t81_558_class_14_01_automl.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "id": "LSIM-PITWYFa"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_14_01_automl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDTXd8-Lmp8Q"
   },
   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 14: Other Neural Network Techniques**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ncNrAEpzmp8S"
   },
   "source": [
    "# Module 14 Video Material\n",
    "\n",
    "* **Part 14.1: What is AutoML** [[Video]](https://www.youtube.com/watch?v=1mB_5iurqzw&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_14_01_automl.ipynb)\n",
    "* Part 14.2: Using Denoising AutoEncoders in Keras [[Video]](https://www.youtube.com/watch?v=4bTSu6_fucc&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_14_02_auto_encode.ipynb)\n",
    "* Part 14.3: Training an Intrusion Detection System with KDD99 [[Video]](https://www.youtube.com/watch?v=1ySn6h2A68I&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_14_03_anomaly.ipynb)\n",
    "* Part 14.4: Anomaly Detection in Keras [[Video]](https://www.youtube.com/watch?v=VgyKQ5MTDFc&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_14_04_ids_kdd99.ipynb)\n",
    "* Part 14.5: The Deep Learning Technologies I am Excited About [[Video]]() [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_14_05_new_tech.ipynb)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lux_6KOXMU94"
   },
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running the correct version of TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fU9UhAxTmp8S",
    "outputId": "8b05ccdb-10f3-460b-c356-f299191aae47"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "# Detect Colab if present\n",
    "try:\n",
    "    from google.colab import drive\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "    %tensorflow_version 2.x\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q09yMGGcmp9N"
   },
   "source": [
    "# Part 14.1: What is AutoML\n",
    "\n",
    "Automatic Machine Learning (AutoML) attempts to use machine learning to automate itself.  Data is passed to the AutoML application in raw form, and models are automatically generated.\n",
    "\n",
    "## AutoML from your Local Computer\n",
    "\n",
    "The following AutoML applications are free:\n",
    "\n",
    "* [AutoKeras](https://autokeras.com/)\n",
    "* [Auto-SKLearn](https://automl.github.io/auto-sklearn/master/)\n",
    "* [Auto PyTorch](https://github.com/automl/Auto-PyTorch)\n",
    "* [TPOT](http://epistasislab.github.io/tpot/)\n",
    "\n",
    "The following AutoML applications are commercial:\n",
    "\n",
    "* [Rapid Miner](https://rapidminer.com/educational-program/) - Free student version available.\n",
    "* [Dataiku](https://www.dataiku.com/dss/editions/) - Free community version available.\n",
    "* [DataRobot](https://www.datarobot.com/) - Commercial\n",
    "* [H2O Driverless](https://www.h2o.ai/products/h2o-driverless-ai/) - Commercial\n",
    "\n",
    "### AutoML from Google Cloud\n",
    "\n",
    "There are also cloud-hosted AutoML platforms:\n",
    "\n",
    "* [Google Cloud AutoML Tutorial](https://cloud.google.com/vision/automl/docs/tutorial)\n",
    "* [Azure AutoML](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models)\n",
    "\n",
    "This module will show how to use [AutoKeras](https://autokeras.com/). First, we download the paperclips counting dataset that you saw previously in this book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c8ixjIi5p8Uy",
    "outputId": "c4997f98-7a51-4aa7-b25b-82af2cc34c10"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-03-13 01:41:01--  https://github.com/jeffheaton/data-mirror/releases/download/v1/paperclips.zip\n",
      "Resolving github.com (github.com)... 140.82.121.3\n",
      "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/408419764/25830812-b9e6-4ddf-93b6-7932d9ef5982?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220313%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220313T014101Z&X-Amz-Expires=300&X-Amz-Signature=edbdd98bfc59943d608486a50d27f92b73b068604860792e5ca2f7a37c7ce6ed&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=408419764&response-content-disposition=attachment%3B%20filename%3Dpaperclips.zip&response-content-type=application%2Foctet-stream [following]\n",
      "--2022-03-13 01:41:01--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/408419764/25830812-b9e6-4ddf-93b6-7932d9ef5982?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220313%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220313T014101Z&X-Amz-Expires=300&X-Amz-Signature=edbdd98bfc59943d608486a50d27f92b73b068604860792e5ca2f7a37c7ce6ed&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=408419764&response-content-disposition=attachment%3B%20filename%3Dpaperclips.zip&response-content-type=application%2Foctet-stream\n",
      "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\n",
      "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.109.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 163590691 (156M) [application/octet-stream]\n",
      "Saving to: ‘/content/paperclips.zip’\n",
      "\n",
      "/content/paperclips 100%[===================>] 156.01M  15.7MB/s    in 8.0s    \n",
      "\n",
      "2022-03-13 01:41:10 (19.6 MB/s) - ‘/content/paperclips.zip’ saved [163590691/163590691]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "URL = \"https://github.com/jeffheaton/data-mirror/\"\n",
    "DOWNLOAD_SOURCE = URL+\"releases/download/v1/paperclips.zip\"\n",
    "DOWNLOAD_NAME = DOWNLOAD_SOURCE[DOWNLOAD_SOURCE.rfind('/')+1:]\n",
    "\n",
    "if COLAB:\n",
    "  PATH = \"/content\"\n",
    "else:\n",
    "  # I used this locally on my machine, you may need different\n",
    "  PATH = \"/Users/jeff/temp\"\n",
    "\n",
    "EXTRACT_TARGET = os.path.join(PATH,\"clips\")\n",
    "SOURCE = os.path.join(EXTRACT_TARGET, \"paperclips\")\n",
    "\n",
    "# Download paperclip data\n",
    "!wget -O {os.path.join(PATH,DOWNLOAD_NAME)} {DOWNLOAD_SOURCE}\n",
    "!mkdir -p {SOURCE}\n",
    "!mkdir -p {TARGET}\n",
    "!mkdir -p {EXTRACT_TARGET}\n",
    "!unzip -o -j -d {SOURCE} {os.path.join(PATH, DOWNLOAD_NAME)} >/dev/null\n",
    "\n",
    "# Process training data \n",
    "df_train = pd.read_csv(os.path.join(SOURCE, \"train.csv\"))\n",
    "df_train['filename'] = \"clips-\" + df_train.id.astype(str) + \".jpg\"\n",
    "\n",
    "# Use only the first 1000 images\n",
    "df_train = df_train[0:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xjU8vkP2ohHM"
   },
   "source": [
    "One limitation of AutoKeras is that it cannot directly utilize generators. Without resorting to complex techniques, all training data must reside in RAM. We will use the following code to load the image data to RAM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GYiMObEJsoof",
    "outputId": "779f7e38-4796-44cc-ebf8-6751f0fb75d5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:02<00:00, 456.16it/s]\n"
     ]
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "import tensorflow as tf\n",
    "import keras_preprocessing\n",
    "import glob, os\n",
    "import tqdm\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "IMG_SHAPE = (128,128)\n",
    "\n",
    "def load_images(files, img_shape):\n",
    "  cnt = len(files)\n",
    "  x = np.zeros((cnt,)+img_shape+(3,))\n",
    "  i = 0\n",
    "  for file in tqdm.tqdm(files):\n",
    "    img = Image.open(file)\n",
    "    img = img.resize(img_shape)\n",
    "    img = np.array(img)\n",
    "    img = img/255\n",
    "    x[i,:,:,:] = img\n",
    "    i+=1\n",
    "  return x\n",
    "\n",
    "images = [os.path.join(SOURCE,x) for x in df_train.filename]\n",
    "x = load_images(images, IMG_SHAPE)\n",
    "y = df_train.clip_count.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mKWkNJOzjD09"
   },
   "source": [
    "## Using AutoKeras\n",
    "\n",
    "[AutoKeras](https://autokeras.com/) is an AutoML system based on Keras. The goal of AutoKeras is to make machine learning accessible to everyone. [DATA Lab](http://people.tamu.edu/~guangzhou92/Data_Lab/) develops it at [Texas A&M University](https://www.tamu.edu/). We will see how to provide the paperclips dataset to AutoKeras and create an automatically tuned Keras deep learning model from this dataset. This automatic process frees you from choosing layer types and neuron counts.\n",
    "\n",
    "We begin by installing AutoKeras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4jI_4yo8Rmj1",
    "outputId": "748ec093-a166-4cb2-9b2a-f89711586e79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting autokeras\n",
      "  Downloading autokeras-1.0.18-py3-none-any.whl (160 kB)\n",
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      "Installing collected packages: tf-estimator-nightly, kt-legacy, keras-tuner, autokeras\n",
      "Successfully installed autokeras-1.0.18 keras-tuner-1.1.0 kt-legacy-1.0.4 tf-estimator-nightly-2.8.0.dev2021122109\n"
     ]
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "!pip install autokeras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9MOm41vpka9"
   },
   "source": [
    "AutoKeras contains several [examples](https://autokeras.com/tutorial/overview/) demonstrating image, tabular, and time-series data. We will make use of the **ImageRegressor**. Refer to the AutoKeras documentation for other classifiers and regressors to fit specific uses.  \n",
    "\n",
    "We define several variables to determine the AutoKeras operation:\n",
    "\n",
    "* **MAX_TRIALS** - Determines how many different models to see.\n",
    "* **SEED** - You can try different random seeds to obtain different results.\n",
    "* **VAL_SPLIT** - What percent of the dataset should we use for validation.\n",
    "* **EPOCHS** - How many epochs to try each model for training.\n",
    "* **BATCH_SIZE** - Training batch size.\n",
    "\n",
    "Setting MAX_TRIALS and EPOCHS will have a great impact on your total runtime. You must balance how many models to try (MAX_TRIALS) and how deeply to try to train each (EPOCHS). AutoKeras utilize early stopping, so setting EPOCHS too high will mean early stopping will prevent you from reaching the EPOCHS number of epochs. \n",
    "\n",
    "One strategy is to do a broad, shallow search. Set TRIALS high and EPOCHS low. The resulting model likely has the best hyperparameters. Finally, train this resulting model fully.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xZ_Qqr2qJvam",
    "outputId": "f6a5266a-c56a-4c92-c903-347807ebbacb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 2 Complete [00h 04m 17s]\n",
      "val_loss: 36.5126953125\n",
      "\n",
      "Best val_loss So Far: 36.123992919921875\n",
      "Total elapsed time: 01h 05m 46s\n",
      "INFO:tensorflow:Oracle triggered exit\n",
      "Epoch 1/1000\n",
      "32/32 [==============================] - 5s 81ms/step - loss: 1027.5819 - mean_squared_error: 1027.5819\n",
      "Epoch 2/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 479.9224 - mean_squared_error: 479.9224\n",
      "Epoch 3/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 464.9632 - mean_squared_error: 464.9632\n",
      "Epoch 4/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 458.4697 - mean_squared_error: 458.4697\n",
      "Epoch 5/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 451.7429 - mean_squared_error: 451.7429\n",
      "Epoch 6/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 444.7328 - mean_squared_error: 444.7328\n",
      "Epoch 7/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 437.4072 - mean_squared_error: 437.4072\n",
      "Epoch 8/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 429.8520 - mean_squared_error: 429.8520\n",
      "Epoch 9/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 422.1240 - mean_squared_error: 422.1240\n",
      "Epoch 10/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 414.2693 - mean_squared_error: 414.2693\n",
      "Epoch 11/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 406.3270 - mean_squared_error: 406.3270\n",
      "Epoch 12/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 398.3294 - mean_squared_error: 398.3294\n",
      "Epoch 13/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 390.3044 - mean_squared_error: 390.3044\n",
      "Epoch 14/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 382.2757 - mean_squared_error: 382.2757\n",
      "Epoch 15/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 374.2636 - mean_squared_error: 374.2636\n",
      "Epoch 16/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 366.2859 - mean_squared_error: 366.2859\n",
      "Epoch 17/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 358.3580 - mean_squared_error: 358.3580\n",
      "Epoch 18/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 350.4934 - mean_squared_error: 350.4934\n",
      "Epoch 19/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 342.7036 - mean_squared_error: 342.7036\n",
      "Epoch 20/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 334.9989 - mean_squared_error: 334.9989\n",
      "Epoch 21/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 327.3882 - mean_squared_error: 327.3882\n",
      "Epoch 22/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 319.8793 - mean_squared_error: 319.8793\n",
      "Epoch 23/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 312.4789 - mean_squared_error: 312.4789\n",
      "Epoch 24/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 305.1928 - mean_squared_error: 305.1928\n",
      "Epoch 25/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 298.0260 - mean_squared_error: 298.0260\n",
      "Epoch 26/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 290.9829 - mean_squared_error: 290.9829\n",
      "Epoch 27/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 284.0670 - mean_squared_error: 284.0670\n",
      "Epoch 28/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 277.2815 - mean_squared_error: 277.2815\n",
      "Epoch 29/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 270.6289 - mean_squared_error: 270.6289\n",
      "Epoch 30/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 264.1111 - mean_squared_error: 264.1111\n",
      "Epoch 31/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 257.7301 - mean_squared_error: 257.7301\n",
      "Epoch 32/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 251.4868 - mean_squared_error: 251.4868\n",
      "Epoch 33/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 245.3823 - mean_squared_error: 245.3823\n",
      "Epoch 34/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 239.4170 - mean_squared_error: 239.4170\n",
      "Epoch 35/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 233.5912 - mean_squared_error: 233.5912\n",
      "Epoch 36/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 227.9050 - mean_squared_error: 227.9050\n",
      "Epoch 37/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 222.3581 - mean_squared_error: 222.3581\n",
      "Epoch 38/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 216.9500 - mean_squared_error: 216.9500\n",
      "Epoch 39/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 211.6801 - mean_squared_error: 211.6801\n",
      "Epoch 40/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 206.5475 - mean_squared_error: 206.5475\n",
      "Epoch 41/1000\n",
      "32/32 [==============================] - 3s 87ms/step - loss: 201.5512 - mean_squared_error: 201.5512\n",
      "Epoch 42/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 196.6900 - mean_squared_error: 196.6900\n",
      "Epoch 43/1000\n",
      "32/32 [==============================] - 2s 66ms/step - loss: 191.9626 - mean_squared_error: 191.9626\n",
      "Epoch 44/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 187.3676 - mean_squared_error: 187.3676\n",
      "Epoch 45/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 182.9034 - mean_squared_error: 182.9034\n",
      "Epoch 46/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 178.5682 - mean_squared_error: 178.5682\n",
      "Epoch 47/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 174.3605 - mean_squared_error: 174.3605\n",
      "Epoch 48/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 170.2783 - mean_squared_error: 170.2783\n",
      "Epoch 49/1000\n",
      "32/32 [==============================] - 2s 60ms/step - loss: 166.3197 - mean_squared_error: 166.3197\n",
      "Epoch 50/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 162.4828 - mean_squared_error: 162.4828\n",
      "Epoch 51/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 158.7654 - mean_squared_error: 158.7654\n",
      "Epoch 52/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 155.1655 - mean_squared_error: 155.1655\n",
      "Epoch 53/1000\n",
      "32/32 [==============================] - 2s 65ms/step - loss: 151.6808 - mean_squared_error: 151.6808\n",
      "Epoch 54/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 148.3092 - mean_squared_error: 148.3092\n",
      "Epoch 55/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 145.0485 - mean_squared_error: 145.0485\n",
      "Epoch 56/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 141.8963 - mean_squared_error: 141.8963\n",
      "Epoch 57/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 138.8504 - mean_squared_error: 138.8504\n",
      "Epoch 58/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 135.9085 - mean_squared_error: 135.9085\n",
      "Epoch 59/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 133.0681 - mean_squared_error: 133.0681\n",
      "Epoch 60/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 130.3270 - mean_squared_error: 130.3270\n",
      "Epoch 61/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 127.6828 - mean_squared_error: 127.6828\n",
      "Epoch 62/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 125.1331 - mean_squared_error: 125.1331\n",
      "Epoch 63/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 122.6755 - mean_squared_error: 122.6755\n",
      "Epoch 64/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 120.3077 - mean_squared_error: 120.3077\n",
      "Epoch 65/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 118.0272 - mean_squared_error: 118.0272\n",
      "Epoch 66/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 115.8318 - mean_squared_error: 115.8318\n",
      "Epoch 67/1000\n",
      "32/32 [==============================] - 2s 63ms/step - loss: 113.7189 - mean_squared_error: 113.7189\n",
      "Epoch 68/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 111.6864 - mean_squared_error: 111.6864\n",
      "Epoch 69/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 109.7318 - mean_squared_error: 109.7318\n",
      "Epoch 70/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 107.8529 - mean_squared_error: 107.8529\n",
      "Epoch 71/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 106.0473 - mean_squared_error: 106.0473\n",
      "Epoch 72/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 104.3128 - mean_squared_error: 104.3128\n",
      "Epoch 73/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 102.6471 - mean_squared_error: 102.6471\n",
      "Epoch 74/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 101.0480 - mean_squared_error: 101.0480\n",
      "Epoch 75/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 99.5132 - mean_squared_error: 99.5132\n",
      "Epoch 76/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 98.0406 - mean_squared_error: 98.0406\n",
      "Epoch 77/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 96.6280 - mean_squared_error: 96.6280\n",
      "Epoch 78/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 95.2733 - mean_squared_error: 95.2733\n",
      "Epoch 79/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 93.9745 - mean_squared_error: 93.9745\n",
      "Epoch 80/1000\n",
      "32/32 [==============================] - 2s 65ms/step - loss: 92.7295 - mean_squared_error: 92.7295\n",
      "Epoch 81/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 91.5361 - mean_squared_error: 91.5361\n",
      "Epoch 82/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 90.3926 - mean_squared_error: 90.3926\n",
      "Epoch 83/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 89.2969 - mean_squared_error: 89.2969\n",
      "Epoch 84/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 88.2472 - mean_squared_error: 88.2472\n",
      "Epoch 85/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 87.2415 - mean_squared_error: 87.2415\n",
      "Epoch 86/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 86.2780 - mean_squared_error: 86.2780\n",
      "Epoch 87/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 85.3550 - mean_squared_error: 85.3550\n",
      "Epoch 88/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 84.4706 - mean_squared_error: 84.4706\n",
      "Epoch 89/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 83.6233 - mean_squared_error: 83.6233\n",
      "Epoch 90/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 82.8113 - mean_squared_error: 82.8113\n",
      "Epoch 91/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 82.0331 - mean_squared_error: 82.0331\n",
      "Epoch 92/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 81.2870 - mean_squared_error: 81.2870\n",
      "Epoch 93/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 80.5716 - mean_squared_error: 80.5716\n",
      "Epoch 94/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 79.8853 - mean_squared_error: 79.8853\n",
      "Epoch 95/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 79.2267 - mean_squared_error: 79.2267\n",
      "Epoch 96/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 78.5945 - mean_squared_error: 78.5945\n",
      "Epoch 97/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 77.9873 - mean_squared_error: 77.9873\n",
      "Epoch 98/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 77.4038 - mean_squared_error: 77.4038\n",
      "Epoch 99/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 76.8428 - mean_squared_error: 76.8428\n",
      "Epoch 100/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 76.3031 - mean_squared_error: 76.3031\n",
      "Epoch 101/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 75.7834 - mean_squared_error: 75.7834\n",
      "Epoch 102/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 75.2828 - mean_squared_error: 75.2828\n",
      "Epoch 103/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 74.8001 - mean_squared_error: 74.8001\n",
      "Epoch 104/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 74.3343 - mean_squared_error: 74.3343\n",
      "Epoch 105/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 73.8845 - mean_squared_error: 73.8845\n",
      "Epoch 106/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 73.4496 - mean_squared_error: 73.4496\n",
      "Epoch 107/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 73.0289 - mean_squared_error: 73.0289\n",
      "Epoch 108/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 72.6215 - mean_squared_error: 72.6215\n",
      "Epoch 109/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 72.2265 - mean_squared_error: 72.2265\n",
      "Epoch 110/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 71.8433 - mean_squared_error: 71.8433\n",
      "Epoch 111/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 71.4710 - mean_squared_error: 71.4710\n",
      "Epoch 112/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 71.1090 - mean_squared_error: 71.1090\n",
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      "Epoch 255/1000\n",
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      "Epoch 256/1000\n",
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      "Epoch 260/1000\n",
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      "Epoch 270/1000\n",
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      "Epoch 271/1000\n",
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      "Epoch 273/1000\n",
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      "Epoch 300/1000\n",
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      "Epoch 335/1000\n",
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      "Epoch 338/1000\n",
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      "Epoch 339/1000\n",
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      "Epoch 340/1000\n",
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      "Epoch 342/1000\n",
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      "Epoch 350/1000\n",
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      "Epoch 353/1000\n",
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      "Epoch 355/1000\n",
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      "Epoch 356/1000\n",
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      "Epoch 359/1000\n",
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      "Epoch 360/1000\n",
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      "Epoch 361/1000\n",
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      "Epoch 362/1000\n",
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      "Epoch 363/1000\n",
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      "Epoch 364/1000\n",
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      "Epoch 365/1000\n",
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      "Epoch 367/1000\n",
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      "Epoch 368/1000\n",
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      "Epoch 370/1000\n",
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      "Epoch 371/1000\n",
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      "Epoch 372/1000\n",
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      "Epoch 373/1000\n",
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      "Epoch 374/1000\n",
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      "Epoch 375/1000\n",
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      "Epoch 376/1000\n",
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      "Epoch 377/1000\n",
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      "Epoch 378/1000\n",
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      "Epoch 379/1000\n",
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      "Epoch 380/1000\n",
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      "Epoch 381/1000\n",
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      "Epoch 382/1000\n",
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      "Epoch 383/1000\n",
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      "Epoch 384/1000\n",
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      "Epoch 385/1000\n",
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      "Epoch 386/1000\n",
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      "Epoch 387/1000\n",
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      "Epoch 388/1000\n",
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      "Epoch 389/1000\n",
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      "Epoch 390/1000\n",
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      "Epoch 391/1000\n",
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      "Epoch 392/1000\n",
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      "Epoch 393/1000\n",
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      "Epoch 394/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 35.0305 - mean_squared_error: 35.0305\n",
      "Epoch 395/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 34.9885 - mean_squared_error: 34.9885\n",
      "Epoch 396/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 34.9467 - mean_squared_error: 34.9467\n",
      "Epoch 397/1000\n",
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      "Epoch 398/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 34.8638 - mean_squared_error: 34.8638\n",
      "Epoch 399/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 34.8227 - mean_squared_error: 34.8227\n",
      "Epoch 400/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 34.7818 - mean_squared_error: 34.7818\n",
      "Epoch 401/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 34.7412 - mean_squared_error: 34.7412\n",
      "Epoch 402/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 34.7007 - mean_squared_error: 34.7007\n",
      "Epoch 403/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 34.6606 - mean_squared_error: 34.6606\n",
      "Epoch 404/1000\n",
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      "Epoch 405/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 34.5808 - mean_squared_error: 34.5808\n",
      "Epoch 406/1000\n",
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      "Epoch 407/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 34.5020 - mean_squared_error: 34.5020\n",
      "Epoch 408/1000\n",
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      "Epoch 409/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 34.4240 - mean_squared_error: 34.4240\n",
      "Epoch 410/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 34.3853 - mean_squared_error: 34.3853\n",
      "Epoch 411/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 34.3468 - mean_squared_error: 34.3468\n",
      "Epoch 412/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 34.3086 - mean_squared_error: 34.3086\n",
      "Epoch 413/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 34.2705 - mean_squared_error: 34.2705\n",
      "Epoch 414/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 34.2327 - mean_squared_error: 34.2327\n",
      "Epoch 415/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 34.1950 - mean_squared_error: 34.1950\n",
      "Epoch 416/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 34.1576 - mean_squared_error: 34.1576\n",
      "Epoch 417/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 34.1203 - mean_squared_error: 34.1203\n",
      "Epoch 418/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 34.0833 - mean_squared_error: 34.0833\n",
      "Epoch 419/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 34.0464 - mean_squared_error: 34.0464\n",
      "Epoch 420/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 34.0097 - mean_squared_error: 34.0097\n",
      "Epoch 421/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 33.9733 - mean_squared_error: 33.9733\n",
      "Epoch 422/1000\n",
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      "Epoch 423/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 33.9009 - mean_squared_error: 33.9009\n",
      "Epoch 424/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 33.8650 - mean_squared_error: 33.8650\n",
      "Epoch 425/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 33.8293 - mean_squared_error: 33.8293\n",
      "Epoch 426/1000\n",
      "32/32 [==============================] - 3s 78ms/step - loss: 33.7937 - mean_squared_error: 33.7937\n",
      "Epoch 427/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 33.7584 - mean_squared_error: 33.7584\n",
      "Epoch 428/1000\n",
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      "Epoch 429/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 33.6882 - mean_squared_error: 33.6882\n",
      "Epoch 430/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 33.6534 - mean_squared_error: 33.6534\n",
      "Epoch 431/1000\n",
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      "Epoch 432/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 33.5843 - mean_squared_error: 33.5843\n",
      "Epoch 433/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 33.5500 - mean_squared_error: 33.5500\n",
      "Epoch 434/1000\n",
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      "Epoch 435/1000\n",
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      "Epoch 436/1000\n",
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      "Epoch 437/1000\n",
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      "Epoch 446/1000\n",
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      "Epoch 447/1000\n",
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      "Epoch 448/1000\n",
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      "Epoch 449/1000\n",
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      "Epoch 450/1000\n",
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      "Epoch 451/1000\n",
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      "Epoch 452/1000\n",
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      "Epoch 453/1000\n",
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      "Epoch 455/1000\n",
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      "Epoch 457/1000\n",
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      "Epoch 458/1000\n",
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      "Epoch 459/1000\n",
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      "Epoch 460/1000\n",
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      "Epoch 461/1000\n",
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      "Epoch 462/1000\n",
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      "Epoch 463/1000\n",
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      "Epoch 464/1000\n",
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      "Epoch 465/1000\n",
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      "Epoch 466/1000\n",
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      "Epoch 467/1000\n",
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      "Epoch 468/1000\n",
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      "Epoch 469/1000\n",
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      "Epoch 470/1000\n",
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      "Epoch 471/1000\n",
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      "Epoch 472/1000\n",
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      "Epoch 473/1000\n",
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      "Epoch 474/1000\n",
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      "Epoch 475/1000\n",
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      "Epoch 476/1000\n",
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      "Epoch 477/1000\n",
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      "Epoch 478/1000\n",
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      "Epoch 479/1000\n",
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      "Epoch 480/1000\n",
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      "Epoch 481/1000\n",
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      "Epoch 482/1000\n",
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      "Epoch 483/1000\n",
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      "Epoch 484/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 32.0031 - mean_squared_error: 32.0031\n",
      "Epoch 485/1000\n",
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      "Epoch 486/1000\n",
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      "Epoch 487/1000\n",
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      "Epoch 488/1000\n",
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      "Epoch 489/1000\n",
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      "Epoch 490/1000\n",
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      "Epoch 491/1000\n",
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      "Epoch 492/1000\n",
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      "Epoch 493/1000\n",
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      "Epoch 494/1000\n",
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      "Epoch 495/1000\n",
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      "Epoch 496/1000\n",
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      "Epoch 497/1000\n",
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      "Epoch 498/1000\n",
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      "Epoch 499/1000\n",
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      "Epoch 500/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 31.5863 - mean_squared_error: 31.5863\n",
      "Epoch 501/1000\n",
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      "Epoch 502/1000\n",
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      "Epoch 503/1000\n",
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      "Epoch 504/1000\n",
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      "Epoch 505/1000\n",
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      "Epoch 506/1000\n",
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      "Epoch 507/1000\n",
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      "Epoch 508/1000\n",
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      "Epoch 509/1000\n",
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      "Epoch 510/1000\n",
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      "Epoch 511/1000\n",
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      "Epoch 512/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 31.2912 - mean_squared_error: 31.2912\n",
      "Epoch 513/1000\n",
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      "Epoch 514/1000\n",
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      "Epoch 515/1000\n",
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      "Epoch 516/1000\n",
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      "Epoch 517/1000\n",
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      "Epoch 518/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 31.1489 - mean_squared_error: 31.1489\n",
      "Epoch 519/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 31.1255 - mean_squared_error: 31.1255\n",
      "Epoch 520/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 31.1022 - mean_squared_error: 31.1022\n",
      "Epoch 521/1000\n",
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      "Epoch 522/1000\n",
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      "Epoch 523/1000\n",
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      "Epoch 524/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 31.0098 - mean_squared_error: 31.0098\n",
      "Epoch 525/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 30.9870 - mean_squared_error: 30.9870\n",
      "Epoch 526/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 30.9642 - mean_squared_error: 30.9642\n",
      "Epoch 527/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 30.9415 - mean_squared_error: 30.9415\n",
      "Epoch 528/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 30.9189 - mean_squared_error: 30.9189\n",
      "Epoch 529/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 30.8964 - mean_squared_error: 30.8964\n",
      "Epoch 530/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 30.8739 - mean_squared_error: 30.8739\n",
      "Epoch 531/1000\n",
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      "Epoch 532/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 30.8293 - mean_squared_error: 30.8293\n",
      "Epoch 533/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 30.8071 - mean_squared_error: 30.8071\n",
      "Epoch 534/1000\n",
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      "Epoch 535/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 30.7630 - mean_squared_error: 30.7630\n",
      "Epoch 536/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 30.7410 - mean_squared_error: 30.7410\n",
      "Epoch 537/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 30.7192 - mean_squared_error: 30.7192\n",
      "Epoch 538/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 30.6974 - mean_squared_error: 30.6974\n",
      "Epoch 539/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 30.6757 - mean_squared_error: 30.6757\n",
      "Epoch 540/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 30.6541 - mean_squared_error: 30.6541\n",
      "Epoch 541/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 30.6325 - mean_squared_error: 30.6325\n",
      "Epoch 542/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 30.6110 - mean_squared_error: 30.6110\n",
      "Epoch 543/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 30.5896 - mean_squared_error: 30.5896\n",
      "Epoch 544/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 30.5683 - mean_squared_error: 30.5683\n",
      "Epoch 545/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 30.5471 - mean_squared_error: 30.5471\n",
      "Epoch 546/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 30.5259 - mean_squared_error: 30.5259\n",
      "Epoch 547/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 30.5048 - mean_squared_error: 30.5048\n",
      "Epoch 548/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 30.4838 - mean_squared_error: 30.4838\n",
      "Epoch 549/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 30.4629 - mean_squared_error: 30.4629\n",
      "Epoch 550/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 30.4420 - mean_squared_error: 30.4420\n",
      "Epoch 551/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 30.4212 - mean_squared_error: 30.4212\n",
      "Epoch 552/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 30.4005 - mean_squared_error: 30.4005\n",
      "Epoch 553/1000\n",
      "32/32 [==============================] - 3s 78ms/step - loss: 30.3799 - mean_squared_error: 30.3799\n",
      "Epoch 554/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 30.3593 - mean_squared_error: 30.3593\n",
      "Epoch 555/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 30.3388 - mean_squared_error: 30.3388\n",
      "Epoch 556/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 30.3184 - mean_squared_error: 30.3184\n",
      "Epoch 557/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 30.2980 - mean_squared_error: 30.2980\n",
      "Epoch 558/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 30.2777 - mean_squared_error: 30.2777\n",
      "Epoch 559/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 30.2575 - mean_squared_error: 30.2575\n",
      "Epoch 560/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 30.2373 - mean_squared_error: 30.2373\n",
      "Epoch 561/1000\n",
      "32/32 [==============================] - 3s 88ms/step - loss: 30.2173 - mean_squared_error: 30.2173\n",
      "Epoch 562/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 30.1972 - mean_squared_error: 30.1972\n",
      "Epoch 563/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 30.1773 - mean_squared_error: 30.1773\n",
      "Epoch 564/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 30.1574 - mean_squared_error: 30.1574\n",
      "Epoch 565/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 30.1376 - mean_squared_error: 30.1376\n",
      "Epoch 566/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 30.1179 - mean_squared_error: 30.1179\n",
      "Epoch 567/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 30.0982 - mean_squared_error: 30.0982\n",
      "Epoch 568/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 30.0786 - mean_squared_error: 30.0786\n",
      "Epoch 569/1000\n",
      "32/32 [==============================] - 2s 63ms/step - loss: 30.0590 - mean_squared_error: 30.0590\n",
      "Epoch 570/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 30.0395 - mean_squared_error: 30.0395\n",
      "Epoch 571/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 30.0201 - mean_squared_error: 30.0201\n",
      "Epoch 572/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 30.0008 - mean_squared_error: 30.0008\n",
      "Epoch 573/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 29.9815 - mean_squared_error: 29.9815\n",
      "Epoch 574/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 29.9622 - mean_squared_error: 29.9622\n",
      "Epoch 575/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 29.9431 - mean_squared_error: 29.9431\n",
      "Epoch 576/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 29.9240 - mean_squared_error: 29.9240\n",
      "Epoch 577/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 29.9050 - mean_squared_error: 29.9050\n",
      "Epoch 578/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 29.8860 - mean_squared_error: 29.8860\n",
      "Epoch 579/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 29.8671 - mean_squared_error: 29.8671\n",
      "Epoch 580/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 29.8482 - mean_squared_error: 29.8482\n",
      "Epoch 581/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 29.8294 - mean_squared_error: 29.8294\n",
      "Epoch 582/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 29.8107 - mean_squared_error: 29.8107\n",
      "Epoch 583/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 29.7920 - mean_squared_error: 29.7920\n",
      "Epoch 584/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 29.7734 - mean_squared_error: 29.7734\n",
      "Epoch 585/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 29.7549 - mean_squared_error: 29.7549\n",
      "Epoch 586/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 29.7364 - mean_squared_error: 29.7364\n",
      "Epoch 587/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 29.7179 - mean_squared_error: 29.7179\n",
      "Epoch 588/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 29.6995 - mean_squared_error: 29.6995\n",
      "Epoch 589/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 29.6812 - mean_squared_error: 29.6812\n",
      "Epoch 590/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 29.6630 - mean_squared_error: 29.6630\n",
      "Epoch 591/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 29.6448 - mean_squared_error: 29.6448\n",
      "Epoch 592/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 29.6266 - mean_squared_error: 29.6266\n",
      "Epoch 593/1000\n",
      "32/32 [==============================] - 2s 64ms/step - loss: 29.6085 - mean_squared_error: 29.6085\n",
      "Epoch 594/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 29.5905 - mean_squared_error: 29.5905\n",
      "Epoch 595/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 29.5725 - mean_squared_error: 29.5725\n",
      "Epoch 596/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 29.5546 - mean_squared_error: 29.5546\n",
      "Epoch 597/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 29.5367 - mean_squared_error: 29.5367\n",
      "Epoch 598/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 29.5189 - mean_squared_error: 29.5189\n",
      "Epoch 599/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 29.5012 - mean_squared_error: 29.5012\n",
      "Epoch 600/1000\n",
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      "Epoch 601/1000\n",
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      "Epoch 603/1000\n",
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      "Epoch 604/1000\n",
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      "Epoch 607/1000\n",
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      "Epoch 608/1000\n",
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      "Epoch 609/1000\n",
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      "Epoch 610/1000\n",
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      "Epoch 611/1000\n",
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      "Epoch 612/1000\n",
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      "Epoch 613/1000\n",
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      "Epoch 614/1000\n",
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      "Epoch 615/1000\n",
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      "Epoch 616/1000\n",
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      "Epoch 617/1000\n",
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      "Epoch 618/1000\n",
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      "Epoch 619/1000\n",
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      "Epoch 620/1000\n",
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      "Epoch 621/1000\n",
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      "Epoch 622/1000\n",
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      "Epoch 623/1000\n",
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      "Epoch 624/1000\n",
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      "Epoch 625/1000\n",
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      "Epoch 626/1000\n",
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      "Epoch 627/1000\n",
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      "Epoch 628/1000\n",
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      "Epoch 629/1000\n",
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      "Epoch 630/1000\n",
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      "Epoch 631/1000\n",
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      "Epoch 632/1000\n",
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      "Epoch 633/1000\n",
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      "Epoch 634/1000\n",
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      "Epoch 635/1000\n",
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      "Epoch 636/1000\n",
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      "Epoch 637/1000\n",
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      "Epoch 638/1000\n",
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      "Epoch 639/1000\n",
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      "Epoch 640/1000\n",
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      "Epoch 641/1000\n",
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      "Epoch 642/1000\n",
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      "Epoch 643/1000\n",
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      "Epoch 644/1000\n",
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      "Epoch 645/1000\n",
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      "Epoch 646/1000\n",
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      "Epoch 647/1000\n",
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      "Epoch 648/1000\n",
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      "Epoch 649/1000\n",
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      "Epoch 650/1000\n",
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      "Epoch 651/1000\n",
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      "Epoch 652/1000\n",
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      "Epoch 653/1000\n",
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      "Epoch 654/1000\n",
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      "Epoch 655/1000\n",
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      "Epoch 656/1000\n",
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      "Epoch 657/1000\n",
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      "Epoch 658/1000\n",
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      "Epoch 659/1000\n",
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      "Epoch 660/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 28.5105 - mean_squared_error: 28.5105\n",
      "Epoch 661/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 28.4956 - mean_squared_error: 28.4956\n",
      "Epoch 662/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 28.4808 - mean_squared_error: 28.4808\n",
      "Epoch 663/1000\n",
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      "Epoch 664/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 28.4512 - mean_squared_error: 28.4512\n",
      "Epoch 665/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 28.4364 - mean_squared_error: 28.4364\n",
      "Epoch 666/1000\n",
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      "Epoch 667/1000\n",
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      "Epoch 668/1000\n",
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      "Epoch 669/1000\n",
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      "Epoch 670/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 28.3634 - mean_squared_error: 28.3634\n",
      "Epoch 671/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 28.3489 - mean_squared_error: 28.3489\n",
      "Epoch 672/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 28.3344 - mean_squared_error: 28.3344\n",
      "Epoch 673/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 28.3200 - mean_squared_error: 28.3200\n",
      "Epoch 674/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 28.3056 - mean_squared_error: 28.3056\n",
      "Epoch 675/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 28.2912 - mean_squared_error: 28.2912\n",
      "Epoch 676/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 28.2769 - mean_squared_error: 28.2769\n",
      "Epoch 677/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 28.2626 - mean_squared_error: 28.2626\n",
      "Epoch 678/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 28.2483 - mean_squared_error: 28.2483\n",
      "Epoch 679/1000\n",
      "32/32 [==============================] - 3s 78ms/step - loss: 28.2341 - mean_squared_error: 28.2341\n",
      "Epoch 680/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 28.2200 - mean_squared_error: 28.2200\n",
      "Epoch 681/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 28.2058 - mean_squared_error: 28.2058\n",
      "Epoch 682/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 28.1917 - mean_squared_error: 28.1917\n",
      "Epoch 683/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 28.1776 - mean_squared_error: 28.1776\n",
      "Epoch 684/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 28.1636 - mean_squared_error: 28.1636\n",
      "Epoch 685/1000\n",
      "32/32 [==============================] - 3s 88ms/step - loss: 28.1496 - mean_squared_error: 28.1496\n",
      "Epoch 686/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 28.1356 - mean_squared_error: 28.1356\n",
      "Epoch 687/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 28.1217 - mean_squared_error: 28.1217\n",
      "Epoch 688/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 28.1078 - mean_squared_error: 28.1078\n",
      "Epoch 689/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 28.0939 - mean_squared_error: 28.0939\n",
      "Epoch 690/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 28.0801 - mean_squared_error: 28.0801\n",
      "Epoch 691/1000\n",
      "32/32 [==============================] - 2s 63ms/step - loss: 28.0663 - mean_squared_error: 28.0663\n",
      "Epoch 692/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 28.0525 - mean_squared_error: 28.0525\n",
      "Epoch 693/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 28.0388 - mean_squared_error: 28.0388\n",
      "Epoch 694/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 28.0251 - mean_squared_error: 28.0251\n",
      "Epoch 695/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 28.0115 - mean_squared_error: 28.0115\n",
      "Epoch 696/1000\n",
      "32/32 [==============================] - 3s 87ms/step - loss: 27.9978 - mean_squared_error: 27.9978\n",
      "Epoch 697/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 27.9842 - mean_squared_error: 27.9842\n",
      "Epoch 698/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 27.9707 - mean_squared_error: 27.9707\n",
      "Epoch 699/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 27.9572 - mean_squared_error: 27.9572\n",
      "Epoch 700/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 27.9437 - mean_squared_error: 27.9437\n",
      "Epoch 701/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 27.9302 - mean_squared_error: 27.9302\n",
      "Epoch 702/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 27.9168 - mean_squared_error: 27.9168\n",
      "Epoch 703/1000\n",
      "32/32 [==============================] - 3s 87ms/step - loss: 27.9034 - mean_squared_error: 27.9034\n",
      "Epoch 704/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 27.8900 - mean_squared_error: 27.8900\n",
      "Epoch 705/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 27.8767 - mean_squared_error: 27.8767\n",
      "Epoch 706/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 27.8634 - mean_squared_error: 27.8634\n",
      "Epoch 707/1000\n",
      "32/32 [==============================] - 3s 89ms/step - loss: 27.8501 - mean_squared_error: 27.8501\n",
      "Epoch 708/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 27.8368 - mean_squared_error: 27.8368\n",
      "Epoch 709/1000\n",
      "32/32 [==============================] - 2s 63ms/step - loss: 27.8236 - mean_squared_error: 27.8236\n",
      "Epoch 710/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 27.8105 - mean_squared_error: 27.8105\n",
      "Epoch 711/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 27.7973 - mean_squared_error: 27.7973\n",
      "Epoch 712/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 27.7842 - mean_squared_error: 27.7842\n",
      "Epoch 713/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 27.7711 - mean_squared_error: 27.7711\n",
      "Epoch 714/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 27.7581 - mean_squared_error: 27.7581\n",
      "Epoch 715/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 27.7450 - mean_squared_error: 27.7450\n",
      "Epoch 716/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 27.7320 - mean_squared_error: 27.7320\n",
      "Epoch 717/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 27.7191 - mean_squared_error: 27.7191\n",
      "Epoch 718/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 27.7061 - mean_squared_error: 27.7061\n",
      "Epoch 719/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 27.6932 - mean_squared_error: 27.6932\n",
      "Epoch 720/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 27.6803 - mean_squared_error: 27.6803\n",
      "Epoch 721/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 27.6675 - mean_squared_error: 27.6675\n",
      "Epoch 722/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 27.6547 - mean_squared_error: 27.6547\n",
      "Epoch 723/1000\n",
      "32/32 [==============================] - 3s 89ms/step - loss: 27.6419 - mean_squared_error: 27.6419\n",
      "Epoch 724/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 27.6292 - mean_squared_error: 27.6292\n",
      "Epoch 725/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 27.6164 - mean_squared_error: 27.6164\n",
      "Epoch 726/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 27.6037 - mean_squared_error: 27.6037\n",
      "Epoch 727/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 27.5911 - mean_squared_error: 27.5911\n",
      "Epoch 728/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 27.5784 - mean_squared_error: 27.5784\n",
      "Epoch 729/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 27.5658 - mean_squared_error: 27.5658\n",
      "Epoch 730/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 27.5532 - mean_squared_error: 27.5532\n",
      "Epoch 731/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 27.5407 - mean_squared_error: 27.5407\n",
      "Epoch 732/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 27.5281 - mean_squared_error: 27.5281\n",
      "Epoch 733/1000\n",
      "32/32 [==============================] - 3s 87ms/step - loss: 27.5156 - mean_squared_error: 27.5156\n",
      "Epoch 734/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 27.5032 - mean_squared_error: 27.5032\n",
      "Epoch 735/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 27.4907 - mean_squared_error: 27.4907\n",
      "Epoch 736/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 27.4783 - mean_squared_error: 27.4783\n",
      "Epoch 737/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 27.4659 - mean_squared_error: 27.4659\n",
      "Epoch 738/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 27.4536 - mean_squared_error: 27.4536\n",
      "Epoch 739/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 27.4412 - mean_squared_error: 27.4412\n",
      "Epoch 740/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 27.4289 - mean_squared_error: 27.4289\n",
      "Epoch 741/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 27.4166 - mean_squared_error: 27.4166\n",
      "Epoch 742/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 27.4044 - mean_squared_error: 27.4044\n",
      "Epoch 743/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 27.3922 - mean_squared_error: 27.3922\n",
      "Epoch 744/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 27.3800 - mean_squared_error: 27.3800\n",
      "Epoch 745/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 27.3678 - mean_squared_error: 27.3678\n",
      "Epoch 746/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 27.3557 - mean_squared_error: 27.3557\n",
      "Epoch 747/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 27.3435 - mean_squared_error: 27.3435\n",
      "Epoch 748/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 27.3314 - mean_squared_error: 27.3314\n",
      "Epoch 749/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 27.3194 - mean_squared_error: 27.3194\n",
      "Epoch 750/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 27.3073 - mean_squared_error: 27.3073\n",
      "Epoch 751/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 27.2953 - mean_squared_error: 27.2953\n",
      "Epoch 752/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 27.2833 - mean_squared_error: 27.2833\n",
      "Epoch 753/1000\n",
      "32/32 [==============================] - 3s 91ms/step - loss: 27.2714 - mean_squared_error: 27.2714\n",
      "Epoch 754/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 27.2595 - mean_squared_error: 27.2595\n",
      "Epoch 755/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 27.2475 - mean_squared_error: 27.2475\n",
      "Epoch 756/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 27.2357 - mean_squared_error: 27.2357\n",
      "Epoch 757/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 27.2238 - mean_squared_error: 27.2238\n",
      "Epoch 758/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 27.2120 - mean_squared_error: 27.2120\n",
      "Epoch 759/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 27.2002 - mean_squared_error: 27.2002\n",
      "Epoch 760/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 27.1884 - mean_squared_error: 27.1884\n",
      "Epoch 761/1000\n",
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      "Epoch 762/1000\n",
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      "Epoch 763/1000\n",
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      "Epoch 764/1000\n",
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      "Epoch 765/1000\n",
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      "Epoch 766/1000\n",
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      "Epoch 767/1000\n",
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      "Epoch 768/1000\n",
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      "Epoch 769/1000\n",
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      "Epoch 770/1000\n",
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      "Epoch 771/1000\n",
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      "Epoch 772/1000\n",
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      "Epoch 773/1000\n",
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      "Epoch 774/1000\n",
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      "Epoch 775/1000\n",
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      "Epoch 776/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 27.0032 - mean_squared_error: 27.0032\n",
      "Epoch 777/1000\n",
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      "Epoch 778/1000\n",
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      "Epoch 779/1000\n",
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      "Epoch 780/1000\n",
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      "Epoch 781/1000\n",
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      "Epoch 782/1000\n",
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      "Epoch 783/1000\n",
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      "Epoch 784/1000\n",
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      "Epoch 785/1000\n",
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      "Epoch 786/1000\n",
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      "Epoch 787/1000\n",
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      "Epoch 788/1000\n",
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      "Epoch 789/1000\n",
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      "Epoch 790/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 26.8462 - mean_squared_error: 26.8462\n",
      "Epoch 791/1000\n",
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      "Epoch 792/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 26.8241 - mean_squared_error: 26.8241\n",
      "Epoch 793/1000\n",
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      "Epoch 794/1000\n",
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      "Epoch 795/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 26.7911 - mean_squared_error: 26.7911\n",
      "Epoch 796/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 26.7802 - mean_squared_error: 26.7802\n",
      "Epoch 797/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 26.7693 - mean_squared_error: 26.7693\n",
      "Epoch 798/1000\n",
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      "Epoch 799/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 26.7475 - mean_squared_error: 26.7475\n",
      "Epoch 800/1000\n",
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      "Epoch 801/1000\n",
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      "Epoch 802/1000\n",
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      "Epoch 803/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 26.7042 - mean_squared_error: 26.7042\n",
      "Epoch 804/1000\n",
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      "Epoch 805/1000\n",
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      "Epoch 806/1000\n",
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      "Epoch 807/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 26.6613 - mean_squared_error: 26.6613\n",
      "Epoch 808/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 26.6506 - mean_squared_error: 26.6506\n",
      "Epoch 809/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 26.6400 - mean_squared_error: 26.6400\n",
      "Epoch 810/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 26.6293 - mean_squared_error: 26.6293\n",
      "Epoch 811/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 26.6187 - mean_squared_error: 26.6187\n",
      "Epoch 812/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 26.6081 - mean_squared_error: 26.6081\n",
      "Epoch 813/1000\n",
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      "Epoch 814/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 26.5870 - mean_squared_error: 26.5870\n",
      "Epoch 815/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 26.5764 - mean_squared_error: 26.5764\n",
      "Epoch 816/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 26.5659 - mean_squared_error: 26.5659\n",
      "Epoch 817/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 26.5554 - mean_squared_error: 26.5554\n",
      "Epoch 818/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 26.5449 - mean_squared_error: 26.5449\n",
      "Epoch 819/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 26.5345 - mean_squared_error: 26.5345\n",
      "Epoch 820/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 26.5240 - mean_squared_error: 26.5240\n",
      "Epoch 821/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 26.5136 - mean_squared_error: 26.5136\n",
      "Epoch 822/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 26.5032 - mean_squared_error: 26.5032\n",
      "Epoch 823/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 26.4928 - mean_squared_error: 26.4928\n",
      "Epoch 824/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 26.4825 - mean_squared_error: 26.4825\n",
      "Epoch 825/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 26.4721 - mean_squared_error: 26.4721\n",
      "Epoch 826/1000\n",
      "32/32 [==============================] - 3s 78ms/step - loss: 26.4618 - mean_squared_error: 26.4618\n",
      "Epoch 827/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 26.4515 - mean_squared_error: 26.4515\n",
      "Epoch 828/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 26.4413 - mean_squared_error: 26.4413\n",
      "Epoch 829/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 26.4310 - mean_squared_error: 26.4310\n",
      "Epoch 830/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 26.4208 - mean_squared_error: 26.4208\n",
      "Epoch 831/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 26.4105 - mean_squared_error: 26.4105\n",
      "Epoch 832/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 26.4003 - mean_squared_error: 26.4003\n",
      "Epoch 833/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 26.3901 - mean_squared_error: 26.3901\n",
      "Epoch 834/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 26.3800 - mean_squared_error: 26.3800\n",
      "Epoch 835/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 26.3698 - mean_squared_error: 26.3698\n",
      "Epoch 836/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 26.3597 - mean_squared_error: 26.3597\n",
      "Epoch 837/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 26.3496 - mean_squared_error: 26.3496\n",
      "Epoch 838/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 26.3395 - mean_squared_error: 26.3395\n",
      "Epoch 839/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 26.3294 - mean_squared_error: 26.3294\n",
      "Epoch 840/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 26.3194 - mean_squared_error: 26.3194\n",
      "Epoch 841/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 26.3094 - mean_squared_error: 26.3094\n",
      "Epoch 842/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 26.2993 - mean_squared_error: 26.2993\n",
      "Epoch 843/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 26.2894 - mean_squared_error: 26.2894\n",
      "Epoch 844/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 26.2794 - mean_squared_error: 26.2794\n",
      "Epoch 845/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 26.2694 - mean_squared_error: 26.2694\n",
      "Epoch 846/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 26.2595 - mean_squared_error: 26.2595\n",
      "Epoch 847/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 26.2496 - mean_squared_error: 26.2496\n",
      "Epoch 848/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 26.2396 - mean_squared_error: 26.2396\n",
      "Epoch 849/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 26.2298 - mean_squared_error: 26.2298\n",
      "Epoch 850/1000\n",
      "32/32 [==============================] - 2s 67ms/step - loss: 26.2199 - mean_squared_error: 26.2199\n",
      "Epoch 851/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 26.2100 - mean_squared_error: 26.2100\n",
      "Epoch 852/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 26.2002 - mean_squared_error: 26.2002\n",
      "Epoch 853/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 26.1904 - mean_squared_error: 26.1904\n",
      "Epoch 854/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 26.1806 - mean_squared_error: 26.1806\n",
      "Epoch 855/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 26.1708 - mean_squared_error: 26.1708\n",
      "Epoch 856/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 26.1611 - mean_squared_error: 26.1611\n",
      "Epoch 857/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 26.1513 - mean_squared_error: 26.1513\n",
      "Epoch 858/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 26.1416 - mean_squared_error: 26.1416\n",
      "Epoch 859/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 26.1319 - mean_squared_error: 26.1319\n",
      "Epoch 860/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 26.1222 - mean_squared_error: 26.1222\n",
      "Epoch 861/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 26.1125 - mean_squared_error: 26.1125\n",
      "Epoch 862/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 26.1029 - mean_squared_error: 26.1029\n",
      "Epoch 863/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 26.0932 - mean_squared_error: 26.0932\n",
      "Epoch 864/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 26.0836 - mean_squared_error: 26.0836\n",
      "Epoch 865/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 26.0740 - mean_squared_error: 26.0740\n",
      "Epoch 866/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 26.0644 - mean_squared_error: 26.0644\n",
      "Epoch 867/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 26.0548 - mean_squared_error: 26.0548\n",
      "Epoch 868/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 26.0453 - mean_squared_error: 26.0453\n",
      "Epoch 869/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 26.0358 - mean_squared_error: 26.0358\n",
      "Epoch 870/1000\n",
      "32/32 [==============================] - 2s 62ms/step - loss: 26.0262 - mean_squared_error: 26.0262\n",
      "Epoch 871/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 26.0167 - mean_squared_error: 26.0167\n",
      "Epoch 872/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 26.0072 - mean_squared_error: 26.0072\n",
      "Epoch 873/1000\n",
      "32/32 [==============================] - 3s 90ms/step - loss: 25.9978 - mean_squared_error: 25.9978\n",
      "Epoch 874/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.9883 - mean_squared_error: 25.9883\n",
      "Epoch 875/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 25.9789 - mean_squared_error: 25.9789\n",
      "Epoch 876/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.9695 - mean_squared_error: 25.9695\n",
      "Epoch 877/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.9601 - mean_squared_error: 25.9601\n",
      "Epoch 878/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 25.9507 - mean_squared_error: 25.9507\n",
      "Epoch 879/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 25.9413 - mean_squared_error: 25.9413\n",
      "Epoch 880/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.9319 - mean_squared_error: 25.9319\n",
      "Epoch 881/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 25.9226 - mean_squared_error: 25.9226\n",
      "Epoch 882/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 25.9133 - mean_squared_error: 25.9133\n",
      "Epoch 883/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 25.9040 - mean_squared_error: 25.9040\n",
      "Epoch 884/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.8947 - mean_squared_error: 25.8947\n",
      "Epoch 885/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 25.8854 - mean_squared_error: 25.8854\n",
      "Epoch 886/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.8762 - mean_squared_error: 25.8762\n",
      "Epoch 887/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.8669 - mean_squared_error: 25.8669\n",
      "Epoch 888/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 25.8577 - mean_squared_error: 25.8577\n",
      "Epoch 889/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 25.8485 - mean_squared_error: 25.8485\n",
      "Epoch 890/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.8393 - mean_squared_error: 25.8393\n",
      "Epoch 891/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 25.8301 - mean_squared_error: 25.8301\n",
      "Epoch 892/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 25.8209 - mean_squared_error: 25.8209\n",
      "Epoch 893/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.8118 - mean_squared_error: 25.8118\n",
      "Epoch 894/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 25.8027 - mean_squared_error: 25.8027\n",
      "Epoch 895/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 25.7936 - mean_squared_error: 25.7936\n",
      "Epoch 896/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 25.7845 - mean_squared_error: 25.7845\n",
      "Epoch 897/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.7754 - mean_squared_error: 25.7754\n",
      "Epoch 898/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 25.7663 - mean_squared_error: 25.7663\n",
      "Epoch 899/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 25.7572 - mean_squared_error: 25.7572\n",
      "Epoch 900/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.7482 - mean_squared_error: 25.7482\n",
      "Epoch 901/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.7392 - mean_squared_error: 25.7392\n",
      "Epoch 902/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 25.7302 - mean_squared_error: 25.7302\n",
      "Epoch 903/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 25.7212 - mean_squared_error: 25.7212\n",
      "Epoch 904/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 25.7122 - mean_squared_error: 25.7122\n",
      "Epoch 905/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 25.7032 - mean_squared_error: 25.7032\n",
      "Epoch 906/1000\n",
      "32/32 [==============================] - 2s 79ms/step - loss: 25.6943 - mean_squared_error: 25.6943\n",
      "Epoch 907/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.6854 - mean_squared_error: 25.6854\n",
      "Epoch 908/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 25.6764 - mean_squared_error: 25.6764\n",
      "Epoch 909/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 25.6675 - mean_squared_error: 25.6675\n",
      "Epoch 910/1000\n",
      "32/32 [==============================] - 3s 89ms/step - loss: 25.6586 - mean_squared_error: 25.6586\n",
      "Epoch 911/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 25.6498 - mean_squared_error: 25.6498\n",
      "Epoch 912/1000\n",
      "32/32 [==============================] - 3s 78ms/step - loss: 25.6409 - mean_squared_error: 25.6409\n",
      "Epoch 913/1000\n",
      "32/32 [==============================] - 2s 66ms/step - loss: 25.6321 - mean_squared_error: 25.6321\n",
      "Epoch 914/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 25.6232 - mean_squared_error: 25.6232\n",
      "Epoch 915/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.6144 - mean_squared_error: 25.6144\n",
      "Epoch 916/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 25.6056 - mean_squared_error: 25.6056\n",
      "Epoch 917/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.5968 - mean_squared_error: 25.5968\n",
      "Epoch 918/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 25.5881 - mean_squared_error: 25.5881\n",
      "Epoch 919/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 25.5793 - mean_squared_error: 25.5793\n",
      "Epoch 920/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.5706 - mean_squared_error: 25.5706\n",
      "Epoch 921/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.5618 - mean_squared_error: 25.5618\n",
      "Epoch 922/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 25.5531 - mean_squared_error: 25.5531\n",
      "Epoch 923/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 25.5444 - mean_squared_error: 25.5444\n",
      "Epoch 924/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.5357 - mean_squared_error: 25.5357\n",
      "Epoch 925/1000\n",
      "32/32 [==============================] - 2s 61ms/step - loss: 25.5271 - mean_squared_error: 25.5271\n",
      "Epoch 926/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.5184 - mean_squared_error: 25.5184\n",
      "Epoch 927/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.5098 - mean_squared_error: 25.5098\n",
      "Epoch 928/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.5011 - mean_squared_error: 25.5011\n",
      "Epoch 929/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 25.4925 - mean_squared_error: 25.4925\n",
      "Epoch 930/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.4839 - mean_squared_error: 25.4839\n",
      "Epoch 931/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.4753 - mean_squared_error: 25.4753\n",
      "Epoch 932/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 25.4668 - mean_squared_error: 25.4668\n",
      "Epoch 933/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 25.4582 - mean_squared_error: 25.4582\n",
      "Epoch 934/1000\n",
      "32/32 [==============================] - 2s 66ms/step - loss: 25.4497 - mean_squared_error: 25.4497\n",
      "Epoch 935/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.4411 - mean_squared_error: 25.4411\n",
      "Epoch 936/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 25.4326 - mean_squared_error: 25.4326\n",
      "Epoch 937/1000\n",
      "32/32 [==============================] - 2s 66ms/step - loss: 25.4241 - mean_squared_error: 25.4241\n",
      "Epoch 938/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 25.4156 - mean_squared_error: 25.4156\n",
      "Epoch 939/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 25.4071 - mean_squared_error: 25.4071\n",
      "Epoch 940/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 25.3987 - mean_squared_error: 25.3987\n",
      "Epoch 941/1000\n",
      "32/32 [==============================] - 3s 87ms/step - loss: 25.3902 - mean_squared_error: 25.3902\n",
      "Epoch 942/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 25.3818 - mean_squared_error: 25.3818\n",
      "Epoch 943/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.3733 - mean_squared_error: 25.3733\n",
      "Epoch 944/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.3649 - mean_squared_error: 25.3649\n",
      "Epoch 945/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 25.3565 - mean_squared_error: 25.3565\n",
      "Epoch 946/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.3482 - mean_squared_error: 25.3482\n",
      "Epoch 947/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.3398 - mean_squared_error: 25.3398\n",
      "Epoch 948/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.3314 - mean_squared_error: 25.3314\n",
      "Epoch 949/1000\n",
      "32/32 [==============================] - 2s 71ms/step - loss: 25.3231 - mean_squared_error: 25.3231\n",
      "Epoch 950/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.3147 - mean_squared_error: 25.3147\n",
      "Epoch 951/1000\n",
      "32/32 [==============================] - 2s 73ms/step - loss: 25.3064 - mean_squared_error: 25.3064\n",
      "Epoch 952/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.2981 - mean_squared_error: 25.2981\n",
      "Epoch 953/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.2898 - mean_squared_error: 25.2898\n",
      "Epoch 954/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.2815 - mean_squared_error: 25.2815\n",
      "Epoch 955/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 25.2733 - mean_squared_error: 25.2733\n",
      "Epoch 956/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 25.2650 - mean_squared_error: 25.2650\n",
      "Epoch 957/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 25.2568 - mean_squared_error: 25.2568\n",
      "Epoch 958/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 25.2486 - mean_squared_error: 25.2486\n",
      "Epoch 959/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 25.2403 - mean_squared_error: 25.2403\n",
      "Epoch 960/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 25.2322 - mean_squared_error: 25.2322\n",
      "Epoch 961/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.2240 - mean_squared_error: 25.2240\n",
      "Epoch 962/1000\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 25.2158 - mean_squared_error: 25.2158\n",
      "Epoch 963/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 25.2076 - mean_squared_error: 25.2076\n",
      "Epoch 964/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.1995 - mean_squared_error: 25.1995\n",
      "Epoch 965/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 25.1913 - mean_squared_error: 25.1913\n",
      "Epoch 966/1000\n",
      "32/32 [==============================] - 2s 69ms/step - loss: 25.1832 - mean_squared_error: 25.1832\n",
      "Epoch 967/1000\n",
      "32/32 [==============================] - 2s 77ms/step - loss: 25.1751 - mean_squared_error: 25.1751\n",
      "Epoch 968/1000\n",
      "32/32 [==============================] - 3s 89ms/step - loss: 25.1670 - mean_squared_error: 25.1670\n",
      "Epoch 969/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 25.1589 - mean_squared_error: 25.1589\n",
      "Epoch 970/1000\n",
      "32/32 [==============================] - 2s 70ms/step - loss: 25.1508 - mean_squared_error: 25.1508\n",
      "Epoch 971/1000\n",
      "32/32 [==============================] - 3s 82ms/step - loss: 25.1427 - mean_squared_error: 25.1427\n",
      "Epoch 972/1000\n",
      "32/32 [==============================] - 2s 68ms/step - loss: 25.1347 - mean_squared_error: 25.1347\n",
      "Epoch 973/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 25.1267 - mean_squared_error: 25.1267\n",
      "Epoch 974/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.1186 - mean_squared_error: 25.1186\n",
      "Epoch 975/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 25.1106 - mean_squared_error: 25.1106\n",
      "Epoch 976/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 25.1026 - mean_squared_error: 25.1026\n",
      "Epoch 977/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.0946 - mean_squared_error: 25.0946\n",
      "Epoch 978/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 25.0866 - mean_squared_error: 25.0866\n",
      "Epoch 979/1000\n",
      "32/32 [==============================] - 3s 87ms/step - loss: 25.0787 - mean_squared_error: 25.0787\n",
      "Epoch 980/1000\n",
      "32/32 [==============================] - 3s 89ms/step - loss: 25.0707 - mean_squared_error: 25.0707\n",
      "Epoch 981/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 25.0628 - mean_squared_error: 25.0628\n",
      "Epoch 982/1000\n",
      "32/32 [==============================] - 2s 75ms/step - loss: 25.0548 - mean_squared_error: 25.0548\n",
      "Epoch 983/1000\n",
      "32/32 [==============================] - 3s 91ms/step - loss: 25.0469 - mean_squared_error: 25.0469\n",
      "Epoch 984/1000\n",
      "32/32 [==============================] - 3s 79ms/step - loss: 25.0390 - mean_squared_error: 25.0390\n",
      "Epoch 985/1000\n",
      "32/32 [==============================] - 3s 84ms/step - loss: 25.0311 - mean_squared_error: 25.0311\n",
      "Epoch 986/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 25.0232 - mean_squared_error: 25.0232\n",
      "Epoch 987/1000\n",
      "32/32 [==============================] - 2s 72ms/step - loss: 25.0154 - mean_squared_error: 25.0154\n",
      "Epoch 988/1000\n",
      "32/32 [==============================] - 2s 76ms/step - loss: 25.0075 - mean_squared_error: 25.0075\n",
      "Epoch 989/1000\n",
      "32/32 [==============================] - 2s 74ms/step - loss: 24.9997 - mean_squared_error: 24.9997\n",
      "Epoch 990/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 24.9918 - mean_squared_error: 24.9918\n",
      "Epoch 991/1000\n",
      "32/32 [==============================] - 2s 63ms/step - loss: 24.9840 - mean_squared_error: 24.9840\n",
      "Epoch 992/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 24.9762 - mean_squared_error: 24.9762\n",
      "Epoch 993/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 24.9684 - mean_squared_error: 24.9684\n",
      "Epoch 994/1000\n",
      "32/32 [==============================] - 3s 81ms/step - loss: 24.9606 - mean_squared_error: 24.9606\n",
      "Epoch 995/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 24.9528 - mean_squared_error: 24.9528\n",
      "Epoch 996/1000\n",
      "32/32 [==============================] - 3s 90ms/step - loss: 24.9451 - mean_squared_error: 24.9451\n",
      "Epoch 997/1000\n",
      "32/32 [==============================] - 3s 83ms/step - loss: 24.9373 - mean_squared_error: 24.9373\n",
      "Epoch 998/1000\n",
      "32/32 [==============================] - 3s 80ms/step - loss: 24.9296 - mean_squared_error: 24.9296\n",
      "Epoch 999/1000\n",
      "32/32 [==============================] - 3s 85ms/step - loss: 24.9218 - mean_squared_error: 24.9218\n",
      "Epoch 1000/1000\n",
      "32/32 [==============================] - 2s 78ms/step - loss: 24.9141 - mean_squared_error: 24.9141\n",
      "INFO:tensorflow:Assets written to: ./image_regressor/best_model/assets\n",
      "32/32 [==============================] - 2s 30ms/step - loss: 24.9077 - mean_squared_error: 24.9077\n",
      "[24.90774917602539, 24.90774917602539]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import autokeras as ak\n",
    "\n",
    "MAX_TRIALS = 2\n",
    "SEED = 42\n",
    "VAL_SPLIT = 0.1\n",
    "EPOCHS = 1000\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "auto_reg = ak.ImageRegressor(overwrite=True, \n",
    "  max_trials=MAX_TRIALS,\n",
    "  seed=42)\n",
    "auto_reg.fit(x, y, validation_split=VAL_SPLIT, batch_size=BATCH_SIZE, \n",
    "        epochs=EPOCHS)\n",
    "print(auto_reg.evaluate(x,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "My1KzK2ApqCz"
   },
   "source": [
    "We can now display the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LJGhmWHNLhZN",
    "outputId": "e71fa5b6-ffa3-4c1a-ac4e-77de9b217dea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'autokeras.tasks.image.ImageRegressor'>\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 128, 128, 3)]     0         \n",
      "                                                                 \n",
      " cast_to_float32 (CastToFloa  (None, 128, 128, 3)      0         \n",
      " t32)                                                            \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, None, None, 2048)  23587712  \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 32768)             0         \n",
      "                                                                 \n",
      " regression_head_1 (Dense)   (None, 1)                 32769     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,620,481\n",
      "Trainable params: 32,769\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print(type(auto_reg))\n",
    "model = auto_reg.export_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZxmVTxKWpvL9"
   },
   "source": [
    "This top model can be saved and either utilized or trained further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NtYaXjNAQ3dT",
    "outputId": "4e6cc42e-4212-491a-9b06-b927a5866e1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'keras.engine.functional.Functional'>\n",
      "INFO:tensorflow:Assets written to: model_autokeras/assets\n",
      "32/32 [==============================] - 2s 21ms/step - loss: 24.9077 - mean_squared_error: 24.9077\n",
      "[24.90774917602539, 24.90774917602539]\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "print(type(model))  \n",
    "\n",
    "try:\n",
    "    model.save(\"model_autokeras\", save_format=\"tf\")\n",
    "except Exception:\n",
    "    model.save(\"model_autokeras.h5\")\n",
    "\n",
    "\n",
    "loaded_model = load_model(\"model_autokeras\",\\\n",
    "    custom_objects=ak.CUSTOM_OBJECTS)\n",
    "print(loaded_model.evaluate(x,y))"
   ]
  }
 ],
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