AutoEncoder.ipynb

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      "name": "AutoEncoder.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shishir-joshi/notebooks/blob/master/AutoEncoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mc8DLU5Cyp3u",
        "colab_type": "code",
        "outputId": "93905e53-3a19-4dd9-ab1f-abaf93175bff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-alpha0\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow import keras\n",
        "import tarfile\n",
        "import random\n",
        "import tensorflow_datasets as tfds\n",
        "from pathlib import Path\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding,Dense,Flatten,Conv2D,MaxPool2D,BatchNormalization\n",
        "from keras.optimizers import Adamax,Nadam,Adam,Adagrad,RMSprop\n",
        "from keras.losses import mean_squared_logarithmic_error,categorical_crossentropy\n",
        "from keras.utils import to_categorical\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.callbacks import TensorBoard\n",
        "from sklearn.model_selection import train_test_split\n",
        "from random import shuffle"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow-gpu==2.0.0-alpha0 in /usr/local/lib/python3.6/dist-packages (2.0.0a0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.1.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.1.7)\n",
            "Requirement already satisfied: tb-nightly<1.14.0a20190302,>=1.14.0a20190301 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.0a20190301)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.15.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.12.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.8.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.2.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.33.4)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.9)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (3.7.1)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.16.4)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.7)\n",
            "Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.0.dev2019030115)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (0.15.4)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (3.1.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-alpha0) (41.0.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-alpha0) (2.8.0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyHQyj2UJo1T",
        "colab_type": "text"
      },
      "source": [
        "##Load and Serialise Data (into TF Records)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGccsz3ey9OP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tfds.list_builders()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzefAx6zBsHZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist, info = tfds.load(name=\"mnist\", with_info=True)\n",
        "# print(info)\n",
        "train = mnist['train']\n",
        "test = mnist['test']\n",
        "print(train)\n",
        "print(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmKDQyysBsb1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTGRMteGIwjH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# AutoEncoders need the same input image and output image, \n",
        "# Which means the dataset in its current form needs some modifications \n",
        "# to be usable to train the autoencoder\n",
        "# So, since 'train' and 'test' are iterators, lets break them apart and \n",
        "# rejoin them into tf.Data Datasets -\n",
        "train_images = list()\n",
        "train_labels = list()\n",
        "for rec in train:\n",
        "  train_images.append(rec['image'])\n",
        "#   train_labels.append(rec['label'])\n",
        "\n",
        "test_images = list()\n",
        "test_labels = list()\n",
        "for rec in test:\n",
        "  test_images.append(rec['image'])\n",
        "#   test_labels.append(rec['label'])\n",
        "\n",
        "# No need for labels either, \n",
        "trainds = tf.data.Dataset.from_tensor_slices((train_images,train_images))\n",
        "testds = tf.data.Dataset.from_tensor_slices((test_images,test_images))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcuxMXgYsco6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# need to normalise, wont converge without that\n",
        "norm_train = trainds\n",
        "norm_test = testds\n",
        "def norm(x,y):\n",
        "  return (x/255,y/255)\n",
        "norm_train = norm_train.map(norm)\n",
        "norm_test = norm_test.map(norm)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-67IQ9diBsf5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Batching and shuffling\n",
        "trainds = trainds.shuffle(1024).batch(32)\n",
        "testds = testds.shuffle(1024).batch(32)\n",
        "norm_train = norm_train.repeat().shuffle(1024).batch(32)\n",
        "norm_test = norm_test.repeat().shuffle(1024).batch(32)\n",
        "trainds.prefetch(tf.data.experimental.AUTOTUNE)\n",
        "testds.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62gpF7-2JmPG",
        "colab_type": "text"
      },
      "source": [
        "## The Model\n",
        "Keras Functional API used:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtXvoStHYybm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.InputLayer(input_shape=(28,28,1)),\n",
        "    keras.layers.Conv2D(filters=16,kernel_size=3,padding='same',activation='relu'),\n",
        "    keras.layers.MaxPool2D(pool_size=2,strides=2),\n",
        "    keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation='tanh'),\n",
        "    keras.layers.MaxPool2D(pool_size=2,strides=2),\n",
        "    keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation='tanh'),\n",
        "    #decoder\n",
        "    keras.layers.Conv2D(filters=64,kernel_size=3,activation='tanh',padding='same'),\n",
        "    keras.layers.UpSampling2D(size=2),\n",
        "    keras.layers.Conv2D(filters=32,kernel_size=3,activation='tanh',padding='same'),\n",
        "    keras.layers.UpSampling2D(size=2),\n",
        "    keras.layers.Conv2D(filters=1,kernel_size=1,activation='relu',padding='same'),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZtrW2CTGAQ3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer = tf.keras.optimizers.RMSprop(),\n",
        "              loss = 'binary_crossentropy')\n",
        "#               metric = tf.keras.metrics.Accuracy)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EEYOq_Qtxp9",
        "colab_type": "code",
        "outputId": "7d7d02b6-72fe-4222-e080-195ffaa8aab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 28, 28, 16)        160       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 14, 14, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 14, 14, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 7, 7, 64)          18496     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 7, 7, 64)          36928     \n",
            "_________________________________________________________________\n",
            "up_sampling2d (UpSampling2D) (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 14, 14, 32)        18464     \n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 28, 28, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 28, 28, 1)         33        \n",
            "=================================================================\n",
            "Total params: 78,721\n",
            "Trainable params: 78,721\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RvwwpssKCR4w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir logs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2UOb5Y5XdNp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filepath=\"weights-improvement-{epoch:02d}-{loss:.4f}.hdf5\"\n",
        "model_checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')\n",
        "tensor_board = TensorBoard(log_dir='logs', batch_size=32, histogram_freq= 2)\n",
        "callbacks_list = [model_checkpoint, tensorboard]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVkZJzAbJtfq",
        "colab_type": "code",
        "outputId": "68761f84-dc76-4497-9d7a-01c4b24a2959",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 27501
        }
      },
      "source": [
        "model.fit(norm_train,\n",
        "          validation_data=norm_test,\n",
        "          epochs = 10000,\n",
        "          callbacks = callbacks_list,\n",
        "          steps_per_epoch=1000,\n",
        "          validation_steps=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1706\n",
            "Epoch 00001: loss improved from 13.32041 to 0.17051, saving model to weights-improvement-01-0.1705.hdf5\n",
            "1000/1000 [==============================] - 11s 11ms/step - loss: 0.1705 - val_loss: 0.1367\n",
            "Epoch 2/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1424\n",
            "Epoch 00002: loss improved from 0.17051 to 0.14237, saving model to weights-improvement-02-0.1424.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1424 - val_loss: 0.1445\n",
            "Epoch 3/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1355\n",
            "Epoch 00003: loss improved from 0.14237 to 0.13553, saving model to weights-improvement-03-0.1355.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1355 - val_loss: 0.1271\n",
            "Epoch 4/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1354\n",
            "Epoch 00004: loss improved from 0.13553 to 0.13533, saving model to weights-improvement-04-0.1353.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1353 - val_loss: 0.1276\n",
            "Epoch 5/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1306\n",
            "Epoch 00005: loss improved from 0.13533 to 0.13060, saving model to weights-improvement-05-0.1306.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1306 - val_loss: 0.1291\n",
            "Epoch 6/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1578\n",
            "Epoch 00006: loss did not improve from 0.13060\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1578 - val_loss: 0.2021\n",
            "Epoch 7/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1532\n",
            "Epoch 00007: loss did not improve from 0.13060\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1532 - val_loss: 0.1327\n",
            "Epoch 8/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1364\n",
            "Epoch 00008: loss did not improve from 0.13060\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1364 - val_loss: 0.1319\n",
            "Epoch 9/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1439\n",
            "Epoch 00009: loss did not improve from 0.13060\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1439 - val_loss: 0.1302\n",
            "Epoch 10/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1277\n",
            "Epoch 00010: loss improved from 0.13060 to 0.12773, saving model to weights-improvement-10-0.1277.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1277 - val_loss: 0.1244\n",
            "Epoch 11/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1352\n",
            "Epoch 00011: loss did not improve from 0.12773\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1351 - val_loss: 0.1283\n",
            "Epoch 12/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1285\n",
            "Epoch 00012: loss did not improve from 0.12773\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1284 - val_loss: 0.1232\n",
            "Epoch 13/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1231\n",
            "Epoch 00013: loss improved from 0.12773 to 0.12312, saving model to weights-improvement-13-0.1231.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1231 - val_loss: 0.1253\n",
            "Epoch 14/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1204\n",
            "Epoch 00014: loss improved from 0.12312 to 0.12046, saving model to weights-improvement-14-0.1205.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1205 - val_loss: 0.1487\n",
            "Epoch 15/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1253\n",
            "Epoch 00015: loss did not improve from 0.12046\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1254 - val_loss: 0.1319\n",
            "Epoch 16/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1247\n",
            "Epoch 00016: loss did not improve from 0.12046\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1247 - val_loss: 0.1223\n",
            "Epoch 17/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1183\n",
            "Epoch 00017: loss improved from 0.12046 to 0.11832, saving model to weights-improvement-17-0.1183.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1183 - val_loss: 0.1176\n",
            "Epoch 18/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1180\n",
            "Epoch 00018: loss improved from 0.11832 to 0.11805, saving model to weights-improvement-18-0.1180.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1180 - val_loss: 0.1166\n",
            "Epoch 19/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1202\n",
            "Epoch 00019: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1202 - val_loss: 0.1201\n",
            "Epoch 20/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1269\n",
            "Epoch 00020: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1269 - val_loss: 0.1206\n",
            "Epoch 21/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1279\n",
            "Epoch 00021: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1279 - val_loss: 0.1246\n",
            "Epoch 22/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1404\n",
            "Epoch 00022: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1404 - val_loss: 0.1280\n",
            "Epoch 23/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1234\n",
            "Epoch 00023: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1234 - val_loss: 0.1254\n",
            "Epoch 24/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1216\n",
            "Epoch 00024: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1216 - val_loss: 0.1191\n",
            "Epoch 25/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1262\n",
            "Epoch 00025: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1261 - val_loss: 0.1217\n",
            "Epoch 26/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1232\n",
            "Epoch 00026: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1233 - val_loss: 0.1330\n",
            "Epoch 27/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1255\n",
            "Epoch 00027: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1255 - val_loss: 0.1281\n",
            "Epoch 28/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1266\n",
            "Epoch 00028: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1266 - val_loss: 0.1227\n",
            "Epoch 29/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1199\n",
            "Epoch 00029: loss did not improve from 0.11805\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1199 - val_loss: 0.1202\n",
            "Epoch 30/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1168\n",
            "Epoch 00030: loss improved from 0.11805 to 0.11681, saving model to weights-improvement-30-0.1168.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1168 - val_loss: 0.1162\n",
            "Epoch 31/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1273\n",
            "Epoch 00031: loss did not improve from 0.11681\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1273 - val_loss: 0.1418\n",
            "Epoch 32/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1216\n",
            "Epoch 00032: loss did not improve from 0.11681\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1216 - val_loss: 0.1164\n",
            "Epoch 33/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1243\n",
            "Epoch 00033: loss did not improve from 0.11681\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1243 - val_loss: 0.1248\n",
            "Epoch 34/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1180\n",
            "Epoch 00034: loss did not improve from 0.11681\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1180 - val_loss: 0.1165\n",
            "Epoch 35/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1154\n",
            "Epoch 00035: loss improved from 0.11681 to 0.11538, saving model to weights-improvement-35-0.1154.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1154 - val_loss: 0.1145\n",
            "Epoch 36/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1174\n",
            "Epoch 00036: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1174 - val_loss: 0.1163\n",
            "Epoch 37/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1161\n",
            "Epoch 00037: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1161 - val_loss: 0.1135\n",
            "Epoch 38/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1154\n",
            "Epoch 00038: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1154 - val_loss: 0.1142\n",
            "Epoch 39/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1237\n",
            "Epoch 00039: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1238 - val_loss: 0.1407\n",
            "Epoch 40/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1217\n",
            "Epoch 00040: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1217 - val_loss: 0.1171\n",
            "Epoch 41/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1232\n",
            "Epoch 00041: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1231 - val_loss: 0.1165\n",
            "Epoch 42/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1241\n",
            "Epoch 00042: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1240 - val_loss: 0.1196\n",
            "Epoch 43/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1198\n",
            "Epoch 00043: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1197 - val_loss: 0.1163\n",
            "Epoch 44/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1268\n",
            "Epoch 00044: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1268 - val_loss: 0.1254\n",
            "Epoch 45/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1222\n",
            "Epoch 00045: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1222 - val_loss: 0.1336\n",
            "Epoch 46/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1242\n",
            "Epoch 00046: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1242 - val_loss: 0.1192\n",
            "Epoch 47/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1175\n",
            "Epoch 00047: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1175 - val_loss: 0.1173\n",
            "Epoch 48/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1173\n",
            "Epoch 00048: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1173 - val_loss: 0.1163\n",
            "Epoch 49/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1201\n",
            "Epoch 00049: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1201 - val_loss: 0.1151\n",
            "Epoch 50/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1159\n",
            "Epoch 00050: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1159 - val_loss: 0.1146\n",
            "Epoch 51/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1172\n",
            "Epoch 00051: loss did not improve from 0.11538\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1172 - val_loss: 0.1187\n",
            "Epoch 52/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1152\n",
            "Epoch 00052: loss improved from 0.11538 to 0.11522, saving model to weights-improvement-52-0.1152.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1152 - val_loss: 0.1142\n",
            "Epoch 53/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1202\n",
            "Epoch 00053: loss did not improve from 0.11522\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1202 - val_loss: 0.1165\n",
            "Epoch 54/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1194\n",
            "Epoch 00054: loss did not improve from 0.11522\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1194 - val_loss: 0.1155\n",
            "Epoch 55/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1137\n",
            "Epoch 00055: loss improved from 0.11522 to 0.11373, saving model to weights-improvement-55-0.1137.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1137 - val_loss: 0.1148\n",
            "Epoch 56/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1239\n",
            "Epoch 00056: loss did not improve from 0.11373\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1239 - val_loss: 0.1227\n",
            "Epoch 57/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1196\n",
            "Epoch 00057: loss did not improve from 0.11373\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1197 - val_loss: 0.1289\n",
            "Epoch 58/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1202\n",
            "Epoch 00058: loss did not improve from 0.11373\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1201 - val_loss: 0.1152\n",
            "Epoch 59/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1128\n",
            "Epoch 00059: loss improved from 0.11373 to 0.11275, saving model to weights-improvement-59-0.1128.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1128 - val_loss: 0.1131\n",
            "Epoch 60/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1137\n",
            "Epoch 00060: loss did not improve from 0.11275\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1137 - val_loss: 0.1122\n",
            "Epoch 61/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1115\n",
            "Epoch 00061: loss improved from 0.11275 to 0.11155, saving model to weights-improvement-61-0.1115.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1115 - val_loss: 0.1114\n",
            "Epoch 62/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1305\n",
            "Epoch 00062: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1305 - val_loss: 0.1185\n",
            "Epoch 63/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1183\n",
            "Epoch 00063: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1183 - val_loss: 0.1164\n",
            "Epoch 64/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1199\n",
            "Epoch 00064: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1199 - val_loss: 0.1266\n",
            "Epoch 65/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1249\n",
            "Epoch 00065: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1249 - val_loss: 0.1176\n",
            "Epoch 66/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1148\n",
            "Epoch 00066: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1149 - val_loss: 0.1135\n",
            "Epoch 67/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1123\n",
            "Epoch 00067: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1123 - val_loss: 0.1125\n",
            "Epoch 68/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1122\n",
            "Epoch 00068: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1123 - val_loss: 0.1131\n",
            "Epoch 69/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1140\n",
            "Epoch 00069: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1140 - val_loss: 0.1121\n",
            "Epoch 70/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1119\n",
            "Epoch 00070: loss did not improve from 0.11155\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1119 - val_loss: 0.1143\n",
            "Epoch 71/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1110\n",
            "Epoch 00071: loss improved from 0.11155 to 0.11105, saving model to weights-improvement-71-0.1110.hdf5\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1110 - val_loss: 0.1108\n",
            "Epoch 72/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1123\n",
            "Epoch 00072: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1123 - val_loss: 0.1113\n",
            "Epoch 73/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1232\n",
            "Epoch 00073: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1232 - val_loss: 0.1221\n",
            "Epoch 74/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1142\n",
            "Epoch 00074: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1142 - val_loss: 0.1128\n",
            "Epoch 75/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1123\n",
            "Epoch 00075: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1123 - val_loss: 0.1116\n",
            "Epoch 76/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1172\n",
            "Epoch 00076: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1171 - val_loss: 0.1123\n",
            "Epoch 77/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1138\n",
            "Epoch 00077: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1138 - val_loss: 0.1119\n",
            "Epoch 78/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1113\n",
            "Epoch 00078: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1113 - val_loss: 0.1111\n",
            "Epoch 79/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1121\n",
            "Epoch 00079: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1124 - val_loss: 0.2847\n",
            "Epoch 80/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1202\n",
            "Epoch 00080: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1202 - val_loss: 0.1250\n",
            "Epoch 81/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1119\n",
            "Epoch 00081: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1119 - val_loss: 0.1124\n",
            "Epoch 82/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1130\n",
            "Epoch 00082: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1130 - val_loss: 0.1117\n",
            "Epoch 83/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1138\n",
            "Epoch 00083: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1138 - val_loss: 0.1116\n",
            "Epoch 84/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1263\n",
            "Epoch 00084: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1263 - val_loss: 0.1245\n",
            "Epoch 85/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1166\n",
            "Epoch 00085: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1165 - val_loss: 0.1133\n",
            "Epoch 86/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1121\n",
            "Epoch 00086: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1121 - val_loss: 0.1115\n",
            "Epoch 87/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1112\n",
            "Epoch 00087: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1112 - val_loss: 0.1116\n",
            "Epoch 88/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1125\n",
            "Epoch 00088: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1125 - val_loss: 0.1229\n",
            "Epoch 89/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1119\n",
            "Epoch 00089: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1119 - val_loss: 0.1110\n",
            "Epoch 90/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1238\n",
            "Epoch 00090: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1238 - val_loss: 0.1209\n",
            "Epoch 91/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1144\n",
            "Epoch 00091: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1143 - val_loss: 0.1125\n",
            "Epoch 92/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1167\n",
            "Epoch 00092: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1169 - val_loss: 0.1976\n",
            "Epoch 93/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1433\n",
            "Epoch 00093: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1433 - val_loss: 0.1242\n",
            "Epoch 94/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1237\n",
            "Epoch 00094: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1236 - val_loss: 0.1164\n",
            "Epoch 95/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1291\n",
            "Epoch 00095: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1290 - val_loss: 0.1168\n",
            "Epoch 96/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1170\n",
            "Epoch 00096: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1170 - val_loss: 0.1146\n",
            "Epoch 97/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1416\n",
            "Epoch 00097: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1415 - val_loss: 0.1293\n",
            "Epoch 98/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1221\n",
            "Epoch 00098: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1221 - val_loss: 0.1174\n",
            "Epoch 99/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1163\n",
            "Epoch 00099: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1164 - val_loss: 0.1167\n",
            "Epoch 100/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1159\n",
            "Epoch 00100: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1159 - val_loss: 0.1146\n",
            "Epoch 101/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1539\n",
            "Epoch 00101: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1537 - val_loss: 0.1254\n",
            "Epoch 102/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1286\n",
            "Epoch 00102: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1287 - val_loss: 0.2151\n",
            "Epoch 103/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1345\n",
            "Epoch 00103: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1344 - val_loss: 0.1203\n",
            "Epoch 104/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1177\n",
            "Epoch 00104: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1177 - val_loss: 0.1165\n",
            "Epoch 105/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1171\n",
            "Epoch 00105: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1171 - val_loss: 0.1153\n",
            "Epoch 106/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1274\n",
            "Epoch 00106: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1273 - val_loss: 0.1167\n",
            "Epoch 107/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1152\n",
            "Epoch 00107: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1152 - val_loss: 0.1156\n",
            "Epoch 108/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1208\n",
            "Epoch 00108: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1209 - val_loss: 0.1480\n",
            "Epoch 109/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1467\n",
            "Epoch 00109: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1467 - val_loss: 0.1273\n",
            "Epoch 110/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1383\n",
            "Epoch 00110: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1382 - val_loss: 0.1294\n",
            "Epoch 111/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1263\n",
            "Epoch 00111: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1263 - val_loss: 0.1206\n",
            "Epoch 112/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1243\n",
            "Epoch 00112: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1244 - val_loss: 0.1404\n",
            "Epoch 113/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1505\n",
            "Epoch 00113: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1507 - val_loss: 0.2561\n",
            "Epoch 114/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1603\n",
            "Epoch 00114: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1602 - val_loss: 0.1280\n",
            "Epoch 115/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1229\n",
            "Epoch 00115: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1229 - val_loss: 0.1203\n",
            "Epoch 116/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1303\n",
            "Epoch 00116: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 0.1305 - val_loss: 0.2269\n",
            "Epoch 117/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1380\n",
            "Epoch 00117: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1380 - val_loss: 0.1212\n",
            "Epoch 118/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1168\n",
            "Epoch 00118: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1168 - val_loss: 0.1164\n",
            "Epoch 119/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1266\n",
            "Epoch 00119: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1268 - val_loss: 0.1665\n",
            "Epoch 120/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1280\n",
            "Epoch 00120: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1280 - val_loss: 0.1185\n",
            "Epoch 121/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1305\n",
            "Epoch 00121: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1305 - val_loss: 0.1286\n",
            "Epoch 122/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1193\n",
            "Epoch 00122: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1193 - val_loss: 0.1157\n",
            "Epoch 123/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1184\n",
            "Epoch 00123: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1184 - val_loss: 0.1201\n",
            "Epoch 124/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1148\n",
            "Epoch 00124: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1148 - val_loss: 0.1144\n",
            "Epoch 125/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1133\n",
            "Epoch 00125: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1133 - val_loss: 0.1131\n",
            "Epoch 126/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1141\n",
            "Epoch 00126: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1141 - val_loss: 0.1130\n",
            "Epoch 127/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1357\n",
            "Epoch 00127: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1356 - val_loss: 0.1218\n",
            "Epoch 128/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1278\n",
            "Epoch 00128: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1279 - val_loss: 0.1489\n",
            "Epoch 129/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1320\n",
            "Epoch 00129: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1320 - val_loss: 0.1221\n",
            "Epoch 130/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1210\n",
            "Epoch 00130: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1209 - val_loss: 0.1167\n",
            "Epoch 131/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1160\n",
            "Epoch 00131: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1160 - val_loss: 0.1152\n",
            "Epoch 132/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1528\n",
            "Epoch 00132: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1528 - val_loss: 0.1376\n",
            "Epoch 133/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1496\n",
            "Epoch 00133: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1495 - val_loss: 0.1452\n",
            "Epoch 134/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1326\n",
            "Epoch 00134: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1326 - val_loss: 0.1259\n",
            "Epoch 135/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1225\n",
            "Epoch 00135: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1225 - val_loss: 0.1192\n",
            "Epoch 136/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1481\n",
            "Epoch 00136: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1482 - val_loss: 0.1569\n",
            "Epoch 137/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1293\n",
            "Epoch 00137: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1293 - val_loss: 0.1204\n",
            "Epoch 138/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1224\n",
            "Epoch 00138: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1224 - val_loss: 0.1310\n",
            "Epoch 139/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1259\n",
            "Epoch 00139: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1262 - val_loss: 0.3643\n",
            "Epoch 140/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1484\n",
            "Epoch 00140: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1482 - val_loss: 0.1241\n",
            "Epoch 141/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1204\n",
            "Epoch 00141: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1204 - val_loss: 0.1184\n",
            "Epoch 142/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1166\n",
            "Epoch 00142: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1166 - val_loss: 0.1159\n",
            "Epoch 143/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1152\n",
            "Epoch 00143: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1152 - val_loss: 0.1145\n",
            "Epoch 144/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1133\n",
            "Epoch 00144: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1133 - val_loss: 0.1142\n",
            "Epoch 145/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1187\n",
            "Epoch 00145: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1187 - val_loss: 0.1354\n",
            "Epoch 146/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1237\n",
            "Epoch 00146: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1238 - val_loss: 0.1542\n",
            "Epoch 147/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1287\n",
            "Epoch 00147: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1287 - val_loss: 0.1170\n",
            "Epoch 148/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1264\n",
            "Epoch 00148: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1265 - val_loss: 0.1304\n",
            "Epoch 149/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1255\n",
            "Epoch 00149: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1255 - val_loss: 0.1254\n",
            "Epoch 150/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1220\n",
            "Epoch 00150: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1220 - val_loss: 0.1965\n",
            "Epoch 151/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1266\n",
            "Epoch 00151: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1265 - val_loss: 0.1170\n",
            "Epoch 152/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1158\n",
            "Epoch 00152: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1158 - val_loss: 0.1148\n",
            "Epoch 153/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1536\n",
            "Epoch 00153: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1538 - val_loss: 0.2006\n",
            "Epoch 154/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1338\n",
            "Epoch 00154: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1338 - val_loss: 0.1198\n",
            "Epoch 155/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1229\n",
            "Epoch 00155: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1229 - val_loss: 0.1224\n",
            "Epoch 156/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1185\n",
            "Epoch 00156: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1185 - val_loss: 0.1160\n",
            "Epoch 157/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1192\n",
            "Epoch 00157: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1192 - val_loss: 0.1152\n",
            "Epoch 158/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1181\n",
            "Epoch 00158: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1181 - val_loss: 0.1288\n",
            "Epoch 159/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1180\n",
            "Epoch 00159: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1180 - val_loss: 0.1156\n",
            "Epoch 160/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1158\n",
            "Epoch 00160: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1158 - val_loss: 0.1152\n",
            "Epoch 161/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1147\n",
            "Epoch 00161: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1147 - val_loss: 0.1134\n",
            "Epoch 162/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1130\n",
            "Epoch 00162: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1130 - val_loss: 0.1131\n",
            "Epoch 163/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1250\n",
            "Epoch 00163: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1250 - val_loss: 0.1367\n",
            "Epoch 164/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1233\n",
            "Epoch 00164: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1233 - val_loss: 0.1194\n",
            "Epoch 165/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1147\n",
            "Epoch 00165: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1147 - val_loss: 0.1137\n",
            "Epoch 166/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1568\n",
            "Epoch 00166: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 10s 10ms/step - loss: 0.1567 - val_loss: 0.1263\n",
            "Epoch 167/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1197\n",
            "Epoch 00167: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1197 - val_loss: 0.1154\n",
            "Epoch 168/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1276\n",
            "Epoch 00168: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1276 - val_loss: 0.1228\n",
            "Epoch 169/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1203\n",
            "Epoch 00169: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1203 - val_loss: 0.1414\n",
            "Epoch 170/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1254\n",
            "Epoch 00170: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1254 - val_loss: 0.1163\n",
            "Epoch 171/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1163\n",
            "Epoch 00171: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1162 - val_loss: 0.1162\n",
            "Epoch 172/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1223\n",
            "Epoch 00172: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1224 - val_loss: 0.1431\n",
            "Epoch 173/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1484\n",
            "Epoch 00173: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1485 - val_loss: 0.1847\n",
            "Epoch 174/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1800\n",
            "Epoch 00174: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1800 - val_loss: 0.1557\n",
            "Epoch 175/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1315\n",
            "Epoch 00175: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1315 - val_loss: 0.1228\n",
            "Epoch 176/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1351\n",
            "Epoch 00176: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1353 - val_loss: 0.1850\n",
            "Epoch 177/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1788\n",
            "Epoch 00177: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1785 - val_loss: 0.1330\n",
            "Epoch 178/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1291\n",
            "Epoch 00178: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1291 - val_loss: 0.1391\n",
            "Epoch 179/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1243\n",
            "Epoch 00179: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1243 - val_loss: 0.1200\n",
            "Epoch 180/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1424\n",
            "Epoch 00180: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1427 - val_loss: 0.1948\n",
            "Epoch 181/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1605\n",
            "Epoch 00181: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1604 - val_loss: 0.1334\n",
            "Epoch 182/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1259\n",
            "Epoch 00182: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1259 - val_loss: 0.1223\n",
            "Epoch 183/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1349\n",
            "Epoch 00183: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1349 - val_loss: 0.1437\n",
            "Epoch 184/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1280\n",
            "Epoch 00184: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1279 - val_loss: 0.1228\n",
            "Epoch 185/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1184\n",
            "Epoch 00185: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1184 - val_loss: 0.1158\n",
            "Epoch 186/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1770\n",
            "Epoch 00186: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1769 - val_loss: 0.1603\n",
            "Epoch 187/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1475\n",
            "Epoch 00187: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1474 - val_loss: 0.1383\n",
            "Epoch 188/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1307\n",
            "Epoch 00188: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1307 - val_loss: 0.1273\n",
            "Epoch 189/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1244\n",
            "Epoch 00189: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1244 - val_loss: 0.1213\n",
            "Epoch 190/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1712\n",
            "Epoch 00190: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1709 - val_loss: 0.1323\n",
            "Epoch 191/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1257\n",
            "Epoch 00191: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1257 - val_loss: 0.1224\n",
            "Epoch 192/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1177\n",
            "Epoch 00192: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1177 - val_loss: 0.1158\n",
            "Epoch 193/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1164\n",
            "Epoch 00193: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1164 - val_loss: 0.1145\n",
            "Epoch 194/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1162\n",
            "Epoch 00194: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1162 - val_loss: 0.1140\n",
            "Epoch 195/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1150\n",
            "Epoch 00195: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1150 - val_loss: 0.1141\n",
            "Epoch 196/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1280\n",
            "Epoch 00196: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1280 - val_loss: 0.1204\n",
            "Epoch 197/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1229\n",
            "Epoch 00197: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1229 - val_loss: 0.1177\n",
            "Epoch 198/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1391\n",
            "Epoch 00198: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1390 - val_loss: 0.1227\n",
            "Epoch 199/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1229\n",
            "Epoch 00199: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1235 - val_loss: 0.2065\n",
            "Epoch 200/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1854\n",
            "Epoch 00200: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1855 - val_loss: 0.1893\n",
            "Epoch 201/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1536\n",
            "Epoch 00201: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1535 - val_loss: 0.1246\n",
            "Epoch 202/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1223\n",
            "Epoch 00202: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1223 - val_loss: 0.1179\n",
            "Epoch 203/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1181\n",
            "Epoch 00203: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1181 - val_loss: 0.1151\n",
            "Epoch 204/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1188\n",
            "Epoch 00204: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1188 - val_loss: 0.1147\n",
            "Epoch 205/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1143\n",
            "Epoch 00205: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1143 - val_loss: 0.1144\n",
            "Epoch 206/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1689\n",
            "Epoch 00206: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1690 - val_loss: 0.1872\n",
            "Epoch 207/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1735\n",
            "Epoch 00207: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1733 - val_loss: 0.1449\n",
            "Epoch 208/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1719\n",
            "Epoch 00208: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1720 - val_loss: 0.2221\n",
            "Epoch 209/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1869\n",
            "Epoch 00209: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1869 - val_loss: 0.1862\n",
            "Epoch 210/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1796\n",
            "Epoch 00210: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1796 - val_loss: 0.1778\n",
            "Epoch 211/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1765\n",
            "Epoch 00211: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1765 - val_loss: 0.1759\n",
            "Epoch 212/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1441\n",
            "Epoch 00212: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1441 - val_loss: 0.1270\n",
            "Epoch 213/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1226\n",
            "Epoch 00213: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1225 - val_loss: 0.1192\n",
            "Epoch 214/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1184\n",
            "Epoch 00214: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1184 - val_loss: 0.1172\n",
            "Epoch 215/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1161\n",
            "Epoch 00215: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1161 - val_loss: 0.1146\n",
            "Epoch 216/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1665\n",
            "Epoch 00216: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1663 - val_loss: 0.1295\n",
            "Epoch 217/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1498\n",
            "Epoch 00217: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1498 - val_loss: 0.1344\n",
            "Epoch 218/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1272\n",
            "Epoch 00218: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1272 - val_loss: 0.1216\n",
            "Epoch 219/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1169\n",
            "Epoch 00219: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1169 - val_loss: 0.1149\n",
            "Epoch 220/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1140\n",
            "Epoch 00220: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1140 - val_loss: 0.1140\n",
            "Epoch 221/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1216\n",
            "Epoch 00221: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1216 - val_loss: 0.1175\n",
            "Epoch 222/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1279\n",
            "Epoch 00222: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1278 - val_loss: 0.1188\n",
            "Epoch 223/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1165\n",
            "Epoch 00223: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1165 - val_loss: 0.1143\n",
            "Epoch 224/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1280\n",
            "Epoch 00224: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1283 - val_loss: 0.1926\n",
            "Epoch 225/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1476\n",
            "Epoch 00225: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1476 - val_loss: 0.1310\n",
            "Epoch 226/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1258\n",
            "Epoch 00226: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1258 - val_loss: 0.1184\n",
            "Epoch 227/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1191\n",
            "Epoch 00227: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1191 - val_loss: 0.1175\n",
            "Epoch 228/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1211\n",
            "Epoch 00228: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1214 - val_loss: 0.1710\n",
            "Epoch 229/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1291\n",
            "Epoch 00229: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1291 - val_loss: 0.1205\n",
            "Epoch 230/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1163\n",
            "Epoch 00230: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1163 - val_loss: 0.1143\n",
            "Epoch 231/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1368\n",
            "Epoch 00231: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1368 - val_loss: 0.1297\n",
            "Epoch 232/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1226\n",
            "Epoch 00232: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1226 - val_loss: 0.1180\n",
            "Epoch 233/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1161\n",
            "Epoch 00233: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1160 - val_loss: 0.1143\n",
            "Epoch 234/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1432\n",
            "Epoch 00234: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1432 - val_loss: 0.1687\n",
            "Epoch 235/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1859\n",
            "Epoch 00235: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1858 - val_loss: 0.1797\n",
            "Epoch 236/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1648\n",
            "Epoch 00236: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1648 - val_loss: 0.1437\n",
            "Epoch 237/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1332\n",
            "Epoch 00237: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1331 - val_loss: 0.1278\n",
            "Epoch 238/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1238\n",
            "Epoch 00238: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1237 - val_loss: 0.1193\n",
            "Epoch 239/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1191\n",
            "Epoch 00239: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1191 - val_loss: 0.1160\n",
            "Epoch 240/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1155\n",
            "Epoch 00240: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1155 - val_loss: 0.1146\n",
            "Epoch 241/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1152\n",
            "Epoch 00241: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1152 - val_loss: 0.1141\n",
            "Epoch 242/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1164\n",
            "Epoch 00242: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1164 - val_loss: 0.1169\n",
            "Epoch 243/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1627\n",
            "Epoch 00243: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1627 - val_loss: 0.1457\n",
            "Epoch 244/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1248\n",
            "Epoch 00244: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1248 - val_loss: 0.1305\n",
            "Epoch 245/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1160\n",
            "Epoch 00245: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1160 - val_loss: 0.1146\n",
            "Epoch 246/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1196\n",
            "Epoch 00246: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1196 - val_loss: 0.1139\n",
            "Epoch 247/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1773\n",
            "Epoch 00247: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1773 - val_loss: 0.1928\n",
            "Epoch 248/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1599\n",
            "Epoch 00248: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1597 - val_loss: 0.1350\n",
            "Epoch 249/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1237\n",
            "Epoch 00249: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1237 - val_loss: 0.1193\n",
            "Epoch 250/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1189\n",
            "Epoch 00250: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 9s 9ms/step - loss: 0.1189 - val_loss: 0.1176\n",
            "Epoch 251/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1199\n",
            "Epoch 00251: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1199 - val_loss: 0.1180\n",
            "Epoch 252/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1654\n",
            "Epoch 00252: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1653 - val_loss: 0.1454\n",
            "Epoch 253/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1386\n",
            "Epoch 00253: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1386 - val_loss: 0.1336\n",
            "Epoch 254/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1303\n",
            "Epoch 00254: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1303 - val_loss: 0.1230\n",
            "Epoch 255/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1543\n",
            "Epoch 00255: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1545 - val_loss: 0.1886\n",
            "Epoch 256/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.2094\n",
            "Epoch 00256: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2093 - val_loss: 0.1818\n",
            "Epoch 257/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1634\n",
            "Epoch 00257: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1633 - val_loss: 0.1461\n",
            "Epoch 258/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1405\n",
            "Epoch 00258: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1405 - val_loss: 0.1379\n",
            "Epoch 259/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1315\n",
            "Epoch 00259: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1315 - val_loss: 0.1285\n",
            "Epoch 260/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1252\n",
            "Epoch 00260: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1252 - val_loss: 0.1239\n",
            "Epoch 261/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1278\n",
            "Epoch 00261: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1279 - val_loss: 0.1863\n",
            "Epoch 262/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1535\n",
            "Epoch 00262: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1534 - val_loss: 0.1246\n",
            "Epoch 263/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1260\n",
            "Epoch 00263: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1260 - val_loss: 0.1236\n",
            "Epoch 264/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1198\n",
            "Epoch 00264: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1198 - val_loss: 0.1182\n",
            "Epoch 265/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1550\n",
            "Epoch 00265: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1549 - val_loss: 0.1258\n",
            "Epoch 266/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1221\n",
            "Epoch 00266: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1221 - val_loss: 0.1200\n",
            "Epoch 267/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1189\n",
            "Epoch 00267: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1189 - val_loss: 0.1216\n",
            "Epoch 268/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1248\n",
            "Epoch 00268: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1248 - val_loss: 0.1228\n",
            "Epoch 269/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1171\n",
            "Epoch 00269: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1171 - val_loss: 0.1156\n",
            "Epoch 270/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1157\n",
            "Epoch 00270: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1157 - val_loss: 0.1143\n",
            "Epoch 271/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1318\n",
            "Epoch 00271: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1318 - val_loss: 0.1284\n",
            "Epoch 272/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1220\n",
            "Epoch 00272: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1220 - val_loss: 0.1188\n",
            "Epoch 273/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1220\n",
            "Epoch 00273: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1220 - val_loss: 0.1180\n",
            "Epoch 274/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1197\n",
            "Epoch 00274: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1197 - val_loss: 0.1163\n",
            "Epoch 275/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1213\n",
            "Epoch 00275: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1212 - val_loss: 0.1170\n",
            "Epoch 276/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1183\n",
            "Epoch 00276: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1183 - val_loss: 0.1167\n",
            "Epoch 277/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1168\n",
            "Epoch 00277: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1168 - val_loss: 0.1419\n",
            "Epoch 278/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1157\n",
            "Epoch 00278: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1161 - val_loss: 0.2272\n",
            "Epoch 279/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1476\n",
            "Epoch 00279: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1476 - val_loss: 0.1237\n",
            "Epoch 280/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1191\n",
            "Epoch 00280: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1191 - val_loss: 0.1182\n",
            "Epoch 281/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1200\n",
            "Epoch 00281: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1200 - val_loss: 0.1169\n",
            "Epoch 282/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1216\n",
            "Epoch 00282: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1218 - val_loss: 0.1484\n",
            "Epoch 283/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1310\n",
            "Epoch 00283: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1309 - val_loss: 0.1202\n",
            "Epoch 284/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1362\n",
            "Epoch 00284: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1362 - val_loss: 0.1237\n",
            "Epoch 285/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1208\n",
            "Epoch 00285: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1208 - val_loss: 0.1192\n",
            "Epoch 286/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1595\n",
            "Epoch 00286: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1593 - val_loss: 0.1289\n",
            "Epoch 287/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1304\n",
            "Epoch 00287: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1309 - val_loss: 0.2832\n",
            "Epoch 288/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1460\n",
            "Epoch 00288: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1459 - val_loss: 0.1228\n",
            "Epoch 289/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1236\n",
            "Epoch 00289: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1236 - val_loss: 0.1232\n",
            "Epoch 290/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1209\n",
            "Epoch 00290: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1209 - val_loss: 0.1188\n",
            "Epoch 291/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1295\n",
            "Epoch 00291: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1296 - val_loss: 0.1545\n",
            "Epoch 292/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1674\n",
            "Epoch 00292: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1673 - val_loss: 0.1511\n",
            "Epoch 293/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1401\n",
            "Epoch 00293: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1401 - val_loss: 0.1433\n",
            "Epoch 294/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1322\n",
            "Epoch 00294: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1322 - val_loss: 0.1264\n",
            "Epoch 295/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1233\n",
            "Epoch 00295: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1232 - val_loss: 0.1209\n",
            "Epoch 296/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1679\n",
            "Epoch 00296: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1678 - val_loss: 0.1545\n",
            "Epoch 297/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1429\n",
            "Epoch 00297: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1429 - val_loss: 0.1375\n",
            "Epoch 298/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1334\n",
            "Epoch 00298: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1333 - val_loss: 0.1305\n",
            "Epoch 299/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1265\n",
            "Epoch 00299: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1265 - val_loss: 0.1257\n",
            "Epoch 300/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1215\n",
            "Epoch 00300: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1215 - val_loss: 0.1197\n",
            "Epoch 301/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1188\n",
            "Epoch 00301: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1190 - val_loss: 0.1509\n",
            "Epoch 302/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1848\n",
            "Epoch 00302: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1848 - val_loss: 0.1956\n",
            "Epoch 303/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1818\n",
            "Epoch 00303: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1816 - val_loss: 0.1476\n",
            "Epoch 304/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1333\n",
            "Epoch 00304: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1332 - val_loss: 0.1375\n",
            "Epoch 305/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1520\n",
            "Epoch 00305: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1524 - val_loss: 0.2205\n",
            "Epoch 306/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1951\n",
            "Epoch 00306: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1951 - val_loss: 0.1967\n",
            "Epoch 307/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1872\n",
            "Epoch 00307: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1873 - val_loss: 0.1929\n",
            "Epoch 308/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1842\n",
            "Epoch 00308: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1842 - val_loss: 0.1896\n",
            "Epoch 309/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1873\n",
            "Epoch 00309: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1873 - val_loss: 0.1868\n",
            "Epoch 310/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1909\n",
            "Epoch 00310: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1909 - val_loss: 0.2146\n",
            "Epoch 311/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.2135\n",
            "Epoch 00311: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2135 - val_loss: 0.2208\n",
            "Epoch 312/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.2069\n",
            "Epoch 00312: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2068 - val_loss: 0.2045\n",
            "Epoch 313/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1944\n",
            "Epoch 00313: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1944 - val_loss: 0.1913\n",
            "Epoch 314/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1958\n",
            "Epoch 00314: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1957 - val_loss: 0.1926\n",
            "Epoch 315/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.2012\n",
            "Epoch 00315: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2012 - val_loss: 0.1970\n",
            "Epoch 316/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.2000\n",
            "Epoch 00316: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1999 - val_loss: 0.1979\n",
            "Epoch 317/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1946\n",
            "Epoch 00317: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1946 - val_loss: 0.2001\n",
            "Epoch 318/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.2011\n",
            "Epoch 00318: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2011 - val_loss: 0.2034\n",
            "Epoch 319/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1905\n",
            "Epoch 00319: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1905 - val_loss: 0.1963\n",
            "Epoch 320/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1871\n",
            "Epoch 00320: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1871 - val_loss: 0.1838\n",
            "Epoch 321/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1849\n",
            "Epoch 00321: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1848 - val_loss: 0.1511\n",
            "Epoch 322/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1531\n",
            "Epoch 00322: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1532 - val_loss: 0.1934\n",
            "Epoch 323/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1944\n",
            "Epoch 00323: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1944 - val_loss: 0.1949\n",
            "Epoch 324/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1903\n",
            "Epoch 00324: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1903 - val_loss: 0.1905\n",
            "Epoch 325/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1895\n",
            "Epoch 00325: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1895 - val_loss: 0.1887\n",
            "Epoch 326/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1659\n",
            "Epoch 00326: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1658 - val_loss: 0.1463\n",
            "Epoch 327/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1404\n",
            "Epoch 00327: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1403 - val_loss: 0.1377\n",
            "Epoch 328/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1367\n",
            "Epoch 00328: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1367 - val_loss: 0.1333\n",
            "Epoch 329/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1301\n",
            "Epoch 00329: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1301 - val_loss: 0.1289\n",
            "Epoch 330/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1258\n",
            "Epoch 00330: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1258 - val_loss: 0.1243\n",
            "Epoch 331/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1227\n",
            "Epoch 00331: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1227 - val_loss: 0.1211\n",
            "Epoch 332/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1916\n",
            "Epoch 00332: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1917 - val_loss: 0.2129\n",
            "Epoch 333/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1929\n",
            "Epoch 00333: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1929 - val_loss: 0.1867\n",
            "Epoch 334/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1872\n",
            "Epoch 00334: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1872 - val_loss: 0.1937\n",
            "Epoch 335/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1782\n",
            "Epoch 00335: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1781 - val_loss: 0.1493\n",
            "Epoch 336/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1384\n",
            "Epoch 00336: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1383 - val_loss: 0.1339\n",
            "Epoch 337/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1330\n",
            "Epoch 00337: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1330 - val_loss: 0.1302\n",
            "Epoch 338/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1269\n",
            "Epoch 00338: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1269 - val_loss: 0.1243\n",
            "Epoch 339/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1721\n",
            "Epoch 00339: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1720 - val_loss: 0.1577\n",
            "Epoch 340/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1479\n",
            "Epoch 00340: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1478 - val_loss: 0.1399\n",
            "Epoch 341/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1366\n",
            "Epoch 00341: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1366 - val_loss: 0.1344\n",
            "Epoch 342/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1305\n",
            "Epoch 00342: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1305 - val_loss: 0.1278\n",
            "Epoch 343/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1273\n",
            "Epoch 00343: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1273 - val_loss: 0.1274\n",
            "Epoch 344/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1306\n",
            "Epoch 00344: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1308 - val_loss: 0.2305\n",
            "Epoch 345/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1693\n",
            "Epoch 00345: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1692 - val_loss: 0.1433\n",
            "Epoch 346/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1360\n",
            "Epoch 00346: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1361 - val_loss: 0.1386\n",
            "Epoch 347/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1287\n",
            "Epoch 00347: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1287 - val_loss: 0.1256\n",
            "Epoch 348/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1248\n",
            "Epoch 00348: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1249 - val_loss: 0.1240\n",
            "Epoch 349/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1570\n",
            "Epoch 00349: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1572 - val_loss: 0.1999\n",
            "Epoch 350/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1903\n",
            "Epoch 00350: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1903 - val_loss: 0.1847\n",
            "Epoch 351/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1938\n",
            "Epoch 00351: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1938 - val_loss: 0.1925\n",
            "Epoch 352/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1491\n",
            "Epoch 00352: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1491 - val_loss: 0.1384\n",
            "Epoch 353/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1359\n",
            "Epoch 00353: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1358 - val_loss: 0.1337\n",
            "Epoch 354/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1286\n",
            "Epoch 00354: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1286 - val_loss: 0.1262\n",
            "Epoch 355/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1836\n",
            "Epoch 00355: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1837 - val_loss: 0.2059\n",
            "Epoch 356/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1892\n",
            "Epoch 00356: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1892 - val_loss: 0.1896\n",
            "Epoch 357/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1849\n",
            "Epoch 00357: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1849 - val_loss: 0.1860\n",
            "Epoch 358/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1854\n",
            "Epoch 00358: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1854 - val_loss: 0.1900\n",
            "Epoch 359/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1900\n",
            "Epoch 00359: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1900 - val_loss: 0.1855\n",
            "Epoch 360/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1907\n",
            "Epoch 00360: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1907 - val_loss: 0.1866\n",
            "Epoch 361/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1876\n",
            "Epoch 00361: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1876 - val_loss: 0.1987\n",
            "Epoch 362/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1860\n",
            "Epoch 00362: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1860 - val_loss: 0.1853\n",
            "Epoch 363/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1845\n",
            "Epoch 00363: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1845 - val_loss: 0.1883\n",
            "Epoch 364/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1725\n",
            "Epoch 00364: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1723 - val_loss: 0.1480\n",
            "Epoch 365/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1718\n",
            "Epoch 00365: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1718 - val_loss: 0.1890\n",
            "Epoch 366/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1882\n",
            "Epoch 00366: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1882 - val_loss: 0.1856\n",
            "Epoch 367/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1880\n",
            "Epoch 00367: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1880 - val_loss: 0.1976\n",
            "Epoch 368/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1904\n",
            "Epoch 00368: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1904 - val_loss: 0.1900\n",
            "Epoch 369/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1886\n",
            "Epoch 00369: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1886 - val_loss: 0.1935\n",
            "Epoch 370/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1938\n",
            "Epoch 00370: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1937 - val_loss: 0.1882\n",
            "Epoch 371/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1851\n",
            "Epoch 00371: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1851 - val_loss: 0.2038\n",
            "Epoch 372/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1894\n",
            "Epoch 00372: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1894 - val_loss: 0.1866\n",
            "Epoch 373/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1838\n",
            "Epoch 00373: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1838 - val_loss: 0.1843\n",
            "Epoch 374/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1845\n",
            "Epoch 00374: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1844 - val_loss: 0.1901\n",
            "Epoch 375/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1845\n",
            "Epoch 00375: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1846 - val_loss: 0.1899\n",
            "Epoch 376/10000\n",
            " 997/1000 [============================>.] - ETA: 0s - loss: 0.1838\n",
            "Epoch 00376: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1838 - val_loss: 0.1903\n",
            "Epoch 377/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1859\n",
            "Epoch 00377: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1858 - val_loss: 0.1895\n",
            "Epoch 378/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1838\n",
            "Epoch 00378: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1839 - val_loss: 0.1836\n",
            "Epoch 379/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1831\n",
            "Epoch 00379: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1831 - val_loss: 0.1860\n",
            "Epoch 380/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1829\n",
            "Epoch 00380: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1829 - val_loss: 0.1875\n",
            "Epoch 381/10000\n",
            " 996/1000 [============================>.] - ETA: 0s - loss: 0.1847\n",
            "Epoch 00381: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1847 - val_loss: 0.1890\n",
            "Epoch 382/10000\n",
            " 998/1000 [============================>.] - ETA: 0s - loss: 0.1838\n",
            "Epoch 00382: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1838 - val_loss: 0.1917\n",
            "Epoch 383/10000\n",
            " 999/1000 [============================>.] - ETA: 0s - loss: 0.1826\n",
            "Epoch 00383: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1826 - val_loss: 0.1826\n",
            "Epoch 384/10000\n",
            " 994/1000 [============================>.] - ETA: 0s - loss: 0.1563\n",
            "Epoch 00384: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1563 - val_loss: 0.1498\n",
            "Epoch 385/10000\n",
            " 993/1000 [============================>.] - ETA: 0s - loss: 0.1472\n",
            "Epoch 00385: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1472 - val_loss: 0.1468\n",
            "Epoch 386/10000\n",
            " 995/1000 [============================>.] - ETA: 0s - loss: 0.1565\n",
            "Epoch 00386: loss did not improve from 0.11105\n",
            "1000/1000 [==============================] - 8s 8ms/step - loss: 0.1565 - val_loss: 0.1484\n",
            "Epoch 387/10000\n",
            " 127/1000 [==>...........................] - ETA: 7s - loss: 0.1465Buffered data was truncated after reaching the output size limit."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qKaGcwFqLRU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8t18QFu9zqw",
        "colab_type": "text"
      },
      "source": [
        "###The Results after 60 epochs (loss did not improve after nearly 60 epochs) :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKq68i-4ksDS",
        "colab_type": "code",
        "outputId": "8ad7eebf-0226-44a9-aae1-fedd7a32ab8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# load the network weights weights-improvement-37-0.0005.hdf5\n",
        "filename = !ls -Art | tail -n 1\n",
        "print(filename[0])\n",
        "model.load_weights(filename[0])\n",
        "model.compile(loss='binary_crossentropy', optimizer=Adam(),metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "weights-improvement-71-0.1110.hdf5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SlwJNbfVaQX",
        "colab_type": "code",
        "outputId": "6343fd72-ea8a-4504-e6be-d0064512d8f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "test_eval = model.evaluate(norm_test,callbacks=callbacks_list,steps=1000)\n",
        "print(\" Final Loss: \",test_eval[0])\n",
        "print(\" Final Accuracy: {}%\".format(test_eval[1]*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1000/1000 [==============================] - 7s 7ms/step - loss: 0.1096 - accuracy: 0.8112\n",
            " Final Loss:  0.10963563667237759\n",
            " Final Accuracy: 81.1174750328064%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BiYY3jKdtEWC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8HuM1jOYb_m",
        "colab_type": "code",
        "outputId": "f7b34c87-08db-4893-fbcb-027fec833a64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "# Noise Input:\n",
        "\n",
        "noise = np.random.rand(1,28,28,3)\n",
        "print(noise.shape)\n",
        "plt.imshow(noise[0,:,:,2],cmap='Greys')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 28, 28, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f34ebed4cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8AKCriLQFoAByANg9lgkhcUdU+/YnJyfr4MGDnX748OFm/f79+51u0qRJZu3IkSNN79sz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\nIYHC8BMSKAw/IYHyX2AHsPuU+7/eAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WrOxNzMYrEw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#loading model weights\n",
        "contrived = np.reshape(noise,(-1,28,28,3))\n",
        "pred = model.predict(contrived[:0,:,:,1:2])\n",
        "print(len(pred))\n",
        "plt.imshow(pred[:],cmap='Greys')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6tM9UGPYrK3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "im_list = list()\n",
        "for im_gen in tfds.as_numpy(norm_test.take(2)):\n",
        "  print(len(im_gen[1]))\n",
        "  im_list.append(im_gen[1])\n",
        "#   plt.imshow(im_gen[1][12][:,:,0],cmap='Greys')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_djQMrjUYrH8",
        "colab_type": "code",
        "outputId": "ad07549c-c817-434e-959c-298b220841f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        }
      },
      "source": [
        "preds = model.predict(im_list,steps = 10)\n",
        "m=0\n",
        "plt.figure(figsize = (16,16))\n",
        "for i in preds:\n",
        "#   print(i.shape)\n",
        "  plt.subplot(20, 20, m+1)\n",
        "  m += 1 \n",
        "  plt.imshow(i[:,:,0],cmap='Greys')\n",
        "#   break\n",
        "#   plt.subplot(20, 20, m+1)\n",
        "#   plt.imshow(i[:,:,0],cmap='Greys')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w7CLUMAzDMAzDMAzD8Ic3O26+vPHGGwAcfPDBrk8S1XTa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QltiFy1apIIOtBIQIqe+ZE77Xohvq7NJHdN9N15n0gAlK5XusMOOwDRekxJz35qRIvMvEAmyEDP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18/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\n+lXZtBSDjN5KOTvInG/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IQrO+OzV48GAAbrvtNteX7+5U3Jo//PBDAL766ivXt/feexe9\nvPXayq75gw8+cO0hQ4YAMG/ePNfXt29f1x4+fDhQvzt/SWwb8+fPd+2OHTsCsGLFCtfXpEmTOt8f\nh2Y9gij7ENlGIDo6u++++wKw0UYbFbz8uNfz999/D0BlZaXrW7JkiWsvXrwYgKZNm1Io5dQsd7D1\ntqu11oV+T7t27Vw72367nJq/++47IHr8aNOmDVD/u+t1kcRvUEYRIPN7PP30ws+zktCsR0Huuece\nIDPCD3Duuee69p/+9KdUaC4V0+yHcmrebLPNAPjoo49c3xZbbFE2rUrb/9x6bt06M81x/QUiq1ev\ndn1vv/02AL1793Z9jz76qGvX5/hXLs1JkEtzbcp3FKubt4BdPX1WfekHzMzSn2bNW2CafWCa/WCa\n/WCa/WCa/WCa/WCa/WCa/WCa/ZBLcwQvF6HKuptGegIX1u5MuebNMM0+MM1+MM1+MM1+MM1+MM1+\nMM1+MM1+MM1+yKq5NqUEE9WLMAyf7datW8GvnzZtGgDbbbed69PD4ML777/v2p07dwaitrYtt9wy\nn66+dTyXV/OiRZk5wbvssgsAm266qevbeuutAWjWrJnr+/LLL1379ddfB+Dmm2+u83NqMS8MwyXZ\nnqjvehb++9//urZYzIqdyC5/Xy27WNk151iWaw8aNMi1v/7662ya8lF2zcuWLXNtsWLPmjXL9WnN\nw4YNA2DzzTevz0eUTbP8tn7yk5+4vvPPPx+APn36uD5tQZNABD0pvwC8bBva9tyrVy/XHjduHJDf\ngluLsmn+5JNPALjkkktc3ymnnALAscce6/q0peeJJ54A4MYbbyxccczrWeyJenu+7777XLsYGxJl\n1PzWW28Bme8b4M9//nNB77388std+84773TtHMeXsmmWbfKpp55yfQ888AAAb7zxhusrQ+iIl9/g\n2rVrXfu0005z7bvvvruYxZVN86effgpE7e+y/3v66add3z//+U/XPvroowGYPj1Tvq+A/V7ZNEvA\n17PPZsrwde/eHYCVK1e6vm233da1xTos4SkFEuu2IdM9Hn74YdcnNnSAzz77DMhMBYHMuVYdlE3z\nH/7wByBzbAPYcccdN3jdwoULXVumAolds0DKplnOczxMt/Oy3ygzJWnW37M+rxfmzp0LZH6LAEOH\nDnXtep7rC2Vfz3q62t///ncgOrVAX6f84he/AOChhx5yfQVMIcypWePLjmsYhmEYhmEYhmEYdhFq\nGIZhGIZhGIZh+MObHbcQdJJijx49AJg4caLry2bHFYsV1MQaAzRv3jwuiRuw/fbbu/a33367wfON\nGjXaoE9b6MRyOXJkJsm4nKmH+RBL1OOPP+76NtlkEyCTGgpRy0+2RM4FCxa49q233grAH//4x/KK\nLQBtdbj//vtdW6dbJsG//vUvAI466ijXJ4lqervW9sU5c+YAUSte3OhtWCyrv/71r12f2DX0962t\n3FKX7KqrropVZ30QW522rOhaldpanARiR7vrrrtcn1j6JXEWMtZ+qF8qbhyIzeuxxx5zfWLVHjFi\nhOvLVRM0CSQpUq/HfKxZswaIWl/1d+ILbYkX+7tOZJR9dtqZMWOGa+upLPvvv7+Xz9frTFs6RYu2\n0x522GFAJoka4I477nDtfFN94mbdunVA9Jgixzx9DqGnJ1188cVA1PqaBO++m6nmt+eeNeGZO+yw\ng+vTUyfkmDR69GhP6qLIcePll192fTJdQiPp35A53ut9oU8kCVdSctPENddc49q33HKLay9fXlNO\nUlv25Tgn2zpEz6lPOOEEAP7617/GojUb2Sy4ml13rckJkuMhwLXXXuvakpqd1DFczjfkGgsy09V2\n331317fVVlu5tlwf6OlkeqpkKdhIqGEYhmEYhmEYhuGNVI2E6ppGcqd9jz32qPM9+o6eBAXUp+Zm\nqehACLn7KBPpAf79738D0ZE4GS2CzN0Sn6Ofmv/85z9AtG6iTFjWoR3ZJrjrO9n6buyLL74I1Dvo\npSTkLtqVV17p+uSuL0Rrs/pC39GTbVMHLsjo8sEHH+z6pK4swH777QdkQjMAttlmm1i0CjJBHTLr\nVN+5lO1Uj9rJaBHAlClTgOjoaBKjNDpgREZZ5DsAuOGGG1xbRrb0RP04aqrlQr5nvS958803ARg1\napTr08EYxx13nCd12ZE7pyeddJLrO+CAA4BouEE2J0hSyN1oHQCVDxnx1/s/n9uGoEfoZF92wQUX\nuL6//OUvviUVxYQJE1xb6/e1j9DnBgcddJBry51+2YYBzj77bCB6DuLz3CIfcsz9xz/+4fqynUfo\nYKKkR0DlWHH88ce7vtdeew2IjjjrETIZfUlqtF+CzPQxLdtIqB6Nk1q+SSFatU7Zhz3zzDOuT2/b\ncr4kDgCodyhiQeiQt8suu8y1ZbRTr2c5JupRQ70P0SFxaUOvW32OJH+fz5FQXd/4xz/+MQBXXHGF\n6zvzzDOB6P5Dn3vIMT0OzTYSahiGYRiGYRiGYXjDLkINwzAMwzAMwzAMb6TKjqutW2IdEEsgZCwO\n2jYndTYhagPzhQ5ykTqlOrRF/g5tTX3uuedcW1uCkkACL3T9n7POOguIhsvodS52Yl0PUOyDEK2T\n6ovhw4cD0K5dO9cn9b2SQodgiB1C210PP/zwDd6jbcPyHl3X8Lzz4q1LrCeeSzCVDvoSO7G2xEgN\nKYC9994biIZIaFu0L7Jtu9oSs9dee7m2rocl6G1bB2bEgViwTz/9dNcnwRba+qmDUMRiV896f2Xj\npptuAqLrVOxoervXtU1l29L107T9sQy1LjdA27I///xzAH71q19t8DqtWdvfJQTot7/9retLwhao\n96kSkNK+fXvXd/XVV7t2q1at/AkrENlvXHfdda5PHzOSQB/zJKxDh4mI3bVly5auT1vixYqe1Ppu\n0aIFEN0Xy/QfvV+Q16WBqVOnAtEAFJmOoIMpJbARMlOGkkLszno9i3V00qRJrk/qJEPy5x5yzHvk\nkUdcn6xzvb2+8847ri1TPPbZZx/XF8dvNNfUMzn/z2f51FPHkpgaUSg6fEv/zUkcP/Q5juyL9b5M\njuPnnHOO69Pbjnw3cdizbSTUMAzDMAzDMAzD8EaqRkL13TEZVezXr5/rk5EjPclWj9DJKIxP9B1q\nubOqRz0nT54MRMNddFmDpEdCJaxDT6KWdd+pUyfXd9FFF7m2RDPrUjNxjGLkQ0pvANx+++1AJhgH\n0hUicemllwLZR2E0+o6Z3H3yeSe7f//+ri3frw6MkNEkHSigww/kbrYuKTFo0CDXjnsy/ooVK4Bo\n+MLKlSsBePLJJ12fHt2V0k563WcrBxUXTZs2BaLBSddffz0Q/V3qwAb5brQTQ48qxoEO2pKRUL2P\nEIeELjelg7iknJXeF0r4S+3+cqEDe2TkUN+hls986aWXXJ8uwSLtJMpN5UJG5vX3oUdv0zgSKqO3\nuryBHGfSgGwbeh8h5xbjx493fbqUmWw7uvyCdjPEjbg5dMiPhN3ddtttrk+XkZPyEEkdG+VYpn+D\nsi/RvzE9kpv0diIj4TJiCxnXhw7o0068pEto6XNp4ZVXXgGi370efRaXgi4Td/fdd7u2z227Nnqf\nrEOr/u///i8JOXUyd+5cAJ544gnXp8+PZfRZO4LiRgdaSokyKSUDmfMmjS6LI+dzcew3bCTUMAzD\nMAzDMAzD8IZdhBqGYRiGYRiGYRjeSJUdV9sOX331VSBjS4OM5eiTTz5xfXrIWGyiPtHD7D169Njg\nebGV6PCW3XbbzbVlGPzee+91fT5r62211VYAzJkzx/X99Kc/BWDGjBmu78ILL3RtsSElYcHVaPuO\n2IwqKyuTkrMB2ip+2mmnAfnXmQ7ikm3bp7VH1zeTwBwJu4CMhUSH5Ggk4EXbA3U9WR2mUi60VWfA\ngAFAxjYMGSuKtrvusssuri2WI13nzWeNW7HHSCBYLsS2C9CrVy8AxowZ4/rituNOnDjRtcUKWlVV\n5frEzqVrBt9///2uLfs1bbvV20a5ePvtt11bHz/ESvT73//e9UlNPF2HUweMyDQE/XcmjexDtA1+\n+vTpri2BYmlCrNr6OCI2eJ9kCyGDTOCGPvbKVBtdC/c3v/mNaw8ePBiAESNGuD6flsUdd9wRiIbd\nCTowTB8npR6g/l36RPa72pYtU1X09qAt0EmfZ8i2ocO/ZNvQ9mxd/33x4sWe1GVHtGgrs4Qa6qlV\nsg1BJszv6aefdn16OkOSdlwd9qmnwO25555JyHHIdDsJsIPM8UfXltbb8IEHHghEp5Pp7yEOdBjS\n7NmzgegUFDnX1OefAwcOdO0465LbSKhhGIZhGIZhGIbhDbsINQzDMAzDMAzDMLyRKjuuRoaPdb0o\nQSc56TpBOokxbYjlDmDatGmu3aFDBwCOPPJI13fMMcf4E7YenVYnNdN0UuoNN9zg2knbY+T7l/pd\nkEnYSxOFWnL0dnvGGWe4tqRgaiu3T8SS2rt374LfIxZundz59ddfl1cY0YRsbZcTe5S2dkm64Qsv\nvOD6tB1NrEu6PloakdQ9yNhjdBp03Oi0P7Ea6d+gpBDrlGFtjZWaodo+KPVQy4m2NolNHzJWSqmx\nCpl9ma43LUnbEE12Thv6b9M2V52WmiS69qrYxsUKlhRiR4VoSv2hhx4KRLfxbPX89O9NklEPPvjg\ncsssGb3/06mtkpyclB1XdGlrv/zG9BQOneaZNFJ1QdJlIZP2rKdtLF261LXTko57xRVXuD6xjT/w\nwAOuT+uU70ZPI8o3RcQXOkVdb88+p65lQyzOut6qHP/0tYk+lxaLs65/G7cdVyPfebbzultvvdW1\n9f77hBNOiE2PjYQahmEYhmEYhmEY3kjtSGhd5KpVk0TQgUYCUvTIj9xp1xPxdW0muWui7877RAKg\ndL3SnXbaCYjWY0p69FMjWmTkBTJBBnokMWl06IrUrRw9erTrk79DartBdGRo6tSpQHQiftx8+OGH\nri137HTNT6lbqe8A6yCDyy+/HIjWptO1LsuFBAJAdORN7kzqcBkJANChVTr4IokwK72P2GyzzTZ4\nXkYa582b5/p0vdaxY8cC0fUcN9oNIfsIHXIh4QZSLxkyOgH69u0LwGuvveb6dCBQudB1Mi+55JI6\nXyuazznnHNenR8R1UFfa0PuFOEaUS2XChAmuLSNbujZdEuhtc/78+a4tI6Q61KlLly5A1NHy1ltv\nuXbPnj2BzAh/WtH1hXVd4STR+2xxXunapmlCbzN1oYPCnnvuuZjU1I8rr7zStSWY76mnnnJ9ekRa\nvhPtPkw6kE3OT7VTRdcATxo5N9IOCQkFO/bYY13f8OHDXVvOPXQ91qTdK6JZB/kdccQRri0BpnFg\nI6GGYRiGYRiGYRiGN+wi1DAMwzAMwzAMw/BGg7Tj6np5e+21l2snMZldT6aXmkW6RpdYdHU9U10/\nSEJnunXrFqvOXLRt2xbI2B4A9ttvPyB5e3MuxL44atQo19e8efOk5OREampCxu6n61fKpHo9uV7b\nL3WYlS8kDAmgX79+APztb39zfWLRFgsVZLYhyNgfdU2xOGpu6jCQbMEgabV2CdomJfZPbdEV+462\nRiUdfJHN9iz/NlTE2vXee++5Pl2TLk3TEGqjjyM6TERCwZIO7XjzzTdd++STTwaSX596X9SpUyfX\nlqAZbdP/4osvNniPDq2SGuBJIYF22qq4//77A9FQET1FRQfVJIkOZZHahHFM2/BJRUWFa995550A\nDBkyJCE1GyLbu97u045YoXVtZ581vPMhNa/Fmg/QunVrIHpOr4+dYsOV/UsamDRpEhC9npG633GT\ndyQ0CIIdgiB4OQiC94IgeDcIgvPX948IgmBxEATvrH8ckW9Zvli0aBE9e/akY8eOdOrUyV2sjBgx\ngrZt21JZWUllZWUkZTdpFi1aRK9evaisrKxTs54/mDSFrmfTXBoNUfPixYvp168fBxxwQIPR3BDX\ns2n2g2n2g2n2g2n2g2n2Q0PUvGzZMoYNG8b555/fYDTHQSFDXWuAi8MwfDsIghbAlCAIJAXh5jAM\nb4xPXnE0btyYkSNH0rVrV6qrq6mqquKwww4DaqLsBw8eDCQ3+piNxo0bc/3119OlSxdWr16dU3Oa\nKHQ9pwnT7IfGjRtz1VVX0blzZ5o2bdpgNDfE9Wya48c0+8E0+8E0+8E0+6Eham7UqBGnnHIK7du3\n57DDDmsQmuMg70VoGIZLgCXr29VBEMwC2tb9Ln/odEZJC2zVqpVLNWzRogUdOnQouGZjfdEWYLHL\nacuR2GL03QydOKbrA4mdY5NNNolVs0bSKYtJqWzTpo239ZyNYmr4+dQstgzI2P2ef/551ycWbUkN\nhezJzz41a9uI2ESLrZ+prfK+t41i8LmedRqe2BZ1gvacOXMA2HzzzetcTtK/wWJIk2bZF+vEXm1J\nF9KkWdBTEGR7gYwNN2nNOl29UJLSLPb2YlIqk9IstkRtze/RowcAs2bNcn1iGYTo+VKS24a2C4tV\nOx9Jb8/50PZ4SVZOu+ZsJK1Z21QLta4m/Rvs06dPwe+RShS6IgUke44kKck6gV8nhcdJvYKJgiCo\nALoAk9Z3nRcEwfQgCO4LgqBlmbWVhYULFzJ16lR3Ij169Gg6d+7MgAEDInM300RdmmX+RNowzX4w\nzX4wzX4wzX4wzX4wzX4wzX4wzX5oiJrLRhiGBT2A5sAU4Oj1/98GaETNhew1wH053ncGMBmY3K5d\nu9An1dXVYdeuXcPx48eHYRiGS5cuDdesWROuXbs2HDp0aNiqVauwoWk+9dRTQ2CyaTbNptk0m2bT\nbJpNc23Wrl3rHg1Fc6GY5h+25tmzZ7vHoEGDwkGDBqVecyk0RM2FkE1ztkehF6AbAS8AF+V4vgKY\nmW85VVVVHv70GlavXh326tUrHDlyZNbnFyxYEDZp0iTMpjNMseZOnTrl/XJNc92YZj+YZj+YZj+Y\nZj+Y5tIp5CI0bZoLwTT7IUnNxV6E2npOF4VehBaSjhsA9wKzwjC8SfW3US/rB8zMtyxfhGHIwIED\n6dChQ2QOxJIlS1z7sccei5R6SZpCNUsZmDRgmv1gmv1gmv1gmv1gmv1gmv1gmv1gmv3QEDXHQVBz\nwVrHC4Jgf+A1YAYgxS6HAicAlUAILATODGtCjOpa1ufAt8CyklTnpzmwO7BK9S0GtgRaUJP4uxpY\nG4Zhq7oWlELNHwLbhWHYeoMlrMc014lpNs25MM2mORem2TTnwjSb5lyYZtOci7o0N6UmOHYl6dKc\nj62Uhh3r0izkvQgtN0EQTA7DMLHaKMV8vmmuP6bZD6bZD6bZD6bZD6bZD6bZD6bZD6bZH/XV0BA1\nQz3TcQ3DMAzDMAzDMAyjFOwi1DAMwzAMwzAMw/BGSRehQRAcHgTB7CAI5gVBcFmBb7urlM8sAxNN\nsxdMsx9Msx9Msx9Msx8aoua7ijjnMM31xzT7wTT7oSFqhvrvo9Oguf4aConQzfagpkbofKA9sDEw\nDehY7PJ8PEyzaTbNptk0p/Nhmk3zD023aTbNptk0/y9oLvZRykjoj4F5YRh+EIbhamAMcFQJy/OB\nafaDafaDafaDafaDafZDQ9QMDVO3afaDafaDafZDQ9RcFI1LeG9bYJH6/8fAPrleHATB4a1atXqu\noqKihI8sjfbt27NixQq6desWAjRv3pxvvvlmWRAE08IwfLb269OouaKigoULF4amubyYZj+YZj+Y\nZj+YZn/U1k1NybgN9IJpLgXT7AfT7AfTnAxTpkxZC/TNdkyJUMJw8bHAPer/JwGjs7zuDGAy8F27\ndu3CJBk3blw4cOBA9/+KioqwoWl+8MEHQ+Cz0DSXFdPsB9PsB9PsB9Psj9q6gQ9qH79Nc+mYZj+Y\nZj+Y5mQAJocx23EXAzuo/2+/vi9CGIZ3Ab8F/t26dd66pbHStm1bFi3KDN6uXr0aGpjmjz/+GGqK\n7kYwzaVhmv1gmv1gmv1gmv1RWzc1c6Uix2/TXDqm2Q+m2Q+mOd2UchH6FrBrEAQ7BUGwMXA88GSO\n19a27iZC9+7dmTt3LgsWLGD16tUsX74coHsQBC2zvDyVmseMGQPQzLfm+fPnu8e0adOYNm1a6jXX\nB9PsB9PsB9PsB9Psj9q6gW2BV7K81DSXgGn2g2n2Q9o09+/fn/79+zN16lT3qE3aNBdJRY5jSoSi\nL0LDMFwDnAe8AMwCHg3D8N1il+eDxo0bM3r0aHr37k2HDh1o2bIl1GgfmbC0nNTW/Mtf/hLgO0xz\nWTHNfjDNfjDNfjDN/qitG1gFnJ6wrDoxzX4wzX4wzX5oiJqz8D0FHFOCGutuvARBsB8woqqqqtfk\nyZNj/7xC6datG1OmTNkJeDoMwz31c2nVDBAEwQzgR3FrXrZsmWtvvfXWrn377bcDcPbZZxe8rDg0\nf/fdd6598803AzBhwgTXd+ONN7r2QQcdVK9lx6W5GPSo8/Drp7MAACAASURBVA47ZBzwW2655Qav\nLafm77//HoBLL73U9cn61ZPgDznkENceMmQIAJtssklBn1EOzV9++aVrn3jiiQAsWLDA9VVVVQHQ\npk0b13fUUZmguQMOOEA+z5vmJCin5k8++QSAM8880/WNHTsWgE033bQ8grH17Is4NH/wwQeuvd7y\nS7t27VxfviANbUeT3/OBBx4Yq+a4Kafm6upqACZNmuT6ZD1/8cUXrk+v5yOPPBKAjTfeOBHNvkhC\n86pVq1x78ODBANx2222u70c/qnvMJ27NH374IQBfffWV69t7772LXt56bbHuN+R8Yt68ea6vb9++\nrj18+HAg/7qNW3M+5s+f79odO3YEYMWKFa6vSZMmdb4/Ds1Lly51bdmHyDYC0KVLF9fed999Adho\no40KXn4uzbUpxY5bH94CdvX0WfWlHzAzS3+aNW+BafaBafaDafaDafaDafaDafaDafaDafaDafZD\nLs0RvFyEKutuGukJXFi7M+WaN8M0+8A0+8E0+8E0+8E0+8E0+8E0+8E0+8E0+yGr5tqUUie0XoRh\n+Gy3bt3q/T6xlwA88sgjADz00EOub+bMzIV2ixYtgKj1cvz48a6dzYIShmHfDTqL1PzOO+8A8Mwz\nz7i+Tz/9FIBDDz3U9f385z937UaNGhW8fMW8MAyXZHui2PWcjaFDh7r2Oeec49pnnXVWMYsrm+Y1\na9YAcMopp7i+cePGAbBu3TrX16dPH9cWW4zua9WqlWvnsGLGup7ffbdmCrW2Nffq1WsDfWLtASKT\n2LPZcSmj5lGjRgHw/PPPu74nnngCiP6W1q5d69r1seEqStKsf0ONG9fs0rSVT+w9etu45ZZbXPvl\nl18Gola/uDXn47///S8AK1eudH1z58517dmzZwMZ+zEUtC8pm+bttvv/9s49zMqq3uOf90gdOYKX\n0JRGEDE1RGkAL2l6io5CcjwWpie1tLx20VAU74pYmVlhapjmraIoxBBF85JH05TSB/ACCIo3kngg\n1Dw5Qh0U3/PHzG/t3zvsmb33zH7Xu6e+n+fZD4s1s/f+zrvXfm/ru76/DwDw0Y9+NPTtt99+QGk/\nWCfqptns5V6fWdOam5tDXx2SBus+NtrCKQB44oknAHj11VdDn1nOofTZ1EjdNJt1dtSoUaHvvPPO\nA+DBBx8MfRdeeGFo2/fW/jYo2eQhu8wiD8225MFvu3Lj4Nlnnw3tYcOGAVlbWwf7ZE+3NPv92gc/\n+EEga3+3ZTObbbZZ6PPLFR599FGgtHylSuo+nm3/BqVjSS3LITz297WzZEY5R/LL2caPHx/ab775\nZjlNlai7Zr+kyqzYS5cuDX1e86RJkwDYYostanmLumm275Y/ppx22mlA9rztlFNOCe1jjz0WgJ12\n2qkGyXHGhrc9+/M6O1etZMFtR90021Kas846K/TZufThhx8e+mbOnBnadt7nl7hVQYeaPbHsuEII\nIYQQQgghhC5ChRBCCCGEEELEI5odtxq8re/cc88FYMqUUsLvuHHjALj++utDn0/3MmuEn873r1kv\nVq0qzTB7O8M999wDwJZbbhn6zNrgLcRtpWEAsESrKmw80TC72m9+85vQt2zZstDuqm2mXlx99dVA\nydYAJault794C9txxx0HZC2Ln/vc50L7pptuAkq2sLzw9nGzq516asnS7+22d955JwCnn3566Ntl\nl11y1ee5++67gex30NsWGwVvH7rrrruAkmXbt72VxKxHkP0+x2LRokWhbbZ3b+tbsmQJkLWM+7Ft\nKXZ+Xzd48OB8xHaCt8R7e2UjYt8jn0poyyW8Rfeoo44K7e9+97tAl23m3Wbt2rVA9jhn49Xby/1n\n//vf/x6o2VZXN2z/7Pcbhx12GABvvfVW6PPfu/vuuw8o2Xah9F2GrLW3Xvj3Nyv53LlzQ185O+68\nefNC244Vffr0qbu2jth+++1D28aGp5wlv9x+z382NVpGu8WJJ54IwO233x767LtlqaFQOvZA+URO\nn35u4+2yyy6rr9gq8LbmH//4x6Ht01yL4P777weyKfB2PuTH9c033xzado5n5x0x8GPYLKv+vOyC\nCy4Asp+3t3JPmzYNgEsuuSRXnbVgtmJvkfXLZry1uAhseZK/jjJLvy1ZgWxFjFpScWtFM6FCCCGE\nEEIIIaLRUDOhP/zhD0Pb7oz6hdWVZgutbqSftfN3vXv37l0Xnf6uuQ+E+OlPfwqUr5PnFyl/6EMf\nCm0LHil6JtRmBKB059SHRNRSVywP/Ayh3c31d8ws7MmHWVhQlX++1TgF+OUvfxnaNkviayPlgV8M\nbjOgPgDqW9/6Vmhb3bFLL700V00dYSEdfkbcaoL6Gq3+O7bddtsBxY8XP6NtDgkfKuKxgI+Y7Lrr\nrqE9efJkANasWbPRz31Qih+7dge7Uq3FvPF1xT7zmc8UqKQ8PtjJZgf8/ttmYbxrwgK5oBRgZfXq\nYmOhYCtXrgx9ti+zgAmAMWPGhLbNou+7774xJG6EBWiZcwlKs21+9vYLX/hCaNt+2/+dee9DXnnl\nldC2fYQ/Npdj+vTpoW2zuzH3dd6FZNvU7zcefvhhIDsTZ7NFUAp6iTn76fnDH/4AZOsm2nHOn/+V\nq1/vQ5n8DN8DDzwA1Bz00i3eeOMNAC666KLQd+aZZ4Z2Eftl7/qzsemP0za7/PGPfzz0+eBP21/4\nc8Ftt902F62GPwezberPd2yc+lk773JasGABkJ0dLcK14o+D+++/P1D6DKDkqIHSbKOvK+vdk3lj\nn7Pflzz22GNA9thnoU8ARxxxRG56NBMqhBBCCCGEECIauggVQgghhBBCCBGNhrLj+kWxY8eOBbIh\nPoa39Hgb1Te/+U0ga7vNIzTg4IMPLtvuDG8NtRpSUKr15gMHisACAwA+/elPA1mLgLc7mEUiz8XK\n7Tn55JNDe5999gFK9meoHJZkNSD9YnFfz9UWa+dtx21qagpts1paKBKU6rgBzJkzB4i7nT1ml/M1\nrqze6tZbbx36Bg4cGNpm5bFwFChmbHv7pFl5vCXeB3j472MsvIXPW/rb4793FsABJRtTUbY649BD\nS2WW/fepUfDb2aziPiTCvlu2z4Pscg6zthZlx7XgKT9e7fvox7PfR+S9D6uELW245ZZbQp/Zoq+9\n9trQZ9Z+KC1JiBl657ep2T/NEgjQv39/IGub8/tnC02JiQ9ysTqlPrTF/g5vTbXARMjWUC8CG88+\nqNHqjvtwGb/NzU7s61uafRDKL3/Km4svvhjIHvu+8Y1vRNfh8efCFgDm7a6f/OQnN3qOtw3bc3zg\now9NzAO/3M6Cqfw5u9mJfZ1gv6+2wLapU6eGPm+LjkW5settw3vssUdo+7q9hh/bAwYMyENiwCzY\nJ510UuizpSr+nN8vEbRj54gRI+quRzOhQgghhBBCCCGi0VAzoTb7CTBkyBCgFFAEpdkXHzbhY9bt\n7t/y5ctDX8z49M7w4S3+DkkRd/E8djfd31m0RdRPP/106PNBAHbHykIz8sJvs2eeeSa0bdaylrvm\n9nf6ECAfVX7QQQd1WWct+Lt0Njv7iU98IvT5sgRFlYUwTJ/NyEJpVnP33XcPfT4EyGLqr7jiitDn\n27HwwRfmpvDjxc9+2l35PMpAdBdfAsXftbaSEkVh4Tj+7np375LaLE49Z8P82LTQFguzgFLAkw/f\n8kEun/3sZ+umpSvYHWg/Q2fBP36/4cNGYga0lOPAAw8EYK+99gp9tt/wJVr8LE0RZb98OJjNKvow\nJRvbXrOfofNlc2Lhzxdsv+U/byv55sNdZs+eHdpFz4Ra8J4PgLJtP3To0NB3xhlnhLY5hnypmSLG\niw+2s5A4vy8pOozPc/bZZwOV91/eSWPOAO/ayxsfTmaf70c+8pHQZ05BK9kI2WOinf/7UmW+bGLe\nLjI7R/UuILs+8edN/jrEjkl+25crB5UX5hT1wUmXX345kP1eeheWfTbeieFDQLuDZkKFEEIIIYQQ\nQkRDF6FCCCGEEEIIIaLRUHZcPxXc0tICwOLFi0Of2R28BeKrX/1qaFsdpJh2gnL4oACrH3TDDTeE\nvr333ju0d9ppp3jCymDBG7vsskvos7qJPrjhvPPOC23b5nnXZlq4cGFo+xCL97///VU93weMHHLI\nIQC8/vrroc/seQCbb755l3XWgrfV2aJ8v0C8aAuuxz5/23ZQ2QZltpLHH388P2FV8Ktf/Sq0zebp\na82Ws/w0ElYT78477wx93h5fNGbV9jZ+25d4m7631FvtQh94YNZSKIUj+M+untjY9eFk1vZhd3Yc\nATjllFNy0VIr3lZmyyG8naoIe2IlfI3CzTbbLPMvZOt0VqrPmQf+POF3v/sdULKlQSlkzddj9ePE\n1/CNhf+cy1nyzRLvw1v8sd2+jz4Mzx/n88aWVPljs9nKFy1aFPomTJgQ2mYtLnqMX3bZZaH9ne98\nB4Dm5uai5GyEt4pb0GSlbebPR2xsxwxCtPAvKAXm+PMys/T7Y4bHwph8bVNfT3bw4MH1E9uGD1A9\n/vjjgew5hNVW9ftnX4vcrgV+8pOfhL6YSyjs/NkCwTrCB7xaGN6MGTNCn+y4QgghhBBCCCF6HLoI\nFUIIIYQQQggRjYay43osTcrb5syi69M2LQUM4iWcVsJb6CwN1Sfi+tqnn//854Hs37l69erQtrQ+\nq41Zbyw11lv9zOLgk/h8TTpLP8zbOjpv3rzQfvDBB0Pb6sH6Gl1r1qwB4I477gh9TzzxRGib7cPS\nAwF23HHHOiuujK9tagnQ3n5i27YRWLlyJZC11flEScNbLq1GoE/rjIlZisyCCyX9Pmnb/9ynjBaJ\nt/qdddZZQNaqXbR132P7CJ9KaJYpvx/2icM777wzkK2tHDMVsDO8lc2sxpD9+xqNmTNnlm0XjX3f\nfP1bS0X19SH9/rkIO67HjmWTJk3a6Gd+/+br6Pn9YqPhl6z4Y7sdc/7rv/4r9H3mM5+JJ6yNcmn3\nPinVEvqheBuuff5+mUARie+VsON1Jfy49fXXrT6lt3LHxCypY8aMqfo5ZuHesGFD6Muj7rdPyD7m\nmGNC+/bbbweyKex2fu/PN6xqAJTS1/M6p68XfqnMEUccAWSXQtYLzYQKIYQQQgghhIhGw86EGn52\n4Ctf+QqQDazxC+wbBV+nyBb/+hk4m9GF0p0cf6fF1x+zGlp5YYuL/Z1oW2zvZ70eeOCB0PY1GPPE\n1w2zmReAiy++eKPftcX0PmDIL6K22ZeianlZUI+vJ3XqqacC8OijjxaiqRIWWLHvvvuGPltU7++6\n+tqr9jmU+4zywtfE+/rXvw5k70wavr5wv379QtvCBYpm1qxZoX3rrbcC8NBDDxWkpnN8KNk/AhMn\nTgxtP/vpa7k1Cnas+Mtf/hL6/L6yaMzZ4d0zFo5js14Ahx56aFxhXaSjY0a5fUxMLCDFz/zYrKEP\ngPK11G08W/3Z2FgAlK9Xao6kK6+8MvQVPfvpMS3++GH7aj+TWDQ+kMfOM6ZOnRr67O/wwWsWBgel\n4L6YtestuBPg2muvBbL7Xzsv9cE9vpa6BWZaHWL/nHriz9/9McHO4W688cbQZ/VkfWiVP7YXEWbl\n9xHlQjjNGWbhgZB1Z95yyy1AdjvXi8Y7wgohhBBCCCGE+IdFF6FCCCGEEEIIIaLR8HZcHxRgNgJf\nX6yR6iqWw6w85Wp6QW2LsPPAaqV5u4EtSPbbefr06aEdq6am1SaCbIiFWR+8ZcesXX47x6y9VAmz\nP3kLnQUseStHI2HWC1/z0+qPeYvXOeecE9oWqBNz2x999NGhPWfOHCBrKRo+fDiQDQc78sgjQ3uL\nLbbIW2KnmC5vKx47dizQ+OEFPR2z+fsasldffXVRcqrC7Lg+XKtoa6jHLPlr164NfWZbu+aaa0Kf\nhQ82Or5e3h577BHaVg8wJr4e4u677w5k92tm0fXLmPw4sdAZXys3Jk1NTUDJlgul5R6NNIY9dr5z\n1VVXhb5GHLtWUxNKtdx9/UpbXuPrwnr7ZbX11+uJhSFBKYTz5z//eegzi7a38dsYgvIBfnmce/ig\nxXKhiz/4wQ/q/p715KKLLgptO6fwFl1bPuWvt3xAap61YyvOhCZJMiBJkt8mSbIkSZJnkiQ5ra1/\ncpIkK5MkeartMTY3lTWyYsUKRo0axW677cbQoUPDzmPy5Mk0NTXR3NxMc3NzJvWuaKrV7P38RSPN\ncZDmOEhzHKQ5DtIch56qefTo0TQ3N/cozT1xO0tz/qxcuZJx48ZxwAEH9BjNPXE750E1t57eAc5M\n0/SJJEn6AguSJLHVzN9P0/R7+cnrGr169WLKlCmMGDGClpYWRo4cGcoGTJgwIYRQFHU3sBzVam4k\npDkO0hwHaY6DNMdBmuPQUzVffvnlDB8+nPXr1/cYzT1xO0tz/vTq1YtLLrmEYcOG0bt37x6juadt\n5zyoeBGapukqYFVbuyVJkqVAU+fPqh/XXXddaN92221AZStE//79Q13Ivn37MmTIkKprKBVFI2m2\nen72b0fkrdnbbb0NyttiaqWo7bzDDjsA2WQ6s434FLJyFD02/DjwNU07I6ZmX+/VW4drpajtbPbK\nxYsXhz6rmVcpzbnosdEVGkmz2bfNvgblrUeNpNlb4Y1y46QozbYkwtdsttqrvs5mORppO5fjjDPO\nCG2z/Pfr1y+aZm8BNrucP06avdwfZ3y9VjsOAQwaNAhoXdIUaztbreOu1Dwuemz4qgfVElOzr7m8\nZMkSAO69997QZxZtn0pd9H7DJ82aTbSrS1D8OWKj7TfKEXM7++VHjz32GJBN0F62bBlQzNKkmoKJ\nkiQZBAwH7Ezv1CRJFiZJcnOSJFvVWVtdWL58OU8++WQY2FOnTmXYsGEcf/zxmbUUjURnmt94442C\n1ZVHmuMgzXGQ5jhIcxykOQ7SHAdpjoM0x6Enaq4baZpW9QD6AAuAw9r+vy2wCa0XspcCN3fwvJOB\n+cD8gQMHpjFpaWlJR4wYkc6aNStN0zRdvXp1+s4776QbNmxIzz///LRfv35pT9N83HHHpcB8aZZm\naf7H0jx37tzwWLduXbpu3bqG19wdGkHzs88+mz777LPp22+/HR6NrrmlpSVtaWlJZ8yYER6NrrlW\npFmapVmapbkxNVdDOc3lHtVegL4HuA84o4OfDwIWV3qdkSNHRvjTW1m/fn06evTodMqUKWV//vLL\nL6ebbrppWk5n2sCahw4dWvHDlebOkeY4SHNtdPUiVNu569RyEdoommu5CG0UzbUgzXGQ5jhIcxyk\nubGo9iK0mnTcBLgJWJqm6RWuv7/7tXHA4vbPLYo0TTnhhBMYMmRIZg2HX0sze/bsTPR60VSr2WLZ\nGwFpjoM0x0Ga4yDNcZDmOEhzHKQ5DtIch56oOQ+S1gvWTn4hSfYHHgEWAVZ86nzgKKAZSIHlwJfS\n1hCjzl7rVWAt8Fq3VFemD7Ar8DfXtxJ4H9CX1sTf9cCGNE37dfZCDaj5j8AH0jTdZqNXaEOaO0Wa\npbkjpFmaO0KapbkjpFmaO0KapbkjOtPcm9bg2HU0luZKbO007NCZZqPiRWi9SZJkfpqmhdVG6cr7\nS3PtSHMcpDkO0hwHaY6DNMdBmuMgzXGQ5njUqqEnaoYa03GFEEIIIYQQQojuoItQIYQQQgghhBDR\n6NZFaJIkn0yS5LkkSV5IkuTcKp92fXfesw7MleYoSHMcpDkO0hwHaY5DT9R8fRfOOaS5dqQ5DtIc\nh56oGWrfRzeC5to1VBOhW+5Ba43QF4HBwHuBp4Hduvp6MR7SLM3SLM3S3JgPaZbmfzTd0izN0izN\n/wyau/rozkzo3sALaZq+lKbpemAG8KluvF4MpDkO0hwHaY6DNMdBmuPQEzVDz9QtzXGQ5jhIcxx6\nouYu0asbz20CVrj//wnYp6NfTpLkk/369btn0KBB3XjL7jF48GD++te/sueee6YAffr04a233not\nSZKn0zS9u/3vN6LmQYMGsXz58lSa64s0x0Ga4yDNcZDmeLTXTWvJuI30gjR3B2mOgzTHQZqLYcGC\nBRuAQ8sdUzJ0Y7r4cOBG9/9jgKllfu9kYD7w94EDB6ZFcuutt6YnnHBC+P+gQYPSnqZ52rRpKbAm\nlea6Is1xkOY45K353XffTd99990epTkPpDke7XUDL7U/fktz95HmOEhzHKS5GID5ac523JXAAPf/\n7dv6MqRpej3wNeDhbbapWLc0V5qamlixojR5u379euhhmv/0pz9Ba9HdDNLcPaQ5DtIcB2mOgzTH\no71uWtdKZY7f0tx9pDkO0hwHaW5sumPHnQfsnCTJjrRunCOBozv43fbW3W6xevXq0P6P//iP0L7g\nggsAOPro8jL22msvnn/+eV5++WWampp44403APZKkmSrNE3fyFNzV2mvecaMGQCbSXN9aWTNLS0t\nob1kyZLQbmTNHSHNcchb87HHHgvAtGnTQl+SJA2tOQ/y0PzWW2+F9oMPPgjA3/72t9A3YsQIAHba\naafQ9y//Uv395J64nWFj3cB2wENlflWau0EjaH7xxRdD274PH/7whzv8/UbQXCvSHAdpLoxBHRxT\nMnR5JjRN03eAU4H7gKXAzDRNn+nq68WgV69eTJ06lTFjxjBkyBC22moraNU+pWBpHdJe83//938D\n/B1privSHAdpjoM0x0Ga49FeN/A34KSCZXWKNMdBmuMgzXHoiZrL8DZVHFOSVutuviRJsi8weeTI\nkaPnz5/f5dd5++23AWhubg59q1atCu2VK1tnq3v37l3V6+25554sWLBgR+CuNE13765mu2P3wgsv\nhD6vtTP8cwYOHBja733vezf63SRJFgH/Ug/Nf//73wF4/fXXQ1///v2B2u6uV6KemqvFZhGgdGf1\npJOq/x4XodnPgtx4441AaYYf4JRTTgnt73znOw2hubtIcxzqqXnzzTcH4JVXXgl9W265Zd20Om3/\ndNvZW6vaTkBs6QgATzzxBABjxowJfTNnzgztao9/9dRcBNIch1iaX3vttdB+//vfH9rXXHMNAF/5\nyleqfq08NNu5EsD3v/99AG677bbQ973vfS+0P/axj9X02nlp7gpPP/10aA8YUFpx9773vW+j362n\nZju/P/vss0OfbV8fuuPdj+eccw4A//qv/1rVe9RD81/+8pfQNtflyy+/HPpGjhwJlM6jAT71qVKw\n7QEHHGDvF01zEXSkuT31u8ronHnAzpHeq1bGAYvL9Dey5i2R5hhIcxykOQ7SHAdpjoM0x0Ga4yDN\ncZDmOHSkOUOUi1Bn3W1ERgET2nc2uObNkeYYSHMcpDkO0hwHaY6DNMdBmuMgzXGQ5jiU1dye7gQT\n1USapnfvueee3XoNsycuXbo09N18882h3RUbUpqmh3bys5o0z5s3D4Bbb7019P3whz+s6rnnnXde\naP/oRz8K7XIWCFqL2K4q94NaNW+66aYA3HnnnaHvpz/9KQC///3vQ193Q0eoo+bO2LBhQ2ifeOKJ\noX3DDTd05eXqpvnPf/4zAH/84x9D37PPPgvAXXfdFfp+85vfhPZhhx0GwMKFC0OfDyPJW7NZTe6+\nu1Tmaa+99gJg3bp1oW+77bYLbbMOW3hKleQ6Nl566SUApk+fHvq8tWrNmjVAydoD8MEPfrDSy9ZN\n8ze+8Q2gFPYDsMMOO2z0e8uXLw9tC6oxu2aV1E2zWfUjLOeIst+oM93S7D/nzTbbbKOfP//880Dp\nuwhw/vnnh7ZZBWuk7tvZhyn98pe/BLJLC7yt7dOf/jQAP/vZz0JfuaUo7ai75rakYAB+8YtfbKRp\n8eLSjf2+ffsCWevlrFmzQrsD/XXT/NRTTwHw61//OvTZcebAAw8Mff/5n/8Z2ptssknVr++I8h30\nY/irX/1qaH/5y1/uysvVTfM777wDwBe/+MXQZ+d47777bug75JBDQvsHP/jBRn39+vUL7Q7Op3Ld\nzs880xrZ4m3No0eP3kjfxIkTQ9+TTz4Z2nmfi1511VUA3HvvvaHvjjvuALLfJX+OV4sN19Etzf47\n1KtX6yWUT7K18w0/Nq688srQ/u1vfwvAv//7v0fTXIn/+7//A7LndXacAXjuueeAbOhrFfuSDjV7\nYtlxhRBCCCGEEEIIXYQKIYQQQgghhIhHNDturZjNa/bs2aHvtNNOA2Dy5Mmhr6OaoEVgSZE+2a0S\nZvXw1ldLCYvJRz/60dC2BFafyNhF20N0Fi1aFNreIrH//vtHeX+/zbyl07R4O+1BBx0EwEc+8pHQ\nd91114V2B/aXaJidxCe7/fjHPwayycm+bu+ZZ54JZK2vRWDWI4Ddd28NZ/NJf//7v/8b2mvXrgVg\n6tSpkdRlMSuN2XQga/0yLP0b4P777wey+8KYWBKupeQ2EpdeemloextUW13ojJ3rPe95D5C1Tnmb\n0VFHHQXAT37yk1y0lqOcBdez886tORR2PAT41re+FdqWmm1/W2zMjrbffvuFvjfffBOAXXfdNfRt\nvfXWoX377bcD2YTUD3zgA7nq9OPg3HPPBWDKlFJFgXHjxgFw/fXXhz5fq9LOUfyx079mvfAVAMaP\nHx/a99xzD5BNpd5iiy2ArIW4rRQdAJagWfSxxWO2Yr8UZdmyZaFdh6VA3eLqq68GssusbH/hj4P+\n2H/ccccB2X3J5z73udC+6aabgJKdMy+8fXzUqFEAnHpqaQmht9vakqzTTz899O2yyy656vPYsh//\nHay2ukRM7DsGpaVUdh7v2z4tedKkSaHtv8+x8OfFZnv3521Wf95bxv3YtmVkfl83ePDgumjTTKgQ\nQgghhBBCiGg07Eyo3Tk95phjQp/V1/HhBl1caJ8Ldjf6rLPOqvo5Vl/IB3zkUW+vEn6Gzmoy+Tti\n1157bWxJXcLX7fL6Y83k+gX0PrDC7vTbGIZSQMCHPvShss8vGpsB/dWvfhX6ytWO9cFERc+A2l3I\nI488MvQ98sgjQHbG2c+Q2exLUbP9dofdwgGg/EyoAdMQywAAEatJREFUn43zNciKwLR6nbYP80Ep\nfmzbLLk5ACB7V7le+JA3m+GC0uyF3842y+JnDf0+xIfENRp+2/o78fb3xZwJ9fWN9957bwAuvPDC\n0PelL30JyO4/LIgESsf0mJp9aOB9990HZGdiK80WWriZn7X761//GtpdCUosh80UQqkGIZQCBP/t\n3/5to+d4p4cfJxY8UvRMqAUoQWnGyOrfQvHHQT9DaLNYfibTwp788dyCqvzzrcYplMK5oHRuMnz4\n8HrK3gh/LmozoD4AyjsoLEjMO0liYs4HPyNuNUF9kKD/jtm5R9Hjxc9o23HQgifbU0XoYd3xDhRz\nT1kgo/+5d5/4sWsBsL5ea73QTKgQQgghhBBCiGjoIlQIIYQQQgghRDQa1o57xRVXAFmbkdnR/ALw\nmTNnhrZZaXz9NG+XyGOBu6/7+OqrrwLw2c9+dqPf85q9FcVCgL72ta+FviJsgd7SYwEpfuHxN7/5\nzdD2i5cbBbNrfPvb3w59jz32WFFygGw4hIV1+DARs7v64AhfK9Ks6EVtb7MXeXviww8/DGTrgHob\nUtGYDcrbT/bdd18gGwjggwL+8Ic/RFJXHrM7++1s1tHHH3889FmdZCjVFi0Ks25ZLUUobXM/Xr2V\n8IgjjgBgn332CX15fEfLWcahtHSjkuXzxRdfDO0ilkZUiw/f8n9zEccPX/PT9sV+X2bHcV//0Y8d\n+2zysGd3hA8AHDt2LJDdFxs+tMofx+2Y6G23ffr0qbvOgw8+uGy7M/w+2ZY2QekcZfvtt6+Tuq7h\na3hbjVj/XfOWeRvbMa3aJ598cmjb/srsz1D5XNJqQPr6jX6Zgp0P5G3HbWpqCm2zWlooEsCjjz4a\n2nPmzAGKCzT7whe+AGRrl1q9VR9iNnDgwNC2c2kf7FnE2PY1xi3IzFvi/bJB/32Mhbcre0t/e/z3\nzgK5oGQl7+jY2h00EyqEEEIIIYQQIhoNNRPq481tJnTo0KGhb9q0aUA2wtkvWLY7IH5htYW/tO+v\nFz6wx2YO/R1qe88HH3ww9Pk7sNa+7LLL6q6tq1gpC/95+NnbRpwJtdlbX95g2LBhRcnZCBsbdrcR\nSrNJs2bNCn0WYASlsePDA0466aRcdXr22GMPIBvyYzHqdocSsncerTxEUUEBNgPgv4O2L/HfMT+T\nW/Q4sdkXm7GFkuvDl4fwgWxF3a02/Eyz8dBDDwHZz97PPptLwUIOAG644YbQjjm22+P3yT606n/+\n53+KkNMpzz//PAB33HFH6PMzMzb77B1BebPtttuGtpUos1IykA0TMfwMo0X/x9xv2OwnwJAhQ4BS\nQBGUZl8szAey43nTTTcFYPny5aEvj5nQruC3t3eTlQsxionNDnkHxHe/+10Ann766dDny4Lttttu\nANx77725avPbzB8/bNayFied/Z0+BGibbbYJbR/OlicWBgel2dlPfOIToc9KjEDxZfhMnz9HsnML\nK7MG2RAgKxlnx/j27Vh494wdz/148bOf5pCzkjmNhA8a9CFEvtxWvdFMqBBCCCGEEEKIaOgiVAgh\nhBBCCCFENBrKjjt37tzQNiuoX0Rrdi5v1bDpeCgt/vW22xUrVtRdp69ndfnll4e2WYm+/vWvhz6z\nXfg6nD5g5IwzzgA6XywcG7MR+Kn5hQsXhrbZYxoJs2pPmDAh9HnbRix8QIfHAjf8AnWzRvlauJ//\n/OdDe+LEiUCprhPEtSzusMMOQLa+meEtXt7mavUA/fcyJlaDy9uyzz77bCA7HrwFOo/AslqwseHD\nv2xseHu2jXGAlStXRlJXHtPircxWh872aVAaQwCXXHIJkLWA+eUMRdpxrV4zZC2L3gZWBPPnzwdK\nAXZQOv742tJ+DJutzdep859DHngr33PPPQdkl6CY9faNN94IfSeccEJoW2hVTHz9zJaWFgAWL14c\n+ux4vmDBgtDng5VsaULRwWx+7FpQore5W91WyJ6HFMGSJUsA2GWXXUKf7bP9sdHX+rVt7kNT8rCO\n+nMcq08JJXt5JXy92EMOOQSA119/PfRZqB/A5ptv3mWdteC/bxbc6fezRVtwPfb527aDysdmszj7\nAL8i8LXUbb/sa836GuUWDtZI2HXSnXfeGfq8PT5PNBMqhBBCCCGEECIauggVQgghhBBCCBGNhrLj\n+rQ/m9L209yWUudr1XhrrNUM9fbB+++/v+46vbXJ0kChZKXcbrvtQp/ZCXw9pmuuuSa0x48fX3d9\n9cL/bd7m6tNSi8TXbJsxYwZQsoIVhdlRAWbPnh3aBx54IJAd4+WsMN76ZcmoH//4x+sts9t4a6tP\nbbXk5KLsuKbLW/vtO+YTnn2aZ9F8+MMfBkrpslBKe7YEToDVq1eHdqOk41544YWhz2zjvp6e12mf\njaVCA3z5y1/OU2bV+BR1P569RbAIzOLs663a8c/XVbQae1CyOPv6t3nbcT32mY8ZM2ajn/nac37/\nfdRRR+UvrBMs1dbb5syi69M2zdoP8RJOK+EtdJaG6pdL+Nqndo7i/06/Xxk3bhyQreVbTyw11lv9\nbBx4G7yvsWjHzryto/PmzQttbyW3ZRK+PuWaNWuA7PHcn4v2798fKNnpAXbcccc6K66M3xdbArS3\nBdu2bQRsiYmveFEuhd2nGJ9++ulAcedItszAL40w/T5p2//cpxMXiU8nP+uss4CsVTuWdV8zoUII\nIYQQQgghotFQM6FWLwpKd418yIVduVudHYBbbrkltA899FAAHnnkkdCXx9W8r5NpdxA6wjT7QAN/\nl9HumDUi/s5kHjPK3eW2224LbZvZ8rXpisCPTV87ymZIfajT8OHDgWzIjL8ba3WkbIa/UfGBEWvX\nri1QSQnvlrA6b762aSPhx0xn+KCwe+65Jyc1tXHRRReF9mGHHQZkZ2b8jLR9JpMmTQp9RQey/e53\nvwOyTpVf//rXRcnZCKuf6R0SNst1+OGHh76LL744tC10y9djLdq9Ypp9kJ+v02k1OYvGzw7Y7LgP\nrLnpppuia6qEd1OZs8DPwNmMLsCiRYsAeOutt0KfOTEg/5rJBxxwAJANhWpubgays14PPPBAaPvj\naJ74mvR+lt5/twyb7fcBQ+bGAjj44IOB4uplW1CP35eZq8Lv6xoJc534etkWsOXPkXztVfscyn1G\neeGDTy2EtFwIpq8v7K8Zjj/++BzVVY8PZ7z11luBrBsrFpoJFUIIIYQQQggRDV2ECiGEEEIIIYSI\nRkPZcb2FzqwD9m9PxewQVh8LsnW9iq5R2Bl+MbUPE7EarkWHdjz22GOhfeyxxwLFb08fJOPtPRY0\n4y0QVkPMP8eHVo0YMSI3ndVgC+y9fWf//fcHsnalk08+ObR9UE2R+FAWq5XmLWA9kUGDBoX2j370\nIwDOOeecgtRsjI13P+4bHbNC+9rO/vtYNGb9Mms+lGrj7bnnnqHPHzvNhutrFBaN2QN9OJjV/W4k\nvFXc6oC+8soroa+R6iqWw+yf++23X9mflwuLionVVPV24eeffx7Ibufp06eHdqyamqNHjw5tHzJk\ndd39uYUt/fLbuZH2GxaO6euWW8CSr1PfSGy//fZAtubniSeeCMCqVatCnz/m2XK4mNv+6KOPDu05\nc+YA2aVrtszKh4P55RBWF7woTJe3FdvSiLwCyTqj4kxokiQDkiT5bZIkS5IkeSZJktPa+icnSbIy\nSZKn2h5jK71WLFasWMGoUaPYbbfdGDp0KFdddRXQmt7Y1NREc3Mzzc3NmZStoqlWsx0YGwFpjoM0\nx0Ga4yDNcXjttdeYNGkSp512Wo/R3BO3szTHQZrjIM1x6Ima86CamdB3gDPTNH0iSZK+wIIkSSyl\n5vtpmn4vP3ldo1evXkyZMoURI0bQ0tLCyJEjQ5z6hAkTmDhxIpC9k1w01WpuJKQ5DtIcB2mOgzTH\nYZNNNuGLX/wigwcP5qCDDuoRmnvidpbmOEhzHKQ5Dj1Rcx5UvAhN03QVsKqt3ZIkyVKgKW9h3aF/\n//4hdbZv374MGTIkk64VE0t/9Im9AwYM2Oj3GkmzYbXTAJYtWxbaZsMtWvOVV15Z83OK0mwpbl1J\nqSxKs1lcfK1Hsx8tXbo09Hlbh0+zLnJseLuwWbUrUfR4roS3x5vlp9E1l6Nozd6mWq11tejv4CGH\nHFL1cz72sY9l/jWKHBuWkuzrU/qkcKPosXHdddeFtqWv++NgOYrW3BUaSbMl2ldKts9bs7fb7rHH\nHqFtM1RdoajtbHWB/Sya7Uv8d7AcRY8NPw58TdPOiKnZ13v11uFaKWo7WzL24sWLQ5/VmS4izbmm\nYKIkSQYBwwHb8qcmSbIwSZKbkyTZqsMnFsjy5ct58skng9d56tSpDBs2jOOPPz7j2W4kOtNs69sa\nDWmOgzTHQZrjIM1xkOY4SHMcpDkO0hyHnqi5bqRpWtUD6AMsAA5r+/+2wCa0XsheCtzcwfNOBuYD\n8wcOHJjGpKWlJR0xYkQ6a9asNE3TdPXq1ek777yTbtiwIT3//PPTfv36pT1N83HHHZcC86VZmovU\nvGHDhvDoKZqrRZr/sTU/99xz4TF+/Ph0/PjxDa+5O0izNEuzNEuzNHvmzp0bHuvWrUvXrVtXzz+r\nrOZyj2ovQN8D3Aec0cHPBwGLK73OyJEj6/pHdsb69evT0aNHp1OmTCn785dffjnddNNN03I60wbW\nPHTo0IofrjR3jjR3n2ouQhtNczVIcxyK1NzVi1Bt5zhIcxykOQ7SHAdpro1GuQitJh03AW4ClqZp\neoXr7+9+bRywuP1ziyJNU0444QSGDBmSWaPmY55nz55N7969i5BXlmo177777kXIK4s0x0Ga4yDN\ncZDmOEhzHKQ5DtIcB2mOQ0/UnAdJ6wVrJ7+QJPsDjwCLgHfbus8HjgKagRRYDnwpbQ0x6uy1XgXW\nAq91S3Vl+gC7An9zfSuB9wF9aU38XQ9sSNO0X2cv1ICa/wh8IE3TbTp6IWnuFGmW5o6QZmnuCGmW\n5o6QZmnuCGmW5o7oTHNvWoNj19FYmiuxtdOwQ2eajYoXofUmSZL5aZoWVhulK+8vzbUjzXGQ5jhI\ncxykOQ7SHAdpjoM0x0Ga41Grhp6oGWpMxxVCCCGEEEIIIbqDLkKFEEIIIYQQQkSjiIvQ6wt4z+6+\nvzTHeX9pjvP+0hzn/aU5zvtLc5z3l+Y47y/Ncd5fmuO8vzR3jVo19ETN8deECiGEEEIIIYT450V2\nXCGEEEIIIYQQ0Yh2EZokySeTJHkuSZIXkiQ5N9J7DkiS5LdJkixJkuSZJElOa+ufnCTJyiRJnmp7\njJVmaZZmaZZmaZZmaZZmaZZmaZbmfDRnSNM09wewCfAiMBh4L/A0sFuE9+0PjGhr9wWWAbsBk4GJ\n0izN0izN0izN0izN0izN0izN0pyv5vaPWDOhewMvpGn6Upqm64EZwKfyftM0TVelafpEW7sFWAo0\nVfl0aa4SaZbmjpBmae4IaZbmjpBmae4IaZbmjpDmHqE5Q6yL0CZghfv/n+ii4K6SJMkgYDjweFvX\nqUmSLEyS5OYkSbYq8xRp7gLSHAdpjoM0x0Ga4yDNcZDmOEhzHKQ5Dv8kmjP8UwQTJUnSB5gFnJ6m\n6ZvAtcBOQDOwCphSoLyySHMcpDkO0hwHaY6DNMdBmuMgzXGQ5jhIcxzqoTnWRehKYID7//ZtfbmT\nJMl7aN1I09M0vQ0gTdM/p2m6IU3Td4EbaJ3Sbo8014A0S3NHSLM0d4Q0S3NHSLM0d4Q0S3NHSHPD\na86S5ryANW1duNoLeAnYkdLi2aER3jcBpgFXtuvv79oTgBnSLM3SLM3SLM3SLM3SLM3SLM3SXH/N\nG71W3mKdoLG0Jii9CFwQ6T33B1JgIfBU22Ms8DNgUVv/HL/hpFmapVmapVmapVmapVmapVmapbm+\nmv0jaXtBIYQQQgghhBAid/4pgomEEEIIIYQQQjQGuggVQgghhBBCCBENXYQKIYQQQgghhIiGLkKF\nEEIIIYQQQkRDF6FCCCGEEEIIIaKhi1AhhBBCCCGEENHQRagQQgghhBBCiGjoIlQIIYQQQgghRDT+\nH5DqeLTg9p9jAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x1152 with 320 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DT90PpEYrBT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}