoch 5/50\n",
      "71/71 [==============================] - 4s 56ms/step - loss: 0.8656 - acc: 0.7658 - disc_loss: 4.9793 - val_loss: 2.0189 - val_acc: 0.4981\n",
      "Epoch 6/50\n",
      "71/71 [==============================] - 4s 56ms/step - loss: 0.7520 - acc: 0.7909 - disc_loss: 4.3038 - val_loss: 2.0078 - val_acc: 0.4730\n",
      "Epoch 7/50\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.6110 - acc: 0.8393 - disc_loss: 3.8673 - val_loss: 2.0799 - val_acc: 0.5031\n",
      "Epoch 8/50\n",
      "71/71 [==============================] - 4s 58ms/step - loss: 0.4696 - acc: 0.8724 - disc_loss: 3.3101 - val_loss: 1.7728 - val_acc: 0.5925\n",
      "Epoch 9/50\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.4419 - acc: 0.8873 - disc_loss: 3.0773 - val_loss: 2.0565 - val_acc: 0.4956\n",
      "Epoch 10/50\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 0.3757 - acc: 0.9058 - disc_loss: 2.8416 - val_loss: 2.3294 - val_acc: 0.5107\n",
      "Epoch 11/50\n",
      "71/71 [==============================] - 4s 56ms/step - loss: 0.2926 - acc: 0.9239 - disc_loss: 2.7525 - val_loss: 1.9341 - val_acc: 0.5862\n",
      "Epoch 12/50\n",
      "71/71 [==============================] - 4s 56ms/step - loss: 0.2152 - acc: 0.9428 - disc_loss: 2.7566 - val_loss: 2.2483 - val_acc: 0.5761\n",
      "Epoch 13/50\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.1624 - acc: 0.9648 - disc_loss: 2.9224 - val_loss: 2.6861 - val_acc: 0.5874\n",
      "Epoch 14/50\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 0.1547 - acc: 0.9665 - disc_loss: 3.1766 - val_loss: 2.9834 - val_acc: 0.5874\n",
      "Epoch 15/50\n",
      "71/71 [==============================] - 4s 58ms/step - loss: 0.1280 - acc: 0.9740 - disc_loss: 3.4739 - val_loss: 2.9884 - val_acc: 0.6075\n",
      "Epoch 16/50\n",
      "71/71 [==============================] - 4s 58ms/step - loss: 0.1501 - acc: 0.9674 - disc_loss: 3.7624 - val_loss: 3.6081 - val_acc: 0.5648\n",
      "Epoch 17/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.1791 - acc: 0.9604 - disc_loss: 3.6178 - val_loss: 3.0777 - val_acc: 0.5987\n",
      "Epoch 18/50\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.2074 - acc: 0.9551 - disc_loss: 3.6645 - val_loss: 4.1626 - val_acc: 0.5522\n",
      "Epoch 19/50\n",
      "71/71 [==============================] - 4s 57ms/step - loss: 0.2284 - acc: 0.9555 - disc_loss: 3.4961 - val_loss: 4.9788 - val_acc: 0.5396\n",
      "Epoch 20/50\n",
      "71/71 [==============================] - 4s 58ms/step - loss: 0.2392 - acc: 0.9498 - disc_loss: 3.3149 - val_loss: 4.0270 - val_acc: 0.6176\n",
      "Epoch 21/50\n",
      "71/71 [==============================] - 4s 58ms/step - loss: 0.2286 - acc: 0.9604 - disc_loss: 3.3962 - val_loss: 4.3935 - val_acc: 0.5711\n",
      "Epoch 22/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.1820 - acc: 0.9604 - disc_loss: 3.4481 - val_loss: 4.7817 - val_acc: 0.5987\n",
      "Epoch 23/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.1604 - acc: 0.9670 - disc_loss: 3.4992 - val_loss: 5.1246 - val_acc: 0.5824\n",
      "Epoch 24/50\n",
      "71/71 [==============================] - 5s 64ms/step - loss: 0.1728 - acc: 0.9665 - disc_loss: 3.6277 - val_loss: 5.7745 - val_acc: 0.5686\n",
      "Epoch 25/50\n",
      "71/71 [==============================] - 4s 53ms/step - loss: 0.3831 - acc: 0.9159 - disc_loss: 3.6940 - val_loss: 3.8723 - val_acc: 0.6214\n",
      "Epoch 26/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.1994 - acc: 0.9573 - disc_loss: 3.5767 - val_loss: 5.5462 - val_acc: 0.5522\n",
      "Epoch 27/50\n",
      "71/71 [==============================] - 4s 57ms/step - loss: 0.1861 - acc: 0.9639 - disc_loss: 3.6581 - val_loss: 4.6228 - val_acc: 0.6013\n",
      "Epoch 28/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.2846 - acc: 0.9379 - disc_loss: 3.6176 - val_loss: 4.3204 - val_acc: 0.6214\n",
      "Epoch 29/50\n",
      "71/71 [==============================] - 4s 57ms/step - loss: 0.2285 - acc: 0.9525 - disc_loss: 3.5272 - val_loss: 4.9658 - val_acc: 0.6176\n",
      "Epoch 30/50\n",
      "71/71 [==============================] - 4s 58ms/step - loss: 0.1397 - acc: 0.9710 - disc_loss: 3.5457 - val_loss: 6.4727 - val_acc: 0.6063\n",
      "Epoch 31/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.3276 - acc: 0.9450 - disc_loss: 3.9350 - val_loss: 6.0114 - val_acc: 0.5623\n",
      "Epoch 32/50\n",
      "71/71 [==============================] - 5s 64ms/step - loss: 0.3092 - acc: 0.9357 - disc_loss: 3.4947 - val_loss: 4.7175 - val_acc: 0.6289\n",
      "Epoch 33/50\n",
      "71/71 [==============================] - 4s 53ms/step - loss: 0.2839 - acc: 0.9450 - disc_loss: 3.4465 - val_loss: 4.6049 - val_acc: 0.6126\n",
      "Epoch 34/50\n",
      "71/71 [==============================] - 3s 46ms/step - loss: 0.1951 - acc: 0.9635 - disc_loss: 3.5649 - val_loss: 6.4033 - val_acc: 0.5623\n",
      "Epoch 35/50\n",
      "71/71 [==============================] - 4s 50ms/step - loss: 0.4804 - acc: 0.8904 - disc_loss: 3.8366 - val_loss: 5.5151 - val_acc: 0.5560\n",
      "Epoch 36/50\n",
      "71/71 [==============================] - 3s 48ms/step - loss: 0.2815 - acc: 0.9362 - disc_loss: 3.4471 - val_loss: 4.7744 - val_acc: 0.6226\n",
      "Epoch 37/50\n",
      "71/71 [==============================] - 4s 51ms/step - loss: 0.1950 - acc: 0.9591 - disc_loss: 3.3060 - val_loss: 5.5707 - val_acc: 0.5987\n",
      "Epoch 38/50\n",
      "71/71 [==============================] - 4s 52ms/step - loss: 0.1268 - acc: 0.9837 - disc_loss: 3.4041 - val_loss: 5.6056 - val_acc: 0.6201\n",
      "Epoch 39/50\n",
      "71/71 [==============================] - 4s 55ms/step - loss: 0.2663 - acc: 0.9586 - disc_loss: 3.7014 - val_loss: 5.5689 - val_acc: 0.6038\n",
      "Epoch 40/50\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1119 - acc: 0.9749 - disc_loss: 3.4972 - val_loss: 6.6409 - val_acc: 0.6126\n",
      "Epoch 41/50\n",
      "71/71 [==============================] - 4s 57ms/step - loss: 0.1701 - acc: 0.9621 - disc_loss: 4.1863 - val_loss: 7.7062 - val_acc: 0.6428\n",
      "Epoch 42/50\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1188 - acc: 0.9771 - disc_loss: 3.8941 - val_loss: 7.5871 - val_acc: 0.6176\n",
      "Epoch 43/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.5638 - acc: 0.9265 - disc_loss: 4.4041 - val_loss: 6.5325 - val_acc: 0.5459\n",
      "Epoch 44/50\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 0.4141 - acc: 0.9305 - disc_loss: 3.7344 - val_loss: 6.1255 - val_acc: 0.6239\n",
      "Epoch 45/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.2632 - acc: 0.9604 - disc_loss: 3.4305 - val_loss: 6.7876 - val_acc: 0.6277\n",
      "Epoch 46/50\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 0.2809 - acc: 0.9498 - disc_loss: 3.4746 - val_loss: 5.7009 - val_acc: 0.6528\n",
      "Epoch 47/50\n",
      "71/71 [==============================] - 4s 58ms/step - loss: 0.3646 - acc: 0.9366 - disc_loss: 3.4981 - val_loss: 5.4116 - val_acc: 0.6491\n",
      "Epoch 48/50\n",
      "71/71 [==============================] - 4s 59ms/step - loss: 0.1865 - acc: 0.9661 - disc_loss: 3.1507 - val_loss: 6.9634 - val_acc: 0.6415\n",
      "Epoch 49/50\n",
      "71/71 [==============================] - 4s 56ms/step - loss: 0.1857 - acc: 0.9665 - disc_loss: 3.1067 - val_loss: 8.2365 - val_acc: 0.6377\n",
      "Epoch 50/50\n",
      "71/71 [==============================] - 4s 56ms/step - loss: 0.0918 - acc: 0.9828 - disc_loss: 3.3619 - val_loss: 8.9936 - val_acc: 0.6440\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<adapt.feature_based._mdd.MDD at 0x7ff93a75bf10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdd.fit(X=X_source[:-1], y=y_source[:-1], Xt=X_target, epochs=50, batch_size=32, validation_data=(X_target, y_target))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = mdd.history.history[\"acc\"]\n",
    "val_acc = mdd.history.history[\"val_acc\"]\n",
    "plt.plot(acc, label=\"Train acc - final value: %.3f\"%acc[-1])\n",
    "plt.plot(val_acc, label=\"Test acc - final value: %.3f\"%val_acc[-1])\n",
    "plt.legend(); plt.xlabel(\"Epochs\"); plt.ylabel(\"Acc\"); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "Xs_enc = mdd.transform(X_source)\n",
    "Xt_enc = mdd.transform(X_target)\n",
    "\n",
    "np.random.seed(0)\n",
    "X_ = np.concatenate((Xs_enc, Xt_enc))\n",
    "X_tsne = TSNE(2).fit_transform(X_)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(X_tsne[:len(X_source), 0], X_tsne[:len(X_source), 1], '.', label=\"source\")\n",
    "plt.plot(X_tsne[len(X_source):, 0], X_tsne[len(X_source):, 1], '.', label=\"target\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.title(\"Encoded Space tSNE for the MDD model\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CherkNevis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dt_from_tensorflow(path):\n",
    "    validation_dataset =tf.keras.preprocessing.image_dataset_from_directory(path,\n",
    "                                              shuffle=True,\n",
    "                                              batch_size=32,\n",
    "                                              image_size=(224,224))\n",
    "    return validation_dataset\n",
    "\n",
    "def load_image_from_tensorflow(path):\n",
    "    validation_dataset =tf.keras.preprocessing.image_dataset_from_directory(path,\n",
    "                                              shuffle=False,\n",
    "                                              batch_size=32,\n",
    "                                              image_size=(224,224))\n",
    "    for image, _ in validation_dataset.take(1):\n",
    "        img = image\n",
    "    return img\n",
    "\n",
    "def load_image_from_pil(path):\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((224, 224),Image.BILINEAR)\n",
    "    img = np.expand_dims(img, axis=0)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<BatchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int32)>\n",
      "Found 2 files belonging to 2 classes.\n",
      "[255. 255. 255.] [168.79688 158.79688 146.79688]\n",
      "[1 0]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[255. 255. 255.] [168.79688 158.79688 146.79688]\n",
      "[1 0]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[255. 255. 255.] [168.79688 158.79688 146.79688]\n",
      "[1 0]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[255. 255. 255.] [168.79688 158.79688 146.79688]\n",
      "[1 0]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[168.79688 158.79688 146.79688] [255. 255. 255.]\n",
      "[0 1]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[168.79688 158.79688 146.79688] [255. 255. 255.]\n",
      "[0 1]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[168.79688 158.79688 146.79688] [255. 255. 255.]\n",
      "[0 1]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[168.79688 158.79688 146.79688] [255. 255. 255.]\n",
      "[0 1]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[255. 255. 255.] [168.79688 158.79688 146.79688]\n",
      "[1 0]\n",
      "Found 2 files belonging to 2 classes.\n",
      "[255. 255. 255.] [168.79688 158.79688 146.79688]\n",
      "[1 0]\n",
      "0:00:01.247278\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import datetime\n",
    "print(d)\n",
    "\n",
    "s = datetime.datetime.now()\n",
    "for i in range(10):\n",
    "    d = load_dt_from_tensorflow('datasets/temp')\n",
    "    xy = [(x, y) for x, y in d]\n",
    "    xx = np.concatenate([x for x, y in xy], axis=0)\n",
    "    yy = np.concatenate([y for x, y in xy], axis=0)\n",
    "\n",
    "    print(xx[0][0][0], xx[1][0][0])\n",
    "    print(yy)\n",
    "\n",
    "e = datetime.datetime.now()\n",
    "print(e-s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([168.79688 158.79688 146.79688], shape=(3,), dtype=float32) tf.Tensor([255. 255. 255.], shape=(3,), dtype=float32)\n",
      "[<tf.Tensor: shape=(), dtype=int32, numpy=1>, <tf.Tensor: shape=(), dtype=int32, numpy=0>]\n",
      "0:00:00.197981\n"
     ]
    }
   ],
   "source": [
    "s = datetime.datetime.now()\n",
    "dd = d.unbatch()\n",
    "xy = dd.map(lambda x, y: (x,y))\n",
    "xx = list(xy.map(lambda x, y: x))\n",
    "yy = list(xy.map(lambda x, y: y))\n",
    "print(xx[0][0][0], xx[1][0][0])\n",
    "print(yy)\n",
    "e = datetime.datetime.now()\n",
    "print(e-s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1 files belonging to 1 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_12245/1478134846.py:12: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.\n",
      "  img = img.resize((224, 224),Image.BILINEAR)\n"
     ]
    }
   ],
   "source": [
    "i1 = load_image_from_tensorflow('datasets/temp')\n",
    "i2 = load_image_from_pil('datasets/temp/saba/frame_0001.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([36.0625 37.0625 32.0625], shape=(3,), dtype=float32)\n",
      "[36 36 34]\n"
     ]
    }
   ],
   "source": [
    "idx = 150\n",
    "print(i1[0][idx][idx])\n",
    "print(i2[0][idx][idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'astype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[55], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m p1 \u001b[39m=\u001b[39m first_blocks\u001b[39m.\u001b[39mpredict(preprocess_input(i1\u001b[39m.\u001b[39;49mastype(np\u001b[39m.\u001b[39mint32)))\n\u001b[1;32m      2\u001b[0m p2 \u001b[39m=\u001b[39m first_blocks\u001b[39m.\u001b[39mpredict(preprocess_input(i2))\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'astype'"
     ]
    }
   ],
   "source": [
    "p1 = first_blocks.predict(preprocess_input(i1.astype(np.int32)))\n",
    "p2 = first_blocks.predict(preprocess_input(i2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 7, 7, 2048)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.01094218 0.         0.         0.         0.\n",
      " 0.         0.08283848 0.         0.        ]\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.10250913 0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "idx, idx2 = 1, 1045\n",
    "print(p1[0][idx][idx][0:10])\n",
    "print(p2[0][idx][idx][0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer mdd is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because its dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-05-13 09:48:13.580128: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer flatten is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because its dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 0.1545 - acc: 0.9100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.15446723997592926"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from adapt.utils import make_classification_da\n",
    "from adapt.feature_based import MDD\n",
    "Xs, ys, Xt, yt = make_classification_da()\n",
    "model = MDD(lambda_=0.1, gamma=4., Xt=Xt, metrics=[\"acc\"], random_state=0)\n",
    "model.fit(Xs, ys, epochs=100, verbose=0)\n",
    "model.score(Xt, yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit transform...\n",
      "Previous covariance difference: 0.013181\n",
      "New covariance difference: 0.000004\n",
      "Fit Estimator...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.86"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeClassifier\n",
    "from adapt.feature_based import CORAL\n",
    "\n",
    "model = CORAL(RidgeClassifier(), Xt=Xt, random_state=0)\n",
    "model.fit(Xs, ys)\n",
    "model.score(Xt, yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'X_source' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[18], line 30\u001b[0m\n\u001b[1;32m     19\u001b[0m optimizer_disc \u001b[39m=\u001b[39m SGD(learning_rate\u001b[39m=\u001b[39mMyDecay(mu_0\u001b[39m=\u001b[39mlr\u001b[39m/\u001b[39m\u001b[39m10.\u001b[39m, alpha\u001b[39m=\u001b[39malpha))\n\u001b[1;32m     22\u001b[0m mdd \u001b[39m=\u001b[39m CORAL(encoder, task,\n\u001b[1;32m     23\u001b[0m             loss\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcategorical_crossentropy\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m     24\u001b[0m             metrics\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39macc\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     27\u001b[0m             optimizer\u001b[39m=\u001b[39moptimizer_task,\n\u001b[1;32m     28\u001b[0m             callbacks\u001b[39m=\u001b[39m[UpdateLambda(lambda_max\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m)])\n\u001b[0;32m---> 30\u001b[0m model\u001b[39m.\u001b[39mfit(X_source, y_source)\n\u001b[1;32m     31\u001b[0m model\u001b[39m.\u001b[39mscore(X_target, y_target)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X_source' is not defined"
     ]
    }
   ],
   "source": [
    "from adapt.feature_based import CORAL\n",
    "from adapt.utils import UpdateLambda\n",
    "import tensorflow as tf \n",
    "\n",
    "np.random.seed(123)\n",
    "tf.random.set_seed(123)\n",
    "\n",
    "lr = 0.04\n",
    "momentum = 0.9\n",
    "alpha = 0.0002\n",
    "\n",
    "encoder = load_resnet50()\n",
    "task = get_task()\n",
    "\n",
    "optimizer_task = SGD(learning_rate=MyDecay(mu_0=lr, alpha=alpha),\n",
    "                     momentum=momentum, nesterov=True)\n",
    "optimizer_enc = SGD(learning_rate=MyDecay(mu_0=lr/10., alpha=alpha),\n",
    "                    momentum=momentum, nesterov=True)\n",
    "optimizer_disc = SGD(learning_rate=MyDecay(mu_0=lr/10., alpha=alpha))\n",
    "\n",
    "\n",
    "mdd = CORAL(encoder, task,\n",
    "            loss=\"categorical_crossentropy\",\n",
    "            metrics=[\"acc\"],\n",
    "            copy=False,\n",
    "            lambda_=tf.Variable(0.),\n",
    "            optimizer=optimizer_task,\n",
    "            callbacks=[UpdateLambda(lambda_max=0.1)])\n",
    "\n",
    "model.fit(X_source, y_source)\n",
    "model.score(X_target, y_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot([1,2,3,4], label=\"Train acc - final value: %.3f\"%1)\n",
    "plt.plot([3,4,10, -1], label=\"Test acc - final value: %.3f\"%3)\n",
    "plt.legend()\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Acc\")\n",
    "plt.savefig('image.png') "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.constraints import MaxNorm\n",
    "from tensorflow.keras.optimizers.schedules import LearningRateSchedule\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "\n",
    "def get_resnet():\n",
    "    # if you want to download weights, remove weights param in ResNet40 and remove this line\n",
    "    WEIGHTS_PATH = 'model-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "\n",
    "    resnet50 = ResNet50(include_top=False, input_shape=(224, 224, 3), pooling=\"avg\", weights=WEIGHTS_PATH, classes=31)\n",
    "\n",
    "    first_layer = resnet50.get_layer('conv5_block2_out')\n",
    "    inputs = Input(first_layer.output_shape[1:])\n",
    "\n",
    "    for layer in resnet50.layers[resnet50.layers.index(first_layer)+1:]:\n",
    "        if layer.name == \"conv5_block3_1_conv\":\n",
    "            x = layer(inputs)\n",
    "        elif layer.name == \"conv5_block3_add\":\n",
    "            x = layer([inputs, x])\n",
    "        else:\n",
    "            x = layer(x)\n",
    "\n",
    "    first_blocks = Model(resnet50.input, first_layer.output)\n",
    "    last_block = Model(inputs, x)\n",
    "\n",
    "\n",
    "    def load_resnet50(path=\"model-weights/resnet50_last_block.hdf5\"):\n",
    "        model = load_model(path)\n",
    "        for i in range(len(model.layers)):\n",
    "            if model.layers[i].__class__.__name__ == \"BatchNormalization\":\n",
    "                model.layers[i].trainable = False\n",
    "        return model\n",
    "\n",
    "    # last_block.summary()\n",
    "    last_block.save(\"model-weights/resnet50_last_block.hdf5\")\n",
    "\n",
    "    return first_blocks, last_block, load_resnet50\n",
    "\n",
    "\n",
    "def get_task(dropout=0.5, max_norm=0.5):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(1024, activation=\"relu\",\n",
    "                kernel_constraint=MaxNorm(max_norm),\n",
    "                bias_constraint=MaxNorm(max_norm)))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(1024, activation=\"relu\",\n",
    "                kernel_constraint=MaxNorm(max_norm),\n",
    "                bias_constraint=MaxNorm(max_norm)))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(31, activation=\"softmax\",\n",
    "                kernel_constraint=MaxNorm(max_norm),\n",
    "                bias_constraint=MaxNorm(max_norm)))\n",
    "    return model\n",
    "\n",
    "\n",
    "class MyDecay(LearningRateSchedule):\n",
    "    def __init__(self, max_steps=1000, mu_0=0.01, alpha=10, beta=0.75):\n",
    "        self.mu_0 = mu_0\n",
    "        self.alpha = alpha\n",
    "        self.beta = beta\n",
    "        self.max_steps = float(max_steps)\n",
    "\n",
    "    def __call__(self, step):\n",
    "        p = step / self.max_steps\n",
    "        return self.mu_0 / (1+self.alpha * p)**self.beta\n",
    "\n",
    "\n",
    "def get_input_and_target_for_head(first_blocks, Xs, ys, Xv, yv, Xt, yt):\n",
    "    X_source = first_blocks.predict(preprocess_input(Xs))\n",
    "    X_val = first_blocks.predict(preprocess_input(Xv))\n",
    "    X_target = first_blocks.predict(preprocess_input(Xt))\n",
    "\n",
    "    one = OneHotEncoder(sparse=False)\n",
    "    one.fit(np.array(ys).reshape(-1, 1))\n",
    "\n",
    "    y_source = one.transform(np.array(ys).reshape(-1, 1))\n",
    "    y_val = one.transform(np.array(yv).reshape(-1, 1))\n",
    "    y_target = one.transform(np.array(yt).reshape(-1, 1))\n",
    "\n",
    "    print(\"X source shape: %s\"%str(X_source.shape))\n",
    "    print(\"X target shape: %s\"%str(X_target.shape))\n",
    "\n",
    "    return X_source, y_source, X_val, y_val, X_target, y_target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2817 files belonging to 31 classes.\n",
      "Using 2254 files for training.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-05-13 12:58:05.917553: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\n",
      "2021-05-13 12:58:05.925685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.92GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-13 12:58:05.926565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties: \n",
      "pciBusID: 0000:0a:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.93GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-13 12:58:05.926598: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
      "2021-05-13 12:58:05.959468: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
      "2021-05-13 12:58:05.978114: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
      "2021-05-13 12:58:05.982830: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
      "2021-05-13 12:58:06.017680: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
      "2021-05-13 12:58:06.022805: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
      "2021-05-13 12:58:06.084876: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
      "2021-05-13 12:58:06.088343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1\n",
      "2021-05-13 12:58:06.089695: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-05-13 12:58:06.103866: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 3298130000 Hz\n",
      "2021-05-13 12:58:06.104753: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5352d60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "2021-05-13 12:58:06.104782: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
      "2021-05-13 12:58:06.264450: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x53bf060 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2021-05-13 12:58:06.264490: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX TITAN X, Compute Capability 5.2\n",
      "2021-05-13 12:58:06.264502: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): GeForce GTX TITAN X, Compute Capability 5.2\n",
      "2021-05-13 12:58:06.266633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.92GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-13 12:58:06.267263: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties: \n",
      "pciBusID: 0000:0a:00.0 name: GeForce GTX TITAN X computeCapability: 5.2\n",
      "coreClock: 1.076GHz coreCount: 24 deviceMemorySize: 11.93GiB deviceMemoryBandwidth: 313.37GiB/s\n",
      "2021-05-13 12:58:06.267315: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
      "2021-05-13 12:58:06.267351: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
      "2021-05-13 12:58:06.267380: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
      "2021-05-13 12:58:06.267406: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
      "2021-05-13 12:58:06.267433: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
      "2021-05-13 12:58:06.267459: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
      "2021-05-13 12:58:06.267486: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
      "2021-05-13 12:58:06.270367: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1\n",
      "2021-05-13 12:58:06.270829: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
      "2021-05-13 12:58:08.309679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-05-13 12:58:08.309710: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 1 \n",
      "2021-05-13 12:58:08.309717: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N Y \n",
      "2021-05-13 12:58:08.309721: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 1:   Y N \n",
      "2021-05-13 12:58:08.314308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11021 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0, compute capability: 5.2)\n",
      "2021-05-13 12:58:08.315883: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 4063 MB memory) -> physical GPU (device: 1, name: GeForce GTX TITAN X, pci bus id: 0000:0a:00.0, compute capability: 5.2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2817 files belonging to 31 classes.\n",
      "Using 563 files for validation.\n",
      "Found 795 files belonging to 31 classes.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import tensorflow as tf \n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "from adapt.feature_based import MDD\n",
    "from adapt.utils import UpdateLambda\n",
    "from data_loader import load_data, get_input_and_labels_from_batch_ds\n",
    "\n",
    "epochs=2\n",
    "batch_size=32\n",
    "\n",
    "train_ds, val_ds, target_ds = load_data(\"amazon\", \"webcam\", image_size=(224,224))\n",
    "Xs, ys = get_input_and_labels_from_batch_ds(train_ds)\n",
    "Xv, yv = get_input_and_labels_from_batch_ds(val_ds)\n",
    "Xt, yt = get_input_and_labels_from_batch_ds(target_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X source shape: (2254, 7, 7, 2048)\n",
      "X target shape: (795, 7, 7, 2048)\n",
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shashemi/miniconda3/envs/tf/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL_NAME: fine_tuning\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "fit() got multiple values for argument 'X'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[38], line 123\u001b[0m\n\u001b[1;32m    113\u001b[0m     model \u001b[39m=\u001b[39m RegularTransferNN(encoder, task,\n\u001b[1;32m    114\u001b[0m                 loss\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcategorical_crossentropy\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    115\u001b[0m                 metrics\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39macc\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    119\u001b[0m                 \n\u001b[1;32m    120\u001b[0m                 )\n\u001b[1;32m    122\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mMODEL_NAME:\u001b[39m\u001b[39m\"\u001b[39m, model\u001b[39m.\u001b[39mname)\n\u001b[0;32m--> 123\u001b[0m model\u001b[39m.\u001b[39;49mfit(X\u001b[39m=\u001b[39;49mX_source[:\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m], y\u001b[39m=\u001b[39;49my_source[:\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m], Xt\u001b[39m=\u001b[39;49mX_target, yt\u001b[39m=\u001b[39;49my_target, epochs\u001b[39m=\u001b[39;49mepochs, batch_size\u001b[39m=\u001b[39;49mbatch_size, validation_data\u001b[39m=\u001b[39;49m(X_val, y_val))\n\u001b[1;32m    125\u001b[0m acc \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mhistory\u001b[39m.\u001b[39mhistory\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39macc\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m) \u001b[39mor\u001b[39;00m model\u001b[39m.\u001b[39mhistory\u001b[39m.\u001b[39mhistory\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mdisc_acc\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m    126\u001b[0m val_acc \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mhistory\u001b[39m.\u001b[39mhistory[\u001b[39m\"\u001b[39m\u001b[39mval_acc\u001b[39m\u001b[39m\"\u001b[39m]\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.8/site-packages/adapt/parameter_based/_finetuning.py:120\u001b[0m, in \u001b[0;36mFineTuning.fit\u001b[0;34m(self, Xt, yt, **fit_params)\u001b[0m\n\u001b[1;32m    118\u001b[0m Xs \u001b[39m=\u001b[39m Xt\n\u001b[1;32m    119\u001b[0m ys \u001b[39m=\u001b[39m yt\n\u001b[0;32m--> 120\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mfit(Xs, ys, Xt\u001b[39m=\u001b[39;49mXt, yt\u001b[39m=\u001b[39;49myt, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params)\n",
      "\u001b[0;31mTypeError\u001b[0m: fit() got multiple values for argument 'X'"
     ]
    }
   ],
   "source": [
    "from adapt.feature_based import DeepCORAL,MCD,MDD, WDGRL ,CDAN ,CCSA ,DANN,ADDA\n",
    "from adapt.parameter_based import RegularTransferNN, FineTuning\n",
    "\n",
    "first_blocks, _, load_resnet50 = get_resnet()\n",
    "X_source, y_source, X_val, y_val, X_target, y_target = get_input_and_target_for_head(first_blocks, Xs, ys, Xv, yv, Xt, yt)\n",
    "model_name =\"FineTuning\"\n",
    "\n",
    "lr = 0.04\n",
    "momentum = 0.9\n",
    "alpha = 0.0002\n",
    "\n",
    "encoder = load_resnet50()\n",
    "task = get_task()\n",
    "\n",
    "optimizer_task = SGD(learning_rate=MyDecay(mu_0=lr, alpha=alpha),\n",
    "                    momentum=momentum, nesterov=True)\n",
    "optimizer_enc = SGD(learning_rate=MyDecay(mu_0=lr/10., alpha=alpha),\n",
    "                    momentum=momentum, nesterov=True)\n",
    "optimizer_disc = SGD(learning_rate=MyDecay(mu_0=lr/10., alpha=alpha))\n",
    "\n",
    "if model_name == \"MDD\":\n",
    "    model = MDD(encoder, task,\n",
    "            loss=\"categorical_crossentropy\",\n",
    "            metrics=[\"acc\"],\n",
    "            copy=False,\n",
    "            gamma=2.,\n",
    "            lambda_=tf.Variable(0.),\n",
    "            optimizer=optimizer_task,\n",
    "            optimizer_enc=optimizer_enc,\n",
    "            optimizer_disc=optimizer_disc,\n",
    "            callbacks=[UpdateLambda(lambda_max=0.1)])\n",
    "elif model_name ==\"DeepCORAL\":\n",
    "    model = DeepCORAL(encoder, task,\n",
    "                loss=\"categorical_crossentropy\",\n",
    "                metrics=[\"acc\"],\n",
    "                copy=False,\n",
    "                lambda_=tf.Variable(0.),\n",
    "                optimizer=optimizer_task,\n",
    "                optimizer_enc=optimizer_enc,\n",
    "                optimizer_disc=optimizer_disc,\n",
    "                callbacks=[UpdateLambda(lambda_max=0.1)])\n",
    "elif model_name ==\"MCD\": \n",
    "    model = MCD(encoder, task,\n",
    "                loss=\"categorical_crossentropy\",\n",
    "                metrics=[\"acc\"],\n",
    "                copy=False,\n",
    "                optimizer=optimizer_task,\n",
    "                optimizer_enc=optimizer_enc,\n",
    "                optimizer_disc=optimizer_disc)\n",
    "elif model_name == \"WDGRL\":\n",
    "    model = WDGRL(encoder, task,\n",
    "                loss=\"categorical_crossentropy\",\n",
    "                metrics=[\"acc\"],\n",
    "                copy=False,\n",
    "                optimizer=optimizer_task,\n",
    "                optimizer_enc=optimizer_enc,\n",
    "                optimizer_disc=optimizer_disc,\n",
    "                )\n",
    "elif model_name == \"CDAN\":\n",
    "    model = CDAN(encoder, task,\n",
    "                loss=\"categorical_crossentropy\",\n",
    "                metrics=[\"acc\"],\n",
    "                copy=False,\n",
    "                optimizer=optimizer_task,\n",
    "                optimizer_enc=optimizer_enc,\n",
    "                optimizer_disc=optimizer_disc,\n",
    "                )\n",
    "elif model_name == \"CCSA\":\n",
    "    model = CCSA(encoder, task,\n",
    "                loss=\"categorical_crossentropy\",\n",
    "                metrics=[\"acc\"],\n",
    "                copy=False,\n",
    "                optimizer=optimizer_task,\n",
    "                optimizer_enc=optimizer_enc,\n",
    "                optimizer_disc=optimizer_disc,\n",
    "                )\n",
    "elif model_name == \"DANN\":\n",
    "    model = DANN(encoder, task,\n",
    "                loss=\"categorical_crossentropy\",\n",
    "                metrics=[\"acc\"],\n",
    "                copy=False,\n",
    "                optimizer=optimizer_task,\n",
    "                optimizer_enc=optimizer_enc,\n",
    "                optimizer_disc=optimizer_disc,\n",
    "                )\n",
    "elif model_name == \"ADDA\":\n",
    "    model = ADDA(encoder, task,\n",
    "                loss=\"categorical_crossentropy\",\n",
    "                metrics=[\"acc\"],\n",
    "                copy=False,\n",
    "                optimizer=optimizer_task,\n",
    "                optimizer_enc=optimizer_enc,\n",
    "                optimizer_disc=optimizer_disc,\n",
    "                )\n",
    "\n",
    "print(\"MODEL_NAME:\", model.name)\n",
    "model.fit(X=X_source[:-1], y=y_source[:-1], Xt=X_target, yt=y_target, epochs=epochs, batch_size=batch_size, validation_data=(X_val, y_val))\n",
    "\n",
    "acc = model.history.history.get(\"acc\", None) or model.history.history.get(\"disc_acc\", None)\n",
    "val_acc = model.history.history[\"val_acc\"]\n",
    "print(model.score(X_source, y_source))\n",
    "print(model.score(X_val, y_val))\n",
    "print(model.score(X_target, y_target))\n",
    "\n",
    "plt.plot(acc, label=\"Train acc - final value: %.3f\"%acc[-1])\n",
    "plt.plot(val_acc, label=\"Test acc - final value: %.3f\"%val_acc[-1])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Acc\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5, 0.5]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.history.history.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cdtrans",
   "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": "3.8.15 (default, Nov 24 2022, 15:19:38) \n[GCC 11.2.0]"
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  "orig_nbformat": 4,
  "vscode": {
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}