{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "arduino_tinyml_workshop.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f92-4Hjy7kA8"
      },
      "source": [
        "<a href=\"https://www.aimldevfest.com\"><img src=\"https://www.aimldevfest.com/files/2019/09/AI-ML-devfest-AZ-2019-horz.png\" width=200/></a>\n",
        "\n",
        "# TinyML on Arduino Workshop\n",
        "[AI/ML Devfest](https://aimldevfest.com) September 28, 2019\n",
        "\n",
        "\n",
        " * Sandeep Mistry - Arduino\n",
        " * Don Coleman - Chariot Solutions\n",
        " \n",
        "https://github.com/sandeepmistry/aimldevfest-workshop-2019"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvDA8AK7QOq-"
      },
      "source": [
        "## Setup Python Environment \n",
        "\n",
        "The next cell sets up the dependencies in required for the notebook, run it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2gs-PL4xDkZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f793ffaa-e61b-4c2a-ea9e-3562cffb5f0e"
      },
      "source": [
        "# Setup environment\n",
        "!apt-get -qq install xxd\n",
        "!pip install pandas numpy matplotlib\n",
        "!pip install tensorflow==2.0.0-rc1"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.21.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2022.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.1.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow==2.0.0-rc1 (from versions: 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0rc0, 2.6.0rc1, 2.6.0rc2, 2.6.0, 2.6.0+zzzcolab20220506153740, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.4+zzzcolab20220516125453, 2.6.5, 2.6.5+zzzcolab20220523104206, 2.7.0rc0, 2.7.0rc1, 2.7.0, 2.7.0+zzzcolab20220506150900, 2.7.1, 2.7.2, 2.7.2+zzzcolab20220516114640, 2.7.3, 2.7.3+zzzcolab20220523111007, 2.7.4, 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.0+zzzcolab20220506162203, 2.8.1, 2.8.1+zzzcolab20220516111314, 2.8.1+zzzcolab20220518083849, 2.8.2, 2.8.2+zzzcolab20220523105045, 2.8.2+zzzcolab20220527125636, 2.8.2+zzzcolab20220629235552, 2.8.2+zzzcolab20220714152931, 2.8.2+zzzcolab20220714162028, 2.8.2+zzzcolab20220719082949, 2.8.2+zzzcolab20220822160911, 2.8.2+zzzcolab20220929150707, 2.8.3, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for tensorflow==2.0.0-rc1\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lwkeshJk7dg"
      },
      "source": [
        "# Upload Data\n",
        "\n",
        "1. Open the panel on the left side of Colab by clicking on the __>__\n",
        "1. Select the files tab\n",
        "1. Drag `punch.csv` and `flex.csv` files from your computer to the tab to upload them into colab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eh9yve14gUyD"
      },
      "source": [
        "# Graph Data (optional)\n",
        "\n",
        "We'll graph the input files on two separate graphs, acceleration and gyroscope, as each data set has different units and scale"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I65ukChEgyNp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8f9a5681-7907-419f-ebdd-082b02c7dd55"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "filename = \"up.csv\"\n",
        "\n",
        "df = pd.read_csv(\"/content/\" + filename)\n",
        "\n",
        "index = range(1, len(df['aX']) + 1)\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
        "\n",
        "plt.plot(index, df['aX'], 'g.', label='x', linestyle='solid', marker=',')\n",
        "plt.plot(index, df['aY'], 'b.', label='y', linestyle='solid', marker=',')\n",
        "plt.plot(index, df['aZ'], 'r.', label='z', linestyle='solid', marker=',')\n",
        "plt.title(\"Acceleration\")\n",
        "plt.xlabel(\"Sample #\")\n",
        "plt.ylabel(\"Acceleration (G)\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "plt.plot(index, df['gX'], 'g.', label='x', linestyle='solid', marker=',')\n",
        "plt.plot(index, df['gY'], 'b.', label='y', linestyle='solid', marker=',')\n",
        "plt.plot(index, df['gZ'], 'r.', label='z', linestyle='solid', marker=',')\n",
        "plt.title(\"Gyroscope\")\n",
        "plt.xlabel(\"Sample #\")\n",
        "plt.ylabel(\"Gyroscope (deg/sec)\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSxUeYPNQbOg"
      },
      "source": [
        "# Train Neural Network\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gxk414PU3oy3"
      },
      "source": [
        "## Parse and prepare the data\n",
        "\n",
        "The next cell parses the csv files and transforms them to a format that will be used to train the full connected neural network.\n",
        "\n",
        "Update the `GESTURES` list with the gesture data you've collected in `.csv` format.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGChd1FAk5_j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adcc4e1c-31cb-4c5e-f599-8c15578a1b02"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "print(f\"TensorFlow version = {tf.__version__}\\n\")\n",
        "\n",
        "# Set a fixed random seed value, for reproducibility, this will allow us to get\n",
        "# the same random numbers each time the notebook is run\n",
        "SEED = 1337\n",
        "np.random.seed(SEED)\n",
        "tf.random.set_seed(SEED)\n",
        "\n",
        "# the list of gestures that data is available for\n",
        "GESTURES = [\n",
        "    \"down\",\n",
        "    \"up\",\n",
        "]\n",
        "\n",
        "SAMPLES_PER_GESTURE = 119\n",
        "\n",
        "NUM_GESTURES = len(GESTURES)\n",
        "\n",
        "# create a one-hot encoded matrix that is used in the output\n",
        "ONE_HOT_ENCODED_GESTURES = np.eye(NUM_GESTURES)\n",
        "\n",
        "inputs = []\n",
        "outputs = []\n",
        "\n",
        "# read each csv file and push an input and output\n",
        "for gesture_index in range(NUM_GESTURES):\n",
        "  gesture = GESTURES[gesture_index]\n",
        "  print(f\"Processing index {gesture_index} for gesture '{gesture}'.\")\n",
        "  \n",
        "  output = ONE_HOT_ENCODED_GESTURES[gesture_index]\n",
        "  \n",
        "  df = pd.read_csv(\"/content/\" + gesture + \".csv\")\n",
        "  \n",
        "  # calculate the number of gesture recordings in the file\n",
        "  num_recordings = int(df.shape[0] / SAMPLES_PER_GESTURE)\n",
        "  \n",
        "  print(f\"\\tThere are {num_recordings} recordings of the {gesture} gesture.\")\n",
        "  \n",
        "  for i in range(num_recordings):\n",
        "    tensor = []\n",
        "    for j in range(SAMPLES_PER_GESTURE):\n",
        "      index = i * SAMPLES_PER_GESTURE + j\n",
        "      # normalize the input data, between 0 to 1:\n",
        "      # - acceleration is between: -4 to +4\n",
        "      # - gyroscope is between: -2000 to +2000\n",
        "      tensor += [\n",
        "          (df['aX'][index] + 4) / 8,\n",
        "          (df['aY'][index] + 4) / 8,\n",
        "          (df['aZ'][index] + 4) / 8,\n",
        "          (df['gX'][index] + 2000) / 4000,\n",
        "          (df['gY'][index] + 2000) / 4000,\n",
        "          (df['gZ'][index] + 2000) / 4000\n",
        "      ]\n",
        "\n",
        "    inputs.append(tensor)\n",
        "    outputs.append(output)\n",
        "\n",
        "# convert the list to numpy array\n",
        "inputs = np.array(inputs)\n",
        "outputs = np.array(outputs)\n",
        "\n",
        "print(\"Data set parsing and preparation complete.\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version = 2.8.2\n",
            "\n",
            "Processing index 0 for gesture 'down'.\n",
            "\tThere are 20 recordings of the down gesture.\n",
            "Processing index 1 for gesture 'up'.\n",
            "\tThere are 21 recordings of the up gesture.\n",
            "Data set parsing and preparation complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5_61831d5AM"
      },
      "source": [
        "## Randomize and split the input and output pairs for training\n",
        "\n",
        "Randomly sprint the input and ouput pairs into sets of data. 60% for training, 20% for validation, and 20% for testing.\n",
        "\n",
        "  - the training set is used to train the model\n",
        "  - the validatiin set is used to measure how well the model is performing during training\n",
        "  - the testing set is used test the model after training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfNEmUZMeIEx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fff89b46-2212-4ccf-fc51-e28384b73d4f"
      },
      "source": [
        "# Randomize the order of the inputs, so they can be evenly distributed for training, testing, and validation\n",
        "# https://stackoverflow.com/a/37710486/2020087\n",
        "num_inputs = len(inputs)\n",
        "randomize = np.arange(num_inputs)\n",
        "np.random.shuffle(randomize)\n",
        "\n",
        "# Swap the consecutive indexes (0, 1, 2, etc) with the randomized indexes\n",
        "inputs = inputs[randomize]\n",
        "outputs = outputs[randomize]\n",
        "\n",
        "# Split the recordings (group of samples) into three sets: training, testing and validation\n",
        "TRAIN_SPLIT = int(0.6 * num_inputs)\n",
        "TEST_SPLIT = int(0.2 * num_inputs + TRAIN_SPLIT)\n",
        "\n",
        "inputs_train, inputs_test, inputs_validate = np.split(inputs, [TRAIN_SPLIT, TEST_SPLIT])\n",
        "outputs_train, outputs_test, outputs_validate = np.split(outputs, [TRAIN_SPLIT, TEST_SPLIT])\n",
        "\n",
        "print(\"Data set randomization and splitting complete.\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data set randomization and splitting complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9g2n41p24nR"
      },
      "source": [
        "## Build & Train the Model\n",
        "\n",
        "Build and train a [TensorFlow](https://www.tensorflow.org) model using the high-level [Keras](https://www.tensorflow.org/guide/keras) API."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGNFa-lX24Qo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50640e8f-df53-4ed0-c453-e5fe3dadbf01"
      },
      "source": [
        "# build the model and train it\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Dense(50, activation='relu')) # relu is used for performance\n",
        "model.add(tf.keras.layers.Dense(15, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(NUM_GESTURES, activation='softmax')) # softmax is used, because we only expect one gesture to occur per input\n",
        "model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
        "history = model.fit(inputs_train, outputs_train, epochs=600, batch_size=1, validation_data=(inputs_validate, outputs_validate))\n",
        "\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/600\n",
            "24/24 [==============================] - 1s 8ms/step - loss: 0.3165 - mae: 0.5324 - val_loss: 0.2522 - val_mae: 0.5006\n",
            "Epoch 2/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2434 - mae: 0.4840 - val_loss: 0.2671 - val_mae: 0.5059\n",
            "Epoch 3/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2361 - mae: 0.4667 - val_loss: 0.2341 - val_mae: 0.4836\n",
            "Epoch 4/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2420 - mae: 0.4807 - val_loss: 0.2543 - val_mae: 0.4963\n",
            "Epoch 5/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2380 - mae: 0.4747 - val_loss: 0.2565 - val_mae: 0.4961\n",
            "Epoch 6/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2332 - mae: 0.4627 - val_loss: 0.2421 - val_mae: 0.4850\n",
            "Epoch 7/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.2280 - mae: 0.4652 - val_loss: 0.2301 - val_mae: 0.4730\n",
            "Epoch 8/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2179 - mae: 0.4547 - val_loss: 0.2297 - val_mae: 0.4660\n",
            "Epoch 9/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.2104 - mae: 0.4480 - val_loss: 0.2332 - val_mae: 0.4602\n",
            "Epoch 10/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1960 - mae: 0.4215 - val_loss: 0.1821 - val_mae: 0.4267\n",
            "Epoch 11/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1871 - mae: 0.4136 - val_loss: 0.1736 - val_mae: 0.4116\n",
            "Epoch 12/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1869 - mae: 0.4238 - val_loss: 0.2121 - val_mae: 0.4290\n",
            "Epoch 13/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1704 - mae: 0.3905 - val_loss: 0.1790 - val_mae: 0.4031\n",
            "Epoch 14/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1636 - mae: 0.3888 - val_loss: 0.1774 - val_mae: 0.3946\n",
            "Epoch 15/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3780 - val_loss: 0.1235 - val_mae: 0.3512\n",
            "Epoch 16/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1358 - mae: 0.3496 - val_loss: 0.1140 - val_mae: 0.3325\n",
            "Epoch 17/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1148 - mae: 0.3167 - val_loss: 0.1207 - val_mae: 0.3218\n",
            "Epoch 18/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1267 - mae: 0.3452 - val_loss: 0.1094 - val_mae: 0.3142\n",
            "Epoch 19/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1082 - mae: 0.3085 - val_loss: 0.0757 - val_mae: 0.2745\n",
            "Epoch 20/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0920 - mae: 0.2834 - val_loss: 0.0663 - val_mae: 0.2564\n",
            "Epoch 21/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0826 - mae: 0.2694 - val_loss: 0.0768 - val_mae: 0.2586\n",
            "Epoch 22/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0749 - mae: 0.2487 - val_loss: 0.0485 - val_mae: 0.2171\n",
            "Epoch 23/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0620 - mae: 0.2206 - val_loss: 0.0493 - val_mae: 0.2073\n",
            "Epoch 24/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0506 - mae: 0.2023 - val_loss: 0.0675 - val_mae: 0.2268\n",
            "Epoch 25/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0552 - mae: 0.2096 - val_loss: 0.0283 - val_mae: 0.1664\n",
            "Epoch 26/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0429 - mae: 0.1757 - val_loss: 0.0376 - val_mae: 0.1693\n",
            "Epoch 27/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0381 - mae: 0.1687 - val_loss: 0.0267 - val_mae: 0.1476\n",
            "Epoch 28/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0348 - mae: 0.1521 - val_loss: 0.0230 - val_mae: 0.1363\n",
            "Epoch 29/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0343 - mae: 0.1517 - val_loss: 0.0139 - val_mae: 0.1159\n",
            "Epoch 30/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0316 - mae: 0.1377 - val_loss: 0.0144 - val_mae: 0.1131\n",
            "Epoch 31/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0283 - mae: 0.1287 - val_loss: 0.0284 - val_mae: 0.1404\n",
            "Epoch 32/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0257 - mae: 0.1172 - val_loss: 0.0081 - val_mae: 0.0884\n",
            "Epoch 33/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0193 - mae: 0.1111 - val_loss: 0.0232 - val_mae: 0.1250\n",
            "Epoch 34/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.0225 - mae: 0.1078 - val_loss: 0.0101 - val_mae: 0.0886\n",
            "Epoch 35/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0155 - mae: 0.0946 - val_loss: 0.0061 - val_mae: 0.0732\n",
            "Epoch 36/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0145 - mae: 0.0940 - val_loss: 0.0050 - val_mae: 0.0661\n",
            "Epoch 37/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0153 - mae: 0.0937 - val_loss: 0.0038 - val_mae: 0.0594\n",
            "Epoch 38/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0096 - mae: 0.0746 - val_loss: 0.0030 - val_mae: 0.0533\n",
            "Epoch 39/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0803 - val_loss: 0.0024 - val_mae: 0.0475\n",
            "Epoch 40/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0063 - mae: 0.0647 - val_loss: 0.0024 - val_mae: 0.0456\n",
            "Epoch 41/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.0153 - mae: 0.0783 - val_loss: 0.0046 - val_mae: 0.0552\n",
            "Epoch 42/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0034 - mae: 0.0470 - val_loss: 0.0018 - val_mae: 0.0388\n",
            "Epoch 43/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.0074 - mae: 0.0588 - val_loss: 0.0014 - val_mae: 0.0350\n",
            "Epoch 44/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0109 - mae: 0.0584 - val_loss: 0.0010 - val_mae: 0.0311\n",
            "Epoch 45/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0043 - mae: 0.0480 - val_loss: 9.5806e-04 - val_mae: 0.0293\n",
            "Epoch 46/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0045 - mae: 0.0458 - val_loss: 0.0881 - val_mae: 0.2188\n",
            "Epoch 47/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0095 - mae: 0.0536 - val_loss: 8.4704e-04 - val_mae: 0.0263\n",
            "Epoch 48/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0060 - mae: 0.0465 - val_loss: 7.4013e-04 - val_mae: 0.0246\n",
            "Epoch 49/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0139 - mae: 0.0508 - val_loss: 5.8524e-04 - val_mae: 0.0226\n",
            "Epoch 50/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0040 - mae: 0.0372 - val_loss: 0.0016 - val_mae: 0.0317\n",
            "Epoch 51/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0315 - val_loss: 4.2324e-04 - val_mae: 0.0198\n",
            "Epoch 52/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0087 - mae: 0.0466 - val_loss: 3.7560e-04 - val_mae: 0.0182\n",
            "Epoch 53/600\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.0023 - mae: 0.0297 - val_loss: 4.5315e-04 - val_mae: 0.0189\n",
            "Epoch 54/600\n",
            "24/24 [==============================] - 0s 13ms/step - loss: 0.0033 - mae: 0.0342 - val_loss: 5.2626e-04 - val_mae: 0.0195\n",
            "Epoch 55/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0027 - mae: 0.0313 - val_loss: 3.9071e-04 - val_mae: 0.0173\n",
            "Epoch 56/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0043 - mae: 0.0343 - val_loss: 2.3193e-04 - val_mae: 0.0145\n",
            "Epoch 57/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0044 - mae: 0.0305 - val_loss: 2.1586e-04 - val_mae: 0.0140\n",
            "Epoch 58/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.6450e-04 - mae: 0.0197 - val_loss: 0.0244 - val_mae: 0.1110\n",
            "Epoch 59/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0035 - mae: 0.0342 - val_loss: 1.6779e-04 - val_mae: 0.0123\n",
            "Epoch 60/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.2016e-04 - mae: 0.0169 - val_loss: 0.0089 - val_mae: 0.0661\n",
            "Epoch 61/600\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.0038 - mae: 0.0308 - val_loss: 1.3059e-04 - val_mae: 0.0107\n",
            "Epoch 62/600\n",
            "24/24 [==============================] - 0s 12ms/step - loss: 0.0023 - mae: 0.0282 - val_loss: 1.1179e-04 - val_mae: 0.0101\n",
            "Epoch 63/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 0.0010 - mae: 0.0178 - val_loss: 0.0632 - val_mae: 0.1797\n",
            "Epoch 64/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0036 - mae: 0.0297 - val_loss: 1.1087e-04 - val_mae: 0.0097\n",
            "Epoch 65/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 9.6311e-04 - mae: 0.0173 - val_loss: 8.3219e-05 - val_mae: 0.0087\n",
            "Epoch 66/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.4814e-04 - mae: 0.0123 - val_loss: 0.0331 - val_mae: 0.1272\n",
            "Epoch 67/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0033 - mae: 0.0276 - val_loss: 7.3034e-05 - val_mae: 0.0079\n",
            "Epoch 68/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0193 - val_loss: 1.6646e-04 - val_mae: 0.0102\n",
            "Epoch 69/600\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.0034 - mae: 0.0249 - val_loss: 7.3084e-05 - val_mae: 0.0076\n",
            "Epoch 70/600\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.0039 - mae: 0.0260 - val_loss: 5.1566e-05 - val_mae: 0.0067\n",
            "Epoch 71/600\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0177 - val_loss: 9.7238e-04 - val_mae: 0.0215\n",
            "Epoch 72/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0163 - val_loss: 0.0241 - val_mae: 0.1066\n",
            "Epoch 73/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.0019 - mae: 0.0182 - val_loss: 0.0067 - val_mae: 0.0550\n",
            "Epoch 74/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.1241e-04 - mae: 0.0166 - val_loss: 5.0726e-05 - val_mae: 0.0063\n",
            "Epoch 75/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.9801e-04 - mae: 0.0100 - val_loss: 0.0596 - val_mae: 0.1713\n",
            "Epoch 76/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0051 - mae: 0.0232 - val_loss: 1.6528e-04 - val_mae: 0.0095\n",
            "Epoch 77/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.1953e-04 - mae: 0.0101 - val_loss: 3.3275e-05 - val_mae: 0.0053\n",
            "Epoch 78/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.0036 - mae: 0.0235 - val_loss: 3.8282e-05 - val_mae: 0.0054\n",
            "Epoch 79/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0161 - val_loss: 2.9326e-05 - val_mae: 0.0050\n",
            "Epoch 80/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.6093e-04 - mae: 0.0132 - val_loss: 2.3404e-05 - val_mae: 0.0045\n",
            "Epoch 81/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.2496e-04 - mae: 0.0122 - val_loss: 1.2699e-04 - val_mae: 0.0083\n",
            "Epoch 82/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4579e-04 - mae: 0.0091 - val_loss: 5.5905e-05 - val_mae: 0.0059\n",
            "Epoch 83/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.1134e-04 - mae: 0.0095 - val_loss: 2.1097e-05 - val_mae: 0.0041\n",
            "Epoch 84/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0019 - mae: 0.0160 - val_loss: 1.7562e-05 - val_mae: 0.0038\n",
            "Epoch 85/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.5487e-04 - mae: 0.0094 - val_loss: 1.6543e-04 - val_mae: 0.0089\n",
            "Epoch 86/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.3033e-04 - mae: 0.0087 - val_loss: 8.4287e-05 - val_mae: 0.0066\n",
            "Epoch 87/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0027 - mae: 0.0186 - val_loss: 0.0012 - val_mae: 0.0222\n",
            "Epoch 88/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.0381e-04 - mae: 0.0109 - val_loss: 6.6104e-05 - val_mae: 0.0059\n",
            "Epoch 89/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.5629e-04 - mae: 0.0062 - val_loss: 0.0018 - val_mae: 0.0269\n",
            "Epoch 90/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.0011 - mae: 0.0131 - val_loss: 9.0530e-06 - val_mae: 0.0028\n",
            "Epoch 91/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.2686e-04 - mae: 0.0112 - val_loss: 8.5655e-06 - val_mae: 0.0027\n",
            "Epoch 92/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.6275e-04 - mae: 0.0100 - val_loss: 9.6210e-06 - val_mae: 0.0028\n",
            "Epoch 93/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.0284e-04 - mae: 0.0091 - val_loss: 1.3956e-05 - val_mae: 0.0031\n",
            "Epoch 94/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.8814e-04 - mae: 0.0068 - val_loss: 1.5888e-05 - val_mae: 0.0032\n",
            "Epoch 95/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0133 - val_loss: 6.1345e-06 - val_mae: 0.0023\n",
            "Epoch 96/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.4137e-04 - mae: 0.0094 - val_loss: 5.5397e-06 - val_mae: 0.0022\n",
            "Epoch 97/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.1376e-04 - mae: 0.0101 - val_loss: 5.5335e-06 - val_mae: 0.0021\n",
            "Epoch 98/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.1787e-04 - mae: 0.0104 - val_loss: 5.2307e-06 - val_mae: 0.0021\n",
            "Epoch 99/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.3656e-04 - mae: 0.0071 - val_loss: 6.5152e-06 - val_mae: 0.0022\n",
            "Epoch 100/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.6347e-04 - mae: 0.0077 - val_loss: 3.9798e-06 - val_mae: 0.0018\n",
            "Epoch 101/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.1443e-04 - mae: 0.0097 - val_loss: 3.8556e-06 - val_mae: 0.0018\n",
            "Epoch 102/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.9371e-04 - mae: 0.0066 - val_loss: 7.0529e-05 - val_mae: 0.0055\n",
            "Epoch 103/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.2793e-04 - mae: 0.0071 - val_loss: 2.5772e-05 - val_mae: 0.0036\n",
            "Epoch 104/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.3965e-04 - mae: 0.0099 - val_loss: 2.8883e-06 - val_mae: 0.0016\n",
            "Epoch 105/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.0034e-04 - mae: 0.0059 - val_loss: 1.4804e-05 - val_mae: 0.0029\n",
            "Epoch 106/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.4657e-05 - mae: 0.0032 - val_loss: 4.0744e-06 - val_mae: 0.0018\n",
            "Epoch 107/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.1573e-04 - mae: 0.0068 - val_loss: 2.3682e-06 - val_mae: 0.0014\n",
            "Epoch 108/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.4400e-04 - mae: 0.0048 - val_loss: 1.4889e-04 - val_mae: 0.0074\n",
            "Epoch 109/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.6638e-05 - mae: 0.0040 - val_loss: 2.1344e-06 - val_mae: 0.0013\n",
            "Epoch 110/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0100 - val_loss: 1.9304e-06 - val_mae: 0.0013\n",
            "Epoch 111/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1716e-04 - mae: 0.0044 - val_loss: 2.1766e-05 - val_mae: 0.0032\n",
            "Epoch 112/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.3544e-05 - mae: 0.0027 - val_loss: 1.8122e-06 - val_mae: 0.0012\n",
            "Epoch 113/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.1591e-04 - mae: 0.0073 - val_loss: 1.6018e-06 - val_mae: 0.0012\n",
            "Epoch 114/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.9190e-05 - mae: 0.0034 - val_loss: 0.0010 - val_mae: 0.0187\n",
            "Epoch 115/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.0952e-04 - mae: 0.0091 - val_loss: 2.2355e-06 - val_mae: 0.0013\n",
            "Epoch 116/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.2307e-05 - mae: 0.0036 - val_loss: 1.5389e-06 - val_mae: 0.0011\n",
            "Epoch 117/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.1832e-04 - mae: 0.0065 - val_loss: 1.2437e-06 - val_mae: 9.9853e-04\n",
            "Epoch 118/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4227e-04 - mae: 0.0056 - val_loss: 2.7304e-05 - val_mae: 0.0033\n",
            "Epoch 119/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3848e-04 - mae: 0.0052 - val_loss: 1.2676e-06 - val_mae: 0.0010\n",
            "Epoch 120/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.4551e-05 - mae: 0.0023 - val_loss: 0.0038 - val_mae: 0.0358\n",
            "Epoch 121/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1870e-04 - mae: 0.0050 - val_loss: 9.3515e-06 - val_mae: 0.0021\n",
            "Epoch 122/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.8701e-04 - mae: 0.0050 - val_loss: 1.1202e-06 - val_mae: 8.5479e-04\n",
            "Epoch 123/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.5785e-05 - mae: 0.0032 - val_loss: 0.0034 - val_mae: 0.0336\n",
            "Epoch 124/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.4681e-05 - mae: 0.0039 - val_loss: 6.2751e-07 - val_mae: 7.0529e-04\n",
            "Epoch 125/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.7447e-04 - mae: 0.0045 - val_loss: 5.9414e-07 - val_mae: 6.9038e-04\n",
            "Epoch 126/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.7759e-04 - mae: 0.0085 - val_loss: 1.3987e-06 - val_mae: 8.7944e-04\n",
            "Epoch 127/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.5586e-04 - mae: 0.0060 - val_loss: 1.6325e-04 - val_mae: 0.0072\n",
            "Epoch 128/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.4770e-05 - mae: 0.0039 - val_loss: 7.4636e-07 - val_mae: 7.6134e-04\n",
            "Epoch 129/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.4724e-05 - mae: 0.0023 - val_loss: 1.2721e-06 - val_mae: 8.2854e-04\n",
            "Epoch 130/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0112 - val_loss: 6.6547e-07 - val_mae: 6.6134e-04\n",
            "Epoch 131/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3333e-04 - mae: 0.0033 - val_loss: 1.2151e-04 - val_mae: 0.0062\n",
            "Epoch 132/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.0042e-05 - mae: 0.0031 - val_loss: 4.9400e-07 - val_mae: 5.9761e-04\n",
            "Epoch 133/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.6105e-04 - mae: 0.0080 - val_loss: 1.1945e-06 - val_mae: 7.9160e-04\n",
            "Epoch 134/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.9576e-04 - mae: 0.0084 - val_loss: 6.6490e-07 - val_mae: 7.0593e-04\n",
            "Epoch 135/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.9572e-06 - mae: 0.0013 - val_loss: 1.0713e-06 - val_mae: 8.3882e-04\n",
            "Epoch 136/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.9309e-06 - mae: 0.0013 - val_loss: 2.1528e-06 - val_mae: 0.0011\n",
            "Epoch 137/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.3346e-05 - mae: 0.0034 - val_loss: 3.7243e-07 - val_mae: 5.2753e-04\n",
            "Epoch 138/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0092 - val_loss: 6.2194e-07 - val_mae: 6.0052e-04\n",
            "Epoch 139/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.7338e-04 - mae: 0.0036 - val_loss: 1.6408e-05 - val_mae: 0.0024\n",
            "Epoch 140/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.7681e-05 - mae: 0.0018 - val_loss: 1.9389e-06 - val_mae: 0.0010\n",
            "Epoch 141/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.7612e-06 - mae: 0.0012 - val_loss: 2.1744e-06 - val_mae: 0.0011\n",
            "Epoch 142/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.5039e-05 - mae: 0.0018 - val_loss: 3.2204e-07 - val_mae: 4.7007e-04\n",
            "Epoch 143/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.7003e-05 - mae: 0.0028 - val_loss: 3.0205e-07 - val_mae: 4.8894e-04\n",
            "Epoch 144/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3230e-05 - mae: 0.0018 - val_loss: 4.0918e-06 - val_mae: 0.0013\n",
            "Epoch 145/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1100e-05 - mae: 0.0019 - val_loss: 3.3614e-06 - val_mae: 0.0012\n",
            "Epoch 146/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.1086e-06 - mae: 0.0011 - val_loss: 1.8938e-07 - val_mae: 3.7896e-04\n",
            "Epoch 147/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1843e-04 - mae: 0.0030 - val_loss: 1.6143e-07 - val_mae: 3.5156e-04\n",
            "Epoch 148/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.2929e-06 - mae: 9.8962e-04 - val_loss: 3.2651e-07 - val_mae: 4.5300e-04\n",
            "Epoch 149/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.2616e-06 - mae: 7.3290e-04 - val_loss: 1.8388e-06 - val_mae: 8.7211e-04\n",
            "Epoch 150/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.6114e-05 - mae: 0.0024 - val_loss: 0.0010 - val_mae: 0.0169\n",
            "Epoch 151/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.2993e-04 - mae: 0.0036 - val_loss: 3.1269e-07 - val_mae: 3.9607e-04\n",
            "Epoch 152/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0439e-04 - mae: 0.0035 - val_loss: 8.8268e-05 - val_mae: 0.0049\n",
            "Epoch 153/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.5031e-05 - mae: 0.0029 - val_loss: 9.4469e-05 - val_mae: 0.0050\n",
            "Epoch 154/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.2898e-04 - mae: 0.0064 - val_loss: 1.1754e-04 - val_mae: 0.0056\n",
            "Epoch 155/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4782e-04 - mae: 0.0033 - val_loss: 1.5960e-07 - val_mae: 3.3214e-04\n",
            "Epoch 156/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.9452e-06 - mae: 6.3371e-04 - val_loss: 2.3205e-07 - val_mae: 3.7869e-04\n",
            "Epoch 157/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3705e-06 - mae: 5.8146e-04 - val_loss: 1.3239e-06 - val_mae: 7.3404e-04\n",
            "Epoch 158/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.0971e-06 - mae: 0.0010 - val_loss: 2.1883e-06 - val_mae: 8.8438e-04\n",
            "Epoch 159/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.0421e-06 - mae: 0.0013 - val_loss: 1.9980e-06 - val_mae: 8.3534e-04\n",
            "Epoch 160/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.9840e-04 - mae: 0.0070 - val_loss: 3.0059e-05 - val_mae: 0.0028\n",
            "Epoch 161/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.6127e-05 - mae: 0.0020 - val_loss: 7.4566e-07 - val_mae: 5.5524e-04\n",
            "Epoch 162/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1128e-06 - mae: 5.4568e-04 - val_loss: 6.1305e-07 - val_mae: 5.1393e-04\n",
            "Epoch 163/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 9.8943e-07 - mae: 5.2605e-04 - val_loss: 3.9224e-07 - val_mae: 4.3143e-04\n",
            "Epoch 164/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.4089e-07 - mae: 5.2705e-04 - val_loss: 3.3372e-07 - val_mae: 4.0257e-04\n",
            "Epoch 165/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.4923e-05 - mae: 0.0017 - val_loss: 1.8239e-07 - val_mae: 3.1553e-04\n",
            "Epoch 166/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.9719e-07 - mae: 4.3604e-04 - val_loss: 2.5106e-07 - val_mae: 3.5469e-04\n",
            "Epoch 167/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.5510e-07 - mae: 4.4656e-04 - val_loss: 4.3500e-08 - val_mae: 1.7711e-04\n",
            "Epoch 168/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.7021e-05 - mae: 0.0020 - val_loss: 4.0174e-08 - val_mae: 1.7380e-04\n",
            "Epoch 169/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.0218e-06 - mae: 7.0340e-04 - val_loss: 4.2044e-05 - val_mae: 0.0032\n",
            "Epoch 170/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 9.5414e-05 - mae: 0.0040 - val_loss: 5.0010e-05 - val_mae: 0.0035\n",
            "Epoch 171/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.9967e-05 - mae: 0.0018 - val_loss: 7.1464e-08 - val_mae: 1.9239e-04\n",
            "Epoch 172/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.7490e-05 - mae: 0.0030 - val_loss: 4.7271e-08 - val_mae: 1.8314e-04\n",
            "Epoch 173/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.7142e-07 - mae: 3.9491e-04 - val_loss: 5.8517e-08 - val_mae: 1.9722e-04\n",
            "Epoch 174/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.4446e-07 - mae: 3.8287e-04 - val_loss: 9.7327e-08 - val_mae: 2.3476e-04\n",
            "Epoch 175/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0001e-06 - mae: 4.5134e-04 - val_loss: 3.9022e-08 - val_mae: 1.4951e-04\n",
            "Epoch 176/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.9583e-07 - mae: 3.1458e-04 - val_loss: 1.3058e-06 - val_mae: 6.3053e-04\n",
            "Epoch 177/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.4477e-07 - mae: 4.6631e-04 - val_loss: 2.2564e-07 - val_mae: 2.8692e-04\n",
            "Epoch 178/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.4179e-05 - mae: 0.0012 - val_loss: 1.0060e-08 - val_mae: 8.3123e-05\n",
            "Epoch 179/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.7886e-06 - mae: 5.8648e-04 - val_loss: 8.6112e-06 - val_mae: 0.0014\n",
            "Epoch 180/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0402e-04 - mae: 0.0032 - val_loss: 3.5377e-05 - val_mae: 0.0028\n",
            "Epoch 181/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.0567e-05 - mae: 0.0017 - val_loss: 9.1544e-09 - val_mae: 7.8722e-05\n",
            "Epoch 182/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.2486e-06 - mae: 5.8231e-04 - val_loss: 5.0366e-08 - val_mae: 1.5251e-04\n",
            "Epoch 183/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.3498e-07 - mae: 1.9671e-04 - val_loss: 5.0222e-08 - val_mae: 1.5221e-04\n",
            "Epoch 184/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.5807e-07 - mae: 2.0720e-04 - val_loss: 5.2378e-08 - val_mae: 1.5414e-04\n",
            "Epoch 185/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.4623e-07 - mae: 3.5061e-04 - val_loss: 1.3474e-07 - val_mae: 2.1667e-04\n",
            "Epoch 186/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.4146e-07 - mae: 4.1537e-04 - val_loss: 1.7461e-07 - val_mae: 2.3641e-04\n",
            "Epoch 187/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.7094e-07 - mae: 4.1942e-04 - val_loss: 5.3078e-06 - val_mae: 0.0011\n",
            "Epoch 188/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.1257e-06 - mae: 9.0941e-04 - val_loss: 4.7892e-09 - val_mae: 5.8496e-05\n",
            "Epoch 189/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0116e-06 - mae: 3.7800e-04 - val_loss: 1.1391e-06 - val_mae: 5.2108e-04\n",
            "Epoch 190/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.1073e-06 - mae: 6.6860e-04 - val_loss: 4.0313e-08 - val_mae: 1.2480e-04\n",
            "Epoch 191/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.1559e-08 - mae: 1.4065e-04 - val_loss: 4.3718e-09 - val_mae: 5.5917e-05\n",
            "Epoch 192/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.1370e-06 - mae: 6.2132e-04 - val_loss: 3.5046e-09 - val_mae: 4.9882e-05\n",
            "Epoch 193/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.1823e-07 - mae: 2.5945e-04 - val_loss: 1.6064e-07 - val_mae: 2.1395e-04\n",
            "Epoch 194/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.0509e-07 - mae: 3.1410e-04 - val_loss: 1.6776e-06 - val_mae: 6.1126e-04\n",
            "Epoch 195/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.4446e-06 - mae: 7.7070e-04 - val_loss: 1.5820e-04 - val_mae: 0.0057\n",
            "Epoch 196/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1091e-05 - mae: 0.0012 - val_loss: 3.0340e-05 - val_mae: 0.0025\n",
            "Epoch 197/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.4384e-06 - mae: 5.2229e-04 - val_loss: 5.2206e-09 - val_mae: 5.4769e-05\n",
            "Epoch 198/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.3523e-08 - mae: 9.8265e-05 - val_loss: 9.1921e-09 - val_mae: 6.6361e-05\n",
            "Epoch 199/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.5164e-08 - mae: 1.0492e-04 - val_loss: 4.6336e-09 - val_mae: 5.2173e-05\n",
            "Epoch 200/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.0380e-07 - mae: 2.0887e-04 - val_loss: 1.9592e-09 - val_mae: 3.5859e-05\n",
            "Epoch 201/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.5833e-06 - mae: 4.0496e-04 - val_loss: 2.3524e-09 - val_mae: 3.9606e-05\n",
            "Epoch 202/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.1571e-08 - mae: 1.0861e-04 - val_loss: 1.1974e-06 - val_mae: 5.0326e-04\n",
            "Epoch 203/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.0302e-05 - mae: 0.0018 - val_loss: 4.0949e-06 - val_mae: 9.0947e-04\n",
            "Epoch 204/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.7460e-06 - mae: 7.1729e-04 - val_loss: 4.6868e-07 - val_mae: 3.2761e-04\n",
            "Epoch 205/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.8780e-07 - mae: 2.8032e-04 - val_loss: 7.5122e-08 - val_mae: 1.4543e-04\n",
            "Epoch 206/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4498e-07 - mae: 1.3615e-04 - val_loss: 2.7218e-08 - val_mae: 9.5495e-05\n",
            "Epoch 207/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.4419e-08 - mae: 9.7550e-05 - val_loss: 1.3379e-08 - val_mae: 7.1926e-05\n",
            "Epoch 208/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.8211e-08 - mae: 8.6197e-05 - val_loss: 2.5348e-09 - val_mae: 3.9671e-05\n",
            "Epoch 209/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3778e-06 - mae: 4.1565e-04 - val_loss: 3.6996e-07 - val_mae: 2.9145e-04\n",
            "Epoch 210/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.7364e-06 - mae: 7.9586e-04 - val_loss: 2.9645e-06 - val_mae: 7.6972e-04\n",
            "Epoch 211/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.6395e-05 - mae: 0.0019 - val_loss: 3.1567e-06 - val_mae: 7.9274e-04\n",
            "Epoch 212/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.4446e-06 - mae: 5.8733e-04 - val_loss: 2.1503e-09 - val_mae: 3.3002e-05\n",
            "Epoch 213/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.3866e-06 - mae: 3.8756e-04 - val_loss: 4.8928e-06 - val_mae: 9.7657e-04\n",
            "Epoch 214/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.7099e-06 - mae: 5.9153e-04 - val_loss: 4.9504e-09 - val_mae: 4.6828e-05\n",
            "Epoch 215/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 9.2626e-06 - mae: 8.6087e-04 - val_loss: 9.0068e-06 - val_mae: 0.0013\n",
            "Epoch 216/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.7649e-05 - mae: 0.0015 - val_loss: 1.4343e-06 - val_mae: 5.3901e-04\n",
            "Epoch 217/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.8730e-06 - mae: 4.2023e-04 - val_loss: 5.9962e-09 - val_mae: 5.0293e-05\n",
            "Epoch 218/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.2025e-08 - mae: 6.0259e-05 - val_loss: 5.6575e-09 - val_mae: 4.9208e-05\n",
            "Epoch 219/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1975e-08 - mae: 5.9744e-05 - val_loss: 4.4200e-09 - val_mae: 4.4946e-05\n",
            "Epoch 220/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0892e-08 - mae: 5.5988e-05 - val_loss: 2.5350e-09 - val_mae: 3.7001e-05\n",
            "Epoch 221/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.4501e-08 - mae: 6.0413e-05 - val_loss: 1.3054e-09 - val_mae: 2.9268e-05\n",
            "Epoch 222/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.0078e-08 - mae: 1.0347e-04 - val_loss: 6.4033e-09 - val_mae: 4.9275e-05\n",
            "Epoch 223/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0706e-06 - mae: 3.4242e-04 - val_loss: 7.7137e-10 - val_mae: 2.1854e-05\n",
            "Epoch 224/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.7160e-07 - mae: 1.2968e-04 - val_loss: 3.9645e-09 - val_mae: 4.0248e-05\n",
            "Epoch 225/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0199e-08 - mae: 5.2073e-05 - val_loss: 2.9341e-09 - val_mae: 3.5783e-05\n",
            "Epoch 226/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.2916e-08 - mae: 6.1766e-05 - val_loss: 7.7550e-09 - val_mae: 5.7354e-05\n",
            "Epoch 227/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.6283e-05 - mae: 0.0019 - val_loss: 3.1601e-07 - val_mae: 2.6234e-04\n",
            "Epoch 228/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.8606e-07 - mae: 2.4152e-04 - val_loss: 3.7930e-08 - val_mae: 1.0190e-04\n",
            "Epoch 229/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.2684e-08 - mae: 1.0187e-04 - val_loss: 1.7040e-08 - val_mae: 7.2692e-05\n",
            "Epoch 230/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.5873e-08 - mae: 7.2062e-05 - val_loss: 9.5815e-09 - val_mae: 5.7117e-05\n",
            "Epoch 231/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.8210e-08 - mae: 6.1106e-05 - val_loss: 1.7365e-09 - val_mae: 3.0509e-05\n",
            "Epoch 232/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.5800e-09 - mae: 4.7173e-05 - val_loss: 3.1534e-09 - val_mae: 3.6799e-05\n",
            "Epoch 233/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0989e-08 - mae: 5.0250e-05 - val_loss: 3.8964e-08 - val_mae: 9.8943e-05\n",
            "Epoch 234/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.6617e-05 - mae: 0.0019 - val_loss: 3.5416e-07 - val_mae: 2.7377e-04\n",
            "Epoch 235/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.9992e-07 - mae: 2.5561e-04 - val_loss: 1.2767e-07 - val_mae: 1.7177e-04\n",
            "Epoch 236/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.8517e-07 - mae: 1.6736e-04 - val_loss: 3.5662e-08 - val_mae: 9.7938e-05\n",
            "Epoch 237/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.0154e-08 - mae: 9.5946e-05 - val_loss: 2.1151e-08 - val_mae: 7.8556e-05\n",
            "Epoch 238/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.6992e-08 - mae: 7.5843e-05 - val_loss: 5.8283e-09 - val_mae: 4.6144e-05\n",
            "Epoch 239/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0984e-08 - mae: 5.1462e-05 - val_loss: 2.7125e-09 - val_mae: 3.4737e-05\n",
            "Epoch 240/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.5987e-09 - mae: 4.4674e-05 - val_loss: 2.7894e-09 - val_mae: 3.4606e-05\n",
            "Epoch 241/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.0093e-08 - mae: 7.4813e-05 - val_loss: 1.2243e-08 - val_mae: 5.9281e-05\n",
            "Epoch 242/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0114e-07 - mae: 1.2026e-04 - val_loss: 1.4742e-08 - val_mae: 6.4168e-05\n",
            "Epoch 243/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 9.9461e-07 - mae: 3.5864e-04 - val_loss: 4.8769e-10 - val_mae: 1.6477e-05\n",
            "Epoch 244/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0231e-07 - mae: 1.0964e-04 - val_loss: 2.2866e-09 - val_mae: 2.9654e-05\n",
            "Epoch 245/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.7894e-09 - mae: 3.5381e-05 - val_loss: 1.6627e-09 - val_mae: 2.6353e-05\n",
            "Epoch 246/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.3828e-09 - mae: 3.4548e-05 - val_loss: 1.2142e-09 - val_mae: 2.3193e-05\n",
            "Epoch 247/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.9666e-09 - mae: 3.6232e-05 - val_loss: 2.6805e-09 - val_mae: 3.0141e-05\n",
            "Epoch 248/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.2672e-09 - mae: 3.9570e-05 - val_loss: 1.4078e-09 - val_mae: 2.4751e-05\n",
            "Epoch 249/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 1.5357e-07 - mae: 1.3248e-04 - val_loss: 2.1681e-10 - val_mae: 1.1110e-05\n",
            "Epoch 250/600\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 1.7547e-07 - mae: 1.5254e-04 - val_loss: 3.0924e-09 - val_mae: 3.6052e-05\n",
            "Epoch 251/600\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 8.1321e-06 - mae: 8.4548e-04 - val_loss: 5.6379e-10 - val_mae: 1.5730e-05\n",
            "Epoch 252/600\n",
            "24/24 [==============================] - 0s 14ms/step - loss: 1.7077e-06 - mae: 2.8072e-04 - val_loss: 2.1037e-10 - val_mae: 1.1455e-05\n",
            "Epoch 253/600\n",
            "24/24 [==============================] - 0s 10ms/step - loss: 1.9929e-09 - mae: 2.2186e-05 - val_loss: 2.1418e-10 - val_mae: 1.1504e-05\n",
            "Epoch 254/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.9368e-09 - mae: 2.2013e-05 - val_loss: 2.1997e-10 - val_mae: 1.1590e-05\n",
            "Epoch 255/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.9226e-09 - mae: 2.2373e-05 - val_loss: 2.5706e-10 - val_mae: 1.2095e-05\n",
            "Epoch 256/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.7600e-09 - mae: 2.2181e-05 - val_loss: 2.7668e-10 - val_mae: 1.2237e-05\n",
            "Epoch 257/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.8477e-09 - mae: 2.1760e-05 - val_loss: 9.0618e-10 - val_mae: 1.7905e-05\n",
            "Epoch 258/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.7104e-09 - mae: 2.4275e-05 - val_loss: 1.6709e-10 - val_mae: 9.5276e-06\n",
            "Epoch 259/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.6619e-08 - mae: 8.7488e-05 - val_loss: 1.0826e-10 - val_mae: 8.4374e-06\n",
            "Epoch 260/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.7289e-09 - mae: 2.1286e-05 - val_loss: 3.2012e-10 - val_mae: 1.1844e-05\n",
            "Epoch 261/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0528e-09 - mae: 1.6841e-05 - val_loss: 1.4913e-10 - val_mae: 9.2458e-06\n",
            "Epoch 262/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.8045e-09 - mae: 2.0487e-05 - val_loss: 9.5717e-11 - val_mae: 7.8996e-06\n",
            "Epoch 263/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.4987e-08 - mae: 6.3756e-05 - val_loss: 1.2975e-10 - val_mae: 8.3489e-06\n",
            "Epoch 264/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.3274e-10 - mae: 1.4811e-05 - val_loss: 2.0028e-10 - val_mae: 9.4812e-06\n",
            "Epoch 265/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.9488e-10 - mae: 1.4527e-05 - val_loss: 1.2056e-10 - val_mae: 8.0193e-06\n",
            "Epoch 266/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.1442e-09 - mae: 1.6164e-05 - val_loss: 7.0461e-11 - val_mae: 6.6863e-06\n",
            "Epoch 267/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.4412e-09 - mae: 1.7116e-05 - val_loss: 1.7813e-09 - val_mae: 2.1103e-05\n",
            "Epoch 268/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.2588e-09 - mae: 2.5223e-05 - val_loss: 1.2762e-10 - val_mae: 7.6991e-06\n",
            "Epoch 269/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.3724e-07 - mae: 1.3385e-04 - val_loss: 1.8332e-07 - val_mae: 1.7638e-04\n",
            "Epoch 270/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.7452e-07 - mae: 1.5879e-04 - val_loss: 3.5808e-10 - val_mae: 1.2290e-05\n",
            "Epoch 271/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.3411e-07 - mae: 2.1407e-04 - val_loss: 1.2622e-06 - val_mae: 4.5303e-04\n",
            "Epoch 272/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.9356e-06 - mae: 5.6792e-04 - val_loss: 1.1293e-10 - val_mae: 7.3003e-06\n",
            "Epoch 273/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.4203e-10 - mae: 1.1302e-05 - val_loss: 1.1296e-10 - val_mae: 7.3002e-06\n",
            "Epoch 274/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.4269e-10 - mae: 1.1306e-05 - val_loss: 1.1214e-10 - val_mae: 7.2778e-06\n",
            "Epoch 275/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.4332e-10 - mae: 1.1278e-05 - val_loss: 1.1275e-10 - val_mae: 7.2843e-06\n",
            "Epoch 276/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.4582e-10 - mae: 1.1258e-05 - val_loss: 1.1643e-10 - val_mae: 7.3401e-06\n",
            "Epoch 277/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.7910e-10 - mae: 1.1529e-05 - val_loss: 1.0212e-10 - val_mae: 6.9989e-06\n",
            "Epoch 278/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.7257e-10 - mae: 1.1268e-05 - val_loss: 1.1340e-10 - val_mae: 7.1517e-06\n",
            "Epoch 279/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.9864e-10 - mae: 1.1251e-05 - val_loss: 6.8824e-11 - val_mae: 6.0831e-06\n",
            "Epoch 280/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.9696e-10 - mae: 1.0628e-05 - val_loss: 1.3134e-10 - val_mae: 7.3266e-06\n",
            "Epoch 281/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.0909e-10 - mae: 1.0995e-05 - val_loss: 5.4897e-11 - val_mae: 5.5339e-06\n",
            "Epoch 282/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.9932e-10 - mae: 1.0909e-05 - val_loss: 5.9081e-11 - val_mae: 5.5872e-06\n",
            "Epoch 283/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.2300e-10 - mae: 1.0180e-05 - val_loss: 1.3441e-10 - val_mae: 7.1878e-06\n",
            "Epoch 284/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.8457e-10 - mae: 1.0596e-05 - val_loss: 5.0627e-11 - val_mae: 5.2355e-06\n",
            "Epoch 285/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.8619e-10 - mae: 1.0493e-05 - val_loss: 5.5226e-11 - val_mae: 5.3395e-06\n",
            "Epoch 286/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.5017e-10 - mae: 9.5339e-06 - val_loss: 5.0444e-11 - val_mae: 5.1419e-06\n",
            "Epoch 287/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.1660e-10 - mae: 1.0194e-05 - val_loss: 5.1830e-11 - val_mae: 5.1484e-06\n",
            "Epoch 288/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.4950e-10 - mae: 9.2780e-06 - val_loss: 4.9867e-11 - val_mae: 5.0450e-06\n",
            "Epoch 289/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.3465e-10 - mae: 8.8124e-06 - val_loss: 9.4669e-11 - val_mae: 6.1231e-06\n",
            "Epoch 290/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.5653e-10 - mae: 9.2846e-06 - val_loss: 4.6851e-11 - val_mae: 4.8669e-06\n",
            "Epoch 291/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5144e-10 - mae: 9.2061e-06 - val_loss: 4.7744e-11 - val_mae: 4.8658e-06\n",
            "Epoch 292/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.0495e-10 - mae: 8.7647e-06 - val_loss: 4.5304e-11 - val_mae: 4.7605e-06\n",
            "Epoch 293/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.9341e-10 - mae: 8.4771e-06 - val_loss: 7.6759e-11 - val_mae: 5.5810e-06\n",
            "Epoch 294/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.0923e-10 - mae: 8.7581e-06 - val_loss: 7.6005e-11 - val_mae: 5.5472e-06\n",
            "Epoch 295/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.0435e-10 - mae: 8.6758e-06 - val_loss: 7.2685e-11 - val_mae: 5.4498e-06\n",
            "Epoch 296/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.9014e-10 - mae: 8.4791e-06 - val_loss: 7.1581e-11 - val_mae: 5.3912e-06\n",
            "Epoch 297/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.8146e-10 - mae: 8.3119e-06 - val_loss: 4.2302e-11 - val_mae: 4.5344e-06\n",
            "Epoch 298/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.4856e-10 - mae: 7.9667e-06 - val_loss: 6.4256e-11 - val_mae: 5.1472e-06\n",
            "Epoch 299/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.5961e-10 - mae: 7.9869e-06 - val_loss: 4.0432e-11 - val_mae: 4.4210e-06\n",
            "Epoch 300/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.5072e-10 - mae: 7.7800e-06 - val_loss: 6.0331e-11 - val_mae: 4.9917e-06\n",
            "Epoch 301/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.4554e-10 - mae: 7.6480e-06 - val_loss: 3.9441e-11 - val_mae: 4.3471e-06\n",
            "Epoch 302/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.4841e-10 - mae: 7.8424e-06 - val_loss: 3.7607e-11 - val_mae: 4.2651e-06\n",
            "Epoch 303/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.4641e-10 - mae: 7.6923e-06 - val_loss: 3.7405e-11 - val_mae: 4.2454e-06\n",
            "Epoch 304/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.3031e-10 - mae: 7.4880e-06 - val_loss: 5.3950e-11 - val_mae: 4.7445e-06\n",
            "Epoch 305/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.3004e-10 - mae: 7.4284e-06 - val_loss: 5.5546e-11 - val_mae: 4.7738e-06\n",
            "Epoch 306/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.2456e-10 - mae: 7.3569e-06 - val_loss: 3.6840e-11 - val_mae: 4.1686e-06\n",
            "Epoch 307/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.1692e-10 - mae: 7.2293e-06 - val_loss: 4.9860e-11 - val_mae: 4.5607e-06\n",
            "Epoch 308/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.0680e-10 - mae: 7.1255e-06 - val_loss: 3.5280e-11 - val_mae: 4.0706e-06\n",
            "Epoch 309/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.1498e-10 - mae: 7.2739e-06 - val_loss: 3.4347e-11 - val_mae: 4.0278e-06\n",
            "Epoch 310/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.0617e-10 - mae: 7.0392e-06 - val_loss: 4.6264e-11 - val_mae: 4.4061e-06\n",
            "Epoch 311/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.9453e-10 - mae: 6.9613e-06 - val_loss: 3.3951e-11 - val_mae: 3.9813e-06\n",
            "Epoch 312/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 2.0270e-10 - mae: 7.0133e-06 - val_loss: 3.2660e-11 - val_mae: 3.9141e-06\n",
            "Epoch 313/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 2.0035e-10 - mae: 7.0208e-06 - val_loss: 3.2414e-11 - val_mae: 3.8940e-06\n",
            "Epoch 314/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.9703e-10 - mae: 6.9336e-06 - val_loss: 3.1677e-11 - val_mae: 3.8516e-06\n",
            "Epoch 315/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.8832e-10 - mae: 6.8315e-06 - val_loss: 4.1563e-11 - val_mae: 4.1991e-06\n",
            "Epoch 316/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.8566e-10 - mae: 6.7549e-06 - val_loss: 4.3668e-11 - val_mae: 4.2398e-06\n",
            "Epoch 317/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.8570e-10 - mae: 6.7821e-06 - val_loss: 4.3648e-11 - val_mae: 4.2336e-06\n",
            "Epoch 318/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.8422e-10 - mae: 6.7335e-06 - val_loss: 4.3037e-11 - val_mae: 4.2045e-06\n",
            "Epoch 319/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.7587e-10 - mae: 6.5103e-06 - val_loss: 3.1424e-11 - val_mae: 3.7853e-06\n",
            "Epoch 320/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.7212e-10 - mae: 6.5750e-06 - val_loss: 3.8267e-11 - val_mae: 4.0219e-06\n",
            "Epoch 321/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.6355e-10 - mae: 6.3791e-06 - val_loss: 2.9588e-11 - val_mae: 3.6797e-06\n",
            "Epoch 322/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.6887e-10 - mae: 6.3662e-06 - val_loss: 3.6576e-11 - val_mae: 3.9319e-06\n",
            "Epoch 323/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.6683e-10 - mae: 6.3660e-06 - val_loss: 3.8242e-11 - val_mae: 3.9800e-06\n",
            "Epoch 324/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.5918e-10 - mae: 6.2443e-06 - val_loss: 2.9045e-11 - val_mae: 3.6368e-06\n",
            "Epoch 325/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.6304e-10 - mae: 6.2992e-06 - val_loss: 2.7885e-11 - val_mae: 3.5880e-06\n",
            "Epoch 326/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.6025e-10 - mae: 6.2148e-06 - val_loss: 3.3998e-11 - val_mae: 3.7913e-06\n",
            "Epoch 327/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.4704e-10 - mae: 6.0403e-06 - val_loss: 2.7218e-11 - val_mae: 3.5186e-06\n",
            "Epoch 328/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.5590e-10 - mae: 6.0880e-06 - val_loss: 3.2999e-11 - val_mae: 3.7288e-06\n",
            "Epoch 329/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4261e-10 - mae: 5.9055e-06 - val_loss: 2.6631e-11 - val_mae: 3.4734e-06\n",
            "Epoch 330/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.5395e-10 - mae: 6.0608e-06 - val_loss: 2.5957e-11 - val_mae: 3.4437e-06\n",
            "Epoch 331/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.5045e-10 - mae: 5.9859e-06 - val_loss: 3.1213e-11 - val_mae: 3.6409e-06\n",
            "Epoch 332/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3601e-10 - mae: 5.8058e-06 - val_loss: 2.5732e-11 - val_mae: 3.4080e-06\n",
            "Epoch 333/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4624e-10 - mae: 5.9291e-06 - val_loss: 3.0479e-11 - val_mae: 3.5818e-06\n",
            "Epoch 334/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4379e-10 - mae: 5.8846e-06 - val_loss: 3.1804e-11 - val_mae: 3.6301e-06\n",
            "Epoch 335/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4409e-10 - mae: 5.8723e-06 - val_loss: 3.2071e-11 - val_mae: 3.6353e-06\n",
            "Epoch 336/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4250e-10 - mae: 5.8674e-06 - val_loss: 3.1728e-11 - val_mae: 3.6092e-06\n",
            "Epoch 337/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.4107e-10 - mae: 5.8170e-06 - val_loss: 3.1528e-11 - val_mae: 3.5932e-06\n",
            "Epoch 338/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3948e-10 - mae: 5.7663e-06 - val_loss: 3.1168e-11 - val_mae: 3.5773e-06\n",
            "Epoch 339/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3752e-10 - mae: 5.7833e-06 - val_loss: 3.0993e-11 - val_mae: 3.5612e-06\n",
            "Epoch 340/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3589e-10 - mae: 5.7310e-06 - val_loss: 3.0573e-11 - val_mae: 3.5375e-06\n",
            "Epoch 341/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.2728e-10 - mae: 5.5996e-06 - val_loss: 2.4434e-11 - val_mae: 3.2849e-06\n",
            "Epoch 342/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.3149e-10 - mae: 5.6278e-06 - val_loss: 2.3416e-11 - val_mae: 3.2461e-06\n",
            "Epoch 343/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.3091e-10 - mae: 5.5760e-06 - val_loss: 2.2957e-11 - val_mae: 3.2189e-06\n",
            "Epoch 344/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.2829e-10 - mae: 5.5153e-06 - val_loss: 2.6416e-11 - val_mae: 3.3408e-06\n",
            "Epoch 345/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.2617e-10 - mae: 5.5017e-06 - val_loss: 2.3649e-11 - val_mae: 3.2305e-06\n",
            "Epoch 346/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.2558e-10 - mae: 5.5414e-06 - val_loss: 2.2816e-11 - val_mae: 3.1933e-06\n",
            "Epoch 347/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.2398e-10 - mae: 5.4548e-06 - val_loss: 2.5641e-11 - val_mae: 3.2917e-06\n",
            "Epoch 348/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1815e-10 - mae: 5.3818e-06 - val_loss: 2.2494e-11 - val_mae: 3.1616e-06\n",
            "Epoch 349/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.2215e-10 - mae: 5.4143e-06 - val_loss: 2.1782e-11 - val_mae: 3.1174e-06\n",
            "Epoch 350/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.2143e-10 - mae: 5.3684e-06 - val_loss: 2.1400e-11 - val_mae: 3.0983e-06\n",
            "Epoch 351/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.2045e-10 - mae: 5.4017e-06 - val_loss: 2.1284e-11 - val_mae: 3.0789e-06\n",
            "Epoch 352/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1900e-10 - mae: 5.3445e-06 - val_loss: 2.0980e-11 - val_mae: 3.0631e-06\n",
            "Epoch 353/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1811e-10 - mae: 5.3658e-06 - val_loss: 2.0859e-11 - val_mae: 3.0501e-06\n",
            "Epoch 354/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1577e-10 - mae: 5.2437e-06 - val_loss: 2.3600e-11 - val_mae: 3.1735e-06\n",
            "Epoch 355/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.1392e-10 - mae: 5.2742e-06 - val_loss: 2.4642e-11 - val_mae: 3.2069e-06\n",
            "Epoch 356/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1360e-10 - mae: 5.2560e-06 - val_loss: 2.4957e-11 - val_mae: 3.2108e-06\n",
            "Epoch 357/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.1296e-10 - mae: 5.2173e-06 - val_loss: 2.4970e-11 - val_mae: 3.2086e-06\n",
            "Epoch 358/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0610e-10 - mae: 5.1015e-06 - val_loss: 2.0921e-11 - val_mae: 3.0156e-06\n",
            "Epoch 359/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.1030e-10 - mae: 5.1440e-06 - val_loss: 2.0289e-11 - val_mae: 2.9908e-06\n",
            "Epoch 360/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0965e-10 - mae: 5.1368e-06 - val_loss: 1.9693e-11 - val_mae: 2.9522e-06\n",
            "Epoch 361/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0887e-10 - mae: 5.0829e-06 - val_loss: 1.9555e-11 - val_mae: 2.9432e-06\n",
            "Epoch 362/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0752e-10 - mae: 5.0594e-06 - val_loss: 2.1814e-11 - val_mae: 3.0433e-06\n",
            "Epoch 363/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0596e-10 - mae: 5.0466e-06 - val_loss: 2.0213e-11 - val_mae: 2.9670e-06\n",
            "Epoch 364/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0530e-10 - mae: 5.0480e-06 - val_loss: 1.9536e-11 - val_mae: 2.9296e-06\n",
            "Epoch 365/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0469e-10 - mae: 5.0237e-06 - val_loss: 1.9158e-11 - val_mae: 2.9103e-06\n",
            "Epoch 366/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0390e-10 - mae: 4.9960e-06 - val_loss: 1.8853e-11 - val_mae: 2.8943e-06\n",
            "Epoch 367/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0277e-10 - mae: 4.9506e-06 - val_loss: 2.0714e-11 - val_mae: 2.9630e-06\n",
            "Epoch 368/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 1.0113e-10 - mae: 4.9603e-06 - val_loss: 2.1657e-11 - val_mae: 3.0062e-06\n",
            "Epoch 369/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 1.0050e-10 - mae: 4.9273e-06 - val_loss: 1.9722e-11 - val_mae: 2.9103e-06\n",
            "Epoch 370/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.9591e-11 - mae: 4.8976e-06 - val_loss: 1.8960e-11 - val_mae: 2.8742e-06\n",
            "Epoch 371/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.9191e-11 - mae: 4.8891e-06 - val_loss: 1.8413e-11 - val_mae: 2.8415e-06\n",
            "Epoch 372/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 9.8698e-11 - mae: 4.8756e-06 - val_loss: 1.8117e-11 - val_mae: 2.8258e-06\n",
            "Epoch 373/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 9.7322e-11 - mae: 4.8430e-06 - val_loss: 1.9878e-11 - val_mae: 2.9023e-06\n",
            "Epoch 374/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 9.6141e-11 - mae: 4.8124e-06 - val_loss: 1.8648e-11 - val_mae: 2.8460e-06\n",
            "Epoch 375/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.5675e-11 - mae: 4.8057e-06 - val_loss: 1.8052e-11 - val_mae: 2.8109e-06\n",
            "Epoch 376/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.4871e-11 - mae: 4.7836e-06 - val_loss: 1.9517e-11 - val_mae: 2.8750e-06\n",
            "Epoch 377/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.3937e-11 - mae: 4.7772e-06 - val_loss: 1.8317e-11 - val_mae: 2.8186e-06\n",
            "Epoch 378/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.3472e-11 - mae: 4.7419e-06 - val_loss: 1.7679e-11 - val_mae: 2.7819e-06\n",
            "Epoch 379/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.2583e-11 - mae: 4.7386e-06 - val_loss: 1.9084e-11 - val_mae: 2.8443e-06\n",
            "Epoch 380/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.1672e-11 - mae: 4.7081e-06 - val_loss: 1.9697e-11 - val_mae: 2.8636e-06\n",
            "Epoch 381/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.1299e-11 - mae: 4.7069e-06 - val_loss: 1.9976e-11 - val_mae: 2.8734e-06\n",
            "Epoch 382/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.0813e-11 - mae: 4.6773e-06 - val_loss: 2.0017e-11 - val_mae: 2.8734e-06\n",
            "Epoch 383/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 9.0260e-11 - mae: 4.6854e-06 - val_loss: 1.9989e-11 - val_mae: 2.8701e-06\n",
            "Epoch 384/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.9599e-11 - mae: 4.6576e-06 - val_loss: 1.9886e-11 - val_mae: 2.8530e-06\n",
            "Epoch 385/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.9055e-11 - mae: 4.6448e-06 - val_loss: 1.9650e-11 - val_mae: 2.8411e-06\n",
            "Epoch 386/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.4308e-11 - mae: 4.5435e-06 - val_loss: 1.7133e-11 - val_mae: 2.7167e-06\n",
            "Epoch 387/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.7195e-11 - mae: 4.5670e-06 - val_loss: 1.6564e-11 - val_mae: 2.6881e-06\n",
            "Epoch 388/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 8.6958e-11 - mae: 4.5724e-06 - val_loss: 1.6281e-11 - val_mae: 2.6727e-06\n",
            "Epoch 389/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 8.6061e-11 - mae: 4.5308e-06 - val_loss: 1.7382e-11 - val_mae: 2.7125e-06\n",
            "Epoch 390/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.5105e-11 - mae: 4.5398e-06 - val_loss: 1.7993e-11 - val_mae: 2.7501e-06\n",
            "Epoch 391/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.4644e-11 - mae: 4.5113e-06 - val_loss: 1.6949e-11 - val_mae: 2.6883e-06\n",
            "Epoch 392/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.4020e-11 - mae: 4.5130e-06 - val_loss: 1.6362e-11 - val_mae: 2.6587e-06\n",
            "Epoch 393/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8.3401e-11 - mae: 4.4959e-06 - val_loss: 1.7216e-11 - val_mae: 2.6918e-06\n",
            "Epoch 394/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.2685e-11 - mae: 4.4795e-06 - val_loss: 1.6383e-11 - val_mae: 2.6562e-06\n",
            "Epoch 395/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 8.2255e-11 - mae: 4.4321e-06 - val_loss: 1.7169e-11 - val_mae: 2.6859e-06\n",
            "Epoch 396/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.1619e-11 - mae: 4.4416e-06 - val_loss: 1.6293e-11 - val_mae: 2.6429e-06\n",
            "Epoch 397/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.1203e-11 - mae: 4.4200e-06 - val_loss: 1.5723e-11 - val_mae: 2.6083e-06\n",
            "Epoch 398/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 8.0525e-11 - mae: 4.4236e-06 - val_loss: 1.6583e-11 - val_mae: 2.6478e-06\n",
            "Epoch 399/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.9934e-11 - mae: 4.3941e-06 - val_loss: 1.5931e-11 - val_mae: 2.6147e-06\n",
            "Epoch 400/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.9450e-11 - mae: 4.3811e-06 - val_loss: 1.6525e-11 - val_mae: 2.6367e-06\n",
            "Epoch 401/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.8821e-11 - mae: 4.3723e-06 - val_loss: 1.5706e-11 - val_mae: 2.5953e-06\n",
            "Epoch 402/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.8428e-11 - mae: 4.3598e-06 - val_loss: 1.5316e-11 - val_mae: 2.5679e-06\n",
            "Epoch 403/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.7951e-11 - mae: 4.3585e-06 - val_loss: 1.5015e-11 - val_mae: 2.5454e-06\n",
            "Epoch 404/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.7541e-11 - mae: 4.3176e-06 - val_loss: 1.5812e-11 - val_mae: 2.5848e-06\n",
            "Epoch 405/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.6783e-11 - mae: 4.3083e-06 - val_loss: 1.6196e-11 - val_mae: 2.6074e-06\n",
            "Epoch 406/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.6367e-11 - mae: 4.3074e-06 - val_loss: 1.6452e-11 - val_mae: 2.6179e-06\n",
            "Epoch 407/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.5981e-11 - mae: 4.2891e-06 - val_loss: 1.6483e-11 - val_mae: 2.6126e-06\n",
            "Epoch 408/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.4026e-11 - mae: 4.2344e-06 - val_loss: 1.5211e-11 - val_mae: 2.5421e-06\n",
            "Epoch 409/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.4981e-11 - mae: 4.2516e-06 - val_loss: 1.4684e-11 - val_mae: 2.5084e-06\n",
            "Epoch 410/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.4400e-11 - mae: 4.2430e-06 - val_loss: 1.5275e-11 - val_mae: 2.5434e-06\n",
            "Epoch 411/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.4054e-11 - mae: 4.2260e-06 - val_loss: 1.5732e-11 - val_mae: 2.5639e-06\n",
            "Epoch 412/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.3491e-11 - mae: 4.2058e-06 - val_loss: 1.5803e-11 - val_mae: 2.5660e-06\n",
            "Epoch 413/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.3173e-11 - mae: 4.2030e-06 - val_loss: 1.5814e-11 - val_mae: 2.5655e-06\n",
            "Epoch 414/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.2879e-11 - mae: 4.1865e-06 - val_loss: 1.4893e-11 - val_mae: 2.5059e-06\n",
            "Epoch 415/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.2121e-11 - mae: 4.1917e-06 - val_loss: 1.4344e-11 - val_mae: 2.4768e-06\n",
            "Epoch 416/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.1828e-11 - mae: 4.1717e-06 - val_loss: 1.4011e-11 - val_mae: 2.4527e-06\n",
            "Epoch 417/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 7.1347e-11 - mae: 4.1504e-06 - val_loss: 1.3766e-11 - val_mae: 2.4387e-06\n",
            "Epoch 418/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.0860e-11 - mae: 4.1413e-06 - val_loss: 1.4497e-11 - val_mae: 2.4751e-06\n",
            "Epoch 419/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 7.0484e-11 - mae: 4.1159e-06 - val_loss: 1.4796e-11 - val_mae: 2.4833e-06\n",
            "Epoch 420/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.9978e-11 - mae: 4.1194e-06 - val_loss: 1.5032e-11 - val_mae: 2.4935e-06\n",
            "Epoch 421/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.9663e-11 - mae: 4.0699e-06 - val_loss: 1.4226e-11 - val_mae: 2.4517e-06\n",
            "Epoch 422/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.9112e-11 - mae: 4.0843e-06 - val_loss: 1.3707e-11 - val_mae: 2.4181e-06\n",
            "Epoch 423/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.8817e-11 - mae: 4.0623e-06 - val_loss: 1.4217e-11 - val_mae: 2.4431e-06\n",
            "Epoch 424/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.8415e-11 - mae: 4.0710e-06 - val_loss: 1.4481e-11 - val_mae: 2.4551e-06\n",
            "Epoch 425/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 6.8141e-11 - mae: 4.0491e-06 - val_loss: 1.3856e-11 - val_mae: 2.4161e-06\n",
            "Epoch 426/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.7712e-11 - mae: 4.0392e-06 - val_loss: 1.3388e-11 - val_mae: 2.3906e-06\n",
            "Epoch 427/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.7087e-11 - mae: 4.0218e-06 - val_loss: 1.3886e-11 - val_mae: 2.4151e-06\n",
            "Epoch 428/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.6794e-11 - mae: 4.0216e-06 - val_loss: 1.4178e-11 - val_mae: 2.4343e-06\n",
            "Epoch 429/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 6.6441e-11 - mae: 4.0127e-06 - val_loss: 1.4354e-11 - val_mae: 2.4364e-06\n",
            "Epoch 430/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.6224e-11 - mae: 3.9948e-06 - val_loss: 1.4376e-11 - val_mae: 2.4363e-06\n",
            "Epoch 431/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.5872e-11 - mae: 3.9859e-06 - val_loss: 1.3574e-11 - val_mae: 2.3884e-06\n",
            "Epoch 432/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.5282e-11 - mae: 3.9713e-06 - val_loss: 1.3835e-11 - val_mae: 2.4006e-06\n",
            "Epoch 433/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.4945e-11 - mae: 3.9672e-06 - val_loss: 1.3275e-11 - val_mae: 2.3701e-06\n",
            "Epoch 434/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.4658e-11 - mae: 3.9596e-06 - val_loss: 1.2950e-11 - val_mae: 2.3453e-06\n",
            "Epoch 435/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.4265e-11 - mae: 3.9348e-06 - val_loss: 1.3261e-11 - val_mae: 2.3619e-06\n",
            "Epoch 436/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.3886e-11 - mae: 3.9312e-06 - val_loss: 1.3499e-11 - val_mae: 2.3730e-06\n",
            "Epoch 437/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.3665e-11 - mae: 3.9244e-06 - val_loss: 1.2962e-11 - val_mae: 2.3373e-06\n",
            "Epoch 438/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.3192e-11 - mae: 3.9255e-06 - val_loss: 1.3249e-11 - val_mae: 2.3575e-06\n",
            "Epoch 439/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.2833e-11 - mae: 3.9125e-06 - val_loss: 1.3462e-11 - val_mae: 2.3673e-06\n",
            "Epoch 440/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.2738e-11 - mae: 3.8869e-06 - val_loss: 1.3490e-11 - val_mae: 2.3676e-06\n",
            "Epoch 441/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.2435e-11 - mae: 3.8736e-06 - val_loss: 1.3499e-11 - val_mae: 2.3669e-06\n",
            "Epoch 442/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.2060e-11 - mae: 3.8752e-06 - val_loss: 1.3463e-11 - val_mae: 2.3584e-06\n",
            "Epoch 443/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.1741e-11 - mae: 3.8579e-06 - val_loss: 1.3441e-11 - val_mae: 2.3560e-06\n",
            "Epoch 444/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.0380e-11 - mae: 3.8257e-06 - val_loss: 1.2619e-11 - val_mae: 2.3098e-06\n",
            "Epoch 445/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 6.0860e-11 - mae: 3.8447e-06 - val_loss: 1.2326e-11 - val_mae: 2.2925e-06\n",
            "Epoch 446/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.0695e-11 - mae: 3.8061e-06 - val_loss: 1.2595e-11 - val_mae: 2.3007e-06\n",
            "Epoch 447/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.0193e-11 - mae: 3.7991e-06 - val_loss: 1.2797e-11 - val_mae: 2.3049e-06\n",
            "Epoch 448/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 6.0149e-11 - mae: 3.8100e-06 - val_loss: 1.2309e-11 - val_mae: 2.2774e-06\n",
            "Epoch 449/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.9682e-11 - mae: 3.8031e-06 - val_loss: 1.2033e-11 - val_mae: 2.2609e-06\n",
            "Epoch 450/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.9475e-11 - mae: 3.7815e-06 - val_loss: 1.1794e-11 - val_mae: 2.2404e-06\n",
            "Epoch 451/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.9215e-11 - mae: 3.7790e-06 - val_loss: 1.1589e-11 - val_mae: 2.2279e-06\n",
            "Epoch 452/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.8814e-11 - mae: 3.7502e-06 - val_loss: 1.2014e-11 - val_mae: 2.2511e-06\n",
            "Epoch 453/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.8431e-11 - mae: 3.7606e-06 - val_loss: 1.2280e-11 - val_mae: 2.2702e-06\n",
            "Epoch 454/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.8126e-11 - mae: 3.7479e-06 - val_loss: 1.1820e-11 - val_mae: 2.2383e-06\n",
            "Epoch 455/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.7938e-11 - mae: 3.7399e-06 - val_loss: 1.1556e-11 - val_mae: 2.2163e-06\n",
            "Epoch 456/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.7701e-11 - mae: 3.7348e-06 - val_loss: 1.1467e-11 - val_mae: 2.2099e-06\n",
            "Epoch 457/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.7420e-11 - mae: 3.7223e-06 - val_loss: 1.1701e-11 - val_mae: 2.2119e-06\n",
            "Epoch 458/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.7161e-11 - mae: 3.7212e-06 - val_loss: 1.1943e-11 - val_mae: 2.2304e-06\n",
            "Epoch 459/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.6861e-11 - mae: 3.7055e-06 - val_loss: 1.2133e-11 - val_mae: 2.2396e-06\n",
            "Epoch 460/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.6475e-11 - mae: 3.6860e-06 - val_loss: 1.1655e-11 - val_mae: 2.2061e-06\n",
            "Epoch 461/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.6274e-11 - mae: 3.6776e-06 - val_loss: 1.1389e-11 - val_mae: 2.1900e-06\n",
            "Epoch 462/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.5936e-11 - mae: 3.6872e-06 - val_loss: 1.1171e-11 - val_mae: 2.1766e-06\n",
            "Epoch 463/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.5625e-11 - mae: 3.6557e-06 - val_loss: 1.1438e-11 - val_mae: 2.1909e-06\n",
            "Epoch 464/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.5426e-11 - mae: 3.6611e-06 - val_loss: 1.1653e-11 - val_mae: 2.2019e-06\n",
            "Epoch 465/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.5156e-11 - mae: 3.6470e-06 - val_loss: 1.1361e-11 - val_mae: 2.1843e-06\n",
            "Epoch 466/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.4951e-11 - mae: 3.6587e-06 - val_loss: 1.1455e-11 - val_mae: 2.1888e-06\n",
            "Epoch 467/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.4659e-11 - mae: 3.6513e-06 - val_loss: 1.1635e-11 - val_mae: 2.1978e-06\n",
            "Epoch 468/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 5.4412e-11 - mae: 3.6226e-06 - val_loss: 1.1199e-11 - val_mae: 2.1723e-06\n",
            "Epoch 469/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.4217e-11 - mae: 3.6159e-06 - val_loss: 1.0951e-11 - val_mae: 2.1569e-06\n",
            "Epoch 470/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.3903e-11 - mae: 3.6069e-06 - val_loss: 1.1186e-11 - val_mae: 2.1695e-06\n",
            "Epoch 471/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.3739e-11 - mae: 3.6106e-06 - val_loss: 1.1374e-11 - val_mae: 2.1791e-06\n",
            "Epoch 472/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.3480e-11 - mae: 3.5815e-06 - val_loss: 1.1092e-11 - val_mae: 2.1619e-06\n",
            "Epoch 473/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.3200e-11 - mae: 3.5749e-06 - val_loss: 1.0857e-11 - val_mae: 2.1473e-06\n",
            "Epoch 474/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.3066e-11 - mae: 3.5681e-06 - val_loss: 1.0648e-11 - val_mae: 2.1342e-06\n",
            "Epoch 475/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.2799e-11 - mae: 3.5623e-06 - val_loss: 1.0891e-11 - val_mae: 2.1475e-06\n",
            "Epoch 476/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.2471e-11 - mae: 3.5519e-06 - val_loss: 1.1067e-11 - val_mae: 2.1512e-06\n",
            "Epoch 477/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.2348e-11 - mae: 3.5450e-06 - val_loss: 1.0821e-11 - val_mae: 2.1414e-06\n",
            "Epoch 478/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.1936e-11 - mae: 3.5384e-06 - val_loss: 1.0578e-11 - val_mae: 2.1207e-06\n",
            "Epoch 479/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.1700e-11 - mae: 3.5355e-06 - val_loss: 1.0776e-11 - val_mae: 2.1261e-06\n",
            "Epoch 480/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.1515e-11 - mae: 3.5303e-06 - val_loss: 1.0525e-11 - val_mae: 2.1049e-06\n",
            "Epoch 481/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.1414e-11 - mae: 3.5212e-06 - val_loss: 1.0726e-11 - val_mae: 2.1162e-06\n",
            "Epoch 482/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.1138e-11 - mae: 3.5094e-06 - val_loss: 1.0469e-11 - val_mae: 2.0947e-06\n",
            "Epoch 483/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.0939e-11 - mae: 3.5045e-06 - val_loss: 1.0572e-11 - val_mae: 2.1056e-06\n",
            "Epoch 484/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.0690e-11 - mae: 3.5057e-06 - val_loss: 1.0721e-11 - val_mae: 2.1080e-06\n",
            "Epoch 485/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 5.0547e-11 - mae: 3.4900e-06 - val_loss: 1.0753e-11 - val_mae: 2.1091e-06\n",
            "Epoch 486/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.0330e-11 - mae: 3.4814e-06 - val_loss: 1.0767e-11 - val_mae: 2.1090e-06\n",
            "Epoch 487/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 5.0197e-11 - mae: 3.4784e-06 - val_loss: 1.0463e-11 - val_mae: 2.0846e-06\n",
            "Epoch 488/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.9849e-11 - mae: 3.4704e-06 - val_loss: 1.0525e-11 - val_mae: 2.0931e-06\n",
            "Epoch 489/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.9540e-11 - mae: 3.4562e-06 - val_loss: 1.0246e-11 - val_mae: 2.0701e-06\n",
            "Epoch 490/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.9426e-11 - mae: 3.4639e-06 - val_loss: 1.0025e-11 - val_mae: 2.0496e-06\n",
            "Epoch 491/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.9295e-11 - mae: 3.4459e-06 - val_loss: 9.9494e-12 - val_mae: 2.0440e-06\n",
            "Epoch 492/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.9046e-11 - mae: 3.4373e-06 - val_loss: 9.7766e-12 - val_mae: 2.0328e-06\n",
            "Epoch 493/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.8902e-11 - mae: 3.4257e-06 - val_loss: 9.9935e-12 - val_mae: 2.0451e-06\n",
            "Epoch 494/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.8611e-11 - mae: 3.4164e-06 - val_loss: 1.0179e-11 - val_mae: 2.0552e-06\n",
            "Epoch 495/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.8519e-11 - mae: 3.4185e-06 - val_loss: 9.9524e-12 - val_mae: 2.0408e-06\n",
            "Epoch 496/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.8271e-11 - mae: 3.4098e-06 - val_loss: 9.7561e-12 - val_mae: 2.0281e-06\n",
            "Epoch 497/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.8000e-11 - mae: 3.4008e-06 - val_loss: 9.9559e-12 - val_mae: 2.0393e-06\n",
            "Epoch 498/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.7859e-11 - mae: 3.3999e-06 - val_loss: 1.0010e-11 - val_mae: 2.0419e-06\n",
            "Epoch 499/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.7544e-11 - mae: 3.3969e-06 - val_loss: 1.0158e-11 - val_mae: 2.0497e-06\n",
            "Epoch 500/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.7399e-11 - mae: 3.3755e-06 - val_loss: 9.9104e-12 - val_mae: 2.0339e-06\n",
            "Epoch 501/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.7330e-11 - mae: 3.3830e-06 - val_loss: 9.7029e-12 - val_mae: 2.0205e-06\n",
            "Epoch 502/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.7038e-11 - mae: 3.3754e-06 - val_loss: 9.7725e-12 - val_mae: 2.0242e-06\n",
            "Epoch 503/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.6807e-11 - mae: 3.3717e-06 - val_loss: 9.6785e-12 - val_mae: 2.0173e-06\n",
            "Epoch 504/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.6656e-11 - mae: 3.3564e-06 - val_loss: 9.4837e-12 - val_mae: 1.9984e-06\n",
            "Epoch 505/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4.6508e-11 - mae: 3.3486e-06 - val_loss: 9.6800e-12 - val_mae: 2.0158e-06\n",
            "Epoch 506/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.6290e-11 - mae: 3.3503e-06 - val_loss: 9.7294e-12 - val_mae: 2.0182e-06\n",
            "Epoch 507/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.6127e-11 - mae: 3.3283e-06 - val_loss: 9.7579e-12 - val_mae: 2.0193e-06\n",
            "Epoch 508/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.5922e-11 - mae: 3.3230e-06 - val_loss: 9.8681e-12 - val_mae: 2.0199e-06\n",
            "Epoch 509/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.5785e-11 - mae: 3.3185e-06 - val_loss: 9.6297e-12 - val_mae: 2.0045e-06\n",
            "Epoch 510/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.5651e-11 - mae: 3.3223e-06 - val_loss: 9.6643e-12 - val_mae: 2.0060e-06\n",
            "Epoch 511/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.5457e-11 - mae: 3.3022e-06 - val_loss: 9.4365e-12 - val_mae: 1.9849e-06\n",
            "Epoch 512/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.5213e-11 - mae: 3.2994e-06 - val_loss: 9.2436e-12 - val_mae: 1.9722e-06\n",
            "Epoch 513/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.4983e-11 - mae: 3.2901e-06 - val_loss: 9.1807e-12 - val_mae: 1.9673e-06\n",
            "Epoch 514/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.4911e-11 - mae: 3.2864e-06 - val_loss: 9.0225e-12 - val_mae: 1.9567e-06\n",
            "Epoch 515/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.4678e-11 - mae: 3.2862e-06 - val_loss: 9.2110e-12 - val_mae: 1.9678e-06\n",
            "Epoch 516/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.4603e-11 - mae: 3.2709e-06 - val_loss: 9.0351e-12 - val_mae: 1.9560e-06\n",
            "Epoch 517/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.4398e-11 - mae: 3.2682e-06 - val_loss: 8.9644e-12 - val_mae: 1.9450e-06\n",
            "Epoch 518/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.4174e-11 - mae: 3.2588e-06 - val_loss: 9.1422e-12 - val_mae: 1.9557e-06\n",
            "Epoch 519/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.4061e-11 - mae: 3.2618e-06 - val_loss: 9.1719e-12 - val_mae: 1.9515e-06\n",
            "Epoch 520/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.3845e-11 - mae: 3.2505e-06 - val_loss: 9.1861e-12 - val_mae: 1.9465e-06\n",
            "Epoch 521/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.3743e-11 - mae: 3.2442e-06 - val_loss: 9.1080e-12 - val_mae: 1.9458e-06\n",
            "Epoch 522/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.3567e-11 - mae: 3.2422e-06 - val_loss: 8.9106e-12 - val_mae: 1.9271e-06\n",
            "Epoch 523/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.3375e-11 - mae: 3.2367e-06 - val_loss: 8.7374e-12 - val_mae: 1.9093e-06\n",
            "Epoch 524/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.3234e-11 - mae: 3.2335e-06 - val_loss: 8.8948e-12 - val_mae: 1.9194e-06\n",
            "Epoch 525/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.2983e-11 - mae: 3.2222e-06 - val_loss: 8.9553e-12 - val_mae: 1.9282e-06\n",
            "Epoch 526/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.2868e-11 - mae: 3.2108e-06 - val_loss: 8.8475e-12 - val_mae: 1.9087e-06\n",
            "Epoch 527/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.2774e-11 - mae: 3.2131e-06 - val_loss: 8.6755e-12 - val_mae: 1.8971e-06\n",
            "Epoch 528/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.2663e-11 - mae: 3.2089e-06 - val_loss: 8.8385e-12 - val_mae: 1.9067e-06\n",
            "Epoch 529/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.2407e-11 - mae: 3.1939e-06 - val_loss: 8.8870e-12 - val_mae: 1.9153e-06\n",
            "Epoch 530/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.2367e-11 - mae: 3.1984e-06 - val_loss: 8.6884e-12 - val_mae: 1.8959e-06\n",
            "Epoch 531/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.2117e-11 - mae: 3.1795e-06 - val_loss: 8.6232e-12 - val_mae: 1.8907e-06\n",
            "Epoch 532/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.2061e-11 - mae: 3.1805e-06 - val_loss: 8.4669e-12 - val_mae: 1.8800e-06\n",
            "Epoch 533/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.1927e-11 - mae: 3.1818e-06 - val_loss: 8.6310e-12 - val_mae: 1.8899e-06\n",
            "Epoch 534/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.1619e-11 - mae: 3.1792e-06 - val_loss: 8.6714e-12 - val_mae: 1.8920e-06\n",
            "Epoch 535/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.1495e-11 - mae: 3.1637e-06 - val_loss: 8.8057e-12 - val_mae: 1.8997e-06\n",
            "Epoch 536/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.1445e-11 - mae: 3.1655e-06 - val_loss: 8.8207e-12 - val_mae: 1.9000e-06\n",
            "Epoch 537/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.1270e-11 - mae: 3.1528e-06 - val_loss: 8.6202e-12 - val_mae: 1.8865e-06\n",
            "Epoch 538/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.1070e-11 - mae: 3.1461e-06 - val_loss: 8.4446e-12 - val_mae: 1.8744e-06\n",
            "Epoch 539/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.1000e-11 - mae: 3.1443e-06 - val_loss: 8.5914e-12 - val_mae: 1.8831e-06\n",
            "Epoch 540/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.0801e-11 - mae: 3.1359e-06 - val_loss: 8.4144e-12 - val_mae: 1.8710e-06\n",
            "Epoch 541/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4.0734e-11 - mae: 3.1427e-06 - val_loss: 8.2537e-12 - val_mae: 1.8599e-06\n",
            "Epoch 542/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 4.0577e-11 - mae: 3.1319e-06 - val_loss: 8.4062e-12 - val_mae: 1.8691e-06\n",
            "Epoch 543/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4.0418e-11 - mae: 3.1075e-06 - val_loss: 8.2423e-12 - val_mae: 1.8578e-06\n",
            "Epoch 544/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4.0306e-11 - mae: 3.1114e-06 - val_loss: 8.3892e-12 - val_mae: 1.8667e-06\n",
            "Epoch 545/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4.0129e-11 - mae: 3.1116e-06 - val_loss: 8.3968e-12 - val_mae: 1.8616e-06\n",
            "Epoch 546/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.9923e-11 - mae: 3.1018e-06 - val_loss: 8.4139e-12 - val_mae: 1.8622e-06\n",
            "Epoch 547/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.9847e-11 - mae: 3.0946e-06 - val_loss: 8.4228e-12 - val_mae: 1.8621e-06\n",
            "Epoch 548/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.9797e-11 - mae: 3.0928e-06 - val_loss: 8.5331e-12 - val_mae: 1.8684e-06\n",
            "Epoch 549/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.9067e-11 - mae: 3.0709e-06 - val_loss: 8.1699e-12 - val_mae: 1.8437e-06\n",
            "Epoch 550/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.9432e-11 - mae: 3.0818e-06 - val_loss: 8.1175e-12 - val_mae: 1.8394e-06\n",
            "Epoch 551/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.9365e-11 - mae: 3.0719e-06 - val_loss: 8.1583e-12 - val_mae: 1.8416e-06\n",
            "Epoch 552/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.9200e-11 - mae: 3.0704e-06 - val_loss: 8.1868e-12 - val_mae: 1.8430e-06\n",
            "Epoch 553/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.9011e-11 - mae: 3.0634e-06 - val_loss: 8.3091e-12 - val_mae: 1.8502e-06\n",
            "Epoch 554/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.8961e-11 - mae: 3.0571e-06 - val_loss: 8.1298e-12 - val_mae: 1.8378e-06\n",
            "Epoch 555/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.8787e-11 - mae: 3.0600e-06 - val_loss: 8.1527e-12 - val_mae: 1.8388e-06\n",
            "Epoch 556/600\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 3.8684e-11 - mae: 3.0475e-06 - val_loss: 8.1656e-12 - val_mae: 1.8391e-06\n",
            "Epoch 557/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.8506e-11 - mae: 3.0365e-06 - val_loss: 8.1740e-12 - val_mae: 1.8390e-06\n",
            "Epoch 558/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.8477e-11 - mae: 3.0468e-06 - val_loss: 8.1767e-12 - val_mae: 1.8386e-06\n",
            "Epoch 559/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.8224e-11 - mae: 3.0295e-06 - val_loss: 8.0938e-12 - val_mae: 1.8322e-06\n",
            "Epoch 560/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.8241e-11 - mae: 3.0329e-06 - val_loss: 7.9274e-12 - val_mae: 1.8205e-06\n",
            "Epoch 561/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.8044e-11 - mae: 3.0268e-06 - val_loss: 7.8753e-12 - val_mae: 1.8162e-06\n",
            "Epoch 562/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.7990e-11 - mae: 3.0225e-06 - val_loss: 7.7325e-12 - val_mae: 1.8061e-06\n",
            "Epoch 563/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.7748e-11 - mae: 3.0087e-06 - val_loss: 7.8566e-12 - val_mae: 1.8084e-06\n",
            "Epoch 564/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.7728e-11 - mae: 3.0049e-06 - val_loss: 7.7083e-12 - val_mae: 1.7978e-06\n",
            "Epoch 565/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.7467e-11 - mae: 2.9996e-06 - val_loss: 7.6705e-12 - val_mae: 1.7945e-06\n",
            "Epoch 566/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.7418e-11 - mae: 2.9969e-06 - val_loss: 7.5405e-12 - val_mae: 1.7852e-06\n",
            "Epoch 567/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.7252e-11 - mae: 2.9942e-06 - val_loss: 7.4991e-12 - val_mae: 1.7760e-06\n",
            "Epoch 568/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.7202e-11 - mae: 2.9894e-06 - val_loss: 7.4764e-12 - val_mae: 1.7738e-06\n",
            "Epoch 569/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.7144e-11 - mae: 2.9889e-06 - val_loss: 7.4566e-12 - val_mae: 1.7718e-06\n",
            "Epoch 570/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.6934e-11 - mae: 2.9790e-06 - val_loss: 7.5018e-12 - val_mae: 1.7748e-06\n",
            "Epoch 571/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.6794e-11 - mae: 2.9741e-06 - val_loss: 7.4534e-12 - val_mae: 1.7652e-06\n",
            "Epoch 572/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.6752e-11 - mae: 2.9717e-06 - val_loss: 7.4276e-12 - val_mae: 1.7628e-06\n",
            "Epoch 573/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.6715e-11 - mae: 2.9770e-06 - val_loss: 7.4042e-12 - val_mae: 1.7605e-06\n",
            "Epoch 574/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.6592e-11 - mae: 2.9659e-06 - val_loss: 7.4336e-12 - val_mae: 1.7567e-06\n",
            "Epoch 575/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.6372e-11 - mae: 2.9593e-06 - val_loss: 7.4018e-12 - val_mae: 1.7538e-06\n",
            "Epoch 576/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.6288e-11 - mae: 2.9488e-06 - val_loss: 7.2736e-12 - val_mae: 1.7444e-06\n",
            "Epoch 577/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.6186e-11 - mae: 2.9474e-06 - val_loss: 7.4129e-12 - val_mae: 1.7535e-06\n",
            "Epoch 578/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.6021e-11 - mae: 2.9501e-06 - val_loss: 7.4441e-12 - val_mae: 1.7552e-06\n",
            "Epoch 579/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5845e-11 - mae: 2.9441e-06 - val_loss: 7.4804e-12 - val_mae: 1.7628e-06\n",
            "Epoch 580/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5871e-11 - mae: 2.9351e-06 - val_loss: 7.4133e-12 - val_mae: 1.7518e-06\n",
            "Epoch 581/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5695e-11 - mae: 2.9238e-06 - val_loss: 7.4344e-12 - val_mae: 1.7529e-06\n",
            "Epoch 582/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5546e-11 - mae: 2.9202e-06 - val_loss: 7.3835e-12 - val_mae: 1.7486e-06\n",
            "Epoch 583/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5485e-11 - mae: 2.9189e-06 - val_loss: 7.2461e-12 - val_mae: 1.7386e-06\n",
            "Epoch 584/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5450e-11 - mae: 2.9171e-06 - val_loss: 7.3744e-12 - val_mae: 1.7468e-06\n",
            "Epoch 585/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5291e-11 - mae: 2.9141e-06 - val_loss: 7.2335e-12 - val_mae: 1.7366e-06\n",
            "Epoch 586/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5129e-11 - mae: 2.9089e-06 - val_loss: 7.1970e-12 - val_mae: 1.7333e-06\n",
            "Epoch 587/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5078e-11 - mae: 2.9083e-06 - val_loss: 7.1672e-12 - val_mae: 1.7306e-06\n",
            "Epoch 588/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.5015e-11 - mae: 2.9027e-06 - val_loss: 7.0459e-12 - val_mae: 1.7216e-06\n",
            "Epoch 589/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.4847e-11 - mae: 2.8924e-06 - val_loss: 7.0245e-12 - val_mae: 1.7195e-06\n",
            "Epoch 590/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.4814e-11 - mae: 2.8949e-06 - val_loss: 7.0055e-12 - val_mae: 1.7175e-06\n",
            "Epoch 591/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.4650e-11 - mae: 2.8867e-06 - val_loss: 6.9654e-12 - val_mae: 1.7091e-06\n",
            "Epoch 592/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.4608e-11 - mae: 2.8822e-06 - val_loss: 6.9493e-12 - val_mae: 1.7073e-06\n",
            "Epoch 593/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.4461e-11 - mae: 2.8783e-06 - val_loss: 6.9356e-12 - val_mae: 1.7058e-06\n",
            "Epoch 594/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.4445e-11 - mae: 2.8708e-06 - val_loss: 6.9786e-12 - val_mae: 1.7085e-06\n",
            "Epoch 595/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.4198e-11 - mae: 2.8741e-06 - val_loss: 7.0098e-12 - val_mae: 1.7104e-06\n",
            "Epoch 596/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.4110e-11 - mae: 2.8684e-06 - val_loss: 6.9783e-12 - val_mae: 1.7075e-06\n",
            "Epoch 597/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.4149e-11 - mae: 2.8665e-06 - val_loss: 6.9504e-12 - val_mae: 1.7048e-06\n",
            "Epoch 598/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.4020e-11 - mae: 2.8579e-06 - val_loss: 6.9805e-12 - val_mae: 1.7066e-06\n",
            "Epoch 599/600\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3.3864e-11 - mae: 2.8560e-06 - val_loss: 7.0021e-12 - val_mae: 1.7077e-06\n",
            "Epoch 600/600\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3.3782e-11 - mae: 2.8432e-06 - val_loss: 6.9618e-12 - val_mae: 1.7042e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NUDPvaJE1wRE"
      },
      "source": [
        "## Verify \n",
        "\n",
        "Graph the models performance vs validation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxA0zCOaS35v"
      },
      "source": [
        "### Graph the loss\n",
        "\n",
        "Graph the loss to see when the model stops improving."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvFNHXoQzmcM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "dffa6ddc-bc7a-4243-e8ec-b37b58aa89d5"
      },
      "source": [
        "# increase the size of the graphs. The default size is (6,4).\n",
        "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
        "\n",
        "# graph the loss, the model above is configure to use \"mean squared error\" as the loss function\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "print(plt.rcParams[\"figure.figsize\"])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[20.0, 10.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DG3m-VpE1zOd"
      },
      "source": [
        "### Graph the loss again, skipping a bit of the start\n",
        "\n",
        "We'll graph the same data as the previous code cell, but start at index 100 so we can further zoom in once the model starts to converge."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3xT7ue2zovd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "b2f54091-e8e3-425f-904b-877737d0d966"
      },
      "source": [
        "# graph the loss again skipping a bit of the start\n",
        "SKIP = 100\n",
        "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n",
        "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CRjvkFQy2RgS"
      },
      "source": [
        "### Graph the mean absolute error\n",
        "\n",
        "[Mean absolute error](https://en.wikipedia.org/wiki/Mean_absolute_error), is another metric to judge the performance of the model.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBjCf1-2zx9C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "559f2fb9-122c-4812-bd6c-f32b95a73e02"
      },
      "source": [
        "# graph of mean absolute error\n",
        "mae = history.history['mae']\n",
        "val_mae = history.history['val_mae']\n",
        "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n",
        "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n",
        "plt.title('Training and validation mean absolute error')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('MAE')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guMjtfa42ahM"
      },
      "source": [
        "### Run with Test Data\n",
        "Put our test data into the model and plot the predictions\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3Y0CCWJz2EK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "35d9b055-e2a1-4aef-ed1b-c484c1065a43"
      },
      "source": [
        "# use the model to predict the test inputs\n",
        "predictions = model.predict(inputs_test)\n",
        "\n",
        "# print the predictions and the expected ouputs\n",
        "print(\"predictions =\\n\", np.round(predictions, decimals=3))\n",
        "print(\"actual =\\n\", outputs_test)\n",
        "\n",
        "# Plot the predictions along with to the test data\n",
        "plt.clf()\n",
        "plt.title('Training data predicted vs actual values')\n",
        "plt.plot(inputs_test, outputs_test, 'b.', label='Actual')\n",
        "plt.plot(inputs_test, predictions, 'r.', label='Predicted')\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predictions =\n",
            " [[0.    1.   ]\n",
            " [0.    1.   ]\n",
            " [0.    1.   ]\n",
            " [1.    0.   ]\n",
            " [0.001 0.999]\n",
            " [0.    1.   ]\n",
            " [0.    1.   ]\n",
            " [0.    1.   ]]\n",
            "actual =\n",
            " [[0. 1.]\n",
            " [0. 1.]\n",
            " [0. 1.]\n",
            " [1. 0.]\n",
            " [0. 1.]\n",
            " [0. 1.]\n",
            " [0. 1.]\n",
            " [0. 1.]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: MatplotlibDeprecationWarning: cycling among columns of inputs with non-matching shapes is deprecated.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: cycling among columns of inputs with non-matching shapes is deprecated.\n",
            "  if sys.path[0] == '':\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j7DO6xxXVCym"
      },
      "source": [
        "# Convert the Trained Model to Tensor Flow Lite\n",
        "\n",
        "The next cell converts the model to TFlite format. The size in bytes of the model is also printed out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Xn1-Rn9Cp_8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0e57e11-5c57-4bd6-be94-6cb23b7374b7"
      },
      "source": [
        "# Convert the model to the TensorFlow Lite format without quantization\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model to disk\n",
        "open(\"gesture_model.tflite\", \"wb\").write(tflite_model)\n",
        "  \n",
        "import os\n",
        "basic_model_size = os.path.getsize(\"gesture_model.tflite\")\n",
        "print(\"Model is %d bytes\" % basic_model_size)\n",
        "  \n",
        "  "
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model is 148032 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykccQn7SXrUX"
      },
      "source": [
        "## Encode the Model in an Arduino Header File \n",
        "\n",
        "The next cell creates a constant byte array that contains the TFlite model. Import it as a tab with the sketch below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9J33uwpNtAku",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e361c4d-9980-4b32-fb17-1d0d5416354d"
      },
      "source": [
        "!echo \"const unsigned char model[] = {\" > /content/model.h\n",
        "!cat gesture_model.tflite | xxd -i      >> /content/model.h\n",
        "!echo \"};\"                              >> /content/model.h\n",
        "\n",
        "import os\n",
        "model_h_size = os.path.getsize(\"model.h\")\n",
        "print(f\"Header file, model.h, is {model_h_size:,} bytes.\")\n",
        "print(\"\\nOpen the side panel (refresh if needed). Double click model.h to download the file.\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Header file, model.h, is 912,898 bytes.\n",
            "\n",
            "Open the side panel (refresh if needed). Double click model.h to download the file.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1eSkHZaLzMId"
      },
      "source": [
        "# Classifying IMU Data\n",
        "\n",
        "Now it's time to switch back to the instructions on Github and run our new model on the Arduino Nano 33 BLE Sense to classify the accelerometer and gyroscope data.\n",
        "\n",
        "[Exercise 6:Classifying IMU Data](https://github.com/sandeepmistry/aimldevfest-workshop-2019/blob/master/exercises/exercise6.md)"
      ]
    }
  ]
}