Wheat_Seeds_Analysis_Pytorch_blogs.ipynb

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  "metadata": {
    "colab": {
      "name": "Wheat_Seeds_Analysis_Pytorch_blogs.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kRsv8iFUMQx"
      },
      "source": [
        "# Wheat Seed Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2wpuYcUURPX"
      },
      "source": [
        "# 1: Introduction\n",
        "\n",
        "The Wheat Seeds Dataset involves the prediction of species given measurements of seeds from different varieties of wheat.\n",
        "\n",
        "It is a classification problem. The number of observations for each class is balanced. There are 199 observations with 7 input variables and 1 output variable. The variable names are as follows:\n",
        "\n",
        "<br> 1. Area.\n",
        "<br> 2. Perimeter.\n",
        "<br> 3. Compactness\n",
        "<br> 4. Length of kernel.\n",
        "<br> 5. Width of kernel.\n",
        "<br> 6. Asymmetry coefficient.\n",
        "<br> 7. Length of kernel groove.\n",
        "<br> 8. Class (1, 2, 3).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhAVA_Z7UV5P"
      },
      "source": [
        "# 2: Data Pre processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4RBiFFxvUYbN"
      },
      "source": [
        "## 2.1 Loading the required Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRu08zJPNLzo"
      },
      "source": [
        "import sys\n",
        "assert sys.version_info >= (3, 5)\n",
        "\n",
        "# Scikit-Learn ≥0.20 is required\n",
        "import sklearn\n",
        "assert sklearn.__version__ >= \"0.20\"\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "#Pytorch Lib\n",
        "import torch\n",
        "import random\n",
        "import torch.utils.data as data_utils\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrOjdz_MUkFM"
      },
      "source": [
        "## 2.2 Get Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDTOvBAwUL4u"
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "XYg3hT_tUL1n",
        "outputId": "6304d92e-6083-45a5-954d-5935077e7d65"
      },
      "source": [
        "## Loading the dataset from github repo\n",
        "url = 'https://raw.githubusercontent.com/hargurjeet/MachineLearning/Wheat-Seeds/seeds.csv'\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>Compactness</th>\n",
              "      <th>Kernel.Length</th>\n",
              "      <th>Kernel.Width</th>\n",
              "      <th>Asymmetry.Coeff</th>\n",
              "      <th>Kernel.Groove</th>\n",
              "      <th>Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.26</td>\n",
              "      <td>14.84</td>\n",
              "      <td>0.8710</td>\n",
              "      <td>5.763</td>\n",
              "      <td>3.312</td>\n",
              "      <td>2.221</td>\n",
              "      <td>5.220</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14.88</td>\n",
              "      <td>14.57</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>5.554</td>\n",
              "      <td>3.333</td>\n",
              "      <td>1.018</td>\n",
              "      <td>4.956</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.29</td>\n",
              "      <td>14.09</td>\n",
              "      <td>0.9050</td>\n",
              "      <td>5.291</td>\n",
              "      <td>3.337</td>\n",
              "      <td>2.699</td>\n",
              "      <td>4.825</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.84</td>\n",
              "      <td>13.94</td>\n",
              "      <td>0.8955</td>\n",
              "      <td>5.324</td>\n",
              "      <td>3.379</td>\n",
              "      <td>2.259</td>\n",
              "      <td>4.805</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16.14</td>\n",
              "      <td>14.99</td>\n",
              "      <td>0.9034</td>\n",
              "      <td>5.658</td>\n",
              "      <td>3.562</td>\n",
              "      <td>1.355</td>\n",
              "      <td>5.175</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Area  Perimeter  Compactness  ...  Asymmetry.Coeff  Kernel.Groove  Type\n",
              "0  15.26      14.84       0.8710  ...            2.221          5.220     1\n",
              "1  14.88      14.57       0.8811  ...            1.018          4.956     1\n",
              "2  14.29      14.09       0.9050  ...            2.699          4.825     1\n",
              "3  13.84      13.94       0.8955  ...            2.259          4.805     1\n",
              "4  16.14      14.99       0.9034  ...            1.355          5.175     1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3slJBZdULy0",
        "outputId": "5c4bff45-7eec-47d9-c0ce-0ad6793b8a33"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(199, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWiklxPiULv6",
        "outputId": "f082bce2-903d-455b-e209-0f6170e48baa"
      },
      "source": [
        "df.Type.value_counts()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    68\n",
              "1    66\n",
              "3    65\n",
              "Name: Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LiJSAEDmULsD",
        "outputId": "76cf1a12-8e0d-49be-9ab6-62c1d3fe6598"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 199 entries, 0 to 198\n",
            "Data columns (total 8 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Area             199 non-null    float64\n",
            " 1   Perimeter        199 non-null    float64\n",
            " 2   Compactness      199 non-null    float64\n",
            " 3   Kernel.Length    199 non-null    float64\n",
            " 4   Kernel.Width     199 non-null    float64\n",
            " 5   Asymmetry.Coeff  199 non-null    float64\n",
            " 6   Kernel.Groove    199 non-null    float64\n",
            " 7   Type             199 non-null    int64  \n",
            "dtypes: float64(7), int64(1)\n",
            "memory usage: 12.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qB44nl59U0Ro"
      },
      "source": [
        "## 2.3 Feature Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "id": "U2kmPbMlULpi",
        "outputId": "960c2b24-bea8-405a-ad49-927b86080f92"
      },
      "source": [
        "df.hist(figsize=(20,15));"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfAxZB37U6Ml"
      },
      "source": [
        "From the above graph it is very difficult to conclude anything. Hence I see the co relation below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "9rnpQ6WAULmq",
        "outputId": "ccef9b8e-4c13-4ac8-cc66-625e150a0282"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Perimeter</th>\n",
              "      <th>Compactness</th>\n",
              "      <th>Kernel.Length</th>\n",
              "      <th>Kernel.Width</th>\n",
              "      <th>Asymmetry.Coeff</th>\n",
              "      <th>Kernel.Groove</th>\n",
              "      <th>Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Area</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994437</td>\n",
              "      <td>0.609887</td>\n",
              "      <td>0.951126</td>\n",
              "      <td>0.970980</td>\n",
              "      <td>-0.222820</td>\n",
              "      <td>0.862727</td>\n",
              "      <td>-0.340664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perimeter</th>\n",
              "      <td>0.994437</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.531841</td>\n",
              "      <td>0.972944</td>\n",
              "      <td>0.945496</td>\n",
              "      <td>-0.211034</td>\n",
              "      <td>0.889543</td>\n",
              "      <td>-0.322223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Compactness</th>\n",
              "      <td>0.609887</td>\n",
              "      <td>0.531841</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.374011</td>\n",
              "      <td>0.762167</td>\n",
              "      <td>-0.329400</td>\n",
              "      <td>0.227016</td>\n",
              "      <td>-0.539261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kernel.Length</th>\n",
              "      <td>0.951126</td>\n",
              "      <td>0.972944</td>\n",
              "      <td>0.374011</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.862680</td>\n",
              "      <td>-0.169704</td>\n",
              "      <td>0.932124</td>\n",
              "      <td>-0.249422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kernel.Width</th>\n",
              "      <td>0.970980</td>\n",
              "      <td>0.945496</td>\n",
              "      <td>0.762167</td>\n",
              "      <td>0.862680</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.253122</td>\n",
              "      <td>0.748228</td>\n",
              "      <td>-0.421961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asymmetry.Coeff</th>\n",
              "      <td>-0.222820</td>\n",
              "      <td>-0.211034</td>\n",
              "      <td>-0.329400</td>\n",
              "      <td>-0.169704</td>\n",
              "      <td>-0.253122</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003349</td>\n",
              "      <td>0.573956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kernel.Groove</th>\n",
              "      <td>0.862727</td>\n",
              "      <td>0.889543</td>\n",
              "      <td>0.227016</td>\n",
              "      <td>0.932124</td>\n",
              "      <td>0.748228</td>\n",
              "      <td>-0.003349</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.035508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type</th>\n",
              "      <td>-0.340664</td>\n",
              "      <td>-0.322223</td>\n",
              "      <td>-0.539261</td>\n",
              "      <td>-0.249422</td>\n",
              "      <td>-0.421961</td>\n",
              "      <td>0.573956</td>\n",
              "      <td>0.035508</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Area  Perimeter  ...  Kernel.Groove      Type\n",
              "Area             1.000000   0.994437  ...       0.862727 -0.340664\n",
              "Perimeter        0.994437   1.000000  ...       0.889543 -0.322223\n",
              "Compactness      0.609887   0.531841  ...       0.227016 -0.539261\n",
              "Kernel.Length    0.951126   0.972944  ...       0.932124 -0.249422\n",
              "Kernel.Width     0.970980   0.945496  ...       0.748228 -0.421961\n",
              "Asymmetry.Coeff -0.222820  -0.211034  ...      -0.003349  0.573956\n",
              "Kernel.Groove    0.862727   0.889543  ...       1.000000  0.035508\n",
              "Type            -0.340664  -0.322223  ...       0.035508  1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSSl0zfaU_wn"
      },
      "source": [
        "Following are my observation\n",
        "\n",
        "- Kernel Length and width seems to have strong relation with Area\n",
        "- Kernel Length and width seems to have strong relation with Perimeter\n",
        "- Area and Perimeter have a strong relation with Groove\n",
        "- Groove is very close to Zero(0.03) againt Type, Hence confirming there is no linear relation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jKYFwHAnVJ4G"
      },
      "source": [
        "I now plot all the above mentioned point for each Type. Plotting Area and Kernal Lenght for each Type."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMYT01JjVk3V"
      },
      "source": [
        "### 2.3.1 Area Vs Kernal length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "oTY2gtSQULkH",
        "outputId": "b1c6fd92-bc92-4f5d-ffe7-7cb0a37c4f53"
      },
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (20,6)\n",
        "sns.scatterplot(data=df, x=\"Area\", y=\"Kernel.Length\", hue=\"Type\",  palette=\"deep\");"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2thRdE3lVPQ5"
      },
      "source": [
        "### 2.3.2 Area Vs Kernal Width\n",
        "Plotting Area and Kernal Width for each Type."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "5EbdlWylVMqY",
        "outputId": "9dda1bdb-1c0d-404c-88a2-8b2763488b6c"
      },
      "source": [
        "sns.scatterplot(data=df, x=\"Area\", y=\"Kernel.Width\", hue=\"Type\", palette=\"deep\");"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3MkiC5GVVLt"
      },
      "source": [
        "I observe the data overlap between Type 1 and Type 2 and minimal overlap with type 3. Hence i conclude while Typel 1 and Type 2 seeds might be similar in Width but Type 3 seeds are definately greater in size."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbdU6Ht2VbAt"
      },
      "source": [
        "### 2.3.3 Plotting Groove Vs Perimeter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "JwSpU8RJVRpJ",
        "outputId": "66a8b7ea-5db4-4539-8ed0-35f60361e37b"
      },
      "source": [
        "sns.scatterplot(data=df, x=\"Kernel.Groove\", y=\"Perimeter\", hue=\"Type\", palette=\"deep\");"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ka8pPcjBV9UJ"
      },
      "source": [
        "### 2.3.4 Area Vs Kernal Groove"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "_iFss160VStd",
        "outputId": "f68a5204-7aeb-4361-e4ae-5b0d7352a4bc"
      },
      "source": [
        "sns.scatterplot(data=df, x=\"Kernel.Groove\", y=\"Area\", hue=\"Type\", palette=\"deep\");"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31Dx8FFqWIpV"
      },
      "source": [
        "I observe cluster's for Type 1,2 and 3 for the data points. This situation is ideal for neural network as relations of features against the labels is not clear."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jtrm2Z6wWPBg"
      },
      "source": [
        "# 3: Preparing the data for ML model using PyTorch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijp08EkYWO2t"
      },
      "source": [
        "We need to convert the data from the Pandas dataframe into a PyTorch tensors for training. To do this, the first step is to convert it numpy arrays. This following function will perform the conversion to numpy arrays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2N4B2WpVSlT",
        "outputId": "972104fa-caab-4cf2-8b28-b6194539c309"
      },
      "source": [
        "# Converting the data to tensors\n",
        "from torch.utils.data import random_split\n",
        "import torch\n",
        "import random\n",
        "from torch.utils.data import TensorDataset\n",
        "\n",
        "# this is to update the labels from 1, 2, 3 to 0, 1, 2\n",
        "def update_values_(df):\n",
        "  for i in range(len(df)):\n",
        "    df[i] = df[i] - 1\n",
        "\n",
        "  return df\n",
        "\n",
        "train_target = torch.tensor(df['Type'].values.astype(np.int64))\n",
        "train = torch.tensor(df.drop('Type', axis = 1).values.astype(np.float32))\n",
        "\n",
        "\n",
        "train_tensor = TensorDataset(train, update_values_(train_target))\n",
        "\n",
        "train_ds, val_ds = random_split(train_tensor, [180, 19])\n",
        "len(train_ds), len(val_ds)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180, 19)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCGUChe6VSii"
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "batch_size = 64\n",
        "\n",
        "train_loader = DataLoader(train_ds, batch_size, shuffle = True)\n",
        "val_loader = DataLoader(val_ds, batch_size)"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6UJPSTdWIYi"
      },
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "input_size = 1*7\n",
        "num_classes = 3\n",
        "\n",
        "model = nn.Linear(input_size, num_classes)"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0Wk9U2OWIU3"
      },
      "source": [
        "class Wheatseed(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.linear = nn.Linear(input_size, num_classes)\n",
        "        \n",
        "    def forward(self, xb):\n",
        "        # xb = xb.reshape(-1, 7)\n",
        "        out = self.linear(xb)\n",
        "        return out\n",
        "    \n",
        "model = Wheatseed()"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rEr6QSHPWISZ",
        "outputId": "6e517597-a6c3-45f5-98c4-95a29f5f9e17"
      },
      "source": [
        "model.linear"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Linear(in_features=7, out_features=3, bias=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgsoS2peX6QK",
        "outputId": "00dbb105-d016-4f79-e181-2f3ab48091c3"
      },
      "source": [
        "for images, labels in train_loader:\n",
        "    print(images.shape)\n",
        "    outputs = model(images)\n",
        "    print(labels.shape)\n",
        "    break\n",
        "\n",
        "print('outputs.shape : ', outputs.shape)\n",
        "print('Sample outputs :\\n', outputs[:2].data)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([64, 7])\n",
            "torch.Size([64])\n",
            "outputs.shape :  torch.Size([64, 3])\n",
            "Sample outputs :\n",
            " tensor([[5.7391, 0.3601, 2.7418],\n",
            "        [7.3783, 0.0444, 3.0794]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9E93jumYBDd"
      },
      "source": [
        "def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
        "    optimizer = opt_func(model.parameters(), lr)\n",
        "    history = [] # for recording epoch-wise results\n",
        "    \n",
        "    for epoch in range(epochs):\n",
        "        \n",
        "        # Training Phase \n",
        "        for batch in train_loader:\n",
        "            loss = model.training_step(batch)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "        \n",
        "        # Validation phase\n",
        "        result = evaluate(model, val_loader)\n",
        "        model.epoch_end(epoch, result)\n",
        "        history.append(result)\n",
        "\n",
        "    return history\n",
        "\n",
        "\n",
        "def evaluate(model, val_loader):\n",
        "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
        "    return model.validation_epoch_end(outputs)"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TzVcmwaYMKJ"
      },
      "source": [
        "class Wheatseed(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.linear = nn.Linear(input_size, num_classes)\n",
        "        \n",
        "    def forward(self, xb):\n",
        "        # xb = xb.reshape(-1, 7)\n",
        "        out = self.linear(xb)\n",
        "        return out\n",
        "    \n",
        "    def training_step(self, batch):\n",
        "        images, labels = batch \n",
        "        out = self(images)                  # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels) # Calculate loss\n",
        "        return loss\n",
        "    \n",
        "    def validation_step(self, batch):\n",
        "        images, labels = batch \n",
        "        out = self(images)                    # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels)   # Calculate loss\n",
        "        acc = accuracy(out, labels)           # Calculate accuracy\n",
        "        return {'val_loss': loss, 'val_acc': acc}\n",
        "        \n",
        "    def validation_epoch_end(self, outputs):\n",
        "        batch_losses = [x['val_loss'] for x in outputs]\n",
        "        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses\n",
        "        batch_accs = [x['val_acc'] for x in outputs]\n",
        "        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies\n",
        "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
        "    \n",
        "    def epoch_end(self, epoch, result):\n",
        "        print(\"Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}\".format(epoch, result['val_loss'], result['val_acc']))\n",
        "    \n",
        "model = Wheatseed()"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jhGlaWCY1Ht"
      },
      "source": [
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))"
      ],
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoktiWezcuAI"
      },
      "source": [
        "Before we train the model, let's see how the model performs on the validation set with the initial set of randomly initialized weights & biases.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQs0xUmNYWj1",
        "outputId": "6115caaa-b7aa-41a8-817d-07a6753c69c8"
      },
      "source": [
        "result0 = evaluate(model, val_loader)\n",
        "result0"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'val_acc': 0.31578946113586426, 'val_loss': 1.8727011680603027}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4Wyr6iUcyaW"
      },
      "source": [
        "The initial accuracy is around 52%, which one might expect from a randomly initialized model.\n",
        "\n",
        "We are now ready to train the model. Let's train for five epochs and look at the results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIFg-uIgYiiL"
      },
      "source": [
        "model = Wheatseed()"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7GZc-Ipaame",
        "outputId": "323537f0-f92f-466f-9bad-ba7840e7a362"
      },
      "source": [
        "history1 = fit(5, 0.001, model, train_loader, val_loader)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0], val_loss: 1.4956, val_acc: 0.4737\n",
            "Epoch [1], val_loss: 1.2353, val_acc: 0.5789\n",
            "Epoch [2], val_loss: 1.0488, val_acc: 0.5789\n",
            "Epoch [3], val_loss: 0.9282, val_acc: 0.6316\n",
            "Epoch [4], val_loss: 0.8510, val_acc: 0.7368\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlI-0ODNaaha",
        "outputId": "970e3bb7-85ce-4f1f-f47e-2360572ed1e0"
      },
      "source": [
        "history2 = fit(5, 0.001, model, train_loader, val_loader)"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0], val_loss: 0.8104, val_acc: 0.7368\n",
            "Epoch [1], val_loss: 0.7880, val_acc: 0.7368\n",
            "Epoch [2], val_loss: 0.7761, val_acc: 0.7368\n",
            "Epoch [3], val_loss: 0.7705, val_acc: 0.7895\n",
            "Epoch [4], val_loss: 0.7658, val_acc: 0.7895\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mulD6EUcaad-",
        "outputId": "de7c0e59-de01-47b8-e2f6-9e78884d0773"
      },
      "source": [
        "history3 = fit(5, 0.001, model, train_loader, val_loader)"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0], val_loss: 0.7627, val_acc: 0.7368\n",
            "Epoch [1], val_loss: 0.7595, val_acc: 0.7368\n",
            "Epoch [2], val_loss: 0.7578, val_acc: 0.7368\n",
            "Epoch [3], val_loss: 0.7566, val_acc: 0.7895\n",
            "Epoch [4], val_loss: 0.7566, val_acc: 0.7895\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pzSz9tFdyev"
      },
      "source": [
        "While the accuracy does continue to increase as we train for more epochs, the improvements get smaller with every epoch. Let's visualize this using a line graph."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "ULEBjAqFaaVv",
        "outputId": "581d4f09-8528-4312-ccfc-3782748f4831"
      },
      "source": [
        "history = [result0] + history1 + history2 + history3 + history4\n",
        "accuracies = [result['val_acc'] for result in history]\n",
        "plt.rcParams[\"figure.figsize\"] = (6,6)\n",
        "plt.plot(accuracies, '-x')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.grid()\n",
        "plt.title('Accuracy vs. No. of epochs')\n",
        "plt.show();"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmM2n65M0dcg"
      },
      "source": [
        "It's quite clear from the above picture that the model probably won't cross the accuracy threshold of 80% even after training for a very long time. One possible reason for this is that the learning rate might be too high. The model's parameters may be \"bouncing\" around the optimal set of parameters for the lowest loss. You can try reducing the learning rate and training for a few more epochs to see if it helps.\n",
        "\n",
        "The more likely reason that the model just isn't powerful enough. There are variour technique to improve the performace of the model but that might be beyond the scope of this notebook. "
      ]
    }
  ]
}