SVM, Decision Tree, CNN on RAVDESS dataset.ipynb

{
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  "metadata": {
    "colab": {
      "name": "FINAL SER USING RAVDESS.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNi7YcxHMm6d/5bc212lazg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ShireenPatel/speech-emotion-recognition/blob/master/SVM%2C%20Decision%20Tree%2C%20CNN%20on%20RAVDESS%20dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFSLPYFEA7sw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "793ed4af-37f7-4c24-e47f-5a543acd634a"
      },
      "source": [
        "!pip install tensorflow==1.14.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==1.14.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/f0/96fb2e0412ae9692dbf400e5b04432885f677ad6241c088ccc5fe7724d69/tensorflow-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (109.2MB)\n",
            "\u001b[K     |████████████████████████████████| 109.2MB 86kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.18.3)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.3.3)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.9.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (3.10.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.28.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.8.1)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.2.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.34.2)\n",
            "Collecting tensorboard<1.15.0,>=1.14.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 36.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.0.8)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.12.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.12.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
            "Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488kB)\n",
            "\u001b[K     |████████████████████████████████| 491kB 48.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==1.14.0) (46.1.3)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.2.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (1.0.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==1.14.0) (2.10.0)\n",
            "Installing collected packages: tensorboard, tensorflow-estimator, tensorflow\n",
            "  Found existing installation: tensorboard 2.2.1\n",
            "    Uninstalling tensorboard-2.2.1:\n",
            "      Successfully uninstalled tensorboard-2.2.1\n",
            "  Found existing installation: tensorflow-estimator 2.2.0\n",
            "    Uninstalling tensorflow-estimator-2.2.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.2.0\n",
            "  Found existing installation: tensorflow 2.2.0rc3\n",
            "    Uninstalling tensorflow-2.2.0rc3:\n",
            "      Successfully uninstalled tensorflow-2.2.0rc3\n",
            "Successfully installed tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OY05xozXBNqa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "281f5818-4157-4408-e403-4458f8bccdf8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCtVYHq9BaC8",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
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                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "4272591f-5bea-4594-bbe0-fe78bdffa947"
      },
      "source": [
        "from google.colab import files\n",
        "!pip install -q kaggle\n",
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a2e125d7-4d1e-4424-bb9d-30f3b8d22cd1\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-a2e125d7-4d1e-4424-bb9d-30f3b8d22cd1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"patelshireendharmesh\",\"key\":\"a383b88dc708b648ed3d3741fbd96af0\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqlwWjbFBhPI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RX_42v5Bt8D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "1c00b789-dac4-41f5-e0de-e5d43a11e2f7"
      },
      "source": [
        "!kaggle datasets download -d uwrfkaggler/ravdess-emotional-speech-audio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading ravdess-emotional-speech-audio.zip to /content\n",
            " 99% 425M/429M [00:06<00:00, 96.0MB/s]\n",
            "100% 429M/429M [00:06<00:00, 69.8MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1scbcZ3hB3BJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1aeed135-45d3-42e0-960e-87db5d2960af"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  kaggle.json  ravdess-emotional-speech-audio.zip\tsample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMxhAmHDB7T1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c7a8fcab-f1f3-46b4-eefb-5e51c0d36401"
      },
      "source": [
        "!mkdir ravdess\n",
        "!unzip ravdess-emotional-speech-audio.zip -d ravdess"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  ravdess-emotional-speech-audio.zip\n",
            "  inflating: ravdess/Actor_01/03-01-01-01-01-01-01.wav  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sv8CDqXaCHTU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "e3bdcb6f-8609-4fc6-9c60-131f30c2c1cf"
      },
      "source": [
        "!pip install librosa\n",
        "import librosa\n",
        "from librosa import display\n",
        "import os\n",
        "import pandas as pd\n",
        "import glob\n",
        "import numpy as np "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: librosa in /usr/local/lib/python3.6/dist-packages (0.6.3)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.22.2.post1)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.4.2)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.14.1)\n",
            "Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.2)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.8)\n",
            "Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.48.0)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.4.1)\n",
            "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.12.0)\n",
            "Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.18.3)\n",
            "Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.31.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (46.1.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEbA9BHsCrzZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "200aef24-fe78-429f-d10e-1a5d306fb429"
      },
      "source": [
        "import time\n",
        "\n",
        "path = '/content/ravdess'\n",
        "lst = []\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "for subdir, dirs, files in os.walk(path):\n",
        "  for file in files:\n",
        "      try:\n",
        "        #Load librosa array, obtain mfcss, store the file and the mcss information in a new array\n",
        "        X, sample_rate = librosa.load(os.path.join(subdir,file), res_type='kaiser_fast')\n",
        "        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=200).T,axis=0) \n",
        "        # The instruction below converts the labels (from 1 to 8) to a series from 0 to 7\n",
        "        # This is because our predictor needs to start from 0 otherwise it will try to predict also 0.\n",
        "        file = int(file[7:8]) - 1 \n",
        "        arr = mfccs, file\n",
        "        lst.append(arr)\n",
        "      # If the file is not valid, skip it\n",
        "      except ValueError:\n",
        "        continue\n",
        "\n",
        "print(\"--- Data loaded. Loading time: %s seconds ---\" % (time.time() - start_time))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Data loaded. Loading time: 466.6064302921295 seconds ---\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAMtgHvbE9UW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6047bc22-20a8-43b8-c07e-c7901c6733ca"
      },
      "source": [
        "X, y = zip(*lst)\n",
        "X = np.asarray(X)\n",
        "y = np.asarray(y)\n",
        "\n",
        "X.shape, y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2880, 128), (2880,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YG-UM15FFL6Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9s6vUPbHFZi4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "b0c73783-ccdd-4f71-e824-5dbe98443108"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dtree = DecisionTreeClassifier()\n",
        "\n",
        "dtree.fit(X_train, y_train)\n",
        "\n",
        "predictions = dtree.predict(X_test)\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.84      0.82        64\n",
            "           1       0.84      0.81      0.83       135\n",
            "           2       0.72      0.77      0.75       122\n",
            "           3       0.64      0.70      0.67       115\n",
            "           4       0.76      0.76      0.76       134\n",
            "           5       0.78      0.83      0.80       126\n",
            "           6       0.78      0.75      0.76       121\n",
            "           7       0.86      0.72      0.78       134\n",
            "\n",
            "    accuracy                           0.77       951\n",
            "   macro avg       0.77      0.77      0.77       951\n",
            "weighted avg       0.77      0.77      0.77       951\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSPsoDcRFiN-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a5408351-4755-4815-cfd8-e4779ef933e7"
      },
      "source": [
        "from sklearn.svm import SVC \n",
        "from sklearn.metrics import accuracy_score \n",
        "# Building the SVM model \n",
        "svmclf = SVC() \n",
        "svmclf.fit(X_train, y_train) \n",
        "  \n",
        "# Storing the predictions of the non-linear model \n",
        "y_pred_svmclf = svmclf.predict(X_test) \n",
        "  \n",
        "# Evaluating the performance of the non-linear model \n",
        "print('Accuracy : '+str(accuracy_score(y_test, y_pred_svmclf))) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.3017875920084122\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VcC0aMgLx1e",
        "colab_type": "text"
      },
      "source": [
        "CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oz2u9AEMLqYB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a84cb592-d8c9-4cce-d302-046bcf3947a0"
      },
      "source": [
        "x_traincnn = np.expand_dims(X_train, axis=2)\n",
        "x_testcnn = np.expand_dims(X_test, axis=2)\n",
        "x_traincnn.shape, x_testcnn.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1929, 128, 1), (951, 128, 1), (1929,), (951,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqOjC8pXLyvs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "f01284db-252f-4836-ff2e-f112ad9f3ac5"
      },
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from keras.preprocessing import sequence\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Embedding\n",
        "from keras.utils import to_categorical\n",
        "from keras.layers import Input, Flatten, Dropout, Activation\n",
        "from keras.layers import Conv1D, MaxPooling1D\n",
        "from keras.models import Model\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv1D(32, 5,padding='same',\n",
        "                 input_shape=(128,1)))\n",
        "model.add(Conv1D(64, 5,padding='same', activation='relu'))\n",
        "#model.add(Activation('relu'))\n",
        "model.add(MaxPooling1D(pool_size=(8)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(8, activation='softmax'))\n",
        "opt = keras.optimizers.rmsprop(lr=0.00005, rho=0.9, epsilon=None, decay=0.0)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_3 (Conv1D)            (None, 128, 32)           192       \n",
            "_________________________________________________________________\n",
            "conv1d_4 (Conv1D)            (None, 128, 64)           10304     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_2 (MaxPooling1 (None, 16, 64)            0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 16, 64)            0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 128)               131200    \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 8)                 1032      \n",
            "=================================================================\n",
            "Total params: 142,728\n",
            "Trainable params: 142,728\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXEE4SSKL4L6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5aa5d238-7140-45fc-ebe2-64bd03475e71"
      },
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=keras.optimizers.Adadelta(),\n",
        "              metrics=['accuracy'])\n",
        "cnnhistory=model.fit(x_traincnn, y_train, batch_size=128, epochs=500, validation_data=(x_testcnn, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1929 samples, validate on 951 samples\n",
            "Epoch 1/500\n",
            "1929/1929 [==============================] - 1s 553us/step - loss: 12.5559 - accuracy: 0.1374 - val_loss: 2.1740 - val_accuracy: 0.1409\n",
            "Epoch 2/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 2.6215 - accuracy: 0.1436 - val_loss: 2.0785 - val_accuracy: 0.1209\n",
            "Epoch 3/500\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 2.2102 - accuracy: 0.1436 - val_loss: 2.0778 - val_accuracy: 0.1209\n",
            "Epoch 4/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 2.1391 - accuracy: 0.1514 - val_loss: 2.0766 - val_accuracy: 0.1220\n",
            "Epoch 5/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 2.1027 - accuracy: 0.1457 - val_loss: 2.0747 - val_accuracy: 0.1230\n",
            "Epoch 6/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 2.0905 - accuracy: 0.1633 - val_loss: 2.0592 - val_accuracy: 0.1209\n",
            "Epoch 7/500\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 2.0693 - accuracy: 0.1721 - val_loss: 2.0462 - val_accuracy: 0.1409\n",
            "Epoch 8/500\n",
            "1929/1929 [==============================] - 1s 473us/step - loss: 2.0700 - accuracy: 0.1550 - val_loss: 2.0029 - val_accuracy: 0.1819\n",
            "Epoch 9/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 2.0468 - accuracy: 0.1612 - val_loss: 2.0272 - val_accuracy: 0.1609\n",
            "Epoch 10/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 2.0344 - accuracy: 0.1685 - val_loss: 2.0783 - val_accuracy: 0.1767\n",
            "Epoch 11/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 2.0608 - accuracy: 0.1685 - val_loss: 2.0399 - val_accuracy: 0.1588\n",
            "Epoch 12/500\n",
            "1929/1929 [==============================] - 1s 480us/step - loss: 2.0429 - accuracy: 0.1783 - val_loss: 1.9630 - val_accuracy: 0.2156\n",
            "Epoch 13/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 2.0254 - accuracy: 0.1783 - val_loss: 1.9459 - val_accuracy: 0.1893\n",
            "Epoch 14/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 2.0191 - accuracy: 0.1820 - val_loss: 1.9549 - val_accuracy: 0.2103\n",
            "Epoch 15/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 2.0082 - accuracy: 0.1877 - val_loss: 1.9788 - val_accuracy: 0.2303\n",
            "Epoch 16/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.9858 - accuracy: 0.2182 - val_loss: 1.9453 - val_accuracy: 0.2240\n",
            "Epoch 17/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.9964 - accuracy: 0.2094 - val_loss: 1.9210 - val_accuracy: 0.2576\n",
            "Epoch 18/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.9897 - accuracy: 0.2125 - val_loss: 1.9409 - val_accuracy: 0.2177\n",
            "Epoch 19/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.9851 - accuracy: 0.2131 - val_loss: 1.9506 - val_accuracy: 0.2419\n",
            "Epoch 20/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.9574 - accuracy: 0.2182 - val_loss: 1.9320 - val_accuracy: 0.2103\n",
            "Epoch 21/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.9719 - accuracy: 0.2177 - val_loss: 1.9120 - val_accuracy: 0.2576\n",
            "Epoch 22/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.9447 - accuracy: 0.2136 - val_loss: 1.8766 - val_accuracy: 0.2618\n",
            "Epoch 23/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.9397 - accuracy: 0.2240 - val_loss: 1.9858 - val_accuracy: 0.2198\n",
            "Epoch 24/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.9379 - accuracy: 0.2328 - val_loss: 1.8636 - val_accuracy: 0.2934\n",
            "Epoch 25/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.9109 - accuracy: 0.2411 - val_loss: 1.8524 - val_accuracy: 0.2839\n",
            "Epoch 26/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.9097 - accuracy: 0.2354 - val_loss: 1.8510 - val_accuracy: 0.2692\n",
            "Epoch 27/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.8890 - accuracy: 0.2447 - val_loss: 1.8678 - val_accuracy: 0.2808\n",
            "Epoch 28/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.8646 - accuracy: 0.2592 - val_loss: 1.9508 - val_accuracy: 0.2387\n",
            "Epoch 29/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.8924 - accuracy: 0.2696 - val_loss: 1.8527 - val_accuracy: 0.2534\n",
            "Epoch 30/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.8528 - accuracy: 0.2711 - val_loss: 1.8517 - val_accuracy: 0.2976\n",
            "Epoch 31/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.8622 - accuracy: 0.2639 - val_loss: 1.9332 - val_accuracy: 0.2345\n",
            "Epoch 32/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.8628 - accuracy: 0.2680 - val_loss: 1.7661 - val_accuracy: 0.3396\n",
            "Epoch 33/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.8449 - accuracy: 0.2608 - val_loss: 1.8310 - val_accuracy: 0.2986\n",
            "Epoch 34/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.8053 - accuracy: 0.2898 - val_loss: 1.7751 - val_accuracy: 0.3060\n",
            "Epoch 35/500\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 1.8274 - accuracy: 0.2789 - val_loss: 1.7551 - val_accuracy: 0.3333\n",
            "Epoch 36/500\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.8073 - accuracy: 0.2836 - val_loss: 1.7362 - val_accuracy: 0.3565\n",
            "Epoch 37/500\n",
            "1929/1929 [==============================] - 1s 471us/step - loss: 1.8178 - accuracy: 0.2846 - val_loss: 1.7667 - val_accuracy: 0.3018\n",
            "Epoch 38/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.7877 - accuracy: 0.2996 - val_loss: 1.7387 - val_accuracy: 0.3260\n",
            "Epoch 39/500\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 1.7662 - accuracy: 0.2893 - val_loss: 1.7634 - val_accuracy: 0.3281\n",
            "Epoch 40/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.7696 - accuracy: 0.2960 - val_loss: 1.7135 - val_accuracy: 0.3870\n",
            "Epoch 41/500\n",
            "1929/1929 [==============================] - 1s 471us/step - loss: 1.7625 - accuracy: 0.2945 - val_loss: 1.7350 - val_accuracy: 0.3375\n",
            "Epoch 42/500\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.7521 - accuracy: 0.3162 - val_loss: 1.7250 - val_accuracy: 0.3680\n",
            "Epoch 43/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 1.7520 - accuracy: 0.3059 - val_loss: 1.7078 - val_accuracy: 0.3659\n",
            "Epoch 44/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.7409 - accuracy: 0.3043 - val_loss: 1.7229 - val_accuracy: 0.3512\n",
            "Epoch 45/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.7288 - accuracy: 0.3142 - val_loss: 1.7212 - val_accuracy: 0.3481\n",
            "Epoch 46/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.7269 - accuracy: 0.3090 - val_loss: 1.6804 - val_accuracy: 0.3807\n",
            "Epoch 47/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.7340 - accuracy: 0.3214 - val_loss: 1.6943 - val_accuracy: 0.3070\n",
            "Epoch 48/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.7401 - accuracy: 0.3162 - val_loss: 1.6671 - val_accuracy: 0.4122\n",
            "Epoch 49/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.7217 - accuracy: 0.3261 - val_loss: 1.7367 - val_accuracy: 0.3249\n",
            "Epoch 50/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.7222 - accuracy: 0.3147 - val_loss: 1.6733 - val_accuracy: 0.3985\n",
            "Epoch 51/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.6958 - accuracy: 0.3292 - val_loss: 1.6840 - val_accuracy: 0.3680\n",
            "Epoch 52/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.6815 - accuracy: 0.3370 - val_loss: 1.6267 - val_accuracy: 0.3670\n",
            "Epoch 53/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.6792 - accuracy: 0.3302 - val_loss: 1.6669 - val_accuracy: 0.3649\n",
            "Epoch 54/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.6926 - accuracy: 0.3390 - val_loss: 1.6570 - val_accuracy: 0.4322\n",
            "Epoch 55/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.6567 - accuracy: 0.3593 - val_loss: 1.6898 - val_accuracy: 0.3680\n",
            "Epoch 56/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.6633 - accuracy: 0.3510 - val_loss: 1.6201 - val_accuracy: 0.4080\n",
            "Epoch 57/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.6597 - accuracy: 0.3453 - val_loss: 1.6352 - val_accuracy: 0.3628\n",
            "Epoch 58/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.6606 - accuracy: 0.3530 - val_loss: 1.5739 - val_accuracy: 0.4374\n",
            "Epoch 59/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.6491 - accuracy: 0.3473 - val_loss: 1.5766 - val_accuracy: 0.4311\n",
            "Epoch 60/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.6359 - accuracy: 0.3650 - val_loss: 1.6058 - val_accuracy: 0.3849\n",
            "Epoch 61/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.6297 - accuracy: 0.3696 - val_loss: 1.5743 - val_accuracy: 0.4332\n",
            "Epoch 62/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.6340 - accuracy: 0.3525 - val_loss: 1.5563 - val_accuracy: 0.4332\n",
            "Epoch 63/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.6231 - accuracy: 0.3629 - val_loss: 1.6226 - val_accuracy: 0.4048\n",
            "Epoch 64/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.6181 - accuracy: 0.3639 - val_loss: 1.5871 - val_accuracy: 0.3743\n",
            "Epoch 65/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.6159 - accuracy: 0.3675 - val_loss: 1.5282 - val_accuracy: 0.4322\n",
            "Epoch 66/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.6132 - accuracy: 0.3712 - val_loss: 1.5197 - val_accuracy: 0.4290\n",
            "Epoch 67/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.5952 - accuracy: 0.3790 - val_loss: 1.5343 - val_accuracy: 0.4175\n",
            "Epoch 68/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.5618 - accuracy: 0.3976 - val_loss: 1.5262 - val_accuracy: 0.4416\n",
            "Epoch 69/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.5572 - accuracy: 0.4069 - val_loss: 1.5490 - val_accuracy: 0.3901\n",
            "Epoch 70/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.5914 - accuracy: 0.3826 - val_loss: 1.4926 - val_accuracy: 0.4501\n",
            "Epoch 71/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.5848 - accuracy: 0.3810 - val_loss: 1.5836 - val_accuracy: 0.4248\n",
            "Epoch 72/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.5590 - accuracy: 0.3841 - val_loss: 1.5002 - val_accuracy: 0.4648\n",
            "Epoch 73/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.5524 - accuracy: 0.3779 - val_loss: 1.4829 - val_accuracy: 0.4574\n",
            "Epoch 74/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.5287 - accuracy: 0.4152 - val_loss: 1.4617 - val_accuracy: 0.4669\n",
            "Epoch 75/500\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.5514 - accuracy: 0.3872 - val_loss: 1.4726 - val_accuracy: 0.4690\n",
            "Epoch 76/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.5298 - accuracy: 0.4080 - val_loss: 1.4753 - val_accuracy: 0.4837\n",
            "Epoch 77/500\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.5423 - accuracy: 0.3940 - val_loss: 1.4579 - val_accuracy: 0.4753\n",
            "Epoch 78/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.5331 - accuracy: 0.4028 - val_loss: 1.4325 - val_accuracy: 0.4658\n",
            "Epoch 79/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.5027 - accuracy: 0.4147 - val_loss: 1.4252 - val_accuracy: 0.4826\n",
            "Epoch 80/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.5250 - accuracy: 0.4142 - val_loss: 1.3946 - val_accuracy: 0.5089\n",
            "Epoch 81/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.5079 - accuracy: 0.4220 - val_loss: 1.4136 - val_accuracy: 0.4784\n",
            "Epoch 82/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.4935 - accuracy: 0.4158 - val_loss: 1.4536 - val_accuracy: 0.4753\n",
            "Epoch 83/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.5145 - accuracy: 0.4090 - val_loss: 1.4676 - val_accuracy: 0.4774\n",
            "Epoch 84/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.4844 - accuracy: 0.4116 - val_loss: 1.4470 - val_accuracy: 0.4837\n",
            "Epoch 85/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.4733 - accuracy: 0.4277 - val_loss: 1.4158 - val_accuracy: 0.4763\n",
            "Epoch 86/500\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.4721 - accuracy: 0.4329 - val_loss: 1.4078 - val_accuracy: 0.5121\n",
            "Epoch 87/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.5011 - accuracy: 0.4303 - val_loss: 1.4755 - val_accuracy: 0.4911\n",
            "Epoch 88/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.4884 - accuracy: 0.4401 - val_loss: 1.5404 - val_accuracy: 0.4406\n",
            "Epoch 89/500\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 1.4736 - accuracy: 0.4266 - val_loss: 1.3994 - val_accuracy: 0.5079\n",
            "Epoch 90/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.4697 - accuracy: 0.4204 - val_loss: 1.4650 - val_accuracy: 0.4311\n",
            "Epoch 91/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.4767 - accuracy: 0.4318 - val_loss: 1.3635 - val_accuracy: 0.4869\n",
            "Epoch 92/500\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.4495 - accuracy: 0.4365 - val_loss: 1.3355 - val_accuracy: 0.5142\n",
            "Epoch 93/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.4270 - accuracy: 0.4334 - val_loss: 1.4499 - val_accuracy: 0.4795\n",
            "Epoch 94/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.4686 - accuracy: 0.4386 - val_loss: 1.3858 - val_accuracy: 0.4932\n",
            "Epoch 95/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.4547 - accuracy: 0.4334 - val_loss: 1.3771 - val_accuracy: 0.4826\n",
            "Epoch 96/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.4477 - accuracy: 0.4323 - val_loss: 1.3734 - val_accuracy: 0.4826\n",
            "Epoch 97/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.4521 - accuracy: 0.4272 - val_loss: 1.4235 - val_accuracy: 0.4522\n",
            "Epoch 98/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.4266 - accuracy: 0.4256 - val_loss: 1.3790 - val_accuracy: 0.4932\n",
            "Epoch 99/500\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.4323 - accuracy: 0.4427 - val_loss: 1.3410 - val_accuracy: 0.5016\n",
            "Epoch 100/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.4106 - accuracy: 0.4438 - val_loss: 1.5940 - val_accuracy: 0.3922\n",
            "Epoch 101/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.4315 - accuracy: 0.4422 - val_loss: 1.3823 - val_accuracy: 0.5100\n",
            "Epoch 102/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.4172 - accuracy: 0.4588 - val_loss: 1.4196 - val_accuracy: 0.4826\n",
            "Epoch 103/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.4159 - accuracy: 0.4567 - val_loss: 1.3208 - val_accuracy: 0.5394\n",
            "Epoch 104/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.4166 - accuracy: 0.4495 - val_loss: 1.3694 - val_accuracy: 0.5195\n",
            "Epoch 105/500\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.3783 - accuracy: 0.4624 - val_loss: 1.3679 - val_accuracy: 0.5058\n",
            "Epoch 106/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.3902 - accuracy: 0.4666 - val_loss: 1.3860 - val_accuracy: 0.5089\n",
            "Epoch 107/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.4090 - accuracy: 0.4562 - val_loss: 1.2976 - val_accuracy: 0.5289\n",
            "Epoch 108/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.4066 - accuracy: 0.4536 - val_loss: 1.3533 - val_accuracy: 0.4858\n",
            "Epoch 109/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.4152 - accuracy: 0.4624 - val_loss: 1.4027 - val_accuracy: 0.4858\n",
            "Epoch 110/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.3792 - accuracy: 0.4769 - val_loss: 1.3068 - val_accuracy: 0.5163\n",
            "Epoch 111/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.3940 - accuracy: 0.4583 - val_loss: 1.2760 - val_accuracy: 0.5205\n",
            "Epoch 112/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.3700 - accuracy: 0.4619 - val_loss: 1.4563 - val_accuracy: 0.4469\n",
            "Epoch 113/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.4008 - accuracy: 0.4531 - val_loss: 1.2840 - val_accuracy: 0.5394\n",
            "Epoch 114/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.3457 - accuracy: 0.4759 - val_loss: 1.3495 - val_accuracy: 0.4921\n",
            "Epoch 115/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.3806 - accuracy: 0.4702 - val_loss: 1.2732 - val_accuracy: 0.5352\n",
            "Epoch 116/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.3806 - accuracy: 0.4712 - val_loss: 1.2430 - val_accuracy: 0.5405\n",
            "Epoch 117/500\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.3749 - accuracy: 0.4754 - val_loss: 1.5350 - val_accuracy: 0.4469\n",
            "Epoch 118/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.3851 - accuracy: 0.4619 - val_loss: 1.2848 - val_accuracy: 0.5226\n",
            "Epoch 119/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.3566 - accuracy: 0.4676 - val_loss: 1.5555 - val_accuracy: 0.4196\n",
            "Epoch 120/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.3802 - accuracy: 0.4738 - val_loss: 1.4356 - val_accuracy: 0.4742\n",
            "Epoch 121/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.3553 - accuracy: 0.4717 - val_loss: 1.2919 - val_accuracy: 0.5310\n",
            "Epoch 122/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.3578 - accuracy: 0.4811 - val_loss: 1.2470 - val_accuracy: 0.5542\n",
            "Epoch 123/500\n",
            "1929/1929 [==============================] - 1s 472us/step - loss: 1.3288 - accuracy: 0.4878 - val_loss: 1.2932 - val_accuracy: 0.5310\n",
            "Epoch 124/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.3707 - accuracy: 0.4681 - val_loss: 1.3638 - val_accuracy: 0.4837\n",
            "Epoch 125/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.3630 - accuracy: 0.4785 - val_loss: 1.3751 - val_accuracy: 0.4942\n",
            "Epoch 126/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.3823 - accuracy: 0.4588 - val_loss: 1.2861 - val_accuracy: 0.5373\n",
            "Epoch 127/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.3534 - accuracy: 0.4826 - val_loss: 1.2541 - val_accuracy: 0.5258\n",
            "Epoch 128/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.3268 - accuracy: 0.4863 - val_loss: 1.2604 - val_accuracy: 0.5247\n",
            "Epoch 129/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.3544 - accuracy: 0.4764 - val_loss: 1.2863 - val_accuracy: 0.5342\n",
            "Epoch 130/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.3327 - accuracy: 0.4816 - val_loss: 1.2392 - val_accuracy: 0.5300\n",
            "Epoch 131/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.3487 - accuracy: 0.4671 - val_loss: 1.2277 - val_accuracy: 0.5584\n",
            "Epoch 132/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.3215 - accuracy: 0.4800 - val_loss: 1.3311 - val_accuracy: 0.5100\n",
            "Epoch 133/500\n",
            "1929/1929 [==============================] - 1s 483us/step - loss: 1.3150 - accuracy: 0.5003 - val_loss: 1.2813 - val_accuracy: 0.5373\n",
            "Epoch 134/500\n",
            "1929/1929 [==============================] - 1s 490us/step - loss: 1.3379 - accuracy: 0.4909 - val_loss: 1.2090 - val_accuracy: 0.5489\n",
            "Epoch 135/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.3128 - accuracy: 0.5013 - val_loss: 1.2087 - val_accuracy: 0.5542\n",
            "Epoch 136/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.3149 - accuracy: 0.4904 - val_loss: 1.2616 - val_accuracy: 0.5352\n",
            "Epoch 137/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.3219 - accuracy: 0.4920 - val_loss: 1.2045 - val_accuracy: 0.5573\n",
            "Epoch 138/500\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.2907 - accuracy: 0.5049 - val_loss: 1.1785 - val_accuracy: 0.5542\n",
            "Epoch 139/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.3306 - accuracy: 0.4728 - val_loss: 1.2208 - val_accuracy: 0.5373\n",
            "Epoch 140/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.3378 - accuracy: 0.4733 - val_loss: 1.2014 - val_accuracy: 0.5594\n",
            "Epoch 141/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.3010 - accuracy: 0.4821 - val_loss: 1.2455 - val_accuracy: 0.5584\n",
            "Epoch 142/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.3079 - accuracy: 0.4951 - val_loss: 1.2530 - val_accuracy: 0.5384\n",
            "Epoch 143/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.3343 - accuracy: 0.4878 - val_loss: 1.3249 - val_accuracy: 0.5279\n",
            "Epoch 144/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.3084 - accuracy: 0.4935 - val_loss: 1.2728 - val_accuracy: 0.5310\n",
            "Epoch 145/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.3013 - accuracy: 0.4909 - val_loss: 1.2442 - val_accuracy: 0.5741\n",
            "Epoch 146/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.2846 - accuracy: 0.4914 - val_loss: 1.2355 - val_accuracy: 0.5605\n",
            "Epoch 147/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.3054 - accuracy: 0.4925 - val_loss: 1.3574 - val_accuracy: 0.4763\n",
            "Epoch 148/500\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.2971 - accuracy: 0.4930 - val_loss: 1.1967 - val_accuracy: 0.5636\n",
            "Epoch 149/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.2772 - accuracy: 0.5049 - val_loss: 1.3532 - val_accuracy: 0.4879\n",
            "Epoch 150/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.2704 - accuracy: 0.5117 - val_loss: 1.1921 - val_accuracy: 0.5563\n",
            "Epoch 151/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.2448 - accuracy: 0.5060 - val_loss: 1.1701 - val_accuracy: 0.5910\n",
            "Epoch 152/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.2779 - accuracy: 0.5065 - val_loss: 1.2042 - val_accuracy: 0.5552\n",
            "Epoch 153/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.2618 - accuracy: 0.5101 - val_loss: 1.2630 - val_accuracy: 0.5373\n",
            "Epoch 154/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.2774 - accuracy: 0.5137 - val_loss: 1.1798 - val_accuracy: 0.5594\n",
            "Epoch 155/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.2758 - accuracy: 0.5148 - val_loss: 1.1795 - val_accuracy: 0.5678\n",
            "Epoch 156/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.2766 - accuracy: 0.5132 - val_loss: 1.2046 - val_accuracy: 0.5783\n",
            "Epoch 157/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.2583 - accuracy: 0.5008 - val_loss: 1.1698 - val_accuracy: 0.5846\n",
            "Epoch 158/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.2339 - accuracy: 0.5334 - val_loss: 1.1269 - val_accuracy: 0.5773\n",
            "Epoch 159/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.2601 - accuracy: 0.5148 - val_loss: 1.2002 - val_accuracy: 0.5310\n",
            "Epoch 160/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.2709 - accuracy: 0.5086 - val_loss: 1.1888 - val_accuracy: 0.5710\n",
            "Epoch 161/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.2803 - accuracy: 0.5029 - val_loss: 1.2354 - val_accuracy: 0.5636\n",
            "Epoch 162/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.2446 - accuracy: 0.5189 - val_loss: 1.1614 - val_accuracy: 0.5910\n",
            "Epoch 163/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.2731 - accuracy: 0.5013 - val_loss: 1.1799 - val_accuracy: 0.5741\n",
            "Epoch 164/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.2499 - accuracy: 0.5101 - val_loss: 1.2892 - val_accuracy: 0.5415\n",
            "Epoch 165/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.2535 - accuracy: 0.5127 - val_loss: 1.2283 - val_accuracy: 0.5478\n",
            "Epoch 166/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.2577 - accuracy: 0.5096 - val_loss: 1.2548 - val_accuracy: 0.5289\n",
            "Epoch 167/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.2553 - accuracy: 0.5231 - val_loss: 1.1697 - val_accuracy: 0.5626\n",
            "Epoch 168/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.2438 - accuracy: 0.5241 - val_loss: 1.1754 - val_accuracy: 0.5773\n",
            "Epoch 169/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.2473 - accuracy: 0.5174 - val_loss: 1.1355 - val_accuracy: 0.5825\n",
            "Epoch 170/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.2400 - accuracy: 0.5303 - val_loss: 1.2423 - val_accuracy: 0.5436\n",
            "Epoch 171/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.2463 - accuracy: 0.5101 - val_loss: 1.1484 - val_accuracy: 0.5783\n",
            "Epoch 172/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.2419 - accuracy: 0.5158 - val_loss: 1.1862 - val_accuracy: 0.5773\n",
            "Epoch 173/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.2494 - accuracy: 0.5158 - val_loss: 1.1357 - val_accuracy: 0.5941\n",
            "Epoch 174/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.2358 - accuracy: 0.5226 - val_loss: 1.1479 - val_accuracy: 0.5857\n",
            "Epoch 175/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.2340 - accuracy: 0.5163 - val_loss: 1.1303 - val_accuracy: 0.5941\n",
            "Epoch 176/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.2186 - accuracy: 0.5334 - val_loss: 1.1560 - val_accuracy: 0.5773\n",
            "Epoch 177/500\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.2372 - accuracy: 0.5184 - val_loss: 1.1212 - val_accuracy: 0.5994\n",
            "Epoch 178/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.2055 - accuracy: 0.5272 - val_loss: 1.1999 - val_accuracy: 0.5626\n",
            "Epoch 179/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.2197 - accuracy: 0.5422 - val_loss: 1.1372 - val_accuracy: 0.5868\n",
            "Epoch 180/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.2034 - accuracy: 0.5236 - val_loss: 1.1616 - val_accuracy: 0.5931\n",
            "Epoch 181/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.2033 - accuracy: 0.5329 - val_loss: 1.2577 - val_accuracy: 0.5174\n",
            "Epoch 182/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.2247 - accuracy: 0.5174 - val_loss: 1.1464 - val_accuracy: 0.5605\n",
            "Epoch 183/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.1746 - accuracy: 0.5391 - val_loss: 1.1815 - val_accuracy: 0.5931\n",
            "Epoch 184/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.2181 - accuracy: 0.5402 - val_loss: 1.1389 - val_accuracy: 0.5815\n",
            "Epoch 185/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.2193 - accuracy: 0.5262 - val_loss: 1.1921 - val_accuracy: 0.5846\n",
            "Epoch 186/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.2300 - accuracy: 0.5153 - val_loss: 1.1851 - val_accuracy: 0.5605\n",
            "Epoch 187/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.2343 - accuracy: 0.5215 - val_loss: 1.1772 - val_accuracy: 0.5941\n",
            "Epoch 188/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.2095 - accuracy: 0.5365 - val_loss: 1.1282 - val_accuracy: 0.6057\n",
            "Epoch 189/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.1902 - accuracy: 0.5412 - val_loss: 1.1107 - val_accuracy: 0.6078\n",
            "Epoch 190/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.1908 - accuracy: 0.5355 - val_loss: 1.0946 - val_accuracy: 0.6046\n",
            "Epoch 191/500\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.1777 - accuracy: 0.5443 - val_loss: 1.1275 - val_accuracy: 0.6067\n",
            "Epoch 192/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.1910 - accuracy: 0.5386 - val_loss: 1.1056 - val_accuracy: 0.6225\n",
            "Epoch 193/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.1542 - accuracy: 0.5417 - val_loss: 1.1231 - val_accuracy: 0.6151\n",
            "Epoch 194/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.1934 - accuracy: 0.5526 - val_loss: 1.1046 - val_accuracy: 0.6151\n",
            "Epoch 195/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.1806 - accuracy: 0.5397 - val_loss: 1.1809 - val_accuracy: 0.5678\n",
            "Epoch 196/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.2113 - accuracy: 0.5371 - val_loss: 1.2309 - val_accuracy: 0.5678\n",
            "Epoch 197/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.1621 - accuracy: 0.5557 - val_loss: 1.1371 - val_accuracy: 0.5889\n",
            "Epoch 198/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.1976 - accuracy: 0.5391 - val_loss: 1.1039 - val_accuracy: 0.5931\n",
            "Epoch 199/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.2034 - accuracy: 0.5262 - val_loss: 1.1472 - val_accuracy: 0.5741\n",
            "Epoch 200/500\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 1.1654 - accuracy: 0.5495 - val_loss: 1.0924 - val_accuracy: 0.6141\n",
            "Epoch 201/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.1743 - accuracy: 0.5609 - val_loss: 1.1065 - val_accuracy: 0.6204\n",
            "Epoch 202/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.1525 - accuracy: 0.5526 - val_loss: 1.0662 - val_accuracy: 0.6341\n",
            "Epoch 203/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.1564 - accuracy: 0.5454 - val_loss: 1.0735 - val_accuracy: 0.6193\n",
            "Epoch 204/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.1686 - accuracy: 0.5407 - val_loss: 1.1582 - val_accuracy: 0.5783\n",
            "Epoch 205/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.2090 - accuracy: 0.5246 - val_loss: 1.1067 - val_accuracy: 0.6141\n",
            "Epoch 206/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.1612 - accuracy: 0.5526 - val_loss: 1.1146 - val_accuracy: 0.6078\n",
            "Epoch 207/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.1555 - accuracy: 0.5526 - val_loss: 1.0824 - val_accuracy: 0.6183\n",
            "Epoch 208/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.1569 - accuracy: 0.5505 - val_loss: 1.1128 - val_accuracy: 0.5941\n",
            "Epoch 209/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.1814 - accuracy: 0.5428 - val_loss: 1.0503 - val_accuracy: 0.6267\n",
            "Epoch 210/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.1151 - accuracy: 0.5630 - val_loss: 1.0343 - val_accuracy: 0.6141\n",
            "Epoch 211/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 1.1960 - accuracy: 0.5448 - val_loss: 1.1662 - val_accuracy: 0.5668\n",
            "Epoch 212/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.1453 - accuracy: 0.5630 - val_loss: 1.1011 - val_accuracy: 0.5962\n",
            "Epoch 213/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.1544 - accuracy: 0.5485 - val_loss: 1.1153 - val_accuracy: 0.5910\n",
            "Epoch 214/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.1474 - accuracy: 0.5552 - val_loss: 1.0866 - val_accuracy: 0.6162\n",
            "Epoch 215/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.1332 - accuracy: 0.5583 - val_loss: 1.1309 - val_accuracy: 0.6004\n",
            "Epoch 216/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.1455 - accuracy: 0.5640 - val_loss: 1.1266 - val_accuracy: 0.5731\n",
            "Epoch 217/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.1890 - accuracy: 0.5376 - val_loss: 1.0654 - val_accuracy: 0.6267\n",
            "Epoch 218/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.1284 - accuracy: 0.5635 - val_loss: 1.0807 - val_accuracy: 0.6172\n",
            "Epoch 219/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.1285 - accuracy: 0.5661 - val_loss: 1.0526 - val_accuracy: 0.6215\n",
            "Epoch 220/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.1413 - accuracy: 0.5480 - val_loss: 1.0395 - val_accuracy: 0.6193\n",
            "Epoch 221/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.1579 - accuracy: 0.5594 - val_loss: 1.1160 - val_accuracy: 0.6183\n",
            "Epoch 222/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.1358 - accuracy: 0.5531 - val_loss: 1.0458 - val_accuracy: 0.6393\n",
            "Epoch 223/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.1919 - accuracy: 0.5433 - val_loss: 1.1042 - val_accuracy: 0.5762\n",
            "Epoch 224/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.1541 - accuracy: 0.5537 - val_loss: 1.1157 - val_accuracy: 0.6236\n",
            "Epoch 225/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.1346 - accuracy: 0.5557 - val_loss: 1.1353 - val_accuracy: 0.5868\n",
            "Epoch 226/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.1518 - accuracy: 0.5516 - val_loss: 1.0496 - val_accuracy: 0.6383\n",
            "Epoch 227/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.1158 - accuracy: 0.5801 - val_loss: 1.0319 - val_accuracy: 0.6362\n",
            "Epoch 228/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.1418 - accuracy: 0.5537 - val_loss: 1.0260 - val_accuracy: 0.6446\n",
            "Epoch 229/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.1053 - accuracy: 0.5697 - val_loss: 1.0408 - val_accuracy: 0.6130\n",
            "Epoch 230/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.1375 - accuracy: 0.5780 - val_loss: 1.0866 - val_accuracy: 0.6267\n",
            "Epoch 231/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.1321 - accuracy: 0.5666 - val_loss: 1.0782 - val_accuracy: 0.6025\n",
            "Epoch 232/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.1500 - accuracy: 0.5562 - val_loss: 1.1658 - val_accuracy: 0.5952\n",
            "Epoch 233/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.1460 - accuracy: 0.5469 - val_loss: 1.0798 - val_accuracy: 0.6257\n",
            "Epoch 234/500\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.1101 - accuracy: 0.5816 - val_loss: 0.9999 - val_accuracy: 0.6299\n",
            "Epoch 235/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.1335 - accuracy: 0.5547 - val_loss: 1.0285 - val_accuracy: 0.6299\n",
            "Epoch 236/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.1191 - accuracy: 0.5718 - val_loss: 1.1054 - val_accuracy: 0.6078\n",
            "Epoch 237/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.1182 - accuracy: 0.5588 - val_loss: 1.0272 - val_accuracy: 0.6477\n",
            "Epoch 238/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.0810 - accuracy: 0.5749 - val_loss: 0.9950 - val_accuracy: 0.6498\n",
            "Epoch 239/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.1053 - accuracy: 0.5697 - val_loss: 1.0172 - val_accuracy: 0.6562\n",
            "Epoch 240/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.0633 - accuracy: 0.5816 - val_loss: 1.0273 - val_accuracy: 0.6362\n",
            "Epoch 241/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0887 - accuracy: 0.5931 - val_loss: 1.0890 - val_accuracy: 0.6036\n",
            "Epoch 242/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.1058 - accuracy: 0.5801 - val_loss: 1.0002 - val_accuracy: 0.6551\n",
            "Epoch 243/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.1037 - accuracy: 0.5744 - val_loss: 1.0771 - val_accuracy: 0.6120\n",
            "Epoch 244/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.1318 - accuracy: 0.5630 - val_loss: 1.0115 - val_accuracy: 0.6299\n",
            "Epoch 245/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.0814 - accuracy: 0.5702 - val_loss: 1.0012 - val_accuracy: 0.6572\n",
            "Epoch 246/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.0775 - accuracy: 0.5848 - val_loss: 1.0476 - val_accuracy: 0.6477\n",
            "Epoch 247/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0875 - accuracy: 0.5889 - val_loss: 1.0834 - val_accuracy: 0.6015\n",
            "Epoch 248/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.0865 - accuracy: 0.5894 - val_loss: 1.0580 - val_accuracy: 0.6414\n",
            "Epoch 249/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.0898 - accuracy: 0.5734 - val_loss: 1.0559 - val_accuracy: 0.6341\n",
            "Epoch 250/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.0886 - accuracy: 0.5765 - val_loss: 1.0668 - val_accuracy: 0.6215\n",
            "Epoch 251/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.0733 - accuracy: 0.5925 - val_loss: 0.9933 - val_accuracy: 0.6498\n",
            "Epoch 252/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.0471 - accuracy: 0.6081 - val_loss: 0.9539 - val_accuracy: 0.6814\n",
            "Epoch 253/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.0816 - accuracy: 0.5759 - val_loss: 1.0050 - val_accuracy: 0.6446\n",
            "Epoch 254/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0834 - accuracy: 0.5728 - val_loss: 1.1153 - val_accuracy: 0.5825\n",
            "Epoch 255/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.0934 - accuracy: 0.5827 - val_loss: 0.9945 - val_accuracy: 0.6562\n",
            "Epoch 256/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.0632 - accuracy: 0.5868 - val_loss: 1.0254 - val_accuracy: 0.6604\n",
            "Epoch 257/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.0609 - accuracy: 0.5827 - val_loss: 0.9953 - val_accuracy: 0.6257\n",
            "Epoch 258/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.0796 - accuracy: 0.5811 - val_loss: 0.9402 - val_accuracy: 0.6751\n",
            "Epoch 259/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.0791 - accuracy: 0.5894 - val_loss: 0.9624 - val_accuracy: 0.6625\n",
            "Epoch 260/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.0961 - accuracy: 0.5910 - val_loss: 1.0138 - val_accuracy: 0.6299\n",
            "Epoch 261/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.0785 - accuracy: 0.5884 - val_loss: 1.0336 - val_accuracy: 0.6509\n",
            "Epoch 262/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.0908 - accuracy: 0.5671 - val_loss: 0.9754 - val_accuracy: 0.6635\n",
            "Epoch 263/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.0701 - accuracy: 0.5723 - val_loss: 0.9711 - val_accuracy: 0.6572\n",
            "Epoch 264/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.0690 - accuracy: 0.5863 - val_loss: 1.0261 - val_accuracy: 0.6562\n",
            "Epoch 265/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.0397 - accuracy: 0.6096 - val_loss: 1.2614 - val_accuracy: 0.5584\n",
            "Epoch 266/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.0850 - accuracy: 0.5905 - val_loss: 0.9764 - val_accuracy: 0.6667\n",
            "Epoch 267/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.0789 - accuracy: 0.5972 - val_loss: 1.0077 - val_accuracy: 0.6540\n",
            "Epoch 268/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0497 - accuracy: 0.5946 - val_loss: 0.9334 - val_accuracy: 0.6803\n",
            "Epoch 269/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.0441 - accuracy: 0.5889 - val_loss: 1.0234 - val_accuracy: 0.6414\n",
            "Epoch 270/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.0453 - accuracy: 0.6008 - val_loss: 0.9356 - val_accuracy: 0.6793\n",
            "Epoch 271/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.0789 - accuracy: 0.5868 - val_loss: 1.0080 - val_accuracy: 0.6551\n",
            "Epoch 272/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0913 - accuracy: 0.5837 - val_loss: 1.0547 - val_accuracy: 0.6288\n",
            "Epoch 273/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.0867 - accuracy: 0.5858 - val_loss: 0.9627 - val_accuracy: 0.6667\n",
            "Epoch 274/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.0454 - accuracy: 0.5868 - val_loss: 0.9990 - val_accuracy: 0.6635\n",
            "Epoch 275/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.0488 - accuracy: 0.5946 - val_loss: 0.9382 - val_accuracy: 0.6866\n",
            "Epoch 276/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0480 - accuracy: 0.5946 - val_loss: 0.9749 - val_accuracy: 0.6593\n",
            "Epoch 277/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.0243 - accuracy: 0.6008 - val_loss: 1.0535 - val_accuracy: 0.6393\n",
            "Epoch 278/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.0534 - accuracy: 0.5910 - val_loss: 1.0330 - val_accuracy: 0.6509\n",
            "Epoch 279/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.0507 - accuracy: 0.5951 - val_loss: 0.9971 - val_accuracy: 0.6730\n",
            "Epoch 280/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.0667 - accuracy: 0.5894 - val_loss: 1.0596 - val_accuracy: 0.6309\n",
            "Epoch 281/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.0530 - accuracy: 0.5998 - val_loss: 0.9423 - val_accuracy: 0.6719\n",
            "Epoch 282/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.0010 - accuracy: 0.6091 - val_loss: 0.9398 - val_accuracy: 0.6761\n",
            "Epoch 283/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.0428 - accuracy: 0.5931 - val_loss: 0.9819 - val_accuracy: 0.6751\n",
            "Epoch 284/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.0558 - accuracy: 0.5801 - val_loss: 0.9394 - val_accuracy: 0.6961\n",
            "Epoch 285/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.0457 - accuracy: 0.6019 - val_loss: 1.0335 - val_accuracy: 0.6562\n",
            "Epoch 286/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.0472 - accuracy: 0.6045 - val_loss: 0.9521 - val_accuracy: 0.6751\n",
            "Epoch 287/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0433 - accuracy: 0.5941 - val_loss: 0.9269 - val_accuracy: 0.6877\n",
            "Epoch 288/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.0782 - accuracy: 0.5962 - val_loss: 0.9777 - val_accuracy: 0.6761\n",
            "Epoch 289/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.0217 - accuracy: 0.6024 - val_loss: 0.9619 - val_accuracy: 0.6719\n",
            "Epoch 290/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.0416 - accuracy: 0.5822 - val_loss: 0.9628 - val_accuracy: 0.6740\n",
            "Epoch 291/500\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 1.0637 - accuracy: 0.5816 - val_loss: 1.0334 - val_accuracy: 0.6288\n",
            "Epoch 292/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.0520 - accuracy: 0.6122 - val_loss: 0.9795 - val_accuracy: 0.6456\n",
            "Epoch 293/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.0028 - accuracy: 0.6143 - val_loss: 1.1544 - val_accuracy: 0.5804\n",
            "Epoch 294/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.0692 - accuracy: 0.5879 - val_loss: 0.9529 - val_accuracy: 0.6951\n",
            "Epoch 295/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.9884 - accuracy: 0.6216 - val_loss: 1.0128 - val_accuracy: 0.6572\n",
            "Epoch 296/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.0479 - accuracy: 0.5931 - val_loss: 0.9353 - val_accuracy: 0.6730\n",
            "Epoch 297/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.0233 - accuracy: 0.5988 - val_loss: 1.0501 - val_accuracy: 0.6393\n",
            "Epoch 298/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0595 - accuracy: 0.5863 - val_loss: 1.0073 - val_accuracy: 0.6519\n",
            "Epoch 299/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.0288 - accuracy: 0.6003 - val_loss: 0.8958 - val_accuracy: 0.6919\n",
            "Epoch 300/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.9841 - accuracy: 0.6122 - val_loss: 0.9377 - val_accuracy: 0.6698\n",
            "Epoch 301/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.0177 - accuracy: 0.6195 - val_loss: 0.9913 - val_accuracy: 0.6404\n",
            "Epoch 302/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.0285 - accuracy: 0.6008 - val_loss: 0.9590 - val_accuracy: 0.6646\n",
            "Epoch 303/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.0015 - accuracy: 0.6174 - val_loss: 0.9389 - val_accuracy: 0.6604\n",
            "Epoch 304/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.0062 - accuracy: 0.6236 - val_loss: 0.9210 - val_accuracy: 0.6824\n",
            "Epoch 305/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.0200 - accuracy: 0.5982 - val_loss: 0.9740 - val_accuracy: 0.6498\n",
            "Epoch 306/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.0203 - accuracy: 0.5982 - val_loss: 0.9170 - val_accuracy: 0.6814\n",
            "Epoch 307/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.0266 - accuracy: 0.5972 - val_loss: 0.9656 - val_accuracy: 0.6845\n",
            "Epoch 308/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.9988 - accuracy: 0.6055 - val_loss: 0.9842 - val_accuracy: 0.6488\n",
            "Epoch 309/500\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.0066 - accuracy: 0.6117 - val_loss: 0.9105 - val_accuracy: 0.6845\n",
            "Epoch 310/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.0394 - accuracy: 0.6076 - val_loss: 0.9013 - val_accuracy: 0.6751\n",
            "Epoch 311/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.9920 - accuracy: 0.6081 - val_loss: 0.9094 - val_accuracy: 0.6751\n",
            "Epoch 312/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.0149 - accuracy: 0.6034 - val_loss: 0.9420 - val_accuracy: 0.6782\n",
            "Epoch 313/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.0190 - accuracy: 0.6107 - val_loss: 1.0031 - val_accuracy: 0.6456\n",
            "Epoch 314/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.0299 - accuracy: 0.6034 - val_loss: 0.9228 - val_accuracy: 0.6887\n",
            "Epoch 315/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.0245 - accuracy: 0.6024 - val_loss: 0.9049 - val_accuracy: 0.7024\n",
            "Epoch 316/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.0142 - accuracy: 0.5910 - val_loss: 1.1144 - val_accuracy: 0.6046\n",
            "Epoch 317/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.0224 - accuracy: 0.6216 - val_loss: 0.9345 - val_accuracy: 0.6677\n",
            "Epoch 318/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.0179 - accuracy: 0.6117 - val_loss: 0.9072 - val_accuracy: 0.6856\n",
            "Epoch 319/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.0040 - accuracy: 0.6164 - val_loss: 1.0148 - val_accuracy: 0.6530\n",
            "Epoch 320/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.0059 - accuracy: 0.6148 - val_loss: 0.8773 - val_accuracy: 0.7129\n",
            "Epoch 321/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.0078 - accuracy: 0.6055 - val_loss: 0.9417 - val_accuracy: 0.6930\n",
            "Epoch 322/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.9816 - accuracy: 0.6190 - val_loss: 0.8748 - val_accuracy: 0.6782\n",
            "Epoch 323/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.0121 - accuracy: 0.6148 - val_loss: 0.9341 - val_accuracy: 0.6877\n",
            "Epoch 324/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.0246 - accuracy: 0.6081 - val_loss: 0.9363 - val_accuracy: 0.6803\n",
            "Epoch 325/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.0025 - accuracy: 0.6205 - val_loss: 0.9151 - val_accuracy: 0.6761\n",
            "Epoch 326/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 0.9776 - accuracy: 0.6174 - val_loss: 0.9041 - val_accuracy: 0.6909\n",
            "Epoch 327/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.9558 - accuracy: 0.6309 - val_loss: 0.9222 - val_accuracy: 0.6835\n",
            "Epoch 328/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.9959 - accuracy: 0.6138 - val_loss: 0.8770 - val_accuracy: 0.7161\n",
            "Epoch 329/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.9818 - accuracy: 0.6273 - val_loss: 0.9373 - val_accuracy: 0.6824\n",
            "Epoch 330/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 0.9621 - accuracy: 0.6340 - val_loss: 0.9393 - val_accuracy: 0.6709\n",
            "Epoch 331/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.0061 - accuracy: 0.6086 - val_loss: 0.9505 - val_accuracy: 0.6583\n",
            "Epoch 332/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.0002 - accuracy: 0.6169 - val_loss: 0.9062 - val_accuracy: 0.6772\n",
            "Epoch 333/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 0.9555 - accuracy: 0.6257 - val_loss: 0.8626 - val_accuracy: 0.7024\n",
            "Epoch 334/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.9928 - accuracy: 0.6314 - val_loss: 0.9140 - val_accuracy: 0.7024\n",
            "Epoch 335/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 0.9783 - accuracy: 0.6252 - val_loss: 1.0131 - val_accuracy: 0.6309\n",
            "Epoch 336/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.0058 - accuracy: 0.6159 - val_loss: 0.8871 - val_accuracy: 0.7203\n",
            "Epoch 337/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.9860 - accuracy: 0.6112 - val_loss: 0.9098 - val_accuracy: 0.6740\n",
            "Epoch 338/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.9806 - accuracy: 0.6236 - val_loss: 0.8595 - val_accuracy: 0.7382\n",
            "Epoch 339/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9826 - accuracy: 0.6262 - val_loss: 0.8771 - val_accuracy: 0.7150\n",
            "Epoch 340/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 0.9739 - accuracy: 0.6174 - val_loss: 0.9446 - val_accuracy: 0.6856\n",
            "Epoch 341/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 0.9787 - accuracy: 0.6299 - val_loss: 0.8763 - val_accuracy: 0.7066\n",
            "Epoch 342/500\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.9518 - accuracy: 0.6314 - val_loss: 0.9241 - val_accuracy: 0.6919\n",
            "Epoch 343/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.9487 - accuracy: 0.6319 - val_loss: 0.9692 - val_accuracy: 0.6614\n",
            "Epoch 344/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.9916 - accuracy: 0.6195 - val_loss: 0.8490 - val_accuracy: 0.7014\n",
            "Epoch 345/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9546 - accuracy: 0.6402 - val_loss: 0.8714 - val_accuracy: 0.7003\n",
            "Epoch 346/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.9799 - accuracy: 0.6345 - val_loss: 0.9347 - val_accuracy: 0.6793\n",
            "Epoch 347/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.0020 - accuracy: 0.6273 - val_loss: 0.8528 - val_accuracy: 0.7098\n",
            "Epoch 348/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.9419 - accuracy: 0.6293 - val_loss: 0.8521 - val_accuracy: 0.7171\n",
            "Epoch 349/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.9629 - accuracy: 0.6314 - val_loss: 0.9226 - val_accuracy: 0.6814\n",
            "Epoch 350/500\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.9744 - accuracy: 0.6356 - val_loss: 0.8363 - val_accuracy: 0.7256\n",
            "Epoch 351/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 0.9447 - accuracy: 0.6387 - val_loss: 0.8461 - val_accuracy: 0.7329\n",
            "Epoch 352/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9359 - accuracy: 0.6376 - val_loss: 0.9045 - val_accuracy: 0.7066\n",
            "Epoch 353/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.9473 - accuracy: 0.6293 - val_loss: 0.8866 - val_accuracy: 0.7014\n",
            "Epoch 354/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.9622 - accuracy: 0.6423 - val_loss: 0.8903 - val_accuracy: 0.6877\n",
            "Epoch 355/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.9674 - accuracy: 0.6205 - val_loss: 0.8940 - val_accuracy: 0.7035\n",
            "Epoch 356/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.9279 - accuracy: 0.6506 - val_loss: 0.8446 - val_accuracy: 0.7140\n",
            "Epoch 357/500\n",
            "1929/1929 [==============================] - 1s 466us/step - loss: 0.9603 - accuracy: 0.6361 - val_loss: 0.8669 - val_accuracy: 0.7035\n",
            "Epoch 358/500\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.9297 - accuracy: 0.6392 - val_loss: 0.8133 - val_accuracy: 0.7245\n",
            "Epoch 359/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9683 - accuracy: 0.6293 - val_loss: 0.8212 - val_accuracy: 0.7256\n",
            "Epoch 360/500\n",
            "1929/1929 [==============================] - 1s 473us/step - loss: 0.9483 - accuracy: 0.6314 - val_loss: 0.9000 - val_accuracy: 0.6951\n",
            "Epoch 361/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9456 - accuracy: 0.6210 - val_loss: 0.8619 - val_accuracy: 0.7340\n",
            "Epoch 362/500\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 0.9601 - accuracy: 0.6464 - val_loss: 0.8780 - val_accuracy: 0.7171\n",
            "Epoch 363/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.9344 - accuracy: 0.6433 - val_loss: 0.8681 - val_accuracy: 0.7077\n",
            "Epoch 364/500\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 0.9643 - accuracy: 0.6231 - val_loss: 0.8690 - val_accuracy: 0.7213\n",
            "Epoch 365/500\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.9483 - accuracy: 0.6356 - val_loss: 0.8705 - val_accuracy: 0.7182\n",
            "Epoch 366/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.9498 - accuracy: 0.6356 - val_loss: 0.8407 - val_accuracy: 0.7171\n",
            "Epoch 367/500\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.9497 - accuracy: 0.6418 - val_loss: 0.8783 - val_accuracy: 0.6909\n",
            "Epoch 368/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.9331 - accuracy: 0.6366 - val_loss: 0.8802 - val_accuracy: 0.7014\n",
            "Epoch 369/500\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.9644 - accuracy: 0.6325 - val_loss: 0.8787 - val_accuracy: 0.7182\n",
            "Epoch 370/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.9244 - accuracy: 0.6485 - val_loss: 0.8928 - val_accuracy: 0.7066\n",
            "Epoch 371/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.9642 - accuracy: 0.6345 - val_loss: 0.8092 - val_accuracy: 0.7329\n",
            "Epoch 372/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.9223 - accuracy: 0.6330 - val_loss: 0.9745 - val_accuracy: 0.6751\n",
            "Epoch 373/500\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.9392 - accuracy: 0.6413 - val_loss: 0.8140 - val_accuracy: 0.7319\n",
            "Epoch 374/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.9160 - accuracy: 0.6376 - val_loss: 0.8099 - val_accuracy: 0.7361\n",
            "Epoch 375/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9725 - accuracy: 0.6179 - val_loss: 0.8097 - val_accuracy: 0.7413\n",
            "Epoch 376/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.9282 - accuracy: 0.6350 - val_loss: 0.9315 - val_accuracy: 0.6751\n",
            "Epoch 377/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.9844 - accuracy: 0.6345 - val_loss: 0.8442 - val_accuracy: 0.7182\n",
            "Epoch 378/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 0.9368 - accuracy: 0.6350 - val_loss: 0.8128 - val_accuracy: 0.7497\n",
            "Epoch 379/500\n",
            "1929/1929 [==============================] - 1s 482us/step - loss: 0.9454 - accuracy: 0.6402 - val_loss: 0.8194 - val_accuracy: 0.7277\n",
            "Epoch 380/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 0.9218 - accuracy: 0.6439 - val_loss: 0.9009 - val_accuracy: 0.6782\n",
            "Epoch 381/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.9573 - accuracy: 0.6267 - val_loss: 0.8487 - val_accuracy: 0.7182\n",
            "Epoch 382/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.9439 - accuracy: 0.6361 - val_loss: 1.0609 - val_accuracy: 0.6414\n",
            "Epoch 383/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.9601 - accuracy: 0.6402 - val_loss: 0.8597 - val_accuracy: 0.7182\n",
            "Epoch 384/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.9476 - accuracy: 0.6242 - val_loss: 0.8151 - val_accuracy: 0.7382\n",
            "Epoch 385/500\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.9113 - accuracy: 0.6433 - val_loss: 0.8547 - val_accuracy: 0.7256\n",
            "Epoch 386/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9240 - accuracy: 0.6522 - val_loss: 0.8297 - val_accuracy: 0.7445\n",
            "Epoch 387/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 0.9281 - accuracy: 0.6361 - val_loss: 0.8711 - val_accuracy: 0.7140\n",
            "Epoch 388/500\n",
            "1929/1929 [==============================] - 1s 473us/step - loss: 0.9419 - accuracy: 0.6330 - val_loss: 0.8604 - val_accuracy: 0.6835\n",
            "Epoch 389/500\n",
            "1929/1929 [==============================] - 1s 473us/step - loss: 0.9384 - accuracy: 0.6459 - val_loss: 0.7918 - val_accuracy: 0.7445\n",
            "Epoch 390/500\n",
            "1929/1929 [==============================] - 1s 471us/step - loss: 0.9392 - accuracy: 0.6392 - val_loss: 0.8431 - val_accuracy: 0.7277\n",
            "Epoch 391/500\n",
            "1929/1929 [==============================] - 1s 489us/step - loss: 0.9349 - accuracy: 0.6413 - val_loss: 0.8339 - val_accuracy: 0.7234\n",
            "Epoch 392/500\n",
            "1929/1929 [==============================] - 1s 483us/step - loss: 0.9390 - accuracy: 0.6407 - val_loss: 0.8346 - val_accuracy: 0.7234\n",
            "Epoch 393/500\n",
            "1929/1929 [==============================] - 1s 471us/step - loss: 0.9136 - accuracy: 0.6366 - val_loss: 0.7968 - val_accuracy: 0.7203\n",
            "Epoch 394/500\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 0.9385 - accuracy: 0.6402 - val_loss: 0.8348 - val_accuracy: 0.7171\n",
            "Epoch 395/500\n",
            "1929/1929 [==============================] - 1s 479us/step - loss: 0.9276 - accuracy: 0.6537 - val_loss: 0.8938 - val_accuracy: 0.6845\n",
            "Epoch 396/500\n",
            "1929/1929 [==============================] - 1s 478us/step - loss: 0.9407 - accuracy: 0.6304 - val_loss: 0.8382 - val_accuracy: 0.7140\n",
            "Epoch 397/500\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.9213 - accuracy: 0.6496 - val_loss: 0.8768 - val_accuracy: 0.7150\n",
            "Epoch 398/500\n",
            "1929/1929 [==============================] - 1s 466us/step - loss: 0.9193 - accuracy: 0.6599 - val_loss: 0.8521 - val_accuracy: 0.7224\n",
            "Epoch 399/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.9137 - accuracy: 0.6444 - val_loss: 0.8323 - val_accuracy: 0.7256\n",
            "Epoch 400/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.9179 - accuracy: 0.6553 - val_loss: 0.8208 - val_accuracy: 0.7161\n",
            "Epoch 401/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.9135 - accuracy: 0.6506 - val_loss: 0.8727 - val_accuracy: 0.7014\n",
            "Epoch 402/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.9115 - accuracy: 0.6568 - val_loss: 0.8979 - val_accuracy: 0.6951\n",
            "Epoch 403/500\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.9617 - accuracy: 0.6267 - val_loss: 0.8873 - val_accuracy: 0.7129\n",
            "Epoch 404/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 0.9246 - accuracy: 0.6418 - val_loss: 0.8346 - val_accuracy: 0.7298\n",
            "Epoch 405/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.9555 - accuracy: 0.6428 - val_loss: 0.9650 - val_accuracy: 0.6635\n",
            "Epoch 406/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.9269 - accuracy: 0.6470 - val_loss: 0.8347 - val_accuracy: 0.7413\n",
            "Epoch 407/500\n",
            "1929/1929 [==============================] - 1s 466us/step - loss: 0.9242 - accuracy: 0.6433 - val_loss: 0.8097 - val_accuracy: 0.7434\n",
            "Epoch 408/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.8858 - accuracy: 0.6636 - val_loss: 0.7930 - val_accuracy: 0.7445\n",
            "Epoch 409/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 0.9075 - accuracy: 0.6501 - val_loss: 0.8914 - val_accuracy: 0.7119\n",
            "Epoch 410/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.9031 - accuracy: 0.6558 - val_loss: 0.8325 - val_accuracy: 0.7192\n",
            "Epoch 411/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.9389 - accuracy: 0.6267 - val_loss: 0.8202 - val_accuracy: 0.7361\n",
            "Epoch 412/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.8792 - accuracy: 0.6568 - val_loss: 0.8566 - val_accuracy: 0.7140\n",
            "Epoch 413/500\n",
            "1929/1929 [==============================] - 1s 471us/step - loss: 0.9522 - accuracy: 0.6371 - val_loss: 0.8319 - val_accuracy: 0.7245\n",
            "Epoch 414/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.9290 - accuracy: 0.6325 - val_loss: 0.8521 - val_accuracy: 0.7119\n",
            "Epoch 415/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.9038 - accuracy: 0.6584 - val_loss: 0.8634 - val_accuracy: 0.7319\n",
            "Epoch 416/500\n",
            "1929/1929 [==============================] - 1s 472us/step - loss: 0.8918 - accuracy: 0.6579 - val_loss: 0.8458 - val_accuracy: 0.7213\n",
            "Epoch 417/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.8980 - accuracy: 0.6641 - val_loss: 0.8429 - val_accuracy: 0.7129\n",
            "Epoch 418/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 0.9081 - accuracy: 0.6402 - val_loss: 0.9162 - val_accuracy: 0.6972\n",
            "Epoch 419/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.8846 - accuracy: 0.6573 - val_loss: 0.8821 - val_accuracy: 0.7182\n",
            "Epoch 420/500\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.9004 - accuracy: 0.6522 - val_loss: 0.8153 - val_accuracy: 0.7487\n",
            "Epoch 421/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.8944 - accuracy: 0.6641 - val_loss: 0.9661 - val_accuracy: 0.6593\n",
            "Epoch 422/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.9285 - accuracy: 0.6382 - val_loss: 0.7973 - val_accuracy: 0.7476\n",
            "Epoch 423/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 0.8636 - accuracy: 0.6744 - val_loss: 0.8026 - val_accuracy: 0.7350\n",
            "Epoch 424/500\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.9156 - accuracy: 0.6599 - val_loss: 0.9405 - val_accuracy: 0.6593\n",
            "Epoch 425/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 0.9572 - accuracy: 0.6402 - val_loss: 0.7855 - val_accuracy: 0.7550\n",
            "Epoch 426/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.9091 - accuracy: 0.6537 - val_loss: 0.8300 - val_accuracy: 0.7560\n",
            "Epoch 427/500\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.8866 - accuracy: 0.6573 - val_loss: 0.8073 - val_accuracy: 0.7424\n",
            "Epoch 428/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.9047 - accuracy: 0.6490 - val_loss: 0.7941 - val_accuracy: 0.7476\n",
            "Epoch 429/500\n",
            "1929/1929 [==============================] - 1s 474us/step - loss: 0.8811 - accuracy: 0.6563 - val_loss: 0.9593 - val_accuracy: 0.6835\n",
            "Epoch 430/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9009 - accuracy: 0.6744 - val_loss: 0.9997 - val_accuracy: 0.6646\n",
            "Epoch 431/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.8900 - accuracy: 0.6584 - val_loss: 0.7811 - val_accuracy: 0.7676\n",
            "Epoch 432/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.8682 - accuracy: 0.6781 - val_loss: 0.8378 - val_accuracy: 0.7266\n",
            "Epoch 433/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.9047 - accuracy: 0.6651 - val_loss: 1.2402 - val_accuracy: 0.6109\n",
            "Epoch 434/500\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.9316 - accuracy: 0.6459 - val_loss: 0.7813 - val_accuracy: 0.7571\n",
            "Epoch 435/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.9056 - accuracy: 0.6625 - val_loss: 0.8079 - val_accuracy: 0.7287\n",
            "Epoch 436/500\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.8713 - accuracy: 0.6568 - val_loss: 0.7880 - val_accuracy: 0.7634\n",
            "Epoch 437/500\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 0.8919 - accuracy: 0.6620 - val_loss: 0.7878 - val_accuracy: 0.7624\n",
            "Epoch 438/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.9059 - accuracy: 0.6330 - val_loss: 0.8605 - val_accuracy: 0.7287\n",
            "Epoch 439/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.9191 - accuracy: 0.6625 - val_loss: 0.8508 - val_accuracy: 0.7077\n",
            "Epoch 440/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.9024 - accuracy: 0.6516 - val_loss: 0.8428 - val_accuracy: 0.7466\n",
            "Epoch 441/500\n",
            "1929/1929 [==============================] - 1s 473us/step - loss: 0.8846 - accuracy: 0.6594 - val_loss: 0.8515 - val_accuracy: 0.7192\n",
            "Epoch 442/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.9220 - accuracy: 0.6397 - val_loss: 0.8252 - val_accuracy: 0.7445\n",
            "Epoch 443/500\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.8950 - accuracy: 0.6604 - val_loss: 0.9586 - val_accuracy: 0.6887\n",
            "Epoch 444/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 0.8978 - accuracy: 0.6615 - val_loss: 0.8567 - val_accuracy: 0.7045\n",
            "Epoch 445/500\n",
            "1929/1929 [==============================] - 1s 479us/step - loss: 0.9071 - accuracy: 0.6459 - val_loss: 0.7991 - val_accuracy: 0.7361\n",
            "Epoch 446/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 0.8843 - accuracy: 0.6630 - val_loss: 0.8562 - val_accuracy: 0.7434\n",
            "Epoch 447/500\n",
            "1929/1929 [==============================] - 1s 466us/step - loss: 0.8502 - accuracy: 0.6703 - val_loss: 0.9226 - val_accuracy: 0.6972\n",
            "Epoch 448/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.8506 - accuracy: 0.6941 - val_loss: 0.7967 - val_accuracy: 0.7371\n",
            "Epoch 449/500\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 0.8794 - accuracy: 0.6739 - val_loss: 0.7933 - val_accuracy: 0.7529\n",
            "Epoch 450/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.8617 - accuracy: 0.6687 - val_loss: 0.8130 - val_accuracy: 0.7529\n",
            "Epoch 451/500\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 0.8739 - accuracy: 0.6682 - val_loss: 0.8271 - val_accuracy: 0.7350\n",
            "Epoch 452/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.9000 - accuracy: 0.6604 - val_loss: 0.7764 - val_accuracy: 0.7634\n",
            "Epoch 453/500\n",
            "1929/1929 [==============================] - 1s 476us/step - loss: 0.8819 - accuracy: 0.6630 - val_loss: 0.7683 - val_accuracy: 0.7581\n",
            "Epoch 454/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.8420 - accuracy: 0.6739 - val_loss: 1.4166 - val_accuracy: 0.5762\n",
            "Epoch 455/500\n",
            "1929/1929 [==============================] - 1s 475us/step - loss: 0.9752 - accuracy: 0.6620 - val_loss: 0.7975 - val_accuracy: 0.7560\n",
            "Epoch 456/500\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 0.9012 - accuracy: 0.6579 - val_loss: 0.8380 - val_accuracy: 0.7413\n",
            "Epoch 457/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 0.9025 - accuracy: 0.6444 - val_loss: 0.8655 - val_accuracy: 0.7287\n",
            "Epoch 458/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 0.9145 - accuracy: 0.6449 - val_loss: 0.8297 - val_accuracy: 0.7382\n",
            "Epoch 459/500\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 0.9023 - accuracy: 0.6501 - val_loss: 0.7883 - val_accuracy: 0.7361\n",
            "Epoch 460/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 0.8841 - accuracy: 0.6599 - val_loss: 0.7828 - val_accuracy: 0.7771\n",
            "Epoch 461/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.8831 - accuracy: 0.6667 - val_loss: 0.8312 - val_accuracy: 0.7382\n",
            "Epoch 462/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 0.8621 - accuracy: 0.6755 - val_loss: 0.8034 - val_accuracy: 0.7424\n",
            "Epoch 463/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 0.8367 - accuracy: 0.6693 - val_loss: 0.8090 - val_accuracy: 0.7466\n",
            "Epoch 464/500\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 0.8405 - accuracy: 0.6900 - val_loss: 0.7801 - val_accuracy: 0.7466\n",
            "Epoch 465/500\n",
            "1929/1929 [==============================] - 1s 475us/step - loss: 0.8661 - accuracy: 0.6739 - val_loss: 0.7573 - val_accuracy: 0.7603\n",
            "Epoch 466/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 0.8724 - accuracy: 0.6734 - val_loss: 0.7684 - val_accuracy: 0.7634\n",
            "Epoch 467/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.8898 - accuracy: 0.6620 - val_loss: 0.8422 - val_accuracy: 0.7340\n",
            "Epoch 468/500\n",
            "1929/1929 [==============================] - 1s 474us/step - loss: 0.8961 - accuracy: 0.6589 - val_loss: 0.7833 - val_accuracy: 0.7529\n",
            "Epoch 469/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.8676 - accuracy: 0.6724 - val_loss: 0.7716 - val_accuracy: 0.7687\n",
            "Epoch 470/500\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 0.8424 - accuracy: 0.6791 - val_loss: 0.7749 - val_accuracy: 0.7529\n",
            "Epoch 471/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.8491 - accuracy: 0.6693 - val_loss: 0.7983 - val_accuracy: 0.7550\n",
            "Epoch 472/500\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.8697 - accuracy: 0.6765 - val_loss: 0.8326 - val_accuracy: 0.7308\n",
            "Epoch 473/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 0.8942 - accuracy: 0.6693 - val_loss: 0.8129 - val_accuracy: 0.7508\n",
            "Epoch 474/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 0.8444 - accuracy: 0.6838 - val_loss: 0.8174 - val_accuracy: 0.7529\n",
            "Epoch 475/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 0.8415 - accuracy: 0.6734 - val_loss: 0.7816 - val_accuracy: 0.7687\n",
            "Epoch 476/500\n",
            "1929/1929 [==============================] - 1s 475us/step - loss: 0.8592 - accuracy: 0.6610 - val_loss: 0.7522 - val_accuracy: 0.7739\n",
            "Epoch 477/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 0.8634 - accuracy: 0.6734 - val_loss: 0.7958 - val_accuracy: 0.7350\n",
            "Epoch 478/500\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 0.8212 - accuracy: 0.6900 - val_loss: 0.9293 - val_accuracy: 0.6824\n",
            "Epoch 479/500\n",
            "1929/1929 [==============================] - 1s 473us/step - loss: 0.8791 - accuracy: 0.6558 - val_loss: 0.7705 - val_accuracy: 0.7613\n",
            "Epoch 480/500\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 0.8537 - accuracy: 0.6729 - val_loss: 0.7788 - val_accuracy: 0.7455\n",
            "Epoch 481/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.8790 - accuracy: 0.6724 - val_loss: 0.9186 - val_accuracy: 0.7098\n",
            "Epoch 482/500\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.8844 - accuracy: 0.6646 - val_loss: 0.7851 - val_accuracy: 0.7634\n",
            "Epoch 483/500\n",
            "1929/1929 [==============================] - 1s 466us/step - loss: 0.8802 - accuracy: 0.6765 - val_loss: 0.7574 - val_accuracy: 0.7613\n",
            "Epoch 484/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.9012 - accuracy: 0.6719 - val_loss: 0.7654 - val_accuracy: 0.7645\n",
            "Epoch 485/500\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.8806 - accuracy: 0.6786 - val_loss: 0.7514 - val_accuracy: 0.7729\n",
            "Epoch 486/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.8441 - accuracy: 0.6589 - val_loss: 0.7935 - val_accuracy: 0.7497\n",
            "Epoch 487/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 0.8152 - accuracy: 0.6900 - val_loss: 0.7560 - val_accuracy: 0.7655\n",
            "Epoch 488/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 0.9023 - accuracy: 0.6470 - val_loss: 0.7779 - val_accuracy: 0.7581\n",
            "Epoch 489/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.8344 - accuracy: 0.6744 - val_loss: 0.8540 - val_accuracy: 0.7277\n",
            "Epoch 490/500\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.8375 - accuracy: 0.6734 - val_loss: 0.8745 - val_accuracy: 0.7161\n",
            "Epoch 491/500\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.8472 - accuracy: 0.6807 - val_loss: 0.9050 - val_accuracy: 0.7077\n",
            "Epoch 492/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 0.8789 - accuracy: 0.6558 - val_loss: 0.8462 - val_accuracy: 0.7161\n",
            "Epoch 493/500\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 0.8833 - accuracy: 0.6615 - val_loss: 0.7936 - val_accuracy: 0.7687\n",
            "Epoch 494/500\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 0.8291 - accuracy: 0.6744 - val_loss: 0.8245 - val_accuracy: 0.7350\n",
            "Epoch 495/500\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 0.8579 - accuracy: 0.6781 - val_loss: 0.7544 - val_accuracy: 0.7655\n",
            "Epoch 496/500\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 0.8302 - accuracy: 0.6801 - val_loss: 0.7819 - val_accuracy: 0.7708\n",
            "Epoch 497/500\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.8361 - accuracy: 0.6853 - val_loss: 0.7861 - val_accuracy: 0.7508\n",
            "Epoch 498/500\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 0.8323 - accuracy: 0.6874 - val_loss: 0.7850 - val_accuracy: 0.7624\n",
            "Epoch 499/500\n",
            "1929/1929 [==============================] - 1s 466us/step - loss: 0.8296 - accuracy: 0.6905 - val_loss: 0.8403 - val_accuracy: 0.7329\n",
            "Epoch 500/500\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.8538 - accuracy: 0.6890 - val_loss: 0.8190 - val_accuracy: 0.7371\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HifwPypaNv7o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "a92fdccb-88d5-416e-e8b4-7cc7a7fb5c35"
      },
      "source": [
        "score = model.evaluate(x_testcnn, y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.8190055634947856\n",
            "Test accuracy: 0.7371188402175903\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkNvlKjWN2x0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "outputId": "c46d04a0-d941-4241-dfaf-8a6839912956"
      },
      "source": [
        "predictions = model.predict_classes(x_testcnn)\n",
        "predictions\n",
        "y_test\n",
        "new_Ytest = y_test.astype(int)\n",
        "new_Ytest"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 5, 3, 3, 6, 7, 5, 5, 7, 3, 1, 3, 1, 2, 5, 4, 4, 3, 3, 3, 1, 7,\n",
              "       2, 2, 2, 1, 1, 3, 4, 2, 0, 2, 1, 1, 7, 5, 1, 7, 7, 5, 4, 0, 4, 6,\n",
              "       7, 2, 5, 3, 3, 5, 5, 2, 4, 4, 6, 5, 7, 2, 7, 3, 4, 6, 5, 6, 3, 6,\n",
              "       5, 5, 7, 5, 4, 6, 1, 3, 6, 1, 3, 1, 6, 1, 2, 4, 1, 6, 4, 5, 3, 5,\n",
              "       0, 6, 4, 4, 1, 4, 1, 5, 4, 4, 6, 2, 3, 4, 2, 6, 2, 5, 2, 1, 5, 3,\n",
              "       4, 4, 5, 1, 3, 6, 1, 7, 5, 7, 1, 0, 6, 1, 4, 3, 0, 2, 5, 3, 2, 6,\n",
              "       2, 2, 6, 5, 3, 1, 0, 5, 1, 6, 5, 5, 5, 3, 7, 1, 1, 4, 0, 6, 5, 6,\n",
              "       6, 1, 6, 1, 4, 1, 6, 2, 6, 6, 1, 5, 1, 6, 2, 3, 3, 2, 5, 5, 6, 5,\n",
              "       1, 4, 7, 1, 3, 6, 3, 3, 0, 0, 5, 2, 7, 2, 4, 3, 7, 2, 4, 3, 1, 6,\n",
              "       3, 2, 1, 4, 6, 2, 7, 0, 5, 2, 4, 7, 0, 3, 2, 5, 3, 5, 3, 1, 4, 7,\n",
              "       7, 7, 3, 4, 2, 3, 5, 6, 5, 1, 7, 1, 4, 1, 7, 4, 6, 7, 3, 0, 2, 6,\n",
              "       5, 4, 7, 7, 7, 1, 2, 7, 5, 1, 1, 1, 6, 0, 0, 6, 6, 4, 3, 7, 5, 7,\n",
              "       4, 3, 7, 7, 7, 5, 7, 2, 2, 2, 6, 3, 3, 6, 3, 2, 3, 1, 2, 3, 4, 0,\n",
              "       3, 4, 4, 2, 2, 0, 3, 2, 2, 2, 0, 5, 1, 4, 0, 6, 4, 7, 7, 3, 1, 0,\n",
              "       1, 2, 3, 7, 4, 5, 5, 6, 5, 2, 4, 4, 7, 4, 5, 1, 3, 2, 3, 0, 3, 4,\n",
              "       5, 7, 1, 6, 3, 7, 4, 2, 4, 3, 5, 5, 3, 5, 7, 7, 1, 1, 7, 1, 0, 6,\n",
              "       0, 5, 6, 1, 5, 3, 5, 5, 0, 5, 4, 2, 6, 1, 1, 1, 3, 2, 2, 4, 4, 2,\n",
              "       4, 2, 5, 3, 6, 6, 5, 2, 4, 4, 6, 7, 4, 7, 2, 1, 0, 7, 4, 0, 0, 3,\n",
              "       1, 2, 2, 6, 4, 3, 6, 7, 2, 1, 5, 7, 1, 7, 5, 1, 7, 6, 5, 0, 6, 3,\n",
              "       3, 3, 2, 0, 0, 7, 5, 7, 2, 5, 4, 6, 5, 5, 2, 5, 5, 3, 6, 4, 5, 4,\n",
              "       0, 1, 6, 5, 7, 2, 4, 0, 4, 4, 1, 7, 1, 6, 6, 0, 4, 2, 7, 7, 6, 7,\n",
              "       4, 5, 1, 5, 6, 6, 1, 7, 1, 7, 6, 4, 3, 4, 4, 1, 4, 5, 7, 6, 2, 7,\n",
              "       2, 5, 6, 6, 1, 3, 3, 7, 5, 7, 4, 6, 5, 2, 1, 1, 5, 7, 1, 6, 6, 7,\n",
              "       4, 2, 6, 7, 4, 5, 4, 7, 4, 1, 7, 1, 5, 7, 2, 7, 4, 7, 0, 1, 3, 2,\n",
              "       6, 2, 7, 1, 4, 2, 2, 4, 0, 4, 2, 3, 4, 6, 7, 7, 3, 5, 2, 2, 5, 1,\n",
              "       1, 4, 4, 3, 5, 4, 0, 6, 5, 3, 7, 5, 0, 3, 2, 2, 4, 2, 2, 2, 3, 1,\n",
              "       5, 3, 2, 0, 2, 7, 7, 7, 1, 7, 6, 5, 6, 6, 1, 3, 7, 6, 1, 6, 2, 6,\n",
              "       5, 2, 7, 3, 4, 7, 2, 5, 1, 0, 4, 1, 5, 0, 5, 4, 2, 0, 4, 4, 3, 5,\n",
              "       3, 7, 0, 1, 6, 3, 0, 4, 4, 6, 3, 4, 0, 1, 7, 5, 3, 5, 1, 6, 6, 0,\n",
              "       4, 1, 7, 5, 7, 1, 1, 1, 4, 3, 7, 4, 7, 3, 3, 6, 3, 1, 1, 7, 6, 1,\n",
              "       1, 6, 1, 7, 4, 5, 5, 2, 0, 1, 0, 4, 7, 5, 0, 7, 1, 2, 6, 0, 1, 0,\n",
              "       4, 4, 7, 5, 5, 4, 1, 3, 5, 2, 7, 6, 6, 7, 3, 7, 3, 2, 6, 7, 3, 4,\n",
              "       7, 0, 3, 5, 7, 1, 5, 5, 4, 4, 4, 2, 1, 4, 7, 7, 6, 2, 3, 1, 4, 5,\n",
              "       7, 2, 1, 4, 0, 5, 4, 6, 3, 2, 6, 1, 1, 2, 6, 1, 7, 2, 2, 2, 6, 7,\n",
              "       6, 1, 1, 6, 4, 6, 1, 4, 5, 4, 2, 0, 1, 6, 2, 2, 7, 4, 2, 7, 1, 6,\n",
              "       2, 6, 5, 7, 2, 5, 3, 0, 4, 4, 7, 5, 4, 3, 1, 4, 3, 7, 2, 1, 1, 7,\n",
              "       1, 0, 6, 6, 5, 1, 3, 2, 1, 1, 7, 4, 5, 4, 2, 5, 0, 5, 6, 2, 1, 2,\n",
              "       7, 3, 3, 5, 2, 0, 7, 7, 3, 0, 1, 5, 3, 6, 2, 5, 2, 7, 5, 1, 0, 6,\n",
              "       0, 3, 7, 4, 0, 7, 3, 2, 4, 1, 5, 0, 4, 1, 4, 7, 4, 7, 2, 6, 6, 7,\n",
              "       5, 5, 3, 5, 4, 2, 3, 6, 6, 4, 7, 1, 2, 2, 2, 2, 1, 0, 4, 1, 0, 7,\n",
              "       6, 4, 4, 7, 6, 7, 1, 2, 4, 1, 3, 6, 7, 4, 6, 6, 3, 1, 6, 5, 3, 6,\n",
              "       1, 7, 7, 6, 6, 2, 4, 4, 4, 3, 5, 1, 4, 1, 3, 7, 6, 3, 7, 3, 7, 5,\n",
              "       1, 7, 4, 4, 5, 2, 7, 3, 2, 4, 3, 0, 5, 5, 3, 2, 1, 7, 6, 6, 6, 1,\n",
              "       3, 1, 6, 3, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLFY-Pr0N7R9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "2da8f858-11d6-413d-9546-b6ba449f652a"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "report = classification_report(new_Ytest, predictions)\n",
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      0.59      0.60        64\n",
            "           1       0.77      0.84      0.80       135\n",
            "           2       0.59      0.79      0.67       122\n",
            "           3       0.68      0.68      0.68       115\n",
            "           4       0.93      0.71      0.81       134\n",
            "           5       0.73      0.74      0.74       126\n",
            "           6       0.83      0.70      0.76       121\n",
            "           7       0.79      0.76      0.78       134\n",
            "\n",
            "    accuracy                           0.74       951\n",
            "   macro avg       0.74      0.73      0.73       951\n",
            "weighted avg       0.75      0.74      0.74       951\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yx3RK1lGGfvO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "495c4331-e358-4754-d3b5-a78af20347fa"
      },
      "source": [
        "!pip install pydub\n",
        "from scipy.io import wavfile\n",
        "import wave\n",
        "import struct\n",
        "from struct import *\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install keras==2.1.5\n",
        "from keras.layers import Input, Dense\n",
        "from keras.models import Model\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pydub in /usr/local/lib/python3.6/dist-packages (0.23.1)\n",
            "Requirement already satisfied: keras==2.1.5 in /usr/local/lib/python3.6/dist-packages (2.1.5)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras==2.1.5) (1.4.1)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras==2.1.5) (3.13)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras==2.1.5) (1.18.3)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras==2.1.5) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcjZGkDOGv9R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "988fccc0-469c-483b-bfc0-1dff28334ea3"
      },
      "source": [
        "x_train= MinMaxScaler().fit_transform(X)\n",
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2880, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnNpfRvEG63t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "80dee14f-b046-426a-e29b-a64c44c7d4c2"
      },
      "source": [
        "encoding_dim = 64\n",
        "input_img = Input(shape=(128,))\n",
        "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
        "\n",
        "decoded = Dense(128, activation='sigmoid')(encoded)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "encoder = Model(input=input_img, output=encoded)\n",
        "encoded_input = Input(shape=(encoding_dim,))\n",
        "\n",
        "decoder_layer = autoencoder.layers[-1]\n",
        "\n",
        "decoder = Model(input=encoded_input, output=decoder_layer(encoded_input))\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
        "\n",
        "\n",
        "filepath=\"audio.compress-{loss:.4f}.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='binary_crossentropy', verbose=1, save_best_only=False)\n",
        "autoencoder.summary()\n",
        "autoencoder.fit(x_train, x_train,\n",
        "                epochs=100,\n",
        "                batch_size=256,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_train, x_train),callbacks=[checkpoint],verbose=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 128)               8320      \n",
            "=================================================================\n",
            "Total params: 16,576\n",
            "Trainable params: 16,576\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "\n",
            "Epoch 00001: saving model to audio.compress-0.6970.hdf5\n",
            "\n",
            "Epoch 00002: saving model to audio.compress-0.6890.hdf5\n",
            "\n",
            "Epoch 00003: saving model to audio.compress-0.6860.hdf5\n",
            "\n",
            "Epoch 00004: saving model to audio.compress-0.6836.hdf5\n",
            "\n",
            "Epoch 00005: saving model to audio.compress-0.6814.hdf5\n",
            "\n",
            "Epoch 00006: saving model to audio.compress-0.6797.hdf5\n",
            "\n",
            "Epoch 00007: saving model to audio.compress-0.6784.hdf5\n",
            "\n",
            "Epoch 00008: saving model to audio.compress-0.6774.hdf5\n",
            "\n",
            "Epoch 00009: saving model to audio.compress-0.6767.hdf5\n",
            "\n",
            "Epoch 00010: saving model to audio.compress-0.6763.hdf5\n",
            "\n",
            "Epoch 00011: saving model to audio.compress-0.6760.hdf5\n",
            "\n",
            "Epoch 00012: saving model to audio.compress-0.6758.hdf5\n",
            "\n",
            "Epoch 00013: saving model to audio.compress-0.6756.hdf5\n",
            "\n",
            "Epoch 00014: saving model to audio.compress-0.6755.hdf5\n",
            "\n",
            "Epoch 00015: saving model to audio.compress-0.6754.hdf5\n",
            "\n",
            "Epoch 00016: saving model to audio.compress-0.6753.hdf5\n",
            "\n",
            "Epoch 00017: saving model to audio.compress-0.6752.hdf5\n",
            "\n",
            "Epoch 00018: saving model to audio.compress-0.6751.hdf5\n",
            "\n",
            "Epoch 00019: saving model to audio.compress-0.6750.hdf5\n",
            "\n",
            "Epoch 00020: saving model to audio.compress-0.6749.hdf5\n",
            "\n",
            "Epoch 00021: saving model to audio.compress-0.6749.hdf5\n",
            "\n",
            "Epoch 00022: saving model to audio.compress-0.6748.hdf5\n",
            "\n",
            "Epoch 00023: saving model to audio.compress-0.6747.hdf5\n",
            "\n",
            "Epoch 00024: saving model to audio.compress-0.6746.hdf5\n",
            "\n",
            "Epoch 00025: saving model to audio.compress-0.6745.hdf5\n",
            "\n",
            "Epoch 00026: saving model to audio.compress-0.6744.hdf5\n",
            "\n",
            "Epoch 00027: saving model to audio.compress-0.6743.hdf5\n",
            "\n",
            "Epoch 00028: saving model to audio.compress-0.6742.hdf5\n",
            "\n",
            "Epoch 00029: saving model to audio.compress-0.6741.hdf5\n",
            "\n",
            "Epoch 00030: saving model to audio.compress-0.6741.hdf5\n",
            "\n",
            "Epoch 00031: saving model to audio.compress-0.6740.hdf5\n",
            "\n",
            "Epoch 00032: saving model to audio.compress-0.6739.hdf5\n",
            "\n",
            "Epoch 00033: saving model to audio.compress-0.6738.hdf5\n",
            "\n",
            "Epoch 00034: saving model to audio.compress-0.6737.hdf5\n",
            "\n",
            "Epoch 00035: saving model to audio.compress-0.6736.hdf5\n",
            "\n",
            "Epoch 00036: saving model to audio.compress-0.6735.hdf5\n",
            "\n",
            "Epoch 00037: saving model to audio.compress-0.6734.hdf5\n",
            "\n",
            "Epoch 00038: saving model to audio.compress-0.6733.hdf5\n",
            "\n",
            "Epoch 00039: saving model to audio.compress-0.6732.hdf5\n",
            "\n",
            "Epoch 00040: saving model to audio.compress-0.6731.hdf5\n",
            "\n",
            "Epoch 00041: saving model to audio.compress-0.6730.hdf5\n",
            "\n",
            "Epoch 00042: saving model to audio.compress-0.6729.hdf5\n",
            "\n",
            "Epoch 00043: saving model to audio.compress-0.6728.hdf5\n",
            "\n",
            "Epoch 00044: saving model to audio.compress-0.6727.hdf5\n",
            "\n",
            "Epoch 00045: saving model to audio.compress-0.6726.hdf5\n",
            "\n",
            "Epoch 00046: saving model to audio.compress-0.6725.hdf5\n",
            "\n",
            "Epoch 00047: saving model to audio.compress-0.6723.hdf5\n",
            "\n",
            "Epoch 00048: saving model to audio.compress-0.6722.hdf5\n",
            "\n",
            "Epoch 00049: saving model to audio.compress-0.6721.hdf5\n",
            "\n",
            "Epoch 00050: saving model to audio.compress-0.6720.hdf5\n",
            "\n",
            "Epoch 00051: saving model to audio.compress-0.6719.hdf5\n",
            "\n",
            "Epoch 00052: saving model to audio.compress-0.6718.hdf5\n",
            "\n",
            "Epoch 00053: saving model to audio.compress-0.6717.hdf5\n",
            "\n",
            "Epoch 00054: saving model to audio.compress-0.6716.hdf5\n",
            "\n",
            "Epoch 00055: saving model to audio.compress-0.6714.hdf5\n",
            "\n",
            "Epoch 00056: saving model to audio.compress-0.6713.hdf5\n",
            "\n",
            "Epoch 00057: saving model to audio.compress-0.6712.hdf5\n",
            "\n",
            "Epoch 00058: saving model to audio.compress-0.6711.hdf5\n",
            "\n",
            "Epoch 00059: saving model to audio.compress-0.6710.hdf5\n",
            "\n",
            "Epoch 00060: saving model to audio.compress-0.6708.hdf5\n",
            "\n",
            "Epoch 00061: saving model to audio.compress-0.6707.hdf5\n",
            "\n",
            "Epoch 00062: saving model to audio.compress-0.6706.hdf5\n",
            "\n",
            "Epoch 00063: saving model to audio.compress-0.6705.hdf5\n",
            "\n",
            "Epoch 00064: saving model to audio.compress-0.6704.hdf5\n",
            "\n",
            "Epoch 00065: saving model to audio.compress-0.6702.hdf5\n",
            "\n",
            "Epoch 00066: saving model to audio.compress-0.6701.hdf5\n",
            "\n",
            "Epoch 00067: saving model to audio.compress-0.6700.hdf5\n",
            "\n",
            "Epoch 00068: saving model to audio.compress-0.6699.hdf5\n",
            "\n",
            "Epoch 00069: saving model to audio.compress-0.6697.hdf5\n",
            "\n",
            "Epoch 00070: saving model to audio.compress-0.6696.hdf5\n",
            "\n",
            "Epoch 00071: saving model to audio.compress-0.6695.hdf5\n",
            "\n",
            "Epoch 00072: saving model to audio.compress-0.6694.hdf5\n",
            "\n",
            "Epoch 00073: saving model to audio.compress-0.6692.hdf5\n",
            "\n",
            "Epoch 00074: saving model to audio.compress-0.6691.hdf5\n",
            "\n",
            "Epoch 00075: saving model to audio.compress-0.6690.hdf5\n",
            "\n",
            "Epoch 00076: saving model to audio.compress-0.6689.hdf5\n",
            "\n",
            "Epoch 00077: saving model to audio.compress-0.6688.hdf5\n",
            "\n",
            "Epoch 00078: saving model to audio.compress-0.6686.hdf5\n",
            "\n",
            "Epoch 00079: saving model to audio.compress-0.6685.hdf5\n",
            "\n",
            "Epoch 00080: saving model to audio.compress-0.6684.hdf5\n",
            "\n",
            "Epoch 00081: saving model to audio.compress-0.6683.hdf5\n",
            "\n",
            "Epoch 00082: saving model to audio.compress-0.6681.hdf5\n",
            "\n",
            "Epoch 00083: saving model to audio.compress-0.6680.hdf5\n",
            "\n",
            "Epoch 00084: saving model to audio.compress-0.6679.hdf5\n",
            "\n",
            "Epoch 00085: saving model to audio.compress-0.6678.hdf5\n",
            "\n",
            "Epoch 00086: saving model to audio.compress-0.6677.hdf5\n",
            "\n",
            "Epoch 00087: saving model to audio.compress-0.6675.hdf5\n",
            "\n",
            "Epoch 00088: saving model to audio.compress-0.6674.hdf5\n",
            "\n",
            "Epoch 00089: saving model to audio.compress-0.6673.hdf5\n",
            "\n",
            "Epoch 00090: saving model to audio.compress-0.6672.hdf5\n",
            "\n",
            "Epoch 00091: saving model to audio.compress-0.6671.hdf5\n",
            "\n",
            "Epoch 00092: saving model to audio.compress-0.6670.hdf5\n",
            "\n",
            "Epoch 00093: saving model to audio.compress-0.6669.hdf5\n",
            "\n",
            "Epoch 00094: saving model to audio.compress-0.6667.hdf5\n",
            "\n",
            "Epoch 00095: saving model to audio.compress-0.6666.hdf5\n",
            "\n",
            "Epoch 00096: saving model to audio.compress-0.6665.hdf5\n",
            "\n",
            "Epoch 00097: saving model to audio.compress-0.6664.hdf5\n",
            "\n",
            "Epoch 00098: saving model to audio.compress-0.6663.hdf5\n",
            "\n",
            "Epoch 00099: saving model to audio.compress-0.6662.hdf5\n",
            "\n",
            "Epoch 00100: saving model to audio.compress-0.6661.hdf5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7ffb17979b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktKts8DGHI9n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "97406c5c-6163-446c-fe63-cae61d41b721"
      },
      "source": [
        "filename = \"audio.compress-0.6661.hdf5\"\n",
        "autoencoder.load_weights(filename)\n",
        "autoencoder.compile(loss='mean_squared_error', optimizer='adam')\n",
        "encoded_imgs = encoder.predict(x_train)\n",
        "decoded_imgs = decoder.predict(encoded_imgs)\n",
        "\n",
        "n = 1  # how many files we will display\n",
        "plt.figure(figsize=(10, 5))\n",
        "for i in range(n):\n",
        "    # display original\n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.imshow(x_train[i].reshape(16,8 ))\n",
        "    plt.title(\"ORIGINAL AUDIO\")\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(8, .5))\n",
        "for i in range(n):\n",
        "    ax = plt.subplot(1, n, i+1)\n",
        "    plt.imshow(encoded_imgs.reshape(512,360).T)\n",
        "    plt.title('ENCODED AUDIO')    \n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "for i in range(n):\n",
        "    # display reconstruction\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\n",
        "    plt.imshow(decoded_imgs[i].reshape(16,8 ))\n",
        "    plt.title(\"RECONSTRUCTED AUDIO\")\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()\n",
        "\n",
        "print(\"Accuracy=\",1-np.mean(abs(x_train-decoded_imgs)),'\\n')\n",
        "\n",
        "plt.figure(figsize=(10, 2))\n",
        "plt.plot(x_train[0],color='r',linewidth=3)\n",
        "plt.plot(decoded_imgs[0])\n",
        "plt.title('ORIGINAL (blue) and RECONSTRUCTED AUDIO (red)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x36 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy= 0.9058661228908634 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klUnBTvsILCv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f0996131-e3a8-4794-c630-5e12da214729"
      },
      "source": [
        "decoded_imgs.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2880, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BolZbxP4IQKn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_encoded, X_test_encoded, y_train_encoded, y_test_encoded = train_test_split(decoded_imgs, y, test_size=0.33, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhysXopfIU8Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "83de8f52-f266-4b08-8c02-5d7f47829162"
      },
      "source": [
        "# Building the SVM model \n",
        "from sklearn.svm import SVC \n",
        "from sklearn.metrics import accuracy_score\n",
        "svmclf = SVC() \n",
        "svmclf.fit(X_train_encoded, y_train_encoded) \n",
        "  \n",
        "# Storing the predictions of the non-linear model \n",
        "y_pred_svmclf = svmclf.predict(X_test_encoded) \n",
        "  \n",
        "# Evaluating the performance of the non-linear model \n",
        "print('Accuracy : '+str(accuracy_score(y_test_encoded, y_pred_svmclf))) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.40168243953732913\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1b9U0St6Iefk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "68a3ab12-8171-4724-e283-34c2d0392e99"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dtree = DecisionTreeClassifier()\n",
        "\n",
        "dtree.fit(X_train_encoded, y_train_encoded)\n",
        "\n",
        "predictions = dtree.predict(X_test_encoded)\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(classification_report(y_test_encoded,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.78      0.78        64\n",
            "           1       0.83      0.75      0.79       135\n",
            "           2       0.83      0.80      0.82       122\n",
            "           3       0.67      0.72      0.70       115\n",
            "           4       0.77      0.73      0.75       134\n",
            "           5       0.72      0.83      0.77       126\n",
            "           6       0.77      0.70      0.73       121\n",
            "           7       0.72      0.76      0.74       134\n",
            "\n",
            "    accuracy                           0.76       951\n",
            "   macro avg       0.76      0.76      0.76       951\n",
            "weighted avg       0.76      0.76      0.76       951\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FM_JJ8HiI-B0",
        "colab_type": "text"
      },
      "source": [
        "CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgYEqi8GI8Rd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b10b3b0f-d3f3-44ea-cd50-bd7f34276069"
      },
      "source": [
        "x_traincnn_encoded = np.expand_dims(X_train_encoded, axis=2)\n",
        "x_testcnn_encoded = np.expand_dims(X_test_encoded, axis=2)\n",
        "x_traincnn_encoded.shape, x_testcnn_encoded.shape, y_train_encoded.shape, y_test_encoded.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1929, 128, 1), (951, 128, 1), (1929,), (951,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsbTftTPJE-4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "9203dbac-2bd3-4a6a-b0a6-7c4cc2daa54f"
      },
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from keras.preprocessing import sequence\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Embedding\n",
        "from keras.utils import to_categorical\n",
        "from keras.layers import Input, Flatten, Dropout, Activation\n",
        "from keras.layers import Conv1D, MaxPooling1D\n",
        "from keras.models import Model\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv1D(32, 5,padding='same',\n",
        "                 input_shape=(128,1)))\n",
        "model.add(Conv1D(64, 5,padding='same', activation='relu'))\n",
        "#model.add(Activation('relu'))\n",
        "model.add(MaxPooling1D(pool_size=(8)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(8, activation='softmax'))\n",
        "#opt = keras.optimizers.rmsprop(lr=0.00005, rho=0.9, epsilon=None, decay=0.0)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_11 (Conv1D)           (None, 128, 32)           192       \n",
            "_________________________________________________________________\n",
            "conv1d_12 (Conv1D)           (None, 128, 64)           10304     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_6 (MaxPooling1 (None, 16, 64)            0         \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 16, 64)            0         \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 128)               131200    \n",
            "_________________________________________________________________\n",
            "dropout_12 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 8)                 1032      \n",
            "=================================================================\n",
            "Total params: 142,728\n",
            "Trainable params: 142,728\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyPRVZz9JLjZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d114594f-b3b6-461e-852d-a48a802ea378"
      },
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=keras.optimizers.Adadelta(),\n",
        "              metrics=['accuracy'])\n",
        "cnnhistory_encoded=model.fit(x_traincnn_encoded, y_train_encoded, batch_size=128, epochs=1000, validation_data=(x_testcnn_encoded, y_test_encoded))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1929 samples, validate on 951 samples\n",
            "Epoch 1/1000\n",
            "1929/1929 [==============================] - 1s 643us/step - loss: 2.0721 - accuracy: 0.1369 - val_loss: 2.0612 - val_accuracy: 0.1504\n",
            "Epoch 2/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 2.0681 - accuracy: 0.1426 - val_loss: 2.0585 - val_accuracy: 0.1272\n",
            "Epoch 3/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 2.0640 - accuracy: 0.1467 - val_loss: 2.0588 - val_accuracy: 0.1272\n",
            "Epoch 4/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 2.0626 - accuracy: 0.1322 - val_loss: 2.0621 - val_accuracy: 0.1651\n",
            "Epoch 5/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 2.0628 - accuracy: 0.1395 - val_loss: 2.0535 - val_accuracy: 0.1535\n",
            "Epoch 6/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 2.0568 - accuracy: 0.1457 - val_loss: 2.0549 - val_accuracy: 0.1325\n",
            "Epoch 7/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 2.0579 - accuracy: 0.1545 - val_loss: 2.0519 - val_accuracy: 0.1735\n",
            "Epoch 8/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 2.0509 - accuracy: 0.1529 - val_loss: 2.0523 - val_accuracy: 0.1630\n",
            "Epoch 9/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 2.0483 - accuracy: 0.1643 - val_loss: 2.0373 - val_accuracy: 0.1703\n",
            "Epoch 10/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 2.0351 - accuracy: 0.1882 - val_loss: 2.0423 - val_accuracy: 0.1451\n",
            "Epoch 11/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 2.0379 - accuracy: 0.1643 - val_loss: 2.0206 - val_accuracy: 0.1819\n",
            "Epoch 12/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 2.0135 - accuracy: 0.1996 - val_loss: 2.0169 - val_accuracy: 0.1903\n",
            "Epoch 13/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 2.0135 - accuracy: 0.1954 - val_loss: 2.0139 - val_accuracy: 0.1924\n",
            "Epoch 14/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 1.9939 - accuracy: 0.1861 - val_loss: 2.0192 - val_accuracy: 0.1987\n",
            "Epoch 15/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.9956 - accuracy: 0.1934 - val_loss: 1.9985 - val_accuracy: 0.2166\n",
            "Epoch 16/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.9882 - accuracy: 0.1996 - val_loss: 1.9796 - val_accuracy: 0.1945\n",
            "Epoch 17/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 1.9726 - accuracy: 0.2053 - val_loss: 2.0431 - val_accuracy: 0.1725\n",
            "Epoch 18/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.9740 - accuracy: 0.1882 - val_loss: 2.0042 - val_accuracy: 0.2114\n",
            "Epoch 19/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.9703 - accuracy: 0.1975 - val_loss: 1.9664 - val_accuracy: 0.1840\n",
            "Epoch 20/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.9608 - accuracy: 0.1949 - val_loss: 1.9557 - val_accuracy: 0.2008\n",
            "Epoch 21/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.9543 - accuracy: 0.2084 - val_loss: 2.0106 - val_accuracy: 0.1945\n",
            "Epoch 22/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.9503 - accuracy: 0.2089 - val_loss: 1.9502 - val_accuracy: 0.2124\n",
            "Epoch 23/1000\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.9341 - accuracy: 0.2234 - val_loss: 1.9346 - val_accuracy: 0.1809\n",
            "Epoch 24/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.9342 - accuracy: 0.2188 - val_loss: 1.9636 - val_accuracy: 0.2082\n",
            "Epoch 25/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.9321 - accuracy: 0.2229 - val_loss: 1.9255 - val_accuracy: 0.2208\n",
            "Epoch 26/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.9299 - accuracy: 0.2094 - val_loss: 1.9868 - val_accuracy: 0.1998\n",
            "Epoch 27/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.9275 - accuracy: 0.2157 - val_loss: 1.9684 - val_accuracy: 0.1882\n",
            "Epoch 28/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.9116 - accuracy: 0.2297 - val_loss: 1.9172 - val_accuracy: 0.2408\n",
            "Epoch 29/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 1.9142 - accuracy: 0.2333 - val_loss: 1.9232 - val_accuracy: 0.2198\n",
            "Epoch 30/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.9096 - accuracy: 0.2276 - val_loss: 1.9057 - val_accuracy: 0.2093\n",
            "Epoch 31/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.8942 - accuracy: 0.2286 - val_loss: 1.9528 - val_accuracy: 0.2124\n",
            "Epoch 32/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.9113 - accuracy: 0.2271 - val_loss: 1.9012 - val_accuracy: 0.2429\n",
            "Epoch 33/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.8975 - accuracy: 0.2390 - val_loss: 1.9248 - val_accuracy: 0.2313\n",
            "Epoch 34/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.8929 - accuracy: 0.2302 - val_loss: 1.9556 - val_accuracy: 0.2240\n",
            "Epoch 35/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.8816 - accuracy: 0.2483 - val_loss: 1.9052 - val_accuracy: 0.2240\n",
            "Epoch 36/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.8848 - accuracy: 0.2379 - val_loss: 1.8754 - val_accuracy: 0.2881\n",
            "Epoch 37/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.8710 - accuracy: 0.2519 - val_loss: 1.8757 - val_accuracy: 0.2482\n",
            "Epoch 38/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.8761 - accuracy: 0.2405 - val_loss: 1.8681 - val_accuracy: 0.2860\n",
            "Epoch 39/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.8681 - accuracy: 0.2576 - val_loss: 1.8704 - val_accuracy: 0.2797\n",
            "Epoch 40/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.8688 - accuracy: 0.2509 - val_loss: 1.8770 - val_accuracy: 0.2082\n",
            "Epoch 41/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.8633 - accuracy: 0.2442 - val_loss: 1.9004 - val_accuracy: 0.2324\n",
            "Epoch 42/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.8711 - accuracy: 0.2551 - val_loss: 1.8512 - val_accuracy: 0.2660\n",
            "Epoch 43/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.8536 - accuracy: 0.2488 - val_loss: 1.8700 - val_accuracy: 0.2576\n",
            "Epoch 44/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.8526 - accuracy: 0.2545 - val_loss: 1.8674 - val_accuracy: 0.2450\n",
            "Epoch 45/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.8302 - accuracy: 0.2623 - val_loss: 1.8459 - val_accuracy: 0.2177\n",
            "Epoch 46/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.8333 - accuracy: 0.2727 - val_loss: 1.8696 - val_accuracy: 0.2271\n",
            "Epoch 47/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.8377 - accuracy: 0.2716 - val_loss: 1.8340 - val_accuracy: 0.2860\n",
            "Epoch 48/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.8301 - accuracy: 0.2633 - val_loss: 1.8318 - val_accuracy: 0.2660\n",
            "Epoch 49/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.8313 - accuracy: 0.2732 - val_loss: 1.8511 - val_accuracy: 0.2681\n",
            "Epoch 50/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.8292 - accuracy: 0.2680 - val_loss: 1.9279 - val_accuracy: 0.2492\n",
            "Epoch 51/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.8310 - accuracy: 0.2810 - val_loss: 1.8621 - val_accuracy: 0.3165\n",
            "Epoch 52/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.8143 - accuracy: 0.2820 - val_loss: 1.8111 - val_accuracy: 0.2734\n",
            "Epoch 53/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.8042 - accuracy: 0.2867 - val_loss: 1.8461 - val_accuracy: 0.2366\n",
            "Epoch 54/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.7973 - accuracy: 0.2976 - val_loss: 1.8193 - val_accuracy: 0.2997\n",
            "Epoch 55/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.8072 - accuracy: 0.2836 - val_loss: 1.8397 - val_accuracy: 0.2618\n",
            "Epoch 56/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 1.8084 - accuracy: 0.2799 - val_loss: 1.8130 - val_accuracy: 0.2744\n",
            "Epoch 57/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.7887 - accuracy: 0.3027 - val_loss: 1.8795 - val_accuracy: 0.2492\n",
            "Epoch 58/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 1.8102 - accuracy: 0.2877 - val_loss: 1.7879 - val_accuracy: 0.3218\n",
            "Epoch 59/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.7848 - accuracy: 0.2939 - val_loss: 1.8011 - val_accuracy: 0.3323\n",
            "Epoch 60/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.7728 - accuracy: 0.3152 - val_loss: 1.8034 - val_accuracy: 0.2829\n",
            "Epoch 61/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.7865 - accuracy: 0.2965 - val_loss: 1.8090 - val_accuracy: 0.3123\n",
            "Epoch 62/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.7723 - accuracy: 0.3173 - val_loss: 1.7788 - val_accuracy: 0.3228\n",
            "Epoch 63/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.7708 - accuracy: 0.2970 - val_loss: 1.7996 - val_accuracy: 0.3007\n",
            "Epoch 64/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.7765 - accuracy: 0.3012 - val_loss: 1.7819 - val_accuracy: 0.3270\n",
            "Epoch 65/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.7595 - accuracy: 0.3287 - val_loss: 1.8052 - val_accuracy: 0.3007\n",
            "Epoch 66/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.7565 - accuracy: 0.3193 - val_loss: 1.7586 - val_accuracy: 0.3428\n",
            "Epoch 67/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.7616 - accuracy: 0.3245 - val_loss: 1.8863 - val_accuracy: 0.2713\n",
            "Epoch 68/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.7605 - accuracy: 0.3178 - val_loss: 1.8327 - val_accuracy: 0.2744\n",
            "Epoch 69/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.7494 - accuracy: 0.3209 - val_loss: 1.7782 - val_accuracy: 0.2829\n",
            "Epoch 70/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.7440 - accuracy: 0.3199 - val_loss: 1.8382 - val_accuracy: 0.2440\n",
            "Epoch 71/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.7617 - accuracy: 0.2877 - val_loss: 1.7702 - val_accuracy: 0.3302\n",
            "Epoch 72/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.7317 - accuracy: 0.3162 - val_loss: 1.7697 - val_accuracy: 0.3197\n",
            "Epoch 73/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.7274 - accuracy: 0.3235 - val_loss: 1.7559 - val_accuracy: 0.3544\n",
            "Epoch 74/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 1.7371 - accuracy: 0.3256 - val_loss: 1.7478 - val_accuracy: 0.3502\n",
            "Epoch 75/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.7183 - accuracy: 0.3453 - val_loss: 1.7229 - val_accuracy: 0.3354\n",
            "Epoch 76/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.7401 - accuracy: 0.3364 - val_loss: 1.8154 - val_accuracy: 0.2702\n",
            "Epoch 77/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 1.7186 - accuracy: 0.3453 - val_loss: 1.7321 - val_accuracy: 0.3544\n",
            "Epoch 78/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.7204 - accuracy: 0.3302 - val_loss: 1.7534 - val_accuracy: 0.3438\n",
            "Epoch 79/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.7167 - accuracy: 0.3390 - val_loss: 1.7629 - val_accuracy: 0.3270\n",
            "Epoch 80/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 1.7137 - accuracy: 0.3313 - val_loss: 1.7442 - val_accuracy: 0.3028\n",
            "Epoch 81/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.7111 - accuracy: 0.3318 - val_loss: 1.7358 - val_accuracy: 0.3691\n",
            "Epoch 82/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.7021 - accuracy: 0.3307 - val_loss: 1.7614 - val_accuracy: 0.3039\n",
            "Epoch 83/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.6969 - accuracy: 0.3339 - val_loss: 1.7200 - val_accuracy: 0.3859\n",
            "Epoch 84/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.6918 - accuracy: 0.3375 - val_loss: 1.7517 - val_accuracy: 0.3344\n",
            "Epoch 85/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.6973 - accuracy: 0.3437 - val_loss: 1.8042 - val_accuracy: 0.2965\n",
            "Epoch 86/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.6942 - accuracy: 0.3499 - val_loss: 1.7322 - val_accuracy: 0.3628\n",
            "Epoch 87/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.6848 - accuracy: 0.3593 - val_loss: 1.7978 - val_accuracy: 0.2944\n",
            "Epoch 88/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.6872 - accuracy: 0.3520 - val_loss: 1.7133 - val_accuracy: 0.3712\n",
            "Epoch 89/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.6830 - accuracy: 0.3437 - val_loss: 1.7460 - val_accuracy: 0.3218\n",
            "Epoch 90/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.6812 - accuracy: 0.3536 - val_loss: 1.8363 - val_accuracy: 0.2839\n",
            "Epoch 91/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.6950 - accuracy: 0.3489 - val_loss: 1.7172 - val_accuracy: 0.3638\n",
            "Epoch 92/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.6627 - accuracy: 0.3541 - val_loss: 1.6985 - val_accuracy: 0.3754\n",
            "Epoch 93/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 1.6661 - accuracy: 0.3593 - val_loss: 1.8081 - val_accuracy: 0.2671\n",
            "Epoch 94/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.6741 - accuracy: 0.3510 - val_loss: 1.7678 - val_accuracy: 0.3060\n",
            "Epoch 95/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.6606 - accuracy: 0.3577 - val_loss: 1.6961 - val_accuracy: 0.3575\n",
            "Epoch 96/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.6569 - accuracy: 0.3624 - val_loss: 1.7302 - val_accuracy: 0.3386\n",
            "Epoch 97/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.6496 - accuracy: 0.3618 - val_loss: 1.6809 - val_accuracy: 0.3859\n",
            "Epoch 98/1000\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.6445 - accuracy: 0.3655 - val_loss: 1.6715 - val_accuracy: 0.3722\n",
            "Epoch 99/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.6370 - accuracy: 0.3644 - val_loss: 1.7054 - val_accuracy: 0.3460\n",
            "Epoch 100/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.6365 - accuracy: 0.3753 - val_loss: 1.7944 - val_accuracy: 0.2997\n",
            "Epoch 101/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.6543 - accuracy: 0.3577 - val_loss: 1.6785 - val_accuracy: 0.3691\n",
            "Epoch 102/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.6484 - accuracy: 0.3613 - val_loss: 1.6849 - val_accuracy: 0.3649\n",
            "Epoch 103/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.6405 - accuracy: 0.3733 - val_loss: 1.8311 - val_accuracy: 0.2965\n",
            "Epoch 104/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.6504 - accuracy: 0.3655 - val_loss: 1.7932 - val_accuracy: 0.3028\n",
            "Epoch 105/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.6235 - accuracy: 0.3764 - val_loss: 1.6594 - val_accuracy: 0.3722\n",
            "Epoch 106/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.6338 - accuracy: 0.3753 - val_loss: 1.6444 - val_accuracy: 0.3975\n",
            "Epoch 107/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.6202 - accuracy: 0.3748 - val_loss: 1.8073 - val_accuracy: 0.3081\n",
            "Epoch 108/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.6334 - accuracy: 0.3608 - val_loss: 1.6448 - val_accuracy: 0.4132\n",
            "Epoch 109/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 1.6340 - accuracy: 0.3634 - val_loss: 1.6560 - val_accuracy: 0.3796\n",
            "Epoch 110/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.6268 - accuracy: 0.3805 - val_loss: 1.6426 - val_accuracy: 0.3933\n",
            "Epoch 111/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.6043 - accuracy: 0.3810 - val_loss: 1.7353 - val_accuracy: 0.3344\n",
            "Epoch 112/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.6266 - accuracy: 0.3815 - val_loss: 1.6588 - val_accuracy: 0.3807\n",
            "Epoch 113/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.5946 - accuracy: 0.3992 - val_loss: 1.6446 - val_accuracy: 0.3891\n",
            "Epoch 114/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.5984 - accuracy: 0.3919 - val_loss: 1.6579 - val_accuracy: 0.3849\n",
            "Epoch 115/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.6205 - accuracy: 0.3670 - val_loss: 1.6335 - val_accuracy: 0.4059\n",
            "Epoch 116/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.5929 - accuracy: 0.3909 - val_loss: 1.6263 - val_accuracy: 0.4164\n",
            "Epoch 117/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.5942 - accuracy: 0.3919 - val_loss: 1.6302 - val_accuracy: 0.3901\n",
            "Epoch 118/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.5842 - accuracy: 0.3872 - val_loss: 1.6689 - val_accuracy: 0.3649\n",
            "Epoch 119/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.5927 - accuracy: 0.3836 - val_loss: 1.6611 - val_accuracy: 0.3996\n",
            "Epoch 120/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.5970 - accuracy: 0.3790 - val_loss: 1.6486 - val_accuracy: 0.4038\n",
            "Epoch 121/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.5764 - accuracy: 0.4012 - val_loss: 1.6572 - val_accuracy: 0.3670\n",
            "Epoch 122/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.5925 - accuracy: 0.3935 - val_loss: 1.7691 - val_accuracy: 0.3354\n",
            "Epoch 123/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.5801 - accuracy: 0.3867 - val_loss: 1.6984 - val_accuracy: 0.3523\n",
            "Epoch 124/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.5804 - accuracy: 0.3727 - val_loss: 1.6116 - val_accuracy: 0.4101\n",
            "Epoch 125/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.5713 - accuracy: 0.3935 - val_loss: 1.6465 - val_accuracy: 0.3659\n",
            "Epoch 126/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.5584 - accuracy: 0.3836 - val_loss: 1.6546 - val_accuracy: 0.3638\n",
            "Epoch 127/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.5624 - accuracy: 0.4002 - val_loss: 1.7109 - val_accuracy: 0.3386\n",
            "Epoch 128/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.5581 - accuracy: 0.3914 - val_loss: 1.6935 - val_accuracy: 0.3596\n",
            "Epoch 129/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.5702 - accuracy: 0.3935 - val_loss: 1.6189 - val_accuracy: 0.3996\n",
            "Epoch 130/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 1.5628 - accuracy: 0.4044 - val_loss: 1.6146 - val_accuracy: 0.4017\n",
            "Epoch 131/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.5555 - accuracy: 0.3888 - val_loss: 1.5984 - val_accuracy: 0.4132\n",
            "Epoch 132/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.5432 - accuracy: 0.4194 - val_loss: 1.6187 - val_accuracy: 0.3764\n",
            "Epoch 133/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.5554 - accuracy: 0.3981 - val_loss: 1.5689 - val_accuracy: 0.4238\n",
            "Epoch 134/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.5319 - accuracy: 0.4251 - val_loss: 1.6515 - val_accuracy: 0.3544\n",
            "Epoch 135/1000\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.5507 - accuracy: 0.4085 - val_loss: 1.5853 - val_accuracy: 0.3954\n",
            "Epoch 136/1000\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.5462 - accuracy: 0.4018 - val_loss: 1.6097 - val_accuracy: 0.4206\n",
            "Epoch 137/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.5471 - accuracy: 0.4220 - val_loss: 1.6767 - val_accuracy: 0.3565\n",
            "Epoch 138/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.5192 - accuracy: 0.4152 - val_loss: 1.6254 - val_accuracy: 0.3964\n",
            "Epoch 139/1000\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.5238 - accuracy: 0.4111 - val_loss: 1.6253 - val_accuracy: 0.3638\n",
            "Epoch 140/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.5275 - accuracy: 0.4220 - val_loss: 1.6064 - val_accuracy: 0.4217\n",
            "Epoch 141/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.5266 - accuracy: 0.3955 - val_loss: 1.5950 - val_accuracy: 0.4038\n",
            "Epoch 142/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.5215 - accuracy: 0.4168 - val_loss: 1.5652 - val_accuracy: 0.4290\n",
            "Epoch 143/1000\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.5048 - accuracy: 0.4241 - val_loss: 1.5870 - val_accuracy: 0.4006\n",
            "Epoch 144/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.5133 - accuracy: 0.4230 - val_loss: 1.5979 - val_accuracy: 0.4101\n",
            "Epoch 145/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.5159 - accuracy: 0.4121 - val_loss: 1.5690 - val_accuracy: 0.4164\n",
            "Epoch 146/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.5040 - accuracy: 0.4266 - val_loss: 1.5381 - val_accuracy: 0.4606\n",
            "Epoch 147/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.5096 - accuracy: 0.4292 - val_loss: 1.6092 - val_accuracy: 0.3975\n",
            "Epoch 148/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.4937 - accuracy: 0.4313 - val_loss: 1.5751 - val_accuracy: 0.4206\n",
            "Epoch 149/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.4987 - accuracy: 0.4365 - val_loss: 1.5624 - val_accuracy: 0.4301\n",
            "Epoch 150/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.4893 - accuracy: 0.4230 - val_loss: 1.5381 - val_accuracy: 0.4385\n",
            "Epoch 151/1000\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.5079 - accuracy: 0.4251 - val_loss: 1.6543 - val_accuracy: 0.3712\n",
            "Epoch 152/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.5032 - accuracy: 0.4391 - val_loss: 1.6223 - val_accuracy: 0.3838\n",
            "Epoch 153/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.4822 - accuracy: 0.4546 - val_loss: 1.5929 - val_accuracy: 0.4217\n",
            "Epoch 154/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.4931 - accuracy: 0.4199 - val_loss: 1.5805 - val_accuracy: 0.4111\n",
            "Epoch 155/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.4790 - accuracy: 0.4474 - val_loss: 1.5568 - val_accuracy: 0.4080\n",
            "Epoch 156/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.4878 - accuracy: 0.4303 - val_loss: 1.5325 - val_accuracy: 0.4522\n",
            "Epoch 157/1000\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.4656 - accuracy: 0.4427 - val_loss: 1.5141 - val_accuracy: 0.4364\n",
            "Epoch 158/1000\n",
            "1929/1929 [==============================] - 1s 473us/step - loss: 1.4630 - accuracy: 0.4406 - val_loss: 1.5352 - val_accuracy: 0.4564\n",
            "Epoch 159/1000\n",
            "1929/1929 [==============================] - 1s 486us/step - loss: 1.4771 - accuracy: 0.4349 - val_loss: 1.6617 - val_accuracy: 0.3670\n",
            "Epoch 160/1000\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 1.4875 - accuracy: 0.4225 - val_loss: 1.5766 - val_accuracy: 0.4111\n",
            "Epoch 161/1000\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.4570 - accuracy: 0.4505 - val_loss: 1.5206 - val_accuracy: 0.4501\n",
            "Epoch 162/1000\n",
            "1929/1929 [==============================] - 1s 475us/step - loss: 1.4795 - accuracy: 0.4272 - val_loss: 1.6193 - val_accuracy: 0.4017\n",
            "Epoch 163/1000\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.4633 - accuracy: 0.4552 - val_loss: 1.5512 - val_accuracy: 0.4290\n",
            "Epoch 164/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.4748 - accuracy: 0.4375 - val_loss: 1.5337 - val_accuracy: 0.4343\n",
            "Epoch 165/1000\n",
            "1929/1929 [==============================] - 1s 474us/step - loss: 1.4578 - accuracy: 0.4505 - val_loss: 1.5157 - val_accuracy: 0.4364\n",
            "Epoch 166/1000\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.4583 - accuracy: 0.4546 - val_loss: 1.5666 - val_accuracy: 0.4038\n",
            "Epoch 167/1000\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 1.4327 - accuracy: 0.4557 - val_loss: 1.5840 - val_accuracy: 0.3975\n",
            "Epoch 168/1000\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 1.4431 - accuracy: 0.4572 - val_loss: 1.6625 - val_accuracy: 0.3680\n",
            "Epoch 169/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.4389 - accuracy: 0.4578 - val_loss: 1.5304 - val_accuracy: 0.4175\n",
            "Epoch 170/1000\n",
            "1929/1929 [==============================] - 1s 485us/step - loss: 1.4578 - accuracy: 0.4370 - val_loss: 1.5421 - val_accuracy: 0.4154\n",
            "Epoch 171/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.4096 - accuracy: 0.4609 - val_loss: 1.5021 - val_accuracy: 0.4406\n",
            "Epoch 172/1000\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 1.4606 - accuracy: 0.4448 - val_loss: 1.4910 - val_accuracy: 0.4416\n",
            "Epoch 173/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.4329 - accuracy: 0.4520 - val_loss: 1.6686 - val_accuracy: 0.3733\n",
            "Epoch 174/1000\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 1.4450 - accuracy: 0.4603 - val_loss: 1.4832 - val_accuracy: 0.4742\n",
            "Epoch 175/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.4278 - accuracy: 0.4541 - val_loss: 1.6046 - val_accuracy: 0.3870\n",
            "Epoch 176/1000\n",
            "1929/1929 [==============================] - 1s 475us/step - loss: 1.4320 - accuracy: 0.4453 - val_loss: 1.4986 - val_accuracy: 0.4479\n",
            "Epoch 177/1000\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 1.4184 - accuracy: 0.4531 - val_loss: 1.5851 - val_accuracy: 0.3859\n",
            "Epoch 178/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.4052 - accuracy: 0.4578 - val_loss: 1.5063 - val_accuracy: 0.4406\n",
            "Epoch 179/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.3999 - accuracy: 0.4795 - val_loss: 1.4878 - val_accuracy: 0.4469\n",
            "Epoch 180/1000\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.4214 - accuracy: 0.4510 - val_loss: 1.5524 - val_accuracy: 0.4080\n",
            "Epoch 181/1000\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.3990 - accuracy: 0.4764 - val_loss: 1.4767 - val_accuracy: 0.4616\n",
            "Epoch 182/1000\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 1.4006 - accuracy: 0.4702 - val_loss: 1.5438 - val_accuracy: 0.4122\n",
            "Epoch 183/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.3981 - accuracy: 0.4738 - val_loss: 1.4546 - val_accuracy: 0.4753\n",
            "Epoch 184/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.3870 - accuracy: 0.4743 - val_loss: 1.5397 - val_accuracy: 0.4206\n",
            "Epoch 185/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.3875 - accuracy: 0.4863 - val_loss: 1.5724 - val_accuracy: 0.4154\n",
            "Epoch 186/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.3920 - accuracy: 0.4738 - val_loss: 1.5471 - val_accuracy: 0.4374\n",
            "Epoch 187/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 1.3866 - accuracy: 0.4795 - val_loss: 1.5147 - val_accuracy: 0.4479\n",
            "Epoch 188/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.3987 - accuracy: 0.4681 - val_loss: 1.5102 - val_accuracy: 0.4164\n",
            "Epoch 189/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 1.3560 - accuracy: 0.5018 - val_loss: 1.4598 - val_accuracy: 0.4606\n",
            "Epoch 190/1000\n",
            "1929/1929 [==============================] - 1s 478us/step - loss: 1.3771 - accuracy: 0.4738 - val_loss: 1.4901 - val_accuracy: 0.4301\n",
            "Epoch 191/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.3708 - accuracy: 0.4894 - val_loss: 1.5081 - val_accuracy: 0.4437\n",
            "Epoch 192/1000\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.3907 - accuracy: 0.4800 - val_loss: 1.4845 - val_accuracy: 0.4616\n",
            "Epoch 193/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.3794 - accuracy: 0.4650 - val_loss: 1.5247 - val_accuracy: 0.4385\n",
            "Epoch 194/1000\n",
            "1929/1929 [==============================] - 1s 487us/step - loss: 1.3531 - accuracy: 0.4800 - val_loss: 1.4672 - val_accuracy: 0.4616\n",
            "Epoch 195/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.3827 - accuracy: 0.4904 - val_loss: 1.4510 - val_accuracy: 0.4742\n",
            "Epoch 196/1000\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.3531 - accuracy: 0.4816 - val_loss: 1.4966 - val_accuracy: 0.4532\n",
            "Epoch 197/1000\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.3795 - accuracy: 0.4754 - val_loss: 1.4872 - val_accuracy: 0.4553\n",
            "Epoch 198/1000\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 1.3642 - accuracy: 0.4904 - val_loss: 1.4438 - val_accuracy: 0.4858\n",
            "Epoch 199/1000\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.3504 - accuracy: 0.4852 - val_loss: 1.4373 - val_accuracy: 0.4742\n",
            "Epoch 200/1000\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.3562 - accuracy: 0.4764 - val_loss: 1.4796 - val_accuracy: 0.4364\n",
            "Epoch 201/1000\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 1.3455 - accuracy: 0.4914 - val_loss: 1.4823 - val_accuracy: 0.4606\n",
            "Epoch 202/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.3244 - accuracy: 0.4925 - val_loss: 1.5087 - val_accuracy: 0.4406\n",
            "Epoch 203/1000\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.3516 - accuracy: 0.4878 - val_loss: 1.5787 - val_accuracy: 0.4048\n",
            "Epoch 204/1000\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.3286 - accuracy: 0.5039 - val_loss: 1.5076 - val_accuracy: 0.4343\n",
            "Epoch 205/1000\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.3289 - accuracy: 0.5054 - val_loss: 1.5111 - val_accuracy: 0.4458\n",
            "Epoch 206/1000\n",
            "1929/1929 [==============================] - 1s 471us/step - loss: 1.3377 - accuracy: 0.4961 - val_loss: 1.4533 - val_accuracy: 0.4627\n",
            "Epoch 207/1000\n",
            "1929/1929 [==============================] - 1s 477us/step - loss: 1.3296 - accuracy: 0.4966 - val_loss: 1.4246 - val_accuracy: 0.4721\n",
            "Epoch 208/1000\n",
            "1929/1929 [==============================] - 1s 471us/step - loss: 1.3332 - accuracy: 0.4930 - val_loss: 1.5198 - val_accuracy: 0.4227\n",
            "Epoch 209/1000\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 1.3351 - accuracy: 0.4920 - val_loss: 1.4317 - val_accuracy: 0.4826\n",
            "Epoch 210/1000\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 1.3143 - accuracy: 0.5091 - val_loss: 1.4330 - val_accuracy: 0.4511\n",
            "Epoch 211/1000\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.3247 - accuracy: 0.5018 - val_loss: 1.4358 - val_accuracy: 0.4795\n",
            "Epoch 212/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.3266 - accuracy: 0.4982 - val_loss: 1.4375 - val_accuracy: 0.4784\n",
            "Epoch 213/1000\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.2993 - accuracy: 0.5049 - val_loss: 1.5184 - val_accuracy: 0.4364\n",
            "Epoch 214/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.3216 - accuracy: 0.4982 - val_loss: 1.5439 - val_accuracy: 0.4196\n",
            "Epoch 215/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.3085 - accuracy: 0.5044 - val_loss: 1.4036 - val_accuracy: 0.4879\n",
            "Epoch 216/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.3016 - accuracy: 0.5101 - val_loss: 1.4528 - val_accuracy: 0.4616\n",
            "Epoch 217/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.3065 - accuracy: 0.5049 - val_loss: 1.4124 - val_accuracy: 0.4774\n",
            "Epoch 218/1000\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.2824 - accuracy: 0.5122 - val_loss: 1.4898 - val_accuracy: 0.4469\n",
            "Epoch 219/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.2883 - accuracy: 0.5060 - val_loss: 1.3841 - val_accuracy: 0.4774\n",
            "Epoch 220/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.2889 - accuracy: 0.5127 - val_loss: 1.4006 - val_accuracy: 0.4848\n",
            "Epoch 221/1000\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.2844 - accuracy: 0.5044 - val_loss: 1.4798 - val_accuracy: 0.4543\n",
            "Epoch 222/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.3015 - accuracy: 0.5205 - val_loss: 1.3899 - val_accuracy: 0.4921\n",
            "Epoch 223/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.2886 - accuracy: 0.5086 - val_loss: 1.3913 - val_accuracy: 0.4858\n",
            "Epoch 224/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 1.2739 - accuracy: 0.5257 - val_loss: 1.3784 - val_accuracy: 0.4869\n",
            "Epoch 225/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.2909 - accuracy: 0.5148 - val_loss: 1.3902 - val_accuracy: 0.5047\n",
            "Epoch 226/1000\n",
            "1929/1929 [==============================] - 1s 471us/step - loss: 1.2601 - accuracy: 0.5194 - val_loss: 1.3494 - val_accuracy: 0.5026\n",
            "Epoch 227/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.2769 - accuracy: 0.5236 - val_loss: 1.4216 - val_accuracy: 0.4711\n",
            "Epoch 228/1000\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 1.2945 - accuracy: 0.5106 - val_loss: 1.4302 - val_accuracy: 0.4795\n",
            "Epoch 229/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.2644 - accuracy: 0.5391 - val_loss: 1.3915 - val_accuracy: 0.4900\n",
            "Epoch 230/1000\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.2787 - accuracy: 0.5158 - val_loss: 1.3600 - val_accuracy: 0.4774\n",
            "Epoch 231/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.2695 - accuracy: 0.5365 - val_loss: 1.4906 - val_accuracy: 0.4700\n",
            "Epoch 232/1000\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 1.2622 - accuracy: 0.5210 - val_loss: 1.3823 - val_accuracy: 0.4890\n",
            "Epoch 233/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.2679 - accuracy: 0.5226 - val_loss: 1.3927 - val_accuracy: 0.4942\n",
            "Epoch 234/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 1.2553 - accuracy: 0.5308 - val_loss: 1.3547 - val_accuracy: 0.5026\n",
            "Epoch 235/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.2430 - accuracy: 0.5350 - val_loss: 1.4134 - val_accuracy: 0.4995\n",
            "Epoch 236/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.2762 - accuracy: 0.5360 - val_loss: 1.5308 - val_accuracy: 0.4437\n",
            "Epoch 237/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.2460 - accuracy: 0.5236 - val_loss: 1.3875 - val_accuracy: 0.4963\n",
            "Epoch 238/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.2583 - accuracy: 0.5277 - val_loss: 1.3525 - val_accuracy: 0.5026\n",
            "Epoch 239/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.2552 - accuracy: 0.5231 - val_loss: 1.4020 - val_accuracy: 0.4869\n",
            "Epoch 240/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.2393 - accuracy: 0.5350 - val_loss: 1.4227 - val_accuracy: 0.4564\n",
            "Epoch 241/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 1.2531 - accuracy: 0.5371 - val_loss: 1.3406 - val_accuracy: 0.5058\n",
            "Epoch 242/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.2576 - accuracy: 0.5200 - val_loss: 1.4983 - val_accuracy: 0.4269\n",
            "Epoch 243/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.2514 - accuracy: 0.5355 - val_loss: 1.3771 - val_accuracy: 0.4763\n",
            "Epoch 244/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.2476 - accuracy: 0.5288 - val_loss: 1.4493 - val_accuracy: 0.4690\n",
            "Epoch 245/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.2430 - accuracy: 0.5340 - val_loss: 1.3127 - val_accuracy: 0.5258\n",
            "Epoch 246/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 1.2186 - accuracy: 0.5324 - val_loss: 1.4668 - val_accuracy: 0.4522\n",
            "Epoch 247/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.2288 - accuracy: 0.5360 - val_loss: 1.4164 - val_accuracy: 0.4963\n",
            "Epoch 248/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.2314 - accuracy: 0.5391 - val_loss: 1.4016 - val_accuracy: 0.4711\n",
            "Epoch 249/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.2177 - accuracy: 0.5428 - val_loss: 1.3283 - val_accuracy: 0.5216\n",
            "Epoch 250/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 1.2262 - accuracy: 0.5485 - val_loss: 1.3859 - val_accuracy: 0.4858\n",
            "Epoch 251/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.2013 - accuracy: 0.5542 - val_loss: 1.3563 - val_accuracy: 0.5195\n",
            "Epoch 252/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.1953 - accuracy: 0.5500 - val_loss: 1.3896 - val_accuracy: 0.4900\n",
            "Epoch 253/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.2222 - accuracy: 0.5163 - val_loss: 1.3107 - val_accuracy: 0.5121\n",
            "Epoch 254/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.1897 - accuracy: 0.5562 - val_loss: 1.3726 - val_accuracy: 0.4932\n",
            "Epoch 255/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.2091 - accuracy: 0.5562 - val_loss: 1.3229 - val_accuracy: 0.5100\n",
            "Epoch 256/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.2267 - accuracy: 0.5360 - val_loss: 1.3222 - val_accuracy: 0.5216\n",
            "Epoch 257/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.2141 - accuracy: 0.5360 - val_loss: 1.3182 - val_accuracy: 0.5068\n",
            "Epoch 258/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.1947 - accuracy: 0.5521 - val_loss: 1.4543 - val_accuracy: 0.4553\n",
            "Epoch 259/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.2016 - accuracy: 0.5417 - val_loss: 1.3831 - val_accuracy: 0.4900\n",
            "Epoch 260/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.1750 - accuracy: 0.5599 - val_loss: 1.3462 - val_accuracy: 0.5058\n",
            "Epoch 261/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.1832 - accuracy: 0.5480 - val_loss: 1.3455 - val_accuracy: 0.5079\n",
            "Epoch 262/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.1762 - accuracy: 0.5651 - val_loss: 1.3866 - val_accuracy: 0.4606\n",
            "Epoch 263/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.1913 - accuracy: 0.5547 - val_loss: 1.3355 - val_accuracy: 0.5121\n",
            "Epoch 264/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.1755 - accuracy: 0.5630 - val_loss: 1.3445 - val_accuracy: 0.4984\n",
            "Epoch 265/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.1689 - accuracy: 0.5645 - val_loss: 1.2949 - val_accuracy: 0.5373\n",
            "Epoch 266/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.1745 - accuracy: 0.5609 - val_loss: 1.3345 - val_accuracy: 0.5163\n",
            "Epoch 267/1000\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.1553 - accuracy: 0.5692 - val_loss: 1.2747 - val_accuracy: 0.5321\n",
            "Epoch 268/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.1730 - accuracy: 0.5500 - val_loss: 1.4627 - val_accuracy: 0.4753\n",
            "Epoch 269/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.1695 - accuracy: 0.5557 - val_loss: 1.3323 - val_accuracy: 0.5279\n",
            "Epoch 270/1000\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.1689 - accuracy: 0.5630 - val_loss: 1.3527 - val_accuracy: 0.4921\n",
            "Epoch 271/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 1.1739 - accuracy: 0.5687 - val_loss: 1.3492 - val_accuracy: 0.5142\n",
            "Epoch 272/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.1692 - accuracy: 0.5687 - val_loss: 1.3472 - val_accuracy: 0.5058\n",
            "Epoch 273/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.1535 - accuracy: 0.5796 - val_loss: 1.3176 - val_accuracy: 0.5026\n",
            "Epoch 274/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.1327 - accuracy: 0.5557 - val_loss: 1.2904 - val_accuracy: 0.5363\n",
            "Epoch 275/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.1528 - accuracy: 0.5718 - val_loss: 1.3212 - val_accuracy: 0.5279\n",
            "Epoch 276/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 1.1619 - accuracy: 0.5614 - val_loss: 1.2631 - val_accuracy: 0.5521\n",
            "Epoch 277/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.1419 - accuracy: 0.5661 - val_loss: 1.3333 - val_accuracy: 0.5016\n",
            "Epoch 278/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.1506 - accuracy: 0.5666 - val_loss: 1.2957 - val_accuracy: 0.5279\n",
            "Epoch 279/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.1416 - accuracy: 0.5568 - val_loss: 1.3390 - val_accuracy: 0.4869\n",
            "Epoch 280/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 1.1220 - accuracy: 0.5780 - val_loss: 1.3003 - val_accuracy: 0.5268\n",
            "Epoch 281/1000\n",
            "1929/1929 [==============================] - 1s 628us/step - loss: 1.1315 - accuracy: 0.5848 - val_loss: 1.2785 - val_accuracy: 0.5352\n",
            "Epoch 282/1000\n",
            "1929/1929 [==============================] - 1s 521us/step - loss: 1.1354 - accuracy: 0.5837 - val_loss: 1.3765 - val_accuracy: 0.4879\n",
            "Epoch 283/1000\n",
            "1929/1929 [==============================] - 1s 476us/step - loss: 1.1503 - accuracy: 0.5599 - val_loss: 1.2868 - val_accuracy: 0.5415\n",
            "Epoch 284/1000\n",
            "1929/1929 [==============================] - 1s 473us/step - loss: 1.1085 - accuracy: 0.5853 - val_loss: 1.3185 - val_accuracy: 0.5037\n",
            "Epoch 285/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.1069 - accuracy: 0.5956 - val_loss: 1.2658 - val_accuracy: 0.5352\n",
            "Epoch 286/1000\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 1.1151 - accuracy: 0.5697 - val_loss: 1.3407 - val_accuracy: 0.5205\n",
            "Epoch 287/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 1.1054 - accuracy: 0.5915 - val_loss: 1.2626 - val_accuracy: 0.5415\n",
            "Epoch 288/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.1182 - accuracy: 0.5816 - val_loss: 1.3166 - val_accuracy: 0.5237\n",
            "Epoch 289/1000\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.0967 - accuracy: 0.5858 - val_loss: 1.2851 - val_accuracy: 0.5331\n",
            "Epoch 290/1000\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 1.1203 - accuracy: 0.5791 - val_loss: 1.2754 - val_accuracy: 0.5342\n",
            "Epoch 291/1000\n",
            "1929/1929 [==============================] - 1s 477us/step - loss: 1.1032 - accuracy: 0.6003 - val_loss: 1.2999 - val_accuracy: 0.5068\n",
            "Epoch 292/1000\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.1152 - accuracy: 0.5811 - val_loss: 1.2507 - val_accuracy: 0.5521\n",
            "Epoch 293/1000\n",
            "1929/1929 [==============================] - 1s 476us/step - loss: 1.0935 - accuracy: 0.5962 - val_loss: 1.2293 - val_accuracy: 0.5699\n",
            "Epoch 294/1000\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 1.1026 - accuracy: 0.5925 - val_loss: 1.2344 - val_accuracy: 0.5573\n",
            "Epoch 295/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 1.0859 - accuracy: 0.5863 - val_loss: 1.2854 - val_accuracy: 0.5415\n",
            "Epoch 296/1000\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 1.0707 - accuracy: 0.6153 - val_loss: 1.4691 - val_accuracy: 0.4427\n",
            "Epoch 297/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.1119 - accuracy: 0.5827 - val_loss: 1.2681 - val_accuracy: 0.5499\n",
            "Epoch 298/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 1.0858 - accuracy: 0.5936 - val_loss: 1.2504 - val_accuracy: 0.5405\n",
            "Epoch 299/1000\n",
            "1929/1929 [==============================] - 1s 468us/step - loss: 1.0996 - accuracy: 0.5884 - val_loss: 1.2044 - val_accuracy: 0.5478\n",
            "Epoch 300/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 1.0714 - accuracy: 0.6034 - val_loss: 1.2145 - val_accuracy: 0.5615\n",
            "Epoch 301/1000\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 1.0893 - accuracy: 0.5863 - val_loss: 1.2271 - val_accuracy: 0.5720\n",
            "Epoch 302/1000\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 1.0943 - accuracy: 0.6034 - val_loss: 1.4500 - val_accuracy: 0.4921\n",
            "Epoch 303/1000\n",
            "1929/1929 [==============================] - 1s 466us/step - loss: 1.0934 - accuracy: 0.5962 - val_loss: 1.2148 - val_accuracy: 0.5552\n",
            "Epoch 304/1000\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 1.0801 - accuracy: 0.6076 - val_loss: 1.1945 - val_accuracy: 0.5741\n",
            "Epoch 305/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 1.0672 - accuracy: 0.5920 - val_loss: 1.2481 - val_accuracy: 0.5552\n",
            "Epoch 306/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.0731 - accuracy: 0.5967 - val_loss: 1.2286 - val_accuracy: 0.5699\n",
            "Epoch 307/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 1.0600 - accuracy: 0.6045 - val_loss: 1.2463 - val_accuracy: 0.5510\n",
            "Epoch 308/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.0692 - accuracy: 0.6081 - val_loss: 1.3756 - val_accuracy: 0.5100\n",
            "Epoch 309/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.1043 - accuracy: 0.5801 - val_loss: 1.3212 - val_accuracy: 0.5226\n",
            "Epoch 310/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 1.0705 - accuracy: 0.6008 - val_loss: 1.2027 - val_accuracy: 0.5636\n",
            "Epoch 311/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 1.0426 - accuracy: 0.6122 - val_loss: 1.2207 - val_accuracy: 0.5615\n",
            "Epoch 312/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 1.0471 - accuracy: 0.5993 - val_loss: 1.2321 - val_accuracy: 0.5542\n",
            "Epoch 313/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.0755 - accuracy: 0.6019 - val_loss: 1.3183 - val_accuracy: 0.5279\n",
            "Epoch 314/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.0496 - accuracy: 0.6236 - val_loss: 1.2604 - val_accuracy: 0.5605\n",
            "Epoch 315/1000\n",
            "1929/1929 [==============================] - 1s 470us/step - loss: 1.0416 - accuracy: 0.6164 - val_loss: 1.2694 - val_accuracy: 0.5352\n",
            "Epoch 316/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.0627 - accuracy: 0.6102 - val_loss: 1.2508 - val_accuracy: 0.5689\n",
            "Epoch 317/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.0632 - accuracy: 0.5998 - val_loss: 1.2033 - val_accuracy: 0.5941\n",
            "Epoch 318/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.0424 - accuracy: 0.6133 - val_loss: 1.1912 - val_accuracy: 0.5752\n",
            "Epoch 319/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.0228 - accuracy: 0.6034 - val_loss: 1.2832 - val_accuracy: 0.5268\n",
            "Epoch 320/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.0418 - accuracy: 0.6200 - val_loss: 1.2406 - val_accuracy: 0.5710\n",
            "Epoch 321/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.0254 - accuracy: 0.6205 - val_loss: 1.1837 - val_accuracy: 0.5825\n",
            "Epoch 322/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 1.0686 - accuracy: 0.6174 - val_loss: 1.2575 - val_accuracy: 0.5647\n",
            "Epoch 323/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 1.0491 - accuracy: 0.6086 - val_loss: 1.2265 - val_accuracy: 0.5647\n",
            "Epoch 324/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.0408 - accuracy: 0.6076 - val_loss: 1.1615 - val_accuracy: 0.6067\n",
            "Epoch 325/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 1.0107 - accuracy: 0.6356 - val_loss: 1.1708 - val_accuracy: 0.5846\n",
            "Epoch 326/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 1.0536 - accuracy: 0.6117 - val_loss: 1.2218 - val_accuracy: 0.5521\n",
            "Epoch 327/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 1.0294 - accuracy: 0.6226 - val_loss: 1.3651 - val_accuracy: 0.5195\n",
            "Epoch 328/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.0244 - accuracy: 0.6060 - val_loss: 1.1427 - val_accuracy: 0.6162\n",
            "Epoch 329/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.0277 - accuracy: 0.6257 - val_loss: 1.1640 - val_accuracy: 0.5973\n",
            "Epoch 330/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.0024 - accuracy: 0.6226 - val_loss: 1.1511 - val_accuracy: 0.5868\n",
            "Epoch 331/1000\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 1.0188 - accuracy: 0.6231 - val_loss: 1.1764 - val_accuracy: 0.5952\n",
            "Epoch 332/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.0154 - accuracy: 0.6200 - val_loss: 1.3009 - val_accuracy: 0.5110\n",
            "Epoch 333/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 1.0544 - accuracy: 0.6081 - val_loss: 1.1343 - val_accuracy: 0.5941\n",
            "Epoch 334/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 1.0068 - accuracy: 0.6216 - val_loss: 1.1769 - val_accuracy: 0.5836\n",
            "Epoch 335/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.9977 - accuracy: 0.6330 - val_loss: 1.1861 - val_accuracy: 0.5815\n",
            "Epoch 336/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.9839 - accuracy: 0.6444 - val_loss: 1.1723 - val_accuracy: 0.5689\n",
            "Epoch 337/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.9779 - accuracy: 0.6350 - val_loss: 1.1612 - val_accuracy: 0.5720\n",
            "Epoch 338/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.0110 - accuracy: 0.6236 - val_loss: 1.1556 - val_accuracy: 0.5836\n",
            "Epoch 339/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 1.0007 - accuracy: 0.6164 - val_loss: 1.2001 - val_accuracy: 0.5668\n",
            "Epoch 340/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.9844 - accuracy: 0.6402 - val_loss: 1.2157 - val_accuracy: 0.5552\n",
            "Epoch 341/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.9753 - accuracy: 0.6361 - val_loss: 1.1445 - val_accuracy: 0.6236\n",
            "Epoch 342/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.9822 - accuracy: 0.6252 - val_loss: 1.1580 - val_accuracy: 0.5941\n",
            "Epoch 343/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 1.0014 - accuracy: 0.6278 - val_loss: 1.1718 - val_accuracy: 0.5741\n",
            "Epoch 344/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.9831 - accuracy: 0.6407 - val_loss: 1.3061 - val_accuracy: 0.5342\n",
            "Epoch 345/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 1.0000 - accuracy: 0.6387 - val_loss: 1.2109 - val_accuracy: 0.5762\n",
            "Epoch 346/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.9941 - accuracy: 0.6304 - val_loss: 1.2977 - val_accuracy: 0.5426\n",
            "Epoch 347/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 1.0079 - accuracy: 0.6257 - val_loss: 1.1909 - val_accuracy: 0.5752\n",
            "Epoch 348/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.9721 - accuracy: 0.6407 - val_loss: 1.2039 - val_accuracy: 0.5773\n",
            "Epoch 349/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.9623 - accuracy: 0.6433 - val_loss: 1.1508 - val_accuracy: 0.5762\n",
            "Epoch 350/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.9605 - accuracy: 0.6485 - val_loss: 1.1330 - val_accuracy: 0.5941\n",
            "Epoch 351/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.9850 - accuracy: 0.6459 - val_loss: 1.2283 - val_accuracy: 0.5478\n",
            "Epoch 352/1000\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 0.9723 - accuracy: 0.6335 - val_loss: 1.1232 - val_accuracy: 0.6299\n",
            "Epoch 353/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.9710 - accuracy: 0.6397 - val_loss: 1.1516 - val_accuracy: 0.5836\n",
            "Epoch 354/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.9690 - accuracy: 0.6397 - val_loss: 1.1289 - val_accuracy: 0.6099\n",
            "Epoch 355/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.9665 - accuracy: 0.6387 - val_loss: 1.1120 - val_accuracy: 0.5857\n",
            "Epoch 356/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.9691 - accuracy: 0.6371 - val_loss: 1.1861 - val_accuracy: 0.5962\n",
            "Epoch 357/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 0.9587 - accuracy: 0.6506 - val_loss: 1.0930 - val_accuracy: 0.6130\n",
            "Epoch 358/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.9546 - accuracy: 0.6490 - val_loss: 1.1744 - val_accuracy: 0.5689\n",
            "Epoch 359/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.9670 - accuracy: 0.6433 - val_loss: 1.1103 - val_accuracy: 0.6120\n",
            "Epoch 360/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.9669 - accuracy: 0.6464 - val_loss: 1.0954 - val_accuracy: 0.6257\n",
            "Epoch 361/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.9847 - accuracy: 0.6356 - val_loss: 1.2432 - val_accuracy: 0.5689\n",
            "Epoch 362/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.9266 - accuracy: 0.6672 - val_loss: 1.2683 - val_accuracy: 0.5478\n",
            "Epoch 363/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.9684 - accuracy: 0.6506 - val_loss: 1.1743 - val_accuracy: 0.5741\n",
            "Epoch 364/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 0.9355 - accuracy: 0.6537 - val_loss: 1.2271 - val_accuracy: 0.5573\n",
            "Epoch 365/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.9826 - accuracy: 0.6449 - val_loss: 1.1654 - val_accuracy: 0.6015\n",
            "Epoch 366/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.9419 - accuracy: 0.6527 - val_loss: 1.0964 - val_accuracy: 0.6383\n",
            "Epoch 367/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.9444 - accuracy: 0.6527 - val_loss: 1.1199 - val_accuracy: 0.6036\n",
            "Epoch 368/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.9194 - accuracy: 0.6625 - val_loss: 1.0918 - val_accuracy: 0.6120\n",
            "Epoch 369/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.9064 - accuracy: 0.6672 - val_loss: 1.1926 - val_accuracy: 0.5647\n",
            "Epoch 370/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.9238 - accuracy: 0.6641 - val_loss: 1.2161 - val_accuracy: 0.5689\n",
            "Epoch 371/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.9426 - accuracy: 0.6677 - val_loss: 1.0839 - val_accuracy: 0.6299\n",
            "Epoch 372/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.9338 - accuracy: 0.6568 - val_loss: 1.0810 - val_accuracy: 0.6299\n",
            "Epoch 373/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.9069 - accuracy: 0.6610 - val_loss: 1.0918 - val_accuracy: 0.6330\n",
            "Epoch 374/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.9289 - accuracy: 0.6547 - val_loss: 1.1202 - val_accuracy: 0.5983\n",
            "Epoch 375/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.9190 - accuracy: 0.6573 - val_loss: 1.0869 - val_accuracy: 0.6193\n",
            "Epoch 376/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.9076 - accuracy: 0.6739 - val_loss: 1.1021 - val_accuracy: 0.6078\n",
            "Epoch 377/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.9054 - accuracy: 0.6656 - val_loss: 1.1619 - val_accuracy: 0.6099\n",
            "Epoch 378/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.9224 - accuracy: 0.6454 - val_loss: 1.2230 - val_accuracy: 0.5668\n",
            "Epoch 379/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.9114 - accuracy: 0.6713 - val_loss: 1.3237 - val_accuracy: 0.5489\n",
            "Epoch 380/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.9613 - accuracy: 0.6402 - val_loss: 1.0430 - val_accuracy: 0.6404\n",
            "Epoch 381/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.9059 - accuracy: 0.6667 - val_loss: 1.1488 - val_accuracy: 0.5573\n",
            "Epoch 382/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.9196 - accuracy: 0.6480 - val_loss: 1.1216 - val_accuracy: 0.6151\n",
            "Epoch 383/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.9060 - accuracy: 0.6708 - val_loss: 1.0633 - val_accuracy: 0.6320\n",
            "Epoch 384/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.9114 - accuracy: 0.6677 - val_loss: 1.1160 - val_accuracy: 0.6183\n",
            "Epoch 385/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.9023 - accuracy: 0.6698 - val_loss: 1.0875 - val_accuracy: 0.6162\n",
            "Epoch 386/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.8999 - accuracy: 0.6687 - val_loss: 1.1165 - val_accuracy: 0.6151\n",
            "Epoch 387/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.9326 - accuracy: 0.6610 - val_loss: 1.1002 - val_accuracy: 0.6299\n",
            "Epoch 388/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.8769 - accuracy: 0.6770 - val_loss: 1.0617 - val_accuracy: 0.6446\n",
            "Epoch 389/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.9063 - accuracy: 0.6667 - val_loss: 1.1037 - val_accuracy: 0.6036\n",
            "Epoch 390/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.9010 - accuracy: 0.6713 - val_loss: 1.1696 - val_accuracy: 0.5889\n",
            "Epoch 391/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.8811 - accuracy: 0.6661 - val_loss: 1.0852 - val_accuracy: 0.6215\n",
            "Epoch 392/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.8857 - accuracy: 0.6734 - val_loss: 1.0583 - val_accuracy: 0.6172\n",
            "Epoch 393/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.8948 - accuracy: 0.6693 - val_loss: 1.1284 - val_accuracy: 0.6036\n",
            "Epoch 394/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.8968 - accuracy: 0.6636 - val_loss: 1.1084 - val_accuracy: 0.6046\n",
            "Epoch 395/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.8817 - accuracy: 0.6817 - val_loss: 1.0910 - val_accuracy: 0.6362\n",
            "Epoch 396/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.8860 - accuracy: 0.6765 - val_loss: 1.0396 - val_accuracy: 0.6215\n",
            "Epoch 397/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.8764 - accuracy: 0.6817 - val_loss: 1.0423 - val_accuracy: 0.6477\n",
            "Epoch 398/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.8642 - accuracy: 0.6941 - val_loss: 1.1489 - val_accuracy: 0.6036\n",
            "Epoch 399/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.8700 - accuracy: 0.6604 - val_loss: 1.1181 - val_accuracy: 0.6204\n",
            "Epoch 400/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.8771 - accuracy: 0.6817 - val_loss: 1.1833 - val_accuracy: 0.5962\n",
            "Epoch 401/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.8830 - accuracy: 0.6864 - val_loss: 1.0118 - val_accuracy: 0.6677\n",
            "Epoch 402/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.8728 - accuracy: 0.6786 - val_loss: 1.0431 - val_accuracy: 0.6467\n",
            "Epoch 403/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.8675 - accuracy: 0.6796 - val_loss: 1.0935 - val_accuracy: 0.6130\n",
            "Epoch 404/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.8794 - accuracy: 0.6636 - val_loss: 1.0751 - val_accuracy: 0.6246\n",
            "Epoch 405/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.8708 - accuracy: 0.6833 - val_loss: 1.0098 - val_accuracy: 0.6477\n",
            "Epoch 406/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.8522 - accuracy: 0.6931 - val_loss: 1.0801 - val_accuracy: 0.6046\n",
            "Epoch 407/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.8665 - accuracy: 0.6879 - val_loss: 1.0818 - val_accuracy: 0.6383\n",
            "Epoch 408/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.8353 - accuracy: 0.6801 - val_loss: 1.0595 - val_accuracy: 0.6414\n",
            "Epoch 409/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.8677 - accuracy: 0.6796 - val_loss: 1.1187 - val_accuracy: 0.6236\n",
            "Epoch 410/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.8519 - accuracy: 0.6931 - val_loss: 1.1398 - val_accuracy: 0.5889\n",
            "Epoch 411/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.8653 - accuracy: 0.6776 - val_loss: 1.1559 - val_accuracy: 0.6015\n",
            "Epoch 412/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.8581 - accuracy: 0.6879 - val_loss: 1.1164 - val_accuracy: 0.6351\n",
            "Epoch 413/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.8525 - accuracy: 0.7030 - val_loss: 1.0887 - val_accuracy: 0.6004\n",
            "Epoch 414/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.8392 - accuracy: 0.6998 - val_loss: 1.0630 - val_accuracy: 0.6151\n",
            "Epoch 415/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.8458 - accuracy: 0.6838 - val_loss: 1.0632 - val_accuracy: 0.6498\n",
            "Epoch 416/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.8602 - accuracy: 0.6801 - val_loss: 0.9899 - val_accuracy: 0.6698\n",
            "Epoch 417/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.8216 - accuracy: 0.7004 - val_loss: 1.0906 - val_accuracy: 0.6288\n",
            "Epoch 418/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.8398 - accuracy: 0.7035 - val_loss: 0.9953 - val_accuracy: 0.6604\n",
            "Epoch 419/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.8066 - accuracy: 0.6973 - val_loss: 1.0481 - val_accuracy: 0.6362\n",
            "Epoch 420/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.8488 - accuracy: 0.6833 - val_loss: 1.0728 - val_accuracy: 0.6309\n",
            "Epoch 421/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.8494 - accuracy: 0.6900 - val_loss: 1.0401 - val_accuracy: 0.6383\n",
            "Epoch 422/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.8342 - accuracy: 0.7014 - val_loss: 1.0365 - val_accuracy: 0.6393\n",
            "Epoch 423/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.8267 - accuracy: 0.6874 - val_loss: 1.1040 - val_accuracy: 0.6141\n",
            "Epoch 424/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.8526 - accuracy: 0.6921 - val_loss: 1.0076 - val_accuracy: 0.6372\n",
            "Epoch 425/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.8354 - accuracy: 0.6884 - val_loss: 0.9895 - val_accuracy: 0.6498\n",
            "Epoch 426/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.8347 - accuracy: 0.7024 - val_loss: 1.0336 - val_accuracy: 0.6635\n",
            "Epoch 427/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.8391 - accuracy: 0.6957 - val_loss: 1.0233 - val_accuracy: 0.6362\n",
            "Epoch 428/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.8363 - accuracy: 0.6900 - val_loss: 1.0096 - val_accuracy: 0.6488\n",
            "Epoch 429/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.8230 - accuracy: 0.7071 - val_loss: 1.0952 - val_accuracy: 0.6057\n",
            "Epoch 430/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.8233 - accuracy: 0.6848 - val_loss: 1.0216 - val_accuracy: 0.6635\n",
            "Epoch 431/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.8170 - accuracy: 0.7118 - val_loss: 1.0251 - val_accuracy: 0.6646\n",
            "Epoch 432/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.8247 - accuracy: 0.7024 - val_loss: 1.0230 - val_accuracy: 0.6456\n",
            "Epoch 433/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.8185 - accuracy: 0.6895 - val_loss: 1.0807 - val_accuracy: 0.6309\n",
            "Epoch 434/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.8263 - accuracy: 0.6910 - val_loss: 1.0529 - val_accuracy: 0.6067\n",
            "Epoch 435/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.8304 - accuracy: 0.6978 - val_loss: 0.9824 - val_accuracy: 0.6730\n",
            "Epoch 436/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.8007 - accuracy: 0.7092 - val_loss: 1.0377 - val_accuracy: 0.6498\n",
            "Epoch 437/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.8085 - accuracy: 0.7019 - val_loss: 0.9731 - val_accuracy: 0.6730\n",
            "Epoch 438/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.7870 - accuracy: 0.7133 - val_loss: 0.9782 - val_accuracy: 0.6845\n",
            "Epoch 439/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.7906 - accuracy: 0.7154 - val_loss: 0.9839 - val_accuracy: 0.6519\n",
            "Epoch 440/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.7655 - accuracy: 0.7118 - val_loss: 1.0364 - val_accuracy: 0.6551\n",
            "Epoch 441/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.7896 - accuracy: 0.7211 - val_loss: 1.0636 - val_accuracy: 0.6215\n",
            "Epoch 442/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.8007 - accuracy: 0.7019 - val_loss: 0.9880 - val_accuracy: 0.6540\n",
            "Epoch 443/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.7972 - accuracy: 0.7170 - val_loss: 1.0548 - val_accuracy: 0.6519\n",
            "Epoch 444/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.8207 - accuracy: 0.6895 - val_loss: 1.0124 - val_accuracy: 0.6583\n",
            "Epoch 445/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.7931 - accuracy: 0.7154 - val_loss: 1.0035 - val_accuracy: 0.6551\n",
            "Epoch 446/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.7846 - accuracy: 0.7076 - val_loss: 0.9821 - val_accuracy: 0.6709\n",
            "Epoch 447/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.8144 - accuracy: 0.6998 - val_loss: 1.0224 - val_accuracy: 0.6372\n",
            "Epoch 448/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.7956 - accuracy: 0.7107 - val_loss: 0.9609 - val_accuracy: 0.6824\n",
            "Epoch 449/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.7750 - accuracy: 0.7066 - val_loss: 0.9483 - val_accuracy: 0.6730\n",
            "Epoch 450/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.7903 - accuracy: 0.7128 - val_loss: 1.0154 - val_accuracy: 0.6583\n",
            "Epoch 451/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.8183 - accuracy: 0.6926 - val_loss: 1.0085 - val_accuracy: 0.6425\n",
            "Epoch 452/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.7791 - accuracy: 0.7055 - val_loss: 1.0720 - val_accuracy: 0.6288\n",
            "Epoch 453/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.8034 - accuracy: 0.7144 - val_loss: 1.2148 - val_accuracy: 0.5878\n",
            "Epoch 454/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.7712 - accuracy: 0.7170 - val_loss: 1.0184 - val_accuracy: 0.6519\n",
            "Epoch 455/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.7844 - accuracy: 0.7128 - val_loss: 1.0293 - val_accuracy: 0.6425\n",
            "Epoch 456/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.7867 - accuracy: 0.7097 - val_loss: 0.9565 - val_accuracy: 0.6688\n",
            "Epoch 457/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.7876 - accuracy: 0.7024 - val_loss: 0.9796 - val_accuracy: 0.6593\n",
            "Epoch 458/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.7895 - accuracy: 0.7004 - val_loss: 1.0144 - val_accuracy: 0.6456\n",
            "Epoch 459/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.7264 - accuracy: 0.7263 - val_loss: 1.1124 - val_accuracy: 0.6204\n",
            "Epoch 460/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.7751 - accuracy: 0.7175 - val_loss: 1.0214 - val_accuracy: 0.6656\n",
            "Epoch 461/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.7943 - accuracy: 0.6993 - val_loss: 1.0657 - val_accuracy: 0.6488\n",
            "Epoch 462/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.7433 - accuracy: 0.7252 - val_loss: 1.1761 - val_accuracy: 0.6025\n",
            "Epoch 463/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.7642 - accuracy: 0.7216 - val_loss: 0.9970 - val_accuracy: 0.6688\n",
            "Epoch 464/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.7581 - accuracy: 0.7263 - val_loss: 0.9687 - val_accuracy: 0.6835\n",
            "Epoch 465/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.7357 - accuracy: 0.7289 - val_loss: 1.1162 - val_accuracy: 0.6257\n",
            "Epoch 466/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.7624 - accuracy: 0.7315 - val_loss: 0.9529 - val_accuracy: 0.6540\n",
            "Epoch 467/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.7763 - accuracy: 0.7289 - val_loss: 1.1353 - val_accuracy: 0.6204\n",
            "Epoch 468/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.7585 - accuracy: 0.7299 - val_loss: 0.9662 - val_accuracy: 0.6719\n",
            "Epoch 469/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.7396 - accuracy: 0.7263 - val_loss: 1.1004 - val_accuracy: 0.6467\n",
            "Epoch 470/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.7340 - accuracy: 0.7201 - val_loss: 1.0988 - val_accuracy: 0.6351\n",
            "Epoch 471/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.7387 - accuracy: 0.7382 - val_loss: 0.9254 - val_accuracy: 0.6972\n",
            "Epoch 472/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.7246 - accuracy: 0.7320 - val_loss: 1.0696 - val_accuracy: 0.6204\n",
            "Epoch 473/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.7660 - accuracy: 0.7216 - val_loss: 0.9455 - val_accuracy: 0.6740\n",
            "Epoch 474/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.7440 - accuracy: 0.7258 - val_loss: 0.9528 - val_accuracy: 0.6751\n",
            "Epoch 475/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.7242 - accuracy: 0.7289 - val_loss: 0.9485 - val_accuracy: 0.6877\n",
            "Epoch 476/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.7357 - accuracy: 0.7372 - val_loss: 0.9154 - val_accuracy: 0.6930\n",
            "Epoch 477/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.7380 - accuracy: 0.7242 - val_loss: 1.0024 - val_accuracy: 0.6719\n",
            "Epoch 478/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.7253 - accuracy: 0.7377 - val_loss: 0.9630 - val_accuracy: 0.6730\n",
            "Epoch 479/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.7266 - accuracy: 0.7444 - val_loss: 1.0368 - val_accuracy: 0.6456\n",
            "Epoch 480/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.7453 - accuracy: 0.7278 - val_loss: 0.9565 - val_accuracy: 0.6793\n",
            "Epoch 481/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.7331 - accuracy: 0.7273 - val_loss: 1.0166 - val_accuracy: 0.6540\n",
            "Epoch 482/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.7311 - accuracy: 0.7418 - val_loss: 0.9379 - val_accuracy: 0.7108\n",
            "Epoch 483/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.7504 - accuracy: 0.7247 - val_loss: 0.9149 - val_accuracy: 0.7003\n",
            "Epoch 484/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.7406 - accuracy: 0.7315 - val_loss: 0.9077 - val_accuracy: 0.6982\n",
            "Epoch 485/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.7199 - accuracy: 0.7367 - val_loss: 0.9680 - val_accuracy: 0.6824\n",
            "Epoch 486/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.7006 - accuracy: 0.7330 - val_loss: 0.9235 - val_accuracy: 0.6824\n",
            "Epoch 487/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.7218 - accuracy: 0.7325 - val_loss: 0.9616 - val_accuracy: 0.6866\n",
            "Epoch 488/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.7304 - accuracy: 0.7392 - val_loss: 0.9213 - val_accuracy: 0.7066\n",
            "Epoch 489/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.7215 - accuracy: 0.7268 - val_loss: 0.9515 - val_accuracy: 0.6972\n",
            "Epoch 490/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.7264 - accuracy: 0.7486 - val_loss: 0.9513 - val_accuracy: 0.6709\n",
            "Epoch 491/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.7188 - accuracy: 0.7361 - val_loss: 0.9295 - val_accuracy: 0.6919\n",
            "Epoch 492/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.7113 - accuracy: 0.7346 - val_loss: 0.9201 - val_accuracy: 0.6909\n",
            "Epoch 493/1000\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.7087 - accuracy: 0.7470 - val_loss: 0.9348 - val_accuracy: 0.6793\n",
            "Epoch 494/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.7522 - accuracy: 0.7159 - val_loss: 1.0074 - val_accuracy: 0.6625\n",
            "Epoch 495/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.7194 - accuracy: 0.7486 - val_loss: 0.8904 - val_accuracy: 0.7245\n",
            "Epoch 496/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.7149 - accuracy: 0.7356 - val_loss: 0.9739 - val_accuracy: 0.6667\n",
            "Epoch 497/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.7345 - accuracy: 0.7299 - val_loss: 1.2201 - val_accuracy: 0.5804\n",
            "Epoch 498/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6978 - accuracy: 0.7527 - val_loss: 0.8960 - val_accuracy: 0.6877\n",
            "Epoch 499/1000\n",
            "1929/1929 [==============================] - 1s 451us/step - loss: 0.7007 - accuracy: 0.7413 - val_loss: 1.0613 - val_accuracy: 0.6656\n",
            "Epoch 500/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.7337 - accuracy: 0.7341 - val_loss: 0.9911 - val_accuracy: 0.6677\n",
            "Epoch 501/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.7110 - accuracy: 0.7481 - val_loss: 1.0171 - val_accuracy: 0.6688\n",
            "Epoch 502/1000\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.7000 - accuracy: 0.7486 - val_loss: 0.8704 - val_accuracy: 0.7098\n",
            "Epoch 503/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.6891 - accuracy: 0.7548 - val_loss: 0.9712 - val_accuracy: 0.6940\n",
            "Epoch 504/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.6769 - accuracy: 0.7522 - val_loss: 0.9135 - val_accuracy: 0.6919\n",
            "Epoch 505/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.7103 - accuracy: 0.7403 - val_loss: 0.9017 - val_accuracy: 0.7066\n",
            "Epoch 506/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.7013 - accuracy: 0.7470 - val_loss: 0.9533 - val_accuracy: 0.7003\n",
            "Epoch 507/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.7001 - accuracy: 0.7434 - val_loss: 0.9963 - val_accuracy: 0.6698\n",
            "Epoch 508/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.7020 - accuracy: 0.7258 - val_loss: 0.9045 - val_accuracy: 0.7150\n",
            "Epoch 509/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.6813 - accuracy: 0.7460 - val_loss: 0.9233 - val_accuracy: 0.6845\n",
            "Epoch 510/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.6873 - accuracy: 0.7491 - val_loss: 0.8944 - val_accuracy: 0.6993\n",
            "Epoch 511/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.6773 - accuracy: 0.7496 - val_loss: 0.9219 - val_accuracy: 0.7161\n",
            "Epoch 512/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.6994 - accuracy: 0.7481 - val_loss: 0.8880 - val_accuracy: 0.7213\n",
            "Epoch 513/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.6945 - accuracy: 0.7470 - val_loss: 0.8974 - val_accuracy: 0.7245\n",
            "Epoch 514/1000\n",
            "1929/1929 [==============================] - 1s 454us/step - loss: 0.6917 - accuracy: 0.7543 - val_loss: 0.9107 - val_accuracy: 0.6993\n",
            "Epoch 515/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.7113 - accuracy: 0.7382 - val_loss: 0.8810 - val_accuracy: 0.7298\n",
            "Epoch 516/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6827 - accuracy: 0.7517 - val_loss: 0.9870 - val_accuracy: 0.6751\n",
            "Epoch 517/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.7043 - accuracy: 0.7506 - val_loss: 1.2261 - val_accuracy: 0.6109\n",
            "Epoch 518/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.7171 - accuracy: 0.7377 - val_loss: 0.8585 - val_accuracy: 0.7213\n",
            "Epoch 519/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6751 - accuracy: 0.7491 - val_loss: 0.9535 - val_accuracy: 0.6877\n",
            "Epoch 520/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.6896 - accuracy: 0.7548 - val_loss: 0.9299 - val_accuracy: 0.6972\n",
            "Epoch 521/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.6913 - accuracy: 0.7491 - val_loss: 1.0067 - val_accuracy: 0.6866\n",
            "Epoch 522/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.6668 - accuracy: 0.7729 - val_loss: 1.0181 - val_accuracy: 0.6562\n",
            "Epoch 523/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.7076 - accuracy: 0.7475 - val_loss: 0.9176 - val_accuracy: 0.7014\n",
            "Epoch 524/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6705 - accuracy: 0.7517 - val_loss: 0.8873 - val_accuracy: 0.7234\n",
            "Epoch 525/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.6705 - accuracy: 0.7532 - val_loss: 0.8819 - val_accuracy: 0.7182\n",
            "Epoch 526/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.6811 - accuracy: 0.7558 - val_loss: 1.0718 - val_accuracy: 0.6519\n",
            "Epoch 527/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.6761 - accuracy: 0.7579 - val_loss: 0.9088 - val_accuracy: 0.6940\n",
            "Epoch 528/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6492 - accuracy: 0.7688 - val_loss: 0.8474 - val_accuracy: 0.7445\n",
            "Epoch 529/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.6597 - accuracy: 0.7543 - val_loss: 0.9753 - val_accuracy: 0.6887\n",
            "Epoch 530/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6541 - accuracy: 0.7714 - val_loss: 0.9236 - val_accuracy: 0.7245\n",
            "Epoch 531/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.6464 - accuracy: 0.7740 - val_loss: 0.9718 - val_accuracy: 0.7150\n",
            "Epoch 532/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.6698 - accuracy: 0.7512 - val_loss: 0.9145 - val_accuracy: 0.7171\n",
            "Epoch 533/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.6969 - accuracy: 0.7481 - val_loss: 0.8748 - val_accuracy: 0.7192\n",
            "Epoch 534/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.6675 - accuracy: 0.7506 - val_loss: 0.9933 - val_accuracy: 0.6993\n",
            "Epoch 535/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.6584 - accuracy: 0.7574 - val_loss: 0.9416 - val_accuracy: 0.6856\n",
            "Epoch 536/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.6792 - accuracy: 0.7486 - val_loss: 0.9495 - val_accuracy: 0.6898\n",
            "Epoch 537/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.6626 - accuracy: 0.7703 - val_loss: 0.8672 - val_accuracy: 0.7245\n",
            "Epoch 538/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.6628 - accuracy: 0.7605 - val_loss: 0.8943 - val_accuracy: 0.7277\n",
            "Epoch 539/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.6607 - accuracy: 0.7522 - val_loss: 0.9248 - val_accuracy: 0.7066\n",
            "Epoch 540/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.6714 - accuracy: 0.7558 - val_loss: 0.8977 - val_accuracy: 0.7056\n",
            "Epoch 541/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6445 - accuracy: 0.7579 - val_loss: 0.9146 - val_accuracy: 0.6982\n",
            "Epoch 542/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.6623 - accuracy: 0.7626 - val_loss: 0.8873 - val_accuracy: 0.7024\n",
            "Epoch 543/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6437 - accuracy: 0.7740 - val_loss: 0.9766 - val_accuracy: 0.7119\n",
            "Epoch 544/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6681 - accuracy: 0.7522 - val_loss: 1.0207 - val_accuracy: 0.6982\n",
            "Epoch 545/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.6612 - accuracy: 0.7652 - val_loss: 0.8811 - val_accuracy: 0.6972\n",
            "Epoch 546/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.6501 - accuracy: 0.7657 - val_loss: 0.8635 - val_accuracy: 0.7171\n",
            "Epoch 547/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6621 - accuracy: 0.7460 - val_loss: 0.9437 - val_accuracy: 0.7045\n",
            "Epoch 548/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.6713 - accuracy: 0.7548 - val_loss: 1.0521 - val_accuracy: 0.6625\n",
            "Epoch 549/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.6646 - accuracy: 0.7569 - val_loss: 1.0266 - val_accuracy: 0.6898\n",
            "Epoch 550/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.6776 - accuracy: 0.7538 - val_loss: 0.8893 - val_accuracy: 0.7129\n",
            "Epoch 551/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6407 - accuracy: 0.7667 - val_loss: 0.8956 - val_accuracy: 0.7308\n",
            "Epoch 552/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.6435 - accuracy: 0.7662 - val_loss: 0.8528 - val_accuracy: 0.7340\n",
            "Epoch 553/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6372 - accuracy: 0.7564 - val_loss: 0.9268 - val_accuracy: 0.7161\n",
            "Epoch 554/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.6481 - accuracy: 0.7569 - val_loss: 0.9018 - val_accuracy: 0.7098\n",
            "Epoch 555/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.6287 - accuracy: 0.7833 - val_loss: 0.9216 - val_accuracy: 0.7077\n",
            "Epoch 556/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.6556 - accuracy: 0.7553 - val_loss: 1.0659 - val_accuracy: 0.6572\n",
            "Epoch 557/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.6334 - accuracy: 0.7693 - val_loss: 0.9615 - val_accuracy: 0.6909\n",
            "Epoch 558/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6218 - accuracy: 0.7693 - val_loss: 0.9319 - val_accuracy: 0.7119\n",
            "Epoch 559/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.6225 - accuracy: 0.7662 - val_loss: 0.9211 - val_accuracy: 0.7066\n",
            "Epoch 560/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.6471 - accuracy: 0.7610 - val_loss: 0.8501 - val_accuracy: 0.7361\n",
            "Epoch 561/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6392 - accuracy: 0.7693 - val_loss: 0.9204 - val_accuracy: 0.7361\n",
            "Epoch 562/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.6423 - accuracy: 0.7797 - val_loss: 0.9769 - val_accuracy: 0.6761\n",
            "Epoch 563/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.6275 - accuracy: 0.7828 - val_loss: 0.9139 - val_accuracy: 0.7171\n",
            "Epoch 564/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.6231 - accuracy: 0.7709 - val_loss: 0.9152 - val_accuracy: 0.7171\n",
            "Epoch 565/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.6041 - accuracy: 0.7755 - val_loss: 0.8671 - val_accuracy: 0.7287\n",
            "Epoch 566/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6525 - accuracy: 0.7574 - val_loss: 0.8969 - val_accuracy: 0.7150\n",
            "Epoch 567/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.6081 - accuracy: 0.7797 - val_loss: 0.8987 - val_accuracy: 0.7350\n",
            "Epoch 568/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.6267 - accuracy: 0.7631 - val_loss: 1.1525 - val_accuracy: 0.6309\n",
            "Epoch 569/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6259 - accuracy: 0.7807 - val_loss: 0.9596 - val_accuracy: 0.6782\n",
            "Epoch 570/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.6389 - accuracy: 0.7615 - val_loss: 0.8775 - val_accuracy: 0.7150\n",
            "Epoch 571/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.6041 - accuracy: 0.7802 - val_loss: 0.8858 - val_accuracy: 0.7192\n",
            "Epoch 572/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6235 - accuracy: 0.7771 - val_loss: 0.8668 - val_accuracy: 0.7129\n",
            "Epoch 573/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.6315 - accuracy: 0.7693 - val_loss: 0.9458 - val_accuracy: 0.6982\n",
            "Epoch 574/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.6280 - accuracy: 0.7678 - val_loss: 0.8987 - val_accuracy: 0.7192\n",
            "Epoch 575/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.6086 - accuracy: 0.7662 - val_loss: 0.8832 - val_accuracy: 0.7298\n",
            "Epoch 576/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.6524 - accuracy: 0.7600 - val_loss: 0.8219 - val_accuracy: 0.7603\n",
            "Epoch 577/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.6157 - accuracy: 0.7818 - val_loss: 0.8654 - val_accuracy: 0.7445\n",
            "Epoch 578/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.6223 - accuracy: 0.7755 - val_loss: 0.8957 - val_accuracy: 0.7361\n",
            "Epoch 579/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.6414 - accuracy: 0.7589 - val_loss: 1.0442 - val_accuracy: 0.6982\n",
            "Epoch 580/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.6483 - accuracy: 0.7600 - val_loss: 0.8468 - val_accuracy: 0.7371\n",
            "Epoch 581/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.6144 - accuracy: 0.7657 - val_loss: 0.8129 - val_accuracy: 0.7466\n",
            "Epoch 582/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.6009 - accuracy: 0.7755 - val_loss: 0.9267 - val_accuracy: 0.6993\n",
            "Epoch 583/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.6015 - accuracy: 0.7812 - val_loss: 0.8978 - val_accuracy: 0.7266\n",
            "Epoch 584/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.6140 - accuracy: 0.7755 - val_loss: 0.8907 - val_accuracy: 0.7403\n",
            "Epoch 585/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.6137 - accuracy: 0.7646 - val_loss: 0.8637 - val_accuracy: 0.7319\n",
            "Epoch 586/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.6041 - accuracy: 0.7724 - val_loss: 0.8424 - val_accuracy: 0.7539\n",
            "Epoch 587/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.5892 - accuracy: 0.7875 - val_loss: 0.9558 - val_accuracy: 0.6761\n",
            "Epoch 588/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.6033 - accuracy: 0.7869 - val_loss: 0.8460 - val_accuracy: 0.7455\n",
            "Epoch 589/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.5879 - accuracy: 0.7812 - val_loss: 0.8705 - val_accuracy: 0.7171\n",
            "Epoch 590/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.5956 - accuracy: 0.7709 - val_loss: 0.9846 - val_accuracy: 0.6940\n",
            "Epoch 591/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.6180 - accuracy: 0.7792 - val_loss: 0.9246 - val_accuracy: 0.7308\n",
            "Epoch 592/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.5854 - accuracy: 0.7875 - val_loss: 0.8491 - val_accuracy: 0.7434\n",
            "Epoch 593/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.5864 - accuracy: 0.7859 - val_loss: 0.9396 - val_accuracy: 0.6856\n",
            "Epoch 594/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.6290 - accuracy: 0.7621 - val_loss: 1.1280 - val_accuracy: 0.6677\n",
            "Epoch 595/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.5998 - accuracy: 0.7838 - val_loss: 0.8437 - val_accuracy: 0.7571\n",
            "Epoch 596/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.5872 - accuracy: 0.7792 - val_loss: 0.8525 - val_accuracy: 0.7487\n",
            "Epoch 597/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.6117 - accuracy: 0.7652 - val_loss: 1.0432 - val_accuracy: 0.6677\n",
            "Epoch 598/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.5971 - accuracy: 0.7812 - val_loss: 0.8196 - val_accuracy: 0.7455\n",
            "Epoch 599/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.6166 - accuracy: 0.7755 - val_loss: 0.8671 - val_accuracy: 0.7245\n",
            "Epoch 600/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.5962 - accuracy: 0.7735 - val_loss: 0.9412 - val_accuracy: 0.6961\n",
            "Epoch 601/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.5956 - accuracy: 0.7828 - val_loss: 0.8244 - val_accuracy: 0.7434\n",
            "Epoch 602/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.5904 - accuracy: 0.7740 - val_loss: 0.9208 - val_accuracy: 0.7140\n",
            "Epoch 603/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.5921 - accuracy: 0.7833 - val_loss: 0.9743 - val_accuracy: 0.7024\n",
            "Epoch 604/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.6003 - accuracy: 0.7786 - val_loss: 0.9564 - val_accuracy: 0.7066\n",
            "Epoch 605/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.5894 - accuracy: 0.7875 - val_loss: 0.8561 - val_accuracy: 0.7234\n",
            "Epoch 606/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.6001 - accuracy: 0.7714 - val_loss: 0.8695 - val_accuracy: 0.7308\n",
            "Epoch 607/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.5805 - accuracy: 0.7843 - val_loss: 0.9090 - val_accuracy: 0.7371\n",
            "Epoch 608/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.6062 - accuracy: 0.7849 - val_loss: 0.8408 - val_accuracy: 0.7497\n",
            "Epoch 609/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.5620 - accuracy: 0.8020 - val_loss: 0.8767 - val_accuracy: 0.7550\n",
            "Epoch 610/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.5913 - accuracy: 0.7864 - val_loss: 0.8697 - val_accuracy: 0.7119\n",
            "Epoch 611/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.6038 - accuracy: 0.7786 - val_loss: 0.8953 - val_accuracy: 0.7340\n",
            "Epoch 612/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.5733 - accuracy: 0.7911 - val_loss: 0.8309 - val_accuracy: 0.7592\n",
            "Epoch 613/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.5678 - accuracy: 0.7895 - val_loss: 0.8479 - val_accuracy: 0.7266\n",
            "Epoch 614/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.5671 - accuracy: 0.7973 - val_loss: 0.8988 - val_accuracy: 0.7182\n",
            "Epoch 615/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.5826 - accuracy: 0.7838 - val_loss: 0.9895 - val_accuracy: 0.7108\n",
            "Epoch 616/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.5728 - accuracy: 0.7869 - val_loss: 0.8531 - val_accuracy: 0.7550\n",
            "Epoch 617/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.5824 - accuracy: 0.7921 - val_loss: 0.8887 - val_accuracy: 0.7613\n",
            "Epoch 618/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.5692 - accuracy: 0.7895 - val_loss: 0.8617 - val_accuracy: 0.7592\n",
            "Epoch 619/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.5412 - accuracy: 0.7968 - val_loss: 0.8209 - val_accuracy: 0.7476\n",
            "Epoch 620/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.5711 - accuracy: 0.7932 - val_loss: 0.8545 - val_accuracy: 0.7560\n",
            "Epoch 621/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.5766 - accuracy: 0.7926 - val_loss: 0.8173 - val_accuracy: 0.7508\n",
            "Epoch 622/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.5967 - accuracy: 0.7869 - val_loss: 1.0462 - val_accuracy: 0.7014\n",
            "Epoch 623/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.5986 - accuracy: 0.7786 - val_loss: 0.8428 - val_accuracy: 0.7560\n",
            "Epoch 624/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.5602 - accuracy: 0.7999 - val_loss: 0.7882 - val_accuracy: 0.7603\n",
            "Epoch 625/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.5689 - accuracy: 0.7838 - val_loss: 0.8463 - val_accuracy: 0.7308\n",
            "Epoch 626/1000\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 0.5857 - accuracy: 0.7797 - val_loss: 0.9081 - val_accuracy: 0.7277\n",
            "Epoch 627/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.5987 - accuracy: 0.7703 - val_loss: 0.9714 - val_accuracy: 0.7203\n",
            "Epoch 628/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.5389 - accuracy: 0.7952 - val_loss: 0.8723 - val_accuracy: 0.7508\n",
            "Epoch 629/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.5736 - accuracy: 0.7932 - val_loss: 0.8207 - val_accuracy: 0.7571\n",
            "Epoch 630/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.5815 - accuracy: 0.7812 - val_loss: 0.8632 - val_accuracy: 0.7382\n",
            "Epoch 631/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.5586 - accuracy: 0.7978 - val_loss: 0.8621 - val_accuracy: 0.7361\n",
            "Epoch 632/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.5840 - accuracy: 0.7875 - val_loss: 0.9169 - val_accuracy: 0.7119\n",
            "Epoch 633/1000\n",
            "1929/1929 [==============================] - 1s 476us/step - loss: 0.5843 - accuracy: 0.7792 - val_loss: 0.8261 - val_accuracy: 0.7581\n",
            "Epoch 634/1000\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.5659 - accuracy: 0.7885 - val_loss: 0.8793 - val_accuracy: 0.7560\n",
            "Epoch 635/1000\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.5671 - accuracy: 0.8035 - val_loss: 0.8281 - val_accuracy: 0.7539\n",
            "Epoch 636/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.5619 - accuracy: 0.7963 - val_loss: 0.9438 - val_accuracy: 0.7287\n",
            "Epoch 637/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.5512 - accuracy: 0.7890 - val_loss: 0.8564 - val_accuracy: 0.7434\n",
            "Epoch 638/1000\n",
            "1929/1929 [==============================] - 1s 464us/step - loss: 0.5898 - accuracy: 0.7947 - val_loss: 0.8608 - val_accuracy: 0.7392\n",
            "Epoch 639/1000\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 0.5552 - accuracy: 0.7973 - val_loss: 0.8582 - val_accuracy: 0.7224\n",
            "Epoch 640/1000\n",
            "1929/1929 [==============================] - 1s 461us/step - loss: 0.5608 - accuracy: 0.8056 - val_loss: 0.8118 - val_accuracy: 0.7645\n",
            "Epoch 641/1000\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 0.5494 - accuracy: 0.8056 - val_loss: 0.8884 - val_accuracy: 0.7455\n",
            "Epoch 642/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.5288 - accuracy: 0.8113 - val_loss: 0.9051 - val_accuracy: 0.7340\n",
            "Epoch 643/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.5783 - accuracy: 0.7875 - val_loss: 0.8624 - val_accuracy: 0.7308\n",
            "Epoch 644/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.5736 - accuracy: 0.8035 - val_loss: 0.8079 - val_accuracy: 0.7855\n",
            "Epoch 645/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.5398 - accuracy: 0.7994 - val_loss: 0.8894 - val_accuracy: 0.7066\n",
            "Epoch 646/1000\n",
            "1929/1929 [==============================] - 1s 472us/step - loss: 0.5773 - accuracy: 0.8025 - val_loss: 0.7786 - val_accuracy: 0.7687\n",
            "Epoch 647/1000\n",
            "1929/1929 [==============================] - 1s 459us/step - loss: 0.5442 - accuracy: 0.7994 - val_loss: 0.8033 - val_accuracy: 0.7592\n",
            "Epoch 648/1000\n",
            "1929/1929 [==============================] - 1s 463us/step - loss: 0.5433 - accuracy: 0.8004 - val_loss: 0.8249 - val_accuracy: 0.7739\n",
            "Epoch 649/1000\n",
            "1929/1929 [==============================] - 1s 462us/step - loss: 0.5575 - accuracy: 0.7932 - val_loss: 0.8651 - val_accuracy: 0.7434\n",
            "Epoch 650/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.5524 - accuracy: 0.7952 - val_loss: 0.8571 - val_accuracy: 0.7708\n",
            "Epoch 651/1000\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.5541 - accuracy: 0.7978 - val_loss: 0.9514 - val_accuracy: 0.7298\n",
            "Epoch 652/1000\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.5729 - accuracy: 0.8004 - val_loss: 0.8320 - val_accuracy: 0.7455\n",
            "Epoch 653/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.5533 - accuracy: 0.7973 - val_loss: 0.8079 - val_accuracy: 0.7676\n",
            "Epoch 654/1000\n",
            "1929/1929 [==============================] - 1s 467us/step - loss: 0.5202 - accuracy: 0.8061 - val_loss: 0.8351 - val_accuracy: 0.7487\n",
            "Epoch 655/1000\n",
            "1929/1929 [==============================] - 1s 465us/step - loss: 0.5222 - accuracy: 0.8056 - val_loss: 0.8456 - val_accuracy: 0.7592\n",
            "Epoch 656/1000\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.5266 - accuracy: 0.8087 - val_loss: 0.8311 - val_accuracy: 0.7581\n",
            "Epoch 657/1000\n",
            "1929/1929 [==============================] - 1s 457us/step - loss: 0.5291 - accuracy: 0.8087 - val_loss: 0.8426 - val_accuracy: 0.7571\n",
            "Epoch 658/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.5430 - accuracy: 0.8077 - val_loss: 0.9276 - val_accuracy: 0.7476\n",
            "Epoch 659/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.5342 - accuracy: 0.7983 - val_loss: 0.8523 - val_accuracy: 0.7571\n",
            "Epoch 660/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.5742 - accuracy: 0.7818 - val_loss: 0.8505 - val_accuracy: 0.7550\n",
            "Epoch 661/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.5549 - accuracy: 0.7921 - val_loss: 0.8509 - val_accuracy: 0.7760\n",
            "Epoch 662/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.5565 - accuracy: 0.7921 - val_loss: 0.9003 - val_accuracy: 0.7560\n",
            "Epoch 663/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.5449 - accuracy: 0.7994 - val_loss: 0.8831 - val_accuracy: 0.7581\n",
            "Epoch 664/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.5301 - accuracy: 0.8087 - val_loss: 0.9858 - val_accuracy: 0.7119\n",
            "Epoch 665/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.5349 - accuracy: 0.8020 - val_loss: 0.8656 - val_accuracy: 0.7729\n",
            "Epoch 666/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.5130 - accuracy: 0.8144 - val_loss: 0.8224 - val_accuracy: 0.7603\n",
            "Epoch 667/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.5400 - accuracy: 0.8009 - val_loss: 0.7968 - val_accuracy: 0.7634\n",
            "Epoch 668/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.5307 - accuracy: 0.8103 - val_loss: 0.8733 - val_accuracy: 0.7697\n",
            "Epoch 669/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.5310 - accuracy: 0.7983 - val_loss: 0.8192 - val_accuracy: 0.7655\n",
            "Epoch 670/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.5436 - accuracy: 0.7994 - val_loss: 0.8067 - val_accuracy: 0.7739\n",
            "Epoch 671/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.5293 - accuracy: 0.8108 - val_loss: 0.8574 - val_accuracy: 0.7550\n",
            "Epoch 672/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.5375 - accuracy: 0.8030 - val_loss: 0.8048 - val_accuracy: 0.7802\n",
            "Epoch 673/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.5240 - accuracy: 0.8139 - val_loss: 0.7975 - val_accuracy: 0.7792\n",
            "Epoch 674/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.5654 - accuracy: 0.7968 - val_loss: 0.8659 - val_accuracy: 0.7603\n",
            "Epoch 675/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.5145 - accuracy: 0.8186 - val_loss: 0.8518 - val_accuracy: 0.7550\n",
            "Epoch 676/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.5397 - accuracy: 0.7983 - val_loss: 0.7876 - val_accuracy: 0.7781\n",
            "Epoch 677/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.5518 - accuracy: 0.8025 - val_loss: 0.7961 - val_accuracy: 0.7603\n",
            "Epoch 678/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.5439 - accuracy: 0.8025 - val_loss: 1.0013 - val_accuracy: 0.7161\n",
            "Epoch 679/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.5427 - accuracy: 0.7999 - val_loss: 1.0268 - val_accuracy: 0.7182\n",
            "Epoch 680/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.5548 - accuracy: 0.8025 - val_loss: 0.8239 - val_accuracy: 0.7687\n",
            "Epoch 681/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.5031 - accuracy: 0.8144 - val_loss: 0.8231 - val_accuracy: 0.7907\n",
            "Epoch 682/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.5149 - accuracy: 0.8072 - val_loss: 0.8542 - val_accuracy: 0.7729\n",
            "Epoch 683/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.5277 - accuracy: 0.8154 - val_loss: 0.8098 - val_accuracy: 0.7539\n",
            "Epoch 684/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.5501 - accuracy: 0.7900 - val_loss: 0.7830 - val_accuracy: 0.7718\n",
            "Epoch 685/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.5213 - accuracy: 0.8035 - val_loss: 0.8405 - val_accuracy: 0.7655\n",
            "Epoch 686/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.5584 - accuracy: 0.7942 - val_loss: 0.8590 - val_accuracy: 0.7613\n",
            "Epoch 687/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4977 - accuracy: 0.8175 - val_loss: 0.7574 - val_accuracy: 0.7792\n",
            "Epoch 688/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4992 - accuracy: 0.8191 - val_loss: 0.8313 - val_accuracy: 0.7718\n",
            "Epoch 689/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.5148 - accuracy: 0.8072 - val_loss: 0.9916 - val_accuracy: 0.7392\n",
            "Epoch 690/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.5071 - accuracy: 0.8118 - val_loss: 0.8042 - val_accuracy: 0.7792\n",
            "Epoch 691/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.5201 - accuracy: 0.8004 - val_loss: 0.8928 - val_accuracy: 0.7550\n",
            "Epoch 692/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.5307 - accuracy: 0.8015 - val_loss: 0.8911 - val_accuracy: 0.7455\n",
            "Epoch 693/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.5248 - accuracy: 0.8144 - val_loss: 0.8225 - val_accuracy: 0.7718\n",
            "Epoch 694/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.5211 - accuracy: 0.8082 - val_loss: 0.8364 - val_accuracy: 0.7571\n",
            "Epoch 695/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.5440 - accuracy: 0.8035 - val_loss: 0.7591 - val_accuracy: 0.7718\n",
            "Epoch 696/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.5123 - accuracy: 0.8103 - val_loss: 0.7543 - val_accuracy: 0.7781\n",
            "Epoch 697/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.4969 - accuracy: 0.8331 - val_loss: 0.8731 - val_accuracy: 0.7508\n",
            "Epoch 698/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.5282 - accuracy: 0.7963 - val_loss: 0.7956 - val_accuracy: 0.7823\n",
            "Epoch 699/1000\n",
            "1929/1929 [==============================] - 1s 482us/step - loss: 0.5009 - accuracy: 0.8129 - val_loss: 0.8414 - val_accuracy: 0.7697\n",
            "Epoch 700/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.5203 - accuracy: 0.8072 - val_loss: 0.8310 - val_accuracy: 0.7855\n",
            "Epoch 701/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 0.5298 - accuracy: 0.8108 - val_loss: 0.9662 - val_accuracy: 0.7256\n",
            "Epoch 702/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.5010 - accuracy: 0.8180 - val_loss: 0.8431 - val_accuracy: 0.7760\n",
            "Epoch 703/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 0.5061 - accuracy: 0.8118 - val_loss: 0.8091 - val_accuracy: 0.7676\n",
            "Epoch 704/1000\n",
            "1929/1929 [==============================] - 1s 453us/step - loss: 0.5100 - accuracy: 0.8149 - val_loss: 0.8405 - val_accuracy: 0.7918\n",
            "Epoch 705/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.4907 - accuracy: 0.8232 - val_loss: 0.8589 - val_accuracy: 0.7729\n",
            "Epoch 706/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.5226 - accuracy: 0.8139 - val_loss: 0.7552 - val_accuracy: 0.7823\n",
            "Epoch 707/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.4766 - accuracy: 0.8222 - val_loss: 0.8152 - val_accuracy: 0.7865\n",
            "Epoch 708/1000\n",
            "1929/1929 [==============================] - 1s 452us/step - loss: 0.5006 - accuracy: 0.8165 - val_loss: 0.7793 - val_accuracy: 0.7729\n",
            "Epoch 709/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.5160 - accuracy: 0.8087 - val_loss: 0.7803 - val_accuracy: 0.7865\n",
            "Epoch 710/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.5101 - accuracy: 0.8082 - val_loss: 0.7636 - val_accuracy: 0.7834\n",
            "Epoch 711/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.5412 - accuracy: 0.8072 - val_loss: 0.8983 - val_accuracy: 0.7487\n",
            "Epoch 712/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.5445 - accuracy: 0.8020 - val_loss: 0.8126 - val_accuracy: 0.7729\n",
            "Epoch 713/1000\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.4795 - accuracy: 0.8305 - val_loss: 0.8362 - val_accuracy: 0.7592\n",
            "Epoch 714/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.4901 - accuracy: 0.8191 - val_loss: 0.8429 - val_accuracy: 0.7813\n",
            "Epoch 715/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.4730 - accuracy: 0.8274 - val_loss: 0.8379 - val_accuracy: 0.7624\n",
            "Epoch 716/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.5083 - accuracy: 0.8134 - val_loss: 0.8724 - val_accuracy: 0.7529\n",
            "Epoch 717/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.4870 - accuracy: 0.8310 - val_loss: 0.7774 - val_accuracy: 0.7771\n",
            "Epoch 718/1000\n",
            "1929/1929 [==============================] - 1s 450us/step - loss: 0.5109 - accuracy: 0.8103 - val_loss: 0.8086 - val_accuracy: 0.7676\n",
            "Epoch 719/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.5073 - accuracy: 0.8269 - val_loss: 0.9024 - val_accuracy: 0.7560\n",
            "Epoch 720/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.5199 - accuracy: 0.8072 - val_loss: 1.0394 - val_accuracy: 0.6951\n",
            "Epoch 721/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.4894 - accuracy: 0.8201 - val_loss: 0.8069 - val_accuracy: 0.7697\n",
            "Epoch 722/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.5022 - accuracy: 0.8118 - val_loss: 0.7881 - val_accuracy: 0.7771\n",
            "Epoch 723/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4676 - accuracy: 0.8289 - val_loss: 0.7868 - val_accuracy: 0.7876\n",
            "Epoch 724/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4854 - accuracy: 0.8154 - val_loss: 0.7906 - val_accuracy: 0.7718\n",
            "Epoch 725/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.4796 - accuracy: 0.8232 - val_loss: 0.8502 - val_accuracy: 0.7655\n",
            "Epoch 726/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4867 - accuracy: 0.8196 - val_loss: 0.8017 - val_accuracy: 0.7802\n",
            "Epoch 727/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.4931 - accuracy: 0.8227 - val_loss: 0.7888 - val_accuracy: 0.7834\n",
            "Epoch 728/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4833 - accuracy: 0.8206 - val_loss: 0.8123 - val_accuracy: 0.7865\n",
            "Epoch 729/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.5089 - accuracy: 0.8123 - val_loss: 0.9238 - val_accuracy: 0.7382\n",
            "Epoch 730/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.5338 - accuracy: 0.8040 - val_loss: 0.7880 - val_accuracy: 0.7844\n",
            "Epoch 731/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4723 - accuracy: 0.8279 - val_loss: 0.7590 - val_accuracy: 0.7697\n",
            "Epoch 732/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4923 - accuracy: 0.8222 - val_loss: 0.7609 - val_accuracy: 0.7655\n",
            "Epoch 733/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4803 - accuracy: 0.8279 - val_loss: 0.7760 - val_accuracy: 0.7676\n",
            "Epoch 734/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4965 - accuracy: 0.8170 - val_loss: 0.8438 - val_accuracy: 0.7581\n",
            "Epoch 735/1000\n",
            "1929/1929 [==============================] - 1s 420us/step - loss: 0.4797 - accuracy: 0.8263 - val_loss: 0.7789 - val_accuracy: 0.7886\n",
            "Epoch 736/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.4807 - accuracy: 0.8227 - val_loss: 0.8470 - val_accuracy: 0.7371\n",
            "Epoch 737/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4973 - accuracy: 0.8134 - val_loss: 0.7765 - val_accuracy: 0.7687\n",
            "Epoch 738/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.4995 - accuracy: 0.8180 - val_loss: 0.8113 - val_accuracy: 0.7645\n",
            "Epoch 739/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.4756 - accuracy: 0.8351 - val_loss: 0.8918 - val_accuracy: 0.7718\n",
            "Epoch 740/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.4838 - accuracy: 0.8300 - val_loss: 0.7999 - val_accuracy: 0.7645\n",
            "Epoch 741/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.5097 - accuracy: 0.8160 - val_loss: 0.7553 - val_accuracy: 0.7781\n",
            "Epoch 742/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.4817 - accuracy: 0.8206 - val_loss: 0.8154 - val_accuracy: 0.7729\n",
            "Epoch 743/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4970 - accuracy: 0.8222 - val_loss: 1.0768 - val_accuracy: 0.7171\n",
            "Epoch 744/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4936 - accuracy: 0.8134 - val_loss: 0.9484 - val_accuracy: 0.7487\n",
            "Epoch 745/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4917 - accuracy: 0.8170 - val_loss: 0.8040 - val_accuracy: 0.7792\n",
            "Epoch 746/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4829 - accuracy: 0.8232 - val_loss: 0.7812 - val_accuracy: 0.7855\n",
            "Epoch 747/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4769 - accuracy: 0.8237 - val_loss: 0.7769 - val_accuracy: 0.7823\n",
            "Epoch 748/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4984 - accuracy: 0.8186 - val_loss: 0.7901 - val_accuracy: 0.7823\n",
            "Epoch 749/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4970 - accuracy: 0.8201 - val_loss: 0.8372 - val_accuracy: 0.7571\n",
            "Epoch 750/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.4601 - accuracy: 0.8367 - val_loss: 0.8103 - val_accuracy: 0.7844\n",
            "Epoch 751/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.4712 - accuracy: 0.8362 - val_loss: 0.7690 - val_accuracy: 0.7907\n",
            "Epoch 752/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4751 - accuracy: 0.8289 - val_loss: 0.7970 - val_accuracy: 0.7792\n",
            "Epoch 753/1000\n",
            "1929/1929 [==============================] - 1s 419us/step - loss: 0.4696 - accuracy: 0.8263 - val_loss: 0.7752 - val_accuracy: 0.7918\n",
            "Epoch 754/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.4651 - accuracy: 0.8269 - val_loss: 0.7729 - val_accuracy: 0.7739\n",
            "Epoch 755/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4856 - accuracy: 0.8243 - val_loss: 0.7921 - val_accuracy: 0.7918\n",
            "Epoch 756/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4601 - accuracy: 0.8357 - val_loss: 0.8225 - val_accuracy: 0.7624\n",
            "Epoch 757/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.4991 - accuracy: 0.8175 - val_loss: 0.8775 - val_accuracy: 0.7771\n",
            "Epoch 758/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4741 - accuracy: 0.8320 - val_loss: 0.7954 - val_accuracy: 0.7886\n",
            "Epoch 759/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4703 - accuracy: 0.8305 - val_loss: 0.9332 - val_accuracy: 0.7624\n",
            "Epoch 760/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4860 - accuracy: 0.8206 - val_loss: 0.9074 - val_accuracy: 0.7434\n",
            "Epoch 761/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4965 - accuracy: 0.8103 - val_loss: 0.8221 - val_accuracy: 0.7802\n",
            "Epoch 762/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4902 - accuracy: 0.8331 - val_loss: 0.8254 - val_accuracy: 0.7571\n",
            "Epoch 763/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4491 - accuracy: 0.8393 - val_loss: 0.7626 - val_accuracy: 0.7907\n",
            "Epoch 764/1000\n",
            "1929/1929 [==============================] - 1s 419us/step - loss: 0.4590 - accuracy: 0.8372 - val_loss: 1.0297 - val_accuracy: 0.7392\n",
            "Epoch 765/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.5177 - accuracy: 0.8180 - val_loss: 0.8312 - val_accuracy: 0.7624\n",
            "Epoch 766/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.4674 - accuracy: 0.8294 - val_loss: 0.7900 - val_accuracy: 0.7981\n",
            "Epoch 767/1000\n",
            "1929/1929 [==============================] - 1s 422us/step - loss: 0.4635 - accuracy: 0.8357 - val_loss: 0.8077 - val_accuracy: 0.7855\n",
            "Epoch 768/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4722 - accuracy: 0.8222 - val_loss: 0.7995 - val_accuracy: 0.7624\n",
            "Epoch 769/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4662 - accuracy: 0.8232 - val_loss: 0.8005 - val_accuracy: 0.7666\n",
            "Epoch 770/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.4879 - accuracy: 0.8222 - val_loss: 1.0133 - val_accuracy: 0.7487\n",
            "Epoch 771/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.5065 - accuracy: 0.8154 - val_loss: 0.7999 - val_accuracy: 0.7813\n",
            "Epoch 772/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4741 - accuracy: 0.8191 - val_loss: 0.8099 - val_accuracy: 0.7907\n",
            "Epoch 773/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.4602 - accuracy: 0.8383 - val_loss: 0.8356 - val_accuracy: 0.7928\n",
            "Epoch 774/1000\n",
            "1929/1929 [==============================] - 1s 418us/step - loss: 0.4665 - accuracy: 0.8289 - val_loss: 0.7957 - val_accuracy: 0.7971\n",
            "Epoch 775/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4568 - accuracy: 0.8346 - val_loss: 0.8819 - val_accuracy: 0.7687\n",
            "Epoch 776/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.5045 - accuracy: 0.8175 - val_loss: 0.8070 - val_accuracy: 0.7907\n",
            "Epoch 777/1000\n",
            "1929/1929 [==============================] - 1s 421us/step - loss: 0.4678 - accuracy: 0.8351 - val_loss: 0.7994 - val_accuracy: 0.7750\n",
            "Epoch 778/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4572 - accuracy: 0.8362 - val_loss: 0.7699 - val_accuracy: 0.7981\n",
            "Epoch 779/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4590 - accuracy: 0.8269 - val_loss: 0.7532 - val_accuracy: 0.7981\n",
            "Epoch 780/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4581 - accuracy: 0.8300 - val_loss: 0.9786 - val_accuracy: 0.7361\n",
            "Epoch 781/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4683 - accuracy: 0.8346 - val_loss: 0.7605 - val_accuracy: 0.7928\n",
            "Epoch 782/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4645 - accuracy: 0.8227 - val_loss: 0.8138 - val_accuracy: 0.7781\n",
            "Epoch 783/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4555 - accuracy: 0.8357 - val_loss: 0.7760 - val_accuracy: 0.7844\n",
            "Epoch 784/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.4731 - accuracy: 0.8253 - val_loss: 0.8411 - val_accuracy: 0.7645\n",
            "Epoch 785/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4746 - accuracy: 0.8217 - val_loss: 0.7563 - val_accuracy: 0.7865\n",
            "Epoch 786/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4430 - accuracy: 0.8409 - val_loss: 0.8961 - val_accuracy: 0.7676\n",
            "Epoch 787/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4795 - accuracy: 0.8284 - val_loss: 0.9442 - val_accuracy: 0.7687\n",
            "Epoch 788/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4544 - accuracy: 0.8243 - val_loss: 0.8020 - val_accuracy: 0.7918\n",
            "Epoch 789/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.4583 - accuracy: 0.8372 - val_loss: 0.7719 - val_accuracy: 0.7950\n",
            "Epoch 790/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4541 - accuracy: 0.8269 - val_loss: 0.8128 - val_accuracy: 0.7865\n",
            "Epoch 791/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4693 - accuracy: 0.8217 - val_loss: 0.7997 - val_accuracy: 0.7950\n",
            "Epoch 792/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.4578 - accuracy: 0.8274 - val_loss: 0.8015 - val_accuracy: 0.7813\n",
            "Epoch 793/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4456 - accuracy: 0.8289 - val_loss: 0.8171 - val_accuracy: 0.7739\n",
            "Epoch 794/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.4476 - accuracy: 0.8305 - val_loss: 0.8467 - val_accuracy: 0.7855\n",
            "Epoch 795/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.4736 - accuracy: 0.8175 - val_loss: 0.8948 - val_accuracy: 0.7508\n",
            "Epoch 796/1000\n",
            "1929/1929 [==============================] - 1s 421us/step - loss: 0.4611 - accuracy: 0.8346 - val_loss: 0.8139 - val_accuracy: 0.7697\n",
            "Epoch 797/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4805 - accuracy: 0.8326 - val_loss: 0.7824 - val_accuracy: 0.7760\n",
            "Epoch 798/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4362 - accuracy: 0.8409 - val_loss: 0.8331 - val_accuracy: 0.7529\n",
            "Epoch 799/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4788 - accuracy: 0.8289 - val_loss: 0.8016 - val_accuracy: 0.8118\n",
            "Epoch 800/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.4530 - accuracy: 0.8310 - val_loss: 1.0427 - val_accuracy: 0.7539\n",
            "Epoch 801/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4639 - accuracy: 0.8377 - val_loss: 0.7778 - val_accuracy: 0.7960\n",
            "Epoch 802/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.4504 - accuracy: 0.8341 - val_loss: 0.8012 - val_accuracy: 0.7886\n",
            "Epoch 803/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4355 - accuracy: 0.8377 - val_loss: 0.8090 - val_accuracy: 0.7781\n",
            "Epoch 804/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4492 - accuracy: 0.8346 - val_loss: 0.7936 - val_accuracy: 0.7876\n",
            "Epoch 805/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4689 - accuracy: 0.8336 - val_loss: 0.8563 - val_accuracy: 0.7792\n",
            "Epoch 806/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4678 - accuracy: 0.8253 - val_loss: 0.8362 - val_accuracy: 0.7550\n",
            "Epoch 807/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4491 - accuracy: 0.8248 - val_loss: 0.9074 - val_accuracy: 0.7666\n",
            "Epoch 808/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4333 - accuracy: 0.8424 - val_loss: 0.9566 - val_accuracy: 0.7560\n",
            "Epoch 809/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.4595 - accuracy: 0.8414 - val_loss: 0.8078 - val_accuracy: 0.7792\n",
            "Epoch 810/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4429 - accuracy: 0.8289 - val_loss: 0.7893 - val_accuracy: 0.8181\n",
            "Epoch 811/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.4414 - accuracy: 0.8512 - val_loss: 0.8086 - val_accuracy: 0.7992\n",
            "Epoch 812/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.4291 - accuracy: 0.8491 - val_loss: 0.8751 - val_accuracy: 0.7771\n",
            "Epoch 813/1000\n",
            "1929/1929 [==============================] - 1s 420us/step - loss: 0.4611 - accuracy: 0.8274 - val_loss: 0.8107 - val_accuracy: 0.7950\n",
            "Epoch 814/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.4356 - accuracy: 0.8326 - val_loss: 0.7986 - val_accuracy: 0.7886\n",
            "Epoch 815/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4311 - accuracy: 0.8543 - val_loss: 0.9774 - val_accuracy: 0.7624\n",
            "Epoch 816/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4568 - accuracy: 0.8419 - val_loss: 0.8069 - val_accuracy: 0.7950\n",
            "Epoch 817/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4652 - accuracy: 0.8263 - val_loss: 0.8886 - val_accuracy: 0.7802\n",
            "Epoch 818/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4454 - accuracy: 0.8351 - val_loss: 0.8546 - val_accuracy: 0.7792\n",
            "Epoch 819/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4235 - accuracy: 0.8471 - val_loss: 0.8670 - val_accuracy: 0.7560\n",
            "Epoch 820/1000\n",
            "1929/1929 [==============================] - 1s 421us/step - loss: 0.4777 - accuracy: 0.8258 - val_loss: 0.7673 - val_accuracy: 0.7907\n",
            "Epoch 821/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.4856 - accuracy: 0.8108 - val_loss: 0.8286 - val_accuracy: 0.7844\n",
            "Epoch 822/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4825 - accuracy: 0.8186 - val_loss: 0.8661 - val_accuracy: 0.7834\n",
            "Epoch 823/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4433 - accuracy: 0.8409 - val_loss: 0.7756 - val_accuracy: 0.7792\n",
            "Epoch 824/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.4500 - accuracy: 0.8434 - val_loss: 0.8157 - val_accuracy: 0.7760\n",
            "Epoch 825/1000\n",
            "1929/1929 [==============================] - 1s 460us/step - loss: 0.4551 - accuracy: 0.8263 - val_loss: 0.7920 - val_accuracy: 0.7886\n",
            "Epoch 826/1000\n",
            "1929/1929 [==============================] - 1s 466us/step - loss: 0.4484 - accuracy: 0.8346 - val_loss: 0.8073 - val_accuracy: 0.8065\n",
            "Epoch 827/1000\n",
            "1929/1929 [==============================] - 1s 469us/step - loss: 0.4203 - accuracy: 0.8507 - val_loss: 0.7580 - val_accuracy: 0.8002\n",
            "Epoch 828/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4259 - accuracy: 0.8414 - val_loss: 0.7811 - val_accuracy: 0.7865\n",
            "Epoch 829/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4373 - accuracy: 0.8476 - val_loss: 0.7807 - val_accuracy: 0.8076\n",
            "Epoch 830/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4491 - accuracy: 0.8274 - val_loss: 0.9328 - val_accuracy: 0.7855\n",
            "Epoch 831/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4325 - accuracy: 0.8409 - val_loss: 0.9211 - val_accuracy: 0.7760\n",
            "Epoch 832/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4507 - accuracy: 0.8367 - val_loss: 0.9333 - val_accuracy: 0.7613\n",
            "Epoch 833/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4657 - accuracy: 0.8300 - val_loss: 1.0829 - val_accuracy: 0.7203\n",
            "Epoch 834/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.4737 - accuracy: 0.8269 - val_loss: 0.8566 - val_accuracy: 0.7781\n",
            "Epoch 835/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4403 - accuracy: 0.8512 - val_loss: 0.8855 - val_accuracy: 0.7718\n",
            "Epoch 836/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.4662 - accuracy: 0.8362 - val_loss: 0.8197 - val_accuracy: 0.7771\n",
            "Epoch 837/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4382 - accuracy: 0.8403 - val_loss: 0.8694 - val_accuracy: 0.7718\n",
            "Epoch 838/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4320 - accuracy: 0.8455 - val_loss: 0.9194 - val_accuracy: 0.7802\n",
            "Epoch 839/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.4068 - accuracy: 0.8491 - val_loss: 0.8865 - val_accuracy: 0.7687\n",
            "Epoch 840/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4751 - accuracy: 0.8196 - val_loss: 0.7759 - val_accuracy: 0.7981\n",
            "Epoch 841/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.4129 - accuracy: 0.8512 - val_loss: 0.7994 - val_accuracy: 0.7971\n",
            "Epoch 842/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.4286 - accuracy: 0.8471 - val_loss: 0.9111 - val_accuracy: 0.7634\n",
            "Epoch 843/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4376 - accuracy: 0.8419 - val_loss: 0.7760 - val_accuracy: 0.7981\n",
            "Epoch 844/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4526 - accuracy: 0.8331 - val_loss: 0.9324 - val_accuracy: 0.7634\n",
            "Epoch 845/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4359 - accuracy: 0.8388 - val_loss: 0.7388 - val_accuracy: 0.8065\n",
            "Epoch 846/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4087 - accuracy: 0.8523 - val_loss: 0.7747 - val_accuracy: 0.8034\n",
            "Epoch 847/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.3886 - accuracy: 0.8626 - val_loss: 0.7865 - val_accuracy: 0.8002\n",
            "Epoch 848/1000\n",
            "1929/1929 [==============================] - 1s 421us/step - loss: 0.4288 - accuracy: 0.8476 - val_loss: 0.9320 - val_accuracy: 0.7666\n",
            "Epoch 849/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4333 - accuracy: 0.8372 - val_loss: 0.8333 - val_accuracy: 0.7918\n",
            "Epoch 850/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4202 - accuracy: 0.8393 - val_loss: 0.7302 - val_accuracy: 0.8034\n",
            "Epoch 851/1000\n",
            "1929/1929 [==============================] - 1s 448us/step - loss: 0.3882 - accuracy: 0.8590 - val_loss: 0.7483 - val_accuracy: 0.7928\n",
            "Epoch 852/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.4040 - accuracy: 0.8590 - val_loss: 0.8764 - val_accuracy: 0.7760\n",
            "Epoch 853/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4299 - accuracy: 0.8466 - val_loss: 0.7592 - val_accuracy: 0.7939\n",
            "Epoch 854/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4360 - accuracy: 0.8403 - val_loss: 0.8254 - val_accuracy: 0.7992\n",
            "Epoch 855/1000\n",
            "1929/1929 [==============================] - 1s 443us/step - loss: 0.3984 - accuracy: 0.8600 - val_loss: 0.8375 - val_accuracy: 0.7950\n",
            "Epoch 856/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.4134 - accuracy: 0.8548 - val_loss: 0.7878 - val_accuracy: 0.8002\n",
            "Epoch 857/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4339 - accuracy: 0.8424 - val_loss: 0.8309 - val_accuracy: 0.8034\n",
            "Epoch 858/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.4438 - accuracy: 0.8243 - val_loss: 0.9210 - val_accuracy: 0.7781\n",
            "Epoch 859/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.4100 - accuracy: 0.8574 - val_loss: 0.8488 - val_accuracy: 0.7550\n",
            "Epoch 860/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.4327 - accuracy: 0.8528 - val_loss: 0.7719 - val_accuracy: 0.8076\n",
            "Epoch 861/1000\n",
            "1929/1929 [==============================] - 1s 445us/step - loss: 0.4095 - accuracy: 0.8554 - val_loss: 0.7946 - val_accuracy: 0.7813\n",
            "Epoch 862/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4568 - accuracy: 0.8409 - val_loss: 0.7447 - val_accuracy: 0.7971\n",
            "Epoch 863/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.4169 - accuracy: 0.8419 - val_loss: 0.7982 - val_accuracy: 0.7655\n",
            "Epoch 864/1000\n",
            "1929/1929 [==============================] - 1s 456us/step - loss: 0.4424 - accuracy: 0.8409 - val_loss: 0.8688 - val_accuracy: 0.7876\n",
            "Epoch 865/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4362 - accuracy: 0.8336 - val_loss: 0.7415 - val_accuracy: 0.7960\n",
            "Epoch 866/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.4228 - accuracy: 0.8419 - val_loss: 0.8002 - val_accuracy: 0.8013\n",
            "Epoch 867/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4373 - accuracy: 0.8471 - val_loss: 0.8224 - val_accuracy: 0.7729\n",
            "Epoch 868/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.4246 - accuracy: 0.8502 - val_loss: 0.7654 - val_accuracy: 0.8044\n",
            "Epoch 869/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4059 - accuracy: 0.8605 - val_loss: 0.7605 - val_accuracy: 0.7981\n",
            "Epoch 870/1000\n",
            "1929/1929 [==============================] - 1s 449us/step - loss: 0.4161 - accuracy: 0.8434 - val_loss: 0.7400 - val_accuracy: 0.7971\n",
            "Epoch 871/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4151 - accuracy: 0.8507 - val_loss: 0.7990 - val_accuracy: 0.7918\n",
            "Epoch 872/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4260 - accuracy: 0.8528 - val_loss: 0.7943 - val_accuracy: 0.7813\n",
            "Epoch 873/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4012 - accuracy: 0.8543 - val_loss: 0.7413 - val_accuracy: 0.8023\n",
            "Epoch 874/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4102 - accuracy: 0.8543 - val_loss: 0.7793 - val_accuracy: 0.7981\n",
            "Epoch 875/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.3985 - accuracy: 0.8554 - val_loss: 0.8094 - val_accuracy: 0.7907\n",
            "Epoch 876/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4205 - accuracy: 0.8460 - val_loss: 0.8542 - val_accuracy: 0.7960\n",
            "Epoch 877/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.4401 - accuracy: 0.8476 - val_loss: 0.7910 - val_accuracy: 0.7950\n",
            "Epoch 878/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.4152 - accuracy: 0.8523 - val_loss: 0.7915 - val_accuracy: 0.8086\n",
            "Epoch 879/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4307 - accuracy: 0.8543 - val_loss: 1.1012 - val_accuracy: 0.7455\n",
            "Epoch 880/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4373 - accuracy: 0.8445 - val_loss: 0.8082 - val_accuracy: 0.8118\n",
            "Epoch 881/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4200 - accuracy: 0.8460 - val_loss: 0.8560 - val_accuracy: 0.7876\n",
            "Epoch 882/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4097 - accuracy: 0.8590 - val_loss: 0.8288 - val_accuracy: 0.7939\n",
            "Epoch 883/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4237 - accuracy: 0.8409 - val_loss: 0.8813 - val_accuracy: 0.7960\n",
            "Epoch 884/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.4013 - accuracy: 0.8528 - val_loss: 0.8687 - val_accuracy: 0.7876\n",
            "Epoch 885/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4113 - accuracy: 0.8507 - val_loss: 0.8146 - val_accuracy: 0.8160\n",
            "Epoch 886/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.4134 - accuracy: 0.8497 - val_loss: 0.8819 - val_accuracy: 0.7213\n",
            "Epoch 887/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.4546 - accuracy: 0.8450 - val_loss: 0.8338 - val_accuracy: 0.7897\n",
            "Epoch 888/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.3939 - accuracy: 0.8548 - val_loss: 0.7685 - val_accuracy: 0.8097\n",
            "Epoch 889/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4047 - accuracy: 0.8564 - val_loss: 0.7703 - val_accuracy: 0.8086\n",
            "Epoch 890/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4137 - accuracy: 0.8502 - val_loss: 0.8316 - val_accuracy: 0.7939\n",
            "Epoch 891/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4009 - accuracy: 0.8647 - val_loss: 0.8236 - val_accuracy: 0.8002\n",
            "Epoch 892/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.3860 - accuracy: 0.8533 - val_loss: 0.8118 - val_accuracy: 0.7907\n",
            "Epoch 893/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4057 - accuracy: 0.8440 - val_loss: 0.7282 - val_accuracy: 0.8139\n",
            "Epoch 894/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4022 - accuracy: 0.8616 - val_loss: 0.7359 - val_accuracy: 0.7992\n",
            "Epoch 895/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.3921 - accuracy: 0.8600 - val_loss: 0.8922 - val_accuracy: 0.7865\n",
            "Epoch 896/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4337 - accuracy: 0.8377 - val_loss: 0.7607 - val_accuracy: 0.7823\n",
            "Epoch 897/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.3931 - accuracy: 0.8528 - val_loss: 0.8061 - val_accuracy: 0.8013\n",
            "Epoch 898/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.4325 - accuracy: 0.8351 - val_loss: 0.8773 - val_accuracy: 0.7813\n",
            "Epoch 899/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4222 - accuracy: 0.8476 - val_loss: 0.8478 - val_accuracy: 0.7918\n",
            "Epoch 900/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.4032 - accuracy: 0.8502 - val_loss: 0.7874 - val_accuracy: 0.7907\n",
            "Epoch 901/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.4092 - accuracy: 0.8517 - val_loss: 0.8180 - val_accuracy: 0.8034\n",
            "Epoch 902/1000\n",
            "1929/1929 [==============================] - 1s 455us/step - loss: 0.4200 - accuracy: 0.8476 - val_loss: 0.8355 - val_accuracy: 0.7971\n",
            "Epoch 903/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4067 - accuracy: 0.8574 - val_loss: 0.7645 - val_accuracy: 0.7981\n",
            "Epoch 904/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.4105 - accuracy: 0.8554 - val_loss: 0.8965 - val_accuracy: 0.7960\n",
            "Epoch 905/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.4125 - accuracy: 0.8429 - val_loss: 0.8711 - val_accuracy: 0.7750\n",
            "Epoch 906/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4087 - accuracy: 0.8538 - val_loss: 0.7609 - val_accuracy: 0.8044\n",
            "Epoch 907/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.3930 - accuracy: 0.8574 - val_loss: 0.8133 - val_accuracy: 0.8086\n",
            "Epoch 908/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4220 - accuracy: 0.8502 - val_loss: 0.7874 - val_accuracy: 0.7834\n",
            "Epoch 909/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.3641 - accuracy: 0.8730 - val_loss: 0.8669 - val_accuracy: 0.7992\n",
            "Epoch 910/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4151 - accuracy: 0.8434 - val_loss: 0.8143 - val_accuracy: 0.8023\n",
            "Epoch 911/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.3910 - accuracy: 0.8595 - val_loss: 0.8081 - val_accuracy: 0.7950\n",
            "Epoch 912/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.3571 - accuracy: 0.8782 - val_loss: 0.8851 - val_accuracy: 0.8034\n",
            "Epoch 913/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4391 - accuracy: 0.8377 - val_loss: 0.7544 - val_accuracy: 0.8107\n",
            "Epoch 914/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.3946 - accuracy: 0.8569 - val_loss: 0.8386 - val_accuracy: 0.7897\n",
            "Epoch 915/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.4456 - accuracy: 0.8414 - val_loss: 0.8166 - val_accuracy: 0.8076\n",
            "Epoch 916/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.3986 - accuracy: 0.8600 - val_loss: 0.7775 - val_accuracy: 0.8086\n",
            "Epoch 917/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.4018 - accuracy: 0.8631 - val_loss: 0.7336 - val_accuracy: 0.8118\n",
            "Epoch 918/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.3986 - accuracy: 0.8585 - val_loss: 0.7668 - val_accuracy: 0.8055\n",
            "Epoch 919/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4303 - accuracy: 0.8434 - val_loss: 0.8572 - val_accuracy: 0.7697\n",
            "Epoch 920/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4174 - accuracy: 0.8554 - val_loss: 0.7809 - val_accuracy: 0.7897\n",
            "Epoch 921/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.3984 - accuracy: 0.8580 - val_loss: 0.8133 - val_accuracy: 0.8034\n",
            "Epoch 922/1000\n",
            "1929/1929 [==============================] - 1s 444us/step - loss: 0.4119 - accuracy: 0.8507 - val_loss: 0.8386 - val_accuracy: 0.8086\n",
            "Epoch 923/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.4197 - accuracy: 0.8424 - val_loss: 0.7836 - val_accuracy: 0.8023\n",
            "Epoch 924/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4258 - accuracy: 0.8455 - val_loss: 0.7627 - val_accuracy: 0.7981\n",
            "Epoch 925/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.3939 - accuracy: 0.8564 - val_loss: 0.8483 - val_accuracy: 0.8013\n",
            "Epoch 926/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.3967 - accuracy: 0.8595 - val_loss: 0.8635 - val_accuracy: 0.8002\n",
            "Epoch 927/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4248 - accuracy: 0.8419 - val_loss: 0.8258 - val_accuracy: 0.8055\n",
            "Epoch 928/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.3903 - accuracy: 0.8574 - val_loss: 0.7513 - val_accuracy: 0.8160\n",
            "Epoch 929/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4031 - accuracy: 0.8574 - val_loss: 0.7935 - val_accuracy: 0.8044\n",
            "Epoch 930/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4236 - accuracy: 0.8445 - val_loss: 0.8204 - val_accuracy: 0.7928\n",
            "Epoch 931/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4108 - accuracy: 0.8434 - val_loss: 0.8136 - val_accuracy: 0.8002\n",
            "Epoch 932/1000\n",
            "1929/1929 [==============================] - 1s 440us/step - loss: 0.4056 - accuracy: 0.8569 - val_loss: 0.7341 - val_accuracy: 0.8086\n",
            "Epoch 933/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4071 - accuracy: 0.8491 - val_loss: 0.8521 - val_accuracy: 0.8034\n",
            "Epoch 934/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.3668 - accuracy: 0.8637 - val_loss: 0.7993 - val_accuracy: 0.8023\n",
            "Epoch 935/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4111 - accuracy: 0.8502 - val_loss: 0.9163 - val_accuracy: 0.7781\n",
            "Epoch 936/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.3769 - accuracy: 0.8626 - val_loss: 0.8400 - val_accuracy: 0.7981\n",
            "Epoch 937/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.3981 - accuracy: 0.8543 - val_loss: 0.8003 - val_accuracy: 0.7939\n",
            "Epoch 938/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.3782 - accuracy: 0.8611 - val_loss: 0.9072 - val_accuracy: 0.7950\n",
            "Epoch 939/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.4075 - accuracy: 0.8507 - val_loss: 0.8278 - val_accuracy: 0.7971\n",
            "Epoch 940/1000\n",
            "1929/1929 [==============================] - 1s 446us/step - loss: 0.4339 - accuracy: 0.8372 - val_loss: 0.8841 - val_accuracy: 0.7813\n",
            "Epoch 941/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4195 - accuracy: 0.8486 - val_loss: 0.8683 - val_accuracy: 0.7950\n",
            "Epoch 942/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.4171 - accuracy: 0.8491 - val_loss: 0.8239 - val_accuracy: 0.8128\n",
            "Epoch 943/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.3944 - accuracy: 0.8528 - val_loss: 0.8138 - val_accuracy: 0.7813\n",
            "Epoch 944/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.3960 - accuracy: 0.8512 - val_loss: 0.9291 - val_accuracy: 0.7834\n",
            "Epoch 945/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4042 - accuracy: 0.8595 - val_loss: 0.8160 - val_accuracy: 0.8107\n",
            "Epoch 946/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.3734 - accuracy: 0.8528 - val_loss: 0.8394 - val_accuracy: 0.8086\n",
            "Epoch 947/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.4141 - accuracy: 0.8538 - val_loss: 0.7975 - val_accuracy: 0.8013\n",
            "Epoch 948/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.3873 - accuracy: 0.8580 - val_loss: 0.8021 - val_accuracy: 0.8139\n",
            "Epoch 949/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.3861 - accuracy: 0.8574 - val_loss: 0.7986 - val_accuracy: 0.8065\n",
            "Epoch 950/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4217 - accuracy: 0.8455 - val_loss: 0.8194 - val_accuracy: 0.7760\n",
            "Epoch 951/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.3621 - accuracy: 0.8818 - val_loss: 0.9114 - val_accuracy: 0.7476\n",
            "Epoch 952/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.3926 - accuracy: 0.8663 - val_loss: 0.7703 - val_accuracy: 0.7928\n",
            "Epoch 953/1000\n",
            "1929/1929 [==============================] - 1s 447us/step - loss: 0.3879 - accuracy: 0.8642 - val_loss: 0.8070 - val_accuracy: 0.7981\n",
            "Epoch 954/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4134 - accuracy: 0.8507 - val_loss: 0.8521 - val_accuracy: 0.7624\n",
            "Epoch 955/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4177 - accuracy: 0.8491 - val_loss: 0.8057 - val_accuracy: 0.8139\n",
            "Epoch 956/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.4004 - accuracy: 0.8491 - val_loss: 0.7926 - val_accuracy: 0.8139\n",
            "Epoch 957/1000\n",
            "1929/1929 [==============================] - 1s 432us/step - loss: 0.3585 - accuracy: 0.8730 - val_loss: 0.8271 - val_accuracy: 0.8076\n",
            "Epoch 958/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.3964 - accuracy: 0.8517 - val_loss: 0.8030 - val_accuracy: 0.7928\n",
            "Epoch 959/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.3830 - accuracy: 0.8631 - val_loss: 0.8650 - val_accuracy: 0.8055\n",
            "Epoch 960/1000\n",
            "1929/1929 [==============================] - 1s 437us/step - loss: 0.3892 - accuracy: 0.8595 - val_loss: 0.8401 - val_accuracy: 0.8023\n",
            "Epoch 961/1000\n",
            "1929/1929 [==============================] - 1s 426us/step - loss: 0.4065 - accuracy: 0.8538 - val_loss: 0.7944 - val_accuracy: 0.7981\n",
            "Epoch 962/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.3915 - accuracy: 0.8580 - val_loss: 0.8836 - val_accuracy: 0.8055\n",
            "Epoch 963/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.3872 - accuracy: 0.8517 - val_loss: 0.9263 - val_accuracy: 0.7718\n",
            "Epoch 964/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.3987 - accuracy: 0.8554 - val_loss: 0.8600 - val_accuracy: 0.7928\n",
            "Epoch 965/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4014 - accuracy: 0.8569 - val_loss: 0.7989 - val_accuracy: 0.8076\n",
            "Epoch 966/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.4025 - accuracy: 0.8590 - val_loss: 0.8259 - val_accuracy: 0.7981\n",
            "Epoch 967/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.4188 - accuracy: 0.8409 - val_loss: 0.7856 - val_accuracy: 0.8013\n",
            "Epoch 968/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.3616 - accuracy: 0.8694 - val_loss: 0.8042 - val_accuracy: 0.8023\n",
            "Epoch 969/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.3890 - accuracy: 0.8528 - val_loss: 0.7916 - val_accuracy: 0.7876\n",
            "Epoch 970/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.3763 - accuracy: 0.8673 - val_loss: 0.8080 - val_accuracy: 0.8107\n",
            "Epoch 971/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.3643 - accuracy: 0.8642 - val_loss: 0.7987 - val_accuracy: 0.7802\n",
            "Epoch 972/1000\n",
            "1929/1929 [==============================] - 1s 424us/step - loss: 0.3844 - accuracy: 0.8554 - val_loss: 0.7889 - val_accuracy: 0.8034\n",
            "Epoch 973/1000\n",
            "1929/1929 [==============================] - 1s 434us/step - loss: 0.3821 - accuracy: 0.8704 - val_loss: 0.7918 - val_accuracy: 0.8034\n",
            "Epoch 974/1000\n",
            "1929/1929 [==============================] - 1s 429us/step - loss: 0.4217 - accuracy: 0.8533 - val_loss: 0.8164 - val_accuracy: 0.8076\n",
            "Epoch 975/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.3716 - accuracy: 0.8668 - val_loss: 0.9668 - val_accuracy: 0.7918\n",
            "Epoch 976/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.4065 - accuracy: 0.8559 - val_loss: 0.8779 - val_accuracy: 0.7592\n",
            "Epoch 977/1000\n",
            "1929/1929 [==============================] - 1s 431us/step - loss: 0.4103 - accuracy: 0.8512 - val_loss: 0.7667 - val_accuracy: 0.8107\n",
            "Epoch 978/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.3850 - accuracy: 0.8611 - val_loss: 0.9169 - val_accuracy: 0.7855\n",
            "Epoch 979/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.4030 - accuracy: 0.8460 - val_loss: 0.7707 - val_accuracy: 0.8107\n",
            "Epoch 980/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.3682 - accuracy: 0.8663 - val_loss: 0.8125 - val_accuracy: 0.7950\n",
            "Epoch 981/1000\n",
            "1929/1929 [==============================] - 1s 439us/step - loss: 0.3757 - accuracy: 0.8647 - val_loss: 0.7628 - val_accuracy: 0.8160\n",
            "Epoch 982/1000\n",
            "1929/1929 [==============================] - 1s 442us/step - loss: 0.3800 - accuracy: 0.8621 - val_loss: 0.8255 - val_accuracy: 0.7992\n",
            "Epoch 983/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.3924 - accuracy: 0.8621 - val_loss: 0.8028 - val_accuracy: 0.8013\n",
            "Epoch 984/1000\n",
            "1929/1929 [==============================] - 1s 427us/step - loss: 0.4012 - accuracy: 0.8554 - val_loss: 0.8007 - val_accuracy: 0.8076\n",
            "Epoch 985/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.3472 - accuracy: 0.8787 - val_loss: 0.8113 - val_accuracy: 0.7865\n",
            "Epoch 986/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.3711 - accuracy: 0.8673 - val_loss: 0.7973 - val_accuracy: 0.8034\n",
            "Epoch 987/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.3650 - accuracy: 0.8621 - val_loss: 0.7524 - val_accuracy: 0.7950\n",
            "Epoch 988/1000\n",
            "1929/1929 [==============================] - 1s 428us/step - loss: 0.3794 - accuracy: 0.8590 - val_loss: 0.8669 - val_accuracy: 0.7992\n",
            "Epoch 989/1000\n",
            "1929/1929 [==============================] - 1s 425us/step - loss: 0.3746 - accuracy: 0.8600 - val_loss: 0.8150 - val_accuracy: 0.8181\n",
            "Epoch 990/1000\n",
            "1929/1929 [==============================] - 1s 433us/step - loss: 0.3710 - accuracy: 0.8616 - val_loss: 0.9182 - val_accuracy: 0.7844\n",
            "Epoch 991/1000\n",
            "1929/1929 [==============================] - 1s 458us/step - loss: 0.3820 - accuracy: 0.8626 - val_loss: 0.8269 - val_accuracy: 0.8170\n",
            "Epoch 992/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.3651 - accuracy: 0.8782 - val_loss: 0.7713 - val_accuracy: 0.7971\n",
            "Epoch 993/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.3773 - accuracy: 0.8621 - val_loss: 0.7131 - val_accuracy: 0.8097\n",
            "Epoch 994/1000\n",
            "1929/1929 [==============================] - 1s 436us/step - loss: 0.3821 - accuracy: 0.8688 - val_loss: 0.7736 - val_accuracy: 0.8055\n",
            "Epoch 995/1000\n",
            "1929/1929 [==============================] - 1s 423us/step - loss: 0.3796 - accuracy: 0.8580 - val_loss: 0.7760 - val_accuracy: 0.8044\n",
            "Epoch 996/1000\n",
            "1929/1929 [==============================] - 1s 435us/step - loss: 0.3777 - accuracy: 0.8548 - val_loss: 0.9585 - val_accuracy: 0.7844\n",
            "Epoch 997/1000\n",
            "1929/1929 [==============================] - 1s 438us/step - loss: 0.3856 - accuracy: 0.8574 - val_loss: 0.8176 - val_accuracy: 0.8002\n",
            "Epoch 998/1000\n",
            "1929/1929 [==============================] - 1s 441us/step - loss: 0.3430 - accuracy: 0.8766 - val_loss: 1.1278 - val_accuracy: 0.7613\n",
            "Epoch 999/1000\n",
            "1929/1929 [==============================] - 1s 430us/step - loss: 0.3948 - accuracy: 0.8564 - val_loss: 0.8178 - val_accuracy: 0.7992\n",
            "Epoch 1000/1000\n",
            "1929/1929 [==============================] - 1s 646us/step - loss: 0.3877 - accuracy: 0.8590 - val_loss: 0.8853 - val_accuracy: 0.7981\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glb18ivxLb1l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "bdb13d9e-83e2-4a49-d14a-b4a1b58edd76"
      },
      "source": [
        "score = model.evaluate(x_testcnn_encoded, y_test_encoded, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.8852590443458718\n",
            "Test accuracy: 0.7981072664260864\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVRlv6K1LgEd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "outputId": "0e34c4dc-2c7d-4abc-c874-0a010bb93806"
      },
      "source": [
        "predictions = model.predict_classes(x_testcnn_encoded)\n",
        "predictions\n",
        "y_test_encoded\n",
        "new_Ytest_encoded = y_test_encoded.astype(int)\n",
        "new_Ytest_encoded"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 5, 3, 3, 6, 7, 5, 5, 7, 3, 1, 3, 1, 2, 5, 4, 4, 3, 3, 3, 1, 7,\n",
              "       2, 2, 2, 1, 1, 3, 4, 2, 0, 2, 1, 1, 7, 5, 1, 7, 7, 5, 4, 0, 4, 6,\n",
              "       7, 2, 5, 3, 3, 5, 5, 2, 4, 4, 6, 5, 7, 2, 7, 3, 4, 6, 5, 6, 3, 6,\n",
              "       5, 5, 7, 5, 4, 6, 1, 3, 6, 1, 3, 1, 6, 1, 2, 4, 1, 6, 4, 5, 3, 5,\n",
              "       0, 6, 4, 4, 1, 4, 1, 5, 4, 4, 6, 2, 3, 4, 2, 6, 2, 5, 2, 1, 5, 3,\n",
              "       4, 4, 5, 1, 3, 6, 1, 7, 5, 7, 1, 0, 6, 1, 4, 3, 0, 2, 5, 3, 2, 6,\n",
              "       2, 2, 6, 5, 3, 1, 0, 5, 1, 6, 5, 5, 5, 3, 7, 1, 1, 4, 0, 6, 5, 6,\n",
              "       6, 1, 6, 1, 4, 1, 6, 2, 6, 6, 1, 5, 1, 6, 2, 3, 3, 2, 5, 5, 6, 5,\n",
              "       1, 4, 7, 1, 3, 6, 3, 3, 0, 0, 5, 2, 7, 2, 4, 3, 7, 2, 4, 3, 1, 6,\n",
              "       3, 2, 1, 4, 6, 2, 7, 0, 5, 2, 4, 7, 0, 3, 2, 5, 3, 5, 3, 1, 4, 7,\n",
              "       7, 7, 3, 4, 2, 3, 5, 6, 5, 1, 7, 1, 4, 1, 7, 4, 6, 7, 3, 0, 2, 6,\n",
              "       5, 4, 7, 7, 7, 1, 2, 7, 5, 1, 1, 1, 6, 0, 0, 6, 6, 4, 3, 7, 5, 7,\n",
              "       4, 3, 7, 7, 7, 5, 7, 2, 2, 2, 6, 3, 3, 6, 3, 2, 3, 1, 2, 3, 4, 0,\n",
              "       3, 4, 4, 2, 2, 0, 3, 2, 2, 2, 0, 5, 1, 4, 0, 6, 4, 7, 7, 3, 1, 0,\n",
              "       1, 2, 3, 7, 4, 5, 5, 6, 5, 2, 4, 4, 7, 4, 5, 1, 3, 2, 3, 0, 3, 4,\n",
              "       5, 7, 1, 6, 3, 7, 4, 2, 4, 3, 5, 5, 3, 5, 7, 7, 1, 1, 7, 1, 0, 6,\n",
              "       0, 5, 6, 1, 5, 3, 5, 5, 0, 5, 4, 2, 6, 1, 1, 1, 3, 2, 2, 4, 4, 2,\n",
              "       4, 2, 5, 3, 6, 6, 5, 2, 4, 4, 6, 7, 4, 7, 2, 1, 0, 7, 4, 0, 0, 3,\n",
              "       1, 2, 2, 6, 4, 3, 6, 7, 2, 1, 5, 7, 1, 7, 5, 1, 7, 6, 5, 0, 6, 3,\n",
              "       3, 3, 2, 0, 0, 7, 5, 7, 2, 5, 4, 6, 5, 5, 2, 5, 5, 3, 6, 4, 5, 4,\n",
              "       0, 1, 6, 5, 7, 2, 4, 0, 4, 4, 1, 7, 1, 6, 6, 0, 4, 2, 7, 7, 6, 7,\n",
              "       4, 5, 1, 5, 6, 6, 1, 7, 1, 7, 6, 4, 3, 4, 4, 1, 4, 5, 7, 6, 2, 7,\n",
              "       2, 5, 6, 6, 1, 3, 3, 7, 5, 7, 4, 6, 5, 2, 1, 1, 5, 7, 1, 6, 6, 7,\n",
              "       4, 2, 6, 7, 4, 5, 4, 7, 4, 1, 7, 1, 5, 7, 2, 7, 4, 7, 0, 1, 3, 2,\n",
              "       6, 2, 7, 1, 4, 2, 2, 4, 0, 4, 2, 3, 4, 6, 7, 7, 3, 5, 2, 2, 5, 1,\n",
              "       1, 4, 4, 3, 5, 4, 0, 6, 5, 3, 7, 5, 0, 3, 2, 2, 4, 2, 2, 2, 3, 1,\n",
              "       5, 3, 2, 0, 2, 7, 7, 7, 1, 7, 6, 5, 6, 6, 1, 3, 7, 6, 1, 6, 2, 6,\n",
              "       5, 2, 7, 3, 4, 7, 2, 5, 1, 0, 4, 1, 5, 0, 5, 4, 2, 0, 4, 4, 3, 5,\n",
              "       3, 7, 0, 1, 6, 3, 0, 4, 4, 6, 3, 4, 0, 1, 7, 5, 3, 5, 1, 6, 6, 0,\n",
              "       4, 1, 7, 5, 7, 1, 1, 1, 4, 3, 7, 4, 7, 3, 3, 6, 3, 1, 1, 7, 6, 1,\n",
              "       1, 6, 1, 7, 4, 5, 5, 2, 0, 1, 0, 4, 7, 5, 0, 7, 1, 2, 6, 0, 1, 0,\n",
              "       4, 4, 7, 5, 5, 4, 1, 3, 5, 2, 7, 6, 6, 7, 3, 7, 3, 2, 6, 7, 3, 4,\n",
              "       7, 0, 3, 5, 7, 1, 5, 5, 4, 4, 4, 2, 1, 4, 7, 7, 6, 2, 3, 1, 4, 5,\n",
              "       7, 2, 1, 4, 0, 5, 4, 6, 3, 2, 6, 1, 1, 2, 6, 1, 7, 2, 2, 2, 6, 7,\n",
              "       6, 1, 1, 6, 4, 6, 1, 4, 5, 4, 2, 0, 1, 6, 2, 2, 7, 4, 2, 7, 1, 6,\n",
              "       2, 6, 5, 7, 2, 5, 3, 0, 4, 4, 7, 5, 4, 3, 1, 4, 3, 7, 2, 1, 1, 7,\n",
              "       1, 0, 6, 6, 5, 1, 3, 2, 1, 1, 7, 4, 5, 4, 2, 5, 0, 5, 6, 2, 1, 2,\n",
              "       7, 3, 3, 5, 2, 0, 7, 7, 3, 0, 1, 5, 3, 6, 2, 5, 2, 7, 5, 1, 0, 6,\n",
              "       0, 3, 7, 4, 0, 7, 3, 2, 4, 1, 5, 0, 4, 1, 4, 7, 4, 7, 2, 6, 6, 7,\n",
              "       5, 5, 3, 5, 4, 2, 3, 6, 6, 4, 7, 1, 2, 2, 2, 2, 1, 0, 4, 1, 0, 7,\n",
              "       6, 4, 4, 7, 6, 7, 1, 2, 4, 1, 3, 6, 7, 4, 6, 6, 3, 1, 6, 5, 3, 6,\n",
              "       1, 7, 7, 6, 6, 2, 4, 4, 4, 3, 5, 1, 4, 1, 3, 7, 6, 3, 7, 3, 7, 5,\n",
              "       1, 7, 4, 4, 5, 2, 7, 3, 2, 4, 3, 0, 5, 5, 3, 2, 1, 7, 6, 6, 6, 1,\n",
              "       3, 1, 6, 3, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmA2CeRmLjSy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "3d80d1e5-b07a-43b4-abb9-0bef939c7525"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "report = classification_report(new_Ytest_encoded, predictions)\n",
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.75      0.74        64\n",
            "           1       0.84      0.95      0.89       135\n",
            "           2       0.83      0.71      0.77       122\n",
            "           3       0.84      0.71      0.77       115\n",
            "           4       0.76      0.87      0.81       134\n",
            "           5       0.71      0.81      0.76       126\n",
            "           6       0.79      0.77      0.78       121\n",
            "           7       0.88      0.77      0.82       134\n",
            "\n",
            "    accuracy                           0.80       951\n",
            "   macro avg       0.80      0.79      0.79       951\n",
            "weighted avg       0.80      0.80      0.80       951\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}