14/Version_1_Final_Model.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ],
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        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "!cp /content/drive/MyDrive/Abdulla_OCR/Character/anotation_28x28/Test.csv .\n",
        "!cp /content/drive/MyDrive/Abdulla_OCR/Character/anotation_28x28/Train.csv .\n",
        "\n",
        "!rm -rf sample_data"
      ],
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        {
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
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      "cell_type": "code",
      "source": [
        "!cp /content/drive/MyDrive/abdulla_model/model_1111.h5 ."
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          "metadata": {},
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    {
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        "y_train = train['label']\n",
        "x_train = train.drop('label', axis=1)\n",
        "y_train.shape, x_train.shape"
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          "shell.execute_reply": "2023-04-15T10:30:19.897065Z"
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        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:20.475306Z",
          "iopub.execute_input": "2023-04-15T10:30:20.476203Z",
          "iopub.status.idle": "2023-04-15T10:30:20.494478Z",
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          "shell.execute_reply": "2023-04-15T10:30:20.493539Z"
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      "execution_count": null,
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e2de8eab-8afa-4705-8fc9-26304357518a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e2de8eab-8afa-4705-8fc9-26304357518a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()"
      ],
      "metadata": {
        "_uuid": "4266188b33bba2ca07600fa9dff89949de0d6f6a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlQ6xuR83dqu",
        "outputId": "12e66ff1-9ca8-4362-8bac-5ce6938a145b",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:22.242158Z",
          "iopub.execute_input": "2023-04-15T10:30:22.242516Z",
          "iopub.status.idle": "2023-04-15T10:30:22.255884Z",
          "shell.execute_reply.started": "2023-04-15T10:30:22.242482Z",
          "shell.execute_reply": "2023-04-15T10:30:22.254584Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     6000\n",
              "26    5999\n",
              "20    5999\n",
              "21    5999\n",
              "22    5999\n",
              "23    5999\n",
              "24    5999\n",
              "25    5999\n",
              "27    5999\n",
              "18    5999\n",
              "28    5999\n",
              "29    5999\n",
              "30    5999\n",
              "31    5999\n",
              "32    5999\n",
              "33    5999\n",
              "19    5999\n",
              "17    5999\n",
              "1     5999\n",
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              "2     5999\n",
              "3     5999\n",
              "4     5999\n",
              "5     5999\n",
              "6     5999\n",
              "7     5999\n",
              "9     5999\n",
              "16    5999\n",
              "10    5999\n",
              "11    5999\n",
              "12    5999\n",
              "13    5999\n",
              "14    5999\n",
              "15    5999\n",
              "34    5999\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = test['0']\n",
        "x_test = test.drop('0', axis=1)\n",
        "y_test.shape, x_test.shape"
      ],
      "metadata": {
        "id": "m4EHUjup6L7X",
        "outputId": "a78ae80a-d421-4a41-b161-f5ba6a0232f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((38385,), (38385, 784))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "id": "OsdXTv8o6xf2",
        "outputId": "8ef402ee-9c9f-4103-9c72-d99128eefcc5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1129\n",
              "7     1129\n",
              "1     1129\n",
              "10    1129\n",
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              "8     1129\n",
              "11    1129\n",
              "6     1129\n",
              "5     1129\n",
              "4     1129\n",
              "3     1129\n",
              "2     1129\n",
              "23    1099\n",
              "33    1099\n",
              "32    1099\n",
              "31    1099\n",
              "30    1099\n",
              "29    1099\n",
              "28    1099\n",
              "27    1099\n",
              "26    1099\n",
              "25    1099\n",
              "24    1099\n",
              "34    1099\n",
              "22    1059\n",
              "21    1059\n",
              "20    1059\n",
              "19    1059\n",
              "18    1059\n",
              "16    1059\n",
              "15    1059\n",
              "14    1059\n",
              "13    1059\n",
              "12    1059\n",
              "17    1059\n",
              "Name: 0, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of images:\", len(test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t4u7fe23qALm",
        "outputId": "2485d67c-7b02-48ca-c0b0-da91df758edc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images: 38385\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.isnull().any().describe()"
      ],
      "metadata": {
        "_uuid": "f9ac1b11b280d2448cfd173768ef738893cedf53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lN-Ey70b3dqw",
        "outputId": "4c27fcb4-1e1a-4a77-d5fb-7e0c89d4b0ca",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:24.202603Z",
          "iopub.execute_input": "2023-04-15T10:30:24.202965Z",
          "iopub.status.idle": "2023-04-15T10:30:24.254990Z",
          "shell.execute_reply.started": "2023-04-15T10:30:24.202931Z",
          "shell.execute_reply": "2023-04-15T10:30:24.252816Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       784\n",
              "unique        1\n",
              "top       False\n",
              "freq        784\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.isnull().any().describe()"
      ],
      "metadata": {
        "_uuid": "897132104feba4ccc25d3f5fd2c0059ae9a01d41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ts5H2Z4S3dqy",
        "outputId": "8e5e72a3-7948-4546-feb2-cd48b64818fb",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:25.814757Z",
          "iopub.execute_input": "2023-04-15T10:30:25.815132Z",
          "iopub.status.idle": "2023-04-15T10:30:25.834518Z",
          "shell.execute_reply.started": "2023-04-15T10:30:25.815098Z",
          "shell.execute_reply": "2023-04-15T10:30:25.833585Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       784\n",
              "unique        1\n",
              "top       False\n",
              "freq        784\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " **Normalization**"
      ],
      "metadata": {
        "_uuid": "1c45b2495198a55b27f54a8326eef55fdbeedbcc",
        "id": "8tOypXuM3dqz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train/255.0\n",
        "x_test = x_test/255.0"
      ],
      "metadata": {
        "_uuid": "21159f3cab1dcb62b57a093e34ff8e4dd325d22f",
        "id": "iz89BAdR3dq2",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:29.099480Z",
          "iopub.execute_input": "2023-04-15T10:30:29.100195Z",
          "iopub.status.idle": "2023-04-15T10:30:29.661435Z",
          "shell.execute_reply.started": "2023-04-15T10:30:29.100157Z",
          "shell.execute_reply": "2023-04-15T10:30:29.660399Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reshape**\n",
        "* [np.reshape](https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.reshape.html) detail\n",
        "* x_train is numpy.ndarray format now"
      ],
      "metadata": {
        "_uuid": "f26b64a8db332453962296abea78f5ec87a38a0e",
        "id": "16wTknsH3dq3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.values.reshape(-1,28,28,1)"
      ],
      "metadata": {
        "_uuid": "7ca2954b8a064524c97c8078bb20ab3f39a35a95",
        "id": "kDTHNXoZ3dq4",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:30.937391Z",
          "iopub.execute_input": "2023-04-15T10:30:30.938076Z",
          "iopub.status.idle": "2023-04-15T10:30:30.943088Z",
          "shell.execute_reply.started": "2023-04-15T10:30:30.938037Z",
          "shell.execute_reply": "2023-04-15T10:30:30.941904Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = x_test.values.reshape(-1,28,28,1)"
      ],
      "metadata": {
        "_uuid": "42c444ef34bd5415a05906cbb077247d047c8a83",
        "id": "yntoFc0F3dq5",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:31.421228Z",
          "iopub.execute_input": "2023-04-15T10:30:31.421601Z",
          "iopub.status.idle": "2023-04-15T10:30:31.427026Z",
          "shell.execute_reply.started": "2023-04-15T10:30:31.421565Z",
          "shell.execute_reply": "2023-04-15T10:30:31.425863Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "_uuid": "20b39258892baf82131d657fb9476851c7847f2f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMaZXaQ73dq5",
        "outputId": "f5299c1b-2d7b-4c20-fc1a-29fbbb400328",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:31.923112Z",
          "iopub.execute_input": "2023-04-15T10:30:31.923809Z",
          "iopub.status.idle": "2023-04-15T10:30:31.930137Z",
          "shell.execute_reply.started": "2023-04-15T10:30:31.923770Z",
          "shell.execute_reply": "2023-04-15T10:30:31.929001Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38385, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Label Encoding**\n",
        "* Use the Scikit to make one-hot encoding label (Be careful about the format), which must be used with **np.array**.\n",
        "* Detail [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)"
      ],
      "metadata": {
        "_uuid": "9050d6c41594fe422082ebf5e205a2ada0e01759",
        "id": "E6zw3CGQ3dq6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the Scikit to make one-hot encoding label\n",
        "y_train = pd.DataFrame(data=y_train)\n",
        "one_hot = OneHotEncoder(handle_unknown='ignore')\n",
        "one_hot.fit(y_train.values)\n",
        "y_train = one_hot.transform(y_train.values).toarray()"
      ],
      "metadata": {
        "_uuid": "d091f72d12f3e5aef7162b620a28848e376650cb",
        "id": "ZK9VDd183dq7",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:34.416577Z",
          "iopub.execute_input": "2023-04-15T10:30:34.417480Z",
          "iopub.status.idle": "2023-04-15T10:30:34.447361Z",
          "shell.execute_reply.started": "2023-04-15T10:30:34.417429Z",
          "shell.execute_reply": "2023-04-15T10:30:34.446302Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the Scikit to make one-hot encoding label\n",
        "y_test = pd.DataFrame(data=y_test)\n",
        "one_hot = OneHotEncoder(handle_unknown='ignore')\n",
        "one_hot.fit(y_test.values)\n",
        "y_test = one_hot.transform(y_test.values).toarray()"
      ],
      "metadata": {
        "id": "lSNcM7QQYBfj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Now, y_train becomes** numpy.ndarray**"
      ],
      "metadata": {
        "_uuid": "deb9955e0190b33d3e039aee691eb6e294c49b62",
        "id": "lnQLZswg3dq8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train, y_train.shape"
      ],
      "metadata": {
        "_uuid": "c4c51eb3537e0122b5332bcf53830ea92b308b17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7hurDLS3dq8",
        "outputId": "ddb72308-3000-4843-beea-d993998e9ae6",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:35.812346Z",
          "iopub.execute_input": "2023-04-15T10:30:35.812738Z",
          "iopub.status.idle": "2023-04-15T10:30:35.822250Z",
          "shell.execute_reply.started": "2023-04-15T10:30:35.812702Z",
          "shell.execute_reply": "2023-04-15T10:30:35.820856Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[1., 0., 0., ..., 0., 0., 0.],\n",
              "        [1., 0., 0., ..., 0., 0., 0.],\n",
              "        [1., 0., 0., ..., 0., 0., 0.],\n",
              "        ...,\n",
              "        [0., 0., 0., ..., 0., 0., 1.],\n",
              "        [0., 0., 0., ..., 0., 0., 1.],\n",
              "        [0., 0., 0., ..., 0., 0., 1.]]),\n",
              " (209966, 35))"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting dataset as train data and cross-validation data**\n",
        "* Detail [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)"
      ],
      "metadata": {
        "_uuid": "ec9f6795bf09f76ab273b1e44c2c0dc2b50f69ef",
        "id": "ea8G0BkF3dq9",
        "trusted": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "random_seed = 3\n",
        "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=random_seed)\n",
        "x_train.shape, x_val.shape, y_train.shape, y_val.shape"
      ],
      "metadata": {
        "_uuid": "977adfeaeb2247845521ef0228a7e5bbca23b010",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fr_ckxXo3dq9",
        "outputId": "f1b05a34-4283-49ac-f345-5c12329fad66",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:38.059377Z",
          "iopub.execute_input": "2023-04-15T10:30:38.060345Z",
          "iopub.status.idle": "2023-04-15T10:30:40.039587Z",
          "shell.execute_reply.started": "2023-04-15T10:30:38.060294Z",
          "shell.execute_reply": "2023-04-15T10:30:40.038444Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((188969, 28, 28, 1), (20997, 28, 28, 1), (188969, 35), (20997, 35))"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "random_seed = 3\n",
        "x_te, x_test, y_te, y_test = train_test_split(x_test, y_test, test_size=0.0001, random_state=random_seed)\n",
        "x_te.shape, x_test.shape, y_te.shape, y_test.shape"
      ],
      "metadata": {
        "id": "a6CUkQz_RGrZ",
        "outputId": "071dbc84-d532-4be9-f141-2352fbf7d93a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((38381, 28, 28, 1), (4, 28, 28, 1), (38381, 35), (4, 35))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g = plt.imshow(x_test[0][:,:,0])"
      ],
      "metadata": {
        "_uuid": "57a8c1decf9f369c80ed2c61e7f8285d208b3b27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "CCnM9QmZ3dq-",
        "outputId": "5ac3fd3d-ae68-47c3-b034-2e983da3faf2",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:40.041782Z",
          "iopub.execute_input": "2023-04-15T10:30:40.042182Z",
          "iopub.status.idle": "2023-04-15T10:30:40.269207Z",
          "shell.execute_reply.started": "2023-04-15T10:30:40.042140Z",
          "shell.execute_reply": "2023-04-15T10:30:40.268295Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdsklEQVR4nO3df3BV9f3n8ddNSK6gyaUhJDcpgQZUqCLplkqaRRFLhpDuuIBsB390FhwHVxqcIrX6TVdF286kxR3r6FCd2W2hzoq/viMwMpYuBhPWNtAvKFKmbb6ESSUOJCjfkhuChEA++wfr7fdKQM/He+/7JjwfM2eG3HveOe/74cArJ+fmnZBzzgkAgDTLsm4AAHBpIoAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgYoR1A582MDCgw4cPKy8vT6FQyLodAEBAzjn19PSotLRUWVkXvs7JuAA6fPiwysrKrNsAAHxBHR0dGjdu3AWfz7gAysvLkyTdoG9rhHKMu8FF+Vyh+kx+Stdx0ildrykrO3iNJA2c9avLVL7fTcn08yhDnVG/3tYb8f/PLyRlAbR27Vo98cQT6uzsVEVFhZ555hnNmDHjM+s++bbbCOVoRIgAymhe/6jTFEA+x0mntK2dZwCFhtntYe9v52f4eZSp/v+yfdZtlJScZS+//LJWrVql1atX65133lFFRYVqamp09OjRVBwOADAEpSSAnnzySS1btkx33XWXrrnmGj333HMaNWqUfv3rX6ficACAISjpAXT69Gnt2bNH1dXV/zhIVpaqq6vV0tJy3v59fX2KxWIJGwBg+Et6AH300Uc6e/asiouLEx4vLi5WZ2fnefs3NDQoEonEN94BBwCXBvM7jfX19eru7o5vHR0d1i0BANIg6e+CKywsVHZ2trq6uhIe7+rqUjQaPW//cDiscDic7DYAABku6VdAubm5mj59uhobG+OPDQwMqLGxUVVVVck+HABgiErJzwGtWrVKS5Ys0Te+8Q3NmDFDTz31lHp7e3XXXXel4nAAgCEoJQG0ePFiffjhh3r00UfV2dmpr33ta9q6det5b0wAAFy6Qs5l1qyJWCymSCSi2ZrPJASck8ZRPKERwb8mc2fOeBzI4zX5TCfwHamTweOP0vZ35CuD1y5dzrh+NWmzuru7lZ+ff8H9zN8FBwC4NBFAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADCRkmnYQFL5DGrMyvY7VLqGVvq8JucxWNRzHXyGmHoNCR0Ivg5pHSzqY5gNFk0lroAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACaYho30CoWC1/hMF/aY5uzN5zV5HSf414uhbM+p4B7rl/FTqn34TBNP57k3xHEFBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwATDSJFePoNF0yldw1LTNOTSZ6ioJIXC4cA1x//Lfwhcc9nfg/cXfuNfAtd4D4xlsGhKcQUEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABMNIkfnSNLhTUtqGpU7bPRC45o1X/2PgmvKa9sA1kpSbdSZwzd//V/CBn16DRX2kcwhuugbaDgNcAQEATBBAAAATSQ+gxx57TKFQKGGbMmVKsg8DABjiUnIP6Nprr9Wbb775j4OM4FYTACBRSpJhxIgRikajqfjUAIBhIiX3gA4cOKDS0lJNnDhRd955pw4dOnTBffv6+hSLxRI2AMDwl/QAqqys1Pr167V161Y9++yzam9v14033qienp5B929oaFAkEolvZWVlyW4JAJCBkh5AtbW1+s53vqNp06appqZGb7zxho4fP65XXnll0P3r6+vV3d0d3zo6OpLdEgAgA6X83QGjR4/W1Vdfrba2tkGfD4fDCofDqW4DAJBhUv5zQCdOnNDBgwdVUlKS6kMBAIaQpAfQAw88oObmZv3tb3/TH/7wBy1cuFDZ2dm6/fbbk30oAMAQlvRvwX3wwQe6/fbbdezYMY0dO1Y33HCDdu7cqbFjxyb7UACAISzpAfTSSy8l+1MiQ4VycgPXuP7TwQ/kM1jUZyCk5DUUMuTxg9bv1V0buOb0wuC9nf7vxYFrJKm/5U+Ba0YPtASu8Vk7dyb4oNR0ng+X6mBRH8yCAwCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYCLlv5AOw5fPYNG0DZ/0HAiZrv5CLe8Frpm4Kztwjdcg1zTy+rv1OlBmnw+XKq6AAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmmIaNtMr0ScFuwG9qclBpm7IcCgWvkbynR6dDOidUp23NM3i9U4krIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYYRgp/WdnBa9yAR00aBzUOnA1e47EOPkMus0aNClzz8U3XBq6RpMs6Twauce/9NfiBPM6HtA609TnHfc6hSxRXQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwwjBT+fIYuhkLJ7yOJQjm5gWtc/+nANe//uCpwzcy5fwpcs/3PfoNcJ47vCVyTl1MYuObUnGOBa9I6jJTBoinFFRAAwAQBBAAwETiAduzYoVtuuUWlpaUKhULatGlTwvPOOT366KMqKSnRyJEjVV1drQMHDiSrXwDAMBE4gHp7e1VRUaG1a9cO+vyaNWv09NNP67nnntOuXbt0+eWXq6amRqdOnfrCzQIAho/Ab0Kora1VbW3toM855/TUU0/p4Ycf1vz58yVJzz//vIqLi7Vp0ybddtttX6xbAMCwkdR7QO3t7ers7FR1dXX8sUgkosrKSrW0tAxa09fXp1gslrABAIa/pAZQZ2enJKm4uDjh8eLi4vhzn9bQ0KBIJBLfysrKktkSACBDmb8Lrr6+Xt3d3fGto6PDuiUAQBokNYCi0agkqaurK+Hxrq6u+HOfFg6HlZ+fn7ABAIa/pAZQeXm5otGoGhsb44/FYjHt2rVLVVXBf/IbADB8BX4X3IkTJ9TW1hb/uL29XXv37lVBQYHGjx+vlStX6qc//amuuuoqlZeX65FHHlFpaakWLFiQzL4BAENc4ADavXu3br755vjHq1atkiQtWbJE69ev14MPPqje3l7dc889On78uG644QZt3bpVl112WfK6BgAMeSHnnN+0whSJxWKKRCKarfkaEcqxbgcXk5UdvMYNBC4JZQc/TjoHVh78H98MXuQxk/WqR/YFrhk4eTL4gTwd3nhN4JrI/84LXHP5xt2Ba3yHiqZrOO1wc8b1q0mb1d3dfdH7+ubvggMAXJoIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYC/zoGIM5zwnBQXpOtfSZ1S4otvj5wTWTyvwWuKbzlXwPXBJ8j7i8UDgeuGf188MnW//bdE4FrFPpG4JLL/3lX8OOIydapxhUQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwwjhb9QKHiNc8nvYxChbL9hpHP/6f8GrvmXO6YGrjnrMyz1G9cELukbc1nw40jqrAr+X8PMuX8KXPOr8W8Hrpl4+L8Frpm8xW8dBk6dCl6Uwf8uMg1XQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwwjBTDkus/7VX3+nOzAtd0/1PwgZWhrGmBawbOeAy57PH7GjO/Nfix9rwQ/DXNmFcSuGbMO8Ffk9dQUV+X6GBRH1wBAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMMEwUvjL4KGLoRF+p/bYZ1sC1xT9z+DHcmfOBK5RyGMYacjza8yBs4FLPrqnKnBN3/8ZG7gmuu4PgWuUlR28RvJaB3x+XAEBAEwQQAAAE4EDaMeOHbrllltUWlqqUCikTZs2JTy/dOlShUKhhG3evHnJ6hcAMEwEDqDe3l5VVFRo7dq1F9xn3rx5OnLkSHx78cUXv1CTAIDhJ/Dd09raWtXW1l50n3A4rGg06t0UAGD4S8k9oKamJhUVFWny5Mlavny5jh07dsF9+/r6FIvFEjYAwPCX9ACaN2+enn/+eTU2NurnP/+5mpubVVtbq7NnB387Y0NDgyKRSHwrKytLdksAgAyU9J8Duu222+J/vu666zRt2jRNmjRJTU1NmjNnznn719fXa9WqVfGPY7EYIQQAl4CUvw174sSJKiwsVFtb26DPh8Nh5efnJ2wAgOEv5QH0wQcf6NixYyopKUn1oQAAQ0jgb8GdOHEi4Wqmvb1de/fuVUFBgQoKCvT4449r0aJFikajOnjwoB588EFdeeWVqqmpSWrjAIChLXAA7d69WzfffHP840/u3yxZskTPPvus9u3bp9/85jc6fvy4SktLNXfuXP3kJz9ROBxOXtcAgCEvcADNnj1b7iJDKH/3u999oYYwhPgMx/QZYOoxSNJr2Kfk9Zq8jxWUz2DRNA7TvHxRZ+CavHsHAtd4rbbvOqTrHL9EMQsOAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGAi6b+SG7gon+nCaZzo7DVxWsEnOnvxWQef9ZY0YkJZ4Jre0zmBa0a2/2vgGi+e68Bk69TiCggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJhpHCn8+gxqxsj+OkcRhpOgefBhQaEfyfa3bZl72ONef1PwWuafxP1wauOeNxPoSyg9e4/tOBa3yFcnID16Szv0zCFRAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATDCOFt1A4HLjG9fUFP84wHO7os3YfLvl64Jr/XNccuEaS/vmncwPX5L2/0+tYgWWF0nMcT5l+7mUSroAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYYBgpvPkMFvU6jsdwx/7q6V7HOhnNCVxztMoFrllx07bANevbjgeu2flfKwLXSFLee8EHi6ZraKw7cyZwjUKeA0xDwb9GD3kMS/V6TcMAV0AAABMEEADARKAAamho0PXXX6+8vDwVFRVpwYIFam1tTdjn1KlTqqur05gxY3TFFVdo0aJF6urqSmrTAIChL1AANTc3q66uTjt37tS2bdvU39+vuXPnqre3N77P/fffr9dff12vvvqqmpubdfjwYd16661JbxwAMLQFehPC1q1bEz5ev369ioqKtGfPHs2aNUvd3d361a9+pQ0bNuhb3/qWJGndunX66le/qp07d+qb3/xm8joHAAxpX+geUHd3tySpoKBAkrRnzx719/eruro6vs+UKVM0fvx4tbS0DPo5+vr6FIvFEjYAwPDnHUADAwNauXKlZs6cqalTp0qSOjs7lZubq9GjRyfsW1xcrM7OzkE/T0NDgyKRSHwrKyvzbQkAMIR4B1BdXZ3279+vl1566Qs1UF9fr+7u7vjW0dHxhT4fAGBo8PpB1BUrVmjLli3asWOHxo0bF388Go3q9OnTOn78eMJVUFdXl6LR6KCfKxwOKxwO+7QBABjCAl0BOee0YsUKbdy4Udu3b1d5eXnC89OnT1dOTo4aGxvjj7W2turQoUOqqqpKTscAgGEh0BVQXV2dNmzYoM2bNysvLy9+XycSiWjkyJGKRCK6++67tWrVKhUUFCg/P1/33XefqqqqeAccACBBoAB69tlnJUmzZ89OeHzdunVaunSpJOkXv/iFsrKytGjRIvX19ammpka//OUvk9IsAGD4CDnngk9STKFYLKZIJKLZmq8RoeCDIZFGWdmBS9I1qNFV+Q3h/PtXRwWuueJw8P5Gvvt+4JqzRz8MXOMzTPNcWZoGanoMCQ2NCP7/gs/QU/g74/rVpM3q7u5Wfn7+BfdjFhwAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwITXb0QFJEkDZwOXuAGP4/hM3W55z+NAUkGLR5FHf8FXTpLX4HqfBZfcmTQNyfd4TV6TrT2mbktSKDv4363XVPBLFFdAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATDCMFP48hnD6DDD1m2DqJzQi+D+JtA2f9Bmo6TXA1JNHf2kb9um5Du6s19hYfE5cAQEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADDBMFL48xks6iONAzXTNljURzoHi/rw6C+j11vK/DUf4rgCAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACAiUAB1NDQoOuvv155eXkqKirSggUL1NramrDP7NmzFQqFErZ77703qU0DAIa+QAHU3Nysuro67dy5U9u2bVN/f7/mzp2r3t7ehP2WLVumI0eOxLc1a9YktWkAwNAX6Deibt26NeHj9evXq6ioSHv27NGsWbPij48aNUrRaDQ5HQIAhqUvdA+ou7tbklRQUJDw+AsvvKDCwkJNnTpV9fX1Onny5AU/R19fn2KxWMIGABj+Al0B/XsDAwNauXKlZs6cqalTp8Yfv+OOOzRhwgSVlpZq3759euihh9Ta2qrXXntt0M/T0NCgxx9/3LcNAMAQFXLOOZ/C5cuX67e//a3efvttjRs37oL7bd++XXPmzFFbW5smTZp03vN9fX3q6+uLfxyLxVRWVqbZmq8RoRyf1gAAhs64fjVps7q7u5Wfn3/B/byugFasWKEtW7Zox44dFw0fSaqsrJSkCwZQOBxWOBz2aQMAMIQFCiDnnO677z5t3LhRTU1NKi8v/8yavXv3SpJKSkq8GgQADE+BAqiurk4bNmzQ5s2blZeXp87OTklSJBLRyJEjdfDgQW3YsEHf/va3NWbMGO3bt0/333+/Zs2apWnTpqXkBQAAhqZA94BCodCgj69bt05Lly5VR0eHvvvd72r//v3q7e1VWVmZFi5cqIcffvii3wf892KxmCKRCPeAAGCISsk9oM/KqrKyMjU3Nwf5lACASxSz4AAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJkZYN/BpzjlJ0hn1S864GQBAYGfUL+kf/59fSMYFUE9PjyTpbb1h3AkA4Ivo6elRJBK54PMh91kRlWYDAwM6fPiw8vLyFAqFEp6LxWIqKytTR0eH8vPzjTq0xzqcwzqcwzqcwzqckwnr4JxTT0+PSktLlZV14Ts9GXcFlJWVpXHjxl10n/z8/Ev6BPsE63AO63AO63AO63CO9Tpc7MrnE7wJAQBgggACAJgYUgEUDoe1evVqhcNh61ZMsQ7nsA7nsA7nsA7nDKV1yLg3IQAALg1D6goIADB8EEAAABMEEADABAEEADAxZAJo7dq1+spXvqLLLrtMlZWV+uMf/2jdUto99thjCoVCCduUKVOs20q5HTt26JZbblFpaalCoZA2bdqU8LxzTo8++qhKSko0cuRIVVdX68CBAzbNptBnrcPSpUvPOz/mzZtn02yKNDQ06Prrr1deXp6Kioq0YMECtba2Juxz6tQp1dXVacyYMbriiiu0aNEidXV1GXWcGp9nHWbPnn3e+XDvvfcadTy4IRFAL7/8slatWqXVq1frnXfeUUVFhWpqanT06FHr1tLu2muv1ZEjR+Lb22+/bd1SyvX29qqiokJr164d9Pk1a9bo6aef1nPPPaddu3bp8ssvV01NjU6dOpXmTlPrs9ZBkubNm5dwfrz44otp7DD1mpubVVdXp507d2rbtm3q7+/X3Llz1dvbG9/n/vvv1+uvv65XX31Vzc3NOnz4sG699VbDrpPv86yDJC1btizhfFizZo1RxxfghoAZM2a4urq6+Mdnz551paWlrqGhwbCr9Fu9erWrqKiwbsOUJLdx48b4xwMDAy4ajbonnngi/tjx48ddOBx2L774okGH6fHpdXDOuSVLlrj58+eb9GPl6NGjTpJrbm52zp37u8/JyXGvvvpqfJ+//OUvTpJraWmxajPlPr0Ozjl30003ue9///t2TX0OGX8FdPr0ae3Zs0fV1dXxx7KyslRdXa2WlhbDzmwcOHBApaWlmjhxou68804dOnTIuiVT7e3t6uzsTDg/IpGIKisrL8nzo6mpSUVFRZo8ebKWL1+uY8eOWbeUUt3d3ZKkgoICSdKePXvU39+fcD5MmTJF48ePH9bnw6fX4RMvvPCCCgsLNXXqVNXX1+vkyZMW7V1Qxg0j/bSPPvpIZ8+eVXFxccLjxcXF+utf/2rUlY3KykqtX79ekydP1pEjR/T444/rxhtv1P79+5WXl2fdnonOzk5JGvT8+OS5S8W8efN06623qry8XAcPHtSPfvQj1dbWqqWlRdnZ2dbtJd3AwIBWrlypmTNnaurUqZLOnQ+5ubkaPXp0wr7D+XwYbB0k6Y477tCECRNUWlqqffv26aGHHlJra6tee+01w24TZXwA4R9qa2vjf542bZoqKys1YcIEvfLKK7r77rsNO0MmuO222+J/vu666zRt2jRNmjRJTU1NmjNnjmFnqVFXV6f9+/dfEvdBL+ZC63DPPffE/3zdddeppKREc+bM0cGDBzVp0qR0tzmojP8WXGFhobKzs897F0tXV5ei0ahRV5lh9OjRuvrqq9XW1mbdiplPzgHOj/NNnDhRhYWFw/L8WLFihbZs2aK33nor4de3RKNRnT59WsePH0/Yf7ieDxdah8FUVlZKUkadDxkfQLm5uZo+fboaGxvjjw0MDKixsVFVVVWGndk7ceKEDh48qJKSEutWzJSXlysajSacH7FYTLt27brkz48PPvhAx44dG1bnh3NOK1as0MaNG7V9+3aVl5cnPD99+nTl5OQknA+tra06dOjQsDofPmsdBrN3715JyqzzwfpdEJ/HSy+95MLhsFu/fr3785//7O655x43evRo19nZad1aWv3gBz9wTU1Nrr293f3+97931dXVrrCw0B09etS6tZTq6elx7777rnv33XedJPfkk0+6d999173//vvOOed+9rOfudGjR7vNmze7ffv2ufnz57vy8nL38ccfG3eeXBdbh56eHvfAAw+4lpYW197e7t5880339a9/3V111VXu1KlT1q0nzfLly10kEnFNTU3uyJEj8e3kyZPxfe699143fvx4t337drd7925XVVXlqqqqDLtOvs9ah7a2NvfjH//Y7d6927W3t7vNmze7iRMnulmzZhl3nmhIBJBzzj3zzDNu/PjxLjc3182YMcPt3LnTuqW0W7x4sSspKXG5ubnuy1/+slu8eLFra2uzbivl3nrrLSfpvG3JkiXOuXNvxX7kkUdccXGxC4fDbs6cOa61tdW26RS42DqcPHnSzZ07140dO9bl5OS4CRMmuGXLlg27L9IGe/2S3Lp16+L7fPzxx+573/ue+9KXvuRGjRrlFi5c6I4cOWLXdAp81jocOnTIzZo1yxUUFLhwOOyuvPJK98Mf/tB1d3fbNv4p/DoGAICJjL8HBAAYngggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJj4fwL0pAeOHaZbAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build AlexNet with Keras**"
      ],
      "metadata": {
        "_uuid": "cc25e588a5079f1aceab4c3d608661454398cb81",
        "id": "a1sapTKf3dq-",
        "trusted": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "\n",
        "model = Sequential([\n",
        "    Conv2D(filters = 64, input_shape=(28,28,1), kernel_size=(3,3), strides=(1,1), padding='valid'),\n",
        "    Activation('relu'),\n",
        "    MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='valid'),\n",
        "    BatchNormalization(),\n",
        "    \n",
        "    Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='valid'),\n",
        "    Activation('relu'),\n",
        "    MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='valid'),\n",
        "    BatchNormalization(),\n",
        "    \n",
        "    Conv2D(filters=192, kernel_size=(1,1), strides=(1,1), padding='valid'),\n",
        "    Activation('relu'),\n",
        "    BatchNormalization(),\n",
        "    \n",
        "    Conv2D(filters=192, kernel_size=(3,3), strides=(1,1), padding='valid'),\n",
        "    Activation('relu'),\n",
        "    BatchNormalization(),\n",
        "\n",
        "    Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='valid'),\n",
        "    Activation('relu'),\n",
        "    MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='valid'),\n",
        "    BatchNormalization(),\n",
        "    \n",
        "    Flatten(),\n",
        "    Dense(2048),\n",
        "    Activation('relu'),\n",
        "    Dropout(0.4),\n",
        "    BatchNormalization(),\n",
        "    \n",
        "    Dense(2048),\n",
        "    Activation('relu'),\n",
        "    Dropout(0.4),\n",
        "    BatchNormalization(),\n",
        "\n",
        "    Dense(800),\n",
        "    Activation('relu'),\n",
        "    Dropout(0.4),\n",
        "    BatchNormalization(),\n",
        "    \n",
        "    Dense(35),\n",
        "    Activation('softmax'),\n",
        "])"
      ],
      "metadata": {
        "_uuid": "e41b1c7e84f92a85027835acb87e4c1a533692cc",
        "id": "M9QYr1Ru3dq_",
        "execution": {
          "iopub.status.busy": "2023-04-15T10:30:44.890482Z",
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        {
          "name": "stdout",
          "text": "Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv2d (Conv2D)             (None, 26, 26, 64)        640       \n                                                                 \n activation (Activation)     (None, 26, 26, 64)        0         \n                                                                 \n max_pooling2d (MaxPooling2D  (None, 25, 25, 64)       0         \n )                                                               \n                                                                 \n batch_normalization (BatchN  (None, 25, 25, 64)       256       \n ormalization)                                                   \n                                                                 \n conv2d_1 (Conv2D)           (None, 23, 23, 128)       73856     \n                                                                 \n activation_1 (Activation)   (None, 23, 23, 128)       0         \n                                                                 \n max_pooling2d_1 (MaxPooling  (None, 22, 22, 128)      0         \n 2D)                                                             \n                                                                 \n batch_normalization_1 (Batc  (None, 22, 22, 128)      512       \n hNormalization)                                                 \n                                                                 \n conv2d_2 (Conv2D)           (None, 22, 22, 192)       24768     \n                                                                 \n activation_2 (Activation)   (None, 22, 22, 192)       0         \n                                                                 \n batch_normalization_2 (Batc  (None, 22, 22, 192)      768       \n hNormalization)                                                 \n                                                                 \n conv2d_3 (Conv2D)           (None, 20, 20, 192)       331968    \n                                                                 \n activation_3 (Activation)   (None, 20, 20, 192)       0         \n                                                                 \n batch_normalization_3 (Batc  (None, 20, 20, 192)      768       \n hNormalization)                                                 \n                                                                 \n conv2d_4 (Conv2D)           (None, 18, 18, 128)       221312    \n                                                                 \n activation_4 (Activation)   (None, 18, 18, 128)       0         \n                                                                 \n max_pooling2d_2 (MaxPooling  (None, 17, 17, 128)      0         \n 2D)                                                             \n                                                                 \n batch_normalization_4 (Batc  (None, 17, 17, 128)      512       \n hNormalization)                                                 \n                                                                 \n flatten (Flatten)           (None, 36992)             0         \n                                                                 \n dense (Dense)               (None, 2048)              75761664  \n                                                                 \n activation_5 (Activation)   (None, 2048)              0         \n                                                                 \n dropout (Dropout)           (None, 2048)              0         \n                                                                 \n batch_normalization_5 (Batc  (None, 2048)             8192      \n hNormalization)                                                 \n                                                                 \n dense_1 (Dense)             (None, 2048)              4196352   \n                                                                 \n activation_6 (Activation)   (None, 2048)              0         \n                                                                 \n dropout_1 (Dropout)         (None, 2048)              0         \n                                                                 \n batch_normalization_6 (Batc  (None, 2048)             8192      \n hNormalization)                                                 \n                                                                 \n dense_2 (Dense)             (None, 800)               1639200   \n                                                                 \n activation_7 (Activation)   (None, 800)               0         \n                                                                 \n dropout_2 (Dropout)         (None, 800)               0         \n                                                                 \n batch_normalization_7 (Batc  (None, 800)              3200      \n hNormalization)                                                 \n                                                                 \n dense_3 (Dense)             (None, 35)                28035     \n                                                                 \n activation_8 (Activation)   (None, 35)                0         \n                                                                 \n=================================================================\nTotal params: 82,300,195\nTrainable params: 82,288,995\nNon-trainable params: 11,200\n_________________________________________________________________\n",
          "output_type": "stream"
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      ]
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    {
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        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
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    {
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      "source": [
        "history = model.fit(x_train, y_train, batch_size=200, validation_data=(x_val,y_val), epochs = 200)"
      ],
      "metadata": {
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      "outputs": [
        {
          "name": "stdout",
          "text": "Epoch 1/200\n945/945 [==============================] - 74s 67ms/step - loss: 0.3631 - accuracy: 0.8893 - val_loss: 0.2526 - val_accuracy: 0.9218\nEpoch 2/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.1142 - accuracy: 0.9659 - val_loss: 0.1862 - val_accuracy: 0.9440\nEpoch 3/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0804 - accuracy: 0.9759 - val_loss: 0.1362 - val_accuracy: 0.9600\nEpoch 4/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0636 - accuracy: 0.9814 - val_loss: 0.1490 - val_accuracy: 0.9534\nEpoch 5/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0499 - accuracy: 0.9854 - val_loss: 0.1382 - val_accuracy: 0.9634\nEpoch 6/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0423 - accuracy: 0.9874 - val_loss: 0.0924 - val_accuracy: 0.9782\nEpoch 7/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0358 - accuracy: 0.9894 - val_loss: 0.0940 - val_accuracy: 0.9745\nEpoch 8/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0293 - accuracy: 0.9911 - val_loss: 3.3474 - val_accuracy: 0.4938\nEpoch 9/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0265 - accuracy: 0.9920 - val_loss: 0.2215 - val_accuracy: 0.9425\nEpoch 10/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0229 - accuracy: 0.9930 - val_loss: 0.1336 - val_accuracy: 0.9730\nEpoch 11/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0199 - accuracy: 0.9940 - val_loss: 0.1365 - val_accuracy: 0.9664\nEpoch 12/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0182 - accuracy: 0.9942 - val_loss: 0.0776 - val_accuracy: 0.9823\nEpoch 13/200\n945/945 [==============================] - 62s 65ms/step - loss: 0.0161 - accuracy: 0.9951 - val_loss: 0.0722 - val_accuracy: 0.9848\nEpoch 14/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0148 - accuracy: 0.9953 - val_loss: 0.1250 - val_accuracy: 0.9699\nEpoch 15/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0123 - accuracy: 0.9962 - val_loss: 0.0805 - val_accuracy: 0.9871\nEpoch 16/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0121 - accuracy: 0.9961 - val_loss: 0.0815 - val_accuracy: 0.9847\nEpoch 17/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0101 - accuracy: 0.9966 - val_loss: 0.3832 - val_accuracy: 0.8944\nEpoch 18/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0095 - accuracy: 0.9971 - val_loss: 0.0738 - val_accuracy: 0.9854\nEpoch 19/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0094 - accuracy: 0.9970 - val_loss: 4.1815 - val_accuracy: 0.4639\nEpoch 20/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.1323 - val_accuracy: 0.9694\nEpoch 21/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0080 - accuracy: 0.9973 - val_loss: 0.1570 - val_accuracy: 0.9590\nEpoch 22/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.1075 - val_accuracy: 0.9808\nEpoch 23/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0068 - accuracy: 0.9978 - val_loss: 0.0780 - val_accuracy: 0.9857\nEpoch 24/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0061 - accuracy: 0.9980 - val_loss: 0.1069 - val_accuracy: 0.9810\nEpoch 25/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0061 - accuracy: 0.9980 - val_loss: 0.0819 - val_accuracy: 0.9870\nEpoch 26/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0055 - accuracy: 0.9983 - val_loss: 0.2605 - val_accuracy: 0.9349\nEpoch 27/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0059 - accuracy: 0.9982 - val_loss: 0.1029 - val_accuracy: 0.9828\nEpoch 28/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0052 - accuracy: 0.9985 - val_loss: 0.1081 - val_accuracy: 0.9765\nEpoch 29/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.0885 - val_accuracy: 0.9865\nEpoch 30/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0049 - accuracy: 0.9986 - val_loss: 0.1442 - val_accuracy: 0.9681\nEpoch 31/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.0807 - val_accuracy: 0.9849\nEpoch 32/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.2024 - val_accuracy: 0.9589\nEpoch 33/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0045 - accuracy: 0.9987 - val_loss: 0.0982 - val_accuracy: 0.9853\nEpoch 34/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.1196 - val_accuracy: 0.9784\nEpoch 35/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.0907 - val_accuracy: 0.9885\nEpoch 36/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.1103 - val_accuracy: 0.9774\nEpoch 37/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.1362 - val_accuracy: 0.9721\nEpoch 38/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0029 - accuracy: 0.9991 - val_loss: 0.1006 - val_accuracy: 0.9869\nEpoch 39/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.0992 - val_accuracy: 0.9870\nEpoch 40/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.0875 - val_accuracy: 0.9870\nEpoch 41/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0880 - val_accuracy: 0.9873\nEpoch 42/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0827 - val_accuracy: 0.9888\nEpoch 43/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.4111 - val_accuracy: 0.9126\nEpoch 44/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.1001 - val_accuracy: 0.9877\nEpoch 45/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.1113 - val_accuracy: 0.9868\nEpoch 46/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0021 - accuracy: 0.9993 - val_loss: 0.0913 - val_accuracy: 0.9882\nEpoch 47/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.0981 - val_accuracy: 0.9858\nEpoch 48/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1093 - val_accuracy: 0.9855\nEpoch 49/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 0.0833 - val_accuracy: 0.9876\nEpoch 50/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1347 - val_accuracy: 0.9761\nEpoch 51/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 0.0857 - val_accuracy: 0.9884\nEpoch 52/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.1032 - val_accuracy: 0.9847\nEpoch 53/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0996 - val_accuracy: 0.9864\nEpoch 54/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0941 - val_accuracy: 0.9856\nEpoch 55/200\n945/945 [==============================] - 62s 65ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0956 - val_accuracy: 0.9884\nEpoch 56/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.0971 - val_accuracy: 0.9850\nEpoch 57/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.1344 - val_accuracy: 0.9820\nEpoch 58/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.1370 - val_accuracy: 0.9734\nEpoch 59/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0922 - val_accuracy: 0.9882\nEpoch 60/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1202 - val_accuracy: 0.9879\nEpoch 61/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.0925 - val_accuracy: 0.9875\nEpoch 62/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0918 - val_accuracy: 0.9882\nEpoch 63/200\n945/945 [==============================] - 62s 65ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1155 - val_accuracy: 0.9850\nEpoch 64/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0959 - val_accuracy: 0.9861\nEpoch 65/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1031 - val_accuracy: 0.9888\nEpoch 66/200\n945/945 [==============================] - 62s 65ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.2408 - val_accuracy: 0.9489\nEpoch 67/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1080 - val_accuracy: 0.9880\nEpoch 68/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0020 - accuracy: 0.9993 - val_loss: 0.0875 - val_accuracy: 0.9885\nEpoch 69/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.0875 - val_accuracy: 0.9883\nEpoch 70/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.0829 - val_accuracy: 0.9894\nEpoch 71/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0891 - val_accuracy: 0.9893\nEpoch 72/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0972 - val_accuracy: 0.9888\nEpoch 73/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0951 - val_accuracy: 0.9874\nEpoch 74/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0965 - val_accuracy: 0.9887\nEpoch 75/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.0884 - val_accuracy: 0.9879\nEpoch 76/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1004 - val_accuracy: 0.9818\nEpoch 77/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.1018 - val_accuracy: 0.9879\nEpoch 78/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0937 - val_accuracy: 0.9893\nEpoch 79/200\n945/945 [==============================] - 61s 65ms/step - loss: 8.3302e-04 - accuracy: 0.9997 - val_loss: 0.0949 - val_accuracy: 0.9857\nEpoch 80/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0966 - val_accuracy: 0.9854\nEpoch 81/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1191 - val_accuracy: 0.9857\nEpoch 82/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0965 - val_accuracy: 0.9887\nEpoch 83/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1072 - val_accuracy: 0.9872\nEpoch 84/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.0974 - val_accuracy: 0.9866\nEpoch 85/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1041 - val_accuracy: 0.9874\nEpoch 86/200\n945/945 [==============================] - 61s 64ms/step - loss: 9.6614e-04 - accuracy: 0.9996 - val_loss: 0.0913 - val_accuracy: 0.9899\nEpoch 87/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1127 - val_accuracy: 0.9885\nEpoch 88/200\n945/945 [==============================] - 61s 65ms/step - loss: 9.8198e-04 - accuracy: 0.9997 - val_loss: 0.0959 - val_accuracy: 0.9889\nEpoch 89/200\n945/945 [==============================] - 61s 64ms/step - loss: 9.0100e-04 - accuracy: 0.9997 - val_loss: 0.1257 - val_accuracy: 0.9821\nEpoch 90/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0904 - val_accuracy: 0.9884\nEpoch 91/200\n945/945 [==============================] - 61s 64ms/step - loss: 8.7349e-04 - accuracy: 0.9997 - val_loss: 0.1026 - val_accuracy: 0.9888\nEpoch 92/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0807 - val_accuracy: 0.9897\nEpoch 93/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0856 - val_accuracy: 0.9896\nEpoch 94/200\n945/945 [==============================] - 61s 64ms/step - loss: 9.4593e-04 - accuracy: 0.9997 - val_loss: 0.0896 - val_accuracy: 0.9889\nEpoch 95/200\n945/945 [==============================] - 61s 64ms/step - loss: 8.1282e-04 - accuracy: 0.9998 - val_loss: 0.0900 - val_accuracy: 0.9860\nEpoch 96/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1024 - val_accuracy: 0.9874\nEpoch 97/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1222 - val_accuracy: 0.9869\nEpoch 98/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.0983 - val_accuracy: 0.9852\nEpoch 99/200\n945/945 [==============================] - 61s 65ms/step - loss: 8.6085e-04 - accuracy: 0.9997 - val_loss: 0.1060 - val_accuracy: 0.9889\nEpoch 100/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.0937 - val_accuracy: 0.9892\nEpoch 101/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.1074 - val_accuracy: 0.9889\nEpoch 102/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1138 - val_accuracy: 0.9888\nEpoch 103/200\n945/945 [==============================] - 61s 64ms/step - loss: 7.3798e-04 - accuracy: 0.9997 - val_loss: 0.2339 - val_accuracy: 0.9544\nEpoch 104/200\n945/945 [==============================] - 61s 64ms/step - loss: 8.5059e-04 - accuracy: 0.9997 - val_loss: 0.0912 - val_accuracy: 0.9897\nEpoch 105/200\n945/945 [==============================] - 61s 64ms/step - loss: 8.9077e-04 - accuracy: 0.9997 - val_loss: 0.1237 - val_accuracy: 0.9875\nEpoch 106/200\n945/945 [==============================] - 61s 65ms/step - loss: 8.6593e-04 - accuracy: 0.9997 - val_loss: 0.1053 - val_accuracy: 0.9896\nEpoch 107/200\n945/945 [==============================] - 61s 65ms/step - loss: 9.1344e-04 - accuracy: 0.9997 - val_loss: 0.1041 - val_accuracy: 0.9878\nEpoch 108/200\n945/945 [==============================] - 61s 64ms/step - loss: 8.5077e-04 - accuracy: 0.9997 - val_loss: 0.1029 - val_accuracy: 0.9882\nEpoch 109/200\n945/945 [==============================] - 61s 64ms/step - loss: 9.2657e-04 - accuracy: 0.9997 - val_loss: 0.0877 - val_accuracy: 0.9878\nEpoch 110/200\n945/945 [==============================] - 61s 65ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1129 - val_accuracy: 0.9884\nEpoch 111/200\n945/945 [==============================] - 61s 64ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1506 - val_accuracy: 0.9836\nEpoch 112/200\n945/945 [==============================] - 60s 64ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.1127 - val_accuracy: 0.9885\nEpoch 113/200\n945/945 [==============================] - 61s 64ms/step - loss: 8.7504e-04 - accuracy: 0.9998 - val_loss: 0.1051 - val_accuracy: 0.9870\nEpoch 114/200\n945/945 [==============================] - 61s 65ms/step - loss: 7.5334e-04 - accuracy: 0.9997 - val_loss: 0.0876 - val_accuracy: 0.9900\nEpoch 115/200\n945/945 [==============================] - 62s 65ms/step - loss: 7.4917e-04 - accuracy: 0.9998 - val_loss: 0.1142 - val_accuracy: 0.9829\nEpoch 116/200\n945/945 [==============================] - 62s 65ms/step - loss: 8.6870e-04 - accuracy: 0.9998 - val_loss: 0.1063 - val_accuracy: 0.9896\nEpoch 117/200\n945/945 [==============================] - 61s 65ms/step - loss: 7.0896e-04 - accuracy: 0.9998 - val_loss: 0.0933 - val_accuracy: 0.9901\nEpoch 118/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.7811e-04 - accuracy: 0.9998 - val_loss: 0.0974 - val_accuracy: 0.9890\nEpoch 119/200\n945/945 [==============================] - 62s 66ms/step - loss: 5.4571e-04 - accuracy: 0.9998 - val_loss: 0.1004 - val_accuracy: 0.9900\nEpoch 120/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.3129e-04 - accuracy: 0.9999 - val_loss: 0.1031 - val_accuracy: 0.9887\nEpoch 121/200\n945/945 [==============================] - 61s 65ms/step - loss: 6.9585e-04 - accuracy: 0.9998 - val_loss: 0.1237 - val_accuracy: 0.9872\nEpoch 122/200\n945/945 [==============================] - 61s 65ms/step - loss: 5.5037e-04 - accuracy: 0.9999 - val_loss: 0.0997 - val_accuracy: 0.9879\nEpoch 123/200\n945/945 [==============================] - 62s 66ms/step - loss: 6.5225e-04 - accuracy: 0.9998 - val_loss: 0.1316 - val_accuracy: 0.9880\nEpoch 124/200\n945/945 [==============================] - 62s 66ms/step - loss: 7.0612e-04 - accuracy: 0.9998 - val_loss: 0.1180 - val_accuracy: 0.9885\nEpoch 125/200\n945/945 [==============================] - 62s 65ms/step - loss: 6.8106e-04 - accuracy: 0.9998 - val_loss: 0.1009 - val_accuracy: 0.9899\nEpoch 126/200\n945/945 [==============================] - 61s 65ms/step - loss: 7.7615e-04 - accuracy: 0.9997 - val_loss: 0.0948 - val_accuracy: 0.9893\nEpoch 127/200\n945/945 [==============================] - 61s 65ms/step - loss: 5.1949e-04 - accuracy: 0.9998 - val_loss: 0.1019 - val_accuracy: 0.9887\nEpoch 128/200\n945/945 [==============================] - 62s 66ms/step - loss: 5.8820e-04 - accuracy: 0.9998 - val_loss: 0.1275 - val_accuracy: 0.9875\nEpoch 129/200\n945/945 [==============================] - 61s 65ms/step - loss: 6.9882e-04 - accuracy: 0.9998 - val_loss: 0.1119 - val_accuracy: 0.9898\nEpoch 130/200\n945/945 [==============================] - 62s 66ms/step - loss: 6.2236e-04 - accuracy: 0.9998 - val_loss: 0.0969 - val_accuracy: 0.9900\nEpoch 131/200\n945/945 [==============================] - 61s 65ms/step - loss: 3.9077e-04 - accuracy: 0.9999 - val_loss: 0.1131 - val_accuracy: 0.9888\nEpoch 132/200\n945/945 [==============================] - 62s 66ms/step - loss: 4.8516e-04 - accuracy: 0.9998 - val_loss: 0.1070 - val_accuracy: 0.9888\nEpoch 133/200\n945/945 [==============================] - 61s 65ms/step - loss: 6.1079e-04 - accuracy: 0.9998 - val_loss: 0.0938 - val_accuracy: 0.9878\nEpoch 134/200\n945/945 [==============================] - 62s 65ms/step - loss: 9.1693e-04 - accuracy: 0.9998 - val_loss: 0.1061 - val_accuracy: 0.9891\nEpoch 135/200\n945/945 [==============================] - 61s 65ms/step - loss: 9.0436e-04 - accuracy: 0.9997 - val_loss: 0.0981 - val_accuracy: 0.9890\nEpoch 136/200\n945/945 [==============================] - 61s 65ms/step - loss: 8.2424e-04 - accuracy: 0.9998 - val_loss: 0.1271 - val_accuracy: 0.9825\nEpoch 137/200\n945/945 [==============================] - 62s 65ms/step - loss: 7.7061e-04 - accuracy: 0.9997 - val_loss: 0.0990 - val_accuracy: 0.9883\nEpoch 138/200\n945/945 [==============================] - 62s 66ms/step - loss: 5.5515e-04 - accuracy: 0.9998 - val_loss: 0.1027 - val_accuracy: 0.9888\nEpoch 139/200\n945/945 [==============================] - 62s 65ms/step - loss: 8.5908e-04 - accuracy: 0.9997 - val_loss: 0.0978 - val_accuracy: 0.9888\nEpoch 140/200\n945/945 [==============================] - 62s 65ms/step - loss: 5.2615e-04 - accuracy: 0.9998 - val_loss: 0.1023 - val_accuracy: 0.9892\nEpoch 141/200\n945/945 [==============================] - 61s 64ms/step - loss: 5.2562e-04 - accuracy: 0.9999 - val_loss: 0.0929 - val_accuracy: 0.9898\nEpoch 142/200\n945/945 [==============================] - 61s 64ms/step - loss: 6.2800e-04 - accuracy: 0.9998 - val_loss: 0.0969 - val_accuracy: 0.9894\nEpoch 143/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.7262e-04 - accuracy: 0.9999 - val_loss: 0.1067 - val_accuracy: 0.9894\nEpoch 144/200\n945/945 [==============================] - 61s 64ms/step - loss: 5.6215e-04 - accuracy: 0.9998 - val_loss: 0.1090 - val_accuracy: 0.9892\nEpoch 145/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.0116e-04 - accuracy: 0.9999 - val_loss: 0.1106 - val_accuracy: 0.9900\nEpoch 146/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.3573e-04 - accuracy: 0.9998 - val_loss: 0.0986 - val_accuracy: 0.9895\nEpoch 147/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.7081e-04 - accuracy: 0.9998 - val_loss: 0.1127 - val_accuracy: 0.9894\nEpoch 148/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.3691e-04 - accuracy: 0.9998 - val_loss: 0.1096 - val_accuracy: 0.9900\nEpoch 149/200\n945/945 [==============================] - 62s 66ms/step - loss: 4.5203e-04 - accuracy: 0.9999 - val_loss: 0.1006 - val_accuracy: 0.9887\nEpoch 150/200\n945/945 [==============================] - 62s 66ms/step - loss: 5.1431e-04 - accuracy: 0.9998 - val_loss: 0.1166 - val_accuracy: 0.9893\nEpoch 151/200\n945/945 [==============================] - 61s 65ms/step - loss: 2.6019e-04 - accuracy: 0.9999 - val_loss: 0.1038 - val_accuracy: 0.9906\nEpoch 152/200\n945/945 [==============================] - 61s 65ms/step - loss: 2.7947e-04 - accuracy: 0.9999 - val_loss: 0.1081 - val_accuracy: 0.9901\nEpoch 153/200\n945/945 [==============================] - 62s 66ms/step - loss: 2.9328e-04 - accuracy: 0.9999 - val_loss: 0.1036 - val_accuracy: 0.9902\nEpoch 154/200\n945/945 [==============================] - 62s 66ms/step - loss: 6.5300e-04 - accuracy: 0.9998 - val_loss: 0.1043 - val_accuracy: 0.9896\nEpoch 155/200\n945/945 [==============================] - 62s 65ms/step - loss: 3.8596e-04 - accuracy: 0.9999 - val_loss: 0.1113 - val_accuracy: 0.9899\nEpoch 156/200\n945/945 [==============================] - 61s 65ms/step - loss: 7.0930e-04 - accuracy: 0.9998 - val_loss: 0.1086 - val_accuracy: 0.9894\nEpoch 157/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.6633e-04 - accuracy: 0.9999 - val_loss: 0.0977 - val_accuracy: 0.9900\nEpoch 158/200\n945/945 [==============================] - 61s 65ms/step - loss: 3.8383e-04 - accuracy: 0.9999 - val_loss: 0.1013 - val_accuracy: 0.9896\nEpoch 159/200\n945/945 [==============================] - 61s 65ms/step - loss: 5.2547e-04 - accuracy: 0.9999 - val_loss: 0.1335 - val_accuracy: 0.9865\nEpoch 160/200\n945/945 [==============================] - 61s 65ms/step - loss: 3.0010e-04 - accuracy: 0.9999 - val_loss: 0.1036 - val_accuracy: 0.9886\nEpoch 161/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.5441e-04 - accuracy: 0.9999 - val_loss: 0.1264 - val_accuracy: 0.9878\nEpoch 162/200\n945/945 [==============================] - 61s 65ms/step - loss: 6.3652e-04 - accuracy: 0.9998 - val_loss: 0.1125 - val_accuracy: 0.9900\nEpoch 163/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.6751e-04 - accuracy: 0.9999 - val_loss: 0.1027 - val_accuracy: 0.9906\nEpoch 164/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.2045e-04 - accuracy: 0.9998 - val_loss: 0.0965 - val_accuracy: 0.9895\nEpoch 165/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.2586e-04 - accuracy: 0.9999 - val_loss: 0.1259 - val_accuracy: 0.9887\nEpoch 166/200\n945/945 [==============================] - 62s 66ms/step - loss: 3.1279e-04 - accuracy: 0.9999 - val_loss: 0.1178 - val_accuracy: 0.9857\nEpoch 167/200\n945/945 [==============================] - 62s 65ms/step - loss: 2.6022e-04 - accuracy: 0.9999 - val_loss: 0.0966 - val_accuracy: 0.9897\nEpoch 168/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.1048e-04 - accuracy: 0.9999 - val_loss: 0.1151 - val_accuracy: 0.9896\nEpoch 169/200\n945/945 [==============================] - 61s 65ms/step - loss: 2.4516e-04 - accuracy: 0.9999 - val_loss: 0.1508 - val_accuracy: 0.9855\nEpoch 170/200\n945/945 [==============================] - 62s 65ms/step - loss: 3.3683e-04 - accuracy: 0.9999 - val_loss: 0.1180 - val_accuracy: 0.9892\nEpoch 171/200\n945/945 [==============================] - 62s 66ms/step - loss: 3.2589e-04 - accuracy: 0.9999 - val_loss: 0.1257 - val_accuracy: 0.9899\nEpoch 172/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.7051e-04 - accuracy: 0.9998 - val_loss: 0.1350 - val_accuracy: 0.9876\nEpoch 173/200\n945/945 [==============================] - 62s 65ms/step - loss: 3.3968e-04 - accuracy: 0.9999 - val_loss: 0.1114 - val_accuracy: 0.9894\nEpoch 174/200\n945/945 [==============================] - 62s 65ms/step - loss: 5.5356e-04 - accuracy: 0.9999 - val_loss: 0.1134 - val_accuracy: 0.9889\nEpoch 175/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.3858e-04 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9898\nEpoch 176/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.7838e-04 - accuracy: 0.9998 - val_loss: 0.1061 - val_accuracy: 0.9899\nEpoch 177/200\n945/945 [==============================] - 61s 65ms/step - loss: 5.6572e-04 - accuracy: 0.9998 - val_loss: 0.1148 - val_accuracy: 0.9895\nEpoch 178/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.1707e-04 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9894\nEpoch 179/200\n945/945 [==============================] - 62s 65ms/step - loss: 2.2556e-04 - accuracy: 0.9999 - val_loss: 0.0965 - val_accuracy: 0.9902\nEpoch 180/200\n945/945 [==============================] - 61s 65ms/step - loss: 2.1563e-04 - accuracy: 0.9999 - val_loss: 0.1107 - val_accuracy: 0.9894\nEpoch 181/200\n945/945 [==============================] - 62s 65ms/step - loss: 3.8830e-04 - accuracy: 0.9999 - val_loss: 0.1031 - val_accuracy: 0.9903\nEpoch 182/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.7762e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9895\nEpoch 183/200\n945/945 [==============================] - 61s 65ms/step - loss: 3.0218e-04 - accuracy: 0.9999 - val_loss: 0.0954 - val_accuracy: 0.9897\nEpoch 184/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.4203e-04 - accuracy: 0.9999 - val_loss: 0.1036 - val_accuracy: 0.9901\nEpoch 185/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.5064e-04 - accuracy: 0.9999 - val_loss: 0.1025 - val_accuracy: 0.9900\nEpoch 186/200\n945/945 [==============================] - 62s 65ms/step - loss: 3.1753e-04 - accuracy: 0.9999 - val_loss: 0.1089 - val_accuracy: 0.9900\nEpoch 187/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.0300e-04 - accuracy: 0.9999 - val_loss: 0.0997 - val_accuracy: 0.9880\nEpoch 188/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.6133e-04 - accuracy: 0.9999 - val_loss: 0.1108 - val_accuracy: 0.9892\nEpoch 189/200\n945/945 [==============================] - 62s 65ms/step - loss: 4.3975e-04 - accuracy: 0.9998 - val_loss: 0.1040 - val_accuracy: 0.9901\nEpoch 190/200\n945/945 [==============================] - 61s 65ms/step - loss: 5.4203e-04 - accuracy: 0.9998 - val_loss: 0.0970 - val_accuracy: 0.9903\nEpoch 191/200\n945/945 [==============================] - 61s 65ms/step - loss: 5.2464e-04 - accuracy: 0.9999 - val_loss: 0.1206 - val_accuracy: 0.9883\nEpoch 192/200\n945/945 [==============================] - 61s 65ms/step - loss: 7.0013e-04 - accuracy: 0.9998 - val_loss: 0.1073 - val_accuracy: 0.9882\nEpoch 193/200\n945/945 [==============================] - 61s 65ms/step - loss: 3.7366e-04 - accuracy: 0.9999 - val_loss: 0.1138 - val_accuracy: 0.9870\nEpoch 194/200\n945/945 [==============================] - 61s 65ms/step - loss: 4.2012e-04 - accuracy: 0.9999 - val_loss: 0.0943 - val_accuracy: 0.9907\nEpoch 195/200\n945/945 [==============================] - 62s 66ms/step - loss: 2.0816e-04 - accuracy: 0.9999 - val_loss: 0.1070 - val_accuracy: 0.9902\nEpoch 196/200\n945/945 [==============================] - 61s 65ms/step - loss: 3.6640e-04 - accuracy: 0.9999 - val_loss: 0.0919 - val_accuracy: 0.9901\nEpoch 197/200\n945/945 [==============================] - 62s 66ms/step - loss: 3.4650e-04 - accuracy: 0.9999 - val_loss: 0.1111 - val_accuracy: 0.9892\nEpoch 198/200\n945/945 [==============================] - 62s 65ms/step - loss: 2.6941e-04 - accuracy: 0.9999 - val_loss: 0.0968 - val_accuracy: 0.9908\nEpoch 199/200\n945/945 [==============================] - 61s 65ms/step - loss: 3.7302e-04 - accuracy: 0.9999 - val_loss: 0.1020 - val_accuracy: 0.9898\nEpoch 200/200\n945/945 [==============================] - 62s 66ms/step - loss: 3.2308e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9900\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "model = keras.models.load_model('model_1111.h5')"
      ],
      "metadata": {
        "id": "Uot9s_g6L3Wp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = model.predict(x_test)"
      ],
      "metadata": {
        "_uuid": "6815575df2c5dde4cad956bcb2196bf22e32711d",
        "id": "7EVfGnYN3drB",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:23:02.091251Z",
          "iopub.execute_input": "2023-04-15T14:23:02.091659Z",
          "iopub.status.idle": "2023-04-15T14:23:12.694750Z",
          "shell.execute_reply.started": "2023-04-15T14:23:02.091622Z",
          "shell.execute_reply": "2023-04-15T14:23:12.693544Z"
        },
        "trusted": true,
        "outputId": "24424ef2-09fc-4a14-b10d-f73fa3619fa6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1164/1164 [==============================] - 16s 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.evaluate(x_val, y_val))"
      ],
      "metadata": {
        "id": "fcvOQj6DAjcq",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:23:12.697079Z",
          "iopub.execute_input": "2023-04-15T14:23:12.697554Z",
          "iopub.status.idle": "2023-04-15T14:23:18.024316Z",
          "shell.execute_reply.started": "2023-04-15T14:23:12.697491Z",
          "shell.execute_reply": "2023-04-15T14:23:18.022969Z"
        },
        "trusted": true,
        "outputId": "65fede50-7cbd-47a6-8196-ded2c234c164"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "657/657 [==============================] - 4s 7ms/step - loss: 0.0923 - accuracy: 0.9900\n[0.09234243631362915, 0.989950954914093]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.evaluate(x_train, y_train))"
      ],
      "metadata": {
        "id": "4nXhTOaOAmGW",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:23:18.026476Z",
          "iopub.execute_input": "2023-04-15T14:23:18.026957Z",
          "iopub.status.idle": "2023-04-15T14:24:00.638239Z",
          "shell.execute_reply.started": "2023-04-15T14:23:18.026914Z",
          "shell.execute_reply": "2023-04-15T14:24:00.637112Z"
        },
        "trusted": true,
        "outputId": "30167058-b29c-42ec-a418-1cf72f64a843"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "5906/5906 [==============================] - 39s 7ms/step - loss: 2.4025e-04 - accuracy: 0.9999\n[0.00024024993763305247, 0.9999206066131592]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"model_1111.h5\")"
      ],
      "metadata": {
        "id": "q5Z07qmjCUst",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:24:00.641500Z",
          "iopub.execute_input": "2023-04-15T14:24:00.641855Z",
          "iopub.status.idle": "2023-04-15T14:24:02.562420Z",
          "shell.execute_reply.started": "2023-04-15T14:24:00.641826Z",
          "shell.execute_reply": "2023-04-15T14:24:02.560879Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !cp modelabdulla.h5 /content/drive/MyDrive/abdulla"
      ],
      "metadata": {
        "id": "bZJV8OBmCekF",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:24:02.564130Z",
          "iopub.execute_input": "2023-04-15T14:24:02.564743Z",
          "iopub.status.idle": "2023-04-15T14:24:02.569728Z",
          "shell.execute_reply.started": "2023-04-15T14:24:02.564701Z",
          "shell.execute_reply": "2023-04-15T14:24:02.568314Z"
        },
        "trusted": true,
        "outputId": "5c99a9ff-ed75-4292-b0ba-d4489fbb839b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: cannot stat 'model_1111.h5': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !ls /content/drive/MyDrive/abdulla"
      ],
      "metadata": {
        "id": "WDE8NwOnDmHP",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:24:02.571459Z",
          "iopub.execute_input": "2023-04-15T14:24:02.572383Z",
          "iopub.status.idle": "2023-04-15T14:24:02.591240Z",
          "shell.execute_reply.started": "2023-04-15T14:24:02.572331Z",
          "shell.execute_reply": "2023-04-15T14:24:02.589960Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the loss and accuracy curves for training and validation \n",
        "fig, ax = plt.subplots(2,1)\n",
        "ax[0].plot(history.history['loss'], color='b', label=\"Training loss\")\n",
        "ax[0].plot(history.history['val_loss'], color='r', label=\"validation loss\",axes =ax[0])\n",
        "legend = ax[0].legend(loc='best', shadow=True)\n",
        "\n",
        "ax[1].plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\n",
        "ax[1].plot(history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\n",
        "legend = ax[1].legend(loc='best', shadow=True)"
      ],
      "metadata": {
        "id": "kLJl2UexH_kl",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:24:02.593346Z",
          "iopub.execute_input": "2023-04-15T14:24:02.595313Z",
          "iopub.status.idle": "2023-04-15T14:24:03.054838Z",
          "shell.execute_reply.started": "2023-04-15T14:24:02.595259Z",
          "shell.execute_reply": "2023-04-15T14:24:03.053571Z"
        },
        "trusted": true,
        "outputId": "bcfdf7b7-f46c-4929-ef10-7ae747989e8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the loss and accuracy curves for training and validation \n",
        "fig, ax = plt.subplots(1,1, figsize=(20, 5))\n",
        "ax.plot(history.history['loss'], color='b', label=\"Training loss\")\n",
        "ax.plot(history.history['val_loss'], color='r', label=\"validation loss\",axes = ax)\n",
        "plt.xlabel(\"Number of Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "legend = ax.legend(loc='best', shadow=True)"
      ],
      "metadata": {
        "id": "vew6GSgo5CdH",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:24:03.057729Z",
          "iopub.execute_input": "2023-04-15T14:24:03.058436Z",
          "iopub.status.idle": "2023-04-15T14:24:03.337719Z",
          "shell.execute_reply.started": "2023-04-15T14:24:03.058387Z",
          "shell.execute_reply": "2023-04-15T14:24:03.336609Z"
        },
        "trusted": true,
        "outputId": "59c731e3-d5f6-4e8f-874d-5f2770c05635"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2000x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1,1, figsize=(20, 5))\n",
        "ax.plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\n",
        "ax.plot(history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\n",
        "plt.xlabel(\"Number of Epochs\")\n",
        "plt.ylabel(\"categorical_accuracy\")\n",
        "legend = ax.legend(loc = 'best', shadow = True)"
      ],
      "metadata": {
        "id": "vH1QWHM75WKp",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:24:03.339226Z",
          "iopub.execute_input": "2023-04-15T14:24:03.339812Z",
          "iopub.status.idle": "2023-04-15T14:24:03.630573Z",
          "shell.execute_reply.started": "2023-04-15T14:24:03.339770Z",
          "shell.execute_reply": "2023-04-15T14:24:03.629588Z"
        },
        "trusted": true,
        "outputId": "81cba733-2bb6-46cf-a544-f76486c90089"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2000x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "fig = plt.figure(figsize=(15, 15)) # Set Figure\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "y_pred = model.predict(x_te)\n",
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "y_true = np.argmax(y_te, axis=1)\n",
        "cm = confusion_matrix(y_true, y_pred, labels=range(35))\n",
        "\n",
        "# Plot the confusion matrix using ConfusionMatrixDisplay\n",
        "cmd = ConfusionMatrixDisplay(cm, display_labels=range(35))\n",
        "fig, ax = plt.subplots(figsize=(15, 15))\n",
        "cmd.plot(cmap=plt.cm.Blues, ax=ax)\n",
        "plt.xlabel('Predicted Values')\n",
        "plt.ylabel('True Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SWz56ZTb51y8",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:30:24.038719Z",
          "iopub.execute_input": "2023-04-15T14:30:24.039100Z",
          "iopub.status.idle": "2023-04-15T14:30:34.438446Z",
          "shell.execute_reply.started": "2023-04-15T14:30:24.039063Z",
          "shell.execute_reply": "2023-04-15T14:30:34.437372Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "12824f52-7c4a-41b8-a8e6-c7d431be3d79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1200/1200 [==============================] - 17s 6ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "# Calculate the number of true samples\n",
        "n_true = np.sum(cm)\n",
        "\n",
        "# Calculate the number of classified samples\n",
        "n_classified = np.sum(cm, axis=0)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(y_true, y_pred)\n",
        "\n",
        "# Calculate precision\n",
        "precision = precision_score(y_true, y_pred, average='weighted')\n",
        "\n",
        "# Calculate recall\n",
        "recall = recall_score(y_true, y_pred, average='weighted')\n",
        "\n",
        "# Calculate F1 score\n",
        "f1 = f1_score(y_true, y_pred, average='weighted')\n",
        "\n",
        "print(\"Number of True Samples:\", n_true)\n",
        "print(\"Number of Classified Samples:\", n_classified)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n"
      ],
      "metadata": {
        "id": "AcoGeirnnGO5",
        "outputId": "a18c4155-8e9f-472a-c7c5-407031b35dd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of True Samples: 38381\n",
            "Number of Classified Samples: [1146 1113 1104 1151 1490 1154 1124 1292 1127 1210 1248 1153 1081 1117\n",
            " 1109 1068 1051 1044 1049 1135 1071 1190 1091  993  963  918  782 1092\n",
            " 1076 1000 1077 1051  978 1042 1091]\n",
            "Accuracy: 0.9491936114223183\n",
            "Precision: 0.9533983598536908\n",
            "Recall: 0.9491936114223183\n",
            "F1 Score: 0.9491474709745417\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape,  y_test.shape"
      ],
      "metadata": {
        "id": "7nwI3C5bQ1ZX",
        "outputId": "62cfb237-c7bf-41fc-c291-0443a5a224a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4, 28, 28, 1), (4, 35))"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "zl5UpcpWB58w",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:13:56.757009Z",
          "iopub.execute_input": "2023-04-15T14:13:56.757374Z",
          "iopub.status.idle": "2023-04-15T14:13:56.762761Z",
          "shell.execute_reply.started": "2023-04-15T14:13:56.757339Z",
          "shell.execute_reply": "2023-04-15T14:13:56.761345Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_val)\n",
        "X_test__ = tf.reshape(x_val, [x_val.shape[0], 28, 28])\n",
        "\n",
        "fig, axis = plt.subplots(4, 4, figsize=(12, 14))\n",
        "for i, ax in enumerate(axis.flat):\n",
        "    ax.imshow(X_test__[i], cmap='binary')\n",
        "    ax.set(title = f\"Real Number is {y_val[i].argmax()}\\nPredict Number is {y_pred[i].argmax()}\");"
      ],
      "metadata": {
        "id": "NYm8YdxhBz3O",
        "execution": {
          "iopub.status.busy": "2023-04-15T14:13:59.741259Z",
          "iopub.execute_input": "2023-04-15T14:13:59.741665Z",
          "iopub.status.idle": "2023-04-15T14:14:05.937596Z",
          "shell.execute_reply.started": "2023-04-15T14:13:59.741627Z",
          "shell.execute_reply": "2023-04-15T14:14:05.936358Z"
        },
        "trusted": true,
        "outputId": "899592c6-29ee-4224-b99f-5248728ff0d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "657/657 [==============================] - 4s 6ms/step\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x1400 with 16 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}