assignments/assignment_yourname_class8.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "metadata": {},
   "source": [
    "# Important Note\n",
    "\n",
    "This assignment is from the older (Keras-based) version of this course and is no longer used for my class. You can find the current asignments here: [updated assignments](https://github.com/jeffheaton/app_deep_learning/tree/main/assignments)"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "hTAVwaaOFuEf"
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   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), School of Engineering and Applied Science, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/).\n",
    "\n",
    "**Module 8 Assignment: Feature Engineering**\n",
    "\n",
    "**Student Name: Your Name**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4YUdI4CcFuEg"
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   "source": [
    "# Assignment Instructions\n",
    "\n",
    "This assignment is similar to assignment 5, except that you must use feature engineering to solve it.  I provide you with a dataset that contains dimensions and the quality of items of specific shapes.  With the values of 'height', 'width', 'depth'. 'shape', and 'quality' you should try to predict the cost of these items.  You should be able to match very close to solution file, if you feature engineer correctly.  To get full credit your average cost should not be more than 50 off from the solution.  The autocorrector will let you know if you are in this range.\n",
    "\n",
    "You can find all of the needed CSV files here:\n",
    "\n",
    "* [Shapes - Training](https://data.heatonresearch.com/data/t81-558/datasets/shapes-train.csv)\n",
    "* [Shapes - Submit](https://data.heatonresearch.com/data/t81-558/datasets/shapes-test.csv)\n",
    "\n",
    "Use the training file to train your neural network and submit results for for the data contained in the test/submit file."
   ]
  },
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   "execution_count": 1,
   "metadata": {
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    "id": "9ZvFFOR5A-Wo",
    "outputId": "79139a73-499a-47fd-e623-cfbd26c8f9f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n",
      "Note: using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive', force_remount=True)\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "4qrsT_KZFuEh"
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   "source": [
    "# Assignment Submit Function\n",
    "\n",
    "You will submit the 10 programming assignments electronically.  The following submit function can be used to do this.  My server will perform a basic check of each assignment and let you know if it sees any basic problems. \n",
    "\n",
    "**It is unlikely that should need to modify this function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "43KOAL0OFuEi"
   },
   "outputs": [],
   "source": [
    "import base64\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import PIL\n",
    "import PIL.Image\n",
    "import io\n",
    "\n",
    "# This function submits an assignment.  You can submit an assignment as much as you like, only the final\n",
    "# submission counts.  The paramaters are as follows:\n",
    "# data - List of pandas dataframes or images.\n",
    "# key - Your student key that was emailed to you.\n",
    "# no - The assignment class number, should be 1 through 1.\n",
    "# source_file - The full path to your Python or IPYNB file.  This must have \"_class1\" as part of its name.  \n",
    "# .             The number must match your assignment number.  For example \"_class2\" for class assignment #2.\n",
    "def submit(data,key,no,source_file=None):\n",
    "    raise Exception(\"If you are a current student, you are using an old version of this assignment. Refer to: https://github.com/jeffheaton/app_deep_learning/tree/main/assignments\")\n",
    "    if source_file is None and '__file__' not in globals(): raise Exception('Must specify a filename when a Jupyter notebook.')\n",
    "    if source_file is None: source_file = __file__\n",
    "    suffix = '_class{}'.format(no)\n",
    "    if suffix not in source_file: raise Exception('{} must be part of the filename.'.format(suffix))\n",
    "    with open(source_file, \"rb\") as image_file:\n",
    "        encoded_python = base64.b64encode(image_file.read()).decode('ascii')\n",
    "    ext = os.path.splitext(source_file)[-1].lower()\n",
    "    if ext not in ['.ipynb','.py']: raise Exception(\"Source file is {} must be .py or .ipynb\".format(ext))\n",
    "    payload = []\n",
    "    for item in data:\n",
    "        if type(item) is PIL.Image.Image:\n",
    "            buffered = BytesIO()\n",
    "            item.save(buffered, format=\"PNG\")\n",
    "            payload.append({'PNG':base64.b64encode(buffered.getvalue()).decode('ascii')})\n",
    "        elif type(item) is pd.core.frame.DataFrame:\n",
    "            payload.append({'CSV':base64.b64encode(item.to_csv(index=False).encode('ascii')).decode(\"ascii\")})\n",
    "    r= requests.post(\"https://api.heatonresearch.com/assignment-submit\",\n",
    "        headers={'x-api-key':key}, json={ 'payload': payload,'assignment': no, 'ext':ext, 'py':encoded_python})\n",
    "    if r.status_code==200:\n",
    "        print(\"Success: {}\".format(r.text))\n",
    "    else: print(\"Failure: {}\".format(r.text))"
   ]
  },
  {
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    "collapsed": true,
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   },
   "source": [
    "# Assignment #8 Sample Code\n",
    "\n",
    "The following code provides a starting point for this assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lIB3MmKuFuEn",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from scipy.stats import zscore\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation, Dropout\n",
    "import pandas as pd\n",
    "import io\n",
    "import requests\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import KFold\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from scipy.stats import zscore\n",
    "\n",
    "# This is your student key that I emailed to you at the beginnning of the semester.\n",
    "key = \"Gx5en9cEVvcsjuijujijisdsdZO4PsI32sgldAXj\"  # This is an example key and will not work.\n",
    "\n",
    "# You must also identify your source file.  (modify for your local setup)\n",
    "file='/content/drive/My Drive/Colab Notebooks/assignment_solution_class8.ipynb'  # Google CoLab\n",
    "# file='C:\\\\Users\\\\jeffh\\\\projects\\\\t81_558_deep_learning\\\\assignments\\\\assignment_yourname_class8.ipynb'  # Windows\n",
    "# file='/Users/jheaton/projects/t81_558_deep_learning/assignments/assignment_yourname_class8.ipynb'  # Mac/Linux\n",
    "\n",
    "# Begin assignment\n",
    "df_train = pd.read_csv(\"https://data.heatonresearch.com/data/t81-558/datasets/shapes-train.csv\")\n",
    "df_submit = pd.read_csv(\"https://data.heatonresearch.com/data/t81-558/datasets/shapes-test.csv\")\n",
    "\n",
    "submit(source_file=file,data=[df_submit],key=key,no=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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