precise_training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/salama4ai/Mycroft-project-salama4ai/blob/main/precise_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1RwM7tFLwJd"
      },
      "source": [
        "# **This script is for installation and train mycroft-Precise on Google-Colab**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIErgEpmN_yn"
      },
      "source": [
        "**in the other notebook i will install mycroft-core to exploit the files(hey-savant.pb.params, hey-savant.pb) produced from precise training(this notebook) in production/inference mode**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFImePE-ft6G"
      },
      "source": [
        "####The Wake Word Listener's job is to continually listen to sounds and speech around the Device, and activate when the sounds or speech match a Wake Word. Mycroft provides an open source Wake Word Listener called Precise. which i will use in this project to train a model to wake up when it hears certain word.\n",
        " \n",
        "####Precise is the default Wake Word Listener and handles detection of \"Hey Mycroft\" for the majority of Mycroft Users.\n",
        "####i will train the model in this project to detect the word \"**hey-savant**\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vt5YeeKidYum"
      },
      "source": [
        "### **installing dependencies**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVuDLmcYRRfk",
        "outputId": "5b629c87-55f5-49b0-f060-f2a97d2d629e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\n",
            "  Selection    Path                Priority   Status\n",
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            "  2            /usr/bin/python3.8   1         manual mode\n",
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            "\n",
            "Press <enter> to keep the current choice[*], or type selection number: 1\n",
            "update-alternatives: using /usr/bin/python3.7 to provide /usr/bin/python3 (python3) in manual mode\n",
            "Reading package lists... Done\n",
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            "python3-pip is already the newest version (20.0.2-5ubuntu1.8).\n",
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            "The following NEW packages will be installed:\n",
            "  libportaudio2 libpython3.7 libpython3.7-dev python3-pyaudio python3.7-dev\n",
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          ]
        }
      ],
      "source": [
        "##########################################################################################\n",
        "############################## installing dependencies    #############################\n",
        "# --------------------------------------------------------------------------------------------\n",
        "# installing dependencies\n",
        "# there may be unnecessary packages, but no problem as long as they don't cause issues\n",
        "\n",
        "# -----To install specific version 3.7 of python in Google Colab-----\n",
        "!sudo apt-get install python3.7 \n",
        "!sudo apt-get update && apt-get upgrade python3-pip\n",
        "!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1\n",
        "\n",
        "# to select the working python version\n",
        "!sudo update-alternatives --config python3\n",
        "\n",
        "!sudo apt-get install python3-pip python3.7-distutils python3.7-dev python3-pyaudio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cu1EcoNXdq4N"
      },
      "source": [
        "### **installing mycroft-precise**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "9qFfMtAndIiu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "615bbf0b-9a71-4aef-a11c-83d61aa762c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root\n",
            "Cloning into 'mycroft-precise'...\n",
            "remote: Enumerating objects: 1581, done.\u001b[K\n",
            "remote: Counting objects: 100% (28/28), done.\u001b[K\n",
            "remote: Compressing objects: 100% (20/20), done.\u001b[K\n",
            "remote: Total 1581 (delta 13), reused 19 (delta 8), pack-reused 1553\u001b[K\n",
            "Receiving objects: 100% (1581/1581), 453.85 KiB | 12.27 MiB/s, done.\n",
            "Resolving deltas: 100% (1044/1044), done.\n",
            "/root/mycroft-precise\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting absl-py==0.7.1\n",
            "  Downloading absl-py-0.7.1.tar.gz (99 kB)\n",
            "\u001b[K     |████████████████████████████████| 99 kB 8.1 MB/s \n",
            "\u001b[?25hCollecting astor==0.7.1\n",
            "  Downloading astor-0.7.1-py2.py3-none-any.whl (27 kB)\n",
            "Collecting attrs==19.1.0\n",
            "  Downloading attrs-19.1.0-py2.py3-none-any.whl (35 kB)\n",
            "Collecting cycler==0.10.0\n",
            "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
            "Collecting decorator==4.4.0\n",
            "  Downloading decorator-4.4.0-py2.py3-none-any.whl (8.3 kB)\n",
            "Collecting fitipy==0.1.2\n",
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            "Collecting future==0.17.1\n",
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            "\u001b[K     |████████████████████████████████| 829 kB 47.1 MB/s \n",
            "\u001b[?25hCollecting gast==0.2.2\n",
            "  Downloading gast-0.2.2.tar.gz (10 kB)\n",
            "Collecting grpcio==1.19.0\n",
            "  Downloading grpcio-1.19.0-cp37-cp37m-manylinux1_x86_64.whl (10.8 MB)\n",
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            "\u001b[?25hCollecting h5py==2.9.0\n",
            "  Downloading h5py-2.9.0-cp37-cp37m-manylinux1_x86_64.whl (2.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.8 MB 57.3 MB/s \n",
            "\u001b[?25hCollecting Keras==2.2.4\n",
            "  Downloading Keras-2.2.4-py2.py3-none-any.whl (312 kB)\n",
            "\u001b[K     |████████████████████████████████| 312 kB 68.4 MB/s \n",
            "\u001b[?25hCollecting Keras-Applications==1.0.7\n",
            "  Downloading Keras_Applications-1.0.7-py2.py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 520 kB/s \n",
            "\u001b[?25hCollecting Keras-Preprocessing==1.0.9\n",
            "  Downloading Keras_Preprocessing-1.0.9-py2.py3-none-any.whl (59 kB)\n",
            "\u001b[K     |████████████████████████████████| 59 kB 6.1 MB/s \n",
            "\u001b[?25hCollecting kiwisolver==1.0.1\n",
            "  Downloading kiwisolver-1.0.1-cp37-cp37m-manylinux1_x86_64.whl (89 kB)\n",
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            "\u001b[?25hCollecting Markdown==3.1\n",
            "  Downloading Markdown-3.1-py2.py3-none-any.whl (87 kB)\n",
            "\u001b[K     |████████████████████████████████| 87 kB 5.5 MB/s \n",
            "\u001b[?25hCollecting matplotlib==3.0.3\n",
            "  Downloading matplotlib-3.0.3-cp37-cp37m-manylinux1_x86_64.whl (13.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.0 MB 50.6 MB/s \n",
            "\u001b[?25hCollecting mock==2.0.0\n",
            "  Downloading mock-2.0.0-py2.py3-none-any.whl (56 kB)\n",
            "\u001b[K     |████████████████████████████████| 56 kB 3.0 MB/s \n",
            "\u001b[?25hObtaining mycroft_precise from git+git@github.com:MycroftAI/mycroft-precise@37ef1ab91eeca81fd889bce2967775b2f6918d97#egg=mycroft_precise (from -r /root/mycroft-precise/requirements.txt (line 18))\n",
            "\u001b[33m  DEPRECATION: This form of VCS requirement is being deprecated: git+git@github.com:MycroftAI/mycroft-precise@37ef1ab91eeca81fd889bce2967775b2f6918d97#egg=mycroft_precise. pip 21.0 will remove support for this functionality. A possible replacement is git+https://git@example.com/..., git+ssh://git@example.com/..., or the insecure git+git://git@example.com/.... You can find discussion regarding this at https://github.com/pypa/pip/issues/7554.\u001b[0m\n",
            "  Cloning git@github.com:MycroftAI/mycroft-precise (to revision 37ef1ab91eeca81fd889bce2967775b2f6918d97) to ./src/mycroft-precise\n",
            "  Running command git clone -q git@github.com:MycroftAI/mycroft-precise /root/mycroft-precise/src/mycroft-precise\n",
            "  Host key verification failed.\n",
            "  fatal: Could not read from remote repository.\n",
            "\n",
            "  Please make sure you have the correct access rights\n",
            "  and the repository exists.\n",
            "\u001b[31mERROR: Command errored out with exit status 128: git clone -q git@github.com:MycroftAI/mycroft-precise /root/mycroft-precise/src/mycroft-precise Check the logs for full command output.\u001b[0m\n",
            "running build\n",
            "running build_py\n",
            "creating build\n",
            "creating build/lib\n",
            "creating build/lib/precise\n",
            "copying precise/__init__.py -> build/lib/precise\n",
            "copying precise/model.py -> build/lib/precise\n",
            "copying precise/threshold_decoder.py -> build/lib/precise\n",
            "copying precise/train_data.py -> build/lib/precise\n",
            "copying precise/vectorization.py -> build/lib/precise\n",
            "copying precise/stats.py -> build/lib/precise\n",
            "copying precise/util.py -> build/lib/precise\n",
            "copying precise/functions.py -> build/lib/precise\n",
            "copying precise/params.py -> build/lib/precise\n",
            "copying precise/annoyance_estimator.py -> build/lib/precise\n",
            "copying precise/network_runner.py -> build/lib/precise\n",
            "package init file 'precise/scripts/__init__.py' not found (or not a regular file)\n",
            "creating build/lib/precise/scripts\n",
            "copying precise/scripts/test.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/train_optimize.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/convert.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/train_incremental.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/train_generated.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/calc_threshold.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/base_script.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/collect.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/listen.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/train_sampled.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/train.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/engine.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/graph.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/add_noise.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/simulate.py -> build/lib/precise/scripts\n",
            "copying precise/scripts/eval.py -> build/lib/precise/scripts\n",
            "creating build/lib/precise/pocketsphinx\n",
            "copying precise/pocketsphinx/__init__.py -> build/lib/precise/pocketsphinx\n",
            "copying precise/pocketsphinx/listener.py -> build/lib/precise/pocketsphinx\n",
            "creating build/lib/precise/pocketsphinx/scripts\n",
            "copying precise/pocketsphinx/scripts/__init__.py -> build/lib/precise/pocketsphinx/scripts\n",
            "copying precise/pocketsphinx/scripts/test.py -> build/lib/precise/pocketsphinx/scripts\n",
            "copying precise/pocketsphinx/scripts/listen.py -> build/lib/precise/pocketsphinx/scripts\n",
            "running egg_info\n",
            "creating mycroft_precise.egg-info\n",
            "writing mycroft_precise.egg-info/PKG-INFO\n",
            "writing dependency_links to mycroft_precise.egg-info/dependency_links.txt\n",
            "writing entry points to mycroft_precise.egg-info/entry_points.txt\n",
            "writing requirements to mycroft_precise.egg-info/requires.txt\n",
            "writing top-level names to mycroft_precise.egg-info/top_level.txt\n",
            "writing manifest file 'mycroft_precise.egg-info/SOURCES.txt'\n",
            "reading manifest file 'mycroft_precise.egg-info/SOURCES.txt'\n",
            "reading manifest template 'MANIFEST.in'\n",
            "writing manifest file 'mycroft_precise.egg-info/SOURCES.txt'\n",
            "creating build/lib/precise/data\n",
            "copying precise/data/activate.wav -> build/lib/precise/data\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Processing /root/mycroft-precise\n",
            "Collecting attrs\n",
            "  Downloading attrs-22.2.0-py3-none-any.whl (60 kB)\n",
            "\u001b[K     |████████████████████████████████| 60 kB 5.9 MB/s \n",
            "\u001b[?25hCollecting fitipy<1.0\n",
            "  Using cached fitipy-0.1.2.tar.gz (1.7 kB)\n",
            "Collecting h5py\n",
            "  Downloading h5py-3.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n",
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            "\u001b[?25hCollecting keras<=2.1.5\n",
            "  Downloading Keras-2.1.5-py2.py3-none-any.whl (334 kB)\n",
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            "\u001b[?25hCollecting precise-runner\n",
            "  Downloading precise-runner-0.3.1.tar.gz (4.4 kB)\n",
            "Collecting prettyparse>=1.1.0\n",
            "  Downloading prettyparse-1.2.0.tar.gz (6.1 kB)\n",
            "Collecting pyache\n",
            "  Downloading pyache-0.2.0-py3-none-any.whl (7.6 kB)\n",
            "Requirement already satisfied: pyaudio in /usr/lib/python3/dist-packages (from mycroft-precise==0.3.0) (0.2.11)\n",
            "Collecting sonopy\n",
            "  Downloading sonopy-0.1.2.tar.gz (3.3 kB)\n",
            "Collecting speechpy-fast\n",
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            "\u001b[?25hCollecting gast>=0.2.0\n",
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            "Requirement already satisfied: wheel>=0.26 in /usr/lib/python3/dist-packages (from tensorflow<1.14,>=1.13->mycroft-precise==0.3.0) (0.34.2)\n",
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            "Building wheels for collected packages: mycroft-precise, fitipy, precise-runner, prettyparse, sonopy, speechpy-fast, typing\n",
            "  Building wheel for mycroft-precise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mycroft-precise: filename=mycroft_precise-0.3.0-py3-none-any.whl size=151346 sha256=bd3d9c9dd83c52d3b6b4eb8abe8cb0b22b165c82519d30614eddab0357e7498f\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/97/ac/dadf91a5f2a53de6aaccc715bba39812c37a23d61a824b31ed\n",
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            "  Created wheel for fitipy: filename=fitipy-0.1.2-py3-none-any.whl size=1914 sha256=25447eac7b618ccbb5bd493653746e9b26a80eceb0b9e1dd7e3e874bc4d9a41d\n",
            "  Stored in directory: /root/.cache/pip/wheels/09/5d/0b/19ec5f35099934615605bf977833bc624aa7ab8fcec2c0b202\n",
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            "  Created wheel for precise-runner: filename=precise_runner-0.3.1-py3-none-any.whl size=4702 sha256=154e404f9b31d93e9fbb387af3933eb84f9d822ce9e1d2d6637b9cd306f629d1\n",
            "  Stored in directory: /root/.cache/pip/wheels/f0/04/a8/c32993e57c2deae2f6323dd86e4b87d4ff21a497864c781992\n",
            "  Building wheel for prettyparse (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prettyparse: filename=prettyparse-1.2.0-py3-none-any.whl size=3786 sha256=060f7bace45be76a78796c7e793c833bfee75971497eaeb5a0042f04b9d990b4\n",
            "  Stored in directory: /root/.cache/pip/wheels/b4/64/0d/3acd215fa101eb87f38037bd36a594f83fdf48b25b14478407\n",
            "  Building wheel for sonopy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sonopy: filename=sonopy-0.1.2-py3-none-any.whl size=2877 sha256=a868909d66990e9d76add602bbc96b95cf63023d5a8c71d446f48b43fd7064fd\n",
            "  Stored in directory: /root/.cache/pip/wheels/27/17/02/d430779ebe878618c57368f50b0a7d6f4c9efc957e762290a9\n",
            "  Building wheel for speechpy-fast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for speechpy-fast: filename=speechpy_fast-2.4-py3-none-any.whl size=7993 sha256=0288d0c8f1e3ec65b20b27ab685da02ab041eca8732a6b9a7a268b5e1ac298df\n",
            "  Stored in directory: /root/.cache/pip/wheels/51/b7/f7/0fbba52db2483f9aa2ef43582c5e984b69dc9d0e20e6e854b5\n",
            "  Building wheel for typing (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for typing: filename=typing-3.7.4.3-py3-none-any.whl size=26308 sha256=dd04da6aa6f136a582a8f9352d1964e1755ac8a24721b55ae6bc47439e1ec6eb\n",
            "  Stored in directory: /root/.cache/pip/wheels/35/f3/15/01aa6571f0a72ee6ae7b827c1491c37a1f72d686fd22b43b0e\n",
            "Successfully built mycroft-precise fitipy precise-runner prettyparse sonopy speechpy-fast typing\n",
            "\u001b[31mERROR: scipy 1.7.3 has requirement numpy<1.23.0,>=1.16.5, but you'll have numpy 1.16.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: wavio 0.0.7 has requirement numpy>=1.19.0, but you'll have numpy 1.16.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: attrs, fitipy, numpy, h5py, six, scipy, pyyaml, keras, precise-runner, prettyparse, pyache, sonopy, speechpy-fast, astor, protobuf, keras-preprocessing, MarkupSafe, werkzeug, absl-py, grpcio, typing-extensions, zipp, importlib-metadata, markdown, tensorboard, mock, tensorflow-estimator, keras-applications, gast, termcolor, tensorflow, typing, wavio, mycroft-precise\n",
            "Successfully installed MarkupSafe-2.1.2 absl-py-1.4.0 astor-0.8.1 attrs-22.2.0 fitipy-0.1.2 gast-0.5.3 grpcio-1.53.0 h5py-3.8.0 importlib-metadata-6.3.0 keras-2.1.5 keras-applications-1.0.8 keras-preprocessing-1.1.2 markdown-3.4.3 mock-5.0.1 mycroft-precise-0.3.0 numpy-1.16.0 precise-runner-0.3.1 prettyparse-1.2.0 protobuf-4.22.3 pyache-0.2.0 pyyaml-6.0 scipy-1.7.3 six-1.16.0 sonopy-0.1.2 speechpy-fast-2.4 tensorboard-1.13.1 tensorflow-1.13.2 tensorflow-estimator-1.13.0 termcolor-2.2.0 typing-3.7.4.3 typing-extensions-4.5.0 wavio-0.0.7 werkzeug-2.2.3 zipp-3.15.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "six",
                  "zipp"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "libatlas-base-dev is already the newest version (3.10.3-8ubuntu7).\n",
            "libhdf5-dev is already the newest version (1.10.4+repack-11ubuntu1).\n",
            "curl is already the newest version (7.68.0-1ubuntu2.18).\n",
            "libopenblas-dev is already the newest version (0.3.8+ds-1ubuntu0.20.04.1).\n",
            "python3-pip is already the newest version (20.0.2-5ubuntu1.8).\n",
            "The following additional packages will be installed:\n",
            "  libblkid-dev libffi-dev libglib2.0-dev libglib2.0-dev-bin libimagequant0\n",
            "  liblbfgsb0 libmount-dev libportaudiocpp0 libpulse-mainloop-glib0\n",
            "  libpython2-dev libpython2-stdlib libpython2.7 libpython2.7-dev\n",
            "  libselinux1-dev libsepol1-dev python-dev-is-python2 python-is-python2\n",
            "  python2 python2-dev python2-minimal python2.7-dev python3-decorator\n",
            "  python3-mpi4py python3-numpy python3-olefile python3-pil swig4.0\n",
            "Suggested packages:\n",
            "  cython-doc libgirepository1.0-dev libglib2.0-doc libgdk-pixbuf2.0-bin\n",
            "  | libgdk-pixbuf2.0-dev libxml2-utils portaudio19-doc python2-doc python-tk\n",
            "  python-h5py-doc python-numpy-doc python3-pytest python3-numpy-dbg\n",
            "  python-pil-doc python3-pil-dbg python-scipy-doc swig-doc swig-examples\n",
            "  swig4.0-examples swig4.0-doc\n",
            "The following NEW packages will be installed:\n",
            "  cython libblkid-dev libffi-dev libglib2.0-dev libglib2.0-dev-bin\n",
            "  libimagequant0 liblbfgsb0 libmount-dev libportaudiocpp0 libpulse-dev\n",
            "  libpulse-mainloop-glib0 libpython2-dev libpython2-stdlib libpython2.7\n",
            "  libpython2.7-dev libselinux1-dev libsepol1-dev portaudio19-dev\n",
            "  python-dev-is-python2 python-is-python2 python2 python2-dev python2-minimal\n",
            "  python2.7-dev python3-decorator python3-h5py python3-mpi4py python3-numpy\n",
            "  python3-olefile python3-pil python3-scipy swig swig4.0\n",
            "0 upgraded, 33 newly installed, 0 to remove and 4 not upgraded.\n",
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            "  Running setup.py develop for mycroft-precise\n",
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            "\u001b[?25hCollecting pycparser\n",
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            "\u001b[?25hBuilding wheels for collected packages: pocketsphinx\n",
            "  Building wheel for pocketsphinx (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pocketsphinx: filename=pocketsphinx-5.0.0-cp37-cp37m-linux_x86_64.whl size=29148243 sha256=567fde8afb27f3c88606046ea748d804aa3a8c48eb4d0b4fc9a8bdd3b2cb23d6\n",
            "  Stored in directory: /root/.cache/pip/wheels/10/ac/fa/e8797064bd494f218f69f49a9c56cd8d7880f0842a3444b408\n",
            "Successfully built pocketsphinx\n",
            "Installing collected packages: pycparser, CFFI, sounddevice, pocketsphinx\n",
            "Successfully installed CFFI-1.15.1 pocketsphinx-5.0.0 pycparser-2.21 sounddevice-0.4.6\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting absl-py==0.7.1\n",
            "  Using cached absl-py-0.7.1.tar.gz (99 kB)\n",
            "Collecting astor==0.7.1\n",
            "  Using cached astor-0.7.1-py2.py3-none-any.whl (27 kB)\n",
            "Collecting attrs==19.1.0\n",
            "  Using cached attrs-19.1.0-py2.py3-none-any.whl (35 kB)\n",
            "Collecting cycler==0.10.0\n",
            "  Using cached cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
            "Collecting decorator==4.4.0\n",
            "  Using cached decorator-4.4.0-py2.py3-none-any.whl (8.3 kB)\n",
            "Requirement already satisfied: fitipy==0.1.2 in /usr/local/lib/python3.7/dist-packages (from -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 6)) (0.1.2)\n",
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            "  Using cached future-0.17.1.tar.gz (829 kB)\n",
            "Collecting gast==0.2.2\n",
            "  Using cached gast-0.2.2.tar.gz (10 kB)\n",
            "Collecting grpcio==1.19.0\n",
            "  Using cached grpcio-1.19.0-cp37-cp37m-manylinux1_x86_64.whl (10.8 MB)\n",
            "Collecting h5py==2.9.0\n",
            "  Using cached h5py-2.9.0-cp37-cp37m-manylinux1_x86_64.whl (2.8 MB)\n",
            "Requirement already satisfied: keras==2.1.5 in /usr/local/lib/python3.7/dist-packages (from -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 12)) (2.1.5)\n",
            "Collecting Keras-Applications==1.0.7\n",
            "  Using cached Keras_Applications-1.0.7-py2.py3-none-any.whl (51 kB)\n",
            "Collecting Keras-Preprocessing==1.0.9\n",
            "  Using cached Keras_Preprocessing-1.0.9-py2.py3-none-any.whl (59 kB)\n",
            "Collecting kiwisolver==1.0.1\n",
            "  Using cached kiwisolver-1.0.1-cp37-cp37m-manylinux1_x86_64.whl (89 kB)\n",
            "Collecting Markdown==3.1\n",
            "  Using cached Markdown-3.1-py2.py3-none-any.whl (87 kB)\n",
            "Collecting matplotlib==3.0.3\n",
            "  Using cached matplotlib-3.0.3-cp37-cp37m-manylinux1_x86_64.whl (13.0 MB)\n",
            "Collecting mock==2.0.0\n",
            "  Using cached mock-2.0.0-py2.py3-none-any.whl (56 kB)\n",
            "Requirement already satisfied: numpy==1.16.0 in /usr/local/lib/python3.7/dist-packages (from -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 20)) (1.16.0)\n",
            "Collecting pbr==5.1.3\n",
            "  Downloading pbr-5.1.3-py2.py3-none-any.whl (107 kB)\n",
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            "  Downloading pymongo-3.7.2-cp37-cp37m-manylinux1_x86_64.whl (406 kB)\n",
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            "  Downloading python_dateutil-2.6.0-py2.py3-none-any.whl (194 kB)\n",
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            "  Downloading scikit_learn-0.20.3-cp37-cp37m-manylinux1_x86_64.whl (5.4 MB)\n",
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            "  Downloading scikit_optimize-0.5.2-py2.py3-none-any.whl (74 kB)\n",
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            "\u001b[?25hCollecting scipy==1.2.1\n",
            "  Downloading scipy-1.2.1-cp37-cp37m-manylinux1_x86_64.whl (24.8 MB)\n",
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            "\u001b[?25hCollecting six==1.12.0\n",
            "  Downloading six-1.12.0-py2.py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: sonopy==0.1.2 in /usr/local/lib/python3.7/dist-packages (from -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 38)) (0.1.2)\n",
            "Requirement already satisfied: speechpy-fast==2.4 in /usr/local/lib/python3.7/dist-packages (from -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 39)) (2.4)\n",
            "Requirement already satisfied: tensorboard==1.13.1 in /usr/local/lib/python3.7/dist-packages (from -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 40)) (1.13.1)\n",
            "Requirement already satisfied: tensorflow==1.13.2 in /usr/local/lib/python3.7/dist-packages (from -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 41)) (1.13.2)\n",
            "Requirement already satisfied: tensorflow-estimator==1.13.0 in /usr/local/lib/python3.7/dist-packages (from -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 42)) (1.13.0)\n",
            "Collecting termcolor==1.1.0\n",
            "  Downloading termcolor-1.1.0.tar.gz (3.9 kB)\n",
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            "  Downloading tqdm-4.31.1-py2.py3-none-any.whl (48 kB)\n",
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            "\u001b[?25hCollecting typing==3.6.6\n",
            "  Downloading typing-3.6.6-py3-none-any.whl (25 kB)\n",
            "Collecting wavio==0.0.4\n",
            "  Using cached wavio-0.0.4-py2.py3-none-any.whl (9.0 kB)\n",
            "Collecting Werkzeug==0.15.3\n",
            "  Downloading Werkzeug-0.15.3-py2.py3-none-any.whl (327 kB)\n",
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            "\u001b[?25hCollecting lingua-franca==0.4.2\n",
            "  Downloading lingua_franca-0.4.2-py3-none-any.whl (591 kB)\n",
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            "\u001b[?25hRequirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from kiwisolver==1.0.1->-r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 15)) (45.2.0)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/lib/python3/dist-packages (from tensorboard==1.13.1->-r /content/drive/MyDrive/Alkhwarizmi/requirements.txt (line 40)) (0.34.2)\n",
            "Building wheels for collected packages: absl-py, future, gast, pocketsphinx, prettyparse, PyYAML, termcolor\n",
            "  Building wheel for absl-py (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/de/06/32/01e3d0016e2e4e1b0757ce35f07d75c1033e142fde22239093\n",
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            "  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7539 sha256=d1aec5d49f812860042a9c97bf1dbf0a62188fba4c054bdcb964cfcb38bce489\n",
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            "  Created wheel for pocketsphinx: filename=pocketsphinx-0.1.0-cp37-cp37m-linux_x86_64.whl size=1709895 sha256=e8526454ade774b3002e1730912d4426135c21c17f0ccc250f64734032ec5997\n",
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            "  Created wheel for prettyparse: filename=prettyparse-1.1.0-py3-none-any.whl size=3747 sha256=9b56c802579ff88ce43989cd4f1fac75cd4976978b174f775b93f3b3d7ae11d8\n",
            "  Stored in directory: /root/.cache/pip/wheels/c7/b8/11/e1810ee454a768c9bb7d15e03e1e39468bd594d224bae6cbe3\n",
            "  Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyYAML: filename=PyYAML-5.1-cp37-cp37m-linux_x86_64.whl size=44075 sha256=9cbcc44bc517544b35d1b8aae199306828288f5ce35adb562da4a4b5cfd8e8e8\n",
            "  Stored in directory: /root/.cache/pip/wheels/77/f5/10/d00a2bd30928b972790053b5de0c703ca87324f3fead0f2fd9\n",
            "  Building wheel for termcolor (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4830 sha256=5e683a9c35953081665a238478797a5856a59f336431ca0dc8fbcde3a47286ff\n",
            "  Stored in directory: /root/.cache/pip/wheels/3f/e3/ec/8a8336ff196023622fbcb36de0c5a5c218cbb24111d1d4c7f2\n",
            "Successfully built absl-py future gast pocketsphinx prettyparse PyYAML termcolor\n",
            "Installing collected packages: six, absl-py, astor, attrs, cycler, decorator, future, gast, grpcio, h5py, Keras-Applications, Keras-Preprocessing, kiwisolver, Markdown, pyparsing, python-dateutil, matplotlib, pbr, mock, pocketsphinx, portalocker, prettyparse, protobuf, pymongo, PyYAML, scipy, scikit-learn, scikit-optimize, termcolor, tqdm, typing, wavio, Werkzeug, lingua-franca\n",
            "  Attempting uninstall: six\n",
            "    Found existing installation: six 1.16.0\n",
            "    Uninstalling six-1.16.0:\n",
            "      Successfully uninstalled six-1.16.0\n",
            "  Attempting uninstall: absl-py\n",
            "    Found existing installation: absl-py 1.4.0\n",
            "    Uninstalling absl-py-1.4.0:\n",
            "      Successfully uninstalled absl-py-1.4.0\n",
            "  Attempting uninstall: astor\n",
            "    Found existing installation: astor 0.8.1\n",
            "    Uninstalling astor-0.8.1:\n",
            "      Successfully uninstalled astor-0.8.1\n",
            "  Attempting uninstall: attrs\n",
            "    Found existing installation: attrs 22.2.0\n",
            "    Uninstalling attrs-22.2.0:\n",
            "      Successfully uninstalled attrs-22.2.0\n",
            "  Attempting uninstall: decorator\n",
            "    Found existing installation: decorator 4.4.2\n",
            "    Not uninstalling decorator at /usr/lib/python3/dist-packages, outside environment /usr\n",
            "    Can't uninstall 'decorator'. No files were found to uninstall.\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.5.3\n",
            "    Uninstalling gast-0.5.3:\n",
            "      Successfully uninstalled gast-0.5.3\n",
            "  Attempting uninstall: grpcio\n",
            "    Found existing installation: grpcio 1.53.0\n",
            "    Uninstalling grpcio-1.53.0:\n",
            "      Successfully uninstalled grpcio-1.53.0\n",
            "  Attempting uninstall: h5py\n",
            "    Found existing installation: h5py 3.8.0\n",
            "    Uninstalling h5py-3.8.0:\n",
            "      Successfully uninstalled h5py-3.8.0\n",
            "  Attempting uninstall: Keras-Applications\n",
            "    Found existing installation: Keras-Applications 1.0.8\n",
            "    Uninstalling Keras-Applications-1.0.8:\n",
            "      Successfully uninstalled Keras-Applications-1.0.8\n",
            "  Attempting uninstall: Keras-Preprocessing\n",
            "    Found existing installation: Keras-Preprocessing 1.1.2\n",
            "    Uninstalling Keras-Preprocessing-1.1.2:\n",
            "      Successfully uninstalled Keras-Preprocessing-1.1.2\n",
            "  Attempting uninstall: Markdown\n",
            "    Found existing installation: Markdown 3.4.3\n",
            "    Uninstalling Markdown-3.4.3:\n",
            "      Successfully uninstalled Markdown-3.4.3\n",
            "  Attempting uninstall: mock\n",
            "    Found existing installation: mock 5.0.1\n",
            "    Uninstalling mock-5.0.1:\n",
            "      Successfully uninstalled mock-5.0.1\n",
            "  Attempting uninstall: prettyparse\n",
            "    Found existing installation: prettyparse 1.2.0\n",
            "    Uninstalling prettyparse-1.2.0:\n",
            "      Successfully uninstalled prettyparse-1.2.0\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 4.22.3\n",
            "    Uninstalling protobuf-4.22.3:\n",
            "      Successfully uninstalled protobuf-4.22.3\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
            "      Successfully uninstalled PyYAML-6.0\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.7.3\n",
            "    Uninstalling scipy-1.7.3:\n",
            "      Successfully uninstalled scipy-1.7.3\n",
            "  Attempting uninstall: termcolor\n",
            "    Found existing installation: termcolor 2.2.0\n",
            "    Uninstalling termcolor-2.2.0:\n",
            "      Successfully uninstalled termcolor-2.2.0\n",
            "  Attempting uninstall: typing\n",
            "    Found existing installation: typing 3.7.4.3\n",
            "    Uninstalling typing-3.7.4.3:\n",
            "      Successfully uninstalled typing-3.7.4.3\n",
            "  Attempting uninstall: wavio\n",
            "    Found existing installation: wavio 0.0.7\n",
            "    Uninstalling wavio-0.0.7:\n",
            "      Successfully uninstalled wavio-0.0.7\n",
            "  Attempting uninstall: Werkzeug\n",
            "    Found existing installation: Werkzeug 2.2.3\n",
            "    Uninstalling Werkzeug-2.2.3:\n",
            "      Successfully uninstalled Werkzeug-2.2.3\n",
            "Successfully installed Keras-Applications-1.0.7 Keras-Preprocessing-1.0.9 Markdown-3.1 PyYAML-5.1 Werkzeug-0.15.3 absl-py-0.7.1 astor-0.7.1 attrs-19.1.0 cycler-0.10.0 decorator-4.4.0 future-0.17.1 gast-0.2.2 grpcio-1.19.0 h5py-2.9.0 kiwisolver-1.0.1 lingua-franca-0.4.2 matplotlib-3.0.3 mock-2.0.0 pbr-5.1.3 pocketsphinx-0.1.0 portalocker-1.4.0 prettyparse-1.1.0 protobuf-3.7.1 pymongo-3.7.2 pyparsing-2.3.1 python-dateutil-2.6.0 scikit-learn-0.20.3 scikit-optimize-0.5.2 scipy-1.2.1 six-1.12.0 termcolor-1.1.0 tqdm-4.31.1 typing-3.6.6 wavio-0.0.4\n",
            "pygobject 3.36.0 requires pycairo, which is not installed.\n"
          ]
        }
      ],
      "source": [
        "##########################################################################################\n",
        "############################## installing mycroft-precise    #############################\n",
        "# --------------------------------------------------------------------------------------------\n",
        "# installing mycroft-precise\n",
        "%cd ~/\n",
        "!git clone https://github.com/mycroftai/mycroft-precise \n",
        "%cd mycroft-precise \n",
        "!sudo python3 -m pip install -r ~/mycroft-precise/requirements.txt\n",
        "!python3 ./setup.py build\n",
        "!pip install .\n",
        "!sudo bash ~/mycroft-precise/setup.sh\n",
        "!sudo python3 -m pip install -r /content/drive/MyDrive/Alkhwarizmi/requirements.txt\n",
        "!pip check"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXBLurBAdu9T"
      },
      "source": [
        "### **start training**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WthrXfMhAqU"
      },
      "source": [
        "####The data samples i used in the training is process:-\n",
        "*  Train Data:-\n",
        "    *   100 (wake-word)\n",
        "    *   422 (not-wake-word)\n",
        "*  Test Data:-\n",
        "    *   15 (wake-word)\n",
        "    *   91 (not-wake-word)\n",
        "\n",
        "**Caution you must name the folders as desribed in the mycroft-recise github repository or the model will NOT find that data and will Not notify you**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "3aVsBUptdC3A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92631b29-22d8-4c43-c884-75d796f6505b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow backend.\n",
            "Loading from hey-savant.net...\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:3138: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "2023-04-13 15:11:32.114070: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2023-04-13 15:11:32.120111: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200210000 Hz\n",
            "2023-04-13 15:11:32.120333: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x1e2d840 executing computations on platform Host. Devices:\n",
            "2023-04-13 15:11:32.120360: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "Data: <TrainData wake_words=100 not_wake_words=422 test_wake_words=15 test_not_wake_words=91>\n",
            "Loading wake-word...\n",
            "Loading not-wake-word...\n",
            "Loading wake-word...\n",
            "Loading not-wake-word...\n",
            "Inputs shape: (377, 29, 13)\n",
            "Outputs shape: (377, 1)\n",
            "Test inputs shape: (56, 29, 13)\n",
            "Test outputs shape: (56, 1)\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "net (GRU)                    (None, 20)                2040      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 21        \n",
            "=================================================================\n",
            "Total params: 2,061\n",
            "Trainable params: 2,061\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 377 samples, validate on 56 samples\n",
            "Epoch 3708/7407\n",
            "377/377 [==============================] - 1s 1ms/step - loss: 0.0054 - acc: 0.9920 - val_loss: 0.0738 - val_acc: 0.9643\n",
            "Epoch 3709/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0090 - acc: 0.9920 - val_loss: 0.0801 - val_acc: 0.9286\n",
            "Epoch 3710/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0029 - acc: 0.9947 - val_loss: 0.0715 - val_acc: 0.9643\n",
            "Epoch 3711/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0063 - acc: 0.9894 - val_loss: 0.0676 - val_acc: 0.9643\n",
            "Epoch 3712/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0042 - acc: 0.9947 - val_loss: 0.0695 - val_acc: 0.9643\n",
            "Epoch 3713/7407\n",
            "377/377 [==============================] - 0s 112us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0748 - val_acc: 0.9643\n",
            "Epoch 3714/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0065 - acc: 0.9973 - val_loss: 0.0719 - val_acc: 0.9643\n",
            "Epoch 3715/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0057 - acc: 0.9920 - val_loss: 0.0758 - val_acc: 0.9464\n",
            "Epoch 3716/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0027 - acc: 0.9947 - val_loss: 0.0845 - val_acc: 0.9286\n",
            "Epoch 3717/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0792 - val_acc: 0.9286\n",
            "Epoch 3718/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0057 - acc: 0.9947 - val_loss: 0.0821 - val_acc: 0.9107\n",
            "Epoch 3719/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0081 - acc: 0.9920 - val_loss: 0.0831 - val_acc: 0.9286\n",
            "Epoch 3720/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0046 - acc: 0.9947 - val_loss: 0.0841 - val_acc: 0.9286\n",
            "Epoch 3721/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0052 - acc: 0.9947 - val_loss: 0.0875 - val_acc: 0.9286\n",
            "Epoch 3722/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0050 - acc: 0.9920 - val_loss: 0.0877 - val_acc: 0.9286\n",
            "Epoch 3723/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0945 - val_acc: 0.9286\n",
            "Epoch 3724/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0038 - acc: 0.9947 - val_loss: 0.0979 - val_acc: 0.9286\n",
            "Epoch 3725/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0051 - acc: 0.9920 - val_loss: 0.1010 - val_acc: 0.9286\n",
            "Epoch 3726/7407\n",
            "377/377 [==============================] - 0s 99us/step - loss: 5.5534e-04 - acc: 1.0000 - val_loss: 0.0990 - val_acc: 0.9286\n",
            "Epoch 3727/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0035 - acc: 0.9973 - val_loss: 0.1094 - val_acc: 0.9107\n",
            "Epoch 3728/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0081 - acc: 0.9973 - val_loss: 0.0792 - val_acc: 0.9464\n",
            "Epoch 3729/7407\n",
            "377/377 [==============================] - 0s 87us/step - loss: 0.0100 - acc: 0.9867 - val_loss: 0.0893 - val_acc: 0.9286\n",
            "Epoch 3730/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0090 - acc: 0.9920 - val_loss: 0.0780 - val_acc: 0.9464\n",
            "Epoch 3731/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0801 - val_acc: 0.9464\n",
            "Epoch 3732/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0042 - acc: 0.9947 - val_loss: 0.0792 - val_acc: 0.9464\n",
            "Epoch 3733/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0048 - acc: 0.9894 - val_loss: 0.0806 - val_acc: 0.9464\n",
            "Epoch 3734/7407\n",
            "377/377 [==============================] - 0s 96us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0795 - val_acc: 0.9464\n",
            "Epoch 3735/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0049 - acc: 0.9947 - val_loss: 0.0779 - val_acc: 0.9464\n",
            "Epoch 3736/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0035 - acc: 0.9920 - val_loss: 0.0779 - val_acc: 0.9643\n",
            "Epoch 3737/7407\n",
            "377/377 [==============================] - 0s 92us/step - loss: 0.0025 - acc: 0.9947 - val_loss: 0.0775 - val_acc: 0.9464\n",
            "Epoch 3738/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0043 - acc: 0.9947 - val_loss: 0.0724 - val_acc: 0.9464\n",
            "Epoch 3739/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0025 - acc: 0.9947 - val_loss: 0.0741 - val_acc: 0.9464\n",
            "Epoch 3740/7407\n",
            "377/377 [==============================] - 0s 76us/step - loss: 0.0035 - acc: 0.9947 - val_loss: 0.0754 - val_acc: 0.9464\n",
            "Epoch 3741/7407\n",
            "377/377 [==============================] - 0s 98us/step - loss: 0.0098 - acc: 0.9920 - val_loss: 0.0740 - val_acc: 0.9464\n",
            "Epoch 3742/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0067 - acc: 0.9894 - val_loss: 0.0767 - val_acc: 0.9464\n",
            "Epoch 3743/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0792 - val_acc: 0.9464\n",
            "Epoch 3744/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.0767 - val_acc: 0.9643\n",
            "Epoch 3745/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0018 - acc: 0.9947 - val_loss: 0.0798 - val_acc: 0.9643\n",
            "Epoch 3746/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0023 - acc: 0.9947 - val_loss: 0.0822 - val_acc: 0.9643\n",
            "Epoch 3747/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0079 - acc: 0.9894 - val_loss: 0.0917 - val_acc: 0.9464\n",
            "Epoch 3748/7407\n",
            "377/377 [==============================] - 0s 89us/step - loss: 0.0044 - acc: 0.9947 - val_loss: 0.0801 - val_acc: 0.9464\n",
            "Epoch 3749/7407\n",
            "377/377 [==============================] - 0s 104us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0818 - val_acc: 0.9464\n",
            "Epoch 3750/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0035 - acc: 0.9973 - val_loss: 0.0809 - val_acc: 0.9464\n",
            "Epoch 3751/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0031 - acc: 0.9973 - val_loss: 0.0828 - val_acc: 0.9286\n",
            "Epoch 3752/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0046 - acc: 0.9920 - val_loss: 0.0845 - val_acc: 0.9286\n",
            "Epoch 3753/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0030 - acc: 0.9947 - val_loss: 0.0826 - val_acc: 0.9286\n",
            "Epoch 3754/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0019 - acc: 1.0000 - val_loss: 0.0835 - val_acc: 0.9286\n",
            "Epoch 3755/7407\n",
            "377/377 [==============================] - 0s 93us/step - loss: 0.0238 - acc: 0.9920 - val_loss: 0.0776 - val_acc: 0.9286\n",
            "Epoch 3756/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0076 - acc: 0.9867 - val_loss: 0.0747 - val_acc: 0.9286\n",
            "Epoch 3757/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0067 - acc: 0.9920 - val_loss: 0.0697 - val_acc: 0.9464\n",
            "Epoch 3758/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0720 - val_acc: 0.9464\n",
            "Epoch 3759/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0731 - val_acc: 0.9464\n",
            "Epoch 3760/7407\n",
            "377/377 [==============================] - 0s 92us/step - loss: 0.0040 - acc: 0.9947 - val_loss: 0.0726 - val_acc: 0.9464\n",
            "Epoch 3761/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0070 - acc: 0.9947 - val_loss: 0.0764 - val_acc: 0.9643\n",
            "Epoch 3762/7407\n",
            "377/377 [==============================] - 0s 90us/step - loss: 0.0044 - acc: 0.9973 - val_loss: 0.0715 - val_acc: 0.9643\n",
            "Epoch 3763/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.0692 - val_acc: 0.9464\n",
            "Epoch 3764/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0070 - acc: 0.9841 - val_loss: 0.0727 - val_acc: 0.9464\n",
            "Epoch 3765/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0064 - acc: 0.9947 - val_loss: 0.0718 - val_acc: 0.9464\n",
            "Epoch 3766/7407\n",
            "377/377 [==============================] - 0s 100us/step - loss: 0.0050 - acc: 0.9920 - val_loss: 0.0777 - val_acc: 0.9464\n",
            "Epoch 3767/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0761 - val_acc: 0.9464\n",
            "Epoch 3768/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0035 - acc: 0.9920 - val_loss: 0.0745 - val_acc: 0.9464\n",
            "Epoch 3769/7407\n",
            "377/377 [==============================] - 0s 89us/step - loss: 0.0021 - acc: 0.9947 - val_loss: 0.0728 - val_acc: 0.9464\n",
            "Epoch 3770/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0051 - acc: 0.9920 - val_loss: 0.0714 - val_acc: 0.9464\n",
            "Epoch 3771/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0072 - acc: 0.9920 - val_loss: 0.0714 - val_acc: 0.9464\n",
            "Epoch 3772/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0074 - acc: 0.9920 - val_loss: 0.0721 - val_acc: 0.9464\n",
            "Epoch 3773/7407\n",
            "377/377 [==============================] - 0s 94us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0720 - val_acc: 0.9464\n",
            "Epoch 3774/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0040 - acc: 0.9973 - val_loss: 0.0720 - val_acc: 0.9464\n",
            "Epoch 3775/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0030 - acc: 0.9973 - val_loss: 0.0701 - val_acc: 0.9464\n",
            "Epoch 3776/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0113 - acc: 0.9894 - val_loss: 0.0674 - val_acc: 0.9464\n",
            "Epoch 3777/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0675 - val_acc: 0.9464\n",
            "Epoch 3778/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0030 - acc: 0.9973 - val_loss: 0.0684 - val_acc: 0.9464\n",
            "Epoch 3779/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.0679 - val_acc: 0.9464\n",
            "Epoch 3780/7407\n",
            "377/377 [==============================] - 0s 87us/step - loss: 0.0044 - acc: 0.9973 - val_loss: 0.0736 - val_acc: 0.9464\n",
            "Epoch 3781/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0019 - acc: 0.9973 - val_loss: 0.0730 - val_acc: 0.9464\n",
            "Epoch 3782/7407\n",
            "377/377 [==============================] - 0s 107us/step - loss: 0.0043 - acc: 0.9920 - val_loss: 0.0710 - val_acc: 0.9464\n",
            "Epoch 3783/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0721 - val_acc: 0.9464\n",
            "Epoch 3784/7407\n",
            "377/377 [==============================] - 0s 101us/step - loss: 0.0036 - acc: 0.9947 - val_loss: 0.0706 - val_acc: 0.9464\n",
            "Epoch 3785/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0703 - val_acc: 0.9464\n",
            "Epoch 3786/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0721 - val_acc: 0.9464\n",
            "Epoch 3787/7407\n",
            "377/377 [==============================] - 0s 94us/step - loss: 0.0058 - acc: 0.9894 - val_loss: 0.0718 - val_acc: 0.9464\n",
            "Epoch 3788/7407\n",
            "377/377 [==============================] - 0s 96us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0758 - val_acc: 0.9643\n",
            "Epoch 3789/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0062 - acc: 0.9920 - val_loss: 0.0714 - val_acc: 0.9464\n",
            "Epoch 3790/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0069 - acc: 0.9947 - val_loss: 0.0796 - val_acc: 0.9286\n",
            "Epoch 3791/7407\n",
            "377/377 [==============================] - 0s 109us/step - loss: 0.0023 - acc: 0.9947 - val_loss: 0.0778 - val_acc: 0.9464\n",
            "Epoch 3792/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0085 - acc: 0.9867 - val_loss: 0.0759 - val_acc: 0.9464\n",
            "Epoch 3793/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0022 - acc: 0.9947 - val_loss: 0.0757 - val_acc: 0.9464\n",
            "Epoch 3794/7407\n",
            "377/377 [==============================] - 0s 92us/step - loss: 0.0056 - acc: 0.9920 - val_loss: 0.0746 - val_acc: 0.9464\n",
            "Epoch 3795/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0070 - acc: 0.9920 - val_loss: 0.0760 - val_acc: 0.9464\n",
            "Epoch 3796/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0029 - acc: 0.9947 - val_loss: 0.0759 - val_acc: 0.9464\n",
            "Epoch 3797/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0042 - acc: 0.9947 - val_loss: 0.0756 - val_acc: 0.9464\n",
            "Epoch 3798/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0059 - acc: 0.9920 - val_loss: 0.0764 - val_acc: 0.9464\n",
            "Epoch 3799/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0090 - acc: 0.9867 - val_loss: 0.0736 - val_acc: 0.9643\n",
            "Epoch 3800/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0867 - val_acc: 0.9286\n",
            "Epoch 3801/7407\n",
            "377/377 [==============================] - 0s 90us/step - loss: 0.0088 - acc: 0.9894 - val_loss: 0.0808 - val_acc: 0.9286\n",
            "Epoch 3802/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0050 - acc: 0.9841 - val_loss: 0.0918 - val_acc: 0.9464\n",
            "Epoch 3803/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0842 - val_acc: 0.9286\n",
            "Epoch 3804/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0040 - acc: 0.9920 - val_loss: 0.0823 - val_acc: 0.9286\n",
            "Epoch 3805/7407\n",
            "377/377 [==============================] - 0s 97us/step - loss: 0.0040 - acc: 0.9973 - val_loss: 0.0828 - val_acc: 0.9464\n",
            "Epoch 3806/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0016 - acc: 0.9973 - val_loss: 0.0870 - val_acc: 0.9464\n",
            "Epoch 3807/7407\n",
            "377/377 [==============================] - 0s 107us/step - loss: 0.0086 - acc: 0.9947 - val_loss: 0.0713 - val_acc: 0.9464\n",
            "Epoch 3808/7407\n",
            "377/377 [==============================] - 0s 87us/step - loss: 0.0095 - acc: 0.9920 - val_loss: 0.0725 - val_acc: 0.9464\n",
            "Epoch 3809/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0052 - acc: 0.9920 - val_loss: 0.0672 - val_acc: 0.9464\n",
            "Epoch 3810/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0059 - acc: 0.9947 - val_loss: 0.0678 - val_acc: 0.9643\n",
            "Epoch 3811/7407\n",
            "377/377 [==============================] - 0s 89us/step - loss: 0.0065 - acc: 0.9867 - val_loss: 0.0783 - val_acc: 0.9464\n",
            "Epoch 3812/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0041 - acc: 0.9973 - val_loss: 0.0695 - val_acc: 0.9464\n",
            "Epoch 3813/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0026 - acc: 0.9920 - val_loss: 0.0718 - val_acc: 0.9464\n",
            "Epoch 3814/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0035 - acc: 0.9947 - val_loss: 0.0703 - val_acc: 0.9464\n",
            "Epoch 3815/7407\n",
            "377/377 [==============================] - 0s 93us/step - loss: 0.0031 - acc: 0.9920 - val_loss: 0.0725 - val_acc: 0.9464\n",
            "Epoch 3816/7407\n",
            "377/377 [==============================] - 0s 87us/step - loss: 0.0044 - acc: 0.9973 - val_loss: 0.0684 - val_acc: 0.9464\n",
            "Epoch 3817/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.0694 - val_acc: 0.9464\n",
            "Epoch 3818/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0025 - acc: 0.9947 - val_loss: 0.0696 - val_acc: 0.9464\n",
            "Epoch 3819/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0080 - acc: 0.9894 - val_loss: 0.0749 - val_acc: 0.9643\n",
            "Epoch 3820/7407\n",
            "377/377 [==============================] - 0s 133us/step - loss: 0.0062 - acc: 0.9947 - val_loss: 0.0859 - val_acc: 0.9464\n",
            "Epoch 3821/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0019 - acc: 1.0000 - val_loss: 0.0728 - val_acc: 0.9643\n",
            "Epoch 3822/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0768 - val_acc: 0.9643\n",
            "Epoch 3823/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0290 - acc: 0.9920 - val_loss: 0.0728 - val_acc: 0.9464\n",
            "Epoch 3824/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0733 - val_acc: 0.9464\n",
            "Epoch 3825/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0058 - acc: 0.9920 - val_loss: 0.0732 - val_acc: 0.9464\n",
            "Epoch 3826/7407\n",
            "377/377 [==============================] - 0s 92us/step - loss: 0.0108 - acc: 0.9947 - val_loss: 0.0735 - val_acc: 0.9464\n",
            "Epoch 3827/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0060 - acc: 0.9894 - val_loss: 0.0875 - val_acc: 0.9286\n",
            "Epoch 3828/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0042 - acc: 0.9947 - val_loss: 0.0718 - val_acc: 0.9464\n",
            "Epoch 3829/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0726 - val_acc: 0.9464\n",
            "Epoch 3830/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0740 - val_acc: 0.9464\n",
            "Epoch 3831/7407\n",
            "377/377 [==============================] - 0s 111us/step - loss: 0.0058 - acc: 0.9920 - val_loss: 0.0730 - val_acc: 0.9464\n",
            "Epoch 3832/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0041 - acc: 0.9973 - val_loss: 0.0771 - val_acc: 0.9464\n",
            "Epoch 3833/7407\n",
            "377/377 [==============================] - 0s 90us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0762 - val_acc: 0.9464\n",
            "Epoch 3834/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0749 - val_acc: 0.9464\n",
            "Epoch 3835/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0076 - acc: 0.9894 - val_loss: 0.0751 - val_acc: 0.9464\n",
            "Epoch 3836/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0028 - acc: 0.9973 - val_loss: 0.0750 - val_acc: 0.9464\n",
            "Epoch 3837/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0038 - acc: 0.9947 - val_loss: 0.0723 - val_acc: 0.9643\n",
            "Epoch 3838/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0053 - acc: 0.9947 - val_loss: 0.0742 - val_acc: 0.9643\n",
            "Epoch 3839/7407\n",
            "377/377 [==============================] - 0s 98us/step - loss: 0.0064 - acc: 0.9947 - val_loss: 0.0693 - val_acc: 0.9643\n",
            "Epoch 3840/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0038 - acc: 0.9920 - val_loss: 0.0713 - val_acc: 0.9643\n",
            "Epoch 3841/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0056 - acc: 0.9894 - val_loss: 0.0725 - val_acc: 0.9643\n",
            "Epoch 3842/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0730 - val_acc: 0.9643\n",
            "Epoch 3843/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0095 - acc: 0.9947 - val_loss: 0.0640 - val_acc: 0.9643\n",
            "Epoch 3844/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0016 - acc: 0.9973 - val_loss: 0.0642 - val_acc: 0.9643\n",
            "Epoch 3845/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0088 - acc: 0.9867 - val_loss: 0.1425 - val_acc: 0.9464\n",
            "Epoch 3846/7407\n",
            "377/377 [==============================] - 0s 98us/step - loss: 0.0065 - acc: 0.9947 - val_loss: 0.1410 - val_acc: 0.9464\n",
            "Epoch 3847/7407\n",
            "377/377 [==============================] - 0s 107us/step - loss: 0.0052 - acc: 0.9973 - val_loss: 0.1426 - val_acc: 0.9286\n",
            "Epoch 3848/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0072 - acc: 0.9920 - val_loss: 0.1388 - val_acc: 0.9286\n",
            "Epoch 3849/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0079 - acc: 0.9867 - val_loss: 0.0742 - val_acc: 0.9286\n",
            "Epoch 3850/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0070 - acc: 0.9867 - val_loss: 0.0594 - val_acc: 0.9464\n",
            "Epoch 3851/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0106 - acc: 0.9841 - val_loss: 0.0597 - val_acc: 0.9643\n",
            "Epoch 3852/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0044 - acc: 0.9920 - val_loss: 0.0626 - val_acc: 0.9643\n",
            "Epoch 3853/7407\n",
            "377/377 [==============================] - 0s 94us/step - loss: 0.0076 - acc: 0.9894 - val_loss: 0.0661 - val_acc: 0.9643\n",
            "Epoch 3854/7407\n",
            "377/377 [==============================] - 0s 124us/step - loss: 0.0028 - acc: 0.9973 - val_loss: 0.0657 - val_acc: 0.9643\n",
            "Epoch 3855/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0054 - acc: 0.9920 - val_loss: 0.0697 - val_acc: 0.9643\n",
            "Epoch 3856/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0066 - acc: 0.9947 - val_loss: 0.0685 - val_acc: 0.9643\n",
            "Epoch 3857/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0113 - acc: 0.9894 - val_loss: 0.0631 - val_acc: 0.9464\n",
            "Epoch 3858/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0011 - acc: 1.0000 - val_loss: 0.0629 - val_acc: 0.9464\n",
            "Epoch 3859/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0648 - val_acc: 0.9464\n",
            "Epoch 3860/7407\n",
            "377/377 [==============================] - 0s 97us/step - loss: 0.0069 - acc: 0.9894 - val_loss: 0.0668 - val_acc: 0.9464\n",
            "Epoch 3861/7407\n",
            "377/377 [==============================] - 0s 90us/step - loss: 0.0051 - acc: 0.9894 - val_loss: 0.0636 - val_acc: 0.9464\n",
            "Epoch 3862/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0060 - acc: 0.9947 - val_loss: 0.0655 - val_acc: 0.9464\n",
            "Epoch 3863/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0070 - acc: 0.9920 - val_loss: 0.0684 - val_acc: 0.9464\n",
            "Epoch 3864/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0072 - acc: 0.9920 - val_loss: 0.0683 - val_acc: 0.9464\n",
            "Epoch 3865/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0682 - val_acc: 0.9464\n",
            "Epoch 3866/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0040 - acc: 0.9920 - val_loss: 0.0634 - val_acc: 0.9464\n",
            "Epoch 3867/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0065 - acc: 0.9894 - val_loss: 0.0650 - val_acc: 0.9464\n",
            "Epoch 3868/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0646 - val_acc: 0.9464\n",
            "Epoch 3869/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0035 - acc: 0.9920 - val_loss: 0.0649 - val_acc: 0.9464\n",
            "Epoch 3870/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0650 - val_acc: 0.9464\n",
            "Epoch 3871/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0673 - val_acc: 0.9464\n",
            "Epoch 3872/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0037 - acc: 0.9973 - val_loss: 0.0672 - val_acc: 0.9464\n",
            "Epoch 3873/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0064 - acc: 0.9947 - val_loss: 0.0666 - val_acc: 0.9464\n",
            "Epoch 3874/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0686 - val_acc: 0.9464\n",
            "Epoch 3875/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0016 - acc: 1.0000 - val_loss: 0.0706 - val_acc: 0.9464\n",
            "Epoch 3876/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0031 - acc: 0.9947 - val_loss: 0.0713 - val_acc: 0.9464\n",
            "Epoch 3877/7407\n",
            "377/377 [==============================] - 0s 91us/step - loss: 0.0046 - acc: 0.9920 - val_loss: 0.0719 - val_acc: 0.9464\n",
            "Epoch 3878/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0129 - acc: 0.9894 - val_loss: 0.0702 - val_acc: 0.9464\n",
            "Epoch 3879/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0745 - val_acc: 0.9286\n",
            "Epoch 3880/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0059 - acc: 0.9947 - val_loss: 0.0683 - val_acc: 0.9464\n",
            "Epoch 3881/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0033 - acc: 0.9920 - val_loss: 0.0689 - val_acc: 0.9464\n",
            "Epoch 3882/7407\n",
            "377/377 [==============================] - 0s 99us/step - loss: 0.0033 - acc: 0.9973 - val_loss: 0.0690 - val_acc: 0.9464\n",
            "Epoch 3883/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0064 - acc: 0.9920 - val_loss: 0.0781 - val_acc: 0.9286\n",
            "Epoch 3884/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0073 - acc: 0.9841 - val_loss: 0.0735 - val_acc: 0.9464\n",
            "Epoch 3885/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0090 - acc: 0.9920 - val_loss: 0.0783 - val_acc: 0.9286\n",
            "Epoch 3886/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0029 - acc: 0.9920 - val_loss: 0.0748 - val_acc: 0.9464\n",
            "Epoch 3887/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0053 - acc: 0.9947 - val_loss: 0.0740 - val_acc: 0.9464\n",
            "Epoch 3888/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0070 - acc: 0.9894 - val_loss: 0.0747 - val_acc: 0.9464\n",
            "Epoch 3889/7407\n",
            "377/377 [==============================] - 0s 87us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0758 - val_acc: 0.9464\n",
            "Epoch 3890/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0052 - acc: 0.9920 - val_loss: 0.0794 - val_acc: 0.9464\n",
            "Epoch 3891/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0044 - acc: 0.9920 - val_loss: 0.0854 - val_acc: 0.9286\n",
            "Epoch 3892/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0834 - val_acc: 0.9286\n",
            "Epoch 3893/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0047 - acc: 0.9947 - val_loss: 0.0837 - val_acc: 0.9286\n",
            "Epoch 3894/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0033 - acc: 0.9973 - val_loss: 0.0817 - val_acc: 0.9464\n",
            "Epoch 3895/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0016 - acc: 1.0000 - val_loss: 0.0827 - val_acc: 0.9286\n",
            "Epoch 3896/7407\n",
            "377/377 [==============================] - 0s 99us/step - loss: 0.0087 - acc: 0.9867 - val_loss: 0.0879 - val_acc: 0.9286\n",
            "Epoch 3897/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0119 - acc: 0.9894 - val_loss: 0.0805 - val_acc: 0.9464\n",
            "Epoch 3898/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0086 - acc: 0.9947 - val_loss: 0.0812 - val_acc: 0.9464\n",
            "Epoch 3899/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0811 - val_acc: 0.9464\n",
            "Epoch 3900/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0034 - acc: 0.9920 - val_loss: 0.0778 - val_acc: 0.9464\n",
            "Epoch 3901/7407\n",
            "377/377 [==============================] - 0s 109us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0791 - val_acc: 0.9464\n",
            "Epoch 3902/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0054 - acc: 0.9894 - val_loss: 0.0787 - val_acc: 0.9464\n",
            "Epoch 3903/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0047 - acc: 0.9947 - val_loss: 0.0790 - val_acc: 0.9464\n",
            "Epoch 3904/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0036 - acc: 0.9947 - val_loss: 0.0805 - val_acc: 0.9286\n",
            "Epoch 3905/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0048 - acc: 0.9947 - val_loss: 0.0790 - val_acc: 0.9464\n",
            "Epoch 3906/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0795 - val_acc: 0.9464\n",
            "Epoch 3907/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0806 - val_acc: 0.9464\n",
            "Epoch 3908/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0048 - acc: 0.9894 - val_loss: 0.0879 - val_acc: 0.9286\n",
            "Epoch 3909/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0915 - val_acc: 0.9286\n",
            "Epoch 3910/7407\n",
            "377/377 [==============================] - 0s 96us/step - loss: 0.0050 - acc: 0.9947 - val_loss: 0.0885 - val_acc: 0.9286\n",
            "Epoch 3911/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0023 - acc: 0.9973 - val_loss: 0.0935 - val_acc: 0.9286\n",
            "Epoch 3912/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0040 - acc: 0.9920 - val_loss: 0.0950 - val_acc: 0.9286\n",
            "Epoch 3913/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0923 - val_acc: 0.9286\n",
            "Epoch 3914/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0017 - acc: 0.9973 - val_loss: 0.0972 - val_acc: 0.9286\n",
            "Epoch 3915/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0071 - acc: 0.9894 - val_loss: 0.0976 - val_acc: 0.9286\n",
            "Epoch 3916/7407\n",
            "377/377 [==============================] - 0s 109us/step - loss: 0.0041 - acc: 0.9920 - val_loss: 0.1029 - val_acc: 0.9286\n",
            "Epoch 3917/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0042 - acc: 0.9894 - val_loss: 0.1031 - val_acc: 0.9464\n",
            "Epoch 3918/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 0.0079 - acc: 0.9920 - val_loss: 0.0893 - val_acc: 0.9286\n",
            "Epoch 3919/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0020 - acc: 0.9947 - val_loss: 0.0886 - val_acc: 0.9286\n",
            "Epoch 3920/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0046 - acc: 0.9973 - val_loss: 0.0842 - val_acc: 0.9464\n",
            "Epoch 3921/7407\n",
            "377/377 [==============================] - 0s 152us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0838 - val_acc: 0.9464\n",
            "Epoch 3922/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0830 - val_acc: 0.9464\n",
            "Epoch 3923/7407\n",
            "377/377 [==============================] - 0s 156us/step - loss: 0.0040 - acc: 0.9894 - val_loss: 0.0859 - val_acc: 0.9286\n",
            "Epoch 3924/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0070 - acc: 0.9894 - val_loss: 0.0807 - val_acc: 0.9643\n",
            "Epoch 3925/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0238 - acc: 0.9867 - val_loss: 0.0766 - val_acc: 0.9464\n",
            "Epoch 3926/7407\n",
            "377/377 [==============================] - 0s 162us/step - loss: 0.0064 - acc: 0.9920 - val_loss: 0.0780 - val_acc: 0.9464\n",
            "Epoch 3927/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0051 - acc: 0.9920 - val_loss: 0.0810 - val_acc: 0.9464\n",
            "Epoch 3928/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0067 - acc: 0.9973 - val_loss: 0.0813 - val_acc: 0.9464\n",
            "Epoch 3929/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0072 - acc: 0.9920 - val_loss: 0.0770 - val_acc: 0.9286\n",
            "Epoch 3930/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0135 - acc: 0.9894 - val_loss: 0.0744 - val_acc: 0.9286\n",
            "Epoch 3931/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0041 - acc: 0.9867 - val_loss: 0.0710 - val_acc: 0.9464\n",
            "Epoch 3932/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0049 - acc: 0.9894 - val_loss: 0.0720 - val_acc: 0.9464\n",
            "Epoch 3933/7407\n",
            "377/377 [==============================] - 0s 157us/step - loss: 0.0133 - acc: 0.9894 - val_loss: 0.0755 - val_acc: 0.9464\n",
            "Epoch 3934/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0075 - acc: 0.9814 - val_loss: 0.0783 - val_acc: 0.9464\n",
            "Epoch 3935/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0097 - acc: 0.9894 - val_loss: 0.0726 - val_acc: 0.9643\n",
            "Epoch 3936/7407\n",
            "377/377 [==============================] - 0s 166us/step - loss: 0.0045 - acc: 0.9920 - val_loss: 0.0748 - val_acc: 0.9643\n",
            "Epoch 3937/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0016 - acc: 1.0000 - val_loss: 0.0747 - val_acc: 0.9643\n",
            "Epoch 3938/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0060 - acc: 0.9947 - val_loss: 0.0721 - val_acc: 0.9464\n",
            "Epoch 3939/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0085 - acc: 0.9867 - val_loss: 0.0767 - val_acc: 0.9643\n",
            "Epoch 3940/7407\n",
            "377/377 [==============================] - 0s 157us/step - loss: 0.0040 - acc: 0.9947 - val_loss: 0.0769 - val_acc: 0.9643\n",
            "Epoch 3941/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0026 - acc: 0.9920 - val_loss: 0.0784 - val_acc: 0.9643\n",
            "Epoch 3942/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0783 - val_acc: 0.9464\n",
            "Epoch 3943/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0132 - acc: 0.9894 - val_loss: 0.0808 - val_acc: 0.9286\n",
            "Epoch 3944/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0862 - val_acc: 0.9464\n",
            "Epoch 3945/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0847 - val_acc: 0.9464\n",
            "Epoch 3946/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0215 - acc: 0.9920 - val_loss: 0.0791 - val_acc: 0.9464\n",
            "Epoch 3947/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0099 - acc: 0.9894 - val_loss: 0.0827 - val_acc: 0.9286\n",
            "Epoch 3948/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0083 - acc: 0.9920 - val_loss: 0.0748 - val_acc: 0.9464\n",
            "Epoch 3949/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0070 - acc: 0.9894 - val_loss: 0.0744 - val_acc: 0.9464\n",
            "Epoch 3950/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0022 - acc: 0.9947 - val_loss: 0.0766 - val_acc: 0.9464\n",
            "Epoch 3951/7407\n",
            "377/377 [==============================] - 0s 161us/step - loss: 0.0053 - acc: 0.9920 - val_loss: 0.0927 - val_acc: 0.9286\n",
            "Epoch 3952/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0043 - acc: 0.9973 - val_loss: 0.0839 - val_acc: 0.9286\n",
            "Epoch 3953/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0855 - val_acc: 0.9286\n",
            "Epoch 3954/7407\n",
            "377/377 [==============================] - 0s 155us/step - loss: 0.0033 - acc: 0.9947 - val_loss: 0.0848 - val_acc: 0.9286\n",
            "Epoch 3955/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0912 - val_acc: 0.9286\n",
            "Epoch 3956/7407\n",
            "377/377 [==============================] - 0s 162us/step - loss: 0.0115 - acc: 0.9867 - val_loss: 0.0799 - val_acc: 0.9464\n",
            "Epoch 3957/7407\n",
            "377/377 [==============================] - 0s 164us/step - loss: 0.0010 - acc: 1.0000 - val_loss: 0.0800 - val_acc: 0.9464\n",
            "Epoch 3958/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0103 - acc: 0.9947 - val_loss: 0.0741 - val_acc: 0.9464\n",
            "Epoch 3959/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0082 - acc: 0.9867 - val_loss: 0.0755 - val_acc: 0.9464\n",
            "Epoch 3960/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0086 - acc: 0.9894 - val_loss: 0.0716 - val_acc: 0.9286\n",
            "Epoch 3961/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0045 - acc: 0.9920 - val_loss: 0.0717 - val_acc: 0.9464\n",
            "Epoch 3962/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0026 - acc: 0.9947 - val_loss: 0.0717 - val_acc: 0.9643\n",
            "Epoch 3963/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0030 - acc: 0.9947 - val_loss: 0.0737 - val_acc: 0.9643\n",
            "Epoch 3964/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0732 - val_acc: 0.9643\n",
            "Epoch 3965/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0035 - acc: 0.9920 - val_loss: 0.0773 - val_acc: 0.9643\n",
            "Epoch 3966/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0757 - val_acc: 0.9643\n",
            "Epoch 3967/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0054 - acc: 0.9894 - val_loss: 0.0825 - val_acc: 0.9643\n",
            "Epoch 3968/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0029 - acc: 0.9947 - val_loss: 0.0787 - val_acc: 0.9643\n",
            "Epoch 3969/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0040 - acc: 1.0000 - val_loss: 0.0717 - val_acc: 0.9643\n",
            "Epoch 3970/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0726 - val_acc: 0.9643\n",
            "Epoch 3971/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0129 - acc: 0.9947 - val_loss: 0.0708 - val_acc: 0.9643\n",
            "Epoch 3972/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0035 - acc: 0.9947 - val_loss: 0.0722 - val_acc: 0.9643\n",
            "Epoch 3973/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0075 - acc: 0.9867 - val_loss: 0.0808 - val_acc: 0.9643\n",
            "Epoch 3974/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 9.5069e-04 - acc: 1.0000 - val_loss: 0.0798 - val_acc: 0.9643\n",
            "Epoch 3975/7407\n",
            "377/377 [==============================] - 0s 158us/step - loss: 0.0023 - acc: 0.9973 - val_loss: 0.0773 - val_acc: 0.9464\n",
            "Epoch 3976/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0068 - acc: 0.9973 - val_loss: 0.0694 - val_acc: 0.9643\n",
            "Epoch 3977/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0035 - acc: 0.9894 - val_loss: 0.0760 - val_acc: 0.9643\n",
            "Epoch 3978/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0110 - acc: 0.9894 - val_loss: 0.0689 - val_acc: 0.9643\n",
            "Epoch 3979/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.0692 - val_acc: 0.9464\n",
            "Epoch 3980/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0086 - acc: 0.9920 - val_loss: 0.0719 - val_acc: 0.9643\n",
            "Epoch 3981/7407\n",
            "377/377 [==============================] - 0s 176us/step - loss: 0.0039 - acc: 0.9920 - val_loss: 0.0813 - val_acc: 0.9464\n",
            "Epoch 3982/7407\n",
            "377/377 [==============================] - 0s 154us/step - loss: 0.0033 - acc: 0.9973 - val_loss: 0.0773 - val_acc: 0.9643\n",
            "Epoch 3983/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0054 - acc: 0.9947 - val_loss: 0.0873 - val_acc: 0.9464\n",
            "Epoch 3984/7407\n",
            "377/377 [==============================] - 0s 164us/step - loss: 0.0016 - acc: 0.9973 - val_loss: 0.0859 - val_acc: 0.9464\n",
            "Epoch 3985/7407\n",
            "377/377 [==============================] - 0s 154us/step - loss: 0.0107 - acc: 0.9920 - val_loss: 0.0908 - val_acc: 0.9464\n",
            "Epoch 3986/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0068 - acc: 0.9920 - val_loss: 0.0917 - val_acc: 0.9464\n",
            "Epoch 3987/7407\n",
            "377/377 [==============================] - 0s 152us/step - loss: 0.0022 - acc: 0.9947 - val_loss: 0.0952 - val_acc: 0.9464\n",
            "Epoch 3988/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0060 - acc: 0.9867 - val_loss: 0.0789 - val_acc: 0.9643\n",
            "Epoch 3989/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.0760 - val_acc: 0.9464\n",
            "Epoch 3990/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0032 - acc: 0.9947 - val_loss: 0.0791 - val_acc: 0.9464\n",
            "Epoch 3991/7407\n",
            "377/377 [==============================] - 0s 182us/step - loss: 0.0067 - acc: 0.9894 - val_loss: 0.0725 - val_acc: 0.9464\n",
            "Epoch 3992/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0044 - acc: 0.9947 - val_loss: 0.0732 - val_acc: 0.9643\n",
            "Epoch 3993/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0104 - acc: 0.9867 - val_loss: 0.0778 - val_acc: 0.9643\n",
            "Epoch 3994/7407\n",
            "377/377 [==============================] - 0s 167us/step - loss: 0.0084 - acc: 0.9947 - val_loss: 0.0836 - val_acc: 0.9464\n",
            "Epoch 3995/7407\n",
            "377/377 [==============================] - 0s 172us/step - loss: 0.0050 - acc: 0.9947 - val_loss: 0.0764 - val_acc: 0.9464\n",
            "Epoch 3996/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0030 - acc: 0.9947 - val_loss: 0.0778 - val_acc: 0.9286\n",
            "Epoch 3997/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0019 - acc: 1.0000 - val_loss: 0.0769 - val_acc: 0.9464\n",
            "Epoch 3998/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.0734 - val_acc: 0.9464\n",
            "Epoch 3999/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 0.0033 - acc: 0.9920 - val_loss: 0.0804 - val_acc: 0.9286\n",
            "Epoch 4000/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0795 - val_acc: 0.9286\n",
            "Epoch 4001/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0109 - acc: 0.9920 - val_loss: 0.0710 - val_acc: 0.9464\n",
            "Epoch 4002/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0013 - acc: 0.9973 - val_loss: 0.0713 - val_acc: 0.9464\n",
            "Epoch 4003/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 0.0076 - acc: 0.9867 - val_loss: 0.0769 - val_acc: 0.9464\n",
            "Epoch 4004/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0736 - val_acc: 0.9464\n",
            "Epoch 4005/7407\n",
            "377/377 [==============================] - 0s 129us/step - loss: 0.0016 - acc: 1.0000 - val_loss: 0.0748 - val_acc: 0.9464\n",
            "Epoch 4006/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0033 - acc: 0.9973 - val_loss: 0.0661 - val_acc: 0.9464\n",
            "Epoch 4007/7407\n",
            "377/377 [==============================] - 0s 173us/step - loss: 0.0038 - acc: 0.9920 - val_loss: 0.0669 - val_acc: 0.9464\n",
            "Epoch 4008/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0637 - val_acc: 0.9464\n",
            "Epoch 4009/7407\n",
            "377/377 [==============================] - 0s 154us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.0675 - val_acc: 0.9464\n",
            "Epoch 4010/7407\n",
            "377/377 [==============================] - 0s 167us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.0726 - val_acc: 0.9464\n",
            "Epoch 4011/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0065 - acc: 0.9867 - val_loss: 0.0848 - val_acc: 0.9286\n",
            "Epoch 4012/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0156 - acc: 0.9788 - val_loss: 0.0920 - val_acc: 0.9464\n",
            "Epoch 4013/7407\n",
            "377/377 [==============================] - 0s 170us/step - loss: 0.0081 - acc: 0.9920 - val_loss: 0.1044 - val_acc: 0.9464\n",
            "Epoch 4014/7407\n",
            "377/377 [==============================] - 0s 160us/step - loss: 0.0151 - acc: 0.9867 - val_loss: 0.0849 - val_acc: 0.9286\n",
            "Epoch 4015/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0036 - acc: 0.9920 - val_loss: 0.0902 - val_acc: 0.9464\n",
            "Epoch 4016/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0969 - val_acc: 0.9464\n",
            "Epoch 4017/7407\n",
            "377/377 [==============================] - 0s 162us/step - loss: 0.0054 - acc: 0.9973 - val_loss: 0.0762 - val_acc: 0.9643\n",
            "Epoch 4018/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0032 - acc: 0.9920 - val_loss: 0.0859 - val_acc: 0.9464\n",
            "Epoch 4019/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0021 - acc: 0.9947 - val_loss: 0.0953 - val_acc: 0.9464\n",
            "Epoch 4020/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0898 - val_acc: 0.9464\n",
            "Epoch 4021/7407\n",
            "377/377 [==============================] - 0s 156us/step - loss: 0.0038 - acc: 0.9973 - val_loss: 0.0829 - val_acc: 0.9464\n",
            "Epoch 4022/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0049 - acc: 0.9947 - val_loss: 0.0786 - val_acc: 0.9643\n",
            "Epoch 4023/7407\n",
            "377/377 [==============================] - 0s 156us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0728 - val_acc: 0.9464\n",
            "Epoch 4024/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0048 - acc: 0.9947 - val_loss: 0.0705 - val_acc: 0.9464\n",
            "Epoch 4025/7407\n",
            "377/377 [==============================] - 0s 174us/step - loss: 0.0130 - acc: 0.9867 - val_loss: 0.0698 - val_acc: 0.9464\n",
            "Epoch 4026/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0706 - val_acc: 0.9464\n",
            "Epoch 4027/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0062 - acc: 0.9894 - val_loss: 0.0751 - val_acc: 0.9464\n",
            "Epoch 4028/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0014 - acc: 0.9973 - val_loss: 0.0754 - val_acc: 0.9464\n",
            "Epoch 4029/7407\n",
            "377/377 [==============================] - 0s 156us/step - loss: 0.0068 - acc: 0.9894 - val_loss: 0.0784 - val_acc: 0.9464\n",
            "Epoch 4030/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0077 - acc: 0.9894 - val_loss: 0.0714 - val_acc: 0.9464\n",
            "Epoch 4031/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0708 - val_acc: 0.9464\n",
            "Epoch 4032/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0724 - val_acc: 0.9464\n",
            "Epoch 4033/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0714 - val_acc: 0.9464\n",
            "Epoch 4034/7407\n",
            "377/377 [==============================] - 0s 152us/step - loss: 0.0046 - acc: 0.9973 - val_loss: 0.0729 - val_acc: 0.9464\n",
            "Epoch 4035/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0734 - val_acc: 0.9464\n",
            "Epoch 4036/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0121 - acc: 0.9947 - val_loss: 0.0788 - val_acc: 0.9107\n",
            "Epoch 4037/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 0.0078 - acc: 0.9894 - val_loss: 0.0742 - val_acc: 0.9286\n",
            "Epoch 4038/7407\n",
            "377/377 [==============================] - 0s 187us/step - loss: 0.0040 - acc: 0.9947 - val_loss: 0.0756 - val_acc: 0.9643\n",
            "Epoch 4039/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0034 - acc: 0.9973 - val_loss: 0.0738 - val_acc: 0.9643\n",
            "Epoch 4040/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0053 - acc: 0.9894 - val_loss: 0.0766 - val_acc: 0.9643\n",
            "Epoch 4041/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0754 - val_acc: 0.9643\n",
            "Epoch 4042/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0033 - acc: 0.9894 - val_loss: 0.0775 - val_acc: 0.9643\n",
            "Epoch 4043/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0779 - val_acc: 0.9643\n",
            "Epoch 4044/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0011 - acc: 0.9973 - val_loss: 0.0781 - val_acc: 0.9643\n",
            "Epoch 4045/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.0750 - val_acc: 0.9643\n",
            "Epoch 4046/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0029 - acc: 1.0000 - val_loss: 0.0698 - val_acc: 0.9643\n",
            "Epoch 4047/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0026 - acc: 0.9920 - val_loss: 0.0715 - val_acc: 0.9643\n",
            "Epoch 4048/7407\n",
            "377/377 [==============================] - 0s 129us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0693 - val_acc: 0.9464\n",
            "Epoch 4049/7407\n",
            "377/377 [==============================] - 0s 151us/step - loss: 0.0051 - acc: 0.9867 - val_loss: 0.0762 - val_acc: 0.9643\n",
            "Epoch 4050/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0788 - val_acc: 0.9643\n",
            "Epoch 4051/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0048 - acc: 0.9947 - val_loss: 0.0778 - val_acc: 0.9643\n",
            "Epoch 4052/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0748 - val_acc: 0.9643\n",
            "Epoch 4053/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0718 - val_acc: 0.9464\n",
            "Epoch 4054/7407\n",
            "377/377 [==============================] - 0s 151us/step - loss: 0.0056 - acc: 0.9947 - val_loss: 0.0723 - val_acc: 0.9464\n",
            "Epoch 4055/7407\n",
            "377/377 [==============================] - 0s 157us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0740 - val_acc: 0.9464\n",
            "Epoch 4056/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0751 - val_acc: 0.9643\n",
            "Epoch 4057/7407\n",
            "377/377 [==============================] - 0s 193us/step - loss: 0.0035 - acc: 0.9920 - val_loss: 0.0827 - val_acc: 0.9643\n",
            "Epoch 4058/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0046 - acc: 0.9947 - val_loss: 0.0696 - val_acc: 0.9464\n",
            "Epoch 4059/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0053 - acc: 0.9920 - val_loss: 0.0784 - val_acc: 0.9464\n",
            "Epoch 4060/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0747 - val_acc: 0.9286\n",
            "Epoch 4061/7407\n",
            "377/377 [==============================] - 0s 189us/step - loss: 0.0113 - acc: 0.9947 - val_loss: 0.0645 - val_acc: 0.9464\n",
            "Epoch 4062/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0055 - acc: 0.9894 - val_loss: 0.0679 - val_acc: 0.9464\n",
            "Epoch 4063/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0041 - acc: 0.9920 - val_loss: 0.0761 - val_acc: 0.9643\n",
            "Epoch 4064/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0081 - acc: 0.9920 - val_loss: 0.1004 - val_acc: 0.9286\n",
            "Epoch 4065/7407\n",
            "377/377 [==============================] - 0s 181us/step - loss: 0.0031 - acc: 0.9920 - val_loss: 0.1015 - val_acc: 0.9286\n",
            "Epoch 4066/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0075 - acc: 0.9894 - val_loss: 0.1092 - val_acc: 0.9286\n",
            "Epoch 4067/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0041 - acc: 0.9947 - val_loss: 0.0754 - val_acc: 0.9464\n",
            "Epoch 4068/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0088 - acc: 0.9867 - val_loss: 0.0861 - val_acc: 0.9286\n",
            "Epoch 4069/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0099 - acc: 0.9920 - val_loss: 0.0736 - val_acc: 0.9643\n",
            "Epoch 4070/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0200 - acc: 0.9947 - val_loss: 0.0669 - val_acc: 0.9464\n",
            "Epoch 4071/7407\n",
            "377/377 [==============================] - 0s 135us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0675 - val_acc: 0.9643\n",
            "Epoch 4072/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0099 - acc: 0.9920 - val_loss: 0.0681 - val_acc: 0.9464\n",
            "Epoch 4073/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0062 - acc: 0.9920 - val_loss: 0.0689 - val_acc: 0.9643\n",
            "Epoch 4074/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0718 - val_acc: 0.9643\n",
            "Epoch 4075/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0082 - acc: 0.9920 - val_loss: 0.0742 - val_acc: 0.9643\n",
            "Epoch 4076/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.0720 - val_acc: 0.9643\n",
            "Epoch 4077/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0023 - acc: 0.9973 - val_loss: 0.0706 - val_acc: 0.9643\n",
            "Epoch 4078/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0013 - acc: 0.9973 - val_loss: 0.0702 - val_acc: 0.9643\n",
            "Epoch 4079/7407\n",
            "377/377 [==============================] - 0s 152us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0720 - val_acc: 0.9643\n",
            "Epoch 4080/7407\n",
            "377/377 [==============================] - 0s 152us/step - loss: 0.0044 - acc: 0.9920 - val_loss: 0.0806 - val_acc: 0.9643\n",
            "Epoch 4081/7407\n",
            "377/377 [==============================] - 0s 158us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0782 - val_acc: 0.9643\n",
            "Epoch 4082/7407\n",
            "377/377 [==============================] - 0s 159us/step - loss: 0.0057 - acc: 0.9947 - val_loss: 0.0734 - val_acc: 0.9643\n",
            "Epoch 4083/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0063 - acc: 0.9894 - val_loss: 0.0716 - val_acc: 0.9464\n",
            "Epoch 4084/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0023 - acc: 0.9947 - val_loss: 0.0726 - val_acc: 0.9464\n",
            "Epoch 4085/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0032 - acc: 0.9947 - val_loss: 0.0731 - val_acc: 0.9464\n",
            "Epoch 4086/7407\n",
            "377/377 [==============================] - 0s 191us/step - loss: 0.0056 - acc: 0.9894 - val_loss: 0.0765 - val_acc: 0.9643\n",
            "Epoch 4087/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0155 - acc: 0.9973 - val_loss: 0.0744 - val_acc: 0.9464\n",
            "Epoch 4088/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0759 - val_acc: 0.9643\n",
            "Epoch 4089/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0040 - acc: 0.9973 - val_loss: 0.0764 - val_acc: 0.9643\n",
            "Epoch 4090/7407\n",
            "377/377 [==============================] - 0s 159us/step - loss: 0.0055 - acc: 0.9973 - val_loss: 0.0759 - val_acc: 0.9464\n",
            "Epoch 4091/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0772 - val_acc: 0.9464\n",
            "Epoch 4092/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0035 - acc: 0.9920 - val_loss: 0.0790 - val_acc: 0.9464\n",
            "Epoch 4093/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0107 - acc: 0.9947 - val_loss: 0.0801 - val_acc: 0.9464\n",
            "Epoch 4094/7407\n",
            "377/377 [==============================] - 0s 190us/step - loss: 0.0024 - acc: 0.9947 - val_loss: 0.0804 - val_acc: 0.9464\n",
            "Epoch 4095/7407\n",
            "377/377 [==============================] - 0s 151us/step - loss: 0.0069 - acc: 0.9814 - val_loss: 0.0852 - val_acc: 0.9464\n",
            "Epoch 4096/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0078 - acc: 0.9920 - val_loss: 0.0854 - val_acc: 0.9464\n",
            "Epoch 4097/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0083 - acc: 0.9841 - val_loss: 0.0875 - val_acc: 0.9643\n",
            "Epoch 4098/7407\n",
            "377/377 [==============================] - 0s 157us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0842 - val_acc: 0.9643\n",
            "Epoch 4099/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0049 - acc: 0.9973 - val_loss: 0.0891 - val_acc: 0.9643\n",
            "Epoch 4100/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0016 - acc: 0.9973 - val_loss: 0.0888 - val_acc: 0.9643\n",
            "Epoch 4101/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0842 - val_acc: 0.9643\n",
            "Epoch 4102/7407\n",
            "377/377 [==============================] - 0s 166us/step - loss: 0.0033 - acc: 0.9973 - val_loss: 0.0869 - val_acc: 0.9643\n",
            "Epoch 4103/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0058 - acc: 0.9947 - val_loss: 0.0787 - val_acc: 0.9464\n",
            "Epoch 4104/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0086 - acc: 0.9867 - val_loss: 0.0826 - val_acc: 0.9643\n",
            "Epoch 4105/7407\n",
            "377/377 [==============================] - 0s 167us/step - loss: 0.0045 - acc: 0.9973 - val_loss: 0.0792 - val_acc: 0.9464\n",
            "Epoch 4106/7407\n",
            "377/377 [==============================] - 0s 167us/step - loss: 0.0159 - acc: 0.9841 - val_loss: 0.0764 - val_acc: 0.9643\n",
            "Epoch 4107/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0762 - val_acc: 0.9464\n",
            "Epoch 4108/7407\n",
            "377/377 [==============================] - 0s 159us/step - loss: 0.0032 - acc: 0.9973 - val_loss: 0.0776 - val_acc: 0.9643\n",
            "Epoch 4109/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0043 - acc: 0.9894 - val_loss: 0.0788 - val_acc: 0.9643\n",
            "Epoch 4110/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.0759 - val_acc: 0.9643\n",
            "Epoch 4111/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0757 - val_acc: 0.9643\n",
            "Epoch 4112/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0045 - acc: 0.9920 - val_loss: 0.0815 - val_acc: 0.9643\n",
            "Epoch 4113/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0794 - val_acc: 0.9643\n",
            "Epoch 4114/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0805 - val_acc: 0.9643\n",
            "Epoch 4115/7407\n",
            "377/377 [==============================] - 0s 103us/step - loss: 0.0050 - acc: 0.9947 - val_loss: 0.0755 - val_acc: 0.9643\n",
            "Epoch 4116/7407\n",
            "377/377 [==============================] - 0s 111us/step - loss: 0.0053 - acc: 0.9947 - val_loss: 0.0786 - val_acc: 0.9643\n",
            "Epoch 4117/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0043 - acc: 0.9973 - val_loss: 0.0755 - val_acc: 0.9464\n",
            "Epoch 4118/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0032 - acc: 0.9894 - val_loss: 0.0751 - val_acc: 0.9464\n",
            "Epoch 4119/7407\n",
            "377/377 [==============================] - 0s 117us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0794 - val_acc: 0.9643\n",
            "Epoch 4120/7407\n",
            "377/377 [==============================] - 0s 119us/step - loss: 0.0030 - acc: 0.9920 - val_loss: 0.0857 - val_acc: 0.9464\n",
            "Epoch 4121/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0032 - acc: 0.9894 - val_loss: 0.0917 - val_acc: 0.9464\n",
            "Epoch 4122/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0889 - val_acc: 0.9464\n",
            "Epoch 4123/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0897 - val_acc: 0.9464\n",
            "Epoch 4124/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0046 - acc: 0.9973 - val_loss: 0.0843 - val_acc: 0.9286\n",
            "Epoch 4125/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0016 - acc: 0.9973 - val_loss: 0.0857 - val_acc: 0.9286\n",
            "Epoch 4126/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0880 - val_acc: 0.9286\n",
            "Epoch 4127/7407\n",
            "377/377 [==============================] - 0s 116us/step - loss: 0.0049 - acc: 0.9947 - val_loss: 0.0881 - val_acc: 0.9286\n",
            "Epoch 4128/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0033 - acc: 0.9947 - val_loss: 0.0973 - val_acc: 0.9464\n",
            "Epoch 4129/7407\n",
            "377/377 [==============================] - 0s 106us/step - loss: 0.0049 - acc: 0.9973 - val_loss: 0.0857 - val_acc: 0.9286\n",
            "Epoch 4130/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0084 - acc: 0.9867 - val_loss: 0.0797 - val_acc: 0.9464\n",
            "Epoch 4131/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0882 - val_acc: 0.9286\n",
            "Epoch 4132/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.1018 - val_acc: 0.9286\n",
            "Epoch 4133/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 6.3698e-04 - acc: 1.0000 - val_loss: 0.0996 - val_acc: 0.9286\n",
            "Epoch 4134/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0978 - val_acc: 0.9286\n",
            "Epoch 4135/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0074 - acc: 0.9947 - val_loss: 0.1141 - val_acc: 0.9286\n",
            "Epoch 4136/7407\n",
            "377/377 [==============================] - 0s 98us/step - loss: 0.0035 - acc: 0.9947 - val_loss: 0.1152 - val_acc: 0.9286\n",
            "Epoch 4137/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.1272 - val_acc: 0.9464\n",
            "Epoch 4138/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0238 - acc: 0.9894 - val_loss: 0.1084 - val_acc: 0.9286\n",
            "Epoch 4139/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0141 - acc: 0.9788 - val_loss: 0.1260 - val_acc: 0.9286\n",
            "Epoch 4140/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.1243 - val_acc: 0.9286\n",
            "Epoch 4141/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0049 - acc: 0.9947 - val_loss: 0.1323 - val_acc: 0.9286\n",
            "Epoch 4142/7407\n",
            "377/377 [==============================] - 0s 91us/step - loss: 0.0084 - acc: 0.9973 - val_loss: 0.1166 - val_acc: 0.9286\n",
            "Epoch 4143/7407\n",
            "377/377 [==============================] - 0s 101us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.1193 - val_acc: 0.9286\n",
            "Epoch 4144/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0044 - acc: 0.9947 - val_loss: 0.1214 - val_acc: 0.9286\n",
            "Epoch 4145/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0015 - acc: 0.9973 - val_loss: 0.1151 - val_acc: 0.9286\n",
            "Epoch 4146/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0032 - acc: 0.9973 - val_loss: 0.1312 - val_acc: 0.9464\n",
            "Epoch 4147/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0078 - acc: 0.9920 - val_loss: 0.1069 - val_acc: 0.9286\n",
            "Epoch 4148/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0065 - acc: 0.9894 - val_loss: 0.1394 - val_acc: 0.9286\n",
            "Epoch 4149/7407\n",
            "377/377 [==============================] - 0s 93us/step - loss: 0.0016 - acc: 1.0000 - val_loss: 0.1415 - val_acc: 0.9286\n",
            "Epoch 4150/7407\n",
            "377/377 [==============================] - 0s 127us/step - loss: 0.0034 - acc: 0.9973 - val_loss: 0.1029 - val_acc: 0.9286\n",
            "Epoch 4151/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0033 - acc: 0.9947 - val_loss: 0.1048 - val_acc: 0.9286\n",
            "Epoch 4152/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.1066 - val_acc: 0.9286\n",
            "Epoch 4153/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0101 - acc: 0.9867 - val_loss: 0.1143 - val_acc: 0.9464\n",
            "Epoch 4154/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0015 - acc: 0.9947 - val_loss: 0.1169 - val_acc: 0.9464\n",
            "Epoch 4155/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0076 - acc: 0.9894 - val_loss: 0.0705 - val_acc: 0.9464\n",
            "Epoch 4156/7407\n",
            "377/377 [==============================] - 0s 99us/step - loss: 0.0041 - acc: 0.9947 - val_loss: 0.0719 - val_acc: 0.9464\n",
            "Epoch 4157/7407\n",
            "377/377 [==============================] - 0s 107us/step - loss: 0.0041 - acc: 0.9947 - val_loss: 0.0809 - val_acc: 0.9464\n",
            "Epoch 4158/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0031 - acc: 0.9947 - val_loss: 0.0844 - val_acc: 0.9464\n",
            "Epoch 4159/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0030 - acc: 0.9894 - val_loss: 0.0873 - val_acc: 0.9464\n",
            "Epoch 4160/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0903 - val_acc: 0.9286\n",
            "Epoch 4161/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.1021 - val_acc: 0.9286\n",
            "Epoch 4162/7407\n",
            "377/377 [==============================] - 0s 103us/step - loss: 8.4821e-04 - acc: 1.0000 - val_loss: 0.1029 - val_acc: 0.9464\n",
            "Epoch 4163/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0059 - acc: 0.9920 - val_loss: 0.1240 - val_acc: 0.9464\n",
            "Epoch 4164/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0017 - acc: 0.9973 - val_loss: 0.1092 - val_acc: 0.9464\n",
            "Epoch 4165/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0057 - acc: 0.9947 - val_loss: 0.1554 - val_acc: 0.9286\n",
            "Epoch 4166/7407\n",
            "377/377 [==============================] - 0s 91us/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.1570 - val_acc: 0.9464\n",
            "Epoch 4167/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0072 - acc: 0.9920 - val_loss: 0.0654 - val_acc: 0.9464\n",
            "Epoch 4168/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0014 - acc: 0.9973 - val_loss: 0.0665 - val_acc: 0.9464\n",
            "Epoch 4169/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0034 - acc: 0.9894 - val_loss: 0.0700 - val_acc: 0.9643\n",
            "Epoch 4170/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0034 - acc: 0.9947 - val_loss: 0.0735 - val_acc: 0.9464\n",
            "Epoch 4171/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0020 - acc: 0.9947 - val_loss: 0.0753 - val_acc: 0.9464\n",
            "Epoch 4172/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0089 - acc: 0.9894 - val_loss: 0.0680 - val_acc: 0.9643\n",
            "Epoch 4173/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0017 - acc: 0.9973 - val_loss: 0.0666 - val_acc: 0.9643\n",
            "Epoch 4174/7407\n",
            "377/377 [==============================] - 0s 126us/step - loss: 0.0093 - acc: 0.9894 - val_loss: 0.0648 - val_acc: 0.9643\n",
            "Epoch 4175/7407\n",
            "377/377 [==============================] - 0s 102us/step - loss: 0.0013 - acc: 0.9973 - val_loss: 0.0681 - val_acc: 0.9643\n",
            "Epoch 4176/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.0697 - val_acc: 0.9643\n",
            "Epoch 4177/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0711 - val_acc: 0.9643\n",
            "Epoch 4178/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0061 - acc: 0.9920 - val_loss: 0.0725 - val_acc: 0.9643\n",
            "Epoch 4179/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0098 - acc: 0.9867 - val_loss: 0.0506 - val_acc: 0.9643\n",
            "Epoch 4180/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0011 - acc: 1.0000 - val_loss: 0.0514 - val_acc: 0.9643\n",
            "Epoch 4181/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0050 - acc: 0.9894 - val_loss: 0.0595 - val_acc: 0.9464\n",
            "Epoch 4182/7407\n",
            "377/377 [==============================] - 0s 132us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0587 - val_acc: 0.9643\n",
            "Epoch 4183/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0073 - acc: 0.9920 - val_loss: 0.0527 - val_acc: 0.9643\n",
            "Epoch 4184/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0154 - acc: 0.9920 - val_loss: 0.0476 - val_acc: 0.9464\n",
            "Epoch 4185/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0491 - val_acc: 0.9464\n",
            "Epoch 4186/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0085 - acc: 0.9894 - val_loss: 0.0633 - val_acc: 0.9464\n",
            "Epoch 4187/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0042 - acc: 0.9947 - val_loss: 0.0600 - val_acc: 0.9464\n",
            "Epoch 4188/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0058 - acc: 0.9920 - val_loss: 0.0622 - val_acc: 0.9643\n",
            "Epoch 4189/7407\n",
            "377/377 [==============================] - 0s 121us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0634 - val_acc: 0.9464\n",
            "Epoch 4190/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0011 - acc: 0.9973 - val_loss: 0.0643 - val_acc: 0.9464\n",
            "Epoch 4191/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0034 - acc: 0.9973 - val_loss: 0.0610 - val_acc: 0.9643\n",
            "Epoch 4192/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0041 - acc: 0.9973 - val_loss: 0.0601 - val_acc: 0.9643\n",
            "Epoch 4193/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0033 - acc: 0.9947 - val_loss: 0.0636 - val_acc: 0.9643\n",
            "Epoch 4194/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0036 - acc: 0.9973 - val_loss: 0.0629 - val_acc: 0.9643\n",
            "Epoch 4195/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0041 - acc: 0.9973 - val_loss: 0.0639 - val_acc: 0.9643\n",
            "Epoch 4196/7407\n",
            "377/377 [==============================] - 0s 128us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0658 - val_acc: 0.9464\n",
            "Epoch 4197/7407\n",
            "377/377 [==============================] - 0s 99us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0667 - val_acc: 0.9464\n",
            "Epoch 4198/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0023 - acc: 0.9947 - val_loss: 0.0688 - val_acc: 0.9464\n",
            "Epoch 4199/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 9.4512e-04 - acc: 1.0000 - val_loss: 0.0685 - val_acc: 0.9464\n",
            "Epoch 4200/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0037 - acc: 0.9920 - val_loss: 0.0704 - val_acc: 0.9464\n",
            "Epoch 4201/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0065 - acc: 0.9920 - val_loss: 0.0683 - val_acc: 0.9286\n",
            "Epoch 4202/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0653 - val_acc: 0.9464\n",
            "Epoch 4203/7407\n",
            "377/377 [==============================] - 0s 90us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0626 - val_acc: 0.9643\n",
            "Epoch 4204/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.0660 - val_acc: 0.9643\n",
            "Epoch 4205/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0045 - acc: 0.9947 - val_loss: 0.0652 - val_acc: 0.9643\n",
            "Epoch 4206/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0071 - acc: 0.9920 - val_loss: 0.0576 - val_acc: 0.9464\n",
            "Epoch 4207/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0038 - acc: 0.9973 - val_loss: 0.0598 - val_acc: 0.9643\n",
            "Epoch 4208/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0040 - acc: 0.9947 - val_loss: 0.0619 - val_acc: 0.9464\n",
            "Epoch 4209/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0034 - acc: 0.9947 - val_loss: 0.0638 - val_acc: 0.9643\n",
            "Epoch 4210/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0049 - acc: 0.9867 - val_loss: 0.0679 - val_acc: 0.9643\n",
            "Epoch 4211/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0670 - val_acc: 0.9643\n",
            "Epoch 4212/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0063 - acc: 0.9973 - val_loss: 0.0679 - val_acc: 0.9464\n",
            "Epoch 4213/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0066 - acc: 0.9894 - val_loss: 0.0695 - val_acc: 0.9643\n",
            "Epoch 4214/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0678 - val_acc: 0.9464\n",
            "Epoch 4215/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0092 - acc: 0.9894 - val_loss: 0.0751 - val_acc: 0.9464\n",
            "Epoch 4216/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0069 - acc: 0.9920 - val_loss: 0.0784 - val_acc: 0.9286\n",
            "Epoch 4217/7407\n",
            "377/377 [==============================] - 0s 92us/step - loss: 0.0032 - acc: 0.9947 - val_loss: 0.0848 - val_acc: 0.9464\n",
            "Epoch 4218/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0099 - acc: 0.9814 - val_loss: 0.1009 - val_acc: 0.9464\n",
            "Epoch 4219/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0044 - acc: 0.9973 - val_loss: 0.0636 - val_acc: 0.9464\n",
            "Epoch 4220/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0037 - acc: 0.9920 - val_loss: 0.0697 - val_acc: 0.9464\n",
            "Epoch 4221/7407\n",
            "377/377 [==============================] - 0s 124us/step - loss: 0.0076 - acc: 0.9973 - val_loss: 0.0672 - val_acc: 0.9464\n",
            "Epoch 4222/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0662 - val_acc: 0.9464\n",
            "Epoch 4223/7407\n",
            "377/377 [==============================] - 0s 99us/step - loss: 0.0056 - acc: 0.9867 - val_loss: 0.0722 - val_acc: 0.9464\n",
            "Epoch 4224/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0065 - acc: 0.9947 - val_loss: 0.0752 - val_acc: 0.9464\n",
            "Epoch 4225/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0058 - acc: 0.9920 - val_loss: 0.0857 - val_acc: 0.9464\n",
            "Epoch 4226/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0054 - acc: 0.9973 - val_loss: 0.0862 - val_acc: 0.9464\n",
            "Epoch 4227/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0071 - acc: 0.9973 - val_loss: 0.0725 - val_acc: 0.9643\n",
            "Epoch 4228/7407\n",
            "377/377 [==============================] - 0s 96us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.0727 - val_acc: 0.9643\n",
            "Epoch 4229/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0048 - acc: 0.9973 - val_loss: 0.0709 - val_acc: 0.9643\n",
            "Epoch 4230/7407\n",
            "377/377 [==============================] - 0s 113us/step - loss: 0.0071 - acc: 0.9947 - val_loss: 0.0880 - val_acc: 0.9464\n",
            "Epoch 4231/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0805 - val_acc: 0.9286\n",
            "Epoch 4232/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0052 - acc: 0.9920 - val_loss: 0.0812 - val_acc: 0.9464\n",
            "Epoch 4233/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0038 - acc: 0.9973 - val_loss: 0.0798 - val_acc: 0.9286\n",
            "Epoch 4234/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0033 - acc: 0.9920 - val_loss: 0.0775 - val_acc: 0.9464\n",
            "Epoch 4235/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0049 - acc: 0.9947 - val_loss: 0.0781 - val_acc: 0.9464\n",
            "Epoch 4236/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0060 - acc: 0.9920 - val_loss: 0.0726 - val_acc: 0.9643\n",
            "Epoch 4237/7407\n",
            "377/377 [==============================] - 0s 108us/step - loss: 0.0021 - acc: 0.9947 - val_loss: 0.0752 - val_acc: 0.9643\n",
            "Epoch 4238/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0784 - val_acc: 0.9464\n",
            "Epoch 4239/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0105 - acc: 0.9894 - val_loss: 0.0715 - val_acc: 0.9464\n",
            "Epoch 4240/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0019 - acc: 0.9973 - val_loss: 0.0726 - val_acc: 0.9643\n",
            "Epoch 4241/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0010 - acc: 0.9973 - val_loss: 0.0786 - val_acc: 0.9464\n",
            "Epoch 4242/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0056 - acc: 0.9947 - val_loss: 0.0694 - val_acc: 0.9643\n",
            "Epoch 4243/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0035 - acc: 0.9947 - val_loss: 0.0683 - val_acc: 0.9643\n",
            "Epoch 4244/7407\n",
            "377/377 [==============================] - 0s 115us/step - loss: 0.0104 - acc: 0.9894 - val_loss: 0.0802 - val_acc: 0.9643\n",
            "Epoch 4245/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0788 - val_acc: 0.9464\n",
            "Epoch 4246/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0800 - val_acc: 0.9464\n",
            "Epoch 4247/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0069 - acc: 0.9920 - val_loss: 0.0748 - val_acc: 0.9643\n",
            "Epoch 4248/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0049 - acc: 0.9920 - val_loss: 0.0774 - val_acc: 0.9643\n",
            "Epoch 4249/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0029 - acc: 0.9947 - val_loss: 0.0767 - val_acc: 0.9464\n",
            "Epoch 4250/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0140 - acc: 0.9973 - val_loss: 0.0744 - val_acc: 0.9464\n",
            "Epoch 4251/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0028 - acc: 0.9920 - val_loss: 0.0737 - val_acc: 0.9643\n",
            "Epoch 4252/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0050 - acc: 0.9973 - val_loss: 0.0763 - val_acc: 0.9643\n",
            "Epoch 4253/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0024 - acc: 0.9947 - val_loss: 0.0820 - val_acc: 0.9643\n",
            "Epoch 4254/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 0.0035 - acc: 0.9973 - val_loss: 0.0785 - val_acc: 0.9643\n",
            "Epoch 4255/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0010 - acc: 1.0000 - val_loss: 0.0774 - val_acc: 0.9643\n",
            "Epoch 4256/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.0800 - val_acc: 0.9643\n",
            "Epoch 4257/7407\n",
            "377/377 [==============================] - 0s 117us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0821 - val_acc: 0.9643\n",
            "Epoch 4258/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0019 - acc: 1.0000 - val_loss: 0.0789 - val_acc: 0.9643\n",
            "Epoch 4259/7407\n",
            "377/377 [==============================] - 0s 91us/step - loss: 0.0215 - acc: 0.9947 - val_loss: 0.0809 - val_acc: 0.9464\n",
            "Epoch 4260/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0805 - val_acc: 0.9464\n",
            "Epoch 4261/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0803 - val_acc: 0.9464\n",
            "Epoch 4262/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0084 - acc: 0.9920 - val_loss: 0.0782 - val_acc: 0.9464\n",
            "Epoch 4263/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0050 - acc: 0.9947 - val_loss: 0.0829 - val_acc: 0.9464\n",
            "Epoch 4264/7407\n",
            "377/377 [==============================] - 0s 108us/step - loss: 0.0016 - acc: 0.9973 - val_loss: 0.0822 - val_acc: 0.9464\n",
            "Epoch 4265/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0136 - acc: 0.9867 - val_loss: 0.0812 - val_acc: 0.9464\n",
            "Epoch 4266/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0812 - val_acc: 0.9464\n",
            "Epoch 4267/7407\n",
            "377/377 [==============================] - 0s 113us/step - loss: 0.0049 - acc: 0.9947 - val_loss: 0.0831 - val_acc: 0.9464\n",
            "Epoch 4268/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0040 - acc: 0.9920 - val_loss: 0.0814 - val_acc: 0.9464\n",
            "Epoch 4269/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0816 - val_acc: 0.9464\n",
            "Epoch 4270/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0106 - acc: 0.9920 - val_loss: 0.0800 - val_acc: 0.9464\n",
            "Epoch 4271/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0801 - val_acc: 0.9643\n",
            "Epoch 4272/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0067 - acc: 0.9947 - val_loss: 0.0791 - val_acc: 0.9464\n",
            "Epoch 4273/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0025 - acc: 1.0000 - val_loss: 0.0794 - val_acc: 0.9464\n",
            "Epoch 4274/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0072 - acc: 0.9947 - val_loss: 0.0810 - val_acc: 0.9464\n",
            "Epoch 4275/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0057 - acc: 0.9947 - val_loss: 0.0856 - val_acc: 0.9286\n",
            "Epoch 4276/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0115 - acc: 0.9841 - val_loss: 0.0804 - val_acc: 0.9464\n",
            "Epoch 4277/7407\n",
            "377/377 [==============================] - 0s 104us/step - loss: 0.0083 - acc: 0.9920 - val_loss: 0.0813 - val_acc: 0.9643\n",
            "Epoch 4278/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0816 - val_acc: 0.9643\n",
            "Epoch 4279/7407\n",
            "377/377 [==============================] - 0s 99us/step - loss: 0.0022 - acc: 0.9947 - val_loss: 0.0829 - val_acc: 0.9643\n",
            "Epoch 4280/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0048 - acc: 0.9973 - val_loss: 0.0807 - val_acc: 0.9643\n",
            "Epoch 4281/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0198 - acc: 0.9894 - val_loss: 0.0810 - val_acc: 0.9643\n",
            "Epoch 4282/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0136 - acc: 0.9920 - val_loss: 0.0768 - val_acc: 0.9464\n",
            "Epoch 4283/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0087 - acc: 0.9894 - val_loss: 0.0785 - val_acc: 0.9643\n",
            "Epoch 4284/7407\n",
            "377/377 [==============================] - 0s 134us/step - loss: 9.6856e-04 - acc: 1.0000 - val_loss: 0.0789 - val_acc: 0.9464\n",
            "Epoch 4285/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0019 - acc: 0.9973 - val_loss: 0.0797 - val_acc: 0.9464\n",
            "Epoch 4286/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0023 - acc: 0.9973 - val_loss: 0.0828 - val_acc: 0.9643\n",
            "Epoch 4287/7407\n",
            "377/377 [==============================] - 0s 132us/step - loss: 0.0043 - acc: 0.9973 - val_loss: 0.0848 - val_acc: 0.9643\n",
            "Epoch 4288/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0028 - acc: 0.9973 - val_loss: 0.0833 - val_acc: 0.9643\n",
            "Epoch 4289/7407\n",
            "377/377 [==============================] - 0s 109us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0843 - val_acc: 0.9643\n",
            "Epoch 4290/7407\n",
            "377/377 [==============================] - 0s 104us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0849 - val_acc: 0.9643\n",
            "Epoch 4291/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0083 - acc: 0.9920 - val_loss: 0.0800 - val_acc: 0.9643\n",
            "Epoch 4292/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0035 - acc: 0.9947 - val_loss: 0.0825 - val_acc: 0.9643\n",
            "Epoch 4293/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0046 - acc: 0.9947 - val_loss: 0.0799 - val_acc: 0.9643\n",
            "Epoch 4294/7407\n",
            "377/377 [==============================] - 0s 93us/step - loss: 0.0033 - acc: 0.9947 - val_loss: 0.0839 - val_acc: 0.9643\n",
            "Epoch 4295/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0021 - acc: 0.9947 - val_loss: 0.0884 - val_acc: 0.9643\n",
            "Epoch 4296/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.0843 - val_acc: 0.9643\n",
            "Epoch 4297/7407\n",
            "377/377 [==============================] - 0s 106us/step - loss: 0.0122 - acc: 0.9894 - val_loss: 0.1004 - val_acc: 0.9464\n",
            "Epoch 4298/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0976 - val_acc: 0.9464\n",
            "Epoch 4299/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0973 - val_acc: 0.9464\n",
            "Epoch 4300/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0063 - acc: 0.9894 - val_loss: 0.1094 - val_acc: 0.9286\n",
            "Epoch 4301/7407\n",
            "377/377 [==============================] - 0s 104us/step - loss: 0.0133 - acc: 0.9894 - val_loss: 0.0844 - val_acc: 0.9286\n",
            "Epoch 4302/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0030 - acc: 0.9947 - val_loss: 0.0821 - val_acc: 0.9464\n",
            "Epoch 4303/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0848 - val_acc: 0.9286\n",
            "Epoch 4304/7407\n",
            "377/377 [==============================] - 0s 96us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0880 - val_acc: 0.9643\n",
            "Epoch 4305/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0023 - acc: 0.9947 - val_loss: 0.0881 - val_acc: 0.9643\n",
            "Epoch 4306/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0055 - acc: 0.9947 - val_loss: 0.0884 - val_acc: 0.9464\n",
            "Epoch 4307/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0029 - acc: 0.9947 - val_loss: 0.0883 - val_acc: 0.9464\n",
            "Epoch 4308/7407\n",
            "377/377 [==============================] - 0s 113us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0932 - val_acc: 0.9464\n",
            "Epoch 4309/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0081 - acc: 0.9947 - val_loss: 0.0813 - val_acc: 0.9643\n",
            "Epoch 4310/7407\n",
            "377/377 [==============================] - 0s 90us/step - loss: 0.0091 - acc: 0.9894 - val_loss: 0.0865 - val_acc: 0.9464\n",
            "Epoch 4311/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0068 - acc: 0.9920 - val_loss: 0.0820 - val_acc: 0.9464\n",
            "Epoch 4312/7407\n",
            "377/377 [==============================] - 0s 115us/step - loss: 0.0077 - acc: 0.9973 - val_loss: 0.0747 - val_acc: 0.9464\n",
            "Epoch 4313/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0030 - acc: 0.9947 - val_loss: 0.0756 - val_acc: 0.9643\n",
            "Epoch 4314/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0085 - acc: 0.9867 - val_loss: 0.0768 - val_acc: 0.9643\n",
            "Epoch 4315/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0064 - acc: 0.9867 - val_loss: 0.0808 - val_acc: 0.9643\n",
            "Epoch 4316/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0039 - acc: 0.9973 - val_loss: 0.0808 - val_acc: 0.9464\n",
            "Epoch 4317/7407\n",
            "377/377 [==============================] - 0s 108us/step - loss: 0.0100 - acc: 0.9920 - val_loss: 0.0872 - val_acc: 0.9643\n",
            "Epoch 4318/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0085 - acc: 0.9894 - val_loss: 0.0864 - val_acc: 0.9643\n",
            "Epoch 4319/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0847 - val_acc: 0.9643\n",
            "Epoch 4320/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0043 - acc: 0.9973 - val_loss: 0.0859 - val_acc: 0.9643\n",
            "Epoch 4321/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 9.1309e-04 - acc: 1.0000 - val_loss: 0.0855 - val_acc: 0.9643\n",
            "Epoch 4322/7407\n",
            "377/377 [==============================] - 0s 121us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0869 - val_acc: 0.9643\n",
            "Epoch 4323/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0074 - acc: 0.9947 - val_loss: 0.0902 - val_acc: 0.9286\n",
            "Epoch 4324/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0110 - acc: 0.9973 - val_loss: 0.0820 - val_acc: 0.9464\n",
            "Epoch 4325/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0036 - acc: 0.9947 - val_loss: 0.0815 - val_acc: 0.9464\n",
            "Epoch 4326/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0031 - acc: 0.9920 - val_loss: 0.0818 - val_acc: 0.9464\n",
            "Epoch 4327/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0820 - val_acc: 0.9464\n",
            "Epoch 4328/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0047 - acc: 0.9973 - val_loss: 0.0831 - val_acc: 0.9464\n",
            "Epoch 4329/7407\n",
            "377/377 [==============================] - 0s 101us/step - loss: 0.0073 - acc: 0.9947 - val_loss: 0.0827 - val_acc: 0.9464\n",
            "Epoch 4330/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0028 - acc: 0.9947 - val_loss: 0.0826 - val_acc: 0.9464\n",
            "Epoch 4331/7407\n",
            "377/377 [==============================] - 0s 87us/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0843 - val_acc: 0.9464\n",
            "Epoch 4332/7407\n",
            "377/377 [==============================] - 0s 77us/step - loss: 0.0109 - acc: 0.9920 - val_loss: 0.0841 - val_acc: 0.9464\n",
            "Epoch 4333/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0078 - acc: 0.9973 - val_loss: 0.0823 - val_acc: 0.9464\n",
            "Epoch 4334/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0041 - acc: 0.9894 - val_loss: 0.0845 - val_acc: 0.9464\n",
            "Epoch 4335/7407\n",
            "377/377 [==============================] - 0s 124us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0815 - val_acc: 0.9464\n",
            "Epoch 4336/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0802 - val_acc: 0.9464\n",
            "Epoch 4337/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0803 - val_acc: 0.9464\n",
            "Epoch 4338/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0091 - acc: 0.9920 - val_loss: 0.0791 - val_acc: 0.9464\n",
            "Epoch 4339/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0082 - acc: 0.9920 - val_loss: 0.0811 - val_acc: 0.9464\n",
            "Epoch 4340/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0027 - acc: 0.9947 - val_loss: 0.0804 - val_acc: 0.9464\n",
            "Epoch 4341/7407\n",
            "377/377 [==============================] - 0s 154us/step - loss: 0.0044 - acc: 0.9894 - val_loss: 0.0821 - val_acc: 0.9464\n",
            "Epoch 4342/7407\n",
            "377/377 [==============================] - 0s 151us/step - loss: 0.0098 - acc: 0.9920 - val_loss: 0.0800 - val_acc: 0.9464\n",
            "Epoch 4343/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0047 - acc: 0.9973 - val_loss: 0.0802 - val_acc: 0.9464\n",
            "Epoch 4344/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0056 - acc: 0.9920 - val_loss: 0.0807 - val_acc: 0.9464\n",
            "Epoch 4345/7407\n",
            "377/377 [==============================] - 0s 161us/step - loss: 0.0041 - acc: 0.9973 - val_loss: 0.0813 - val_acc: 0.9464\n",
            "Epoch 4346/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0045 - acc: 0.9973 - val_loss: 0.0842 - val_acc: 0.9464\n",
            "Epoch 4347/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0072 - acc: 0.9894 - val_loss: 0.0913 - val_acc: 0.9107\n",
            "Epoch 4348/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0044 - acc: 0.9920 - val_loss: 0.0849 - val_acc: 0.9464\n",
            "Epoch 4349/7407\n",
            "377/377 [==============================] - 0s 151us/step - loss: 0.0063 - acc: 0.9920 - val_loss: 0.0804 - val_acc: 0.9464\n",
            "Epoch 4350/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0030 - acc: 0.9947 - val_loss: 0.0825 - val_acc: 0.9464\n",
            "Epoch 4351/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0042 - acc: 0.9920 - val_loss: 0.0801 - val_acc: 0.9643\n",
            "Epoch 4352/7407\n",
            "377/377 [==============================] - 0s 155us/step - loss: 0.0031 - acc: 0.9947 - val_loss: 0.0812 - val_acc: 0.9464\n",
            "Epoch 4353/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0030 - acc: 0.9973 - val_loss: 0.0798 - val_acc: 0.9464\n",
            "Epoch 4354/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0212 - acc: 0.9894 - val_loss: 0.0789 - val_acc: 0.9464\n",
            "Epoch 4355/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0019 - acc: 0.9973 - val_loss: 0.0798 - val_acc: 0.9464\n",
            "Epoch 4356/7407\n",
            "377/377 [==============================] - 0s 132us/step - loss: 0.0044 - acc: 0.9920 - val_loss: 0.0781 - val_acc: 0.9464\n",
            "Epoch 4357/7407\n",
            "377/377 [==============================] - 0s 186us/step - loss: 0.0047 - acc: 0.9947 - val_loss: 0.0775 - val_acc: 0.9464\n",
            "Epoch 4358/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0038 - acc: 0.9920 - val_loss: 0.0763 - val_acc: 0.9464\n",
            "Epoch 4359/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0068 - acc: 0.9894 - val_loss: 0.0767 - val_acc: 0.9464\n",
            "Epoch 4360/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0019 - acc: 0.9947 - val_loss: 0.0769 - val_acc: 0.9464\n",
            "Epoch 4361/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0034 - acc: 0.9973 - val_loss: 0.0773 - val_acc: 0.9464\n",
            "Epoch 4362/7407\n",
            "377/377 [==============================] - 0s 155us/step - loss: 0.0050 - acc: 0.9867 - val_loss: 0.0784 - val_acc: 0.9643\n",
            "Epoch 4363/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0044 - acc: 0.9973 - val_loss: 0.0765 - val_acc: 0.9464\n",
            "Epoch 4364/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0043 - acc: 0.9947 - val_loss: 0.0762 - val_acc: 0.9464\n",
            "Epoch 4365/7407\n",
            "377/377 [==============================] - 0s 152us/step - loss: 9.1537e-04 - acc: 1.0000 - val_loss: 0.0770 - val_acc: 0.9464\n",
            "Epoch 4366/7407\n",
            "377/377 [==============================] - 0s 159us/step - loss: 0.0038 - acc: 0.9947 - val_loss: 0.0769 - val_acc: 0.9464\n",
            "Epoch 4367/7407\n",
            "377/377 [==============================] - 0s 173us/step - loss: 0.0029 - acc: 0.9947 - val_loss: 0.0787 - val_acc: 0.9464\n",
            "Epoch 4368/7407\n",
            "377/377 [==============================] - 0s 158us/step - loss: 0.0047 - acc: 0.9973 - val_loss: 0.0802 - val_acc: 0.9464\n",
            "Epoch 4369/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0045 - acc: 0.9947 - val_loss: 0.0805 - val_acc: 0.9464\n",
            "Epoch 4370/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0030 - acc: 0.9973 - val_loss: 0.0847 - val_acc: 0.9643\n",
            "Epoch 4371/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0125 - acc: 0.9947 - val_loss: 0.0848 - val_acc: 0.9286\n",
            "Epoch 4372/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0053 - acc: 0.9920 - val_loss: 0.0853 - val_acc: 0.9286\n",
            "Epoch 4373/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0051 - acc: 0.9947 - val_loss: 0.0844 - val_acc: 0.9286\n",
            "Epoch 4374/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0079 - acc: 0.9920 - val_loss: 0.0854 - val_acc: 0.9643\n",
            "Epoch 4375/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0037 - acc: 0.9973 - val_loss: 0.0804 - val_acc: 0.9643\n",
            "Epoch 4376/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0031 - acc: 0.9973 - val_loss: 0.0798 - val_acc: 0.9286\n",
            "Epoch 4377/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0054 - acc: 0.9947 - val_loss: 0.0799 - val_acc: 0.9464\n",
            "Epoch 4378/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0803 - val_acc: 0.9286\n",
            "Epoch 4379/7407\n",
            "377/377 [==============================] - 0s 135us/step - loss: 0.0022 - acc: 0.9947 - val_loss: 0.0810 - val_acc: 0.9464\n",
            "Epoch 4380/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0781 - val_acc: 0.9464\n",
            "Epoch 4381/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0049 - acc: 0.9973 - val_loss: 0.0823 - val_acc: 0.9107\n",
            "Epoch 4382/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0036 - acc: 0.9920 - val_loss: 0.0807 - val_acc: 0.9286\n",
            "Epoch 4383/7407\n",
            "377/377 [==============================] - 0s 167us/step - loss: 0.0030 - acc: 0.9973 - val_loss: 0.0787 - val_acc: 0.9286\n",
            "Epoch 4384/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0798 - val_acc: 0.9286\n",
            "Epoch 4385/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0084 - acc: 0.9920 - val_loss: 0.0800 - val_acc: 0.9643\n",
            "Epoch 4386/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0044 - acc: 0.9947 - val_loss: 0.0814 - val_acc: 0.9464\n",
            "Epoch 4387/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0031 - acc: 0.9973 - val_loss: 0.0829 - val_acc: 0.9286\n",
            "Epoch 4388/7407\n",
            "377/377 [==============================] - 0s 165us/step - loss: 0.0014 - acc: 0.9973 - val_loss: 0.0827 - val_acc: 0.9286\n",
            "Epoch 4389/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0863 - val_acc: 0.9643\n",
            "Epoch 4390/7407\n",
            "377/377 [==============================] - 0s 182us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0843 - val_acc: 0.9286\n",
            "Epoch 4391/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0030 - acc: 0.9947 - val_loss: 0.0857 - val_acc: 0.9464\n",
            "Epoch 4392/7407\n",
            "377/377 [==============================] - 0s 158us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0850 - val_acc: 0.9286\n",
            "Epoch 4393/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0023 - acc: 0.9947 - val_loss: 0.0804 - val_acc: 0.9286\n",
            "Epoch 4394/7407\n",
            "377/377 [==============================] - 0s 184us/step - loss: 0.0063 - acc: 0.9947 - val_loss: 0.0793 - val_acc: 0.9286\n",
            "Epoch 4395/7407\n",
            "377/377 [==============================] - 0s 161us/step - loss: 0.0048 - acc: 0.9947 - val_loss: 0.0774 - val_acc: 0.9286\n",
            "Epoch 4396/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0774 - val_acc: 0.9286\n",
            "Epoch 4397/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0032 - acc: 0.9947 - val_loss: 0.0769 - val_acc: 0.9464\n",
            "Epoch 4398/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0037 - acc: 0.9920 - val_loss: 0.0764 - val_acc: 0.9286\n",
            "Epoch 4399/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 0.0014 - acc: 0.9973 - val_loss: 0.0771 - val_acc: 0.9286\n",
            "Epoch 4400/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0112 - acc: 0.9920 - val_loss: 0.0862 - val_acc: 0.9286\n",
            "Epoch 4401/7407\n",
            "377/377 [==============================] - 0s 173us/step - loss: 0.0081 - acc: 0.9867 - val_loss: 0.0784 - val_acc: 0.9286\n",
            "Epoch 4402/7407\n",
            "377/377 [==============================] - 0s 134us/step - loss: 0.0023 - acc: 0.9973 - val_loss: 0.0790 - val_acc: 0.9286\n",
            "Epoch 4403/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0019 - acc: 0.9973 - val_loss: 0.0781 - val_acc: 0.9286\n",
            "Epoch 4404/7407\n",
            "377/377 [==============================] - 0s 161us/step - loss: 0.0013 - acc: 0.9973 - val_loss: 0.0782 - val_acc: 0.9286\n",
            "Epoch 4405/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0091 - acc: 0.9920 - val_loss: 0.0950 - val_acc: 0.9107\n",
            "Epoch 4406/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0041 - acc: 0.9867 - val_loss: 0.0826 - val_acc: 0.9286\n",
            "Epoch 4407/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0029 - acc: 0.9920 - val_loss: 0.0831 - val_acc: 0.9464\n",
            "Epoch 4408/7407\n",
            "377/377 [==============================] - 0s 134us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0823 - val_acc: 0.9464\n",
            "Epoch 4409/7407\n",
            "377/377 [==============================] - 0s 172us/step - loss: 0.0045 - acc: 0.9947 - val_loss: 0.0950 - val_acc: 0.9286\n",
            "Epoch 4410/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0023 - acc: 0.9947 - val_loss: 0.0972 - val_acc: 0.9286\n",
            "Epoch 4411/7407\n",
            "377/377 [==============================] - 0s 162us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.0846 - val_acc: 0.9464\n",
            "Epoch 4412/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0037 - acc: 0.9867 - val_loss: 0.0812 - val_acc: 0.9464\n",
            "Epoch 4413/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0035 - acc: 0.9973 - val_loss: 0.0816 - val_acc: 0.9464\n",
            "Epoch 4414/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0020 - acc: 1.0000 - val_loss: 0.0808 - val_acc: 0.9464\n",
            "Epoch 4415/7407\n",
            "377/377 [==============================] - 0s 185us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0817 - val_acc: 0.9464\n",
            "Epoch 4416/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0045 - acc: 0.9947 - val_loss: 0.0800 - val_acc: 0.9464\n",
            "Epoch 4417/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0060 - acc: 0.9947 - val_loss: 0.0819 - val_acc: 0.9464\n",
            "Epoch 4418/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0034 - acc: 0.9947 - val_loss: 0.0819 - val_acc: 0.9464\n",
            "Epoch 4419/7407\n",
            "377/377 [==============================] - 0s 200us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0821 - val_acc: 0.9464\n",
            "Epoch 4420/7407\n",
            "377/377 [==============================] - 0s 197us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.0819 - val_acc: 0.9464\n",
            "Epoch 4421/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0816 - val_acc: 0.9464\n",
            "Epoch 4422/7407\n",
            "377/377 [==============================] - 0s 174us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0824 - val_acc: 0.9464\n",
            "Epoch 4423/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0057 - acc: 0.9947 - val_loss: 0.0826 - val_acc: 0.9464\n",
            "Epoch 4424/7407\n",
            "377/377 [==============================] - 0s 157us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0819 - val_acc: 0.9464\n",
            "Epoch 4425/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0021 - acc: 0.9947 - val_loss: 0.0792 - val_acc: 0.9464\n",
            "Epoch 4426/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.0804 - val_acc: 0.9464\n",
            "Epoch 4427/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0026 - acc: 0.9920 - val_loss: 0.0806 - val_acc: 0.9464\n",
            "Epoch 4428/7407\n",
            "377/377 [==============================] - 0s 160us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0836 - val_acc: 0.9464\n",
            "Epoch 4429/7407\n",
            "377/377 [==============================] - 0s 159us/step - loss: 0.0049 - acc: 0.9894 - val_loss: 0.0812 - val_acc: 0.9464\n",
            "Epoch 4430/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0059 - acc: 0.9947 - val_loss: 0.0840 - val_acc: 0.9464\n",
            "Epoch 4431/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0040 - acc: 0.9947 - val_loss: 0.0824 - val_acc: 0.9464\n",
            "Epoch 4432/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0067 - acc: 0.9947 - val_loss: 0.0905 - val_acc: 0.9286\n",
            "Epoch 4433/7407\n",
            "377/377 [==============================] - 0s 139us/step - loss: 0.0044 - acc: 0.9947 - val_loss: 0.0892 - val_acc: 0.9286\n",
            "Epoch 4434/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0053 - acc: 0.9920 - val_loss: 0.1049 - val_acc: 0.9286\n",
            "Epoch 4435/7407\n",
            "377/377 [==============================] - 0s 136us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.1045 - val_acc: 0.9286\n",
            "Epoch 4436/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0936 - val_acc: 0.9286\n",
            "Epoch 4437/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0055 - acc: 0.9894 - val_loss: 0.0969 - val_acc: 0.9286\n",
            "Epoch 4438/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0023 - acc: 0.9973 - val_loss: 0.0890 - val_acc: 0.9286\n",
            "Epoch 4439/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0049 - acc: 0.9973 - val_loss: 0.0822 - val_acc: 0.9464\n",
            "Epoch 4440/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0857 - val_acc: 0.9464\n",
            "Epoch 4441/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0056 - acc: 0.9920 - val_loss: 0.0869 - val_acc: 0.9286\n",
            "Epoch 4442/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0871 - val_acc: 0.9286\n",
            "Epoch 4443/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0030 - acc: 0.9920 - val_loss: 0.1066 - val_acc: 0.9286\n",
            "Epoch 4444/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0031 - acc: 0.9947 - val_loss: 0.0880 - val_acc: 0.9107\n",
            "Epoch 4445/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0108 - acc: 0.9920 - val_loss: 0.0838 - val_acc: 0.9286\n",
            "Epoch 4446/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0111 - acc: 0.9894 - val_loss: 0.0824 - val_acc: 0.9286\n",
            "Epoch 4447/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0856 - val_acc: 0.9286\n",
            "Epoch 4448/7407\n",
            "377/377 [==============================] - 0s 169us/step - loss: 0.0011 - acc: 1.0000 - val_loss: 0.0844 - val_acc: 0.9286\n",
            "Epoch 4449/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0127 - acc: 0.9947 - val_loss: 0.0840 - val_acc: 0.9286\n",
            "Epoch 4450/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0110 - acc: 0.9920 - val_loss: 0.0881 - val_acc: 0.9286\n",
            "Epoch 4451/7407\n",
            "377/377 [==============================] - 0s 164us/step - loss: 0.0021 - acc: 0.9973 - val_loss: 0.0886 - val_acc: 0.9286\n",
            "Epoch 4452/7407\n",
            "377/377 [==============================] - 0s 125us/step - loss: 9.4641e-04 - acc: 1.0000 - val_loss: 0.0909 - val_acc: 0.9286\n",
            "Epoch 4453/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0081 - acc: 0.9973 - val_loss: 0.0806 - val_acc: 0.9286\n",
            "Epoch 4454/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0117 - acc: 0.9841 - val_loss: 0.0812 - val_acc: 0.9286\n",
            "Epoch 4455/7407\n",
            "377/377 [==============================] - 0s 159us/step - loss: 0.0073 - acc: 0.9867 - val_loss: 0.0791 - val_acc: 0.9286\n",
            "Epoch 4456/7407\n",
            "377/377 [==============================] - 0s 157us/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.0801 - val_acc: 0.9286\n",
            "Epoch 4457/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0040 - acc: 0.9947 - val_loss: 0.0775 - val_acc: 0.9286\n",
            "Epoch 4458/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0017 - acc: 0.9973 - val_loss: 0.0764 - val_acc: 0.9286\n",
            "Epoch 4459/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0036 - acc: 0.9973 - val_loss: 0.0767 - val_acc: 0.9286\n",
            "Epoch 4460/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.0762 - val_acc: 0.9286\n",
            "Epoch 4461/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0050 - acc: 0.9894 - val_loss: 0.0793 - val_acc: 0.9286\n",
            "Epoch 4462/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0805 - val_acc: 0.9286\n",
            "Epoch 4463/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0812 - val_acc: 0.9286\n",
            "Epoch 4464/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0024 - acc: 0.9947 - val_loss: 0.0836 - val_acc: 0.9286\n",
            "Epoch 4465/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0051 - acc: 0.9867 - val_loss: 0.0896 - val_acc: 0.9107\n",
            "Epoch 4466/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0017 - acc: 0.9947 - val_loss: 0.0946 - val_acc: 0.9107\n",
            "Epoch 4467/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0057 - acc: 0.9947 - val_loss: 0.0957 - val_acc: 0.9107\n",
            "Epoch 4468/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.0900 - val_acc: 0.9107\n",
            "Epoch 4469/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0900 - val_acc: 0.9286\n",
            "Epoch 4470/7407\n",
            "377/377 [==============================] - 0s 173us/step - loss: 9.0996e-04 - acc: 1.0000 - val_loss: 0.0921 - val_acc: 0.9286\n",
            "Epoch 4471/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.0903 - val_acc: 0.9107\n",
            "Epoch 4472/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0068 - acc: 0.9894 - val_loss: 0.0944 - val_acc: 0.9286\n",
            "Epoch 4473/7407\n",
            "377/377 [==============================] - 0s 183us/step - loss: 0.0028 - acc: 0.9973 - val_loss: 0.0913 - val_acc: 0.9107\n",
            "Epoch 4474/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0889 - val_acc: 0.9107\n",
            "Epoch 4475/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0027 - acc: 1.0000 - val_loss: 0.0828 - val_acc: 0.9107\n",
            "Epoch 4476/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0031 - acc: 0.9973 - val_loss: 0.0825 - val_acc: 0.9107\n",
            "Epoch 4477/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0088 - acc: 0.9947 - val_loss: 0.0961 - val_acc: 0.9464\n",
            "Epoch 4478/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0015 - acc: 0.9973 - val_loss: 0.0997 - val_acc: 0.9464\n",
            "Epoch 4479/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0147 - acc: 0.9867 - val_loss: 0.0811 - val_acc: 0.9286\n",
            "Epoch 4480/7407\n",
            "377/377 [==============================] - 0s 138us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0813 - val_acc: 0.9286\n",
            "Epoch 4481/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0021 - acc: 0.9947 - val_loss: 0.0819 - val_acc: 0.9286\n",
            "Epoch 4482/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.0806 - val_acc: 0.9286\n",
            "Epoch 4483/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 4.7398e-04 - acc: 1.0000 - val_loss: 0.0812 - val_acc: 0.9286\n",
            "Epoch 4484/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0015 - acc: 0.9973 - val_loss: 0.0814 - val_acc: 0.9286\n",
            "Epoch 4485/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0054 - acc: 0.9920 - val_loss: 0.0830 - val_acc: 0.9464\n",
            "Epoch 4486/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0023 - acc: 0.9973 - val_loss: 0.0838 - val_acc: 0.9286\n",
            "Epoch 4487/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0047 - acc: 0.9920 - val_loss: 0.0862 - val_acc: 0.9286\n",
            "Epoch 4488/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0036 - acc: 0.9947 - val_loss: 0.0825 - val_acc: 0.9286\n",
            "Epoch 4489/7407\n",
            "377/377 [==============================] - 0s 156us/step - loss: 0.0051 - acc: 0.9947 - val_loss: 0.0810 - val_acc: 0.9286\n",
            "Epoch 4490/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0052 - acc: 0.9920 - val_loss: 0.0797 - val_acc: 0.9286\n",
            "Epoch 4491/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0801 - val_acc: 0.9286\n",
            "Epoch 4492/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0039 - acc: 0.9973 - val_loss: 0.0820 - val_acc: 0.9286\n",
            "Epoch 4493/7407\n",
            "377/377 [==============================] - 0s 165us/step - loss: 0.0050 - acc: 0.9973 - val_loss: 0.0860 - val_acc: 0.9286\n",
            "Epoch 4494/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0807 - val_acc: 0.9286\n",
            "Epoch 4495/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0014 - acc: 0.9973 - val_loss: 0.0813 - val_acc: 0.9286\n",
            "Epoch 4496/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0020 - acc: 0.9973 - val_loss: 0.0835 - val_acc: 0.9286\n",
            "Epoch 4497/7407\n",
            "377/377 [==============================] - 0s 172us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0840 - val_acc: 0.9286\n",
            "Epoch 4498/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0055 - acc: 0.9920 - val_loss: 0.0871 - val_acc: 0.9464\n",
            "Epoch 4499/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0856 - val_acc: 0.9286\n",
            "Epoch 4500/7407\n",
            "377/377 [==============================] - 0s 152us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0823 - val_acc: 0.9286\n",
            "Epoch 4501/7407\n",
            "377/377 [==============================] - 0s 148us/step - loss: 0.0043 - acc: 0.9920 - val_loss: 0.0926 - val_acc: 0.9464\n",
            "Epoch 4502/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0104 - acc: 0.9947 - val_loss: 0.0732 - val_acc: 0.9286\n",
            "Epoch 4503/7407\n",
            "377/377 [==============================] - 0s 147us/step - loss: 0.0022 - acc: 0.9920 - val_loss: 0.0750 - val_acc: 0.9286\n",
            "Epoch 4504/7407\n",
            "377/377 [==============================] - 0s 158us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0752 - val_acc: 0.9286\n",
            "Epoch 4505/7407\n",
            "377/377 [==============================] - 0s 153us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0747 - val_acc: 0.9286\n",
            "Epoch 4506/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0068 - acc: 0.9947 - val_loss: 0.0676 - val_acc: 0.9286\n",
            "Epoch 4507/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0021 - acc: 0.9947 - val_loss: 0.0700 - val_acc: 0.9464\n",
            "Epoch 4508/7407\n",
            "377/377 [==============================] - 0s 156us/step - loss: 0.0031 - acc: 0.9947 - val_loss: 0.0725 - val_acc: 0.9464\n",
            "Epoch 4509/7407\n",
            "377/377 [==============================] - 0s 110us/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0712 - val_acc: 0.9464\n",
            "Epoch 4510/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0038 - acc: 0.9973 - val_loss: 0.0705 - val_acc: 0.9464\n",
            "Epoch 4511/7407\n",
            "377/377 [==============================] - 0s 175us/step - loss: 8.1795e-04 - acc: 1.0000 - val_loss: 0.0701 - val_acc: 0.9286\n",
            "Epoch 4512/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0054 - acc: 0.9973 - val_loss: 0.0774 - val_acc: 0.9643\n",
            "Epoch 4513/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0023 - acc: 0.9947 - val_loss: 0.0811 - val_acc: 0.9286\n",
            "Epoch 4514/7407\n",
            "377/377 [==============================] - 0s 194us/step - loss: 0.0063 - acc: 0.9973 - val_loss: 0.0740 - val_acc: 0.9286\n",
            "Epoch 4515/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0028 - acc: 0.9973 - val_loss: 0.0794 - val_acc: 0.9464\n",
            "Epoch 4516/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0036 - acc: 0.9920 - val_loss: 0.0845 - val_acc: 0.9286\n",
            "Epoch 4517/7407\n",
            "377/377 [==============================] - 0s 140us/step - loss: 0.0018 - acc: 0.9947 - val_loss: 0.0854 - val_acc: 0.9286\n",
            "Epoch 4518/7407\n",
            "377/377 [==============================] - 0s 179us/step - loss: 0.0065 - acc: 0.9947 - val_loss: 0.0867 - val_acc: 0.9107\n",
            "Epoch 4519/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0037 - acc: 0.9973 - val_loss: 0.0834 - val_acc: 0.9286\n",
            "Epoch 4520/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0041 - acc: 0.9973 - val_loss: 0.0772 - val_acc: 0.9464\n",
            "Epoch 4521/7407\n",
            "377/377 [==============================] - 0s 145us/step - loss: 0.0059 - acc: 0.9973 - val_loss: 0.0748 - val_acc: 0.9286\n",
            "Epoch 4522/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0081 - acc: 0.9920 - val_loss: 0.0738 - val_acc: 0.9286\n",
            "Epoch 4523/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0084 - acc: 0.9894 - val_loss: 0.0776 - val_acc: 0.9286\n",
            "Epoch 4524/7407\n",
            "377/377 [==============================] - 0s 135us/step - loss: 0.0038 - acc: 0.9920 - val_loss: 0.0773 - val_acc: 0.9286\n",
            "Epoch 4525/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0070 - acc: 0.9920 - val_loss: 0.0795 - val_acc: 0.9286\n",
            "Epoch 4526/7407\n",
            "377/377 [==============================] - 0s 142us/step - loss: 0.0113 - acc: 0.9894 - val_loss: 0.0724 - val_acc: 0.9286\n",
            "Epoch 4527/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0111 - acc: 0.9920 - val_loss: 0.0662 - val_acc: 0.9286\n",
            "Epoch 4528/7407\n",
            "377/377 [==============================] - 0s 141us/step - loss: 0.0053 - acc: 0.9973 - val_loss: 0.0677 - val_acc: 0.9107\n",
            "Epoch 4529/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0034 - acc: 0.9920 - val_loss: 0.0657 - val_acc: 0.9286\n",
            "Epoch 4530/7407\n",
            "377/377 [==============================] - 0s 143us/step - loss: 0.0024 - acc: 0.9947 - val_loss: 0.0657 - val_acc: 0.9286\n",
            "Epoch 4531/7407\n",
            "377/377 [==============================] - 0s 137us/step - loss: 0.0068 - acc: 0.9920 - val_loss: 0.0695 - val_acc: 0.9464\n",
            "Epoch 4532/7407\n",
            "377/377 [==============================] - 0s 135us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0712 - val_acc: 0.9464\n",
            "Epoch 4533/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0713 - val_acc: 0.9464\n",
            "Epoch 4534/7407\n",
            "377/377 [==============================] - 0s 168us/step - loss: 8.7463e-04 - acc: 1.0000 - val_loss: 0.0710 - val_acc: 0.9464\n",
            "Epoch 4535/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0030 - acc: 0.9973 - val_loss: 0.0681 - val_acc: 0.9464\n",
            "Epoch 4536/7407\n",
            "377/377 [==============================] - 0s 144us/step - loss: 0.0068 - acc: 0.9920 - val_loss: 0.0844 - val_acc: 0.9286\n",
            "Epoch 4537/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0042 - acc: 0.9894 - val_loss: 0.0862 - val_acc: 0.9286\n",
            "Epoch 4538/7407\n",
            "377/377 [==============================] - 0s 149us/step - loss: 0.0055 - acc: 0.9973 - val_loss: 0.0806 - val_acc: 0.8929\n",
            "Epoch 4539/7407\n",
            "377/377 [==============================] - 0s 152us/step - loss: 0.0039 - acc: 0.9947 - val_loss: 0.0837 - val_acc: 0.8929\n",
            "Epoch 4540/7407\n",
            "377/377 [==============================] - 0s 150us/step - loss: 0.0072 - acc: 0.9867 - val_loss: 0.0865 - val_acc: 0.9286\n",
            "Epoch 4541/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0017 - acc: 0.9973 - val_loss: 0.0860 - val_acc: 0.9286\n",
            "Epoch 4542/7407\n",
            "377/377 [==============================] - 0s 101us/step - loss: 0.0044 - acc: 0.9973 - val_loss: 0.0708 - val_acc: 0.9107\n",
            "Epoch 4543/7407\n",
            "377/377 [==============================] - 0s 89us/step - loss: 0.0029 - acc: 0.9947 - val_loss: 0.0701 - val_acc: 0.9107\n",
            "Epoch 4544/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0702 - val_acc: 0.9286\n",
            "Epoch 4545/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0054 - acc: 0.9947 - val_loss: 0.0698 - val_acc: 0.9107\n",
            "Epoch 4546/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0056 - acc: 0.9947 - val_loss: 0.0761 - val_acc: 0.9107\n",
            "Epoch 4547/7407\n",
            "377/377 [==============================] - 0s 89us/step - loss: 0.0151 - acc: 0.9814 - val_loss: 0.0771 - val_acc: 0.9107\n",
            "Epoch 4548/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0026 - acc: 0.9947 - val_loss: 0.0746 - val_acc: 0.9107\n",
            "Epoch 4549/7407\n",
            "377/377 [==============================] - 0s 111us/step - loss: 0.0121 - acc: 0.9867 - val_loss: 0.0769 - val_acc: 0.9464\n",
            "Epoch 4550/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0024 - acc: 0.9947 - val_loss: 0.0771 - val_acc: 0.9464\n",
            "Epoch 4551/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0045 - acc: 0.9973 - val_loss: 0.0806 - val_acc: 0.9464\n",
            "Epoch 4552/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0765 - val_acc: 0.9464\n",
            "Epoch 4553/7407\n",
            "377/377 [==============================] - 0s 87us/step - loss: 0.0054 - acc: 0.9947 - val_loss: 0.0740 - val_acc: 0.9643\n",
            "Epoch 4554/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0071 - acc: 0.9947 - val_loss: 0.0666 - val_acc: 0.9464\n",
            "Epoch 4555/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0047 - acc: 0.9947 - val_loss: 0.0669 - val_acc: 0.9286\n",
            "Epoch 4556/7407\n",
            "377/377 [==============================] - 0s 124us/step - loss: 0.0102 - acc: 0.9894 - val_loss: 0.0609 - val_acc: 0.9464\n",
            "Epoch 4557/7407\n",
            "377/377 [==============================] - 0s 110us/step - loss: 9.1611e-04 - acc: 1.0000 - val_loss: 0.0613 - val_acc: 0.9464\n",
            "Epoch 4558/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0033 - acc: 0.9947 - val_loss: 0.0603 - val_acc: 0.9464\n",
            "Epoch 4559/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0041 - acc: 0.9947 - val_loss: 0.0617 - val_acc: 0.9643\n",
            "Epoch 4560/7407\n",
            "377/377 [==============================] - 0s 97us/step - loss: 0.0051 - acc: 0.9867 - val_loss: 0.0674 - val_acc: 0.9643\n",
            "Epoch 4561/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0657 - val_acc: 0.9643\n",
            "Epoch 4562/7407\n",
            "377/377 [==============================] - 0s 106us/step - loss: 0.0075 - acc: 0.9947 - val_loss: 0.0659 - val_acc: 0.9464\n",
            "Epoch 4563/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0021 - acc: 0.9947 - val_loss: 0.0663 - val_acc: 0.9464\n",
            "Epoch 4564/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0053 - acc: 0.9920 - val_loss: 0.0651 - val_acc: 0.9643\n",
            "Epoch 4565/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 9.1907e-04 - acc: 1.0000 - val_loss: 0.0654 - val_acc: 0.9643\n",
            "Epoch 4566/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0642 - val_acc: 0.9643\n",
            "Epoch 4567/7407\n",
            "377/377 [==============================] - 0s 122us/step - loss: 0.0012 - acc: 0.9973 - val_loss: 0.0661 - val_acc: 0.9643\n",
            "Epoch 4568/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0649 - val_acc: 0.9643\n",
            "Epoch 4569/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0056 - acc: 0.9894 - val_loss: 0.0706 - val_acc: 0.9643\n",
            "Epoch 4570/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0672 - val_acc: 0.9643\n",
            "Epoch 4571/7407\n",
            "377/377 [==============================] - 0s 102us/step - loss: 5.0968e-04 - acc: 1.0000 - val_loss: 0.0675 - val_acc: 0.9643\n",
            "Epoch 4572/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0033 - acc: 0.9973 - val_loss: 0.0662 - val_acc: 0.9643\n",
            "Epoch 4573/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0050 - acc: 0.9973 - val_loss: 0.0656 - val_acc: 0.9464\n",
            "Epoch 4574/7407\n",
            "377/377 [==============================] - 0s 99us/step - loss: 0.0030 - acc: 0.9947 - val_loss: 0.0678 - val_acc: 0.9643\n",
            "Epoch 4575/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0038 - acc: 0.9973 - val_loss: 0.0668 - val_acc: 0.9286\n",
            "Epoch 4576/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0036 - acc: 0.9973 - val_loss: 0.0687 - val_acc: 0.9464\n",
            "Epoch 4577/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0011 - acc: 1.0000 - val_loss: 0.0699 - val_acc: 0.9464\n",
            "Epoch 4578/7407\n",
            "377/377 [==============================] - 0s 146us/step - loss: 0.0060 - acc: 0.9947 - val_loss: 0.0710 - val_acc: 0.9286\n",
            "Epoch 4579/7407\n",
            "377/377 [==============================] - 0s 101us/step - loss: 0.0034 - acc: 0.9947 - val_loss: 0.0726 - val_acc: 0.9464\n",
            "Epoch 4580/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0059 - acc: 0.9920 - val_loss: 0.0778 - val_acc: 0.9643\n",
            "Epoch 4581/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0060 - acc: 0.9920 - val_loss: 0.0770 - val_acc: 0.9643\n",
            "Epoch 4582/7407\n",
            "377/377 [==============================] - 0s 111us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0762 - val_acc: 0.9286\n",
            "Epoch 4583/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0049 - acc: 0.9973 - val_loss: 0.0764 - val_acc: 0.9286\n",
            "Epoch 4584/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0783 - val_acc: 0.9464\n",
            "Epoch 4585/7407\n",
            "377/377 [==============================] - 0s 90us/step - loss: 0.0050 - acc: 0.9920 - val_loss: 0.0750 - val_acc: 0.9286\n",
            "Epoch 4586/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0033 - acc: 0.9920 - val_loss: 0.0729 - val_acc: 0.9286\n",
            "Epoch 4587/7407\n",
            "377/377 [==============================] - 0s 125us/step - loss: 0.0021 - acc: 1.0000 - val_loss: 0.0759 - val_acc: 0.9107\n",
            "Epoch 4588/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0057 - acc: 0.9867 - val_loss: 0.0762 - val_acc: 0.9286\n",
            "Epoch 4589/7407\n",
            "377/377 [==============================] - 0s 129us/step - loss: 0.0046 - acc: 0.9947 - val_loss: 0.0770 - val_acc: 0.9286\n",
            "Epoch 4590/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0048 - acc: 0.9947 - val_loss: 0.0755 - val_acc: 0.9464\n",
            "Epoch 4591/7407\n",
            "377/377 [==============================] - 0s 125us/step - loss: 0.0017 - acc: 0.9973 - val_loss: 0.0756 - val_acc: 0.9464\n",
            "Epoch 4592/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0016 - acc: 0.9973 - val_loss: 0.0747 - val_acc: 0.9464\n",
            "Epoch 4593/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0017 - acc: 0.9973 - val_loss: 0.0746 - val_acc: 0.9464\n",
            "Epoch 4594/7407\n",
            "377/377 [==============================] - 0s 96us/step - loss: 0.0053 - acc: 0.9920 - val_loss: 0.0716 - val_acc: 0.9464\n",
            "Epoch 4595/7407\n",
            "377/377 [==============================] - 0s 86us/step - loss: 0.0065 - acc: 0.9920 - val_loss: 0.0808 - val_acc: 0.9107\n",
            "Epoch 4596/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0059 - acc: 0.9947 - val_loss: 0.0743 - val_acc: 0.9286\n",
            "Epoch 4597/7407\n",
            "377/377 [==============================] - 0s 103us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0749 - val_acc: 0.9286\n",
            "Epoch 4598/7407\n",
            "377/377 [==============================] - 0s 89us/step - loss: 0.0023 - acc: 0.9973 - val_loss: 0.0717 - val_acc: 0.9286\n",
            "Epoch 4599/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0065 - acc: 0.9947 - val_loss: 0.0748 - val_acc: 0.9286\n",
            "Epoch 4600/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0064 - acc: 0.9894 - val_loss: 0.0727 - val_acc: 0.9286\n",
            "Epoch 4601/7407\n",
            "377/377 [==============================] - 0s 107us/step - loss: 0.0046 - acc: 0.9973 - val_loss: 0.0728 - val_acc: 0.9286\n",
            "Epoch 4602/7407\n",
            "377/377 [==============================] - 0s 111us/step - loss: 0.0033 - acc: 0.9947 - val_loss: 0.0745 - val_acc: 0.9286\n",
            "Epoch 4603/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0044 - acc: 0.9920 - val_loss: 0.0740 - val_acc: 0.9286\n",
            "Epoch 4604/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0029 - acc: 0.9973 - val_loss: 0.0761 - val_acc: 0.9286\n",
            "Epoch 4605/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0046 - acc: 0.9894 - val_loss: 0.0737 - val_acc: 0.9286\n",
            "Epoch 4606/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0740 - val_acc: 0.9286\n",
            "Epoch 4607/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.0774 - val_acc: 0.9107\n",
            "Epoch 4608/7407\n",
            "377/377 [==============================] - 0s 92us/step - loss: 0.0032 - acc: 0.9947 - val_loss: 0.0769 - val_acc: 0.9286\n",
            "Epoch 4609/7407\n",
            "377/377 [==============================] - 0s 94us/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0790 - val_acc: 0.9286\n",
            "Epoch 4610/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0044 - acc: 0.9947 - val_loss: 0.0738 - val_acc: 0.9286\n",
            "Epoch 4611/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 7.4025e-04 - acc: 1.0000 - val_loss: 0.0742 - val_acc: 0.9286\n",
            "Epoch 4612/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0056 - acc: 0.9920 - val_loss: 0.0710 - val_acc: 0.9286\n",
            "Epoch 4613/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0076 - acc: 0.9920 - val_loss: 0.0737 - val_acc: 0.9464\n",
            "Epoch 4614/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.0715 - val_acc: 0.9286\n",
            "Epoch 4615/7407\n",
            "377/377 [==============================] - 0s 102us/step - loss: 0.0042 - acc: 0.9947 - val_loss: 0.0678 - val_acc: 0.9286\n",
            "Epoch 4616/7407\n",
            "377/377 [==============================] - 0s 87us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0653 - val_acc: 0.9286\n",
            "Epoch 4617/7407\n",
            "377/377 [==============================] - 0s 97us/step - loss: 0.0066 - acc: 0.9973 - val_loss: 0.0759 - val_acc: 0.9107\n",
            "Epoch 4618/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0094 - acc: 0.9867 - val_loss: 0.0694 - val_acc: 0.9286\n",
            "Epoch 4619/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0070 - acc: 0.9947 - val_loss: 0.0740 - val_acc: 0.9107\n",
            "Epoch 4620/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0056 - acc: 0.9920 - val_loss: 0.0701 - val_acc: 0.9286\n",
            "Epoch 4621/7407\n",
            "377/377 [==============================] - 0s 76us/step - loss: 0.0094 - acc: 0.9920 - val_loss: 0.0642 - val_acc: 0.9464\n",
            "Epoch 4622/7407\n",
            "377/377 [==============================] - 0s 125us/step - loss: 0.0043 - acc: 0.9973 - val_loss: 0.0650 - val_acc: 0.9286\n",
            "Epoch 4623/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0066 - acc: 0.9920 - val_loss: 0.0640 - val_acc: 0.9286\n",
            "Epoch 4624/7407\n",
            "377/377 [==============================] - 0s 123us/step - loss: 0.0033 - acc: 0.9947 - val_loss: 0.0650 - val_acc: 0.9286\n",
            "Epoch 4625/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0027 - acc: 0.9973 - val_loss: 0.0661 - val_acc: 0.9286\n",
            "Epoch 4626/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0025 - acc: 0.9973 - val_loss: 0.0662 - val_acc: 0.9286\n",
            "Epoch 4627/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0661 - val_acc: 0.9286\n",
            "Epoch 4628/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0093 - acc: 0.9920 - val_loss: 0.0673 - val_acc: 0.9643\n",
            "Epoch 4629/7407\n",
            "377/377 [==============================] - 0s 101us/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.0653 - val_acc: 0.9464\n",
            "Epoch 4630/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0663 - val_acc: 0.9286\n",
            "Epoch 4631/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0051 - acc: 0.9947 - val_loss: 0.0663 - val_acc: 0.9464\n",
            "Epoch 4632/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0017 - acc: 0.9947 - val_loss: 0.0674 - val_acc: 0.9464\n",
            "Epoch 4633/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 8.0926e-04 - acc: 1.0000 - val_loss: 0.0680 - val_acc: 0.9464\n",
            "Epoch 4634/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0024 - acc: 0.9947 - val_loss: 0.0686 - val_acc: 0.9464\n",
            "Epoch 4635/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0065 - acc: 0.9947 - val_loss: 0.0669 - val_acc: 0.9464\n",
            "Epoch 4636/7407\n",
            "377/377 [==============================] - 0s 93us/step - loss: 0.0096 - acc: 0.9867 - val_loss: 0.0667 - val_acc: 0.9464\n",
            "Epoch 4637/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0011 - acc: 1.0000 - val_loss: 0.0669 - val_acc: 0.9464\n",
            "Epoch 4638/7407\n",
            "377/377 [==============================] - 0s 84us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0688 - val_acc: 0.9464\n",
            "Epoch 4639/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0137 - acc: 0.9947 - val_loss: 0.0746 - val_acc: 0.9464\n",
            "Epoch 4640/7407\n",
            "377/377 [==============================] - 0s 89us/step - loss: 0.0042 - acc: 0.9973 - val_loss: 0.0792 - val_acc: 0.9286\n",
            "Epoch 4641/7407\n",
            "377/377 [==============================] - 0s 103us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0784 - val_acc: 0.9286\n",
            "Epoch 4642/7407\n",
            "377/377 [==============================] - 0s 88us/step - loss: 0.0117 - acc: 0.9894 - val_loss: 0.0795 - val_acc: 0.9464\n",
            "Epoch 4643/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0043 - acc: 0.9973 - val_loss: 0.0801 - val_acc: 0.9464\n",
            "Epoch 4644/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0093 - acc: 0.9920 - val_loss: 0.0753 - val_acc: 0.9464\n",
            "Epoch 4645/7407\n",
            "377/377 [==============================] - 0s 121us/step - loss: 0.0047 - acc: 0.9947 - val_loss: 0.0730 - val_acc: 0.9464\n",
            "Epoch 4646/7407\n",
            "377/377 [==============================] - 0s 114us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0730 - val_acc: 0.9464\n",
            "Epoch 4647/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0055 - acc: 0.9920 - val_loss: 0.0773 - val_acc: 0.9286\n",
            "Epoch 4648/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0055 - acc: 0.9947 - val_loss: 0.0747 - val_acc: 0.9286\n",
            "Epoch 4649/7407\n",
            "377/377 [==============================] - 0s 91us/step - loss: 0.0069 - acc: 0.9867 - val_loss: 0.0721 - val_acc: 0.9107\n",
            "Epoch 4650/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0030 - acc: 0.9973 - val_loss: 0.0689 - val_acc: 0.9464\n",
            "Epoch 4651/7407\n",
            "377/377 [==============================] - 0s 101us/step - loss: 0.0038 - acc: 0.9973 - val_loss: 0.0681 - val_acc: 0.9464\n",
            "Epoch 4652/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0018 - acc: 0.9973 - val_loss: 0.0697 - val_acc: 0.9464\n",
            "Epoch 4653/7407\n",
            "377/377 [==============================] - 0s 92us/step - loss: 0.0019 - acc: 0.9973 - val_loss: 0.0701 - val_acc: 0.9643\n",
            "Epoch 4654/7407\n",
            "377/377 [==============================] - 0s 82us/step - loss: 0.0102 - acc: 0.9894 - val_loss: 0.0689 - val_acc: 0.9286\n",
            "Epoch 4655/7407\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0048 - acc: 0.9920 - val_loss: 0.0678 - val_acc: 0.9464\n",
            "Epoch 4656/7407\n",
            "377/377 [==============================] - 0s 95us/step - loss: 0.0076 - acc: 0.9973 - val_loss: 0.0664 - val_acc: 0.9643\n",
            "Epoch 4657/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0016 - acc: 1.0000 - val_loss: 0.0671 - val_acc: 0.9464\n",
            "Epoch 4658/7407\n",
            "377/377 [==============================] - 0s 120us/step - loss: 0.0050 - acc: 0.9973 - val_loss: 0.0660 - val_acc: 0.9643\n",
            "Epoch 4659/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0022 - acc: 0.9973 - val_loss: 0.0676 - val_acc: 0.9643\n",
            "Epoch 4660/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0013 - acc: 0.9973 - val_loss: 0.0674 - val_acc: 0.9643\n",
            "Epoch 4661/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0675 - val_acc: 0.9643\n",
            "Epoch 4662/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0059 - acc: 0.9947 - val_loss: 0.0707 - val_acc: 0.9286\n",
            "Epoch 4663/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0017 - acc: 0.9973 - val_loss: 0.0706 - val_acc: 0.9286\n",
            "Epoch 4664/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0037 - acc: 0.9947 - val_loss: 0.0700 - val_acc: 0.9464\n",
            "Epoch 4665/7407\n",
            "377/377 [==============================] - 0s 161us/step - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0729 - val_acc: 0.9286\n",
            "Epoch 4666/7407\n",
            "377/377 [==============================] - 0s 83us/step - loss: 0.0035 - acc: 0.9947 - val_loss: 0.0698 - val_acc: 0.9464\n",
            "Epoch 4667/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0057 - acc: 0.9920 - val_loss: 0.0736 - val_acc: 0.9286\n",
            "Epoch 4668/7407\n",
            "377/377 [==============================] - 0s 96us/step - loss: 0.0027 - acc: 0.9947 - val_loss: 0.0730 - val_acc: 0.9286\n",
            "Epoch 4669/7407\n",
            "377/377 [==============================] - 0s 103us/step - loss: 0.0069 - acc: 0.9894 - val_loss: 0.0714 - val_acc: 0.9464\n",
            "Epoch 4670/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0063 - acc: 0.9973 - val_loss: 0.0724 - val_acc: 0.9464\n",
            "Epoch 4671/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0073 - acc: 0.9920 - val_loss: 0.0755 - val_acc: 0.9464\n",
            "Epoch 4672/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0084 - acc: 0.9894 - val_loss: 0.0739 - val_acc: 0.9464\n",
            "Epoch 4673/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 6.6310e-04 - acc: 1.0000 - val_loss: 0.0741 - val_acc: 0.9464\n",
            "Epoch 4674/7407\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0753 - val_acc: 0.9464\n",
            "Epoch 4675/7407\n",
            "377/377 [==============================] - 0s 109us/step - loss: 0.0016 - acc: 0.9947 - val_loss: 0.0764 - val_acc: 0.9464\n",
            "Epoch 4676/7407\n",
            "377/377 [==============================] - 0s 80us/step - loss: 5.6283e-04 - acc: 1.0000 - val_loss: 0.0778 - val_acc: 0.9464\n",
            "Epoch 4677/7407\n",
            "377/377 [==============================] - 0s 112us/step - loss: 0.0036 - acc: 0.9947 - val_loss: 0.0754 - val_acc: 0.9464\n",
            "Epoch 4678/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 7.5409e-04 - acc: 1.0000 - val_loss: 0.0761 - val_acc: 0.9464\n",
            "Epoch 4679/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0755 - val_acc: 0.9464\n",
            "Epoch 4680/7407\n",
            "377/377 [==============================] - 0s 78us/step - loss: 0.0016 - acc: 0.9973 - val_loss: 0.0758 - val_acc: 0.9464\n",
            "Epoch 4681/7407\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0778 - val_acc: 0.9286\n",
            "Epoch 4682/7407\n",
            "\n"
          ]
        }
      ],
      "source": [
        "##########################################################################################\n",
        "############################## start training   #############################\n",
        "# --------------------------------------------------------------------------------------------\n",
        "!source ~/mycroft-precise/.venv/bin/activate; \n",
        "!precise-train -e 3700 hey-savant.net /content/drive/MyDrive/Alkhwarizmi/hey-savant\n",
        "# # # /content/drive/MyDrive/Alkhwarizmi/hey-savant is just the directory where \n",
        "# you save your training and testing data as described in the mycroft documentations\n",
        "# Caution any "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4s-rRTHXdyaT"
      },
      "source": [
        "### further training as the number of epochs so far was very small"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "C43bQ0s7BwV3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50ce6093-2891-4800-9f9b-58b7401d9e9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow backend.\n",
            "Loading from hey-savant.net...\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:3138: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "2023-04-13 15:13:36.523679: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2023-04-13 15:13:36.527888: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200210000 Hz\n",
            "2023-04-13 15:13:36.528147: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x20eb840 executing computations on platform Host. Devices:\n",
            "2023-04-13 15:13:36.528187: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "Data: <TrainData wake_words=100 not_wake_words=422 test_wake_words=15 test_not_wake_words=91>\n",
            "Loading wake-word...\n",
            "Loading not-wake-word...\n",
            "Loading wake-word...\n",
            "Loading not-wake-word...\n",
            "Inputs shape: (377, 29, 13)\n",
            "Outputs shape: (377, 1)\n",
            "Test inputs shape: (56, 29, 13)\n",
            "Test outputs shape: (56, 1)\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "net (GRU)                    (None, 20)                2040      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 21        \n",
            "=================================================================\n",
            "Total params: 2,061\n",
            "Trainable params: 2,061\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 377 samples, validate on 56 samples\n",
            "Epoch 4710/4716\n",
            "377/377 [==============================] - 1s 1ms/step - loss: 0.0024 - acc: 0.9973 - val_loss: 0.0653 - val_acc: 0.9464\n",
            "Epoch 4711/4716\n",
            "377/377 [==============================] - 0s 85us/step - loss: 0.0034 - acc: 0.9973 - val_loss: 0.0596 - val_acc: 0.9286\n",
            "Epoch 4712/4716\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0090 - acc: 0.9947 - val_loss: 0.0563 - val_acc: 0.9464\n",
            "Epoch 4713/4716\n",
            "377/377 [==============================] - 0s 79us/step - loss: 0.0026 - acc: 0.9973 - val_loss: 0.0562 - val_acc: 0.9464\n",
            "Epoch 4714/4716\n",
            "377/377 [==============================] - 0s 81us/step - loss: 0.0067 - acc: 0.9841 - val_loss: 0.0794 - val_acc: 0.9464\n",
            "Epoch 4715/4716\n",
            "377/377 [==============================] - 0s 127us/step - loss: 0.0045 - acc: 0.9973 - val_loss: 0.0612 - val_acc: 0.9464\n",
            "Epoch 4716/4716\n",
            "377/377 [==============================] - 0s 80us/step - loss: 0.0030 - acc: 0.9920 - val_loss: 0.0655 - val_acc: 0.9643\n"
          ]
        }
      ],
      "source": [
        "# further training\n",
        "!source ~/mycroft-precise/.venv/bin/activate;\n",
        "!precise-train -e 7 hey-savant.net /content/drive/MyDrive/Alkhwarizmi/hey-savant"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5QCxd2Fd2Qg"
      },
      "source": [
        "### testing against the samples of the testing set and print the results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Sq_U_d6pX2yA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a5bcafd-3233-4ed3-fe0a-a3e8f4acfad4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading wake-word...\n",
            "\r       \rLoading not-wake-word...\n",
            "\r       \r2023-04-13 15:13:55.376695: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2023-04-13 15:13:55.380415: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200210000 Hz\n",
            "2023-04-13 15:13:55.380653: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x252aab0 executing computations on platform Host. Devices:\n",
            "2023-04-13 15:13:55.380687: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "Using TensorFlow backend.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:3138: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "Data: <TrainData wake_words=100 not_wake_words=422 test_wake_words=15 test_not_wake_words=91>\n",
            "=== False Positives ===\n",
            "/content/drive/MyDrive/Alkhwarizmi/hey-savant/test/not-wake-word/notwakeword-en-0fca3b04-7d88-4fc8-baa6-ea1a11f84176.wav\n",
            "\n",
            "=== False Negatives ===\n",
            "/content/drive/MyDrive/Alkhwarizmi/hey-savant/test/wake-word/heysavant-en-2d98e8f0-4350-4d74-9c74-cd4c16d5aad9.wav\n",
            "\n",
            "=== Counts ===\n",
            "False Positives: 1\n",
            "True Negatives: 40\n",
            "False Negatives: 1\n",
            "True Positives: 14\n",
            "\n",
            "\n",
            "=== Summary ===\n",
            "54 out of 56\n",
            "96.43%\n",
            "\n",
            "2.44% false positives\n",
            "6.67% false negatives\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!precise-test hey-savant.net /content/drive/MyDrive/Alkhwarizmi/hey-savant"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnydIn6uYi86"
      },
      "source": [
        "ofcourse 15 sample for testing is small and the confidence in this result is low due to the small number of testing samples, i suggest increase number of testing samples to get more confidence of the results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KG3QCODSZucI"
      },
      "source": [
        "###now convert the .net model into .pd mode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "hzKiA495Z3Jz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e977611a-d6ca-4fe5-881c-a1da4f2c4b9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Converting hey-savant.net to hey-savant.pb ...\n",
            "Using TensorFlow backend.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "2023-04-13 15:15:45.332850: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2023-04-13 15:15:45.337397: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200210000 Hz\n",
            "2023-04-13 15:15:45.337643: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x24150c0 executing computations on platform Host. Devices:\n",
            "2023-04-13 15:15:45.337677: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "Output node name: net_output\n",
            "Output folder: .\n",
            "Saved readable graph to: hey-savant.pbtxt\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/precise/scripts/convert.py:77: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.compat.v1.graph_util.convert_variables_to_constants\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/graph_util_impl.py:245: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.compat.v1.graph_util.extract_sub_graph\n",
            "Saved graph to: hey-savant.pb\n"
          ]
        }
      ],
      "source": [
        "# now convert the .net model into .pd mode\n",
        "!precise-convert hey-savant.net"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tpHqrFnzd80j"
      },
      "source": [
        "### printing the files inside mycroft-precise folder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "wKazbOb3aIDe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d479d77-56e9-4000-ec61-44337c3f8ffd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "build\t\t       Jenkinsfile\t\t requirements.txt\n",
            "build.sh\t       LICENSE\t\t\t runner\n",
            "export.sh\t       MANIFEST.in\t\t setup.py\n",
            "hey-savant.epoch       mycroft_precise.egg-info  setup.sh\n",
            "hey-savant.logs        precise\t\t\t snap\n",
            "hey-savant.net\t       precise.template.spec\t SNAPCRAFT.md\n",
            "hey-savant.net.params  publish.sh\t\t src\n",
            "hey-savant.pb\t       pylintrc\t\t\t test\n",
            "hey-savant.pb.params   README.md\n",
            "hey-savant.pbtxt       requirements\n"
          ]
        }
      ],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "OxB-pk1khZFF"
      },
      "outputs": [],
      "source": [
        "# #!/usr/bin/env python3\n",
        "\n",
        "# from precise_runner import PreciseEngine, PreciseRunner\n",
        "\n",
        "# engine = PreciseEngine('.venv/bin/precise-engine', 'my_model_file.pb')\n",
        "# runner = PreciseRunner(engine, on_activation=lambda: print('hello'))\n",
        "# runner.start()\n",
        "\n",
        "# # Sleep forever\n",
        "# from time import sleep\n",
        "# while True:\n",
        "#     sleep(10)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yfrEApa6EijJ"
      },
      "outputs": [],
      "source": [
        "# # !python3 -m pip install PySoundCard PyAudioWPatch\n",
        "# !pip install PySoundCard\n",
        "# !pip install PyAudioWPatch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0xHOdrSc8aC"
      },
      "source": [
        "REFERENCES:- \n",
        "\n",
        "*   [mycroft documentation ](https://mycroft-ai.gitbook.io/docs)\n",
        "*   [https://github.com/MycroftAI/mycroft-precise](https://github.com/MycroftAI/mycroft-precise)\n",
        "*   [How to Create a Custom Wake Word for Mycroft AI  Digi-Key Electronics youtube video](https://www.youtube.com/watch?v=CDlpuI_wPME)\n",
        "*   [Training your own wake word](https://github.com/MycroftAI/mycroft-precise/wiki/Training-your-own-wake-word#how-to-train-your-own-wake-word)\n",
        "*   [https://github.com/MycroftAI/mycroft-core](https://github.com/MycroftAI/mycroft-core)\n",
        "*   [Precise Wakeword Model Maker](https://github.com/secretsauceai/precise-wakeword-model-maker#installation)\n",
        "*   [Precise-Community-Data](https://github.com/MycroftAI/precise-community-data)\n",
        "*   [stackoverflow](https://stackoverflow.com/)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKn1LOacgVtW"
      },
      "source": [
        "#\n",
        "this work done by Ahmed Salama in April-2023\n",
        "\n",
        "salama4ai@gmail.com\n",
        "\n",
        "[github.com/salama4ai](https://www.github.com/salama4ai/)\n",
        "\n",
        "[linkedin.com/in/salama4ai](https://www.linkedin.com/in/salama4ai/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sbZJLXa9gnW1"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1d6eA8_VeJFSb24PGRitFAnXC0y74_Ich",
      "authorship_tag": "ABX9TyNzdFUCTdgYjIQ9hB5ot2e0",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}