A1-T1-mlp-multirun.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "9PUXkk5s8Cdp"
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()\n",
    "#create the validation set\n",
    "X_valid, X_train = X_train_full[:6000] / 255.0, X_train_full[6000:] / 255.0\n",
    "y_valid, y_train = y_train_full[:6000], y_train_full[6000:]\n",
    "#add label for class\n",
    "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
    "                   \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "print(tf.config.list_physical_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#variable\n",
    "acts=[\"relu\",\"linear\"]\n",
    "kinits=['he_normal','he_uniform','glorot_normal','glorot_uniform']\n",
    "lrs=[0.5,0.1,1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LZPOym_0EO-0",
    "outputId": "733ec000-f942-4ff6-8f3b-133bfeeb3c63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear he_normal 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n",
      "2024-11-10 21:43:56.895058: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-11-10 21:43:56.895142: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB\n",
      "2024-11-10 21:43:56.895158: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB\n",
      "2024-11-10 21:43:56.895506: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-11-10 21:43:56.895561: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-10 21:43:57.690542: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.6461 - loss: 16502.9941 - val_accuracy: 0.7775 - val_loss: 30.4640\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 7ms/step - accuracy: 0.7089 - loss: 467511.4688 - val_accuracy: 0.7490 - val_loss: 20744.5059\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7575 - loss: 16634.3242 - val_accuracy: 0.7055 - val_loss: 17053.1348\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7317 - loss: 13886.3125 - val_accuracy: 0.7588 - val_loss: 6481.2119\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7233 - loss: 467335.5000 - val_accuracy: 0.7795 - val_loss: 59469.4570\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7675 - loss: 44622.5508 - val_accuracy: 0.7568 - val_loss: 26593.8164\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7599 - loss: 22012.4512 - val_accuracy: 0.7850 - val_loss: 13977.5938\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7552 - loss: 144116.9375 - val_accuracy: 0.7570 - val_loss: 137399.6562\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7706 - loss: 67938.0859 - val_accuracy: 0.7708 - val_loss: 25169.8691\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7624 - loss: 28681.9902 - val_accuracy: 0.7855 - val_loss: 27013.3340\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7501 - loss: 237888.4688 - val_accuracy: 0.7950 - val_loss: 57281.3398\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7811 - loss: 42090.6523 - val_accuracy: 0.7288 - val_loss: 26583.3574\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7669 - loss: 26221.0840 - val_accuracy: 0.7080 - val_loss: 42571.6016\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7411 - loss: 839608.0000 - val_accuracy: 0.7437 - val_loss: 157794.7344\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7865 - loss: 70864.2031 - val_accuracy: 0.7498 - val_loss: 47962.9062\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7727 - loss: 38242.0781 - val_accuracy: 0.7755 - val_loss: 59666.9492\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7552 - loss: 536915.6250 - val_accuracy: 0.7867 - val_loss: 127451.4062\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7920 - loss: 79786.8984 - val_accuracy: 0.7748 - val_loss: 101081.3828\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7751 - loss: 48020.4805 - val_accuracy: 0.7700 - val_loss: 60005.9609\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7597 - loss: 304706.1250 - val_accuracy: 0.7823 - val_loss: 78202.6172\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7898 - loss: 48950.5820 - val_accuracy: 0.7703 - val_loss: 41511.1602\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7658 - loss: 172203.8906 - val_accuracy: 0.7965 - val_loss: 140319.0156\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7888 - loss: 70103.6562 - val_accuracy: 0.7618 - val_loss: 48536.5703\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7759 - loss: 160647.7812 - val_accuracy: 0.7935 - val_loss: 202993.4062\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7934 - loss: 108489.8359 - val_accuracy: 0.8153 - val_loss: 51770.3047\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7809 - loss: 46561.9883 - val_accuracy: 0.7863 - val_loss: 61453.5742\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7623 - loss: 560928.8125 - val_accuracy: 0.8080 - val_loss: 60775.7227\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7861 - loss: 43983.2422 - val_accuracy: 0.8070 - val_loss: 48940.5000\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7571 - loss: 360474.7188 - val_accuracy: 0.7548 - val_loss: 72835.8750\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7941 - loss: 47936.2266 - val_accuracy: 0.7513 - val_loss: 50221.6328\n",
      "linear he_normal 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.6562 - loss: 253.1873 - val_accuracy: 0.7197 - val_loss: 1.9022\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7461 - loss: 3783.1101 - val_accuracy: 0.7707 - val_loss: 585.1681\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7662 - loss: 378.2358 - val_accuracy: 0.7370 - val_loss: 191.0279\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7607 - loss: 122.5209 - val_accuracy: 0.7788 - val_loss: 88.2935\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7519 - loss: 99.2023 - val_accuracy: 0.6328 - val_loss: 46.4958\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7278 - loss: 4341.2046 - val_accuracy: 0.7795 - val_loss: 289.9186\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7731 - loss: 209.0841 - val_accuracy: 0.7522 - val_loss: 107.5458\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7601 - loss: 124.3124 - val_accuracy: 0.7568 - val_loss: 118.5146\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7455 - loss: 3107.8330 - val_accuracy: 0.7983 - val_loss: 630.1599\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7803 - loss: 379.9755 - val_accuracy: 0.8053 - val_loss: 317.4634\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7559 - loss: 260.0003 - val_accuracy: 0.7783 - val_loss: 256.3058\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7573 - loss: 3390.9880 - val_accuracy: 0.7995 - val_loss: 946.0465\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7881 - loss: 662.5161 - val_accuracy: 0.7382 - val_loss: 452.6743\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7741 - loss: 324.8792 - val_accuracy: 0.7782 - val_loss: 242.2603\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7623 - loss: 480.9810 - val_accuracy: 0.7632 - val_loss: 3445.7585\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7818 - loss: 1149.9622 - val_accuracy: 0.7528 - val_loss: 383.1799\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7722 - loss: 251.5170 - val_accuracy: 0.7558 - val_loss: 258.8121\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7455 - loss: 11177.3047 - val_accuracy: 0.7873 - val_loss: 571.8745\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7890 - loss: 424.0201 - val_accuracy: 0.7992 - val_loss: 285.0843\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7792 - loss: 243.4184 - val_accuracy: 0.7622 - val_loss: 330.4053\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7588 - loss: 1241.6783 - val_accuracy: 0.7748 - val_loss: 534.7108\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7881 - loss: 330.2733 - val_accuracy: 0.7377 - val_loss: 519.4704\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7681 - loss: 1647.6792 - val_accuracy: 0.8150 - val_loss: 754.7679\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7881 - loss: 419.5996 - val_accuracy: 0.7425 - val_loss: 323.2971\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7683 - loss: 399.2614 - val_accuracy: 0.7770 - val_loss: 1539.8943\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7616 - loss: 1400.8414 - val_accuracy: 0.7483 - val_loss: 463.6868\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7463 - loss: 12590.4521 - val_accuracy: 0.7595 - val_loss: 838.3166\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7882 - loss: 651.6387 - val_accuracy: 0.7565 - val_loss: 455.0001\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7769 - loss: 427.5620 - val_accuracy: 0.7948 - val_loss: 470.2950\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7661 - loss: 4027.6384 - val_accuracy: 0.8098 - val_loss: 703.6357\n",
      "linear he_normal 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.6300 - loss: 344860.0938 - val_accuracy: 0.6678 - val_loss: 5075635.0000\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7351 - loss: 785519.0000 - val_accuracy: 0.7680 - val_loss: 176045.3125\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7381 - loss: 126287.8281 - val_accuracy: 0.7912 - val_loss: 37434.8320\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7427 - loss: 49062.2344 - val_accuracy: 0.7477 - val_loss: 29542.2344\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7272 - loss: 6353848.5000 - val_accuracy: 0.7313 - val_loss: 381296.4375\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7685 - loss: 240303.9688 - val_accuracy: 0.7687 - val_loss: 219785.5938\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7634 - loss: 127810.2891 - val_accuracy: 0.7028 - val_loss: 294772.2812\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7274 - loss: 3452668.7500 - val_accuracy: 0.7267 - val_loss: 416450.8125\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7750 - loss: 294162.7500 - val_accuracy: 0.7422 - val_loss: 229397.4844\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7605 - loss: 199027.5625 - val_accuracy: 0.6510 - val_loss: 71807584.0000\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7539 - loss: 10342043.0000 - val_accuracy: 0.7548 - val_loss: 263303.5625\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7745 - loss: 245671.6875 - val_accuracy: 0.7392 - val_loss: 334529.4688\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7545 - loss: 2570545.2500 - val_accuracy: 0.7765 - val_loss: 1012239.2500\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7879 - loss: 661793.0625 - val_accuracy: 0.7532 - val_loss: 447750.2188\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7713 - loss: 326138.0625 - val_accuracy: 0.7598 - val_loss: 437322.6562\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7498 - loss: 1296721.5000 - val_accuracy: 0.8022 - val_loss: 479020.6250\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7847 - loss: 354606.1562 - val_accuracy: 0.7972 - val_loss: 239493.1875\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7656 - loss: 2379019.0000 - val_accuracy: 0.7768 - val_loss: 1108622.1250\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7886 - loss: 554290.4375 - val_accuracy: 0.7820 - val_loss: 437250.5312\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7742 - loss: 308995.2500 - val_accuracy: 0.7637 - val_loss: 1076590.0000\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7519 - loss: 3904009.7500 - val_accuracy: 0.7835 - val_loss: 531015.2500\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7832 - loss: 377192.7500 - val_accuracy: 0.7690 - val_loss: 376799.1562\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7686 - loss: 424252.9062 - val_accuracy: 0.4502 - val_loss: 216438496.0000\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7698 - loss: 13684154.0000 - val_accuracy: 0.8022 - val_loss: 387834.1562\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7881 - loss: 362101.0625 - val_accuracy: 0.7410 - val_loss: 669367.1250\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7681 - loss: 414856.7500 - val_accuracy: 0.6012 - val_loss: 21563986.0000\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7638 - loss: 9842320.0000 - val_accuracy: 0.7977 - val_loss: 378758.1562\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7863 - loss: 327190.6250 - val_accuracy: 0.7190 - val_loss: 992758.6875\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7638 - loss: 1882617.2500 - val_accuracy: 0.8000 - val_loss: 356501.9375\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7794 - loss: 911966.5000 - val_accuracy: 0.7797 - val_loss: 3395072.2500\n",
      "linear he_uniform 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.6384 - loss: 24795.0176 - val_accuracy: 0.6713 - val_loss: 105.6416\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7159 - loss: 394816.7500 - val_accuracy: 0.7563 - val_loss: 220028.7188\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7513 - loss: 105428.9531 - val_accuracy: 0.7873 - val_loss: 37489.4453\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7590 - loss: 34111.7695 - val_accuracy: 0.7330 - val_loss: 34799.1797\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7502 - loss: 24598.0859 - val_accuracy: 0.8005 - val_loss: 5662.7578\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7587 - loss: 24284.5488 - val_accuracy: 0.7260 - val_loss: 3980361.5000\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7595 - loss: 561554.1250 - val_accuracy: 0.8092 - val_loss: 44432.6055\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7657 - loss: 37782.0977 - val_accuracy: 0.7953 - val_loss: 17540.8301\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7683 - loss: 17786.6855 - val_accuracy: 0.7505 - val_loss: 10177.1523\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7376 - loss: 344375.6562 - val_accuracy: 0.7588 - val_loss: 126185.8359\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7790 - loss: 83579.1250 - val_accuracy: 0.7900 - val_loss: 39246.2969\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7600 - loss: 65554.3438 - val_accuracy: 0.7357 - val_loss: 25226.6445\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7629 - loss: 55098.1562 - val_accuracy: 0.7402 - val_loss: 972978.6250\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7804 - loss: 183290.0312 - val_accuracy: 0.8003 - val_loss: 25801.9688\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7763 - loss: 20913.9824 - val_accuracy: 0.7977 - val_loss: 15055.1572\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7514 - loss: 237523.0469 - val_accuracy: 0.7937 - val_loss: 80091.4219\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7794 - loss: 45418.4883 - val_accuracy: 0.7940 - val_loss: 40639.6055\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7692 - loss: 36175.4805 - val_accuracy: 0.7932 - val_loss: 45568.7578\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7532 - loss: 1053600.1250 - val_accuracy: 0.8092 - val_loss: 57823.6836\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7823 - loss: 43534.9609 - val_accuracy: 0.7888 - val_loss: 29794.5098\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7685 - loss: 33061.8711 - val_accuracy: 0.7453 - val_loss: 37960.2969\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7492 - loss: 796832.0625 - val_accuracy: 0.7847 - val_loss: 65938.2734\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7873 - loss: 44397.0469 - val_accuracy: 0.7842 - val_loss: 42371.0781\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7714 - loss: 82598.7656 - val_accuracy: 0.7977 - val_loss: 86701.5625\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7845 - loss: 48342.7500 - val_accuracy: 0.7770 - val_loss: 57972.6133\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7561 - loss: 961894.5000 - val_accuracy: 0.7905 - val_loss: 83150.5078\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7856 - loss: 53986.4570 - val_accuracy: 0.8053 - val_loss: 45198.1367\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7732 - loss: 41748.0664 - val_accuracy: 0.7502 - val_loss: 102075.0859\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7550 - loss: 1008594.1250 - val_accuracy: 0.7370 - val_loss: 83917.7656\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7915 - loss: 42798.0000 - val_accuracy: 0.7893 - val_loss: 55583.2383\n",
      "linear he_uniform 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.6496 - loss: 254.9787 - val_accuracy: 0.7593 - val_loss: 0.8142\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7688 - loss: 1289.4215 - val_accuracy: 0.6967 - val_loss: 2424.8235\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7453 - loss: 983.7387 - val_accuracy: 0.7835 - val_loss: 274.3246\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7556 - loss: 179.1014 - val_accuracy: 0.7655 - val_loss: 146.4732\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7468 - loss: 108.9323 - val_accuracy: 0.7117 - val_loss: 79.7007\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7283 - loss: 123.7099 - val_accuracy: 0.5282 - val_loss: 88466.9297\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7362 - loss: 8422.8193 - val_accuracy: 0.7653 - val_loss: 221.5154\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7611 - loss: 200.0809 - val_accuracy: 0.7578 - val_loss: 194.2916\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7576 - loss: 127.0367 - val_accuracy: 0.7240 - val_loss: 382.5994\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7179 - loss: 5875.1436 - val_accuracy: 0.8090 - val_loss: 267.7701\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7690 - loss: 218.9535 - val_accuracy: 0.7602 - val_loss: 144.4289\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.7608 - loss: 188.7350 - val_accuracy: 0.4477 - val_loss: 112966.2969\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7429 - loss: 14379.3516 - val_accuracy: 0.7593 - val_loss: 255.9852\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7734 - loss: 233.3714 - val_accuracy: 0.7318 - val_loss: 424.7133\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7543 - loss: 526.5861 - val_accuracy: 0.8015 - val_loss: 173.4921\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7561 - loss: 3780.1191 - val_accuracy: 0.7902 - val_loss: 427.7921\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7835 - loss: 314.5966 - val_accuracy: 0.7892 - val_loss: 281.9660\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7715 - loss: 291.6510 - val_accuracy: 0.6868 - val_loss: 9083.6504\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7440 - loss: 6745.3066 - val_accuracy: 0.7533 - val_loss: 323.0639\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7773 - loss: 249.6099 - val_accuracy: 0.6690 - val_loss: 6204.5498\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7512 - loss: 8970.2383 - val_accuracy: 0.7658 - val_loss: 393.8013\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7782 - loss: 307.9129 - val_accuracy: 0.7282 - val_loss: 1194.1331\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7631 - loss: 2287.3184 - val_accuracy: 0.7927 - val_loss: 1392.3567\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7948 - loss: 654.4024 - val_accuracy: 0.7882 - val_loss: 416.7375\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7803 - loss: 282.5173 - val_accuracy: 0.7418 - val_loss: 403.7301\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7615 - loss: 6447.3291 - val_accuracy: 0.8038 - val_loss: 799.5212\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7952 - loss: 516.2310 - val_accuracy: 0.8195 - val_loss: 455.5818\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7824 - loss: 369.4223 - val_accuracy: 0.7997 - val_loss: 421.9329\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7682 - loss: 3595.6211 - val_accuracy: 0.8123 - val_loss: 874.9653\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7900 - loss: 459.5462 - val_accuracy: 0.7943 - val_loss: 339.1328\n",
      "linear he_uniform 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.6379 - loss: 189882.5312 - val_accuracy: 0.7615 - val_loss: 319.9666\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7154 - loss: 1269466.7500 - val_accuracy: 0.7860 - val_loss: 455194.6250\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7491 - loss: 354552.6250 - val_accuracy: 0.6198 - val_loss: 584527.5625\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7121 - loss: 2924590.5000 - val_accuracy: 0.7648 - val_loss: 283658.7500\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7692 - loss: 215600.2031 - val_accuracy: 0.7853 - val_loss: 133197.8906\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7598 - loss: 188463.6719 - val_accuracy: 0.6873 - val_loss: 230673.9844\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7395 - loss: 2947597.7500 - val_accuracy: 0.8048 - val_loss: 703090.1250\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7856 - loss: 471482.6875 - val_accuracy: 0.7597 - val_loss: 294877.2812\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7688 - loss: 239672.9844 - val_accuracy: 0.8028 - val_loss: 154064.4219\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7570 - loss: 172534.5938 - val_accuracy: 0.6607 - val_loss: 871100.9375\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7242 - loss: 17374890.0000 - val_accuracy: 0.7770 - val_loss: 525533.8750\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7734 - loss: 346098.0312 - val_accuracy: 0.7767 - val_loss: 225595.3750\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7659 - loss: 263423.5312 - val_accuracy: 0.6588 - val_loss: 10108636.0000\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7482 - loss: 8945117.0000 - val_accuracy: 0.7400 - val_loss: 296840.9375\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7649 - loss: 279610.9688 - val_accuracy: 0.7810 - val_loss: 280968.3750\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7483 - loss: 3713585.2500 - val_accuracy: 0.8017 - val_loss: 553245.1875\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7831 - loss: 359058.6875 - val_accuracy: 0.7922 - val_loss: 350716.2500\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7662 - loss: 482397.5625 - val_accuracy: 0.7803 - val_loss: 579672.3750\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7786 - loss: 319619.8750 - val_accuracy: 0.6375 - val_loss: 4906257.5000\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7379 - loss: 15575083.0000 - val_accuracy: 0.8037 - val_loss: 443520.9375\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7854 - loss: 337649.5312 - val_accuracy: 0.7613 - val_loss: 394956.8125\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7685 - loss: 752793.3125 - val_accuracy: 0.7862 - val_loss: 503375.5000\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7852 - loss: 376263.6250 - val_accuracy: 0.7338 - val_loss: 18696000.0000\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7841 - loss: 3568911.7500 - val_accuracy: 0.7802 - val_loss: 369241.9375\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7778 - loss: 286051.8750 - val_accuracy: 0.7833 - val_loss: 349112.4688\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7601 - loss: 3279449.7500 - val_accuracy: 0.7880 - val_loss: 754147.0000\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7913 - loss: 489931.0938 - val_accuracy: 0.7630 - val_loss: 484640.6875\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7754 - loss: 378660.8438 - val_accuracy: 0.7848 - val_loss: 1356846.1250\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7790 - loss: 1616402.1250 - val_accuracy: 0.8120 - val_loss: 1549040.2500\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7972 - loss: 803063.6250 - val_accuracy: 0.7570 - val_loss: 477580.4688\n",
      "linear glorot_normal 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.6312 - loss: 23053.9121 - val_accuracy: 0.7107 - val_loss: 52.3231\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7215 - loss: 292725.7188 - val_accuracy: 0.7108 - val_loss: 56906.6602\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7466 - loss: 36759.1328 - val_accuracy: 0.7458 - val_loss: 12597.3662\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7339 - loss: 20622.0605 - val_accuracy: 0.7130 - val_loss: 23124.7715\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7377 - loss: 89810.9375 - val_accuracy: 0.7623 - val_loss: 243184.7500\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7625 - loss: 98816.5312 - val_accuracy: 0.7610 - val_loss: 27152.3945\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7642 - loss: 19017.6582 - val_accuracy: 0.7363 - val_loss: 23208.4609\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7480 - loss: 21978.5156 - val_accuracy: 0.7288 - val_loss: 22228.0918\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7269 - loss: 921291.9375 - val_accuracy: 0.8092 - val_loss: 24722.9980\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7793 - loss: 26710.4551 - val_accuracy: 0.7787 - val_loss: 24416.2695\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7623 - loss: 19934.4805 - val_accuracy: 0.7582 - val_loss: 23116.6953\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7303 - loss: 903456.8125 - val_accuracy: 0.7273 - val_loss: 38600.6680\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7750 - loss: 35519.5742 - val_accuracy: 0.8133 - val_loss: 27732.4316\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7619 - loss: 147243.6875 - val_accuracy: 0.7413 - val_loss: 144844.2500\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7841 - loss: 73837.3203 - val_accuracy: 0.7577 - val_loss: 40597.1992\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7693 - loss: 32188.9961 - val_accuracy: 0.7360 - val_loss: 54392.0156\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7441 - loss: 1048198.4375 - val_accuracy: 0.8107 - val_loss: 65981.3203\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7863 - loss: 54115.7422 - val_accuracy: 0.7755 - val_loss: 54786.2148\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7723 - loss: 44566.0000 - val_accuracy: 0.8048 - val_loss: 45520.9922\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7499 - loss: 514840.5312 - val_accuracy: 0.8248 - val_loss: 50906.7891\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7906 - loss: 50288.4922 - val_accuracy: 0.7668 - val_loss: 36577.9531\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7727 - loss: 43007.1328 - val_accuracy: 0.7908 - val_loss: 100158.7109\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7486 - loss: 506011.0625 - val_accuracy: 0.8053 - val_loss: 39353.7891\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7781 - loss: 43568.3359 - val_accuracy: 0.6155 - val_loss: 3140270.0000\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7670 - loss: 830844.0625 - val_accuracy: 0.7927 - val_loss: 37770.5156\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7765 - loss: 41588.2578 - val_accuracy: 0.7597 - val_loss: 92413.5547\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7618 - loss: 753579.8750 - val_accuracy: 0.7918 - val_loss: 46455.5430\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7893 - loss: 38428.1211 - val_accuracy: 0.7443 - val_loss: 56963.2227\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7639 - loss: 157869.8438 - val_accuracy: 0.7482 - val_loss: 60267.1289\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7789 - loss: 282876.4688 - val_accuracy: 0.8165 - val_loss: 198461.3281\n",
      "linear glorot_normal 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.6628 - loss: 299.7073 - val_accuracy: 0.8002 - val_loss: 0.9350\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7756 - loss: 820.3441 - val_accuracy: 0.7415 - val_loss: 1303.9468\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7491 - loss: 686.6799 - val_accuracy: 0.7890 - val_loss: 113.9179\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7454 - loss: 259.4383 - val_accuracy: 0.7590 - val_loss: 168.4489\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7379 - loss: 149.9687 - val_accuracy: 0.7307 - val_loss: 147.0617\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7130 - loss: 6042.7559 - val_accuracy: 0.7967 - val_loss: 156.6257\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7720 - loss: 138.6693 - val_accuracy: 0.7823 - val_loss: 77.1268\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7476 - loss: 936.1297 - val_accuracy: 0.7497 - val_loss: 1736.9315\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7691 - loss: 735.5507 - val_accuracy: 0.7932 - val_loss: 225.7316\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7643 - loss: 226.4825 - val_accuracy: 0.7898 - val_loss: 163.9896\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7558 - loss: 1162.7308 - val_accuracy: 0.7580 - val_loss: 1146.2047\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7767 - loss: 685.9636 - val_accuracy: 0.7610 - val_loss: 270.9862\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7663 - loss: 240.4512 - val_accuracy: 0.8067 - val_loss: 195.8037\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7495 - loss: 1042.1271 - val_accuracy: 0.8102 - val_loss: 771.2908\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7838 - loss: 454.5202 - val_accuracy: 0.7760 - val_loss: 270.0096\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7591 - loss: 1220.5969 - val_accuracy: 0.8208 - val_loss: 1046.5519\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7871 - loss: 624.1376 - val_accuracy: 0.7565 - val_loss: 415.8612\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7713 - loss: 305.8503 - val_accuracy: 0.7382 - val_loss: 465.2492\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7565 - loss: 4486.1685 - val_accuracy: 0.7658 - val_loss: 345.7629\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7761 - loss: 303.3779 - val_accuracy: 0.7325 - val_loss: 419.0219\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7534 - loss: 4471.6479 - val_accuracy: 0.7997 - val_loss: 555.1248\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7887 - loss: 363.5746 - val_accuracy: 0.7727 - val_loss: 352.2488\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7713 - loss: 962.6332 - val_accuracy: 0.7957 - val_loss: 3585.3040\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7968 - loss: 1432.0209 - val_accuracy: 0.8073 - val_loss: 341.8755\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7817 - loss: 341.2709 - val_accuracy: 0.7850 - val_loss: 299.8353\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7663 - loss: 773.9296 - val_accuracy: 0.8037 - val_loss: 2611.3101\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7940 - loss: 1140.6892 - val_accuracy: 0.7820 - val_loss: 438.4576\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7750 - loss: 350.6816 - val_accuracy: 0.7203 - val_loss: 691.7618\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7609 - loss: 4720.4707 - val_accuracy: 0.7632 - val_loss: 526.3718\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7863 - loss: 324.8257 - val_accuracy: 0.7420 - val_loss: 463.7949\n",
      "linear glorot_normal 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.6444 - loss: 178325.0781 - val_accuracy: 0.7447 - val_loss: 848.7839\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7206 - loss: 772.1750 - val_accuracy: 0.5217 - val_loss: 5949.5479\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.6624 - loss: 41121064.0000 - val_accuracy: 0.7677 - val_loss: 323883.5000\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7618 - loss: 232834.2812 - val_accuracy: 0.6560 - val_loss: 270876.0000\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7398 - loss: 156926.6719 - val_accuracy: 0.7698 - val_loss: 75316.5625\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7477 - loss: 640072.5000 - val_accuracy: 0.7948 - val_loss: 1790225.1250\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7592 - loss: 800643.6875 - val_accuracy: 0.7773 - val_loss: 227629.0781\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7612 - loss: 187598.2656 - val_accuracy: 0.7408 - val_loss: 98944.7031\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7550 - loss: 100874.4141 - val_accuracy: 0.4195 - val_loss: 297190304.0000\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7331 - loss: 23845610.0000 - val_accuracy: 0.7858 - val_loss: 355135.9688\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7744 - loss: 271891.7188 - val_accuracy: 0.7508 - val_loss: 234780.5938\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7539 - loss: 198156.3281 - val_accuracy: 0.6838 - val_loss: 223303.3594\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7486 - loss: 2243491.5000 - val_accuracy: 0.7612 - val_loss: 248694.0625\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7709 - loss: 203104.4688 - val_accuracy: 0.7735 - val_loss: 223791.6562\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7544 - loss: 678840.0625 - val_accuracy: 0.7660 - val_loss: 4931865.5000\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7846 - loss: 1416667.0000 - val_accuracy: 0.7505 - val_loss: 292736.0938\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7744 - loss: 258809.6875 - val_accuracy: 0.7667 - val_loss: 331752.1562\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7660 - loss: 924512.2500 - val_accuracy: 0.7852 - val_loss: 3591363.2500\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7857 - loss: 1646082.7500 - val_accuracy: 0.7677 - val_loss: 435639.9688\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7868 - loss: 350061.1250 - val_accuracy: 0.7253 - val_loss: 390785.9688\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7704 - loss: 331791.2812 - val_accuracy: 0.6463 - val_loss: 1676001.2500\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7414 - loss: 20450766.0000 - val_accuracy: 0.8092 - val_loss: 671389.5000\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7919 - loss: 597993.0000 - val_accuracy: 0.7880 - val_loss: 642546.0625\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7789 - loss: 365868.2812 - val_accuracy: 0.7472 - val_loss: 322521.9688\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7568 - loss: 2230313.7500 - val_accuracy: 0.7740 - val_loss: 445578.5000\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7843 - loss: 314138.3750 - val_accuracy: 0.7355 - val_loss: 428149.8438\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7591 - loss: 1042695.9375 - val_accuracy: 0.7825 - val_loss: 604852.6250\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7854 - loss: 378336.1875 - val_accuracy: 0.7940 - val_loss: 305006.6875\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7633 - loss: 8218761.5000 - val_accuracy: 0.8123 - val_loss: 1000177.3125\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7919 - loss: 663539.6875 - val_accuracy: 0.7842 - val_loss: 721659.7500\n",
      "linear glorot_uniform 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.6265 - loss: 24034.0859 - val_accuracy: 0.7470 - val_loss: 128.1668\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7064 - loss: 936024.6875 - val_accuracy: 0.7693 - val_loss: 46127.6055\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7574 - loss: 28210.4141 - val_accuracy: 0.7130 - val_loss: 20575.2930\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7440 - loss: 15864.8076 - val_accuracy: 0.7343 - val_loss: 8533.5508\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7426 - loss: 6269.1431 - val_accuracy: 0.7967 - val_loss: 8122.9492\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7094 - loss: 1814196.2500 - val_accuracy: 0.7528 - val_loss: 60962.6875\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7704 - loss: 33894.2891 - val_accuracy: 0.7053 - val_loss: 32591.0293\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7532 - loss: 23027.7891 - val_accuracy: 0.7223 - val_loss: 27004.8008\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7323 - loss: 265226.8750 - val_accuracy: 0.7933 - val_loss: 74780.6250\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7767 - loss: 48277.3633 - val_accuracy: 0.7272 - val_loss: 31530.0645\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7706 - loss: 21572.1680 - val_accuracy: 0.7723 - val_loss: 27435.3086\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7453 - loss: 557615.1250 - val_accuracy: 0.7875 - val_loss: 83976.2812\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7799 - loss: 55215.4375 - val_accuracy: 0.7642 - val_loss: 33914.0547\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7721 - loss: 25994.7305 - val_accuracy: 0.7477 - val_loss: 18457.4062\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7548 - loss: 229424.2500 - val_accuracy: 0.8203 - val_loss: 156006.2969\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7826 - loss: 89095.2188 - val_accuracy: 0.7770 - val_loss: 37703.7266\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7737 - loss: 33208.0859 - val_accuracy: 0.7422 - val_loss: 36650.7266\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7581 - loss: 320178.8125 - val_accuracy: 0.7675 - val_loss: 110951.7891\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7871 - loss: 54848.3555 - val_accuracy: 0.7450 - val_loss: 37210.1133\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7741 - loss: 30162.5000 - val_accuracy: 0.4735 - val_loss: 1564743.1250\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7359 - loss: 2782231.7500 - val_accuracy: 0.8123 - val_loss: 64358.4258\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7908 - loss: 44846.6289 - val_accuracy: 0.7793 - val_loss: 58191.0664\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7728 - loss: 39563.8711 - val_accuracy: 0.7895 - val_loss: 58275.2422\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7411 - loss: 484987.3750 - val_accuracy: 0.8080 - val_loss: 72692.5859\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7870 - loss: 48406.7188 - val_accuracy: 0.7790 - val_loss: 55480.8633\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7643 - loss: 109563.1641 - val_accuracy: 0.7690 - val_loss: 243626.8438\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7935 - loss: 113060.6406 - val_accuracy: 0.7947 - val_loss: 37722.7695\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7782 - loss: 37130.6992 - val_accuracy: 0.7197 - val_loss: 135471.8906\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7486 - loss: 586656.7500 - val_accuracy: 0.7973 - val_loss: 50851.7500\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7885 - loss: 46006.1641 - val_accuracy: 0.7575 - val_loss: 70538.0078\n",
      "linear glorot_uniform 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.6578 - loss: 194.3953 - val_accuracy: 0.7810 - val_loss: 0.9503\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7188 - loss: 6277.1064 - val_accuracy: 0.7893 - val_loss: 118.3067\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7605 - loss: 119.3483 - val_accuracy: 0.7857 - val_loss: 66.0058\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7380 - loss: 95.7589 - val_accuracy: 0.6538 - val_loss: 125.9874\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7305 - loss: 604.5133 - val_accuracy: 0.7427 - val_loss: 1389.6263\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7621 - loss: 647.8839 - val_accuracy: 0.7783 - val_loss: 180.4883\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7602 - loss: 151.5225 - val_accuracy: 0.7725 - val_loss: 108.0999\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7444 - loss: 865.0085 - val_accuracy: 0.8062 - val_loss: 2080.1064\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7809 - loss: 1080.0819 - val_accuracy: 0.6930 - val_loss: 516.3877\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7711 - loss: 312.6332 - val_accuracy: 0.8143 - val_loss: 213.0539\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7586 - loss: 494.4822 - val_accuracy: 0.7400 - val_loss: 7732.6113\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7763 - loss: 2218.9319 - val_accuracy: 0.8070 - val_loss: 285.9916\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7773 - loss: 266.9089 - val_accuracy: 0.7458 - val_loss: 325.4369\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7586 - loss: 814.2660 - val_accuracy: 0.7312 - val_loss: 1403.4452\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7860 - loss: 662.8011 - val_accuracy: 0.7802 - val_loss: 248.1195\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7777 - loss: 204.7387 - val_accuracy: 0.7870 - val_loss: 231.1467\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7548 - loss: 5048.3701 - val_accuracy: 0.7882 - val_loss: 674.8621\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7885 - loss: 430.9815 - val_accuracy: 0.8018 - val_loss: 283.8713\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7701 - loss: 289.1760 - val_accuracy: 0.7397 - val_loss: 273.0934\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7630 - loss: 1249.1406 - val_accuracy: 0.8213 - val_loss: 409.9653\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7854 - loss: 325.5088 - val_accuracy: 0.6658 - val_loss: 472.4160\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7629 - loss: 2169.7900 - val_accuracy: 0.7845 - val_loss: 1100.8328\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7981 - loss: 651.9677 - val_accuracy: 0.7832 - val_loss: 403.3407\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7782 - loss: 288.7599 - val_accuracy: 0.7715 - val_loss: 317.6161\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7516 - loss: 1480.1577 - val_accuracy: 0.7947 - val_loss: 251.5111\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7716 - loss: 886.8712 - val_accuracy: 0.7938 - val_loss: 2962.4170\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7924 - loss: 1154.1232 - val_accuracy: 0.7995 - val_loss: 359.6128\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7821 - loss: 330.5584 - val_accuracy: 0.7858 - val_loss: 418.4391\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7631 - loss: 1055.4607 - val_accuracy: 0.7778 - val_loss: 220.1417\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7635 - loss: 4013.9463 - val_accuracy: 0.7482 - val_loss: 1269.4161\n",
      "linear glorot_uniform 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caroldong/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.6309 - loss: 1578687.5000 - val_accuracy: 0.7742 - val_loss: 213974.2656\n",
      "Epoch 2/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7502 - loss: 152515.9844 - val_accuracy: 0.7365 - val_loss: 55227.2148\n",
      "Epoch 3/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7500 - loss: 53027.6680 - val_accuracy: 0.7690 - val_loss: 17605.5527\n",
      "Epoch 4/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7375 - loss: 26734.8398 - val_accuracy: 0.7478 - val_loss: 16795.5859\n",
      "Epoch 5/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7156 - loss: 10737229.0000 - val_accuracy: 0.7730 - val_loss: 397431.7500\n",
      "Epoch 6/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7750 - loss: 317983.2500 - val_accuracy: 0.7348 - val_loss: 398319.5000\n",
      "Epoch 7/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7584 - loss: 189003.7656 - val_accuracy: 0.7733 - val_loss: 97720.1953\n",
      "Epoch 8/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7525 - loss: 283138.4375 - val_accuracy: 0.7567 - val_loss: 16974768.0000\n",
      "Epoch 9/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7674 - loss: 3015073.2500 - val_accuracy: 0.7368 - val_loss: 433619.0312\n",
      "Epoch 10/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7712 - loss: 221716.4531 - val_accuracy: 0.7815 - val_loss: 123862.3750\n",
      "Epoch 11/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7625 - loss: 131494.0312 - val_accuracy: 0.7372 - val_loss: 352549.1562\n",
      "Epoch 12/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7393 - loss: 6019097.5000 - val_accuracy: 0.7863 - val_loss: 594544.1875\n",
      "Epoch 13/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7848 - loss: 436711.1250 - val_accuracy: 0.7793 - val_loss: 219726.9375\n",
      "Epoch 14/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7731 - loss: 221663.4844 - val_accuracy: 0.7398 - val_loss: 347750.6250\n",
      "Epoch 15/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7555 - loss: 1820282.8750 - val_accuracy: 0.7902 - val_loss: 1252025.1250\n",
      "Epoch 16/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7905 - loss: 677201.7500 - val_accuracy: 0.7390 - val_loss: 692085.8750\n",
      "Epoch 17/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7736 - loss: 312683.0625 - val_accuracy: 0.8082 - val_loss: 239465.4062\n",
      "Epoch 18/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7640 - loss: 2027829.2500 - val_accuracy: 0.7670 - val_loss: 1294404.7500\n",
      "Epoch 19/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7893 - loss: 623891.5000 - val_accuracy: 0.7685 - val_loss: 432617.6875\n",
      "Epoch 20/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7733 - loss: 282280.7500 - val_accuracy: 0.7402 - val_loss: 428107.0625\n",
      "Epoch 21/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7529 - loss: 5079950.5000 - val_accuracy: 0.7782 - val_loss: 1322572.5000\n",
      "Epoch 22/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7911 - loss: 820684.6875 - val_accuracy: 0.7287 - val_loss: 623631.8125\n",
      "Epoch 23/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7798 - loss: 389222.9688 - val_accuracy: 0.7767 - val_loss: 419062.7812\n",
      "Epoch 24/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7686 - loss: 666963.8125 - val_accuracy: 0.7435 - val_loss: 5232452.5000\n",
      "Epoch 25/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.7908 - loss: 1520953.5000 - val_accuracy: 0.7827 - val_loss: 395689.4375\n",
      "Epoch 26/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7846 - loss: 282812.7500 - val_accuracy: 0.7635 - val_loss: 313212.9062\n",
      "Epoch 27/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7677 - loss: 2284723.7500 - val_accuracy: 0.8205 - val_loss: 710278.1250\n",
      "Epoch 28/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7920 - loss: 489527.1562 - val_accuracy: 0.7957 - val_loss: 410680.4375\n",
      "Epoch 29/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7781 - loss: 445019.8750 - val_accuracy: 0.6458 - val_loss: 1466429.7500\n",
      "Epoch 30/30\n",
      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.7628 - loss: 4245788.0000 - val_accuracy: 0.8032 - val_loss: 478174.6875\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aeGwjb298G4DYtrG0CW3D+Pbjefz3x/lk2yfcUPMGWlRr4dAyFdmKeHnVyxgY3FTnJtqEtrnkbVrMFpoENaFJUBMeavYQB9IP8MjCRziUeYi7593NtK7TaBrctBxKf/lIyU3hrXVvsTZzLfXT6tMwuKGzi1RuDMPgldWvsC55Hd6u3rzd5e1LupraZDLhbnHH3dO9TNsxDIOcwpziUHo6mOan2V+n5aeRmpvK/sT9VK1aFYvZgslkwoQJs8lc4rkZM5jAjPmMeSWenzo7UWQrotAopNBWSJFRVPzaVkihUUiRrYgi4xyv/768UYi7xZ0OYR3oWrMrHcI6nLOFqbw1DmrMhKAJPN36aebum8s3u79h98ndzImfw5z4OUT5RzGw4UBurXfrVdmSlVGQwdrEtcWBM3EVBzMOlpjv6eJJm9A2tKvejvbV258R1oa1GMaCgwv4etfXbDy20T4CQaRfJLfXv52+UX0r/CKd/KJ8NiRvYFXiKlYlrmJHyg4MDDj1Vvzd/ekY3pFONTrRIazDRZenU41OvHTtS/xr2b/4fMfnVPWsygNNHyjHd+I8+9P3M2LxCP593b9pXLWxs4tTQpmvoBg5ciRDhgyhdevWtG3blilTppCdnW2/+n3w4MGEh4czceJEAPr06cPkyZNp0aKF/bT72LFj6dOnjz2ESun8cfgP3t/0PjHVY3i8xeNO/XaWlpfGM0ufocgoonft3vSv1x+A6yOup2/dvvwU/xPPL3ueb/p8g5er4/rpfrfnO7albMPH1YdRrUdVyD4iq0Tyee/PGbZoGNtTtnP/b/fzRqc36BzRuUL250g2w8b3e77nrfVvkVFQfKHfY4sf49NenxLhG+Hk0pWPT7d/ynd7vsNsMvN6p9epF1DPKeUwmUz2i5Vq+NY46zJWq5V58+bRu1vvK7LFraL5uvly5zV3MqDBADaf2Mw3u77h1wO/sjdtLy+ufJE1iWt4uePLl9QF6EpgLbISdzzOHtS2ntha4qpui6n4C3T7sPa0q96OZkHNcLWc+/PkbnHnpjo3cVOdm9iVuotvdn/Dz/E/cyDjAG+se4P/bPwPPSJ7MKDBAJoGNS2X/0U2w8bO1J2sSlzFyqMr2XhsI/lF+SWWCTWH0rthb26oeQNNg5qWW4ten7p9SMtP4/W1rzN1w1QC3AO4tf6t5bJtZzmUeYgHfn2AY7nHeGXF68y66ZPLqkW3zOFzwIABHD9+nHHjxpGUlETz5s2ZP38+ISHFY2UlJCSUaOl8/vnnMZlMPP/88xw5coTg4GD69OnDK6+8Un7vopJLyk7itTWvsTBhIQCbT2wG4ImWTzilPDbDxnPLnyM5J5lIv0jGtR9X4kM9pu0YViWuIiEzgSkbpvCvmH85pFwpuSlM2TAFgMdbPE6QZ1CF7SvIM4iZPWYyaukolh1ZxhOLn2Bsu7HcVv+2CttnRduVuouXVr3EpuObAGgQ0ICT6Sc5lnuMh357iE97fXpZ9P26FEsPLWXSukkAPN36aTrV6ERiei7frjuMv7cbLSL8aRDqi6vl6mstu5KZTCaig6OJDo7mmTbPMHv7N3ywZRrzD8wnKTuJqV2mXhb9s9Pz03ltzWtsOr7J3iptMVnsP0+3YpvNZnsrttlkLrFMiWVNZrKt2Ww6voncwtwS+4r0i7SHzTahbfB1872oMjcIbMDz7Z7nqVZPMXffXL7e9TW7Tu6ytzI3DGzIHQ3uoHft3mVuaDiSdYSVR4tbZ1cnriYtP63E/Gqe1WgX1o521dvRKrgVaxevpXd0xXwRu6fRPaTmpfLRlo94cdWL+Lv707VW13LfT0UpKLSxKymTzUfSWH0wnj+yJlBkSaUovxr1zI9dVsETynjBkbNkZGRQpUqVC3ZgLS/21obezm1tKLQVMmvHLKbFTSO3MBeLyUKnGp1YfGgxUPzPc0jjIQ4v1ydbP2HS+km4md2Y/X+zaRDY4IxlVhxZwcMLHwbgoxs/IqZ6TJn2cTF18Pyy5/kp/icaBjbki//7otz7uZyN1WblxZUv8uPeHwF4JPoRHou+/A7088mx5vDepvf4bPtnFBlFeLl4MbzFcG6rexvfzfuOWbZZHM46TJR/1BU9Jt6u1F0M/mUwOYU53Fb/Nsa1G8fq/akMm7WBlOwC+3IermaahFWheYQ/zWv60zzCn3B/T6fU6eXyt+hyl19YxPqDJ1m+9wTL9pxgy5F0TJ7xeNf8HMOUS3WvGrx/47vUrlL7orZfHvWwI2UHTy15iiNZRy5q/QvxtvjTsUZ7OtboQLvq7Qj1Pvt1GJfKMAw2Hd/E17u+5tcDv1JgKz52fFx9uKnOTQxoMIAA1wh+ijvKliPphPl7UCfIh9rB3gT72diZvoFVR4sv5jyUeajEtk/f+KNdWHF3gNpVatuPu4utg9yCIuKPZ7H3WBa7kzPZc6z4eXZ+IS1q+hNTuyrt6lTlmlBfTCaYsHIC3+35DjezG9O7Ty+XrlvlrchmEH88i02H0th8OJ3NR9LZkZhBQaENk0sGXrXex+yWgi0/CM+U4dzVqgmjbjzz/3RZlaYOSpvXrq6BC68gccfieGnVS+w+uRuA5sHNeb7d8zQIbMBHWz5i6oapvLnuTfzc/Lil3i0OLdfp1sUxbcecNXgCdAjvwB317+Dr3V8zdvlYvr/5e3zcfCqsXBuSN/BT/E8APNfuOYcETwBXsysvdniREK8Q3t/8PtM3TScpO4lx7ceV+Qp7Z/g94XcmrplIUnbxxS7da3VndJvRhHqHYrVa8TX78u7173L/gvvZm7aXYYuG8UH3DxzalaI8nMg9weO/P05OYQ5tQ9sS2zaWmcsP8Mq8HRTZDBqE+FLNz524Q2lk5hWy7uBJ1h08aV8/yMed5hH+tDgVRpvVqIKvx+Vfv5WVzWawMymT5XtP8OfeE6zZn0KeteTtOM25dcna9yieEZ+QyGH6/XAnt9V4nodjulPN1zF9U0/7ce+PvLzqZfKL8gn3CedfMf/C29W7eCSC8z2wFfexLbJxICWTTYfT2HzkJCey8jCZDMAATBTlRpCZH8qve1zxaBFGlKcnod4V815MJhPNqzWnebXmjG4zmp/if+LrXV+TkJnAl7u+5MtdX1KUU5uCkzEUZl2DxeMIFu89uHjvxexx5FS5T20LM7W8G9I2NIYb61xHy9Doi/67mZ1fSPzxLPYkZ7HnWBZ7TgXNQydzOFcT26/bkvl1W/EF1FU8XWkTGUjb2oNoE3yctcf/4PHfH2dmj5k0rOq8Pu+GYXAwJYdNh9PYcjidzYfT2Xo0nZyCojOW9fPOxy1iJvmmFALcQnnnxg9pGnp53rlP4fMyk5aXxpQNU/huz3cAVHGvwshWI+kX1c/ecf7+JveTkZ/BzG0zeWHlC/i6+dKtVrcKL1t6fjrP/FHcz7NXZC9ur3/7eZcf1XoUy48u50jWEd5c9yYvdHihQspVaCvk5dUvA3BrvVuJDo6ukP2ci8lkYniL4YR4h/Dyqpf5ce+PnMg9waTOky7bkHY06ygT10xkyaElAPZ/iJ1qdLIvsyI+hZ8OmDkWUMT9URN5Z+dTbDq+iaeWPMXbXd7GzXJljE+ZX5TPiMUjSMxOpJZfLV7p8Aajv9nGj3FHAejXPIyJ/Zvh6WbBZjPYdyKbuENpxB06yaZDxS0KJ7LyWbgjmYU7iv9RmUwQFexTonW0QYgvLpfp6XrDMPh1WxLv/7GPIptBy5oBtKoVQOvIAKpXuTIG1z6alsuyPSdYtvcEK+JPcCKroMT8IB93OkZVpWO9YK6Nqoq7i4X5W5P4YVM423KngOchvjo8lk/XbqJNUDf6RIfRs3EoAd4V9zkuKCpg4pqJfLu7ePSNTjU68e+O/y7V2YPCIhtrD5xk/tZEft2WTFKGAfgANXB3MdOpfjA9G4cSHeHPr9uS+HJtAodSc/l8VQKfr0qgSbgfd7apSd/mYRX2RamKexVa+fdjn6UlR44tJc9jGS6+O7B47cfTa/9Z1ynKr0ZRdhSF2VEU5dRhi82DLcBMUzI1ApZRO8ibOsHe1Anypk6wD7WDvAn1++vLQmZeIQcTs+wtmLuTM9mTnMWRtNyz7g8gwMuVeiG+1KvmU/wI8cXNxcya/ams3p/K+gOppOda/zrGTd3xrXWYbPYxZN6DvNR2Ol2jGlX48Z2VX0hSeh57j2Wx+XAaW44Uh8303DMHbvdys9AkvArRNarQtIY/darBC2ufYNfJRKp5VeO/PWeesy/55UCn3c/CGae6DMPgx70/8tb6tziZX9zi0i+qHyNbjTzrnTwMw+CFlS/w/Z7vcTW7Mq3rNNqHtT9jufIs3xO/P8GSw0uo6VuTr276qlQtmeuS1nHfr/dhYDCt67QS4eZ8ylIHn277lDfWvYG/uz8/9/sZfw//Uu2jIiw9tJSnlz5NXlEejao2YlrXaRXa97SsrDYrn2//nPc2vUduYS4uJhfubXIvDzV7yH6Hj/zCIl79ZSczlx8osa7ZIwGvWh9iMlvxt7Xm+oCniAzypXaQF7WqehMR4IWby+UVvgzDIHZZLHP3zcXXzZdJ187gpR+Osz0xA4vZxHO9GzL02vMPUp5nLWLb0XQ2JqSdCqVpHD555j86D1czTcOLT9d3rBfMdVFBmM2X1uJQHn+LVuw9wWvzd7LpcPpZ54dV8aBVZCCtavrTOjKQa0IvjxCdkWdlZXyK/VT6vhPZJeZ7uVmIqR3ItVFBXFcvmPohPuesx4STaTy5aAx7s1cAkH+8GwUnuuJiLg5xfaKr071RKD7uZ2+PuZh6SMxKZOSSkWxN2YoJE481f4yHmj103qvv8wuLWLE3hflbk1iwI5nUv3UH8Xaz0KVhCD0bh3J9g2C8/1FWm81g5b4UvliTwG/bkikoKm4J9nS1cFOz6tzZNoKWNQPKpRXsWGYeP208yrfrD7Mr+a8hj4J93enZzBP3wLUsPvIzx3KPEeQZRLvqxf02m1VtQ06uN/uOZ7P/RDb7jmed+plNZv65x8r1dLVQM9CT5JOZpBWcu/xBPm7Uq+ZLvZDikBl16nmQz/kvOCsssrH1aAar96Wwen8qa/enkmnNwqvW+1g8ErEVBMLRYbSKiCSmdiDt6gTSNNy/1H/vimwGJ7LySUrPIykjj+SMvDOeJ2fkk3WO34Gbi5lG1f1oVqMKzWr4E12jCnWCfbCc+vuSWZDJQ789xNaUrVT1qMonPT8hskpkqcpWFuV52l3h8ywcHT73nNzDy6teZsOxDUDx7efGthtLy5CW512vyFbEM388w4KDC/B08eSjGz+iWXCzCinjf7f9lzfXvYmb2Y3Pe39eptMQr699nc+2f0awZzA/9P2hVN/6S1sHydnJ3PzjzeQU5jChwwT7Vfd/t2pfCp+tOojFZMLfyxV/T1eqeLnh7+la/NrLlSqebqd+ul7yxSabj29m+KLhnMw/SbhPONO7Ta+QPwRltfHYRl5c+SJ70/YCxQPlj203lqiAKPsy8cezeHz2RrYnFl/p3qKqjeCQUBJSczmYkkOB2048Iz7BZCqi4GRb8pNuAYr/AJpNEB7gSWRVbyKrelOrqhe1g7yLg2mgJ+4ujh/d4oPNH/D2xrexmCwMa/ga034xkZZjpaq3G+8MbEn7uhc3xNLxzHw2HforjG46lHbGP8961Xx48Lo69G0RdtHv/VL+Fm0+nMYbv+7izz0ngOKwdn/H2tQL8WXDwZOsO5jKjsRMimwl/wV4uVloHuFPq1rFraMtagZQxbPi/w4WFNrYmHDSfip906E0/l40swmiI/zpGBVEx6ggWtQMKNOXHZthY8r6KczcNhMAr4IYkuP7cPoEoLuLmS7XVKNPdBhdrqmGh+tfdVbWelhxdAVj/hhDWn4aVdyr8Np1r3Ft+LVnXTanoJClu44zf1sSv+84VuJz5O/lSveGIfRsEsq1UUElynQ+qdkFfL/hMF+uPcTeY1n26fVDfBjQpib9W4SXucU3z1rEoh3H+G7DYZbuPm7/3Li5mOneKITbWtXguqgg+xeXQlshJ3JPEOIVcsHAaxgGJ7IK/gqjpwLpvhNZJKTkUPiPz2g1X3fqh/gSVc3nVNAsfh5YTq3YRTaD7Ucz+H1PPJ8lPEM+xyjKCyXn4MNgK/6S7ulqoVWtAGJqB9I6MhDDMEjKOBUoTwXLpIx8ktPzOJ6Vf8Zxdi6+7i5EBHrRrEYVmtaoQnQNf+qfaqk9mxxrDo8sfISNxzbi7+7PjB4zKmwED4XPCuao8JljzWH65ul8tu0zCo1CPF08eTT6Ue5udHep+70UFBUwfNFwViauxM/Nj//2/G+JMFEeNh3fxL2/3EuhUcjzMc8z4JoBZVo/rzCP23++nQMZB/i/Ov/Hq9e9esF1SlsHzyx9hvkH5hMdHM2nvT4t0aqQZy3i9fm7mLH87Kd/zsXbzYK/lxtVzhJOTwfWKp6u+Hme+unhShUvV3zdXex/ZA9mHOTRhY9yKPMQ/u7+vNP1HYd3BzgtPT+dt9a/Ze/K4e/uz6jWo+hbt6+9vIZh8M36w4z/aRu51iICvd149ZbG5MavtdeBYRgcy8zn2x1zeX/nixjYqGHqDSdv4mBK9ln7IJ1mNkGYvyc9G4fyUOc6Dulz99uB3xi1tHi4rc6BjzBvRSQ2A6JrVOG9u1sR5l9+p5v/frp+/cFUft6UaG/FCPZ1594OkQyKqYm/V9n+OV7M36L441lM+m0X87YU9+N1tZgYFFOLYTdEEexbsgUoO7+QTYfSWH+qj+uGhJNk5pUM0SYT1K/mS8taAbQ+daq+ZqDXBQNFbkERqTkFnMwuICW7+GdqdgEnc/76mZJ1+rWVkzkFZ/yDrhPkTcd6QVwbFUS7OlXLJQR/vetr/r363xQZRTQJbEm025P8tjWDfcf/aln1drNwY+NQ+kRXp2NUMCajqFT1YDNsfLTlI97Z+A4GBo2qNmLy9ZMJ9yl5u+H0XCu/70xm/tYklu4+XqK/ajVfd3o0DqVnk1BiagdeUiu0YRisP3iSL9YcYu6Wo/b9uFnM9GwSyp1tImhXp+o5W+gNw2DT4XS+XX+Inzclljj926KmP7e1qsFNTcOo4lVx/ycLi2wcOplLfHI6Wzeu5e6buxPk57juTIcyDzH4l8GcyD1BuEcjauQ/yfoDWZzMKd09zE+zmE0E+7gTUsWDUD93Qv08Tj33KPH8ny3a55NXmMewRcNYk7QGXzdfPr7x4wrtn6rwWcEcET4XJyxm4pqJJGYnAtAlogvPtn2W6j7Vy7ytHGsODy54kM3HN1PNsxr/7fXfcuvrkZ6fzh0/38HR7KP0iOzBG53euKjTNpuPb+aeX+7BZth46/q3LthHtTR1sPLoSh5aUHwa66ubvuKawGvs8zYmnGTUN5vs/1Bub1WDBqG+pOdaScuxkpZrJS2n4K/XOQVk5hees2N6aZhNlAikXp65JLi+TRb7seBG9+BRNK96LVX+Hlo9Xanq41YhfbIMw+DnfT/z5to37V05+tfrz1MtnyrRNSEjz8pzP2zl503FfSCvjarKW3c0J8DTcs46+H7P94xfMR6AES1HcF+T+ziemc+BlBwOnMjmQEo2B1Ny2H8im4Mp2WT/LZi6u5i5u10tHq7AELotZRv3/nIveUV5hJm6s2t78ZApd7SuwYt9m5S6BeliZeRZ+XJNAjOWHSApo/jWhV5uFu5oHcH9HWsTEVi6f55l+VuUmJ7L1IV7+Gb9YYpsBiYT3NI8nKe61y/1/mw2gz3Hsk6F0VQ2HDzJgZScM5YL8nGjZc0AGoT6kplXaA+UqadDZk7BGRcAlUZVbzeuPdWyeW29IMLL8QvC3y0/spxRS0eRbc0m0i+Sd7u+S0aWHz9vSuTnTUdL9B+s4ulK94bVSE8+RGTtOtgwUWQzsBbZTv00KLLZyC3KYkfhB5wkDoBA23WEWO/EsLmcWqZ4nUKbwcGUbKxFf/2xiQgs/mLWs0l1WkT4X3J3jbNJz7UyZ9NRvlyTwLajGfbptap6MaBNBLe1qmE/HpPS8/h+42G+W3+Y+L+F8upVPLilRTi3tqpB3eCKu4D0bJw58sOu1F0MnT+UTGsm19e4nkmdJ7P/RB6r96ewel8qcYfS8HSzEOLnTsipMBlaxaPE8yAfd/tp8vJQUFTAE4ufYPmR5Xi7evNB9w8q7MznaQqfFawiP+T/vNAjzDuM2JhYro+4/pK2m56fzr3z72Vv2l4ifCP4tNenl9zX0DAMnlj8BEsOLSHCN4Kvb/r6kq5Yn7phKh9t+YhAj0B+6PvDecfdu1AdFBQVcOucWzmQcYBBDQfxbNtni6cX2pi6aDfvLYnHZkCInzuv3tqMGxpUu2D5imwGmXnnCqdW0nILSD81L/0fj4LCc/yjNeXjWWM2Lj67MAwT+Un9sKaVHHbKZIKbmoXxRJco6oVc3Fh8/7QvbR8vrXqJdcnrgHN35diQcJInvtjI4ZO5WMwmRt1Yn0c61cVsNl2wDk4PuQUwvv34c45xahgGx7Py2XQonXeX7GVjQhpQHEIHxdTikc51qOZXfiE0OTuZgXMHciz3GO4FjTgRPwhXiwvj+zRmUExNh175WVBoY+6Wo3zwx352nOrKYDZBr6bVeei6OkRH+J93/dL8LTqZXcB7S+P5ZMUB++ewW8NqPN2jAdeEXvrfy+OZ+WxIOMn6g8WPLYfT7f0JL8TNYibA25UALzcCvf96nH4d4O1G4N/mVfN1r5DgdTa7UncxbNEwknOSCfQIZOoNU2lerTmGYbDxUBpz4o4yd0sixzPzL7gts/tRPGvMwuyWgmFzIT+pL9b08w/RU6+aDz2bFLdwNqru59DP5ZbD6XyxNoE5cUftLfQWs4mu11Qjr9DGsj3H7V0ePFzN9Gwcyq2tatChblC5BqiycPawY+uT1/PwgofJL8qnb92+vHTtS067itxqszJqySgWH1qMp4sn73V7j1YhrSp+vwqfFasiPuRWm5VPt33K+5vft1/oMbjxYB5u9nC5XRF9LOcYg38ZzJGsI9QPqM+MHjMuaUzGz7Z/xutrX8fV7MrnvT+nUdVGl1S+gqIC7px7J3tO7qF7re5M6jzpnAfvhergw80f8p+N/yHIM4g5/ebg6+bL9qMZjPw6jp1JxR3g+zUPY8LNTSr0lNBpedYiMv4RSDPyrKTnWDmZk8/ilHfZn188PmvVwv/DPbMXGbmFpOda7aerTSbo3bQ6T3SpR4PQiwuheYV5fLD5A2Zum0mhrRAPiwePNn+UexrdU6Irh81m8N7SeCYv2E2RzSAi0JOpd7agZc2/Lm4rzXFw+guFCRNvdH6DHpE9zls+wzD4c88JpizczYYKCKG5hbncO/9etqdsh4IQMvc/SrB3Fabf3ZJWtZw3yLhhGCzbe4IP/thn74MJ0LZ2IA93qsMNDaqdNXSdrw6y8wuZuXw/7y/dZ+8n2DYykNE9G9A6suLe6+kLsNYdOMnB1Bz8PV3PCJRVT/30drNclsO8nHYs5xjDFw1nR+oO3Mxu/Pu6f5f4DBfZDFbvT2HR9iT2xO+nXt3auLq44GI24WIx4WI2EZ+7lCUp71FoFFDFNYT+4bFEeNc/Nd+Mi9mExWzC1WLGcmq96lU8qR1UQWMhlUF2fiFztyTy5ZoE+/F4WpvIAG5rVYPeTatfFsOKOTt8QvEZy6eWPEWRUcTQJkMZ2Wqkw8tQZCvi2T+fZf6B+biZ3ZjWbRrtqrdzyL4VPitYeX/I1yWt4+VVLxOfHg9Aq5BWPB/zfLn3zQQ4lHGIwfOL+6c0D27O+93fv6hwu+X4FgbPH0yhrZB/xfyLu665q1zKtyNlBwPnDqTQKOS1616jd53eZ13ufHVwJOsI/X7sR15RHq9e9yo9avXi/T/2MWXhbqxFBoHebrzSrwm9mpa9C0NFMQyD6Zum8+6mdwG4ue7NvNDhBVzNrmw7ms5/Fu2xjzcH0KtJKE90rUfD6uf+vGcVZBGfHs++tH3Ep8UTnx7P9pTtpOalAtC5RmdiY2LP6G+WnJHHyK/jWL43BYA+0WG8cksT/P7xD6Y0x4FhGLy06iW+2f0NLmYXpnWZRofwDqX6fSzbe4K3FpQMoQNjavJo57oXFUJtho1RS0axMGEhRqE32Qceo2VYFO8NalmuLauXakdiBh/+uY85cUftF1LUDfbmwevq0K9F+AUvdCkotPHFmgTe/n0vJ7KKW+UaVvdjdM8GXF8/+LIOe5ejHGsOY/4Yw5LDS4C/upH8/fd41nooKuD1ta/z1a6vALg2/Fpe7fiqU0fbuBS7kjL5Ke4I7i4W+jYPI/IyCMd/dzmETyges3Xs8rEAjGo1inub3OuwfdsMG2OXj2VO/BxczC5MvWFqqUeQKQ8aZP4K8vdTkwHuAYxqPYqb695cYf8gIvwieL/7+9w7/17ijscxcslI3u7y9nnv4/tPGQUZPPPHMxTaCuleqzt3Nriz3MrXsGpDHop+iHfj3uWV1a/QOrQ11bwufEr8715d8yp5RXm0DW1Lfe9O3Dp9JZsOpQFwY6MQ/t2/6QWH1nA0k8nEo80fJcQ7hBdXvsic+Dmk5KYw6fpJNA6rwvv3tGZHYgZv/76HeVuS+GVr8aNH4xDu7xSKm+cJ4tPi2Zu2l33pxWEzOSf5rPsK8QohNiaWLhFdzvic/b4zmae/2UxqdgGerhZe7NuY21rVuOjPo8lk4rmY58goyODXA78yYskIPuj+Ac2rNb/getfVC6ZjVBDL9p5gysI9rD94kpnLDzB7dcJFhdC31r1dHDwNC7mH72ZQqxaMu6nxZTf8U8Pqfky+oznP9GjAJ6feb/zxbJ79fgtv/raLIe0jubtdrTOuRi6yGczZdITJC3ZzKLW4T2LNQC9G3VifPs3CHHa6urLxcvViyg1TeH3t68zeOZspG6ZwKPMQz7V77pwXfiZlJzFqySg2n9iMCROPRD/Cw80edtjNLSpCg1BfRve85sILXuX6RfXjZN5JJq+fzKT1k/D38KdfVL8K369hGLy86mXmxM/BYrLwZqc3HRo8y5vCZwU6kH6AqRunAsUXeoxsNdIhtyasH1Cfd7u+y0MLHmL50eXELovlteteK/GHsbDIRq61CLPJVOLqOsMwGLd8HEeyjlDDpwYTOkwo96D8QNMHWHJoCdtTtjNh5QTe6fJOqfex5NASlhxagovJhQaug7np7WXkF9rw9XBhws2NuaVF+GXd8tO/Xn+CPIN4eunTLD+6nKHzh/Jut3cJ8gwiLNDg/m7QsH4q/9u5kYTM/SzLT2bFksxzbq+aZzXq+Nehrn9d6lQp/tkkqAnulpLh+59jdzaq7sfbA1uUy0UDFrOFiR0nkmXNYvmR5Ty26DFm9ph5zrtf/d3fQ+jyvSm8tXC3PYTOWp3AwLY1efT6uoRcIIR+vPE7Ptn+EQCFybcysXdf7mgTccnvrSJVr+JJbO+GDO8SxVdrDzFj2X6OpucxacFu3l0Szx2tazC4XQSGAb/vOs5bC/fau5QE+7rzRNd6DGgdcdmF6yuRxWwhNiaWmn41eW3Na3y35zsSsxN5s/ObZ9wTfXXiakb/MZrUvFT83PyYeN3EKzoESNkNbTKU1LxUPtn2CS+seAF/d/9Lvm7jfAzD4PW1r/PN7m8wYeLfHf99Rd13/mx02v0syqN53zAMHl30KMuPLOfa8Gt5r+t75RKKth5J58CpYW3yrEXkFhSRaz31KPjrdZ61iGOFm9lveRtMRbjndsCceht5BTbyrLYSFwz4ebgQHuBFuL8HuZ5L2Zz7XywmF8a2fJfrarYgyKf8LwLYe3Ivd/zvjuJ7o3d48YxbhJ6tDnILc7nlp1s4knWEqoU9ObDnegCuqxfE67c1u2Lu1AKw9cRWhi0aRmpeKoEegZgwkZKXcs7lbdYq2PKrEe5dm5saNuf62k2pXaV2qb7M/HPszvuurc2YXg0uOPZkWY+DHGsODy94mLjjcQR5BvFpz0+J8CtbADQMg+V7U5iycLf99pZuLmYGtq3JI53rElrlzBD6/urfeXv7KEzmQlwzuzKj7ws0v8CFPJcja5GNeVsSeX/pPntdmU0Q5G5wLK/4+PP1cOGRznUZem0kXm5qO6gIixMWM+bPMeQW5hLlH8W7Xd8lyD2IuXPncizyGNM2T8Nm2GgY2JDJ10++rO8iU5lcLqfdTzMMg7HLx/JT/E+4W9wZ1XoUHcM6UsP34s8knWs/UzdM5eOtHwOc9f+lo6jPZwUrjw/54oTFPLH4CVzMLvxw8w/lMsj4e0vieW3+zjKt4+K7GY/wLzCZDPJPXE/B8Z7nXNbscRivyPcwmYrIS+qD9WTxoMhuFjNh/h6E+XsS7u9Z/DPAkxqnnlf397ioQbRnbp3J5PWT8Xb15vubvyfMJ8w+72x18PaGt/lgywcYVn+y4kfi5erJc//XkIFtHXsFc3k5lHGIRxY+QkJmgn1amHdYcUtmlbrU9S9+UBDCzGWJ/BR3xH4F6vUNgnmyaz1a/O0CoX8629idb97ejC7XhJSqfBdzHKTnp3Pfr/ex++Ruwn3C+bTXp2XuVnG67CviU3hrwblDaJHN4KX5y/km8RnMLln4FjXnx9s/oJrvlfMl5GwMw2BlfArv/7GPpbuPA8X9Ye+9NpJHO9ct81ihUnbbUrYxfNFwTuSeIMgziJfbv8zUP6eyw7oDKD71+lzMc3i4XD59iSu7yy18QvFA+iMWj2Dp4aX2aWHeYbSt3paY6jHEhMYQ7BV8Sft4b9N7vBtXfK3AczHPcec15dcNrqwUPivYpX7I8wrz6PdTP45kHeH+JvczotWISy7T+0vjmfhLcfBsWdOfKp6ueLpZ8HR1wdPNjKerBU9XCx5uFvtzz1PP15/8hdnxkwEYcs0wBjYYgpebBQ9XC4U2g6Npuew5cZyJmx4i3ZpMsLk1AVkPkJhWfJeG0tyYoZqvO2H+ntQJ9qZ+iC/1T911Itzf85ytpkW2Invf1JjqMXzQ/QP7IPH/rIN1R3Zx34I7MUyF5B6+m5ZVO/Hm7dHUrHp53ju9tLIKslibtJZgr2BqV6mNt+u5O/nvO57FO4v38lPcUftg3NfVC2JEt3pnXMl9rrE7y9KH8mKPgxO5Jxj8y2AOZR4iyj+KT3p+ctHdTU6H0CkLd7P2wF8h9K42EexLTWF9wUtYPJLxt0Tyv9u+oIqHY8cerGjbDqfy+S/LeKz/DURULZ9huKR0ErMSGfb7MPac3GOf5mp25V8x/+LWerdekV94r2SXY/iE4v/3n+/4nD8P/8nmE5sptJW8SUPdKnXtYbRNaBv83EqfYU430AA83fpphjQeUq5lLyuFzwp2qR/y099UqnlV4+d+P1/yUEof/rGPV+YVf+Me2b0+T3Qt+62zZmydwVvr3wLghfYvcGv9W+3zDMNg1NJRLDi4gHCfcL7u87X9ALEW2UhKz+NoWi5H0nI5cjKXo+m5HD6Za592vsGkvdws1KvmQ73TgTTEl/ohvoRV8cBkMnEw4yC3zbmNvKK8Et/qTtdBr169mLv1GOPWPAmeuynKbsBTTV7j/o51rtoLLA6cyGba4r18v/GIPYR2jAriia71aFs78Lxjd5bFpRwHhzMPM+SXIRzLPUaz4GZ82P3DSzoOTrcGTlm4hzUHUgEbnjU+xcV3Jz4uAfzQ72tCvUMvevuXq8v1H+7VIqsgi1FLR7Hi6AqqmKrwzo3v0Dy0ubOLdVW6Eo6FHGsOG45tYE3iGlYlrmJn6k4M/opYZpOZRoGN7GG0RbUWeLqc/UzNrB2zeHVN8d0AH2/xOA81e8gh7+F8dLX7ZexI1hE+3lLcN+OZ1s9ccvD86M+/gueIbvUuKngC3NfkPtLz05mxdQYvrnoRXzdfboy8EYAvdn7BgoMLcDG78GbnN0t8M3O1mIkI9DrnHVIMwyA1u4CjaXkcOpnD3mNZ7E7OZE9yFvtOZJFTUMSmw+lsOpxeYj0fdxeiqvlQP8SHGP97WJryIW+um0T76u2pVaUWAFlWePzLTSw6tBDPGrsxGS683+slrqtd96J+B5VFZJA3b9wezeNd6vHukr18u/4wy/aeYNneE0TXqMLWoxnnHLvTUWr41igedeHXe9l8fDMjFo/gna7v4GYp3SljwzDItmaTkpdCSm5K8c/CFDrFnCC4diLrj+4k27wbV7MbH9w4rVIGT3E+HzcfpnWdxorDKzi6/iiNqzZ2dpHkMubl6kXH8I50DO8IQFpeGmuT17I6cTWrE1dzIOMAW1O2sjVlKzO2zsDV7Erzas2JCY0hpnoMjYMa42p25dvd39qD54NNH7wsgmd5U/gsZ2+sfYP8onzahra94IDbFzJz+X5enlscPJ/oEsWIbvUvaXsjWo4gPT+d7/Z8x5g/x+Dj5oO/uz9vrnsTgJGtRtIkqEmZtmkymajq405VH3ea1ih5atVaZONgSja7k/8KpLuTM9l/Ipus/ELiDqURdygNqI1nzTrgvY+bvnycqKJnqBvky/zNFrJsh/Cp8z8AHo5+kOtqV9x9a680Nat68eqtzRh2QxTvLonn2/WH7CH/XGN3OlJUQPHFGg/89gArE1fy7J/P8ny75zmZd5KU3BRS81JLhsvcv56n5qWSX3SeO8ucusD73x1foWlwU8e8IbkquZhdaF+9PfPM85xdFLnC+Hv4071Wd7rX6g4UD9G1NmktqxJXsTpxNck5yaxNWsvapLW8E/cOXi5eNA1uyprENQAMbjSYx1s87sy3UGEUPsvR8iPLWZSwCIvJQmzb2EvqE/TfFQeY8PN2AIbfEMVT3S8teEJxUBzbbiyZBZn8dvA3Riwegb+7P1ablRsibuDuhndf8j7+ztViJqqaL1HVfOn9twHfCwptHEjJZndyJruTs9iTnMmO44M54flvTB772Zr8PzYmdAJMhEYuJds1gwjfCB5odn+5lq+yiAj0YmL/pgzvEsWsVQdpEOrLzdFhl0WftGbBzZh6w1SGLRrGgoMLWHBwQZnW93LxoqpnVQI9AqnqUZWqnqceHlVpGtSUxkFqiRKRK0Oodyh96vahT90+GIbBwYyDrEkqPkW/NmktaflprE5cDcCABgN4uvXTl8Xf8Yqg8FlOrEVWezP5wIYDL+nuRZ+uPMD4OdsAeOz6uoy6sX65fQAtZguvXvcqWdYsVhxdQW5hLmHeYQ69T62bi/nURUl/v4CiFV/uLOKV1S/hE7qA3g27cfzoftZ4LgUDYtvGnjF2pZQU7u95WQ4S3T6sPW90eoOxy8eSac3Ez83PHiDPCJYeVQn0/Ov1ufpDiYhcyUwmE5FVIomsEskdDe7AZtjYlbqLNUlrcDW7cuc1d1ba4AkKn+Xmsx2fcSDjAFU9qvJo9KMXvZ3PVx1k3E/FwfORznV5pkeDcv8Aulpceev6t3ji9yfYkbqDNzq/4ZDB7y9kQIPbWXL4d5YfWc5ePiLbO5uioiK61ezGdTWuc3bx5BJ0rdWV62pchwlTme62JSJyNTCbzDSs2pCGVa+OrmUKn+UgOTuZ6ZumAzCy9cgz7ohRWrNXJ/D8j1sBeKhTHcb0LP/geZqXqxcf3vghBbaCy6ZF0WQyMaH9BG6ZcwvbU4u7HHhYPBjTdoyTSyblobQXG4mISOWm+7KVg0nrJ5FbmEvz4ObcVOemi9rGl2sS+NcPWwB4oGNtYntdU+FN7iaT6bIJnqeFeIcQ2zbW/vqhpg/pSmYREZFKRC2fl2ht0lp+2f8LJkz8K+Zf9kHSy+LrtYd49vvi4HnftbV57v8aVuq+HhdyU52b2JO6h7i9cQxqMMjZxREREZFypPB5CQpthfx79b8BuKPBHRfVV+ObdYcY8/1mAO7tEMnYm67u4AnFLbKPN3+ceUfnqX+giIhIJaPT7pfgq11fsTdtL/7u/hc1Ftd36w8z+rvNGAYMaV+L8X0aXfXBU0RERCo3hc+LdCL3BO9sfAeAJ1o+UearxX/YeJinv92EYcDd7Wryws2NFTxFRESk0lP4vEhTN0wly5pFo6qN6B/Vv0zr/hR3hFFfFwfPQTE1efHmJgqeIiIiclVQ+LwIm45v4se9PwLwr5h/YTFbSr3unE1HeeqrOGwG3NW2Ji/1bYLZrOApIiIiVweFzzIqshXxyqpXAOgX1Y/o4OhSr/vzpqOM+HIjNgPubBPBK/0UPEVEROTqovBZRt/t+Y4dqTvwdfXlyZZPlnq9uZsTGXGqxfOO1jX49y1NFTxFRETkqqPwWQZpeWn8Z+N/ABjWYhhBnkGlWu+XLYk88eVGimwGt7Wqwav9myl4ioiIyFVJ43yWwdsb3yY9P50o/ygGNBhQYl5+YRHHM/NJzsjnWEYexzLzSc7IIykjjzlxRymyGfRvGc5rtyp4ioiIyNVL4fMCTofK1Uc2883ubwCoY7qbZ7/bRnJG3qnAmcfJHOt5t3NLi3DeuC0ai4KniIiIXMUUPv/hYEo2z/2whfijFl7YtPhUqLThVWs6Fi8Da3o03+3wAA6fsa6bxUw1P3eq+boT4udBiJ8Hwb7u1A32oXujEAVPERERueopfP6DxWxi2d4UwAQUt2Z6BMRh8UrAZLjTIeBeakZWJ/hUwPwraLpTxdNV43WKiIiInIfC5z9U8/Xgtf6NObBjMzd17UgVX4NB818nNQ9GtH6M+5p0c3YRRURERK5Yutr9H9xczPRvEc41/gb1Q3yZtesjUvNSifSL5J6G9zi7eCIiIiJXNIXP89ibtpcvdn4BQGzbWFwtrk4ukYiIiMiVTeHzHAzD4PV1r1NkFNG1Zlc6hHdwdpFERERErnjq83kOW61bWZe+DneLO8+0ecbZxRERERGpFNTyeRY51hx+yf0FgAeaPkC4T7iTSyQiIiJSOSh8nsXH2z4mw8ighk8NhjYZ6uziiIiIiFQaCp//cCD9AJ/t/AyAUS1H4W5xd3KJRERERCoP9fn8h0JbIdcEXENBegGdwjs5uzgiIiIilYpaPv8hKiCKT278hNu9btfdikRERETKmcLnWZhNZjzNns4uhoiIiEilo/ApIiIiIg6j8CkiIiIiDqPwKSIiIiIOo/ApIiIiIg6j8CkiIiIiDqPwKSIiIiIOo/ApIiIiIg6j8CkiIiIiDqPwKSIiIiIOo/ApIiIiIg6j8CkiIiIiDqPwKSIiIiIOo/ApIiIiIg6j8CkiIiIiDqPwKSIiIiIOo/ApIiIiIg6j8CkiIiIiDqPwKSIiIiIOo/ApIiIiIg6j8CkiIiIiDnNR4XPatGlERkbi4eFBTEwMa9asOe/yaWlpDBs2jOrVq+Pu7k79+vWZN2/eRRVYRERERK5cLmVd4auvvmLkyJFMnz6dmJgYpkyZQo8ePdi1axfVqlU7Y/mCggK6d+9OtWrV+PbbbwkPD+fgwYP4+/uXR/lFRERE5ApS5vA5efJkHnzwQYYOHQrA9OnTmTt3LjNmzODZZ589Y/kZM2aQmprKihUrcHV1BSAyMvLSSi0iIiIiV6Qyhc+CggLWr19PbGysfZrZbKZbt26sXLnyrOvMmTOH9u3bM2zYMH766SeCg4MZOHAgY8aMwWKxnHWd/Px88vPz7a8zMjIAsFqtWK3WshT5opzehyP2JWenOnA+1YHzqQ4uD6oH51MdOF9p6qC09VOm8HnixAmKiooICQkpMT0kJISdO3eedZ19+/bx+++/M2jQIObNm8fevXt57LHHsFqtjB8//qzrTJw4kQkTJpwx/bfffsPLy6ssRb4kCxYscNi+5OxUB86nOnA+1cHlQfXgfKoD5ztfHeTk5JRqG2U+7V5WNpuNatWq8cEHH2CxWGjVqhVHjhzhjTfeOGf4jI2NZeTIkfbXGRkZREREcOONN+Ln51fRRcZqtbJgwQK6d+9u7yogjqU6cD7VgfOpDi4PqgfnUx04X2nq4PSZ6gspU/gMCgrCYrGQnJxcYnpycjKhoaFnXad69eq4urqWOMXesGFDkpKSKCgowM3N7Yx13N3dcXd3P2O6q6urQz90jt6fnEl14HyqA+dTHVweVA/OpzpwvvPVQWnrpkxDLbm5udGqVSsWLVpkn2az2Vi0aBHt27c/6zrXXnste/fuxWaz2aft3r2b6tWrnzV4ioiIiEjlVeZxPkeOHMmHH37If//7X3bs2MGjjz5Kdna2/er3wYMHl7gg6dFHHyU1NZUnn3yS3bt3M3fuXP79738zbNiw8nsXIiIiInJFKHOfzwEDBnD8+HHGjRtHUlISzZs3Z/78+faLkBISEjCb/8q0ERER/Prrrzz11FM0a9aM8PBwnnzyScaMGVN+70JERERErggXdcHR8OHDGT58+FnnLVmy5Ixp7du3Z9WqVRezKxERERGpRHRvdxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcZiLCp/Tpk0jMjISDw8PYmJiWLNmTanW+/LLLzGZTPTr1+9idisiIiIiV7gyh8+vvvqKkSNHMn78eDZs2EB0dDQ9evTg2LFj513vwIEDPP3001x33XUXXVgRERERubKVOXxOnjyZBx98kKFDh9KoUSOmT5+Ol5cXM2bMOOc6RUVFDBo0iAkTJlCnTp1LKrCIiIiIXLlcyrJwQUEB69evJzY21j7NbDbTrVs3Vq5cec71XnzxRapVq8b999/Pn3/+ecH95Ofnk5+fb3+dkZEBgNVqxWq1lqXIF+X0PhyxLzk71YHzqQ6cT3VweVA9OJ/qwPlKUwelrZ8yhc8TJ05QVFRESEhIiekhISHs3LnzrOssW7aMjz/+mLi4uFLvZ+LEiUyYMOGM6b/99hteXl5lKfIlWbBggcP2JWenOnA+1YHzqQ4uD6oH51MdON/56iAnJ6dU2yhT+CyrzMxM7rnnHj788EOCgoJKvV5sbCwjR460v87IyCAiIoIbb7wRPz+/iihqCVarlQULFtC9e3dcXV0rfH9yJtWB86kOnE91cHlQPTif6sD5SlMHp89UX0iZwmdQUBAWi4Xk5OQS05OTkwkNDT1j+fj4eA4cOECfPn3s02w2W/GOXVzYtWsXdevWPWM9d3d33N3dz5ju6urq0A+do/cnZ1IdOJ/qwPlUB5cH1YPzqQ6c73x1UNq6KdMFR25ubrRq1YpFixbZp9lsNhYtWkT79u3PWP6aa65hy5YtxMXF2R8333wzN9xwA3FxcURERJRl9yIiIiJyhSvzafeRI0cyZMgQWrduTdu2bZkyZQrZ2dkMHToUgMGDBxMeHs7EiRPx8PCgSZMmJdb39/cHOGO6iIiIiFR+ZQ6fAwYM4Pjx44wbN46kpCSaN2/O/Pnz7RchJSQkYDbrxkkiIiIicqaLuuBo+PDhDB8+/KzzlixZct51P/nkk4vZpYiIiIhUAmqiFBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh1H4FBERERGHUfgUEREREYdR+BQRERERh3FxdgHKi81mo6CgoFy2ZbVacXFxIS8vj6KionLZppRNRdWBm5sbZrO+c4mIiDhLpQifBQUF7N+/H5vNVi7bMwyD0NBQDh06hMlkKpdtStlUVB2YzWZq166Nm5tbuW1TRERESu+KD5+GYZCYmIjFYiEiIqJcWrVsNhtZWVn4+PiolcxJKqIObDYbR48eJTExkZo1a+qLhYiIiBNc8eGzsLCQnJwcwsLC8PLyKpdtnj6F7+HhofDpJBVVB8HBwRw9epTCwkJcXV3LbbsiIiJSOld8sjrdH1CnUaU0Tn9O1JdXRETEOa748HmaTqFKaehzIiIi4lyVJnyKiIiIyOVP4VNEREREHEbhU0REREQcRuFT7KxWq7OLICIiIpWcwqcTzZ8/n44dO+Lv70/VqlW56aabiI+Pt88/fPgwd911F4GBgXh7e9O6dWtWr15tn//zzz/Tpk0bPDw8CAoK4pZbbrHPM5lM/PjjjyX25+/vzyeffALAgQMHMJlMfPXVV3Tu3BkPDw9mzZpFSkoKd911F+Hh4Xh5edG0aVO++OKLEtux2Wy8/vrrREVF4e7uTs2aNXnllVcA6NKlC8OHDy+x/PHjx3Fzc2PRokXl8WsTERGRK9gVP87nPxmGQa710obRsdls5BYU4VJQWKYxJj1dLWW6mjo7O5uRI0fSrFkzsrKyGDduHLfccgtxcXHk5OTQuXNnwsPDmTNnDqGhoWzYsMF+F6e5c+dyyy238Nxzz/Hpp59SUFDAvHnzyvxen332WSZNmkSLFi3w8PAgLy+PVq1aMWbMGPz8/Jg7dy733HMPdevWpW3btgDExsby4Ycf8tZbb9GxY0cSExPZuXMnAA888ADDhw9n0qRJuLu7A/D5558THh5Oly5dylw+ERERqVwqXfjMtRbRaNyvTtn39hd74OVW+l/prbfeWuL1jBkzCA4OZvv27axYsYLjx4+zdu1aAgMDAYiKirIv+8orr3DnnXcyYcIE+7To6Ogyl3nEiBH079+/xLSnn37a/vzxxx/n119/5euvv6Zt27ZkZmYydepU3nnnHYYMGQJA3bp16dixIwD9+/dn+PDh/PTTT9xxxx0AfPLJJ9x7770a5khERER02t2Z9uzZw1133UWdOnXw8/MjMjISgISEBOLi4mjRooU9eP5TXFwcXbt2veQytG7dusTroqIiXnrpJZo2bUpgYCA+Pj78+uuvJCQkALBjxw7y8/PPuW8PDw/uueceZsyYAcCGDRvYunUr99577yWXVURERK58la7l09PVwvYXe1zSNmw2G5kZmfj6+Zb5tHtZ9OnTh1q1avHhhx8SFhaGzWajSZMmFBQU4Onpef59XWC+yWTCMIwS0852QZG3t3eJ12+88QZTp05lypQpNG3aFG9vb0aMGEFBQUGp9gvFp96bN2/O4cOHmTlzJl26dKFWrVoXXE9EREQqv4tq+Zw2bRqRkZF4eHgQExPDmjVrzrnshx9+yHXXXUdAQAABAQF069btvMtfKpPJhJebyyU/PN0sZV6nLKeVU1JS2LVrF88//zxdu3alYcOGnDx50j6/WbNmxMXFkZqaetb1mzVrdt4LeIKDg0lMTLS/3rNnDzk5ORcs1/Lly+nbty9333030dHR1KlTh927d9vn16tXD09Pz/Puu2nTprRu3ZoPP/yQ2bNnc999911wvyIiInJ1KHP4/Oqrrxg5ciTjx49nw4YNREdH06NHD44dO3bW5ZcsWcJdd93F4sWLWblyJREREdx4440cOXLkkgt/JQsICKBq1ap88MEH7N27l99//52RI0fa5991112EhobSr18/li9fzr59+/juu+9YuXIlAOPHj+eLL75g/Pjx7Nixgy1btvDaa6/Z1+/SpQvvvPMOGzduZN26dTzyyCO4urpesFz16tVjwYIFrFixgh07dvDwww+TnJxsn+/h4cGYMWMYPXo0n376KfHx8axatYqPP/64xHYeeOABXn31VQzDKHEVvoiIiFzdyhw+J0+ezIMPPsjQoUNp1KgR06dPx8vLy97H759mzZrFY489RvPmzbnmmmv46KOPsNlsV/2wO2azmS+//JL169fTpEkTnnrqKd544w37fDc3N3777TeqVatG7969adq0Ka+++ioWS/Gp/euvv55vvvmGOXPm0Lx5c7p06VKiRXnSpElERERw3XXXMXDgQJ5++mm8vLwuWK7nn3+eli1b0qNHD66//np7AP67sWPHMmrUKMaNG0fDhg0ZMGDAGV8+7rrrLlxcXLjrrrvw8PC4hN+UiIiIVCZl6vNZUFDA+vXriY2NtU8zm81069bN3iJ3ITk5OVit1nNeSAOQn59Pfn6+/XVGRgZQ3Gfxn/0WrVYrhmFgs9nswxBdqtN9JU9vt6J06dKFrVu3lphWVFQ8TJTNZiMiIoKvv/76jPVOl6lfv35nBMPT80JDQ/nll19KzDt9Ct9ms1GzZs0S+zrN39+f77///qzl/ftysbGxJT4H/5x/7Ngx8vLyGDp06EX9DiuqDmw2G4ZhYLVa7UFezu70saabDziP6uDyoHpwPtWB85WmDkpbP2UKnydOnKCoqIiQkJAS00NCQuzjPF7ImDFjCAsLo1u3budcZuLEiSWGEDrtt99+O6P1zsXFhdDQULKysuwXxZSXzMzMct3e1cBqtZKamsrYsWNp3bo1UVFR9i8PF6O866CgoIDc3Fz++OMPCgsLy3XbldWCBQucXYSrnurg8qB6cD7VgfOdrw5Kc20JOPhq91dffZUvv/ySJUuWnPdUbGxsbIn+jxkZGfa+on5+fiWWzcvL49ChQ/j4+JTb6V3DMMjMzMTX11djU5bRkiVL6Nq1K/Xr1+frr78+o75Kq6LqIC8vD09PTzp16qTuABdgtVpZsGAB3bt3L1V/YSl/qoPLg+rB+VQHzleaOihtY1OZwmdQUBAWi6XEBSgAycnJhIaGnnfdN998k1dffZWFCxfSrFmz8y7r7u5uvzvO37m6up7xhouKijCZTJjN5jINi3Q+p0/znt6ulF6XLl3OGOLpYlRUHZjNZkwm01k/S3J2+l05n+rg8qB6cD7VgfOdrw5KWzdl+q/u5uZGq1atSlwsdPriofbt259zvddff52XXnqJ+fPnnzGouYiIiIhcPcp82n3kyJEMGTKE1q1b07ZtW6ZMmUJ2djZDhw4FYPDgwYSHhzNx4kQAXnvtNcaNG8fs2bOJjIwkKSkJAB8fH3x8fMrxrYiIiIjI5a7M4XPAgAEcP36ccePGkZSURPPmzZk/f779IqSEhIQSp0nfe+89CgoKuO2220psZ/z48bzwwguXVnoRERERuaJc1AVHw4cPZ/jw4Wedt2TJkhKvDxw4cDG7EBEREZFKSFfTiIiIiIjDKHyKiIiIiMMofDrJ9ddfz4gRI5xdDBERERGHUvgUEREREYdR+BQRERERh1H4vAycPHmSwYMHExAQgJeXF7169WLPnj32+QcPHqRPnz4EBATg7e1N48aNmTdvnn3dQYMGERwcjKenJ/Xq1WPmzJnOeisiIiIi5+XQe7s7hGGAtXQ3tj8nm614GwUWKMutHV294CLuQ37vvfeyZ88e5syZg5+fH2PGjKF3795s374dV1dXhg0bRkFBAX/88Qfe3t5s377dPkD/2LFj2b59O7/88gtBQUHs3buX3NzcMpdBRERExBEqX/i05sC/wy5pE2bA/2JW/NdRcPMu0yqnQ+fy5cvp0KEDALNmzSIiIoIff/yR22+/nYSEBG699VaaNm0KQJ06dezrJyQk0KJFC/ttSyMjIy+m5CIiIiIOodPuTrZjxw5cXFyIiYmxT6tatSoNGjRgx44dADzxxBO8/PLLXHvttYwfP57Nmzfbl3300Uf58ssvad68OaNHj2bFihUOfw8iIiIipVX5Wj5dvYpbIC+BzWYjIzMTP1/fErcKLdW+K8ADDzxAjx49mDt3Lr/99hsTJ05k0qRJPP744/Tq1YuDBw8yb948FixYQNeuXRk2bBhvvvlmhZRFRERE5FJUvpZPk6n41PelPly9yr7ORfT3bNiwIYWFhaxevdo+LSUlhV27dtGoUSP7tIiICB555BG+//57Ro0axYcffmifFxwczJAhQ/j888+ZMmUKH3zwwaX9DkVEREQqSOVr+bzC1KtXj759+/Lggw/y/vvv4+vry7PPPkt4eDh9+/YFYMSIEfTq1Yv69etz8uRJFi9eTMOGDQEYN24crVq1onHjxuTn5/O///3PPk9ERETkclP5Wj6vQDNnzqRVq1bcdNNNtG/fHsMwmDdvHq6urgAUFRUxbNgwGjZsSM+ePalfvz7vvvsuAG5ubsTGxtKsWTM6deqExWLhyy+/dObbERERETkntXw6yZIlS+zPAwIC+PTTT8+57Ntvv33Oec8//zzPP/98eRZNREREpMKo5VNEREREHEbhU0REREQcRuFTRERERBxG4VNEREREHEbhU0REREQcRuFTRERERBxG4VNEREREHEbhU0REREQcRuFTRERERBxG4fMKFhkZyZQpU5xdDBEREZFSU/gUEREREYdR+BSnKCoqwmazObsYIiIi4mAKn07ywQcfEBYWdkYA69u3L/fddx/x8fH07duXkJAQfHx8aNOmDQsXLrzo/U2ePJmmTZvi7e1NREQEjz32GFlZWSWWWb58Oddffz1eXl4EBATQo0cPTp48CYDNZuP1118nKioKd3d3atasySuvvALAkiVLMJlMpKWl2bcVFxeHyWTiwIEDAHzyySf4+/szZ84cGjVqhLu7OwkJCaxdu5bu3bsTFBRElSpV6Ny5Mxs2bChRrrS0NB5++GFCQkLw8PCgSZMm/O9//yM7Oxs/Pz++/fbbEsv/+OOPeHt7k5mZedG/LxEREakYlS58GoZBjjXnkh+5hbllXscwjFKX8/bbbyclJYXFixfbp6WmpjJ//nwGDRpEVlYWvXv3ZtGiRWzcuJGePXvSp08fEhISLur3Yjab+c9//sO2bdv473//y++//87o0aPt8+Pi4ujatSuNGjVi5cqVLFu2jD59+lBUVARAbGwsr776KmPHjmX79u3Mnj2bkJCQMpUhJyeH1157jY8++oht27ZRrVo1MjMzGTJkCMuWLWPVqlXUq1eP3r1724OjzWajV69eLF++nM8//5zt27fz6quvYrFY8Pb25s4772TmzJkl9jNz5kxuu+02fH19L+p3JSIiIhXHxdkFKG+5hbnEzI5xyr5XD1yNl6tXqZYNCAigV69ezJ49m65duwLw7bffEhQUxA033IDZbCY6Otq+/EsvvcQPP/zAnDlzGD58eJnLNmLECPvzyMhIXn75ZR555BHeffddAF5//XVat25tfw3QuHFjADIzM5k6dSrvvPMOQ4YMAaBu3bp07NixTGWwWq28++67Jd5Xly5dSizzwQcf4O/vz9KlS+nUqRMLFy5kzZo17Nixg/r16wNQp04d+/IPPPAAHTp0IDExkerVq3Ps2DHmzZt3Sa3EIiIiUnEqXcvnlWTQoEF899135OfnAzBr1izuvPNOzGYzWVlZPP300zRs2BB/f398fHzYsWPHRbd8Lly4kK5duxIeHo6vry/33HMPKSkp5OTkAH+1fJ7Njh07yM/PP+f80nJzc6NZs2YlpiUnJ/Pggw9Sr149qlSpgp+fH1lZWRw6dAiATZs2UaNGDXvw/Ke2bdvSuHFj/vvf/wLw+eefU6tWLTp16nRJZRUREZGKUelaPj1dPFk9cPUlbcNms5GZmYmvry9mc+nzuaeLZ5n206dPHwzDYO7cubRp04Y///yTt956C4Cnn36aBQsW8OabbxIVFYWnpye33XYbBQUFZdoHwIEDB7jpppt49NFHeeWVVwgMDGTZsmXcf//9FBQU4OXlhafnuct+vnmA/Xf0924HVqv1rNsxmUwlpg0ZMoSUlBSmTp1KrVq1cHd3p3379vb3eaF9Q3Hr57Rp03j22WeZOXMmQ4cOPWM/IiIicnmodOHTZDKV+tT3udhsNgpdCvFy9SpT+CwrDw8P+vfvz6xZs9i7dy8NGjSgZcuWQPHFP/feey+33HILAFlZWfaLd8pq/fr12Gw2Jk2aZH8/X3/9dYllmjVrxqJFi5gwYcIZ69erVw9PT08WLVrEAw88cMb84OBgABITEwkICACKW1JLY/ny5bz77rv07t0bgEOHDnHixAn7/KZNm3L48GF27959ztbPu+++m9GjR/Of//yH7du327sGiIiIyOVHp92dbNCgQcydO5cZM2YwaNAg+/R69erx/fffExcXx6ZNmxg4cOBFD00UFRWF1Wrl7bffZt++fXz22WdMnz69xDKxsbGsXbuWxx57jM2bN7Nz507ee+89Tpw4gYeHB2PGjGH06NF8+umnxMfHs2rVKj7++GP79iMiInjhhRfYs2cPc+fOZdKkSaUqW7169fjss8/YsWMHq1evZtCgQSVaOzt37kynTp249dZbWbBgAfv37+eXX35h/vz59mUCAgLo378/zzzzDDfeeCM1atS4qN+TiIiIVDyFTyfr0qULgYGB7Nq1i4EDB9qnT548mYCAADp06ECfPn3o0aOHvVW0rKKjo5k8eTKvvfYaTZo0YdasWUycOLHEMvXr1+e3335j06ZNtG3blvbt2/PTTz/h4lLcOD527FhGjRrFuHHjaNiwIQMGDODYsWMAuLq68sUXX7Bz506aNWvGa6+9xssvv1yqsn388cecPHmSli1bcs899/DEE09QrVq1Est89913tGnThrvuuotGjRoxevRo+1X4p53uQnDfffdd1O9IREREHMNklGV8ICfJyMigSpUqpKen4+fnV2JeXl4e+/fvp3bt2nh4eJTL/mw2GxkZGfj5+VXoaXc5t7LWwWeffcZTTz3F0aNHcXNzO+dyFfF5qaysVivz5s2jd+/euLq6Ors4VyXVweVB9eB8qgPnK00dnC+v/V2l6/MpV5ecnBwSExN59dVXefjhh88bPEVERMT51KxXCcyaNQsfH5+zPk6P1VlZvf7661xzzTWEhoYSGxvr7OKIiIjIBajlsxK4+eabiYk5+8D6lf30xAsvvMALL7zg7GKIiIhIKSl8VgK+vr66laSIiIhcEXTaXUREREQcRuFTRERERBxG4VNEREREHEbhU0REREQcRuFTRERERBxG4fMKFhkZyZQpU0q1rMlk4scff6zQ8oiIiIhciMKniIiIiDiMwqeIiIiIOIzCp5N88MEHhIWFYbPZSkzv27cv9913H/Hx8fTt25eQkBB8fHxo06YNCxcuLLf9b9myhS5duuDp6UnVqlV56KGHyMrKss9fsmQJbdu2xdvbG39/f6699loOHjwIwKZNm7jhhhvw9fXFz8+PVq1asW7dunIrm4iIiFRelS58GoaBLSfn0h+5uWVexzCMUpfz9ttvJyUlhcWLF9unpaamMn/+fAYNGkRWVha9e/dm0aJFbNy4kZ49e9KnTx8SEhIu+XeUnZ1Njx49CAgIYO3atXzzzTcsXLiQ4cOHA1BYWEi/fv3o3LkzmzdvZuXKlTz00EOYTCYABg0aRI0aNVi7di3r16/n2WefrfS38RQREZHyUelur2nk5rKrZaty2VZyGZdvsGE9Ji+vUi0bEBBAr169mD17Nl27dgXg22+/JSgoiBtuuAGz2Ux0dLR9+ZdeeokffviBOXPm2EPixZo9ezZ5eXl8+umneHt7A/DOO+/Qp08fXnvtNVxdXUlPT+emm26ibt26ADRs2NC+fkJCAs888wzXXHMNAPXq1buk8oiIiMjVo9K1fF5JBg0axHfffUd+fj4As2bN4s4778RsNpOVlcXTTz9Nw4YN8ff3x8fHhx07dpRLy+eOHTuIjo62B0+Aa6+9FpvNxq5duwgMDOTee++lR48e9OnTh6lTp5KYmGhfduTIkTzwwAN069aNV199lfj4+Esuk4iIiFwdKl3Lp8nTkwYb1l/SNmw2GxmZmfj5+mI2lz6fmzw9y7SfPn36YBgGc+fOpU2bNvz555+89dZbADz99NMsWLCAN998k6ioKDw9PbntttsoKCgo0z4u1syZM3niiSeYP38+X331Fc8//zwLFiygXbt2vPDCCwwcOJC5c+fyyy+/MH78eL788ktuueUWh5RNRERErlyVL3yaTKU+9X1ONhvmwkLMXl5lCp9l5eHhQf/+/Zk1axZ79+6lQYMGtGzZEoDly5dz77332gNdVlYWBw4cKJf9NmzYkE8++YTs7Gx76+fy5csxm800aNDAvlyLFi1o0aIFsbGxtG/fntmzZ9OuXTsA6tevT/369Xnqqae46667mDlzpsKniIiIXJBOuzvZoEGDmDt3LjNmzGDQoEH26fXq1eP7778nLi6OTZs2MXDgwDOujL+UfXp4eDBkyBC2bt3K4sWLefzxx7nnnnsICQlh//79xMbGsnLlSg4ePMhvv/3Gnj17aNiwIbm5uQwfPpwlS5Zw8OBBli9fztq1a0v0CRURERE5l0rX8nml6dKlC4GBgezatYuBAwfap0+ePJn77ruPDh06EBQUxJgxY8jIyCiXfXp5efHrr7/y5JNP0qZNG7y8vLj11luZPHmyff7OnTv573//S0pKCtWrV2fYsGE8/PDDFBYWkpKSwuDBg0lOTiYoKIj+/fszYcKEcimbiIiIVG4Kn05mNps5evToGdMjIyP5/fffS0wbNmxYiddlOQ3/z2GgmjZtesb2TwsJCeGHH3446zw3Nze++OKLUu9XRERE5O902l1EREREHEbhsxKYNWsWPj4+Z300btzY2cUTERERsdNp90rg5ptvJiYm5qzzdOchERERuZwofFYCvr6++Pr6OrsYIiIiIhek0+4iIiIi4jCVJnz+82pukbPR50RERMS5rvjT7q6urphMJo4fP05wcDAmk+mSt2mz2SgoKCAvL69C73Ak51YRdWAYBsePH8dkMqkvrIiIiJNc8eHTYrFQo0YNDh8+XG63nzQMg9zcXDw9PcslzErZVVQdmEwmatSogcViKbdtioiISOld8eETwMfHh3r16mG1Wstle1arlT/++INOnTqphcxJKqoOXF1dFTxFREScqFKETyhuAS2vUGGxWCgsLMTDw0Ph00lUByIiIpXTRXWmmzZtGpGRkXh4eBATE8OaNWvOu/w333zDNddcg4eHB02bNmXevHkXVVgRERERubKVOXx+9dVXjBw5kvHjx7Nhwwaio6Pp0aMHx44dO+vyK1as4K677uL+++9n48aN9OvXj379+rF169ZLLryIiIiIXFnKHD4nT57Mgw8+yNChQ2nUqBHTp0/Hy8uLGTNmnHX5qVOn0rNnT5555hkaNmzISy+9RMuWLXnnnXcuufAiIiIicmUpU5/PgoIC1q9fT2xsrH2a2WymW7durFy58qzrrFy5kpEjR5aY1qNHD3788cdz7ic/P5/8/Hz76/T0dABSU1PL7aKi87FareTk5JCSkqL+hk6iOnA+1YHzqQ4uD6oH51MdOF9p6iAzMxO48JjaZQqfJ06coKioiJCQkBLTQ0JC2Llz51nXSUpKOuvySUlJ59zPxIkTmTBhwhnTa9euXZbiioiIiIiDZWZmUqVKlXPOvyyvdo+NjS3RWmqz2UhNTaVq1aoOGXczIyODiIgIDh06hJ+fX4XvT86kOnA+1YHzqQ4uD6oH51MdOF9p6sAwDDIzMwkLCzvvtsoUPoOCgrBYLCQnJ5eYnpycTGho6FnXCQ0NLdPyAO7u7ri7u5eY5u/vX5ailgs/Pz99yJ1MdeB8qgPnUx1cHlQPzqc6cL4L1cH5WjxPK9MFR25ubrRq1YpFixbZp9lsNhYtWkT79u3Puk779u1LLA+wYMGCcy4vIiIiIpVXmU+7jxw5kiFDhtC6dWvatm3LlClTyM7OZujQoQAMHjyY8PBwJk6cCMCTTz5J586dmTRpEv/3f//Hl19+ybp16/jggw/K952IiIiIyGWvzOFzwIABHD9+nHHjxpGUlETz5s2ZP3++/aKihIQEzOa/GlQ7dOjA7Nmzef755/nXv/5FvXr1+PHHH2nSpEn5vYty5u7uzvjx48849S+OozpwPtWB86kOLg+qB+dTHThfedaBybjQ9fAiIiIiIuXkom6vKSIiIiJyMRQ+RURERMRhFD5FRERExGEUPkVERETEYRQ+/2HatGlERkbi4eFBTEwMa9ascXaRriovvPACJpOpxOOaa65xdrEqtT/++IM+ffoQFhaGyWTixx9/LDHfMAzGjRtH9erV8fT0pFu3buzZs8c5ha2kLlQH99577xnHRc+ePZ1T2Epq4sSJtGnTBl9fX6pVq0a/fv3YtWtXiWXy8vIYNmwYVatWxcfHh1tvvfWMm6jIxStNHVx//fVnHAuPPPKIk0pc+bz33ns0a9bMPpB8+/bt+eWXX+zzy+sYUPj8m6+++oqRI0cyfvx4NmzYQHR0ND169ODYsWPOLtpVpXHjxiQmJtofy5Ytc3aRKrXs7Gyio6OZNm3aWee//vrr/Oc//2H69OmsXr0ab29vevToQV5enoNLWnldqA4AevbsWeK4+OKLLxxYwspv6dKlDBs2jFWrVrFgwQKsVis33ngj2dnZ9mWeeuopfv75Z7755huWLl3K0aNH6d+/vxNLXbmUpg4AHnzwwRLHwuuvv+6kElc+NWrU4NVXX2X9+vWsW7eOLl260LdvX7Zt2waU4zFgiF3btm2NYcOG2V8XFRUZYWFhxsSJE51YqqvL+PHjjejoaGcX46oFGD/88IP9tc1mM0JDQ4033njDPi0tLc1wd3c3vvjiCyeUsPL7Zx0YhmEMGTLE6Nu3r1PKc7U6duyYARhLly41DKP4c+/q6mp888039mV27NhhAMbKlSudVcxK7Z91YBiG0blzZ+PJJ590XqGuQgEBAcZHH31UrseAWj5PKSgoYP369XTr1s0+zWw2061bN1auXOnEkl199uzZQ1hYGHXq1GHQoEEkJCQ4u0hXrf3795OUlFTiuKhSpQoxMTE6LhxsyZIlVKtWjQYNGvDoo4+SkpLi7CJVaunp6QAEBgYCsH79eqxWa4lj4ZprrqFmzZo6FirIP+vgtFmzZhEUFESTJk2IjY0lJyfHGcWr9IqKivjyyy/Jzs6mffv25XoMlPkOR5XViRMnKCoqst+p6bSQkBB27tzppFJdfWJiYvjkk09o0KABiYmJTJgwgeuuu46tW7fi6+vr7OJddZKSkgDOelycnicVr2fPnvTv35/atWsTHx/Pv/71L3r16sXKlSuxWCzOLl6lY7PZGDFiBNdee639bnxJSUm4ubnh7+9fYlkdCxXjbHUAMHDgQGrVqkVYWBibN29mzJgx7Nq1i++//96Jpa1ctmzZQvv27cnLy8PHx4cffviBRo0aERcXV27HgMKnXFZ69eplf96sWTNiYmKoVasWX3/9Nffff78TSybiPHfeeaf9edOmTWnWrBl169ZlyZIldO3a1Yklq5yGDRvG1q1b1d/cic5VBw899JD9edOmTalevTpdu3YlPj6eunXrOrqYlVKDBg2Ii4sjPT2db7/9liFDhrB06dJy3YdOu58SFBSExWI546qt5ORkQkNDnVQq8ff3p379+uzdu9fZRbkqnf7s67i4vNSpU4egoCAdFxVg+PDh/O9//2Px4sXUqFHDPj00NJSCggLS0tJKLK9jofydqw7OJiYmBkDHQjlyc3MjKiqKVq1aMXHiRKKjo5k6dWq5HgMKn6e4ubnRqlUrFi1aZJ9ms9lYtGgR7du3d2LJrm5ZWVnEx8dTvXp1ZxflqlS7dm1CQ0NLHBcZGRmsXr1ax4UTHT58mJSUFB0X5cgwDIYPH84PP/zA77//Tu3atUvMb9WqFa6uriWOhV27dpGQkKBjoZxcqA7OJi4uDkDHQgWy2Wzk5+eX6zGg0+5/M3LkSIYMGULr1q1p27YtU6ZMITs7m6FDhzq7aFeNp59+mj59+lCrVi2OHj3K+PHjsVgs3HXXXc4uWqWVlZVVotVg//79xMXFERgYSM2aNRkxYgQvv/wy9erVo3bt2owdO5awsDD69evnvEJXMuerg8DAQCZMmMCtt95KaGgo8fHxjB49mqioKHr06OHEUlcuw4YNY/bs2fz000/4+vra+7BVqVIFT09PqlSpwv3338/IkSMJDAzEz8+Pxx9/nPbt29OuXTsnl75yuFAdxMfHM3v2bHr37k3VqlXZvHkzTz31FJ06daJZs2ZOLn3lEBsbS69evahZsyaZmZnMnj2bJUuW8Ouvv5bvMVC+F+Rf+d5++22jZs2ahpubm9G2bVtj1apVzi7SVWXAgAFG9erVDTc3NyM8PNwYMGCAsXfvXmcXq1JbvHixAZzxGDJkiGEYxcMtjR071ggJCTHc3d2Nrl27Grt27XJuoSuZ89VBTk6OceONNxrBwcGGq6urUatWLePBBx80kpKSnF3sSuVsv3/AmDlzpn2Z3Nxc47HHHjMCAgIMLy8v45ZbbjESExOdV+hK5kJ1kJCQYHTq1MkIDAw03N3djaioKOOZZ54x0tPTnVvwSuS+++4zatWqZbi5uRnBwcFG165djd9++80+v7yOAZNhGMalJmURERERkdJQn08RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXEYhU8RERERcRiFTxERERFxGIVPEREREXGY/wefnjuFNEXm0AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#A multi-layer perceptron described in detail in Ch. 10, pp. 299-307\n",
    "d1=300\n",
    "d2=100\n",
    "for act in acts:\n",
    "    for kinit in kinits:\n",
    "        for lr in lrs:\n",
    "            print(act,kinit,lr)\n",
    "            model = keras.models.Sequential()\n",
    "            model.add(keras.layers.Flatten(input_shape=[28, 28]))# convert each input image into a 1D array\n",
    "            model.add(keras.layers.Dense(d1, activation=act,kernel_initializer=kinit))# a Dense hidden layer with 300 neurons.\n",
    "            #model.add(keras.layers.Dropout(0.5))\n",
    "            model.add(keras.layers.Dense(d2, activation=act,kernel_initializer=kinit))# a Dense hidden layer with 100 neurons.\n",
    "            #model.add(keras.layers.Dropout(0.5))\n",
    "            model.add(keras.layers.Dense(10, activation=\"softmax\"))#a Dense output layer with 10 neurons (one per class)\n",
    "\n",
    "            opt=keras.optimizers.Adam(learning_rate=lr)\n",
    "            model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "                            optimizer=opt,\n",
    "                            metrics=[\"accuracy\"])\n",
    "\n",
    "            history = model.fit(X_train, y_train, epochs=30,\n",
    "                                validation_data=(X_valid, y_valid))\n",
    "            df=pd.DataFrame(history.history)\n",
    "            df.to_csv(f'mlp/{d1}_{d2}_{kinit}_{act}_Adam_{lr}.csv')\n",
    "            df.plot(figsize=(8, 5))\n",
    "            plt.grid(True)\n",
    "            plt.gca().set_ylim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "files=os.listdir('mlp/')\n",
    "saving=[]\n",
    "for filename in files:\n",
    "    if '.csv' in filename:\n",
    "        data=pd.read_csv(f'mlp/{filename}')[-1:]\n",
    "        d1=float(filename.split('_')[0])\n",
    "        d2=float(filename.split('_')[1])\n",
    "        kinit=filename.split('_')[2]+'_'+filename.split('_')[3]\n",
    "        act=filename.split('_')[4]\n",
    "        opt=filename.split('_')[5]\n",
    "        if 'd0.5' in opt:\n",
    "            opt=opt[:-4]\n",
    "            reg='d0.5'\n",
    "        elif 'l10.01' in opt:\n",
    "            opt=opt[:-6]\n",
    "            reg='l10.01'\n",
    "        elif 'l20.01' in opt:\n",
    "            opt=opt[:-6]\n",
    "            reg='l20.01'\n",
    "        else:\n",
    "            reg='None'\n",
    "        lr=float(filename.split('_')[6][:-4])\n",
    "        acc=np.array(data['accuracy'])[0]\n",
    "        loss=np.array(data['loss'])[0]\n",
    "        val_acc=np.array(data['val_accuracy'])[0]\n",
    "        val_loss=np.array(data['val_loss'])[0]\n",
    "        read_out=[d1,d2,kinit,act,opt,reg,lr,acc,loss,val_acc,val_loss]\n",
    "        saving.append(read_out)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(saving,\n",
    "                columns=['d1','d2','kernal_init','activation','optimizer','regularization','learning_rate',\n",
    "                         'accuracy','loss','val_accuracy','val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>kernal_init</th>\n",
       "      <th>activation</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>regularization</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>Adam</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.952093</td>\n",
       "      <td>0.124428</td>\n",
       "      <td>0.897000</td>\n",
       "      <td>0.429704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>Adam</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.951778</td>\n",
       "      <td>0.123696</td>\n",
       "      <td>0.896833</td>\n",
       "      <td>0.447729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>Adam</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.951426</td>\n",
       "      <td>0.123032</td>\n",
       "      <td>0.894000</td>\n",
       "      <td>0.457067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>Adam</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.950833</td>\n",
       "      <td>0.126743</td>\n",
       "      <td>0.896167</td>\n",
       "      <td>0.422312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.949148</td>\n",
       "      <td>0.132726</td>\n",
       "      <td>0.886500</td>\n",
       "      <td>0.377233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.948444</td>\n",
       "      <td>0.135212</td>\n",
       "      <td>0.896833</td>\n",
       "      <td>0.354425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.947741</td>\n",
       "      <td>0.139111</td>\n",
       "      <td>0.894833</td>\n",
       "      <td>0.331195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.947444</td>\n",
       "      <td>0.136881</td>\n",
       "      <td>0.898000</td>\n",
       "      <td>0.344440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.947426</td>\n",
       "      <td>0.141965</td>\n",
       "      <td>0.897833</td>\n",
       "      <td>0.318618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.947204</td>\n",
       "      <td>0.140832</td>\n",
       "      <td>0.898500</td>\n",
       "      <td>0.314652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        d1     d2     kernal_init activation optimizer regularization  \\\n",
       "171  300.0  100.0       he_normal       relu      Adam           None   \n",
       "76   300.0  100.0   glorot_normal       relu      Adam           None   \n",
       "146  300.0  100.0      he_uniform       relu      Adam           None   \n",
       "98   300.0  100.0  glorot_uniform       relu      Adam           None   \n",
       "3    300.0  100.0      he_uniform       relu       SGD           None   \n",
       "151  300.0  100.0   glorot_normal       relu       SGD           None   \n",
       "188  300.0  100.0  glorot_uniform       relu       SGD           None   \n",
       "94   300.0  100.0  glorot_uniform       relu       SGD           None   \n",
       "4    300.0  100.0      he_uniform       relu       SGD           None   \n",
       "14   300.0  100.0       he_normal       relu       SGD           None   \n",
       "\n",
       "     learning_rate  accuracy      loss  val_accuracy  val_loss  \n",
       "171          0.001  0.952093  0.124428      0.897000  0.429704  \n",
       "76           0.001  0.951778  0.123696      0.896833  0.447729  \n",
       "146          0.001  0.951426  0.123032      0.894000  0.457067  \n",
       "98           0.001  0.950833  0.126743      0.896167  0.422312  \n",
       "3            0.100  0.949148  0.132726      0.886500  0.377233  \n",
       "151          0.100  0.948444  0.135212      0.896833  0.354425  \n",
       "188          0.050  0.947741  0.139111      0.894833  0.331195  \n",
       "94           0.100  0.947444  0.136881      0.898000  0.344440  \n",
       "4            0.050  0.947426  0.141965      0.897833  0.318618  \n",
       "14           0.050  0.947204  0.140832      0.898500  0.314652  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nlargest(10, 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>kernal_init</th>\n",
       "      <th>activation</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>regularization</th>\n",
       "      <th>learning_rate</th>\n",
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       "      <th>val_accuracy</th>\n",
       "      <th>val_loss</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.947204</td>\n",
       "      <td>0.140832</td>\n",
       "      <td>0.898500</td>\n",
       "      <td>0.314652</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.947444</td>\n",
       "      <td>0.136881</td>\n",
       "      <td>0.898000</td>\n",
       "      <td>0.344440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.947426</td>\n",
       "      <td>0.141965</td>\n",
       "      <td>0.897833</td>\n",
       "      <td>0.318618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>Adam</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.952093</td>\n",
       "      <td>0.124428</td>\n",
       "      <td>0.897000</td>\n",
       "      <td>0.429704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>Adam</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.951778</td>\n",
       "      <td>0.123696</td>\n",
       "      <td>0.896833</td>\n",
       "      <td>0.447729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.948444</td>\n",
       "      <td>0.135212</td>\n",
       "      <td>0.896833</td>\n",
       "      <td>0.354425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>Adam</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.950833</td>\n",
       "      <td>0.126743</td>\n",
       "      <td>0.896167</td>\n",
       "      <td>0.422312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.947741</td>\n",
       "      <td>0.139111</td>\n",
       "      <td>0.894833</td>\n",
       "      <td>0.331195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>he_uniform</td>\n",
       "      <td>relu</td>\n",
       "      <td>Adam</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.951426</td>\n",
       "      <td>0.123032</td>\n",
       "      <td>0.894000</td>\n",
       "      <td>0.457067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>300.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>glorot_normal</td>\n",
       "      <td>relu</td>\n",
       "      <td>SGD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.945685</td>\n",
       "      <td>0.143688</td>\n",
       "      <td>0.893500</td>\n",
       "      <td>0.327351</td>\n",
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      "text/plain": [
       "        d1     d2     kernal_init activation optimizer regularization  \\\n",
       "14   300.0  100.0       he_normal       relu       SGD           None   \n",
       "94   300.0  100.0  glorot_uniform       relu       SGD           None   \n",
       "4    300.0  100.0      he_uniform       relu       SGD           None   \n",
       "171  300.0  100.0       he_normal       relu      Adam           None   \n",
       "76   300.0  100.0   glorot_normal       relu      Adam           None   \n",
       "151  300.0  100.0   glorot_normal       relu       SGD           None   \n",
       "98   300.0  100.0  glorot_uniform       relu      Adam           None   \n",
       "188  300.0  100.0  glorot_uniform       relu       SGD           None   \n",
       "146  300.0  100.0      he_uniform       relu      Adam           None   \n",
       "88   300.0  100.0   glorot_normal       relu       SGD           None   \n",
       "\n",
       "     learning_rate  accuracy      loss  val_accuracy  val_loss  \n",
       "14           0.050  0.947204  0.140832      0.898500  0.314652  \n",
       "94           0.100  0.947444  0.136881      0.898000  0.344440  \n",
       "4            0.050  0.947426  0.141965      0.897833  0.318618  \n",
       "171          0.001  0.952093  0.124428      0.897000  0.429704  \n",
       "76           0.001  0.951778  0.123696      0.896833  0.447729  \n",
       "151          0.100  0.948444  0.135212      0.896833  0.354425  \n",
       "98           0.001  0.950833  0.126743      0.896167  0.422312  \n",
       "188          0.050  0.947741  0.139111      0.894833  0.331195  \n",
       "146          0.001  0.951426  0.123032      0.894000  0.457067  \n",
       "88           0.050  0.945685  0.143688      0.893500  0.327351  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nlargest(10, 'val_accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'learning rate')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#learning_rate \n",
    "subset0=df[(df['kernal_init']=='glorot_uniform')\n",
    "           *(df['regularization']=='None')\n",
    "           *(df['activation']=='relu')\n",
    "           *(df['optimizer']=='SGD')]\n",
    "plt.scatter(subset0['learning_rate'],subset0['accuracy'],label='accuracy')\n",
    "plt.scatter(subset0['learning_rate'],subset0['val_accuracy'],label='val_accuracy')\n",
    "plt.scatter(subset0['learning_rate'],subset0['loss'],label='accuracy')\n",
    "plt.scatter(subset0['learning_rate'],subset0['val_loss'],label='val_accuracy')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.ylim(0,1)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('learning rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'learning rate')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#optimizer influence\n",
    "subset1=df[(df['kernal_init']=='glorot_uniform')\n",
    "           *(df['regularization']=='None')\n",
    "           *(df['activation']=='relu')\n",
    "           *(df['optimizer']=='Adam')]\n",
    "plt.scatter(subset1['learning_rate'],subset1['accuracy'],label='accuracy')\n",
    "plt.scatter(subset1['learning_rate'],subset1['val_accuracy'],label='val_accuracy')\n",
    "plt.scatter(subset1['learning_rate'],subset1['loss'],label='accuracy')\n",
    "plt.scatter(subset1['learning_rate'],subset1['val_loss'],label='val_accuracy')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.ylim(0,1)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('learning rate')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2r2ULo1YtozBD74jNrUSIyK/MR0ZhRgtWRbeqTEvXO2KjQxRRm5+PyrT0liuKiLQYbprh5p9XTNqPCACKKotM2o9Mr7aocb/7xvYjItNiuGkGR7f2Ju1HBACeCk+T9iPTs/Fs3O++sf2IyLQYbpqhW3g0CuAOTQNPZdSIQD7c0S08umULo1Yt2CsYSoVS72nT9QQI8FZ4I9gruIUro3r1D2TVe7p2PUHgA1mJLIjhphnkNja4GhELAHoBp345LyKW892QUeQyOeaFzQMAvYBTvzw3bC7nu7Gg+gey1i3cFnD+WuYDWYksh+GmmfpGT0JW/49RJLjrtBcK7sjq/zHnuaEmifKPwurI1fBSeOm0KxVKrI5czXlurAAfyEpkvXhIwQT6Rk+C+tHxOHU0AbihxpmBX6BHxFB484gNNUOUfxQG+g1E2tU05KfnY82jazhDsZXhA1mJrBOP3JiI3MYG3R6ou7am2wPRPBVFJiGXyRGsrLu2JlgZzGBjheofyNrm8eFwCg9jsCGyAgw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpFg83a9asQUBAABwcHBAeHo7U1NQ79o+Pj0fXrl3h6OgIPz8/vPbaa6iqqmqhaomIiMjaWTTcbNu2DTExMYiNjUVGRgYCAwMRHR2NwsJCg/23bNmCefPmITY2FqdPn8YXX3yBbdu2YcGCBS1cOREREVkriz66evXq1XjhhRcwZcoUAMDatWuxe/dubNiwAfPmzdPrf+TIEQwYMADPPvssACAgIADPPPMMjh071uA+qqurUV1drV0uLS0FAKhUKqhUKlO+He32TL1durdxXJE5cFyRuZhrbBmzPYuFm5qaGqSnp2P+/PnaNplMhqioKBw9etTgOv3798e//vUvpKamIiwsDBcvXsSePXswYcKEBvezbNkyxMXF6bXv378fCoWi+W/EgMTERLNsl+5tHFdkDhxXZC6mHluVlZWN7muxcHPt2jWo1WoolUqddqVSiTNnzhhc59lnn8W1a9fw4IMPQhRF1NbW4qWXXrrjaan58+cjJiZGu1xaWgo/Pz8MGTIErq6upnkzf1GpVEhMTMTgwYNha2tr0m3TvYvjisyB44rMxVxjq/7MS2NY9LSUsZKTk/Hee+/hn//8J8LDw3H+/HnMmjULb7/9NhYtWmRwHXt7e9jb2+u129ramu1/aHNum+5dHFdkDhxXZC6mHlvGbMti4cbDwwNyuRwFBQU67QUFBfD29ja4zqJFizBhwgQ8//zzAIDevXujoqIC06ZNw1tvvQWZzOI3fxEREZGFWSwN2NnZISQkBElJSdo2jUaDpKQkREREGFynsrJSL8DI5XIAgCiK5iuWiIiIWg2LnpaKiYnBpEmTEBoairCwMMTHx6OiokJ799TEiRPRvn17LFu2DAAwYsQIrF69Gn379tWellq0aBFGjBihDTlERER0b7NouBk3bhyKioqwePFi5OfnIygoCAkJCdqLjHNycnSO1CxcuBCCIGDhwoW4cuUKPD09MWLECLz77ruWegtERERkZSx+QfGMGTMwY8YMg68lJyfrLNvY2CA2NhaxsbEtUBkRERG1RrwCl4iIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCTFxtIFEBFR66TRaFBTU2PpMsjKqFQq2NjYoKqqCmq12qh17ezsIJM1/7gLww0RERmtpqYG2dnZ0Gg0li6FrIwoivD29kZubi4EQTBqXZlMhk6dOsHOzq5ZNTDcEBGRUURRRF5eHuRyOfz8/EzyTZukQ6PRoLy8HM7OzkaNDY1Gg6tXryIvLw8dO3Y0OhjdiuGGiIiMUltbi8rKSvj6+kKhUFi6HLIy9acrHRwcjA6+np6euHr1Kmpra2Fra9vkGhi3iYjIKPXXUTT31AHR7erHlLHX6tzO4uFmzZo1CAgIgIODA8LDw5GamnrH/sXFxZg+fTp8fHxgb2+P+++/H3v27GmhaomIqF5zThsQGWKqMWXR01Lbtm1DTEwM1q5di/DwcMTHxyM6Ohpnz56Fl5eXXv+amhoMHjwYXl5e+O6779C+fXtcvnwZbdu2bfniiYiIyCpZNNysXr0aL7zwAqZMmQIAWLt2LXbv3o0NGzZg3rx5ev03bNiAGzdu4MiRI9pzcQEBAXfcR3V1Naqrq7XLpaWlAOpuVVOpVCZ6J9Bu89Z/EpkCxxWZQ3PGlUqlgiiK0Gg0vFuK9IiiqP2nseNDo9FAFEWoVCrI5XKd14wZq4JYX0ULq6mpgUKhwHfffYfRo0dr2ydNmoTi4mLs3LlTb51hw4ahXbt2UCgU2LlzJzw9PfHss89i7ty5er+EekuWLEFcXJxe+5YtW3ghHBFRE9jY2MDb2xt+fn6Su+6mT58+ePnll/Hyyy9bupRWLycnB4GBgTh06BB69+7dqHVqamqQm5uL/Px81NbW6rxWWVmJZ599FiUlJXB1db3jdix25ObatWtQq9VQKpU67UqlEmfOnDG4zsWLF/Hzzz9j/Pjx2LNnD86fP49XXnkFKpUKsbGxBteZP38+YmJitMulpaXw8/PDkCFD7vrLMZZKpUJiYiIGDx7crKu8iW7FcUXm0JxxVVVVhdzcXDg7O8PBwaHJNag1Io5fuoHCsmp4udijX0A7yGWWvY5HJpPBwcHB5J8Pt4uLi8POnTuRkZFh1v1YgiiKKCsrg5OTEwDAycmp0b/PqqoqODo64uGHH9YbW/VnXhqjVd0KrtFo4OXlhc8//xxyuRwhISG4cuUKVqxY0WC4sbe3h729vV67ra2t2T4ozLltundxXJE5NGVcqdVqCIIAmUzW5DluEk7mIe7HU8grqdK2+bRxQOyIHhjay6dJ2zSV+vfWFDU1NY06mlV/4ayl5whqbL3GqD8Vdet7bOz7lMlkEATB4Lg0Zpxa7Lfq4eEBuVyOgoICnfaCggJ4e3sbXMfHxwf333+/zimo7t27Iz8/n1OAExG1Egkn8/DyvzJ0gg0A5JdU4eV/ZSDhZJ7Z9l1WVobx48fDyckJPj4++PDDDxEZGYnZs2cb7J+Tk4NRo0bB2dkZrq6uGDt2rM7n1pIlSxAUFIT169ejU6dO2qMNd1pv48aNiIuLQ1ZWFgRBgCAI2Lhx411rFwQB69evx5gxY6BQKNClSxfs2rVLp89//vMfhIWFwd7eHj4+Ppg3b57O6Z3IyEjMmDEDs2fPhoeHB6Kjo5GcnAxBELBv3z707dsXjo6OGDRoEAoLC7F37150794drq6uePbZZ1FZWandVkJCAh588EG0bdsW7u7uePzxx3HhwoXG/qcwK4uFGzs7O4SEhCApKUnbptFokJSUhIiICIPrDBgwAOfPn9e5QOncuXPw8fGR3HlfIiIpUmtExP14CoYu9qxvi/vxFNQa81wOGhMTg5SUFOzatQuJiYk4fPhwg6eGNBoNRo0ahRs3buA///kPEhMTcfHiRYwbN06n3/nz5/H999/j3//+NzIzM++63rhx4/D666+jZ8+eyMvLQ15ent42GxIXF4exY8fit99+w7BhwzB+/HjcuHEDAHDlyhUMGzYM/fr1Q1ZWFj799FN88cUXeOedd3S2sWnTJtjZ2SElJQVr167Vti9ZsgSffPIJjhw5gtzcXIwdOxbx8fHYsmULdu/ejf379+Mf//iHtn9FRQViYmKQlpaGpKQkyGQyjBkzxjouMhctaOvWraK9vb24ceNG8dSpU+K0adPEtm3bivn5+aIoiuKECRPEefPmafvn5OSILi4u4owZM8SzZ8+KP/30k+jl5SW+8847jd5nSUmJCEAsKSkx+fupqakRd+zYIdbU1Jh823Tv4rgic2jOuLp586Z46tQp8ebNm0ave+T8NdF/7k93/Tly/prR276b0tJS0dbWVty+fbu2rbi4WFQoFOKsWbNEURRFf39/8cMPPxRFURT3798vyuVyMScnR9v/v//9rwhATE1NFUVRFGNjY0VbW1uxsLBQ26ex6wUGBhpVPwBx4cKF2uXy8nIRgLh3715RFEVxwYIFYteuXUWNRqPts2bNGtHZ2VlUq9WiKIriI488Ivbt21dnuwcPHhQBiAcOHNC2LVu2TAQgXrhwQdv24osvitHR0Q3WV1RUJAIQs7KyxD///FO8cOGCCEA8ceJEo9/jncaWMZ/fFj3ZN27cOKxcuRKLFy9GUFAQMjMzkZCQoL3IOCcnB3l5/3d40s/PD/v27cPx48fRp08fzJw5E7NmzTJ42zgREVmfwrKqu3cyop8xLl68CJVKhbCwMG1bmzZt0LVrV4P9T58+DT8/P/j5+WnbevTogbZt2+L06dPaNn9/f3h6ehq9XlP06dNH++/1F+oWFhZq9xsREaEzEd6AAQNQXl6O//3vf9q2kJCQu25bqVRCoVCgc+fOOm31+wKAP/74A8888ww6d+4MV1dX7dQsOTk5zXqPpmDxC4pnzJiBGTNmGHwtOTlZry0iIgK//vqrmasiIiJz8HJp3N1Vje1nDervCmoJt19UKwiC0aeBGqr31m3XX9R7p32NGDEC/v7+WLduHXx9faHRaNCrVy+ruAbW4o9fICKie0dYp3bwaeOAhm74FlB311RYp3Ym33fnzp1ha2uL48ePa9tKSkpw7tw5g/27d++O3Nxc5ObmattOnTqF4uJi9OjRo8H9NGY9Ozu7Zj8/ydB+jx49qp1EDwBSUlLg4uKCDh06mHRf169fx9mzZ7Fw4UI8+uij6N69O/7880+T7qM5jA43AQEBWLp0qVUcdiIiotZFLhMQO6LuA/72gFO/HDuih1nmu3FxccGkSZPwxhtv4ODBg/jvf/+LqVOnam8/vl1UVBR69+6N8ePHIyMjA6mpqZg4cSIeeeQRhIaGNrifxqwXEBCA7OxsZGZm4tq1azoz6TfVK6+8gtzcXLz66qs4c+YMdu7cidjYWMTExJj8lnM3Nze4u7vj888/x/nz5/Hzzz/rzClnaUa/29mzZ+Pf//43OnfujMGDB2Pr1q0m+Y9CRET3hqG9fPDp/wuGdxvdU0/ebRzw6f8LNus8N6tXr0ZERAQef/xxREVFYcCAAejevbvByQgFQcDOnTvh5uaGhx9+GFFRUejcuTO2bdt2x300Zr0nn3wSQ4cOxcCBA+Hp6Ylvvvmm2e+tffv22LNnD1JTUxEYGIiXXnoJU6dOxcKFC5u97dvJZDJs3boV6enp6NWrF1577TWsWLHC5PtpqiY/fiEjIwMbN27EN998A7VajWeffRbPPfccgoODTV2jSZWWlqJNmzaNmr7ZWCqVCnv27MGwYcM42RqZDMcVmUNzxlVVVRWys7N15nVpCrVGRGr2DRSWVcHLpe5UVEvPUFxRUYH27dtj1apVmDp1aovuW6o0Gg1KS0vh6upq9BGjO40tYz6/m3ycKjg4GB9//DGuXr2K2NhYrF+/Hv369UNQUBA2bNiAJmYmIiK6R8hlAiLuc8eooPaIuM+9RYLNiRMn8M033+DChQvIyMjA+PHjAQCjRo0y+76p5TQ53KhUKnz77bcYOXIkXn/9dYSGhmL9+vV48sknsWDBAu2AISIisiYrV65EYGAgoqKiUFFRgcOHD8PDw8OiNW3evBnOzs4Gf3r27GnR2lojo28Fz8jIwJdffolvvvkGMpkMEydOxIcffohu3bpp+4wZMwb9+vUzaaFERETN1bdvX6Snp1u6DD0jR45EeHi4wdd4Otp4Roebfv36YfDgwfj0008xevRog7/0Tp064emnnzZJgURERFLn4uICFxcXS5chGUaHm4sXL8Lf3/+OfZycnPDll182uSgiIiKipjL6mpvCwkIcO3ZMr/3YsWNIS0szSVFERERETWV0uJk+fbrOrIv1rly5gunTp5ukKCIiIqKmMjrcnDp1yuBcNn379sWpU6dMUhQRERFRUxkdbuzt7VFQUKDXnpeXBxsbiz+Hk4iIiO5xRoebIUOGYP78+SgpKdG2FRcXY8GCBRg8eLBJiyMiImpJAQEBiI+Pt3QZZrNkyRIEBQXptSmVSgiCgB07dlikLlMz+lDLypUr8fDDD8Pf3x99+/YFAGRmZkKpVOLrr782eYFERCRRGjVw+QhQXgA4KwH//oBMbumqWsSSJUuwY8cOZGZmtuh+58yZg1dffVW7fPr0acTFxeGHH37AAw88ADc3txatx1yMDjft27fHb7/9hs2bNyMrKwuOjo6YMmUKnnnmGU40REREjXNqF5AwFyi9+n9trr7A0PeBHiMtV1cz1dTUwM7OztJlNKh+1uN6Fy5cAFD3+AlDT0ZvLJVKZVUZoEmPX3BycsK0adOwZs0arFy5EhMnTrSqN0VERFbs1C7g24m6wQYASvPq2k/tMtuuy8rKMH78eDg5OcHHxwcffvghIiMjMXv2bIP9c3JyMGrUKDg7O8PV1RVjx47Vue60/jTP+vXrdR72eKf1Nm7ciLi4OGRlZUEQBAiCgI0bN96x7kuXLkEQBJ0jPcXFxRAEAcnJyQCA5ORkCIKApKQkhIaGQqFQoH///jh79qxevfX/PmLECAB1T/muDzcajQZLly5Fhw4dYG9vj6CgICQkJOjVsm3bNjzyyCNwcHDA5s2bMXnyZIwePRrLli3D/fffj3bt2mHp0qWora3FG2+8gXbt2qFDhw4tMg9ek58tderUKSQkJGDXrl06P0RERA3SqOuO2MDQw5X/akuYV9fPDGJiYpCSkoJdu3YhMTERhw8fRkZGhuFSNRqMGjUKN27cwH/+8x8kJibi4sWLGDdunE6/8+fP4/vvv8e///1vZGZm3nW9cePG4fXXX0fPnj2Rl5eHvLw8vW02x1tvvYVVq1YhLS0NNjY2eO655wz2mzNnjjZo1NcBAB999BFWrVqFlStX4rfffkN0dDRGjhyJP/74Q2f9efPmYdasWTh9+jSio6MBAD///DOuXr2K3bt3Y+XKlYiNjcXjjz8ONzc3HDt2DC+99BJefPFF/O9//zPZ+zWkSTMUjxkzBr///jsEQdA+/bs+8anV5hmQREQkAZeP6B+x0SECpVfq+nV6yKS7Lisrw6ZNm7BlyxY8+uijAIAvv/wSvr6+BvsnJSXh999/R3Z2Nvz8/AAAX331FXr27Injx49rn6FYU1ODr776Cp6engCAxMTEu67n7OwMGxsbeHt7m/Q9AsC7776LRx55BEBdABk+fDiqqqq0R5XqOTs7o23btgCgU8fKlSsxd+5c7WOU3n//fRw8eBDx8fFYs2aNtt/s2bPxxBNP6GyzXbt2+Oijj1BeXo6QkBCsXLkSlZWVWLBgAQBg/vz5WL58OX755RezPqbJ6CM3s2bNQqdOnVBYWAiFQoH//ve/OHToEEJDQ7WHxoiIiAwq159KpFn9jHDx4kWoVCqEhYVp29q0aYOuXbsa7H/69Gn4+flpAwoA9OjRA23btsXp06e1bf7+/tpgY8x65tKnTx/tv/v4+ACoe7pAY5SWluLq1asYMGCATvuAAQP0ag8NDdVbv2fPnpDJ/i9aKJVK9O7dW7ssl8vh7u7e6HqayugjN0ePHsXPP/8MDw8PyGQyyGQyPPjgg1i2bBlmzpyJEydOmKNOIiKSAmelaftZAScnJ7Pvoz4w1J8tAeou4jXk1mtgb72OxtQMve/br78VBMFgmznquZXRR27UarX2yaUeHh64erXu8KK/v7/ORUtERER6/PvX3RWFhu7MEQDX9nX9TKxz586wtbXF8ePHtW0lJSU4d+6cwf7du3dHbm6uziOHTp06heLiYvTo0aPB/TRmPTs7O6Mu46g/MlR/XQwAs9xG7urqCl9fX6SkpOi0p6Sk3PE9Wxujj9z06tULWVlZ6NSpE8LDw/HBBx/Azs4On3/+OTp37myOGomISCpk8rrbvb+diLqAc+uFxX8FnqHLzTLfjYuLCyZNmqS9c8fLywuxsbE6dwrdKioqCr1798b48eMRHx+P2tpavPLKK3jkkUcMnpIxZr2AgABkZ2cjMzMTHTp0gIuLC+zt7RvcpqOjIx544AEsX75ce2nIwoULm/9LMeCNN95AbGws7rvvPgQFBeHLL79EZmYmNm/ebJb9mYPRR24WLlyoPZy0dOlSZGdn46GHHsKePXvw8ccfm7xAIiKSmB4jgbFfAa4+uu2uvnXtZpznZvXq1YiIiMDjjz+OqKgoDBgwAN27d9e72BaoO32yc+dOuLm54eGHH0ZUVBQ6d+6Mbdu23XEfjVnvySefxNChQzFw4EB4enrim2++uWvtGzZsQG1tLUJCQjB79my88847xv8CGmHmzJmIiYnB66+/jt69e2vvjO7SpYtZ9mcOgnjrCbwmunHjBtzc3Jo1AVBLKS0tRZs2bVBSUgJXV1eTblulUmHPnj0YNmwY5/0hk+G4InNozriqqqpCdna2zrwuTWIFMxRXVFSgffv2WLVqFaZOndqi+5YqjUaD0tJSuLq66lxc3Bh3GlvGfH4bdVpKpVLB0dERmZmZ6NWrl7a9Xbt2xmyGiIioLsiY+Hbvuzlx4gTOnDmDsLAwlJSUYOnSpQDqZugl6TAqUtna2qJjx46cy4aIiFqtlStXIjAwEFFRUaioqMDhw4fh4eFh0Zo2b96sfTTC7T89e/a0aG2tkdEXFL/11ltYsGABvv76ax6xISKiVqVv375IT0+3dBl6Ro4cifDwcIOv8XS08YwON5988gnOnz8PX19f+Pv7693n3tA01kRERGSYi4uLdpoVaj6jw83o0aPNUAYRERGRaRgdbmJjY81RBxEREZFJNPmp4ERERETWyOgjNw3N5FiPd1IRERGRJRkdbn744QedZZVKhRMnTmDTpk2Ii4szWWFERERETWF0uDE00dHf//539OzZE9u2beMMj0REZJUiIyMRFBSE+Ph4S5diFSZPnozi4mLs2LHD0qWYnMmuuXnggQeQlJRkqs0REZHEqTVqHM8/jj0X9+B4/nGoNbysgUzD6CM3hty8eRMff/wx2rdvb4rNERGRxB24fADLU5ejoLJA26ZUKDEvbB6i/KMsWJn1EEURarUaNjYm+ai+pxh95MbNzQ3t2rXT/ri5ucHFxQUbNmzAihUrzFEjERFJyIHLBxCTHKMTbACgsLIQMckxOHD5gNn2rdFo8Oabb6Jdu3bw9vbGkiVLtK8VFxfj+eefh6enJ1xdXTFo0CBkZWU1artLlixBUFAQvv76awQEBKBNmzZ4+umnUVZWpu1TXV2NmTNnwsvLCw4ODnjwwQdx/Phx7evJyckQBAF79+5FSEgI7O3t8csvvyAyMhKvvvoqZs+eDTc3NyiVSqxbtw4VFRWYMmUKXFxc8Le//Q179+7VbkutVmPq1Kno1KkTHB0d0bVrV3z00UfN/wW2EkbHwQ8//FDnbimZTAZPT0+Eh4fDzc3NpMUREZG0qDVqLE9dDhGi3msiRAgQ8H7q+xjoNxByMzwhfNOmTYiJicGxY8dw9OhRTJ48GQMGDMDgwYPx1FNPwdHREXv37kWbNm3w2Wef4dFHH8W5c+ca9bihCxcuYMeOHfjpp5/w559/YuzYsVi+fDneffddAMCbb76J77//Hps2bYK/vz8++OADREdH4/z58zrbnzdvHlauXInOnTtrP1c3bdqEN998E6mpqdi2bRtefvll/PDDDxgzZgwWLFiADz/8EBMmTEBOTg4UCgU0Gg06dOiA7du3w93dHUeOHMG0adPg4+ODsWPHmvz3am2MDjeTJ082QxlERHQvyCjM0DticysRIvIr85FRmIF+3v1Mvv8+ffpoJ6Pt0qULPvnkEyQlJcHR0RGpqakoLCyEvb09gLoHbO7YsQPfffcdpk2bdtdtazQabNy4UfsYhQkTJiApKQnvvvsuKioq8Omnn2Ljxo147LHHAADr1q1DYmIivvjiC7zxxhva7SxduhSDBw/W2XZgYCAWLlwIAJg/fz6WL18ODw8PvPDCCwCAxYsX49NPP8Vvv/2GBx54ALa2tjp3MHfq1AlHjx7Ft99+y3BjyJdffglnZ2c89dRTOu3bt29HZWUlJk2aZLLiiIhIWooqi0zaz1h9+vTRWfbx8UFhYSGysrJQXl4Od3d3nddv3ryJCxcuNGrbAQEBOs+Hqt82UHdUR6VSYcCAAdrXbW1tERYWhtOnT+tsJzQ09I51y+VyuLu7o3fv3to2pVIJANr9AcCaNWuwYcMG5OTk4ObNm6ipqUFQUFCj3ktrZ3S4WbZsGT777DO9di8vL0ybNo3hhoiIGuSp8DRpP2Pd/oRtQRCg0WhQXl4OHx8fJCcn663Ttm3bZm3bWLc/kLqhbd/aVn+5SP3+tm7dijlz5mDVqlWIiIiAi4sLVqxYgWPHjhldT2tkdLjJyclBp06d9Nr9/f2Rk5NjkqKIiEiagr2CoVQoUVhZaPC6GwEClAolgr2CW7au4GDk5+fDxsYGAQEBJt/+fffdBzs7O6SkpMDf3x9A3SS4x48fx+zZs02+v5SUFPTv3x+vvPKKtq2xR6CkwOi7pby8vPDbb7/ptWdlZekdziMiIrqVXCbHvLB5AOqCzK3ql+eGzTXLxcR3EhUVhYiICIwePRr79+/HpUuXcOTIEbz11ltIS0tr9vadnJzw8ssv44033kBCQgJOnTqFF154AZWVlWaZ/LZLly5IS0vDvn37cO7cOSxatEjnziypMzrcPPPMM5g5cyYOHjwItVoNtVqNn3/+GbNmzcLTTz9tjhqJiEhCovyjsDpyNbwUXjrtSoUSqyNXW2SeG0EQsGfPHjz88MOYMmUK7r//fjz99NO4fPmy9nqW5lq+fDmefPJJTJgwAcHBwTh//jz27dtnljuNX3zxRTzxxBMYN24cwsPDcf36dZ2jOFIniKKof1zwDmpqajBhwgRs375dO7GQRqPBxIkTsXbtWtjZ2ZmlUFMpLS1FmzZtUFJSAldXV5NuW6VSYc+ePRg2bJje+VGipuK4InNozriqqqpCdnY2OnXqBAcHhybXoNaokVGYgaLKIngqPBHsFdziR2zI9DQaDUpLS+Hq6gqZzLhjKHcaW8Z8fht9zY2dnR22bduGd955B5mZmXB0dETv3r215xCJiIgaQy6Tm+V2b6Imz+ncpUsXdOnSxZS1EBERWaWePXvi8uXLBl/77LPPMH78+BauiO7E6HDz5JNPIiwsDHPnztVp/+CDD3D8+HFs377dZMURERFZgz179kClUhl8zVTX5JDpGB1uDh06pPMsjnqPPfYYVq1aZYqaiIiIrAovvWhdjL5bqry83OBFw7a2tigtLTVJUURERERNZXS46d27N7Zt26bXvnXrVvTo0cMkRRERERE1ldGnpRYtWoQnnngCFy5cwKBBgwAASUlJ2LJlC7777juTF0hERERkDKPDzYgRI7Bjxw689957+O677+Do6IjAwED8/PPPjXokPBEREZE5NelW8OHDh2P48OEA6ibV+eabbzBnzhykp6dDrVabtEAiIiIiYxh9zU29Q4cOYdKkSfD19cWqVaswaNAg/Prrr6asjYiIyGQiIyPN8pBKUxIEATt27NAunzlzBg888AAcHBwQFBRksbpaG6OO3OTn52Pjxo344osvUFpairFjx6K6uho7duzgxcRERGQUUa1GZVo6aouKYOPpCUVoCAT5vf34hby8PJ1nTcXGxsLJyQlnz56Fs7OzBStrXRodbkaMGIFDhw5h+PDhiI+Px9ChQyGXy7F27Vpz1kdERBJUun8/Ct5bhtr8fG2bjbc3lAvmw3XIEAtWZlne3t46yxcuXMDw4cObNc9OTU2N1T/30dQafVpq7969mDp1KuLi4jB8+HDI7/F0TURETVO6fz+uzJqtE2wAoLagAFdmzUbp/v1m27dGo8Gbb76Jdu3awdvbW2dS2uLiYjz//PPw9PSEq6srBg0ahKysrEZtd/LkyRg9erRO2+zZsxEZGaldjoyMxMyZMxvcP6B7WkoQBKSnp2Pp0qUQBEHb9/fff8egQYPg6OgId3d3TJs2DeXl5Xq1vPvuu/D19UXXrl1x6dIlCIKAb7/9Fg899BAcHR3Rr18/nDt3DsePH0doaCicnZ3x2GOPoaioqLG/TqvV6HDzyy+/oKysDCEhIQgPD8cnn3yCa9eumbM2IiKSGFGtRsF7ywBRNPBiXVvBe8sgmunmlE2bNsHJyQnHjh3DBx98gKVLlyIxMREA8NRTT6GwsBB79+5Feno6goOD8eijj+LGjRstsv/b5eXloWfPnnj99deRl5eHOXPmoKKiAtHR0XBzc9M+8ujAgQOYMWOGzrpJSUk4e/YsEhMT8dNPP2nbY2NjsXDhQmRkZMDGxgbPPvss3nzzTXz00Uc4fPgwzp8/j8WLF5vs/VpKo8PNAw88gHXr1iEvLw8vvvgitm7dCl9fX2g0GiQmJqKsrMycdRIRkQRUpqXrHbHRIYqozc9HZVq6Wfbfp08fxMbGokuXLpg4cSJCQ0ORlJSEX375Bampqdi+fTtCQ0PRpUsXrFy5Em3btjXpHG4N7d8Qb29v2NjYwNnZGd7e3nB2dsaWLVtQVVWFr776Cr169cKgQYPwySef4Ouvv0ZBQYF2XScnJ6xfvx49e/ZEz549te1z5sxBdHQ0unfvjlmzZiE9PR2LFi3CgAED0LdvX0ydOhUHDx402fu1FKPvlnJycsJzzz2HX375Bb///jtef/11LF++HF5eXhg5cqQ5aiQiIomobeQpj8b2M1afPn10ln18fFBYWIisrCyUl5fD3d0dzs7O2p/s7GxcuHDB7PtvrNOnTyMwMBBOTk7atgEDBkCj0eDs2bPatt69exu8zubW/dc/8LN37946bcbUY62aNM9Nva5du+KDDz7AsmXL8OOPP2LDhg2mqouIiCTIxtPTpP2MZWtrq7MsCAI0Gg3Ky8vh4+OD5ORkvXXatm171+3KZDKIt51qM/QU8Yb2b2q3hp+G9i8IgsE2c9TT0po8z82t5HI5Ro8ejV27djVp/TVr1iAgIAAODg4IDw9Hampqo9bbunUrBEHQu4iLiIiskyI0BDbe3sBfH6x6BAE23t5QhIa0aF3BwcHIz8+HjY0N/va3v+n8eHh43HV9T09P5OXl6bRlZmaavM7u3bsjKysLFRUV2raUlBTIZDJ07drV5PtrrUwSbppj27ZtiImJQWxsLDIyMhAYGIjo6Oi7Hha7dOkS5syZg4ceeqiFKiUiouYS5HIoF8z/a+G2gPPXsnLB/Baf7yYqKgoREREYPXo09u/fj0uXLuHIkSN46623kJaWdtf1Bw0ahLS0NHz11Vf4448/EBsbi5MnT5q8zvHjx8PBwQGTJk3CyZMncfDgQbz66quYMGGC9jQTNfO0lCmsXr0aL7zwAqZMmQIAWLt2LXbv3o0NGzZg3rx5BtdRq9UYP3484uLicPjwYRQXFze4/erqalRXV2uXS0tLAdQdLjR0yLA56rdn6u3SvY3jisyhOeNKpVJBFEVoNJomncJwjoqCb/yHKHxvGWpvuQjWRqmE1/x5cI6KMtupkfq6b12u//npp5+wcOFCTJkyBUVFRfD29sZDDz0ET0/Pu9YzePBgLFy4EG+++SaqqqowZcoUTJgwASdPntTbn6H939p2++/11tcdHBywd+9evPbaa+jXrx8UCgWeeOIJrFq1StunoW3evu27tTVV/em522toDI1GA1EUoVKp9KacMWasCuLtJwlbUE1NDRQKBb777judU0uTJk1CcXExdu7caXC92NhY/Pbbb/jhhx8wefJkFBcX60xXfaslS5YgLi5Or33Lli1QKBSmeBtERPcUGxsbeHt7w8/Pr1mTw4lqNWoys6C5fg0ydw/YBQXe8zMU3+tqamqQm5uL/Px81NbW6rxWWVmJZ599FiUlJXB1db3jdix65ObatWtQq9V6h9KUSiXOnDljcJ1ffvkFX3zxRaPPZc6fPx8xMTHa5dLSUvj5+WHIkCF3/eUYS6VSITExEYMHD9a7aIyoqTiuyByaM66qqqqQm5sLZ2dnODg4NK+QgZHNW5+sjiiKKCsrg4uLi/ai5caqqqqCo6MjHn74Yb2xVX/mpTEsflrKGGVlZZgwYQLWrVvXqAu8AMDe3h729vZ67ba2tmb7oDDntunexXFF5tCUcaVWqyEIAmQyGWQyi1+62SJ69uyJy5cvG3zts88+w/jx41u4IutVfyqqfowYQyaTQRAEg+PSmHFq0XDj4eEBuVyuM/EQABQUFOg9XwOoe8bGpUuXMGLECG1b/S/RxsYGZ8+exX333WfeoomI6J6zZ8+eBq/54IW81sei4cbOzg4hISFISkrSXnOj0WiQlJSkN5U0AHTr1g2///67TtvChQtRVlaGjz76CH5+fi1RNhER3WOa8+BKankWPy0VExODSZMmITQ0FGFhYYiPj0dFRYX27qmJEyeiffv2WLZsGRwcHNCrVy+d9esnV7q9nYiIzMuC96OQRJlqTFk83IwbNw5FRUVYvHgx8vPzERQUhISEBO1hvpycnHvmnC4RUWtQf4tuTU0NHB0dLVwNSUlNTQ0A6N0GbiyLhxsAmDFjhsHTUAAMToV9q40bN5q+ICIiapCNjQ0UCgWKiopga2vLL6CkQ6PRoKamBlVVVUaNDY1Gg6KiIigUCtjYNC+eWEW4ISKi1kMQBPj4+CA7O7vBO4jo3iWKIm7evAlHR0ejbwWXyWTo2LGj0evdjuGGiIiMZmdnhy5dumhPIxDVU6lUOHToEB5++GGjpxmws7MzyZFAhhsiImoSmUzW/En8SHLkcjlqa2vh4OBgsbm5eKKUiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkxSrCzZo1axAQEAAHBweEh4cjNTW1wb7r1q3DQw89BDc3N7i5uSEqKuqO/YmIiOjeYvFws23bNsTExCA2NhYZGRkIDAxEdHQ0CgsLDfZPTk7GM888g4MHD+Lo0aPw8/PDkCFDcOXKlRaunIiIiKyRjaULWL16NV544QVMmTIFALB27Vrs3r0bGzZswLx58/T6b968WWd5/fr1+P7775GUlISJEyfq9a+urkZ1dbV2ubS0FACgUqmgUqlM+Va02zP1dunexnFF5sBxReZirrFlzPYsGm5qamqQnp6O+fPna9tkMhmioqJw9OjRRm2jsrISKpUK7dq1M/j6smXLEBcXp9e+f/9+KBSKphV+F4mJiWbZLt3bOK7IHDiuyFxMPbYqKysb3dei4ebatWtQq9VQKpU67UqlEmfOnGnUNubOnQtfX19ERUUZfH3+/PmIiYnRLpeWlmpPZbm6uja9eANUKhUSExMxePBg2NramnTbdO/iuCJz4LgiczHX2Ko/89IYFj8t1RzLly/H1q1bkZycDAcHB4N97O3tYW9vr9dua2trtv+hzbltundxXJE5cFyRuZh6bBmzLYuGGw8PD8jlchQUFOi0FxQUwNvb+47rrly5EsuXL8eBAwfQp08fc5ZJRERErYhF75ays7NDSEgIkpKStG0ajQZJSUmIiIhocL0PPvgAb7/9NhISEhAaGtoSpRIREVErYfHTUjExMZg0aRJCQ0MRFhaG+Ph4VFRUaO+emjhxItq3b49ly5YBAN5//30sXrwYW7ZsQUBAAPLz8wEAzs7OcHZ2ttj7ICIiIutg8XAzbtw4FBUVYfHixcjPz0dQUBASEhK0Fxnn5ORAJvu/A0yffvopampq8Pe//11nO7GxsViyZElLlk5ERERWyOLhBgBmzJiBGTNmGHwtOTlZZ/nSpUvmL4iIiIhaLYvPUExERERkSgw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3JqLWqJFRkAEAyCjIgFqjtnBFJAWiWo3KtHQAQGVaOkQ1xxURWS+1RkRq9g0AQGr2Dag1okXqYLgxgQOXDyD6+2hMT5oOAJieNB3R30fjwOUDFq6MWrPS/ftx/tEo5L70EgAg96WXcP7RKJTu32/hyoiI9CWczMOD7/+M5zYdBwA8t+k4Hnz/ZySczGvxWhhumunA5QOISY5BQWWBTnthZSFikmMYcKhJSvfvx5VZs1Gbn6/TXltQgCuzZjPgEJFVSTiZh5f/lYG8kiqd9vySKrz8r4wWDzgMN82g1qixPHU5ROgfdqtvez/1fZ6iIqOIajUK3lsGiAYO5/7VVvDeMp6iIiKroNaIiPvxlIFPQmjb4n481aKnqBhumiGjMEPviM2tRIjIr8xHRmFGC1ZFrV1lWrreERsdooja/HzttThERJaUmn1D74jNrUQAeSVV2mtxWgLDTTMUVRaZtB8RANQWNW68NLYfEZE5FZY1HGya0s8UGG6awVPhadJ+RABg49m48dLYfkRE5uTl4mDSfqbAcNMMwV7BUCqUECAYfF2AAG+FN4K9glu4MmrNFKEhsPH2BgTD4wqCABtvbyhCQ1q2MCIiA8I6tYNPG4cGPgkBAYBPGweEdWrXYjUx3DSDXCbHvLB5AKAXcOqX54bNhVwmb/HaqPUS5HIoF8z/a+G2Pxd/LSsXzIcg57giIsuTywTEjugBAHoBp345dkQPyGUNxR/TY7hppij/KKyOXA0vhZdOu1KhxOrI1Yjyj7JQZdSauQ4ZgvYfxcNGqdRpt1Eq0f6jeLgOGWKhyoiI9A3t5YNP/18wvNvonnrybuOAT/9fMIb28mnRegRRNHS/qXSVlpaiTZs2KCkpgaurq8m2q9aokXY1Dfnp+fAO8UaobyiP2FCziWo1SlOPI7mwAJFeSriG9eMRGzIJlUqFPXv2YNiwYbC1tbV0OSQRao2IX88X4trpX+HR/QE88Dcvkx2xMebzm0duTEQukyNYWXdtTbAymMGGTEKQy7XX1ihCQxhsiMiqyWWC9tqasE7tWvRU1K0YboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiMg0NGrg8tG6f798tG7ZAhhuiIiIqPlO7QLiewFbnqpb3vJU3fKpXS1eilWEmzVr1iAgIAAODg4IDw9HamrqHftv374d3bp1g4ODA3r37o09e/a0UKVERESk59Qu4NuJQOlV3fbSvLr2Fg44Fg8327ZtQ0xMDGJjY5GRkYHAwEBER0ejsLDQYP8jR47gmWeewdSpU3HixAmMHj0ao0ePxsmTJ1u4ciIiIoJGDSTMBSAaePGvtoR5LXqKyqbF9tSA1atX44UXXsCUKVMAAGvXrsXu3buxYcMGzJs3T6//Rx99hKFDh+KNN94AALz99ttITEzEJ598grVr1+r1r66uRnV1tXa5pKQEAHDjxg2oVCqTvheVSoXKykpcv34dtra2Jt023bs4rsgcOK7IZHJSgWvXANgBAFQyu7qxVWMHW42mrs+1IiBrP9AxrMm7KSsrAwCIoqEQdRvRgqqrq0W5XC7+8MMPOu0TJ04UR44caXAdPz8/8cMPP9RpW7x4sdinTx+D/WNjY0XURUf+8Ic//OEPf/jTyn9yc3Pvmi8seuTm2rVrUKvVUCqVOu1KpRJnzpwxuE5+fr7B/vn5+Qb7z58/HzExMdpljUaDGzduwN3dHYIgGFynX79+OH78eIN1N/R6aWkp/Pz8kJubC1dX1wbXtzZ3e7/WuK/mbMfYdRvbvzH97tSH48o69tXUbVnruGro9dY6roCWG1scV9b1WSiKIsrKyuDr63vXvhY/LWVu9vb2sLe312lr27btHdeRy+V3/A9yt9ddXV1b1R+Lu70fa9xXc7Zj7LqN7d+Yfnfqw3FlHftq6rasdVzd7fXWNq6AlhtbHFfW91nYpk2bRvWz6AXFHh4ekMvlKCgo0GkvKCiAt7e3wXW8vb2N6t8U06dPb9brrU1Lvh9T7as52zF23cb2b0y/O/XhuLKOfTV1W9Y6rozZV2vRUu+H46r1jitBFBtzZY75hIeHIywsDP/4xz8A1J026tixI2bMmGHwguJx48ahsrISP/74o7atf//+6NOnj8ELiltSaWkp2rRpg5KSklb3TYisF8cVmQPHFZmLNYwti5+WiomJwaRJkxAaGoqwsDDEx8ejoqJCe/fUxIkT0b59eyxbtgwAMGvWLDzyyCNYtWoVhg8fjq1btyItLQ2ff/65Jd8GgLpTYLGxsXqnwYiag+OKzIHjiszFGsaWxY/cAMAnn3yCFStWID8/H0FBQfj4448RHh4OAIiMjERAQAA2btyo7b99+3YsXLgQly5dQpcuXfDBBx9g2LBhFqqeiIiIrIlVhBsiIiIiU7H4DMVEREREpsRwQ0RERJLCcENERESSwnBDREREksJwYwHFxcUIDQ1FUFAQevXqhXXr1lm6JJKI3NxcREZGokePHujTpw+2b99u6ZJIIsaMGQM3Nzf8/e9/t3Qp1Ir99NNP6Nq1K7p06YL169ebbT+8W8oC1Go1qquroVAoUFFRgV69eiEtLQ3u7u6WLo1auby8PBQUFCAoKAj5+fkICQnBuXPn4OTkZOnSqJVLTk5GWVkZNm3ahO+++87S5VArVFtbix49euDgwYNo06YNQkJCcOTIEbN89vHIjQXI5XIoFAoAQHV1NURRbNwj3InuwsfHB0FBQQDqHlXi4eGBGzduWLYokoTIyEi4uLhYugxqxVJTU9GzZ0+0b98ezs7OeOyxx7B//36z7IvhxoBDhw5hxIgR8PX1hSAI2LFjh16fNWvWICAgAA4ODggPD0dqaqpR+yguLkZgYCA6dOiAN954Ax4eHiaqnqxZS4yteunp6VCr1fDz82tm1WTtWnJc0b2ruePs6tWraN++vXa5ffv2uHLlillqZbgxoKKiAoGBgVizZo3B17dt24aYmBjExsYiIyMDgYGBiI6ORmFhobZP/fU0t/9cvXoVQN2TybOyspCdnY0tW7boPQyUpKklxhYA3LhxAxMnTrSKx5KQ+bXUuKJ7mynGWYsR6Y4AiD/88INOW1hYmDh9+nTtslqtFn19fcVly5Y1aR8vv/yyuH379uaUSa2QucZWVVWV+NBDD4lfffWVqUqlVsScf7MOHjwoPvnkk6Yok1q5poyzlJQUcfTo0drXZ82aJW7evNks9fHIjZFqamqQnp6OqKgobZtMJkNUVBSOHj3aqG0UFBSgrKwMAFBSUoJDhw6ha9euZqmXWg9TjC1RFDF58mQMGjQIEyZMMFep1IqYYlwR3U1jxllYWBhOnjyJK1euoLy8HHv37kV0dLRZ6rH4U8Fbm2vXrkGtVkOpVOq0K5VKnDlzplHbuHz5MqZNm6a9kPjVV19F7969zVEutSKmGFspKSnYtm0b+vTpoz0f/vXXX3N83cNMMa4AICoqCllZWaioqECHDh2wfft2REREmLpcaqUaM85sbGywatUqDBw4EBqNBm+++abZ7hJmuLGAsLAwZGZmWroMkqAHH3wQGo3G0mWQBB04cMDSJZAEjBw5EiNHjjT7fnhaykgeHh6Qy+V6FwAXFBTA29vbQlWRFHBskTlwXFFLsLZxxnBjJDs7O4SEhCApKUnbptFokJSUxEO01CwcW2QOHFfUEqxtnPG0lAHl5eU4f/68djk7OxuZmZlo164dOnbsiJiYGEyaNAmhoaEICwtDfHw8KioqMGXKFAtWTa0BxxaZA8cVtYRWNc7Mcg9WK3fw4EERgN7PpEmTtH3+8Y9/iB07dhTt7OzEsLAw8ddff7VcwdRqcGyROXBcUUtoTeOMz5YiIiIiSeE1N0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0TUKgUEBCA+Pt7SZRCRFeLjF4ioQZMnT0ZxcTF27Nhh6VL0FBUVwcnJCQqFwtKlGGTNvzsiqeORGyKyKiqVqlH9PD09LRJsGlsfEVkOww0RNdnJkyfx2GOPwdnZGUqlEhMmTMC1a9e0ryckJODBBx9E27Zt4e7ujscffxwXLlzQvn7p0iUIgoBt27bhkUcegYODAzZv3ozJkydj9OjRWLlyJXx8fODu7o7p06frBIvbT0sJgoD169djzJgxUCgU6NKlC3bt2qVT765du9ClSxc4ODhg4MCB2LRpEwRBQHFxcYPvURAEfPrppxg5ciScnJzw7rvvQq1WY+rUqejUqRMcHR3RtWtXfPTRR9p1lixZgk2bNmHnzp0QBAGCICA5ORkAkJubi7Fjx6Jt27Zo164dRo0ahUuXLjXtPwARGcRwQ0RNUlxcjEGDBqFv375IS0tDQkICCgoKMHbsWG2fiooKxMTEIC0tDUlJSZDJZBgzZgw0Go3OtubNm4dZs2bh9OnTiI6OBgAcPHgQFy5cwMGDB7Fp0yZs3LgRGzduvGNNcXFxGDt2LH777TcMGzYM48ePx40bNwAA2dnZ+Pvf/47Ro0cjKysLL774It56661GvdclS5ZgzJgx+P333/Hcc89Bo9GgQ4cO2L59O06dOoXFixdjwYIF+PbbbwEAc+bMwdixYzF06FDk5eUhLy8P/fv3h0qlQnR0NFxcXHD48GGkpKTA2dkZQ4cORU1NTWN/9UR0NyIRUQMmTZokjho1yuBrb7/9tjhkyBCdttzcXBGAePbsWYPrFBUViQDE33//XRRFUczOzhYBiPHx8Xr79ff3F2tra7VtTz31lDhu3Djtsr+/v/jhhx9qlwGICxcu1C6Xl5eLAMS9e/eKoiiKc+fOFXv16qWzn7feeksEIP7555+GfwF/bXf27NkNvl5v+vTp4pNPPqnzHm7/3X399ddi165dRY1Go22rrq4WHR0dxX379t11H0TUODxyQ0RNkpWVhYMHD8LZ2Vn7061bNwDQnnr6448/8Mwzz6Bz585wdXVFQEAAACAnJ0dnW6GhoXrb79mzJ+RyuXbZx8cHhYWFd6ypT58+2n93cnKCq6urdp2zZ8+iX79+Ov3DwsIa9V4N1bdmzRqEhITA09MTzs7O+Pzzz/Xe1+2ysrJw/vx5uLi4aH9n7dq1Q1VVlc7pOiJqHhtLF0BErVN5eTlGjBiB999/X+81Hx8fAMCIESPg7++PdevWwdfXFxqNBr169dI7BePk5KS3DVtbW51lQRD0TmeZYp3GuL2+rVu3Ys6cOVi1ahUiIiLg4uKCFStW4NixY3fcTnl5OUJCQrB582a91zw9PZtdJxHVYbghoiYJDg7G999/j4CAANjY6P8puX79Os6ePYt169bhoYceAgD88ssvLV2mVteuXbFnzx6dtuPHjzdpWykpKejfvz9eeeUVbdvtR17s7OygVqt12oKDg7Ft2zZ4eXnB1dW1SfsmorvjaSkiuqOSkhJkZmbq/OTm5mL69Om4ceMGnnnmGRw/fhwXLlzAvn37MGXKFKjVari5ucHd3R2ff/45zp8/j59//hkxMTEWex8vvvgizpw5g7lz5+LcuXP49ttvtRcoC4Jg1La6dOmCtLQ07Nu3D+fOncOiRYv0glJAQAB+++03nD17FteuXYNKpcL48ePh4eGBUaNG4fDhw8jOzkZycjJmzpyJ//3vf6Z6q0T3PIYbIrqj5ORk9O3bV+cnLi4Ovr6+SElJgVqtxpAhQ9C7d2/Mnj0bbdu2hUwmg0wmw9atW5Geno5evXrhtddew4oVKyz2Pjp16oTvvvsO//73v9GnTx98+umn2rul7O3tjdrWiy++iCeeeALjxo1DeHg4rl+/rnMUBwBeeOEFdO3aFaGhofD09ERKSgoUCgUOHTqEjh074oknnkD37t0xdepUVFVV8UgOkQlxhmIiume9++67WLt2LXJzcy1dChGZEK+5IaJ7xj//+U/069cP7u7uSElJwYoVKzBjxgxLl0VEJsZwQ0T3jD/++APvvPMObty4gY4dO+L111/H/PnzLV0WEZkYT0sRERGRpPCCYiIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSlP8PJFasb6Xg6dgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#learning_rate \n",
    "subset0=df[(df['kernal_init']=='glorot_uniform')\n",
    "           *(df['regularization']=='None')\n",
    "           *(df['activation']=='relu')\n",
    "           *(df['optimizer']=='SGD')]\n",
    "subset2=df[(df['kernal_init']=='glorot_normal')\n",
    "           *(df['regularization']=='None')\n",
    "           *(df['activation']=='relu')\n",
    "           *(df['optimizer']=='SGD')]\n",
    "subset3=df[(df['kernal_init']=='he_normal')\n",
    "           *(df['regularization']=='None')\n",
    "           *(df['activation']=='relu')\n",
    "           *(df['optimizer']=='SGD')]\n",
    "subset4=df[(df['kernal_init']=='he_uniform')\n",
    "           *(df['regularization']=='None')\n",
    "           *(df['activation']=='relu')\n",
    "           *(df['optimizer']=='SGD')]\n",
    "plt.scatter(subset2['learning_rate'],subset2['accuracy'],label='glorot_normal')\n",
    "plt.scatter(subset0['learning_rate'],subset0['accuracy'],label='glorot_uniform')\n",
    "plt.scatter(subset3['learning_rate'],subset2['accuracy'],label='he_normal')\n",
    "plt.scatter(subset4['learning_rate'],subset2['accuracy'],label='he_uniform')\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.ylim(0,1)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Learning rate')\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cifar10=keras.datasets.cifar10.load_data()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}