From be129649f89cc473996a443f2a3af53f595fb7ac Mon Sep 17 00:00:00 2001
From: Jay K Chandran <raider6604@gmail.com>
Date: Mon, 22 Feb 2021 11:32:31 -0500
Subject: [PATCH] Add files via upload

---
 COVID Neural Net.ipynb | 1985 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1985 insertions(+)
 create mode 100644 COVID Neural Net.ipynb

diff --git a/COVID Neural Net.ipynb b/COVID Neural Net.ipynb
new file mode 100644
index 0000000..ef502bb
--- /dev/null
+++ b/COVID Neural Net.ipynb	
@@ -0,0 +1,1985 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "advance-demonstration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "hollow-edinburgh",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import keras\n",
+    "import sklearn\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import os, os.path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "innovative-preliminary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = tf.compat.v1.ConfigProto()\n",
+    "config.gpu_options.allow_growth = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "hundred-elevation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sess = tf.compat.v1.Session(config=config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "premium-symbol",
+   "metadata": {},
+   "source": [
+    "# Loading in Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "sharp-swiss",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_dir = r'C:\\Users\\jaych\\Resilio Sync\\SDP\\ct images'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "worthy-helmet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covid_dir = os.path.join(base_dir, 'CT_COVID')\n",
+    "non_covid_dir = os.path.join(base_dir, 'CT_NonCOVID')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "introductory-heart",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Covid Positive Images: 349\n",
+      "Total Non-Covid Images: 397\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total Covid Positive Images:\" ,len(os.listdir(covid_dir)))\n",
+    "print(\"Total Non-Covid Images:\" ,len(os.listdir(non_covid_dir)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "compliant-australia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covidImgs = []\n",
+    "nonCovidImgs = []\n",
+    "def load_images(path, imgs):\n",
+    "    for f in os.listdir(path):\n",
+    "        imgs.append(Image.open(os.path.join(path,f)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "perfect-intelligence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_images(covid_dir, covidImgs)\n",
+    "load_images(non_covid_dir, nonCovidImgs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "collectible-minute",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1e80dc25190>"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "plt.imshow(covidImgs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "sophisticated-passage",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1e81ac064c0>"
+      ]
+     },
+     "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": [
+    "plt.imshow(nonCovidImgs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "extended-answer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allImgs = covidImgs + nonCovidImgs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "eleven-recycling",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels=[]\n",
+    "def label (imgs, string):\n",
+    "    for i,image in enumerate(imgs):\n",
+    "        labels.append(string)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "broad-momentum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label(covidImgs, 'Covid')\n",
+    "label(nonCovidImgs, 'Non-Covid')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "earlier-procurement",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
+       " 'Covid',\n",
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+       " 'Non-Covid',\n",
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+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
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+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
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+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
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+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid',\n",
+       " 'Non-Covid']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "relevant-trick",
+   "metadata": {},
+   "source": [
+    "# Loading in Data 2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "technological-dover",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_dir = r'C:\\Users\\jaych\\Documents\\Senior Design\\output'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "proof-constraint",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Directories for our training,\n",
+    "# validation and test splits\n",
+    "train_dir = os.path.join(base_dir, 'train')\n",
+    "validation_dir = os.path.join(base_dir, 'val')\n",
+    "test_dir = os.path.join(base_dir, 'test')\n",
+    "\n",
+    "# Directory with our training male pictures\n",
+    "train_covid_dir = os.path.join(train_dir, 'CT_COVID')\n",
+    "\n",
+    "# Directory with our training female pictures\n",
+    "train_non_dir = os.path.join(train_dir, 'CT_NonCOVID')\n",
+    "\n",
+    "# Directory with our validation male pictures\n",
+    "validation_covid_dir = os.path.join(validation_dir, 'CT_COVID')\n",
+    "\n",
+    "# Directory with our validation female pictures\n",
+    "validation_non_dir = os.path.join(validation_dir, 'CT_NonCOVID')\n",
+    "\n",
+    "# Directory with our validation male pictures\n",
+    "test_covid_dir = os.path.join(test_dir, 'CT_COVID')\n",
+    "\n",
+    "# Directory with our validation female pictures\n",
+    "test_non_dir = os.path.join(test_dir, 'CT_NonCOVID')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "persistent-looking",
+   "metadata": {},
+   "source": [
+    "Fairly Balanced in the Training Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "adjustable-robertson",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total training covid: 279\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('total training covid:', len(os.listdir(train_covid_dir)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "desperate-elimination",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total training non-covid: 317\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('total training non-covid:', len(os.listdir(train_non_dir)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "forced-contractor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total validation covid: 34\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('total validation covid:', len(os.listdir(validation_covid_dir)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bacterial-reform",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total validation non-covid: 39\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('total validation non-covid:', len(os.listdir(validation_non_dir)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "portuguese-lighting",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "funded-separation",
+   "metadata": {},
+   "source": [
+    "# Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "industrial-catholic",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 596 images belonging to 2 classes.\n",
+      "Found 73 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "# All images will be rescaled by 1./255\n",
+    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "        # This is the target directory\n",
+    "        train_dir,\n",
+    "        # All images will be resized to 150x150\n",
+    "        target_size=(218, 178),\n",
+    "        batch_size=20,\n",
+    "        # Since we use binary_crossentropy loss, we need binary labels\n",
+    "        class_mode='binary')\n",
+    "\n",
+    "validation_generator = test_datagen.flow_from_directory(\n",
+    "        validation_dir,\n",
+    "        target_size=(218, 178),\n",
+    "        batch_size=20,\n",
+    "        class_mode='binary')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "experimental-hardware",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data batch shape: (20, 218, 178, 3)\n",
+      "labels batch shape: (20,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for data_batch, labels_batch in train_generator:\n",
+    "    print('data batch shape:', data_batch.shape)\n",
+    "    print('labels batch shape:', labels_batch.shape)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "printable-burns",
+   "metadata": {},
+   "source": [
+    "# Building the Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "protected-inventory",
+   "metadata": {},
+   "source": [
+    "We will be using a VGG-16 pre-trained model as the base for our image classification. I am keeping the base of the model, but am setting include_top to be False so that we can use our own densely connected classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "boxed-supplement",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.applications import VGG16\n",
+    "\n",
+    "conv_base = VGG16(weights='imagenet',\n",
+    "                  include_top=False,\n",
+    "                  input_shape=(218, 178, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "processed-confidence",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"vgg16\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         [(None, 218, 178, 3)]     0         \n",
+      "_________________________________________________________________\n",
+      "block1_conv1 (Conv2D)        (None, 218, 178, 64)      1792      \n",
+      "_________________________________________________________________\n",
+      "block1_conv2 (Conv2D)        (None, 218, 178, 64)      36928     \n",
+      "_________________________________________________________________\n",
+      "block1_pool (MaxPooling2D)   (None, 109, 89, 64)       0         \n",
+      "_________________________________________________________________\n",
+      "block2_conv1 (Conv2D)        (None, 109, 89, 128)      73856     \n",
+      "_________________________________________________________________\n",
+      "block2_conv2 (Conv2D)        (None, 109, 89, 128)      147584    \n",
+      "_________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)   (None, 54, 44, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "block3_conv1 (Conv2D)        (None, 54, 44, 256)       295168    \n",
+      "_________________________________________________________________\n",
+      "block3_conv2 (Conv2D)        (None, 54, 44, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_conv3 (Conv2D)        (None, 54, 44, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)   (None, 27, 22, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "block4_conv1 (Conv2D)        (None, 27, 22, 512)       1180160   \n",
+      "_________________________________________________________________\n",
+      "block4_conv2 (Conv2D)        (None, 27, 22, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_conv3 (Conv2D)        (None, 27, 22, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)   (None, 13, 11, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "block5_conv1 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv2 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv3 (Conv2D)        (None, 13, 11, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_pool (MaxPooling2D)   (None, 6, 5, 512)         0         \n",
+      "=================================================================\n",
+      "Total params: 14,714,688\n",
+      "Trainable params: 14,714,688\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "conv_base.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "charged-puzzle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 596 images belonging to 2 classes.\n",
+      "Found 73 images belonging to 2 classes.\n",
+      "Found 77 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "datagen = ImageDataGenerator(rescale=1./255)\n",
+    "batch_size = 20\n",
+    "\n",
+    "def extract_features(directory, sample_count):\n",
+    "    features = np.zeros(shape=(sample_count, 6, 5, 512))\n",
+    "    labels = np.zeros(shape=(sample_count))\n",
+    "    generator = datagen.flow_from_directory(\n",
+    "        directory,\n",
+    "        target_size=(218, 178),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='binary')\n",
+    "    i = 0\n",
+    "    for inputs_batch, labels_batch in generator:\n",
+    "        features_batch = conv_base.predict(inputs_batch)\n",
+    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
+    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
+    "        i += 1\n",
+    "        if i * batch_size >= sample_count:\n",
+    "            # Note that since generators yield data indefinitely in a loop,\n",
+    "            # we must `break` after every image has been seen once.\n",
+    "            break\n",
+    "    return features, labels\n",
+    "\n",
+    "train_features, train_labels = extract_features(train_dir, 596)\n",
+    "validation_features, validation_labels = extract_features(validation_dir, 73)\n",
+    "test_features, test_labels = extract_features(test_dir, 77)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adjusted-circumstances",
+   "metadata": {},
+   "source": [
+    "flatten the features so they can be processed in a densely connected classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "radio-profile",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_features = np.reshape(train_features, (596, 6 * 5 * 512))\n",
+    "validation_features = np.reshape(validation_features, (73, 6 * 5 * 512))\n",
+    "test_features = np.reshape(test_features, (77, 6 * 5 * 512))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "determined-distance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras import models\n",
+    "from keras import layers\n",
+    "\n",
+    "model = models.Sequential()\n",
+    "model.add(conv_base)\n",
+    "model.add(layers.Flatten())\n",
+    "model.add(layers.Dense(256, activation='relu'))\n",
+    "model.add(layers.Dense(1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "european-hands",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "vgg16 (Functional)           (None, 6, 5, 512)         14714688  \n",
+      "_________________________________________________________________\n",
+      "flatten (Flatten)            (None, 15360)             0         \n",
+      "_________________________________________________________________\n",
+      "dense (Dense)                (None, 256)               3932416   \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 1)                 257       \n",
+      "=================================================================\n",
+      "Total params: 18,647,361\n",
+      "Trainable params: 18,647,361\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corrected-snake",
+   "metadata": {},
+   "source": [
+    "Before we compile and train our model, a very important thing to do is to freeze the convolutional base. \"Freezing\" a layer or set of \n",
+    "layers means preventing their weights from getting updated during training. If we don't do this, then the representations that were \n",
+    "previously learned by the convolutional base would get modified during training. Since the `Dense` layers on top are randomly initialized, \n",
+    "very large weight updates would be propagated through the network, effectively destroying the representations previously learned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "neither-array",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is the number of trainable weights before freezing the conv base: 30\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('This is the number of trainable weights '\n",
+    "      'before freezing the conv base:', len(model.trainable_weights))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "prerequisite-defeat",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conv_base.trainable = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "metropolitan-reproduction",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is the number of trainable weights before freezing the conv base: 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('This is the number of trainable weights '\n",
+    "      'before freezing the conv base:', len(model.trainable_weights))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "jewish-congress",
+   "metadata": {},
+   "source": [
+    "Two weights in each dense layer, the bias and the weight vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "sorted-competition",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 596 images belonging to 2 classes.\n",
+      "Found 73 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras import optimizers\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "        # This is the target directory\n",
+    "        train_dir,\n",
+    "        # All images will be resized to 150x150\n",
+    "        target_size=(218, 178),\n",
+    "        batch_size=20,\n",
+    "        # Since we use binary_crossentropy loss, we need binary labels\n",
+    "        class_mode='binary')\n",
+    "\n",
+    "validation_generator = test_datagen.flow_from_directory(\n",
+    "        validation_dir,\n",
+    "        target_size=(218, 178),\n",
+    "        batch_size=20,\n",
+    "        class_mode='binary')\n",
+    "\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer=optimizers.RMSprop(lr=2e-5),\n",
+    "              metrics=['acc'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "after-arkansas",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(596//batch_size, 73//batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "meaningful-gothic",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/35\n",
+      "29/29 [==============================] - 3s 87ms/step - loss: 0.6882 - acc: 0.5747 - val_loss: 0.5551 - val_acc: 0.7333\n",
+      "Epoch 2/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.5422 - acc: 0.7626 - val_loss: 0.4502 - val_acc: 0.7833\n",
+      "Epoch 3/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.4692 - acc: 0.7938 - val_loss: 0.4454 - val_acc: 0.7833\n",
+      "Epoch 4/35\n",
+      "29/29 [==============================] - 2s 56ms/step - loss: 0.4024 - acc: 0.8498 - val_loss: 0.4209 - val_acc: 0.8167\n",
+      "Epoch 5/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.3938 - acc: 0.8698 - val_loss: 0.4033 - val_acc: 0.8000\n",
+      "Epoch 6/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.3605 - acc: 0.8829 - val_loss: 0.4256 - val_acc: 0.7667\n",
+      "Epoch 7/35\n",
+      "29/29 [==============================] - 2s 59ms/step - loss: 0.3432 - acc: 0.8648 - val_loss: 0.3596 - val_acc: 0.8667\n",
+      "Epoch 8/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.3261 - acc: 0.8838 - val_loss: 0.3304 - val_acc: 0.8500\n",
+      "Epoch 9/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.2657 - acc: 0.9221 - val_loss: 0.3691 - val_acc: 0.8333\n",
+      "Epoch 10/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.2475 - acc: 0.9347 - val_loss: 0.3405 - val_acc: 0.8500\n",
+      "Epoch 11/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.2289 - acc: 0.9241 - val_loss: 0.3436 - val_acc: 0.8500\n",
+      "Epoch 12/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.2160 - acc: 0.9419 - val_loss: 0.3527 - val_acc: 0.8333\n",
+      "Epoch 13/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.1889 - acc: 0.9524 - val_loss: 0.2974 - val_acc: 0.9000\n",
+      "Epoch 14/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.1774 - acc: 0.9664 - val_loss: 0.3310 - val_acc: 0.8500\n",
+      "Epoch 15/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.1863 - acc: 0.9649 - val_loss: 0.2959 - val_acc: 0.9167\n",
+      "Epoch 16/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.1669 - acc: 0.9717 - val_loss: 0.2850 - val_acc: 0.9000\n",
+      "Epoch 17/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.1625 - acc: 0.9688 - val_loss: 0.3316 - val_acc: 0.8333\n",
+      "Epoch 18/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.1520 - acc: 0.9830 - val_loss: 0.3502 - val_acc: 0.8500\n",
+      "Epoch 19/35\n",
+      "29/29 [==============================] - 2s 56ms/step - loss: 0.1374 - acc: 0.9851 - val_loss: 0.2882 - val_acc: 0.8833\n",
+      "Epoch 20/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.1346 - acc: 0.9763 - val_loss: 0.2851 - val_acc: 0.8667\n",
+      "Epoch 21/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.1216 - acc: 0.9816 - val_loss: 0.3446 - val_acc: 0.8500\n",
+      "Epoch 22/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.1077 - acc: 0.9891 - val_loss: 0.2455 - val_acc: 0.8667\n",
+      "Epoch 23/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0943 - acc: 0.9947 - val_loss: 0.2579 - val_acc: 0.9000\n",
+      "Epoch 24/35\n",
+      "29/29 [==============================] - 2s 56ms/step - loss: 0.0939 - acc: 0.9986 - val_loss: 0.2669 - val_acc: 0.8833\n",
+      "Epoch 25/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0812 - acc: 0.9907 - val_loss: 0.3305 - val_acc: 0.8667\n",
+      "Epoch 26/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0765 - acc: 0.9998 - val_loss: 0.2578 - val_acc: 0.8833\n",
+      "Epoch 27/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0765 - acc: 1.0000 - val_loss: 0.2883 - val_acc: 0.8667\n",
+      "Epoch 28/35\n",
+      "29/29 [==============================] - 2s 58ms/step - loss: 0.0670 - acc: 0.9990 - val_loss: 0.2817 - val_acc: 0.8833\n",
+      "Epoch 29/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0618 - acc: 0.9980 - val_loss: 0.2338 - val_acc: 0.9167\n",
+      "Epoch 30/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0627 - acc: 0.9958 - val_loss: 0.2536 - val_acc: 0.8833\n",
+      "Epoch 31/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0581 - acc: 1.0000 - val_loss: 0.2244 - val_acc: 0.9000\n",
+      "Epoch 32/35\n",
+      "29/29 [==============================] - 2s 56ms/step - loss: 0.0541 - acc: 1.0000 - val_loss: 0.2464 - val_acc: 0.8833\n",
+      "Epoch 33/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0490 - acc: 0.9981 - val_loss: 0.3140 - val_acc: 0.8500\n",
+      "Epoch 34/35\n",
+      "29/29 [==============================] - 2s 59ms/step - loss: 0.0405 - acc: 1.0000 - val_loss: 0.3190 - val_acc: 0.8333\n",
+      "Epoch 35/35\n",
+      "29/29 [==============================] - 2s 57ms/step - loss: 0.0393 - acc: 1.0000 - val_loss: 0.2224 - val_acc: 0.9000\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(\n",
+    "      train_generator,\n",
+    "      steps_per_epoch=29,\n",
+    "      epochs=35,\n",
+    "      validation_data=validation_generator,\n",
+    "      validation_steps=3,\n",
+    "      verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "vanilla-radio",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[name: \"/device:CPU:0\"\n",
+      "device_type: \"CPU\"\n",
+      "memory_limit: 268435456\n",
+      "locality {\n",
+      "}\n",
+      "incarnation: 16838192518558930312\n",
+      ", name: \"/device:GPU:0\"\n",
+      "device_type: \"GPU\"\n",
+      "memory_limit: 6910041152\n",
+      "locality {\n",
+      "  bus_id: 1\n",
+      "  links {\n",
+      "  }\n",
+      "}\n",
+      "incarnation: 5839872070457136224\n",
+      "physical_device_desc: \"device: 0, name: GeForce RTX 3070, pci bus id: 0000:09:00.0, compute capability: 8.6\"\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.python.client import device_lib\n",
+    "print(device_lib.list_local_devices())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "monthly-judge",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Device mapping:\n",
+      "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce RTX 3070, pci bus id: 0000:09:00.0, compute capability: 8.6\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "sunrise-football",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "acc = history.history['acc']\n",
+    "val_acc = history.history['val_acc']\n",
+    "loss = history.history['loss']\n",
+    "val_loss = history.history['val_loss']\n",
+    "\n",
+    "epochs = range(len(acc))\n",
+    "\n",
+    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
+    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
+    "plt.title('Training and validation accuracy')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.figure()\n",
+    "\n",
+    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
+    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
+    "plt.title('Training and validation loss')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "white-rider",
+   "metadata": {},
+   "source": [
+    "Lots of overfitting even from the start, data augmentation can help fix this. But it requires GPU usage in order to be feasible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "composed-doubt",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 596 images belonging to 2 classes.\n",
+      "Found 73 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "train_datagen = ImageDataGenerator(\n",
+    "      rescale=1./255,\n",
+    "      rotation_range=40,\n",
+    "      width_shift_range=0.2,# data augmentation transformations\n",
+    "      height_shift_range=0.2,\n",
+    "      shear_range=0.2,\n",
+    "      zoom_range=0.2,\n",
+    "      horizontal_flip=True,\n",
+    "      fill_mode='nearest')\n",
+    "\n",
+    "# Note that the validation data should not be augmented!\n",
+    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "        # This is the target directory\n",
+    "        train_dir,\n",
+    "        # All images will be resized to 218x178\n",
+    "        target_size=(218, 178),\n",
+    "        batch_size=20,\n",
+    "        # Since we use binary_crossentropy loss, we need binary labels\n",
+    "        class_mode='binary')\n",
+    "\n",
+    "validation_generator = test_datagen.flow_from_directory(\n",
+    "        validation_dir,\n",
+    "        target_size=(218, 178),\n",
+    "        batch_size=20,\n",
+    "        class_mode='binary')\n",
+    "\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer=optimizers.RMSprop(lr=2e-5),\n",
+    "              metrics=['acc'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "eastern-melissa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/30\n",
+      "29/29 [==============================] - 5s 179ms/step - loss: 0.5866 - acc: 0.7069 - val_loss: 0.3745 - val_acc: 0.8333\n",
+      "Epoch 2/30\n",
+      "29/29 [==============================] - 5s 173ms/step - loss: 0.5038 - acc: 0.7535 - val_loss: 0.3365 - val_acc: 0.8500\n",
+      "Epoch 3/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.5150 - acc: 0.7292 - val_loss: 0.3872 - val_acc: 0.8167\n",
+      "Epoch 4/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4991 - acc: 0.7604 - val_loss: 0.3126 - val_acc: 0.8167\n",
+      "Epoch 5/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.5020 - acc: 0.7500 - val_loss: 0.3045 - val_acc: 0.9000\n",
+      "Epoch 6/30\n",
+      "29/29 [==============================] - 5s 170ms/step - loss: 0.4615 - acc: 0.7830 - val_loss: 0.3367 - val_acc: 0.8500\n",
+      "Epoch 7/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4737 - acc: 0.7812 - val_loss: 0.3246 - val_acc: 0.8833\n",
+      "Epoch 8/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.4718 - acc: 0.7691 - val_loss: 0.4177 - val_acc: 0.8333\n",
+      "Epoch 9/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4742 - acc: 0.7604 - val_loss: 0.3013 - val_acc: 0.8667\n",
+      "Epoch 10/30\n",
+      "29/29 [==============================] - 5s 170ms/step - loss: 0.4541 - acc: 0.7726 - val_loss: 0.3413 - val_acc: 0.8500\n",
+      "Epoch 11/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.4457 - acc: 0.7934 - val_loss: 0.3308 - val_acc: 0.8667\n",
+      "Epoch 12/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4183 - acc: 0.7914 - val_loss: 0.4123 - val_acc: 0.8167\n",
+      "Epoch 13/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.4291 - acc: 0.7882 - val_loss: 0.3907 - val_acc: 0.8333\n",
+      "Epoch 14/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4276 - acc: 0.7969 - val_loss: 0.3833 - val_acc: 0.8333\n",
+      "Epoch 15/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.4648 - acc: 0.7830 - val_loss: 0.2974 - val_acc: 0.8333\n",
+      "Epoch 16/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4490 - acc: 0.7899 - val_loss: 0.3490 - val_acc: 0.8500\n",
+      "Epoch 17/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4138 - acc: 0.8160 - val_loss: 0.2703 - val_acc: 0.9167\n",
+      "Epoch 18/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.4213 - acc: 0.7969 - val_loss: 0.3588 - val_acc: 0.8333\n",
+      "Epoch 19/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.4459 - acc: 0.7899 - val_loss: 0.3524 - val_acc: 0.8000\n",
+      "Epoch 20/30\n",
+      "29/29 [==============================] - 5s 173ms/step - loss: 0.4354 - acc: 0.7897 - val_loss: 0.3583 - val_acc: 0.8500\n",
+      "Epoch 21/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4161 - acc: 0.7969 - val_loss: 0.3354 - val_acc: 0.8500\n",
+      "Epoch 22/30\n",
+      "29/29 [==============================] - 5s 175ms/step - loss: 0.4113 - acc: 0.8108 - val_loss: 0.3735 - val_acc: 0.8333\n",
+      "Epoch 23/30\n",
+      "29/29 [==============================] - 5s 176ms/step - loss: 0.4326 - acc: 0.8090 - val_loss: 0.3121 - val_acc: 0.8333\n",
+      "Epoch 24/30\n",
+      "29/29 [==============================] - 5s 183ms/step - loss: 0.3955 - acc: 0.8207 - val_loss: 0.3023 - val_acc: 0.8833\n",
+      "Epoch 25/30\n",
+      "29/29 [==============================] - 5s 174ms/step - loss: 0.4033 - acc: 0.8056 - val_loss: 0.3970 - val_acc: 0.8167\n",
+      "Epoch 26/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4049 - acc: 0.8177 - val_loss: 0.3546 - val_acc: 0.8333\n",
+      "Epoch 27/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.3811 - acc: 0.8438 - val_loss: 0.3010 - val_acc: 0.8500\n",
+      "Epoch 28/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.3983 - acc: 0.8177 - val_loss: 0.3496 - val_acc: 0.8333\n",
+      "Epoch 29/30\n",
+      "29/29 [==============================] - 5s 172ms/step - loss: 0.4177 - acc: 0.8090 - val_loss: 0.3825 - val_acc: 0.8333\n",
+      "Epoch 30/30\n",
+      "29/29 [==============================] - 5s 171ms/step - loss: 0.3906 - acc: 0.8073 - val_loss: 0.3535 - val_acc: 0.8333\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(\n",
+    "      train_generator,\n",
+    "      steps_per_epoch=29,\n",
+    "      epochs=30,\n",
+    "      validation_data=validation_generator,\n",
+    "      validation_steps=3,\n",
+    "      verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "moderate-bottle",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save('covid_data_aug.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "addressed-stretch",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "acc = history.history['acc']\n",
+    "val_acc = history.history['val_acc']\n",
+    "loss = history.history['loss']\n",
+    "val_loss = history.history['val_loss']\n",
+    "\n",
+    "epochs = range(len(acc))\n",
+    "\n",
+    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
+    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
+    "plt.title('Training and validation accuracy')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.figure()\n",
+    "\n",
+    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
+    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
+    "plt.title('Training and validation loss')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "positive-silly",
+   "metadata": {},
+   "source": [
+    "## Fine-tuning\n",
+    "\n",
+    "Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
+    "Fine-tuning consists in unfreezing a few of the top layers \n",
+    "of a frozen model base used for feature extraction, and jointly training both the newly added part of the model (in our case, the \n",
+    "fully-connected classifier) and these top layers. This is called \"fine-tuning\" because it slightly adjusts the more abstract \n",
+    "representations of the model being reused, in order to make them more relevant for the problem at hand.\n",
+    "\n",
+    "![fine-tuning VGG16](https://s3.amazonaws.com/book.keras.io/img/ch5/vgg16_fine_tuning.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "played-emerald",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}