linear_classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of linear_classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "d2a5817a2fdc43a997c8843dde178219": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_c1caae73433140bcb93f6c0e1d4050e0",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_c3ea522524774199bd8de347a0c00293",
              "IPY_MODEL_270f5a7455604a3a8d3112cb2167c458"
            ]
          }
        },
        "c1caae73433140bcb93f6c0e1d4050e0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "c3ea522524774199bd8de347a0c00293": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_61f4c02a41ac4cfa96223e904864629f",
            "_dom_classes": [],
            "description": "",
            "_model_name": "IntProgressModel",
            "bar_style": "info",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e78a625dea6447c5939f504e1f81a322"
          }
        },
        "270f5a7455604a3a8d3112cb2167c458": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_b6cdf54d72f744df9978779cb8b7d457",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 170500096/? [00:20&lt;00:00, 54904017.39it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_27f37d1efcdc4835b69558887c82339a"
          }
        },
        "61f4c02a41ac4cfa96223e904864629f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "e78a625dea6447c5939f504e1f81a322": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "b6cdf54d72f744df9978779cb8b7d457": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "27f37d1efcdc4835b69558887c82339a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pure-water/utility_ml/blob/master/linear_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vTOX0d3cmLQf"
      },
      "source": [
        "# EECS 498-007/598-005 Assignment 2-1: Linear Classifiers\n",
        "\n",
        "Before we start, please put your name and UMID in following format\n",
        "\n",
        ": Firstname LASTNAME, #00000000   //   e.g.) Justin JOHNSON, #12345678"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tt7vq1h6mRto"
      },
      "source": [
        "**Your Answer:**   \n",
        "Hello WORLD, #XXXXXXXX"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "FrfeHl_-m4V-"
      },
      "source": [
        "## Install starter code\n",
        "We will continue using the utility functions that we've used for Assignment 1: [`coutils` package](https://github.com/deepvision-class/starter-code). Run this cell to download and install it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VyQblYp0nEZq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "72fa0239-891e-4d47-e29b-0b05cbb83de0"
      },
      "source": [
        "!pip install git+https://github.com/deepvision-class/starter-code"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/deepvision-class/starter-code\n",
            "  Cloning https://github.com/deepvision-class/starter-code to /tmp/pip-req-build-hwdt819f\n",
            "  Running command git clone -q https://github.com/deepvision-class/starter-code /tmp/pip-req-build-hwdt819f\n",
            "Requirement already satisfied (use --upgrade to upgrade): Colab-Utils==0.1.dev0 from git+https://github.com/deepvision-class/starter-code in /usr/local/lib/python3.6/dist-packages\n",
            "Requirement already satisfied: pydrive in /usr/local/lib/python3.6/dist-packages (from Colab-Utils==0.1.dev0) (1.3.1)\n",
            "Requirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (1.7.12)\n",
            "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (4.1.3)\n",
            "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (3.13)\n",
            "Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (0.0.3)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (3.0.1)\n",
            "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (1.12.0)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (0.17.2)\n",
            "Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (1.7.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (0.2.8)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (4.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (0.4.8)\n",
            "Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth>=1.4.1->google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (3.1.1)\n",
            "Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.6/dist-packages (from google-auth>=1.4.1->google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (46.1.3)\n",
            "Building wheels for collected packages: Colab-Utils\n",
            "  Building wheel for Colab-Utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Colab-Utils: filename=Colab_Utils-0.1.dev0-cp36-none-any.whl size=10323 sha256=dc18cda75ff8387d511515ba9d375db754ce84b2aef9e313ff05043fb99f20c1\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-tky4mgoq/wheels/63/d1/27/a208931527abb98d326d00209f46c80c9d745851d6a1defd10\n",
            "Successfully built Colab-Utils\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "28bBK1v7nJOu"
      },
      "source": [
        "## Setup code\n",
        "Run some setup code for this notebook: Import some useful packages and increase the default figure size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VUCKw4Tl1ddj",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "from __future__ import division\n",
        "\n",
        "import torch\n",
        "import coutils\n",
        "import random\n",
        "import time\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision.utils import make_grid\n",
        "\n",
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
        "plt.rcParams['image.interpolation'] = 'nearest'\n",
        "plt.rcParams['image.cmap'] = 'gray'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lhqpd2IN2O-K"
      },
      "source": [
        "Starting in this assignment, we will use the GPU to accelerate our computation. Run this cell to make sure you are using a GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "SGDxdBIMRX6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "be9ad380-506b-42a7-b9c0-e39556f77d38"
      },
      "source": [
        "if torch.cuda.is_available:\n",
        "  print('Good to go!')\n",
        "else:\n",
        "  print('Please set GPU via Edit -> Notebook Settings.')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Good to go!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fdMnUocj7Srw"
      },
      "source": [
        "Now, we will load CIFAR10 dataset, with normalization.\n",
        "\n",
        "In this notebook we will use the **bias trick**: By adding an extra constant feature of ones to each image, we avoid the need to keep track of a bias vector; the bias will be encoded as the part of the weight matrix that interacts with the constant ones in the input.\n",
        "\n",
        "In the `two_layer_net.ipynb` notebook that follows this one, we will not use the bias trick."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V2mFlFmQ1ddm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638,
          "referenced_widgets": [
            "d2a5817a2fdc43a997c8843dde178219",
            "c1caae73433140bcb93f6c0e1d4050e0",
            "c3ea522524774199bd8de347a0c00293",
            "270f5a7455604a3a8d3112cb2167c458",
            "61f4c02a41ac4cfa96223e904864629f",
            "e78a625dea6447c5939f504e1f81a322",
            "b6cdf54d72f744df9978779cb8b7d457",
            "27f37d1efcdc4835b69558887c82339a"
          ]
        },
        "outputId": "75882952-7cf5-4311-c884-8c8be6f5ebd1"
      },
      "source": [
        "def get_CIFAR10_data(validation_ratio = 0.02):\n",
        "  \"\"\"\n",
        "  Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
        "  it for the linear classifier. These are the same steps as we used for the\n",
        "  SVM, but condensed to a single function.  \n",
        "  \"\"\"\n",
        "  X_train, y_train, X_test, y_test = coutils.data.cifar10()\n",
        "\n",
        "  # Move all the data to the GPU\n",
        "  X_train = X_train.cuda()\n",
        "  y_train = y_train.cuda()\n",
        "  X_test = X_test.cuda()\n",
        "  y_test = y_test.cuda()\n",
        "\n",
        "  # 0. Visualize some examples from the dataset.\n",
        "  class_names = [\n",
        "      'plane', 'car', 'bird', 'cat', 'deer',\n",
        "      'dog', 'frog', 'horse', 'ship', 'truck'\n",
        "  ]\n",
        "  img = coutils.utils.visualize_dataset(X_train, y_train, 12, class_names)\n",
        "  plt.imshow(img)\n",
        "  plt.axis('off')\n",
        "  plt.show()\n",
        "\n",
        "  # 1. Normalize the data: subtract the mean RGB (zero mean)\n",
        "  mean_image = X_train.mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)\n",
        "  X_train -= mean_image\n",
        "  X_test -= mean_image\n",
        "\n",
        "  # 2. Reshape the image data into rows\n",
        "  X_train = X_train.reshape(X_train.shape[0], -1)\n",
        "  X_test = X_test.reshape(X_test.shape[0], -1)\n",
        "\n",
        "  # 3. Add bias dimension and transform into columns\n",
        "  ones_train = torch.ones(X_train.shape[0], 1, device=X_train.device)\n",
        "  X_train = torch.cat([X_train, ones_train], dim=1)\n",
        "  ones_test = torch.ones(X_test.shape[0], 1, device=X_test.device)\n",
        "  X_test = torch.cat([X_test, ones_test], dim=1)\n",
        "\n",
        "  # 4. Carve out part of the training set to use for validation.\n",
        "  # For random permutation, you can use torch.randperm or torch.randint\n",
        "  # But, for this homework, we use slicing instead.\n",
        "  num_training = int( X_train.shape[0] * (1.0 - validation_ratio) )\n",
        "  num_validation = X_train.shape[0] - num_training\n",
        "\n",
        "  # Return the dataset as a dictionary\n",
        "  data_dict = {}\n",
        "  data_dict['X_val'] = X_train[num_training:num_training + num_validation]\n",
        "  data_dict['y_val'] = y_train[num_training:num_training + num_validation]\n",
        "  data_dict['X_train'] = X_train[0:num_training]\n",
        "  data_dict['y_train'] = y_train[0:num_training]\n",
        "\n",
        "  data_dict['X_test'] = X_test\n",
        "  data_dict['y_test'] = y_test\n",
        "  return data_dict\n",
        "\n",
        "# Invoke the above function to get our data.\n",
        "data_dict = get_CIFAR10_data()\n",
        "print('Train data shape: ', data_dict['X_train'].shape)\n",
        "print('Train labels shape: ', data_dict['y_train'].shape)\n",
        "print('Validation data shape: ', data_dict['X_val'].shape)\n",
        "print('Validation labels shape: ', data_dict['y_val'].shape)\n",
        "print('Test data shape: ', data_dict['X_test'].shape)\n",
        "print('Test labels shape: ', data_dict['y_test'].shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2a5817a2fdc43a997c8843dde178219",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./cifar-10-python.tar.gz to .\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Train data shape:  torch.Size([49000, 3073])\n",
            "Train labels shape:  torch.Size([49000])\n",
            "Validation data shape:  torch.Size([1000, 3073])\n",
            "Validation labels shape:  torch.Size([1000])\n",
            "Test data shape:  torch.Size([10000, 3073])\n",
            "Test labels shape:  torch.Size([10000])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MhOuO4zE9ONc"
      },
      "source": [
        "For Softmax and SVM, we will analytically compute the gradient, as a sanity check."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "biGULpeRxEhe",
        "colab": {}
      },
      "source": [
        "def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-7):\n",
        "  \"\"\"\n",
        "  Utility function to perform numeric gradient checking. We use the centered\n",
        "  difference formula to compute a numeric derivative:\n",
        "  \n",
        "  f'(x) =~ (f(x + h) - f(x - h)) / (2h)\n",
        "\n",
        "  Rather than computing a full numeric gradient, we sparsely sample a few\n",
        "  dimensions along which to compute numeric derivatives.\n",
        "\n",
        "  Inputs:\n",
        "  - f: A function that inputs a torch tensor and returns a torch scalar\n",
        "  - x: A torch tensor giving the point at which to evaluate the numeric gradient\n",
        "  - analytic_grad: A torch tensor giving the analytic gradient of f at x\n",
        "  - num_checks: The number of dimensions along which to check\n",
        "  - h: Step size for computing numeric derivatives\n",
        "  \"\"\"\n",
        "  # fix random seed for \n",
        "  coutils.utils.fix_random_seed()\n",
        "\n",
        "  for i in range(num_checks):\n",
        "    \n",
        "    ix = tuple([random.randrange(m) for m in x.shape])\n",
        "    \n",
        "    oldval = x[ix].item()\n",
        "    x[ix] = oldval + h # increment by h\n",
        "    fxph = f(x).item() # evaluate f(x + h)\n",
        "    x[ix] = oldval - h # increment by h\n",
        "    fxmh = f(x).item() # evaluate f(x - h)\n",
        "    x[ix] = oldval     # reset\n",
        "\n",
        "    grad_numerical = (fxph - fxmh) / (2 * h)\n",
        "    grad_analytic = analytic_grad[ix]\n",
        "    rel_error_top = abs(grad_numerical - grad_analytic)\n",
        "    rel_error_bot = (abs(grad_numerical) + abs(grad_analytic) + 1e-12)\n",
        "    rel_error = rel_error_top / rel_error_bot\n",
        "    msg = 'numerical: %f analytic: %f, relative error: %e'\n",
        "    print(msg % (grad_numerical, grad_analytic, rel_error))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0Lvdm4fm7iJC"
      },
      "source": [
        "## SVM Classifier\n",
        "\n",
        "In this section, you will:\n",
        "    \n",
        "- implement a fully-vectorized **loss function** for the SVM\n",
        "- implement the fully-vectorized expression for its **analytic gradient**\n",
        "- **check your implementation** using numerical gradient\n",
        "- use a validation set to **tune the learning rate and regularization** strength\n",
        "- **optimize** the loss function with **SGD**\n",
        "- **visualize** the final learned weights\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3CFWcoJF8hti",
        "colab": {}
      },
      "source": [
        "def svm_loss_naive(W, X, y, reg):\n",
        "  \"\"\"\n",
        "  Structured SVM loss function, naive implementation (with loops).\n",
        "\n",
        "  Inputs have dimension D, there are C classes, and we operate on minibatches\n",
        "  of N examples. When you implment the regularization over W, please DO NOT\n",
        "  multiply the regularization term by 1/2 (no coefficient). \n",
        "\n",
        "  Inputs:\n",
        "  - W: A PyTorch tensor of shape (D, C) containing weights.\n",
        "  - X: A PyTorch tensor of shape (N, D) containing a minibatch of data.\n",
        "  - y: A PyTorch tensor of shape (N,) containing training labels; y[i] = c means\n",
        "    that X[i] has label c, where 0 <= c < C.\n",
        "  - reg: (float) regularization strength\n",
        "\n",
        "  Returns a tuple of:\n",
        "  - loss as torch scalar\n",
        "  - gradient of loss with respect to weights W; a tensor of same shape as W\n",
        "  \"\"\"\n",
        "  dW = torch.zeros_like(W) # initialize the gradient as zero\n",
        "\n",
        "  # compute the loss and the gradient\n",
        "  num_classes = W.shape[1]\n",
        "  num_train = X.shape[0]\n",
        "  loss = 0.0\n",
        "  for i in range(num_train):\n",
        "    scores = W.t().mv(X[i])\n",
        "    correct_class_score = scores[y[i]]\n",
        "    for j in range(num_classes):\n",
        "      if j == y[i]:\n",
        "        continue\n",
        "      margin = scores[j] - correct_class_score + 1 # note delta = 1\n",
        "      if margin > 0:\n",
        "        loss += margin\n",
        "        #######################################################################\n",
        "        # TODO:                                                               #\n",
        "        # Compute the gradient of the loss function and store it dW. (part 1) #\n",
        "        # Rather that first computing the loss and then computing the         #\n",
        "        # derivative, it is simple to compute the derivative at the same time #\n",
        "        # that the loss is being computed.                                    #\n",
        "        #######################################################################\n",
        "        # Replace \"pass\" statement with your code\n",
        "        # Note, this one is wrong\n",
        "        #dW[i,j] += X[i][j]\n",
        "        #dW[i,y[i]] -= X[i][y[i]] \n",
        "        # semiclone on X can be ignored\n",
        "        dW[:,j] += X[i,:]\n",
        "        dW[:,y[i]] -= X[i,:] \n",
        "        \n",
        "        #######################################################################\n",
        "        #                       END OF YOUR CODE                              #\n",
        "        #######################################################################\n",
        "        \n",
        "\n",
        "  # Right now the loss is a sum over all training examples, but we want it\n",
        "  # to be an average instead so we divide by num_train.\n",
        "  loss /= num_train\n",
        "\n",
        "  # Add regularization to the loss.\n",
        "  loss += reg * torch.sum(W * W)\n",
        "\n",
        "  #############################################################################\n",
        "  # TODO:                                                                     #\n",
        "  # Compute the gradient of the loss function and store it in dW. (part 2)    #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  dW = dW/num_train + 2.0 * reg * W \n",
        "  #############################################################################\n",
        "  #                             END OF YOUR CODE                              #\n",
        "  #############################################################################\n",
        "\n",
        "  return loss, dW"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xTI4qN7S9aTr"
      },
      "source": [
        "Evaluate the naive implementation of the loss we provided for you. You will get around 9.000175."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Hxzu2uZq9P8P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7e898406-d63e-47a2-f0a6-30eb21035e21"
      },
      "source": [
        "# generate a random SVM weight tensor of small numbers\n",
        "coutils.utils.fix_random_seed()\n",
        "W = torch.randn(3073, 10, device=data_dict['X_val'].device) * 0.0001 \n",
        "\n",
        "loss, grad = svm_loss_naive(W, data_dict['X_val'], data_dict['y_val'], 0.000005)\n",
        "print('loss: %f' % (loss, ))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss: 9.000433\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9LbRTXJ39Yp8"
      },
      "source": [
        "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
        "\n",
        "To check that you have implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you (The relative errors should be less than `1e-6`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "o3sMha4i9p_V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "outputId": "55378a99-e5fe-4a6e-929c-23d621164ed3"
      },
      "source": [
        "# Once you've implemented the gradient, recompute it with the code below\n",
        "# and gradient check it with the function we provided for you\n",
        "\n",
        "# Use a random W and a minibatch of data from the val set for gradient checking\n",
        "# For numeric gradient checking it is a good idea to use 64-bit floating point\n",
        "# numbers for increased numeric precision; however when actually training models\n",
        "# we usually use 32-bit floating point numbers for increased speed.\n",
        "coutils.utils.fix_random_seed()\n",
        "W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n",
        "batch_size = 64\n",
        "X_batch = data_dict['X_val'][:64].double()\n",
        "y_batch = data_dict['y_val'][:64]\n",
        "\n",
        "# Compute the loss and its gradient at W.\n",
        "loss, grad = svm_loss_naive(W.double(), X_batch, y_batch, reg=0.0) \n",
        "\n",
        "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
        "# compare them with your analytically computed gradient. The numbers should\n",
        "# match almost exactly along all dimensions.\n",
        "f = lambda w: svm_loss_naive(w, X_batch, y_batch, reg=0.0)[0]\n",
        "grad_numerical = grad_check_sparse(f, W.double(), grad)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "numerical: -0.034577 analytic: -0.034577, relative error: 1.088088e-07\n",
            "numerical: 0.126951 analytic: 0.126951, relative error: 5.721190e-08\n",
            "numerical: -0.068597 analytic: -0.068597, relative error: 1.195002e-07\n",
            "numerical: 0.025717 analytic: 0.025717, relative error: 4.062602e-08\n",
            "numerical: 0.048266 analytic: 0.048266, relative error: 1.932111e-07\n",
            "numerical: 0.052260 analytic: 0.052260, relative error: 1.179863e-07\n",
            "numerical: 0.096133 analytic: 0.096133, relative error: 1.389181e-07\n",
            "numerical: 0.032702 analytic: 0.032702, relative error: 1.787458e-07\n",
            "numerical: -0.117158 analytic: -0.117158, relative error: 7.364778e-08\n",
            "numerical: -0.154093 analytic: -0.154093, relative error: 1.236371e-07\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lSdsG-L292Ww"
      },
      "source": [
        "Let's do the gradient check once again with regularization turned on. (You didn't forget the regularization gradient, did you?)\n",
        "\n",
        "You should see relative errors less than `1e-5`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bH6lXxVn9xZk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "outputId": "794d87e8-82d9-442f-c310-55fec5ebf567"
      },
      "source": [
        "# Use a minibatch of data from the val set for gradient checking\n",
        "coutils.utils.fix_random_seed()\n",
        "W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n",
        "batch_size = 64\n",
        "X_batch = data_dict['X_val'][:64].double()\n",
        "y_batch = data_dict['y_val'][:64]\n",
        "\n",
        "# Compute the loss and its gradient at W.\n",
        "loss, grad = svm_loss_naive(W.double(), X_batch, y_batch, reg=1e3) \n",
        "\n",
        "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
        "# compare them with your analytically computed gradient. The numbers should\n",
        "# match almost exactly along all dimensions.\n",
        "f = lambda w: svm_loss_naive(w, X_batch, y_batch, reg=1e3)[0]\n",
        "grad_numerical = grad_check_sparse(f, W.double(), grad)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "numerical: -0.121624 analytic: -0.121624, relative error: 3.509551e-08\n",
            "numerical: 0.020923 analytic: 0.020923, relative error: 3.331613e-07\n",
            "numerical: -0.076604 analytic: -0.076604, relative error: 1.173771e-07\n",
            "numerical: 0.256069 analytic: 0.256069, relative error: 8.151044e-09\n",
            "numerical: -0.330089 analytic: -0.330089, relative error: 2.561535e-08\n",
            "numerical: 0.004713 analytic: 0.004713, relative error: 1.481242e-06\n",
            "numerical: 0.101968 analytic: 0.101968, relative error: 1.098650e-07\n",
            "numerical: 0.097235 analytic: 0.097235, relative error: 6.655515e-08\n",
            "numerical: -0.117112 analytic: -0.117112, relative error: 8.427538e-08\n",
            "numerical: -0.257260 analytic: -0.257260, relative error: 7.655009e-08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OeOlF_YJ-ApZ"
      },
      "source": [
        "Now, let's implement vectorized version of SVM: `svm_loss_vectorized`. It should compute the same inputs and outputs as the naive version before, but it should involve **no explicit loops**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yYrN2JA0-Aoa",
        "colab": {}
      },
      "source": [
        "def svm_loss_vectorized(W, X, y, reg):\n",
        "  \"\"\"\n",
        "  Structured SVM loss function, vectorized implementation. When you implment \n",
        "  the regularization over W, please DO NOT multiply the regularization term by \n",
        "  1/2 (no coefficient). \n",
        "\n",
        "  Inputs and outputs are the same as svm_loss_naive.\n",
        "  \"\"\"\n",
        "  loss = 0.0\n",
        "  dW = torch.zeros_like(W) # initialize the gradient as zero\n",
        "\n",
        "  #############################################################################\n",
        "  # TODO:                                                                     #\n",
        "  # Implement a vectorized version of the structured SVM loss, storing the    #\n",
        "  # result in loss.                                                           #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  scores = X.mm(W)\n",
        "  \n",
        "  # compute the margins for all classes in one vector operation\n",
        "  #margins = np.maximum(0, scores - scores[y] + delta)\n",
        "  # on y-th position scores[y] - scores[y] canceled and gave delta. We want\n",
        "  # to ignore the y-th position and only consider margin on max wrong class\n",
        "  #margins[y] = 0\n",
        "  #loss_i = np.sum(margins)\n",
        "  #############################################################################\n",
        "  #                             END OF YOUR CODE                              #\n",
        "  #############################################################################\n",
        "\n",
        "\n",
        "  #############################################################################\n",
        "  # TODO:                                                                     #\n",
        "  # Implement a vectorized version of the gradient for the structured SVM     #\n",
        "  # loss, storing the result in dW.                                           #\n",
        "  #                                                                           #\n",
        "  # Hint: Instead of computing the gradient from scratch, it may be easier    #\n",
        "  # to reuse some of the intermediate values that you used to compute the     #\n",
        "  # loss.                                                                     #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  pass\n",
        "  #############################################################################\n",
        "  #                             END OF YOUR CODE                              #\n",
        "  #############################################################################\n",
        "\n",
        "  return loss, dW"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "sc5Wtu-e-WlI"
      },
      "source": [
        "Let's first check the speed and performance bewteen the non-vectorized and the vectorized version. You should see a speedup of more than 100x.\n",
        "\n",
        "(Note: It may have some difference, but should be less than 1e-6)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pBLqLTAGo1Rs",
        "colab": {}
      },
      "source": [
        "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
        "# we will implement the gradient in a moment.\n",
        "\n",
        "# Use random weights and a minibatch of val data for gradient checking\n",
        "coutils.utils.fix_random_seed()\n",
        "W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n",
        "X_batch = data_dict['X_val'][:128].double()\n",
        "y_batch = data_dict['y_val'][:128]\n",
        "reg = 0.000005\n",
        "\n",
        "# Run and time the naive version\n",
        "torch.cuda.synchronize()\n",
        "tic = time.time()\n",
        "loss_naive, grad_naive = svm_loss_naive(W, X_batch, y_batch, reg)\n",
        "torch.cuda.synchronize()\n",
        "toc = time.time()\n",
        "ms_naive = 1000.0 * (toc - tic)\n",
        "print('Naive loss: %e computed in %.2fms' % (loss_naive, ms_naive))\n",
        "\n",
        "# Run and time the vectorized version\n",
        "torch.cuda.synchronize()\n",
        "tic = time.time()\n",
        "loss_vec, _ = svm_loss_vectorized(W, X_batch, y_batch, reg)\n",
        "torch.cuda.synchronize()\n",
        "toc = time.time()\n",
        "ms_vec = 1000.0 * (toc - tic)\n",
        "print('Vectorized loss: %e computed in %.2fms' % (loss_vec, ms_vec))\n",
        "\n",
        "# The losses should match but your vectorized implementation should be much faster.\n",
        "print('Difference: %.2e' % (loss_naive - loss_vec))\n",
        "print('Speedup: %.2fX' % (ms_naive / ms_vec))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cRDPpAMl-0WD"
      },
      "source": [
        "Then, let's compute the gradient of the loss function. We can check the difference of gradient as well. (The error should be less than 1e-6)\n",
        "\n",
        "Now implement a vectorized version of the gradient computation in `svm_loss_vectorize` above. Run the cell below to compare the gradient of your naive and vectorized implementations. The difference between the gradients should be less than `1e-6`, and the vectorized version should run at least 100x faster.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3_SyWrTJ-OfX",
        "colab": {}
      },
      "source": [
        "# The naive implementation and the vectorized implementation should match, but\n",
        "# the vectorized version should still be much faster.\n",
        "\n",
        "# Use random weights and a minibatch of val data for gradient checking\n",
        "coutils.utils.fix_random_seed()\n",
        "W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n",
        "X_batch = data_dict['X_val'][:128].double()\n",
        "y_batch = data_dict['y_val'][:128]\n",
        "reg = 0.000005\n",
        "\n",
        "# Run and time the naive version\n",
        "torch.cuda.synchronize()\n",
        "tic = time.time()\n",
        "_, grad_naive = svm_loss_naive(W, X_batch, y_batch, 0.000005)\n",
        "torch.cuda.synchronize()\n",
        "toc = time.time()\n",
        "ms_naive = 1000.0 * (toc - tic)\n",
        "print('Naive loss and gradient: computed in %.2fms' % ms_naive)\n",
        "\n",
        "# Run and time the vectorized version\n",
        "torch.cuda.synchronize()\n",
        "tic = time.time()\n",
        "_, grad_vec = svm_loss_vectorized(W, X_batch, y_batch, 0.000005)\n",
        "torch.cuda.synchronize()\n",
        "toc = time.time()\n",
        "ms_vec = 1000.0 * (toc - tic)\n",
        "print('Vectorized loss and gradient: computed in %.2fms' % ms_vec)\n",
        "\n",
        "# The loss is a single number, so it is easy to compare the values computed\n",
        "# by the two implementations. The gradient on the other hand is a tensor, so\n",
        "# we use the Frobenius norm to compare them.\n",
        "grad_difference = torch.norm(grad_naive - grad_vec, p='fro')\n",
        "print('Gradient difference: %.2e' % grad_difference)\n",
        "print('Speedup: %.2fX' % (ms_naive / ms_vec))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uU852IitCtrC"
      },
      "source": [
        "Now that we have an efficient vectorized implementation of the SVM loss and its gradient, we can implement a training pipeline for linear classifiers.\n",
        "\n",
        "Complete the implementation of the following function:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YeRAY8TZAK22",
        "colab": {}
      },
      "source": [
        "def train_linear_classifier(loss_func, W, X, y, learning_rate=1e-3, \n",
        "                            reg=1e-5, num_iters=100, batch_size=200, verbose=False):\n",
        "  \"\"\"\n",
        "  Train this linear classifier using stochastic gradient descent.\n",
        "\n",
        "  Inputs:\n",
        "  - loss_func: loss function to use when training. It should take W, X, y\n",
        "    and reg as input, and output a tuple of (loss, dW)\n",
        "  - W: A PyTorch tensor of shape (D, C) giving the initial weights of the\n",
        "    classifier. If W is None then it will be initialized here.\n",
        "  - X: A PyTorch tensor of shape (N, D) containing training data; there are N\n",
        "    training samples each of dimension D.\n",
        "  - y: A PyTorch tensor of shape (N,) containing training labels; y[i] = c\n",
        "    means that X[i] has label 0 <= c < C for C classes.\n",
        "  - learning_rate: (float) learning rate for optimization.\n",
        "  - reg: (float) regularization strength.\n",
        "  - num_iters: (integer) number of steps to take when optimizing\n",
        "  - batch_size: (integer) number of training examples to use at each step.\n",
        "  - verbose: (boolean) If true, print progress during optimization.\n",
        "\n",
        "  Returns: A tuple of:\n",
        "  - W: The final value of the weight matrix and the end of optimization\n",
        "  - loss_history: A list of Python scalars giving the values of the loss at each\n",
        "    training iteration.\n",
        "  \"\"\"\n",
        "  # assume y takes values 0...K-1 where K is number of classes\n",
        "  num_classes = torch.max(y) + 1\n",
        "  num_train, dim = X.shape\n",
        "  if W is None:\n",
        "    # lazily initialize W\n",
        "    W = 0.000001 * torch.randn(dim, num_classes, device=X.device, dtype=X.dtype)\n",
        "\n",
        "  # Run stochastic gradient descent to optimize W\n",
        "  loss_history = []\n",
        "  for it in range(num_iters):\n",
        "    X_batch = None\n",
        "    y_batch = None\n",
        "    #########################################################################\n",
        "    # TODO:                                                                 #\n",
        "    # Sample batch_size elements from the training data and their           #\n",
        "    # corresponding labels to use in this round of gradient descent.        #\n",
        "    # Store the data in X_batch and their corresponding labels in           #\n",
        "    # y_batch; after sampling, X_batch should have shape (batch_size, dim)  #\n",
        "    # and y_batch should have shape (batch_size,)                           #\n",
        "    #                                                                       #\n",
        "    # Hint: Use torch.randint to generate indices.                          #\n",
        "    #########################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    pass\n",
        "    #########################################################################\n",
        "    #                       END OF YOUR CODE                                #\n",
        "    #########################################################################\n",
        "\n",
        "    # evaluate loss and gradient\n",
        "    loss, grad = loss_func(W, X_batch, y_batch, reg)\n",
        "    loss_history.append(loss.item())\n",
        "\n",
        "    # perform parameter update\n",
        "    #########################################################################\n",
        "    # TODO:                                                                 #\n",
        "    # Update the weights using the gradient and the learning rate.          #\n",
        "    #########################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    pass\n",
        "    #########################################################################\n",
        "    #                       END OF YOUR CODE                                #\n",
        "    #########################################################################\n",
        "\n",
        "    if verbose and it % 100 == 0:\n",
        "      print('iteration %d / %d: loss %f' % (it, num_iters, loss))\n",
        "\n",
        "  return W, loss_history"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "c6IL1_D9wCbF"
      },
      "source": [
        "Once you have implemented the training function, run the following cell to train a linear classifier using some default hyperparameters:\n",
        "\n",
        "(You should see a final loss close to 9.0, and your training loop should run in about two seconds)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QaEZkCe3-kOu",
        "colab": {}
      },
      "source": [
        "# fix random seed before we perform this operation\n",
        "coutils.utils.fix_random_seed()\n",
        "\n",
        "torch.cuda.synchronize()\n",
        "tic = time.time()\n",
        "\n",
        "W, loss_hist = train_linear_classifier(svm_loss_vectorized, None, \n",
        "                                       data_dict['X_train'], \n",
        "                                       data_dict['y_train'], \n",
        "                                       learning_rate=3e-11, reg=2.5e4,\n",
        "                                       num_iters=1500, verbose=True)\n",
        "\n",
        "torch.cuda.synchronize()\n",
        "toc = time.time()\n",
        "print('That took %fs' % (toc - tic))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "n8mz9aXfDrsF"
      },
      "source": [
        "A useful debugging strategy is to plot the loss as a function of iteration number. In this case it seems our hyperparameters are not good, since the training loss is not decreasing very fast.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JJ8GjaZS_MLe",
        "colab": {}
      },
      "source": [
        "plt.plot(loss_hist, 'o')\n",
        "plt.xlabel('Iteration number')\n",
        "plt.ylabel('Loss value')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hBzZNGNoD18s"
      },
      "source": [
        "Let's move on to the prediction stage:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UdvAk57DD0xT",
        "colab": {}
      },
      "source": [
        "def predict_linear_classifier(W, X):\n",
        "  \"\"\"\n",
        "  Use the trained weights of this linear classifier to predict labels for\n",
        "  data points.\n",
        "\n",
        "  Inputs:\n",
        "  - W: A PyTorch tensor of shape (D, C), containing weights of a model\n",
        "  - X: A PyTorch tensor of shape (N, D) containing training data; there are N\n",
        "    training samples each of dimension D.\n",
        "\n",
        "  Returns:\n",
        "  - y_pred: PyTorch int64 tensor of shape (N,) giving predicted labels for each\n",
        "    elemment of X. Each element of y_pred should be between 0 and C - 1.\n",
        "  \"\"\"\n",
        "  y_pred = torch.zeros(X.shape[0])\n",
        "  ###########################################################################\n",
        "  # TODO:                                                                   #\n",
        "  # Implement this method. Store the predicted labels in y_pred.            #\n",
        "  ###########################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  pass\n",
        "  ###########################################################################\n",
        "  #                           END OF YOUR CODE                              #\n",
        "  ###########################################################################\n",
        "  return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DRdfknKsE6F2"
      },
      "source": [
        "Then, let's evaluate the performance our trained model on both the training and validation set. You should see validation accuracy less than 10%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YfToPzce_OBH",
        "colab": {}
      },
      "source": [
        "# evaluate the performance on both the training and validation set\n",
        "y_train_pred = predict_linear_classifier(W, data_dict['X_train'])\n",
        "train_acc = 100.0 * (data_dict['y_train'] == y_train_pred).float().mean().item()\n",
        "print('Training accuracy: %.2f%%' % train_acc)\n",
        "y_val_pred = predict_linear_classifier(W, data_dict['X_val'])\n",
        "val_acc = 100.0 * (data_dict['y_val'] == y_val_pred).float().mean().item()\n",
        "print('Validation accuracy: %.2f%%' % val_acc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VWIyGnMOFOV8"
      },
      "source": [
        "Unfortunately, the performance of our initial model is quite bad. To find a better hyperparamters, let's first modulize the functions that we've implemented."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "A1SfNxVs8OBv",
        "colab": {}
      },
      "source": [
        "# Note: We will re-use `LinearClassifier' in Softmax section\n",
        "class LinearClassifier(object):\n",
        "  \n",
        "  def __init__(self):\n",
        "    self.W = None\n",
        "    \n",
        "  def train(self, X_train, y_train, learning_rate=1e-3, reg=1e-5, num_iters=100,\n",
        "            batch_size=200, verbose=False):\n",
        "    train_args = (self.loss, self.W, X_train, y_train, learning_rate, reg,\n",
        "                  num_iters, batch_size, verbose)\n",
        "    self.W, loss_history = train_linear_classifier(*train_args)\n",
        "    return loss_history\n",
        "\n",
        "  def predict(self, X):\n",
        "    return predict_linear_classifier(self.W, X) \n",
        "  \n",
        "  def loss(self, W, X_batch, y_batch, reg):\n",
        "    \"\"\"\n",
        "    Compute the loss function and its derivative. \n",
        "    Subclasses will override this.\n",
        "\n",
        "    Inputs:\n",
        "    - W: A PyTorch tensor of shape (D, C) containing (trained) weight of a model.\n",
        "    - X_batch: A PyTorch tensor of shape (N, D) containing a minibatch of N\n",
        "      data points; each point has dimension D.\n",
        "    - y_batch: A PyTorch tensor of shape (N,) containing labels for the minibatch.\n",
        "    - reg: (float) regularization strength.\n",
        "\n",
        "    Returns: A tuple containing:\n",
        "    - loss as a single float\n",
        "    - gradient with respect to self.W; an tensor of the same shape as W\n",
        "    \"\"\"\n",
        "    pass\n",
        "  def _loss(self, X_batch, y_batch, reg):\n",
        "    self.loss(self.W, X_batch, y_batch, reg)\n",
        "\n",
        "  \n",
        "class LinearSVM(LinearClassifier):\n",
        "  \"\"\" A subclass that uses the Multiclass SVM loss function \"\"\"\n",
        "\n",
        "  def loss(self, W, X_batch, y_batch, reg):\n",
        "    return svm_loss_vectorized(W, X_batch, y_batch, reg)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "taNmjt2wGJQr"
      },
      "source": [
        "Now, please use the validation set to tune hyperparameters (regularization strength and learning rate). You should experiment with different ranges for the learning rates and regularization strengths.\n",
        "\n",
        "To get full credit for the assignment your best model found through cross-validation should achieve an accuracy of at least 37% on the validation set.\n",
        "\n",
        "(Our best model got over 40% -- did you beat us?)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oVMsJ9Ti_Ude",
        "colab": {}
      },
      "source": [
        "# results is dictionary mapping tuples of the form\n",
        "# (learning_rate, regularization_strength) to tuples of the form\n",
        "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
        "# of data points that are correctly classified.\n",
        "results = {}\n",
        "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
        "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
        "learning_rates = [] # learning rate candidates, e.g. [1e-3, 1e-2, ...]\n",
        "regularization_strengths = [] # regularization strengths candidates e.g. [1e0, 1e1, ...]\n",
        "\n",
        "################################################################################\n",
        "# TODO:                                                                        #\n",
        "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
        "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
        "# training set, compute its accuracy on the training and validation sets, and  #\n",
        "# store these numbers in the results dictionary. In addition, store the best   #\n",
        "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
        "# accuracy in best_svm.                                                        #\n",
        "#                                                                              #\n",
        "# Hint: You should use a small value for num_iters as you develop your         #\n",
        "# validation code so that the SVMs don't take much time to train; once you are #\n",
        "# confident that your validation code works, you should rerun the validation   #\n",
        "# code with a larger value for num_iters.                                      #\n",
        "################################################################################\n",
        "# Replace \"pass\" statement with your code\n",
        "pass\n",
        "################################################################################\n",
        "#                              END OF YOUR CODE                                #\n",
        "################################################################################\n",
        "    \n",
        "# Print out results.\n",
        "for lr, reg in sorted(results):\n",
        "  train_accuracy, val_accuracy = results[(lr, reg)]\n",
        "  print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
        "         lr, reg, train_accuracy, val_accuracy))\n",
        "    \n",
        "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wBbvJvMeGZ-7"
      },
      "source": [
        "Visualize the cross-validation results. You can use this as a debugging tool -- after examining the cross-validation results here, you may want to go back and rerun your cross-validation from above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QbPffK9H_ZGj",
        "colab": {}
      },
      "source": [
        "x_scatter = [math.log10(x[0]) for x in results]\n",
        "y_scatter = [math.log10(x[1]) for x in results]\n",
        "\n",
        "# plot training accuracy\n",
        "marker_size = 100\n",
        "colors = [results[x][0] for x in results]\n",
        "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap='viridis')\n",
        "plt.colorbar()\n",
        "plt.xlabel('log learning rate')\n",
        "plt.ylabel('log regularization strength')\n",
        "plt.title('CIFAR-10 training accuracy')\n",
        "plt.gcf().set_size_inches(8, 5)\n",
        "plt.show()\n",
        "\n",
        "# plot validation accuracy\n",
        "colors = [results[x][1] for x in results] # default size of markers is 20\n",
        "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap='viridis')\n",
        "plt.colorbar()\n",
        "plt.xlabel('log learning rate')\n",
        "plt.ylabel('log regularization strength')\n",
        "plt.title('CIFAR-10 validation accuracy')\n",
        "plt.gcf().set_size_inches(8, 5)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MCMVzxquGf1O"
      },
      "source": [
        "Evaluate the best svm on test set. To get full credit for the assignment you should achieve a test-set accuracy above 35%.\n",
        "\n",
        "(Our best was over 38% -- did you beat us?)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "maJ7use3_soL",
        "colab": {}
      },
      "source": [
        "y_test_pred = best_svm.predict(data_dict['X_test'])\n",
        "test_accuracy = torch.mean((data_dict['y_test'] == y_test_pred).float())\n",
        "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "I-QVIG4fGiqJ"
      },
      "source": [
        "Visualize the learned weights for each class. Depending on your choice of learning rate and regularization strength, these may or may not be nice to look at."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "McLHYtFd_vSI",
        "colab": {}
      },
      "source": [
        "w = best_svm.W[:-1,:] # strip out the bias\n",
        "w = w.reshape(3, 32, 32, 10)\n",
        "w = w.transpose(0, 2).transpose(1, 0)\n",
        "\n",
        "w_min, w_max = torch.min(w), torch.max(w)\n",
        "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "for i in range(10):\n",
        "  plt.subplot(2, 5, i + 1)\n",
        "\n",
        "  # Rescale the weights to be between 0 and 255\n",
        "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
        "  plt.imshow(wimg.type(torch.uint8).cpu())\n",
        "  plt.axis('off')\n",
        "  plt.title(classes[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DkuwyMY27RxS"
      },
      "source": [
        "## Softmax Classifier\n",
        "\n",
        "Similar to the SVM, you will:\n",
        "\n",
        "- implement a fully-vectorized **loss function** for the Softmax classifier\n",
        "- implement the fully-vectorized expression for its **analytic gradient**\n",
        "- **check your implementation** with numerical gradient\n",
        "- use a validation set to **tune the learning rate and regularization** strength\n",
        "- **optimize** the loss function with **SGD**\n",
        "- **visualize** the final learned weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hLJMVGtvIgo3"
      },
      "source": [
        "First, let's start from implementing the naive softmax loss function with nested loops.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w3MDCs7Cwss8",
        "colab": {}
      },
      "source": [
        "def softmax_loss_naive(W, X, y, reg):\n",
        "  \"\"\"\n",
        "  Softmax loss function, naive implementation (with loops).  When you implment \n",
        "  the regularization over W, please DO NOT multiply the regularization term by \n",
        "  1/2 (no coefficient). \n",
        "\n",
        "  Inputs have dimension D, there are C classes, and we operate on minibatches\n",
        "  of N examples.\n",
        "\n",
        "  Inputs:\n",
        "  - W: A PyTorch tensor of shape (D, C) containing weights.\n",
        "  - X: A PyTorch tensor of shape (N, D) containing a minibatch of data.\n",
        "  - y: A PyTorch tensor of shape (N,) containing training labels; y[i] = c means\n",
        "    that X[i] has label c, where 0 <= c < C.\n",
        "  - reg: (float) regularization strength\n",
        "\n",
        "  Returns a tuple of:\n",
        "  - loss as single float\n",
        "  - gradient with respect to weights W; an tensor of same shape as W\n",
        "  \"\"\"\n",
        "  # Initialize the loss and gradient to zero.\n",
        "  loss = 0.0\n",
        "  dW = torch.zeros_like(W)\n",
        "\n",
        "  #############################################################################\n",
        "  # TODO: Compute the softmax loss and its gradient using explicit loops.     #\n",
        "  # Store the loss in loss and the gradient in dW. If you are not careful     #\n",
        "  # here, it is easy to run into numeric instability (Check Numeric Stability #\n",
        "  # in http://cs231n.github.io/linear-classify/). Plus, don't forget the      #\n",
        "  # regularization!                                                           #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  pass\n",
        "  #############################################################################\n",
        "  #                          END OF YOUR CODE                                 #\n",
        "  #############################################################################\n",
        "\n",
        "  return loss, dW"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cER8fiSq7Ys-"
      },
      "source": [
        "As a sanity check to see whether we have implemented the loss correctly, run the softmax classifier with a small random weight matrix and no regularization. You should see loss near log(10) = 2.3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V9q77O7F7VI6",
        "colab": {}
      },
      "source": [
        "# Generate a random softmax weight tensor and use it to compute the loss.\n",
        "coutils.utils.fix_random_seed()\n",
        "W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n",
        "\n",
        "X_batch = data_dict['X_val'][:128].double()\n",
        "y_batch = data_dict['y_val'][:128]\n",
        "\n",
        "# Complete the implementation of softmax_loss_naive and implement a (naive)\n",
        "# version of the gradient that uses nested loops.\n",
        "loss, grad = softmax_loss_naive(W, X_batch, y_batch, reg=0.0)\n",
        "\n",
        "# As a rough sanity check, our loss should be something close to log(10.0).\n",
        "print('loss: %f' % loss)\n",
        "print('sanity check: %f' % (math.log(10.0)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5QJzUHl5I0HH"
      },
      "source": [
        "Next, we use gradient checking to debug the analytic gradient of our naive softmax loss function. If you've implemented the gradient correctly, you should see relative errors less than `1e-6`.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Lj6YpN3q1hVG",
        "colab": {}
      },
      "source": [
        "coutils.utils.fix_random_seed()\n",
        "W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n",
        "X_batch = data_dict['X_val'][:128].double()\n",
        "y_batch = data_dict['y_val'][:128]\n",
        "\n",
        "loss, grad = softmax_loss_naive(W, X_batch, y_batch, reg=0.0)\n",
        "\n",
        "f = lambda w: softmax_loss_naive(w, X_batch, y_batch, reg=0.0)[0]\n",
        "grad_check_sparse(f, W, grad, 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cFcgeajBI-L3"
      },
      "source": [
        "Let's perform another gradient check with regularization enabled. Again you should see relative errors less than `1e-6`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ik0i21sszZzg",
        "colab": {}
      },
      "source": [
        "coutils.utils.fix_random_seed()\n",
        "W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n",
        "reg = 10.0\n",
        "\n",
        "X_batch = data_dict['X_val'][:128].double()\n",
        "y_batch = data_dict['y_val'][:128]\n",
        "\n",
        "loss, grad = softmax_loss_naive(W, X_batch, y_batch, reg)\n",
        "\n",
        "f = lambda w: softmax_loss_naive(w, X_batch, y_batch, reg)[0]\n",
        "grad_check_sparse(f, W, grad, 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JQgRzrdRJAm7"
      },
      "source": [
        "Then, let's move on to the vectorized form"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bYlTPinzwv3x",
        "colab": {}
      },
      "source": [
        "def softmax_loss_vectorized(W, X, y, reg):\n",
        "  \"\"\"\n",
        "  Softmax loss function, vectorized version.  When you implment the \n",
        "  regularization over W, please DO NOT multiply the regularization term by 1/2 \n",
        "  (no coefficient). \n",
        "\n",
        "  Inputs and outputs are the same as softmax_loss_naive.\n",
        "  \"\"\"\n",
        "  # Initialize the loss and gradient to zero.\n",
        "  loss = 0.0\n",
        "  dW = torch.zeros_like(W)\n",
        "\n",
        "  #############################################################################\n",
        "  # TODO: Compute the softmax loss and its gradient using no explicit loops.  #\n",
        "  # Store the loss in loss and the gradient in dW. If you are not careful     #\n",
        "  # here, it is easy to run into numeric instability (Check Numeric Stability #\n",
        "  # in http://cs231n.github.io/linear-classify/). Don't forget the            #\n",
        "  # regularization!                                                           #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  pass\n",
        "  #############################################################################\n",
        "  #                          END OF YOUR CODE                                 #\n",
        "  #############################################################################\n",
        "\n",
        "  return loss, dW"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "88xZ0rbLJGKV"
      },
      "source": [
        "Now that we have a naive implementation of the softmax loss function and its gradient, implement a vectorized version in softmax_loss_vectorized. The two versions should compute the same results, but the vectorized version should be much faster.\n",
        "\n",
        "The differences between the naive and vectorized losses and gradients should both be less than `1e-6`, and your vectorized implementation should be at least 100x faster than the naive implementation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lGNAe-oP1dds",
        "colab": {}
      },
      "source": [
        "coutils.utils.fix_random_seed()\n",
        "W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device)\n",
        "reg = 0.05\n",
        "\n",
        "X_batch = data_dict['X_val'][:128]\n",
        "y_batch = data_dict['y_val'][:128]\n",
        "\n",
        "# Run and time the naive version\n",
        "torch.cuda.synchronize()\n",
        "tic = time.time()\n",
        "loss_naive, grad_naive = softmax_loss_naive(W, X_batch, y_batch, reg)\n",
        "torch.cuda.synchronize()\n",
        "toc = time.time()\n",
        "ms_naive = 1000.0 * (toc - tic)\n",
        "print('naive loss: %e computed in %fs' % (loss_naive, ms_naive))\n",
        "\n",
        "# Run and time the vectorized version\n",
        "torch.cuda.synchronize()\n",
        "tic = time.time()\n",
        "loss_vec, grad_vec = softmax_loss_vectorized(W, X_batch, y_batch, reg)\n",
        "torch.cuda.synchronize()\n",
        "toc = time.time()\n",
        "ms_vec = 1000.0 * (toc - tic)\n",
        "print('vectorized loss: %e computed in %fs' % (loss_vec, ms_vec))\n",
        "\n",
        "# we use the Frobenius norm to compare the two versions of the gradient.\n",
        "loss_diff = (loss_naive - loss_vec).abs().item()\n",
        "grad_diff = torch.norm(grad_naive - grad_vec, p='fro')\n",
        "print('Loss difference: %.2e' % loss_diff)\n",
        "print('Gradient difference: %.2e' % grad_diff)\n",
        "print('Speedup: %.2fX' % (ms_naive / ms_vec))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bqZScXKyq6WB"
      },
      "source": [
        "Let's check that your implementation of the softmax loss is numerically stable.\n",
        "\n",
        "If either of the following print `nan` then you should double-check the numeric stability of your implementations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bCyFPWxxq58R",
        "colab": {}
      },
      "source": [
        "device = data_dict['X_train'].device\n",
        "dtype = torch.float32\n",
        "D = data_dict['X_train'].shape[1]\n",
        "C = 10\n",
        "\n",
        "W_ones = torch.ones(D, C, device=device, dtype=dtype)\n",
        "W, loss_hist = train_linear_classifier(softmax_loss_naive, W_ones, \n",
        "                                       data_dict['X_train'], \n",
        "                                       data_dict['y_train'], \n",
        "                                       learning_rate=1e-8, reg=2.5e4,\n",
        "                                       num_iters=1, verbose=True)\n",
        "\n",
        "\n",
        "W_ones = torch.ones(D, C, device=device, dtype=dtype)\n",
        "W, loss_hist = train_linear_classifier(softmax_loss_vectorized, W_ones, \n",
        "                                       data_dict['X_train'], \n",
        "                                       data_dict['y_train'], \n",
        "                                       learning_rate=1e-8, reg=2.5e4,\n",
        "                                       num_iters=1, verbose=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kR4JGKoek8FB"
      },
      "source": [
        "Now lets train a softmax classifier with some default hyperparameters:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Kqga1rvjk7b8",
        "colab": {}
      },
      "source": [
        "# fix random seed before we perform this operation\n",
        "coutils.utils.fix_random_seed(10)\n",
        "\n",
        "torch.cuda.synchronize()\n",
        "tic = time.time()\n",
        "\n",
        "W, loss_hist = train_linear_classifier(softmax_loss_vectorized, None, \n",
        "                                       data_dict['X_train'], \n",
        "                                       data_dict['y_train'], \n",
        "                                       learning_rate=1e-10, reg=2.5e4,\n",
        "                                       num_iters=1500, verbose=True)\n",
        "\n",
        "torch.cuda.synchronize()\n",
        "toc = time.time()\n",
        "print('That took %fs' % (toc - tic))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QKjxCGwkorCc"
      },
      "source": [
        "Plot the loss curve:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "K29x-DWNoujL",
        "colab": {}
      },
      "source": [
        "plt.plot(loss_hist, 'o')\n",
        "plt.xlabel('Iteration number')\n",
        "plt.ylabel('Loss value')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7WvpBuJWSwfd"
      },
      "source": [
        "Let's compute the accuracy of current model. It should be less than 10%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zb8kY2MjSvfH",
        "colab": {}
      },
      "source": [
        "# evaluate the performance on both the training and validation set\n",
        "y_train_pred = predict_linear_classifier(W, data_dict['X_train'])\n",
        "train_acc = 100.0 * (data_dict['y_train'] == y_train_pred).float().mean().item()\n",
        "print('training accuracy: %.2f%%' % train_acc)\n",
        "y_val_pred = predict_linear_classifier(W, data_dict['X_val'])\n",
        "val_acc = 100.0 * (data_dict['y_val'] == y_val_pred).float().mean().item()\n",
        "print('validation accuracy: %.2f%%' % val_acc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "IuV0BZvzJirI"
      },
      "source": [
        "Now use the validation set to tune hyperparameters (regularization strength and learning rate). You should experiment with different ranges for the learning rates and regularization strengths.\n",
        "\n",
        "To get full credit for the assignment, your best model found through cross-validation should achieve an accuracy above 0.37 on the validation set.\n",
        "\n",
        "(Our best model was above 0.40 -- did you beat us?)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TGyf3TkWB-Er",
        "colab": {}
      },
      "source": [
        "class Softmax(LinearClassifier):\n",
        "  \"\"\" A subclass that uses the Softmax + Cross-entropy loss function \"\"\"\n",
        "  def loss(self, W, X_batch, y_batch, reg):\n",
        "    return softmax_loss_vectorized(W, X_batch, y_batch, reg)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "68lmNVj31ddu",
        "colab": {}
      },
      "source": [
        "results = {}\n",
        "best_val = -1\n",
        "best_softmax = None\n",
        "\n",
        "learning_rates = [] # learning rate candidates\n",
        "regularization_strengths = [] # regularization strengths candidates\n",
        "\n",
        "# As before, store your cross-validation results in this dictionary.\n",
        "# The keys should be tuples of (learning_rate, regularization_strength) and\n",
        "# the values should be tuples (train_accuracy, val_accuracy)\n",
        "results = {}\n",
        "\n",
        "################################################################################\n",
        "# TODO:                                                                        #\n",
        "# Use the validation set to set the learning rate and regularization strength. #\n",
        "# This should be similar to the cross-validation that you used for the SVM,    #\n",
        "# but you may need to select different hyperparameters to achieve good         #\n",
        "# performance with the softmax classifier. Save your best trained softmax      #\n",
        "# classifer in best_softmax.                                                   # \n",
        "################################################################################\n",
        "# Replace \"pass\" statement with your code\n",
        "pass\n",
        "################################################################################\n",
        "#                              END OF YOUR CODE                                #\n",
        "################################################################################\n",
        "    \n",
        "# Print out results.\n",
        "for lr, reg in sorted(results):\n",
        "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
        "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
        "                lr, reg, train_accuracy, val_accuracy))\n",
        "    \n",
        "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "efougAmNCFLo"
      },
      "source": [
        "Run the following to visualize your cross-validation results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IVhRe3-DBjPr",
        "colab": {}
      },
      "source": [
        "x_scatter = [math.log10(x[0]) for x in results]\n",
        "y_scatter = [math.log10(x[1]) for x in results]\n",
        "\n",
        "# plot training accuracy\n",
        "marker_size = 100\n",
        "colors = [results[x][0] for x in results]\n",
        "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap='viridis')\n",
        "plt.colorbar()\n",
        "plt.xlabel('log learning rate')\n",
        "plt.ylabel('log regularization strength')\n",
        "plt.title('CIFAR-10 training accuracy')\n",
        "plt.gcf().set_size_inches(8, 5)\n",
        "plt.show()\n",
        "\n",
        "# plot validation accuracy\n",
        "colors = [results[x][1] for x in results] # default size of markers is 20\n",
        "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap='viridis')\n",
        "plt.colorbar()\n",
        "plt.xlabel('log learning rate')\n",
        "plt.ylabel('log regularization strength')\n",
        "plt.title('CIFAR-10 validation accuracy')\n",
        "plt.gcf().set_size_inches(8, 5)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fbOlUcv6J7MM"
      },
      "source": [
        "Them, evaluate the performance of your best model on test set. To get full credit for this assignment you should achieve a test-set accuracy above 0.36.\n",
        "\n",
        "(Our best was just over 0.40 -- did you beat us?)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-wxkVdB-1ddx",
        "colab": {}
      },
      "source": [
        "y_test_pred = best_softmax.predict(data_dict['X_test'])\n",
        "test_accuracy = torch.mean((data_dict['y_test'] == y_test_pred).float())\n",
        "print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Joo4RbeoKECC"
      },
      "source": [
        "Finally, let's visualize the learned weights for each class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XDfxI7mR1ddz",
        "colab": {}
      },
      "source": [
        "w = best_softmax.W[:-1,:] # strip out the bias\n",
        "w = w.reshape(3, 32, 32, 10)\n",
        "w = w.transpose(0, 2).transpose(1, 0)\n",
        "\n",
        "w_min, w_max = torch.min(w), torch.max(w)\n",
        "\n",
        "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "for i in range(10):\n",
        "  plt.subplot(2, 5, i + 1)\n",
        "\n",
        "  # Rescale the weights to be between 0 and 255\n",
        "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
        "  plt.imshow(wimg.type(torch.uint8).cpu())\n",
        "  plt.axis('off')\n",
        "  plt.title(classes[i])"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}