CSV_Classification.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import pandas as pd "
      ],
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        "_uuid": "a31348a5eba01332c755b5d244ff9d8a5f1d3ee7",
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
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          "base_uri": "https://localhost:8080/",
          "height": 73
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
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              "\n",
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              "</script> "
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving colon - labled-1 C.csv to colon - labled-1 C.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "df = pd.read_csv(io.BytesIO(uploaded['colon - labled-1 C.csv']))\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEghRXyS4me8",
        "outputId": "27cc3a9b-f8b0-4463-f825-87130d650436"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "    Unnamed: 0     H55933     R39465    R39465_     R85482     U14973  \\\n",
            "0            1  8589.4160  5468.2407  4263.4077  4064.9358  1997.8930   \n",
            "1            2  9164.2540  6719.5293  4883.4487  3718.1590  2015.2214   \n",
            "2            3  3825.7050  6970.3613  5369.9688  4705.6500  1166.5536   \n",
            "3            4  6246.4487  7823.5340  5955.8350  3975.5642  2002.6132   \n",
            "4            5  3230.3286  3694.4500  3400.7400  3463.5857  2181.4202   \n",
            "..         ...        ...        ...        ...        ...        ...   \n",
            "57          58  4972.1660  4173.9180  3668.5337  1567.5554  1570.4406   \n",
            "58          59  9112.3720  6824.4863  5982.8460  3147.0430  4847.3080   \n",
            "59          60  6730.6250  3472.1250  2559.4624  2624.6892  1596.2179   \n",
            "60          61  6234.6226  4005.3000  3093.6750  3183.0857  1795.3107   \n",
            "61          62  7472.0100  3653.9340  2728.2163  3494.4805  2404.6655   \n",
            "\n",
            "        R02593     T51496     H80240     T65938  ...     R70790     L11706  \\\n",
            "0    5282.3250  2169.7200  2773.4211  7526.3860  ...   67.56125  259.91250   \n",
            "1    5569.9070  3849.0588  2793.3875  7017.7340  ...   92.23875   96.27625   \n",
            "2    1572.1678  1325.4025  1472.2587  3296.9512  ...   82.71500   31.10250   \n",
            "3    2130.5430  1531.1425  1714.6312  3869.7850  ...   41.68375    5.92500   \n",
            "4    2922.7820  2069.2463  2948.5750  3303.3710  ...   76.60375  161.35000   \n",
            "..         ...        ...        ...        ...  ...        ...        ...   \n",
            "57   3466.8108  2106.4988  1535.7574  1887.6212  ...    6.44750  156.44500   \n",
            "58  11222.6820  6333.4727  4299.5024  4644.4424  ...   35.87875  342.65000   \n",
            "59   4372.7890  3798.5261  1026.4775  3512.3337  ...  119.96250  111.60375   \n",
            "60   3940.8464  2019.8500  1406.6500  2927.7937  ...   85.25875  206.53625   \n",
            "61   5791.6070  2876.4211  2150.9587  3767.0024  ...   63.63500  437.65250   \n",
            "\n",
            "       T90549     D17390      M33210     H18490     H40891    R77780  \\\n",
            "0   138.89874   88.23250   39.667860   67.82875   75.67750  83.52250   \n",
            "1   150.59000   82.23750   85.033330  152.19500  186.56750  44.47250   \n",
            "2   193.92000   76.97250  224.620240   31.22500   42.65625  16.09250   \n",
            "3   183.00626   74.52875   67.710720   48.33875   42.52000  49.98250   \n",
            "4    61.70125   54.56375  223.359530   73.09875   57.59875   7.48875   \n",
            "..        ...        ...         ...        ...        ...       ...   \n",
            "57   93.95000   86.90875   95.442856   31.18750   57.15250  13.96000   \n",
            "58   84.75125   60.27375  143.877380    8.99625  106.87875  23.20250   \n",
            "59  166.75500  130.45500  124.253570   96.46500  133.52126  93.09875   \n",
            "60   80.44875  104.80500  180.941670   68.93375  118.20125  32.68750   \n",
            "61  178.27250  153.84875  269.436900   67.86250   77.21500  49.86250   \n",
            "\n",
            "      T49647  Class  \n",
            "0   28.70125      1  \n",
            "1   16.77375      0  \n",
            "2   15.15625      1  \n",
            "3   16.08500      0  \n",
            "4   31.81250      1  \n",
            "..       ...    ...  \n",
            "57  10.54750      1  \n",
            "58  32.16625      1  \n",
            "59   7.43250      0  \n",
            "60  23.26500      1  \n",
            "61  39.63125      0  \n",
            "\n",
            "[62 rows x 2002 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "y = df.Class\n",
        "X = df.drop('Class', axis=1)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2)\n",
        "print(\"\\nX_train:\\n\")\n",
        "print(X_train.head())\n",
        "print(X_train.shape)\n",
        "\n",
        "print(\"\\nX_test:\\n\")\n",
        "print(X_test.head())\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_fvqxA1dW_f",
        "outputId": "0a73227c-0431-44c1-aa47-752ce22bf9fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "X_train:\n",
            "\n",
            "    Unnamed: 0     H55933      R39465    R39465_     R85482     U14973  \\\n",
            "59          60  6730.6250   3472.1250  2559.4624  2624.6892  1596.2179   \n",
            "41          42  6194.2450   5202.0000  4079.6338  3951.1982  2382.2214   \n",
            "6            7  7126.5986   3779.0680  3705.5537  6594.5140  2460.9048   \n",
            "31          32  5012.0200   1383.4886  1269.6487  3168.7124  1945.4929   \n",
            "36          37  5466.9300  10152.2730  7760.9175  7258.2500  1469.6906   \n",
            "\n",
            "       R02593     T51496     H80240     T65938  ...     X67699     R70790  \\\n",
            "59  4372.7890  3798.5261  1026.4775  3512.3337  ...  254.86548  119.96250   \n",
            "41  1949.1643  1909.0975  3415.1038  3965.9700  ...  214.36070  103.07875   \n",
            "6   3775.6821  2621.4187  2047.2812  6411.2676  ...  147.80476   51.03250   \n",
            "31  2726.8037  3300.5938  1672.8600  4185.6226  ...  107.97976   96.21375   \n",
            "36  5695.6465  4662.8550  2949.1812  6223.8600  ...  247.62262  303.73750   \n",
            "\n",
            "       L11706     T90549     D17390     M33210     H18490     H40891  \\\n",
            "59  111.60375  166.75500  130.45500  124.25357   96.46500  133.52126   \n",
            "41  321.14500  140.93625  200.44000  164.53929   54.96250  113.40875   \n",
            "6    80.19000   76.48500   98.53750   54.62143   30.54000   52.96125   \n",
            "31  365.51376   34.02000  125.69625  562.30835  127.43625  171.84250   \n",
            "36  117.94750  333.41876  464.93000  118.31548   73.37375  114.95500   \n",
            "\n",
            "      R77780    T49647  \n",
            "59  93.09875   7.43250  \n",
            "41  76.41750  43.76875  \n",
            "6   37.23625  24.44500  \n",
            "31  36.49500  48.37500  \n",
            "36  47.55875   6.73125  \n",
            "\n",
            "[5 rows x 2001 columns]\n",
            "(49, 2001)\n",
            "\n",
            "X_test:\n",
            "\n",
            "    Unnamed: 0     H55933     R39465    R39465_     R85482     U14973  \\\n",
            "25          26  5934.8887  3744.9885  3528.8337  2721.7036  1556.6190   \n",
            "39          40  9128.1190  5502.7160  4590.1340  3234.4019  3978.5452   \n",
            "9           10  5271.5176  4740.7680  3318.5137  6792.3480  2632.8894   \n",
            "0            1  8589.4160  5468.2407  4263.4077  4064.9358  1997.8930   \n",
            "33          34  8347.9830  9852.2980  8178.9650  4467.5020  3388.4463   \n",
            "\n",
            "       R02593     T51496     H80240     T65938  ...      X67699     R70790  \\\n",
            "25  3584.2393  3484.4850  1385.9625  3634.3838  ...   99.955956  146.80250   \n",
            "39  7676.7144  4644.3887  2518.6975  5460.9360  ...  134.852390  129.21375   \n",
            "9   5449.2070  4623.2124  3277.4038  4488.0600  ...  401.770230  141.37250   \n",
            "0   5282.3250  2169.7200  2773.4211  7526.3860  ...   99.110720   67.56125   \n",
            "33  5632.5605  3754.5150  4869.5560  5019.0537  ...  159.278560  149.71124   \n",
            "\n",
            "       L11706     T90549     D17390      M33210     H18490     H40891  \\\n",
            "25  260.70874   80.30500  120.27625   52.714287   40.53125  113.16000   \n",
            "39  440.22626  102.51000  140.32500  173.289290  110.22500  155.92375   \n",
            "9   215.88750   71.03750   93.52125  136.040480   43.23000   67.15000   \n",
            "0   259.91250  138.89874   88.23250   39.667860   67.82875   75.67750   \n",
            "33  363.45750  184.26000  132.87750  379.829770   50.03500  127.05000   \n",
            "\n",
            "      R77780    T49647  \n",
            "25  17.11500  19.75000  \n",
            "39  40.18125  37.45125  \n",
            "9   65.61000  44.04250  \n",
            "0   83.52250  28.70125  \n",
            "33  45.84000  25.99500  \n",
            "\n",
            "[5 rows x 2001 columns]\n",
            "(13, 2001)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.to_numpy()\n",
        "X_test = X_test.to_numpy()"
      ],
      "metadata": {
        "id": "q-YysNZElbiH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4JPfgF0m4s7",
        "outputId": "99b192c1-a6b0-445b-dc77-f88fb9fca7cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.0000000e+01 6.7306250e+03 3.4721250e+03 ... 1.3352126e+02\n",
            "  9.3098750e+01 7.4325000e+00]\n",
            " [4.2000000e+01 6.1942450e+03 5.2020000e+03 ... 1.1340875e+02\n",
            "  7.6417500e+01 4.3768750e+01]\n",
            " [7.0000000e+00 7.1265986e+03 3.7790680e+03 ... 5.2961250e+01\n",
            "  3.7236250e+01 2.4445000e+01]\n",
            " ...\n",
            " [2.0000000e+00 9.1642540e+03 6.7195293e+03 ... 1.8656750e+02\n",
            "  4.4472500e+01 1.6773750e+01]\n",
            " [1.2000000e+01 1.4173054e+04 8.4118610e+03 ... 2.0543875e+02\n",
            "  5.8243750e+01 4.4057500e+01]\n",
            " [9.0000000e+00 9.3306790e+03 7.0172295e+03 ... 1.2244125e+02\n",
            "  4.0391250e+01 2.6847500e+01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainX = X_train[:,1:].reshape(X_train.shape[0],1,40, 50).astype('float32')\n",
        "X_train = trainX/255.0\n",
        "\n",
        "testX = X_test[:,1:].reshape(X_test.shape[0],1, 40, 50).astype('float32')\n",
        "X_test = testX/255.0"
      ],
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "fepRy7p_7Jpv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "lb = preprocessing.LabelBinarizer()\n",
        "y_train = lb.fit_transform(y_train)\n",
        "y_test = lb.fit_transform(y_test)"
      ],
      "metadata": {
        "_cell_guid": "04bd1d50-0dee-409d-938e-678bcfb5a944",
        "_uuid": "ba10ccfb03cf8b78ad5b0672cb1cba91d3afb8f1",
        "scrolled": true,
        "id": "vZzIJgkg7Jpv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import TensorBoard\n",
        "num_classes = 10\n",
        "epochs = 20\n",
        "cnn_model = Sequential([\n",
        "    Conv2D(filters=32,kernel_size=3,activation='relu', input_shape=(1, 40, 50),data_format='channels_first'),\n",
        "    MaxPooling2D(pool_size=2) ,\n",
        "    Dropout(0.2),\n",
        "    Flatten(),\n",
        "    Dense(32,activation='relu'),\n",
        "    Dense(10,activation = 'softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "iDTfai_Zy3hP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn_model.compile(loss ='sparse_categorical_crossentropy', optimizer=Adam(lr=0.001),metrics =['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxLUgRj3aXTt",
        "outputId": "e29fda6a-f5ce-4be6-be14-192a91728498"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = cnn_model.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    batch_size=4096,\n",
        "    epochs=75,\n",
        "    verbose=1,\n",
        "    validation_data=(X_test,y_test),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DaUwUBiwae_u",
        "outputId": "de1747a4-dc1d-4752-95e4-ab4e4b948e00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/75\n",
            "1/1 [==============================] - 9s 9s/step - loss: 4.1062 - accuracy: 0.0000e+00 - val_loss: 0.8458 - val_accuracy: 0.6923\n",
            "Epoch 2/75\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.9905 - accuracy: 0.6327 - val_loss: 2.1203 - val_accuracy: 0.3077\n",
            "Epoch 3/75\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 1.5802 - accuracy: 0.3673 - val_loss: 0.4551 - val_accuracy: 0.6923\n",
            "Epoch 4/75\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.5253 - accuracy: 0.6531 - val_loss: 0.6912 - val_accuracy: 0.6923\n",
            "Epoch 5/75\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.8215 - accuracy: 0.6327 - val_loss: 0.4685 - val_accuracy: 0.6923\n",
            "Epoch 6/75\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5808 - accuracy: 0.6939 - val_loss: 0.4357 - val_accuracy: 0.9231\n",
            "Epoch 7/75\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.3571 - accuracy: 0.8776 - val_loss: 0.9122 - val_accuracy: 0.6923\n",
            "Epoch 8/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.4644 - accuracy: 0.8163 - val_loss: 0.9248 - val_accuracy: 0.7692\n",
            "Epoch 9/75\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.4856 - accuracy: 0.7755 - val_loss: 0.5480 - val_accuracy: 0.8462\n",
            "Epoch 10/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.3252 - accuracy: 0.8980 - val_loss: 0.3255 - val_accuracy: 0.8462\n",
            "Epoch 11/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.2725 - accuracy: 0.8980 - val_loss: 0.2611 - val_accuracy: 0.7692\n",
            "Epoch 12/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.3192 - accuracy: 0.8776 - val_loss: 0.2637 - val_accuracy: 0.8462\n",
            "Epoch 13/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.3630 - accuracy: 0.8571 - val_loss: 0.2526 - val_accuracy: 0.8462\n",
            "Epoch 14/75\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.3417 - accuracy: 0.8571 - val_loss: 0.2496 - val_accuracy: 0.8462\n",
            "Epoch 15/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.3016 - accuracy: 0.8776 - val_loss: 0.2938 - val_accuracy: 0.8462\n",
            "Epoch 16/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.2646 - accuracy: 0.8776 - val_loss: 0.3806 - val_accuracy: 0.8462\n",
            "Epoch 17/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.2504 - accuracy: 0.8980 - val_loss: 0.4879 - val_accuracy: 0.9231\n",
            "Epoch 18/75\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.2294 - accuracy: 0.9184 - val_loss: 0.5810 - val_accuracy: 0.9231\n",
            "Epoch 19/75\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.2483 - accuracy: 0.9592 - val_loss: 0.6134 - val_accuracy: 0.9231\n",
            "Epoch 20/75\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.2552 - accuracy: 0.9592 - val_loss: 0.5818 - val_accuracy: 0.9231\n",
            "Epoch 21/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.2655 - accuracy: 0.9388 - val_loss: 0.4964 - val_accuracy: 0.9231\n",
            "Epoch 22/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.2293 - accuracy: 0.9184 - val_loss: 0.4059 - val_accuracy: 0.9231\n",
            "Epoch 23/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.2000 - accuracy: 0.9184 - val_loss: 0.3288 - val_accuracy: 0.9231\n",
            "Epoch 24/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.1799 - accuracy: 0.9184 - val_loss: 0.2687 - val_accuracy: 0.8462\n",
            "Epoch 25/75\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.1879 - accuracy: 0.8980 - val_loss: 0.2323 - val_accuracy: 0.8462\n",
            "Epoch 26/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.1879 - accuracy: 0.8980 - val_loss: 0.2168 - val_accuracy: 0.8462\n",
            "Epoch 27/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.1907 - accuracy: 0.9184 - val_loss: 0.2122 - val_accuracy: 0.8462\n",
            "Epoch 28/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.1781 - accuracy: 0.8980 - val_loss: 0.2163 - val_accuracy: 0.8462\n",
            "Epoch 29/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.1672 - accuracy: 0.9184 - val_loss: 0.2327 - val_accuracy: 0.8462\n",
            "Epoch 30/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.1409 - accuracy: 0.9184 - val_loss: 0.2591 - val_accuracy: 0.9231\n",
            "Epoch 31/75\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.1405 - accuracy: 0.9184 - val_loss: 0.2903 - val_accuracy: 0.9231\n",
            "Epoch 32/75\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.1227 - accuracy: 0.9388 - val_loss: 0.3204 - val_accuracy: 0.9231\n",
            "Epoch 33/75\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.1339 - accuracy: 0.9592 - val_loss: 0.3369 - val_accuracy: 0.9231\n",
            "Epoch 34/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.1271 - accuracy: 0.9592 - val_loss: 0.3346 - val_accuracy: 0.9231\n",
            "Epoch 35/75\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.1300 - accuracy: 0.9592 - val_loss: 0.3156 - val_accuracy: 0.9231\n",
            "Epoch 36/75\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.1210 - accuracy: 0.9592 - val_loss: 0.2848 - val_accuracy: 0.9231\n",
            "Epoch 37/75\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.1141 - accuracy: 0.9592 - val_loss: 0.2489 - val_accuracy: 0.9231\n",
            "Epoch 38/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.1046 - accuracy: 0.9592 - val_loss: 0.2189 - val_accuracy: 0.9231\n",
            "Epoch 39/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.1174 - accuracy: 0.9592 - val_loss: 0.2015 - val_accuracy: 0.9231\n",
            "Epoch 40/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0951 - accuracy: 0.9796 - val_loss: 0.1941 - val_accuracy: 0.9231\n",
            "Epoch 41/75\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.0905 - accuracy: 1.0000 - val_loss: 0.1954 - val_accuracy: 0.9231\n",
            "Epoch 42/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0929 - accuracy: 0.9592 - val_loss: 0.2068 - val_accuracy: 0.9231\n",
            "Epoch 43/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0941 - accuracy: 0.9796 - val_loss: 0.2286 - val_accuracy: 0.9231\n",
            "Epoch 44/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0800 - accuracy: 0.9796 - val_loss: 0.2523 - val_accuracy: 0.9231\n",
            "Epoch 45/75\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0824 - accuracy: 0.9592 - val_loss: 0.2729 - val_accuracy: 0.9231\n",
            "Epoch 46/75\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0720 - accuracy: 0.9796 - val_loss: 0.2841 - val_accuracy: 0.9231\n",
            "Epoch 47/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0763 - accuracy: 0.9796 - val_loss: 0.2878 - val_accuracy: 0.9231\n",
            "Epoch 48/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0810 - accuracy: 0.9592 - val_loss: 0.2848 - val_accuracy: 0.9231\n",
            "Epoch 49/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0696 - accuracy: 0.9796 - val_loss: 0.2756 - val_accuracy: 0.9231\n",
            "Epoch 50/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0624 - accuracy: 0.9796 - val_loss: 0.2602 - val_accuracy: 0.9231\n",
            "Epoch 51/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0638 - accuracy: 0.9796 - val_loss: 0.2450 - val_accuracy: 0.9231\n",
            "Epoch 52/75\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.0524 - accuracy: 1.0000 - val_loss: 0.2305 - val_accuracy: 0.9231\n",
            "Epoch 53/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0570 - accuracy: 1.0000 - val_loss: 0.2213 - val_accuracy: 0.9231\n",
            "Epoch 54/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0492 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9231\n",
            "Epoch 55/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0589 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9231\n",
            "Epoch 56/75\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.0530 - accuracy: 1.0000 - val_loss: 0.2218 - val_accuracy: 0.9231\n",
            "Epoch 57/75\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0595 - accuracy: 1.0000 - val_loss: 0.2360 - val_accuracy: 0.9231\n",
            "Epoch 58/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0507 - accuracy: 1.0000 - val_loss: 0.2537 - val_accuracy: 0.9231\n",
            "Epoch 59/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0464 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 0.9231\n",
            "Epoch 60/75\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.0483 - accuracy: 1.0000 - val_loss: 0.2911 - val_accuracy: 0.9231\n",
            "Epoch 61/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0491 - accuracy: 1.0000 - val_loss: 0.2984 - val_accuracy: 0.9231\n",
            "Epoch 62/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0460 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9231\n",
            "Epoch 63/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0377 - accuracy: 1.0000 - val_loss: 0.2889 - val_accuracy: 0.9231\n",
            "Epoch 64/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0370 - accuracy: 1.0000 - val_loss: 0.2772 - val_accuracy: 0.9231\n",
            "Epoch 65/75\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0347 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9231\n",
            "Epoch 66/75\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.0363 - accuracy: 1.0000 - val_loss: 0.2541 - val_accuracy: 0.9231\n",
            "Epoch 67/75\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.0369 - accuracy: 1.0000 - val_loss: 0.2441 - val_accuracy: 0.9231\n",
            "Epoch 68/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0363 - accuracy: 1.0000 - val_loss: 0.2384 - val_accuracy: 0.9231\n",
            "Epoch 69/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.2369 - val_accuracy: 0.9231\n",
            "Epoch 70/75\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0330 - accuracy: 1.0000 - val_loss: 0.2435 - val_accuracy: 0.9231\n",
            "Epoch 71/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0280 - accuracy: 1.0000 - val_loss: 0.2545 - val_accuracy: 0.9231\n",
            "Epoch 72/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0324 - accuracy: 1.0000 - val_loss: 0.2678 - val_accuracy: 0.9231\n",
            "Epoch 73/75\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.0326 - accuracy: 1.0000 - val_loss: 0.2811 - val_accuracy: 0.9231\n",
            "Epoch 74/75\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.0291 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9231\n",
            "Epoch 75/75\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.0291 - accuracy: 1.0000 - val_loss: 0.3055 - val_accuracy: 0.9231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.plot(history.history['loss'], label='Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.legend()\n",
        "plt.title('Training - Loss Function')\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.plot(history.history['accuracy'], label='Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.legend()\n",
        "plt.title('Train - Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "Fy9DCuXDa1c2",
        "outputId": "04a48870-8e6b-43e8-be2c-05587b1f841e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Train - Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Other lines of code."
      ],
      "metadata": {
        "id": "mWpWG093OQTu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''solutions = []\n",
        "\n",
        "for x in range(1, 2000 + 1):\n",
        "    for y in range(1, 2000 + 1):\n",
        "        if x * y == 2000:\n",
        "            solutions.append((x, y))\n",
        "\n",
        "print(solutions)'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIMuW85RUUS1",
        "outputId": "46a5e572-59ff-472c-da74-7b355045a6a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, 2000), (2, 1000), (4, 500), (5, 400), (8, 250), (10, 200), (16, 125), (20, 100), (25, 80), (40, 50), (50, 40), (80, 25), (100, 20), (125, 16), (200, 10), (250, 8), (400, 5), (500, 4), (1000, 2), (2000, 1)]\n"
          ]
        }
      ]
    }
  ]
}