1_introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to our Submission System\n",
    "\n",
    "In this exercise you will:\n",
    "\n",
    "- Learn how to work with IPython notebooks\n",
    "- Get a small numpy introduction\n",
    "- Learn about our submission system\n",
    "\n",
    "Run cells with `Shift + Enter`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. IPython Notebooks\n",
    "An IPython notebook is made up of a number of cells. Each cell can contain Python code. You can execute a cell by clicking on it and pressing `Shift-Enter`. When you do so, the code in the cell will be executed, and the output of the cell will be displayed beneath the cell. For example, after running the first cell the notebook looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "x = 5\n",
    "print(x+3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Global variables are shared between cells. Therefore, we can access `x` in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "print(x+10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By convention, IPython notebooks are expected to be run from top to bottom. Failing to execute some cells or executing cells out of order can result in errors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-e6faf7a9fd0a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# This does not work\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y' is not defined"
     ]
    }
   ],
   "source": [
    "# This does not work\n",
    "print(x+y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After executing this cell, you can re-evaluate the cell above\n",
    "y = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can edit cells by double clicking them (the color on the left changes from blue to green) and change the text (not really useful) or code (will be useful)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have not changed the cell\n"
     ]
    }
   ],
   "source": [
    "print('I have not changed the cell')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please make sure that you are using the correct python environment. You can print out the environment path with the following cell. If you use python2 to work on the exercises and submit your model, you will encounter errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.6 (default, Jan  8 2020, 13:42:34) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.version)\n",
    "if sys.version_info <= (3, 7):\n",
    "    print(\"you are using an older version of python. We expect python 3.7.x \\nYou can continue with your version but it might cause some issues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you have modified an IPython notebook for one of the assignments by modifying or executing some of its cells, remember to __save your changes__!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 2. Imports\n",
    "\n",
    "We start every notebook with a cell containg the important imports. So the first cell of every notebook is the import cell, as this needs to be executed before running any code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "import random\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 5.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Numpy Introduction\n",
    "\n",
    "Numpy is a high performance framework for operations on multi dimensional arrays. It would be a nice tool in your kitty for working on later exercises and your life as a Data Scientist in general. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n",
      "(2, 3)\n"
     ]
    }
   ],
   "source": [
    "# Create a simple (2, 3) numpy array\n",
    "# [[ 1  2  3]\n",
    "#  [ 4  5  6]]\n",
    "a = np.array([[1,2,3], [4,5,6]])\n",
    "\n",
    "# Print the data type of this array\n",
    "print(a.dtype)\n",
    "\n",
    "# print the shape of this array\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [4 5]]\n",
      "(2, 2)\n"
     ]
    }
   ],
   "source": [
    "# Slicing operation \n",
    "# Let us take the first two columns from the original array and save it in a new one\n",
    "b = a[:, :2]\n",
    "print(b)\n",
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select elements which satisfy a condition\n",
    "# Using numpy array makes such a selection trivial\n",
    "mask = a > 1\n",
    "new_array = a[mask]\n",
    "new_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do the same thing in a single step\n",
    "c = a[a>1]\n",
    "assert np.all(new_array == c) #  np.all() to indicate that all the values need to match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6  8]\n",
      " [10 12]]\n",
      "[[ 6  8]\n",
      " [10 12]]\n",
      "[[-4 -4]\n",
      " [-4 -4]]\n",
      "[[-4 -4]\n",
      " [-4 -4]]\n",
      "[[ 5 12]\n",
      " [21 32]]\n",
      "[[ 5 12]\n",
      " [21 32]]\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "# Mathematical operations\n",
    "x = np.array([[1,2],[3,4]])\n",
    "y = np.array([[5,6],[7,8]])\n",
    "\n",
    "# Elementwise sum; both produce the array\n",
    "# [[ 6.0  8.0]\n",
    "#  [10.0 12.0]]\n",
    "print(x + y)\n",
    "print(np.add(x, y))\n",
    "\n",
    "# Elementwise difference; both produce the array\n",
    "# [[-4.0 -4.0]\n",
    "#  [-4.0 -4.0]]\n",
    "print(x - y)\n",
    "print(np.subtract(x, y))\n",
    "\n",
    "# Elementwise product; both produce the array\n",
    "# [[ 5.0 12.0]\n",
    "#  [21.0 32.0]]\n",
    "print(x * y)\n",
    "print(np.multiply(x, y))\n",
    "\n",
    "# Elementwise division; both produce the array\n",
    "# [[ 0.2         0.33333333]\n",
    "#  [ 0.42857143  0.5       ]]\n",
    "print(x / y)\n",
    "print(np.divide(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         1.41421356]\n",
      " [1.73205081 2.        ]]\n",
      "[[ 2.71828183  7.3890561 ]\n",
      " [20.08553692 54.59815003]]\n"
     ]
    }
   ],
   "source": [
    "# Element wise operations\n",
    "# Elementwise square root; produces the array\n",
    "# [[ 1.          1.41421356]\n",
    "#  [ 1.73205081  2.        ]]\n",
    "print(np.sqrt(x))\n",
    "\n",
    "# Element wise exponential operation\n",
    "#[[ 2.71828183  7.3890561 ]\n",
    "#  [20.08553692 54.59815003]] \n",
    "print(np.exp(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A detailed summary of numpy is beyond the scope of this assignment. Please take a look at http://docs.scipy.org/doc/numpy/reference/ for further reference. For this exercise, the tasks we have covered should be enough."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Displaying an Image #\n",
    "One of the convenient things about jupyter notebooks is the ability to display images. You will not have any new clanky windows opening up and everything shall be inline. Let us take a look at a simple example here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbe0c754850>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the image first\n",
    "image = Image.open('images/sample_image.jpg')\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <h3>Inline Question</h3>\n",
    "    <p>Sometimes we ask inline questions. They should help you to think about the subject that are relevant for the exam.</p>\n",
    "    <p>**Your answer:** You can write your answer here to save it in the ipython notebook so you can review it before the exam. Double click a cell to edit it and enter your answer.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. External Files\n",
    "Most of your work will involve implementing certain functions in external python files. These are contained in the `exercise_code` folder. Check out the `Dummy` class in `exercise_code/networks/dummy.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from exercise_code.networks.dummy import Dummy\n",
    "# Instantiate a model\n",
    "model = Dummy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Dummy Machine\n",
    "\n",
    "Usually, we ask you to train and submit models. You don't need to train anything fancy in this exercise, this is just a sample code to familarize you with file structure of our codebase.\n",
    "\n",
    "You need to make some changes to the `forward()` method in `exercise_01/exercise_code/networks/dummy.py`and set `x` to any integer that is larger than 59. We highlight all locations where you should modify the code with boxes.\n",
    "\n",
    "To edit these files you can use any IDE or text editor but __make sure that you don't rename or remove classes or functions__.\n",
    "\n",
    "In this submission you get a score depending upon the number of test cases you pass. Usually, the total points you can secure is between __0 - 100__. For this exercise, you have __one test cases__ which depends on the output of the `forward()` function. You can check the `exercise_01/exercise_code/tests` folder to have a general feel about the unit tests. Please remember that these test cases are just a sample and the submission server has a set of different test cases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DummyTest passed.\n",
      "The score of your dummy machine is: 66\n"
     ]
    }
   ],
   "source": [
    "from exercise_code.tests.dummy_tests import DummyTest\n",
    "\n",
    "test_cases = DummyTest(model)\n",
    "test_cases()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Model Saving\n",
    "Now you need to save the model. We provide you with all the functionality, so you will only need to execute the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "    <h3>Warning</h3>\n",
    "    <p>You might get an error like this:</p>\n",
    "    <p>PicklingError: Can't pickle `<class 'exercise_code.test_model.TestModel'>`: it's not the same object as exercise_code.test_class.TestSubmission</p>\n",
    "    <p>The reason is that we are using autoreload and working on this class during the notebook session. If you get this error simply restart the kernel and rerun the whole script (Kernel -> Restart & Run All) or only the important cells for generating your model.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Zip File Creation\n",
    "Run the following cell to create the zip file for submission, which contains all the exercise code, models, and ipynb notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "relevant folders: ['exercise_code', 'models']\n",
      "notebooks files: ['2_colab_introduction.ipynb', '1_introduction.ipynb']\n",
      "Adding folder exercise_code\n",
      "Adding folder models\n",
      "Adding notebook 2_colab_introduction.ipynb\n",
      "Adding notebook 1_introduction.ipynb\n",
      "Zipping successful! Zip is stored under: /Users/meiqiliu/Downloads/exercise_01/exercise_01/exercise1.zip\n"
     ]
    }
   ],
   "source": [
    "from exercise_code.submit import submit_exercise\n",
    "\n",
    "submit_exercise('exercise1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission Goals\n",
    "\n",
    "- Goal: implement dummy machine \n",
    "- Threshold to this exercise: 60\n",
    "- Submission start: __Apr 22, 2021 13:00__.\n",
    "- Submission deadline: __Apr 28, 2021 15:59__. \n",
    "- You can make multiple submission until the deadline. Your __best submission__ will be considered for bonus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission Instructions\n",
    "\n",
    "Now, that you have completed the necessary parts in the notebook, you can go on and submit your files.\n",
    "\n",
    "1. Go on [our submission page](https://i2dl.vc.in.tum.de/), register for an account and login. We use your matriculation number and send an email with the login details to the mail account associated. When in doubt, login into tum-online and check your mails there. You will get an id which we need in the next step.\n",
    "2. Log into [our submission page](https://i2dl.vc.in.tum.de/) with your account details and upload the zip file.\n",
    "3. Your submission will be evaluated by our system and you will get feedback about the performance of it. You will get an email with your score as well as a message if you have surpassed the threshold.\n",
    "4. Within the working period, you can submit as many solutions as you want to get the best possible score."
   ]
  },
  {
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   "metadata": {},
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    "<img src=\"./images/i2dlsubmission.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Colab\n",
    "\n",
    "We have also uploaded a notebook which introduces you to basic commands of Google Colab. Although you would not need it for this exercise but it will come in handy for the future exercises. If you are interested, you can check `2_colab_introduction.ipynb` notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "Congratulations!! You have finished the introductory notebook.\n",
    "Some references to learn how to use basic python libraries (Numpy, Scipy and Matplotlib) that you would need for this course:\n",
    "\n",
    "[1] https://docs.python.org/3/tutorial/\n",
    "\n",
    "[2] http://cs231n.github.io/python-numpy-tutorial/"
   ]
  }
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