material/dnn_in_keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": " dnn_in_keras.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mte4_QlwwrlF",
        "colab_type": "text"
      },
      "source": [
        "# 모듈 임포팅"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YegBF6qExut",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNGWUXDmv2nF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVWB0cOhwvs_",
        "colab_type": "text"
      },
      "source": [
        "# 디바이스"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwA83M6Dw3gd",
        "colab_type": "text"
      },
      "source": [
        "## 리스트 보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8qfdb0UUJPv",
        "colab_type": "code",
        "outputId": "40cbbf3c-5e26-4c43-f385-1159b4c9494c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from tensorflow.python.client import device_lib\n",
        "print(tf.test.gpu_device_name())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Aasznk1w5eq",
        "colab_type": "text"
      },
      "source": [
        "## NVIDIA GPU 상태 보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sBfV0eAxAg_",
        "colab_type": "code",
        "outputId": "1aa393a9-774b-4529-ee9f-1def81d4c599",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Oct 17 11:08:16 2019       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 430.40       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    57W / 149W |     69MiB / 11441MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E18r6et9tfxU",
        "colab_type": "text"
      },
      "source": [
        "# Keras DNN 최소 코드\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJBqaE5oyaIi",
        "colab_type": "text"
      },
      "source": [
        "# 데이터 준비\n",
        "\n",
        "1차원 데이터 x와 x**2의 1차원 데이타 y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEj2Fb6HMrnt",
        "colab_type": "code",
        "outputId": "4c35e765-252d-4361-e9d6-cde864931909",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.arange(-1,1,0.01)\n",
        "y = x**2\n",
        "\n",
        "plt.scatter(x,y)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7efc5007fb00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HuG3XxX0eUbM53Gticnys40WVwIurVn29FlFdZy+ew71GurWH+cqRVlXtk5WyjI2ud7CX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\nA/1//EPhMWOcrjsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSlhN2bS2pwj",
        "colab_type": "code",
        "outputId": "98dd5f0a-c89f-4216-e0d2-d08cb8b37c79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(len(x))\n",
        "print(x.shape)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200\n",
            "(200,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o5Z0WwGByiBd",
        "colab_type": "text"
      },
      "source": [
        "## 최소 코드\n",
        "\n",
        "아래의 코드는 최소 코드."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0ti9sKoMrrE",
        "colab_type": "code",
        "outputId": "47c801c9-901c-44cc-d1df-6954766510ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "\n",
        "import time\n",
        "\n",
        "\n",
        "# 모델 정의\n",
        "model = keras.Sequential()\n",
        "model.add(Input(1))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "# 컴파일 : 모델 + optimizer + loss\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\")\n",
        "\n",
        "\n",
        "# 학습\n",
        "model.fit(x, y, epochs=1000, verbose=0, batch_size=20)\n",
        "\n",
        "\n",
        "# 예측\n",
        "y_ = model.predict(x)\n",
        "\n",
        "\n",
        "# 결과 그래프로 보기\n",
        "plt.scatter(x,y) # 정답\n",
        "plt.scatter(x,y_,color='r') # 결과\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dwHjg5XjNlaZ3661w8skV9eKDAD3ita0sv+9Glt93Y82Dfmbvej738Cre9dpW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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EXLaSAcy5HI",
        "colab_type": "text"
      },
      "source": [
        "# Keras DNN 최소 코드에 기본 정도만 추가\n",
        "\n",
        "최소는 아니고, 이정도는 매번 사용하는 코드"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0e9tc-p1NjN8",
        "colab_type": "text"
      },
      "source": [
        "## model.evaluate() - 성능 평가하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAT9UZiXMrwY",
        "colab_type": "code",
        "outputId": "5f879044-ead2-49c7-86c1-64a43e1d1f25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "\n",
        "model = keras.Sequential()\n",
        "#model.add(Input(1))\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,) ))\n",
        "model.add(Dense(10, activation='tanh' ))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "#model.compile(optimizer=\"SGD\", loss=\"mse\")\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "\n",
        "\n",
        "model.fit(x, y, epochs=1000, verbose=0, batch_size=20)\n",
        "\n",
        "loss, mse = model.evaluate(x, y) # ADD\n",
        "print(\"loss=\",loss) # ADD\n",
        "print(\"mse=\",mse) # ADD\n",
        "\n",
        "\n",
        "y_ = model.predict(x)\n",
        "\n",
        "\n",
        "plt.scatter(x,y)\n",
        "plt.scatter(x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200/200 [==============================] - 0s 203us/sample - loss: 6.3815e-04 - mean_squared_error: 6.3815e-04\n",
            "loss= 0.0006381518929265439\n",
            "mse= 0.0006381519\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lvqoTepFG61JB3OQeZ22ZVuCl0OPNwEeijnH3XjN7HRgN/DFeuCIRbr4ZJk/u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\n/wergdxiLv4F0wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEKtss5Oz4-L",
        "colab_type": "text"
      },
      "source": [
        "출력에 loss와 mse가 추가되었다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jp3u-snhSL6W",
        "colab_type": "text"
      },
      "source": [
        "## 테스트셋으로 평가하기\n",
        "\n",
        "데이터를 train과 test으로 나누어 준비한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bd8YIGMLSvJ1",
        "colab_type": "code",
        "outputId": "9b2cbd0b-0029-4a74-b3c2-f59ebe80a5b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "x = np.arange(-1,1,0.01)\n",
        "np.random.shuffle(x)\n",
        "y = x**2\n",
        "\n",
        "split_index = int(x.shape[0]*0.6)\n",
        "\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]\n",
        "\n",
        "plt.scatter(train_x,train_y)\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ly0jRJLNiPvnIHhxSli2woBfPttXwi13wgb8oYCvLTF8nbL+NtuGXxxYktdhh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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8M503BOSOu-",
        "colab_type": "code",
        "outputId": "85419eb4-6b49-49f0-c73c-487488fe6656",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "\n",
        "\n",
        "# model.fit(x, y, epochs=1000, verbose=0, batch_size=20)\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "\n",
        "\n",
        "# loss, mse = model.evaluate(x, y)\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "# y_ = model.predict(x)\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(x,y)\n",
        "plt.scatter(test_x,test_y)\n",
        "# plt.scatter(x,y_,color='r')\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 0s 428us/sample - loss: 0.0022 - mean_squared_error: 0.0022\n",
            "loss= 0.002159204240888357\n",
            "mse= 0.0021592043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vvQWTJ2tTlTKlYRnJuuDmH36dk7b5i2mbvzhtXfFy23TbX4jUOYoibHU1VdH3\n6Q9/gNdS5LhLyVLPXXKiYUE9O5su5o7Lzh6Wluf34vst/EexZ3pd2RQd84dfRhPYB2v6RI2ja3y9\nbCm4S041LKin8fyZIwL85z702RHj8D0TJ/Hjmed4Y+8HDoBzJZtNk7yZRhw1VZVDNX3CFoppfL2s\nKbhLzn2l4Sy+nig85guOw5/AODi5lqalq/jvv3m6LIqOBTfTSMeAK86fyXO3fGCovo82LZck2kNV\n8ipqX1bfCxs+zISQsFeMe3wmF/YKbi4+p+mxWMH98t/9nC8+/SDVhw95Qy4qulZ24u6hqglVyat0\nNeIPT6ml7o0jI5736pRankkRLAtJa0fniP9fco56XU1VyrF2AzYc382lT/yL6uJLLOq5S8EI69nu\nuPnrrEvKpumZOImmpas4aeIEPrP9PuqOvcahydPYeOFK2uYvxgCHV2c+XwE/mPXitydMfU0VO5su\nTvkOpr6mijsGnufcWz7j5f8nmzXLW3EqZSFuz13BXQraovXbOWfn49z41P3DgvgEM776438ZFvQd\n8HrVn3Lre6+lbf5iwOvxNp4/k680nJXVdgb/ME2pquSPb/XTN5D+d8uAF9d/aMRrDHsn8ulPw7e+\n5U0oh76IQZENUcnYZTS4m9lS4E6gArjbObc+6fwk4H7gHOAocJlzbn+q11RwlzjCerRVlRX85Bsr\nmXGsK/Q5/k905+RaNl64kh/OX0zj+TPZ8X+7xjyEk2q8PN28QSp+zz1US4tXCvno0dQvop57WcnY\nmLuZVQB3Ae8HDgK7zKzNOfd84LKrgdedc+8wsxXABuCysTVdZEhwAVQwsNZ9JXphjp9mOeNYF3f8\n6Gucc/A33Mqnh4J+0nh3UHA4pcKMAeeoqpxAb99Qzzj5+WG11ONIue9sut66T+mOEiHOhOp5wD7n\n3AsAZvYQcAkQDO6XALcmPn8E2Gxm5vI15iMlJWxLv57T66h+pTPtcycAVz73OM/O+PPBoRoYqskS\nfN3kHvhA4sc3GNjDnh9WSz2dU6orueXD84f/v+L21H3aHk9SiJPnXg+8HHh8MHEs9BrnXD/wBjA1\nEw0UCVO9aQP9J4+sYR5mAl5t82TJQXm0PXD/+WG11JP57ybqa6q447Kz6bj5AyMD+8c/Hj+wm2l7\nPEkpp6mQZnYtcC3ATC2LlvFobPR+eK+/Hnf0aPTmzwl1x7r4xTc/PmxS9tlFy4ZdM9oeuB/UVy+Z\nO2LMvXKC8ScnT6S7py/eGP/atdA3supjKDO47joFdkkpTnDvBM4IPJ6ROBZ2zUEzmwhMwZtYHcY5\ntwXYAt6E6lgaLDKosREaG7F2Z0I3AAAIeElEQVSY49P+BOyMY11s2LaZX58/GxiazEyXax5kMDhe\nHjUvMKoUzLgF0qZO9bbGU2CXNOIMy+wCzjSzOWZ2ErACaEu6pg1I7K7AR4HtGm+XnPnXf4UHHvCy\nRsKYjfhBr+o7zrnfuX3YsbDdjEJfDi+9Mhi8/cJoL67/EDubLh455DJ7ttfjnjjR+ze5AFq6d7Jm\n8KlPeRUeFdglDudc2g9gGfBb4PfA2sSx24Dlic9PBr4P7AP+E3h7utc855xznEhWPPigc7NmOWfm\n/ev16Ud+eHP+w/znV/7FvVJzqhsA12cT3AlwnVNOdU0fucnNvulH7i/X/cz94P8cjN8G/+uEff3q\nau86//rKyvDrpk4duk7KHtDu4sTtOBdl40PBXXImKsDPmjX8ugcf9AJu1B+DVME2+Adl6lTnTjop\n/eskt+HBB73nKqhLCnGDu1aoSulrafFqsASrS1ZXj0wjnD3bq9cSR2Ul3Huv9/yw149Lq0tllOIu\nYlLJXyl9ccvhjmbXp76+obLDYVvcxaWsMckSVYWU8pDIrElp5sz4PXcY+mMw1q0AtbpUskg9dxFf\n2G5Gqfi97ji9b0tk4lcksnG0mYZkmYK7iC84fANDgThMZeVQrzvsj0JlpZeT7g8DPfCAN03a3+/9\nu3+/ArtklYK7SFBjoxd4g4H4wQe9QO2bOnVoMtV/TvKY/r33ejnpJ04okEteKFtGRKSIKFtGRKSM\nKbiLiJQgBXcRkRKk4C4iUoIU3EVESpCCu4hICVJwFxEpQQruIiIlKG+LmMysCxhFlaZQ04DXMtCc\nTCvEdhVim0DtGi21a3RKsV2znHO16S7KW3DPBDNrj7NSK9cKsV2F2CZQu0ZL7Rqdcm6XhmVEREqQ\ngruISAkq9uC+Jd8NiFCI7SrENoHaNVpq1+iUbbuKesxdRETCFXvPXUREQhR8cDez/2Vme8zshJlF\nzi6b2VIz22tm+8ysKXB8jpn9R+L4w2Z2Ugba9DYz+4mZ/S7x7ykh1yw2s+cCH2+aWUPi3H1m9mLg\n3NnjbVPcdiWuGwh87bbA8Yzfq7jtMrOzzezpxPf6V2Z2WeBcRu9X1M9K4PykxP9/X+J+zA6cW5M4\nvtfMloynHWNo1+fM7PnE/fmZmc0KnAv9nuaoXX9rZl2Br39N4NxVie/778zsqhy26euB9vzWzLoD\n57J5r+4xsyNm9uuI82Zm30i0+1dm9p7AuczeK+dcQX8Afw7MBZ4EFkZcUwH8Hng7cBLwS2Be4tz3\ngBWJz78FfCoDbdoINCU+bwI2pLn+bcAfgOrE4/uAj2bhXsVqF/BfEcczfq/itgt4J3Bm4vM64BWg\nJtP3K9XPSuCaTwPfSny+Ang48fm8xPWTgDmJ16nIYbsWB36GPuW3K9X3NEft+ltgc8hz3wa8kPj3\nlMTnp+SiTUnX/wNwT7bvVeK1LwTeA/w64vwy4MeAAecD/5Gte1XwPXfn3G+cc3vTXHYesM8594Jz\n7i3gIeASMzPgYuCRxHVbgYYMNOuSxGvFfc2PAj92zvVk4GunMtp2DcrivYrVLufcb51zv0t8fgg4\nAqRdqDEGoT8rKdr7CPDexP25BHjIOXfcOfcisC/xejlpl3NuR+Bn6BlgRoa+9rjalcIS4CfOuT84\n514HfgIszUObLge+m4Gvm5Zz7im8jlyUS4D7necZoMbMTicL96rgg3tM9cDLgccHE8emAt3Ouf6k\n4+N1mnPulcTnh4HT0ly/gpE/XM2Jt2VfN7NJGWjTaNp1spm1m9kz/lAR2btXo2kXAGZ2Hl6P7PeB\nw5m6X1E/K6HXJO7HG3j3J85zs9muoKvxeoC+sO9pLtv1N4nvzyNmdsYon5utNpEYupoDbA8czta9\niiOq7Rm/VxPH8+RMMbOfAtNDTq11zv3vXLcHUrcp+MA558wsMuUo8Vf5LGBb4PAavCB3El5K1E3A\nbTls1yznXKeZvR3Ybma78QLYmGX4fj0AXOWcO5E4POb7VYrM7ApgIfBXgcMjvqfOud+Hv0LG/RD4\nrnPuuJn9Hd67notz9LXTWQE84pwbCBzL573KmYII7s65943zJTqBMwKPZySOHcV72zMx0QPzj4+r\nTWb2qpmd7px7JRGMjqR4qUuBHzjn+gKv7fdij5vZvcANcdqUqXY55zoT/75gZk8CC4B/Y4z3KlPt\nMrPJwGN4f9SfCbz2mO9XiKiflbBrDprZRGAK3s9SnOdms12Y2fvw/mD+lXPuuH884nuaiYCVtl3O\nuaOBh3fjzbH4z70o6blP5qJNASuAvw8eyOK9iiOq7Rm/V6UyLLMLONO8bI+T8L6hbc6bqdiBN+YN\ncBWQiXcCbYnXivOaI8b7EgHOH+duAEJn1rPRLjM7xR/WMLNpwCLg+Szeq7jtOgn4Ad545CNJ5zJ5\nv0J/VlK096PA9sT9aQNWmJdNMwc4E/jPcbRlVO0yswXAt4HlzrkjgeOh39Mctuv0wMPlwG8Sn28D\nPpBo3ynABxj+DjZrbUq06114k5NPB45l817F0QasTGTNnA+8kei8ZP5eZXq2ONMfwP/EG386DrwK\nbEscrwMeD1y3DPgt3l/gtYHjb8f7BdwHfB+YlIE2TQV+BvwO+CnwtsTxhcDdgetm4/1FnpD0/O3A\nbrwg9SDwJxm6V2nbBfxl4mv/MvHv1dm8V6No1xVAH/Bc4OPsbNyvsJ8VvGGe5YnPT078//cl7sfb\nA89dm3jeXuCDGf5ZT9eunyZ+B/z705bue5qjdq0D9iS+/g7gXYHnfiJxH/cBH89VmxKPbwXWJz0v\n2/fqu3iZXn14cetq4DrgusR5A+5KtHs3gQzATN8rrVAVESlBpTIsIyIiAQruIiIlSMFdRKQEKbiL\niJQgBXcRkRKk4C4iUoIU3EVESpCCu4hICfr/VS7NeYzFRg0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LujK_3NBz9N5",
        "colab_type": "text"
      },
      "source": [
        "train_x, train_y로 학습하였고,\n",
        "evaluate()와 predict()에는 test_x, test_y를 사용하였다.\n",
        "\n",
        "그려진 그림을 보면 test_x의 점사이에 빈 간격이 보인다. 그 간격이 train_x에 해당한다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pnd5Gq51aExN",
        "colab_type": "text"
      },
      "source": [
        "## Base Model 결과 저장\n",
        "\n",
        "이후 비교를 위해 그래프를 저장해 둔다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyZ_5XlbaAk8",
        "colab_type": "code",
        "outputId": "d8f518a9-b24f-4af1-9632-1443497cd772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        }
      },
      "source": [
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.savefig(\"base_result.png\")\n",
        "\n",
        "from IPython.display import Image\n",
        "display(Image(\"base_result.png\"))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FpGJDaYJlzMtsdryA+4hpSmW9CRuqT6G98zoA5fEtAR10bFnTILAFRh7lbN3E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\np0/HtGnTAACRkZEYP3482tr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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vvQWTJ2tTlTKlYRnJuuDmH36dk7b5i2mbvzhtXfFy23TbX4jUOYoibHU1VdH3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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0o-2M3BYsGr",
        "colab_type": "text"
      },
      "source": [
        "## 학습 시간을 출력"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z66QeNbxYukl",
        "colab_type": "code",
        "outputId": "26148caf-b24d-4d7f-a0dc-37f3dfc090ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "\n",
        "start_time = time.time() # ADD\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time)) # ADD \n",
        "    \n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "elapsed : 16.960872411727905\n",
            "80/80 [==============================] - 0s 463us/sample - loss: 0.0025 - mean_squared_error: 0.0025\n",
            "loss= 0.0024685786105692387\n",
            "mse= 0.0024685785\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sz2tPpOCeBfHhmcS3h9Wv7Q9+gvLfRZIL+R0Z/+p+urp7ldceQMMyWTKkLEFcyESQ8t9F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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDrqq-7b0L1E",
        "colab_type": "text"
      },
      "source": [
        "elapsed로 경과된 시간이 초 단위로 보인다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQkpZHJWckjq",
        "colab_type": "text"
      },
      "source": [
        "## model.summary() 네트웤 모양 보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBZdqkq7cqeO",
        "colab_type": "code",
        "outputId": "5066fea5-39dd-4155-d557-864f51216601",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary() # ADD\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_19 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 17.029454469680786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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Onl7m3vxDHswQ2CHwySi9xMNxx4U/QHVjKoKCu+TdormN3HL+KTSGTPrdeuZi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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbW6vjIb0TJI",
        "colab_type": "text"
      },
      "source": [
        "네트웤의 모양이 보인다.\n",
        "\n",
        "노드 10개, 10개를 갖는 은닉층 2개가 있다.\n",
        "\n",
        "\n",
        "모든 레이어는 activation(w\\*x+b) 의 형태로, \n",
        "\n",
        "첫 번 째 은닉층은 20개( = ((1+1)\\*10) = (입력수+1)\\*10)의 웨이트를, \n",
        "\n",
        "두 번 째 은닉층은 110개( = ( (10+1)\\*10) )의 웨이트가 있다.. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31Xxr4IckI9R",
        "colab_type": "text"
      },
      "source": [
        "# Keras DNN Template\n",
        "\n",
        "본 실습에 사용되는 코든 코드의 템플릿.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eq_eO6o9kMA1",
        "colab_type": "code",
        "outputId": "06fb622d-579a-4683-cb7b-b5f486858603",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "import time\n",
        "\n",
        "\n",
        "# 모델 정의\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "# 모델 컴파일\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "# 학습\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))\n",
        "\n",
        "\n",
        "# 평가\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "# 예측\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_22 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 17.042150259017944\n",
            "80/80 [==============================] - 0s 524us/sample - loss: 0.0031 - mean_squared_error: 0.0031\n",
            "loss= 0.003101612813770771\n",
            "mse= 0.003101613\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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95KYG7rr4DLb//QcV2CWWqkJK8dXVxU8WtrWx5aobuOrouzjUfyTjrsaGekaP\nqhtyHCps5aVf0dEdPJjxh+zomEZvr9r2dqZ2PJpop6SaTxcVVYWUFMlVUGz3bt7z+U+z/bfd3HXx\nGUMW4bwaEdjh+HDN7M5NTOl4lN9btiGdqZRdXRy98irICuwAow7307f0JiD3pOjJTQ185eIzFNhl\nWNRzl+JLUifFDB54YEgZg9kxW8Q1Nzbw+tGBnJtRBOeVY1x6y4rVTPrSbbz90P6cw04DGHVuIHLX\nJPXWJUrSnruCu5RGV5c39p5rsw/wNvwIFSGLCnqNDfWMaajjlb7oXn22hjrj4rMm8fj/OVC0Sdnw\npO+iX/2Yz/fcNWRLwih7x7Yw8dX9Q56jWieOZeQKGtzNbB6wCqgH1jrnOrPuHw3cD5wJHAQuds7t\nyvWcCu41Kt9uToFx42DVqsgdhpbOncZnHtqec4w6W1Rd86jaK8Fr9R7qp96MY87R3NjAkWMD/L83\nvD8w2Z8Gsv8APfm1jzHxtQN529Q3ajR3XvRZbnnoi8P4n0itSxrc86ZCmlk9cA/wAWAvsMXMepxz\n4Q0zrwBecc6908wWAXcAF59Y06WqrVgBH/1o3tWYHDzoDeUAC9vbhwTh4SyIAob8IQhy6MPPmx2k\nj/ltzJ7QPdR/hKXf/pnXtpmtrNy4gw9s/wE3PnE/E157GcvzZ8cBrzS+mS/OvYZzOq5N/H8QGY4k\nE6pnATudc887594AHgQuzDrnQmCd//0jwPvMLNdQo9Sq9na45hpvjD0ffwu5KMMpaRAne9l+vg2l\nw44MuMEFVrM2b6DzsdVMfO0AdbjYMXaHNwyz5MOf430dj3DOzUs07CJFk2QRUyvwYuj2XuCP485x\nzh01s1eBccDL4ZPM7GrgaoDJ2pKtdn31qzB7drIx+Jhl+SdS0iBbdobKcGu0BOcve/KBvOPrfaNG\n0zHvOp6ePZ+lc6dxt4K6FFlJUyGdc2ucc7Occ7NaWlpK+dKSNsFmH+vX5950e/Lk2MU/C2e2srnj\nfHZ1XjCYRgmZHwqaGxu49OzJiTayGG6NluD8t78aPb7u8LJhftP8Np695Uvc/b0vaWNqKZkkPfde\nYFLo9kT/WNQ5e81sFPAWvIlVkdyC1MclS4ZuUNHUBPPnZ6ZR7t49OBYfTpvMV8t8Vttb82aiLJ07\nbUhmDsCCZx/nlh+u4eT+/wK86oz/8MG/4b23XA+ATZ4c+QnE2tqwXbs4BTgl33UQKTTnXM5/eH8A\nngemAicBPwNmZJ1zLfB1//tFwMP5nvfMM890IhnWr3eurc05M+9rcNubfs38F74/fP4Iffd/73V/\ncvsP3ac+/Dm3d2yLOwZuIOKC6k1gAAAHxElEQVT1j45qOP5669c719SUeU5TU0HaI5IN2OryxFfn\nvQsTnATzgV8CvwaW+8duBRb4348Bvg3sBP4DeEe+51Rwl0TMooN7EECjjgePOZGAv369c+PGxb9m\n9h+Y8OMK/IdGJErS4K5FTJJucXnx9fVwLEFmi5mXnRNM4O7Z443jhxZKDUqykjb7uQcGkp0rUiCq\nLSPVYcWKoROuTU3JAjt4feyvfQ0+/nHvj4Rzx8ftQ1UZgcjqlTkp40tSTMFd0q29Hdas8coSmHlf\ng9vD8cYbmbejcujz7IaUoaHB+8MjklLarEPSr7196BAKDG8IJUp2MI/JehkiVBpBJK3Uc5fKFPTo\nx4078efIHlaJGgIC7zXWrz8+lfryywrsknoK7lK52tu9QLt+fe4g39AAJ52UeaypaeiwStQQ0Pr1\nCuZSkRTcpfIFQT7oWa9fnxmgv/lNuPfeoeP2UQE7WDk7MOB9VVCXCqVUSBGRCqJUSBGRGqbgLiJS\nhRTcRUSqkIK7iEgVUnAXEalCCu4iIlVIwV1EpAopuIuIVKGyLWIyswNAgipNOY0naxPulEhju9LY\nJlC7hkvtGp5qbFebcy7vJtRlC+6FYGZbk6zUKrU0tiuNbQK1a7jUruGp5XZpWEZEpAopuIuIVKFK\nD+5ryt2AGGlsVxrbBGrXcKldw1Oz7aroMXcREYlW6T13ERGJkPrgbmZ/ZWbPmtmAmcXOLpvZPDPb\nYWY7zawjdHyqmf3UP/6QmZ0U9xzDaNNbzezfzexX/teTI86ZY2bbQ/8Om9lC/777zOyF0H1njLRN\nSdvln3cs9No9oeMFv1ZJ22VmZ5jZT/yf9X+a2cWh+wp6veLeK6H7R/v//53+9ZgSum+Zf3yHmc0d\nSTtOoF2fNbPn/OvzQzNrC90X+TMtUbsuN7MDode/MnTfZf7P/VdmdlkJ2/SVUHt+aWaHQvcV81rd\na2b7zeznMfebmd3tt/s/zezdofsKe62cc6n+B/wBMA34ETAr5px64NfAO4CTgJ8B0/37HgYW+d9/\nHfhEAdp0J9Dhf98B3JHn/LcCvwOa/Nv3AX9ZhGuVqF3A/405XvBrlbRdwLuA0/zvJwAvAc2Fvl65\n3iuhcz4JfN3/fhHwkP/9dP/80cBU/3nqS9iuOaH30CeCduX6mZaoXZcDqyMe+1bgef/ryf73J5ei\nTVnnfwq4t9jXyn/uc4F3Az+PuX8+8K+AAWcDPy3WtUp9z9059wvn3I48p50F7HTOPe+cewN4ELjQ\nzAw4H3jEP28dsLAAzbrQf66kz/mXwL865/oK8Nq5DLddg4p4rRK1yzn3S+fcr/zv9wH7gbwLNU5A\n5HslR3sfAd7nX58LgQedc687514AdvrPV5J2OeceD72HngImFui1R9SuHOYC/+6c+51z7hXg34F5\nZWjTJcC3CvC6eTnnnsDryMW5ELjfeZ4Cms3sVIpwrVIf3BNqBV4M3d7rHxsHHHLOHc06PlJvd869\n5H//G+Dtec5fxNA31wr/Y9lXzGx0Ado0nHaNMbOtZvZUMFRE8a7VcNoFgJmdhdcj+3XocKGuV9x7\nJfIc/3q8ind9kjy2mO0KuwKvBxiI+pmWsl1/4f98HjGzScN8bLHahD90NRXYFDpcrGuVRFzbC36t\nRo3kwYViZj8ATom4a7lz7l9K3R7I3abwDeecM7PYlCP/r/LpwMbQ4WV4Qe4kvJSom4BbS9iuNudc\nr5m9A9hkZs/gBbATVuDr9QBwmXNuwD98wterGpnZpcAs4L2hw0N+ps65X0c/Q8F9D/iWc+51M/sb\nvE8955fotfNZBDzinDsWOlbOa1UyqQjuzrn3j/ApeoFJodsT/WMH8T72jPJ7YMHxEbXJzH5rZqc6\n517yg9H+HE/1EeC7zrkjoecOerGvm9k3gRuStKlQ7XLO9fpfnzezHwEzgX/mBK9VodplZmOBR/H+\nqD8Veu4Tvl4R4t4rUefsNbNRwFvw3ktJHlvMdmFm78f7g/le59zrwfGYn2khAlbedjnnDoZursWb\nYwkee17WY39UijaFLAKuDR8o4rVKIq7tBb9W1TIsswU4zbxsj5PwfqA9zpupeBxvzBvgMqAQnwR6\n/OdK8pxDxvv8ABeMcy8EImfWi9EuMzs5GNYws/HAbOC5Il6rpO06Cfgu3njkI1n3FfJ6Rb5XcrT3\nL4FN/vXpARaZl00zFTgN+I8RtGVY7TKzmcA/AQucc/tDxyN/piVs16mhmwuAX/jfbwQ+6LfvZOCD\nZH6CLVqb/Hb9Pt7k5E9Cx4p5rZLoARb7WTNnA6/6nZfCX6tCzxYX+h/w53jjT68DvwU2+scnABtC\n580Hfon3F3h56Pg78H4BdwLfBkYXoE3jgB8CvwJ+ALzVPz4LWBs6bwreX+S6rMdvAp7BC1LrgTcV\n6FrlbRfwJ/5r/8z/ekUxr9Uw2nUpcATYHvp3RjGuV9R7BW+YZ4H//Rj//7/Tvx7vCD12uf+4HcCH\nCvxez9euH/i/A8H16cn3My1Ru24HnvVf/3Hg90OP/bh/HXcCHytVm/zbtwCdWY8r9rX6Fl6m1xG8\nuHUFcA1wjX+/Aff47X6GUAZgoa+VVqiKiFShahmWERGREAV3EZEqpOAuIlKFFNxFRKqQgruISBVS\ncBcRqUIK7iIiVUjBXUSkCv1/EjbFsHCR7ZoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhiGYL5x1-FF",
        "colab_type": "text"
      },
      "source": [
        "1개의 경우 거의 학습이 되지 않는다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAK87lLiNmTF",
        "colab_type": "text"
      },
      "source": [
        "# 학습 추가 진행"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Ty3h3ICmF8U",
        "colab_type": "text"
      },
      "source": [
        "모델은 그대로 두고 데이터만 sin 데이터로 변경한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VT4QUNhPmIej",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_sin_data(start=0, end=10, step=0.1):\n",
        "  x = np.arange(start,end,step)\n",
        "  np.random.shuffle(x)\n",
        "  y = np.sin(x)\n",
        "\n",
        "  split_index = int(x.shape[0]*0.6)\n",
        "\n",
        "  train_x, test_x = x[:split_index], x[split_index:]\n",
        "  train_y, test_y = y[:split_index], y[split_index:]\n",
        "\n",
        "  return (train_x, train_y), (test_x, test_y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIkL36cWvoCU",
        "colab_type": "code",
        "outputId": "ab9a7692-5e8b-476f-e3eb-8aba461fbb90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "plt.scatter(train_x,train_y)\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(test_x,test_y,color=\"r\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ByYjYA3wR+FNJh4DjNIMHyXn3Ak8Bp4CPRsTrHV+oR/5CFEPZ8rnN6kgRkXcZFqzRaMTk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\nwGBmZi3+P2lwALNLJBfZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NAK4Jg2mInV",
        "colab_type": "code",
        "outputId": "9c4d6645-f1d3-46f9-9dd7-3aa67eff8f11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_15\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_52 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_53 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_54 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 7.029408693313599\n",
            "40/40 [==============================] - 0s 1ms/sample - loss: 0.1624 - mean_squared_error: 0.1624\n",
            "loss= 0.16241912245750428\n",
            "mse= 0.16241913\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prz5bv1o2MrY",
        "colab_type": "text"
      },
      "source": [
        "학습이 충분히 되지 않은 듯 하다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fgZIrh0-nW8t",
        "colab_type": "text"
      },
      "source": [
        "## 추가 학습"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFzO7p3qnlKk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fit_one_more(model, train_x, train_y, test_x, test_y, batch_size=20):\n",
        "\n",
        "  start_time = time.time()\n",
        "  model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=batch_size)\n",
        "  print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "  y_ = model.predict(test_x)\n",
        "\n",
        "  plt.scatter(test_x,test_y)\n",
        "  plt.scatter(test_x,y_,color='r')\n",
        "  plt.show()\n",
        "\n",
        "def fit_n_times(model, train_x, train_y, test_x, test_y, n):\n",
        "  \n",
        "  for i in range(n):\n",
        "    print(\"{} times fitting\".format(i))\n",
        "    fit_one_more(model, train_x, train_y, test_x, test_y)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgMhQ2wMn9ct",
        "colab_type": "code",
        "outputId": "9d755697-5a52-4a00-a6bb-9749e76f484d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit_n_times(model, train_x, train_y, test_x, test_y, 10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 times fitting\n",
            "elapsed : 6.860821008682251\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qwc079zF0zi9x1zm/VFb+jZ37/E2piuLP5aQE8f55ZVY7rui12jgwVOB/iOlptyl7eefu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\nZmZlHBjMzKzM/welVDn8NtSprgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1 times fitting\n",
            "elapsed : 6.830053806304932\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RK8sOD40wsPQyti29bEy7qcQ5EmcNDU6rPWr1SPu0AgfLnh8qtlXcxt1PAB8Cn6nDvqvS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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2 times fitting\n",
            "elapsed : 6.776483535766602\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v845ertm6m96+gWNpFz/4E9bdvp6JR14cWnrkJW2Mv+5buTkOe/eO/Amq0Pxk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\nGMzMrIgDg5mZFXFgMDOzIg4MZmZW5P8DaTpj+wYdu5UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3 times fitting\n",
            "elapsed : 6.757455110549927\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rgN8BrgQuAjJ2Q7l7i7s3uHvDtGmaky4iki8jzkpy92szPWZmvzCzS939ePjB\nf3KYl/ow8F13P5P02omrjdfN7JvAp0YZt4iI5Em2XUltwMrw/krge8Oc+1EGdSOFyQQzM4Lxieey\njEdkdMq1AqtIDmSbGDYB15nZ88C14TFm1mBm2xInmdks4DLgfw96fquZPQs8C0wFvpBlPCIjS1Rg\nPXoU3M9XYFVyEAGynK4aFU1XlazMmhUkg8Hq6+HFFwsdjUjBFGq6qkjp0d7JIsNSYpD4ybRHsrvG\nG0RQYpA4Srd3coLGG0SUGCSGGhvZ9+l7OFFzcfqFN/ne8U0zoqTIKTFI7OzY38VNp9/CO/74fhxL\nf9LRo/n50G5tZWDVJ1JmRA2s+oSSgxQVJQaJnc3th+g/8wYA3ZOnZj4xD91KfWvvovJ0f0pb5el+\n+tZmXPQvUnBKDBI73b3nP5jvvfqmITu+pejrg49/PGdXDxce7x5Tu0gUlBgkdmprqs7db5u3+NyO\nb8Ou6MnR1UOmK5Rhr1xECkyJQWJn7ZI5VE2sOHfcNm8x7/wv36Rr8gjFGXMwKL1t6aq0e1JvW7oq\nq9cVySUlBomd5F3eko3YrQTnF8ElzyyaOjW4Db6fpvtpQdNtrL/+9pQ9qddffzsLmm7L3RsUyZJK\nYkiszW76YUoX0rIDu7nzRw9Sd6on/Xyl+vpgHcTq1cEVxEiqq6GlBRobzzXt2N/F5vZDdPf2U1tT\nxdolc7T1qBTEaEtiKDFIrC3a9CRdvf1D2pcd2M097VupOvP6+cbEh3xzc/paS5moBpMUCdVKEhmF\nweMNCW3zFnPXkjV0vfli3Cz4cE/85T/WmkqqwSQlZsSNekTKWaILZ3P7oSFXDm3zFtM2bzEAdTVV\nrJ07J9jUfObMsV0xZKrNJFKkdMUgsbdiYR17m67JtAYagK7eftY9+iw79ncNX2tpkIELq4LzRUpI\nVonBzP6zmR0ws7NmlrHfysyWmtkhMztsZk1J7bPN7Cdh+3fM7IJs4hHJRu2gWUqD9Z95g83th4Lu\npJaWoHvJjNffPIXeqt/kLMYvL/xNXgnvd06eRtPSNeyY+57CvAGRHMm2K+k54AbgrzOdYGYVwNeB\n64BOYJ+Ztbn7QeAe4Cvu/rCZ/XfgVuC+LGMSGZe1S+aw7tFnz5XLSCexanrH3Pew+U/up6u3H4Nh\nF8f9uP2QZh1JScnqisHdf+7uh0Y47SrgsLsfcfdfAw8Dy8N9nq8BHgnP206w77NIJDKtb0hWW1PF\njv1drHv02XNjEiPN6+tOM+tJpJgVYoyhDjiWdNwZtv0W0OvuA4PaRSKTGG/Y8pEFQ2YrVU2sYO2S\nOSlF+EZjpC4qkWIzYmIws8fwGo2JAAAEcUlEQVTN7Lk0t+WFCDApjtVm1mFmHT09PYX81hJDyVcP\nRjAraeMN81mxsG5MVwCJZCJSSkYcY3D3a7P8Hl3AZUnHM8K2XwI1ZlYZXjUk2jPF0QK0QLDALcuY\nREa0YmFd2rGB2pqqtIviEhJjDnVa1SwlqhDrGPYBl5vZbIIP/huBj7m7m9lu4EME4w4rge8VIB6R\nrKQbpFYykHKSVWIws/8IfA2YBvzQzJ5x9yVmVgtsc/f3u/uAma0B2oEK4H53PxC+xF3Aw2b2BWA/\n8DfZxCNSCMmL4lTvSMqRaiWJiMSEaiWJiMi4KDGIiEgKJQYREUmhxCAiIimUGEREJIUSg4iIpCjJ\n6apm1gOMYaeUjKYCL+fgdUpNHN93HN8zxPN9x/E9w+jed727TxvphUoyMeSKmXWMZk5vuYnj+47j\ne4Z4vu84vmfI7ftWV5KIiKRQYhARkRRxTwwtUQcQkTi+7zi+Z4jn+47je4Ycvu9YjzGIiMhQcb9i\nEBGRQWKZGMxsqZkdMrPDZtYUdTyFYGaXmdluMztoZgfM7I6oYyoUM6sws/1m9oOoYykUM6sxs0fM\n7P+Y2c/N7A+ijqkQzOzPwv/fz5nZt83swqhjygczu9/MTprZc0ltF5nZY2b2fPh1ynhfP3aJwcwq\ngK8D7wPmAh81s7nRRlUQA8BfuPtc4B3AbTF53wB3AD+POogC+yqwy91/B/g9YvD+zawOuB1ocPe3\nEez/cmO0UeXNA8DSQW1NwBPufjnwRHg8LrFLDMBVwGF3P+LuvybYPa6g+1dHwd2Pu/s/h/f/L8EH\nRdnvLGNmM4APANuijqVQzOzNwNWEG1+5+6/dvTfaqAqmEqgys0qgGuiOOJ68cPcfAa8Mal4ObA/v\nbwdWjPf145gY6oBjScedxOADMpmZzQIWAj+JNpKC2ALcCZyNOpACmg30AN8Mu9C2mdmbog4q39y9\nC/gS8BJwHHjN3f9XtFEV1HR3Px7ePwFMH+8LxTExxJqZ/Qbwd8An3f1U1PHkk5ldD5x096ejjqXA\nKoHfB+5z94XAr8iiW6FUhH3qywkSYy3wJjP7eLRRRcOD6abjnnIax8TQBVyWdDwjbCt7ZjaRICm0\nuvujUcdTAIuAZWb2IkGX4TVm9lC0IRVEJ9Dp7okrwkcIEkW5uxZ4wd173P0M8CjwHyKOqZB+YWaX\nAoRfT473heKYGPYBl5vZbDO7gGBwqi3imPLOzIygz/nn7v7lqOMpBHdf5+4z3H0Wwc/5SXcv+78g\n3f0EcMzM5oRN7wUORhhSobwEvMPMqsP/7+8lBoPuSdqAleH9lcD3xvtClTkJp4S4+4CZrQHaCWYt\n3O/uByIOqxAWAX8EPGtmz4Rtn3b3nRHGJPnzp0Br+MfPEeCWiOPJO3f/iZk9AvwzwSy8/ZTpKmgz\n+zbwHmCqmXUCnwU2AX9rZrcSVJ/+8LhfXyufRUQkWRy7kkREZBhKDCIikkKJQUREUigxiIhICiUG\nERFJocQgIiIplBhERCSFEoOIiKT4/2sp77uIqNW9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "4 times fitting\n",
            "elapsed : 6.848926305770874\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AA8AM4FtZxiMiIlnKKjG4+9vu/il3v8jdr3T3d4LyDndflXTea+5e6+5nhr3+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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "5 times fitting\n",
            "elapsed : 6.840904951095581\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DLgJaE4pXEfQ5XAycB6RthnL3ZnevcfeaysrKs4UtIiIjdNbOZ3e/Ot1zZva2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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "6 times fitting\n",
            "elapsed : 6.881314039230347\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DJI6HfDsPKy+o5ctzLvLH8uXFf22RQVJgkMR584yQzHn3oo83rD/vMprnLObg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\ngUFERLIoMIiISBYFBhERyfL/AW5Hjd6dOe2qAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "7 times fitting\n",
            "elapsed : 6.782005310058594\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3uHuVu1eVl2uEiYhIrgw689ndv5DuOTN7y8xmuPuJ8If/1ABv9VXgr9z9XMJ7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\nEUny/wGkmCEIRsCOQQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "8 times fitting\n",
            "elapsed : 6.755645513534546\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HGDYCC4L7C4CfnOLczwA/Sm0IkknyMtR84KU84xERkTzlmxhagcvM7FXg0uAY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\nUWIQEZE0/x9uXRDdJHZcHgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "9 times fitting\n",
            "elapsed : 6.758832693099976\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iaUprET13/xnwmSTtrcCauO03gcokxy1L5/NFRCTzNPNZREQSKDGIiEgCJQaJ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\nBEUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udoykOBX2SVg",
        "colab_type": "text"
      },
      "source": [
        "학습이 더 진행되면서 거의 완벽하게 학습된 것을 볼 수 있다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Txmz0VqaqcMm",
        "colab_type": "text"
      },
      "source": [
        "# model.fit()의 batch_size\n",
        "\n",
        "batch_size는 GPU와 관련된 옵션이다.\n",
        "\n",
        "한번에 GPU에 보내는 데이터의 수이다.  \n",
        "\n",
        "<br>\n",
        "\n",
        "batch_size가 1일 경우 1개를 보내고, 1개의 결과를 받고, 1번 웨이트를 업데이트 한다.\n",
        "\n",
        "batch_size가 10일 경우 10개를 보내고, 10개의 결과를 한 번에 받고, 1번 웨이트를 업데이트 한다.\n",
        "\n",
        "<br>\n",
        "\n",
        "GPU는 보통 수천개의 코어를 가지고 있다. 동시에 꽤 많은 연산을 처리할 수 있다. 그런데 데이터가 적으면 대부분은 사용하지 못하고 일부만 연산에 사용된다. \n",
        "\n",
        "복수의 데이터를 한번에 보내어 한번에 연산을 할수 있고, 그 결과를 반환할 수 있다. 이런 방법으로 연산 시간을 줄일 수 있다.\n",
        "\n",
        "<br>\n",
        "\n",
        "하지만, 복수의 데이터를 한번에 보내는 경우 한번에 보낸 결과가 한번에 오고 1번 업데이트 되면서 업데이트 되는 사항이 뭉개지는 단점이 있다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF6qxEfLriZy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_model():\n",
        "\n",
        "  model = keras.Sequential()\n",
        "  model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "  model.add(Dense(10, activation='tanh'))\n",
        "  model.add(Dense(1))\n",
        "\n",
        "  model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "\n",
        "  return model\n",
        "\n",
        "def fit_with_batch_sizes(train_x, train_y, test_x, test_y, batch_sizes):\n",
        "\n",
        "  for batch_size in batch_sizes:\n",
        "    model = build_model()\n",
        "    print(\"batch_size={}\".format(batch_size))\n",
        "    fit_one_more(model, train_x, train_y, test_x, test_y, batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtN9WexdvEaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONkh2C1GuRsg",
        "colab_type": "text"
      },
      "source": [
        "## batch_size 적용 : [1,2,5,10,20,50,100,200,500]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbW3bysfrTcS",
        "colab_type": "code",
        "outputId": "c890eb3b-fe1d-4e60-f67f-d8f3963949d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit_with_batch_sizes(train_x, train_y, test_x, test_y, batch_sizes=[1,2,5,10,20,50,100,200,500])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "batch_size=1\n",
            "elapsed : 100.3012764453888\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3trjjdwAjAQP2Bcf8W3fvq0I90lNeVIQl+T/uZlhbW5IjwtUwfDRjmjqfeDeU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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=2\n",
            "elapsed : 51.838141679763794\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wp5m9R3yI71Yz+2t/Q/JMB9DhnBv6hreb+IdB2H0B+JVzrts5dwF4Gfh3Psfk\npffNbC5A4vZMJv84zMn/EHCVmS00s8uIXwja63NMeWdmRnzs9x3n3F/4HY9XnHMbnXOVzrkFxP9b\n73fORaIX6Jw7DZw0s6pE023A2z6G5JUTwHVmVpL4u7+NCFzoTrIXuDdx/17gR5n84+KchxMQzrkB\nM1sHtBKvAtjpnHvL57C8UAPcA7xpZm8k2r7jnHvFx5gk//4z0JLo6LwLfM3nePLOOfdLM9sN/APx\nKrcjhHS2r5n9ALgZmGVmHcDjwCbgb8zs68RXQP5yRq+pGb4iItET5mEfERFJQ8lfRCSClPxFRCJI\nyV9EJIKU/EVEIkjJX0QkgpT8RUQiSMlfRCSC/j/tdSntq4qbegAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=5\n",
            "elapsed : 22.21281623840332\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a21PAr8IP/MQH//F+3urzwI/c/VTSex/1wLvAPwBXZfftiKRaM2s+JytTL0RP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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=10\n",
            "elapsed : 12.134780406951904\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JIjGzaE6dEoFESktiiIhICiUGERFJocQgIiIplBhERCSFEoOIiKRQYhARkRRK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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=20\n",
            "elapsed : 7.339964866638184\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=50\n",
            "elapsed : 5.389750957489014\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m3djYWIyP1y2ZMDOzBiQ9HBFjzc7zInpmZlbDgcHMzGo4MJiZWQ0HBjMzq+HA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\nwGBmZjX+P/z2NIGuoEQjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=100\n",
            "elapsed : 3.670841932296753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D9n43oE/8e5dPWX93/94LUOo5BjMzm6vsPQYzM2tQysAg6VJJeyTtlbS53+3J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\ntMQ21/MbjAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=200\n",
            "elapsed : 3.6978063583374023\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CSfsk/UW/y1IUSQsl3SPpf0t6QtKb+12mIkj6aPo3/qikuySd3e8y9Yqk2yQ9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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=500\n",
            "elapsed : 3.7025442123413086\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L20zKtWltMHKRSdaHtY/LqtvA4KYkW5hqs1J1IZFG7m8onIGM5WnWTJSjtY/LyoHB0hmS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\nZmZ1/j/Ca1GhVk3TAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXHdl5Ie3dUd",
        "colab_type": "text"
      },
      "source": [
        "맨 앞의 결과가 batch_size 1인 경우이다. 100개의 데이터를 매번 1개 씩 보냈고, 매번 업데이트 했다. \n",
        "\n",
        "batch_size를 키우면, 시간은 줄어들지만 학습이 빠르게 진행되지 않는다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3YdUyA9xnZo",
        "colab_type": "text"
      },
      "source": [
        "# 학습 진행된 내역 보기\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FEMCvHjzs2P",
        "colab_type": "text"
      },
      "source": [
        "## model.fit()의 반환값 history\n",
        "\n",
        "반환된 history의 loss로 진행 상황을 확인."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K69Avyero8Ee",
        "colab_type": "code",
        "outputId": "1cf03c0b-d7a2-4b81-e9c0-9caeafea6fe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "# model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_25\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_82 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_83 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_84 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 7.361281156539917\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SN6iFgw9o8B5",
        "colab_type": "code",
        "outputId": "ac765d8a-f01f-457b-d812-094045529512",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "\n",
        "plt.plot(history.history['loss'])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f343630bc50>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FJACMB0YCxcA8EZlojLGPIXvDGPO8tf9lwDPAqHoor2pCju/Shse+O5DB+e3p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\nqTSkwV0ppdLQ/wOsmJ/h6k4fCwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRYHL9fp5STU",
        "colab_type": "text"
      },
      "source": [
        "## model.fit() verbose\n",
        "\n",
        "- 0 : X\n",
        "- 1: progress bar\n",
        "- 2 : 1 line per epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7JfvwoF4bVT",
        "colab_type": "code",
        "outputId": "d5c050b4-31bd-47ee-800e-1b7be96be026",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "# (train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.01)\n",
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.0001)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "print(train_x.shape)\n",
        "\n",
        "start_time = time.time()\n",
        "# history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_27\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_88 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_89 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_90 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "(60000,)\n",
            "Epoch 1/5\n",
            "60000/60000 - 6s - loss: 0.2209 - mean_squared_error: 0.2209\n",
            "Epoch 2/5\n",
            "60000/60000 - 5s - loss: 0.1077 - mean_squared_error: 0.1077\n",
            "Epoch 3/5\n",
            "60000/60000 - 5s - loss: 0.0547 - mean_squared_error: 0.0547\n",
            "Epoch 4/5\n",
            "60000/60000 - 5s - loss: 0.0192 - mean_squared_error: 0.0192\n",
            "Epoch 5/5\n",
            "60000/60000 - 5s - loss: 0.0086 - mean_squared_error: 0.0086\n",
            "elapsed : 27.608429431915283\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hob_7N00L61C",
        "colab_type": "text"
      },
      "source": [
        "# 학습 시에 validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vR-m3FJ7zoiK",
        "colab_type": "text"
      },
      "source": [
        "## model.fit()의 validation_data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1QFN0UCzlGA",
        "colab_type": "code",
        "outputId": "c5ff6d06-678a-47c6-d046-3f6d7588c248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "# history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=1000, verbose=1, batch_size=20, validation_data=(test_x, test_y))\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_28\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_91 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_92 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_93 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 60 samples, validate on 40 samples\n",
            "Epoch 1/1000\n",
            "60/60 [==============================] - 0s 7ms/sample - loss: 1.7064 - mean_squared_error: 1.7064 - val_loss: 0.4434 - val_mean_squared_error: 0.4434\n",
            "Epoch 2/1000\n",
            "60/60 [==============================] - 0s 325us/sample - loss: 0.4610 - mean_squared_error: 0.4610 - val_loss: 0.4652 - val_mean_squared_error: 0.4652\n",
            "Epoch 3/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3946 - mean_squared_error: 0.3946 - val_loss: 0.4812 - val_mean_squared_error: 0.4812\n",
            "Epoch 4/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.3914 - mean_squared_error: 0.3914 - val_loss: 0.4740 - val_mean_squared_error: 0.4740\n",
            "Epoch 5/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.3911 - mean_squared_error: 0.3911 - val_loss: 0.4743 - val_mean_squared_error: 0.4743\n",
            "Epoch 6/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.3918 - mean_squared_error: 0.3918 - val_loss: 0.4620 - val_mean_squared_error: 0.4620\n",
            "Epoch 7/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.3835 - mean_squared_error: 0.3835 - val_loss: 0.4804 - val_mean_squared_error: 0.4804\n",
            "Epoch 8/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.3900 - mean_squared_error: 0.3900 - val_loss: 0.4791 - val_mean_squared_error: 0.4791\n",
            "Epoch 9/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.3971 - mean_squared_error: 0.3971 - val_loss: 0.5152 - val_mean_squared_error: 0.5152\n",
            "Epoch 10/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.3817 - mean_squared_error: 0.3817 - val_loss: 0.4650 - val_mean_squared_error: 0.4650\n",
            "Epoch 11/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.3768 - mean_squared_error: 0.3768 - val_loss: 0.4663 - val_mean_squared_error: 0.4663\n",
            "Epoch 12/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.3841 - mean_squared_error: 0.3841 - val_loss: 0.4497 - val_mean_squared_error: 0.4497\n",
            "Epoch 13/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.3830 - mean_squared_error: 0.3830 - val_loss: 0.4617 - val_mean_squared_error: 0.4617\n",
            "Epoch 14/1000\n",
            "60/60 [==============================] - 0s 327us/sample - loss: 0.3759 - mean_squared_error: 0.3759 - val_loss: 0.4642 - val_mean_squared_error: 0.4642\n",
            "Epoch 15/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.3715 - mean_squared_error: 0.3715 - val_loss: 0.4776 - val_mean_squared_error: 0.4776\n",
            "Epoch 16/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.3726 - mean_squared_error: 0.3726 - val_loss: 0.4858 - val_mean_squared_error: 0.4858\n",
            "Epoch 17/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.3721 - mean_squared_error: 0.3721 - val_loss: 0.4733 - val_mean_squared_error: 0.4733\n",
            "Epoch 18/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.3667 - mean_squared_error: 0.3667 - val_loss: 0.4692 - val_mean_squared_error: 0.4692\n",
            "Epoch 19/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.3652 - mean_squared_error: 0.3652 - val_loss: 0.4629 - val_mean_squared_error: 0.4629\n",
            "Epoch 20/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.3694 - mean_squared_error: 0.3694 - val_loss: 0.4458 - val_mean_squared_error: 0.4458\n",
            "Epoch 21/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.3637 - mean_squared_error: 0.3637 - val_loss: 0.4675 - val_mean_squared_error: 0.4675\n",
            "Epoch 22/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.3613 - mean_squared_error: 0.3613 - val_loss: 0.4562 - val_mean_squared_error: 0.4562\n",
            "Epoch 23/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.3665 - mean_squared_error: 0.3665 - val_loss: 0.4607 - val_mean_squared_error: 0.4607\n",
            "Epoch 24/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.3670 - mean_squared_error: 0.3670 - val_loss: 0.4686 - val_mean_squared_error: 0.4686\n",
            "Epoch 25/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.3742 - mean_squared_error: 0.3742 - val_loss: 0.4501 - val_mean_squared_error: 0.4501\n",
            "Epoch 26/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.3609 - mean_squared_error: 0.3609 - val_loss: 0.4516 - val_mean_squared_error: 0.4516\n",
            "Epoch 27/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.3582 - mean_squared_error: 0.3582 - val_loss: 0.4521 - val_mean_squared_error: 0.4521\n",
            "Epoch 28/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.3570 - mean_squared_error: 0.3570 - val_loss: 0.4465 - val_mean_squared_error: 0.4465\n",
            "Epoch 29/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.3604 - mean_squared_error: 0.3604 - val_loss: 0.4773 - val_mean_squared_error: 0.4773\n",
            "Epoch 30/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.3551 - mean_squared_error: 0.3551 - val_loss: 0.4534 - val_mean_squared_error: 0.4534\n",
            "Epoch 31/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3631 - mean_squared_error: 0.3631 - val_loss: 0.4526 - val_mean_squared_error: 0.4526\n",
            "Epoch 32/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.3561 - mean_squared_error: 0.3561 - val_loss: 0.4521 - val_mean_squared_error: 0.4521\n",
            "Epoch 33/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.3546 - mean_squared_error: 0.3546 - val_loss: 0.4489 - val_mean_squared_error: 0.4489\n",
            "Epoch 34/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.3571 - mean_squared_error: 0.3571 - val_loss: 0.4600 - val_mean_squared_error: 0.4600\n",
            "Epoch 35/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.3512 - mean_squared_error: 0.3512 - val_loss: 0.4561 - val_mean_squared_error: 0.4561\n",
            "Epoch 36/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.3529 - mean_squared_error: 0.3529 - val_loss: 0.4557 - val_mean_squared_error: 0.4557\n",
            "Epoch 37/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.3552 - mean_squared_error: 0.3552 - val_loss: 0.4698 - val_mean_squared_error: 0.4698\n",
            "Epoch 38/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.3505 - mean_squared_error: 0.3505 - val_loss: 0.4487 - val_mean_squared_error: 0.4487\n",
            "Epoch 39/1000\n",
            "60/60 [==============================] - 0s 204us/sample - loss: 0.3509 - mean_squared_error: 0.3509 - val_loss: 0.4318 - val_mean_squared_error: 0.4318\n",
            "Epoch 40/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.3610 - mean_squared_error: 0.3610 - val_loss: 0.4633 - val_mean_squared_error: 0.4633\n",
            "Epoch 41/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.3518 - mean_squared_error: 0.3518 - val_loss: 0.4299 - val_mean_squared_error: 0.4299\n",
            "Epoch 42/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.3484 - mean_squared_error: 0.3484 - val_loss: 0.4351 - val_mean_squared_error: 0.4351\n",
            "Epoch 43/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.3542 - mean_squared_error: 0.3542 - val_loss: 0.4500 - val_mean_squared_error: 0.4500\n",
            "Epoch 44/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.3498 - mean_squared_error: 0.3498 - val_loss: 0.4572 - val_mean_squared_error: 0.4572\n",
            "Epoch 45/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.3457 - mean_squared_error: 0.3457 - val_loss: 0.4338 - val_mean_squared_error: 0.4338\n",
            "Epoch 46/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.3464 - mean_squared_error: 0.3464 - val_loss: 0.4481 - val_mean_squared_error: 0.4481\n",
            "Epoch 47/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3448 - mean_squared_error: 0.3448 - val_loss: 0.4535 - val_mean_squared_error: 0.4535\n",
            "Epoch 48/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.3434 - mean_squared_error: 0.3434 - val_loss: 0.4415 - val_mean_squared_error: 0.4415\n",
            "Epoch 49/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.3441 - mean_squared_error: 0.3441 - val_loss: 0.4442 - val_mean_squared_error: 0.4442\n",
            "Epoch 50/1000\n",
            "60/60 [==============================] - 0s 314us/sample - loss: 0.3540 - mean_squared_error: 0.3540 - val_loss: 0.4197 - val_mean_squared_error: 0.4197\n",
            "Epoch 51/1000\n",
            "60/60 [==============================] - 0s 287us/sample - loss: 0.3483 - mean_squared_error: 0.3483 - val_loss: 0.4337 - val_mean_squared_error: 0.4337\n",
            "Epoch 52/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.3442 - mean_squared_error: 0.3442 - val_loss: 0.4275 - val_mean_squared_error: 0.4275\n",
            "Epoch 53/1000\n",
            "60/60 [==============================] - 0s 200us/sample - loss: 0.3452 - mean_squared_error: 0.3452 - val_loss: 0.4293 - val_mean_squared_error: 0.4293\n",
            "Epoch 54/1000\n",
            "60/60 [==============================] - 0s 214us/sample - loss: 0.3419 - mean_squared_error: 0.3419 - val_loss: 0.4292 - val_mean_squared_error: 0.4292\n",
            "Epoch 55/1000\n",
            "60/60 [==============================] - 0s 310us/sample - loss: 0.3498 - mean_squared_error: 0.3498 - val_loss: 0.4509 - val_mean_squared_error: 0.4509\n",
            "Epoch 56/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.3557 - mean_squared_error: 0.3557 - val_loss: 0.4625 - val_mean_squared_error: 0.4625\n",
            "Epoch 57/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.3490 - mean_squared_error: 0.3490 - val_loss: 0.4432 - val_mean_squared_error: 0.4432\n",
            "Epoch 58/1000\n",
            "60/60 [==============================] - 0s 413us/sample - loss: 0.3598 - mean_squared_error: 0.3598 - val_loss: 0.4667 - val_mean_squared_error: 0.4667\n",
            "Epoch 59/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.3454 - mean_squared_error: 0.3454 - val_loss: 0.4345 - val_mean_squared_error: 0.4345\n",
            "Epoch 60/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.3441 - mean_squared_error: 0.3441 - val_loss: 0.4271 - val_mean_squared_error: 0.4271\n",
            "Epoch 61/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.3436 - mean_squared_error: 0.3436 - val_loss: 0.4597 - val_mean_squared_error: 0.4597\n",
            "Epoch 62/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.3428 - mean_squared_error: 0.3428 - val_loss: 0.4241 - val_mean_squared_error: 0.4241\n",
            "Epoch 63/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3387 - mean_squared_error: 0.3387 - val_loss: 0.4429 - val_mean_squared_error: 0.4429\n",
            "Epoch 64/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.3371 - mean_squared_error: 0.3371 - val_loss: 0.4347 - val_mean_squared_error: 0.4347\n",
            "Epoch 65/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.3401 - mean_squared_error: 0.3401 - val_loss: 0.4488 - val_mean_squared_error: 0.4488\n",
            "Epoch 66/1000\n",
            "60/60 [==============================] - 0s 203us/sample - loss: 0.3521 - mean_squared_error: 0.3521 - val_loss: 0.4130 - val_mean_squared_error: 0.4130\n",
            "Epoch 67/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.3424 - mean_squared_error: 0.3424 - val_loss: 0.4139 - val_mean_squared_error: 0.4139\n",
            "Epoch 68/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.3443 - mean_squared_error: 0.3443 - val_loss: 0.4112 - val_mean_squared_error: 0.4112\n",
            "Epoch 69/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.3563 - mean_squared_error: 0.3563 - val_loss: 0.4486 - val_mean_squared_error: 0.4486\n",
            "Epoch 70/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.3358 - mean_squared_error: 0.3358 - val_loss: 0.4382 - val_mean_squared_error: 0.4382\n",
            "Epoch 71/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.3396 - mean_squared_error: 0.3396 - val_loss: 0.4133 - val_mean_squared_error: 0.4133\n",
            "Epoch 72/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.3355 - mean_squared_error: 0.3355 - val_loss: 0.4285 - val_mean_squared_error: 0.4285\n",
            "Epoch 73/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3464 - mean_squared_error: 0.3464 - val_loss: 0.4052 - val_mean_squared_error: 0.4052\n",
            "Epoch 74/1000\n",
            "60/60 [==============================] - 0s 272us/sample - loss: 0.3647 - mean_squared_error: 0.3647 - val_loss: 0.3998 - val_mean_squared_error: 0.3998\n",
            "Epoch 75/1000\n",
            "60/60 [==============================] - 0s 273us/sample - loss: 0.3357 - mean_squared_error: 0.3357 - val_loss: 0.4343 - val_mean_squared_error: 0.4343\n",
            "Epoch 76/1000\n",
            "60/60 [==============================] - 0s 279us/sample - loss: 0.3385 - mean_squared_error: 0.3385 - val_loss: 0.4107 - val_mean_squared_error: 0.4107\n",
            "Epoch 77/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.3419 - mean_squared_error: 0.3419 - val_loss: 0.4381 - val_mean_squared_error: 0.4381\n",
            "Epoch 78/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.3378 - mean_squared_error: 0.3378 - val_loss: 0.4544 - val_mean_squared_error: 0.4544\n",
            "Epoch 79/1000\n",
            "60/60 [==============================] - 0s 279us/sample - loss: 0.3486 - mean_squared_error: 0.3486 - val_loss: 0.4304 - val_mean_squared_error: 0.4304\n",
            "Epoch 80/1000\n",
            "60/60 [==============================] - 0s 270us/sample - loss: 0.3342 - mean_squared_error: 0.3342 - val_loss: 0.4330 - val_mean_squared_error: 0.4330\n",
            "Epoch 81/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.3390 - mean_squared_error: 0.3390 - val_loss: 0.4114 - val_mean_squared_error: 0.4114\n",
            "Epoch 82/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.3326 - mean_squared_error: 0.3326 - val_loss: 0.4241 - val_mean_squared_error: 0.4241\n",
            "Epoch 83/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.3440 - mean_squared_error: 0.3440 - val_loss: 0.4175 - val_mean_squared_error: 0.4175\n",
            "Epoch 84/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.3326 - mean_squared_error: 0.3326 - val_loss: 0.4143 - val_mean_squared_error: 0.4143\n",
            "Epoch 85/1000\n",
            "60/60 [==============================] - 0s 211us/sample - loss: 0.3587 - mean_squared_error: 0.3587 - val_loss: 0.4568 - val_mean_squared_error: 0.4568\n",
            "Epoch 86/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.3354 - mean_squared_error: 0.3354 - val_loss: 0.4240 - val_mean_squared_error: 0.4240\n",
            "Epoch 87/1000\n",
            "60/60 [==============================] - 0s 264us/sample - loss: 0.3323 - mean_squared_error: 0.3323 - val_loss: 0.4217 - val_mean_squared_error: 0.4217\n",
            "Epoch 88/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.3334 - mean_squared_error: 0.3334 - val_loss: 0.4262 - val_mean_squared_error: 0.4262\n",
            "Epoch 89/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.3298 - mean_squared_error: 0.3298 - val_loss: 0.4178 - val_mean_squared_error: 0.4178\n",
            "Epoch 90/1000\n",
            "60/60 [==============================] - 0s 267us/sample - loss: 0.3307 - mean_squared_error: 0.3307 - val_loss: 0.4248 - val_mean_squared_error: 0.4248\n",
            "Epoch 91/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.3403 - mean_squared_error: 0.3403 - val_loss: 0.4681 - val_mean_squared_error: 0.4681\n",
            "Epoch 92/1000\n",
            "60/60 [==============================] - 0s 263us/sample - loss: 0.3368 - mean_squared_error: 0.3368 - val_loss: 0.4102 - val_mean_squared_error: 0.4102\n",
            "Epoch 93/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.3300 - mean_squared_error: 0.3300 - val_loss: 0.4369 - val_mean_squared_error: 0.4369\n",
            "Epoch 94/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.3348 - mean_squared_error: 0.3348 - val_loss: 0.4319 - val_mean_squared_error: 0.4319\n",
            "Epoch 95/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.3322 - mean_squared_error: 0.3322 - val_loss: 0.4179 - val_mean_squared_error: 0.4179\n",
            "Epoch 96/1000\n",
            "60/60 [==============================] - 0s 286us/sample - loss: 0.3752 - mean_squared_error: 0.3752 - val_loss: 0.4120 - val_mean_squared_error: 0.4120\n",
            "Epoch 97/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.3328 - mean_squared_error: 0.3328 - val_loss: 0.4053 - val_mean_squared_error: 0.4053\n",
            "Epoch 98/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.3292 - mean_squared_error: 0.3292 - val_loss: 0.4320 - val_mean_squared_error: 0.4320\n",
            "Epoch 99/1000\n",
            "60/60 [==============================] - 0s 272us/sample - loss: 0.3343 - mean_squared_error: 0.3343 - val_loss: 0.4502 - val_mean_squared_error: 0.4502\n",
            "Epoch 100/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.3347 - mean_squared_error: 0.3347 - val_loss: 0.4138 - val_mean_squared_error: 0.4138\n",
            "Epoch 101/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.3264 - mean_squared_error: 0.3264 - val_loss: 0.4171 - val_mean_squared_error: 0.4171\n",
            "Epoch 102/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.3288 - mean_squared_error: 0.3288 - val_loss: 0.4065 - val_mean_squared_error: 0.4065\n",
            "Epoch 103/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.3279 - mean_squared_error: 0.3279 - val_loss: 0.4250 - val_mean_squared_error: 0.4250\n",
            "Epoch 104/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.3278 - mean_squared_error: 0.3278 - val_loss: 0.4156 - val_mean_squared_error: 0.4156\n",
            "Epoch 105/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.3276 - mean_squared_error: 0.3276 - val_loss: 0.4098 - val_mean_squared_error: 0.4098\n",
            "Epoch 106/1000\n",
            "60/60 [==============================] - 0s 347us/sample - loss: 0.3267 - mean_squared_error: 0.3267 - val_loss: 0.4156 - val_mean_squared_error: 0.4156\n",
            "Epoch 107/1000\n",
            "60/60 [==============================] - 0s 292us/sample - loss: 0.3304 - mean_squared_error: 0.3304 - val_loss: 0.4006 - val_mean_squared_error: 0.4006\n",
            "Epoch 108/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.3315 - mean_squared_error: 0.3315 - val_loss: 0.3995 - val_mean_squared_error: 0.3995\n",
            "Epoch 109/1000\n",
            "60/60 [==============================] - 0s 272us/sample - loss: 0.3250 - mean_squared_error: 0.3250 - val_loss: 0.4223 - val_mean_squared_error: 0.4223\n",
            "Epoch 110/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.3256 - mean_squared_error: 0.3256 - val_loss: 0.4077 - val_mean_squared_error: 0.4077\n",
            "Epoch 111/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.3290 - mean_squared_error: 0.3290 - val_loss: 0.4348 - val_mean_squared_error: 0.4348\n",
            "Epoch 112/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.3291 - mean_squared_error: 0.3291 - val_loss: 0.4348 - val_mean_squared_error: 0.4348\n",
            "Epoch 113/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.3273 - mean_squared_error: 0.3273 - val_loss: 0.4346 - val_mean_squared_error: 0.4346\n",
            "Epoch 114/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.3250 - mean_squared_error: 0.3250 - val_loss: 0.4323 - val_mean_squared_error: 0.4323\n",
            "Epoch 115/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.3285 - mean_squared_error: 0.3285 - val_loss: 0.4342 - val_mean_squared_error: 0.4342\n",
            "Epoch 116/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.3472 - mean_squared_error: 0.3472 - val_loss: 0.4235 - val_mean_squared_error: 0.4235\n",
            "Epoch 117/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.3265 - mean_squared_error: 0.3265 - val_loss: 0.4431 - val_mean_squared_error: 0.4431\n",
            "Epoch 118/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.3251 - mean_squared_error: 0.3251 - val_loss: 0.4080 - val_mean_squared_error: 0.4080\n",
            "Epoch 119/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3241 - mean_squared_error: 0.3241 - val_loss: 0.4386 - val_mean_squared_error: 0.4386\n",
            "Epoch 120/1000\n",
            "60/60 [==============================] - 0s 214us/sample - loss: 0.3348 - mean_squared_error: 0.3348 - val_loss: 0.4649 - val_mean_squared_error: 0.4649\n",
            "Epoch 121/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.3351 - mean_squared_error: 0.3351 - val_loss: 0.4346 - val_mean_squared_error: 0.4346\n",
            "Epoch 122/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.3239 - mean_squared_error: 0.3239 - val_loss: 0.4007 - val_mean_squared_error: 0.4007\n",
            "Epoch 123/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.3355 - mean_squared_error: 0.3355 - val_loss: 0.4067 - val_mean_squared_error: 0.4067\n",
            "Epoch 124/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.3203 - mean_squared_error: 0.3203 - val_loss: 0.4128 - val_mean_squared_error: 0.4128\n",
            "Epoch 125/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.3207 - mean_squared_error: 0.3207 - val_loss: 0.4051 - val_mean_squared_error: 0.4051\n",
            "Epoch 126/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.3246 - mean_squared_error: 0.3246 - val_loss: 0.3972 - val_mean_squared_error: 0.3972\n",
            "Epoch 127/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.3261 - mean_squared_error: 0.3261 - val_loss: 0.4296 - val_mean_squared_error: 0.4296\n",
            "Epoch 128/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.3231 - mean_squared_error: 0.3231 - val_loss: 0.3954 - val_mean_squared_error: 0.3954\n",
            "Epoch 129/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.3262 - mean_squared_error: 0.3262 - val_loss: 0.4496 - val_mean_squared_error: 0.4496\n",
            "Epoch 130/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.3247 - mean_squared_error: 0.3247 - val_loss: 0.4329 - val_mean_squared_error: 0.4329\n",
            "Epoch 131/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.3224 - mean_squared_error: 0.3224 - val_loss: 0.4172 - val_mean_squared_error: 0.4172\n",
            "Epoch 132/1000\n",
            "60/60 [==============================] - 0s 274us/sample - loss: 0.3206 - mean_squared_error: 0.3206 - val_loss: 0.4284 - val_mean_squared_error: 0.4284\n",
            "Epoch 133/1000\n",
            "60/60 [==============================] - 0s 281us/sample - loss: 0.3221 - mean_squared_error: 0.3221 - val_loss: 0.4245 - val_mean_squared_error: 0.4245\n",
            "Epoch 134/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.3211 - mean_squared_error: 0.3211 - val_loss: 0.4319 - val_mean_squared_error: 0.4319\n",
            "Epoch 135/1000\n",
            "60/60 [==============================] - 0s 266us/sample - loss: 0.3276 - mean_squared_error: 0.3276 - val_loss: 0.4047 - val_mean_squared_error: 0.4047\n",
            "Epoch 136/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.3205 - mean_squared_error: 0.3205 - val_loss: 0.4095 - val_mean_squared_error: 0.4095\n",
            "Epoch 137/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.3176 - mean_squared_error: 0.3176 - val_loss: 0.4212 - val_mean_squared_error: 0.4212\n",
            "Epoch 138/1000\n",
            "60/60 [==============================] - 0s 276us/sample - loss: 0.3257 - mean_squared_error: 0.3257 - val_loss: 0.4507 - val_mean_squared_error: 0.4507\n",
            "Epoch 139/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.3323 - mean_squared_error: 0.3323 - val_loss: 0.4160 - val_mean_squared_error: 0.4160\n",
            "Epoch 140/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.3198 - mean_squared_error: 0.3198 - val_loss: 0.4105 - val_mean_squared_error: 0.4105\n",
            "Epoch 141/1000\n",
            "60/60 [==============================] - 0s 261us/sample - loss: 0.3217 - mean_squared_error: 0.3217 - val_loss: 0.4454 - val_mean_squared_error: 0.4454\n",
            "Epoch 142/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.3212 - mean_squared_error: 0.3212 - val_loss: 0.4283 - val_mean_squared_error: 0.4283\n",
            "Epoch 143/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3236 - mean_squared_error: 0.3236 - val_loss: 0.4012 - val_mean_squared_error: 0.4012\n",
            "Epoch 144/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.3255 - mean_squared_error: 0.3255 - val_loss: 0.4546 - val_mean_squared_error: 0.4546\n",
            "Epoch 145/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.3226 - mean_squared_error: 0.3226 - val_loss: 0.3830 - val_mean_squared_error: 0.3830\n",
            "Epoch 146/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.3311 - mean_squared_error: 0.3311 - val_loss: 0.4057 - val_mean_squared_error: 0.4057\n",
            "Epoch 147/1000\n",
            "60/60 [==============================] - 0s 202us/sample - loss: 0.3214 - mean_squared_error: 0.3214 - val_loss: 0.4370 - val_mean_squared_error: 0.4370\n",
            "Epoch 148/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.3209 - mean_squared_error: 0.3209 - val_loss: 0.4170 - val_mean_squared_error: 0.4170\n",
            "Epoch 149/1000\n",
            "60/60 [==============================] - 0s 272us/sample - loss: 0.3215 - mean_squared_error: 0.3215 - val_loss: 0.4396 - val_mean_squared_error: 0.4396\n",
            "Epoch 150/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.3234 - mean_squared_error: 0.3234 - val_loss: 0.4202 - val_mean_squared_error: 0.4202\n",
            "Epoch 151/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.3169 - mean_squared_error: 0.3169 - val_loss: 0.4235 - val_mean_squared_error: 0.4235\n",
            "Epoch 152/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.3136 - mean_squared_error: 0.3136 - val_loss: 0.3967 - val_mean_squared_error: 0.3967\n",
            "Epoch 153/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.3154 - mean_squared_error: 0.3154 - val_loss: 0.3992 - val_mean_squared_error: 0.3992\n",
            "Epoch 154/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.3125 - mean_squared_error: 0.3125 - val_loss: 0.4018 - val_mean_squared_error: 0.4018\n",
            "Epoch 155/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.3241 - mean_squared_error: 0.3241 - val_loss: 0.3782 - val_mean_squared_error: 0.3782\n",
            "Epoch 156/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.3278 - mean_squared_error: 0.3278 - val_loss: 0.4440 - val_mean_squared_error: 0.4440\n",
            "Epoch 157/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.3166 - mean_squared_error: 0.3166 - val_loss: 0.3872 - val_mean_squared_error: 0.3872\n",
            "Epoch 158/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.3130 - mean_squared_error: 0.3130 - val_loss: 0.4004 - val_mean_squared_error: 0.4004\n",
            "Epoch 159/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.3172 - mean_squared_error: 0.3172 - val_loss: 0.3825 - val_mean_squared_error: 0.3825\n",
            "Epoch 160/1000\n",
            "60/60 [==============================] - 0s 209us/sample - loss: 0.3153 - mean_squared_error: 0.3153 - val_loss: 0.4044 - val_mean_squared_error: 0.4044\n",
            "Epoch 161/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.3143 - mean_squared_error: 0.3143 - val_loss: 0.3973 - val_mean_squared_error: 0.3973\n",
            "Epoch 162/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.3116 - mean_squared_error: 0.3116 - val_loss: 0.4112 - val_mean_squared_error: 0.4112\n",
            "Epoch 163/1000\n",
            "60/60 [==============================] - 0s 327us/sample - loss: 0.3211 - mean_squared_error: 0.3211 - val_loss: 0.3799 - val_mean_squared_error: 0.3799\n",
            "Epoch 164/1000\n",
            "60/60 [==============================] - 0s 331us/sample - loss: 0.3121 - mean_squared_error: 0.3121 - val_loss: 0.4144 - val_mean_squared_error: 0.4144\n",
            "Epoch 165/1000\n",
            "60/60 [==============================] - 0s 284us/sample - loss: 0.3136 - mean_squared_error: 0.3136 - val_loss: 0.3900 - val_mean_squared_error: 0.3900\n",
            "Epoch 166/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.3097 - mean_squared_error: 0.3097 - val_loss: 0.4027 - val_mean_squared_error: 0.4027\n",
            "Epoch 167/1000\n",
            "60/60 [==============================] - 0s 259us/sample - loss: 0.3127 - mean_squared_error: 0.3127 - val_loss: 0.3840 - val_mean_squared_error: 0.3840\n",
            "Epoch 168/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.3114 - mean_squared_error: 0.3114 - val_loss: 0.3895 - val_mean_squared_error: 0.3895\n",
            "Epoch 169/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.3093 - mean_squared_error: 0.3093 - val_loss: 0.4089 - val_mean_squared_error: 0.4089\n",
            "Epoch 170/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.3085 - mean_squared_error: 0.3085 - val_loss: 0.3910 - val_mean_squared_error: 0.3910\n",
            "Epoch 171/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.3158 - mean_squared_error: 0.3158 - val_loss: 0.3854 - val_mean_squared_error: 0.3854\n",
            "Epoch 172/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.3082 - mean_squared_error: 0.3082 - val_loss: 0.4014 - val_mean_squared_error: 0.4014\n",
            "Epoch 173/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.3140 - mean_squared_error: 0.3140 - val_loss: 0.3722 - val_mean_squared_error: 0.3722\n",
            "Epoch 174/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.3111 - mean_squared_error: 0.3111 - val_loss: 0.4177 - val_mean_squared_error: 0.4177\n",
            "Epoch 175/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.3098 - mean_squared_error: 0.3098 - val_loss: 0.4091 - val_mean_squared_error: 0.4091\n",
            "Epoch 176/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.3120 - mean_squared_error: 0.3120 - val_loss: 0.3791 - val_mean_squared_error: 0.3791\n",
            "Epoch 177/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.3165 - mean_squared_error: 0.3165 - val_loss: 0.3882 - val_mean_squared_error: 0.3882\n",
            "Epoch 178/1000\n",
            "60/60 [==============================] - 0s 325us/sample - loss: 0.3144 - mean_squared_error: 0.3144 - val_loss: 0.3863 - val_mean_squared_error: 0.3863\n",
            "Epoch 179/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.3071 - mean_squared_error: 0.3071 - val_loss: 0.3987 - val_mean_squared_error: 0.3987\n",
            "Epoch 180/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.3113 - mean_squared_error: 0.3113 - val_loss: 0.3902 - val_mean_squared_error: 0.3902\n",
            "Epoch 181/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.3109 - mean_squared_error: 0.3109 - val_loss: 0.3759 - val_mean_squared_error: 0.3759\n",
            "Epoch 182/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.3113 - mean_squared_error: 0.3113 - val_loss: 0.3913 - val_mean_squared_error: 0.3913\n",
            "Epoch 183/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.3174 - mean_squared_error: 0.3174 - val_loss: 0.3759 - val_mean_squared_error: 0.3759\n",
            "Epoch 184/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.3081 - mean_squared_error: 0.3081 - val_loss: 0.3824 - val_mean_squared_error: 0.3824\n",
            "Epoch 185/1000\n",
            "60/60 [==============================] - 0s 292us/sample - loss: 0.3189 - mean_squared_error: 0.3189 - val_loss: 0.3738 - val_mean_squared_error: 0.3738\n",
            "Epoch 186/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.3189 - mean_squared_error: 0.3189 - val_loss: 0.4220 - val_mean_squared_error: 0.4220\n",
            "Epoch 187/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.3122 - mean_squared_error: 0.3122 - val_loss: 0.4237 - val_mean_squared_error: 0.4237\n",
            "Epoch 188/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.3191 - mean_squared_error: 0.3191 - val_loss: 0.3922 - val_mean_squared_error: 0.3922\n",
            "Epoch 189/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.3085 - mean_squared_error: 0.3085 - val_loss: 0.3778 - val_mean_squared_error: 0.3778\n",
            "Epoch 190/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.3087 - mean_squared_error: 0.3087 - val_loss: 0.4322 - val_mean_squared_error: 0.4322\n",
            "Epoch 191/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.3252 - mean_squared_error: 0.3252 - val_loss: 0.3565 - val_mean_squared_error: 0.3565\n",
            "Epoch 192/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.3157 - mean_squared_error: 0.3157 - val_loss: 0.3801 - val_mean_squared_error: 0.3801\n",
            "Epoch 193/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.3091 - mean_squared_error: 0.3091 - val_loss: 0.3751 - val_mean_squared_error: 0.3751\n",
            "Epoch 194/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.3083 - mean_squared_error: 0.3083 - val_loss: 0.3869 - val_mean_squared_error: 0.3869\n",
            "Epoch 195/1000\n",
            "60/60 [==============================] - 0s 277us/sample - loss: 0.3124 - mean_squared_error: 0.3124 - val_loss: 0.3977 - val_mean_squared_error: 0.3977\n",
            "Epoch 196/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.3161 - mean_squared_error: 0.3161 - val_loss: 0.4249 - val_mean_squared_error: 0.4249\n",
            "Epoch 197/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.3055 - mean_squared_error: 0.3055 - val_loss: 0.4030 - val_mean_squared_error: 0.4030\n",
            "Epoch 198/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.3159 - mean_squared_error: 0.3159 - val_loss: 0.4125 - val_mean_squared_error: 0.4125\n",
            "Epoch 199/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.3020 - mean_squared_error: 0.3020 - val_loss: 0.3880 - val_mean_squared_error: 0.3880\n",
            "Epoch 200/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.3090 - mean_squared_error: 0.3090 - val_loss: 0.3818 - val_mean_squared_error: 0.3818\n",
            "Epoch 201/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.3099 - mean_squared_error: 0.3099 - val_loss: 0.3672 - val_mean_squared_error: 0.3672\n",
            "Epoch 202/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3058 - mean_squared_error: 0.3058 - val_loss: 0.4262 - val_mean_squared_error: 0.4262\n",
            "Epoch 203/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.3042 - mean_squared_error: 0.3042 - val_loss: 0.3939 - val_mean_squared_error: 0.3939\n",
            "Epoch 204/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.3016 - mean_squared_error: 0.3016 - val_loss: 0.3777 - val_mean_squared_error: 0.3777\n",
            "Epoch 205/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.3620 - mean_squared_error: 0.3620 - val_loss: 0.3781 - val_mean_squared_error: 0.3781\n",
            "Epoch 206/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.3105 - mean_squared_error: 0.3105 - val_loss: 0.3866 - val_mean_squared_error: 0.3866\n",
            "Epoch 207/1000\n",
            "60/60 [==============================] - 0s 273us/sample - loss: 0.3000 - mean_squared_error: 0.3000 - val_loss: 0.3885 - val_mean_squared_error: 0.3885\n",
            "Epoch 208/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.3032 - mean_squared_error: 0.3032 - val_loss: 0.3719 - val_mean_squared_error: 0.3719\n",
            "Epoch 209/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.3044 - mean_squared_error: 0.3044 - val_loss: 0.4213 - val_mean_squared_error: 0.4213\n",
            "Epoch 210/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.3033 - mean_squared_error: 0.3033 - val_loss: 0.4058 - val_mean_squared_error: 0.4058\n",
            "Epoch 211/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.2999 - mean_squared_error: 0.2999 - val_loss: 0.3951 - val_mean_squared_error: 0.3951\n",
            "Epoch 212/1000\n",
            "60/60 [==============================] - 0s 209us/sample - loss: 0.3149 - mean_squared_error: 0.3149 - val_loss: 0.4114 - val_mean_squared_error: 0.4114\n",
            "Epoch 213/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.3041 - mean_squared_error: 0.3041 - val_loss: 0.3642 - val_mean_squared_error: 0.3642\n",
            "Epoch 214/1000\n",
            "60/60 [==============================] - 0s 214us/sample - loss: 0.3220 - mean_squared_error: 0.3220 - val_loss: 0.4470 - val_mean_squared_error: 0.4470\n",
            "Epoch 215/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.3210 - mean_squared_error: 0.3210 - val_loss: 0.4496 - val_mean_squared_error: 0.4496\n",
            "Epoch 216/1000\n",
            "60/60 [==============================] - 0s 264us/sample - loss: 0.3065 - mean_squared_error: 0.3065 - val_loss: 0.4004 - val_mean_squared_error: 0.4004\n",
            "Epoch 217/1000\n",
            "60/60 [==============================] - 0s 266us/sample - loss: 0.3029 - mean_squared_error: 0.3029 - val_loss: 0.3698 - val_mean_squared_error: 0.3698\n",
            "Epoch 218/1000\n",
            "60/60 [==============================] - 0s 272us/sample - loss: 0.3010 - mean_squared_error: 0.3010 - val_loss: 0.4004 - val_mean_squared_error: 0.4004\n",
            "Epoch 219/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.3006 - mean_squared_error: 0.3006 - val_loss: 0.3640 - val_mean_squared_error: 0.3640\n",
            "Epoch 220/1000\n",
            "60/60 [==============================] - 0s 357us/sample - loss: 0.3007 - mean_squared_error: 0.3007 - val_loss: 0.3952 - val_mean_squared_error: 0.3952\n",
            "Epoch 221/1000\n",
            "60/60 [==============================] - 0s 316us/sample - loss: 0.2979 - mean_squared_error: 0.2979 - val_loss: 0.3931 - val_mean_squared_error: 0.3931\n",
            "Epoch 222/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.2986 - mean_squared_error: 0.2986 - val_loss: 0.3834 - val_mean_squared_error: 0.3834\n",
            "Epoch 223/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.3242 - mean_squared_error: 0.3242 - val_loss: 0.3548 - val_mean_squared_error: 0.3548\n",
            "Epoch 224/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.2992 - mean_squared_error: 0.2992 - val_loss: 0.3877 - val_mean_squared_error: 0.3877\n",
            "Epoch 225/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.3149 - mean_squared_error: 0.3149 - val_loss: 0.4608 - val_mean_squared_error: 0.4608\n",
            "Epoch 226/1000\n",
            "60/60 [==============================] - 0s 279us/sample - loss: 0.3096 - mean_squared_error: 0.3096 - val_loss: 0.4064 - val_mean_squared_error: 0.4064\n",
            "Epoch 227/1000\n",
            "60/60 [==============================] - 0s 267us/sample - loss: 0.2982 - mean_squared_error: 0.2982 - val_loss: 0.3723 - val_mean_squared_error: 0.3723\n",
            "Epoch 228/1000\n",
            "60/60 [==============================] - 0s 282us/sample - loss: 0.2968 - mean_squared_error: 0.2968 - val_loss: 0.3745 - val_mean_squared_error: 0.3745\n",
            "Epoch 229/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.3163 - mean_squared_error: 0.3163 - val_loss: 0.3575 - val_mean_squared_error: 0.3575\n",
            "Epoch 230/1000\n",
            "60/60 [==============================] - 0s 259us/sample - loss: 0.2977 - mean_squared_error: 0.2977 - val_loss: 0.4163 - val_mean_squared_error: 0.4163\n",
            "Epoch 231/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.3038 - mean_squared_error: 0.3038 - val_loss: 0.4108 - val_mean_squared_error: 0.4108\n",
            "Epoch 232/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.2969 - mean_squared_error: 0.2969 - val_loss: 0.3664 - val_mean_squared_error: 0.3664\n",
            "Epoch 233/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.2983 - mean_squared_error: 0.2983 - val_loss: 0.3689 - val_mean_squared_error: 0.3689\n",
            "Epoch 234/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.3102 - mean_squared_error: 0.3102 - val_loss: 0.4199 - val_mean_squared_error: 0.4199\n",
            "Epoch 235/1000\n",
            "60/60 [==============================] - 0s 266us/sample - loss: 0.3369 - mean_squared_error: 0.3369 - val_loss: 0.3959 - val_mean_squared_error: 0.3959\n",
            "Epoch 236/1000\n",
            "60/60 [==============================] - 0s 278us/sample - loss: 0.2947 - mean_squared_error: 0.2947 - val_loss: 0.3811 - val_mean_squared_error: 0.3811\n",
            "Epoch 237/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2955 - mean_squared_error: 0.2955 - val_loss: 0.3724 - val_mean_squared_error: 0.3724\n",
            "Epoch 238/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.3003 - mean_squared_error: 0.3003 - val_loss: 0.3563 - val_mean_squared_error: 0.3563\n",
            "Epoch 239/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.3015 - mean_squared_error: 0.3015 - val_loss: 0.3648 - val_mean_squared_error: 0.3648\n",
            "Epoch 240/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.2982 - mean_squared_error: 0.2982 - val_loss: 0.3804 - val_mean_squared_error: 0.3804\n",
            "Epoch 241/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.3062 - mean_squared_error: 0.3062 - val_loss: 0.4128 - val_mean_squared_error: 0.4128\n",
            "Epoch 242/1000\n",
            "60/60 [==============================] - 0s 205us/sample - loss: 0.2952 - mean_squared_error: 0.2952 - val_loss: 0.3631 - val_mean_squared_error: 0.3631\n",
            "Epoch 243/1000\n",
            "60/60 [==============================] - 0s 206us/sample - loss: 0.2950 - mean_squared_error: 0.2950 - val_loss: 0.3680 - val_mean_squared_error: 0.3680\n",
            "Epoch 244/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.3150 - mean_squared_error: 0.3150 - val_loss: 0.3575 - val_mean_squared_error: 0.3575\n",
            "Epoch 245/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2928 - mean_squared_error: 0.2928 - val_loss: 0.3926 - val_mean_squared_error: 0.3926\n",
            "Epoch 246/1000\n",
            "60/60 [==============================] - 0s 279us/sample - loss: 0.3000 - mean_squared_error: 0.3000 - val_loss: 0.3492 - val_mean_squared_error: 0.3492\n",
            "Epoch 247/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.2941 - mean_squared_error: 0.2941 - val_loss: 0.3863 - val_mean_squared_error: 0.3863\n",
            "Epoch 248/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.2922 - mean_squared_error: 0.2922 - val_loss: 0.3625 - val_mean_squared_error: 0.3625\n",
            "Epoch 249/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.3007 - mean_squared_error: 0.3007 - val_loss: 0.3542 - val_mean_squared_error: 0.3542\n",
            "Epoch 250/1000\n",
            "60/60 [==============================] - 0s 351us/sample - loss: 0.2931 - mean_squared_error: 0.2931 - val_loss: 0.3835 - val_mean_squared_error: 0.3835\n",
            "Epoch 251/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2956 - mean_squared_error: 0.2956 - val_loss: 0.3683 - val_mean_squared_error: 0.3683\n",
            "Epoch 252/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.2931 - mean_squared_error: 0.2931 - val_loss: 0.3670 - val_mean_squared_error: 0.3670\n",
            "Epoch 253/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.3052 - mean_squared_error: 0.3052 - val_loss: 0.3651 - val_mean_squared_error: 0.3651\n",
            "Epoch 254/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.2930 - mean_squared_error: 0.2930 - val_loss: 0.3650 - val_mean_squared_error: 0.3650\n",
            "Epoch 255/1000\n",
            "60/60 [==============================] - 0s 266us/sample - loss: 0.2925 - mean_squared_error: 0.2925 - val_loss: 0.4121 - val_mean_squared_error: 0.4121\n",
            "Epoch 256/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.3000 - mean_squared_error: 0.3000 - val_loss: 0.3646 - val_mean_squared_error: 0.3646\n",
            "Epoch 257/1000\n",
            "60/60 [==============================] - 0s 280us/sample - loss: 0.3094 - mean_squared_error: 0.3094 - val_loss: 0.4025 - val_mean_squared_error: 0.4025\n",
            "Epoch 258/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.2967 - mean_squared_error: 0.2967 - val_loss: 0.3752 - val_mean_squared_error: 0.3752\n",
            "Epoch 259/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.3121 - mean_squared_error: 0.3121 - val_loss: 0.3562 - val_mean_squared_error: 0.3562\n",
            "Epoch 260/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.2948 - mean_squared_error: 0.2948 - val_loss: 0.4196 - val_mean_squared_error: 0.4196\n",
            "Epoch 261/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2961 - mean_squared_error: 0.2961 - val_loss: 0.3744 - val_mean_squared_error: 0.3744\n",
            "Epoch 262/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.2941 - mean_squared_error: 0.2941 - val_loss: 0.3637 - val_mean_squared_error: 0.3637\n",
            "Epoch 263/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.2930 - mean_squared_error: 0.2930 - val_loss: 0.3757 - val_mean_squared_error: 0.3757\n",
            "Epoch 264/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.2923 - mean_squared_error: 0.2923 - val_loss: 0.3660 - val_mean_squared_error: 0.3660\n",
            "Epoch 265/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.3200 - mean_squared_error: 0.3200 - val_loss: 0.3618 - val_mean_squared_error: 0.3618\n",
            "Epoch 266/1000\n",
            "60/60 [==============================] - 0s 344us/sample - loss: 0.2938 - mean_squared_error: 0.2938 - val_loss: 0.3623 - val_mean_squared_error: 0.3623\n",
            "Epoch 267/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.3143 - mean_squared_error: 0.3143 - val_loss: 0.4282 - val_mean_squared_error: 0.4282\n",
            "Epoch 268/1000\n",
            "60/60 [==============================] - 0s 276us/sample - loss: 0.2961 - mean_squared_error: 0.2961 - val_loss: 0.3696 - val_mean_squared_error: 0.3696\n",
            "Epoch 269/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.2935 - mean_squared_error: 0.2935 - val_loss: 0.3670 - val_mean_squared_error: 0.3670\n",
            "Epoch 270/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.3362 - mean_squared_error: 0.3362 - val_loss: 0.4239 - val_mean_squared_error: 0.4239\n",
            "Epoch 271/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.2931 - mean_squared_error: 0.2931 - val_loss: 0.3862 - val_mean_squared_error: 0.3862\n",
            "Epoch 272/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.2954 - mean_squared_error: 0.2954 - val_loss: 0.3630 - val_mean_squared_error: 0.3630\n",
            "Epoch 273/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2905 - mean_squared_error: 0.2905 - val_loss: 0.4012 - val_mean_squared_error: 0.4012\n",
            "Epoch 274/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.3005 - mean_squared_error: 0.3005 - val_loss: 0.3766 - val_mean_squared_error: 0.3766\n",
            "Epoch 275/1000\n",
            "60/60 [==============================] - 0s 321us/sample - loss: 0.2864 - mean_squared_error: 0.2864 - val_loss: 0.3667 - val_mean_squared_error: 0.3667\n",
            "Epoch 276/1000\n",
            "60/60 [==============================] - 0s 292us/sample - loss: 0.3059 - mean_squared_error: 0.3059 - val_loss: 0.3918 - val_mean_squared_error: 0.3918\n",
            "Epoch 277/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.2892 - mean_squared_error: 0.2892 - val_loss: 0.3891 - val_mean_squared_error: 0.3891\n",
            "Epoch 278/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.3257 - mean_squared_error: 0.3257 - val_loss: 0.3883 - val_mean_squared_error: 0.3883\n",
            "Epoch 279/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2866 - mean_squared_error: 0.2866 - val_loss: 0.3548 - val_mean_squared_error: 0.3548\n",
            "Epoch 280/1000\n",
            "60/60 [==============================] - 0s 275us/sample - loss: 0.2870 - mean_squared_error: 0.2870 - val_loss: 0.4021 - val_mean_squared_error: 0.4021\n",
            "Epoch 281/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.3087 - mean_squared_error: 0.3087 - val_loss: 0.3692 - val_mean_squared_error: 0.3692\n",
            "Epoch 282/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.2953 - mean_squared_error: 0.2953 - val_loss: 0.3770 - val_mean_squared_error: 0.3770\n",
            "Epoch 283/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.2854 - mean_squared_error: 0.2854 - val_loss: 0.3550 - val_mean_squared_error: 0.3550\n",
            "Epoch 284/1000\n",
            "60/60 [==============================] - 0s 270us/sample - loss: 0.2853 - mean_squared_error: 0.2853 - val_loss: 0.3864 - val_mean_squared_error: 0.3864\n",
            "Epoch 285/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.2998 - mean_squared_error: 0.2998 - val_loss: 0.3887 - val_mean_squared_error: 0.3887\n",
            "Epoch 286/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.2939 - mean_squared_error: 0.2939 - val_loss: 0.3410 - val_mean_squared_error: 0.3410\n",
            "Epoch 287/1000\n",
            "60/60 [==============================] - 0s 264us/sample - loss: 0.2853 - mean_squared_error: 0.2853 - val_loss: 0.3850 - val_mean_squared_error: 0.3850\n",
            "Epoch 288/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.2954 - mean_squared_error: 0.2954 - val_loss: 0.3985 - val_mean_squared_error: 0.3985\n",
            "Epoch 289/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.2933 - mean_squared_error: 0.2933 - val_loss: 0.3828 - val_mean_squared_error: 0.3828\n",
            "Epoch 290/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2939 - mean_squared_error: 0.2939 - val_loss: 0.3418 - val_mean_squared_error: 0.3418\n",
            "Epoch 291/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2847 - mean_squared_error: 0.2847 - val_loss: 0.4106 - val_mean_squared_error: 0.4106\n",
            "Epoch 292/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.2975 - mean_squared_error: 0.2975 - val_loss: 0.4126 - val_mean_squared_error: 0.4126\n",
            "Epoch 293/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2892 - mean_squared_error: 0.2892 - val_loss: 0.3885 - val_mean_squared_error: 0.3885\n",
            "Epoch 294/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2849 - mean_squared_error: 0.2849 - val_loss: 0.3822 - val_mean_squared_error: 0.3822\n",
            "Epoch 295/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.3171 - mean_squared_error: 0.3171 - val_loss: 0.3963 - val_mean_squared_error: 0.3963\n",
            "Epoch 296/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.2855 - mean_squared_error: 0.2855 - val_loss: 0.3723 - val_mean_squared_error: 0.3723\n",
            "Epoch 297/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2810 - mean_squared_error: 0.2810 - val_loss: 0.3657 - val_mean_squared_error: 0.3657\n",
            "Epoch 298/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.2912 - mean_squared_error: 0.2912 - val_loss: 0.3370 - val_mean_squared_error: 0.3370\n",
            "Epoch 299/1000\n",
            "60/60 [==============================] - 0s 206us/sample - loss: 0.2847 - mean_squared_error: 0.2847 - val_loss: 0.3629 - val_mean_squared_error: 0.3629\n",
            "Epoch 300/1000\n",
            "60/60 [==============================] - 0s 211us/sample - loss: 0.2808 - mean_squared_error: 0.2808 - val_loss: 0.3706 - val_mean_squared_error: 0.3706\n",
            "Epoch 301/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2814 - mean_squared_error: 0.2814 - val_loss: 0.3689 - val_mean_squared_error: 0.3689\n",
            "Epoch 302/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.3084 - mean_squared_error: 0.3084 - val_loss: 0.4796 - val_mean_squared_error: 0.4796\n",
            "Epoch 303/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.3166 - mean_squared_error: 0.3166 - val_loss: 0.4264 - val_mean_squared_error: 0.4264\n",
            "Epoch 304/1000\n",
            "60/60 [==============================] - 0s 206us/sample - loss: 0.2827 - mean_squared_error: 0.2827 - val_loss: 0.3486 - val_mean_squared_error: 0.3486\n",
            "Epoch 305/1000\n",
            "60/60 [==============================] - 0s 209us/sample - loss: 0.3072 - mean_squared_error: 0.3072 - val_loss: 0.4810 - val_mean_squared_error: 0.4810\n",
            "Epoch 306/1000\n",
            "60/60 [==============================] - 0s 267us/sample - loss: 0.3192 - mean_squared_error: 0.3192 - val_loss: 0.3515 - val_mean_squared_error: 0.3515\n",
            "Epoch 307/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.3108 - mean_squared_error: 0.3108 - val_loss: 0.3680 - val_mean_squared_error: 0.3680\n",
            "Epoch 308/1000\n",
            "60/60 [==============================] - 0s 371us/sample - loss: 0.2837 - mean_squared_error: 0.2837 - val_loss: 0.3386 - val_mean_squared_error: 0.3386\n",
            "Epoch 309/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.2918 - mean_squared_error: 0.2918 - val_loss: 0.3336 - val_mean_squared_error: 0.3336\n",
            "Epoch 310/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.2833 - mean_squared_error: 0.2833 - val_loss: 0.4174 - val_mean_squared_error: 0.4174\n",
            "Epoch 311/1000\n",
            "60/60 [==============================] - 0s 336us/sample - loss: 0.3581 - mean_squared_error: 0.3581 - val_loss: 0.5357 - val_mean_squared_error: 0.5357\n",
            "Epoch 312/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.2976 - mean_squared_error: 0.2976 - val_loss: 0.3452 - val_mean_squared_error: 0.3452\n",
            "Epoch 313/1000\n",
            "60/60 [==============================] - 0s 214us/sample - loss: 0.2919 - mean_squared_error: 0.2919 - val_loss: 0.3465 - val_mean_squared_error: 0.3465\n",
            "Epoch 314/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.2849 - mean_squared_error: 0.2849 - val_loss: 0.3857 - val_mean_squared_error: 0.3857\n",
            "Epoch 315/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.2821 - mean_squared_error: 0.2821 - val_loss: 0.3385 - val_mean_squared_error: 0.3385\n",
            "Epoch 316/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2782 - mean_squared_error: 0.2782 - val_loss: 0.3691 - val_mean_squared_error: 0.3691\n",
            "Epoch 317/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2778 - mean_squared_error: 0.2778 - val_loss: 0.3598 - val_mean_squared_error: 0.3598\n",
            "Epoch 318/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.2801 - mean_squared_error: 0.2801 - val_loss: 0.3856 - val_mean_squared_error: 0.3856\n",
            "Epoch 319/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.2805 - mean_squared_error: 0.2805 - val_loss: 0.3480 - val_mean_squared_error: 0.3480\n",
            "Epoch 320/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.2816 - mean_squared_error: 0.2816 - val_loss: 0.4040 - val_mean_squared_error: 0.4040\n",
            "Epoch 321/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.3092 - mean_squared_error: 0.3092 - val_loss: 0.4251 - val_mean_squared_error: 0.4251\n",
            "Epoch 322/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.3082 - mean_squared_error: 0.3082 - val_loss: 0.4024 - val_mean_squared_error: 0.4024\n",
            "Epoch 323/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2815 - mean_squared_error: 0.2815 - val_loss: 0.3673 - val_mean_squared_error: 0.3673\n",
            "Epoch 324/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.2975 - mean_squared_error: 0.2975 - val_loss: 0.4673 - val_mean_squared_error: 0.4673\n",
            "Epoch 325/1000\n",
            "60/60 [==============================] - 0s 270us/sample - loss: 0.2895 - mean_squared_error: 0.2895 - val_loss: 0.3634 - val_mean_squared_error: 0.3634\n",
            "Epoch 326/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.2878 - mean_squared_error: 0.2878 - val_loss: 0.4014 - val_mean_squared_error: 0.4014\n",
            "Epoch 327/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.2793 - mean_squared_error: 0.2793 - val_loss: 0.3515 - val_mean_squared_error: 0.3515\n",
            "Epoch 328/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.2773 - mean_squared_error: 0.2773 - val_loss: 0.3632 - val_mean_squared_error: 0.3632\n",
            "Epoch 329/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.2851 - mean_squared_error: 0.2851 - val_loss: 0.3802 - val_mean_squared_error: 0.3802\n",
            "Epoch 330/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.2855 - mean_squared_error: 0.2855 - val_loss: 0.3905 - val_mean_squared_error: 0.3905\n",
            "Epoch 331/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.2753 - mean_squared_error: 0.2753 - val_loss: 0.3393 - val_mean_squared_error: 0.3393\n",
            "Epoch 332/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.2769 - mean_squared_error: 0.2769 - val_loss: 0.3527 - val_mean_squared_error: 0.3527\n",
            "Epoch 333/1000\n",
            "60/60 [==============================] - 0s 310us/sample - loss: 0.2819 - mean_squared_error: 0.2819 - val_loss: 0.3763 - val_mean_squared_error: 0.3763\n",
            "Epoch 334/1000\n",
            "60/60 [==============================] - 0s 324us/sample - loss: 0.2801 - mean_squared_error: 0.2801 - val_loss: 0.3239 - val_mean_squared_error: 0.3239\n",
            "Epoch 335/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.2989 - mean_squared_error: 0.2989 - val_loss: 0.3326 - val_mean_squared_error: 0.3326\n",
            "Epoch 336/1000\n",
            "60/60 [==============================] - 0s 266us/sample - loss: 0.2801 - mean_squared_error: 0.2801 - val_loss: 0.3759 - val_mean_squared_error: 0.3759\n",
            "Epoch 337/1000\n",
            "60/60 [==============================] - 0s 264us/sample - loss: 0.2750 - mean_squared_error: 0.2750 - val_loss: 0.3676 - val_mean_squared_error: 0.3676\n",
            "Epoch 338/1000\n",
            "60/60 [==============================] - 0s 272us/sample - loss: 0.2823 - mean_squared_error: 0.2823 - val_loss: 0.3891 - val_mean_squared_error: 0.3891\n",
            "Epoch 339/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.2743 - mean_squared_error: 0.2743 - val_loss: 0.3326 - val_mean_squared_error: 0.3326\n",
            "Epoch 340/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2797 - mean_squared_error: 0.2797 - val_loss: 0.3410 - val_mean_squared_error: 0.3410\n",
            "Epoch 341/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2768 - mean_squared_error: 0.2768 - val_loss: 0.3362 - val_mean_squared_error: 0.3362\n",
            "Epoch 342/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2920 - mean_squared_error: 0.2920 - val_loss: 0.3378 - val_mean_squared_error: 0.3378\n",
            "Epoch 343/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.2772 - mean_squared_error: 0.2772 - val_loss: 0.3937 - val_mean_squared_error: 0.3937\n",
            "Epoch 344/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2889 - mean_squared_error: 0.2889 - val_loss: 0.3504 - val_mean_squared_error: 0.3504\n",
            "Epoch 345/1000\n",
            "60/60 [==============================] - 0s 295us/sample - loss: 0.2718 - mean_squared_error: 0.2718 - val_loss: 0.3626 - val_mean_squared_error: 0.3626\n",
            "Epoch 346/1000\n",
            "60/60 [==============================] - 0s 309us/sample - loss: 0.2712 - mean_squared_error: 0.2712 - val_loss: 0.3588 - val_mean_squared_error: 0.3588\n",
            "Epoch 347/1000\n",
            "60/60 [==============================] - 0s 261us/sample - loss: 0.2964 - mean_squared_error: 0.2964 - val_loss: 0.4184 - val_mean_squared_error: 0.4184\n",
            "Epoch 348/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.2915 - mean_squared_error: 0.2915 - val_loss: 0.3779 - val_mean_squared_error: 0.3779\n",
            "Epoch 349/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2833 - mean_squared_error: 0.2833 - val_loss: 0.4018 - val_mean_squared_error: 0.4018\n",
            "Epoch 350/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.2915 - mean_squared_error: 0.2915 - val_loss: 0.4094 - val_mean_squared_error: 0.4094\n",
            "Epoch 351/1000\n",
            "60/60 [==============================] - 0s 292us/sample - loss: 0.2850 - mean_squared_error: 0.2850 - val_loss: 0.3956 - val_mean_squared_error: 0.3956\n",
            "Epoch 352/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.2830 - mean_squared_error: 0.2830 - val_loss: 0.3372 - val_mean_squared_error: 0.3372\n",
            "Epoch 353/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2760 - mean_squared_error: 0.2760 - val_loss: 0.4176 - val_mean_squared_error: 0.4176\n",
            "Epoch 354/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.3033 - mean_squared_error: 0.3033 - val_loss: 0.4070 - val_mean_squared_error: 0.4070\n",
            "Epoch 355/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2748 - mean_squared_error: 0.2748 - val_loss: 0.3472 - val_mean_squared_error: 0.3472\n",
            "Epoch 356/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.2746 - mean_squared_error: 0.2746 - val_loss: 0.3374 - val_mean_squared_error: 0.3374\n",
            "Epoch 357/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.2736 - mean_squared_error: 0.2736 - val_loss: 0.3235 - val_mean_squared_error: 0.3235\n",
            "Epoch 358/1000\n",
            "60/60 [==============================] - 0s 273us/sample - loss: 0.2805 - mean_squared_error: 0.2805 - val_loss: 0.3370 - val_mean_squared_error: 0.3370\n",
            "Epoch 359/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.2782 - mean_squared_error: 0.2782 - val_loss: 0.3287 - val_mean_squared_error: 0.3287\n",
            "Epoch 360/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.2885 - mean_squared_error: 0.2885 - val_loss: 0.3152 - val_mean_squared_error: 0.3152\n",
            "Epoch 361/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2825 - mean_squared_error: 0.2825 - val_loss: 0.3783 - val_mean_squared_error: 0.3783\n",
            "Epoch 362/1000\n",
            "60/60 [==============================] - 0s 275us/sample - loss: 0.2686 - mean_squared_error: 0.2686 - val_loss: 0.3439 - val_mean_squared_error: 0.3439\n",
            "Epoch 363/1000\n",
            "60/60 [==============================] - 0s 387us/sample - loss: 0.2702 - mean_squared_error: 0.2702 - val_loss: 0.3653 - val_mean_squared_error: 0.3653\n",
            "Epoch 364/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.2900 - mean_squared_error: 0.2900 - val_loss: 0.3539 - val_mean_squared_error: 0.3539\n",
            "Epoch 365/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2782 - mean_squared_error: 0.2782 - val_loss: 0.3683 - val_mean_squared_error: 0.3683\n",
            "Epoch 366/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2684 - mean_squared_error: 0.2684 - val_loss: 0.3293 - val_mean_squared_error: 0.3293\n",
            "Epoch 367/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.2774 - mean_squared_error: 0.2774 - val_loss: 0.4115 - val_mean_squared_error: 0.4115\n",
            "Epoch 368/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.2705 - mean_squared_error: 0.2705 - val_loss: 0.3396 - val_mean_squared_error: 0.3396\n",
            "Epoch 369/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2707 - mean_squared_error: 0.2707 - val_loss: 0.3435 - val_mean_squared_error: 0.3435\n",
            "Epoch 370/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.3107 - mean_squared_error: 0.3107 - val_loss: 0.3380 - val_mean_squared_error: 0.3380\n",
            "Epoch 371/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.2684 - mean_squared_error: 0.2684 - val_loss: 0.3539 - val_mean_squared_error: 0.3539\n",
            "Epoch 372/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2679 - mean_squared_error: 0.2679 - val_loss: 0.3646 - val_mean_squared_error: 0.3646\n",
            "Epoch 373/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.2687 - mean_squared_error: 0.2687 - val_loss: 0.3635 - val_mean_squared_error: 0.3635\n",
            "Epoch 374/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.2689 - mean_squared_error: 0.2689 - val_loss: 0.3134 - val_mean_squared_error: 0.3134\n",
            "Epoch 375/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.2754 - mean_squared_error: 0.2754 - val_loss: 0.3200 - val_mean_squared_error: 0.3200\n",
            "Epoch 376/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.2772 - mean_squared_error: 0.2772 - val_loss: 0.3352 - val_mean_squared_error: 0.3352\n",
            "Epoch 377/1000\n",
            "60/60 [==============================] - 0s 297us/sample - loss: 0.2643 - mean_squared_error: 0.2643 - val_loss: 0.3311 - val_mean_squared_error: 0.3311\n",
            "Epoch 378/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.2635 - mean_squared_error: 0.2635 - val_loss: 0.3342 - val_mean_squared_error: 0.3342\n",
            "Epoch 379/1000\n",
            "60/60 [==============================] - 0s 293us/sample - loss: 0.2666 - mean_squared_error: 0.2666 - val_loss: 0.3242 - val_mean_squared_error: 0.3242\n",
            "Epoch 380/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.3076 - mean_squared_error: 0.3076 - val_loss: 0.3204 - val_mean_squared_error: 0.3204\n",
            "Epoch 381/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.2695 - mean_squared_error: 0.2695 - val_loss: 0.3153 - val_mean_squared_error: 0.3153\n",
            "Epoch 382/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.2648 - mean_squared_error: 0.2648 - val_loss: 0.3900 - val_mean_squared_error: 0.3900\n",
            "Epoch 383/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.2662 - mean_squared_error: 0.2662 - val_loss: 0.3346 - val_mean_squared_error: 0.3346\n",
            "Epoch 384/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.2615 - mean_squared_error: 0.2615 - val_loss: 0.3481 - val_mean_squared_error: 0.3481\n",
            "Epoch 385/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2869 - mean_squared_error: 0.2869 - val_loss: 0.3201 - val_mean_squared_error: 0.3201\n",
            "Epoch 386/1000\n",
            "60/60 [==============================] - 0s 335us/sample - loss: 0.2805 - mean_squared_error: 0.2805 - val_loss: 0.3237 - val_mean_squared_error: 0.3237\n",
            "Epoch 387/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.2839 - mean_squared_error: 0.2839 - val_loss: 0.3111 - val_mean_squared_error: 0.3111\n",
            "Epoch 388/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2665 - mean_squared_error: 0.2665 - val_loss: 0.3274 - val_mean_squared_error: 0.3274\n",
            "Epoch 389/1000\n",
            "60/60 [==============================] - 0s 358us/sample - loss: 0.2652 - mean_squared_error: 0.2652 - val_loss: 0.3561 - val_mean_squared_error: 0.3561\n",
            "Epoch 390/1000\n",
            "60/60 [==============================] - 0s 300us/sample - loss: 0.2659 - mean_squared_error: 0.2659 - val_loss: 0.3262 - val_mean_squared_error: 0.3262\n",
            "Epoch 391/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.2606 - mean_squared_error: 0.2606 - val_loss: 0.3272 - val_mean_squared_error: 0.3272\n",
            "Epoch 392/1000\n",
            "60/60 [==============================] - 0s 286us/sample - loss: 0.2634 - mean_squared_error: 0.2634 - val_loss: 0.3135 - val_mean_squared_error: 0.3135\n",
            "Epoch 393/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.2693 - mean_squared_error: 0.2693 - val_loss: 0.3920 - val_mean_squared_error: 0.3920\n",
            "Epoch 394/1000\n",
            "60/60 [==============================] - 0s 206us/sample - loss: 0.2638 - mean_squared_error: 0.2638 - val_loss: 0.3151 - val_mean_squared_error: 0.3151\n",
            "Epoch 395/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2708 - mean_squared_error: 0.2708 - val_loss: 0.3063 - val_mean_squared_error: 0.3063\n",
            "Epoch 396/1000\n",
            "60/60 [==============================] - 0s 287us/sample - loss: 0.2660 - mean_squared_error: 0.2660 - val_loss: 0.3195 - val_mean_squared_error: 0.3195\n",
            "Epoch 397/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.2824 - mean_squared_error: 0.2824 - val_loss: 0.3874 - val_mean_squared_error: 0.3874\n",
            "Epoch 398/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.2625 - mean_squared_error: 0.2625 - val_loss: 0.3308 - val_mean_squared_error: 0.3308\n",
            "Epoch 399/1000\n",
            "60/60 [==============================] - 0s 288us/sample - loss: 0.2670 - mean_squared_error: 0.2670 - val_loss: 0.4028 - val_mean_squared_error: 0.4028\n",
            "Epoch 400/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.2606 - mean_squared_error: 0.2606 - val_loss: 0.3169 - val_mean_squared_error: 0.3169\n",
            "Epoch 401/1000\n",
            "60/60 [==============================] - 0s 211us/sample - loss: 0.2764 - mean_squared_error: 0.2764 - val_loss: 0.3916 - val_mean_squared_error: 0.3916\n",
            "Epoch 402/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3047 - mean_squared_error: 0.3047 - val_loss: 0.4807 - val_mean_squared_error: 0.4807\n",
            "Epoch 403/1000\n",
            "60/60 [==============================] - 0s 307us/sample - loss: 0.2911 - mean_squared_error: 0.2911 - val_loss: 0.3665 - val_mean_squared_error: 0.3665\n",
            "Epoch 404/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.2593 - mean_squared_error: 0.2593 - val_loss: 0.3313 - val_mean_squared_error: 0.3313\n",
            "Epoch 405/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.2604 - mean_squared_error: 0.2604 - val_loss: 0.3231 - val_mean_squared_error: 0.3231\n",
            "Epoch 406/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.2699 - mean_squared_error: 0.2699 - val_loss: 0.3128 - val_mean_squared_error: 0.3128\n",
            "Epoch 407/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.2556 - mean_squared_error: 0.2556 - val_loss: 0.3577 - val_mean_squared_error: 0.3577\n",
            "Epoch 408/1000\n",
            "60/60 [==============================] - 0s 291us/sample - loss: 0.2611 - mean_squared_error: 0.2611 - val_loss: 0.3613 - val_mean_squared_error: 0.3613\n",
            "Epoch 409/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.2553 - mean_squared_error: 0.2553 - val_loss: 0.3220 - val_mean_squared_error: 0.3220\n",
            "Epoch 410/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.2578 - mean_squared_error: 0.2578 - val_loss: 0.3279 - val_mean_squared_error: 0.3279\n",
            "Epoch 411/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.3182 - mean_squared_error: 0.3182 - val_loss: 0.3068 - val_mean_squared_error: 0.3068\n",
            "Epoch 412/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.2703 - mean_squared_error: 0.2703 - val_loss: 0.3741 - val_mean_squared_error: 0.3741\n",
            "Epoch 413/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.2635 - mean_squared_error: 0.2635 - val_loss: 0.3024 - val_mean_squared_error: 0.3024\n",
            "Epoch 414/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.2569 - mean_squared_error: 0.2569 - val_loss: 0.3332 - val_mean_squared_error: 0.3332\n",
            "Epoch 415/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.2539 - mean_squared_error: 0.2539 - val_loss: 0.3254 - val_mean_squared_error: 0.3254\n",
            "Epoch 416/1000\n",
            "60/60 [==============================] - 0s 280us/sample - loss: 0.2757 - mean_squared_error: 0.2757 - val_loss: 0.3323 - val_mean_squared_error: 0.3323\n",
            "Epoch 417/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.2560 - mean_squared_error: 0.2560 - val_loss: 0.3251 - val_mean_squared_error: 0.3251\n",
            "Epoch 418/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.2556 - mean_squared_error: 0.2556 - val_loss: 0.3165 - val_mean_squared_error: 0.3165\n",
            "Epoch 419/1000\n",
            "60/60 [==============================] - 0s 282us/sample - loss: 0.2626 - mean_squared_error: 0.2626 - val_loss: 0.3009 - val_mean_squared_error: 0.3009\n",
            "Epoch 420/1000\n",
            "60/60 [==============================] - 0s 274us/sample - loss: 0.2551 - mean_squared_error: 0.2551 - val_loss: 0.3541 - val_mean_squared_error: 0.3541\n",
            "Epoch 421/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.2592 - mean_squared_error: 0.2592 - val_loss: 0.3134 - val_mean_squared_error: 0.3134\n",
            "Epoch 422/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.2520 - mean_squared_error: 0.2520 - val_loss: 0.3318 - val_mean_squared_error: 0.3318\n",
            "Epoch 423/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.2535 - mean_squared_error: 0.2535 - val_loss: 0.3003 - val_mean_squared_error: 0.3003\n",
            "Epoch 424/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.2673 - mean_squared_error: 0.2673 - val_loss: 0.3050 - val_mean_squared_error: 0.3050\n",
            "Epoch 425/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.2517 - mean_squared_error: 0.2517 - val_loss: 0.3339 - val_mean_squared_error: 0.3339\n",
            "Epoch 426/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.2533 - mean_squared_error: 0.2533 - val_loss: 0.3158 - val_mean_squared_error: 0.3158\n",
            "Epoch 427/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.2581 - mean_squared_error: 0.2581 - val_loss: 0.3107 - val_mean_squared_error: 0.3107\n",
            "Epoch 428/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.2833 - mean_squared_error: 0.2833 - val_loss: 0.3796 - val_mean_squared_error: 0.3796\n",
            "Epoch 429/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.2524 - mean_squared_error: 0.2524 - val_loss: 0.3112 - val_mean_squared_error: 0.3112\n",
            "Epoch 430/1000\n",
            "60/60 [==============================] - 0s 276us/sample - loss: 0.2634 - mean_squared_error: 0.2634 - val_loss: 0.2998 - val_mean_squared_error: 0.2998\n",
            "Epoch 431/1000\n",
            "60/60 [==============================] - 0s 303us/sample - loss: 0.2504 - mean_squared_error: 0.2504 - val_loss: 0.3440 - val_mean_squared_error: 0.3440\n",
            "Epoch 432/1000\n",
            "60/60 [==============================] - 0s 289us/sample - loss: 0.2741 - mean_squared_error: 0.2741 - val_loss: 0.4175 - val_mean_squared_error: 0.4175\n",
            "Epoch 433/1000\n",
            "60/60 [==============================] - 0s 276us/sample - loss: 0.2723 - mean_squared_error: 0.2723 - val_loss: 0.3175 - val_mean_squared_error: 0.3175\n",
            "Epoch 434/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.2484 - mean_squared_error: 0.2484 - val_loss: 0.3116 - val_mean_squared_error: 0.3116\n",
            "Epoch 435/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.2511 - mean_squared_error: 0.2511 - val_loss: 0.3053 - val_mean_squared_error: 0.3053\n",
            "Epoch 436/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.2492 - mean_squared_error: 0.2492 - val_loss: 0.3585 - val_mean_squared_error: 0.3585\n",
            "Epoch 437/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.2526 - mean_squared_error: 0.2526 - val_loss: 0.3311 - val_mean_squared_error: 0.3311\n",
            "Epoch 438/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2873 - mean_squared_error: 0.2873 - val_loss: 0.2986 - val_mean_squared_error: 0.2986\n",
            "Epoch 439/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.2653 - mean_squared_error: 0.2653 - val_loss: 0.3217 - val_mean_squared_error: 0.3217\n",
            "Epoch 440/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.2489 - mean_squared_error: 0.2489 - val_loss: 0.2974 - val_mean_squared_error: 0.2974\n",
            "Epoch 441/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.2470 - mean_squared_error: 0.2470 - val_loss: 0.3657 - val_mean_squared_error: 0.3657\n",
            "Epoch 442/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.2531 - mean_squared_error: 0.2531 - val_loss: 0.3405 - val_mean_squared_error: 0.3405\n",
            "Epoch 443/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.2469 - mean_squared_error: 0.2469 - val_loss: 0.3058 - val_mean_squared_error: 0.3058\n",
            "Epoch 444/1000\n",
            "60/60 [==============================] - 0s 438us/sample - loss: 0.2474 - mean_squared_error: 0.2474 - val_loss: 0.3223 - val_mean_squared_error: 0.3223\n",
            "Epoch 445/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.2471 - mean_squared_error: 0.2471 - val_loss: 0.2952 - val_mean_squared_error: 0.2952\n",
            "Epoch 446/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.2584 - mean_squared_error: 0.2584 - val_loss: 0.3302 - val_mean_squared_error: 0.3302\n",
            "Epoch 447/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2476 - mean_squared_error: 0.2476 - val_loss: 0.3393 - val_mean_squared_error: 0.3393\n",
            "Epoch 448/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2658 - mean_squared_error: 0.2658 - val_loss: 0.3453 - val_mean_squared_error: 0.3453\n",
            "Epoch 449/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2618 - mean_squared_error: 0.2618 - val_loss: 0.3423 - val_mean_squared_error: 0.3423\n",
            "Epoch 450/1000\n",
            "60/60 [==============================] - 0s 340us/sample - loss: 0.2546 - mean_squared_error: 0.2546 - val_loss: 0.3301 - val_mean_squared_error: 0.3301\n",
            "Epoch 451/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2565 - mean_squared_error: 0.2565 - val_loss: 0.3459 - val_mean_squared_error: 0.3459\n",
            "Epoch 452/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.2560 - mean_squared_error: 0.2560 - val_loss: 0.3545 - val_mean_squared_error: 0.3545\n",
            "Epoch 453/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2525 - mean_squared_error: 0.2525 - val_loss: 0.3295 - val_mean_squared_error: 0.3295\n",
            "Epoch 454/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2538 - mean_squared_error: 0.2538 - val_loss: 0.3555 - val_mean_squared_error: 0.3555\n",
            "Epoch 455/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.2782 - mean_squared_error: 0.2782 - val_loss: 0.4292 - val_mean_squared_error: 0.4292\n",
            "Epoch 456/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2538 - mean_squared_error: 0.2538 - val_loss: 0.2875 - val_mean_squared_error: 0.2875\n",
            "Epoch 457/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2424 - mean_squared_error: 0.2424 - val_loss: 0.3860 - val_mean_squared_error: 0.3860\n",
            "Epoch 458/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.2537 - mean_squared_error: 0.2537 - val_loss: 0.3055 - val_mean_squared_error: 0.3055\n",
            "Epoch 459/1000\n",
            "60/60 [==============================] - 0s 259us/sample - loss: 0.2429 - mean_squared_error: 0.2429 - val_loss: 0.3347 - val_mean_squared_error: 0.3347\n",
            "Epoch 460/1000\n",
            "60/60 [==============================] - 0s 214us/sample - loss: 0.2520 - mean_squared_error: 0.2520 - val_loss: 0.2833 - val_mean_squared_error: 0.2833\n",
            "Epoch 461/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2508 - mean_squared_error: 0.2508 - val_loss: 0.3094 - val_mean_squared_error: 0.3094\n",
            "Epoch 462/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.2412 - mean_squared_error: 0.2412 - val_loss: 0.3152 - val_mean_squared_error: 0.3152\n",
            "Epoch 463/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2573 - mean_squared_error: 0.2573 - val_loss: 0.3245 - val_mean_squared_error: 0.3245\n",
            "Epoch 464/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2645 - mean_squared_error: 0.2645 - val_loss: 0.3011 - val_mean_squared_error: 0.3011\n",
            "Epoch 465/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.2478 - mean_squared_error: 0.2478 - val_loss: 0.2869 - val_mean_squared_error: 0.2869\n",
            "Epoch 466/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2447 - mean_squared_error: 0.2447 - val_loss: 0.3104 - val_mean_squared_error: 0.3104\n",
            "Epoch 467/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.2453 - mean_squared_error: 0.2453 - val_loss: 0.3190 - val_mean_squared_error: 0.3190\n",
            "Epoch 468/1000\n",
            "60/60 [==============================] - 0s 207us/sample - loss: 0.2460 - mean_squared_error: 0.2460 - val_loss: 0.3389 - val_mean_squared_error: 0.3389\n",
            "Epoch 469/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2413 - mean_squared_error: 0.2413 - val_loss: 0.2914 - val_mean_squared_error: 0.2914\n",
            "Epoch 470/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.2400 - mean_squared_error: 0.2400 - val_loss: 0.3002 - val_mean_squared_error: 0.3002\n",
            "Epoch 471/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.2418 - mean_squared_error: 0.2418 - val_loss: 0.3421 - val_mean_squared_error: 0.3421\n",
            "Epoch 472/1000\n",
            "60/60 [==============================] - 0s 285us/sample - loss: 0.2392 - mean_squared_error: 0.2392 - val_loss: 0.3048 - val_mean_squared_error: 0.3048\n",
            "Epoch 473/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.2393 - mean_squared_error: 0.2393 - val_loss: 0.2891 - val_mean_squared_error: 0.2891\n",
            "Epoch 474/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.2374 - mean_squared_error: 0.2374 - val_loss: 0.3384 - val_mean_squared_error: 0.3384\n",
            "Epoch 475/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.2394 - mean_squared_error: 0.2394 - val_loss: 0.2868 - val_mean_squared_error: 0.2868\n",
            "Epoch 476/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.2379 - mean_squared_error: 0.2379 - val_loss: 0.3052 - val_mean_squared_error: 0.3052\n",
            "Epoch 477/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2581 - mean_squared_error: 0.2581 - val_loss: 0.2807 - val_mean_squared_error: 0.2807\n",
            "Epoch 478/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.2542 - mean_squared_error: 0.2542 - val_loss: 0.2966 - val_mean_squared_error: 0.2966\n",
            "Epoch 479/1000\n",
            "60/60 [==============================] - 0s 288us/sample - loss: 0.2427 - mean_squared_error: 0.2427 - val_loss: 0.3391 - val_mean_squared_error: 0.3391\n",
            "Epoch 480/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.2384 - mean_squared_error: 0.2384 - val_loss: 0.2938 - val_mean_squared_error: 0.2938\n",
            "Epoch 481/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.2420 - mean_squared_error: 0.2420 - val_loss: 0.2920 - val_mean_squared_error: 0.2920\n",
            "Epoch 482/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.2564 - mean_squared_error: 0.2564 - val_loss: 0.3464 - val_mean_squared_error: 0.3464\n",
            "Epoch 483/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.2408 - mean_squared_error: 0.2408 - val_loss: 0.2988 - val_mean_squared_error: 0.2988\n",
            "Epoch 484/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.2374 - mean_squared_error: 0.2374 - val_loss: 0.3099 - val_mean_squared_error: 0.3099\n",
            "Epoch 485/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.2433 - mean_squared_error: 0.2433 - val_loss: 0.2959 - val_mean_squared_error: 0.2959\n",
            "Epoch 486/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.2450 - mean_squared_error: 0.2450 - val_loss: 0.3504 - val_mean_squared_error: 0.3504\n",
            "Epoch 487/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.2409 - mean_squared_error: 0.2409 - val_loss: 0.3157 - val_mean_squared_error: 0.3157\n",
            "Epoch 488/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.2396 - mean_squared_error: 0.2396 - val_loss: 0.3253 - val_mean_squared_error: 0.3253\n",
            "Epoch 489/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.2866 - mean_squared_error: 0.2866 - val_loss: 0.3535 - val_mean_squared_error: 0.3535\n",
            "Epoch 490/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.2479 - mean_squared_error: 0.2479 - val_loss: 0.3162 - val_mean_squared_error: 0.3162\n",
            "Epoch 491/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2336 - mean_squared_error: 0.2336 - val_loss: 0.3038 - val_mean_squared_error: 0.3038\n",
            "Epoch 492/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.2368 - mean_squared_error: 0.2368 - val_loss: 0.2809 - val_mean_squared_error: 0.2809\n",
            "Epoch 493/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.2316 - mean_squared_error: 0.2316 - val_loss: 0.3290 - val_mean_squared_error: 0.3290\n",
            "Epoch 494/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.2493 - mean_squared_error: 0.2493 - val_loss: 0.3223 - val_mean_squared_error: 0.3223\n",
            "Epoch 495/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2320 - mean_squared_error: 0.2320 - val_loss: 0.2847 - val_mean_squared_error: 0.2847\n",
            "Epoch 496/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.3050 - mean_squared_error: 0.3050 - val_loss: 0.2900 - val_mean_squared_error: 0.2900\n",
            "Epoch 497/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.2375 - mean_squared_error: 0.2375 - val_loss: 0.3329 - val_mean_squared_error: 0.3329\n",
            "Epoch 498/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.2374 - mean_squared_error: 0.2374 - val_loss: 0.2825 - val_mean_squared_error: 0.2825\n",
            "Epoch 499/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.2365 - mean_squared_error: 0.2365 - val_loss: 0.2960 - val_mean_squared_error: 0.2960\n",
            "Epoch 500/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.2309 - mean_squared_error: 0.2309 - val_loss: 0.3071 - val_mean_squared_error: 0.3071\n",
            "Epoch 501/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.2315 - mean_squared_error: 0.2315 - val_loss: 0.3164 - val_mean_squared_error: 0.3164\n",
            "Epoch 502/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2378 - mean_squared_error: 0.2378 - val_loss: 0.2697 - val_mean_squared_error: 0.2697\n",
            "Epoch 503/1000\n",
            "60/60 [==============================] - 0s 298us/sample - loss: 0.2508 - mean_squared_error: 0.2508 - val_loss: 0.3126 - val_mean_squared_error: 0.3126\n",
            "Epoch 504/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.2360 - mean_squared_error: 0.2360 - val_loss: 0.2746 - val_mean_squared_error: 0.2746\n",
            "Epoch 505/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.2452 - mean_squared_error: 0.2452 - val_loss: 0.2700 - val_mean_squared_error: 0.2700\n",
            "Epoch 506/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.2327 - mean_squared_error: 0.2327 - val_loss: 0.2943 - val_mean_squared_error: 0.2943\n",
            "Epoch 507/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.2298 - mean_squared_error: 0.2298 - val_loss: 0.3036 - val_mean_squared_error: 0.3036\n",
            "Epoch 508/1000\n",
            "60/60 [==============================] - 0s 279us/sample - loss: 0.2324 - mean_squared_error: 0.2324 - val_loss: 0.3083 - val_mean_squared_error: 0.3083\n",
            "Epoch 509/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.2331 - mean_squared_error: 0.2331 - val_loss: 0.3283 - val_mean_squared_error: 0.3283\n",
            "Epoch 510/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.2312 - mean_squared_error: 0.2312 - val_loss: 0.3082 - val_mean_squared_error: 0.3082\n",
            "Epoch 511/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2399 - mean_squared_error: 0.2399 - val_loss: 0.3893 - val_mean_squared_error: 0.3893\n",
            "Epoch 512/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2409 - mean_squared_error: 0.2409 - val_loss: 0.2952 - val_mean_squared_error: 0.2952\n",
            "Epoch 513/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.2272 - mean_squared_error: 0.2272 - val_loss: 0.3069 - val_mean_squared_error: 0.3069\n",
            "Epoch 514/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.2341 - mean_squared_error: 0.2341 - val_loss: 0.2673 - val_mean_squared_error: 0.2673\n",
            "Epoch 515/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.2382 - mean_squared_error: 0.2382 - val_loss: 0.2707 - val_mean_squared_error: 0.2707\n",
            "Epoch 516/1000\n",
            "60/60 [==============================] - 0s 292us/sample - loss: 0.2366 - mean_squared_error: 0.2366 - val_loss: 0.2688 - val_mean_squared_error: 0.2688\n",
            "Epoch 517/1000\n",
            "60/60 [==============================] - 0s 301us/sample - loss: 0.2281 - mean_squared_error: 0.2281 - val_loss: 0.2899 - val_mean_squared_error: 0.2899\n",
            "Epoch 518/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.2482 - mean_squared_error: 0.2482 - val_loss: 0.4429 - val_mean_squared_error: 0.4429\n",
            "Epoch 519/1000\n",
            "60/60 [==============================] - 0s 263us/sample - loss: 0.2923 - mean_squared_error: 0.2923 - val_loss: 0.2782 - val_mean_squared_error: 0.2782\n",
            "Epoch 520/1000\n",
            "60/60 [==============================] - 0s 264us/sample - loss: 0.2340 - mean_squared_error: 0.2340 - val_loss: 0.2696 - val_mean_squared_error: 0.2696\n",
            "Epoch 521/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.2286 - mean_squared_error: 0.2286 - val_loss: 0.2864 - val_mean_squared_error: 0.2864\n",
            "Epoch 522/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.2272 - mean_squared_error: 0.2272 - val_loss: 0.3319 - val_mean_squared_error: 0.3319\n",
            "Epoch 523/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.2325 - mean_squared_error: 0.2325 - val_loss: 0.2639 - val_mean_squared_error: 0.2639\n",
            "Epoch 524/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.2394 - mean_squared_error: 0.2394 - val_loss: 0.2864 - val_mean_squared_error: 0.2864\n",
            "Epoch 525/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.2271 - mean_squared_error: 0.2271 - val_loss: 0.3113 - val_mean_squared_error: 0.3113\n",
            "Epoch 526/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.2232 - mean_squared_error: 0.2232 - val_loss: 0.2820 - val_mean_squared_error: 0.2820\n",
            "Epoch 527/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.2312 - mean_squared_error: 0.2312 - val_loss: 0.2616 - val_mean_squared_error: 0.2616\n",
            "Epoch 528/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.2270 - mean_squared_error: 0.2270 - val_loss: 0.2976 - val_mean_squared_error: 0.2976\n",
            "Epoch 529/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.2237 - mean_squared_error: 0.2237 - val_loss: 0.2841 - val_mean_squared_error: 0.2841\n",
            "Epoch 530/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.2293 - mean_squared_error: 0.2293 - val_loss: 0.2621 - val_mean_squared_error: 0.2621\n",
            "Epoch 531/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.2221 - mean_squared_error: 0.2221 - val_loss: 0.3137 - val_mean_squared_error: 0.3137\n",
            "Epoch 532/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.2312 - mean_squared_error: 0.2312 - val_loss: 0.2601 - val_mean_squared_error: 0.2601\n",
            "Epoch 533/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.2279 - mean_squared_error: 0.2279 - val_loss: 0.2935 - val_mean_squared_error: 0.2935\n",
            "Epoch 534/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2669 - mean_squared_error: 0.2669 - val_loss: 0.2879 - val_mean_squared_error: 0.2879\n",
            "Epoch 535/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.2223 - mean_squared_error: 0.2223 - val_loss: 0.2701 - val_mean_squared_error: 0.2701\n",
            "Epoch 536/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.2274 - mean_squared_error: 0.2274 - val_loss: 0.2598 - val_mean_squared_error: 0.2598\n",
            "Epoch 537/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.2389 - mean_squared_error: 0.2389 - val_loss: 0.2583 - val_mean_squared_error: 0.2583\n",
            "Epoch 538/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.2210 - mean_squared_error: 0.2210 - val_loss: 0.4179 - val_mean_squared_error: 0.4179\n",
            "Epoch 539/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.2314 - mean_squared_error: 0.2314 - val_loss: 0.2582 - val_mean_squared_error: 0.2582\n",
            "Epoch 540/1000\n",
            "60/60 [==============================] - 0s 271us/sample - loss: 0.2253 - mean_squared_error: 0.2253 - val_loss: 0.2909 - val_mean_squared_error: 0.2909\n",
            "Epoch 541/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.2409 - mean_squared_error: 0.2409 - val_loss: 0.2815 - val_mean_squared_error: 0.2815\n",
            "Epoch 542/1000\n",
            "60/60 [==============================] - 0s 269us/sample - loss: 0.2313 - mean_squared_error: 0.2313 - val_loss: 0.2596 - val_mean_squared_error: 0.2596\n",
            "Epoch 543/1000\n",
            "60/60 [==============================] - 0s 261us/sample - loss: 0.2651 - mean_squared_error: 0.2651 - val_loss: 0.3152 - val_mean_squared_error: 0.3152\n",
            "Epoch 544/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.2236 - mean_squared_error: 0.2236 - val_loss: 0.2752 - val_mean_squared_error: 0.2752\n",
            "Epoch 545/1000\n",
            "60/60 [==============================] - 0s 263us/sample - loss: 0.2353 - mean_squared_error: 0.2353 - val_loss: 0.2620 - val_mean_squared_error: 0.2620\n",
            "Epoch 546/1000\n",
            "60/60 [==============================] - 0s 352us/sample - loss: 0.2228 - mean_squared_error: 0.2228 - val_loss: 0.3068 - val_mean_squared_error: 0.3068\n",
            "Epoch 547/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.2307 - mean_squared_error: 0.2307 - val_loss: 0.2562 - val_mean_squared_error: 0.2562\n",
            "Epoch 548/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.2415 - mean_squared_error: 0.2415 - val_loss: 0.2556 - val_mean_squared_error: 0.2556\n",
            "Epoch 549/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.2305 - mean_squared_error: 0.2305 - val_loss: 0.2740 - val_mean_squared_error: 0.2740\n",
            "Epoch 550/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2194 - mean_squared_error: 0.2194 - val_loss: 0.2830 - val_mean_squared_error: 0.2830\n",
            "Epoch 551/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.2194 - mean_squared_error: 0.2194 - val_loss: 0.2641 - val_mean_squared_error: 0.2641\n",
            "Epoch 552/1000\n",
            "60/60 [==============================] - 0s 271us/sample - loss: 0.2168 - mean_squared_error: 0.2168 - val_loss: 0.2760 - val_mean_squared_error: 0.2760\n",
            "Epoch 553/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.2209 - mean_squared_error: 0.2209 - val_loss: 0.2570 - val_mean_squared_error: 0.2570\n",
            "Epoch 554/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2280 - mean_squared_error: 0.2280 - val_loss: 0.3166 - val_mean_squared_error: 0.3166\n",
            "Epoch 555/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2269 - mean_squared_error: 0.2269 - val_loss: 0.2717 - val_mean_squared_error: 0.2717\n",
            "Epoch 556/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.2199 - mean_squared_error: 0.2199 - val_loss: 0.2524 - val_mean_squared_error: 0.2524\n",
            "Epoch 557/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.2645 - mean_squared_error: 0.2645 - val_loss: 0.2784 - val_mean_squared_error: 0.2784\n",
            "Epoch 558/1000\n",
            "60/60 [==============================] - 0s 296us/sample - loss: 0.2199 - mean_squared_error: 0.2199 - val_loss: 0.2549 - val_mean_squared_error: 0.2549\n",
            "Epoch 559/1000\n",
            "60/60 [==============================] - 0s 331us/sample - loss: 0.2168 - mean_squared_error: 0.2168 - val_loss: 0.2828 - val_mean_squared_error: 0.2828\n",
            "Epoch 560/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.2171 - mean_squared_error: 0.2171 - val_loss: 0.2994 - val_mean_squared_error: 0.2994\n",
            "Epoch 561/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2539 - mean_squared_error: 0.2539 - val_loss: 0.2725 - val_mean_squared_error: 0.2725\n",
            "Epoch 562/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.2154 - mean_squared_error: 0.2154 - val_loss: 0.2836 - val_mean_squared_error: 0.2836\n",
            "Epoch 563/1000\n",
            "60/60 [==============================] - 0s 294us/sample - loss: 0.2565 - mean_squared_error: 0.2565 - val_loss: 0.3141 - val_mean_squared_error: 0.3141\n",
            "Epoch 564/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.2231 - mean_squared_error: 0.2231 - val_loss: 0.2511 - val_mean_squared_error: 0.2511\n",
            "Epoch 565/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.2177 - mean_squared_error: 0.2177 - val_loss: 0.2651 - val_mean_squared_error: 0.2651\n",
            "Epoch 566/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.2198 - mean_squared_error: 0.2198 - val_loss: 0.3064 - val_mean_squared_error: 0.3064\n",
            "Epoch 567/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2151 - mean_squared_error: 0.2151 - val_loss: 0.2676 - val_mean_squared_error: 0.2676\n",
            "Epoch 568/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.2228 - mean_squared_error: 0.2228 - val_loss: 0.2484 - val_mean_squared_error: 0.2484\n",
            "Epoch 569/1000\n",
            "60/60 [==============================] - 0s 303us/sample - loss: 0.2333 - mean_squared_error: 0.2333 - val_loss: 0.2547 - val_mean_squared_error: 0.2547\n",
            "Epoch 570/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.2125 - mean_squared_error: 0.2125 - val_loss: 0.2858 - val_mean_squared_error: 0.2858\n",
            "Epoch 571/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.2364 - mean_squared_error: 0.2364 - val_loss: 0.3292 - val_mean_squared_error: 0.3292\n",
            "Epoch 572/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.2249 - mean_squared_error: 0.2249 - val_loss: 0.2771 - val_mean_squared_error: 0.2771\n",
            "Epoch 573/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.2437 - mean_squared_error: 0.2437 - val_loss: 0.2671 - val_mean_squared_error: 0.2671\n",
            "Epoch 574/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.2152 - mean_squared_error: 0.2152 - val_loss: 0.3172 - val_mean_squared_error: 0.3172\n",
            "Epoch 575/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2116 - mean_squared_error: 0.2116 - val_loss: 0.2463 - val_mean_squared_error: 0.2463\n",
            "Epoch 576/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2154 - mean_squared_error: 0.2154 - val_loss: 0.2817 - val_mean_squared_error: 0.2817\n",
            "Epoch 577/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.2121 - mean_squared_error: 0.2121 - val_loss: 0.2835 - val_mean_squared_error: 0.2835\n",
            "Epoch 578/1000\n",
            "60/60 [==============================] - 0s 209us/sample - loss: 0.2162 - mean_squared_error: 0.2162 - val_loss: 0.2704 - val_mean_squared_error: 0.2704\n",
            "Epoch 579/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.2465 - mean_squared_error: 0.2465 - val_loss: 0.2457 - val_mean_squared_error: 0.2457\n",
            "Epoch 580/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.2279 - mean_squared_error: 0.2279 - val_loss: 0.2472 - val_mean_squared_error: 0.2472\n",
            "Epoch 581/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.2128 - mean_squared_error: 0.2128 - val_loss: 0.2881 - val_mean_squared_error: 0.2881\n",
            "Epoch 582/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2429 - mean_squared_error: 0.2429 - val_loss: 0.2661 - val_mean_squared_error: 0.2661\n",
            "Epoch 583/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.2304 - mean_squared_error: 0.2304 - val_loss: 0.2991 - val_mean_squared_error: 0.2991\n",
            "Epoch 584/1000\n",
            "60/60 [==============================] - 0s 209us/sample - loss: 0.2208 - mean_squared_error: 0.2208 - val_loss: 0.2734 - val_mean_squared_error: 0.2734\n",
            "Epoch 585/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.2202 - mean_squared_error: 0.2202 - val_loss: 0.3545 - val_mean_squared_error: 0.3545\n",
            "Epoch 586/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.2182 - mean_squared_error: 0.2182 - val_loss: 0.2725 - val_mean_squared_error: 0.2725\n",
            "Epoch 587/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.2110 - mean_squared_error: 0.2110 - val_loss: 0.3126 - val_mean_squared_error: 0.3126\n",
            "Epoch 588/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.2437 - mean_squared_error: 0.2437 - val_loss: 0.2650 - val_mean_squared_error: 0.2650\n",
            "Epoch 589/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.2733 - mean_squared_error: 0.2733 - val_loss: 0.2830 - val_mean_squared_error: 0.2830\n",
            "Epoch 590/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.2081 - mean_squared_error: 0.2081 - val_loss: 0.2474 - val_mean_squared_error: 0.2474\n",
            "Epoch 591/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.2062 - mean_squared_error: 0.2062 - val_loss: 0.3113 - val_mean_squared_error: 0.3113\n",
            "Epoch 592/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.2067 - mean_squared_error: 0.2067 - val_loss: 0.2448 - val_mean_squared_error: 0.2448\n",
            "Epoch 593/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2395 - mean_squared_error: 0.2395 - val_loss: 0.3096 - val_mean_squared_error: 0.3096\n",
            "Epoch 594/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2269 - mean_squared_error: 0.2269 - val_loss: 0.2621 - val_mean_squared_error: 0.2621\n",
            "Epoch 595/1000\n",
            "60/60 [==============================] - 0s 267us/sample - loss: 0.2046 - mean_squared_error: 0.2046 - val_loss: 0.2713 - val_mean_squared_error: 0.2713\n",
            "Epoch 596/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2086 - mean_squared_error: 0.2086 - val_loss: 0.2756 - val_mean_squared_error: 0.2756\n",
            "Epoch 597/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.2076 - mean_squared_error: 0.2076 - val_loss: 0.2395 - val_mean_squared_error: 0.2395\n",
            "Epoch 598/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.2357 - mean_squared_error: 0.2357 - val_loss: 0.3306 - val_mean_squared_error: 0.3306\n",
            "Epoch 599/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.2186 - mean_squared_error: 0.2186 - val_loss: 0.2657 - val_mean_squared_error: 0.2657\n",
            "Epoch 600/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.2170 - mean_squared_error: 0.2170 - val_loss: 0.2399 - val_mean_squared_error: 0.2399\n",
            "Epoch 601/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.2041 - mean_squared_error: 0.2041 - val_loss: 0.2811 - val_mean_squared_error: 0.2811\n",
            "Epoch 602/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.2034 - mean_squared_error: 0.2034 - val_loss: 0.2559 - val_mean_squared_error: 0.2559\n",
            "Epoch 603/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.2218 - mean_squared_error: 0.2218 - val_loss: 0.3685 - val_mean_squared_error: 0.3685\n",
            "Epoch 604/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2327 - mean_squared_error: 0.2327 - val_loss: 0.2525 - val_mean_squared_error: 0.2525\n",
            "Epoch 605/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.2031 - mean_squared_error: 0.2031 - val_loss: 0.3054 - val_mean_squared_error: 0.3054\n",
            "Epoch 606/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.2075 - mean_squared_error: 0.2075 - val_loss: 0.2722 - val_mean_squared_error: 0.2722\n",
            "Epoch 607/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.2212 - mean_squared_error: 0.2212 - val_loss: 0.2623 - val_mean_squared_error: 0.2623\n",
            "Epoch 608/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.2153 - mean_squared_error: 0.2153 - val_loss: 0.3100 - val_mean_squared_error: 0.3100\n",
            "Epoch 609/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2227 - mean_squared_error: 0.2227 - val_loss: 0.3235 - val_mean_squared_error: 0.3235\n",
            "Epoch 610/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.2068 - mean_squared_error: 0.2068 - val_loss: 0.2493 - val_mean_squared_error: 0.2493\n",
            "Epoch 611/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2040 - mean_squared_error: 0.2040 - val_loss: 0.2410 - val_mean_squared_error: 0.2410\n",
            "Epoch 612/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.2332 - mean_squared_error: 0.2332 - val_loss: 0.3082 - val_mean_squared_error: 0.3082\n",
            "Epoch 613/1000\n",
            "60/60 [==============================] - 0s 211us/sample - loss: 0.2157 - mean_squared_error: 0.2157 - val_loss: 0.2891 - val_mean_squared_error: 0.2891\n",
            "Epoch 614/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.2076 - mean_squared_error: 0.2076 - val_loss: 0.2514 - val_mean_squared_error: 0.2514\n",
            "Epoch 615/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2947 - mean_squared_error: 0.2947 - val_loss: 0.3127 - val_mean_squared_error: 0.3127\n",
            "Epoch 616/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.2047 - mean_squared_error: 0.2047 - val_loss: 0.2555 - val_mean_squared_error: 0.2555\n",
            "Epoch 617/1000\n",
            "60/60 [==============================] - 0s 200us/sample - loss: 0.2000 - mean_squared_error: 0.2000 - val_loss: 0.2501 - val_mean_squared_error: 0.2501\n",
            "Epoch 618/1000\n",
            "60/60 [==============================] - 0s 315us/sample - loss: 0.2033 - mean_squared_error: 0.2033 - val_loss: 0.2640 - val_mean_squared_error: 0.2640\n",
            "Epoch 619/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2041 - mean_squared_error: 0.2041 - val_loss: 0.2329 - val_mean_squared_error: 0.2329\n",
            "Epoch 620/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2144 - mean_squared_error: 0.2144 - val_loss: 0.2454 - val_mean_squared_error: 0.2454\n",
            "Epoch 621/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.1988 - mean_squared_error: 0.1988 - val_loss: 0.2739 - val_mean_squared_error: 0.2739\n",
            "Epoch 622/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2034 - mean_squared_error: 0.2034 - val_loss: 0.2662 - val_mean_squared_error: 0.2662\n",
            "Epoch 623/1000\n",
            "60/60 [==============================] - 0s 206us/sample - loss: 0.2528 - mean_squared_error: 0.2528 - val_loss: 0.3003 - val_mean_squared_error: 0.3003\n",
            "Epoch 624/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2135 - mean_squared_error: 0.2135 - val_loss: 0.2965 - val_mean_squared_error: 0.2965\n",
            "Epoch 625/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2016 - mean_squared_error: 0.2016 - val_loss: 0.2584 - val_mean_squared_error: 0.2584\n",
            "Epoch 626/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.2181 - mean_squared_error: 0.2181 - val_loss: 0.2714 - val_mean_squared_error: 0.2714\n",
            "Epoch 627/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.2039 - mean_squared_error: 0.2039 - val_loss: 0.2950 - val_mean_squared_error: 0.2950\n",
            "Epoch 628/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.2003 - mean_squared_error: 0.2003 - val_loss: 0.2524 - val_mean_squared_error: 0.2524\n",
            "Epoch 629/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.1979 - mean_squared_error: 0.1979 - val_loss: 0.2505 - val_mean_squared_error: 0.2505\n",
            "Epoch 630/1000\n",
            "60/60 [==============================] - 0s 271us/sample - loss: 0.2030 - mean_squared_error: 0.2030 - val_loss: 0.2817 - val_mean_squared_error: 0.2817\n",
            "Epoch 631/1000\n",
            "60/60 [==============================] - 0s 274us/sample - loss: 0.2054 - mean_squared_error: 0.2054 - val_loss: 0.3015 - val_mean_squared_error: 0.3015\n",
            "Epoch 632/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.2252 - mean_squared_error: 0.2252 - val_loss: 0.2822 - val_mean_squared_error: 0.2822\n",
            "Epoch 633/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.2028 - mean_squared_error: 0.2028 - val_loss: 0.2872 - val_mean_squared_error: 0.2872\n",
            "Epoch 634/1000\n",
            "60/60 [==============================] - 0s 211us/sample - loss: 0.1998 - mean_squared_error: 0.1998 - val_loss: 0.2668 - val_mean_squared_error: 0.2668\n",
            "Epoch 635/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.1983 - mean_squared_error: 0.1983 - val_loss: 0.2488 - val_mean_squared_error: 0.2488\n",
            "Epoch 636/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.1988 - mean_squared_error: 0.1988 - val_loss: 0.2299 - val_mean_squared_error: 0.2299\n",
            "Epoch 637/1000\n",
            "60/60 [==============================] - 0s 263us/sample - loss: 0.2218 - mean_squared_error: 0.2218 - val_loss: 0.2307 - val_mean_squared_error: 0.2307\n",
            "Epoch 638/1000\n",
            "60/60 [==============================] - 0s 283us/sample - loss: 0.2254 - mean_squared_error: 0.2254 - val_loss: 0.2307 - val_mean_squared_error: 0.2307\n",
            "Epoch 639/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.1938 - mean_squared_error: 0.1938 - val_loss: 0.2648 - val_mean_squared_error: 0.2648\n",
            "Epoch 640/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2030 - mean_squared_error: 0.2030 - val_loss: 0.2693 - val_mean_squared_error: 0.2693\n",
            "Epoch 641/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.1961 - mean_squared_error: 0.1961 - val_loss: 0.2264 - val_mean_squared_error: 0.2264\n",
            "Epoch 642/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.1993 - mean_squared_error: 0.1993 - val_loss: 0.2444 - val_mean_squared_error: 0.2444\n",
            "Epoch 643/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.2254 - mean_squared_error: 0.2254 - val_loss: 0.3057 - val_mean_squared_error: 0.3057\n",
            "Epoch 644/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.2122 - mean_squared_error: 0.2122 - val_loss: 0.2374 - val_mean_squared_error: 0.2374\n",
            "Epoch 645/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.2146 - mean_squared_error: 0.2146 - val_loss: 0.2285 - val_mean_squared_error: 0.2285\n",
            "Epoch 646/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.1932 - mean_squared_error: 0.1932 - val_loss: 0.2669 - val_mean_squared_error: 0.2669\n",
            "Epoch 647/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2067 - mean_squared_error: 0.2067 - val_loss: 0.2603 - val_mean_squared_error: 0.2603\n",
            "Epoch 648/1000\n",
            "60/60 [==============================] - 0s 206us/sample - loss: 0.1977 - mean_squared_error: 0.1977 - val_loss: 0.2719 - val_mean_squared_error: 0.2719\n",
            "Epoch 649/1000\n",
            "60/60 [==============================] - 0s 195us/sample - loss: 0.2233 - mean_squared_error: 0.2233 - val_loss: 0.2254 - val_mean_squared_error: 0.2254\n",
            "Epoch 650/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.2100 - mean_squared_error: 0.2100 - val_loss: 0.2429 - val_mean_squared_error: 0.2429\n",
            "Epoch 651/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.1931 - mean_squared_error: 0.1931 - val_loss: 0.2355 - val_mean_squared_error: 0.2355\n",
            "Epoch 652/1000\n",
            "60/60 [==============================] - 0s 211us/sample - loss: 0.1916 - mean_squared_error: 0.1916 - val_loss: 0.2395 - val_mean_squared_error: 0.2395\n",
            "Epoch 653/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.2006 - mean_squared_error: 0.2006 - val_loss: 0.2493 - val_mean_squared_error: 0.2493\n",
            "Epoch 654/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.1994 - mean_squared_error: 0.1994 - val_loss: 0.2461 - val_mean_squared_error: 0.2461\n",
            "Epoch 655/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1943 - mean_squared_error: 0.1943 - val_loss: 0.2934 - val_mean_squared_error: 0.2934\n",
            "Epoch 656/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1972 - mean_squared_error: 0.1972 - val_loss: 0.2274 - val_mean_squared_error: 0.2274\n",
            "Epoch 657/1000\n",
            "60/60 [==============================] - 0s 214us/sample - loss: 0.2021 - mean_squared_error: 0.2021 - val_loss: 0.2634 - val_mean_squared_error: 0.2634\n",
            "Epoch 658/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.1902 - mean_squared_error: 0.1902 - val_loss: 0.2289 - val_mean_squared_error: 0.2289\n",
            "Epoch 659/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.1908 - mean_squared_error: 0.1908 - val_loss: 0.2717 - val_mean_squared_error: 0.2717\n",
            "Epoch 660/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.1908 - mean_squared_error: 0.1908 - val_loss: 0.2212 - val_mean_squared_error: 0.2212\n",
            "Epoch 661/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.2122 - mean_squared_error: 0.2122 - val_loss: 0.2218 - val_mean_squared_error: 0.2218\n",
            "Epoch 662/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2079 - mean_squared_error: 0.2079 - val_loss: 0.2325 - val_mean_squared_error: 0.2325\n",
            "Epoch 663/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.1934 - mean_squared_error: 0.1934 - val_loss: 0.2306 - val_mean_squared_error: 0.2306\n",
            "Epoch 664/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.1953 - mean_squared_error: 0.1953 - val_loss: 0.2220 - val_mean_squared_error: 0.2220\n",
            "Epoch 665/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.1890 - mean_squared_error: 0.1890 - val_loss: 0.2567 - val_mean_squared_error: 0.2567\n",
            "Epoch 666/1000\n",
            "60/60 [==============================] - 0s 206us/sample - loss: 0.2401 - mean_squared_error: 0.2401 - val_loss: 0.2206 - val_mean_squared_error: 0.2206\n",
            "Epoch 667/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2079 - mean_squared_error: 0.2079 - val_loss: 0.2593 - val_mean_squared_error: 0.2593\n",
            "Epoch 668/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.1948 - mean_squared_error: 0.1948 - val_loss: 0.2664 - val_mean_squared_error: 0.2664\n",
            "Epoch 669/1000\n",
            "60/60 [==============================] - 0s 327us/sample - loss: 0.1886 - mean_squared_error: 0.1886 - val_loss: 0.2351 - val_mean_squared_error: 0.2351\n",
            "Epoch 670/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.1883 - mean_squared_error: 0.1883 - val_loss: 0.2318 - val_mean_squared_error: 0.2318\n",
            "Epoch 671/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.1900 - mean_squared_error: 0.1900 - val_loss: 0.2302 - val_mean_squared_error: 0.2302\n",
            "Epoch 672/1000\n",
            "60/60 [==============================] - 0s 202us/sample - loss: 0.1900 - mean_squared_error: 0.1900 - val_loss: 0.2250 - val_mean_squared_error: 0.2250\n",
            "Epoch 673/1000\n",
            "60/60 [==============================] - 0s 205us/sample - loss: 0.2038 - mean_squared_error: 0.2038 - val_loss: 0.2548 - val_mean_squared_error: 0.2548\n",
            "Epoch 674/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.1992 - mean_squared_error: 0.1992 - val_loss: 0.3280 - val_mean_squared_error: 0.3280\n",
            "Epoch 675/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.1886 - mean_squared_error: 0.1886 - val_loss: 0.2187 - val_mean_squared_error: 0.2187\n",
            "Epoch 676/1000\n",
            "60/60 [==============================] - 0s 307us/sample - loss: 0.1851 - mean_squared_error: 0.1851 - val_loss: 0.3006 - val_mean_squared_error: 0.3006\n",
            "Epoch 677/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.2002 - mean_squared_error: 0.2002 - val_loss: 0.2229 - val_mean_squared_error: 0.2229\n",
            "Epoch 678/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.2115 - mean_squared_error: 0.2115 - val_loss: 0.2179 - val_mean_squared_error: 0.2179\n",
            "Epoch 679/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.1998 - mean_squared_error: 0.1998 - val_loss: 0.2216 - val_mean_squared_error: 0.2216\n",
            "Epoch 680/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1883 - mean_squared_error: 0.1883 - val_loss: 0.2527 - val_mean_squared_error: 0.2527\n",
            "Epoch 681/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.2003 - mean_squared_error: 0.2003 - val_loss: 0.2656 - val_mean_squared_error: 0.2656\n",
            "Epoch 682/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.1993 - mean_squared_error: 0.1993 - val_loss: 0.2191 - val_mean_squared_error: 0.2191\n",
            "Epoch 683/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.1951 - mean_squared_error: 0.1951 - val_loss: 0.2512 - val_mean_squared_error: 0.2512\n",
            "Epoch 684/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.2040 - mean_squared_error: 0.2040 - val_loss: 0.2285 - val_mean_squared_error: 0.2285\n",
            "Epoch 685/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.2017 - mean_squared_error: 0.2017 - val_loss: 0.2158 - val_mean_squared_error: 0.2158\n",
            "Epoch 686/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.1953 - mean_squared_error: 0.1953 - val_loss: 0.2154 - val_mean_squared_error: 0.2154\n",
            "Epoch 687/1000\n",
            "60/60 [==============================] - 0s 356us/sample - loss: 0.1862 - mean_squared_error: 0.1862 - val_loss: 0.2394 - val_mean_squared_error: 0.2394\n",
            "Epoch 688/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.1859 - mean_squared_error: 0.1859 - val_loss: 0.2359 - val_mean_squared_error: 0.2359\n",
            "Epoch 689/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.1990 - mean_squared_error: 0.1990 - val_loss: 0.2331 - val_mean_squared_error: 0.2331\n",
            "Epoch 690/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1969 - mean_squared_error: 0.1969 - val_loss: 0.2219 - val_mean_squared_error: 0.2219\n",
            "Epoch 691/1000\n",
            "60/60 [==============================] - 0s 269us/sample - loss: 0.1911 - mean_squared_error: 0.1911 - val_loss: 0.3528 - val_mean_squared_error: 0.3528\n",
            "Epoch 692/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.2350 - mean_squared_error: 0.2350 - val_loss: 0.2187 - val_mean_squared_error: 0.2187\n",
            "Epoch 693/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.1915 - mean_squared_error: 0.1915 - val_loss: 0.2137 - val_mean_squared_error: 0.2137\n",
            "Epoch 694/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.1923 - mean_squared_error: 0.1923 - val_loss: 0.2133 - val_mean_squared_error: 0.2133\n",
            "Epoch 695/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.1946 - mean_squared_error: 0.1946 - val_loss: 0.2138 - val_mean_squared_error: 0.2138\n",
            "Epoch 696/1000\n",
            "60/60 [==============================] - 0s 196us/sample - loss: 0.1909 - mean_squared_error: 0.1909 - val_loss: 0.2134 - val_mean_squared_error: 0.2134\n",
            "Epoch 697/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.1866 - mean_squared_error: 0.1866 - val_loss: 0.2211 - val_mean_squared_error: 0.2211\n",
            "Epoch 698/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.1885 - mean_squared_error: 0.1885 - val_loss: 0.2347 - val_mean_squared_error: 0.2347\n",
            "Epoch 699/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.1919 - mean_squared_error: 0.1919 - val_loss: 0.2122 - val_mean_squared_error: 0.2122\n",
            "Epoch 700/1000\n",
            "60/60 [==============================] - 0s 261us/sample - loss: 0.1817 - mean_squared_error: 0.1817 - val_loss: 0.2474 - val_mean_squared_error: 0.2474\n",
            "Epoch 701/1000\n",
            "60/60 [==============================] - 0s 308us/sample - loss: 0.1843 - mean_squared_error: 0.1843 - val_loss: 0.2175 - val_mean_squared_error: 0.2175\n",
            "Epoch 702/1000\n",
            "60/60 [==============================] - 0s 261us/sample - loss: 0.1889 - mean_squared_error: 0.1889 - val_loss: 0.2238 - val_mean_squared_error: 0.2238\n",
            "Epoch 703/1000\n",
            "60/60 [==============================] - 0s 288us/sample - loss: 0.1848 - mean_squared_error: 0.1848 - val_loss: 0.2126 - val_mean_squared_error: 0.2126\n",
            "Epoch 704/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.1923 - mean_squared_error: 0.1923 - val_loss: 0.2449 - val_mean_squared_error: 0.2449\n",
            "Epoch 705/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.1801 - mean_squared_error: 0.1801 - val_loss: 0.2119 - val_mean_squared_error: 0.2119\n",
            "Epoch 706/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.1924 - mean_squared_error: 0.1924 - val_loss: 0.2095 - val_mean_squared_error: 0.2095\n",
            "Epoch 707/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.1924 - mean_squared_error: 0.1924 - val_loss: 0.2237 - val_mean_squared_error: 0.2237\n",
            "Epoch 708/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.1798 - mean_squared_error: 0.1798 - val_loss: 0.2330 - val_mean_squared_error: 0.2330\n",
            "Epoch 709/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.1869 - mean_squared_error: 0.1869 - val_loss: 0.2933 - val_mean_squared_error: 0.2933\n",
            "Epoch 710/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.1977 - mean_squared_error: 0.1977 - val_loss: 0.2190 - val_mean_squared_error: 0.2190\n",
            "Epoch 711/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1886 - mean_squared_error: 0.1886 - val_loss: 0.2098 - val_mean_squared_error: 0.2098\n",
            "Epoch 712/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.1827 - mean_squared_error: 0.1827 - val_loss: 0.2319 - val_mean_squared_error: 0.2319\n",
            "Epoch 713/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.1894 - mean_squared_error: 0.1894 - val_loss: 0.3169 - val_mean_squared_error: 0.3169\n",
            "Epoch 714/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.2002 - mean_squared_error: 0.2002 - val_loss: 0.2623 - val_mean_squared_error: 0.2623\n",
            "Epoch 715/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.1850 - mean_squared_error: 0.1850 - val_loss: 0.2519 - val_mean_squared_error: 0.2519\n",
            "Epoch 716/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1873 - mean_squared_error: 0.1873 - val_loss: 0.2597 - val_mean_squared_error: 0.2597\n",
            "Epoch 717/1000\n",
            "60/60 [==============================] - 0s 297us/sample - loss: 0.1784 - mean_squared_error: 0.1784 - val_loss: 0.2080 - val_mean_squared_error: 0.2080\n",
            "Epoch 718/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.2029 - mean_squared_error: 0.2029 - val_loss: 0.2086 - val_mean_squared_error: 0.2086\n",
            "Epoch 719/1000\n",
            "60/60 [==============================] - 0s 263us/sample - loss: 0.1989 - mean_squared_error: 0.1989 - val_loss: 0.2356 - val_mean_squared_error: 0.2356\n",
            "Epoch 720/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.1962 - mean_squared_error: 0.1962 - val_loss: 0.2281 - val_mean_squared_error: 0.2281\n",
            "Epoch 721/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.1768 - mean_squared_error: 0.1768 - val_loss: 0.2193 - val_mean_squared_error: 0.2193\n",
            "Epoch 722/1000\n",
            "60/60 [==============================] - 0s 277us/sample - loss: 0.1816 - mean_squared_error: 0.1816 - val_loss: 0.2502 - val_mean_squared_error: 0.2502\n",
            "Epoch 723/1000\n",
            "60/60 [==============================] - 0s 267us/sample - loss: 0.1883 - mean_squared_error: 0.1883 - val_loss: 0.2222 - val_mean_squared_error: 0.2222\n",
            "Epoch 724/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1807 - mean_squared_error: 0.1807 - val_loss: 0.2146 - val_mean_squared_error: 0.2146\n",
            "Epoch 725/1000\n",
            "60/60 [==============================] - 0s 264us/sample - loss: 0.1809 - mean_squared_error: 0.1809 - val_loss: 0.2350 - val_mean_squared_error: 0.2350\n",
            "Epoch 726/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.1786 - mean_squared_error: 0.1786 - val_loss: 0.2469 - val_mean_squared_error: 0.2469\n",
            "Epoch 727/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.1948 - mean_squared_error: 0.1948 - val_loss: 0.2473 - val_mean_squared_error: 0.2473\n",
            "Epoch 728/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.1829 - mean_squared_error: 0.1829 - val_loss: 0.2409 - val_mean_squared_error: 0.2409\n",
            "Epoch 729/1000\n",
            "60/60 [==============================] - 0s 275us/sample - loss: 0.1844 - mean_squared_error: 0.1844 - val_loss: 0.2707 - val_mean_squared_error: 0.2707\n",
            "Epoch 730/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.1778 - mean_squared_error: 0.1778 - val_loss: 0.2103 - val_mean_squared_error: 0.2103\n",
            "Epoch 731/1000\n",
            "60/60 [==============================] - 0s 273us/sample - loss: 0.1920 - mean_squared_error: 0.1920 - val_loss: 0.2388 - val_mean_squared_error: 0.2388\n",
            "Epoch 732/1000\n",
            "60/60 [==============================] - 0s 400us/sample - loss: 0.1977 - mean_squared_error: 0.1977 - val_loss: 0.2176 - val_mean_squared_error: 0.2176\n",
            "Epoch 733/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.2067 - mean_squared_error: 0.2067 - val_loss: 0.2134 - val_mean_squared_error: 0.2134\n",
            "Epoch 734/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.1756 - mean_squared_error: 0.1756 - val_loss: 0.2208 - val_mean_squared_error: 0.2208\n",
            "Epoch 735/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.1792 - mean_squared_error: 0.1792 - val_loss: 0.2037 - val_mean_squared_error: 0.2037\n",
            "Epoch 736/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.1826 - mean_squared_error: 0.1826 - val_loss: 0.2120 - val_mean_squared_error: 0.2120\n",
            "Epoch 737/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.1925 - mean_squared_error: 0.1925 - val_loss: 0.2111 - val_mean_squared_error: 0.2111\n",
            "Epoch 738/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.1815 - mean_squared_error: 0.1815 - val_loss: 0.2305 - val_mean_squared_error: 0.2305\n",
            "Epoch 739/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.2060 - mean_squared_error: 0.2060 - val_loss: 0.2818 - val_mean_squared_error: 0.2818\n",
            "Epoch 740/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.1886 - mean_squared_error: 0.1886 - val_loss: 0.2623 - val_mean_squared_error: 0.2623\n",
            "Epoch 741/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.2170 - mean_squared_error: 0.2170 - val_loss: 0.2352 - val_mean_squared_error: 0.2352\n",
            "Epoch 742/1000\n",
            "60/60 [==============================] - 0s 343us/sample - loss: 0.2045 - mean_squared_error: 0.2045 - val_loss: 0.2047 - val_mean_squared_error: 0.2047\n",
            "Epoch 743/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2428 - mean_squared_error: 0.2428 - val_loss: 0.2885 - val_mean_squared_error: 0.2885\n",
            "Epoch 744/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1845 - mean_squared_error: 0.1845 - val_loss: 0.2358 - val_mean_squared_error: 0.2358\n",
            "Epoch 745/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.1780 - mean_squared_error: 0.1780 - val_loss: 0.2262 - val_mean_squared_error: 0.2262\n",
            "Epoch 746/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.1787 - mean_squared_error: 0.1787 - val_loss: 0.2063 - val_mean_squared_error: 0.2063\n",
            "Epoch 747/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.2110 - mean_squared_error: 0.2110 - val_loss: 0.2426 - val_mean_squared_error: 0.2426\n",
            "Epoch 748/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.1962 - mean_squared_error: 0.1962 - val_loss: 0.2007 - val_mean_squared_error: 0.2007\n",
            "Epoch 749/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.2026 - mean_squared_error: 0.2026 - val_loss: 0.3340 - val_mean_squared_error: 0.3340\n",
            "Epoch 750/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.1938 - mean_squared_error: 0.1938 - val_loss: 0.2392 - val_mean_squared_error: 0.2392\n",
            "Epoch 751/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1932 - mean_squared_error: 0.1932 - val_loss: 0.2003 - val_mean_squared_error: 0.2003\n",
            "Epoch 752/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.1830 - mean_squared_error: 0.1830 - val_loss: 0.2161 - val_mean_squared_error: 0.2161\n",
            "Epoch 753/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.1773 - mean_squared_error: 0.1773 - val_loss: 0.2643 - val_mean_squared_error: 0.2643\n",
            "Epoch 754/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.1778 - mean_squared_error: 0.1778 - val_loss: 0.2222 - val_mean_squared_error: 0.2222\n",
            "Epoch 755/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.1820 - mean_squared_error: 0.1820 - val_loss: 0.2304 - val_mean_squared_error: 0.2304\n",
            "Epoch 756/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.1771 - mean_squared_error: 0.1771 - val_loss: 0.2450 - val_mean_squared_error: 0.2450\n",
            "Epoch 757/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.1842 - mean_squared_error: 0.1842 - val_loss: 0.2004 - val_mean_squared_error: 0.2004\n",
            "Epoch 758/1000\n",
            "60/60 [==============================] - 0s 289us/sample - loss: 0.1755 - mean_squared_error: 0.1755 - val_loss: 0.2030 - val_mean_squared_error: 0.2030\n",
            "Epoch 759/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.1867 - mean_squared_error: 0.1867 - val_loss: 0.2087 - val_mean_squared_error: 0.2087\n",
            "Epoch 760/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1812 - mean_squared_error: 0.1812 - val_loss: 0.2013 - val_mean_squared_error: 0.2013\n",
            "Epoch 761/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.1751 - mean_squared_error: 0.1751 - val_loss: 0.2267 - val_mean_squared_error: 0.2267\n",
            "Epoch 762/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.1822 - mean_squared_error: 0.1822 - val_loss: 0.2958 - val_mean_squared_error: 0.2958\n",
            "Epoch 763/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.1991 - mean_squared_error: 0.1991 - val_loss: 0.2419 - val_mean_squared_error: 0.2419\n",
            "Epoch 764/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.1716 - mean_squared_error: 0.1716 - val_loss: 0.2022 - val_mean_squared_error: 0.2022\n",
            "Epoch 765/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.1738 - mean_squared_error: 0.1738 - val_loss: 0.2002 - val_mean_squared_error: 0.2002\n",
            "Epoch 766/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.2773 - mean_squared_error: 0.2773 - val_loss: 0.3355 - val_mean_squared_error: 0.3355\n",
            "Epoch 767/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.2204 - mean_squared_error: 0.2204 - val_loss: 0.2464 - val_mean_squared_error: 0.2464\n",
            "Epoch 768/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.1846 - mean_squared_error: 0.1846 - val_loss: 0.2040 - val_mean_squared_error: 0.2040\n",
            "Epoch 769/1000\n",
            "60/60 [==============================] - 0s 207us/sample - loss: 0.1778 - mean_squared_error: 0.1778 - val_loss: 0.2034 - val_mean_squared_error: 0.2034\n",
            "Epoch 770/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1801 - mean_squared_error: 0.1801 - val_loss: 0.2003 - val_mean_squared_error: 0.2003\n",
            "Epoch 771/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.1692 - mean_squared_error: 0.1692 - val_loss: 0.2463 - val_mean_squared_error: 0.2463\n",
            "Epoch 772/1000\n",
            "60/60 [==============================] - 0s 281us/sample - loss: 0.1722 - mean_squared_error: 0.1722 - val_loss: 0.1986 - val_mean_squared_error: 0.1986\n",
            "Epoch 773/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.1695 - mean_squared_error: 0.1695 - val_loss: 0.2161 - val_mean_squared_error: 0.2161\n",
            "Epoch 774/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.1699 - mean_squared_error: 0.1699 - val_loss: 0.2228 - val_mean_squared_error: 0.2228\n",
            "Epoch 775/1000\n",
            "60/60 [==============================] - 0s 203us/sample - loss: 0.1775 - mean_squared_error: 0.1775 - val_loss: 0.1963 - val_mean_squared_error: 0.1963\n",
            "Epoch 776/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.1691 - mean_squared_error: 0.1691 - val_loss: 0.2305 - val_mean_squared_error: 0.2305\n",
            "Epoch 777/1000\n",
            "60/60 [==============================] - 0s 239us/sample - loss: 0.1745 - mean_squared_error: 0.1745 - val_loss: 0.2174 - val_mean_squared_error: 0.2174\n",
            "Epoch 778/1000\n",
            "60/60 [==============================] - 0s 201us/sample - loss: 0.1682 - mean_squared_error: 0.1682 - val_loss: 0.2135 - val_mean_squared_error: 0.2135\n",
            "Epoch 779/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.1814 - mean_squared_error: 0.1814 - val_loss: 0.2304 - val_mean_squared_error: 0.2304\n",
            "Epoch 780/1000\n",
            "60/60 [==============================] - 0s 209us/sample - loss: 0.1689 - mean_squared_error: 0.1689 - val_loss: 0.2061 - val_mean_squared_error: 0.2061\n",
            "Epoch 781/1000\n",
            "60/60 [==============================] - 0s 205us/sample - loss: 0.1735 - mean_squared_error: 0.1735 - val_loss: 0.1939 - val_mean_squared_error: 0.1939\n",
            "Epoch 782/1000\n",
            "60/60 [==============================] - 0s 207us/sample - loss: 0.1879 - mean_squared_error: 0.1879 - val_loss: 0.1936 - val_mean_squared_error: 0.1936\n",
            "Epoch 783/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.1677 - mean_squared_error: 0.1677 - val_loss: 0.2215 - val_mean_squared_error: 0.2215\n",
            "Epoch 784/1000\n",
            "60/60 [==============================] - 0s 207us/sample - loss: 0.1747 - mean_squared_error: 0.1747 - val_loss: 0.2274 - val_mean_squared_error: 0.2274\n",
            "Epoch 785/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.1714 - mean_squared_error: 0.1714 - val_loss: 0.1927 - val_mean_squared_error: 0.1927\n",
            "Epoch 786/1000\n",
            "60/60 [==============================] - 0s 308us/sample - loss: 0.1700 - mean_squared_error: 0.1700 - val_loss: 0.2108 - val_mean_squared_error: 0.2108\n",
            "Epoch 787/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.1701 - mean_squared_error: 0.1701 - val_loss: 0.2405 - val_mean_squared_error: 0.2405\n",
            "Epoch 788/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1913 - mean_squared_error: 0.1913 - val_loss: 0.1942 - val_mean_squared_error: 0.1942\n",
            "Epoch 789/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.1671 - mean_squared_error: 0.1671 - val_loss: 0.2116 - val_mean_squared_error: 0.2116\n",
            "Epoch 790/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.1655 - mean_squared_error: 0.1655 - val_loss: 0.2011 - val_mean_squared_error: 0.2011\n",
            "Epoch 791/1000\n",
            "60/60 [==============================] - 0s 334us/sample - loss: 0.1823 - mean_squared_error: 0.1823 - val_loss: 0.1973 - val_mean_squared_error: 0.1973\n",
            "Epoch 792/1000\n",
            "60/60 [==============================] - 0s 259us/sample - loss: 0.1660 - mean_squared_error: 0.1660 - val_loss: 0.2188 - val_mean_squared_error: 0.2188\n",
            "Epoch 793/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.1805 - mean_squared_error: 0.1805 - val_loss: 0.2197 - val_mean_squared_error: 0.2197\n",
            "Epoch 794/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1819 - mean_squared_error: 0.1819 - val_loss: 0.2737 - val_mean_squared_error: 0.2737\n",
            "Epoch 795/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.1804 - mean_squared_error: 0.1804 - val_loss: 0.2336 - val_mean_squared_error: 0.2336\n",
            "Epoch 796/1000\n",
            "60/60 [==============================] - 0s 258us/sample - loss: 0.1866 - mean_squared_error: 0.1866 - val_loss: 0.1972 - val_mean_squared_error: 0.1972\n",
            "Epoch 797/1000\n",
            "60/60 [==============================] - 0s 198us/sample - loss: 0.1953 - mean_squared_error: 0.1953 - val_loss: 0.2990 - val_mean_squared_error: 0.2990\n",
            "Epoch 798/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.1688 - mean_squared_error: 0.1688 - val_loss: 0.1910 - val_mean_squared_error: 0.1910\n",
            "Epoch 799/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1746 - mean_squared_error: 0.1746 - val_loss: 0.1956 - val_mean_squared_error: 0.1956\n",
            "Epoch 800/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1675 - mean_squared_error: 0.1675 - val_loss: 0.2078 - val_mean_squared_error: 0.2078\n",
            "Epoch 801/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1753 - mean_squared_error: 0.1753 - val_loss: 0.2098 - val_mean_squared_error: 0.2098\n",
            "Epoch 802/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1643 - mean_squared_error: 0.1643 - val_loss: 0.1978 - val_mean_squared_error: 0.1978\n",
            "Epoch 803/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.1643 - mean_squared_error: 0.1643 - val_loss: 0.2065 - val_mean_squared_error: 0.2065\n",
            "Epoch 804/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1734 - mean_squared_error: 0.1734 - val_loss: 0.2456 - val_mean_squared_error: 0.2456\n",
            "Epoch 805/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.1691 - mean_squared_error: 0.1691 - val_loss: 0.2027 - val_mean_squared_error: 0.2027\n",
            "Epoch 806/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1644 - mean_squared_error: 0.1644 - val_loss: 0.2277 - val_mean_squared_error: 0.2277\n",
            "Epoch 807/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1774 - mean_squared_error: 0.1774 - val_loss: 0.1892 - val_mean_squared_error: 0.1892\n",
            "Epoch 808/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.1687 - mean_squared_error: 0.1687 - val_loss: 0.2014 - val_mean_squared_error: 0.2014\n",
            "Epoch 809/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.1647 - mean_squared_error: 0.1647 - val_loss: 0.2019 - val_mean_squared_error: 0.2019\n",
            "Epoch 810/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1715 - mean_squared_error: 0.1715 - val_loss: 0.1910 - val_mean_squared_error: 0.1910\n",
            "Epoch 811/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.1855 - mean_squared_error: 0.1855 - val_loss: 0.1952 - val_mean_squared_error: 0.1952\n",
            "Epoch 812/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.1676 - mean_squared_error: 0.1676 - val_loss: 0.2400 - val_mean_squared_error: 0.2400\n",
            "Epoch 813/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1680 - mean_squared_error: 0.1680 - val_loss: 0.2028 - val_mean_squared_error: 0.2028\n",
            "Epoch 814/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.1678 - mean_squared_error: 0.1678 - val_loss: 0.2253 - val_mean_squared_error: 0.2253\n",
            "Epoch 815/1000\n",
            "60/60 [==============================] - 0s 263us/sample - loss: 0.1660 - mean_squared_error: 0.1660 - val_loss: 0.2109 - val_mean_squared_error: 0.2109\n",
            "Epoch 816/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.1919 - mean_squared_error: 0.1919 - val_loss: 0.2199 - val_mean_squared_error: 0.2199\n",
            "Epoch 817/1000\n",
            "60/60 [==============================] - 0s 275us/sample - loss: 0.1700 - mean_squared_error: 0.1700 - val_loss: 0.2093 - val_mean_squared_error: 0.2093\n",
            "Epoch 818/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.1694 - mean_squared_error: 0.1694 - val_loss: 0.1949 - val_mean_squared_error: 0.1949\n",
            "Epoch 819/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.1632 - mean_squared_error: 0.1632 - val_loss: 0.2261 - val_mean_squared_error: 0.2261\n",
            "Epoch 820/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.1670 - mean_squared_error: 0.1670 - val_loss: 0.1988 - val_mean_squared_error: 0.1988\n",
            "Epoch 821/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.1646 - mean_squared_error: 0.1646 - val_loss: 0.2035 - val_mean_squared_error: 0.2035\n",
            "Epoch 822/1000\n",
            "60/60 [==============================] - 0s 195us/sample - loss: 0.1682 - mean_squared_error: 0.1682 - val_loss: 0.1946 - val_mean_squared_error: 0.1946\n",
            "Epoch 823/1000\n",
            "60/60 [==============================] - 0s 203us/sample - loss: 0.1623 - mean_squared_error: 0.1623 - val_loss: 0.2025 - val_mean_squared_error: 0.2025\n",
            "Epoch 824/1000\n",
            "60/60 [==============================] - 0s 208us/sample - loss: 0.2102 - mean_squared_error: 0.2102 - val_loss: 0.1891 - val_mean_squared_error: 0.1891\n",
            "Epoch 825/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1686 - mean_squared_error: 0.1686 - val_loss: 0.2073 - val_mean_squared_error: 0.2073\n",
            "Epoch 826/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.1875 - mean_squared_error: 0.1875 - val_loss: 0.1927 - val_mean_squared_error: 0.1927\n",
            "Epoch 827/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.1631 - mean_squared_error: 0.1631 - val_loss: 0.1913 - val_mean_squared_error: 0.1913\n",
            "Epoch 828/1000\n",
            "60/60 [==============================] - 0s 315us/sample - loss: 0.1732 - mean_squared_error: 0.1732 - val_loss: 0.1890 - val_mean_squared_error: 0.1890\n",
            "Epoch 829/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.1620 - mean_squared_error: 0.1620 - val_loss: 0.1967 - val_mean_squared_error: 0.1967\n",
            "Epoch 830/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.1613 - mean_squared_error: 0.1613 - val_loss: 0.2174 - val_mean_squared_error: 0.2174\n",
            "Epoch 831/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.2018 - mean_squared_error: 0.2018 - val_loss: 0.2578 - val_mean_squared_error: 0.2578\n",
            "Epoch 832/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.2087 - mean_squared_error: 0.2087 - val_loss: 0.2182 - val_mean_squared_error: 0.2182\n",
            "Epoch 833/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.1745 - mean_squared_error: 0.1745 - val_loss: 0.1863 - val_mean_squared_error: 0.1863\n",
            "Epoch 834/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.1630 - mean_squared_error: 0.1630 - val_loss: 0.2016 - val_mean_squared_error: 0.2016\n",
            "Epoch 835/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.1626 - mean_squared_error: 0.1626 - val_loss: 0.2063 - val_mean_squared_error: 0.2063\n",
            "Epoch 836/1000\n",
            "60/60 [==============================] - 0s 269us/sample - loss: 0.1677 - mean_squared_error: 0.1677 - val_loss: 0.2459 - val_mean_squared_error: 0.2459\n",
            "Epoch 837/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.1691 - mean_squared_error: 0.1691 - val_loss: 0.2206 - val_mean_squared_error: 0.2206\n",
            "Epoch 838/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.1658 - mean_squared_error: 0.1658 - val_loss: 0.2358 - val_mean_squared_error: 0.2358\n",
            "Epoch 839/1000\n",
            "60/60 [==============================] - 0s 227us/sample - loss: 0.1714 - mean_squared_error: 0.1714 - val_loss: 0.2042 - val_mean_squared_error: 0.2042\n",
            "Epoch 840/1000\n",
            "60/60 [==============================] - 0s 263us/sample - loss: 0.1678 - mean_squared_error: 0.1678 - val_loss: 0.2050 - val_mean_squared_error: 0.2050\n",
            "Epoch 841/1000\n",
            "60/60 [==============================] - 0s 302us/sample - loss: 0.1797 - mean_squared_error: 0.1797 - val_loss: 0.1908 - val_mean_squared_error: 0.1908\n",
            "Epoch 842/1000\n",
            "60/60 [==============================] - 0s 321us/sample - loss: 0.2012 - mean_squared_error: 0.2012 - val_loss: 0.1957 - val_mean_squared_error: 0.1957\n",
            "Epoch 843/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.1897 - mean_squared_error: 0.1897 - val_loss: 0.2019 - val_mean_squared_error: 0.2019\n",
            "Epoch 844/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.1602 - mean_squared_error: 0.1602 - val_loss: 0.2074 - val_mean_squared_error: 0.2074\n",
            "Epoch 845/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1712 - mean_squared_error: 0.1712 - val_loss: 0.2466 - val_mean_squared_error: 0.2466\n",
            "Epoch 846/1000\n",
            "60/60 [==============================] - 0s 279us/sample - loss: 0.1727 - mean_squared_error: 0.1727 - val_loss: 0.1933 - val_mean_squared_error: 0.1933\n",
            "Epoch 847/1000\n",
            "60/60 [==============================] - 0s 304us/sample - loss: 0.1615 - mean_squared_error: 0.1615 - val_loss: 0.2282 - val_mean_squared_error: 0.2282\n",
            "Epoch 848/1000\n",
            "60/60 [==============================] - 0s 294us/sample - loss: 0.1743 - mean_squared_error: 0.1743 - val_loss: 0.1835 - val_mean_squared_error: 0.1835\n",
            "Epoch 849/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.1699 - mean_squared_error: 0.1699 - val_loss: 0.1841 - val_mean_squared_error: 0.1841\n",
            "Epoch 850/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.1632 - mean_squared_error: 0.1632 - val_loss: 0.1918 - val_mean_squared_error: 0.1918\n",
            "Epoch 851/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.1724 - mean_squared_error: 0.1724 - val_loss: 0.1898 - val_mean_squared_error: 0.1898\n",
            "Epoch 852/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.1805 - mean_squared_error: 0.1805 - val_loss: 0.2497 - val_mean_squared_error: 0.2497\n",
            "Epoch 853/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.1732 - mean_squared_error: 0.1732 - val_loss: 0.2076 - val_mean_squared_error: 0.2076\n",
            "Epoch 854/1000\n",
            "60/60 [==============================] - 0s 207us/sample - loss: 0.1603 - mean_squared_error: 0.1603 - val_loss: 0.1853 - val_mean_squared_error: 0.1853\n",
            "Epoch 855/1000\n",
            "60/60 [==============================] - 0s 324us/sample - loss: 0.1659 - mean_squared_error: 0.1659 - val_loss: 0.2193 - val_mean_squared_error: 0.2193\n",
            "Epoch 856/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.1788 - mean_squared_error: 0.1788 - val_loss: 0.1937 - val_mean_squared_error: 0.1937\n",
            "Epoch 857/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.1583 - mean_squared_error: 0.1583 - val_loss: 0.2018 - val_mean_squared_error: 0.2018\n",
            "Epoch 858/1000\n",
            "60/60 [==============================] - 0s 304us/sample - loss: 0.1595 - mean_squared_error: 0.1595 - val_loss: 0.2195 - val_mean_squared_error: 0.2195\n",
            "Epoch 859/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.1792 - mean_squared_error: 0.1792 - val_loss: 0.2173 - val_mean_squared_error: 0.2173\n",
            "Epoch 860/1000\n",
            "60/60 [==============================] - 0s 213us/sample - loss: 0.1589 - mean_squared_error: 0.1589 - val_loss: 0.1826 - val_mean_squared_error: 0.1826\n",
            "Epoch 861/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.1723 - mean_squared_error: 0.1723 - val_loss: 0.1806 - val_mean_squared_error: 0.1806\n",
            "Epoch 862/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.2038 - mean_squared_error: 0.2038 - val_loss: 0.1937 - val_mean_squared_error: 0.1937\n",
            "Epoch 863/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.1619 - mean_squared_error: 0.1619 - val_loss: 0.1816 - val_mean_squared_error: 0.1816\n",
            "Epoch 864/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1596 - mean_squared_error: 0.1596 - val_loss: 0.2003 - val_mean_squared_error: 0.2003\n",
            "Epoch 865/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1722 - mean_squared_error: 0.1722 - val_loss: 0.1941 - val_mean_squared_error: 0.1941\n",
            "Epoch 866/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.1639 - mean_squared_error: 0.1639 - val_loss: 0.2387 - val_mean_squared_error: 0.2387\n",
            "Epoch 867/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.1582 - mean_squared_error: 0.1582 - val_loss: 0.1801 - val_mean_squared_error: 0.1801\n",
            "Epoch 868/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.1636 - mean_squared_error: 0.1636 - val_loss: 0.1879 - val_mean_squared_error: 0.1879\n",
            "Epoch 869/1000\n",
            "60/60 [==============================] - 0s 267us/sample - loss: 0.1583 - mean_squared_error: 0.1583 - val_loss: 0.2191 - val_mean_squared_error: 0.2191\n",
            "Epoch 870/1000\n",
            "60/60 [==============================] - 0s 221us/sample - loss: 0.1611 - mean_squared_error: 0.1611 - val_loss: 0.1909 - val_mean_squared_error: 0.1909\n",
            "Epoch 871/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.1586 - mean_squared_error: 0.1586 - val_loss: 0.2298 - val_mean_squared_error: 0.2298\n",
            "Epoch 872/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.1779 - mean_squared_error: 0.1779 - val_loss: 0.1911 - val_mean_squared_error: 0.1911\n",
            "Epoch 873/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.1568 - mean_squared_error: 0.1568 - val_loss: 0.1976 - val_mean_squared_error: 0.1976\n",
            "Epoch 874/1000\n",
            "60/60 [==============================] - 0s 237us/sample - loss: 0.1723 - mean_squared_error: 0.1723 - val_loss: 0.1812 - val_mean_squared_error: 0.1812\n",
            "Epoch 875/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.1619 - mean_squared_error: 0.1619 - val_loss: 0.1860 - val_mean_squared_error: 0.1860\n",
            "Epoch 876/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.1913 - mean_squared_error: 0.1913 - val_loss: 0.1804 - val_mean_squared_error: 0.1804\n",
            "Epoch 877/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.1618 - mean_squared_error: 0.1618 - val_loss: 0.1845 - val_mean_squared_error: 0.1845\n",
            "Epoch 878/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1623 - mean_squared_error: 0.1623 - val_loss: 0.2148 - val_mean_squared_error: 0.2148\n",
            "Epoch 879/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.1922 - mean_squared_error: 0.1922 - val_loss: 0.2153 - val_mean_squared_error: 0.2153\n",
            "Epoch 880/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.1565 - mean_squared_error: 0.1565 - val_loss: 0.1795 - val_mean_squared_error: 0.1795\n",
            "Epoch 881/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.1794 - mean_squared_error: 0.1794 - val_loss: 0.1986 - val_mean_squared_error: 0.1986\n",
            "Epoch 882/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.1641 - mean_squared_error: 0.1641 - val_loss: 0.2237 - val_mean_squared_error: 0.2237\n",
            "Epoch 883/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.1893 - mean_squared_error: 0.1893 - val_loss: 0.1909 - val_mean_squared_error: 0.1909\n",
            "Epoch 884/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.1634 - mean_squared_error: 0.1634 - val_loss: 0.2568 - val_mean_squared_error: 0.2568\n",
            "Epoch 885/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.1714 - mean_squared_error: 0.1714 - val_loss: 0.2040 - val_mean_squared_error: 0.2040\n",
            "Epoch 886/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1989 - mean_squared_error: 0.1989 - val_loss: 0.2056 - val_mean_squared_error: 0.2056\n",
            "Epoch 887/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.1660 - mean_squared_error: 0.1660 - val_loss: 0.2587 - val_mean_squared_error: 0.2587\n",
            "Epoch 888/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.1682 - mean_squared_error: 0.1682 - val_loss: 0.2006 - val_mean_squared_error: 0.2006\n",
            "Epoch 889/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.1886 - mean_squared_error: 0.1886 - val_loss: 0.1777 - val_mean_squared_error: 0.1777\n",
            "Epoch 890/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1574 - mean_squared_error: 0.1574 - val_loss: 0.2679 - val_mean_squared_error: 0.2679\n",
            "Epoch 891/1000\n",
            "60/60 [==============================] - 0s 217us/sample - loss: 0.1656 - mean_squared_error: 0.1656 - val_loss: 0.1850 - val_mean_squared_error: 0.1850\n",
            "Epoch 892/1000\n",
            "60/60 [==============================] - 0s 361us/sample - loss: 0.1542 - mean_squared_error: 0.1542 - val_loss: 0.1953 - val_mean_squared_error: 0.1953\n",
            "Epoch 893/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.1620 - mean_squared_error: 0.1620 - val_loss: 0.1765 - val_mean_squared_error: 0.1765\n",
            "Epoch 894/1000\n",
            "60/60 [==============================] - 0s 329us/sample - loss: 0.1599 - mean_squared_error: 0.1599 - val_loss: 0.1833 - val_mean_squared_error: 0.1833\n",
            "Epoch 895/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.1566 - mean_squared_error: 0.1566 - val_loss: 0.2282 - val_mean_squared_error: 0.2282\n",
            "Epoch 896/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.1626 - mean_squared_error: 0.1626 - val_loss: 0.2114 - val_mean_squared_error: 0.2114\n",
            "Epoch 897/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.1599 - mean_squared_error: 0.1599 - val_loss: 0.2127 - val_mean_squared_error: 0.2127\n",
            "Epoch 898/1000\n",
            "60/60 [==============================] - 0s 222us/sample - loss: 0.1740 - mean_squared_error: 0.1740 - val_loss: 0.1841 - val_mean_squared_error: 0.1841\n",
            "Epoch 899/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.1700 - mean_squared_error: 0.1700 - val_loss: 0.1868 - val_mean_squared_error: 0.1868\n",
            "Epoch 900/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.1560 - mean_squared_error: 0.1560 - val_loss: 0.1943 - val_mean_squared_error: 0.1943\n",
            "Epoch 901/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.1534 - mean_squared_error: 0.1534 - val_loss: 0.1926 - val_mean_squared_error: 0.1926\n",
            "Epoch 902/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1545 - mean_squared_error: 0.1545 - val_loss: 0.1771 - val_mean_squared_error: 0.1771\n",
            "Epoch 903/1000\n",
            "60/60 [==============================] - 0s 236us/sample - loss: 0.1571 - mean_squared_error: 0.1571 - val_loss: 0.1774 - val_mean_squared_error: 0.1774\n",
            "Epoch 904/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.1748 - mean_squared_error: 0.1748 - val_loss: 0.1861 - val_mean_squared_error: 0.1861\n",
            "Epoch 905/1000\n",
            "60/60 [==============================] - 0s 346us/sample - loss: 0.1686 - mean_squared_error: 0.1686 - val_loss: 0.1789 - val_mean_squared_error: 0.1789\n",
            "Epoch 906/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.1638 - mean_squared_error: 0.1638 - val_loss: 0.1858 - val_mean_squared_error: 0.1858\n",
            "Epoch 907/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.1535 - mean_squared_error: 0.1535 - val_loss: 0.2004 - val_mean_squared_error: 0.2004\n",
            "Epoch 908/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.1544 - mean_squared_error: 0.1544 - val_loss: 0.1749 - val_mean_squared_error: 0.1749\n",
            "Epoch 909/1000\n",
            "60/60 [==============================] - 0s 216us/sample - loss: 0.1787 - mean_squared_error: 0.1787 - val_loss: 0.1823 - val_mean_squared_error: 0.1823\n",
            "Epoch 910/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.1714 - mean_squared_error: 0.1714 - val_loss: 0.1750 - val_mean_squared_error: 0.1750\n",
            "Epoch 911/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1647 - mean_squared_error: 0.1647 - val_loss: 0.1791 - val_mean_squared_error: 0.1791\n",
            "Epoch 912/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.1661 - mean_squared_error: 0.1661 - val_loss: 0.2093 - val_mean_squared_error: 0.2093\n",
            "Epoch 913/1000\n",
            "60/60 [==============================] - 0s 232us/sample - loss: 0.1567 - mean_squared_error: 0.1567 - val_loss: 0.1833 - val_mean_squared_error: 0.1833\n",
            "Epoch 914/1000\n",
            "60/60 [==============================] - 0s 267us/sample - loss: 0.1747 - mean_squared_error: 0.1747 - val_loss: 0.1753 - val_mean_squared_error: 0.1753\n",
            "Epoch 915/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.1679 - mean_squared_error: 0.1679 - val_loss: 0.1859 - val_mean_squared_error: 0.1859\n",
            "Epoch 916/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.1531 - mean_squared_error: 0.1531 - val_loss: 0.2009 - val_mean_squared_error: 0.2009\n",
            "Epoch 917/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.1533 - mean_squared_error: 0.1533 - val_loss: 0.1938 - val_mean_squared_error: 0.1938\n",
            "Epoch 918/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.1545 - mean_squared_error: 0.1545 - val_loss: 0.1793 - val_mean_squared_error: 0.1793\n",
            "Epoch 919/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.1529 - mean_squared_error: 0.1529 - val_loss: 0.1959 - val_mean_squared_error: 0.1959\n",
            "Epoch 920/1000\n",
            "60/60 [==============================] - 0s 259us/sample - loss: 0.1556 - mean_squared_error: 0.1556 - val_loss: 0.1738 - val_mean_squared_error: 0.1738\n",
            "Epoch 921/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.1695 - mean_squared_error: 0.1695 - val_loss: 0.1844 - val_mean_squared_error: 0.1844\n",
            "Epoch 922/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.1542 - mean_squared_error: 0.1542 - val_loss: 0.1729 - val_mean_squared_error: 0.1729\n",
            "Epoch 923/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.1806 - mean_squared_error: 0.1806 - val_loss: 0.1774 - val_mean_squared_error: 0.1774\n",
            "Epoch 924/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.1558 - mean_squared_error: 0.1558 - val_loss: 0.1779 - val_mean_squared_error: 0.1779\n",
            "Epoch 925/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.1539 - mean_squared_error: 0.1539 - val_loss: 0.1787 - val_mean_squared_error: 0.1787\n",
            "Epoch 926/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.1560 - mean_squared_error: 0.1560 - val_loss: 0.2345 - val_mean_squared_error: 0.2345\n",
            "Epoch 927/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.1528 - mean_squared_error: 0.1528 - val_loss: 0.1740 - val_mean_squared_error: 0.1740\n",
            "Epoch 928/1000\n",
            "60/60 [==============================] - 0s 269us/sample - loss: 0.1526 - mean_squared_error: 0.1526 - val_loss: 0.1916 - val_mean_squared_error: 0.1916\n",
            "Epoch 929/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.1691 - mean_squared_error: 0.1691 - val_loss: 0.1873 - val_mean_squared_error: 0.1873\n",
            "Epoch 930/1000\n",
            "60/60 [==============================] - 0s 266us/sample - loss: 0.1574 - mean_squared_error: 0.1574 - val_loss: 0.1720 - val_mean_squared_error: 0.1720\n",
            "Epoch 931/1000\n",
            "60/60 [==============================] - 0s 311us/sample - loss: 0.1616 - mean_squared_error: 0.1616 - val_loss: 0.2055 - val_mean_squared_error: 0.2055\n",
            "Epoch 932/1000\n",
            "60/60 [==============================] - 0s 341us/sample - loss: 0.1509 - mean_squared_error: 0.1509 - val_loss: 0.1790 - val_mean_squared_error: 0.1790\n",
            "Epoch 933/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.1592 - mean_squared_error: 0.1592 - val_loss: 0.2339 - val_mean_squared_error: 0.2339\n",
            "Epoch 934/1000\n",
            "60/60 [==============================] - 0s 262us/sample - loss: 0.1816 - mean_squared_error: 0.1816 - val_loss: 0.2282 - val_mean_squared_error: 0.2282\n",
            "Epoch 935/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.1597 - mean_squared_error: 0.1597 - val_loss: 0.1846 - val_mean_squared_error: 0.1846\n",
            "Epoch 936/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.1536 - mean_squared_error: 0.1536 - val_loss: 0.2159 - val_mean_squared_error: 0.2159\n",
            "Epoch 937/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1690 - mean_squared_error: 0.1690 - val_loss: 0.2534 - val_mean_squared_error: 0.2534\n",
            "Epoch 938/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.1778 - mean_squared_error: 0.1778 - val_loss: 0.1925 - val_mean_squared_error: 0.1925\n",
            "Epoch 939/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.1507 - mean_squared_error: 0.1507 - val_loss: 0.1881 - val_mean_squared_error: 0.1881\n",
            "Epoch 940/1000\n",
            "60/60 [==============================] - 0s 277us/sample - loss: 0.1499 - mean_squared_error: 0.1499 - val_loss: 0.1735 - val_mean_squared_error: 0.1735\n",
            "Epoch 941/1000\n",
            "60/60 [==============================] - 0s 226us/sample - loss: 0.1727 - mean_squared_error: 0.1727 - val_loss: 0.2668 - val_mean_squared_error: 0.2668\n",
            "Epoch 942/1000\n",
            "60/60 [==============================] - 0s 268us/sample - loss: 0.1721 - mean_squared_error: 0.1721 - val_loss: 0.1795 - val_mean_squared_error: 0.1795\n",
            "Epoch 943/1000\n",
            "60/60 [==============================] - 0s 205us/sample - loss: 0.1638 - mean_squared_error: 0.1638 - val_loss: 0.1737 - val_mean_squared_error: 0.1737\n",
            "Epoch 944/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.1568 - mean_squared_error: 0.1568 - val_loss: 0.1767 - val_mean_squared_error: 0.1767\n",
            "Epoch 945/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.1560 - mean_squared_error: 0.1560 - val_loss: 0.1875 - val_mean_squared_error: 0.1875\n",
            "Epoch 946/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.1497 - mean_squared_error: 0.1497 - val_loss: 0.1757 - val_mean_squared_error: 0.1757\n",
            "Epoch 947/1000\n",
            "60/60 [==============================] - 0s 212us/sample - loss: 0.1492 - mean_squared_error: 0.1492 - val_loss: 0.1858 - val_mean_squared_error: 0.1858\n",
            "Epoch 948/1000\n",
            "60/60 [==============================] - 0s 206us/sample - loss: 0.1510 - mean_squared_error: 0.1510 - val_loss: 0.1925 - val_mean_squared_error: 0.1925\n",
            "Epoch 949/1000\n",
            "60/60 [==============================] - 0s 244us/sample - loss: 0.1511 - mean_squared_error: 0.1511 - val_loss: 0.1834 - val_mean_squared_error: 0.1834\n",
            "Epoch 950/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.1515 - mean_squared_error: 0.1515 - val_loss: 0.1859 - val_mean_squared_error: 0.1859\n",
            "Epoch 951/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.1587 - mean_squared_error: 0.1587 - val_loss: 0.1999 - val_mean_squared_error: 0.1999\n",
            "Epoch 952/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.1573 - mean_squared_error: 0.1573 - val_loss: 0.2011 - val_mean_squared_error: 0.2011\n",
            "Epoch 953/1000\n",
            "60/60 [==============================] - 0s 223us/sample - loss: 0.1510 - mean_squared_error: 0.1510 - val_loss: 0.1715 - val_mean_squared_error: 0.1715\n",
            "Epoch 954/1000\n",
            "60/60 [==============================] - 0s 219us/sample - loss: 0.1583 - mean_squared_error: 0.1583 - val_loss: 0.1758 - val_mean_squared_error: 0.1758\n",
            "Epoch 955/1000\n",
            "60/60 [==============================] - 0s 220us/sample - loss: 0.1496 - mean_squared_error: 0.1496 - val_loss: 0.1781 - val_mean_squared_error: 0.1781\n",
            "Epoch 956/1000\n",
            "60/60 [==============================] - 0s 230us/sample - loss: 0.1543 - mean_squared_error: 0.1543 - val_loss: 0.1692 - val_mean_squared_error: 0.1692\n",
            "Epoch 957/1000\n",
            "60/60 [==============================] - 0s 245us/sample - loss: 0.1666 - mean_squared_error: 0.1666 - val_loss: 0.1702 - val_mean_squared_error: 0.1702\n",
            "Epoch 958/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.1533 - mean_squared_error: 0.1533 - val_loss: 0.1843 - val_mean_squared_error: 0.1843\n",
            "Epoch 959/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.1508 - mean_squared_error: 0.1508 - val_loss: 0.1774 - val_mean_squared_error: 0.1774\n",
            "Epoch 960/1000\n",
            "60/60 [==============================] - 0s 231us/sample - loss: 0.1543 - mean_squared_error: 0.1543 - val_loss: 0.1871 - val_mean_squared_error: 0.1871\n",
            "Epoch 961/1000\n",
            "60/60 [==============================] - 0s 248us/sample - loss: 0.1503 - mean_squared_error: 0.1503 - val_loss: 0.2011 - val_mean_squared_error: 0.2011\n",
            "Epoch 962/1000\n",
            "60/60 [==============================] - 0s 312us/sample - loss: 0.1551 - mean_squared_error: 0.1551 - val_loss: 0.1699 - val_mean_squared_error: 0.1699\n",
            "Epoch 963/1000\n",
            "60/60 [==============================] - 0s 316us/sample - loss: 0.1674 - mean_squared_error: 0.1674 - val_loss: 0.1687 - val_mean_squared_error: 0.1687\n",
            "Epoch 964/1000\n",
            "60/60 [==============================] - 0s 353us/sample - loss: 0.1501 - mean_squared_error: 0.1501 - val_loss: 0.1850 - val_mean_squared_error: 0.1850\n",
            "Epoch 965/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.1488 - mean_squared_error: 0.1488 - val_loss: 0.1819 - val_mean_squared_error: 0.1819\n",
            "Epoch 966/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1482 - mean_squared_error: 0.1482 - val_loss: 0.1714 - val_mean_squared_error: 0.1714\n",
            "Epoch 967/1000\n",
            "60/60 [==============================] - 0s 256us/sample - loss: 0.1687 - mean_squared_error: 0.1687 - val_loss: 0.1690 - val_mean_squared_error: 0.1690\n",
            "Epoch 968/1000\n",
            "60/60 [==============================] - 0s 253us/sample - loss: 0.1643 - mean_squared_error: 0.1643 - val_loss: 0.1677 - val_mean_squared_error: 0.1677\n",
            "Epoch 969/1000\n",
            "60/60 [==============================] - 0s 271us/sample - loss: 0.1509 - mean_squared_error: 0.1509 - val_loss: 0.1840 - val_mean_squared_error: 0.1840\n",
            "Epoch 970/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.1477 - mean_squared_error: 0.1477 - val_loss: 0.1802 - val_mean_squared_error: 0.1802\n",
            "Epoch 971/1000\n",
            "60/60 [==============================] - 0s 261us/sample - loss: 0.1485 - mean_squared_error: 0.1485 - val_loss: 0.1901 - val_mean_squared_error: 0.1901\n",
            "Epoch 972/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.1548 - mean_squared_error: 0.1548 - val_loss: 0.2056 - val_mean_squared_error: 0.2056\n",
            "Epoch 973/1000\n",
            "60/60 [==============================] - 0s 250us/sample - loss: 0.1472 - mean_squared_error: 0.1472 - val_loss: 0.1680 - val_mean_squared_error: 0.1680\n",
            "Epoch 974/1000\n",
            "60/60 [==============================] - 0s 238us/sample - loss: 0.1642 - mean_squared_error: 0.1642 - val_loss: 0.1667 - val_mean_squared_error: 0.1667\n",
            "Epoch 975/1000\n",
            "60/60 [==============================] - 0s 242us/sample - loss: 0.1597 - mean_squared_error: 0.1597 - val_loss: 0.2370 - val_mean_squared_error: 0.2370\n",
            "Epoch 976/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.1555 - mean_squared_error: 0.1555 - val_loss: 0.1874 - val_mean_squared_error: 0.1874\n",
            "Epoch 977/1000\n",
            "60/60 [==============================] - 0s 274us/sample - loss: 0.1672 - mean_squared_error: 0.1672 - val_loss: 0.2327 - val_mean_squared_error: 0.2327\n",
            "Epoch 978/1000\n",
            "60/60 [==============================] - 0s 282us/sample - loss: 0.1589 - mean_squared_error: 0.1589 - val_loss: 0.1900 - val_mean_squared_error: 0.1900\n",
            "Epoch 979/1000\n",
            "60/60 [==============================] - 0s 265us/sample - loss: 0.1482 - mean_squared_error: 0.1482 - val_loss: 0.1772 - val_mean_squared_error: 0.1772\n",
            "Epoch 980/1000\n",
            "60/60 [==============================] - 0s 307us/sample - loss: 0.1596 - mean_squared_error: 0.1596 - val_loss: 0.1920 - val_mean_squared_error: 0.1920\n",
            "Epoch 981/1000\n",
            "60/60 [==============================] - 0s 251us/sample - loss: 0.1553 - mean_squared_error: 0.1553 - val_loss: 0.1665 - val_mean_squared_error: 0.1665\n",
            "Epoch 982/1000\n",
            "60/60 [==============================] - 0s 243us/sample - loss: 0.1494 - mean_squared_error: 0.1494 - val_loss: 0.2506 - val_mean_squared_error: 0.2506\n",
            "Epoch 983/1000\n",
            "60/60 [==============================] - 0s 234us/sample - loss: 0.1608 - mean_squared_error: 0.1608 - val_loss: 0.1688 - val_mean_squared_error: 0.1688\n",
            "Epoch 984/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.1621 - mean_squared_error: 0.1621 - val_loss: 0.1994 - val_mean_squared_error: 0.1994\n",
            "Epoch 985/1000\n",
            "60/60 [==============================] - 0s 225us/sample - loss: 0.1602 - mean_squared_error: 0.1602 - val_loss: 0.1890 - val_mean_squared_error: 0.1890\n",
            "Epoch 986/1000\n",
            "60/60 [==============================] - 0s 207us/sample - loss: 0.1728 - mean_squared_error: 0.1728 - val_loss: 0.1889 - val_mean_squared_error: 0.1889\n",
            "Epoch 987/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.1509 - mean_squared_error: 0.1509 - val_loss: 0.1985 - val_mean_squared_error: 0.1985\n",
            "Epoch 988/1000\n",
            "60/60 [==============================] - 0s 291us/sample - loss: 0.1480 - mean_squared_error: 0.1480 - val_loss: 0.1751 - val_mean_squared_error: 0.1751\n",
            "Epoch 989/1000\n",
            "60/60 [==============================] - 0s 210us/sample - loss: 0.1617 - mean_squared_error: 0.1617 - val_loss: 0.1673 - val_mean_squared_error: 0.1673\n",
            "Epoch 990/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.1497 - mean_squared_error: 0.1497 - val_loss: 0.1891 - val_mean_squared_error: 0.1891\n",
            "Epoch 991/1000\n",
            "60/60 [==============================] - 0s 308us/sample - loss: 0.1599 - mean_squared_error: 0.1599 - val_loss: 0.1761 - val_mean_squared_error: 0.1761\n",
            "Epoch 992/1000\n",
            "60/60 [==============================] - 0s 278us/sample - loss: 0.1569 - mean_squared_error: 0.1569 - val_loss: 0.1683 - val_mean_squared_error: 0.1683\n",
            "Epoch 993/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.1560 - mean_squared_error: 0.1560 - val_loss: 0.1680 - val_mean_squared_error: 0.1680\n",
            "Epoch 994/1000\n",
            "60/60 [==============================] - 0s 278us/sample - loss: 0.1601 - mean_squared_error: 0.1601 - val_loss: 0.1674 - val_mean_squared_error: 0.1674\n",
            "Epoch 995/1000\n",
            "60/60 [==============================] - 0s 312us/sample - loss: 0.1478 - mean_squared_error: 0.1478 - val_loss: 0.2111 - val_mean_squared_error: 0.2111\n",
            "Epoch 996/1000\n",
            "60/60 [==============================] - 0s 257us/sample - loss: 0.1523 - mean_squared_error: 0.1523 - val_loss: 0.1775 - val_mean_squared_error: 0.1775\n",
            "Epoch 997/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.1488 - mean_squared_error: 0.1488 - val_loss: 0.1655 - val_mean_squared_error: 0.1655\n",
            "Epoch 998/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.1514 - mean_squared_error: 0.1514 - val_loss: 0.2008 - val_mean_squared_error: 0.2008\n",
            "Epoch 999/1000\n",
            "60/60 [==============================] - 0s 224us/sample - loss: 0.1580 - mean_squared_error: 0.1580 - val_loss: 0.1745 - val_mean_squared_error: 0.1745\n",
            "Epoch 1000/1000\n",
            "60/60 [==============================] - 0s 233us/sample - loss: 0.1501 - mean_squared_error: 0.1501 - val_loss: 0.1643 - val_mean_squared_error: 0.1643\n",
            "elapsed : 19.344327926635742\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzbvzCmNzlOI",
        "colab_type": "code",
        "outputId": "3c061821-cfcd-43fd-9482-67cbe3d86109",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.plot(history.history['loss'], label='loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f34369a3b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3ZIyZaoxJMsYkNW/evFIV1pS7UkoFF0pwXwF0E5HOIhKH7TCd41NmJzAUQESOxwb3qjXN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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gaLsARjT1a17",
        "colab_type": "text"
      },
      "source": [
        "## model.fit()의 validation_split\n",
        "\n",
        "따로 validation 데이터를 주지 않고, test 데이터로 준 것의 일부를 validation에 사용.\n",
        "\n",
        "validation에 사용된 데이터는 학습에 사용되지 않는다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sjr-j0WYo7-j",
        "colab_type": "code",
        "outputId": "014989f0-54f3-42d4-e11c-ef2a3554d8ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "# history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20, validation_split=0.1)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_29\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_94 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_95 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_96 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 10.968835353851318\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbPF6kuJo77k",
        "colab_type": "code",
        "outputId": "8233bb23-b543-449d-f544-7843a43d3852",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.plot(history.history['loss'], label='loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3436ee3550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V/17OM7M3n+xiCCHEKeWEO1SVUiHAW8Cfgtj3XqVUolIqMSMj40Tf2mVE91b88Ngo1+Ov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\nhs9h7CTPfZTybL5xr+3neyb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aOO43hN/F28BTgGO1Y1oCudrkJwLPzxRU/qJTXFcgA/jY\n0RT1kVKqCQ34O9ZapwN/B1KBQ5jvbR0N+3t2V93vtta+c6sF9wZPKRUNzAIe01rnuT+nzam8QQxv\nUkpdCRzVWq+r77KcRGHA2cC7WuuzgAIqLtOBhvUdAziaFMZhTmztgSZUbro4LZzs79ZqwT2YPDeW\npZQKxwT2L7TWsx2bjyil2jmebwccdWy3+u9iBHC1UioFk0b6Qkx7dDNHfiLw/Eyuzxts/qJTUBqQ\nprVe7Xg8ExPsG+p3DHARsE9rnaG1LgNmY777hvw9u6vud1tr37nVgrsrz42jd/1GTF4by1NKKUwa\nhx1a67fcnnLm7cFxO8dt++2OXvehwDG3y79Tntb6Ga11vNa6C+Z7XKy1vgVYgslPBJU/r/P3EFT+\nolON1vowcEAp1cuxaQywnQb6HTukAkOVUo0df+POz9xgv2cv1f1uFwKXKKWaO656LnFsq7767oCo\nQYfF5cBuYC/wXH2XpxY/10jMJdtmYKPj53JMe+MiYA/wM9DCsb/CjBzaC2zBjEao989Rw89+PvC9\n4343YA2QBMwAIh3boxyPkxzPd6vvctfwsw4CEh3f83dA84b+HQN/AXYCW4HPgMiG+D0DX2H6Fcow\nV2l31+S7Be5yfP4k4M6alkdmqAohRANktWYZIYQQQZDgLoQQDZAEdyGEaIAkuAshRAMkwV0IIRog\nCe5CCNEASXAXQogGSIK7EEI0QP8PaEJQGBgxsJ4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DsJzdB2tvEYf",
        "colab_type": "text"
      },
      "source": [
        "# model.fit()의 shuffle - 학습 시의 데이터 섞기\n",
        "\n",
        "학습 시에 데이터를 섞어 주지 않으면 특정 데이터 순서로 학습이 일어나 편향이 생길 수있다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0Sw3_tAvp-x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.01)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zz585h4kvp5W",
        "colab_type": "code",
        "outputId": "94169411-a52f-4241-f427-3cc6147b3f1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start_time = time.time()\n",
        "history = model.fit(train_x, train_y, epochs=50, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time)) \n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_31\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_100 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_101 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_102 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 3.699143648147583\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4IRj+LE338OqbpRcCuYuhuLBw5olmPnUwYfCjsxM+//msAzePXW2HuWY61/0quqMjm53t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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDo7dBPqv_t5",
        "colab_type": "code",
        "outputId": "aff3d20e-e31c-41d9-f320-a15bce4c2472",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start_time = time.time()\n",
        "# history = model.fit(train_x, train_y, epochs=50, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=50, verbose=0, batch_size=20, shuffle=True)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time)) \n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_32\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_103 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_104 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_105 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 3.7709591388702393\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PJaLN+v4XiWhc3r4l+va9RDTLjfXY8ZVzxqA4m54AzJs2VtxITijRcrh/2914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\nf0gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLdm5OtU9qdP",
        "colab_type": "text"
      },
      "source": [
        "학습된 결과가 살짝 향상된 것을 볼 수 있다.\n",
        "\n",
        "편향을 방지하기 위해서라도 항상 shuffle하는 것이 좋다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zli-EpQjamVC",
        "colab_type": "text"
      },
      "source": [
        "# 데이터 준비"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZbkr4-ZauCj",
        "colab_type": "text"
      },
      "source": [
        "## Shuffle\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJcDuc_0a8Ju",
        "colab_type": "code",
        "outputId": "b2649c30-747c-4e7b-a7f9-d20f3d980cff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "x = np.arange(0,10,0.1)\n",
        "# np.random.shuffle(x) # COMMENTED\n",
        "y = np.sin(x)\n",
        "\n",
        "split_index = int(x.shape[0]*0.6)\n",
        "print(split_index)\n",
        "\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]\n",
        "\n",
        "plt.scatter(train_x, train_y)\n",
        "plt.scatter(test_x, test_y, color=\"r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fb40bc59860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LOjabNbS29xrHVmArQKPRmOts3mwgrWqlVvlprw7mdq0EtKuPrC7GUa76EHBG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\n/w8VVB89ikehWgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjVgwk63Iypm",
        "colab_type": "code",
        "outputId": "3fb46c01-53c6-4a63-b2e6-cb0f1a5bd2ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start_time = time.time()\n",
        "history = model.fit(train_x, train_y, epochs=50, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time)) \n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0816 10:27:17.681707 140412517930880 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 4.123774290084839\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lJKkD5tHt8xXglIg4OSKOBi4A9s5hvmubdFYA0+0cvJ4gqQsyc8MP4Lcp+vB/AvwAuGk0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\nA2T4S9IAGf6SNECGvyQN0P8DpOIMv0aFrD0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Nhc6VTNbSXQ",
        "colab_type": "text"
      },
      "source": [
        "train 데이터와 test 데이터가 서로 떨어져 있다. \n",
        "\n",
        "train 영역의 데이터로 학습된 모델은 test 영역의 데이터에 적용하지 못한다.\n",
        "\n",
        "전체데이터를 잘 섞어 주고, 이를 train, test로 나누어야 한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UKpP696bktP",
        "colab_type": "code",
        "outputId": "8922dad1-729a-43f1-ac54-a1a0b82b8bd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "x = np.arange(0,10,0.1)\n",
        "np.random.shuffle(x) # UNCOMMENT\n",
        "y = np.sin(x)\n",
        "\n",
        "split_index = int(x.shape[0]*0.6)\n",
        "print(split_index)\n",
        "\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]\n",
        "\n",
        "plt.scatter(train_x, train_y)\n",
        "plt.scatter(test_x, test_y, color=\"r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f342b19fdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uJ939LeCbwL8s+Jjy9HdmdjFA7eurWTxpyMH/GeASM1tpZu8iWSDaXfAxdZSZ\nGck88Avu/kdFH08e3P1Wd1/u7v0k/4/3uHvQo0J3/ylwzMxW1TZdBTxf4CF12lHgw2bWXTvHryLg\nBe4Uu4Hrat9fB3w7iyedn8WTlJG7nzWzbcATJNkB97n7oYIPq9MGgM8Dz5nZs7VtX3X3xwo8JumM\nfw/srA1sXgZuKPh4Osbdf2BmjwA/JMloO0iglb5m9g3gI8ASMxsFfg8YAf7czL5A0un4X2fyWqrw\nFRGJT8jTPiIi0oKCv4hIhBT8RUQipOAvIhIhBX8RkQgp+IuIREjBX0QkQgr+IiIR+v/DeqvCxHqi\n/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RaxWc4VTat58",
        "colab_type": "text"
      },
      "source": [
        "## Normalization/Standardization\n",
        "\n",
        "입력이 여러 차원일 때 각 차원의 입력을 동일한 스케일로 맞추어주면, 학습이 빠르게 진행된다고 한다.\n",
        "\n",
        "- Normalization : 전체 데이터를 0~1로 변환해준다.\n",
        "- Standardization : 평균을 0, 표준편차를 1이되도록 변환해 준다.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NR-I6j5xajoC",
        "colab_type": "code",
        "outputId": "35d07203-654f-48bd-98fe-92edeeb3cc2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=-10, end=10, step=0.1)\n",
        "\n",
        "print(\"min=\",np.min(train_x))\n",
        "print(\"max=\",np.max(train_x))\n",
        "\n",
        "plt.scatter(train_x, train_y)\n",
        "plt.scatter(test_x, test_y, color=\"r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "min= -9.600000000000001\n",
            "max= 9.899999999999928\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7ab01738d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fQ1dHO4vmzaSttZmt/QOsXL0plUhUJbbZo8a/FjLS3asBiiaLlIwan2iySkV6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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XH39z1Cuajfu",
        "colab_type": "code",
        "outputId": "b64d6f86-3cf3-4a18-e708-c9fddab6be30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "min = -10 # ADDED\n",
        "max = 10 # ADDED\n",
        "\n",
        "train_x = (train_x-min)/(max-min) # ADDED\n",
        "test_x = (test_x-min)/(max-min) # ADDED\n",
        "\n",
        "print(\"min=\",np.min(train_x))\n",
        "print(\"max=\",np.max(train_x))\n",
        "\n",
        "plt.scatter(train_x, train_y)\n",
        "plt.scatter(test_x, test_y, color=\"r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "min= 0.019999999999999928\n",
            "max= 0.9949999999999963\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7ab2555ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aJNHIvxZi8t5rFBRM1DVhFaDJxJl1eb17bcJo5F8LMe3tryJUShzZkNd7+Jeh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\nFEXxEBV/RVEUD/n/V+C5iToq0zAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMzrd9hTok1X",
        "colab_type": "code",
        "outputId": "bef0b1ae-4308-4b73-8fb3-1203cd6b5240",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_38\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_125 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_126 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_127 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 8.89617109298706\n",
            "40/40 [==============================] - 0s 5ms/sample - loss: 0.0614 - mean_squared_error: 0.0614\n",
            "loss= 0.061400238424539566\n",
            "mse= 0.061400242\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9inxU7JPdI8I"
      },
      "source": [
        "# 모델 저장과 로딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "acf24a0c-2227-475d-a734-b9f36bf8ca22",
        "id": "byGj84t5dIdH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_35\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_112 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_113 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_114 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 8.070200681686401\n",
            "40/40 [==============================] - 0s 3ms/sample - loss: 0.1501 - mean_squared_error: 0.1501\n",
            "loss= 0.15005545914173127\n",
            "mse= 0.15005545\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZPVD8/bfNrL8Z1wUALE7Dwd/M2iTdJelaSWslvcfM1lac9kFJP3D3n5L0aUmfbPS6iAjF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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d04e660b-940b-4ba6-d5ea-ff68a2f91d82",
        "id": "BRjLRnQudH2J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "model.save('my_model.h5')\n",
        "\n",
        "!ls -al\n",
        "\n",
        "new_model = keras.models.load_model('my_model.h5')\n",
        "\n",
        "y_ = new_model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 52\n",
            "drwxr-xr-x 1 root root  4096 Aug 14 12:47 .\n",
            "drwxr-xr-x 1 root root  4096 Aug 14 11:06 ..\n",
            "-rw-r--r-- 1 root root 12510 Aug 14 11:30 base_result.png\n",
            "drwxr-xr-x 1 root root  4096 Aug  7 16:05 .config\n",
            "-rw-r--r-- 1 root root 19760 Aug 14 12:47 my_model.h5\n",
            "drwxr-xr-x 1 root root  4096 Aug  2 16:06 sample_data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0814 12:47:46.716366 139865081911168 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "W0814 12:47:46.718369 139865081911168 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHU56sKBnLHx",
        "colab_type": "text"
      },
      "source": [
        "# Overfitting 처리\n",
        "\n",
        "- DropOut\n",
        "- BatchNormalization\n",
        "- Regularization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXugk5XL4Vcg",
        "colab_type": "text"
      },
      "source": [
        "## DropOut 레이어"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sOQzyxp3MQb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Dropout"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeS9ewcunK9U",
        "colab_type": "code",
        "outputId": "1be807d3-6830-49aa-8549-c7564ef6fc79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dropout(0.1)) # ADDED\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_47\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_152 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "dense_153 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_154 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 10.028263807296753\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 0.1967 - mean_squared_error: 0.1967\n",
            "loss= 0.19667569696903228\n",
            "mse= 0.19667569\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IOCrpOmAHyXDVuyLi8fQtbgDulfT7wG7gs92Ux6xXplvkxxnOVnRd5TFkxXkM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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InNqbyKae5rY",
        "colab_type": "text"
      },
      "source": [
        "## BatchNormalization 레이어"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m919TDB2e45M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import BatchNormalization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFiIbiccfGlK",
        "colab_type": "code",
        "outputId": "b486656a-1a59-4eed-a4d4-568573a2a760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(BatchNormalization()) # ADDED\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_48\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_155 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 10)                40        \n",
            "_________________________________________________________________\n",
            "dense_156 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_157 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 181\n",
            "Trainable params: 161\n",
            "Non-trainable params: 20\n",
            "_________________________________________________________________\n",
            "elapsed : 12.237880945205688\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 0.0931 - mean_squared_error: 0.0931\n",
            "loss= 0.09305349886417388\n",
            "mse= 0.0930535\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Bh4APRsSLadpkNpGavelXjw8cOIh4ZfNzr2i2PEq1UY+kNwGHgS8DlzUKDJIm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\nrI4Dg5mZ1XFgMDOzOv8fFFRi2DoYtBMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPXW3Kez4Y33",
        "colab_type": "text"
      },
      "source": [
        "## Regularization\n",
        "\n",
        "Dense() 생성시에 kernel_regularization, bias_regularization으로 설정한다.\n",
        "\n",
        "- l1()\n",
        "- l1_l2()\n",
        "- l2()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qitCQF5j7I_J",
        "colab_type": "code",
        "outputId": "7fbec083-aa20-4449-8b4f-e086b5bfeb0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        }
      },
      "source": [
        "from tensorflow.keras.regularizers import l1, l2\n",
        "\n",
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "# model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "# model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,), kernel_regularizer=l2(0.001)))\n",
        "model.add(Dense(10, activation='tanh', kernel_regularizer=l2(0.001)))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_49\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_158 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_159 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_160 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0813 04:29:00.962242 140167977838464 util.py:244] Unresolved object in checkpoint: (root).optimizer.decay\n",
            "W0813 04:29:00.964253 140167977838464 util.py:244] Unresolved object in checkpoint: (root).optimizer.learning_rate\n",
            "W0813 04:29:00.965291 140167977838464 util.py:244] Unresolved object in checkpoint: (root).optimizer.momentum\n",
            "W0813 04:29:00.971273 140167977838464 util.py:252] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.\n",
            "W0813 04:29:00.977120 140167977838464 util.py:244] Unresolved object in checkpoint: (root).optimizer\n",
            "W0813 04:29:00.979098 140167977838464 util.py:244] Unresolved object in checkpoint: (root).optimizer.iter\n",
            "W0813 04:29:00.981695 140167977838464 util.py:244] Unresolved object in checkpoint: (root).optimizer.decay\n",
            "W0813 04:29:00.983344 140167977838464 util.py:244] Unresolved object in checkpoint: (root).optimizer.learning_rate\n",
            "W0813 04:29:00.984951 140167977838464 util.py:244] Unresolved object in checkpoint: (root).optimizer.momentum\n",
            "W0813 04:29:00.986894 140167977838464 util.py:252] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "elapsed : 10.796214580535889\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 0.1749 - mean_squared_error: 0.1590\n",
            "loss= 0.17491161227226257\n",
            "mse= 0.15898657\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8E7gSeJ+ZXTnjtA8CP3D3nwbuA+5Ne11ZJAlW+0K585ulhYQdACm3LIZ93gIccfcX3f1H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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igfl54-jc1PG",
        "colab_type": "text"
      },
      "source": [
        "# 복수개의 입력과 출력"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDlw_B29412O",
        "colab_type": "text"
      },
      "source": [
        "## 2개의 입력, 1개의 출력\n",
        "\n",
        "x1 + x2 -> y의 함수를 학습\n",
        "\n",
        "x1과 x2는 0~10의 범위를 갖는다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxI99iGz4-CT",
        "colab_type": "code",
        "outputId": "071c18bf-667f-44f7-a6ed-acf0a995a981",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "source": [
        "\n",
        "x1 = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x1)\n",
        "x2 = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x2)\n",
        "\n",
        "x = np.ones((x1.shape[0],2))\n",
        "x[:,0] = x1\n",
        "x[:,1] = x2\n",
        "np.random.shuffle(x)\n",
        "\n",
        "y = np.sum(x, axis=1)\n",
        "\n",
        "\n",
        "s = int(x.shape[0]*0.6)\n",
        "train_x, test_x = x[:s], x[s:]\n",
        "train_y, test_y = y[:s], y[s:]\n",
        "\n",
        "\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = keras.Sequential()\n",
        "# model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh', input_shape=(2,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000*5, verbose=0, batch_size=20)\n",
        "print(\"elapsed :\", (time.time() - start))\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mse=\", mse)\n",
        "              \n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(test_x,test_y)\n",
        "# plt.scatter(test_x,y_, color=\"r\")\n",
        "plt.scatter(test_x.T[0],test_y)\n",
        "plt.scatter(test_x.T[0],y_, color=\"r\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_36\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_115 (Dense)            (None, 10)                30        \n",
            "_________________________________________________________________\n",
            "dense_116 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_117 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 151\n",
            "Trainable params: 151\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 35.705761194229126\n",
            "40/40 [==============================] - 0s 2ms/sample - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "loss= 0.006477165594696998\n",
            "mse= 0.006477165\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rJYzUkaMWzN",
        "colab_type": "code",
        "outputId": "11858359-1db7-43b0-afa5-a32d3fc05231",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "print(x.shape)\n",
        "print(x[:5])\n",
        "print(y[:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 2)\n",
            "[[1.5 8.4]\n",
            " [8.6 5.6]\n",
            " [4.7 9.6]\n",
            " [4.9 2.7]\n",
            " [7.5 7.1]]\n",
            "[ 9.9 14.2 14.3  7.6 14.6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IIxLKZWs4NDz",
        "colab_type": "text"
      },
      "source": [
        "## 1개의 입력, 2개의 출력\n",
        "\n",
        "다음과 같이 y는 2개의 값을 갖고, y1 = x%2, y2=x%3인 함수를 학습\n",
        "\n",
        "```\n",
        "x = np.arange(0,10,0.1)\n",
        "y1 = x%2\n",
        "y2 = x%3\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bovsxj6f5-Bz",
        "colab_type": "code",
        "outputId": "a52ce4f6-e1c8-45a0-eb26-8232f8e3fef3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        }
      },
      "source": [
        "x = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x)\n",
        "y1 = x%2\n",
        "y2 = x%3\n",
        "\n",
        "y = np.zeros((y.shape[0],2))\n",
        "y[:,0] = y1\n",
        "y[:,1] = y2\n",
        "\n",
        "\n",
        "s = int(x.shape[0]*0.6)\n",
        "train_x, test_x = x[:s], x[s:]\n",
        "train_y, test_y = y[:s], y[s:]\n",
        "\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "# model.add(Dense(1))\n",
        "model.add(Dense(2))\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000*10, verbose=0, batch_size=20)\n",
        "print(\"elapsed :\", (time.time() - start))\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mse=\", mse)\n",
        "              \n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(test_x,test_y)\n",
        "# plt.scatter(test_x,y_, color=\"r\")\n",
        "plt.scatter(test_x,test_y[:,0])\n",
        "plt.scatter(test_x,y_[:,0], color=\"r\")\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(test_x,test_y[:,1])\n",
        "plt.scatter(test_x,y_[:,1], color=\"r\")\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_52\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_167 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_168 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_169 (Dense)            (None, 2)                 22        \n",
            "=================================================================\n",
            "Total params: 152\n",
            "Trainable params: 152\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 79.75155305862427\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 0.4017 - mean_squared_error: 0.4017\n",
            "loss= 0.4017043113708496\n",
            "mse= 0.4017043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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0S6+4ZM7XAqWb4IpV2xzXjL6dSpvNyn0/OAh/8ifzb2qr8WCGlvy7bpS8/xo08tFqTT2D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\nPedHPx9MAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UO+c/+JuLemP/D5kI9Zphox8iKa1f63nD5Ixbiai18qGYlPvx7im/aPNDPvLF+/yizQ+p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\n0P8Hw6/PZSg7UNsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHtOQg8wPI-1",
        "colab_type": "text"
      },
      "source": [
        "## 2개의 입력, 2개의 출력\n",
        "\n",
        "x1 + x2 -> z, \n",
        "\n",
        "y1 = z%2, y2=z%3인 함수를 학습\n",
        "\n",
        "x1과 x2는 0~10의 범위를 갖는다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlXRZPTzPHZq",
        "colab_type": "code",
        "outputId": "10e80914-17ee-4fc8-dfc7-3b3d5b6f4af5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        }
      },
      "source": [
        "x1 = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x1)\n",
        "x2 = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x2)\n",
        "\n",
        "x = np.ones((x1.shape[0],2))\n",
        "x[:,0] = x1\n",
        "x[:,1] = x2\n",
        "\n",
        "z = x1 + x2\n",
        "y1 = z%2\n",
        "y2 = z%3\n",
        "\n",
        "y = np.zeros((y.shape[0],2))\n",
        "y[:,0] = y1\n",
        "y[:,1] = y2\n",
        "\n",
        "\n",
        "s = int(x.shape[0]*0.6)\n",
        "train_x, test_x = x[:s], x[s:]\n",
        "train_y, test_y = y[:s], y[s:]\n",
        "\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = keras.Sequential()\n",
        "# model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh', input_shape=(2,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "# model.add(Dense(1))\n",
        "model.add(Dense(2))\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000*10, verbose=0, batch_size=20)\n",
        "print(\"elapsed :\", (time.time() - start))\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mse=\", mse)\n",
        "              \n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(test_x,test_y)\n",
        "# plt.scatter(test_x,y_, color=\"r\")\n",
        "plt.scatter(test_x.T[0],test_y[:,0])\n",
        "plt.scatter(test_x.T[0],y_[:,0], color=\"r\")\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(test_x.T[1],test_y[:,1])\n",
        "plt.scatter(test_x.T[1],y_[:,1], color=\"r\")\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_55\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_176 (Dense)            (None, 10)                30        \n",
            "_________________________________________________________________\n",
            "dense_177 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_178 (Dense)            (None, 2)                 22        \n",
            "=================================================================\n",
            "Total params: 162\n",
            "Trainable params: 162\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 80.7371563911438\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 1.0752 - mean_squared_error: 1.0752\n",
            "loss= 1.075191903114319\n",
            "mse= 1.0751919\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}