smart_irrigation_system.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Srujan-rai/smart_irrigation/blob/main/smart_irrigation_system.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iWqO7X-47Okb"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Load data from the uploaded CSV file\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OqLfRcFj7fz9"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKWdLerK-KYV",
        "outputId": "96ac306f-75e2-45a7-fcc7-5725c9bdd064"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      temp  humidity  soil_moisture  pump_status\n",
            "0       27        52              3            0\n",
            "1       27        52              3            0\n",
            "2       27        52              3            0\n",
            "3       27        52              3            0\n",
            "4       27        52              3            0\n",
            "...    ...       ...            ...          ...\n",
            "8547    61        52              4            1\n",
            "8548    61        52              3            1\n",
            "8549    61        52              4            1\n",
            "8550    61        52              3            1\n",
            "8551    61        52              4            1\n",
            "\n",
            "[8552 rows x 4 columns]\n"
          ]
        }
      ],
      "source": [
        "print(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bg5rPeha-uw4"
      },
      "outputs": [],
      "source": [
        "# Split data into features (X) and target (y)\n",
        "X = data[['temp', 'humidity', 'soil_moisture']]\n",
        "y = data['pump_status']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardize features (optional but can help with some models)\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "VUCjMf3_-8DO",
        "outputId": "f40410d2-a6bc-4921-9641-23bc3a8aa3d2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create and train a logistic regression model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1l6W4fDf-_Fn",
        "outputId": "1bf29500-4663-494f-c6bf-42cdebb7d4ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.994155464640561\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99       967\n",
            "           1       1.00      0.99      0.99       744\n",
            "\n",
            "    accuracy                           0.99      1711\n",
            "   macro avg       0.99      0.99      0.99      1711\n",
            "weighted avg       0.99      0.99      0.99      1711\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print(f'Accuracy: {accuracy}')\n",
        "print(report)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "uBevT4mk_HiG",
        "outputId": "fcfd2c6b-52d8-4fdf-9144-faa57ce9b6da"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_9d8d1a44-ee46-42ec-8e1e-18b26a61eec2\", \"your_saved_model.pkl\", 879)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import joblib\n",
        "from google.colab import files\n",
        "\n",
        "# Train your model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Save the trained model to a file\n",
        "joblib.dump(model, 'your_saved_model.pkl')\n",
        "\n",
        "# Download the saved model\n",
        "files.download('your_saved_model.pkl')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2WssSM46_Y5W"
      },
      "outputs": [],
      "source": [
        "import joblib\n",
        "import pandas as pd\n",
        "\n",
        "# Load the trained model\n",
        "model = joblib.load('your_saved_model.pkl')\n",
        "\n",
        "# Input new data\n",
        "new_data = {\n",
        "    'temperature': 40,\n",
        "    'humidity': 0,\n",
        "    'soil_moisture': 25\n",
        "}\n",
        "\n",
        "# Prepare the input data as a DataFrame\n",
        "new_data_df = pd.DataFrame([new_data])\n",
        "\n",
        "# Make predictions\n",
        "prediction = model.predict(new_data_df)\n",
        "\n",
        "# Interpret the prediction\n",
        "if prediction[0] == 0:\n",
        "    print(\"The pump status is OFF.\")\n",
        "else:\n",
        "    print(\"The pump status is ON.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzMRP1gLkFrG"
      },
      "source": [
        "nEURAL NETWORKS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKbG6wTakKAi",
        "outputId": "8fe86439-ff1b-4850-d8fd-b8f64dbb0bc5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "86/86 - 2s - loss: 0.5004 - accuracy: 0.7409 - val_loss: 0.3470 - val_accuracy: 0.8006 - 2s/epoch - 25ms/step\n",
            "Epoch 2/200\n",
            "86/86 - 0s - loss: 0.3033 - accuracy: 0.8653 - val_loss: 0.2473 - val_accuracy: 0.9291 - 336ms/epoch - 4ms/step\n",
            "Epoch 3/200\n",
            "86/86 - 0s - loss: 0.2278 - accuracy: 0.9432 - val_loss: 0.1858 - val_accuracy: 0.9591 - 359ms/epoch - 4ms/step\n",
            "Epoch 4/200\n",
            "86/86 - 0s - loss: 0.1671 - accuracy: 0.9768 - val_loss: 0.1291 - val_accuracy: 0.9963 - 365ms/epoch - 4ms/step\n",
            "Epoch 5/200\n",
            "86/86 - 0s - loss: 0.1234 - accuracy: 0.9947 - val_loss: 0.0976 - val_accuracy: 0.9949 - 435ms/epoch - 5ms/step\n",
            "Epoch 6/200\n",
            "86/86 - 1s - loss: 0.0975 - accuracy: 0.9949 - val_loss: 0.0786 - val_accuracy: 0.9949 - 621ms/epoch - 7ms/step\n",
            "Epoch 7/200\n",
            "86/86 - 0s - loss: 0.0816 - accuracy: 0.9932 - val_loss: 0.0652 - val_accuracy: 0.9949 - 315ms/epoch - 4ms/step\n",
            "Epoch 8/200\n",
            "86/86 - 0s - loss: 0.0704 - accuracy: 0.9951 - val_loss: 0.0596 - val_accuracy: 0.9949 - 273ms/epoch - 3ms/step\n",
            "Epoch 9/200\n",
            "86/86 - 0s - loss: 0.0623 - accuracy: 0.9951 - val_loss: 0.0504 - val_accuracy: 0.9949 - 284ms/epoch - 3ms/step\n",
            "Epoch 10/200\n",
            "86/86 - 0s - loss: 0.0554 - accuracy: 0.9951 - val_loss: 0.0449 - val_accuracy: 0.9949 - 278ms/epoch - 3ms/step\n",
            "Epoch 11/200\n",
            "86/86 - 0s - loss: 0.0509 - accuracy: 0.9954 - val_loss: 0.0409 - val_accuracy: 0.9956 - 290ms/epoch - 3ms/step\n",
            "Epoch 12/200\n",
            "86/86 - 0s - loss: 0.0461 - accuracy: 0.9956 - val_loss: 0.0374 - val_accuracy: 0.9956 - 260ms/epoch - 3ms/step\n",
            "Epoch 13/200\n",
            "86/86 - 0s - loss: 0.0414 - accuracy: 0.9954 - val_loss: 0.0334 - val_accuracy: 0.9963 - 293ms/epoch - 3ms/step\n",
            "Epoch 14/200\n",
            "86/86 - 0s - loss: 0.0382 - accuracy: 0.9962 - val_loss: 0.0318 - val_accuracy: 0.9971 - 291ms/epoch - 3ms/step\n",
            "Epoch 15/200\n",
            "86/86 - 0s - loss: 0.0347 - accuracy: 0.9971 - val_loss: 0.0293 - val_accuracy: 0.9971 - 267ms/epoch - 3ms/step\n",
            "Epoch 16/200\n",
            "86/86 - 0s - loss: 0.0322 - accuracy: 0.9978 - val_loss: 0.0258 - val_accuracy: 0.9971 - 180ms/epoch - 2ms/step\n",
            "Epoch 17/200\n",
            "86/86 - 0s - loss: 0.0296 - accuracy: 0.9982 - val_loss: 0.0239 - val_accuracy: 0.9971 - 181ms/epoch - 2ms/step\n",
            "Epoch 18/200\n",
            "86/86 - 0s - loss: 0.0273 - accuracy: 0.9987 - val_loss: 0.0225 - val_accuracy: 0.9978 - 179ms/epoch - 2ms/step\n",
            "Epoch 19/200\n",
            "86/86 - 0s - loss: 0.0253 - accuracy: 0.9989 - val_loss: 0.0201 - val_accuracy: 0.9985 - 191ms/epoch - 2ms/step\n",
            "Epoch 20/200\n",
            "86/86 - 0s - loss: 0.0233 - accuracy: 0.9991 - val_loss: 0.0193 - val_accuracy: 0.9985 - 215ms/epoch - 3ms/step\n",
            "Epoch 21/200\n",
            "86/86 - 0s - loss: 0.0219 - accuracy: 0.9991 - val_loss: 0.0176 - val_accuracy: 0.9993 - 175ms/epoch - 2ms/step\n",
            "Epoch 22/200\n",
            "86/86 - 0s - loss: 0.0202 - accuracy: 0.9998 - val_loss: 0.0165 - val_accuracy: 0.9985 - 221ms/epoch - 3ms/step\n",
            "Epoch 23/200\n",
            "86/86 - 0s - loss: 0.0189 - accuracy: 0.9996 - val_loss: 0.0161 - val_accuracy: 0.9985 - 228ms/epoch - 3ms/step\n",
            "Epoch 24/200\n",
            "86/86 - 0s - loss: 0.0174 - accuracy: 0.9996 - val_loss: 0.0168 - val_accuracy: 0.9993 - 185ms/epoch - 2ms/step\n",
            "Epoch 25/200\n",
            "86/86 - 0s - loss: 0.0164 - accuracy: 0.9996 - val_loss: 0.0132 - val_accuracy: 0.9993 - 178ms/epoch - 2ms/step\n",
            "Epoch 26/200\n",
            "86/86 - 0s - loss: 0.0155 - accuracy: 0.9996 - val_loss: 0.0130 - val_accuracy: 0.9985 - 219ms/epoch - 3ms/step\n",
            "Epoch 27/200\n",
            "86/86 - 0s - loss: 0.0141 - accuracy: 0.9996 - val_loss: 0.0120 - val_accuracy: 0.9993 - 185ms/epoch - 2ms/step\n",
            "Epoch 28/200\n",
            "86/86 - 0s - loss: 0.0135 - accuracy: 0.9996 - val_loss: 0.0109 - val_accuracy: 0.9993 - 182ms/epoch - 2ms/step\n",
            "Epoch 29/200\n",
            "86/86 - 0s - loss: 0.0125 - accuracy: 0.9996 - val_loss: 0.0103 - val_accuracy: 0.9993 - 215ms/epoch - 2ms/step\n",
            "Epoch 30/200\n",
            "86/86 - 0s - loss: 0.0116 - accuracy: 0.9996 - val_loss: 0.0098 - val_accuracy: 0.9993 - 179ms/epoch - 2ms/step\n",
            "Epoch 31/200\n",
            "86/86 - 0s - loss: 0.0110 - accuracy: 0.9998 - val_loss: 0.0093 - val_accuracy: 0.9993 - 215ms/epoch - 3ms/step\n",
            "Epoch 32/200\n",
            "86/86 - 0s - loss: 0.0100 - accuracy: 0.9996 - val_loss: 0.0092 - val_accuracy: 0.9985 - 215ms/epoch - 2ms/step\n",
            "Epoch 33/200\n",
            "86/86 - 0s - loss: 0.0095 - accuracy: 0.9996 - val_loss: 0.0081 - val_accuracy: 0.9993 - 183ms/epoch - 2ms/step\n",
            "Epoch 34/200\n",
            "86/86 - 0s - loss: 0.0089 - accuracy: 0.9998 - val_loss: 0.0077 - val_accuracy: 0.9993 - 214ms/epoch - 2ms/step\n",
            "Epoch 35/200\n",
            "86/86 - 0s - loss: 0.0081 - accuracy: 0.9998 - val_loss: 0.0070 - val_accuracy: 0.9993 - 185ms/epoch - 2ms/step\n",
            "Epoch 36/200\n",
            "86/86 - 0s - loss: 0.0084 - accuracy: 0.9996 - val_loss: 0.0064 - val_accuracy: 0.9993 - 177ms/epoch - 2ms/step\n",
            "Epoch 37/200\n",
            "86/86 - 0s - loss: 0.0073 - accuracy: 0.9996 - val_loss: 0.0062 - val_accuracy: 0.9993 - 178ms/epoch - 2ms/step\n",
            "Epoch 38/200\n",
            "86/86 - 0s - loss: 0.0066 - accuracy: 0.9998 - val_loss: 0.0063 - val_accuracy: 0.9993 - 224ms/epoch - 3ms/step\n",
            "Epoch 39/200\n",
            "86/86 - 0s - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9985 - 217ms/epoch - 3ms/step\n",
            "Epoch 40/200\n",
            "86/86 - 0s - loss: 0.0057 - accuracy: 0.9998 - val_loss: 0.0053 - val_accuracy: 0.9993 - 214ms/epoch - 2ms/step\n",
            "Epoch 41/200\n",
            "86/86 - 0s - loss: 0.0054 - accuracy: 0.9998 - val_loss: 0.0048 - val_accuracy: 0.9993 - 211ms/epoch - 2ms/step\n",
            "Epoch 42/200\n",
            "86/86 - 0s - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9993 - 214ms/epoch - 2ms/step\n",
            "Epoch 43/200\n",
            "86/86 - 0s - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9993 - 217ms/epoch - 3ms/step\n",
            "Epoch 44/200\n",
            "86/86 - 0s - loss: 0.0044 - accuracy: 0.9998 - val_loss: 0.0051 - val_accuracy: 0.9993 - 228ms/epoch - 3ms/step\n",
            "Epoch 45/200\n",
            "86/86 - 0s - loss: 0.0041 - accuracy: 0.9998 - val_loss: 0.0038 - val_accuracy: 0.9993 - 217ms/epoch - 3ms/step\n",
            "Epoch 46/200\n",
            "86/86 - 0s - loss: 0.0040 - accuracy: 0.9996 - val_loss: 0.0041 - val_accuracy: 0.9993 - 180ms/epoch - 2ms/step\n",
            "Epoch 47/200\n",
            "86/86 - 0s - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 0.9993 - 221ms/epoch - 3ms/step\n",
            "Epoch 48/200\n",
            "86/86 - 0s - loss: 0.0034 - accuracy: 0.9998 - val_loss: 0.0043 - val_accuracy: 0.9993 - 184ms/epoch - 2ms/step\n",
            "Epoch 49/200\n",
            "86/86 - 0s - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 0.9993 - 215ms/epoch - 3ms/step\n",
            "Epoch 50/200\n",
            "86/86 - 0s - loss: 0.0030 - accuracy: 0.9998 - val_loss: 0.0044 - val_accuracy: 0.9993 - 210ms/epoch - 2ms/step\n",
            "Epoch 51/200\n",
            "86/86 - 0s - loss: 0.0033 - accuracy: 0.9996 - val_loss: 0.0043 - val_accuracy: 0.9985 - 190ms/epoch - 2ms/step\n",
            "Epoch 52/200\n",
            "86/86 - 0s - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0028 - val_accuracy: 0.9993 - 219ms/epoch - 3ms/step\n",
            "Epoch 53/200\n",
            "86/86 - 0s - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.0023 - val_accuracy: 1.0000 - 185ms/epoch - 2ms/step\n",
            "Epoch 54/200\n",
            "86/86 - 0s - loss: 0.0024 - accuracy: 0.9998 - val_loss: 0.0030 - val_accuracy: 0.9993 - 185ms/epoch - 2ms/step\n",
            "Epoch 55/200\n",
            "86/86 - 0s - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 0.9993 - 212ms/epoch - 2ms/step\n",
            "Epoch 56/200\n",
            "86/86 - 0s - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 0.9993 - 182ms/epoch - 2ms/step\n",
            "Epoch 57/200\n",
            "86/86 - 0s - loss: 0.0023 - accuracy: 0.9995 - val_loss: 0.0040 - val_accuracy: 0.9985 - 232ms/epoch - 3ms/step\n",
            "Epoch 58/200\n",
            "86/86 - 0s - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 0.9993 - 181ms/epoch - 2ms/step\n",
            "Epoch 59/200\n",
            "86/86 - 0s - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 0.9993 - 184ms/epoch - 2ms/step\n",
            "Epoch 60/200\n",
            "86/86 - 0s - loss: 0.0017 - accuracy: 0.9998 - val_loss: 0.0018 - val_accuracy: 0.9993 - 182ms/epoch - 2ms/step\n",
            "Epoch 61/200\n",
            "86/86 - 0s - loss: 0.0017 - accuracy: 0.9998 - val_loss: 0.0029 - val_accuracy: 0.9993 - 227ms/epoch - 3ms/step\n",
            "Epoch 62/200\n",
            "86/86 - 0s - loss: 0.0022 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 1.0000 - 222ms/epoch - 3ms/step\n",
            "Epoch 63/200\n",
            "86/86 - 0s - loss: 0.0038 - accuracy: 0.9995 - val_loss: 0.0010 - val_accuracy: 1.0000 - 212ms/epoch - 2ms/step\n",
            "Epoch 64/200\n",
            "86/86 - 0s - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 1.0000 - 280ms/epoch - 3ms/step\n",
            "Epoch 65/200\n",
            "86/86 - 0s - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 0.9993 - 260ms/epoch - 3ms/step\n",
            "Epoch 66/200\n",
            "86/86 - 0s - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 0.9993 - 308ms/epoch - 4ms/step\n",
            "Epoch 67/200\n",
            "86/86 - 0s - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.0012 - val_accuracy: 1.0000 - 263ms/epoch - 3ms/step\n",
            "Epoch 68/200\n",
            "86/86 - 0s - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 0.9993 - 287ms/epoch - 3ms/step\n",
            "Epoch 69/200\n",
            "86/86 - 0s - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 0.9993 - 258ms/epoch - 3ms/step\n",
            "Epoch 70/200\n",
            "86/86 - 0s - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 0.9993 - 294ms/epoch - 3ms/step\n",
            "Epoch 71/200\n",
            "86/86 - 0s - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 0.9993 - 281ms/epoch - 3ms/step\n",
            "Epoch 72/200\n",
            "86/86 - 0s - loss: 9.8620e-04 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 0.9993 - 273ms/epoch - 3ms/step\n",
            "Epoch 73/200\n",
            "86/86 - 0s - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0019 - val_accuracy: 0.9993 - 272ms/epoch - 3ms/step\n",
            "Epoch 74/200\n",
            "86/86 - 0s - loss: 9.2047e-04 - accuracy: 0.9998 - val_loss: 0.0022 - val_accuracy: 0.9993 - 239ms/epoch - 3ms/step\n",
            "Epoch 75/200\n",
            "86/86 - 0s - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0017 - val_accuracy: 0.9993 - 208ms/epoch - 2ms/step\n",
            "Epoch 76/200\n",
            "86/86 - 0s - loss: 8.8116e-04 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 0.9993 - 183ms/epoch - 2ms/step\n",
            "Epoch 77/200\n",
            "86/86 - 0s - loss: 8.9888e-04 - accuracy: 0.9998 - val_loss: 0.0022 - val_accuracy: 0.9993 - 210ms/epoch - 2ms/step\n",
            "Epoch 78/200\n",
            "86/86 - 0s - loss: 8.1438e-04 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 0.9993 - 181ms/epoch - 2ms/step\n",
            "Epoch 79/200\n",
            "86/86 - 0s - loss: 7.9308e-04 - accuracy: 0.9998 - val_loss: 0.0030 - val_accuracy: 0.9993 - 189ms/epoch - 2ms/step\n",
            "Epoch 80/200\n",
            "86/86 - 0s - loss: 7.7175e-04 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 0.9993 - 182ms/epoch - 2ms/step\n",
            "Epoch 81/200\n",
            "86/86 - 0s - loss: 7.9687e-04 - accuracy: 0.9998 - val_loss: 0.0023 - val_accuracy: 0.9993 - 178ms/epoch - 2ms/step\n",
            "Epoch 82/200\n",
            "86/86 - 0s - loss: 8.2438e-04 - accuracy: 0.9998 - val_loss: 0.0013 - val_accuracy: 0.9993 - 211ms/epoch - 2ms/step\n",
            "Epoch 83/200\n",
            "86/86 - 0s - loss: 0.0022 - accuracy: 0.9996 - val_loss: 0.0012 - val_accuracy: 0.9993 - 214ms/epoch - 2ms/step\n",
            "Epoch 84/200\n",
            "86/86 - 0s - loss: 7.2476e-04 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 0.9993 - 223ms/epoch - 3ms/step\n",
            "Epoch 85/200\n",
            "86/86 - 0s - loss: 6.7076e-04 - accuracy: 0.9998 - val_loss: 0.0013 - val_accuracy: 0.9993 - 211ms/epoch - 2ms/step\n",
            "Epoch 86/200\n",
            "86/86 - 0s - loss: 7.1852e-04 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 0.9993 - 208ms/epoch - 2ms/step\n",
            "Epoch 87/200\n",
            "86/86 - 0s - loss: 9.7546e-04 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9993 - 187ms/epoch - 2ms/step\n",
            "Epoch 88/200\n",
            "86/86 - 0s - loss: 6.2767e-04 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 0.9993 - 214ms/epoch - 2ms/step\n",
            "Epoch 89/200\n",
            "86/86 - 0s - loss: 6.3488e-04 - accuracy: 0.9998 - val_loss: 0.0027 - val_accuracy: 0.9993 - 221ms/epoch - 3ms/step\n",
            "Epoch 90/200\n",
            "86/86 - 0s - loss: 6.4377e-04 - accuracy: 0.9998 - val_loss: 0.0013 - val_accuracy: 0.9993 - 222ms/epoch - 3ms/step\n",
            "Epoch 91/200\n",
            "86/86 - 0s - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0048 - val_accuracy: 0.9985 - 241ms/epoch - 3ms/step\n",
            "Epoch 92/200\n",
            "86/86 - 0s - loss: 8.8937e-04 - accuracy: 0.9998 - val_loss: 0.0034 - val_accuracy: 0.9993 - 210ms/epoch - 2ms/step\n",
            "Epoch 93/200\n",
            "86/86 - 0s - loss: 7.1383e-04 - accuracy: 0.9998 - val_loss: 6.5692e-04 - val_accuracy: 1.0000 - 198ms/epoch - 2ms/step\n",
            "Epoch 94/200\n",
            "86/86 - 0s - loss: 6.8080e-04 - accuracy: 0.9998 - val_loss: 0.0027 - val_accuracy: 0.9993 - 182ms/epoch - 2ms/step\n",
            "Epoch 95/200\n",
            "86/86 - 0s - loss: 5.7618e-04 - accuracy: 0.9998 - val_loss: 0.0025 - val_accuracy: 0.9993 - 215ms/epoch - 3ms/step\n",
            "Epoch 96/200\n",
            "86/86 - 0s - loss: 6.1415e-04 - accuracy: 0.9996 - val_loss: 0.0024 - val_accuracy: 0.9993 - 213ms/epoch - 2ms/step\n",
            "Epoch 97/200\n",
            "86/86 - 0s - loss: 4.6178e-04 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 0.9993 - 217ms/epoch - 3ms/step\n",
            "Epoch 98/200\n",
            "86/86 - 0s - loss: 5.9344e-04 - accuracy: 0.9998 - val_loss: 0.0022 - val_accuracy: 0.9993 - 187ms/epoch - 2ms/step\n",
            "Epoch 99/200\n",
            "86/86 - 0s - loss: 4.8515e-04 - accuracy: 0.9998 - val_loss: 0.0020 - val_accuracy: 0.9993 - 177ms/epoch - 2ms/step\n",
            "Epoch 100/200\n",
            "86/86 - 0s - loss: 4.5606e-04 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 0.9993 - 193ms/epoch - 2ms/step\n",
            "Epoch 101/200\n",
            "86/86 - 0s - loss: 4.8028e-04 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 0.9993 - 182ms/epoch - 2ms/step\n",
            "Epoch 102/200\n",
            "86/86 - 0s - loss: 5.5764e-04 - accuracy: 0.9998 - val_loss: 0.0016 - val_accuracy: 0.9993 - 220ms/epoch - 3ms/step\n",
            "Epoch 103/200\n",
            "86/86 - 0s - loss: 4.0713e-04 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 0.9993 - 224ms/epoch - 3ms/step\n",
            "Epoch 104/200\n",
            "86/86 - 0s - loss: 4.0768e-04 - accuracy: 0.9998 - val_loss: 0.0022 - val_accuracy: 0.9993 - 176ms/epoch - 2ms/step\n",
            "Epoch 105/200\n",
            "86/86 - 0s - loss: 5.2287e-04 - accuracy: 0.9998 - val_loss: 0.0026 - val_accuracy: 0.9993 - 210ms/epoch - 2ms/step\n",
            "Epoch 106/200\n",
            "86/86 - 0s - loss: 3.5281e-04 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 0.9993 - 216ms/epoch - 3ms/step\n",
            "Epoch 107/200\n",
            "86/86 - 0s - loss: 3.5254e-04 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 0.9993 - 184ms/epoch - 2ms/step\n",
            "Epoch 108/200\n",
            "86/86 - 0s - loss: 3.9898e-04 - accuracy: 0.9998 - val_loss: 0.0010 - val_accuracy: 0.9993 - 224ms/epoch - 3ms/step\n",
            "Epoch 109/200\n",
            "86/86 - 0s - loss: 8.3047e-04 - accuracy: 0.9996 - val_loss: 0.0013 - val_accuracy: 0.9993 - 212ms/epoch - 2ms/step\n",
            "Epoch 110/200\n",
            "86/86 - 0s - loss: 5.5860e-04 - accuracy: 0.9998 - val_loss: 0.0013 - val_accuracy: 0.9993 - 209ms/epoch - 2ms/step\n",
            "Epoch 111/200\n",
            "86/86 - 0s - loss: 4.2948e-04 - accuracy: 0.9998 - val_loss: 0.0021 - val_accuracy: 0.9993 - 219ms/epoch - 3ms/step\n",
            "Epoch 112/200\n",
            "86/86 - 0s - loss: 4.4596e-04 - accuracy: 0.9998 - val_loss: 0.0022 - val_accuracy: 0.9993 - 219ms/epoch - 3ms/step\n",
            "Epoch 113/200\n",
            "86/86 - 0s - loss: 5.2071e-04 - accuracy: 0.9998 - val_loss: 0.0019 - val_accuracy: 0.9993 - 225ms/epoch - 3ms/step\n",
            "Epoch 114/200\n",
            "86/86 - 0s - loss: 5.0130e-04 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 0.9993 - 187ms/epoch - 2ms/step\n",
            "Epoch 115/200\n",
            "86/86 - 0s - loss: 3.7572e-04 - accuracy: 0.9998 - val_loss: 0.0011 - val_accuracy: 0.9993 - 210ms/epoch - 2ms/step\n",
            "Epoch 116/200\n",
            "86/86 - 0s - loss: 4.6645e-04 - accuracy: 0.9998 - val_loss: 0.0015 - val_accuracy: 0.9993 - 212ms/epoch - 2ms/step\n",
            "Epoch 117/200\n",
            "86/86 - 0s - loss: 7.0538e-04 - accuracy: 0.9998 - val_loss: 3.7986e-04 - val_accuracy: 1.0000 - 188ms/epoch - 2ms/step\n",
            "Epoch 118/200\n",
            "86/86 - 0s - loss: 5.0680e-04 - accuracy: 0.9996 - val_loss: 4.5891e-04 - val_accuracy: 1.0000 - 222ms/epoch - 3ms/step\n",
            "Epoch 119/200\n",
            "86/86 - 0s - loss: 0.0056 - accuracy: 0.9993 - val_loss: 9.7818e-04 - val_accuracy: 0.9993 - 220ms/epoch - 3ms/step\n",
            "Epoch 120/200\n",
            "86/86 - 0s - loss: 5.1542e-04 - accuracy: 0.9998 - val_loss: 9.6003e-04 - val_accuracy: 0.9993 - 217ms/epoch - 3ms/step\n",
            "Epoch 121/200\n",
            "86/86 - 0s - loss: 4.6050e-04 - accuracy: 0.9998 - val_loss: 0.0018 - val_accuracy: 0.9993 - 254ms/epoch - 3ms/step\n",
            "Epoch 122/200\n",
            "86/86 - 0s - loss: 4.9465e-04 - accuracy: 0.9998 - val_loss: 0.0024 - val_accuracy: 0.9993 - 273ms/epoch - 3ms/step\n",
            "Epoch 123/200\n",
            "86/86 - 0s - loss: 0.0014 - accuracy: 0.9995 - val_loss: 3.2247e-04 - val_accuracy: 1.0000 - 268ms/epoch - 3ms/step\n",
            "Epoch 124/200\n",
            "86/86 - 0s - loss: 4.1502e-04 - accuracy: 1.0000 - val_loss: 8.5594e-04 - val_accuracy: 0.9993 - 275ms/epoch - 3ms/step\n",
            "Epoch 125/200\n",
            "86/86 - 0s - loss: 4.4398e-04 - accuracy: 0.9998 - val_loss: 0.0010 - val_accuracy: 0.9993 - 314ms/epoch - 4ms/step\n",
            "Epoch 126/200\n",
            "86/86 - 0s - loss: 0.0012 - accuracy: 0.9996 - val_loss: 5.2099e-04 - val_accuracy: 1.0000 - 289ms/epoch - 3ms/step\n",
            "Epoch 127/200\n",
            "86/86 - 0s - loss: 5.2709e-04 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9993 - 326ms/epoch - 4ms/step\n",
            "Epoch 128/200\n",
            "86/86 - 0s - loss: 4.0685e-04 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 0.9993 - 269ms/epoch - 3ms/step\n",
            "Epoch 129/200\n",
            "86/86 - 0s - loss: 8.3003e-04 - accuracy: 0.9995 - val_loss: 0.0029 - val_accuracy: 0.9993 - 287ms/epoch - 3ms/step\n",
            "Epoch 130/200\n",
            "86/86 - 0s - loss: 8.8918e-04 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9993 - 207ms/epoch - 2ms/step\n",
            "Epoch 131/200\n",
            "86/86 - 0s - loss: 0.0016 - accuracy: 0.9996 - val_loss: 3.5933e-04 - val_accuracy: 1.0000 - 212ms/epoch - 2ms/step\n",
            "Epoch 132/200\n",
            "86/86 - 0s - loss: 4.6985e-04 - accuracy: 0.9998 - val_loss: 7.7849e-04 - val_accuracy: 0.9993 - 218ms/epoch - 3ms/step\n",
            "Epoch 133/200\n",
            "86/86 - 0s - loss: 3.2307e-04 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 0.9993 - 217ms/epoch - 3ms/step\n",
            "Epoch 134/200\n",
            "86/86 - 0s - loss: 7.4198e-04 - accuracy: 0.9998 - val_loss: 0.0039 - val_accuracy: 0.9985 - 226ms/epoch - 3ms/step\n",
            "Epoch 135/200\n",
            "86/86 - 0s - loss: 0.0013 - accuracy: 0.9996 - val_loss: 4.1572e-04 - val_accuracy: 1.0000 - 182ms/epoch - 2ms/step\n",
            "Epoch 136/200\n",
            "86/86 - 0s - loss: 0.0017 - accuracy: 0.9993 - val_loss: 0.0014 - val_accuracy: 0.9993 - 182ms/epoch - 2ms/step\n",
            "Epoch 137/200\n",
            "86/86 - 0s - loss: 3.2788e-04 - accuracy: 0.9998 - val_loss: 7.3955e-04 - val_accuracy: 0.9993 - 217ms/epoch - 3ms/step\n",
            "Epoch 138/200\n",
            "86/86 - 0s - loss: 3.7450e-04 - accuracy: 1.0000 - val_loss: 9.5193e-04 - val_accuracy: 0.9993 - 203ms/epoch - 2ms/step\n",
            "Epoch 139/200\n",
            "86/86 - 0s - loss: 3.5852e-04 - accuracy: 1.0000 - val_loss: 4.9203e-04 - val_accuracy: 1.0000 - 244ms/epoch - 3ms/step\n",
            "Epoch 140/200\n",
            "86/86 - 0s - loss: 3.5473e-04 - accuracy: 0.9998 - val_loss: 0.0012 - val_accuracy: 0.9993 - 216ms/epoch - 3ms/step\n",
            "Epoch 141/200\n",
            "86/86 - 0s - loss: 3.0774e-04 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 0.9993 - 184ms/epoch - 2ms/step\n",
            "Epoch 142/200\n",
            "86/86 - 0s - loss: 6.9907e-04 - accuracy: 0.9995 - val_loss: 0.0024 - val_accuracy: 0.9993 - 190ms/epoch - 2ms/step\n",
            "Epoch 143/200\n",
            "86/86 - 0s - loss: 6.9705e-04 - accuracy: 0.9996 - val_loss: 3.0917e-04 - val_accuracy: 1.0000 - 179ms/epoch - 2ms/step\n",
            "Epoch 144/200\n",
            "86/86 - 0s - loss: 7.3542e-04 - accuracy: 0.9998 - val_loss: 5.2872e-04 - val_accuracy: 1.0000 - 228ms/epoch - 3ms/step\n",
            "Epoch 145/200\n",
            "86/86 - 0s - loss: 4.0180e-04 - accuracy: 0.9998 - val_loss: 0.0014 - val_accuracy: 0.9993 - 222ms/epoch - 3ms/step\n",
            "Epoch 146/200\n",
            "86/86 - 0s - loss: 4.8767e-04 - accuracy: 0.9998 - val_loss: 2.9178e-04 - val_accuracy: 1.0000 - 214ms/epoch - 2ms/step\n",
            "Epoch 147/200\n",
            "86/86 - 0s - loss: 9.6182e-04 - accuracy: 0.9995 - val_loss: 0.0013 - val_accuracy: 0.9993 - 179ms/epoch - 2ms/step\n",
            "Epoch 148/200\n",
            "86/86 - 0s - loss: 4.1482e-04 - accuracy: 0.9998 - val_loss: 0.0014 - val_accuracy: 0.9993 - 229ms/epoch - 3ms/step\n",
            "Epoch 149/200\n",
            "86/86 - 0s - loss: 3.2595e-04 - accuracy: 0.9998 - val_loss: 0.0012 - val_accuracy: 0.9993 - 181ms/epoch - 2ms/step\n",
            "Epoch 150/200\n",
            "86/86 - 0s - loss: 3.1158e-04 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 0.9993 - 213ms/epoch - 2ms/step\n",
            "Epoch 151/200\n",
            "86/86 - 0s - loss: 3.5337e-04 - accuracy: 1.0000 - val_loss: 7.6931e-04 - val_accuracy: 0.9993 - 187ms/epoch - 2ms/step\n",
            "Epoch 152/200\n",
            "86/86 - 0s - loss: 2.9347e-04 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 0.9993 - 214ms/epoch - 2ms/step\n",
            "Epoch 153/200\n",
            "86/86 - 0s - loss: 6.6284e-04 - accuracy: 0.9998 - val_loss: 0.0066 - val_accuracy: 0.9985 - 183ms/epoch - 2ms/step\n",
            "Epoch 154/200\n",
            "86/86 - 0s - loss: 0.0026 - accuracy: 0.9995 - val_loss: 0.0032 - val_accuracy: 0.9993 - 193ms/epoch - 2ms/step\n",
            "Epoch 155/200\n",
            "86/86 - 0s - loss: 7.0163e-04 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9993 - 217ms/epoch - 3ms/step\n",
            "Epoch 156/200\n",
            "86/86 - 0s - loss: 2.6791e-04 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 0.9993 - 186ms/epoch - 2ms/step\n",
            "Epoch 157/200\n",
            "86/86 - 0s - loss: 3.1083e-04 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 0.9993 - 232ms/epoch - 3ms/step\n",
            "Epoch 158/200\n",
            "86/86 - 0s - loss: 2.6067e-04 - accuracy: 1.0000 - val_loss: 0.0012 - val_accuracy: 0.9993 - 190ms/epoch - 2ms/step\n",
            "Epoch 159/200\n",
            "86/86 - 0s - loss: 3.1831e-04 - accuracy: 0.9998 - val_loss: 0.0019 - val_accuracy: 0.9993 - 214ms/epoch - 2ms/step\n",
            "Epoch 160/200\n",
            "86/86 - 0s - loss: 4.6538e-04 - accuracy: 0.9998 - val_loss: 0.0027 - val_accuracy: 0.9993 - 203ms/epoch - 2ms/step\n",
            "Epoch 161/200\n",
            "86/86 - 0s - loss: 4.4493e-04 - accuracy: 0.9998 - val_loss: 0.0011 - val_accuracy: 0.9993 - 181ms/epoch - 2ms/step\n",
            "Epoch 162/200\n",
            "86/86 - 0s - loss: 3.7920e-04 - accuracy: 0.9998 - val_loss: 0.0021 - val_accuracy: 0.9993 - 220ms/epoch - 3ms/step\n",
            "Epoch 163/200\n",
            "86/86 - 0s - loss: 3.3410e-04 - accuracy: 0.9998 - val_loss: 0.0025 - val_accuracy: 0.9993 - 226ms/epoch - 3ms/step\n",
            "Epoch 164/200\n",
            "86/86 - 0s - loss: 3.4758e-04 - accuracy: 1.0000 - val_loss: 9.9367e-04 - val_accuracy: 0.9993 - 214ms/epoch - 2ms/step\n",
            "Epoch 165/200\n",
            "86/86 - 0s - loss: 5.4662e-04 - accuracy: 0.9998 - val_loss: 0.0013 - val_accuracy: 0.9993 - 220ms/epoch - 3ms/step\n",
            "Epoch 166/200\n",
            "86/86 - 0s - loss: 2.6508e-04 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 0.9993 - 188ms/epoch - 2ms/step\n",
            "Epoch 167/200\n",
            "86/86 - 0s - loss: 3.3839e-04 - accuracy: 1.0000 - val_loss: 7.0432e-04 - val_accuracy: 0.9993 - 179ms/epoch - 2ms/step\n",
            "Epoch 168/200\n",
            "86/86 - 0s - loss: 2.9658e-04 - accuracy: 0.9998 - val_loss: 0.0032 - val_accuracy: 0.9993 - 224ms/epoch - 3ms/step\n",
            "Epoch 169/200\n",
            "86/86 - 0s - loss: 4.3935e-04 - accuracy: 0.9998 - val_loss: 0.0054 - val_accuracy: 0.9985 - 218ms/epoch - 3ms/step\n",
            "Epoch 170/200\n",
            "86/86 - 0s - loss: 0.0014 - accuracy: 0.9996 - val_loss: 9.1542e-04 - val_accuracy: 0.9993 - 185ms/epoch - 2ms/step\n",
            "Epoch 171/200\n",
            "86/86 - 0s - loss: 3.0242e-04 - accuracy: 0.9998 - val_loss: 0.0011 - val_accuracy: 0.9993 - 213ms/epoch - 2ms/step\n",
            "Epoch 172/200\n",
            "86/86 - 0s - loss: 4.3705e-04 - accuracy: 0.9998 - val_loss: 0.0028 - val_accuracy: 0.9993 - 186ms/epoch - 2ms/step\n",
            "Epoch 173/200\n",
            "86/86 - 1s - loss: 4.0807e-04 - accuracy: 0.9998 - val_loss: 6.3433e-04 - val_accuracy: 1.0000 - 633ms/epoch - 7ms/step\n",
            "Epoch 174/200\n",
            "86/86 - 0s - loss: 5.4853e-04 - accuracy: 0.9998 - val_loss: 0.0029 - val_accuracy: 0.9993 - 212ms/epoch - 2ms/step\n",
            "Epoch 175/200\n",
            "86/86 - 0s - loss: 4.3267e-04 - accuracy: 0.9996 - val_loss: 7.0828e-04 - val_accuracy: 0.9993 - 226ms/epoch - 3ms/step\n",
            "Epoch 176/200\n",
            "86/86 - 1s - loss: 0.0013 - accuracy: 0.9993 - val_loss: 9.8369e-04 - val_accuracy: 0.9993 - 1s/epoch - 12ms/step\n",
            "Epoch 177/200\n",
            "86/86 - 0s - loss: 5.0417e-04 - accuracy: 0.9996 - val_loss: 7.8369e-04 - val_accuracy: 0.9993 - 305ms/epoch - 4ms/step\n",
            "Epoch 178/200\n",
            "86/86 - 0s - loss: 4.4424e-04 - accuracy: 0.9998 - val_loss: 0.0026 - val_accuracy: 0.9993 - 249ms/epoch - 3ms/step\n",
            "Epoch 179/200\n",
            "86/86 - 0s - loss: 4.1111e-04 - accuracy: 0.9998 - val_loss: 0.0014 - val_accuracy: 0.9993 - 261ms/epoch - 3ms/step\n",
            "Epoch 180/200\n",
            "86/86 - 0s - loss: 4.4300e-04 - accuracy: 1.0000 - val_loss: 0.0010 - val_accuracy: 0.9993 - 297ms/epoch - 3ms/step\n",
            "Epoch 181/200\n",
            "86/86 - 0s - loss: 2.9586e-04 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 0.9993 - 294ms/epoch - 3ms/step\n",
            "Epoch 182/200\n",
            "86/86 - 0s - loss: 3.4344e-04 - accuracy: 0.9998 - val_loss: 0.0019 - val_accuracy: 0.9993 - 284ms/epoch - 3ms/step\n",
            "Epoch 183/200\n",
            "86/86 - 0s - loss: 3.1839e-04 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 0.9993 - 188ms/epoch - 2ms/step\n",
            "Epoch 184/200\n",
            "86/86 - 0s - loss: 3.0854e-04 - accuracy: 0.9998 - val_loss: 0.0023 - val_accuracy: 0.9993 - 178ms/epoch - 2ms/step\n",
            "Epoch 185/200\n",
            "86/86 - 0s - loss: 3.0495e-04 - accuracy: 1.0000 - val_loss: 0.0011 - val_accuracy: 0.9993 - 222ms/epoch - 3ms/step\n",
            "Epoch 186/200\n",
            "86/86 - 0s - loss: 3.3889e-04 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 0.9993 - 181ms/epoch - 2ms/step\n",
            "Epoch 187/200\n",
            "86/86 - 0s - loss: 4.7400e-04 - accuracy: 0.9998 - val_loss: 0.0013 - val_accuracy: 0.9993 - 213ms/epoch - 2ms/step\n",
            "Epoch 188/200\n",
            "86/86 - 0s - loss: 3.3532e-04 - accuracy: 0.9998 - val_loss: 0.0015 - val_accuracy: 0.9993 - 185ms/epoch - 2ms/step\n",
            "Epoch 189/200\n",
            "86/86 - 0s - loss: 2.5246e-04 - accuracy: 0.9998 - val_loss: 0.0024 - val_accuracy: 0.9993 - 178ms/epoch - 2ms/step\n",
            "Epoch 190/200\n",
            "86/86 - 0s - loss: 3.1296e-04 - accuracy: 1.0000 - val_loss: 9.9821e-04 - val_accuracy: 0.9993 - 193ms/epoch - 2ms/step\n",
            "Epoch 191/200\n",
            "86/86 - 0s - loss: 2.1747e-04 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 0.9993 - 186ms/epoch - 2ms/step\n",
            "Epoch 192/200\n",
            "86/86 - 0s - loss: 6.8799e-04 - accuracy: 0.9996 - val_loss: 0.0059 - val_accuracy: 0.9985 - 187ms/epoch - 2ms/step\n",
            "Epoch 193/200\n",
            "86/86 - 0s - loss: 6.7737e-04 - accuracy: 0.9996 - val_loss: 1.3931e-04 - val_accuracy: 1.0000 - 213ms/epoch - 2ms/step\n",
            "Epoch 194/200\n",
            "86/86 - 0s - loss: 2.2473e-04 - accuracy: 1.0000 - val_loss: 8.2510e-04 - val_accuracy: 0.9993 - 211ms/epoch - 2ms/step\n",
            "Epoch 195/200\n",
            "86/86 - 0s - loss: 2.3098e-04 - accuracy: 1.0000 - val_loss: 2.5337e-04 - val_accuracy: 1.0000 - 230ms/epoch - 3ms/step\n",
            "Epoch 196/200\n",
            "86/86 - 0s - loss: 5.0820e-04 - accuracy: 0.9996 - val_loss: 1.8053e-04 - val_accuracy: 1.0000 - 211ms/epoch - 2ms/step\n",
            "Epoch 197/200\n",
            "86/86 - 0s - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.0017 - val_accuracy: 0.9993 - 178ms/epoch - 2ms/step\n",
            "Epoch 198/200\n",
            "86/86 - 0s - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0046 - val_accuracy: 0.9985 - 210ms/epoch - 2ms/step\n",
            "Epoch 199/200\n",
            "86/86 - 0s - loss: 8.2382e-04 - accuracy: 0.9998 - val_loss: 3.2207e-04 - val_accuracy: 1.0000 - 195ms/epoch - 2ms/step\n",
            "Epoch 200/200\n",
            "86/86 - 0s - loss: 5.5269e-04 - accuracy: 0.9998 - val_loss: 0.0015 - val_accuracy: 0.9993 - 220ms/epoch - 3ms/step\n",
            "54/54 [==============================] - 0s 1ms/step\n",
            "Accuracy: 1.00\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Load your dataset into a DataFrame (replace 'your_dataset.csv' with your dataset file)\n",
        "df = pd.read_csv('data.csv')\n",
        "\n",
        "# Define X (features) and y (target)\n",
        "X = df[['temp', 'humidity', 'soil_moisture']]\n",
        "y = df['pump_status']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardize the input features (important for neural networks)\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Create a simple neural network model\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(32, activation='relu', input_dim=3),\n",
        "    keras.layers.Dense(16, activation='relu'),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=200, batch_size=64, validation_split=0.2, verbose=2)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred_binary = np.round(y_pred)\n",
        "\n",
        "# Evaluate the model's performance\n",
        "accuracy = accuracy_score(y_test, y_pred_binary)\n",
        "print(f'Accuracy: {accuracy:.2f}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "ycpjMhs2mBxc",
        "outputId": "8beef2e8-72d7-4f0e-81c6-1fbbb1c8ffc5"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_40645b51-1485-4052-b835-3f8fb47db3c7\", \"irrigation.h5\", 38336)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model.save(\"irrigation.h5\")\n",
        "files.download(\"irrigation.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "A8vkPfeHmNGG",
        "outputId": "938ca0f0-442c-4f56-ccae-3b5843aaa451"
      },
      "outputs": [
        {
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-8e9a292436db>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Input values from the user\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mtemperature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter temperature: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mhumidity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter humidity: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0msoil_moisture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter soil moisture: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    849\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m             )\n\u001b[0;32m--> 851\u001b[0;31m         return self._input_request(str(prompt),\n\u001b[0m\u001b[1;32m    852\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "# Load the saved model\n",
        "model = tf.keras.models.load_model('irrigation.h5')  # Replace with the actual path to your saved model\n",
        "\n",
        "\n",
        "\n",
        "# Input values from the user\n",
        "temperature = float(input(\"Enter temperature: \"))\n",
        "humidity = float(input(\"Enter humidity: \"))\n",
        "soil_moisture = float(input(\"Enter soil moisture: \"))\n",
        "\n",
        "# Create an input array with the provided values\n",
        "input_data = np.array([[temperature, humidity, soil_moisture]])\n",
        "\n",
        "# Normalize the input data (if your model requires normalization)\n",
        "# If you previously standardized the data for training, you should apply the same transformation to new data.\n",
        "# Example: input_data = scaler.transform(input_data)\n",
        "\n",
        "# Make a prediction\n",
        "prediction = model.predict(input_data)\n",
        "\n",
        "# Interpret the prediction (assuming it's a binary classification)\n",
        "if prediction >= 0.5:\n",
        "    pump_status = \"On\"\n",
        "else:\n",
        "    pump_status = \"Off\"\n",
        "\n",
        "print(f\"Predicted Pump Status: {pump_status}\")\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM58U9Soeel1SMI3m68ki95",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}