task2_VGG.ipynb

{
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     "id": "1d234e3f-af8b-4757-83f3-241b04b7a511"
    }
   },
   "source": [
    "# Class Challenge: Image Classification of COVID-19 X-rays\n",
    "# Task 2 [Total points: 30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b07b9992-b592-4871-9f9a-3b0ae5fa8b5f"
    }
   },
   "source": [
    "## Setup\n",
    "\n",
    "* This assignment involves the following packages: 'matplotlib', 'numpy', and 'sklearn'. \n",
    "\n",
    "* If you are using conda, use the following commands to install the above packages:<br>\n",
    "```shell\n",
    "conda install matplotlib\n",
    "conda install numpy\n",
    "conda install -c anaconda scikit-learn\n",
    "```\n",
    "\n",
    "* If you are using pip, use use the following commands to install the above packages: <br> \n",
    "```shell\n",
    "pip install matplotlib\n",
    "pip install numpy\n",
    "pip install sklearn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5516f610-f00c-43c3-bf19-4cf7ab9c089f"
    }
   },
   "source": [
    "## Data\n",
    "\n",
    "Please download the data using the following link: [COVID-19](https://drive.google.com/file/d/1Y88tgqpQ1Pjko_7rntcPowOJs_QNOrJ-/view). \n",
    "\n",
    "* After downloading 'Covid_Data_GradientCrescent.zip', unzip the file and you should see the following data structure:\n",
    "\n",
    "\n",
    "|--all<br>\n",
    "|--------train<br>\n",
    "|--------test<br>\n",
    "|--two<br>\n",
    "|--------train<br>\n",
    "|--------test<br>\n",
    "\n",
    "\n",
    "* Put the 'all' folder, the 'two' folder and this python notebook in the **same directory** so that the following code can correctly locate the data.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "## [20 points] Multi-class Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbpresent": {
     "id": "5e05f980-3d14-4367-b3d1-664249145b13"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.6.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# os.environ['OMP_NUM_THREADS'] = '1'\n",
    "# os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   # see issue #152\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n",
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 10053575647669565555\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 6252920832\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 6610614899885848118\n",
      "physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 2080 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n",
    "tf.config.list_physical_devices('GPU')\n",
    "\n",
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "#### Load Image Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "nbpresent": {
     "id": "58317664-8da3-4283-b3e5-35721687e7ab"
    }
   },
   "outputs": [],
   "source": [
    "DATA_LIST = os.listdir('all/train')\n",
    "DATASET_PATH  = 'all/train'\n",
    "TEST_DIR =  'all/test'\n",
    "IMAGE_SIZE    = (224, 224)\n",
    "NUM_CLASSES   = len(DATA_LIST)\n",
    "BATCH_SIZE    = 10  # try reducing batch size or freeze more layers if your GPU runs out of memory\n",
    "NUM_EPOCHS    = 100\n",
    "LEARNING_RATE = 0.0001 # start off with high rate first 0.001 and experiment with reducing it gradually "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "#### Generate Training and Validation Batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "nbpresent": {
     "id": "71760e29-2b08-4259-93f1-ebe68cd74a6d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 216 images belonging to 4 classes.\n",
      "Found 54 images belonging to 4 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Li\\anaconda3\\lib\\site-packages\\keras_preprocessing\\image\\image_data_generator.py:342: UserWarning: This ImageDataGenerator specifies `zca_whitening` which overrides setting of`featurewise_std_normalization`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255,rotation_range=50,featurewise_center = True,\n",
    "                                   featurewise_std_normalization = True,width_shift_range=0.2,\n",
    "                                   height_shift_range=0.2,shear_range=0.25,zoom_range=0.1,\n",
    "                                   zca_whitening = True,channel_shift_range = 20,\n",
    "                                   horizontal_flip = True,vertical_flip = True,\n",
    "                                   validation_split = 0.2,fill_mode='constant')\n",
    "\n",
    "\n",
    "train_batches = train_datagen.flow_from_directory(DATASET_PATH,target_size=IMAGE_SIZE,\n",
    "                                                  shuffle=True,batch_size=BATCH_SIZE,\n",
    "                                                  subset = \"training\",seed=42,\n",
    "                                                  class_mode=\"categorical\")\n",
    "\n",
    "valid_batches = train_datagen.flow_from_directory(DATASET_PATH,target_size=IMAGE_SIZE,\n",
    "                                                  shuffle=True,batch_size=BATCH_SIZE,\n",
    "                                                  subset = \"validation\",\n",
    "                                                  seed=42,class_mode=\"categorical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "#### [10 points] Build Model\n",
    "Hint: Starting from a pre-trained model typically helps performance on a new task, e.g. starting with weights obtained by training on ImageNet. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "nbpresent": {
     "id": "5c52eeb2-1092-4d45-9e16-02219c82cb5e"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Functional)           (None, 7, 7, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               6422784   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 1028      \n",
      "=================================================================\n",
      "Total params: 21,138,500\n",
      "Trainable params: 6,423,812\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import VGG16\n",
    "from keras.layers.core import Flatten, Dense, Dropout, Lambda\n",
    "\n",
    "\n",
    "vgg16 = VGG16(weights='imagenet',include_top=False,pooling = \"None\", classes = 2, input_shape=(224, 224, 3))\n",
    "vgg16.trainable = False\n",
    "\n",
    "model = tf.keras.models.Sequential()\n",
    "model.add(vgg16)\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(4, activation='softmax'))\n",
    "model.summary()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "#### [5 points] Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "nbpresent": {
     "id": "cdb90d24-6a23-4969-8138-f37e7050062d"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Li\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py:1972: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n",
      "C:\\Users\\Li\\anaconda3\\lib\\site-packages\\keras_preprocessing\\image\\image_data_generator.py:720: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "C:\\Users\\Li\\anaconda3\\lib\\site-packages\\keras_preprocessing\\image\\image_data_generator.py:739: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "21/21 [==============================] - 8s 269ms/step - loss: 1.4264 - accuracy: 0.3010 - val_loss: 1.1429 - val_accuracy: 0.4000\n",
      "Epoch 2/100\n",
      "21/21 [==============================] - 5s 237ms/step - loss: 1.1732 - accuracy: 0.4563 - val_loss: 1.1100 - val_accuracy: 0.4400\n",
      "Epoch 3/100\n",
      "21/21 [==============================] - 5s 230ms/step - loss: 1.1180 - accuracy: 0.4563 - val_loss: 1.1234 - val_accuracy: 0.4600\n",
      "Epoch 4/100\n",
      "21/21 [==============================] - 5s 228ms/step - loss: 0.9523 - accuracy: 0.6019 - val_loss: 0.8814 - val_accuracy: 0.6000\n",
      "Epoch 5/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.9605 - accuracy: 0.5680 - val_loss: 0.8906 - val_accuracy: 0.6400\n",
      "Epoch 6/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.8831 - accuracy: 0.6214 - val_loss: 0.9134 - val_accuracy: 0.6000\n",
      "Epoch 7/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.8801 - accuracy: 0.6117 - val_loss: 0.8144 - val_accuracy: 0.6600\n",
      "Epoch 8/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.8398 - accuracy: 0.6845 - val_loss: 0.8069 - val_accuracy: 0.6400\n",
      "Epoch 9/100\n",
      "21/21 [==============================] - 5s 237ms/step - loss: 0.8554 - accuracy: 0.6602 - val_loss: 0.7314 - val_accuracy: 0.7200\n",
      "Epoch 10/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.8012 - accuracy: 0.6359 - val_loss: 0.7197 - val_accuracy: 0.7400\n",
      "Epoch 11/100\n",
      "21/21 [==============================] - 5s 230ms/step - loss: 0.8225 - accuracy: 0.6456 - val_loss: 0.8817 - val_accuracy: 0.5600\n",
      "Epoch 12/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.8391 - accuracy: 0.6214 - val_loss: 0.8488 - val_accuracy: 0.5800\n",
      "Epoch 13/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.8010 - accuracy: 0.6602 - val_loss: 0.7427 - val_accuracy: 0.6200\n",
      "Epoch 14/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.7103 - accuracy: 0.7039 - val_loss: 0.6787 - val_accuracy: 0.7400\n",
      "Epoch 15/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.6754 - accuracy: 0.7087 - val_loss: 0.9397 - val_accuracy: 0.5800\n",
      "Epoch 16/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.7933 - accuracy: 0.6699 - val_loss: 0.7090 - val_accuracy: 0.7000\n",
      "Epoch 17/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.6515 - accuracy: 0.7087 - val_loss: 0.7031 - val_accuracy: 0.6800\n",
      "Epoch 18/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.6871 - accuracy: 0.7087 - val_loss: 0.6270 - val_accuracy: 0.7000\n",
      "Epoch 19/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.6966 - accuracy: 0.7379 - val_loss: 0.8661 - val_accuracy: 0.6600\n",
      "Epoch 20/100\n",
      "21/21 [==============================] - 5s 233ms/step - loss: 0.7016 - accuracy: 0.6699 - val_loss: 0.8272 - val_accuracy: 0.6600\n",
      "Epoch 21/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.7270 - accuracy: 0.6650 - val_loss: 0.7961 - val_accuracy: 0.5400\n",
      "Epoch 22/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.6639 - accuracy: 0.7379 - val_loss: 0.8159 - val_accuracy: 0.5800\n",
      "Epoch 23/100\n",
      "21/21 [==============================] - 5s 223ms/step - loss: 0.7012 - accuracy: 0.6990 - val_loss: 0.8038 - val_accuracy: 0.6200\n",
      "Epoch 24/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.7277 - accuracy: 0.6845 - val_loss: 0.8103 - val_accuracy: 0.6600\n",
      "Epoch 25/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.6329 - accuracy: 0.7330 - val_loss: 0.7182 - val_accuracy: 0.6600\n",
      "Epoch 26/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.6680 - accuracy: 0.7184 - val_loss: 0.5410 - val_accuracy: 0.7800\n",
      "Epoch 27/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.6135 - accuracy: 0.7670 - val_loss: 0.9916 - val_accuracy: 0.5200\n",
      "Epoch 28/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.7011 - accuracy: 0.6893 - val_loss: 0.9131 - val_accuracy: 0.5200\n",
      "Epoch 29/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.6107 - accuracy: 0.7476 - val_loss: 0.6755 - val_accuracy: 0.6400\n",
      "Epoch 30/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.6591 - accuracy: 0.7136 - val_loss: 0.9429 - val_accuracy: 0.5400\n",
      "Epoch 31/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.6792 - accuracy: 0.7039 - val_loss: 0.7686 - val_accuracy: 0.7000\n",
      "Epoch 32/100\n",
      "21/21 [==============================] - 5s 233ms/step - loss: 0.6704 - accuracy: 0.7184 - val_loss: 0.7658 - val_accuracy: 0.6800\n",
      "Epoch 33/100\n",
      "21/21 [==============================] - 5s 227ms/step - loss: 0.6095 - accuracy: 0.7573 - val_loss: 0.7125 - val_accuracy: 0.7000\n",
      "Epoch 34/100\n",
      "21/21 [==============================] - 5s 226ms/step - loss: 0.6457 - accuracy: 0.7233 - val_loss: 0.6751 - val_accuracy: 0.7000\n",
      "Epoch 35/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.6267 - accuracy: 0.7524 - val_loss: 0.7554 - val_accuracy: 0.6400\n",
      "Epoch 36/100\n",
      "21/21 [==============================] - 5s 233ms/step - loss: 0.6632 - accuracy: 0.7282 - val_loss: 0.6110 - val_accuracy: 0.7600\n",
      "Epoch 37/100\n",
      "21/21 [==============================] - 5s 230ms/step - loss: 0.5977 - accuracy: 0.7718 - val_loss: 0.6660 - val_accuracy: 0.6800\n",
      "Epoch 38/100\n",
      "21/21 [==============================] - 5s 233ms/step - loss: 0.5316 - accuracy: 0.7670 - val_loss: 0.7484 - val_accuracy: 0.6000\n",
      "Epoch 39/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.6451 - accuracy: 0.7282 - val_loss: 0.8453 - val_accuracy: 0.5600\n",
      "Epoch 40/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.6425 - accuracy: 0.7476 - val_loss: 0.7557 - val_accuracy: 0.6600\n",
      "Epoch 41/100\n",
      "21/21 [==============================] - 5s 239ms/step - loss: 0.6038 - accuracy: 0.7143 - val_loss: 0.6908 - val_accuracy: 0.6400\n",
      "Epoch 42/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.5543 - accuracy: 0.7670 - val_loss: 0.6859 - val_accuracy: 0.6200\n",
      "Epoch 43/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.5929 - accuracy: 0.7621 - val_loss: 0.6272 - val_accuracy: 0.7200\n",
      "Epoch 44/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.6248 - accuracy: 0.7184 - val_loss: 0.6195 - val_accuracy: 0.7200\n",
      "Epoch 45/100\n",
      "21/21 [==============================] - 5s 228ms/step - loss: 0.5850 - accuracy: 0.7670 - val_loss: 0.8039 - val_accuracy: 0.5600\n",
      "Epoch 46/100\n",
      "21/21 [==============================] - 5s 230ms/step - loss: 0.5371 - accuracy: 0.7621 - val_loss: 0.7563 - val_accuracy: 0.5800\n",
      "Epoch 47/100\n",
      "21/21 [==============================] - 5s 230ms/step - loss: 0.5336 - accuracy: 0.7524 - val_loss: 0.7040 - val_accuracy: 0.7000\n",
      "Epoch 48/100\n",
      "21/21 [==============================] - 5s 230ms/step - loss: 0.5937 - accuracy: 0.7476 - val_loss: 0.6356 - val_accuracy: 0.6800\n",
      "Epoch 49/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.6132 - accuracy: 0.7233 - val_loss: 0.5993 - val_accuracy: 0.6600\n",
      "Epoch 50/100\n",
      "21/21 [==============================] - 5s 229ms/step - loss: 0.6334 - accuracy: 0.7524 - val_loss: 0.7154 - val_accuracy: 0.7200\n",
      "Epoch 51/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.5691 - accuracy: 0.7573 - val_loss: 0.6660 - val_accuracy: 0.6800\n",
      "Epoch 52/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.5078 - accuracy: 0.7961 - val_loss: 0.7096 - val_accuracy: 0.6200\n",
      "Epoch 53/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.5323 - accuracy: 0.7670 - val_loss: 0.5824 - val_accuracy: 0.7600\n",
      "Epoch 54/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.6194 - accuracy: 0.7233 - val_loss: 0.6352 - val_accuracy: 0.6600\n",
      "Epoch 55/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.5684 - accuracy: 0.7816 - val_loss: 0.6507 - val_accuracy: 0.7400\n",
      "Epoch 56/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.5429 - accuracy: 0.7573 - val_loss: 0.6149 - val_accuracy: 0.6800\n",
      "Epoch 57/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.6298 - accuracy: 0.7087 - val_loss: 0.8278 - val_accuracy: 0.6200\n",
      "Epoch 58/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.5504 - accuracy: 0.7573 - val_loss: 0.7283 - val_accuracy: 0.7000\n",
      "Epoch 59/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.5396 - accuracy: 0.7767 - val_loss: 0.6445 - val_accuracy: 0.7000\n",
      "Epoch 60/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.5385 - accuracy: 0.7667 - val_loss: 0.7447 - val_accuracy: 0.5800\n",
      "Epoch 61/100\n",
      "21/21 [==============================] - 5s 230ms/step - loss: 0.5600 - accuracy: 0.7816 - val_loss: 0.5994 - val_accuracy: 0.7200\n",
      "Epoch 62/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.4901 - accuracy: 0.8204 - val_loss: 0.7886 - val_accuracy: 0.5800\n",
      "Epoch 63/100\n",
      "21/21 [==============================] - 5s 233ms/step - loss: 0.5679 - accuracy: 0.7767 - val_loss: 0.7193 - val_accuracy: 0.6200\n",
      "Epoch 64/100\n",
      "21/21 [==============================] - 5s 237ms/step - loss: 0.5666 - accuracy: 0.7427 - val_loss: 0.5512 - val_accuracy: 0.7600\n",
      "Epoch 65/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.5544 - accuracy: 0.7573 - val_loss: 0.6635 - val_accuracy: 0.7000\n",
      "Epoch 66/100\n",
      "21/21 [==============================] - 5s 230ms/step - loss: 0.5992 - accuracy: 0.7330 - val_loss: 0.8694 - val_accuracy: 0.6400\n",
      "Epoch 67/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.6100 - accuracy: 0.7573 - val_loss: 0.7381 - val_accuracy: 0.6200\n",
      "Epoch 68/100\n",
      "21/21 [==============================] - 5s 233ms/step - loss: 0.5367 - accuracy: 0.7864 - val_loss: 0.7385 - val_accuracy: 0.5800\n",
      "Epoch 69/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.5460 - accuracy: 0.7379 - val_loss: 0.6521 - val_accuracy: 0.6800\n",
      "Epoch 70/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.5708 - accuracy: 0.7427 - val_loss: 0.7493 - val_accuracy: 0.6400\n",
      "Epoch 71/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.5353 - accuracy: 0.7427 - val_loss: 0.5695 - val_accuracy: 0.7400\n",
      "Epoch 72/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.5555 - accuracy: 0.7573 - val_loss: 0.5782 - val_accuracy: 0.7000\n",
      "Epoch 73/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.5271 - accuracy: 0.7621 - val_loss: 0.8220 - val_accuracy: 0.6400\n",
      "Epoch 74/100\n",
      "21/21 [==============================] - 5s 237ms/step - loss: 0.5769 - accuracy: 0.7670 - val_loss: 0.7831 - val_accuracy: 0.6800\n",
      "Epoch 75/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.5406 - accuracy: 0.7718 - val_loss: 0.7095 - val_accuracy: 0.7200\n",
      "Epoch 76/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.4809 - accuracy: 0.7816 - val_loss: 0.7204 - val_accuracy: 0.6400\n",
      "Epoch 77/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.4846 - accuracy: 0.8010 - val_loss: 0.8055 - val_accuracy: 0.6600\n",
      "Epoch 78/100\n",
      "21/21 [==============================] - 5s 232ms/step - loss: 0.5305 - accuracy: 0.7718 - val_loss: 0.7984 - val_accuracy: 0.6600\n",
      "Epoch 79/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.5435 - accuracy: 0.7573 - val_loss: 0.7398 - val_accuracy: 0.6800\n",
      "Epoch 80/100\n",
      "21/21 [==============================] - 5s 237ms/step - loss: 0.5985 - accuracy: 0.7573 - val_loss: 0.7189 - val_accuracy: 0.6400\n",
      "Epoch 81/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.6298 - accuracy: 0.7379 - val_loss: 0.6261 - val_accuracy: 0.7400\n",
      "Epoch 82/100\n",
      "21/21 [==============================] - 5s 233ms/step - loss: 0.5582 - accuracy: 0.7621 - val_loss: 0.7064 - val_accuracy: 0.6600\n",
      "Epoch 83/100\n",
      "21/21 [==============================] - 5s 237ms/step - loss: 0.5068 - accuracy: 0.7767 - val_loss: 0.4752 - val_accuracy: 0.7800\n",
      "Epoch 84/100\n",
      "21/21 [==============================] - 5s 224ms/step - loss: 0.5029 - accuracy: 0.7573 - val_loss: 0.7053 - val_accuracy: 0.6600\n",
      "Epoch 85/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.5323 - accuracy: 0.7767 - val_loss: 0.7489 - val_accuracy: 0.6400\n",
      "Epoch 86/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.4766 - accuracy: 0.8010 - val_loss: 0.6302 - val_accuracy: 0.7200\n",
      "Epoch 87/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.4788 - accuracy: 0.8301 - val_loss: 0.6049 - val_accuracy: 0.7000\n",
      "Epoch 88/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.4871 - accuracy: 0.7816 - val_loss: 0.7066 - val_accuracy: 0.6600\n",
      "Epoch 89/100\n",
      "21/21 [==============================] - 5s 228ms/step - loss: 0.4901 - accuracy: 0.7816 - val_loss: 0.7800 - val_accuracy: 0.6400\n",
      "Epoch 90/100\n",
      "21/21 [==============================] - 5s 238ms/step - loss: 0.4343 - accuracy: 0.8350 - val_loss: 0.5082 - val_accuracy: 0.7400\n",
      "Epoch 91/100\n",
      "21/21 [==============================] - 5s 236ms/step - loss: 0.4970 - accuracy: 0.7767 - val_loss: 0.4633 - val_accuracy: 0.7800\n",
      "Epoch 92/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.5019 - accuracy: 0.8058 - val_loss: 0.6147 - val_accuracy: 0.7000\n",
      "Epoch 93/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.5359 - accuracy: 0.7573 - val_loss: 0.7254 - val_accuracy: 0.6400\n",
      "Epoch 94/100\n",
      "21/21 [==============================] - 5s 233ms/step - loss: 0.4413 - accuracy: 0.8010 - val_loss: 0.8067 - val_accuracy: 0.7000\n",
      "Epoch 95/100\n",
      "21/21 [==============================] - 5s 235ms/step - loss: 0.4942 - accuracy: 0.7913 - val_loss: 0.6256 - val_accuracy: 0.7400\n",
      "Epoch 96/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.4537 - accuracy: 0.8010 - val_loss: 0.6329 - val_accuracy: 0.6600\n",
      "Epoch 97/100\n",
      "21/21 [==============================] - 5s 226ms/step - loss: 0.4488 - accuracy: 0.8010 - val_loss: 0.7618 - val_accuracy: 0.6200\n",
      "Epoch 98/100\n",
      "21/21 [==============================] - 5s 234ms/step - loss: 0.4621 - accuracy: 0.7816 - val_loss: 0.6498 - val_accuracy: 0.6800\n",
      "Epoch 99/100\n",
      "21/21 [==============================] - 5s 231ms/step - loss: 0.4938 - accuracy: 0.7767 - val_loss: 0.8265 - val_accuracy: 0.6600\n",
      "Epoch 100/100\n",
      "21/21 [==============================] - 5s 227ms/step - loss: 0.4718 - accuracy: 0.8058 - val_loss: 0.9059 - val_accuracy: 0.6800\n"
     ]
    }
   ],
   "source": [
    "#FIT MODEL\n",
    "print(len(train_batches))\n",
    "print(len(valid_batches))\n",
    "\n",
    "STEP_SIZE_TRAIN=train_batches.n//train_batches.batch_size\n",
    "STEP_SIZE_VALID=valid_batches.n//valid_batches.batch_size\n",
    "\n",
    "history=model.fit_generator(train_batches, steps_per_epoch =STEP_SIZE_TRAIN, validation_data = valid_batches, validation_steps = STEP_SIZE_VALID, epochs= NUM_EPOCHS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "#### [5 points] Plot Accuracy and Loss During Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "nbpresent": {
     "id": "ff342098-784a-4e20-ac34-b74ca8ebe839"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_acc_loss(history, epochs):\n",
    "    acc = history.history['accuracy']\n",
    "    val_acc = history.history['val_accuracy']\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    \n",
    "    plt.figure(figsize=(15, 5))\n",
    "    \n",
    "    plt.subplot(121)\n",
    "    plt.plot(range(1,epochs), acc[1:], label='Train_acc')\n",
    "    plt.plot(range(1,epochs), val_acc[1:], label='Test_acc')\n",
    "    plt.title('Accuracy over ' + str(epochs) + ' Epochs', size=15)\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    plt.plot(range(1,epochs), loss[1:], label='Train_loss')\n",
    "    plt.plot(range(1,epochs), val_loss[1:], label='Test_loss')\n",
    "    plt.title('Loss over ' + str(epochs) + ' Epochs', size=15)\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "plot_acc_loss(history, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "#### Testing Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 36 images belonging to 4 classes.\n",
      "36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Li\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py:2006: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
      "  warnings.warn('`Model.evaluate_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36/36 [==============================] - 1s 16ms/step - loss: 0.7485 - accuracy: 0.7500\n",
      "Test loss: 0.748456597328186\n",
      "Test accuracy: 0.75\n"
     ]
    }
   ],
   "source": [
    "test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
    "\n",
    "eval_generator = test_datagen.flow_from_directory(TEST_DIR,target_size=IMAGE_SIZE,\n",
    "                                                  batch_size=1,shuffle=True,seed=42,class_mode=\"categorical\")\n",
    "eval_generator.reset()\n",
    "print(len(eval_generator))\n",
    "x = model.evaluate_generator(eval_generator,steps = np.ceil(len(eval_generator)),\n",
    "                           use_multiprocessing = False,verbose = 1,workers=1)\n",
    "print('Test loss:' , x[0])\n",
    "print('Test accuracy:',x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "## [10 points] TSNE Plot\n",
    "t-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. After training is complete, extract features from a specific deep layer of your choice, use t-SNE to reduce the dimensionality of your extracted features to 2 dimensions and plot the resulting 2D features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 270 images belonging to 4 classes.\n",
      "{'covid': 0, 'normal': 1, 'pneumonia_bac': 2, 'pneumonia_vir': 3}\n",
      "  1/270 [..............................] - ETA: 29s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Li\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py:2035: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "270/270 [==============================] - 4s 13ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29993192bc8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "intermediate_layer_model = tf.keras.models.Model(inputs=model.input,\n",
    "                                        outputs=model.get_layer('dense').output)\n",
    "tsne_eval_generator = test_datagen.flow_from_directory(DATASET_PATH,target_size=IMAGE_SIZE,\n",
    "                                                  batch_size=1,shuffle=False,seed=42,class_mode=\"categorical\")\n",
    "print(tsne_eval_generator.class_indices)\n",
    "\n",
    "outputs = intermediate_layer_model.predict_generator(tsne_eval_generator,270,verbose=1)\n",
    "features = TSNE(n_components=2).fit_transform(outputs)\n",
    "label = tsne_eval_generator.classes\n",
    "\n",
    "\n",
    "covid_x, covid_y, normal_x, normal_y, pneumonia_bac_x, pneumonia_bac_y, pneumonia_vir_x, pneumonia_vir_y = [],[],[],[],[],[],[],[]\n",
    "\n",
    "plt.figure()\n",
    "for index in range(len(features)):\n",
    "    if label[index] == 0:\n",
    "        covid_x.append(features[index, 0])\n",
    "        covid_y.append(features[index, 1])        \n",
    "    elif label[index] == 1:\n",
    "        normal_x.append(features[index, 0])\n",
    "        normal_y.append(features[index, 1]) \n",
    "    elif label[index] == 2:\n",
    "        pneumonia_bac_x.append(features[index, 0])\n",
    "        pneumonia_bac_y.append(features[index, 1]) \n",
    "    else:\n",
    "        pneumonia_vir_x.append(features[index, 0])\n",
    "        pneumonia_vir_y.append(features[index, 1]) \n",
    "\n",
    "plt.plot(covid_x, covid_y,'bo', ms=4, label=\"COVID-19\")\n",
    "plt.plot(normal_x, normal_y,'yo', ms=4, label=\"Normal\")\n",
    "plt.plot(pneumonia_bac_x, pneumonia_bac_y, 'go', ms=4, label=\"Pneumonia_bac\")\n",
    "plt.plot(pneumonia_vir_x, pneumonia_vir_y, 'ro', ms=4, label=\"Pneumonia_vir\")\n",
    "\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ## Task 2: COVID-19 vs Normal vs Tertiary Pneumonia (Bacterial and Viral) -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "<!-- #### Renew Training Batch and Validation Batch -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "66140980-2aa4-457f-b1df-74c10c234cc2"
    }
   },
   "source": [
    "<!-- #### Renew Model -->"
   ]
  }
 ],
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     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
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    "builtin_function_or_method",
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}