Multivariate_Stock_Price_Prediction_with_Transformer_Encoder.ipynb

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      "cell_type": "markdown",
      "source": [
        "## Detailed article explaination\n",
        "\n",
        "The detailed code explanation for this article is available at the following link:\n",
        "\n",
        "https://www.daniweb.com/programming/computer-science/tutorials/541281/multivariate-stock-price-prediction-with-transformer-encoder-in-tensorflow\n",
        "\n",
        "For my other articles for Daniweb.com, please see this link:\n",
        "\n",
        "https://www.daniweb.com/members/1235222/usmanmalik57\n"
      ],
      "metadata": {
        "id": "DU2cWcqIL7ZW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Required Libraries and Datasets"
      ],
      "metadata": {
        "id": "yhlzUHWBlTWd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras-nlp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFCGSMoCTDeL",
        "outputId": "e04df46f-73a6-46de-eb4c-464f31140ad7"
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    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import datetime\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import datetime, timedelta\n",
        "import tensorflow as tf\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n",
        "from keras_nlp.layers import TransformerEncoder\n",
        "from keras.callbacks import EarlyStopping\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QP4Fqzd8NHHg",
        "outputId": "43f99cc6-1f9f-490a-ded9-e0a8d65d4777"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the ticker symbol for the stock\n",
        "ticker_symbol = \"GOOG\"  # Example: Apple Inc.\n",
        "\n",
        "# Define the start and end dates for the historical data\n",
        "date_string = \"02-Dec-2023\"\n",
        "end_date = datetime.strptime(date_string, \"%d-%b-%Y\")\n",
        "start_date = end_date - timedelta(days=5 * 365)  # 5 years ago\n",
        "\n",
        "# Retrieve historical data\n",
        "data = yf.download(ticker_symbol, start=start_date, end=end_date)\n",
        "\n",
        "# Display the historical data as a Pandas DataFrame\n",
        "print(data.shape)\n",
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "wzpC-_NJMIGV",
        "outputId": "fb4737c2-5062-4043-a985-83c13b0a6922"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "(1258, 6)\n"
          ]
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              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2023-11-27  137.570007  139.630005  137.539993  138.050003  138.050003   \n",
              "2023-11-28  137.630005  138.660004  137.039993  138.619995  138.619995   \n",
              "2023-11-29  138.985001  139.669998  136.294998  136.399994  136.399994   \n",
              "2023-11-30  136.399994  136.960007  132.789993  133.919998  133.919998   \n",
              "2023-12-01  133.320007  133.500000  132.151993  133.320007  133.320007   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2023-11-27  17886400  \n",
              "2023-11-28  17046900  \n",
              "2023-11-29  21014700  \n",
              "2023-11-30  29913500  \n",
              "2023-12-01  24258400  "
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              "      <th>2023-11-27</th>\n",
              "      <td>137.570007</td>\n",
              "      <td>139.630005</td>\n",
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              "\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",
              "\n",
              "\n",
              "<div id=\"df-faf1f9e5-044e-402c-9d06-a55edf13e95e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-faf1f9e5-044e-402c-9d06-a55edf13e95e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-faf1f9e5-044e-402c-9d06-a55edf13e95e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.set_style('darkgrid')\n",
        "sns.lineplot(x=data.index, y=data['Close'])\n",
        "\n",
        "# Set plot labels and title\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Close Price')\n",
        "plt.title(f'{ticker_symbol} Stock Price Over Time')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "rJOEvgT8MKZo",
        "outputId": "6a0a50b1-33fa-4bdb-fb80-f42d155fed67"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing for Creating Multivariate Time Series"
      ],
      "metadata": {
        "id": "h0vD8OHPYkj0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dividing the Data into Training and Test Sets"
      ],
      "metadata": {
        "id": "Xflpphz4ZjsF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Get the number of records in the DataFrame\n",
        "total_records = len(data)\n",
        "\n",
        "# Number of records to keep in the training set\n",
        "train_size = total_records - 60\n",
        "\n",
        "# Create the training set\n",
        "train_data = data.iloc[:train_size]\n",
        "\n",
        "# Create the test set\n",
        "test_data = data.iloc[train_size:]\n",
        "\n",
        "# split each of the training and test sets into features and labels\n",
        "# Features will include all columns except 'Close'\n",
        "# Labels will include only the 'Close' column\n",
        "\n",
        "train_features = train_data.drop('Close', axis=1)\n",
        "train_labels = train_data['Close']\n",
        "\n",
        "test_features = test_data.drop('Close', axis=1)\n",
        "test_labels = test_data['Close']\n",
        "\n",
        "# Print shapes to confirm\n",
        "print(\"Train Features:\", train_features.shape, \"Train Labels:\", train_labels.shape)\n",
        "print(\"Test Features:\", test_features.shape, \"Test Labels:\", test_labels.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFtQV51DNDEN",
        "outputId": "bfe7f606-e922-464d-f023-be214c7e2dc7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Features: (1198, 5) Train Labels: (1198,)\n",
            "Test Features: (60, 5) Test Labels: (60,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Scaling"
      ],
      "metadata": {
        "id": "raQaYeI7Znnb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Fit the scaler to the training features\n",
        "scaler.fit(train_features)\n",
        "\n",
        "# Transform the training and test feature sets\n",
        "train_features_scaled = scaler.transform(train_features)\n",
        "test_features_scaled = scaler.transform(test_features)\n",
        "\n",
        "# Reconstruct dataframes with original columns and indices\n",
        "train_scaled_df = pd.DataFrame(train_features_scaled, columns = train_features.columns, index = train_features.index)\n",
        "test_scaled_df = pd.DataFrame(test_features_scaled, columns = test_features.columns, index = test_features.index)\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Fit the scaler to the training labels and transform them\n",
        "train_labels = scaler.fit_transform(train_labels.values.reshape(-1, 1))\n",
        "\n",
        "# Transform the test labels with the already fitted scaler\n",
        "test_labels = scaler.transform(test_labels.values.reshape(-1, 1))"
      ],
      "metadata": {
        "id": "1lZBltkIUyny"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating Multivariate Training and Test Sequences"
      ],
      "metadata": {
        "id": "EwXaPXuhZrs3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sequence_length = 60\n",
        "\n",
        "def create_train_sequence(train_df, train_labels, sequence_length):\n",
        "    seq_length = sequence_length  # Length of the sequence\n",
        "    features = []  # List to hold feature sequences\n",
        "    labels = []    # List to hold labels\n",
        "\n",
        "    # Iterate through the DataFrame to create sequences\n",
        "    for i in range(seq_length, len(train_df)):\n",
        "        sequence = train_df.iloc[i-seq_length:i]  # Get 60 days sequence\n",
        "        label = train_labels[i]                      # Get the label for the corresponding day\n",
        "\n",
        "        features.append(sequence.values)  # Append sequence to features\n",
        "        labels.append(label)              # Append label\n",
        "\n",
        "    # Convert lists to numpy arrays\n",
        "    X = np.array(features)\n",
        "    y = np.array(labels).reshape(-1, 1)\n",
        "\n",
        "    return X, y"
      ],
      "metadata": {
        "id": "K3dY8ra3Njbo"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = create_train_sequence(train_scaled_df,\n",
        "                                         train_labels,\n",
        "                                         sequence_length)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPD1ApmZZXGK",
        "outputId": "7c1c1d01-6162-43cd-8c19-64ba55d9b746"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1138, 60, 5)\n",
            "(1138, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_test_sequence(train_df, test_df, test_labels, seq_length):\n",
        "    # Concatenate the last part of train features with test features\n",
        "    combined_features_df = pd.concat([train_df.iloc[-seq_length:], test_df])\n",
        "\n",
        "    test_features = []  # List to hold test feature sequences\n",
        "    test_labels_list = []  # List to hold test labels\n",
        "\n",
        "    # Create sequences for the test set\n",
        "    for i in range(seq_length, len(combined_features_df)):\n",
        "        sequence = combined_features_df.iloc[i-seq_length:i]  # Get 60 days sequence\n",
        "        # Use test_labels for label (assuming test_labels is aligned with test_features_df)\n",
        "        label = test_labels[i - seq_length] if i < len(test_df) + seq_length else None\n",
        "\n",
        "        test_features.append(sequence.values)  # Append sequence to test features\n",
        "        test_labels_list.append(label)         # Append label\n",
        "\n",
        "    # Remove the None values at the end (if any)\n",
        "    test_features = [feature for feature, label in zip(test_features, test_labels_list) if label is not None]\n",
        "    test_labels_list = [label for label in test_labels_list if label is not None]\n",
        "\n",
        "    # Convert lists to numpy arrays\n",
        "    X = np.array(test_features)\n",
        "    y = np.array(test_labels_list).reshape(-1, 1)\n",
        "\n",
        "    return X, y\n",
        "\n",
        "X_test, y_test = create_test_sequence(train_scaled_df,\n",
        "                                      test_scaled_df,\n",
        "                                      test_labels,\n",
        "                                      sequence_length)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhi5H2_EPr6g",
        "outputId": "190e14b1-9baf-44a4-ca0a-71d740bf103c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60, 60, 5)\n",
            "(60, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training a Stock Price Prediction Model with Transformer Encoder"
      ],
      "metadata": {
        "id": "JFoYjcAgoo2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# input shape: (60 time steps, 5 features)\n",
        "input_layer = Input(shape=(60, 5))\n",
        "\n",
        "# Transformer Encoder layers\n",
        "transformer_1 = TransformerEncoder(num_heads=2, intermediate_dim=64)(input_layer)\n",
        "dropout_1 = Dropout(0.2)(transformer_1)\n",
        "\n",
        "transformer_2 = TransformerEncoder(num_heads=2, intermediate_dim=64)(dropout_1)\n",
        "dropout_2 = Dropout(0.2)(transformer_2)\n",
        "\n",
        "transformer_3 = TransformerEncoder( num_heads=2, intermediate_dim=64)(dropout_2)\n",
        "dropout_3 = Dropout(0.2)(transformer_3)\n",
        "\n",
        "# Flatten layer\n",
        "flatten = Flatten()(dropout_3)\n",
        "\n",
        "# Dense layers\n",
        "dense_1 = Dense(200, activation='relu')(flatten)\n",
        "\n",
        "# Dense layer\n",
        "dense_2 = Dense(100, activation='relu')(dense_1)\n",
        "\n",
        "# Output layer with a single neuron\n",
        "output_layer = Dense(1, activation='linear')(dense_2)\n",
        "\n",
        "# Create the model\n",
        "model = Model(inputs=input_layer, outputs=output_layer)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "# Display the model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHx_rIdDPwFO",
        "outputId": "480942a3-18d7-4c6f-ff5d-9603523b8c27"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 60, 5)]           0         \n",
            "                                                                 \n",
            " transformer_encoder (Trans  (None, 60, 5)             826       \n",
            " formerEncoder)                                                  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 60, 5)             0         \n",
            "                                                                 \n",
            " transformer_encoder_1 (Tra  (None, 60, 5)             826       \n",
            " nsformerEncoder)                                                \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 60, 5)             0         \n",
            "                                                                 \n",
            " transformer_encoder_2 (Tra  (None, 60, 5)             826       \n",
            " nsformerEncoder)                                                \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 60, 5)             0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 300)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 200)               60200     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 100)               20100     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 82879 (323.75 KB)\n",
            "Trainable params: 82879 (323.75 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss', patience=100, restore_best_weights=True)\n",
        "\n",
        "# Train the model with early stopping\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    epochs=500,\n",
        "    validation_split=0.2,  # 20% of the data for validation\n",
        "    callbacks=[early_stopping],\n",
        "    verbose=1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j4_Y3oXaTSPH",
        "outputId": "a2955e6f-b2e2-4197-f1d8-9edbe15911dc"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "29/29 [==============================] - 12s 39ms/step - loss: 0.1686 - val_loss: 0.0401\n",
            "Epoch 2/500\n",
            "29/29 [==============================] - 1s 20ms/step - loss: 0.0656 - val_loss: 0.0109\n",
            "Epoch 3/500\n",
            "29/29 [==============================] - 1s 18ms/step - loss: 0.0315 - val_loss: 0.0390\n",
            "Epoch 4/500\n",
            "29/29 [==============================] - 1s 21ms/step - loss: 0.0222 - val_loss: 0.0106\n",
            "Epoch 5/500\n",
            "29/29 [==============================] - 1s 18ms/step - loss: 0.0157 - val_loss: 0.0142\n",
            "Epoch 6/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0130 - val_loss: 0.0187\n",
            "Epoch 7/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0128 - val_loss: 0.0089\n",
            "Epoch 8/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0121 - val_loss: 0.0106\n",
            "Epoch 9/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0092 - val_loss: 0.0082\n",
            "Epoch 10/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0089 - val_loss: 0.0135\n",
            "Epoch 11/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0101 - val_loss: 0.0104\n",
            "Epoch 12/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0089 - val_loss: 0.0150\n",
            "Epoch 13/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0076 - val_loss: 0.0101\n",
            "Epoch 14/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0074 - val_loss: 0.0077\n",
            "Epoch 15/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0072 - val_loss: 0.0081\n",
            "Epoch 16/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0070 - val_loss: 0.0069\n",
            "Epoch 17/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0066 - val_loss: 0.0080\n",
            "Epoch 18/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0062 - val_loss: 0.0080\n",
            "Epoch 19/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0073 - val_loss: 0.0087\n",
            "Epoch 20/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0062 - val_loss: 0.0096\n",
            "Epoch 21/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0053 - val_loss: 0.0091\n",
            "Epoch 22/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0055 - val_loss: 0.0080\n",
            "Epoch 23/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0054 - val_loss: 0.0098\n",
            "Epoch 24/500\n",
            "29/29 [==============================] - 1s 18ms/step - loss: 0.0060 - val_loss: 0.0069\n",
            "Epoch 25/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0057 - val_loss: 0.0072\n",
            "Epoch 26/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0049 - val_loss: 0.0095\n",
            "Epoch 27/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0049 - val_loss: 0.0096\n",
            "Epoch 28/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0051 - val_loss: 0.0088\n",
            "Epoch 29/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0045 - val_loss: 0.0099\n",
            "Epoch 30/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0044 - val_loss: 0.0083\n",
            "Epoch 31/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0047 - val_loss: 0.0081\n",
            "Epoch 32/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0046 - val_loss: 0.0074\n",
            "Epoch 33/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0056 - val_loss: 0.0092\n",
            "Epoch 34/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0044 - val_loss: 0.0089\n",
            "Epoch 35/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0045 - val_loss: 0.0074\n",
            "Epoch 36/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0050 - val_loss: 0.0132\n",
            "Epoch 37/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0042 - val_loss: 0.0087\n",
            "Epoch 38/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0046 - val_loss: 0.0111\n",
            "Epoch 39/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0040 - val_loss: 0.0074\n",
            "Epoch 40/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0038 - val_loss: 0.0085\n",
            "Epoch 41/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0039 - val_loss: 0.0102\n",
            "Epoch 42/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0039 - val_loss: 0.0094\n",
            "Epoch 43/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0037 - val_loss: 0.0137\n",
            "Epoch 44/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0039 - val_loss: 0.0132\n",
            "Epoch 45/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0038 - val_loss: 0.0098\n",
            "Epoch 46/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0035 - val_loss: 0.0076\n",
            "Epoch 47/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0038 - val_loss: 0.0075\n",
            "Epoch 48/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0038 - val_loss: 0.0095\n",
            "Epoch 49/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0036 - val_loss: 0.0068\n",
            "Epoch 50/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0034 - val_loss: 0.0106\n",
            "Epoch 51/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0035 - val_loss: 0.0072\n",
            "Epoch 52/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0032 - val_loss: 0.0097\n",
            "Epoch 53/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0032 - val_loss: 0.0087\n",
            "Epoch 54/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0034 - val_loss: 0.0071\n",
            "Epoch 55/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0035 - val_loss: 0.0076\n",
            "Epoch 56/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0031 - val_loss: 0.0085\n",
            "Epoch 57/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0034 - val_loss: 0.0109\n",
            "Epoch 58/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0031 - val_loss: 0.0090\n",
            "Epoch 59/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0028 - val_loss: 0.0104\n",
            "Epoch 60/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0027 - val_loss: 0.0071\n",
            "Epoch 61/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0034 - val_loss: 0.0071\n",
            "Epoch 62/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0032 - val_loss: 0.0144\n",
            "Epoch 63/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0036 - val_loss: 0.0065\n",
            "Epoch 64/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0027 - val_loss: 0.0073\n",
            "Epoch 65/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0032 - val_loss: 0.0120\n",
            "Epoch 66/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0030 - val_loss: 0.0061\n",
            "Epoch 67/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0032 - val_loss: 0.0081\n",
            "Epoch 68/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0037 - val_loss: 0.0060\n",
            "Epoch 69/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0042 - val_loss: 0.0068\n",
            "Epoch 70/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0025 - val_loss: 0.0110\n",
            "Epoch 71/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0025 - val_loss: 0.0070\n",
            "Epoch 72/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0031 - val_loss: 0.0065\n",
            "Epoch 73/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0027 - val_loss: 0.0081\n",
            "Epoch 74/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0024 - val_loss: 0.0091\n",
            "Epoch 75/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0027 - val_loss: 0.0088\n",
            "Epoch 76/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0030 - val_loss: 0.0061\n",
            "Epoch 77/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0025 - val_loss: 0.0072\n",
            "Epoch 78/500\n",
            "29/29 [==============================] - 1s 17ms/step - loss: 0.0028 - val_loss: 0.0061\n",
            "Epoch 79/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0030 - val_loss: 0.0066\n",
            "Epoch 80/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0026 - val_loss: 0.0093\n",
            "Epoch 81/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0027 - val_loss: 0.0094\n",
            "Epoch 82/500\n",
            "29/29 [==============================] - 1s 17ms/step - loss: 0.0028 - val_loss: 0.0058\n",
            "Epoch 83/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0029 - val_loss: 0.0064\n",
            "Epoch 84/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0025 - val_loss: 0.0124\n",
            "Epoch 85/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0027 - val_loss: 0.0084\n",
            "Epoch 86/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0026 - val_loss: 0.0094\n",
            "Epoch 87/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0026 - val_loss: 0.0060\n",
            "Epoch 88/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0024 - val_loss: 0.0057\n",
            "Epoch 89/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0024 - val_loss: 0.0065\n",
            "Epoch 90/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0023 - val_loss: 0.0079\n",
            "Epoch 91/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0027 - val_loss: 0.0068\n",
            "Epoch 92/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0037 - val_loss: 0.0109\n",
            "Epoch 93/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0026 - val_loss: 0.0139\n",
            "Epoch 94/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0025 - val_loss: 0.0087\n",
            "Epoch 95/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0025 - val_loss: 0.0061\n",
            "Epoch 96/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0026 - val_loss: 0.0078\n",
            "Epoch 97/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0023 - val_loss: 0.0066\n",
            "Epoch 98/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0022 - val_loss: 0.0132\n",
            "Epoch 99/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0027 - val_loss: 0.0099\n",
            "Epoch 100/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0020 - val_loss: 0.0078\n",
            "Epoch 101/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0022 - val_loss: 0.0103\n",
            "Epoch 102/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0023 - val_loss: 0.0080\n",
            "Epoch 103/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0022 - val_loss: 0.0059\n",
            "Epoch 104/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0022 - val_loss: 0.0066\n",
            "Epoch 105/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0021 - val_loss: 0.0114\n",
            "Epoch 106/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0022 - val_loss: 0.0081\n",
            "Epoch 107/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0019 - val_loss: 0.0070\n",
            "Epoch 108/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0021 - val_loss: 0.0085\n",
            "Epoch 109/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0022 - val_loss: 0.0078\n",
            "Epoch 110/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0020 - val_loss: 0.0100\n",
            "Epoch 111/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0020 - val_loss: 0.0078\n",
            "Epoch 112/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0021 - val_loss: 0.0141\n",
            "Epoch 113/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0019 - val_loss: 0.0085\n",
            "Epoch 114/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0020 - val_loss: 0.0066\n",
            "Epoch 115/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0018 - val_loss: 0.0071\n",
            "Epoch 116/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0022 - val_loss: 0.0103\n",
            "Epoch 117/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0023 - val_loss: 0.0067\n",
            "Epoch 118/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0025 - val_loss: 0.0069\n",
            "Epoch 119/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0020 - val_loss: 0.0069\n",
            "Epoch 120/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0021 - val_loss: 0.0067\n",
            "Epoch 121/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0019 - val_loss: 0.0077\n",
            "Epoch 122/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0021 - val_loss: 0.0073\n",
            "Epoch 123/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0019 - val_loss: 0.0101\n",
            "Epoch 124/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0020 - val_loss: 0.0096\n",
            "Epoch 125/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0019 - val_loss: 0.0080\n",
            "Epoch 126/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0021 - val_loss: 0.0090\n",
            "Epoch 127/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0018 - val_loss: 0.0077\n",
            "Epoch 128/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0020 - val_loss: 0.0077\n",
            "Epoch 129/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0018 - val_loss: 0.0070\n",
            "Epoch 130/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0018 - val_loss: 0.0081\n",
            "Epoch 131/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0019 - val_loss: 0.0079\n",
            "Epoch 132/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0021 - val_loss: 0.0069\n",
            "Epoch 133/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0018 - val_loss: 0.0070\n",
            "Epoch 134/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0016 - val_loss: 0.0098\n",
            "Epoch 135/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0016 - val_loss: 0.0064\n",
            "Epoch 136/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0020 - val_loss: 0.0083\n",
            "Epoch 137/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0019 - val_loss: 0.0067\n",
            "Epoch 138/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0020 - val_loss: 0.0089\n",
            "Epoch 139/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0019 - val_loss: 0.0090\n",
            "Epoch 140/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0019 - val_loss: 0.0063\n",
            "Epoch 141/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0018 - val_loss: 0.0077\n",
            "Epoch 142/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0020 - val_loss: 0.0103\n",
            "Epoch 143/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0018 - val_loss: 0.0069\n",
            "Epoch 144/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0017 - val_loss: 0.0106\n",
            "Epoch 145/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0016 - val_loss: 0.0058\n",
            "Epoch 146/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0018 - val_loss: 0.0082\n",
            "Epoch 147/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0019 - val_loss: 0.0105\n",
            "Epoch 148/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0017 - val_loss: 0.0052\n",
            "Epoch 149/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0018 - val_loss: 0.0085\n",
            "Epoch 150/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0015 - val_loss: 0.0105\n",
            "Epoch 151/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0016 - val_loss: 0.0077\n",
            "Epoch 152/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0018 - val_loss: 0.0070\n",
            "Epoch 153/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0014 - val_loss: 0.0069\n",
            "Epoch 154/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0016 - val_loss: 0.0069\n",
            "Epoch 155/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0017 - val_loss: 0.0161\n",
            "Epoch 156/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0023 - val_loss: 0.0068\n",
            "Epoch 157/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0023 - val_loss: 0.0102\n",
            "Epoch 158/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0017 - val_loss: 0.0134\n",
            "Epoch 159/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0016 - val_loss: 0.0088\n",
            "Epoch 160/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0015 - val_loss: 0.0067\n",
            "Epoch 161/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0016 - val_loss: 0.0091\n",
            "Epoch 162/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0016 - val_loss: 0.0055\n",
            "Epoch 163/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0016 - val_loss: 0.0075\n",
            "Epoch 164/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0017 - val_loss: 0.0138\n",
            "Epoch 165/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0028 - val_loss: 0.0070\n",
            "Epoch 166/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0022 - val_loss: 0.0062\n",
            "Epoch 167/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0017 - val_loss: 0.0075\n",
            "Epoch 168/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0014 - val_loss: 0.0096\n",
            "Epoch 169/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0013 - val_loss: 0.0072\n",
            "Epoch 170/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0014 - val_loss: 0.0105\n",
            "Epoch 171/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0017 - val_loss: 0.0111\n",
            "Epoch 172/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0018 - val_loss: 0.0107\n",
            "Epoch 173/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0016 - val_loss: 0.0080\n",
            "Epoch 174/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0014 - val_loss: 0.0077\n",
            "Epoch 175/500\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0016 - val_loss: 0.0086\n",
            "Epoch 176/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0014 - val_loss: 0.0103\n",
            "Epoch 177/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0014 - val_loss: 0.0076\n",
            "Epoch 178/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0014 - val_loss: 0.0087\n",
            "Epoch 179/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0014 - val_loss: 0.0101\n",
            "Epoch 180/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0014 - val_loss: 0.0076\n",
            "Epoch 181/500\n",
            "29/29 [==============================] - 1s 17ms/step - loss: 0.0016 - val_loss: 0.0067\n",
            "Epoch 182/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0014 - val_loss: 0.0097\n",
            "Epoch 183/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0013 - val_loss: 0.0076\n",
            "Epoch 184/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0013 - val_loss: 0.0103\n",
            "Epoch 185/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0012 - val_loss: 0.0095\n",
            "Epoch 186/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0014 - val_loss: 0.0062\n",
            "Epoch 187/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0014 - val_loss: 0.0076\n",
            "Epoch 188/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0015 - val_loss: 0.0108\n",
            "Epoch 189/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0017 - val_loss: 0.0104\n",
            "Epoch 190/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0015 - val_loss: 0.0090\n",
            "Epoch 191/500\n",
            "29/29 [==============================] - 1s 18ms/step - loss: 0.0014 - val_loss: 0.0084\n",
            "Epoch 192/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0012 - val_loss: 0.0071\n",
            "Epoch 193/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0013 - val_loss: 0.0105\n",
            "Epoch 194/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0015 - val_loss: 0.0072\n",
            "Epoch 195/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0013 - val_loss: 0.0109\n",
            "Epoch 196/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0014 - val_loss: 0.0119\n",
            "Epoch 197/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0013 - val_loss: 0.0123\n",
            "Epoch 198/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0012 - val_loss: 0.0096\n",
            "Epoch 199/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0014 - val_loss: 0.0069\n",
            "Epoch 200/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0014 - val_loss: 0.0105\n",
            "Epoch 201/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0016 - val_loss: 0.0068\n",
            "Epoch 202/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0014 - val_loss: 0.0057\n",
            "Epoch 203/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0012 - val_loss: 0.0078\n",
            "Epoch 204/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0013 - val_loss: 0.0078\n",
            "Epoch 205/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0013 - val_loss: 0.0076\n",
            "Epoch 206/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0013 - val_loss: 0.0105\n",
            "Epoch 207/500\n",
            "29/29 [==============================] - 1s 18ms/step - loss: 0.0012 - val_loss: 0.0078\n",
            "Epoch 208/500\n",
            "29/29 [==============================] - 1s 18ms/step - loss: 0.0014 - val_loss: 0.0128\n",
            "Epoch 209/500\n",
            "29/29 [==============================] - 1s 18ms/step - loss: 0.0012 - val_loss: 0.0071\n",
            "Epoch 210/500\n",
            "29/29 [==============================] - 1s 17ms/step - loss: 0.0012 - val_loss: 0.0100\n",
            "Epoch 211/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0010 - val_loss: 0.0098\n",
            "Epoch 212/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0011 - val_loss: 0.0066\n",
            "Epoch 213/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0011 - val_loss: 0.0102\n",
            "Epoch 214/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0011 - val_loss: 0.0099\n",
            "Epoch 215/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 9.6590e-04 - val_loss: 0.0106\n",
            "Epoch 216/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0013 - val_loss: 0.0111\n",
            "Epoch 217/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0010 - val_loss: 0.0079\n",
            "Epoch 218/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0012 - val_loss: 0.0123\n",
            "Epoch 219/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0013 - val_loss: 0.0091\n",
            "Epoch 220/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0012 - val_loss: 0.0076\n",
            "Epoch 221/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0012 - val_loss: 0.0114\n",
            "Epoch 222/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0012 - val_loss: 0.0073\n",
            "Epoch 223/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0010 - val_loss: 0.0068\n",
            "Epoch 224/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0012 - val_loss: 0.0077\n",
            "Epoch 225/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0010 - val_loss: 0.0097\n",
            "Epoch 226/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0011 - val_loss: 0.0097\n",
            "Epoch 227/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0011 - val_loss: 0.0076\n",
            "Epoch 228/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0010 - val_loss: 0.0112\n",
            "Epoch 229/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0011 - val_loss: 0.0072\n",
            "Epoch 230/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0010 - val_loss: 0.0081\n",
            "Epoch 231/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0011 - val_loss: 0.0057\n",
            "Epoch 232/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0016 - val_loss: 0.0087\n",
            "Epoch 233/500\n",
            "29/29 [==============================] - 1s 17ms/step - loss: 0.0013 - val_loss: 0.0134\n",
            "Epoch 234/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0011 - val_loss: 0.0089\n",
            "Epoch 235/500\n",
            "29/29 [==============================] - 1s 18ms/step - loss: 0.0010 - val_loss: 0.0077\n",
            "Epoch 236/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0011 - val_loss: 0.0098\n",
            "Epoch 237/500\n",
            "29/29 [==============================] - 0s 17ms/step - loss: 0.0011 - val_loss: 0.0091\n",
            "Epoch 238/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0015 - val_loss: 0.0096\n",
            "Epoch 239/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0014 - val_loss: 0.0085\n",
            "Epoch 240/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0011 - val_loss: 0.0101\n",
            "Epoch 241/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 9.6525e-04 - val_loss: 0.0097\n",
            "Epoch 242/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0010 - val_loss: 0.0125\n",
            "Epoch 243/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0012 - val_loss: 0.0118\n",
            "Epoch 244/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0012 - val_loss: 0.0156\n",
            "Epoch 245/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0013 - val_loss: 0.0088\n",
            "Epoch 246/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0010 - val_loss: 0.0068\n",
            "Epoch 247/500\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0011 - val_loss: 0.0120\n",
            "Epoch 248/500\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0010 - val_loss: 0.0092\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating Model Performance"
      ],
      "metadata": {
        "id": "IPKMvM5eoyRp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "# Calculate Mean Squared Error (MSE)\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "\n",
        "# Calculate Mean Absolute Error (MAE)\n",
        "mae = mean_absolute_error(y_test, predictions)\n",
        "\n",
        "# Print the values\n",
        "print(\"Mean Squared Error (MSE):\", mse)\n",
        "print(\"Mean Absolute Error (MAE):\", mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtHw2BbTWp38",
        "outputId": "8d25af1c-f1d1-4d88-e0e0-1d3030505787"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 7ms/step\n",
            "Mean Squared Error (MSE): 0.0025678165444383786\n",
            "Mean Absolute Error (MAE): 0.0387130112330789\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting predictions and targets to actual values\n",
        "\n",
        "y_test = scaler.inverse_transform(y_test)\n",
        "y_true = scaler.inverse_transform(predictions)"
      ],
      "metadata": {
        "id": "l3Yw5OKEoUIh"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(y_test, color='green', label='True Stock Price')\n",
        "plt.plot(y_true, color='blue', label='Predicted Stock Price')\n",
        "plt.title('Stock Price Prediction')\n",
        "plt.xlabel('Dates')\n",
        "plt.ylabel('Stock Price')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "LEkApznuXTzb",
        "outputId": "a25c9cf9-cc83-47f4-8dd8-74fb2cee7473"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W3mDJHntXZCs"
      },
      "execution_count": 14,
      "outputs": []
    }
  ]
}