full_data_2_cnn3d_pipeline.ipynb

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  "metadata": {
    "colab": {
      "name": "full-data-2-cnn3d-pipeline.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tracijo32/astralar/blob/master/full_data_2_cnn3d_pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrpxSwOZG6LI",
        "colab_type": "text"
      },
      "source": [
        "# OnTarget\n",
        " \n",
        "## Full 3D Convolutional Neural Network Pipeline for Walabot Data Analysis and Firearm Clip Detection\n",
        "\n",
        "### Traci Johnson\n",
        "#### Insight Data Science, in collaboration with Astral AR\n",
        "\n",
        "In this Python notebook, we will import and reduce the data from Walabot 2D image slices to recreate 3D data cubes of radio frequency data and perform some machine learning with a neural network to classify the presence of loaded vs unloaded gun clips vs a control object.\n",
        "\n",
        "I perform this work on Google Colaboratory in order to access its free GPU computing power. Yet, I host all relevant files on my own personal [Github](https://github.com/tracijo32/astralar). Colab has the ability to connect to Github API and commit files to your repo, as well as upload Jupyter notebooks from any github repo to save to your Google Drive. Yay! Cloud computing!\n",
        "\n",
        "Let's get started."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gC5s4bHaGf5j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qPGUozxdGsJM",
        "colab_type": "text"
      },
      "source": [
        "I have copied the data files to my Google Drive for easy access."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyEBE8OdKyg2",
        "colab_type": "code",
        "outputId": "c9a24880-b166-4d6f-bad1-b6353260c7d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsuSVsF8LLFN",
        "colab_type": "text"
      },
      "source": [
        "The following script will read in the data from the raw CSV files dumped from the Walabot detector. Each image is located on a single line of the file, with a list of lists embedding the 2D matrix of the $R$ by $\\phi$ image. There are a few other redundant columns, but the only most important indicates which slice of $\\theta$ the image come froms. The Walabot records which ever 2D slice in $\\theta$ has the highest signal at the moment of recording. Over the course of a 30 minute recording, that can vary, so we have a bunch of images of most of the slices, with the slices with highest signals having the most data. Despite the Walabot settings being identical each time the data are recorded, the slice values for $\\theta$ (recorded in radians times 100) are slightly different, my guess due to the internal calibration that occurs when the Walabot is turned on. Therefore, I convert the image slice values to an integer, ranging from 0 to 11 in order of increasing $\\theta$. Finally, some of the slices produce frames of all zeros, which aren't super useful, so I filter those out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cyhd3rZK3FF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "root_path = 'gdrive/My Drive/Walabot-Data/data-csv/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urVNPwzWLAbZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def read_rawimage(file,slices=np.arange(-45,45,8),nozero=True):\n",
        "    df = pd.read_csv(file,header=None)\n",
        "    image_cols = df.iloc[:,:-4].astype(str)\n",
        "    last_col = df.iloc[:,-2]/100*(180/np.pi)\n",
        "    new_last_col = last_col.apply(lambda x: np.abs(x-slices).argmin())\n",
        "    image = image_cols.apply(lambda col: np.array(eval(','.join(col.to_list()))).astype(float),axis=1)\n",
        "    new_df = pd.DataFrame({'Image':image,'Slice':new_last_col})\n",
        "    if nozero:\n",
        "        new_df = new_df[new_df['Image'].apply(lambda x: np.max(x)) > 0]\n",
        "    return new_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZGBPIGaOqUK",
        "colab_type": "text"
      },
      "source": [
        "Let's import the data the full magazine, empty magazine, and a glass pipe control object. The data were taken on June 19 and 20, 2019, hence the file names."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zK9mhjPtObPZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fullclip1 = read_rawimage(root_path+'data-RawImage-fullMag-0619.csv')\n",
        "emptyclip1 = read_rawimage(root_path+'data-RawImage-empty-0619.csv')\n",
        "pipe1 = read_rawimage(root_path+'data-RawImage-pipe-0619.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sp5XMMR-OejX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fullclip2 = read_rawimage(root_path+'data-RawImage-fullMag-0620.csv')\n",
        "emptyclip2 = read_rawimage(root_path+'data-RawImage-empty-0620.csv')\n",
        "pipe2 = read_rawimage(root_path+'data-RawImage-pipe-0620.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hf7Ba2nSOn-_",
        "colab_type": "text"
      },
      "source": [
        "Let's visualize what these data sets look like by playing each slice in time sequence. This will be similar to what we see in the Rasberry Pi GUI when recording the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iLiubtz0QGkt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTcriRXIfwEr",
        "colab_type": "text"
      },
      "source": [
        "We're going to download the celluloid package, which is a more intuitive wrapper package for matplotlib's animation package. Literally, it's just \"make plot, take snap shot, now put all snaps into a video\". Sadly, mpl's API really fails here with requiring you to start with a shell and write an update function using functions you would normally never use in mpl in designing your plots. Just pointing that out. Celluloid basically just allows you to FOR loop it like normals."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-0oKoPtQT7Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "b44ae6f2-340c-4944-af3a-18e3de57a682"
      },
      "source": [
        "pip install celluloid"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting celluloid\n",
            "  Downloading https://files.pythonhosted.org/packages/60/a7/7fbe80721c6f1b7370c4e50c77abe31b4d5cfeb58873d4d32f48ae5a0bae/celluloid-0.2.0-py3-none-any.whl\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from celluloid) (3.0.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->celluloid) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from matplotlib->celluloid) (1.16.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->celluloid) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->celluloid) (2.4.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->celluloid) (2.5.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->celluloid) (41.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->celluloid) (1.12.0)\n",
            "Installing collected packages: celluloid\n",
            "Successfully installed celluloid-0.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NxhvSL-oQX-u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from celluloid import Camera"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntel8NXjQgb7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dfs = [fullclip1,emptyclip1,pipe1,fullclip2,emptyclip2,pipe2]\n",
        "titles = ['fullclip1','emptyclip1','pipe1','fullclip2','emptyclip2','pipe2']\n",
        "lenframes = [len(df) for df in dfs]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4k6R1z1RMFu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fig,ax = plt.subplots(2,3,sharex=True,sharey=True)\n",
        "cam = Camera(fig)\n",
        "for frame in range(max(lenframes)):\n",
        "    percent = frame/max(lenframes)\n",
        "    for i in range(6):\n",
        "        n = round(lenframes[i]*percent)\n",
        "        if n == lenframes[i]:\n",
        "            n = n-1\n",
        "        _ = ax.flat[i].imshow(dfs[i].iloc[n,0])\n",
        "    cam.snap()\n",
        "_ = [ax.flat[i].set_title(titles[i]) for i in range(6)]\n",
        "_ = [ax.flat[i].set_xticks([]) for i in range(6)]\n",
        "_ = [ax.flat[i].set_yticks([]) for i in range(6)]\n",
        "anim = cam.animate(interval=50,repeat_delay=2000);\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T77yn3ymUND7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "7adb840d-7c5d-48b4-d54d-d98cee61cdef"
      },
      "source": [
        "from IPython.display import HTML\n",
        "HTML(anim.to_html5_video())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hACKo0IU5Jk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "anim.save(root_path + 'timesequence.mp4')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXL9oATw0DAP",
        "colab_type": "text"
      },
      "source": [
        "## Train/test split\n",
        "We have to carefully split the data set because some of the data only have a few images per slice. We have to stratify based on slice."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKa_TdaPksgv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_test_split_images(df,test_size=0.2,limit=5,random_state=123):\n",
        "    train = []\n",
        "    test = []\n",
        "    for slc in df['Slice'].unique():\n",
        "        subset = df[df['Slice']==slc]\n",
        "        if len(subset) < limit:\n",
        "            split = int(len(subset)*0.5)\n",
        "        else:\n",
        "            split = int(len(subset)*test_size)\n",
        "        shuffle = subset.sample(frac=1,replace=False,random_state=random_state)\n",
        "        train.append(shuffle.iloc[split:])\n",
        "        test.append(shuffle.iloc[:split])\n",
        "    return pd.concat(train),pd.concat(test)\n",
        "    \n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EhhSEG-2wqO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fc1_train,fc1_test = train_test_split_images(fullclip1)\n",
        "fc2_train,fc2_test = train_test_split_images(fullclip2)\n",
        "ec1_train,ec1_test = train_test_split_images(emptyclip1)\n",
        "ec2_train,ec2_test = train_test_split_images(emptyclip2)\n",
        "pp1_train,pp1_test = train_test_split_images(pipe1)\n",
        "pp2_train,pp2_test = train_test_split_images(pipe2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuBegcws-Nvx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del fullclip1,fullclip2,emptyclip1,emptyclip2,pipe1,pipe2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LM4R_H2UAza",
        "colab_type": "text"
      },
      "source": [
        "In order to artificially inflate the size of the data set, I perform some translations of the data cube in x and y."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hj8o4d6p3RHn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_augmented_images(df,n_images=1000,n_slices=12,\n",
        "                              xshift=10,yshift=10,zshift=1,\n",
        "                              xflip=False,yflip=False,zflip=False,\n",
        "                              xcrop=[20,80],ycrop=[0,50],zcrop=[1,11]):\n",
        "    xdim,ydim = df['Image'].iloc[0].shape\n",
        "    full_stack = []\n",
        "    for slc in range(n_slices):\n",
        "        df_slc = df[df['Slice']==slc]\n",
        "        if len(df_slc) == 0:\n",
        "            sample_slc = np.zeros((n_images,xdim,ydim))\n",
        "        else:\n",
        "            sample_slc = np.stack(df_slc.sample(n_images,replace=True)['Image'].to_list())\n",
        "        full_stack.append(sample_slc)\n",
        "        \n",
        "    full_stack = np.stack(full_stack,axis=3)\n",
        "    \n",
        "    x = np.random.randint(xshift,size=n_images)\n",
        "    y = np.random.randint(yshift,size=n_images)\n",
        "    z = np.random.randint(zshift,size=n_images)\n",
        "    xx = np.random.randint(2)\n",
        "    yy = np.random.randint(2)\n",
        "    zz = np.random.randint(2)\n",
        "    for i in range(n_images):\n",
        "        orig = full_stack[i,:,:,:]\n",
        "        aug = np.zeros_like(orig)\n",
        "        aug[x[i]:,y[i]:,z[i]:] = orig[:xdim-x[i],:ydim-y[i],:n_slices-z[i]]\n",
        "        if xflip and xx==1:\n",
        "            aug = aug[::-1,:,:]\n",
        "        if yflip and yy==1:\n",
        "            aug = aug[:,::-1,:]\n",
        "        if zflip and zz==1:\n",
        "            aug = aug[:,:,::-1]\n",
        "        full_stack[i,:,:,:] = aug\n",
        "    \n",
        "    full_stack = full_stack[:,xcrop[0]:xcrop[1],ycrop[0]:ycrop[1],zcrop[0]:zcrop[1]]\n",
        "    \n",
        "    return full_stack\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3aCEyXb5_vm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fc1_train_stack = generate_augmented_images(fc1_train,n_images=len(fc1_train)*3)\n",
        "del fc1_train\n",
        "fc2_train_stack = generate_augmented_images(fc2_train,n_images=len(fc2_train)*3)\n",
        "del fc2_train\n",
        "ec1_train_stack = generate_augmented_images(ec1_train,n_images=len(ec1_train)*3)\n",
        "del ec1_train\n",
        "ec2_train_stack = generate_augmented_images(ec2_train,n_images=len(ec2_train)*3)\n",
        "del ec2_train\n",
        "pp1_train_stack = generate_augmented_images(pp1_train,n_images=len(pp1_train)*3)\n",
        "del pp1_train\n",
        "pp2_train_stack = generate_augmented_images(pp2_train,n_images=len(pp2_train)*3)\n",
        "del pp2_train\n",
        "\n",
        "fc1_test_stack = generate_augmented_images(fc1_test,n_images=len(fc1_test)*3)\n",
        "del fc1_test\n",
        "fc2_test_stack = generate_augmented_images(fc2_test,n_images=len(fc2_test)*3)\n",
        "del fc2_test\n",
        "ec1_test_stack = generate_augmented_images(ec1_test,n_images=len(ec1_test)*3)\n",
        "del ec1_test\n",
        "ec2_test_stack = generate_augmented_images(ec2_test,n_images=len(ec2_test)*3)\n",
        "del ec2_test\n",
        "pp1_test_stack = generate_augmented_images(pp1_test,n_images=len(pp1_test)*3)\n",
        "del pp1_test\n",
        "pp2_test_stack = generate_augmented_images(pp2_test,n_images=len(pp2_test)*3)\n",
        "del pp2_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rdYdbHf6EoX",
        "colab_type": "code",
        "outputId": "dbf2bad2-b536-4d2a-c68a-2a37eb695493",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ec1_train_stack.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24, 60, 50, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QacHmpRF6398",
        "colab_type": "code",
        "outputId": "60c29e86-d0bd-4cfc-de20-f3fd349e079f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ec2_train_stack.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1269, 60, 50, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ij9n80b65N-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fc_train = np.vstack([fc1_train_stack,fc2_train_stack])\n",
        "ec_train = np.vstack([ec1_train_stack,ec2_train_stack])\n",
        "pp_train = np.vstack([pp1_train_stack,pp2_train_stack])\n",
        "del fc1_train_stack,fc2_train_stack,ec1_train_stack,ec2_train_stack,pp1_train_stack,pp2_train_stack\n",
        "\n",
        "fc_test = np.vstack([fc1_test_stack,fc2_test_stack])\n",
        "ec_test = np.vstack([ec1_test_stack,ec2_test_stack])\n",
        "pp_test = np.vstack([pp1_test_stack,pp2_test_stack])\n",
        "del fc1_test_stack,fc2_test_stack,ec1_test_stack,ec2_test_stack,pp1_test_stack,pp2_test_stack"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZXH3SvlULlp",
        "colab_type": "text"
      },
      "source": [
        "Now to add the labels to the data frame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVKzNqMo7oMF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = np.array([0]*len(fc_train) + [1]*len(ec_train) + [2]*len(pp_train))\n",
        "y_test = np.array([0]*len(fc_test) + [1]*len(ec_test) + [2]*len(pp_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPJ2iYJgA2ZF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = np.vstack([fc_train,ec_train,pp_train])\n",
        "X_test = np.vstack([fc_test,ec_test,pp_test])\n",
        "del fc_train,ec_train,pp_train,fc_test,ec_test,pp_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3Ia61ZCCDdE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.reshape(X_train.shape + (1,))\n",
        "X_test = X_test.reshape(X_test.shape + (1,))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J93n-iSHTjPI",
        "colab_type": "text"
      },
      "source": [
        "## CNN architecture"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ns-tj00xUSjm",
        "colab_type": "text"
      },
      "source": [
        "I downloaded the settings for google colab from a coursera course I took. The files that are downloaded are not needed, I'm just too lazy to update the code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnGYdOsBBEpl",
        "colab_type": "code",
        "outputId": "5490da35-3f02-438e-e7e7-e253041b59cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "! shred -u setup_google_colab.py\n",
        "! wget https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py -O setup_google_colab.py\n",
        "import setup_google_colab\n",
        "# please, uncomment the week you're working on\n",
        "# setup_google_colab.setup_week1()\n",
        "# setup_google_colab.setup_week2()\n",
        "setup_google_colab.setup_week3()\n",
        "# setup_google_colab.setup_week4()\n",
        "# setup_google_colab.setup_week5()\n",
        "# setup_google_colab.setup_week6()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-07-01 06:29:24--  https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py\n",
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            "Length: 3792 (3.7K) [text/plain]\n",
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            "\n",
            "**************************************************\n",
            "102flowers.tgz\n",
            "**************************************************\n",
            "imagelabels.mat\n",
            "**************************************************\n",
            "inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "**************************************************\n",
            "cifar-10-batches-py.tar.gz\n",
            "**************************************************\n",
            "mnist.npz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NSrnqKSlBFzQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "sys.path.append(\"..\")\n",
        "import download_utils"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Neil-RWSBXeS",
        "colab_type": "code",
        "outputId": "d2a94707-d726-406e-adba-5d21e72b2ab2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import backend as K\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)\n",
        "print(keras.__version__)\n",
        "import grading_utils\n",
        "import keras_utils\n",
        "from keras_utils import reset_tf_session"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.14.0\n",
            "2.0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QsUNxq9Ud4A",
        "colab_type": "text"
      },
      "source": [
        "Convert the labels into one-hot encoded arrays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WU7qIuVBbeH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train2 = keras.utils.to_categorical(y_train)\n",
        "y_test2 = keras.utils.to_categorical(y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBBcWaVNBgOV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import necessary building blocks\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv3D, Conv2D, MaxPooling2D, MaxPooling3D, Flatten, Dense, Activation, Dropout\n",
        "from keras.layers.advanced_activations import LeakyReLU"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYiG3ZOIUnpP",
        "colab_type": "text"
      },
      "source": [
        "Here's where we set up the model: We've got two 3D convolutional layers and a max pool, followed by a flattening, a dense layer, and finally another dense layer of three neurons for the classification."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7InsrXVnBiDu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_model():\n",
        "    \"\"\"\n",
        "    Define your model architecture here.\n",
        "    Returns `Sequential` model.\n",
        "    \"\"\"\n",
        "    model = Sequential()\n",
        "    \n",
        "    model.add(Conv3D(16,(2,2,2),padding='same',input_shape=(60,50,10,1)))\n",
        "    model.add(LeakyReLU(0.1))\n",
        "    model.add(Conv3D(32,(2,2,2),padding='same'))\n",
        "    model.add(LeakyReLU(0.1))\n",
        "    model.add(MaxPooling3D((2,2,2)))\n",
        "              \n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(256))\n",
        "    model.add(Dense(3))\n",
        "    model.add(Activation('softmax'))\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kt_nebdBj8x",
        "colab_type": "code",
        "outputId": "ad1423cb-3b81-48ec-bb05-a7797a82fc7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "# describe model\n",
        "s = reset_tf_session()  # clear default graph\n",
        "model = make_model()\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv3d_1 (Conv3D)            (None, 60, 50, 10, 16)    144       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 60, 50, 10, 16)    0         \n",
            "_________________________________________________________________\n",
            "conv3d_2 (Conv3D)            (None, 60, 50, 10, 32)    4128      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 60, 50, 10, 32)    0         \n",
            "_________________________________________________________________\n",
            "max_pooling3d_1 (MaxPooling3 (None, 30, 25, 5, 32)     0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 120000)            0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 256)               30720256  \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 3)                 771       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 3)                 0         \n",
            "=================================================================\n",
            "Total params: 30,725,299\n",
            "Trainable params: 30,725,299\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvLPT8qxBlef",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "INIT_LR = 5e-3  # initial learning rate\n",
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10\n",
        "\n",
        "s = reset_tf_session()  # clear default graph\n",
        "# don't call K.set_learning_phase() !!! (otherwise will enable dropout in train/test simultaneously)\n",
        "model = make_model()  # define our model\n",
        "\n",
        "# prepare model for fitting (loss, optimizer, etc)\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',  # we train 10-way classification\n",
        "    optimizer=keras.optimizers.adamax(lr=INIT_LR),  # for SGD\n",
        "    metrics=['accuracy']  # report accuracy during training\n",
        ")\n",
        "\n",
        "# scheduler of learning rate (decay with epochs)\n",
        "def lr_scheduler(epoch):\n",
        "    return INIT_LR * 0.9 ** epoch\n",
        "\n",
        "# callback for printing of actual learning rate used by optimizer\n",
        "class LrHistory(keras.callbacks.Callback):\n",
        "    def on_epoch_begin(self, epoch, logs={}):\n",
        "        print(\"Learning rate:\", K.get_value(model.optimizer.lr))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhvjhZrWBrXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# we will save model checkpoints to continue training in case of kernel death\n",
        "model_filename = 'walabot.{0:03d}.hdf5'\n",
        "last_finished_epoch = None\n",
        "\n",
        "#### uncomment below to continue training from model checkpoint\n",
        "#### fill `last_finished_epoch` with your latest finished epoch\n",
        "# from keras.models import load_model\n",
        "# s = reset_tf_session()\n",
        "# last_finished_epoch = 7\n",
        "# model = load_model(model_filename.format(last_finished_epoch))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nB72xucpBuOL",
        "colab_type": "code",
        "outputId": "21e03c59-dab2-4093-f397-d31b4e10cd18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# fit model\n",
        "model.fit(\n",
        "    X_train, y_train2,  # prepared data\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks=[keras.callbacks.LearningRateScheduler(lr_scheduler), \n",
        "               LrHistory(), \n",
        "               keras_utils.TqdmProgressCallback(),\n",
        "               keras_utils.ModelSaveCallback(model_filename)],\n",
        "    validation_data=(X_test, y_test2),\n",
        "    shuffle=True,\n",
        "    verbose=0,\n",
        "    initial_epoch=last_finished_epoch or 0\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate: 0.005\n",
            "\n",
            "Epoch 1/10\n",
            "**************************************************\n",
            "loss: 2.6185; acc: 0.8343; val_loss: 2.8930; val_acc: 0.8205\n",
            "Model saved in walabot.000.hdf5\n",
            "Learning rate: 0.0045\n",
            "\n",
            "Epoch 2/10\n",
            "**************************************************\n",
            "loss: 1.7982; acc: 0.8881; val_loss: 2.6938; val_acc: 0.8322\n",
            "Model saved in walabot.001.hdf5\n",
            "Learning rate: 0.00405\n",
            "\n",
            "Epoch 3/10\n",
            "**************************************************\n",
            "loss: 1.5506; acc: 0.9035; val_loss: 2.1541; val_acc: 0.8664\n",
            "Model saved in walabot.002.hdf5\n",
            "Learning rate: 0.003645\n",
            "\n",
            "Epoch 4/10\n",
            "**************************************************\n",
            "loss: 1.4858; acc: 0.9076; val_loss: 2.1666; val_acc: 0.8656\n",
            "Model saved in walabot.003.hdf5\n",
            "Learning rate: 0.0032805\n",
            "\n",
            "Epoch 5/10\n",
            "**************************************************\n",
            "loss: 1.4633; acc: 0.9091; val_loss: 2.1179; val_acc: 0.8679\n",
            "Model saved in walabot.004.hdf5\n",
            "Learning rate: 0.00295245\n",
            "\n",
            "Epoch 6/10\n",
            "**************************************************\n",
            "loss: 1.4613; acc: 0.9093; val_loss: 2.1179; val_acc: 0.8679\n",
            "Model saved in walabot.005.hdf5\n",
            "Learning rate: 0.002657205\n",
            "\n",
            "Epoch 7/10\n",
            "**************************************************\n",
            "loss: 1.4613; acc: 0.9093; val_loss: 2.1179; val_acc: 0.8679\n",
            "Model saved in walabot.006.hdf5\n",
            "Learning rate: 0.0023914846\n",
            "\n",
            "Epoch 8/10\n",
            "**************************************************\n",
            "loss: 1.4613; acc: 0.9093; val_loss: 2.1179; val_acc: 0.8679\n",
            "Model saved in walabot.007.hdf5\n",
            "Learning rate: 0.002152336\n",
            "\n",
            "Epoch 9/10\n",
            "**************************************************\n",
            "loss: 1.4613; acc: 0.9093; val_loss: 2.1179; val_acc: 0.8679\n",
            "Model saved in walabot.008.hdf5\n",
            "Learning rate: 0.0019371024\n",
            "\n",
            "Epoch 10/10\n",
            "**************************************************\n",
            "loss: 1.4613; acc: 0.9093; val_loss: 2.1179; val_acc: 0.8679\n",
            "Model saved in walabot.009.hdf5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f330116d358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfrU7iJVBwUx",
        "colab_type": "code",
        "outputId": "fdbb527c-305c-4263-dffc-891b00fb908c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred_test = model.predict_proba(X_test)\n",
        "y_pred_test_classes = np.argmax(y_pred_test, axis=1)\n",
        "y_pred_test_max_probas = np.max(y_pred_test, axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1280/1287 [============================>.] - ETA: 0s"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQnAx3YQU_Op",
        "colab_type": "text"
      },
      "source": [
        "Here's the confusion matrix for the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBt9_FHOFzMf",
        "colab_type": "code",
        "outputId": "308eff25-6812-4adc-9239-34f6da534a24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "plt.figure(figsize=(7, 6))\n",
        "plt.title('Confusion matrix', fontsize=16)\n",
        "cm = confusion_matrix(y_test, y_pred_test_classes)\n",
        "plt.xticks(np.arange(3), ['fullclip','emptyclip','control'], rotation=45, fontsize=12)\n",
        "plt.yticks(np.arange(3), ['fullclip','emptyclip','control'], fontsize=12)\n",
        "plt.imshow(cm,cmap='Blues')\n",
        "thresh = cm.max() / 2.\n",
        "for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            plt.text(j, i, cm[i, j],size=14,\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "plt.xlabel('Predicted label',size=16)\n",
        "plt.ylabel('True label',size=16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'True label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tLTMzK61iuwfPBQZLehi4AwhgM0mHA72BnjVUn5mZ2WxFtbSyc6Z6A2sBd5LO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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PprWw_G5Td93",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4QZvZm9Td7m",
        "colab_type": "code",
        "outputId": "f5056e80-7e41-4a34-c455-3b295cade520",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "f1_score(y_test, y_pred_test_classes,average='weighted')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8341108714846476"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTv1UVrjF2NI",
        "colab_type": "code",
        "outputId": "5e0096ea-72c4-441a-d54b-0c9fc6cfb1fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "accuracy_score(y_test, y_pred_test_classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8679098679098679"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iAPcSw7vQuwh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}