2DCNNv2_Hole.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "2DCNNv2 Hole.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sid-vt/DL-Research/blob/main/2DCNNv2_Hole.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZTOsA_Zf-Qf"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import os\n",
        "import glob\n",
        "\n",
        "from PIL import Image\n",
        "import cv2\n",
        "\n",
        "import random\n",
        "import math\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout, BatchNormalization\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "import sklearn as sk\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "pd.options.display.max_rows = 3000\n",
        "\n",
        "# import h5py\n",
        "# import torchvision"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSZb2l7cBK03",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16fffc6b-21bb-4e80-86e3-2f7bc1e07a81"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6irloPy7-OvW"
      },
      "source": [
        "# top left corner +1 and bottom corner as it is \r\n",
        "# img_read[y1+1:y2, x1:x2]\r\n",
        "\r\n",
        "image = []\r\n",
        "for image_path in glob.glob('/content/drive/MyDrive/Colab Notebooks/ResearchCNN/20_Holes/*.png'):\r\n",
        "    img_read = cv2.imread(image_path)\r\n",
        "    crop_img = img_read[82:147, 81:146]\r\n",
        "    # resize = cv2.resize(crop_img, (64, 64))\r\n",
        "    # image.append(resize)\r\n",
        "    image.append(crop_img)\r\n",
        "image = np.asarray(image)"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "F3OvtIqy-aSo",
        "outputId": "3933d791-324e-425b-8ff9-2f9a64daeeeb"
      },
      "source": [
        "data = image[15]\r\n",
        "img_show = Image.fromarray(image[15], 'RGB')\r\n",
        "plt.imshow(data, interpolation='nearest')\r\n",
        "plt.show()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmymEeqXf-Qr"
      },
      "source": [
        "X = image"
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ljnG9f79f-3",
        "outputId": "3500cbb5-ff7b-4df7-a4e6-4e138153ed80"
      },
      "source": [
        "x = (X - X.min())/(X.max() - X.min())\r\n",
        "x[10]"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        ...,\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ]],\n",
              "\n",
              "       [[0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        ...,\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ]],\n",
              "\n",
              "       [[0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        ...,\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0.99607843, 0.99607843, 0.99607843],\n",
              "        [0.99607843, 0.99607843, 0.99607843],\n",
              "        [0.99607843, 0.99607843, 0.99607843],\n",
              "        ...,\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ]],\n",
              "\n",
              "       [[0.99607843, 0.99607843, 0.99607843],\n",
              "        [0.99607843, 0.99607843, 0.99607843],\n",
              "        [0.99607843, 0.99607843, 0.99607843],\n",
              "        ...,\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ]],\n",
              "\n",
              "       [[0.99607843, 0.99607843, 0.99607843],\n",
              "        [0.99607843, 0.99607843, 0.99607843],\n",
              "        [0.99607843, 0.99607843, 0.99607843],\n",
              "        ...,\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ],\n",
              "        [0.78039216, 1.        , 0.        ]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nBnq1dHsCgT1",
        "outputId": "e1833222-d08a-41a5-ba32-dd2f7b0fb5e2"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 65, 65, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bP9DBbkWIq7",
        "outputId": "cc047179-eef1-4104-bb70-6600faf02c40"
      },
      "source": [
        "X_single = X.mean(axis=3).mean(axis=2).mean(axis=1)\r\n",
        "X_single"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([125.51408284, 115.41759369, 144.66635108, 147.26453649,\n",
              "       148.18934911, 144.9512426 , 147.48386588, 132.69767258,\n",
              "       164.19731755, 159.27439842, 147.65364892, 115.60457594,\n",
              "       160.52852071, 136.26824458, 151.1130572 , 148.27061144,\n",
              "       145.60433925, 130.23439842, 136.15242604, 131.81704142])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbAz1fKef-Qt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63e134e6-2a80-4609-f7d0-77bddb04f116"
      },
      "source": [
        "# Scaling with min max\n",
        "Xnorm = (X_single - X_single.min())/(X_single.max() - X_single.min())\n",
        "Xnorm"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.20698127, 0.        , 0.59960892, 0.65287255, 0.67183151,\n",
              "       0.60544928, 0.65736887, 0.35424717, 1.        , 0.89907858,\n",
              "       0.66084948, 0.0038332 , 0.92478849, 0.42744504, 0.73176846,\n",
              "       0.67349741, 0.61883798, 0.30374925, 0.42507072, 0.33619394])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yaA_gJIf-Q2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fac02536-39e9-4ebf-a156-a9fa53a05cf5"
      },
      "source": [
        "y = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/ResearchCNN/20_Holes/results.txt')\r\n",
        "ynorm = (y - y.min())/(y.max() - y.min())\r\n",
        "ynorm"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.93012709, 0.88790645, 0.80744144, 0.21972576,\n",
              "       0.07784484, 0.90057687, 0.88861118, 0.74886359, 0.14661646,\n",
              "       0.90400195, 0.89771009, 0.99636851, 0.93211877, 1.        ,\n",
              "       0.37646926, 0.53243553, 0.20647382, 0.65052266, 0.03129753])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VoFQeqBPf-Q4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "3a3d1b72-76c7-4b6f-b33d-7327969d7ccd"
      },
      "source": [
        "plt.scatter(Xnorm, ynorm)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f533ac25d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "ahR5TTxtPmfD",
        "outputId": "1aa32605-8c2a-4f36-d87c-2d78e4917661"
      },
      "source": [
        "# x = np.random.normal(size = 1000)\r\n",
        "plt.hist(y, bins=100)\r\n",
        "plt.gca().set(title='Frequency Histogram', ylabel='Frequency');"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcwbf5-WUU8c",
        "outputId": "bcb6f200-1085-424a-a6f0-48f5344f8839"
      },
      "source": [
        "(unique, counts) = np.unique(Xnorm, return_counts=True)\r\n",
        "frequencies = np.asarray((unique, counts)).T\r\n",
        "frequencies"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 1.        ],\n",
              "       [0.0038332 , 1.        ],\n",
              "       [0.20698127, 1.        ],\n",
              "       [0.30374925, 1.        ],\n",
              "       [0.33619394, 1.        ],\n",
              "       [0.35424717, 1.        ],\n",
              "       [0.42507072, 1.        ],\n",
              "       [0.42744504, 1.        ],\n",
              "       [0.59960892, 1.        ],\n",
              "       [0.60544928, 1.        ],\n",
              "       [0.61883798, 1.        ],\n",
              "       [0.65287255, 1.        ],\n",
              "       [0.65736887, 1.        ],\n",
              "       [0.66084948, 1.        ],\n",
              "       [0.67183151, 1.        ],\n",
              "       [0.67349741, 1.        ],\n",
              "       [0.73176846, 1.        ],\n",
              "       [0.89907858, 1.        ],\n",
              "       [0.92478849, 1.        ],\n",
              "       [1.        , 1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXLxRBx9IJuH",
        "outputId": "a3952580-4e74-4044-ff1a-a387c298f33d"
      },
      "source": [
        "(unique, counts) = np.unique(y, return_counts=True)\r\n",
        "frequencies = np.asarray((unique, counts)).T\r\n",
        "counts"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTcGNt6ef-Q5"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(x, ynorm , test_size=0.1)"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0PM9nInf-Q6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "648328a4-c1cf-48d6-d12d-66149c69d100"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18, 65, 65, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMr-AT9Ef-Q7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a0fd4fa-8632-4789-a9f4-2e49fde41b4d"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1zZtzDGNYpT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38102cc0-57f3-4da5-ed7d-e9bc7e30d83f"
      },
      "source": [
        "model = Sequential()\r\n",
        "\r\n",
        "model.add(Conv2D(64, kernel_size=(2,2 ), activation='relu', kernel_initializer='glorot_uniform', input_shape=(65,65,3)))\r\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\r\n",
        "model.add(Dropout(0.1))\r\n",
        "\r\n",
        "model.add(Flatten())\r\n",
        "\r\n",
        "model.add(Dense(256, activation='relu', kernel_initializer='glorot_uniform'))\r\n",
        "model.add(Dense(1, activation='linear'))\r\n",
        "\r\n",
        "model.summary()"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_7 (Conv2D)            (None, 64, 64, 64)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_7 (Flatten)          (None, 65536)             0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 256)               16777472  \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 1)                 257       \n",
            "=================================================================\n",
            "Total params: 16,778,561\n",
            "Trainable params: 16,778,561\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzB49ASINfMb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca543007-cf5f-41d7-b25c-5ab1ec6b1e6b"
      },
      "source": [
        "# Compile the model\r\n",
        "model.compile( loss=tf.keras.losses.MeanSquaredError(),\r\n",
        "              optimizer=keras.optimizers.Adam(lr=0.00001),\r\n",
        "              metrics=['mae'])\r\n",
        "\r\n",
        "# Fit data to model\r\n",
        "history = model.fit(X_train ,y_train, validation_data=(X_test,y_test),\r\n",
        "            batch_size=1,\r\n",
        "            epochs=500,\r\n",
        "            verbose=1)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "18/18 [==============================] - 1s 23ms/step - loss: 0.2673 - mae: 0.3856 - val_loss: 0.1058 - val_mae: 0.3160\n",
            "Epoch 2/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1008 - mae: 0.2259 - val_loss: 0.0625 - val_mae: 0.2302\n",
            "Epoch 3/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.3024 - mae: 0.3633 - val_loss: 0.0366 - val_mae: 0.1765\n",
            "Epoch 4/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1596 - mae: 0.3400 - val_loss: 0.4349 - val_mae: 0.6582\n",
            "Epoch 5/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.2106 - mae: 0.3656 - val_loss: 0.0170 - val_mae: 0.1092\n",
            "Epoch 6/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.2349 - mae: 0.4157 - val_loss: 0.0343 - val_mae: 0.1598\n",
            "Epoch 7/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.2773 - mae: 0.4005 - val_loss: 0.0127 - val_mae: 0.0872\n",
            "Epoch 8/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.3303 - mae: 0.4592 - val_loss: 0.0353 - val_mae: 0.1726\n",
            "Epoch 9/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.2161 - mae: 0.3709 - val_loss: 0.1696 - val_mae: 0.4055\n",
            "Epoch 10/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1153 - mae: 0.3135 - val_loss: 0.0511 - val_mae: 0.2103\n",
            "Epoch 11/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1294 - mae: 0.3400 - val_loss: 0.0253 - val_mae: 0.1349\n",
            "Epoch 12/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1695 - mae: 0.3542 - val_loss: 0.0804 - val_mae: 0.2737\n",
            "Epoch 13/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1213 - mae: 0.3373 - val_loss: 0.0264 - val_mae: 0.1418\n",
            "Epoch 14/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1945 - mae: 0.4032 - val_loss: 0.0221 - val_mae: 0.1221\n",
            "Epoch 15/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.2143 - mae: 0.4039 - val_loss: 0.0214 - val_mae: 0.1240\n",
            "Epoch 16/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1532 - mae: 0.3464 - val_loss: 0.1600 - val_mae: 0.3949\n",
            "Epoch 17/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1538 - mae: 0.3537 - val_loss: 0.0296 - val_mae: 0.1534\n",
            "Epoch 18/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1134 - mae: 0.2957 - val_loss: 0.0167 - val_mae: 0.0948\n",
            "Epoch 19/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1993 - mae: 0.3703 - val_loss: 0.0953 - val_mae: 0.3026\n",
            "Epoch 20/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1278 - mae: 0.3100 - val_loss: 0.0421 - val_mae: 0.1945\n",
            "Epoch 21/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0679 - mae: 0.2043 - val_loss: 0.0748 - val_mae: 0.2653\n",
            "Epoch 22/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0958 - mae: 0.2607 - val_loss: 0.0345 - val_mae: 0.1699\n",
            "Epoch 23/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1099 - mae: 0.3066 - val_loss: 0.0188 - val_mae: 0.1142\n",
            "Epoch 24/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1727 - mae: 0.3903 - val_loss: 0.0381 - val_mae: 0.1813\n",
            "Epoch 25/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0892 - mae: 0.1973 - val_loss: 0.1493 - val_mae: 0.3814\n",
            "Epoch 26/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1843 - mae: 0.3664 - val_loss: 0.1279 - val_mae: 0.3519\n",
            "Epoch 27/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1709 - mae: 0.3668 - val_loss: 0.1047 - val_mae: 0.3177\n",
            "Epoch 28/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1156 - mae: 0.2867 - val_loss: 0.2229 - val_mae: 0.4702\n",
            "Epoch 29/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.2534 - mae: 0.4551 - val_loss: 0.1082 - val_mae: 0.3240\n",
            "Epoch 30/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1651 - mae: 0.3418 - val_loss: 0.0856 - val_mae: 0.2852\n",
            "Epoch 31/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1039 - mae: 0.2875 - val_loss: 0.1064 - val_mae: 0.3207\n",
            "Epoch 32/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0931 - mae: 0.2537 - val_loss: 0.0381 - val_mae: 0.1845\n",
            "Epoch 33/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1156 - mae: 0.2835 - val_loss: 0.2308 - val_mae: 0.4775\n",
            "Epoch 34/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1467 - mae: 0.3268 - val_loss: 0.0783 - val_mae: 0.2723\n",
            "Epoch 35/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1368 - mae: 0.3127 - val_loss: 0.1711 - val_mae: 0.4094\n",
            "Epoch 36/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0848 - mae: 0.2565 - val_loss: 0.0162 - val_mae: 0.1096\n",
            "Epoch 37/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1123 - mae: 0.2863 - val_loss: 0.1801 - val_mae: 0.4224\n",
            "Epoch 38/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1502 - mae: 0.3441 - val_loss: 0.0327 - val_mae: 0.1715\n",
            "Epoch 39/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1832 - mae: 0.3642 - val_loss: 0.1015 - val_mae: 0.3153\n",
            "Epoch 40/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1113 - mae: 0.3090 - val_loss: 0.0083 - val_mae: 0.0664\n",
            "Epoch 41/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1706 - mae: 0.2712 - val_loss: 0.0469 - val_mae: 0.2100\n",
            "Epoch 42/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0908 - mae: 0.2577 - val_loss: 0.0551 - val_mae: 0.2287\n",
            "Epoch 43/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0934 - mae: 0.2669 - val_loss: 0.0747 - val_mae: 0.2684\n",
            "Epoch 44/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0991 - mae: 0.2829 - val_loss: 0.0987 - val_mae: 0.3102\n",
            "Epoch 45/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0677 - mae: 0.2112 - val_loss: 0.0129 - val_mae: 0.0973\n",
            "Epoch 46/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0448 - mae: 0.1559 - val_loss: 0.1441 - val_mae: 0.3779\n",
            "Epoch 47/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1390 - mae: 0.3519 - val_loss: 0.0923 - val_mae: 0.3010\n",
            "Epoch 48/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1502 - mae: 0.3449 - val_loss: 0.0356 - val_mae: 0.1823\n",
            "Epoch 49/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1349 - mae: 0.3019 - val_loss: 0.0135 - val_mae: 0.1028\n",
            "Epoch 50/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0917 - mae: 0.2432 - val_loss: 0.0548 - val_mae: 0.2284\n",
            "Epoch 51/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0893 - mae: 0.2496 - val_loss: 0.0144 - val_mae: 0.1016\n",
            "Epoch 52/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1053 - mae: 0.2469 - val_loss: 0.0536 - val_mae: 0.2237\n",
            "Epoch 53/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1260 - mae: 0.3108 - val_loss: 0.0540 - val_mae: 0.2253\n",
            "Epoch 54/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0860 - mae: 0.2611 - val_loss: 0.0261 - val_mae: 0.1512\n",
            "Epoch 55/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0781 - mae: 0.2069 - val_loss: 0.3279 - val_mae: 0.5717\n",
            "Epoch 56/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1804 - mae: 0.3463 - val_loss: 0.0765 - val_mae: 0.2743\n",
            "Epoch 57/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1327 - mae: 0.3269 - val_loss: 0.0042 - val_mae: 0.0544\n",
            "Epoch 58/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0762 - mae: 0.2179 - val_loss: 0.0414 - val_mae: 0.1979\n",
            "Epoch 59/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1002 - mae: 0.2439 - val_loss: 0.0394 - val_mae: 0.1917\n",
            "Epoch 60/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1271 - mae: 0.3136 - val_loss: 0.0594 - val_mae: 0.2390\n",
            "Epoch 61/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1776 - mae: 0.3881 - val_loss: 0.0444 - val_mae: 0.2039\n",
            "Epoch 62/500\n",
            "18/18 [==============================] - 0s 13ms/step - loss: 0.1241 - mae: 0.3033 - val_loss: 0.0124 - val_mae: 0.0934\n",
            "Epoch 63/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0768 - mae: 0.2176 - val_loss: 0.1435 - val_mae: 0.3762\n",
            "Epoch 64/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1059 - mae: 0.2397 - val_loss: 0.0075 - val_mae: 0.0653\n",
            "Epoch 65/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1510 - mae: 0.3294 - val_loss: 0.0152 - val_mae: 0.1116\n",
            "Epoch 66/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0754 - mae: 0.2080 - val_loss: 0.1136 - val_mae: 0.3343\n",
            "Epoch 67/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0939 - mae: 0.2663 - val_loss: 0.0034 - val_mae: 0.0581\n",
            "Epoch 68/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1104 - mae: 0.2377 - val_loss: 0.1709 - val_mae: 0.4114\n",
            "Epoch 69/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1755 - mae: 0.3924 - val_loss: 0.1034 - val_mae: 0.3183\n",
            "Epoch 70/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1096 - mae: 0.2627 - val_loss: 0.0410 - val_mae: 0.1930\n",
            "Epoch 71/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1296 - mae: 0.3058 - val_loss: 0.0172 - val_mae: 0.1061\n",
            "Epoch 72/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1866 - mae: 0.3387 - val_loss: 0.1390 - val_mae: 0.3691\n",
            "Epoch 73/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1293 - mae: 0.2566 - val_loss: 0.1500 - val_mae: 0.3827\n",
            "Epoch 74/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0958 - mae: 0.2461 - val_loss: 0.0305 - val_mae: 0.1564\n",
            "Epoch 75/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1616 - mae: 0.3400 - val_loss: 0.0166 - val_mae: 0.0997\n",
            "Epoch 76/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0943 - mae: 0.2644 - val_loss: 0.1227 - val_mae: 0.3453\n",
            "Epoch 77/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0766 - mae: 0.2326 - val_loss: 0.0180 - val_mae: 0.1143\n",
            "Epoch 78/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1363 - mae: 0.3123 - val_loss: 0.0308 - val_mae: 0.1607\n",
            "Epoch 79/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0798 - mae: 0.2192 - val_loss: 0.0263 - val_mae: 0.1481\n",
            "Epoch 80/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1025 - mae: 0.2967 - val_loss: 0.1657 - val_mae: 0.4020\n",
            "Epoch 81/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1210 - mae: 0.2828 - val_loss: 0.1144 - val_mae: 0.3311\n",
            "Epoch 82/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0889 - mae: 0.2206 - val_loss: 0.0310 - val_mae: 0.1603\n",
            "Epoch 83/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0659 - mae: 0.1995 - val_loss: 0.2369 - val_mae: 0.4835\n",
            "Epoch 84/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1337 - mae: 0.3311 - val_loss: 0.0996 - val_mae: 0.3083\n",
            "Epoch 85/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1004 - mae: 0.2783 - val_loss: 0.0500 - val_mae: 0.2152\n",
            "Epoch 86/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0971 - mae: 0.2762 - val_loss: 0.0077 - val_mae: 0.0685\n",
            "Epoch 87/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1016 - mae: 0.2448 - val_loss: 0.0658 - val_mae: 0.2502\n",
            "Epoch 88/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0597 - mae: 0.1675 - val_loss: 0.1155 - val_mae: 0.3349\n",
            "Epoch 89/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0883 - mae: 0.2385 - val_loss: 0.0360 - val_mae: 0.1768\n",
            "Epoch 90/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0847 - mae: 0.2540 - val_loss: 0.0641 - val_mae: 0.2454\n",
            "Epoch 91/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0882 - mae: 0.2268 - val_loss: 0.2157 - val_mae: 0.4618\n",
            "Epoch 92/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0966 - mae: 0.2568 - val_loss: 0.1172 - val_mae: 0.3397\n",
            "Epoch 93/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1346 - mae: 0.3031 - val_loss: 0.0941 - val_mae: 0.3033\n",
            "Epoch 94/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0610 - mae: 0.1988 - val_loss: 0.0393 - val_mae: 0.1922\n",
            "Epoch 95/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0847 - mae: 0.2587 - val_loss: 0.0336 - val_mae: 0.1776\n",
            "Epoch 96/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0827 - mae: 0.2246 - val_loss: 0.0074 - val_mae: 0.0651\n",
            "Epoch 97/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1106 - mae: 0.2583 - val_loss: 0.0107 - val_mae: 0.0867\n",
            "Epoch 98/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1340 - mae: 0.3083 - val_loss: 0.1672 - val_mae: 0.4067\n",
            "Epoch 99/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0647 - mae: 0.2058 - val_loss: 0.0345 - val_mae: 0.1793\n",
            "Epoch 100/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0859 - mae: 0.2292 - val_loss: 0.0038 - val_mae: 0.0605\n",
            "Epoch 101/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1594 - mae: 0.3332 - val_loss: 0.0648 - val_mae: 0.2496\n",
            "Epoch 102/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1051 - mae: 0.2869 - val_loss: 0.0316 - val_mae: 0.1697\n",
            "Epoch 103/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0718 - mae: 0.2154 - val_loss: 0.0036 - val_mae: 0.0554\n",
            "Epoch 104/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0912 - mae: 0.2484 - val_loss: 0.1601 - val_mae: 0.3989\n",
            "Epoch 105/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0871 - mae: 0.2380 - val_loss: 0.0150 - val_mae: 0.1128\n",
            "Epoch 106/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0911 - mae: 0.2582 - val_loss: 0.1254 - val_mae: 0.3524\n",
            "Epoch 107/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1250 - mae: 0.3155 - val_loss: 0.0639 - val_mae: 0.2493\n",
            "Epoch 108/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1021 - mae: 0.2917 - val_loss: 0.0046 - val_mae: 0.0495\n",
            "Epoch 109/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0692 - mae: 0.1877 - val_loss: 0.1919 - val_mae: 0.4370\n",
            "Epoch 110/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1130 - mae: 0.2703 - val_loss: 0.1012 - val_mae: 0.3164\n",
            "Epoch 111/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1331 - mae: 0.3349 - val_loss: 0.0628 - val_mae: 0.2467\n",
            "Epoch 112/500\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0860 - mae: 0.2683 - val_loss: 0.0431 - val_mae: 0.2019\n",
            "Epoch 113/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0467 - mae: 0.1817 - val_loss: 0.0729 - val_mae: 0.2667\n",
            "Epoch 114/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1200 - mae: 0.2789 - val_loss: 0.0354 - val_mae: 0.1805\n",
            "Epoch 115/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0702 - mae: 0.2257 - val_loss: 0.0726 - val_mae: 0.2659\n",
            "Epoch 116/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1152 - mae: 0.2619 - val_loss: 0.1863 - val_mae: 0.4299\n",
            "Epoch 117/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1343 - mae: 0.3061 - val_loss: 0.0180 - val_mae: 0.1236\n",
            "Epoch 118/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0885 - mae: 0.2446 - val_loss: 0.0269 - val_mae: 0.1526\n",
            "Epoch 119/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0827 - mae: 0.2129 - val_loss: 0.0571 - val_mae: 0.2311\n",
            "Epoch 120/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0990 - mae: 0.2684 - val_loss: 0.0044 - val_mae: 0.0664\n",
            "Epoch 121/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1261 - mae: 0.2577 - val_loss: 0.0353 - val_mae: 0.1817\n",
            "Epoch 122/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1294 - mae: 0.3280 - val_loss: 0.0485 - val_mae: 0.2144\n",
            "Epoch 123/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0787 - mae: 0.2258 - val_loss: 0.0169 - val_mae: 0.1149\n",
            "Epoch 124/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1182 - mae: 0.3048 - val_loss: 0.0306 - val_mae: 0.1644\n",
            "Epoch 125/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0760 - mae: 0.2263 - val_loss: 0.0604 - val_mae: 0.2396\n",
            "Epoch 126/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1252 - mae: 0.3200 - val_loss: 0.0072 - val_mae: 0.0637\n",
            "Epoch 127/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1037 - mae: 0.2878 - val_loss: 0.0318 - val_mae: 0.1723\n",
            "Epoch 128/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0894 - mae: 0.2279 - val_loss: 0.0920 - val_mae: 0.2996\n",
            "Epoch 129/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0966 - mae: 0.2922 - val_loss: 0.0443 - val_mae: 0.2026\n",
            "Epoch 130/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0843 - mae: 0.2472 - val_loss: 0.0256 - val_mae: 0.1484\n",
            "Epoch 131/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0771 - mae: 0.2505 - val_loss: 0.0417 - val_mae: 0.1959\n",
            "Epoch 132/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1017 - mae: 0.2733 - val_loss: 0.1118 - val_mae: 0.3301\n",
            "Epoch 133/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0525 - mae: 0.1927 - val_loss: 0.0272 - val_mae: 0.1544\n",
            "Epoch 134/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0631 - mae: 0.2052 - val_loss: 0.0611 - val_mae: 0.2407\n",
            "Epoch 135/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0703 - mae: 0.2143 - val_loss: 0.0364 - val_mae: 0.1814\n",
            "Epoch 136/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1281 - mae: 0.3056 - val_loss: 0.0054 - val_mae: 0.0615\n",
            "Epoch 137/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0951 - mae: 0.2502 - val_loss: 0.0753 - val_mae: 0.2701\n",
            "Epoch 138/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0841 - mae: 0.2500 - val_loss: 0.0609 - val_mae: 0.2423\n",
            "Epoch 139/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0564 - mae: 0.1900 - val_loss: 0.0311 - val_mae: 0.1690\n",
            "Epoch 140/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1020 - mae: 0.2681 - val_loss: 0.0594 - val_mae: 0.2381\n",
            "Epoch 141/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0671 - mae: 0.1957 - val_loss: 0.0574 - val_mae: 0.2344\n",
            "Epoch 142/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1003 - mae: 0.2633 - val_loss: 0.0129 - val_mae: 0.0983\n",
            "Epoch 143/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0806 - mae: 0.2132 - val_loss: 0.1077 - val_mae: 0.3251\n",
            "Epoch 144/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1370 - mae: 0.3069 - val_loss: 0.1547 - val_mae: 0.3911\n",
            "Epoch 145/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0789 - mae: 0.2325 - val_loss: 0.0181 - val_mae: 0.1240\n",
            "Epoch 146/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0941 - mae: 0.2562 - val_loss: 0.1100 - val_mae: 0.3301\n",
            "Epoch 147/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0944 - mae: 0.2645 - val_loss: 0.0086 - val_mae: 0.0824\n",
            "Epoch 148/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0606 - mae: 0.1889 - val_loss: 0.0792 - val_mae: 0.2794\n",
            "Epoch 149/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0482 - mae: 0.1700 - val_loss: 0.0970 - val_mae: 0.3092\n",
            "Epoch 150/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1010 - mae: 0.2791 - val_loss: 0.1112 - val_mae: 0.3312\n",
            "Epoch 151/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0949 - mae: 0.2412 - val_loss: 0.0550 - val_mae: 0.2318\n",
            "Epoch 152/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0725 - mae: 0.2069 - val_loss: 0.0590 - val_mae: 0.2415\n",
            "Epoch 153/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0715 - mae: 0.2365 - val_loss: 0.0320 - val_mae: 0.1763\n",
            "Epoch 154/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0611 - mae: 0.2168 - val_loss: 0.0050 - val_mae: 0.0593\n",
            "Epoch 155/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1164 - mae: 0.2938 - val_loss: 0.1163 - val_mae: 0.3399\n",
            "Epoch 156/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0719 - mae: 0.2094 - val_loss: 0.1512 - val_mae: 0.3882\n",
            "Epoch 157/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0692 - mae: 0.2119 - val_loss: 0.0025 - val_mae: 0.0376\n",
            "Epoch 158/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1010 - mae: 0.2768 - val_loss: 0.1165 - val_mae: 0.3407\n",
            "Epoch 159/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1281 - mae: 0.2857 - val_loss: 0.0023 - val_mae: 0.0469\n",
            "Epoch 160/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0694 - mae: 0.2116 - val_loss: 0.0187 - val_mae: 0.1233\n",
            "Epoch 161/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1296 - mae: 0.3107 - val_loss: 0.0250 - val_mae: 0.1485\n",
            "Epoch 162/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0991 - mae: 0.2347 - val_loss: 0.0946 - val_mae: 0.3039\n",
            "Epoch 163/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0903 - mae: 0.2752 - val_loss: 0.0373 - val_mae: 0.1859\n",
            "Epoch 164/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0472 - mae: 0.1540 - val_loss: 0.0108 - val_mae: 0.0900\n",
            "Epoch 165/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0971 - mae: 0.2598 - val_loss: 0.1458 - val_mae: 0.3803\n",
            "Epoch 166/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0713 - mae: 0.2187 - val_loss: 0.0212 - val_mae: 0.1377\n",
            "Epoch 167/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1125 - mae: 0.2871 - val_loss: 0.0296 - val_mae: 0.1666\n",
            "Epoch 168/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0424 - mae: 0.1395 - val_loss: 0.0095 - val_mae: 0.0854\n",
            "Epoch 169/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0674 - mae: 0.2049 - val_loss: 0.1109 - val_mae: 0.3305\n",
            "Epoch 170/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0791 - mae: 0.2413 - val_loss: 0.1168 - val_mae: 0.3393\n",
            "Epoch 171/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1183 - mae: 0.2797 - val_loss: 0.1539 - val_mae: 0.3900\n",
            "Epoch 172/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1233 - mae: 0.2993 - val_loss: 0.0744 - val_mae: 0.2683\n",
            "Epoch 173/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0674 - mae: 0.2202 - val_loss: 0.0443 - val_mae: 0.2040\n",
            "Epoch 174/500\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0845 - mae: 0.2514 - val_loss: 0.0307 - val_mae: 0.1681\n",
            "Epoch 175/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0654 - mae: 0.2174 - val_loss: 0.1353 - val_mae: 0.3656\n",
            "Epoch 176/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1371 - mae: 0.3116 - val_loss: 0.2457 - val_mae: 0.4935\n",
            "Epoch 177/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1610 - mae: 0.3195 - val_loss: 0.0234 - val_mae: 0.1315\n",
            "Epoch 178/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0381 - mae: 0.1652 - val_loss: 0.1539 - val_mae: 0.3868\n",
            "Epoch 179/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0908 - mae: 0.2675 - val_loss: 0.1130 - val_mae: 0.3288\n",
            "Epoch 180/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1000 - mae: 0.2783 - val_loss: 0.0799 - val_mae: 0.2699\n",
            "Epoch 181/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0636 - mae: 0.2199 - val_loss: 0.0194 - val_mae: 0.1110\n",
            "Epoch 182/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1203 - mae: 0.3008 - val_loss: 0.0545 - val_mae: 0.2206\n",
            "Epoch 183/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0555 - mae: 0.1842 - val_loss: 0.1549 - val_mae: 0.3897\n",
            "Epoch 184/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1123 - mae: 0.2632 - val_loss: 0.0239 - val_mae: 0.1372\n",
            "Epoch 185/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.1261 - mae: 0.3029 - val_loss: 0.0089 - val_mae: 0.0801\n",
            "Epoch 186/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0486 - mae: 0.1537 - val_loss: 0.1569 - val_mae: 0.3942\n",
            "Epoch 187/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1089 - mae: 0.3009 - val_loss: 0.1011 - val_mae: 0.3134\n",
            "Epoch 188/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0679 - mae: 0.2297 - val_loss: 0.0493 - val_mae: 0.2126\n",
            "Epoch 189/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0585 - mae: 0.1967 - val_loss: 0.0325 - val_mae: 0.1692\n",
            "Epoch 190/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0806 - mae: 0.2092 - val_loss: 0.2381 - val_mae: 0.4864\n",
            "Epoch 191/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.2002 - mae: 0.3848 - val_loss: 0.0505 - val_mae: 0.2202\n",
            "Epoch 192/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0701 - mae: 0.2346 - val_loss: 0.0130 - val_mae: 0.1012\n",
            "Epoch 193/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0741 - mae: 0.2204 - val_loss: 0.0407 - val_mae: 0.1948\n",
            "Epoch 194/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0763 - mae: 0.2344 - val_loss: 0.0851 - val_mae: 0.2875\n",
            "Epoch 195/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0573 - mae: 0.1979 - val_loss: 0.0040 - val_mae: 0.0613\n",
            "Epoch 196/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0679 - mae: 0.2283 - val_loss: 0.0525 - val_mae: 0.2250\n",
            "Epoch 197/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0646 - mae: 0.2052 - val_loss: 0.1667 - val_mae: 0.4074\n",
            "Epoch 198/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1630 - mae: 0.3184 - val_loss: 0.0025 - val_mae: 0.0454\n",
            "Epoch 199/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1386 - mae: 0.3133 - val_loss: 0.0099 - val_mae: 0.0881\n",
            "Epoch 200/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0852 - mae: 0.2350 - val_loss: 0.1185 - val_mae: 0.3426\n",
            "Epoch 201/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0982 - mae: 0.2643 - val_loss: 0.0556 - val_mae: 0.2305\n",
            "Epoch 202/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0667 - mae: 0.2157 - val_loss: 0.0325 - val_mae: 0.1728\n",
            "Epoch 203/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0943 - mae: 0.2697 - val_loss: 0.0066 - val_mae: 0.0584\n",
            "Epoch 204/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0904 - mae: 0.2420 - val_loss: 0.0201 - val_mae: 0.1323\n",
            "Epoch 205/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0566 - mae: 0.1898 - val_loss: 0.0567 - val_mae: 0.2337\n",
            "Epoch 206/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0548 - mae: 0.2101 - val_loss: 0.0657 - val_mae: 0.2528\n",
            "Epoch 207/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0750 - mae: 0.2425 - val_loss: 0.0635 - val_mae: 0.2479\n",
            "Epoch 208/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0532 - mae: 0.1951 - val_loss: 0.0195 - val_mae: 0.1295\n",
            "Epoch 209/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0681 - mae: 0.2058 - val_loss: 0.0392 - val_mae: 0.1903\n",
            "Epoch 210/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0943 - mae: 0.2680 - val_loss: 0.0849 - val_mae: 0.2889\n",
            "Epoch 211/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1079 - mae: 0.2848 - val_loss: 0.0178 - val_mae: 0.1248\n",
            "Epoch 212/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0734 - mae: 0.2105 - val_loss: 0.0450 - val_mae: 0.2077\n",
            "Epoch 213/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0597 - mae: 0.2195 - val_loss: 0.0069 - val_mae: 0.0646\n",
            "Epoch 214/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0959 - mae: 0.2575 - val_loss: 0.0687 - val_mae: 0.2583\n",
            "Epoch 215/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0815 - mae: 0.2364 - val_loss: 0.0721 - val_mae: 0.2652\n",
            "Epoch 216/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0854 - mae: 0.2494 - val_loss: 0.0478 - val_mae: 0.2137\n",
            "Epoch 217/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0613 - mae: 0.1793 - val_loss: 0.0199 - val_mae: 0.1325\n",
            "Epoch 218/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0853 - mae: 0.2489 - val_loss: 0.0734 - val_mae: 0.2677\n",
            "Epoch 219/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0699 - mae: 0.2144 - val_loss: 0.0743 - val_mae: 0.2688\n",
            "Epoch 220/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0988 - mae: 0.2854 - val_loss: 0.0207 - val_mae: 0.1322\n",
            "Epoch 221/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0690 - mae: 0.2189 - val_loss: 0.1066 - val_mae: 0.3233\n",
            "Epoch 222/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0796 - mae: 0.2451 - val_loss: 0.1026 - val_mae: 0.3158\n",
            "Epoch 223/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0923 - mae: 0.2526 - val_loss: 0.1547 - val_mae: 0.3904\n",
            "Epoch 224/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0800 - mae: 0.2481 - val_loss: 0.0157 - val_mae: 0.1106\n",
            "Epoch 225/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0621 - mae: 0.2099 - val_loss: 0.0123 - val_mae: 0.0947\n",
            "Epoch 226/500\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0352 - mae: 0.1406 - val_loss: 0.1855 - val_mae: 0.4283\n",
            "Epoch 227/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1418 - mae: 0.3230 - val_loss: 0.0204 - val_mae: 0.1263\n",
            "Epoch 228/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0552 - mae: 0.1887 - val_loss: 0.0204 - val_mae: 0.1208\n",
            "Epoch 229/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0479 - mae: 0.1717 - val_loss: 0.1372 - val_mae: 0.3659\n",
            "Epoch 230/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0661 - mae: 0.2352 - val_loss: 0.0225 - val_mae: 0.1367\n",
            "Epoch 231/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0320 - mae: 0.1329 - val_loss: 0.0500 - val_mae: 0.2158\n",
            "Epoch 232/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0585 - mae: 0.2042 - val_loss: 0.0152 - val_mae: 0.1009\n",
            "Epoch 233/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0664 - mae: 0.1958 - val_loss: 0.0749 - val_mae: 0.2662\n",
            "Epoch 234/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0540 - mae: 0.2029 - val_loss: 0.0181 - val_mae: 0.1169\n",
            "Epoch 235/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0783 - mae: 0.2345 - val_loss: 0.0195 - val_mae: 0.1215\n",
            "Epoch 236/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0669 - mae: 0.2079 - val_loss: 0.0229 - val_mae: 0.1341\n",
            "Epoch 237/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0571 - mae: 0.1951 - val_loss: 0.0179 - val_mae: 0.1132\n",
            "Epoch 238/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0718 - mae: 0.2136 - val_loss: 0.0631 - val_mae: 0.2412\n",
            "Epoch 239/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0807 - mae: 0.2526 - val_loss: 0.0521 - val_mae: 0.2166\n",
            "Epoch 240/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0503 - mae: 0.1850 - val_loss: 0.0617 - val_mae: 0.2380\n",
            "Epoch 241/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0635 - mae: 0.2127 - val_loss: 0.1394 - val_mae: 0.3673\n",
            "Epoch 242/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0574 - mae: 0.2162 - val_loss: 0.0169 - val_mae: 0.1088\n",
            "Epoch 243/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0516 - mae: 0.1691 - val_loss: 0.0363 - val_mae: 0.1789\n",
            "Epoch 244/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0752 - mae: 0.2228 - val_loss: 0.0765 - val_mae: 0.2709\n",
            "Epoch 245/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0587 - mae: 0.2014 - val_loss: 0.0324 - val_mae: 0.1705\n",
            "Epoch 246/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0698 - mae: 0.2134 - val_loss: 0.0888 - val_mae: 0.2935\n",
            "Epoch 247/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0748 - mae: 0.2325 - val_loss: 0.0449 - val_mae: 0.2046\n",
            "Epoch 248/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0551 - mae: 0.2123 - val_loss: 0.0862 - val_mae: 0.2899\n",
            "Epoch 249/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0475 - mae: 0.1770 - val_loss: 0.0157 - val_mae: 0.1146\n",
            "Epoch 250/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0501 - mae: 0.1756 - val_loss: 0.0194 - val_mae: 0.1283\n",
            "Epoch 251/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0554 - mae: 0.1838 - val_loss: 0.0051 - val_mae: 0.0681\n",
            "Epoch 252/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0804 - mae: 0.2318 - val_loss: 0.0722 - val_mae: 0.2635\n",
            "Epoch 253/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0565 - mae: 0.1965 - val_loss: 0.0335 - val_mae: 0.1756\n",
            "Epoch 254/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0717 - mae: 0.2320 - val_loss: 0.0247 - val_mae: 0.1455\n",
            "Epoch 255/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0850 - mae: 0.2327 - val_loss: 0.0672 - val_mae: 0.2542\n",
            "Epoch 256/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0490 - mae: 0.1864 - val_loss: 0.0310 - val_mae: 0.1658\n",
            "Epoch 257/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0793 - mae: 0.2295 - val_loss: 0.0540 - val_mae: 0.2224\n",
            "Epoch 258/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0615 - mae: 0.1998 - val_loss: 0.0333 - val_mae: 0.1659\n",
            "Epoch 259/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0710 - mae: 0.2273 - val_loss: 0.0279 - val_mae: 0.1504\n",
            "Epoch 260/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0328 - mae: 0.1330 - val_loss: 0.1428 - val_mae: 0.3727\n",
            "Epoch 261/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0862 - mae: 0.2483 - val_loss: 0.0072 - val_mae: 0.0802\n",
            "Epoch 262/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0742 - mae: 0.2274 - val_loss: 0.0517 - val_mae: 0.2163\n",
            "Epoch 263/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0589 - mae: 0.2055 - val_loss: 0.0579 - val_mae: 0.2303\n",
            "Epoch 264/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0349 - mae: 0.1359 - val_loss: 0.0512 - val_mae: 0.2175\n",
            "Epoch 265/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0577 - mae: 0.2124 - val_loss: 0.0249 - val_mae: 0.1398\n",
            "Epoch 266/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0770 - mae: 0.2210 - val_loss: 0.0118 - val_mae: 0.0853\n",
            "Epoch 267/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0408 - mae: 0.1631 - val_loss: 0.0535 - val_mae: 0.2184\n",
            "Epoch 268/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0659 - mae: 0.2186 - val_loss: 0.0622 - val_mae: 0.2357\n",
            "Epoch 269/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0693 - mae: 0.2159 - val_loss: 0.0416 - val_mae: 0.1859\n",
            "Epoch 270/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0338 - mae: 0.1438 - val_loss: 0.0512 - val_mae: 0.2136\n",
            "Epoch 271/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0659 - mae: 0.2084 - val_loss: 0.1240 - val_mae: 0.3461\n",
            "Epoch 272/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0441 - mae: 0.1703 - val_loss: 0.0787 - val_mae: 0.2723\n",
            "Epoch 273/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0393 - mae: 0.1539 - val_loss: 0.0424 - val_mae: 0.1959\n",
            "Epoch 274/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0646 - mae: 0.2143 - val_loss: 0.1364 - val_mae: 0.3660\n",
            "Epoch 275/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0547 - mae: 0.1874 - val_loss: 0.0163 - val_mae: 0.1095\n",
            "Epoch 276/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0824 - mae: 0.2266 - val_loss: 0.0138 - val_mae: 0.0906\n",
            "Epoch 277/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0802 - mae: 0.2310 - val_loss: 0.1431 - val_mae: 0.3700\n",
            "Epoch 278/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0534 - mae: 0.1848 - val_loss: 0.0789 - val_mae: 0.2658\n",
            "Epoch 279/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0550 - mae: 0.2062 - val_loss: 0.0421 - val_mae: 0.1916\n",
            "Epoch 280/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0489 - mae: 0.1850 - val_loss: 0.0767 - val_mae: 0.2700\n",
            "Epoch 281/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0556 - mae: 0.2101 - val_loss: 0.0085 - val_mae: 0.0740\n",
            "Epoch 282/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0860 - mae: 0.2404 - val_loss: 0.0358 - val_mae: 0.1785\n",
            "Epoch 283/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0329 - mae: 0.1324 - val_loss: 0.1005 - val_mae: 0.3117\n",
            "Epoch 284/500\n",
            "18/18 [==============================] - 0s 8ms/step - loss: 0.0530 - mae: 0.1966 - val_loss: 0.0052 - val_mae: 0.0653\n",
            "Epoch 285/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0720 - mae: 0.2180 - val_loss: 0.0355 - val_mae: 0.1816\n",
            "Epoch 286/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0526 - mae: 0.2032 - val_loss: 0.0846 - val_mae: 0.2876\n",
            "Epoch 287/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0505 - mae: 0.1821 - val_loss: 0.1409 - val_mae: 0.3730\n",
            "Epoch 288/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0867 - mae: 0.2358 - val_loss: 0.0121 - val_mae: 0.0946\n",
            "Epoch 289/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0735 - mae: 0.2325 - val_loss: 0.0149 - val_mae: 0.1134\n",
            "Epoch 290/500\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0754 - mae: 0.2250 - val_loss: 0.0060 - val_mae: 0.0601\n",
            "Epoch 291/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0287 - mae: 0.1222 - val_loss: 0.1351 - val_mae: 0.3656\n",
            "Epoch 292/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0615 - mae: 0.2015 - val_loss: 0.0301 - val_mae: 0.1647\n",
            "Epoch 293/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0647 - mae: 0.2227 - val_loss: 0.1003 - val_mae: 0.3132\n",
            "Epoch 294/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0805 - mae: 0.2338 - val_loss: 0.0242 - val_mae: 0.1421\n",
            "Epoch 295/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0548 - mae: 0.1892 - val_loss: 0.1628 - val_mae: 0.3989\n",
            "Epoch 296/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0669 - mae: 0.2274 - val_loss: 0.0376 - val_mae: 0.1741\n",
            "Epoch 297/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0462 - mae: 0.1612 - val_loss: 0.1323 - val_mae: 0.3547\n",
            "Epoch 298/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0466 - mae: 0.1458 - val_loss: 0.1321 - val_mae: 0.3548\n",
            "Epoch 299/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0467 - mae: 0.1807 - val_loss: 0.0573 - val_mae: 0.2222\n",
            "Epoch 300/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0648 - mae: 0.2049 - val_loss: 0.1699 - val_mae: 0.4058\n",
            "Epoch 301/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1170 - mae: 0.3046 - val_loss: 0.0339 - val_mae: 0.1635\n",
            "Epoch 302/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0383 - mae: 0.1488 - val_loss: 0.0745 - val_mae: 0.2633\n",
            "Epoch 303/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0412 - mae: 0.1803 - val_loss: 0.0871 - val_mae: 0.2881\n",
            "Epoch 304/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0708 - mae: 0.2448 - val_loss: 0.0372 - val_mae: 0.1763\n",
            "Epoch 305/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0464 - mae: 0.1950 - val_loss: 0.0636 - val_mae: 0.2419\n",
            "Epoch 306/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0497 - mae: 0.1774 - val_loss: 0.0089 - val_mae: 0.0914\n",
            "Epoch 307/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0530 - mae: 0.1808 - val_loss: 0.0882 - val_mae: 0.2874\n",
            "Epoch 308/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0525 - mae: 0.2005 - val_loss: 0.0915 - val_mae: 0.2935\n",
            "Epoch 309/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0562 - mae: 0.1968 - val_loss: 0.0192 - val_mae: 0.1101\n",
            "Epoch 310/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0513 - mae: 0.1923 - val_loss: 0.0619 - val_mae: 0.2385\n",
            "Epoch 311/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0445 - mae: 0.1884 - val_loss: 0.0093 - val_mae: 0.0845\n",
            "Epoch 312/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0605 - mae: 0.1863 - val_loss: 0.1266 - val_mae: 0.3502\n",
            "Epoch 313/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0557 - mae: 0.1848 - val_loss: 0.0181 - val_mae: 0.1110\n",
            "Epoch 314/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0398 - mae: 0.1611 - val_loss: 0.0180 - val_mae: 0.1156\n",
            "Epoch 315/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0788 - mae: 0.2493 - val_loss: 0.1306 - val_mae: 0.3586\n",
            "Epoch 316/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0809 - mae: 0.1992 - val_loss: 0.0662 - val_mae: 0.2541\n",
            "Epoch 317/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0667 - mae: 0.2309 - val_loss: 0.0168 - val_mae: 0.1186\n",
            "Epoch 318/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0314 - mae: 0.1406 - val_loss: 0.0521 - val_mae: 0.2208\n",
            "Epoch 319/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0519 - mae: 0.1648 - val_loss: 0.0057 - val_mae: 0.0657\n",
            "Epoch 320/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0674 - mae: 0.2227 - val_loss: 0.0179 - val_mae: 0.1146\n",
            "Epoch 321/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0457 - mae: 0.1719 - val_loss: 0.0903 - val_mae: 0.2939\n",
            "Epoch 322/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0532 - mae: 0.1975 - val_loss: 0.0211 - val_mae: 0.1190\n",
            "Epoch 323/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0466 - mae: 0.1978 - val_loss: 0.0439 - val_mae: 0.1963\n",
            "Epoch 324/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0291 - mae: 0.1266 - val_loss: 0.0683 - val_mae: 0.2532\n",
            "Epoch 325/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0476 - mae: 0.1922 - val_loss: 0.0331 - val_mae: 0.1684\n",
            "Epoch 326/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0412 - mae: 0.1363 - val_loss: 0.0352 - val_mae: 0.1742\n",
            "Epoch 327/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0384 - mae: 0.1604 - val_loss: 0.1385 - val_mae: 0.3676\n",
            "Epoch 328/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1019 - mae: 0.2663 - val_loss: 0.1028 - val_mae: 0.3167\n",
            "Epoch 329/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0911 - mae: 0.2555 - val_loss: 0.0479 - val_mae: 0.2109\n",
            "Epoch 330/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0405 - mae: 0.1640 - val_loss: 0.0991 - val_mae: 0.3099\n",
            "Epoch 331/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0427 - mae: 0.1467 - val_loss: 0.0045 - val_mae: 0.0657\n",
            "Epoch 332/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0389 - mae: 0.1716 - val_loss: 0.0861 - val_mae: 0.2910\n",
            "Epoch 333/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0518 - mae: 0.1975 - val_loss: 0.0494 - val_mae: 0.2192\n",
            "Epoch 334/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0499 - mae: 0.1758 - val_loss: 0.0026 - val_mae: 0.0494\n",
            "Epoch 335/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0417 - mae: 0.1700 - val_loss: 0.0332 - val_mae: 0.1755\n",
            "Epoch 336/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0332 - mae: 0.1413 - val_loss: 0.1019 - val_mae: 0.3157\n",
            "Epoch 337/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0446 - mae: 0.1844 - val_loss: 0.0374 - val_mae: 0.1862\n",
            "Epoch 338/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0283 - mae: 0.1263 - val_loss: 0.0722 - val_mae: 0.2638\n",
            "Epoch 339/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0531 - mae: 0.1889 - val_loss: 0.0153 - val_mae: 0.1052\n",
            "Epoch 340/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0547 - mae: 0.1792 - val_loss: 0.0060 - val_mae: 0.0594\n",
            "Epoch 341/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0715 - mae: 0.2169 - val_loss: 0.0157 - val_mae: 0.1121\n",
            "Epoch 342/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0686 - mae: 0.2191 - val_loss: 0.0363 - val_mae: 0.1823\n",
            "Epoch 343/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0439 - mae: 0.1668 - val_loss: 0.0446 - val_mae: 0.2030\n",
            "Epoch 344/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0480 - mae: 0.1842 - val_loss: 0.0281 - val_mae: 0.1550\n",
            "Epoch 345/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0506 - mae: 0.1827 - val_loss: 0.0109 - val_mae: 0.0755\n",
            "Epoch 346/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0416 - mae: 0.1685 - val_loss: 0.0062 - val_mae: 0.0691\n",
            "Epoch 347/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0765 - mae: 0.2312 - val_loss: 0.0742 - val_mae: 0.2695\n",
            "Epoch 348/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0521 - mae: 0.1807 - val_loss: 0.0047 - val_mae: 0.0490\n",
            "Epoch 349/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0483 - mae: 0.1597 - val_loss: 0.0824 - val_mae: 0.2847\n",
            "Epoch 350/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0559 - mae: 0.2084 - val_loss: 0.0281 - val_mae: 0.1632\n",
            "Epoch 351/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0360 - mae: 0.1705 - val_loss: 0.0290 - val_mae: 0.1656\n",
            "Epoch 352/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0291 - mae: 0.1361 - val_loss: 0.0574 - val_mae: 0.2364\n",
            "Epoch 353/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0378 - mae: 0.1663 - val_loss: 0.0103 - val_mae: 0.0792\n",
            "Epoch 354/500\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0496 - mae: 0.1719 - val_loss: 0.0457 - val_mae: 0.1996\n",
            "Epoch 355/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0324 - mae: 0.1533 - val_loss: 0.1059 - val_mae: 0.3167\n",
            "Epoch 356/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0877 - mae: 0.2670 - val_loss: 0.0126 - val_mae: 0.0813\n",
            "Epoch 357/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0346 - mae: 0.1423 - val_loss: 0.0455 - val_mae: 0.2014\n",
            "Epoch 358/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0376 - mae: 0.1769 - val_loss: 0.0516 - val_mae: 0.2193\n",
            "Epoch 359/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0446 - mae: 0.1897 - val_loss: 0.0059 - val_mae: 0.0766\n",
            "Epoch 360/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0727 - mae: 0.2295 - val_loss: 0.0108 - val_mae: 0.0744\n",
            "Epoch 361/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1175 - mae: 0.2886 - val_loss: 0.0347 - val_mae: 0.1729\n",
            "Epoch 362/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0399 - mae: 0.1653 - val_loss: 0.0718 - val_mae: 0.2607\n",
            "Epoch 363/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0329 - mae: 0.1417 - val_loss: 0.0746 - val_mae: 0.2664\n",
            "Epoch 364/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0692 - mae: 0.2038 - val_loss: 0.0615 - val_mae: 0.2423\n",
            "Epoch 365/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0373 - mae: 0.1717 - val_loss: 0.0271 - val_mae: 0.1534\n",
            "Epoch 366/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0433 - mae: 0.1691 - val_loss: 0.0060 - val_mae: 0.0715\n",
            "Epoch 367/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0809 - mae: 0.2279 - val_loss: 0.0140 - val_mae: 0.0988\n",
            "Epoch 368/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0292 - mae: 0.1272 - val_loss: 0.1003 - val_mae: 0.3110\n",
            "Epoch 369/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0232 - mae: 0.1228 - val_loss: 0.0114 - val_mae: 0.0782\n",
            "Epoch 370/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0366 - mae: 0.1484 - val_loss: 0.0310 - val_mae: 0.1617\n",
            "Epoch 371/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0409 - mae: 0.1659 - val_loss: 0.1095 - val_mae: 0.3248\n",
            "Epoch 372/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0423 - mae: 0.1749 - val_loss: 0.0682 - val_mae: 0.2507\n",
            "Epoch 373/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0355 - mae: 0.1493 - val_loss: 0.0179 - val_mae: 0.1058\n",
            "Epoch 374/500\n",
            "18/18 [==============================] - 0s 8ms/step - loss: 0.0414 - mae: 0.1662 - val_loss: 0.0091 - val_mae: 0.0911\n",
            "Epoch 375/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0767 - mae: 0.2292 - val_loss: 0.1314 - val_mae: 0.3552\n",
            "Epoch 376/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0648 - mae: 0.2085 - val_loss: 0.0915 - val_mae: 0.2923\n",
            "Epoch 377/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0484 - mae: 0.1605 - val_loss: 0.0110 - val_mae: 0.0866\n",
            "Epoch 378/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0608 - mae: 0.2316 - val_loss: 0.0105 - val_mae: 0.1007\n",
            "Epoch 379/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0522 - mae: 0.1697 - val_loss: 0.1849 - val_mae: 0.4229\n",
            "Epoch 380/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0723 - mae: 0.2149 - val_loss: 0.0410 - val_mae: 0.1899\n",
            "Epoch 381/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0324 - mae: 0.1362 - val_loss: 0.0271 - val_mae: 0.1474\n",
            "Epoch 382/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0317 - mae: 0.1329 - val_loss: 0.0316 - val_mae: 0.1603\n",
            "Epoch 383/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0472 - mae: 0.1828 - val_loss: 0.0126 - val_mae: 0.0912\n",
            "Epoch 384/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0395 - mae: 0.1696 - val_loss: 0.0808 - val_mae: 0.2721\n",
            "Epoch 385/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0336 - mae: 0.1544 - val_loss: 0.0293 - val_mae: 0.1425\n",
            "Epoch 386/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0614 - mae: 0.2169 - val_loss: 0.0530 - val_mae: 0.2121\n",
            "Epoch 387/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0257 - mae: 0.1238 - val_loss: 0.0546 - val_mae: 0.2189\n",
            "Epoch 388/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0559 - mae: 0.2016 - val_loss: 0.0425 - val_mae: 0.1881\n",
            "Epoch 389/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0328 - mae: 0.1354 - val_loss: 0.1042 - val_mae: 0.3145\n",
            "Epoch 390/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0667 - mae: 0.2136 - val_loss: 0.0468 - val_mae: 0.2006\n",
            "Epoch 391/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0405 - mae: 0.1704 - val_loss: 0.0149 - val_mae: 0.0928\n",
            "Epoch 392/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0596 - mae: 0.1959 - val_loss: 0.0279 - val_mae: 0.1462\n",
            "Epoch 393/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0379 - mae: 0.1637 - val_loss: 0.0950 - val_mae: 0.3022\n",
            "Epoch 394/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0624 - mae: 0.2215 - val_loss: 0.0529 - val_mae: 0.2196\n",
            "Epoch 395/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0278 - mae: 0.1369 - val_loss: 0.0072 - val_mae: 0.0760\n",
            "Epoch 396/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0294 - mae: 0.1511 - val_loss: 0.0236 - val_mae: 0.1370\n",
            "Epoch 397/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0342 - mae: 0.1542 - val_loss: 0.0700 - val_mae: 0.2542\n",
            "Epoch 398/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0476 - mae: 0.1683 - val_loss: 0.0770 - val_mae: 0.2695\n",
            "Epoch 399/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0658 - mae: 0.2121 - val_loss: 0.0520 - val_mae: 0.2177\n",
            "Epoch 400/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0533 - mae: 0.1995 - val_loss: 0.0169 - val_mae: 0.0981\n",
            "Epoch 401/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0466 - mae: 0.1774 - val_loss: 0.0122 - val_mae: 0.0924\n",
            "Epoch 402/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0577 - mae: 0.2117 - val_loss: 0.1081 - val_mae: 0.3224\n",
            "Epoch 403/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0256 - mae: 0.1235 - val_loss: 0.0655 - val_mae: 0.2491\n",
            "Epoch 404/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0464 - mae: 0.1875 - val_loss: 0.0079 - val_mae: 0.0879\n",
            "Epoch 405/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0378 - mae: 0.1226 - val_loss: 0.0899 - val_mae: 0.2903\n",
            "Epoch 406/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0653 - mae: 0.1979 - val_loss: 0.0746 - val_mae: 0.2633\n",
            "Epoch 407/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0347 - mae: 0.1663 - val_loss: 0.0288 - val_mae: 0.1532\n",
            "Epoch 408/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0463 - mae: 0.1916 - val_loss: 0.0800 - val_mae: 0.2770\n",
            "Epoch 409/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0540 - mae: 0.1953 - val_loss: 0.1194 - val_mae: 0.3431\n",
            "Epoch 410/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0915 - mae: 0.2494 - val_loss: 0.0080 - val_mae: 0.0688\n",
            "Epoch 411/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0233 - mae: 0.1206 - val_loss: 0.0110 - val_mae: 0.0766\n",
            "Epoch 412/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0264 - mae: 0.1276 - val_loss: 0.1853 - val_mae: 0.4283\n",
            "Epoch 413/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.1073 - mae: 0.2692 - val_loss: 0.0186 - val_mae: 0.1160\n",
            "Epoch 414/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0330 - mae: 0.1347 - val_loss: 0.0248 - val_mae: 0.1430\n",
            "Epoch 415/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0426 - mae: 0.1719 - val_loss: 0.0145 - val_mae: 0.0926\n",
            "Epoch 416/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0559 - mae: 0.1829 - val_loss: 0.0072 - val_mae: 0.0795\n",
            "Epoch 417/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0613 - mae: 0.2250 - val_loss: 0.0173 - val_mae: 0.1061\n",
            "Epoch 418/500\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0473 - mae: 0.1604 - val_loss: 0.0223 - val_mae: 0.1338\n",
            "Epoch 419/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0193 - mae: 0.1046 - val_loss: 0.0730 - val_mae: 0.2645\n",
            "Epoch 420/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0347 - mae: 0.1542 - val_loss: 0.0091 - val_mae: 0.0777\n",
            "Epoch 421/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0584 - mae: 0.1860 - val_loss: 0.0793 - val_mae: 0.2751\n",
            "Epoch 422/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0410 - mae: 0.1554 - val_loss: 0.0872 - val_mae: 0.2887\n",
            "Epoch 423/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0523 - mae: 0.1896 - val_loss: 0.0261 - val_mae: 0.1358\n",
            "Epoch 424/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0468 - mae: 0.1598 - val_loss: 0.0195 - val_mae: 0.1052\n",
            "Epoch 425/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0573 - mae: 0.2117 - val_loss: 0.0074 - val_mae: 0.0843\n",
            "Epoch 426/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0615 - mae: 0.2074 - val_loss: 0.0080 - val_mae: 0.0727\n",
            "Epoch 427/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0375 - mae: 0.1573 - val_loss: 0.0635 - val_mae: 0.2462\n",
            "Epoch 428/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0710 - mae: 0.2231 - val_loss: 0.0662 - val_mae: 0.2516\n",
            "Epoch 429/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0425 - mae: 0.1523 - val_loss: 0.0417 - val_mae: 0.1899\n",
            "Epoch 430/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0305 - mae: 0.1350 - val_loss: 0.0311 - val_mae: 0.1576\n",
            "Epoch 431/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0251 - mae: 0.1184 - val_loss: 0.0336 - val_mae: 0.1699\n",
            "Epoch 432/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0317 - mae: 0.1432 - val_loss: 0.0487 - val_mae: 0.2107\n",
            "Epoch 433/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0404 - mae: 0.1588 - val_loss: 0.0357 - val_mae: 0.1743\n",
            "Epoch 434/500\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 0.0489 - mae: 0.1811 - val_loss: 0.0145 - val_mae: 0.0900\n",
            "Epoch 435/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0194 - mae: 0.1187 - val_loss: 0.0212 - val_mae: 0.1184\n",
            "Epoch 436/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0588 - mae: 0.1872 - val_loss: 0.0831 - val_mae: 0.2802\n",
            "Epoch 437/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0298 - mae: 0.1460 - val_loss: 0.0099 - val_mae: 0.0934\n",
            "Epoch 438/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0486 - mae: 0.1875 - val_loss: 0.0237 - val_mae: 0.1260\n",
            "Epoch 439/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0265 - mae: 0.1437 - val_loss: 0.0292 - val_mae: 0.1462\n",
            "Epoch 440/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0355 - mae: 0.1713 - val_loss: 0.0225 - val_mae: 0.1201\n",
            "Epoch 441/500\n",
            "18/18 [==============================] - 0s 8ms/step - loss: 0.0375 - mae: 0.1631 - val_loss: 0.0225 - val_mae: 0.1253\n",
            "Epoch 442/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0363 - mae: 0.1431 - val_loss: 0.1107 - val_mae: 0.3240\n",
            "Epoch 443/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0470 - mae: 0.1929 - val_loss: 0.0512 - val_mae: 0.2090\n",
            "Epoch 444/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0510 - mae: 0.2028 - val_loss: 0.0129 - val_mae: 0.1026\n",
            "Epoch 445/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0294 - mae: 0.1401 - val_loss: 0.1695 - val_mae: 0.4038\n",
            "Epoch 446/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0585 - mae: 0.1929 - val_loss: 0.0402 - val_mae: 0.1701\n",
            "Epoch 447/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0343 - mae: 0.1585 - val_loss: 0.0257 - val_mae: 0.1230\n",
            "Epoch 448/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0351 - mae: 0.1652 - val_loss: 0.0620 - val_mae: 0.2321\n",
            "Epoch 449/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0254 - mae: 0.1274 - val_loss: 0.0240 - val_mae: 0.1177\n",
            "Epoch 450/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0668 - mae: 0.2226 - val_loss: 0.0212 - val_mae: 0.1182\n",
            "Epoch 451/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0430 - mae: 0.1504 - val_loss: 0.0203 - val_mae: 0.1200\n",
            "Epoch 452/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0455 - mae: 0.1985 - val_loss: 0.0527 - val_mae: 0.2184\n",
            "Epoch 453/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0274 - mae: 0.1450 - val_loss: 0.0188 - val_mae: 0.1015\n",
            "Epoch 454/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0260 - mae: 0.1287 - val_loss: 0.0328 - val_mae: 0.1599\n",
            "Epoch 455/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0220 - mae: 0.1094 - val_loss: 0.0373 - val_mae: 0.1753\n",
            "Epoch 456/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0411 - mae: 0.1789 - val_loss: 0.0095 - val_mae: 0.0822\n",
            "Epoch 457/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0285 - mae: 0.1371 - val_loss: 0.0528 - val_mae: 0.2210\n",
            "Epoch 458/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0415 - mae: 0.1820 - val_loss: 0.0095 - val_mae: 0.0728\n",
            "Epoch 459/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0521 - mae: 0.1840 - val_loss: 0.0118 - val_mae: 0.0846\n",
            "Epoch 460/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0512 - mae: 0.1895 - val_loss: 0.0419 - val_mae: 0.1955\n",
            "Epoch 461/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0326 - mae: 0.1414 - val_loss: 0.0140 - val_mae: 0.0946\n",
            "Epoch 462/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0342 - mae: 0.1529 - val_loss: 0.1268 - val_mae: 0.3509\n",
            "Epoch 463/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0268 - mae: 0.1348 - val_loss: 0.0186 - val_mae: 0.1021\n",
            "Epoch 464/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0401 - mae: 0.1599 - val_loss: 0.0110 - val_mae: 0.0904\n",
            "Epoch 465/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0405 - mae: 0.1565 - val_loss: 0.0236 - val_mae: 0.1328\n",
            "Epoch 466/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0129 - mae: 0.0985 - val_loss: 0.0370 - val_mae: 0.1743\n",
            "Epoch 467/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0263 - mae: 0.1180 - val_loss: 0.1222 - val_mae: 0.3416\n",
            "Epoch 468/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0337 - mae: 0.1608 - val_loss: 0.0508 - val_mae: 0.2044\n",
            "Epoch 469/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0332 - mae: 0.1344 - val_loss: 0.0316 - val_mae: 0.1453\n",
            "Epoch 470/500\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0163 - mae: 0.1034 - val_loss: 0.1207 - val_mae: 0.3385\n",
            "Epoch 471/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0822 - mae: 0.2413 - val_loss: 0.0642 - val_mae: 0.2413\n",
            "Epoch 472/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0859 - mae: 0.2338 - val_loss: 0.0396 - val_mae: 0.1780\n",
            "Epoch 473/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0578 - mae: 0.2025 - val_loss: 0.0212 - val_mae: 0.1035\n",
            "Epoch 474/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0672 - mae: 0.2349 - val_loss: 0.0111 - val_mae: 0.1043\n",
            "Epoch 475/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0625 - mae: 0.2109 - val_loss: 0.0262 - val_mae: 0.1299\n",
            "Epoch 476/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0308 - mae: 0.1493 - val_loss: 0.0525 - val_mae: 0.2172\n",
            "Epoch 477/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0545 - mae: 0.1902 - val_loss: 0.0858 - val_mae: 0.2832\n",
            "Epoch 478/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0372 - mae: 0.1817 - val_loss: 0.0265 - val_mae: 0.1316\n",
            "Epoch 479/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0375 - mae: 0.1535 - val_loss: 0.0385 - val_mae: 0.1765\n",
            "Epoch 480/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0282 - mae: 0.1283 - val_loss: 0.0286 - val_mae: 0.1494\n",
            "Epoch 481/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0313 - mae: 0.1470 - val_loss: 0.1028 - val_mae: 0.3133\n",
            "Epoch 482/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0502 - mae: 0.1941 - val_loss: 0.0254 - val_mae: 0.1323\n",
            "Epoch 483/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0427 - mae: 0.1721 - val_loss: 0.0502 - val_mae: 0.2068\n",
            "Epoch 484/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0293 - mae: 0.1426 - val_loss: 0.0213 - val_mae: 0.1130\n",
            "Epoch 485/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0206 - mae: 0.1329 - val_loss: 0.0771 - val_mae: 0.2657\n",
            "Epoch 486/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0182 - mae: 0.1197 - val_loss: 0.0546 - val_mae: 0.2196\n",
            "Epoch 487/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0452 - mae: 0.1822 - val_loss: 0.0163 - val_mae: 0.0917\n",
            "Epoch 488/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0271 - mae: 0.1358 - val_loss: 0.0748 - val_mae: 0.2602\n",
            "Epoch 489/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0220 - mae: 0.1199 - val_loss: 0.0524 - val_mae: 0.2131\n",
            "Epoch 490/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0230 - mae: 0.1237 - val_loss: 0.0210 - val_mae: 0.1134\n",
            "Epoch 491/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0344 - mae: 0.1623 - val_loss: 0.0108 - val_mae: 0.0968\n",
            "Epoch 492/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0250 - mae: 0.1310 - val_loss: 0.0336 - val_mae: 0.1648\n",
            "Epoch 493/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0381 - mae: 0.1349 - val_loss: 0.2110 - val_mae: 0.4548\n",
            "Epoch 494/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0814 - mae: 0.2368 - val_loss: 0.0681 - val_mae: 0.2505\n",
            "Epoch 495/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0247 - mae: 0.1302 - val_loss: 0.0104 - val_mae: 0.0756\n",
            "Epoch 496/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0402 - mae: 0.1581 - val_loss: 0.0212 - val_mae: 0.1338\n",
            "Epoch 497/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0182 - mae: 0.1012 - val_loss: 0.0082 - val_mae: 0.0655\n",
            "Epoch 498/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0501 - mae: 0.1911 - val_loss: 0.0417 - val_mae: 0.1910\n",
            "Epoch 499/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0237 - mae: 0.1275 - val_loss: 0.0123 - val_mae: 0.0921\n",
            "Epoch 500/500\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.0553 - mae: 0.1939 - val_loss: 0.0561 - val_mae: 0.2213\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCD5i_1wf-Q-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "e5255a55-9238-4008-b572-ba3d4f31d915"
      },
      "source": [
        "plt.semilogy(history.history['loss'])\n",
        "plt.semilogy(history.history['val_loss'])\n",
        "plt.title('Loss vs Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.yscale('log')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq94hhs0f-Q_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f95096f-c228-428e-d9ce-b87ab998e7b8"
      },
      "source": [
        "predictions_all = model.predict(x)\n",
        "predictions_train = model.predict(X_train)\n",
        "predictions_test = model.predict(X_test)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 10 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f52f0c2a268> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acjwVhl7f-Q_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "30d97ea0-39e6-4414-ce05-fbacf2fcb0e3"
      },
      "source": [
        "xlim = 1\n",
        "x = np.linspace(0,xlim,100)\n",
        "plt.plot(x, x)\n",
        "\n",
        "# x = np.linspace(1,1,10)\n",
        "plt.scatter(predictions_all,y)\n",
        "# plt.plot(x,x)"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f53423cadd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOALfuzcf-RA"
      },
      "source": [
        "import seaborn as snsn"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JV2Z23cMf-RA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "outputId": "d0341f15-1521-4066-d4af-0d822dc671e6"
      },
      "source": [
        "import seaborn as sns\n",
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(predictions_test.ravel(),label=\"\",ax=ax,c='b')\n",
        "sns.kdeplot(y_test.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')\n",
        "\n",
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(predictions_train.ravel(),label=\"\",ax=ax,c='b')\n",
        "sns.kdeplot(y_train.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')\n"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQnuPsSLf-RB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2dbd1ae-3379-4863-a002-03125a3e56dd"
      },
      "source": [
        "sns.kdeplot(y,label=\"\",ax=ax,c='b')"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f539a417be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxIDnQTZf-RB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "10c0e8a7-93ba-4657-e4b4-d76de78af69f"
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(y.ravel(),label=\"\",ax=ax,c='b')\n",
        "# sns.kdeplot(y_test.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UqGxyVcVBrR"
      },
      "source": [
        ""
      ],
      "execution_count": 140,
      "outputs": []
    }
  ]
}