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diff --git a/Lab2/examples/Lab24-iris.ipynb b/Lab2/examples/Lab24-iris.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Load the Iris Dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#Load CSV files\n",
+    "inputs_train=pd.read_csv('datasets/iris_train.csv',usecols = [0,1,2,3],skiprows = None,header=None).values\n",
+    "labels_train = pd.read_csv('datasets/iris_train.csv',usecols = [4],skiprows = None ,header=None).values.reshape(-1)\n",
+    "inputs_test=pd.read_csv('datasets/iris_test.csv',usecols = [0,1,2,3],skiprows = None,header=None).values\n",
+    "labels_test = pd.read_csv('datasets/iris_test.csv',usecols = [4],skiprows = None ,header=None).values.reshape(-1)\n",
+    "\n",
+    "#print(\"Data loaded\")\n",
+    "#print(\"Train set inputs:\",inputs_train)\n",
+    "#print(\"Train set labels:\",labels_train)\n",
+    "#print(\"Test set inputs:\",inputs_test)\n",
+    "#print(\"Test set labels:\",labels_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build the neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_26\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense_78 (Dense)            (None, 20)                100       \n",
+      "                                                                 \n",
+      " dense_79 (Dense)            (None, 20)                420       \n",
+      "                                                                 \n",
+      " dense_80 (Dense)            (None, 3)                 63        \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 583\n",
+      "Trainable params: 583\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "k_l2=0\n",
+    "\n",
+    "keras_model = tf.keras.Sequential([\n",
+    "    tf.keras.layers.Dense(20, activation='tanh',kernel_regularizer=keras.regularizers.l2(k_l2)),\n",
+    "    tf.keras.layers.Dense(20, activation='tanh',kernel_regularizer=keras.regularizers.l2(k_l2)),\n",
+    "    tf.keras.layers.Dense(3, activation='softmax',kernel_regularizer=keras.regularizers.l2(k_l2))\n",
+    "])\n",
+    "\n",
+    "keras_model.build(input_shape=[None,4])\n",
+    "keras_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train the neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "keras_model.compile(\n",
+    "    optimizer=tf.keras.optimizers.Adam(), # Optimizer\n",
+    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(), # Loss function to minimize\n",
+    "    metrics=[keras.metrics.SparseCategoricalAccuracy()] # List of metrics to monitor\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training started ..\n",
+      "Training finished.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train loop\n",
+    "\n",
+    "print(\"Training started ..\")\n",
+    "\n",
+    "history = keras_model.fit(\n",
+    "    inputs_train,\n",
+    "    labels_train,\n",
+    "    batch_size=len(inputs_train),\n",
+    "    epochs=2000,\n",
+    "    validation_data=(inputs_test, labels_test),\n",
+    "    verbose=0\n",
+    ")\n",
+    "\n",
+    "print(\"Training finished.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RkbpY3CiME4qJiIjUxeJGYZxQTEREpC4WNwpzX6GYxQ0REZEqWNwo7NycGx6WIiIiUgOLG4XxsBQREZG6WNwopewUpF+/wB+smwCwuCEiIlILixulFB2G4b8zMKbiKwBANQ9LERERqYLFjVJMIQCAAFEFgCM3REREamFxoxSzXNyYRSUAXueGiIhILSxulGIKlZ+EFXo4OHJDRESkEhY3SqkZuQGAYFTxVHAiIiKVsLhRisEMoTcBkIsbXsSPiIhIHSxulFQzqThYquRhKSIiIpWwuFFSTXETgipOKCYiIlIJixslmc+N3FRy5IaIiEgVLG4UJM4buamw2lWOhoiIqGVicaMkd3FTCZtDwMozpoiIiJocixslnTehGABHb4iIiFTA4kZJNcVNmE6+BUOFlfNuiIiImhqLGwUJs3yV4ki9FQBHboiIiNTA4kZJF4zclFdz5IaIiKipsbhRkjkYABCmqwbAw1JERERqULW4Wb9+PcaMGYPExERIkoRly5Zdcp21a9eiX79+MJvN6NChA5YsWeLzOBusZuQm1D3nhoeliIiImpqqxU15eTl69+6NRYsWNaj/kSNHcMMNN+Daa69FTk4OHnroIdx77734/vvvfRxpw4jzTgUHgHKO3BARETU5g5o7HzVqFEaNGtXg/u+99x7atm2L1157DQDQtWtXbNiwAa+//jpGjBhR5zrV1dWorq52v7dYLAAAm80Gm83WiOhrcxqCYQAQgnIAQFllteL7UJMrFy3ldCGt58j8/J/Wc9R6foD2c/RVft5sT9XixlvZ2dlIT0/3aBsxYgQeeuiheteZN28e5syZU6t91apVCAoKUjS+qLIDuApAgK0EAPBzzk4EF/yq6D6ag8zMTLVD8Dmt58j8/J/Wc9R6foD2c1Q6v4qKigb39aviJj8/H3FxcR5tcXFxsFgsqKysRGBgYK11Zs2ahYyMDPd7i8WC5ORkDB8+HGFhYYrGZ89PAQ78FRGS/AGktu+M0UPbKboPNdlsNmRmZmLYsGEwGo1qh+MTWs+R+fk/reeo9fwA7efoq/xcR14awq+Km8thNpthNptrtRuNRuV/qUJiAAABzjLo4ESVQ2jyF9cnP7tmRus5Mj//p/UctZ4foP0clc7Pm2351ang8fHxKCgo8GgrKChAWFhYnaM2TS4wAgCgg0AYylHJCcVERERNzq+Km7S0NGRlZXm0ZWZmIi0tTaWILqA3waYLAABESGUor+ap4ERERE1N1eKmrKwMOTk5yMnJASCf6p2Tk4Pc3FwA8nyZSZMmuftPmzYNhw8fxuOPP469e/finXfewb/+9S88/PDDaoRfJ5tBPh08AuW8iB8REZEKVC1utm7dir59+6Jv374AgIyMDPTt2xfPPvssACAvL89d6ABA27Zt8d133yEzMxO9e/fGa6+9hr///e/1ngauBqtevkpxhFTKi/gRERGpQNUJxUOHDoUQot7ldV19eOjQofjll198GFXjWM8buTnJkRsiIqIm51dzbvyBrWbkJlIq5ZwbIiIiFbC4UViVMQIAECsVo7SKxQ0REVFTY3GjMFdxEyedRWmVNi+tTURE1JyxuFFYlTESABCHs7BU2S86p4iIiIiUx+JGYa7iJl4qgsMpeDo4ERFRE2NxozD3yI1UDACw8NAUERFRk2Jxo7DKmuImRKpEMCo5qZiIiKiJsbhRmEMfABEQAQBIlk7DUsmRGyIioqbE4sYHRKsOAID20kkeliIiImpiLG58oVUnAEAH6QQPSxERETUxFjc+IKI7AgA66E7wsBQREVETY3HjAyKmCwCgm3QMFo7cEBERNSkWNz4gkgZAQEJ7XR4cJflqh0NERNSisLjxhcBIFAbLh6aii7aqHAwREVHLwuLGRwpaDQQAtC3OVjkSIiKiloXFjY8Utb4OANCtLBtw8hYMRERETYXFjY+I5D/AIoIQ7iwBfuehKSIioqbC4sZHokJDsMbZR36zb4WqsRAREbUkXhc3H3/8Mb777jv3+8cffxwREREYPHgwjh07pmhw/iwy2IgsRz8AgNj3P5WjISIiajm8Lm7mzp2LwMBAAEB2djYWLVqE+fPnIzo6Gg8//LDiAfqrVsFmrHX2gk3oIRXuA84cUjskIiKiFsHr4ub48ePo0EG+d9KyZctw22234b777sO8efPw448/Kh6gvwo06WEzhmOLU76gHw6sUjcgIiKiFsLr4iYkJARnzpwBAKxatQrDhg0DAAQEBKCyslLZ6PxcVLAJm5zd5TcntqkbDBERUQth8HaFYcOG4d5770Xfvn2xf/9+jB49GgDw22+/ITU1Ven4/FpUsAm7LG3lN3k71A2GiIiohfB65GbRokVIS0vD6dOn8dVXX6FVq1YAgG3btmHChAmKB+jPooJN+M2ZKr8pPABUl6kaDxERUUvg9chNREQE3n777Vrtc+bMUSQgLWkVbEIhwlFuikawtRA4tQdIvkLtsIiIiDTN65GblStXYsOGDe73ixYtQp8+fTBx4kScPXtW0eD8XWSwCQBQaG4jNxTxjCkiIiJf87q4eeyxx2CxWAAAO3fuxCOPPILRo0fjyJEjyMjIUDxAf9YqRC5uTuqT5IYzB1WMhoiIqGXw+rDUkSNH0K1bNwDAV199hRtvvBFz587F9u3b3ZOLSRYbGgAAOCISkAbwWjdERERNwOuRG5PJhIqKCgDADz/8gOHDhwMAoqKi3CM6JIsNNQMA9tli5QaO3BAREfmc1yM3V155JTIyMjBkyBBs2bIFS5cuBQDs378frVu3VjxAfxYXJo/c/FoZLTecOQQIAUiSilERERFpm9cjN2+//TYMBgO+/PJLvPvuu0hKkueT/O9//8PIkSMVD9CfuUZudlVGQUg6wFYOlJ1SOSoiIiJt83rkpk2bNvj2229rtb/++uuKBKQlEUFGmPQ6WB0GOILjYSg7CZT8DoTGqR0aERGRZnld3ACAw+HAsmXLsGfPHgBA9+7dMXbsWOj1ekWD83eSJCEm1IwTxZWoCkpASNlJoOQ40Lq/2qERERFpltfFzcGDBzF69GicOHECnTt3BgDMmzcPycnJ+O6779C+fXvFg/RncWFycVNqjkMIII/cEBERkc94PefmwQcfRPv27XH8+HFs374d27dvR25uLtq2bYsHH3zQFzH6Ndfp4Gf0NWdMlRxXMRoiIiLt83rkZt26dfjpp58QFRXlbmvVqhVeeuklDBkyRNHgtCAuTJ5UnC9FowfAkRsiIiIf83rkxmw2o7S0tFZ7WVkZTCaTIkFpSWzN6eC5DvkGoxy5ISIi8i2vi5sbb7wR9913HzZv3gwhBIQQ+OmnnzBt2jSMHTvWFzH6tZia08EPW2tGujhyQ0RE5FNeFzdvvvkm2rdvj7S0NAQEBCAgIABDhgxBhw4dsHDhQh+E6N9cF/LbUxEmN1ScAawVKkZERESkbV7PuYmIiMDy5ctx8OBB96ngXbt2RYcOHRQPTgtcF/I7UmYATKGAtVQevYnppHJkRERE2nRZ17kBgA4dOngUNL/++isGDBgAq9WqSGBaEV8zclNUYYOzdSJ0hfsAC4sbIiIiX/H6sFR9hBBwOBxer7do0SKkpqYiICAAgwYNwpYtWy7af+HChejcuTMCAwORnJyMhx9+GFVVVZcbts9FBBlhNsg/5uqgBLnRclLFiIiIiLRNseLmcixduhQZGRmYPXs2tm/fjt69e2PEiBE4daru+y999tlnePLJJzF79mzs2bMHH374IZYuXYqnnnqqiSNvOEmSEB8uj96Ummtuu1ByQsWIiIiItE3V4mbBggWYOnUqpkyZgm7duuG9995DUFAQFi9eXGf/TZs2YciQIZg4cSJSU1MxfPhwTJgw4ZKjPWpzH5rSx8gNFp4xRURE5CsNnnNjsVguuryua99cjNVqxbZt2zBr1ix3m06nQ3p6OrKzs+tcZ/Dgwfj000+xZcsWDBw4EIcPH8aKFStw55131ruf6upqVFdX18rDZrPBZrN5FfOluLZ34XbjaiYV5zkj0QWAs/gEHArvuynUl5+WaD1H5uf/tJ6j1vMDtJ+jr/LzZnuSEEI0pKNOp4MkSfUuF0JAkqQGz7s5efIkkpKSsGnTJqSlpbnbH3/8caxbtw6bN2+uc70333wTjz76KIQQsNvtmDZtGt5999169/Pcc89hzpw5tdo/++wzBAUFNSjWxvrPMR2yTurwQKsdeLz8ZVgCkrCm67wm2TcREZEWVFRUYOLEiSgpKUFYWNhF+zZ45GbNmjWNDqyx1q5di7lz5+Kdd97BoEGDcPDgQcycORMvvPACnnnmmTrXmTVrFjIyMtzvLRYLkpOTMXz48Ev+cLxls9mQmZmJYcOGwWg0utvP/JSLrJN7UR7ZCSgHQkUpRo8erei+m0J9+WmJ1nNkfv5P6zlqPT9A+zn6Kr9LHUE6X4OLm2uuueaygqlPdHQ09Ho9CgoKPNoLCgoQHx9f5zrPPPMM7rzzTtx7770AgJ49e6K8vBz33Xcfnn76aeh0tacQmc1mmM3mWu1Go9Fnv1QXbjsxMhgAsK8yHAAgVVtgdFYB5lCf7N/XfPmzay60niPz839az1Hr+QHaz1Hp/LzZlmoTik0mE/r374+srCx3m9PpRFZWlsdhqvNVVFTUKmD0ej0A+bBYc5VQc7bUsTIdYJYLHJ4xRURE5BuXfRE/JWRkZGDy5MkYMGAABg4ciIULF6K8vBxTpkwBAEyaNAlJSUmYN0+enzJmzBgsWLAAffv2dR+WeuaZZzBmzBh3kdMcuYqbU6XVEEmJkE6XAJYTQGwXlSMjIiLSHlWLm/Hjx+P06dN49tlnkZ+fjz59+mDlypWIi5OvB5Obm+sxUvOXv/wFkiThL3/5C06cOIGYmBiMGTMGL774olopNEirEDMMOgl2p4A1OBHm03vk4oaIiIgUp2pxAwAzZszAjBkz6ly2du1aj/cGgwGzZ8/G7NmzmyAy5eh1EmJDzThZUoVSUyzMAK9STERE5CNez7n56KOPUFHBu1p7y3WV4rOGmgv5lfBCfkRERL7gdXHz5JNPIj4+Hvfccw82bdrki5g0KSE8EACQjyi5gYeliIiIfMLr4ubEiRP4+OOPUVhYiKFDh6JLly54+eWXkZ+f74v4NMM1cvO7w1Xc8LAUERGRL3hd3BgMBtxyyy1Yvnw5jh8/jqlTp+Kf//wn2rRpg7Fjx2L58uVwOp2+iNWvue4vdcgaITfwVHAiIiKfaNR1buLi4nDllVciLS0NOp0OO3fuxOTJk9G+fftak4FbOtfIzb7ymqsiW0uBqoZfbZGIiIga5rKKm4KCArz66qvo3r07hg4dCovFgm+//RZHjhzBiRMnMG7cOEyePFnpWP3auQv5AQiIkBs574aIiEhxXhc3Y8aMQXJyMpYsWYKpU6fixIkT+Pzzz5Geng4ACA4OxiOPPILjx48rHqw/c43c5JdUQYQlyo08NEVERKQ4r69zExsbi3Xr1tV7iwQAiImJwZEjRxoVmNbEhsrFjdXhhC04ESbs5sgNERGRD3hd3Hz44YeX7CNJElJSUi4rIK0yGXSIDjGjsKwaZeY4+YRwFjdERESKu6w5N1lZWbjxxhvRvn17tG/fHjfeeCN++OEHpWPTnIQLL+TH4oaIiEhxXhc377zzDkaOHInQ0FDMnDkTM2fORFhYGEaPHo1Fixb5IkbNcM27KZBayQ2cc0NERKQ4rw9LzZ07F6+//rrH/aAefPBBDBkyBHPnzsX06dMVDVBLXNe6+d0RKTdw5IaIiEhxXo/cFBcXY+TIkbXahw8fjpKSEkWC0irXyM2h6pripuQEIISKEREREWmP18XN2LFj8c0339RqX758OW688UZFgtIq15yb/ZUhcoOtHKhiQUhERKQkrw9LdevWDS+++CLWrl3rPh38p59+wsaNG/HII4/gzTffdPd98MEHlYtUA1wjN7mlAAIjgcqz8j2mAiNUjYuIiEhLLutU8MjISOzevRu7d+92t0dERHicJi5JEoubC7jm3OSVVEEkJEGqPCvPu4nrpnJkRERE2uF1ccOL810+18hNhdUBe0gijAW7gJLfVY6KiIhIWxp140whBAQnxDZYkMmA8EAjAKDcHCc3Wk6qGBEREZH2XFZx88knn6Bnz54IDAxEYGAgevXqhX/84x9Kx6ZJ7gv5GXkhPyIiIl/w+rDUggUL8Mwzz2DGjBkYMmQIAGDDhg2YNm0aCgsL8fDDDysepJbEhQVgb34pTqEV2gIsboiIiBTmdXHz1ltv4d1338WkSZPcbWPHjkX37t3x3HPPsbi5BNfIzXFHKwwCgGLePZ2IiEhJXh+WysvLw+DBg2u1Dx48GHl5eYoEpWWuScUH7dFyQ3Eu4HSoGBEREZG2eF3cdOjQAf/6179qtS9duhQdO3ZUJCgtc43c7KsIAfQmwGnjGVNEREQK8vqw1Jw5czB+/HisX7/ePedm48aNyMrKqrPoIU/x4YEAgDyLDYhIAc4cAM4eASJTVI6MiIhIG7weubntttuwZcsWREdHY9myZVi2bBmio6OxZcsW3HLLLb6IUVNcF/LLt1QBUe3kxiJeO4iIiEgpXo3c2Gw2/PnPf8YzzzyDTz/91FcxaZprzk1xhQ228BQYAaDosKoxERERaYlXIzdGoxFfffWVr2JpEcIDjQgNkGvKs+YkufEsR26IiIiU4vVhqZtvvhnLli3zQSgtR+vIIABAvj5Bbig6ql4wREREGuP1hOKOHTvi+eefx8aNG9G/f38EBwd7LOfNMi+tdWQg9uRZcMQRi16APHIjBCBJaodGRETk9y7rruARERHYtm0btm3b5rGMdwJvmNaR8hlT+6xRACTAWgaUFwIhMeoGRkREpAG8K7gKXIeljpU4gLAkwPK7PHrD4oaIiKjRvJ5z8/zzz6OioqJWe2VlJZ5//nlFgtK6pAh55Ob3s5VAVFu5kWdMERERKcLr4mbOnDkoKyur1V5RUYE5c+YoEpTWuQ5LnThbAbTqIDcWHlAxIiIiIu3wurgRQkCqY+Lrjh07EBUVpUhQWpdcc1iqsMwKa1QnufH0XhUjIiIi0o4Gz7mJjIyEJEmQJAmdOnXyKHAcDgfKysowbdo0nwSpNWGBBoSaDSittuN0QAqSAKBwv9phERERaUKDi5uFCxdCCIG7774bc+bMQXh4uHuZyWRCamoq0tLSfBKk1kiShKTIQOzNL8VRXbJc3Jw5BNitgMGkdnhERER+rcHFzeTJkwEAbdu2xeDBg2E0Gn0WVEvQOjIIe/NLcbgqDENMoYC1VJ5UHNtF7dCIiIj8mtengl9zzTVwOp3Yv38/Tp06BafT6bH86quvViw4LXNNKv69uBKI6Qyc2CrPu2FxQ0RE1CheFzc//fQTJk6ciGPHjkEI4bFMkiQ4HA7FgtMyd3Fz9rzihvNuiIiIGs3r4mbatGkYMGAAvvvuOyQkJNR55hRdmutCfr+frQRSOsuNPGOKiIio0bwubg4cOIAvv/wSHTp08EU8LUabKLm4yT1TDkS7ihuO3BARETWW19e5GTRoEA4ePOiLWFqUlFZycXO2wgZLaHu5sXA/4LCrGBUREZH/87q4+b//+z888sgjWLJkCbZt24Zff/3V4+GtRYsWITU1FQEBARg0aBC2bNly0f7FxcWYPn06EhISYDab0alTJ6xYscLr/aot2GxATKgZAHDU0QowhQCOauAMC0ciIqLG8Pqw1G233QYAuPvuu91tkiS5r1zszYTipUuXIiMjA++99x4GDRqEhQsXYsSIEdi3bx9iY2Nr9bdarRg2bBhiY2Px5ZdfIikpCceOHUNERIS3aTQLqa2CcLq0GkeLqtArthvw+xagYBfPmCIiImoEVe8KvmDBAkydOhVTpkwBALz33nv47rvvsHjxYjz55JO1+i9evBhFRUXYtGmT+zo7qampisXT1FJaBePno2dxrLAciO8hFzf5O4Gef1Q7NCIiIr/ldXGTkpKiyI6tViu2bduGWbNmudt0Oh3S09ORnZ1d5zr/+c9/kJaWhunTp2P58uWIiYnBxIkT8cQTT0Cv19e5TnV1Naqrq93vLRYLAMBms8FmsymSi4trew3dbnJEAADgcGEZHKndoAfgzNsJh8JxKcXb/PyR1nNkfv5P6zlqPT9A+zn6Kj9vttfg4uaBBx7A/PnzERISAgD4/PPPMXbsWAQHBwOQ58JMnDixwfNfCgsL4XA4EBcX59EeFxeHvXvrPiX68OHDWL16Ne644w6sWLECBw8exAMPPACbzYbZs2fXuc68efPqvFv5qlWrEBQU1KBYvZWZmdmgfkWFEgA9cg6ewEaHBVcDsOZuxffNfA5RQ/PzZ1rPkfn5P63nqPX8AO3nqHR+FRUVDe4riQuvxFcPvV6PvLw891yYsLAw5OTkoF27dgCAgoICJCYmNnjOzcmTJ5GUlIRNmzZ53JPq8ccfx7p167B58+Za63Tq1AlVVVU4cuSIe6RmwYIFeOWVV5CXl1fnfuoauUlOTkZhYSHCwsIaFGtD2Ww2ZGZmYtiwYQ26PcVvJy24+d2fEB1iQvbDV8DwSiokCNge2gMExygamxK8zc8faT1H5uf/tJ6j1vMDtJ+jr/KzWCyIjo5GSUnJJb+/Gzxyc2EN1MCaqF7R0dHQ6/UoKCjwaC8oKEB8fHyd6yQkJMBoNHocguratSvy8/NhtVphMtW+6aTZbIbZbK7VbjQaffZL1dBtt4uTP5zCMiuqDSEwRrUDig7BeGYvEJHok9iU4MufXXOh9RyZn//Teo5azw/Qfo5K5+fNtrw+FVwpJpMJ/fv3R1ZWlrvN6XQiKyur3ruLDxkyBAcPHvS4n9X+/fuRkJBQZ2HT3IUFGNEqWI772JmaScUAkL9LxaiIiIj8m2rFDQBkZGTggw8+wMcff4w9e/bg/vvvR3l5ufvsqUmTJnlMOL7//vtRVFSEmTNnYv/+/fjuu+8wd+5cTJ8+Xa0UGs11Mb+jhRVAXE+5sYDFDRER0eXy6mypZ5991j0J12q14sUXX0R4eDgA7yb6uIwfPx6nT5/Gs88+i/z8fPTp0wcrV650TzLOzc2FTneu/kpOTsb333+Phx9+GL169UJSUhJmzpyJJ554wut9NxeprYKxPbcYR8+UA4kcuSEiImqsBhc3V199Nfbt2+d+P3jwYBw+fLhWH2/NmDEDM2bMqHPZ2rVra7WlpaXhp59+8no/zVVKK/lss6OF5UDfmuKmcB9grwYMtecKERER0cU1uLipq9CgxmsfKxc3h06XAeG9gIBwoKpEvkN4Qm+VoyMiIvI/jZpzs3HjRo/TrMl7HWLl6wYdOFUGAQDxveQFeTtUi4mIiMifNaq4GTVqFE6cOKFULC1S2+hg6CSgtMqO06XVQGIfeQGLGyIiosvSqOKmsde6IcBs0KNNlDxJ++CpMiChj7zgZI5qMREREfkzVU8FJ1mH2FAA8qEpd3FTsAtw2NULioiIyE81qrh5//33a90birznmndz8FQZENUOMIUC9ir5rCkiIiLySqOKm4kTJ8LhcGDZsmXYs2ePUjG1OB7FjU4HJHBSMRER0eXyurgZN24c3n77bQBAZWUlBgwYgHHjxqFXr1746quvFA+wJTj/jCkAnHdDRETUCF4XN+vXr8dVV10FAPjmm28ghEBxcTHefPNN/PWvf1U8wJbAVdwUllWjpMJ27vo2HLkhIiLymtfFTUlJCaKiogAAK1euxG233YagoCDccMMNOHDggOIBtgQhZgMSwgMAAAdPl547HTz/V8DpUC8wIiIiP+R1cZOcnIzs7GyUl5dj5cqVGD58OADg7NmzCAgIUDzAlsI1erO/oAxo1QEwBgO2CuDMQZUjIyIi8i9eFzcPPfQQ7rjjDrRu3RqJiYkYOnQoAPlwVc+ePZWOr8XoHCefDr4vvxTQ6YH4mp8l590QERF5xevi5oEHHkB2djYWL16MDRs2uO/a3a5dO865aYRuiWEAgN0nLXKDe95NjjoBERER+akG3zjzfAMGDMCAAQMAAA6HAzt37sTgwYMRGRmpaHAtibu4ybPA6RTQ8TYMREREl+WyDkt9+OGHAOTC5pprrkG/fv2QnJzMO4c3QvuYEJj0OpRV2/H72crzRm5+BZxOdYMjIiLyI14XN19++SV695a/eP/73//iyJEj2Lt3Lx5++GE8/fTTigfYUhj1OnSMkycV786zANGdAUMgYC3lpGIiIiIveF3cFBYWIj4+HgCwYsUK3H777ejUqRPuvvtu7Ny5U/EAW5JuCecOTUFvOHdK+O8/qxcUERGRn/G6uImLi8Pu3bvhcDiwcuVKDBs2DABQUVEBvV6veIAtSa1Jxa2vkJ9/36JSRERERP7H6wnFU6ZMwbhx45CQkABJkpCeng4A2Lx5M7p06aJ4gC2Ja+RmT15NcZM8UH4+zuKGiIioobwubp577jn06NEDx48fx+233w6z2QwA0Ov1ePLJJxUPsCXpWjNyc6K4EsUVVkS0riluTu0BqkqAgHAVoyMiIvIPl3Uq+B//+MdabZMnT250MC1dWIARyVGBOF5Uid9OWjCkQxwQkQIUHwN+3wp0uF7tEImIiJo9r+fcAMC6deswZswYdOjQAR06dMDYsWPx448/Kh1bi9SrdQQAYMfvxXJD8iD5mZOKiYiIGsTr4ubTTz9Feno6goKC8OCDD+LBBx9EYGAgrr/+enz22We+iLFF6VNT3OTkFssNnHdDRETkFa8PS7344ouYP38+Hn74YXfbgw8+iAULFuCFF17AxIkTFQ2wpemdHAHg/JGbmuLm963yxfx0lzXYRkRE1GJ4/U15+PBhjBkzplb72LFjceTIEUWCasl6JIVBr5NQYKlGfkkVENtdvkN4dQlweq/a4RERETV7Xhc3ycnJyMrKqtX+ww8/IDk5WZGgWrIgkwGdau4QnnP8rHwxv+Sa690c26hiZERERP7B68NSjzzyCB588EHk5ORg8ODBAICNGzdiyZIleOONNxQPsCXqkxyBPXkW5BwvwcgeCUDqlcDhtcDRDcDAqWqHR0RE1Kx5Xdzcf//9iI+Px2uvvYZ//etfAICuXbti6dKluOmmmxQPsCXqkxyOz7cAO44Xyw0pV8rPRzcAQgCSpFpsREREzZ1XxY3dbsfcuXNx9913Y8OGDb6KqcVzTSr+9fdiOJwC+qR+8k00KwqB0/uAWF4JmoiIqD5ezbkxGAyYP38+7Ha7r+IhAB1jQxFqNqDc6pBvxWAwnztr6hiLSiIioovxekLx9ddfj3Xr1vkiFqqh10nolxIJAPj5aJHcmHreoSkiIiKql9dzbkaNGoUnn3wSO3fuRP/+/REcHOyxfOzYsYoF15INbBuFdftP4+ejRZgypK1nccN5N0RERPXyurh54IEHAAALFiyotUySJDgcjsZHRRjYNgoAsOVIEYQQkJL6A4YAoPw0UHgAiOmkcoRERETNk9eHpZxOZ70PFjbK6dU6HCaDDoVlVhwpLJfn3bSuud7NUd7Hi4iIqD68ln8zZTbo3feZ2nLENe/mKvmZxQ0REVG9GlzcrF69Gt26dYPFYqm1rKSkBN27d8f69esVDa6lcx+ack0qbneN/Hx4nXyfKSIiIqqlwcXNwoULMXXqVISFhdVaFh4ejj//+c94/fXXFQ2upbuiprhxnzGV1B8whQKVRUD+DhUjIyIiar4aXNzs2LEDI0eOrHf58OHDsW3bNkWCIln/lEjodRKOF1Xi97MVgN4ItL1aXnhotbrBERERNVMNLm4KCgpgNBrrXW4wGHD69GlFgiJZiNmA3q3DAQAbDxbKje2vlZ8PrVEpKiIiouatwcVNUlISdu3aVe/yX3/9FQkJCYoERedc2TEGAPDjAVdxc538nPsTYC1XKSoiIqLmq8HFzejRo/HMM8+gqqqq1rLKykrMnj0bN954o6LBEXBVx2gAwKZDZ+B0CiCqHRDeBnDagKMbVY6OiIio+WlwcfOXv/wFRUVF6NSpE+bPn4/ly5dj+fLlePnll9G5c2cUFRXh6aef9mWsLVKf5AiEmA0oKrdid55FvjKx+9AU590QERFdqMHFTVxcHDZt2oQePXpg1qxZuOWWW3DLLbfgqaeeQo8ePbBhwwbExcVdVhCLFi1CamoqAgICMGjQIGzZsqVB633xxReQJAk333zzZe3XHxj1OvyhnXzWVK1DU4c574aIiOhCXl3ELyUlBStWrEBhYSE2b96Mn376CYWFhVixYgXatm17WQEsXboUGRkZmD17NrZv347evXtjxIgROHXq1EXXO3r0KB599FFcddVVl7Vff3JlB/nQ1IaDNRO2214NQAJO7wVKTqgXGBERUTN0WVcojoyMxBVXXIGBAwciMjKyUQEsWLAAU6dOxZQpU9CtWze89957CAoKwuLFi+tdx+Fw4I477sCcOXPQrl27Ru3fH7gmFf989CyqbA4gKApoPUBeeGCVipERERE1P17fOFNJVqsV27Ztw6xZs9xtOp0O6enpyM7Orne9559/HrGxsbjnnnvw448XvxVBdXU1qqur3e9dV1i22Wyw2WyNzMCTa3tKb7dNhAnxYWbkW6qx6cApXNUxGrr26dD//jOc+1bC0fv/Kbq/+vgqv+ZE6zkyP/+n9Ry1nh+g/Rx9lZ8321O1uCksLITD4ag1VycuLg579+6tc50NGzbgww8/RE5OToP2MW/ePMyZM6dW+6pVqxAUFOR1zA2RmZmp+DbbBuiQb9FhyaqtKD3gRHhFIIYCcB5ajZXfLoNTZ1J8n/XxRX7NjdZzZH7+T+s5aj0/QPs5Kp1fRUVFg/uqWtx4q7S0FHfeeSc++OADREdHN2idWbNmISMjw/3eYrEgOTkZw4cPr/NWEo1hs9mQmZmJYcOGXfSCh5fDtOcUsj/LweGqIIwadRUkAOLNd2Aoy8eorqEQ7a9XdH918WV+zYXWc2R+/k/rOWo9P0D7Ofoqv7rubVkfVYub6Oho6PV6FBQUeLQXFBQgPj6+Vv9Dhw7h6NGjGDNmjLvNWXMDSYPBgH379qF9+/Ye65jNZpjN5lrbMhqNPvul8sW2r+kSB5NBh9+Lq3D0bDU6xYUCnUYA2z+G4XAW0KX+W2MozZc/u+ZC6zkyP/+n9Ry1nh+g/RyVzs+bbV3WhGKlmEwm9O/fH1lZWe42p9OJrKwspKWl1erfpUsX7Ny5Ezk5Oe7H2LFjce211yInJwfJyclNGX6TCjIZMKR9KwDAD3tqisFOI+Tn/d8DQqgUGRERUfOi+mGpjIwMTJ48GQMGDMDAgQOxcOFClJeXY8qUKQCASZMmISkpCfPmzUNAQAB69OjhsX5ERAQA1GrXouu6xmHNvtNYvecUHhjaAWh7DaA3AcXHgML9QExntUMkIiJSnerFzfjx43H69Gk8++yzyM/PR58+fbBy5Ur3JOPc3FzodKoOMDUb13eJxTMAtueeRVG5FVHBIUDqlfKVivd/z+KGiIgIzaC4AYAZM2ZgxowZdS5bu3btRdddsmSJ8gE1U4kRgeiaEIY9eRas2XsKt/VvDXQaea64GfKg2iESERGpjkMifia9aywAIGtvzbybjsPl59xsoPKsSlERERE1Hyxu/Mz1XeXDdWv3nZavVhzVFojpCggHsJ9XKyYiImJx42d6tw5HUkQgKqwOrNtfc6+prjWnxu/5j3qBERERNRMsbvyMJEkY1UO+BtCKnXlyo6u4OZgFWMtVioyIiKh5YHHjh0b1TAAAZO05JR+aiu8JRKQA9kq5wCEiImrBWNz4ob7JEYgPC0BZtR0bDhQCknTeoan/qhscERGRyljc+CGdTsKonhcemhorP+9fCditKkVGRESkPhY3fmp0zaGpzD0FqLY7gNZXACHxQLUFOLJe5eiIiIjUw+LGT/VvE4nYUDNKq+zYeLAQ0OmArjfKC/csVzc4IiIiFbG48VM63bmzpv6744KzpvZ8CzhsKkVGRESkLhY3fuymvkkAgJW78lFebQdSrwKCY4HKIuDQGpWjIyIiUgeLGz/WNzkCqa2CUGlzYNXufECnB7rfIi/c9aW6wREREamExY0fkyQJN9eM3nzzy0m5secf5ee93wHWCpUiIyIiUg+LGz93cx+5uNlw4DROWarks6Yi2gDWMvm0cCIiohaGxY2fS40ORr82EXAK4D87TsoX9OtRM3qz6yt1gyMiIlIBixsNuKVfawDAN7+ckBtch6YOrAIqi9UJioiISCUsbjTgxp4JMOol/HbSgn35pUBcdyCmK+Cw8nYMRETU4rC40YDIYBOu7xIHAPji51y5sdft8vOOz1WKioiISB0sbjRi/MBkAPKhqSqbA+g9AZB0wLGNwJlDKkdHRETUdFjcaMTVHWOQFBGI4gobvv8tHwhLBNpfJy/M+Uzd4IiIiJoQixuN0Osk3D5Anlj82eaaQ1N9/5/8vONzwOlQKTIiIqKmxeJGQ8YNSIZOAjYfKcLh02VA59FAYCRgOQEc5u0YiIioZWBxoyGJEYG4plMMAGDpz8cBgxnoWTOx+Jd/qhgZERFR02FxozF/GtgGAPDltt/licWuQ1N7vwMqilSMjIiIqGmwuNGY67vEIiE8AGfKrfju1zwgoTcQ3xNwVHNiMRERtQgsbjTGoNfhzrQUAMCSTUchhAAG3CMv3Poh4HSqGB0REZHvsbjRoD9d0QZmgw47T5Rge+5ZoNc4wBwOFB0GDq9WOzwiIiKfYnGjQVHBJvfdwj/aeBQwBQN9JsoLt/xdvcCIiIiaAIsbjZo8OBUA8L9d+cgrqQSuqDk0tX8lcPaYeoERERH5GIsbjeqWGIZBbaPgcAr8I/sYEN0RaDcUgAC2faR2eERERD7D4kbDpgxpCwD49KdjKKu2A1fcKy/Y9jFgrVAxMiIiIt9hcaNhw7vFoV1MMCxVdny+ORfoNAqISAEqi4AcXtSPiIi0icWNhul0EqZd0x4A8PcNh1EtJCBturwwexHvN0VERJrE4kbjbu6ThPiwABRYqrHslxPyFYsDI4GzR4C936odHhERkeJY3GicyaDDvVfJc2/eX3cYDkPQubk3G98EhFAxOiIiIuWxuGkBJgxsg/BAIw4XlmPFzjxg4H2A3gyc2ArkZqsdHhERkaJY3LQAwWYD7rlSHr15/Yf9sAdGA30myAt/XKBiZERERMpjcdNCTBmSisggIw6fLsfynJPAkJmApAcOZgK/b1M7PCIiIsWwuGkhQgOM7jOnFmbthzUsFej9J3nhupfUC4yIiEhhLG5akElpqYgOMeN4USX+ve04cNUj8ujNgVUcvSEiIs1gcdOCBJr0mHGtPHrzVtZBVIamcvSGiIg0h8VNCzNhUBskRQQi31KF99cf8hy9Of6z2uERERE1WrMobhYtWoTU1FQEBARg0KBB2LJlS719P/jgA1x11VWIjIxEZGQk0tPTL9qfPJkNeswa3QUA8N66QzipTwR615w5lfkMr3tDRER+T/XiZunSpcjIyMDs2bOxfft29O7dGyNGjMCpU6fq7L927VpMmDABa9asQXZ2NpKTkzF8+HCcOHGiiSP3Xzf0TMDA1ChU2Zx4eeVe4NqnAEOgfM0bXrWYiIj8nEHtABYsWICpU6diypQpAID33nsP3333HRYvXownn3yyVv9//tPzho9///vf8dVXXyErKwuTJk2q1b+6uhrV1dXu9xaLBQBgs9lgs9mUTMW9PaW36wtPjeqEW977CctzTmLCgCRcMeh+6DcugFj1DOxtrwP0plrr+FN+l0vrOTI//6f1HLWeH6D9HH2Vnzfbk4RQ7ziE1WpFUFAQvvzyS9x8883u9smTJ6O4uBjLly+/5DZKS0sRGxuLf//737jxxhtrLX/uuecwZ86cWu2fffYZgoKCGhW/v/v8kA4/ndIhIVDgye5lGLH3MQTYLdiZ9P9wOHa42uERERG5VVRUYOLEiSgpKUFYWNhF+6o6clNYWAiHw4G4uDiP9ri4OOzdu7dB23jiiSeQmJiI9PT0OpfPmjULGRkZ7vcWi8V9KOtSPxxv2Ww2ZGZmYtiwYTAajYpu2xf+UG7FyDc3Iq/ChsNhfWBMfwZY+Rh6nPkvuvzxKSAk1qO/v+V3ObSeI/Pzf1rPUev5AdrP0Vf5uY68NITqh6Ua46WXXsIXX3yBtWvXIiAgoM4+ZrMZZrO5VrvRaPTZL5Uvt62kuAgjnhvbHTO/yMG7645gzP/djg7xn0DK3wnjmjnArX+rcz1/ya8xtJ4j8/N/Ws9R6/kB2s9R6fy82ZaqE4qjo6Oh1+tRUFDg0V5QUID4+PiLrvvqq6/ipZdewqpVq9CrVy9fhqlpY3sn4rousbA6nHj8691w3LAQgAT8uhQ4vFbl6IiIiLynanFjMpnQv39/ZGVluducTieysrKQlpZW73rz58/HCy+8gJUrV2LAgAFNEapmSZKEv97cAyFmA7bnFmPR/nDginvlhd9mALYqdQMkIiLykuqngmdkZOCDDz7Axx9/jD179uD+++9HeXm5++ypSZMmYdasWe7+L7/8Mp555hksXrwYqampyM/PR35+PsrKytRKwe8lRgTihZu7AwDeyDqAXzrNAELigaJDwOoXVI6OiIjIO6oXN+PHj8err76KZ599Fn369EFOTg5WrlzpnmScm5uLvLw8d/93330XVqsVf/zjH5GQkOB+vPrqq2qloAm39G2NW/omweEUmPHVYZSPeE1ekL0IOLJe3eCIiIi80CwmFM+YMQMzZsyoc9natWs93h89etT3AbVQz9/UHduOnUVuUQVmbo/DB/3ugrR9CfDNNOD+jYAhRO0QiYiILkn1kRtqPkIDjHh7Yl+YDDr8sOcU3jLeBUS1AywngP8+xFszEBGRX2BxQx56tY7AS7f2BAAsWHcSG3vNA3QGYPcy6La8p3J0REREl8bihmq5tV9r3HtlWwDAPVkCuVc8DQDQZT2HVqV71AyNiIjokljcUJ2eHNUF13WJRZXNiRs3d0NJx1sgCQcGHF0ElBxXOzwiIqJ6sbihOhn0Oiya2A/9UyJhqXLgxiO3o7pVNwTYLTB8MR6oKFI7RCIiojqxuKF6BZr0WDz5CnSOC8XxMuCPlpkoM0RCKtwPfHEHL/BHRETNEosbuqjwICP+cc9AdIwNwc7SUEyqfhwOYyiQuwn48m7AblU7RCIiIg8sbuiSYsMC8MV9f0CX+FBst6Vgmj0DTp0J2Pcd8OUUFjhERNSssLihBmkVYsY/pgxASohAZmVn3Gt9BA6dCdj7bU2BU612iERERABY3JAXIoKMmN7NgeHdYrHa3hNTqh6GXaopcD69DagsVjtEIiKi5nH7BfIfZj3w1vjeeH31Yby3DphU/Sj+bl6IoKM/AotHAv/vSyC8tdphEhGRrzidQGURUFYAlJ2SH+Wnat6fhr40Hz1LDQBGqxYiixvymk4n4clRXdAtMQyzvtLjtqpn8LH5FcSe3gP87Vrg9iVA6hC1wyQiooawWwGHVS5OzhyUL/VRfkpuqywGKs4A5afPK2ROA8JR7+Z0AKICU5sq+jqxuKHLNrZ3InomhWP6P4NxU94cLDbNR9fy4xAfj4GU/hww+P8ASVI7TCKilkkIoKoYsJwELHmA5XfgvzOV235gFBASB4TE1jzigOAY2AOjsWvvCQxSbk9eY3FDjdI2OhhfPzAYr61qhT9umIMXDItxq34DkPkMxNEfIY15EwhLUDtMIiJtcDrkkRTXCIrrUXYKKC+sOTzken0acFzmyR7hbYDIFHmaQav2QEi8ZyETHAPojXWuKmw2nPl9RSOSbDwWN9RoAUY9nr6hG0b3TMDj/26FbUWd8KzhHzAfWAX724NguPE1oOcfOYpDRFQXuxWoqClGLHlygVJ+GihzFS81xUrZKbmwgfBu+4FRQFgSEBoPHMysvbz7rUDvPwHxPYHQBE38rWZxQ4rp2yYS3868Cn//sTX+uKY7XhRvo5f1CPD1vSjf+k8Ej30ViO6gdphERL7jdMrFyL7/AauekQuFasu55cZgGAwmpDv0MOx/VO7rNQkIaiWPnoTEyM/BsUBwdM2oSqznMmOgYun5CxY3pCizQY/p13bA7f1b49WVfRG/4x3MMHyD4Nw1sL89CJa+9yFq5FOAOVTtUImIGsZeXTOptlAeYakoOve6vFBeVnEGsJwASk4ATlv927KVQ7KVIxgAzr/+qaSXC5awxPMKlOhzh4Bcj5BYeSRGz6/vi+FPh3wiNiwA88f1x29XLsAL/7sR1x19HdfqdyDql3dQuuNTnO41DakjZ0IXEKJ2qETUkggBWMvOFSXu4uSCQsVdvJwBrKXK7d8QCGfXMdhX6ETHwTfCENupZhQmFtDx0nNKYXFDPtU9MRwv3HMLdp+4Dov++w+MOvk22iEPoTnzcXbH+zjYYQo6jpyBiFYxaodKRP7I6ag5XbnwvJGVM3JR4lGw1BQqFWcub5Kta2QlOFp+dr+ueR/cCghNlCfghsbLozsntwOpVwGmYI95LA6bDftXrECHLqMBY92TcqlxWNxQk+iWFI5u02bg6Kk78c2KDzDgyN+QjAJccWAhyve/i7XhI2AYfD8GXfEHGPX83wtRi2SrBCynEFaRC+noesBqkYuEyiKg4mzNc9G554ozQFUJvJ5gCwCGwLoLleCa90HR5xUvUUBAhHcjK6FxQOdR3sdFimBxQ00qNTYcqXc9irKK6dj43ftI2rsYqY5jGGr5D7DyP9iysjuOJI5F7KDbkdYtFQFGvdohE5G3nE6guqSmEDnrWZB4PJ/xLFrslTACuBYA9nm5z4Dw80ZRLnh2FyrnFTKmYOXzpmaDxQ2pIiQoEENufwgQM3Fs20pUbViEjsUbMBC/YeDJ31D59WvI/HogTiaOQGyfUUjrkoz48AC1wyZqWZxO+UyfqmL50E/l2ZrXZ89dubau4qXyLCCcl7VLIelRrQ+GOSIeUlArefJsUGTNc1T9z/Vcc4VaJhY3pC5JQsqAUcCAUbAXHcPx9R8jeM+/EV2dizHYAORtQOXJF/Djtz3xafAQ6Dpejz7du2JAahTCAvjHjOiShABsFRcUJ8W1C5XzX7uWVZVcdpECADAG1xQgkZ6FiLtoiapVvNh1gfj+f//D6NGjYeR8FLpMLG6o2TBEpSDl5meBm56B+H0rCrP/CdOhlQivzsNw/TYMr9oG7HwTh3YkYLnojmOhAyC1HYIu7dphQGok2kQFQdLAxaeI6mSrkouNskJElh2AdOB7wFZ26UKlqli+R1BjGAKBwAi5SAmIOPf6wqLlwmeD+TLyvMhp1EQNxOKGmh9JgpR8BWKSr5D/11mwC5U7/4Pq3SsQdvY3tNfloT3ygIofgN+AYztjsUO0x78NnVER0xtBKf3QuXUsuieGIbVVMHQ6FjykMtfpx1UlNQ/Ludeuwz51tp/Xv+YMHyOAqwHggJcx6Ay1i5NaryPl9xe+NvKQMPkXFjfUvEkSEN8TgfE9ETjsafl/o8c2oWLfajgOrUOo5QBSdKeQglOAyAZOAY4CCcc2x+GAaI1VujaoiOgEXVxXhCd2RmpCK3SICUFSRCCLHmoYIQBrOVBdKj+spede11uslNQULJZzbY05vOMmQQSEo8JpQmCrROhcBUhDCpULTkcm0jIWN+RfAiOBLjcgqMsN8vvKYuDkL7Dl/ozyI5thzv8FgdYzaCflox3yAWwFSiA/9gN5Igq5IhZbEIfyoNZwRqTA1CoFITHJiI5LQWxUKBxKfAeRuoSQTyuuLpMLC2vZuYLkwod7maWm/4XtpbisU43rojPKZ/UEhAMBYedem12vIzzbPZaFA6YQ2B0O/LBiBUaPHg0d56QQ1YnFDfm3wAig/bUwtr8WEddC/lIrPw2c2g1H/m6UHt8JR/5uhFgOwOwoR4JUhASpCIOwF6gCkF/zqFEsgtFdROKXX19DRUAMRFAMdMGtYApthcDwGIRExCCiVRwiWsXCEBJ9eXMKSOawAfYq+dL2tsqaR7k8SmKtqHldIU+GtZad97r83LP7tdzHYC3H6EoLDDlWhUZKziPp5NuGmELlZ3No7SLlYoWKOUy+x09jR08cDkXSIdIyFjekLZIk33slJBb6dkMR4WoXQj5N9ewROM4chiXvICoLDgFnjyCgIg8htkKYhBURUjkipHJA/A5UQn6cqX931TChWhcEmz4QDmMwhDEYMAVDCgiFISAUpsBQmIPDYDQFAgYToDfLBZHeVPNsrN0m6eWLhUmuh/7ca53rteTZDiF/mQunnKvrGcKzzWZFWEUukP8roNfJp/o6bXKh4bQBDrs8+dT12mPZBf1cbfZqwF5Z81wlT3x1FS32qvMeNUWMq10o/yUtQZ6T4sEUUlOUhJwrSs5/XLQ9rOZ9CGAM4mEdIj/B4oZaBkmSrzwa3Ar61gMQ2RuIPH+5EEBVMaxnjmNd5n/RLikGVWdPwGY5BVFRBF11MczWYgTaLQgRpYhAGfSSgBlWmJ1WwFkM+MFJHpd9gTRf05vkUQ1TTRFhCpJPIzYFyXNFXK+NNe9Nwedeu/qbQmCTTFi36WdcM2wUjEHh8igL79dD1OKwuCEC5OInMBJSXAgqoo6gzbX1X2PD4RQ4U1qJ04WnUXTmNEpKzqKizIKqcgusFRbYK0thryqDsJZBZy1DgKiCCTaYYIdJsrlfm2GDSbLDBBuMsMvLYYMeTugkAR2c0EFADwG95HQ/6yAgQUAPJyQI6OCAgGsER6oZ1ZFHd6SakR1J0gE6HSQA1VYbzAGBNW16+SwavVGeD6I3yIWG67XOWLOspv38fq5lBrN8qrDBLBcoBjNgCDjvUU+7seZZb1auALHZUB5wHAhN4D17iFowFjdEXtLrJMSGByE2PAVon3LRvkIIlFXbUVRuxZlyK86WW2GpsqGw0o6SShsslTb5ucoGi6utSm4rrbL7Jn5JINhuRKBJj0CjHgFGPYJ0es/3rtcmPYKMBgSadOctk98HGOU+gSY9Agx6mI0697PZoIeeZ6MRkUpY3BD5kCRJCA0wIjTAiJRW3t3LxuEUKKuyw1JlQ1m1HeXV9ppnx3mv7Sizys/l1Q5324V9y612OGtO+HEICZYqOyw+Kp5cjHoJZoMeZoMOZoNcDJkMOpiNegTUPJ+/TH6tR0BNcSQXS+f3u3CZ/GzS62Ay6GDU66ATDlgdgNOp0NlNROSXWNwQNVN6nYTwICPCgxp/eEUIgWq7E6UV1VjxfSb+cOU1sAkJVTYHKm0OVFgd8mur/LrSdt57mwNVNW0X9q2sea62O1Ftd8DmOFdU2BwCNocdZdWNDt9LBjy2JRMGneQuekwGuQgyG2peu9r1596bDDqY9ef1N5xbz+PZy9dGvQ5GvVTzrOOIFlETYHFD1AJIkoQAox76ICMizEC7mGCf3LfH7nDC6nCi2uZEtd2JKtu5wqfKJj9fdJldXrfK3e+CZXYnql3r2RyosjthdT0uuECR3SlgtzoANK9TpyUJcqGjk2B0FT81rw26c0WQUS/BUFOAGdzFkQS9BBTk6bBp+W8wGw0w6HQwGiQYdfJ6Br1Uax25XQfTea/PL7hchaBr/wa9BL1OgkEnvza4XuskXvyS/AKLGyJSjKHmizPI1PT7FkKgosqKb/+3EkOvS4eQ9DVFjwNWu4DVcX4h5Kh5Fufa7I4L+gjPvjUF1LltOc5rc8JW07/6gm1deIRMCMjrAYD1cgsvHbacPtHIn9jlkSTAqNPVFD9STSFUU3i52+RCSH/ea4++7mXniibXMh0Efs/V4deV+2A0GNzbdY16ubalv2C7ru24+upqlukkuc39uPC9q00vP+t0gEGnq7NNJ4H3r/MTLG6ISBMkSR59CNADUcGmZnNHaYdTwOZwwu4UsNmdsDnlQsjucMLmcNYcvjv3bHe/P39ZzTpOJ6qsduz6bTfadewMJ6SadTz72h1yAXb+a/d2a+KwO+tYxy7H6XAK2JxO+VJJFxAC8iiZTwfEdFiXf8yXO7hsOqmm0NHhgkJJB72rzV0UnSuwDOe16SWg+KweXxRshaGmaKu36Kp57bGt89rObVOCToL8WlfzWnK9PtdHr5P/rbi2LUlw70ty76+OPtK5feqkc0Wfxz5qtuV0OGBp5L1aG4vFDRGRD8lfFnr5jQIXtLbZbFhR/BtGD23n8wLO6RTy4T1nTdHjkIseh1PA7hA1hZBcJDlcfWsKOXtNMSYXd67lchHlKp4c5/Vzbb/aZse+AweR0rYdBCR3cVj39uX9u967tut67RRyH6dTwCHkPhe2ORw1z85zOVz0Z6JYcSfhoKWosRtptlJD9PiTivtncUNERHXS6SSYdBJMaLoLIdpsNqyo3o/RIzqpNvrmLnxqCh6HEO5C72KFkqvNvd5565//3mqzY+u27ejVpw8kna72tlz7EgIOJ+BwOuVnIc69rnl27c8pzt+3fJjWtV8h4JGHUwg4BM699niG5+ta6wn5wuZ1refqKwQMOt+ejXkpLG6IiIjOo9NJ0EGCUe+b7dtsNjiOCYzuldBsDp8qyWazYcWKFarGwOuSExERkaY0i+Jm0aJFSE1NRUBAAAYNGoQtW7ZctP+///1vdOnSBQEBAejZs6fqFSIRERE1H6oXN0uXLkVGRgZmz56N7du3o3fv3hgxYgROnTpVZ/9NmzZhwoQJuOeee/DLL7/g5ptvxs0334xdu3Y1ceRERETUHKle3CxYsABTp07FlClT0K1bN7z33nsICgrC4sWL6+z/xhtvYOTIkXjsscfQtWtXvPDCC+jXrx/efvvtJo6ciIiImiNVJxRbrVZs27YNs2bNcrfpdDqkp6cjOzu7znWys7ORkZHh0TZixAgsW7aszv7V1dWorj53/XeLxQJAnvBks9kamYEn1/aU3m5zofX8AO3nyPz8n9Zz1Hp+gPZz9FV+3mxP1eKmsLAQDocDcXFxHu1xcXHYu3dvnevk5+fX2T8/P7/O/vPmzcOcOXNqta9atQpBQUGXGfnFZWZm+mS7zYXW8wO0nyPz839az1Hr+QHaz1Hp/CoqKhrcV/Ongs+aNctjpMdisSA5ORnDhw9HWFiYovuy2WzIzMzEsGHDNHt6n5bzA7SfI/Pzf1rPUev5AdrP0Vf5uY68NISqxU10dDT0ej0KCgo82gsKChAfH1/nOvHx8V71N5vNMJtrXxbUaDT67JfKl9tuDrSeH6D9HJmf/9N6jlrPD9B+jkrn5822VJ1QbDKZ0L9/f2RlZbnbnE4nsrKykJaWVuc6aWlpHv0Beeirvv5ERETUsqh+WCojIwOTJ0/GgAEDMHDgQCxcuBDl5eWYMmUKAGDSpElISkrCvHnzAAAzZ87ENddcg9deew033HADvvjiC2zduhV/+9vf1EyDiIiImgnVi5vx48fj9OnTePbZZ5Gfn48+ffpg5cqV7knDubm50OnODTANHjwYn332Gf7yl7/gqaeeQseOHbFs2TL06NFDrRSIiIioGVG9uAGAGTNmYMaMGXUuW7t2ba2222+/HbfffruPoyIiIiJ/pPpF/IiIiIiUxOKGiIiINKVZHJZqSkIIAN6dL99QNpsNFRUVsFgsmjy9T+v5AdrPkfn5P63nqPX8AO3n6Kv8XN/bru/xi2lxxU1paSkAIDk5WeVIiIiIyFulpaUIDw+/aB9JNKQE0hCn04mTJ08iNDQUkiQpum3X1Y+PHz+u+NWPmwOt5wdoP0fm5/+0nqPW8wO0n6Ov8hNCoLS0FImJiR5nUdelxY3c6HQ6tG7d2qf7CAsL0+QvrIvW8wO0nyPz839az1Hr+QHaz9EX+V1qxMaFE4qJiIhIU1jcEBERkaawuFGQ2WzG7Nmz67xRpxZoPT9A+zkyP/+n9Ry1nh+g/RybQ34tbkIxERERaRtHboiIiEhTWNwQERGRprC4ISIiIk1hcUNERESawuJGIYsWLUJqaioCAgIwaNAgbNmyRe2QGmTevHm44oorEBoaitjYWNx8883Yt2+fR5+hQ4dCkiSPx7Rp0zz65Obm4oYbbkBQUBBiY2Px2GOPwW63N2Uq9Xruuedqxd+lSxf38qqqKkyfPh2tWrVCSEgIbrvtNhQUFHhsoznnl5qaWis/SZIwffp0AP73+a1fvx5jxoxBYmIiJEnCsmXLPJYLIfDss88iISEBgYGBSE9Px4EDBzz6FBUV4Y477kBYWBgiIiJwzz33oKyszKPPr7/+iquuugoBAQFITk7G/PnzfZ2a28VytNlseOKJJ9CzZ08EBwcjMTERkyZNwsmTJz22Udfn/tJLL3n0USvHS32Gd911V63YR44c6dHHnz9DAHX+m5QkCa+88oq7T3P9DBvyvaDU3821a9eiX79+MJvN6NChA5YsWaJMEoIa7YsvvhAmk0ksXrxY/Pbbb2Lq1KkiIiJCFBQUqB3aJY0YMUJ89NFHYteuXSInJ0eMHj1atGnTRpSVlbn7XHPNNWLq1KkiLy/P/SgpKXEvt9vtokePHiI9PV388ssvYsWKFSI6OlrMmjVLjZRqmT17tujevbtH/KdPn3YvnzZtmkhOThZZWVli69at4g9/+IMYPHiwe3lzz+/UqVMeuWVmZgoAYs2aNUII//v8VqxYIZ5++mnx9ddfCwDim2++8Vj+0ksvifDwcLFs2TKxY8cOMXbsWNG2bVtRWVnp7jNy5EjRu3dv8dNPP4kff/xRdOjQQUyYMMG9vKSkRMTFxYk77rhD7Nq1S3z++eciMDBQvP/++6rnWFxcLNLT08XSpUvF3r17RXZ2thg4cKDo37+/xzZSUlLE888/7/G5nv/vVs0cL/UZTp48WYwcOdIj9qKiIo8+/vwZCiE8csvLyxOLFy8WkiSJQ4cOufs018+wId8LSvzdPHz4sAgKChIZGRli9+7d4q233hJ6vV6sXLmy0TmwuFHAwIEDxfTp093vHQ6HSExMFPPmzVMxqstz6tQpAUCsW7fO3XbNNdeImTNn1rvOihUrhE6nE/n5+e62d999V4SFhYnq6mpfhtsgs2fPFr17965zWXFxsTAajeLf//63u23Pnj0CgMjOzhZCNP/8LjRz5kzRvn174XQ6hRD+/fld+KXhdDpFfHy8eOWVV9xtxcXFwmw2i88//1wIIcTu3bsFAPHzzz+7+/zvf/8TkiSJEydOCCGEeOedd0RkZKRHfk888YTo3LmzjzOqra4vxgtt2bJFABDHjh1zt6WkpIjXX3+93nWaS471FTc33XRTveto8TO86aabxHXXXefR5i+f4YXfC0r93Xz88cdF9+7dPfY1fvx4MWLEiEbHzMNSjWS1WrFt2zakp6e723Q6HdLT05Gdna1iZJenpKQEABAVFeXR/s9//hPR0dHo0aMHZs2ahYqKCvey7Oxs9OzZE3Fxce62ESNGwGKx4LfffmuawC/hwIEDSExMRLt27XDHHXcgNzcXALBt2zbYbDaPz69Lly5o06aN+/Pzh/xcrFYrPv30U9x9990eN4b198/P5ciRI8jPz/f4vMLDwzFo0CCPzysiIgIDBgxw90lPT4dOp8PmzZvdfa6++mqYTCZ3nxEjRmDfvn04e/ZsE2XTcCUlJZAkCRERER7tL730Elq1aoW+ffvilVde8Rjyb+45rl27FrGxsejcuTPuv/9+nDlzxr1Ma59hQUEBvvvuO9xzzz21lvnDZ3jh94JSfzezs7M9tuHqo8R3Z4u7cabSCgsL4XA4PD5AAIiLi8PevXtViuryOJ1OPPTQQxgyZAh69Ojhbp84cSJSUlKQmJiIX3/9FU888QT27duHr7/+GgCQn59fZ/6uZWobNGgQlixZgs6dOyMvLw9z5szBVVddhV27diE/Px8mk6nWl0ZcXJw79uae3/mWLVuG4uJi3HXXXe42f//8zueKp654z/+8YmNjPZYbDAZERUV59Gnbtm2tbbiWRUZG+iT+y1FVVYUnnngCEyZM8LgJ4YMPPoh+/fohKioKmzZtwqxZs5CXl4cFCxYAaN45jhw5Erfeeivatm2LQ4cO4amnnsKoUaOQnZ0NvV6vuc/w448/RmhoKG699VaPdn/4DOv6XlDq72Z9fSwWCyorKxEYGHjZcbO4Ibfp06dj165d2LBhg0f7fffd537ds2dPJCQk4Prrr8ehQ4fQvn37pg7Ta6NGjXK/7tWrFwYNGoSUlBT861//atQ/nuboww8/xKhRo5CYmOhu8/fPryWz2WwYN24chBB49913PZZlZGS4X/fq1Qsmkwl//vOfMW/evGZ/Wf8//elP7tc9e/ZEr1690L59e6xduxbXX3+9ipH5xuLFi3HHHXcgICDAo90fPsP6vheaOx6WaqTo6Gjo9fpas8QLCgoQHx+vUlTemzFjBr799lusWbMGrVu3vmjfQYMGAQAOHjwIAIiPj68zf9ey5iYiIgKdOnXCwYMHER8fD6vViuLiYo8+539+/pLfsWPH8MMPP+Dee++9aD9//vxc8Vzs31t8fDxOnTrlsdxut6OoqMivPlNXYXPs2DFkZmZ6jNrUZdCgQbDb7Th69CgA/8jRpV27doiOjvb4ndTCZwgAP/74I/bt23fJf5dA8/sM6/teUOrvZn19wsLCGv0fTxY3jWQymdC/f39kZWW525xOJ7KyspCWlqZiZA0jhMCMGTPwzTffYPXq1bWGQOuSk5MDAEhISAAApKWlYefOnR5/jFx/jLt16+aTuBujrKwMhw4dQkJCAvr37w+j0ejx+e3btw+5ubnuz89f8vvoo48QGxuLG2644aL9/Pnza9u2LeLj4z0+L4vFgs2bN3t8XsXFxdi2bZu7z+rVq+F0Ot2FXVpaGtavXw+bzebuk5mZic6dOzeLwxmuwubAgQP44Ycf0KpVq0uuk5OTA51O5z6c09xzPN/vv/+OM2fOePxO+vtn6PLhhx+if//+6N279yX7NpfP8FLfC0r93UxLS/PYhquPIt+djZ6STOKLL74QZrNZLFmyROzevVvcd999IiIiwmOWeHN1//33i/DwcLF27VqP0xErKiqEEEIcPHhQPP/882Lr1q3iyJEjYvny5aJdu3bi6quvdm/Ddcrf8OHDRU5Ojli5cqWIiYlpNqdKP/LII2Lt2rXiyJEjYuPGjSI9PV1ER0eLU6dOCSHkUxrbtGkjVq9eLbZu3SrS0tJEWlqae/3mnp8Q8hl6bdq0EU888YRHuz9+fqWlpeKXX34Rv/zyiwAgFixYIH755Rf3mUIvvfSSiIiIEMuXLxe//vqruOmmm+o8Fbxv375i8+bNYsOGDaJjx44epxEXFxeLuLg4ceedd4pdu3aJL774QgQFBTXZacQXy9FqtYqxY8eK1q1bi5ycHI9/l66zTDZt2iRef/11kZOTIw4dOiQ+/fRTERMTIyZNmtQscrxYfqWlpeLRRx8V2dnZ4siRI+KHH34Q/fr1Ex07dhRVVVXubfjzZ+hSUlIigoKCxLvvvltr/eb8GV7qe0EIZf5uuk4Ff+yxx8SePXvEokWLeCp4c/PWW2+JNm3aCJPJJAYOHCh++ukntUNqEAB1Pj766CMhhBC5ubni6quvFlFRUcJsNosOHTqIxx57zOM6KUIIcfToUTFq1CgRGBgooqOjxSOPPCJsNpsKGdU2fvx4kZCQIEwmk0hKShLjx48XBw8edC+vrKwUDzzwgIiMjBRBQUHilltuEXl5eR7baM75CSHE999/LwCIffv2ebT74+e3Zs2aOn8nJ0+eLISQTwd/5plnRFxcnDCbzeL666+vlfeZM2fEhAkTREhIiAgLCxNTpkwRpaWlHn127NghrrzySmE2m0VSUpJ46aWXmirFi+Z45MiRev9duq5dtG3bNjFo0CARHh4uAgICRNeuXcXcuXM9igM1c7xYfhUVFWL48OEiJiZGGI1GkZKSIqZOnVrrP4P+/Bm6vP/++yIwMFAUFxfXWr85f4aX+l4QQrm/m2vWrBF9+vQRJpNJtGvXzmMfjSHVJEJERESkCZxzQ0RERJrC4oaIiIg0hcUNERERaQqLGyIiItIUFjdERESkKSxuiIiISFNY3BAREZGmsLghIiIiTWFxQ0QtTmpqKhYuXKh2GETkIyxuiMin7rrrLtx8880AgKFDh+Khhx5qsn0vWbIEERERtdp//vln3HfffU0WBxE1LYPaARARectqtcJkMl32+jExMQpGQ0TNDUduiKhJ3HXXXVi3bh3eeOMNSJIESZJw9OhRAMCuXbswatQohISEIC4uDnfeeScKCwvd6w4dOhQzZszAQw89hOjoaIwYMQIAsGDBAvTs2RPBwcFITk7GAw88gLKyMgDA2rVrMWXKFJSUlLj399xzzwGofVgqNzcXN910E0JCQhAWFoZx48ahoKDAvfy5555Dnz598I9//AOpqakIDw/Hn/70J5SWlvr2h0ZEl4XFDRE1iTfeeANpaWmYOnUq8vLykJeXh+TkZBQXF+O6665D3759sXXrVqxcuRIFBQUYN26cx/off/wxTCYTNm7ciPfeew8AoNPp8Oabb+K3337Dxx9/jNWrV+Pxxx8HAAwePBgLFy5EWFiYe3+PPvporbicTiduuukmFBUVYd26dcjMzMThw4cxfvx4j36HDh3CsmXL8O233+Lbb7/FunXr8NJLL/nop0VEjcHDUkTUJMLDw2EymRAUFIT4+Hh3+9tvv42+ffti7ty57rbFixcjOTkZ+/fvR6dOnQAAHTt2xPz58z22ef78ndTUVPz1r3/FtGnT8M4778BkMiE8PBySJHns70JZWVnYuXMnjhw5guTkZADAJ598gu7du+Pnn3/GFVdcAUAugpYsWYLQ0FAAwJ133omsrCy8+OKLjfvBEJHiOHJDRKrasWMH1qxZg5CQEPejS5cuAOTREpf+/fvXWveHH37A9ddfj6SkJISGhuLOO+/EmTNnUFFR0eD979mzB8nJye7CBgC6deuGiIgI7Nmzx92WmprqLmwAICEhAadOnfIqVyJqGhy5ISJVlZWVYcyYMXj55ZdrLUtISHC/Dg4O9lh29OhR3Hjjjbj//vvx4osvIioqChs2bMA999wDq9WKoKAgReM0Go0e7yVJgtPpVHQfRKQMFjdE1GRMJhMcDodHW79+/fDVV18hNTUVBkPD/yRt27YNTqcTr732GnQ6eRD6X//61yX3d6GuXbvi+PHjOH78uHv0Zvfu3SguLka3bt0aHA8RNR88LEVETSY1NRWbN2/G0aNHUVhYCKfTienTp6OoqAgTJkzAzz//jEOHDuH777/HlClTLlqYdOjQATabDW+99RYOHz6Mf/zjH+6Jxufvr6ysDFlZWSgsLKzzcFV6ejp69uyJO+64A9u3b8eWLVswadIkXHPNNRgwYIDiPwMi8j0WN0TUZB599FHo9Xp069YNMTExyM3NRWJiIjZu3AiHw4Hhw4ejZ8+eeOihhxAREeEekalL7969sWDBArz88svo0aMH/vnPf2LevHkefQYPHoxp06Zh/PjxiImJqTUhGZAPLy1fvhyRkZG4+uqrkZ6ejnbt2mHp0qWK509ETUMSQgi1gyAiIiJSCkduiIiISFNY3BAREZGmsLghIiIiTWFxQ0RERJrC4oaIiIg0hcUNERERaQqLGyIiItIUFjdERESkKSxuiIiISFNY3BAREZGmsLghIiIiTfn/ZOQ1BgdgA4MAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(history.history[\"loss\"],label=\"train\")\n",
+    "plt.plot(history.history[\"val_loss\"],label=\"test\")\n",
+    "plt.xlabel(\"Iteration\")\n",
+    "plt.ylabel('Cross-Entropy Loss')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#print(\"history\",history.history)\n",
+    "plt.plot(history.history['sparse_categorical_accuracy'],label=\"train\")\n",
+    "plt.plot(history.history['val_sparse_categorical_accuracy'],label=\"validation\")\n",
+    "plt.title('Model Accuracy')\n",
+    "#plt.yscale('log')\n",
+    "plt.ylabel('Acc')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the trained model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: IrisModel/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "keras_model.save(\"IrisModel\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check we can re-load our saved model back in\n",
+    "- And check it still works (we will print its accuracy on the test set!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_26\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense_78 (Dense)            (None, 20)                100       \n",
+      "                                                                 \n",
+      " dense_79 (Dense)            (None, 20)                420       \n",
+      "                                                                 \n",
+      " dense_80 (Dense)            (None, 3)                 63        \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 583\n",
+      "Trainable params: 583\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Accuracy of saved model on test set 0.96666664\n"
+     ]
+    }
+   ],
+   "source": [
+    "model2 = keras.models.load_model('IrisModel') # just need to give it a folder name here.\n",
+    "model2.summary()\n",
+    "predictions=model2(inputs_test)\n",
+    "accuracy_metric=keras.metrics.SparseCategoricalAccuracy()\n",
+    "print(\"Accuracy of saved model on test set\",accuracy_metric(labels_test,predictions).numpy())"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Lab2/examples/Lab24-mnist.ipynb b/Lab2/examples/Lab24-mnist.ipynb
new file mode 100644
index 0000000..0efd4e5
--- /dev/null
+++ b/Lab2/examples/Lab24-mnist.ipynb
@@ -0,0 +1,770 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MNIST digits dataset\n",
+    "\n",
+    "- First load and view the MNIST digits dataset\n",
+    "- There are 60000 images in this dataset, but we will only view the first 25 of them:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test_images shape (10000, 28, 28) train_images shape (60000, 28, 28)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load and visualise the MNIST digits\n",
+    "import tensorflow as tf\n",
+    "tf.config.experimental.set_visible_devices([], \"GPU\")\n",
+    "\n",
+    "mnist = tf.keras.datasets.mnist\n",
+    "(train_images0, train_labels0),(test_images0, test_labels0) = mnist.load_data()\n",
+    "\n",
+    "print(\"test_images shape\",test_images0.shape,\"train_images shape\",train_images0.shape)\n",
+    "class_names=[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]\n",
+    "import matplotlib.pyplot as plt\n",
+    "# plot first few images\n",
+    "plt.figure(figsize=(10,10))\n",
+    "for i in range(25):\n",
+    "    # define subplot\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    # plot raw pixel data\n",
+    "    plt.imshow(train_images0[i], cmap=plt.get_cmap('gray'))\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.grid(False)\n",
+    "    # Add a label underneath...\n",
+    "    plt.xlabel(class_names[train_labels0[i]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Next build a neural-network classifier for these digits.\n",
+    "\n",
+    "- We will build a keras model, with the higher-level API concepts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From f:\\KTU\\Neuroninių tinklų metodai\\venv\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow import keras\n",
+    "# Each MNIST images are 28*28.  Therefore if there are N images, then the\n",
+    "# shape of the numpy array holding the images is N*28*28\n",
+    "# We will reshape that here to be N*784, using a numpy reshape.\n",
+    "# Note that this flattens each image into a single vector length 784.\n",
+    "test_images=test_images0.reshape(10000,784) # 10000 test patterns\n",
+    "train_images=train_images0.reshape(60000,784) # 60000 train patterns\n",
+    "\n",
+    "# Also rescale greyscale from 8 bit to floating point (by dividing by 255)\n",
+    "test_images=test_images/255.0\n",
+    "train_images=train_images/255.0\n",
+    "\n",
+    "# Create the model\n",
+    "\n",
+    "keras_model = tf.keras.models.Sequential([\n",
+    "  tf.keras.layers.Dense(10, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "keras_model.build(input_shape=[None,784])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## View the keras model summary information\n",
+    "\n",
+    "- This shows you how many layers your neural network has, and how many weights, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_2\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense_6 (Dense)             (None, 10)                7850      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 7,850\n",
+      "Trainable params: 7,850\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# View the model summary information...\n",
+    "keras_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Keras model\n",
+    "\n",
+    "- We will use SGD optimiser (ordinary gradient descent)\n",
+    "- We will use Cross Entropy loss (\"SparseCategoricalCrossentropy\")\n",
+    "- We will run 200 training iterations (epochs)..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/200\n",
+      "WARNING:tensorflow:From f:\\KTU\\Neuroninių tinklų metodai\\venv\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\KTU\\Neuroninių tinklų metodai\\venv\\Lib\\site-packages\\keras\\src\\backend.py:5727: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
+      "  output, from_logits = _get_logits(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 3s 3s/step - loss: 2.3981 - sparse_categorical_accuracy: 0.1408 - val_loss: 2.3445 - val_sparse_categorical_accuracy: 0.1587\n",
+      "Epoch 2/200\n",
+      "1/1 [==============================] - 0s 104ms/step - loss: 2.3408 - sparse_categorical_accuracy: 0.1616 - val_loss: 2.2886 - val_sparse_categorical_accuracy: 0.1820\n",
+      "Epoch 3/200\n",
+      "1/1 [==============================] - 0s 90ms/step - loss: 2.2859 - sparse_categorical_accuracy: 0.1848 - val_loss: 2.2350 - val_sparse_categorical_accuracy: 0.2089\n",
+      "Epoch 4/200\n",
+      "1/1 [==============================] - 0s 95ms/step - loss: 2.2332 - sparse_categorical_accuracy: 0.2104 - val_loss: 2.1834 - val_sparse_categorical_accuracy: 0.2361\n",
+      "Epoch 5/200\n",
+      "1/1 [==============================] - 0s 90ms/step - loss: 2.1824 - sparse_categorical_accuracy: 0.2393 - val_loss: 2.1336 - val_sparse_categorical_accuracy: 0.2635\n",
+      "Epoch 6/200\n",
+      "1/1 [==============================] - 0s 95ms/step - loss: 2.1335 - sparse_categorical_accuracy: 0.2682 - val_loss: 2.0856 - val_sparse_categorical_accuracy: 0.2916\n",
+      "Epoch 7/200\n",
+      "1/1 [==============================] - 0s 93ms/step - loss: 2.0862 - sparse_categorical_accuracy: 0.2971 - val_loss: 2.0391 - val_sparse_categorical_accuracy: 0.3199\n",
+      "Epoch 8/200\n",
+      "1/1 [==============================] - 0s 86ms/step - loss: 2.0405 - sparse_categorical_accuracy: 0.3268 - val_loss: 1.9941 - val_sparse_categorical_accuracy: 0.3460\n",
+      "Epoch 9/200\n",
+      "1/1 [==============================] - 0s 86ms/step - loss: 1.9963 - sparse_categorical_accuracy: 0.3544 - val_loss: 1.9504 - val_sparse_categorical_accuracy: 0.3704\n",
+      "Epoch 10/200\n",
+      "1/1 [==============================] - 0s 85ms/step - loss: 1.9534 - sparse_categorical_accuracy: 0.3800 - val_loss: 1.9081 - val_sparse_categorical_accuracy: 0.3965\n",
+      "Epoch 11/200\n",
+      "1/1 [==============================] - 0s 113ms/step - loss: 1.9119 - sparse_categorical_accuracy: 0.4030 - val_loss: 1.8671 - val_sparse_categorical_accuracy: 0.4210\n",
+      "Epoch 12/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 1.8716 - sparse_categorical_accuracy: 0.4265 - val_loss: 1.8272 - val_sparse_categorical_accuracy: 0.4426\n",
+      "Epoch 13/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 1.8325 - sparse_categorical_accuracy: 0.4490 - val_loss: 1.7885 - val_sparse_categorical_accuracy: 0.4659\n",
+      "Epoch 14/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 1.7946 - sparse_categorical_accuracy: 0.4730 - val_loss: 1.7509 - val_sparse_categorical_accuracy: 0.4950\n",
+      "Epoch 15/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 1.7578 - sparse_categorical_accuracy: 0.4976 - val_loss: 1.7144 - val_sparse_categorical_accuracy: 0.5188\n",
+      "Epoch 16/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 1.7220 - sparse_categorical_accuracy: 0.5228 - val_loss: 1.6788 - val_sparse_categorical_accuracy: 0.5444\n",
+      "Epoch 17/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 1.6872 - sparse_categorical_accuracy: 0.5471 - val_loss: 1.6442 - val_sparse_categorical_accuracy: 0.5684\n",
+      "Epoch 18/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 1.6533 - sparse_categorical_accuracy: 0.5695 - val_loss: 1.6105 - val_sparse_categorical_accuracy: 0.5938\n",
+      "Epoch 19/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 1.6204 - sparse_categorical_accuracy: 0.5916 - val_loss: 1.5778 - val_sparse_categorical_accuracy: 0.6146\n",
+      "Epoch 20/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 1.5884 - sparse_categorical_accuracy: 0.6111 - val_loss: 1.5458 - val_sparse_categorical_accuracy: 0.6345\n",
+      "Epoch 21/200\n",
+      "1/1 [==============================] - 0s 138ms/step - loss: 1.5572 - sparse_categorical_accuracy: 0.6291 - val_loss: 1.5148 - val_sparse_categorical_accuracy: 0.6516\n",
+      "Epoch 22/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 1.5269 - sparse_categorical_accuracy: 0.6466 - val_loss: 1.4846 - val_sparse_categorical_accuracy: 0.6677\n",
+      "Epoch 23/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 1.4974 - sparse_categorical_accuracy: 0.6613 - val_loss: 1.4553 - val_sparse_categorical_accuracy: 0.6809\n",
+      "Epoch 24/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 1.4688 - sparse_categorical_accuracy: 0.6744 - val_loss: 1.4268 - val_sparse_categorical_accuracy: 0.6935\n",
+      "Epoch 25/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 1.4410 - sparse_categorical_accuracy: 0.6866 - val_loss: 1.3992 - val_sparse_categorical_accuracy: 0.7060\n",
+      "Epoch 26/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 1.4141 - sparse_categorical_accuracy: 0.6972 - val_loss: 1.3724 - val_sparse_categorical_accuracy: 0.7174\n",
+      "Epoch 27/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 1.3880 - sparse_categorical_accuracy: 0.7064 - val_loss: 1.3465 - val_sparse_categorical_accuracy: 0.7265\n",
+      "Epoch 28/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 1.3627 - sparse_categorical_accuracy: 0.7157 - val_loss: 1.3215 - val_sparse_categorical_accuracy: 0.7355\n",
+      "Epoch 29/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 1.3382 - sparse_categorical_accuracy: 0.7234 - val_loss: 1.2972 - val_sparse_categorical_accuracy: 0.7442\n",
+      "Epoch 30/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 1.3146 - sparse_categorical_accuracy: 0.7301 - val_loss: 1.2738 - val_sparse_categorical_accuracy: 0.7500\n",
+      "Epoch 31/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 1.2917 - sparse_categorical_accuracy: 0.7363 - val_loss: 1.2512 - val_sparse_categorical_accuracy: 0.7567\n",
+      "Epoch 32/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 1.2696 - sparse_categorical_accuracy: 0.7422 - val_loss: 1.2294 - val_sparse_categorical_accuracy: 0.7617\n",
+      "Epoch 33/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 1.2483 - sparse_categorical_accuracy: 0.7472 - val_loss: 1.2083 - val_sparse_categorical_accuracy: 0.7674\n",
+      "Epoch 34/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 1.2276 - sparse_categorical_accuracy: 0.7525 - val_loss: 1.1880 - val_sparse_categorical_accuracy: 0.7722\n",
+      "Epoch 35/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 1.2077 - sparse_categorical_accuracy: 0.7570 - val_loss: 1.1683 - val_sparse_categorical_accuracy: 0.7759\n",
+      "Epoch 36/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 1.1885 - sparse_categorical_accuracy: 0.7608 - val_loss: 1.1494 - val_sparse_categorical_accuracy: 0.7793\n",
+      "Epoch 37/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 1.1699 - sparse_categorical_accuracy: 0.7648 - val_loss: 1.1311 - val_sparse_categorical_accuracy: 0.7843\n",
+      "Epoch 38/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 1.1520 - sparse_categorical_accuracy: 0.7684 - val_loss: 1.1134 - val_sparse_categorical_accuracy: 0.7881\n",
+      "Epoch 39/200\n",
+      "1/1 [==============================] - 0s 139ms/step - loss: 1.1346 - sparse_categorical_accuracy: 0.7716 - val_loss: 1.0963 - val_sparse_categorical_accuracy: 0.7904\n",
+      "Epoch 40/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 1.1178 - sparse_categorical_accuracy: 0.7745 - val_loss: 1.0797 - val_sparse_categorical_accuracy: 0.7930\n",
+      "Epoch 41/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 1.1016 - sparse_categorical_accuracy: 0.7776 - val_loss: 1.0638 - val_sparse_categorical_accuracy: 0.7955\n",
+      "Epoch 42/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 1.0860 - sparse_categorical_accuracy: 0.7805 - val_loss: 1.0483 - val_sparse_categorical_accuracy: 0.7971\n",
+      "Epoch 43/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 1.0708 - sparse_categorical_accuracy: 0.7832 - val_loss: 1.0334 - val_sparse_categorical_accuracy: 0.7984\n",
+      "Epoch 44/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 1.0561 - sparse_categorical_accuracy: 0.7859 - val_loss: 1.0189 - val_sparse_categorical_accuracy: 0.7997\n",
+      "Epoch 45/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 1.0420 - sparse_categorical_accuracy: 0.7886 - val_loss: 1.0049 - val_sparse_categorical_accuracy: 0.8026\n",
+      "Epoch 46/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 1.0282 - sparse_categorical_accuracy: 0.7909 - val_loss: 0.9914 - val_sparse_categorical_accuracy: 0.8045\n",
+      "Epoch 47/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 1.0150 - sparse_categorical_accuracy: 0.7932 - val_loss: 0.9783 - val_sparse_categorical_accuracy: 0.8065\n",
+      "Epoch 48/200\n",
+      "1/1 [==============================] - 0s 133ms/step - loss: 1.0021 - sparse_categorical_accuracy: 0.7953 - val_loss: 0.9657 - val_sparse_categorical_accuracy: 0.8093\n",
+      "Epoch 49/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.9896 - sparse_categorical_accuracy: 0.7975 - val_loss: 0.9534 - val_sparse_categorical_accuracy: 0.8111\n",
+      "Epoch 50/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.9776 - sparse_categorical_accuracy: 0.7994 - val_loss: 0.9416 - val_sparse_categorical_accuracy: 0.8124\n",
+      "Epoch 51/200\n",
+      "1/1 [==============================] - 0s 112ms/step - loss: 0.9659 - sparse_categorical_accuracy: 0.8015 - val_loss: 0.9301 - val_sparse_categorical_accuracy: 0.8144\n",
+      "Epoch 52/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.9546 - sparse_categorical_accuracy: 0.8034 - val_loss: 0.9190 - val_sparse_categorical_accuracy: 0.8153\n",
+      "Epoch 53/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.9437 - sparse_categorical_accuracy: 0.8054 - val_loss: 0.9082 - val_sparse_categorical_accuracy: 0.8168\n",
+      "Epoch 54/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.9330 - sparse_categorical_accuracy: 0.8072 - val_loss: 0.8978 - val_sparse_categorical_accuracy: 0.8190\n",
+      "Epoch 55/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.9227 - sparse_categorical_accuracy: 0.8092 - val_loss: 0.8877 - val_sparse_categorical_accuracy: 0.8216\n",
+      "Epoch 56/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.9127 - sparse_categorical_accuracy: 0.8106 - val_loss: 0.8779 - val_sparse_categorical_accuracy: 0.8230\n",
+      "Epoch 57/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.9031 - sparse_categorical_accuracy: 0.8123 - val_loss: 0.8684 - val_sparse_categorical_accuracy: 0.8245\n",
+      "Epoch 58/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.8937 - sparse_categorical_accuracy: 0.8135 - val_loss: 0.8592 - val_sparse_categorical_accuracy: 0.8269\n",
+      "Epoch 59/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.8845 - sparse_categorical_accuracy: 0.8151 - val_loss: 0.8503 - val_sparse_categorical_accuracy: 0.8294\n",
+      "Epoch 60/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.8757 - sparse_categorical_accuracy: 0.8163 - val_loss: 0.8416 - val_sparse_categorical_accuracy: 0.8309\n",
+      "Epoch 61/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.8671 - sparse_categorical_accuracy: 0.8177 - val_loss: 0.8332 - val_sparse_categorical_accuracy: 0.8324\n",
+      "Epoch 62/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.8587 - sparse_categorical_accuracy: 0.8189 - val_loss: 0.8250 - val_sparse_categorical_accuracy: 0.8336\n",
+      "Epoch 63/200\n",
+      "1/1 [==============================] - 0s 156ms/step - loss: 0.8505 - sparse_categorical_accuracy: 0.8201 - val_loss: 0.8171 - val_sparse_categorical_accuracy: 0.8346\n",
+      "Epoch 64/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.8426 - sparse_categorical_accuracy: 0.8214 - val_loss: 0.8093 - val_sparse_categorical_accuracy: 0.8364\n",
+      "Epoch 65/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.8349 - sparse_categorical_accuracy: 0.8228 - val_loss: 0.8018 - val_sparse_categorical_accuracy: 0.8373\n",
+      "Epoch 66/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.8274 - sparse_categorical_accuracy: 0.8242 - val_loss: 0.7945 - val_sparse_categorical_accuracy: 0.8389\n",
+      "Epoch 67/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.8202 - sparse_categorical_accuracy: 0.8255 - val_loss: 0.7874 - val_sparse_categorical_accuracy: 0.8395\n",
+      "Epoch 68/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.8131 - sparse_categorical_accuracy: 0.8264 - val_loss: 0.7804 - val_sparse_categorical_accuracy: 0.8404\n",
+      "Epoch 69/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.8061 - sparse_categorical_accuracy: 0.8274 - val_loss: 0.7737 - val_sparse_categorical_accuracy: 0.8418\n",
+      "Epoch 70/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.7994 - sparse_categorical_accuracy: 0.8286 - val_loss: 0.7671 - val_sparse_categorical_accuracy: 0.8428\n",
+      "Epoch 71/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.7928 - sparse_categorical_accuracy: 0.8294 - val_loss: 0.7607 - val_sparse_categorical_accuracy: 0.8441\n",
+      "Epoch 72/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.7864 - sparse_categorical_accuracy: 0.8305 - val_loss: 0.7544 - val_sparse_categorical_accuracy: 0.8451\n",
+      "Epoch 73/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.7802 - sparse_categorical_accuracy: 0.8316 - val_loss: 0.7483 - val_sparse_categorical_accuracy: 0.8459\n",
+      "Epoch 74/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.7741 - sparse_categorical_accuracy: 0.8325 - val_loss: 0.7424 - val_sparse_categorical_accuracy: 0.8467\n",
+      "Epoch 75/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.7681 - sparse_categorical_accuracy: 0.8335 - val_loss: 0.7365 - val_sparse_categorical_accuracy: 0.8476\n",
+      "Epoch 76/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 0.7623 - sparse_categorical_accuracy: 0.8343 - val_loss: 0.7309 - val_sparse_categorical_accuracy: 0.8484\n",
+      "Epoch 77/200\n",
+      "1/1 [==============================] - 0s 132ms/step - loss: 0.7566 - sparse_categorical_accuracy: 0.8350 - val_loss: 0.7253 - val_sparse_categorical_accuracy: 0.8493\n",
+      "Epoch 78/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.7511 - sparse_categorical_accuracy: 0.8359 - val_loss: 0.7199 - val_sparse_categorical_accuracy: 0.8500\n",
+      "Epoch 79/200\n",
+      "1/1 [==============================] - 0s 132ms/step - loss: 0.7457 - sparse_categorical_accuracy: 0.8368 - val_loss: 0.7146 - val_sparse_categorical_accuracy: 0.8516\n",
+      "Epoch 80/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.7404 - sparse_categorical_accuracy: 0.8378 - val_loss: 0.7095 - val_sparse_categorical_accuracy: 0.8526\n",
+      "Epoch 81/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.7352 - sparse_categorical_accuracy: 0.8387 - val_loss: 0.7044 - val_sparse_categorical_accuracy: 0.8534\n",
+      "Epoch 82/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.7301 - sparse_categorical_accuracy: 0.8393 - val_loss: 0.6995 - val_sparse_categorical_accuracy: 0.8545\n",
+      "Epoch 83/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.7252 - sparse_categorical_accuracy: 0.8401 - val_loss: 0.6947 - val_sparse_categorical_accuracy: 0.8553\n",
+      "Epoch 84/200\n",
+      "1/1 [==============================] - 0s 122ms/step - loss: 0.7203 - sparse_categorical_accuracy: 0.8409 - val_loss: 0.6900 - val_sparse_categorical_accuracy: 0.8559\n",
+      "Epoch 85/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.7156 - sparse_categorical_accuracy: 0.8415 - val_loss: 0.6853 - val_sparse_categorical_accuracy: 0.8560\n",
+      "Epoch 86/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.7110 - sparse_categorical_accuracy: 0.8425 - val_loss: 0.6808 - val_sparse_categorical_accuracy: 0.8572\n",
+      "Epoch 87/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.7064 - sparse_categorical_accuracy: 0.8435 - val_loss: 0.6764 - val_sparse_categorical_accuracy: 0.8575\n",
+      "Epoch 88/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.7020 - sparse_categorical_accuracy: 0.8442 - val_loss: 0.6721 - val_sparse_categorical_accuracy: 0.8575\n",
+      "Epoch 89/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.6976 - sparse_categorical_accuracy: 0.8448 - val_loss: 0.6678 - val_sparse_categorical_accuracy: 0.8590\n",
+      "Epoch 90/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.6933 - sparse_categorical_accuracy: 0.8457 - val_loss: 0.6637 - val_sparse_categorical_accuracy: 0.8594\n",
+      "Epoch 91/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.6891 - sparse_categorical_accuracy: 0.8466 - val_loss: 0.6596 - val_sparse_categorical_accuracy: 0.8598\n",
+      "Epoch 92/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.6850 - sparse_categorical_accuracy: 0.8472 - val_loss: 0.6556 - val_sparse_categorical_accuracy: 0.8605\n",
+      "Epoch 93/200\n",
+      "1/1 [==============================] - 0s 137ms/step - loss: 0.6810 - sparse_categorical_accuracy: 0.8478 - val_loss: 0.6517 - val_sparse_categorical_accuracy: 0.8610\n",
+      "Epoch 94/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.6771 - sparse_categorical_accuracy: 0.8486 - val_loss: 0.6478 - val_sparse_categorical_accuracy: 0.8618\n",
+      "Epoch 95/200\n",
+      "1/1 [==============================] - 0s 122ms/step - loss: 0.6732 - sparse_categorical_accuracy: 0.8491 - val_loss: 0.6441 - val_sparse_categorical_accuracy: 0.8623\n",
+      "Epoch 96/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.6694 - sparse_categorical_accuracy: 0.8496 - val_loss: 0.6404 - val_sparse_categorical_accuracy: 0.8626\n",
+      "Epoch 97/200\n",
+      "1/1 [==============================] - 0s 137ms/step - loss: 0.6657 - sparse_categorical_accuracy: 0.8501 - val_loss: 0.6368 - val_sparse_categorical_accuracy: 0.8628\n",
+      "Epoch 98/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.6620 - sparse_categorical_accuracy: 0.8505 - val_loss: 0.6332 - val_sparse_categorical_accuracy: 0.8634\n",
+      "Epoch 99/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.6584 - sparse_categorical_accuracy: 0.8511 - val_loss: 0.6297 - val_sparse_categorical_accuracy: 0.8642\n",
+      "Epoch 100/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.6549 - sparse_categorical_accuracy: 0.8521 - val_loss: 0.6263 - val_sparse_categorical_accuracy: 0.8649\n",
+      "Epoch 101/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.6514 - sparse_categorical_accuracy: 0.8526 - val_loss: 0.6229 - val_sparse_categorical_accuracy: 0.8650\n",
+      "Epoch 102/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.6480 - sparse_categorical_accuracy: 0.8532 - val_loss: 0.6196 - val_sparse_categorical_accuracy: 0.8659\n",
+      "Epoch 103/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.6446 - sparse_categorical_accuracy: 0.8538 - val_loss: 0.6164 - val_sparse_categorical_accuracy: 0.8669\n",
+      "Epoch 104/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.6414 - sparse_categorical_accuracy: 0.8543 - val_loss: 0.6132 - val_sparse_categorical_accuracy: 0.8671\n",
+      "Epoch 105/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 0.6381 - sparse_categorical_accuracy: 0.8550 - val_loss: 0.6101 - val_sparse_categorical_accuracy: 0.8677\n",
+      "Epoch 106/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.6349 - sparse_categorical_accuracy: 0.8556 - val_loss: 0.6070 - val_sparse_categorical_accuracy: 0.8684\n",
+      "Epoch 107/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.6318 - sparse_categorical_accuracy: 0.8561 - val_loss: 0.6040 - val_sparse_categorical_accuracy: 0.8684\n",
+      "Epoch 108/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.6288 - sparse_categorical_accuracy: 0.8568 - val_loss: 0.6010 - val_sparse_categorical_accuracy: 0.8693\n",
+      "Epoch 109/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.6257 - sparse_categorical_accuracy: 0.8573 - val_loss: 0.5981 - val_sparse_categorical_accuracy: 0.8697\n",
+      "Epoch 110/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 0.6228 - sparse_categorical_accuracy: 0.8578 - val_loss: 0.5952 - val_sparse_categorical_accuracy: 0.8696\n",
+      "Epoch 111/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.6198 - sparse_categorical_accuracy: 0.8583 - val_loss: 0.5924 - val_sparse_categorical_accuracy: 0.8701\n",
+      "Epoch 112/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.6170 - sparse_categorical_accuracy: 0.8587 - val_loss: 0.5896 - val_sparse_categorical_accuracy: 0.8706\n",
+      "Epoch 113/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 0.6141 - sparse_categorical_accuracy: 0.8594 - val_loss: 0.5868 - val_sparse_categorical_accuracy: 0.8714\n",
+      "Epoch 114/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.6113 - sparse_categorical_accuracy: 0.8599 - val_loss: 0.5841 - val_sparse_categorical_accuracy: 0.8720\n",
+      "Epoch 115/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.6086 - sparse_categorical_accuracy: 0.8603 - val_loss: 0.5815 - val_sparse_categorical_accuracy: 0.8726\n",
+      "Epoch 116/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.6059 - sparse_categorical_accuracy: 0.8609 - val_loss: 0.5789 - val_sparse_categorical_accuracy: 0.8734\n",
+      "Epoch 117/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.6033 - sparse_categorical_accuracy: 0.8613 - val_loss: 0.5763 - val_sparse_categorical_accuracy: 0.8744\n",
+      "Epoch 118/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.6006 - sparse_categorical_accuracy: 0.8616 - val_loss: 0.5738 - val_sparse_categorical_accuracy: 0.8747\n",
+      "Epoch 119/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.5981 - sparse_categorical_accuracy: 0.8620 - val_loss: 0.5713 - val_sparse_categorical_accuracy: 0.8753\n",
+      "Epoch 120/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.5955 - sparse_categorical_accuracy: 0.8624 - val_loss: 0.5688 - val_sparse_categorical_accuracy: 0.8757\n",
+      "Epoch 121/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 0.5930 - sparse_categorical_accuracy: 0.8629 - val_loss: 0.5664 - val_sparse_categorical_accuracy: 0.8759\n",
+      "Epoch 122/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5906 - sparse_categorical_accuracy: 0.8634 - val_loss: 0.5640 - val_sparse_categorical_accuracy: 0.8762\n",
+      "Epoch 123/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.5881 - sparse_categorical_accuracy: 0.8637 - val_loss: 0.5617 - val_sparse_categorical_accuracy: 0.8761\n",
+      "Epoch 124/200\n",
+      "1/1 [==============================] - 0s 122ms/step - loss: 0.5857 - sparse_categorical_accuracy: 0.8641 - val_loss: 0.5594 - val_sparse_categorical_accuracy: 0.8763\n",
+      "Epoch 125/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.5834 - sparse_categorical_accuracy: 0.8646 - val_loss: 0.5571 - val_sparse_categorical_accuracy: 0.8764\n",
+      "Epoch 126/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5810 - sparse_categorical_accuracy: 0.8650 - val_loss: 0.5548 - val_sparse_categorical_accuracy: 0.8766\n",
+      "Epoch 127/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.5788 - sparse_categorical_accuracy: 0.8655 - val_loss: 0.5526 - val_sparse_categorical_accuracy: 0.8771\n",
+      "Epoch 128/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.5765 - sparse_categorical_accuracy: 0.8659 - val_loss: 0.5504 - val_sparse_categorical_accuracy: 0.8774\n",
+      "Epoch 129/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.5743 - sparse_categorical_accuracy: 0.8664 - val_loss: 0.5483 - val_sparse_categorical_accuracy: 0.8780\n",
+      "Epoch 130/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.5721 - sparse_categorical_accuracy: 0.8666 - val_loss: 0.5462 - val_sparse_categorical_accuracy: 0.8782\n",
+      "Epoch 131/200\n",
+      "1/1 [==============================] - 0s 140ms/step - loss: 0.5699 - sparse_categorical_accuracy: 0.8671 - val_loss: 0.5441 - val_sparse_categorical_accuracy: 0.8785\n",
+      "Epoch 132/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.5678 - sparse_categorical_accuracy: 0.8674 - val_loss: 0.5420 - val_sparse_categorical_accuracy: 0.8787\n",
+      "Epoch 133/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.5657 - sparse_categorical_accuracy: 0.8677 - val_loss: 0.5400 - val_sparse_categorical_accuracy: 0.8790\n",
+      "Epoch 134/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.5636 - sparse_categorical_accuracy: 0.8683 - val_loss: 0.5380 - val_sparse_categorical_accuracy: 0.8795\n",
+      "Epoch 135/200\n",
+      "1/1 [==============================] - 0s 159ms/step - loss: 0.5615 - sparse_categorical_accuracy: 0.8687 - val_loss: 0.5360 - val_sparse_categorical_accuracy: 0.8797\n",
+      "Epoch 136/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.5595 - sparse_categorical_accuracy: 0.8690 - val_loss: 0.5340 - val_sparse_categorical_accuracy: 0.8796\n",
+      "Epoch 137/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.5575 - sparse_categorical_accuracy: 0.8694 - val_loss: 0.5321 - val_sparse_categorical_accuracy: 0.8803\n",
+      "Epoch 138/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.5555 - sparse_categorical_accuracy: 0.8697 - val_loss: 0.5302 - val_sparse_categorical_accuracy: 0.8806\n",
+      "Epoch 139/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.5536 - sparse_categorical_accuracy: 0.8701 - val_loss: 0.5283 - val_sparse_categorical_accuracy: 0.8812\n",
+      "Epoch 140/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5517 - sparse_categorical_accuracy: 0.8703 - val_loss: 0.5265 - val_sparse_categorical_accuracy: 0.8814\n",
+      "Epoch 141/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5498 - sparse_categorical_accuracy: 0.8706 - val_loss: 0.5247 - val_sparse_categorical_accuracy: 0.8820\n",
+      "Epoch 142/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.5479 - sparse_categorical_accuracy: 0.8710 - val_loss: 0.5229 - val_sparse_categorical_accuracy: 0.8824\n",
+      "Epoch 143/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.5460 - sparse_categorical_accuracy: 0.8713 - val_loss: 0.5211 - val_sparse_categorical_accuracy: 0.8825\n",
+      "Epoch 144/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.5442 - sparse_categorical_accuracy: 0.8717 - val_loss: 0.5193 - val_sparse_categorical_accuracy: 0.8825\n",
+      "Epoch 145/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.5424 - sparse_categorical_accuracy: 0.8722 - val_loss: 0.5176 - val_sparse_categorical_accuracy: 0.8829\n",
+      "Epoch 146/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5406 - sparse_categorical_accuracy: 0.8725 - val_loss: 0.5159 - val_sparse_categorical_accuracy: 0.8831\n",
+      "Epoch 147/200\n",
+      "1/1 [==============================] - 0s 122ms/step - loss: 0.5389 - sparse_categorical_accuracy: 0.8728 - val_loss: 0.5142 - val_sparse_categorical_accuracy: 0.8833\n",
+      "Epoch 148/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5371 - sparse_categorical_accuracy: 0.8731 - val_loss: 0.5125 - val_sparse_categorical_accuracy: 0.8839\n",
+      "Epoch 149/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 0.5354 - sparse_categorical_accuracy: 0.8734 - val_loss: 0.5109 - val_sparse_categorical_accuracy: 0.8841\n",
+      "Epoch 150/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5337 - sparse_categorical_accuracy: 0.8736 - val_loss: 0.5092 - val_sparse_categorical_accuracy: 0.8847\n",
+      "Epoch 151/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.5320 - sparse_categorical_accuracy: 0.8737 - val_loss: 0.5076 - val_sparse_categorical_accuracy: 0.8848\n",
+      "Epoch 152/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.5304 - sparse_categorical_accuracy: 0.8739 - val_loss: 0.5060 - val_sparse_categorical_accuracy: 0.8850\n",
+      "Epoch 153/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.5287 - sparse_categorical_accuracy: 0.8740 - val_loss: 0.5045 - val_sparse_categorical_accuracy: 0.8853\n",
+      "Epoch 154/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.5271 - sparse_categorical_accuracy: 0.8744 - val_loss: 0.5029 - val_sparse_categorical_accuracy: 0.8856\n",
+      "Epoch 155/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.5255 - sparse_categorical_accuracy: 0.8747 - val_loss: 0.5014 - val_sparse_categorical_accuracy: 0.8863\n",
+      "Epoch 156/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.5239 - sparse_categorical_accuracy: 0.8749 - val_loss: 0.4998 - val_sparse_categorical_accuracy: 0.8861\n",
+      "Epoch 157/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.5224 - sparse_categorical_accuracy: 0.8752 - val_loss: 0.4983 - val_sparse_categorical_accuracy: 0.8864\n",
+      "Epoch 158/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.5208 - sparse_categorical_accuracy: 0.8755 - val_loss: 0.4969 - val_sparse_categorical_accuracy: 0.8864\n",
+      "Epoch 159/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.5193 - sparse_categorical_accuracy: 0.8756 - val_loss: 0.4954 - val_sparse_categorical_accuracy: 0.8868\n",
+      "Epoch 160/200\n",
+      "1/1 [==============================] - 0s 113ms/step - loss: 0.5178 - sparse_categorical_accuracy: 0.8759 - val_loss: 0.4940 - val_sparse_categorical_accuracy: 0.8872\n",
+      "Epoch 161/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.5163 - sparse_categorical_accuracy: 0.8761 - val_loss: 0.4925 - val_sparse_categorical_accuracy: 0.8873\n",
+      "Epoch 162/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.5148 - sparse_categorical_accuracy: 0.8764 - val_loss: 0.4911 - val_sparse_categorical_accuracy: 0.8875\n",
+      "Epoch 163/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.5134 - sparse_categorical_accuracy: 0.8766 - val_loss: 0.4897 - val_sparse_categorical_accuracy: 0.8878\n",
+      "Epoch 164/200\n",
+      "1/1 [==============================] - 0s 147ms/step - loss: 0.5119 - sparse_categorical_accuracy: 0.8767 - val_loss: 0.4883 - val_sparse_categorical_accuracy: 0.8878\n",
+      "Epoch 165/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.5105 - sparse_categorical_accuracy: 0.8770 - val_loss: 0.4870 - val_sparse_categorical_accuracy: 0.8879\n",
+      "Epoch 166/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.5091 - sparse_categorical_accuracy: 0.8772 - val_loss: 0.4856 - val_sparse_categorical_accuracy: 0.8881\n",
+      "Epoch 167/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.5077 - sparse_categorical_accuracy: 0.8775 - val_loss: 0.4843 - val_sparse_categorical_accuracy: 0.8882\n",
+      "Epoch 168/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.5063 - sparse_categorical_accuracy: 0.8776 - val_loss: 0.4829 - val_sparse_categorical_accuracy: 0.8887\n",
+      "Epoch 169/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.5049 - sparse_categorical_accuracy: 0.8777 - val_loss: 0.4816 - val_sparse_categorical_accuracy: 0.8888\n",
+      "Epoch 170/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5036 - sparse_categorical_accuracy: 0.8779 - val_loss: 0.4803 - val_sparse_categorical_accuracy: 0.8891\n",
+      "Epoch 171/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5022 - sparse_categorical_accuracy: 0.8781 - val_loss: 0.4791 - val_sparse_categorical_accuracy: 0.8892\n",
+      "Epoch 172/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.5009 - sparse_categorical_accuracy: 0.8785 - val_loss: 0.4778 - val_sparse_categorical_accuracy: 0.8893\n",
+      "Epoch 173/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.4996 - sparse_categorical_accuracy: 0.8787 - val_loss: 0.4765 - val_sparse_categorical_accuracy: 0.8895\n",
+      "Epoch 174/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.4983 - sparse_categorical_accuracy: 0.8790 - val_loss: 0.4753 - val_sparse_categorical_accuracy: 0.8897\n",
+      "Epoch 175/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.4970 - sparse_categorical_accuracy: 0.8792 - val_loss: 0.4741 - val_sparse_categorical_accuracy: 0.8899\n",
+      "Epoch 176/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.4957 - sparse_categorical_accuracy: 0.8794 - val_loss: 0.4729 - val_sparse_categorical_accuracy: 0.8903\n",
+      "Epoch 177/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.4945 - sparse_categorical_accuracy: 0.8795 - val_loss: 0.4717 - val_sparse_categorical_accuracy: 0.8904\n",
+      "Epoch 178/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.4932 - sparse_categorical_accuracy: 0.8798 - val_loss: 0.4705 - val_sparse_categorical_accuracy: 0.8905\n",
+      "Epoch 179/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.4920 - sparse_categorical_accuracy: 0.8801 - val_loss: 0.4693 - val_sparse_categorical_accuracy: 0.8908\n",
+      "Epoch 180/200\n",
+      "1/1 [==============================] - 0s 132ms/step - loss: 0.4908 - sparse_categorical_accuracy: 0.8803 - val_loss: 0.4681 - val_sparse_categorical_accuracy: 0.8913\n",
+      "Epoch 181/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 0.4896 - sparse_categorical_accuracy: 0.8806 - val_loss: 0.4670 - val_sparse_categorical_accuracy: 0.8916\n",
+      "Epoch 182/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.4884 - sparse_categorical_accuracy: 0.8807 - val_loss: 0.4658 - val_sparse_categorical_accuracy: 0.8915\n",
+      "Epoch 183/200\n",
+      "1/1 [==============================] - 0s 117ms/step - loss: 0.4872 - sparse_categorical_accuracy: 0.8809 - val_loss: 0.4647 - val_sparse_categorical_accuracy: 0.8919\n",
+      "Epoch 184/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.4860 - sparse_categorical_accuracy: 0.8812 - val_loss: 0.4636 - val_sparse_categorical_accuracy: 0.8921\n",
+      "Epoch 185/200\n",
+      "1/1 [==============================] - 0s 118ms/step - loss: 0.4848 - sparse_categorical_accuracy: 0.8814 - val_loss: 0.4625 - val_sparse_categorical_accuracy: 0.8925\n",
+      "Epoch 186/200\n",
+      "1/1 [==============================] - 0s 119ms/step - loss: 0.4837 - sparse_categorical_accuracy: 0.8816 - val_loss: 0.4614 - val_sparse_categorical_accuracy: 0.8927\n",
+      "Epoch 187/200\n",
+      "1/1 [==============================] - 0s 120ms/step - loss: 0.4826 - sparse_categorical_accuracy: 0.8818 - val_loss: 0.4603 - val_sparse_categorical_accuracy: 0.8927\n",
+      "Epoch 188/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.4814 - sparse_categorical_accuracy: 0.8820 - val_loss: 0.4592 - val_sparse_categorical_accuracy: 0.8927\n",
+      "Epoch 189/200\n",
+      "1/1 [==============================] - 0s 114ms/step - loss: 0.4803 - sparse_categorical_accuracy: 0.8821 - val_loss: 0.4582 - val_sparse_categorical_accuracy: 0.8934\n",
+      "Epoch 190/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.4792 - sparse_categorical_accuracy: 0.8823 - val_loss: 0.4571 - val_sparse_categorical_accuracy: 0.8934\n",
+      "Epoch 191/200\n",
+      "1/1 [==============================] - 0s 116ms/step - loss: 0.4781 - sparse_categorical_accuracy: 0.8826 - val_loss: 0.4561 - val_sparse_categorical_accuracy: 0.8935\n",
+      "Epoch 192/200\n",
+      "1/1 [==============================] - 0s 135ms/step - loss: 0.4770 - sparse_categorical_accuracy: 0.8827 - val_loss: 0.4550 - val_sparse_categorical_accuracy: 0.8935\n",
+      "Epoch 193/200\n",
+      "1/1 [==============================] - 0s 143ms/step - loss: 0.4759 - sparse_categorical_accuracy: 0.8828 - val_loss: 0.4540 - val_sparse_categorical_accuracy: 0.8934\n",
+      "Epoch 194/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.4749 - sparse_categorical_accuracy: 0.8829 - val_loss: 0.4530 - val_sparse_categorical_accuracy: 0.8936\n",
+      "Epoch 195/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.4738 - sparse_categorical_accuracy: 0.8832 - val_loss: 0.4520 - val_sparse_categorical_accuracy: 0.8937\n",
+      "Epoch 196/200\n",
+      "1/1 [==============================] - 0s 115ms/step - loss: 0.4728 - sparse_categorical_accuracy: 0.8834 - val_loss: 0.4510 - val_sparse_categorical_accuracy: 0.8939\n",
+      "Epoch 197/200\n",
+      "1/1 [==============================] - 0s 87ms/step - loss: 0.4717 - sparse_categorical_accuracy: 0.8835 - val_loss: 0.4500 - val_sparse_categorical_accuracy: 0.8940\n",
+      "Epoch 198/200\n",
+      "1/1 [==============================] - 0s 90ms/step - loss: 0.4707 - sparse_categorical_accuracy: 0.8837 - val_loss: 0.4490 - val_sparse_categorical_accuracy: 0.8941\n",
+      "Epoch 199/200\n",
+      "1/1 [==============================] - 0s 86ms/step - loss: 0.4697 - sparse_categorical_accuracy: 0.8839 - val_loss: 0.4481 - val_sparse_categorical_accuracy: 0.8942\n",
+      "Epoch 200/200\n",
+      "1/1 [==============================] - 0s 88ms/step - loss: 0.4687 - sparse_categorical_accuracy: 0.8842 - val_loss: 0.4471 - val_sparse_categorical_accuracy: 0.8946\n"
+     ]
+    }
+   ],
+   "source": [
+    "keras_model.compile(\n",
+    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
+    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
+    "    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n",
+    ")\n",
+    "\n",
+    "# Train loop\n",
+    "history = keras_model.fit(\n",
+    "    train_images,\n",
+    "    train_labels0,\n",
+    "    batch_size=len(train_images),\n",
+    "    epochs=200,\n",
+    "    validation_data=(test_images, test_labels0),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## View the training performance\n",
+    "\n",
+    "- When the Keras fit loop runs, it returns a \"history\" object, which includes a dictionary of the trianing history.\n",
+    "\n",
+    "- Hence we can plot graphs of the training performance (Accuracy, Loss), for both the \"Training\" and \"Validation\" sets...."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key: loss\n",
+      "Key: sparse_categorical_accuracy\n",
+      "Key: val_loss\n",
+      "Key: val_sparse_categorical_accuracy\n"
+     ]
+    }
+   ],
+   "source": [
+    "# first show keys for data series recorded by fit loop:\n",
+    "for item in history.history:\n",
+    "    print(\"Key:\",item)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.plot(history.history['loss'],label=\"train\")\n",
+    "plt.plot(history.history['val_loss'],label=\"validation\")\n",
+    "plt.title('Model Loss')\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Cross Entropy')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "#print(\"history\",history.history)\n",
+    "plt.plot(history.history['sparse_categorical_accuracy'],label=\"train\")\n",
+    "plt.plot(history.history['val_sparse_categorical_accuracy'],label=\"validation\")\n",
+    "plt.title('Model Accuracy')\n",
+    "#plt.yscale('log')\n",
+    "plt.ylabel('Acc')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspect how well the system is working on a sample of 25 new images (from the test set)...\n",
+    "- The test set has a lot of images in it, but we can only view 25 at a time.\n",
+    "- Hence rerun this code block several times, to get a different random set of samples from the test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "plt.figure(figsize=(10,10))\n",
+    "# plot 25 random images from the test set.\n",
+    "first_index=np.random.randint(len(test_images)-25)\n",
+    "for i in range(first_index,first_index+25):\n",
+    "    # define subplot\n",
+    "    plt.subplot(5,5,i+1-first_index)\n",
+    "    # plot raw pixel data\n",
+    "    plt.imshow(test_images0[i], cmap=plt.get_cmap('gray'))\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.grid(False)\n",
+    "    if class_names!=None:\n",
+    "        prediction=keras_model(test_images[i:i+1])[0,:] # This will be a vector of length 10\n",
+    "        prediction_class=np.argmax(prediction)  # Pick the index of the largest element of the length-10 vector\n",
+    "        # Add a label underneath...\n",
+    "        true_label=test_labels0[i]\n",
+    "        class_name=class_names[prediction_class]\n",
+    "        plt.xlabel(class_name+\" \"+(\"CORRECT\" if prediction_class==true_label else \"WRONG\"))\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Lab2/examples/Lab24-xor.ipynb b/Lab2/examples/Lab24-xor.ipynb
new file mode 100644
index 0000000..13d1d3d
--- /dev/null
+++ b/Lab2/examples/Lab24-xor.ipynb
@@ -0,0 +1,127 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ab7d7dbd-f2e0-4384-8c55-d4aeee74dd7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The tensorboard extension is already loaded. To reload it, use:\n",
+      "  %reload_ext tensorboard\n",
+      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe3e8248550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
+      "1/1 [==============================] - 0s 33ms/step\n",
+      "[[0.03452728]\n",
+      " [0.9867295 ]\n",
+      " [0.9883936 ]\n",
+      " [0.01205833]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Reusing TensorBoard on port 6008 (pid 911540), started 0:22:54 ago. (Use '!kill 911540' to kill it.)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "      <iframe id=\"tensorboard-frame-e941beefef1b33a0\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
+       "      </iframe>\n",
+       "      <script>\n",
+       "        (function() {\n",
+       "          const frame = document.getElementById(\"tensorboard-frame-e941beefef1b33a0\");\n",
+       "          const url = new URL(\"/\", window.location);\n",
+       "          const port = 6008;\n",
+       "          if (port) {\n",
+       "            url.port = port;\n",
+       "          }\n",
+       "          frame.src = url;\n",
+       "        })();\n",
+       "      </script>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%load_ext tensorboard\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "import numpy as np \n",
+    "import datetime, os\n",
+    "\n",
+    "tf.config.experimental.set_visible_devices([], \"GPU\") \n",
+    "\n",
+    "X = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
+    "y = np.array([[0],[1],[1],[0]])\n",
+    "    \n",
+    "model = tf.keras.models.Sequential([\n",
+    "    tf.keras.layers.Dense(8, activation='relu', name='layers_dense_1'),\n",
+    "    tf.keras.layers.Dense(8, activation='relu', name='layers_dense_2'),\n",
+    "    tf.keras.layers.Dense(1, activation='sigmoid', name='layers_dense_3')\n",
+    "])\n",
+    "    \n",
+    "loss_fn = tf.keras.losses.binary_crossentropy\n",
+    "\n",
+    "simple = True\n",
+    "\n",
+    "if simple == True:\n",
+    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "    \n",
+    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
+    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
+    "    model.fit(X, y, batch_size=4, epochs=1000, verbose=0, callbacks = [tensorboard_callback])\n",
+    "else:\n",
+    "    for i in range(100):\n",
+    "        with tf.GradientTape() as tape:\n",
+    "            # Forward pass.\n",
+    "            predictions = model(X)\n",
+    "            # Compute the loss value for this batch.\n",
+    "            loss_value = loss_fn(y, predictions)\n",
+    "\n",
+    "        # Get gradients of loss wrt the weights.\n",
+    "        gradients = tape.gradient(loss_value, model.trainable_weights)\n",
+    "        # Update the weights of the model.\n",
+    "        optimizer.apply_gradients(zip(gradients, model.trainable_weights))\n",
+    "\n",
+    "print(model.predict(X))\n",
+    "\n",
+    "%tensorboard --logdir logs"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lab2/examples/datasets/iris_test.csv b/Lab2/examples/datasets/iris_test.csv
new file mode 100644
index 0000000..e4939d2
--- /dev/null
+++ b/Lab2/examples/datasets/iris_test.csv
@@ -0,0 +1,30 @@
+5.9,3,4.2,1.5,1
+6.9,3.1,5.4,2.1,2
+5.1,3.3,1.7,0.5,0
+6,3.4,4.5,1.6,1
+5.5,2.5,4,1.3,1
+6.2,2.9,4.3,1.3,1
+5.5,4.2,1.4,0.2,0
+6.3,2.8,5.1,1.5,2
+5.6,3,4.1,1.3,1
+6.7,2.5,5.8,1.8,2
+7.1,3,5.9,2.1,2
+4.3,3,1.1,0.1,0
+5.6,2.8,4.9,2,2
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+6,2.2,4,1,1
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+5.7,2.6,3.5,1,1
+4.8,3.4,1.9,0.2,0
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+5.7,2.5,5,2,2
+5.4,3.4,1.7,0.2,0
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+6.3,2.9,5.6,1.8,2
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+5.8,2.7,3.9,1.2,1
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+6.7,3.1,4.7,1.5,1
+6.7,3.3,5.7,2.5,2
+6.4,2.9,4.3,1.3,1
diff --git a/Lab2/examples/datasets/iris_train.csv b/Lab2/examples/datasets/iris_train.csv
new file mode 100644
index 0000000..710f8a1
--- /dev/null
+++ b/Lab2/examples/datasets/iris_train.csv
@@ -0,0 +1,120 @@
+6.4,2.8,5.6,2.2,2
+5,2.3,3.3,1,1
+4.9,2.5,4.5,1.7,2
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diff --git a/Lab2/main.ipynb b/Lab2/main.ipynb
new file mode 100644
index 0000000..c6e0893
--- /dev/null
+++ b/Lab2/main.ipynb
@@ -0,0 +1,1433 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lab24"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import imageio.v3 as imageio\n",
+    "import numpy as np\n",
+    "from tensorflow import keras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.\n",
+    "Pakeisti modelių hiperparametrus, optimizavimo funkcijas, tinklo architektūras, aktyvacijos funkcijas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_29\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense_41 (Dense)            (None, 20)                15700     \n",
+      "                                                                 \n",
+      " dense_42 (Dense)            (None, 10)                210       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 15910 (62.15 KB)\n",
+      "Trainable params: 15910 (62.15 KB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "_________________________________________________________________\n",
+      "Epoch 1/200\n",
+      "1/1 [==============================] - 2s 2s/step - loss: 0.1158 - sparse_categorical_accuracy: 0.1325 - val_loss: 0.1145 - val_sparse_categorical_accuracy: 0.1317\n",
+      "Epoch 2/200\n",
+      "1/1 [==============================] - 0s 99ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.1294 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.1297\n",
+      "Epoch 3/200\n",
+      "1/1 [==============================] - 0s 105ms/step - loss: 0.1146 - sparse_categorical_accuracy: 0.1279 - val_loss: 0.1135 - val_sparse_categorical_accuracy: 0.1270\n",
+      "Epoch 4/200\n",
+      "1/1 [==============================] - 0s 140ms/step - loss: 0.1142 - sparse_categorical_accuracy: 0.1258 - val_loss: 0.1131 - val_sparse_categorical_accuracy: 0.1266\n",
+      "Epoch 5/200\n",
+      "1/1 [==============================] - 0s 108ms/step - loss: 0.1138 - sparse_categorical_accuracy: 0.1235 - val_loss: 0.1128 - val_sparse_categorical_accuracy: 0.1255\n",
+      "Epoch 6/200\n",
+      "1/1 [==============================] - 0s 104ms/step - loss: 0.1135 - sparse_categorical_accuracy: 0.1222 - val_loss: 0.1125 - val_sparse_categorical_accuracy: 0.1228\n",
+      "Epoch 7/200\n",
+      "1/1 [==============================] - 0s 99ms/step - loss: 0.1133 - sparse_categorical_accuracy: 0.1203 - val_loss: 0.1123 - val_sparse_categorical_accuracy: 0.1217\n",
+      "Epoch 8/200\n",
+      "1/1 [==============================] - 0s 99ms/step - loss: 0.1131 - sparse_categorical_accuracy: 0.1185 - val_loss: 0.1121 - val_sparse_categorical_accuracy: 0.1203\n",
+      "Epoch 9/200\n",
+      "1/1 [==============================] - 0s 98ms/step - loss: 0.1129 - sparse_categorical_accuracy: 0.1167 - val_loss: 0.1120 - val_sparse_categorical_accuracy: 0.1182\n",
+      "Epoch 10/200\n",
+      "1/1 [==============================] - 0s 104ms/step - loss: 0.1128 - sparse_categorical_accuracy: 0.1156 - val_loss: 0.1119 - val_sparse_categorical_accuracy: 0.1181\n",
+      "Epoch 11/200\n",
+      "1/1 [==============================] - 0s 96ms/step - loss: 0.1126 - sparse_categorical_accuracy: 0.1148 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.1166\n",
+      "Epoch 12/200\n",
+      "1/1 [==============================] - 0s 147ms/step - loss: 0.1125 - sparse_categorical_accuracy: 0.1134 - val_loss: 0.1117 - val_sparse_categorical_accuracy: 0.1161\n",
+      "Epoch 13/200\n",
+      "1/1 [==============================] - 0s 157ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.1123 - val_loss: 0.1116 - val_sparse_categorical_accuracy: 0.1150\n",
+      "Epoch 14/200\n",
+      "1/1 [==============================] - 0s 133ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.1116 - val_loss: 0.1115 - val_sparse_categorical_accuracy: 0.1143\n",
+      "Epoch 15/200\n",
+      "1/1 [==============================] - 0s 132ms/step - loss: 0.1123 - sparse_categorical_accuracy: 0.1108 - val_loss: 0.1114 - val_sparse_categorical_accuracy: 0.1141\n",
+      "Epoch 16/200\n",
+      "1/1 [==============================] - 0s 133ms/step - loss: 0.1122 - sparse_categorical_accuracy: 0.1104 - val_loss: 0.1114 - val_sparse_categorical_accuracy: 0.1136\n",
+      "Epoch 17/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.1098 - val_loss: 0.1113 - val_sparse_categorical_accuracy: 0.1132\n",
+      "Epoch 18/200\n",
+      "1/1 [==============================] - 0s 136ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.1095 - val_loss: 0.1112 - val_sparse_categorical_accuracy: 0.1126\n",
+      "Epoch 19/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1120 - sparse_categorical_accuracy: 0.1089 - val_loss: 0.1112 - val_sparse_categorical_accuracy: 0.1127\n",
+      "Epoch 20/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1120 - sparse_categorical_accuracy: 0.1085 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.1127\n",
+      "Epoch 21/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1119 - sparse_categorical_accuracy: 0.1082 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.1119\n",
+      "Epoch 22/200\n",
+      "1/1 [==============================] - 0s 141ms/step - loss: 0.1118 - sparse_categorical_accuracy: 0.1075 - val_loss: 0.1110 - val_sparse_categorical_accuracy: 0.1108\n",
+      "Epoch 23/200\n",
+      "1/1 [==============================] - 0s 143ms/step - loss: 0.1118 - sparse_categorical_accuracy: 0.1073 - val_loss: 0.1110 - val_sparse_categorical_accuracy: 0.1098\n",
+      "Epoch 24/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.1117 - sparse_categorical_accuracy: 0.1071 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.1096\n",
+      "Epoch 25/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1117 - sparse_categorical_accuracy: 0.1065 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.1085\n",
+      "Epoch 26/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1117 - sparse_categorical_accuracy: 0.1064 - val_loss: 0.1108 - val_sparse_categorical_accuracy: 0.1079\n",
+      "Epoch 27/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1116 - sparse_categorical_accuracy: 0.1062 - val_loss: 0.1108 - val_sparse_categorical_accuracy: 0.1078\n",
+      "Epoch 28/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1116 - sparse_categorical_accuracy: 0.1061 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.1078\n",
+      "Epoch 29/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1115 - sparse_categorical_accuracy: 0.1058 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.1074\n",
+      "Epoch 30/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1115 - sparse_categorical_accuracy: 0.1055 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.1066\n",
+      "Epoch 31/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1114 - sparse_categorical_accuracy: 0.1051 - val_loss: 0.1106 - val_sparse_categorical_accuracy: 0.1067\n",
+      "Epoch 32/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1114 - sparse_categorical_accuracy: 0.1047 - val_loss: 0.1106 - val_sparse_categorical_accuracy: 0.1071\n",
+      "Epoch 33/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1114 - sparse_categorical_accuracy: 0.1045 - val_loss: 0.1106 - val_sparse_categorical_accuracy: 0.1072\n",
+      "Epoch 34/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1113 - sparse_categorical_accuracy: 0.1041 - val_loss: 0.1105 - val_sparse_categorical_accuracy: 0.1068\n",
+      "Epoch 35/200\n",
+      "1/1 [==============================] - 0s 143ms/step - loss: 0.1113 - sparse_categorical_accuracy: 0.1039 - val_loss: 0.1105 - val_sparse_categorical_accuracy: 0.1064\n",
+      "Epoch 36/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1113 - sparse_categorical_accuracy: 0.1035 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.1061\n",
+      "Epoch 37/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1112 - sparse_categorical_accuracy: 0.1034 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.1067\n",
+      "Epoch 38/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1112 - sparse_categorical_accuracy: 0.1032 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.1065\n",
+      "Epoch 39/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1112 - sparse_categorical_accuracy: 0.1029 - val_loss: 0.1103 - val_sparse_categorical_accuracy: 0.1060\n",
+      "Epoch 40/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1111 - sparse_categorical_accuracy: 0.1027 - val_loss: 0.1103 - val_sparse_categorical_accuracy: 0.1057\n",
+      "Epoch 41/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.1111 - sparse_categorical_accuracy: 0.1024 - val_loss: 0.1103 - val_sparse_categorical_accuracy: 0.1050\n",
+      "Epoch 42/200\n",
+      "1/1 [==============================] - 0s 135ms/step - loss: 0.1111 - sparse_categorical_accuracy: 0.1022 - val_loss: 0.1103 - val_sparse_categorical_accuracy: 0.1051\n",
+      "Epoch 43/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1110 - sparse_categorical_accuracy: 0.1020 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.1043\n",
+      "Epoch 44/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1110 - sparse_categorical_accuracy: 0.1017 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.1037\n",
+      "Epoch 45/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1110 - sparse_categorical_accuracy: 0.1012 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.1026\n",
+      "Epoch 46/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.1010 - val_loss: 0.1101 - val_sparse_categorical_accuracy: 0.1027\n",
+      "Epoch 47/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.1009 - val_loss: 0.1101 - val_sparse_categorical_accuracy: 0.1020\n",
+      "Epoch 48/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.1006 - val_loss: 0.1101 - val_sparse_categorical_accuracy: 0.1017\n",
+      "Epoch 49/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.1005 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.1018\n",
+      "Epoch 50/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1108 - sparse_categorical_accuracy: 0.1005 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.1013\n",
+      "Epoch 51/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1108 - sparse_categorical_accuracy: 0.1003 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.1015\n",
+      "Epoch 52/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1108 - sparse_categorical_accuracy: 0.1001 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.1014\n",
+      "Epoch 53/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.0997 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.1018\n",
+      "Epoch 54/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.0994 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.1016\n",
+      "Epoch 55/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.0994 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.1017\n",
+      "Epoch 56/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.0994 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.1014\n",
+      "Epoch 57/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.0992 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.1012\n",
+      "Epoch 58/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.0990 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.1010\n",
+      "Epoch 59/200\n",
+      "1/1 [==============================] - 0s 138ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.0988 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.1007\n",
+      "Epoch 60/200\n",
+      "1/1 [==============================] - 0s 139ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.0986 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.1009\n",
+      "Epoch 61/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.0985 - val_loss: 0.1097 - val_sparse_categorical_accuracy: 0.1004\n",
+      "Epoch 62/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.0983 - val_loss: 0.1097 - val_sparse_categorical_accuracy: 0.1001\n",
+      "Epoch 63/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.0981 - val_loss: 0.1097 - val_sparse_categorical_accuracy: 0.0993\n",
+      "Epoch 64/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.0976 - val_loss: 0.1097 - val_sparse_categorical_accuracy: 0.0991\n",
+      "Epoch 65/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.0972 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.0991\n",
+      "Epoch 66/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.0969 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.0989\n",
+      "Epoch 67/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.0967 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.0981\n",
+      "Epoch 68/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.0967 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.0981\n",
+      "Epoch 69/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1103 - sparse_categorical_accuracy: 0.0965 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.0981\n",
+      "Epoch 70/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1103 - sparse_categorical_accuracy: 0.0961 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.0977\n",
+      "Epoch 71/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1103 - sparse_categorical_accuracy: 0.0957 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.0973\n",
+      "Epoch 72/200\n",
+      "1/1 [==============================] - 0s 143ms/step - loss: 0.1103 - sparse_categorical_accuracy: 0.0955 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.0972\n",
+      "Epoch 73/200\n",
+      "1/1 [==============================] - 0s 146ms/step - loss: 0.1102 - sparse_categorical_accuracy: 0.0954 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0972\n",
+      "Epoch 74/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1102 - sparse_categorical_accuracy: 0.0951 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0977\n",
+      "Epoch 75/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1102 - sparse_categorical_accuracy: 0.0948 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0975\n",
+      "Epoch 76/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.1102 - sparse_categorical_accuracy: 0.0948 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0976\n",
+      "Epoch 77/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0946 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0974\n",
+      "Epoch 78/200\n",
+      "1/1 [==============================] - 0s 135ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0945 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0970\n",
+      "Epoch 79/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0946 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0968\n",
+      "Epoch 80/200\n",
+      "1/1 [==============================] - 0s 140ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0946 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0967\n",
+      "Epoch 81/200\n",
+      "1/1 [==============================] - 0s 142ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0943 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0964\n",
+      "Epoch 82/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1100 - sparse_categorical_accuracy: 0.0938 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0958\n",
+      "Epoch 83/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1100 - sparse_categorical_accuracy: 0.0934 - val_loss: 0.1092 - val_sparse_categorical_accuracy: 0.0957\n",
+      "Epoch 84/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.1100 - sparse_categorical_accuracy: 0.0931 - val_loss: 0.1092 - val_sparse_categorical_accuracy: 0.0948\n",
+      "Epoch 85/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1100 - sparse_categorical_accuracy: 0.0929 - val_loss: 0.1092 - val_sparse_categorical_accuracy: 0.0945\n",
+      "Epoch 86/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1100 - sparse_categorical_accuracy: 0.0926 - val_loss: 0.1092 - val_sparse_categorical_accuracy: 0.0947\n",
+      "Epoch 87/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.0922 - val_loss: 0.1091 - val_sparse_categorical_accuracy: 0.0943\n",
+      "Epoch 88/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.0922 - val_loss: 0.1091 - val_sparse_categorical_accuracy: 0.0945\n",
+      "Epoch 89/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.0923 - val_loss: 0.1091 - val_sparse_categorical_accuracy: 0.0946\n",
+      "Epoch 90/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.0920 - val_loss: 0.1091 - val_sparse_categorical_accuracy: 0.0947\n",
+      "Epoch 91/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.0921 - val_loss: 0.1091 - val_sparse_categorical_accuracy: 0.0931\n",
+      "Epoch 92/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.0921 - val_loss: 0.1091 - val_sparse_categorical_accuracy: 0.0927\n",
+      "Epoch 93/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.0921 - val_loss: 0.1090 - val_sparse_categorical_accuracy: 0.0920\n",
+      "Epoch 94/200\n",
+      "1/1 [==============================] - 0s 133ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.0919 - val_loss: 0.1090 - val_sparse_categorical_accuracy: 0.0925\n",
+      "Epoch 95/200\n",
+      "1/1 [==============================] - 0s 139ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.0918 - val_loss: 0.1090 - val_sparse_categorical_accuracy: 0.0927\n",
+      "Epoch 96/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.0919 - val_loss: 0.1090 - val_sparse_categorical_accuracy: 0.0929\n",
+      "Epoch 97/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1097 - sparse_categorical_accuracy: 0.0917 - val_loss: 0.1090 - val_sparse_categorical_accuracy: 0.0932\n",
+      "Epoch 98/200\n",
+      "1/1 [==============================] - 0s 133ms/step - loss: 0.1097 - sparse_categorical_accuracy: 0.0916 - val_loss: 0.1090 - val_sparse_categorical_accuracy: 0.0930\n",
+      "Epoch 99/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1097 - sparse_categorical_accuracy: 0.0913 - val_loss: 0.1089 - val_sparse_categorical_accuracy: 0.0927\n",
+      "Epoch 100/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1097 - sparse_categorical_accuracy: 0.0912 - val_loss: 0.1089 - val_sparse_categorical_accuracy: 0.0929\n",
+      "Epoch 101/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1097 - sparse_categorical_accuracy: 0.0909 - val_loss: 0.1089 - val_sparse_categorical_accuracy: 0.0927\n",
+      "Epoch 102/200\n",
+      "1/1 [==============================] - 0s 137ms/step - loss: 0.1097 - sparse_categorical_accuracy: 0.0910 - val_loss: 0.1089 - val_sparse_categorical_accuracy: 0.0928\n",
+      "Epoch 103/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.0908 - val_loss: 0.1089 - val_sparse_categorical_accuracy: 0.0931\n",
+      "Epoch 104/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.0906 - val_loss: 0.1089 - val_sparse_categorical_accuracy: 0.0927\n",
+      "Epoch 105/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.0903 - val_loss: 0.1088 - val_sparse_categorical_accuracy: 0.0933\n",
+      "Epoch 106/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.0905 - val_loss: 0.1088 - val_sparse_categorical_accuracy: 0.0924\n",
+      "Epoch 107/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.0900 - val_loss: 0.1088 - val_sparse_categorical_accuracy: 0.0930\n",
+      "Epoch 108/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.0901 - val_loss: 0.1088 - val_sparse_categorical_accuracy: 0.0921\n",
+      "Epoch 109/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.0897 - val_loss: 0.1088 - val_sparse_categorical_accuracy: 0.0921\n",
+      "Epoch 110/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0900 - val_loss: 0.1088 - val_sparse_categorical_accuracy: 0.0904\n",
+      "Epoch 111/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0896 - val_loss: 0.1088 - val_sparse_categorical_accuracy: 0.0916\n",
+      "Epoch 112/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0899 - val_loss: 0.1088 - val_sparse_categorical_accuracy: 0.0902\n",
+      "Epoch 113/200\n",
+      "1/1 [==============================] - 0s 135ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0892 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0911\n",
+      "Epoch 114/200\n",
+      "1/1 [==============================] - 0s 136ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0894 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0898\n",
+      "Epoch 115/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0889 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0907\n",
+      "Epoch 116/200\n",
+      "1/1 [==============================] - 0s 146ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0894 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0893\n",
+      "Epoch 117/200\n",
+      "1/1 [==============================] - 0s 148ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0889 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0889\n",
+      "Epoch 118/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.0891 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0901\n",
+      "Epoch 119/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0894 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0887\n",
+      "Epoch 120/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0889 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0899\n",
+      "Epoch 121/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0893 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.0885\n",
+      "Epoch 122/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0887 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0893\n",
+      "Epoch 123/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0893 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0890\n",
+      "Epoch 124/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0886 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0894\n",
+      "Epoch 125/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0889 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0883\n",
+      "Epoch 126/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0880 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0898\n",
+      "Epoch 127/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.0887 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0887\n",
+      "Epoch 128/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0881 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0892\n",
+      "Epoch 129/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0887 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0880\n",
+      "Epoch 130/200\n",
+      "1/1 [==============================] - 0s 132ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0880 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0882\n",
+      "Epoch 131/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0880 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.0898\n",
+      "Epoch 132/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0885 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0884\n",
+      "Epoch 133/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0875 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0891\n",
+      "Epoch 134/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0884 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0878\n",
+      "Epoch 135/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0876 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0889\n",
+      "Epoch 136/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0884 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0881\n",
+      "Epoch 137/200\n",
+      "1/1 [==============================] - 0s 135ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0875 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0888\n",
+      "Epoch 138/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0884 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0880\n",
+      "Epoch 139/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1093 - sparse_categorical_accuracy: 0.0874 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0890\n",
+      "Epoch 140/200\n",
+      "1/1 [==============================] - 0s 130ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0882 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0876\n",
+      "Epoch 141/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0871 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0890\n",
+      "Epoch 142/200\n",
+      "1/1 [==============================] - 0s 123ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0876 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0877\n",
+      "Epoch 143/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0867 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0890\n",
+      "Epoch 144/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0875 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.0879\n",
+      "Epoch 145/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0863 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0893\n",
+      "Epoch 146/200\n",
+      "1/1 [==============================] - 0s 121ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0872 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0883\n",
+      "Epoch 147/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0861 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0895\n",
+      "Epoch 148/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0869 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0886\n",
+      "Epoch 149/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0860 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0893\n",
+      "Epoch 150/200\n",
+      "1/1 [==============================] - 0s 133ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0868 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0880\n",
+      "Epoch 151/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0859 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0891\n",
+      "Epoch 152/200\n",
+      "1/1 [==============================] - 0s 137ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0866 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0883\n",
+      "Epoch 153/200\n",
+      "1/1 [==============================] - 0s 157ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.0856 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0892\n",
+      "Epoch 154/200\n",
+      "1/1 [==============================] - 0s 137ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0865 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0883\n",
+      "Epoch 155/200\n",
+      "1/1 [==============================] - 0s 137ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0854 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0892\n",
+      "Epoch 156/200\n",
+      "1/1 [==============================] - 0s 135ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0865 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0883\n",
+      "Epoch 157/200\n",
+      "1/1 [==============================] - 0s 137ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0853 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0864\n",
+      "Epoch 158/200\n",
+      "1/1 [==============================] - 0s 141ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0840 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0890\n",
+      "Epoch 159/200\n",
+      "1/1 [==============================] - 0s 135ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0858 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0874\n",
+      "Epoch 160/200\n",
+      "1/1 [==============================] - 0s 132ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0846 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.0860\n",
+      "Epoch 161/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0836 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0881\n",
+      "Epoch 162/200\n",
+      "1/1 [==============================] - 0s 131ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0856 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0866\n",
+      "Epoch 163/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0844 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0875\n",
+      "Epoch 164/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0856 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0862\n",
+      "Epoch 165/200\n",
+      "1/1 [==============================] - 0s 139ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0843 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0840\n",
+      "Epoch 166/200\n",
+      "1/1 [==============================] - 0s 137ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0831 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0877\n",
+      "Epoch 167/200\n",
+      "1/1 [==============================] - 0s 132ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0853 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0856\n",
+      "Epoch 168/200\n",
+      "1/1 [==============================] - 0s 134ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0842 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0840\n",
+      "Epoch 169/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0829 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0863\n",
+      "Epoch 170/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0842 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0882\n",
+      "Epoch 171/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1091 - sparse_categorical_accuracy: 0.0853 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0837\n",
+      "Epoch 172/200\n",
+      "1/1 [==============================] - 0s 155ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0826 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0867\n",
+      "Epoch 173/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0841 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0880\n",
+      "Epoch 174/200\n",
+      "1/1 [==============================] - 0s 140ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0852 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0841\n",
+      "Epoch 175/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0825 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0866\n",
+      "Epoch 176/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0840 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0884\n",
+      "Epoch 177/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0855 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0846\n",
+      "Epoch 178/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0824 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0870\n",
+      "Epoch 179/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0840 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0849\n",
+      "Epoch 180/200\n",
+      "1/1 [==============================] - 0s 124ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0829 - val_loss: 0.1083 - val_sparse_categorical_accuracy: 0.0894\n",
+      "Epoch 181/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0854 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0873\n",
+      "Epoch 182/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0840 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0856\n",
+      "Epoch 183/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0838 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0841\n",
+      "Epoch 184/200\n",
+      "1/1 [==============================] - 0s 146ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0819 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0867\n",
+      "Epoch 185/200\n",
+      "1/1 [==============================] - 0s 152ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0838 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0886\n",
+      "Epoch 186/200\n",
+      "1/1 [==============================] - 0s 133ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0850 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0843\n",
+      "Epoch 187/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0815 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0854\n",
+      "Epoch 188/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0828 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0864\n",
+      "Epoch 189/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0834 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0876\n",
+      "Epoch 190/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0847 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0833\n",
+      "Epoch 191/200\n",
+      "1/1 [==============================] - 0s 139ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0811 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0843\n",
+      "Epoch 192/200\n",
+      "1/1 [==============================] - 0s 128ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0817 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0859\n",
+      "Epoch 193/200\n",
+      "1/1 [==============================] - 0s 127ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0829 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0867\n",
+      "Epoch 194/200\n",
+      "1/1 [==============================] - 0s 125ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0834 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0836\n",
+      "Epoch 195/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0808 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0877\n",
+      "Epoch 196/200\n",
+      "1/1 [==============================] - 0s 126ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.0844 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0850\n",
+      "Epoch 197/200\n",
+      "1/1 [==============================] - 0s 129ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.0822 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0863\n",
+      "Epoch 198/200\n",
+      "1/1 [==============================] - 0s 98ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.0828 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0833\n",
+      "Epoch 199/200\n",
+      "1/1 [==============================] - 0s 92ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.0800 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0881\n",
+      "Epoch 200/200\n",
+      "1/1 [==============================] - 0s 99ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.0841 - val_loss: 0.1082 - val_sparse_categorical_accuracy: 0.0842\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train mnist\n",
+    "\n",
+    "mnist = tf.keras.datasets.mnist\n",
+    "(train_images0, train_labels0), (test_images0, test_labels0) = mnist.load_data()\n",
+    "\n",
+    "test_images  = test_images0.reshape(10000, 784)\n",
+    "train_images = train_images0.reshape(60000, 784)\n",
+    "\n",
+    "test_images  = test_images/255.0\n",
+    "train_images = train_images/255.0\n",
+    "\n",
+    "keras_model = tf.keras.models.Sequential([\n",
+    "    tf.keras.layers.Dense(20, activation='relu'),\n",
+    "  tf.keras.layers.Dense(10, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "keras_model.build(input_shape=[None,784])\n",
+    "\n",
+    "keras_model.summary()\n",
+    "\n",
+    "keras_model.compile(\n",
+    "    optimizer=tf.keras.optimizers.SGD(0.2),\n",
+    "    loss=tf.keras.losses.CategoricalHinge(),\n",
+    "    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n",
+    ")\n",
+    "\n",
+    "# Train loop\n",
+    "history = keras_model.fit(\n",
+    "    train_images,\n",
+    "    train_labels0,\n",
+    "    batch_size=len(train_images),\n",
+    "    epochs=200,\n",
+    "    validation_data=(test_images, test_labels0),\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key: loss\n",
+      "Key: sparse_categorical_accuracy\n",
+      "Key: val_loss\n",
+      "Key: val_sparse_categorical_accuracy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# vizualize mnist\n",
+    "\n",
+    "for item in history.history:\n",
+    "    print(\"Key:\",item)\n",
+    "\n",
+    "plt.plot(history.history['loss'],label=\"train\")\n",
+    "plt.plot(history.history['val_loss'],label=\"validation\")\n",
+    "plt.title('Model Loss')\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Cross Entropy')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "#print(\"history\",history.history)\n",
+    "plt.plot(history.history['sparse_categorical_accuracy'],label=\"train\")\n",
+    "plt.plot(history.history['val_sparse_categorical_accuracy'],label=\"validation\")\n",
+    "plt.title('Model Accuracy')\n",
+    "#plt.yscale('log')\n",
+    "plt.ylabel('Acc')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.\n",
+    "Pamėginti MNIST duomenims gauti kuo didesnį tikslumą."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# train mnist\n",
+    "\n",
+    "mnist = tf.keras.datasets.mnist\n",
+    "(train_images0, train_labels0), (test_images0, test_labels0) = mnist.load_data()\n",
+    "\n",
+    "test_images  = test_images0.reshape(10000, 784)\n",
+    "train_images = train_images0.reshape(60000, 784)\n",
+    "\n",
+    "test_images  = test_images/255.0\n",
+    "train_images = train_images/255.0\n",
+    "\n",
+    "keras_model = tf.keras.models.Sequential([\n",
+    "  tf.keras.layers.Dense(10, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "keras_model.build(input_shape=[None,784])\n",
+    "\n",
+    "keras_model.summary()\n",
+    "\n",
+    "keras_model.compile(\n",
+    "    optimizer=tf.keras.optimizers.SGD(0.4, use_ema=True),\n",
+    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
+    "    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n",
+    ")\n",
+    "\n",
+    "# Train loop\n",
+    "history = keras_model.fit(\n",
+    "    train_images,\n",
+    "    train_labels0,\n",
+    "    batch_size=len(train_images),\n",
+    "    epochs=300,\n",
+    "    validation_data=(test_images, test_labels0),\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key: loss\n",
+      "Key: sparse_categorical_accuracy\n",
+      "Key: val_loss\n",
+      "Key: val_sparse_categorical_accuracy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# vizualize mnist\n",
+    "\n",
+    "for item in history.history:\n",
+    "    print(\"Key:\",item)\n",
+    "\n",
+    "plt.plot(history.history['loss'],label=\"train\")\n",
+    "plt.plot(history.history['val_loss'],label=\"validation\")\n",
+    "plt.title('Model Loss')\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Cross Entropy')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "#print(\"history\",history.history)\n",
+    "plt.plot(history.history['sparse_categorical_accuracy'],label=\"train\")\n",
+    "plt.plot(history.history['val_sparse_categorical_accuracy'],label=\"validation\")\n",
+    "plt.title('Model Accuracy')\n",
+    "#plt.yscale('log')\n",
+    "plt.ylabel('Acc')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.\n",
+    "Rasti internete duomenų rinkinį su vaizdais ir pritaikyti turimą kodą."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load datasets\n",
+    "\n",
+    "# https://www.kaggle.com/datasets/zalando-research/fashionmnist\n",
+    "train_dataset = pd.read_csv('assets/fashion-mnist_train.csv', skiprows = [0], header=None).values\n",
+    "train_input = train_dataset[:, 1:]\n",
+    "train_label = train_dataset[:, :1]\n",
+    "\n",
+    "test_dataset = pd.read_csv('assets/fashion-mnist_test.csv', skiprows = [0], header=None).values\n",
+    "test_input = test_dataset[:, 1:]\n",
+    "test_label = test_dataset[:, :1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_37\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense_56 (Dense)            (None, 10)                7850      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 7850 (30.66 KB)\n",
+      "Trainable params: 7850 (30.66 KB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "_________________________________________________________________\n",
+      "Epoch 1/300\n",
+      "1/1 [==============================] - 11s 11s/step - loss: 200.8899 - sparse_categorical_accuracy: 0.0993 - val_loss: 135.2529 - val_sparse_categorical_accuracy: 0.1143\n",
+      "Epoch 2/300\n",
+      "1/1 [==============================] - 0s 393ms/step - loss: 136.3396 - sparse_categorical_accuracy: 0.1098 - val_loss: 113.3213 - val_sparse_categorical_accuracy: 0.1422\n",
+      "Epoch 3/300\n",
+      "1/1 [==============================] - 0s 212ms/step - loss: 114.0896 - sparse_categorical_accuracy: 0.1405 - val_loss: 104.0747 - val_sparse_categorical_accuracy: 0.1805\n",
+      "Epoch 4/300\n",
+      "1/1 [==============================] - 0s 206ms/step - loss: 104.5925 - sparse_categorical_accuracy: 0.1804 - val_loss: 95.5190 - val_sparse_categorical_accuracy: 0.2200\n",
+      "Epoch 5/300\n",
+      "1/1 [==============================] - 0s 190ms/step - loss: 95.9215 - sparse_categorical_accuracy: 0.2210 - val_loss: 87.2152 - val_sparse_categorical_accuracy: 0.2655\n",
+      "Epoch 6/300\n",
+      "1/1 [==============================] - 0s 187ms/step - loss: 87.7617 - sparse_categorical_accuracy: 0.2698 - val_loss: 77.3627 - val_sparse_categorical_accuracy: 0.3068\n",
+      "Epoch 7/300\n",
+      "1/1 [==============================] - 0s 167ms/step - loss: 78.1472 - sparse_categorical_accuracy: 0.3109 - val_loss: 66.3837 - val_sparse_categorical_accuracy: 0.3583\n",
+      "Epoch 8/300\n",
+      "1/1 [==============================] - 0s 216ms/step - loss: 67.2765 - sparse_categorical_accuracy: 0.3551 - val_loss: 56.1239 - val_sparse_categorical_accuracy: 0.4027\n",
+      "Epoch 9/300\n",
+      "1/1 [==============================] - 0s 236ms/step - loss: 57.0718 - sparse_categorical_accuracy: 0.4009 - val_loss: 48.2395 - val_sparse_categorical_accuracy: 0.4350\n",
+      "Epoch 10/300\n",
+      "1/1 [==============================] - 0s 198ms/step - loss: 49.2051 - sparse_categorical_accuracy: 0.4325 - val_loss: 44.2210 - val_sparse_categorical_accuracy: 0.4495\n",
+      "Epoch 11/300\n",
+      "1/1 [==============================] - 0s 278ms/step - loss: 45.0963 - sparse_categorical_accuracy: 0.4487 - val_loss: 43.6507 - val_sparse_categorical_accuracy: 0.4584\n",
+      "Epoch 12/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 44.3950 - sparse_categorical_accuracy: 0.4548 - val_loss: 43.7409 - val_sparse_categorical_accuracy: 0.4641\n",
+      "Epoch 13/300\n",
+      "1/1 [==============================] - 0s 467ms/step - loss: 44.4352 - sparse_categorical_accuracy: 0.4608 - val_loss: 41.6169 - val_sparse_categorical_accuracy: 0.4838\n",
+      "Epoch 14/300\n",
+      "1/1 [==============================] - 0s 224ms/step - loss: 42.2697 - sparse_categorical_accuracy: 0.4777 - val_loss: 37.5434 - val_sparse_categorical_accuracy: 0.5103\n",
+      "Epoch 15/300\n",
+      "1/1 [==============================] - 0s 313ms/step - loss: 38.2246 - sparse_categorical_accuracy: 0.5052 - val_loss: 34.0593 - val_sparse_categorical_accuracy: 0.5321\n",
+      "Epoch 16/300\n",
+      "1/1 [==============================] - 0s 329ms/step - loss: 34.7490 - sparse_categorical_accuracy: 0.5330 - val_loss: 32.4519 - val_sparse_categorical_accuracy: 0.5513\n",
+      "Epoch 17/300\n",
+      "1/1 [==============================] - 0s 309ms/step - loss: 33.1500 - sparse_categorical_accuracy: 0.5514 - val_loss: 31.9879 - val_sparse_categorical_accuracy: 0.5625\n",
+      "Epoch 18/300\n",
+      "1/1 [==============================] - 0s 319ms/step - loss: 32.7047 - sparse_categorical_accuracy: 0.5631 - val_loss: 31.5895 - val_sparse_categorical_accuracy: 0.5743\n",
+      "Epoch 19/300\n",
+      "1/1 [==============================] - 0s 261ms/step - loss: 32.2759 - sparse_categorical_accuracy: 0.5731 - val_loss: 30.6852 - val_sparse_categorical_accuracy: 0.5836\n",
+      "Epoch 20/300\n",
+      "1/1 [==============================] - 0s 356ms/step - loss: 31.3555 - sparse_categorical_accuracy: 0.5829 - val_loss: 29.3388 - val_sparse_categorical_accuracy: 0.5963\n",
+      "Epoch 21/300\n",
+      "1/1 [==============================] - 0s 294ms/step - loss: 29.9866 - sparse_categorical_accuracy: 0.5944 - val_loss: 27.9010 - val_sparse_categorical_accuracy: 0.6068\n",
+      "Epoch 22/300\n",
+      "1/1 [==============================] - 0s 290ms/step - loss: 28.5080 - sparse_categorical_accuracy: 0.6052 - val_loss: 26.8371 - val_sparse_categorical_accuracy: 0.6170\n",
+      "Epoch 23/300\n",
+      "1/1 [==============================] - 0s 292ms/step - loss: 27.4188 - sparse_categorical_accuracy: 0.6167 - val_loss: 26.3897 - val_sparse_categorical_accuracy: 0.6260\n",
+      "Epoch 24/300\n",
+      "1/1 [==============================] - 0s 288ms/step - loss: 26.9111 - sparse_categorical_accuracy: 0.6238 - val_loss: 26.0771 - val_sparse_categorical_accuracy: 0.6314\n",
+      "Epoch 25/300\n",
+      "1/1 [==============================] - 0s 269ms/step - loss: 26.5634 - sparse_categorical_accuracy: 0.6270 - val_loss: 25.4394 - val_sparse_categorical_accuracy: 0.6357\n",
+      "Epoch 26/300\n",
+      "1/1 [==============================] - 0s 261ms/step - loss: 25.9387 - sparse_categorical_accuracy: 0.6301 - val_loss: 24.5883 - val_sparse_categorical_accuracy: 0.6359\n",
+      "Epoch 27/300\n",
+      "1/1 [==============================] - 0s 246ms/step - loss: 25.1048 - sparse_categorical_accuracy: 0.6338 - val_loss: 23.8745 - val_sparse_categorical_accuracy: 0.6366\n",
+      "Epoch 28/300\n",
+      "1/1 [==============================] - 0s 272ms/step - loss: 24.4025 - sparse_categorical_accuracy: 0.6360 - val_loss: 23.4751 - val_sparse_categorical_accuracy: 0.6351\n",
+      "Epoch 29/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 23.9629 - sparse_categorical_accuracy: 0.6384 - val_loss: 23.1136 - val_sparse_categorical_accuracy: 0.6405\n",
+      "Epoch 30/300\n",
+      "1/1 [==============================] - 0s 252ms/step - loss: 23.5747 - sparse_categorical_accuracy: 0.6417 - val_loss: 22.6080 - val_sparse_categorical_accuracy: 0.6459\n",
+      "Epoch 31/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 23.0632 - sparse_categorical_accuracy: 0.6481 - val_loss: 22.0341 - val_sparse_categorical_accuracy: 0.6530\n",
+      "Epoch 32/300\n",
+      "1/1 [==============================] - 0s 266ms/step - loss: 22.5126 - sparse_categorical_accuracy: 0.6557 - val_loss: 21.6302 - val_sparse_categorical_accuracy: 0.6590\n",
+      "Epoch 33/300\n",
+      "1/1 [==============================] - 0s 233ms/step - loss: 22.1279 - sparse_categorical_accuracy: 0.6616 - val_loss: 21.4182 - val_sparse_categorical_accuracy: 0.6655\n",
+      "Epoch 34/300\n",
+      "1/1 [==============================] - 0s 252ms/step - loss: 21.9163 - sparse_categorical_accuracy: 0.6659 - val_loss: 21.1914 - val_sparse_categorical_accuracy: 0.6709\n",
+      "Epoch 35/300\n",
+      "1/1 [==============================] - 0s 235ms/step - loss: 21.6713 - sparse_categorical_accuracy: 0.6700 - val_loss: 20.7828 - val_sparse_categorical_accuracy: 0.6756\n",
+      "Epoch 36/300\n",
+      "1/1 [==============================] - 0s 215ms/step - loss: 21.2508 - sparse_categorical_accuracy: 0.6733 - val_loss: 20.2652 - val_sparse_categorical_accuracy: 0.6813\n",
+      "Epoch 37/300\n",
+      "1/1 [==============================] - 0s 242ms/step - loss: 20.7232 - sparse_categorical_accuracy: 0.6778 - val_loss: 19.8116 - val_sparse_categorical_accuracy: 0.6835\n",
+      "Epoch 38/300\n",
+      "1/1 [==============================] - 0s 225ms/step - loss: 20.2661 - sparse_categorical_accuracy: 0.6806 - val_loss: 19.5268 - val_sparse_categorical_accuracy: 0.6848\n",
+      "Epoch 39/300\n",
+      "1/1 [==============================] - 0s 236ms/step - loss: 19.9708 - sparse_categorical_accuracy: 0.6821 - val_loss: 19.3480 - val_sparse_categorical_accuracy: 0.6851\n",
+      "Epoch 40/300\n",
+      "1/1 [==============================] - 0s 254ms/step - loss: 19.7887 - sparse_categorical_accuracy: 0.6814 - val_loss: 19.1417 - val_sparse_categorical_accuracy: 0.6858\n",
+      "Epoch 41/300\n",
+      "1/1 [==============================] - 0s 232ms/step - loss: 19.5808 - sparse_categorical_accuracy: 0.6820 - val_loss: 18.8611 - val_sparse_categorical_accuracy: 0.6890\n",
+      "Epoch 42/300\n",
+      "1/1 [==============================] - 0s 230ms/step - loss: 19.2956 - sparse_categorical_accuracy: 0.6829 - val_loss: 18.5724 - val_sparse_categorical_accuracy: 0.6895\n",
+      "Epoch 43/300\n",
+      "1/1 [==============================] - 0s 224ms/step - loss: 18.9996 - sparse_categorical_accuracy: 0.6857 - val_loss: 18.3326 - val_sparse_categorical_accuracy: 0.6923\n",
+      "Epoch 44/300\n",
+      "1/1 [==============================] - 0s 234ms/step - loss: 18.7523 - sparse_categorical_accuracy: 0.6887 - val_loss: 18.1063 - val_sparse_categorical_accuracy: 0.6952\n",
+      "Epoch 45/300\n",
+      "1/1 [==============================] - 0s 231ms/step - loss: 18.5292 - sparse_categorical_accuracy: 0.6921 - val_loss: 17.8600 - val_sparse_categorical_accuracy: 0.6983\n",
+      "Epoch 46/300\n",
+      "1/1 [==============================] - 0s 256ms/step - loss: 18.2929 - sparse_categorical_accuracy: 0.6942 - val_loss: 17.6157 - val_sparse_categorical_accuracy: 0.7013\n",
+      "Epoch 47/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 18.0623 - sparse_categorical_accuracy: 0.6962 - val_loss: 17.4156 - val_sparse_categorical_accuracy: 0.7024\n",
+      "Epoch 48/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 17.8715 - sparse_categorical_accuracy: 0.6980 - val_loss: 17.2511 - val_sparse_categorical_accuracy: 0.7028\n",
+      "Epoch 49/300\n",
+      "1/1 [==============================] - 0s 232ms/step - loss: 17.7106 - sparse_categorical_accuracy: 0.6991 - val_loss: 17.0785 - val_sparse_categorical_accuracy: 0.7055\n",
+      "Epoch 50/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 17.5329 - sparse_categorical_accuracy: 0.7005 - val_loss: 16.8799 - val_sparse_categorical_accuracy: 0.7073\n",
+      "Epoch 51/300\n",
+      "1/1 [==============================] - 0s 237ms/step - loss: 17.3180 - sparse_categorical_accuracy: 0.7023 - val_loss: 16.6840 - val_sparse_categorical_accuracy: 0.7105\n",
+      "Epoch 52/300\n",
+      "1/1 [==============================] - 0s 230ms/step - loss: 17.0982 - sparse_categorical_accuracy: 0.7030 - val_loss: 16.5309 - val_sparse_categorical_accuracy: 0.7110\n",
+      "Epoch 53/300\n",
+      "1/1 [==============================] - 0s 230ms/step - loss: 16.9205 - sparse_categorical_accuracy: 0.7032 - val_loss: 16.4061 - val_sparse_categorical_accuracy: 0.7117\n",
+      "Epoch 54/300\n",
+      "1/1 [==============================] - 0s 231ms/step - loss: 16.7789 - sparse_categorical_accuracy: 0.7033 - val_loss: 16.2694 - val_sparse_categorical_accuracy: 0.7112\n",
+      "Epoch 55/300\n",
+      "1/1 [==============================] - 0s 240ms/step - loss: 16.6307 - sparse_categorical_accuracy: 0.7042 - val_loss: 16.1078 - val_sparse_categorical_accuracy: 0.7120\n",
+      "Epoch 56/300\n",
+      "1/1 [==============================] - 0s 244ms/step - loss: 16.4600 - sparse_categorical_accuracy: 0.7050 - val_loss: 15.9454 - val_sparse_categorical_accuracy: 0.7137\n",
+      "Epoch 57/300\n",
+      "1/1 [==============================] - 0s 231ms/step - loss: 16.2947 - sparse_categorical_accuracy: 0.7066 - val_loss: 15.8032 - val_sparse_categorical_accuracy: 0.7137\n",
+      "Epoch 58/300\n",
+      "1/1 [==============================] - 0s 229ms/step - loss: 16.1502 - sparse_categorical_accuracy: 0.7081 - val_loss: 15.6730 - val_sparse_categorical_accuracy: 0.7143\n",
+      "Epoch 59/300\n",
+      "1/1 [==============================] - 0s 236ms/step - loss: 16.0130 - sparse_categorical_accuracy: 0.7093 - val_loss: 15.5380 - val_sparse_categorical_accuracy: 0.7161\n",
+      "Epoch 60/300\n",
+      "1/1 [==============================] - 0s 215ms/step - loss: 15.8696 - sparse_categorical_accuracy: 0.7104 - val_loss: 15.4090 - val_sparse_categorical_accuracy: 0.7182\n",
+      "Epoch 61/300\n",
+      "1/1 [==============================] - 0s 233ms/step - loss: 15.7291 - sparse_categorical_accuracy: 0.7122 - val_loss: 15.2951 - val_sparse_categorical_accuracy: 0.7203\n",
+      "Epoch 62/300\n",
+      "1/1 [==============================] - 0s 222ms/step - loss: 15.6048 - sparse_categorical_accuracy: 0.7139 - val_loss: 15.1881 - val_sparse_categorical_accuracy: 0.7224\n",
+      "Epoch 63/300\n",
+      "1/1 [==============================] - 0s 238ms/step - loss: 15.4892 - sparse_categorical_accuracy: 0.7150 - val_loss: 15.0698 - val_sparse_categorical_accuracy: 0.7237\n",
+      "Epoch 64/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 15.3673 - sparse_categorical_accuracy: 0.7162 - val_loss: 14.9416 - val_sparse_categorical_accuracy: 0.7250\n",
+      "Epoch 65/300\n",
+      "1/1 [==============================] - 0s 266ms/step - loss: 15.2410 - sparse_categorical_accuracy: 0.7167 - val_loss: 14.8211 - val_sparse_categorical_accuracy: 0.7247\n",
+      "Epoch 66/300\n",
+      "1/1 [==============================] - 0s 275ms/step - loss: 15.1234 - sparse_categorical_accuracy: 0.7171 - val_loss: 14.7128 - val_sparse_categorical_accuracy: 0.7266\n",
+      "Epoch 67/300\n",
+      "1/1 [==============================] - 0s 246ms/step - loss: 15.0171 - sparse_categorical_accuracy: 0.7182 - val_loss: 14.6074 - val_sparse_categorical_accuracy: 0.7275\n",
+      "Epoch 68/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 14.9121 - sparse_categorical_accuracy: 0.7195 - val_loss: 14.4997 - val_sparse_categorical_accuracy: 0.7297\n",
+      "Epoch 69/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 14.8028 - sparse_categorical_accuracy: 0.7206 - val_loss: 14.3956 - val_sparse_categorical_accuracy: 0.7313\n",
+      "Epoch 70/300\n",
+      "1/1 [==============================] - 0s 246ms/step - loss: 14.6953 - sparse_categorical_accuracy: 0.7217 - val_loss: 14.2986 - val_sparse_categorical_accuracy: 0.7324\n",
+      "Epoch 71/300\n",
+      "1/1 [==============================] - 0s 338ms/step - loss: 14.5937 - sparse_categorical_accuracy: 0.7230 - val_loss: 14.2029 - val_sparse_categorical_accuracy: 0.7323\n",
+      "Epoch 72/300\n",
+      "1/1 [==============================] - 0s 283ms/step - loss: 14.4943 - sparse_categorical_accuracy: 0.7243 - val_loss: 14.1052 - val_sparse_categorical_accuracy: 0.7333\n",
+      "Epoch 73/300\n",
+      "1/1 [==============================] - 0s 261ms/step - loss: 14.3949 - sparse_categorical_accuracy: 0.7255 - val_loss: 14.0099 - val_sparse_categorical_accuracy: 0.7336\n",
+      "Epoch 74/300\n",
+      "1/1 [==============================] - 0s 262ms/step - loss: 14.2991 - sparse_categorical_accuracy: 0.7263 - val_loss: 13.9184 - val_sparse_categorical_accuracy: 0.7351\n",
+      "Epoch 75/300\n",
+      "1/1 [==============================] - 0s 273ms/step - loss: 14.2084 - sparse_categorical_accuracy: 0.7267 - val_loss: 13.8273 - val_sparse_categorical_accuracy: 0.7352\n",
+      "Epoch 76/300\n",
+      "1/1 [==============================] - 0s 261ms/step - loss: 14.1187 - sparse_categorical_accuracy: 0.7277 - val_loss: 13.7361 - val_sparse_categorical_accuracy: 0.7365\n",
+      "Epoch 77/300\n",
+      "1/1 [==============================] - 0s 269ms/step - loss: 14.0274 - sparse_categorical_accuracy: 0.7285 - val_loss: 13.6494 - val_sparse_categorical_accuracy: 0.7375\n",
+      "Epoch 78/300\n",
+      "1/1 [==============================] - 0s 282ms/step - loss: 13.9381 - sparse_categorical_accuracy: 0.7294 - val_loss: 13.5701 - val_sparse_categorical_accuracy: 0.7388\n",
+      "Epoch 79/300\n",
+      "1/1 [==============================] - 0s 308ms/step - loss: 13.8536 - sparse_categorical_accuracy: 0.7304 - val_loss: 13.4938 - val_sparse_categorical_accuracy: 0.7389\n",
+      "Epoch 80/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 13.7718 - sparse_categorical_accuracy: 0.7315 - val_loss: 13.4165 - val_sparse_categorical_accuracy: 0.7390\n",
+      "Epoch 81/300\n",
+      "1/1 [==============================] - 0s 261ms/step - loss: 13.6900 - sparse_categorical_accuracy: 0.7324 - val_loss: 13.3395 - val_sparse_categorical_accuracy: 0.7407\n",
+      "Epoch 82/300\n",
+      "1/1 [==============================] - 0s 266ms/step - loss: 13.6093 - sparse_categorical_accuracy: 0.7333 - val_loss: 13.2646 - val_sparse_categorical_accuracy: 0.7416\n",
+      "Epoch 83/300\n",
+      "1/1 [==============================] - 0s 284ms/step - loss: 13.5312 - sparse_categorical_accuracy: 0.7342 - val_loss: 13.1913 - val_sparse_categorical_accuracy: 0.7418\n",
+      "Epoch 84/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 13.4549 - sparse_categorical_accuracy: 0.7351 - val_loss: 13.1195 - val_sparse_categorical_accuracy: 0.7427\n",
+      "Epoch 85/300\n",
+      "1/1 [==============================] - 0s 252ms/step - loss: 13.3794 - sparse_categorical_accuracy: 0.7357 - val_loss: 13.0506 - val_sparse_categorical_accuracy: 0.7437\n",
+      "Epoch 86/300\n",
+      "1/1 [==============================] - 0s 263ms/step - loss: 13.3061 - sparse_categorical_accuracy: 0.7363 - val_loss: 12.9844 - val_sparse_categorical_accuracy: 0.7443\n",
+      "Epoch 87/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 13.2355 - sparse_categorical_accuracy: 0.7367 - val_loss: 12.9177 - val_sparse_categorical_accuracy: 0.7446\n",
+      "Epoch 88/300\n",
+      "1/1 [==============================] - 0s 327ms/step - loss: 13.1651 - sparse_categorical_accuracy: 0.7371 - val_loss: 12.8496 - val_sparse_categorical_accuracy: 0.7448\n",
+      "Epoch 89/300\n",
+      "1/1 [==============================] - 0s 252ms/step - loss: 13.0944 - sparse_categorical_accuracy: 0.7376 - val_loss: 12.7826 - val_sparse_categorical_accuracy: 0.7442\n",
+      "Epoch 90/300\n",
+      "1/1 [==============================] - 0s 252ms/step - loss: 13.0254 - sparse_categorical_accuracy: 0.7385 - val_loss: 12.7189 - val_sparse_categorical_accuracy: 0.7451\n",
+      "Epoch 91/300\n",
+      "1/1 [==============================] - 0s 261ms/step - loss: 12.9589 - sparse_categorical_accuracy: 0.7395 - val_loss: 12.6576 - val_sparse_categorical_accuracy: 0.7457\n",
+      "Epoch 92/300\n",
+      "1/1 [==============================] - 0s 253ms/step - loss: 12.8934 - sparse_categorical_accuracy: 0.7402 - val_loss: 12.5982 - val_sparse_categorical_accuracy: 0.7464\n",
+      "Epoch 93/300\n",
+      "1/1 [==============================] - 0s 240ms/step - loss: 12.8282 - sparse_categorical_accuracy: 0.7410 - val_loss: 12.5400 - val_sparse_categorical_accuracy: 0.7470\n",
+      "Epoch 94/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 12.7640 - sparse_categorical_accuracy: 0.7416 - val_loss: 12.4820 - val_sparse_categorical_accuracy: 0.7474\n",
+      "Epoch 95/300\n",
+      "1/1 [==============================] - 0s 262ms/step - loss: 12.7009 - sparse_categorical_accuracy: 0.7422 - val_loss: 12.4235 - val_sparse_categorical_accuracy: 0.7474\n",
+      "Epoch 96/300\n",
+      "1/1 [==============================] - 0s 244ms/step - loss: 12.6387 - sparse_categorical_accuracy: 0.7426 - val_loss: 12.3656 - val_sparse_categorical_accuracy: 0.7477\n",
+      "Epoch 97/300\n",
+      "1/1 [==============================] - 0s 269ms/step - loss: 12.5777 - sparse_categorical_accuracy: 0.7426 - val_loss: 12.3098 - val_sparse_categorical_accuracy: 0.7485\n",
+      "Epoch 98/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 12.5179 - sparse_categorical_accuracy: 0.7429 - val_loss: 12.2564 - val_sparse_categorical_accuracy: 0.7493\n",
+      "Epoch 99/300\n",
+      "1/1 [==============================] - 0s 257ms/step - loss: 12.4585 - sparse_categorical_accuracy: 0.7434 - val_loss: 12.2053 - val_sparse_categorical_accuracy: 0.7497\n",
+      "Epoch 100/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 12.3994 - sparse_categorical_accuracy: 0.7443 - val_loss: 12.1561 - val_sparse_categorical_accuracy: 0.7500\n",
+      "Epoch 101/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 12.3415 - sparse_categorical_accuracy: 0.7448 - val_loss: 12.1070 - val_sparse_categorical_accuracy: 0.7502\n",
+      "Epoch 102/300\n",
+      "1/1 [==============================] - 0s 262ms/step - loss: 12.2845 - sparse_categorical_accuracy: 0.7454 - val_loss: 12.0567 - val_sparse_categorical_accuracy: 0.7508\n",
+      "Epoch 103/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 12.2280 - sparse_categorical_accuracy: 0.7460 - val_loss: 12.0055 - val_sparse_categorical_accuracy: 0.7510\n",
+      "Epoch 104/300\n",
+      "1/1 [==============================] - 0s 255ms/step - loss: 12.1720 - sparse_categorical_accuracy: 0.7463 - val_loss: 11.9552 - val_sparse_categorical_accuracy: 0.7511\n",
+      "Epoch 105/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 12.1167 - sparse_categorical_accuracy: 0.7468 - val_loss: 11.9066 - val_sparse_categorical_accuracy: 0.7515\n",
+      "Epoch 106/300\n",
+      "1/1 [==============================] - 0s 256ms/step - loss: 12.0620 - sparse_categorical_accuracy: 0.7471 - val_loss: 11.8599 - val_sparse_categorical_accuracy: 0.7517\n",
+      "Epoch 107/300\n",
+      "1/1 [==============================] - 0s 286ms/step - loss: 12.0079 - sparse_categorical_accuracy: 0.7478 - val_loss: 11.8144 - val_sparse_categorical_accuracy: 0.7525\n",
+      "Epoch 108/300\n",
+      "1/1 [==============================] - 0s 255ms/step - loss: 11.9545 - sparse_categorical_accuracy: 0.7481 - val_loss: 11.7689 - val_sparse_categorical_accuracy: 0.7527\n",
+      "Epoch 109/300\n",
+      "1/1 [==============================] - 0s 275ms/step - loss: 11.9018 - sparse_categorical_accuracy: 0.7485 - val_loss: 11.7227 - val_sparse_categorical_accuracy: 0.7532\n",
+      "Epoch 110/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 11.8496 - sparse_categorical_accuracy: 0.7491 - val_loss: 11.6758 - val_sparse_categorical_accuracy: 0.7537\n",
+      "Epoch 111/300\n",
+      "1/1 [==============================] - 0s 268ms/step - loss: 11.7978 - sparse_categorical_accuracy: 0.7498 - val_loss: 11.6294 - val_sparse_categorical_accuracy: 0.7534\n",
+      "Epoch 112/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 11.7467 - sparse_categorical_accuracy: 0.7502 - val_loss: 11.5841 - val_sparse_categorical_accuracy: 0.7540\n",
+      "Epoch 113/300\n",
+      "1/1 [==============================] - 0s 262ms/step - loss: 11.6962 - sparse_categorical_accuracy: 0.7506 - val_loss: 11.5399 - val_sparse_categorical_accuracy: 0.7543\n",
+      "Epoch 114/300\n",
+      "1/1 [==============================] - 0s 265ms/step - loss: 11.6461 - sparse_categorical_accuracy: 0.7510 - val_loss: 11.4964 - val_sparse_categorical_accuracy: 0.7550\n",
+      "Epoch 115/300\n",
+      "1/1 [==============================] - 0s 328ms/step - loss: 11.5966 - sparse_categorical_accuracy: 0.7515 - val_loss: 11.4529 - val_sparse_categorical_accuracy: 0.7552\n",
+      "Epoch 116/300\n",
+      "1/1 [==============================] - 0s 272ms/step - loss: 11.5475 - sparse_categorical_accuracy: 0.7519 - val_loss: 11.4092 - val_sparse_categorical_accuracy: 0.7558\n",
+      "Epoch 117/300\n",
+      "1/1 [==============================] - 0s 289ms/step - loss: 11.4990 - sparse_categorical_accuracy: 0.7523 - val_loss: 11.3654 - val_sparse_categorical_accuracy: 0.7559\n",
+      "Epoch 118/300\n",
+      "1/1 [==============================] - 0s 280ms/step - loss: 11.4509 - sparse_categorical_accuracy: 0.7526 - val_loss: 11.3225 - val_sparse_categorical_accuracy: 0.7565\n",
+      "Epoch 119/300\n",
+      "1/1 [==============================] - 0s 257ms/step - loss: 11.4034 - sparse_categorical_accuracy: 0.7528 - val_loss: 11.2807 - val_sparse_categorical_accuracy: 0.7564\n",
+      "Epoch 120/300\n",
+      "1/1 [==============================] - 0s 253ms/step - loss: 11.3562 - sparse_categorical_accuracy: 0.7533 - val_loss: 11.2400 - val_sparse_categorical_accuracy: 0.7567\n",
+      "Epoch 121/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 11.3095 - sparse_categorical_accuracy: 0.7539 - val_loss: 11.2000 - val_sparse_categorical_accuracy: 0.7566\n",
+      "Epoch 122/300\n",
+      "1/1 [==============================] - 0s 259ms/step - loss: 11.2633 - sparse_categorical_accuracy: 0.7543 - val_loss: 11.1602 - val_sparse_categorical_accuracy: 0.7568\n",
+      "Epoch 123/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 11.2176 - sparse_categorical_accuracy: 0.7547 - val_loss: 11.1202 - val_sparse_categorical_accuracy: 0.7571\n",
+      "Epoch 124/300\n",
+      "1/1 [==============================] - 0s 263ms/step - loss: 11.1722 - sparse_categorical_accuracy: 0.7551 - val_loss: 11.0806 - val_sparse_categorical_accuracy: 0.7574\n",
+      "Epoch 125/300\n",
+      "1/1 [==============================] - 0s 256ms/step - loss: 11.1274 - sparse_categorical_accuracy: 0.7555 - val_loss: 11.0417 - val_sparse_categorical_accuracy: 0.7581\n",
+      "Epoch 126/300\n",
+      "1/1 [==============================] - 0s 272ms/step - loss: 11.0830 - sparse_categorical_accuracy: 0.7559 - val_loss: 11.0037 - val_sparse_categorical_accuracy: 0.7584\n",
+      "Epoch 127/300\n",
+      "1/1 [==============================] - 0s 253ms/step - loss: 11.0391 - sparse_categorical_accuracy: 0.7563 - val_loss: 10.9663 - val_sparse_categorical_accuracy: 0.7589\n",
+      "Epoch 128/300\n",
+      "1/1 [==============================] - 0s 274ms/step - loss: 10.9955 - sparse_categorical_accuracy: 0.7564 - val_loss: 10.9290 - val_sparse_categorical_accuracy: 0.7595\n",
+      "Epoch 129/300\n",
+      "1/1 [==============================] - 0s 276ms/step - loss: 10.9525 - sparse_categorical_accuracy: 0.7570 - val_loss: 10.8914 - val_sparse_categorical_accuracy: 0.7598\n",
+      "Epoch 130/300\n",
+      "1/1 [==============================] - 0s 263ms/step - loss: 10.9098 - sparse_categorical_accuracy: 0.7573 - val_loss: 10.8537 - val_sparse_categorical_accuracy: 0.7601\n",
+      "Epoch 131/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 10.8675 - sparse_categorical_accuracy: 0.7578 - val_loss: 10.8164 - val_sparse_categorical_accuracy: 0.7600\n",
+      "Epoch 132/300\n",
+      "1/1 [==============================] - 0s 275ms/step - loss: 10.8257 - sparse_categorical_accuracy: 0.7582 - val_loss: 10.7798 - val_sparse_categorical_accuracy: 0.7600\n",
+      "Epoch 133/300\n",
+      "1/1 [==============================] - 0s 237ms/step - loss: 10.7842 - sparse_categorical_accuracy: 0.7586 - val_loss: 10.7436 - val_sparse_categorical_accuracy: 0.7603\n",
+      "Epoch 134/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 10.7432 - sparse_categorical_accuracy: 0.7590 - val_loss: 10.7076 - val_sparse_categorical_accuracy: 0.7608\n",
+      "Epoch 135/300\n",
+      "1/1 [==============================] - 0s 240ms/step - loss: 10.7025 - sparse_categorical_accuracy: 0.7594 - val_loss: 10.6716 - val_sparse_categorical_accuracy: 0.7610\n",
+      "Epoch 136/300\n",
+      "1/1 [==============================] - 0s 238ms/step - loss: 10.6622 - sparse_categorical_accuracy: 0.7598 - val_loss: 10.6355 - val_sparse_categorical_accuracy: 0.7607\n",
+      "Epoch 137/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 10.6222 - sparse_categorical_accuracy: 0.7602 - val_loss: 10.5999 - val_sparse_categorical_accuracy: 0.7609\n",
+      "Epoch 138/300\n",
+      "1/1 [==============================] - 0s 265ms/step - loss: 10.5827 - sparse_categorical_accuracy: 0.7607 - val_loss: 10.5649 - val_sparse_categorical_accuracy: 0.7612\n",
+      "Epoch 139/300\n",
+      "1/1 [==============================] - 0s 231ms/step - loss: 10.5434 - sparse_categorical_accuracy: 0.7612 - val_loss: 10.5305 - val_sparse_categorical_accuracy: 0.7618\n",
+      "Epoch 140/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 10.5045 - sparse_categorical_accuracy: 0.7616 - val_loss: 10.4964 - val_sparse_categorical_accuracy: 0.7621\n",
+      "Epoch 141/300\n",
+      "1/1 [==============================] - 0s 228ms/step - loss: 10.4660 - sparse_categorical_accuracy: 0.7616 - val_loss: 10.4624 - val_sparse_categorical_accuracy: 0.7625\n",
+      "Epoch 142/300\n",
+      "1/1 [==============================] - 0s 232ms/step - loss: 10.4278 - sparse_categorical_accuracy: 0.7620 - val_loss: 10.4285 - val_sparse_categorical_accuracy: 0.7629\n",
+      "Epoch 143/300\n",
+      "1/1 [==============================] - 0s 255ms/step - loss: 10.3899 - sparse_categorical_accuracy: 0.7624 - val_loss: 10.3950 - val_sparse_categorical_accuracy: 0.7633\n",
+      "Epoch 144/300\n",
+      "1/1 [==============================] - 0s 298ms/step - loss: 10.3523 - sparse_categorical_accuracy: 0.7628 - val_loss: 10.3620 - val_sparse_categorical_accuracy: 0.7635\n",
+      "Epoch 145/300\n",
+      "1/1 [==============================] - 0s 277ms/step - loss: 10.3151 - sparse_categorical_accuracy: 0.7631 - val_loss: 10.3296 - val_sparse_categorical_accuracy: 0.7638\n",
+      "Epoch 146/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 10.2782 - sparse_categorical_accuracy: 0.7634 - val_loss: 10.2973 - val_sparse_categorical_accuracy: 0.7640\n",
+      "Epoch 147/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 10.2416 - sparse_categorical_accuracy: 0.7636 - val_loss: 10.2651 - val_sparse_categorical_accuracy: 0.7641\n",
+      "Epoch 148/300\n",
+      "1/1 [==============================] - 0s 257ms/step - loss: 10.2053 - sparse_categorical_accuracy: 0.7638 - val_loss: 10.2330 - val_sparse_categorical_accuracy: 0.7646\n",
+      "Epoch 149/300\n",
+      "1/1 [==============================] - 0s 259ms/step - loss: 10.1693 - sparse_categorical_accuracy: 0.7641 - val_loss: 10.2011 - val_sparse_categorical_accuracy: 0.7649\n",
+      "Epoch 150/300\n",
+      "1/1 [==============================] - 0s 236ms/step - loss: 10.1336 - sparse_categorical_accuracy: 0.7643 - val_loss: 10.1697 - val_sparse_categorical_accuracy: 0.7654\n",
+      "Epoch 151/300\n",
+      "1/1 [==============================] - 0s 340ms/step - loss: 10.0982 - sparse_categorical_accuracy: 0.7645 - val_loss: 10.1387 - val_sparse_categorical_accuracy: 0.7662\n",
+      "Epoch 152/300\n",
+      "1/1 [==============================] - 0s 273ms/step - loss: 10.0630 - sparse_categorical_accuracy: 0.7649 - val_loss: 10.1079 - val_sparse_categorical_accuracy: 0.7666\n",
+      "Epoch 153/300\n",
+      "1/1 [==============================] - 0s 271ms/step - loss: 10.0281 - sparse_categorical_accuracy: 0.7654 - val_loss: 10.0772 - val_sparse_categorical_accuracy: 0.7666\n",
+      "Epoch 154/300\n",
+      "1/1 [==============================] - 0s 262ms/step - loss: 9.9935 - sparse_categorical_accuracy: 0.7657 - val_loss: 10.0466 - val_sparse_categorical_accuracy: 0.7669\n",
+      "Epoch 155/300\n",
+      "1/1 [==============================] - 0s 265ms/step - loss: 9.9592 - sparse_categorical_accuracy: 0.7660 - val_loss: 10.0164 - val_sparse_categorical_accuracy: 0.7674\n",
+      "Epoch 156/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 9.9251 - sparse_categorical_accuracy: 0.7660 - val_loss: 9.9866 - val_sparse_categorical_accuracy: 0.7676\n",
+      "Epoch 157/300\n",
+      "1/1 [==============================] - 0s 298ms/step - loss: 9.8913 - sparse_categorical_accuracy: 0.7662 - val_loss: 9.9571 - val_sparse_categorical_accuracy: 0.7679\n",
+      "Epoch 158/300\n",
+      "1/1 [==============================] - 0s 259ms/step - loss: 9.8577 - sparse_categorical_accuracy: 0.7666 - val_loss: 9.9277 - val_sparse_categorical_accuracy: 0.7682\n",
+      "Epoch 159/300\n",
+      "1/1 [==============================] - 0s 259ms/step - loss: 9.8244 - sparse_categorical_accuracy: 0.7668 - val_loss: 9.8985 - val_sparse_categorical_accuracy: 0.7683\n",
+      "Epoch 160/300\n",
+      "1/1 [==============================] - 0s 254ms/step - loss: 9.7913 - sparse_categorical_accuracy: 0.7668 - val_loss: 9.8695 - val_sparse_categorical_accuracy: 0.7687\n",
+      "Epoch 161/300\n",
+      "1/1 [==============================] - 0s 231ms/step - loss: 9.7585 - sparse_categorical_accuracy: 0.7670 - val_loss: 9.8408 - val_sparse_categorical_accuracy: 0.7688\n",
+      "Epoch 162/300\n",
+      "1/1 [==============================] - 0s 242ms/step - loss: 9.7258 - sparse_categorical_accuracy: 0.7675 - val_loss: 9.8126 - val_sparse_categorical_accuracy: 0.7691\n",
+      "Epoch 163/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 9.6934 - sparse_categorical_accuracy: 0.7678 - val_loss: 9.7846 - val_sparse_categorical_accuracy: 0.7691\n",
+      "Epoch 164/300\n",
+      "1/1 [==============================] - 0s 231ms/step - loss: 9.6613 - sparse_categorical_accuracy: 0.7680 - val_loss: 9.7568 - val_sparse_categorical_accuracy: 0.7693\n",
+      "Epoch 165/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 9.6293 - sparse_categorical_accuracy: 0.7682 - val_loss: 9.7291 - val_sparse_categorical_accuracy: 0.7695\n",
+      "Epoch 166/300\n",
+      "1/1 [==============================] - 0s 240ms/step - loss: 9.5976 - sparse_categorical_accuracy: 0.7686 - val_loss: 9.7016 - val_sparse_categorical_accuracy: 0.7694\n",
+      "Epoch 167/300\n",
+      "1/1 [==============================] - 0s 255ms/step - loss: 9.5660 - sparse_categorical_accuracy: 0.7689 - val_loss: 9.6745 - val_sparse_categorical_accuracy: 0.7699\n",
+      "Epoch 168/300\n",
+      "1/1 [==============================] - 0s 265ms/step - loss: 9.5347 - sparse_categorical_accuracy: 0.7692 - val_loss: 9.6477 - val_sparse_categorical_accuracy: 0.7701\n",
+      "Epoch 169/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 9.5037 - sparse_categorical_accuracy: 0.7695 - val_loss: 9.6211 - val_sparse_categorical_accuracy: 0.7703\n",
+      "Epoch 170/300\n",
+      "1/1 [==============================] - 0s 252ms/step - loss: 9.4728 - sparse_categorical_accuracy: 0.7697 - val_loss: 9.5947 - val_sparse_categorical_accuracy: 0.7703\n",
+      "Epoch 171/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 9.4421 - sparse_categorical_accuracy: 0.7700 - val_loss: 9.5685 - val_sparse_categorical_accuracy: 0.7704\n",
+      "Epoch 172/300\n",
+      "1/1 [==============================] - 0s 238ms/step - loss: 9.4117 - sparse_categorical_accuracy: 0.7702 - val_loss: 9.5425 - val_sparse_categorical_accuracy: 0.7704\n",
+      "Epoch 173/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 9.3814 - sparse_categorical_accuracy: 0.7705 - val_loss: 9.5168 - val_sparse_categorical_accuracy: 0.7705\n",
+      "Epoch 174/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 9.3514 - sparse_categorical_accuracy: 0.7707 - val_loss: 9.4913 - val_sparse_categorical_accuracy: 0.7709\n",
+      "Epoch 175/300\n",
+      "1/1 [==============================] - 0s 261ms/step - loss: 9.3216 - sparse_categorical_accuracy: 0.7710 - val_loss: 9.4661 - val_sparse_categorical_accuracy: 0.7708\n",
+      "Epoch 176/300\n",
+      "1/1 [==============================] - 0s 238ms/step - loss: 9.2919 - sparse_categorical_accuracy: 0.7711 - val_loss: 9.4409 - val_sparse_categorical_accuracy: 0.7709\n",
+      "Epoch 177/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 9.2625 - sparse_categorical_accuracy: 0.7714 - val_loss: 9.4159 - val_sparse_categorical_accuracy: 0.7711\n",
+      "Epoch 178/300\n",
+      "1/1 [==============================] - 0s 292ms/step - loss: 9.2332 - sparse_categorical_accuracy: 0.7715 - val_loss: 9.3911 - val_sparse_categorical_accuracy: 0.7713\n",
+      "Epoch 179/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 9.2041 - sparse_categorical_accuracy: 0.7718 - val_loss: 9.3666 - val_sparse_categorical_accuracy: 0.7714\n",
+      "Epoch 180/300\n",
+      "1/1 [==============================] - 0s 231ms/step - loss: 9.1752 - sparse_categorical_accuracy: 0.7721 - val_loss: 9.3421 - val_sparse_categorical_accuracy: 0.7718\n",
+      "Epoch 181/300\n",
+      "1/1 [==============================] - 0s 260ms/step - loss: 9.1465 - sparse_categorical_accuracy: 0.7724 - val_loss: 9.3178 - val_sparse_categorical_accuracy: 0.7719\n",
+      "Epoch 182/300\n",
+      "1/1 [==============================] - 0s 296ms/step - loss: 9.1179 - sparse_categorical_accuracy: 0.7728 - val_loss: 9.2937 - val_sparse_categorical_accuracy: 0.7722\n",
+      "Epoch 183/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 9.0895 - sparse_categorical_accuracy: 0.7728 - val_loss: 9.2697 - val_sparse_categorical_accuracy: 0.7722\n",
+      "Epoch 184/300\n",
+      "1/1 [==============================] - 0s 239ms/step - loss: 9.0613 - sparse_categorical_accuracy: 0.7732 - val_loss: 9.2459 - val_sparse_categorical_accuracy: 0.7721\n",
+      "Epoch 185/300\n",
+      "1/1 [==============================] - 0s 254ms/step - loss: 9.0333 - sparse_categorical_accuracy: 0.7736 - val_loss: 9.2224 - val_sparse_categorical_accuracy: 0.7725\n",
+      "Epoch 186/300\n",
+      "1/1 [==============================] - 0s 238ms/step - loss: 9.0054 - sparse_categorical_accuracy: 0.7738 - val_loss: 9.1990 - val_sparse_categorical_accuracy: 0.7730\n",
+      "Epoch 187/300\n",
+      "1/1 [==============================] - 0s 280ms/step - loss: 8.9777 - sparse_categorical_accuracy: 0.7739 - val_loss: 9.1758 - val_sparse_categorical_accuracy: 0.7730\n",
+      "Epoch 188/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 8.9502 - sparse_categorical_accuracy: 0.7743 - val_loss: 9.1527 - val_sparse_categorical_accuracy: 0.7731\n",
+      "Epoch 189/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 8.9228 - sparse_categorical_accuracy: 0.7744 - val_loss: 9.1299 - val_sparse_categorical_accuracy: 0.7730\n",
+      "Epoch 190/300\n",
+      "1/1 [==============================] - 0s 238ms/step - loss: 8.8957 - sparse_categorical_accuracy: 0.7747 - val_loss: 9.1072 - val_sparse_categorical_accuracy: 0.7731\n",
+      "Epoch 191/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 8.8686 - sparse_categorical_accuracy: 0.7750 - val_loss: 9.0846 - val_sparse_categorical_accuracy: 0.7738\n",
+      "Epoch 192/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 8.8418 - sparse_categorical_accuracy: 0.7751 - val_loss: 9.0622 - val_sparse_categorical_accuracy: 0.7744\n",
+      "Epoch 193/300\n",
+      "1/1 [==============================] - 0s 270ms/step - loss: 8.8151 - sparse_categorical_accuracy: 0.7753 - val_loss: 9.0399 - val_sparse_categorical_accuracy: 0.7747\n",
+      "Epoch 194/300\n",
+      "1/1 [==============================] - 0s 244ms/step - loss: 8.7886 - sparse_categorical_accuracy: 0.7756 - val_loss: 9.0177 - val_sparse_categorical_accuracy: 0.7750\n",
+      "Epoch 195/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 8.7622 - sparse_categorical_accuracy: 0.7758 - val_loss: 8.9957 - val_sparse_categorical_accuracy: 0.7751\n",
+      "Epoch 196/300\n",
+      "1/1 [==============================] - 0s 254ms/step - loss: 8.7360 - sparse_categorical_accuracy: 0.7758 - val_loss: 8.9738 - val_sparse_categorical_accuracy: 0.7756\n",
+      "Epoch 197/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 8.7099 - sparse_categorical_accuracy: 0.7760 - val_loss: 8.9520 - val_sparse_categorical_accuracy: 0.7755\n",
+      "Epoch 198/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 8.6840 - sparse_categorical_accuracy: 0.7763 - val_loss: 8.9303 - val_sparse_categorical_accuracy: 0.7754\n",
+      "Epoch 199/300\n",
+      "1/1 [==============================] - 0s 288ms/step - loss: 8.6582 - sparse_categorical_accuracy: 0.7763 - val_loss: 8.9088 - val_sparse_categorical_accuracy: 0.7755\n",
+      "Epoch 200/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 8.6326 - sparse_categorical_accuracy: 0.7764 - val_loss: 8.8875 - val_sparse_categorical_accuracy: 0.7762\n",
+      "Epoch 201/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 8.6072 - sparse_categorical_accuracy: 0.7768 - val_loss: 8.8663 - val_sparse_categorical_accuracy: 0.7761\n",
+      "Epoch 202/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 8.5818 - sparse_categorical_accuracy: 0.7771 - val_loss: 8.8452 - val_sparse_categorical_accuracy: 0.7766\n",
+      "Epoch 203/300\n",
+      "1/1 [==============================] - 0s 277ms/step - loss: 8.5567 - sparse_categorical_accuracy: 0.7772 - val_loss: 8.8242 - val_sparse_categorical_accuracy: 0.7763\n",
+      "Epoch 204/300\n",
+      "1/1 [==============================] - 0s 254ms/step - loss: 8.5317 - sparse_categorical_accuracy: 0.7774 - val_loss: 8.8034 - val_sparse_categorical_accuracy: 0.7764\n",
+      "Epoch 205/300\n",
+      "1/1 [==============================] - 0s 238ms/step - loss: 8.5068 - sparse_categorical_accuracy: 0.7778 - val_loss: 8.7828 - val_sparse_categorical_accuracy: 0.7765\n",
+      "Epoch 206/300\n",
+      "1/1 [==============================] - 0s 246ms/step - loss: 8.4820 - sparse_categorical_accuracy: 0.7780 - val_loss: 8.7622 - val_sparse_categorical_accuracy: 0.7764\n",
+      "Epoch 207/300\n",
+      "1/1 [==============================] - 0s 264ms/step - loss: 8.4574 - sparse_categorical_accuracy: 0.7784 - val_loss: 8.7417 - val_sparse_categorical_accuracy: 0.7767\n",
+      "Epoch 208/300\n",
+      "1/1 [==============================] - 0s 310ms/step - loss: 8.4330 - sparse_categorical_accuracy: 0.7785 - val_loss: 8.7214 - val_sparse_categorical_accuracy: 0.7770\n",
+      "Epoch 209/300\n",
+      "1/1 [==============================] - 0s 291ms/step - loss: 8.4086 - sparse_categorical_accuracy: 0.7786 - val_loss: 8.7011 - val_sparse_categorical_accuracy: 0.7776\n",
+      "Epoch 210/300\n",
+      "1/1 [==============================] - 0s 310ms/step - loss: 8.3844 - sparse_categorical_accuracy: 0.7788 - val_loss: 8.6810 - val_sparse_categorical_accuracy: 0.7780\n",
+      "Epoch 211/300\n",
+      "1/1 [==============================] - 0s 317ms/step - loss: 8.3604 - sparse_categorical_accuracy: 0.7790 - val_loss: 8.6610 - val_sparse_categorical_accuracy: 0.7780\n",
+      "Epoch 212/300\n",
+      "1/1 [==============================] - 0s 240ms/step - loss: 8.3365 - sparse_categorical_accuracy: 0.7793 - val_loss: 8.6411 - val_sparse_categorical_accuracy: 0.7781\n",
+      "Epoch 213/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 8.3127 - sparse_categorical_accuracy: 0.7793 - val_loss: 8.6213 - val_sparse_categorical_accuracy: 0.7784\n",
+      "Epoch 214/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 8.2890 - sparse_categorical_accuracy: 0.7794 - val_loss: 8.6017 - val_sparse_categorical_accuracy: 0.7786\n",
+      "Epoch 215/300\n",
+      "1/1 [==============================] - 0s 219ms/step - loss: 8.2654 - sparse_categorical_accuracy: 0.7795 - val_loss: 8.5821 - val_sparse_categorical_accuracy: 0.7786\n",
+      "Epoch 216/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 8.2420 - sparse_categorical_accuracy: 0.7797 - val_loss: 8.5627 - val_sparse_categorical_accuracy: 0.7789\n",
+      "Epoch 217/300\n",
+      "1/1 [==============================] - 0s 234ms/step - loss: 8.2187 - sparse_categorical_accuracy: 0.7798 - val_loss: 8.5434 - val_sparse_categorical_accuracy: 0.7787\n",
+      "Epoch 218/300\n",
+      "1/1 [==============================] - 0s 252ms/step - loss: 8.1955 - sparse_categorical_accuracy: 0.7800 - val_loss: 8.5242 - val_sparse_categorical_accuracy: 0.7790\n",
+      "Epoch 219/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 8.1724 - sparse_categorical_accuracy: 0.7799 - val_loss: 8.5050 - val_sparse_categorical_accuracy: 0.7791\n",
+      "Epoch 220/300\n",
+      "1/1 [==============================] - 0s 257ms/step - loss: 8.1494 - sparse_categorical_accuracy: 0.7802 - val_loss: 8.4860 - val_sparse_categorical_accuracy: 0.7794\n",
+      "Epoch 221/300\n",
+      "1/1 [==============================] - 0s 230ms/step - loss: 8.1266 - sparse_categorical_accuracy: 0.7803 - val_loss: 8.4671 - val_sparse_categorical_accuracy: 0.7793\n",
+      "Epoch 222/300\n",
+      "1/1 [==============================] - 0s 242ms/step - loss: 8.1039 - sparse_categorical_accuracy: 0.7806 - val_loss: 8.4482 - val_sparse_categorical_accuracy: 0.7795\n",
+      "Epoch 223/300\n",
+      "1/1 [==============================] - 0s 242ms/step - loss: 8.0812 - sparse_categorical_accuracy: 0.7807 - val_loss: 8.4295 - val_sparse_categorical_accuracy: 0.7796\n",
+      "Epoch 224/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 8.0588 - sparse_categorical_accuracy: 0.7807 - val_loss: 8.4108 - val_sparse_categorical_accuracy: 0.7798\n",
+      "Epoch 225/300\n",
+      "1/1 [==============================] - 0s 237ms/step - loss: 8.0364 - sparse_categorical_accuracy: 0.7809 - val_loss: 8.3922 - val_sparse_categorical_accuracy: 0.7798\n",
+      "Epoch 226/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 8.0141 - sparse_categorical_accuracy: 0.7811 - val_loss: 8.3737 - val_sparse_categorical_accuracy: 0.7798\n",
+      "Epoch 227/300\n",
+      "1/1 [==============================] - 0s 235ms/step - loss: 7.9920 - sparse_categorical_accuracy: 0.7812 - val_loss: 8.3553 - val_sparse_categorical_accuracy: 0.7795\n",
+      "Epoch 228/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 7.9700 - sparse_categorical_accuracy: 0.7814 - val_loss: 8.3370 - val_sparse_categorical_accuracy: 0.7794\n",
+      "Epoch 229/300\n",
+      "1/1 [==============================] - 0s 258ms/step - loss: 7.9480 - sparse_categorical_accuracy: 0.7816 - val_loss: 8.3189 - val_sparse_categorical_accuracy: 0.7792\n",
+      "Epoch 230/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 7.9262 - sparse_categorical_accuracy: 0.7817 - val_loss: 8.3008 - val_sparse_categorical_accuracy: 0.7790\n",
+      "Epoch 231/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 7.9046 - sparse_categorical_accuracy: 0.7818 - val_loss: 8.2829 - val_sparse_categorical_accuracy: 0.7789\n",
+      "Epoch 232/300\n",
+      "1/1 [==============================] - 0s 229ms/step - loss: 7.8830 - sparse_categorical_accuracy: 0.7819 - val_loss: 8.2651 - val_sparse_categorical_accuracy: 0.7792\n",
+      "Epoch 233/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 7.8615 - sparse_categorical_accuracy: 0.7821 - val_loss: 8.2474 - val_sparse_categorical_accuracy: 0.7793\n",
+      "Epoch 234/300\n",
+      "1/1 [==============================] - 0s 237ms/step - loss: 7.8402 - sparse_categorical_accuracy: 0.7822 - val_loss: 8.2298 - val_sparse_categorical_accuracy: 0.7793\n",
+      "Epoch 235/300\n",
+      "1/1 [==============================] - 0s 242ms/step - loss: 7.8189 - sparse_categorical_accuracy: 0.7824 - val_loss: 8.2123 - val_sparse_categorical_accuracy: 0.7795\n",
+      "Epoch 236/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 7.7978 - sparse_categorical_accuracy: 0.7825 - val_loss: 8.1949 - val_sparse_categorical_accuracy: 0.7791\n",
+      "Epoch 237/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 7.7768 - sparse_categorical_accuracy: 0.7828 - val_loss: 8.1776 - val_sparse_categorical_accuracy: 0.7791\n",
+      "Epoch 238/300\n",
+      "1/1 [==============================] - 0s 265ms/step - loss: 7.7559 - sparse_categorical_accuracy: 0.7830 - val_loss: 8.1603 - val_sparse_categorical_accuracy: 0.7792\n",
+      "Epoch 239/300\n",
+      "1/1 [==============================] - 0s 285ms/step - loss: 7.7350 - sparse_categorical_accuracy: 0.7832 - val_loss: 8.1432 - val_sparse_categorical_accuracy: 0.7791\n",
+      "Epoch 240/300\n",
+      "1/1 [==============================] - 0s 263ms/step - loss: 7.7143 - sparse_categorical_accuracy: 0.7833 - val_loss: 8.1261 - val_sparse_categorical_accuracy: 0.7793\n",
+      "Epoch 241/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 7.6937 - sparse_categorical_accuracy: 0.7836 - val_loss: 8.1090 - val_sparse_categorical_accuracy: 0.7795\n",
+      "Epoch 242/300\n",
+      "1/1 [==============================] - 0s 254ms/step - loss: 7.6732 - sparse_categorical_accuracy: 0.7837 - val_loss: 8.0921 - val_sparse_categorical_accuracy: 0.7797\n",
+      "Epoch 243/300\n",
+      "1/1 [==============================] - 0s 257ms/step - loss: 7.6528 - sparse_categorical_accuracy: 0.7838 - val_loss: 8.0752 - val_sparse_categorical_accuracy: 0.7800\n",
+      "Epoch 244/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 7.6324 - sparse_categorical_accuracy: 0.7839 - val_loss: 8.0583 - val_sparse_categorical_accuracy: 0.7805\n",
+      "Epoch 245/300\n",
+      "1/1 [==============================] - 0s 233ms/step - loss: 7.6122 - sparse_categorical_accuracy: 0.7841 - val_loss: 8.0416 - val_sparse_categorical_accuracy: 0.7807\n",
+      "Epoch 246/300\n",
+      "1/1 [==============================] - 0s 232ms/step - loss: 7.5921 - sparse_categorical_accuracy: 0.7840 - val_loss: 8.0248 - val_sparse_categorical_accuracy: 0.7811\n",
+      "Epoch 247/300\n",
+      "1/1 [==============================] - 0s 252ms/step - loss: 7.5720 - sparse_categorical_accuracy: 0.7841 - val_loss: 8.0082 - val_sparse_categorical_accuracy: 0.7813\n",
+      "Epoch 248/300\n",
+      "1/1 [==============================] - 0s 244ms/step - loss: 7.5521 - sparse_categorical_accuracy: 0.7843 - val_loss: 7.9916 - val_sparse_categorical_accuracy: 0.7817\n",
+      "Epoch 249/300\n",
+      "1/1 [==============================] - 0s 264ms/step - loss: 7.5322 - sparse_categorical_accuracy: 0.7844 - val_loss: 7.9751 - val_sparse_categorical_accuracy: 0.7818\n",
+      "Epoch 250/300\n",
+      "1/1 [==============================] - 0s 246ms/step - loss: 7.5124 - sparse_categorical_accuracy: 0.7847 - val_loss: 7.9586 - val_sparse_categorical_accuracy: 0.7820\n",
+      "Epoch 251/300\n",
+      "1/1 [==============================] - 0s 270ms/step - loss: 7.4928 - sparse_categorical_accuracy: 0.7848 - val_loss: 7.9423 - val_sparse_categorical_accuracy: 0.7822\n",
+      "Epoch 252/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 7.4732 - sparse_categorical_accuracy: 0.7850 - val_loss: 7.9260 - val_sparse_categorical_accuracy: 0.7820\n",
+      "Epoch 253/300\n",
+      "1/1 [==============================] - 0s 244ms/step - loss: 7.4536 - sparse_categorical_accuracy: 0.7850 - val_loss: 7.9098 - val_sparse_categorical_accuracy: 0.7821\n",
+      "Epoch 254/300\n",
+      "1/1 [==============================] - 0s 234ms/step - loss: 7.4342 - sparse_categorical_accuracy: 0.7853 - val_loss: 7.8937 - val_sparse_categorical_accuracy: 0.7820\n",
+      "Epoch 255/300\n",
+      "1/1 [==============================] - 0s 238ms/step - loss: 7.4149 - sparse_categorical_accuracy: 0.7854 - val_loss: 7.8777 - val_sparse_categorical_accuracy: 0.7819\n",
+      "Epoch 256/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 7.3957 - sparse_categorical_accuracy: 0.7856 - val_loss: 7.8617 - val_sparse_categorical_accuracy: 0.7819\n",
+      "Epoch 257/300\n",
+      "1/1 [==============================] - 0s 255ms/step - loss: 7.3765 - sparse_categorical_accuracy: 0.7858 - val_loss: 7.8458 - val_sparse_categorical_accuracy: 0.7821\n",
+      "Epoch 258/300\n",
+      "1/1 [==============================] - 0s 240ms/step - loss: 7.3575 - sparse_categorical_accuracy: 0.7860 - val_loss: 7.8300 - val_sparse_categorical_accuracy: 0.7826\n",
+      "Epoch 259/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 7.3385 - sparse_categorical_accuracy: 0.7861 - val_loss: 7.8143 - val_sparse_categorical_accuracy: 0.7831\n",
+      "Epoch 260/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 7.3196 - sparse_categorical_accuracy: 0.7862 - val_loss: 7.7986 - val_sparse_categorical_accuracy: 0.7829\n",
+      "Epoch 261/300\n",
+      "1/1 [==============================] - 0s 285ms/step - loss: 7.3008 - sparse_categorical_accuracy: 0.7864 - val_loss: 7.7830 - val_sparse_categorical_accuracy: 0.7831\n",
+      "Epoch 262/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 7.2821 - sparse_categorical_accuracy: 0.7866 - val_loss: 7.7675 - val_sparse_categorical_accuracy: 0.7833\n",
+      "Epoch 263/300\n",
+      "1/1 [==============================] - 0s 317ms/step - loss: 7.2635 - sparse_categorical_accuracy: 0.7869 - val_loss: 7.7521 - val_sparse_categorical_accuracy: 0.7835\n",
+      "Epoch 264/300\n",
+      "1/1 [==============================] - 0s 245ms/step - loss: 7.2450 - sparse_categorical_accuracy: 0.7872 - val_loss: 7.7367 - val_sparse_categorical_accuracy: 0.7839\n",
+      "Epoch 265/300\n",
+      "1/1 [==============================] - 0s 253ms/step - loss: 7.2265 - sparse_categorical_accuracy: 0.7874 - val_loss: 7.7213 - val_sparse_categorical_accuracy: 0.7841\n",
+      "Epoch 266/300\n",
+      "1/1 [==============================] - 0s 294ms/step - loss: 7.2081 - sparse_categorical_accuracy: 0.7875 - val_loss: 7.7061 - val_sparse_categorical_accuracy: 0.7843\n",
+      "Epoch 267/300\n",
+      "1/1 [==============================] - 0s 241ms/step - loss: 7.1899 - sparse_categorical_accuracy: 0.7878 - val_loss: 7.6909 - val_sparse_categorical_accuracy: 0.7844\n",
+      "Epoch 268/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 7.1717 - sparse_categorical_accuracy: 0.7879 - val_loss: 7.6757 - val_sparse_categorical_accuracy: 0.7846\n",
+      "Epoch 269/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 7.1536 - sparse_categorical_accuracy: 0.7879 - val_loss: 7.6607 - val_sparse_categorical_accuracy: 0.7851\n",
+      "Epoch 270/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 7.1355 - sparse_categorical_accuracy: 0.7880 - val_loss: 7.6457 - val_sparse_categorical_accuracy: 0.7851\n",
+      "Epoch 271/300\n",
+      "1/1 [==============================] - 0s 254ms/step - loss: 7.1176 - sparse_categorical_accuracy: 0.7881 - val_loss: 7.6308 - val_sparse_categorical_accuracy: 0.7852\n",
+      "Epoch 272/300\n",
+      "1/1 [==============================] - 0s 226ms/step - loss: 7.0997 - sparse_categorical_accuracy: 0.7883 - val_loss: 7.6160 - val_sparse_categorical_accuracy: 0.7856\n",
+      "Epoch 273/300\n",
+      "1/1 [==============================] - 0s 239ms/step - loss: 7.0819 - sparse_categorical_accuracy: 0.7885 - val_loss: 7.6013 - val_sparse_categorical_accuracy: 0.7855\n",
+      "Epoch 274/300\n",
+      "1/1 [==============================] - 0s 229ms/step - loss: 7.0642 - sparse_categorical_accuracy: 0.7886 - val_loss: 7.5867 - val_sparse_categorical_accuracy: 0.7858\n",
+      "Epoch 275/300\n",
+      "1/1 [==============================] - 0s 236ms/step - loss: 7.0465 - sparse_categorical_accuracy: 0.7886 - val_loss: 7.5721 - val_sparse_categorical_accuracy: 0.7859\n",
+      "Epoch 276/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 7.0290 - sparse_categorical_accuracy: 0.7886 - val_loss: 7.5576 - val_sparse_categorical_accuracy: 0.7862\n",
+      "Epoch 277/300\n",
+      "1/1 [==============================] - 0s 237ms/step - loss: 7.0115 - sparse_categorical_accuracy: 0.7888 - val_loss: 7.5432 - val_sparse_categorical_accuracy: 0.7863\n",
+      "Epoch 278/300\n",
+      "1/1 [==============================] - 0s 250ms/step - loss: 6.9941 - sparse_categorical_accuracy: 0.7891 - val_loss: 7.5288 - val_sparse_categorical_accuracy: 0.7862\n",
+      "Epoch 279/300\n",
+      "1/1 [==============================] - 0s 225ms/step - loss: 6.9767 - sparse_categorical_accuracy: 0.7893 - val_loss: 7.5145 - val_sparse_categorical_accuracy: 0.7863\n",
+      "Epoch 280/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 6.9595 - sparse_categorical_accuracy: 0.7893 - val_loss: 7.5002 - val_sparse_categorical_accuracy: 0.7863\n",
+      "Epoch 281/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 6.9423 - sparse_categorical_accuracy: 0.7895 - val_loss: 7.4860 - val_sparse_categorical_accuracy: 0.7864\n",
+      "Epoch 282/300\n",
+      "1/1 [==============================] - 0s 251ms/step - loss: 6.9252 - sparse_categorical_accuracy: 0.7895 - val_loss: 7.4719 - val_sparse_categorical_accuracy: 0.7866\n",
+      "Epoch 283/300\n",
+      "1/1 [==============================] - 0s 264ms/step - loss: 6.9081 - sparse_categorical_accuracy: 0.7895 - val_loss: 7.4578 - val_sparse_categorical_accuracy: 0.7867\n",
+      "Epoch 284/300\n",
+      "1/1 [==============================] - 0s 243ms/step - loss: 6.8912 - sparse_categorical_accuracy: 0.7895 - val_loss: 7.4438 - val_sparse_categorical_accuracy: 0.7862\n",
+      "Epoch 285/300\n",
+      "1/1 [==============================] - 0s 284ms/step - loss: 6.8743 - sparse_categorical_accuracy: 0.7897 - val_loss: 7.4299 - val_sparse_categorical_accuracy: 0.7863\n",
+      "Epoch 286/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 6.8575 - sparse_categorical_accuracy: 0.7898 - val_loss: 7.4160 - val_sparse_categorical_accuracy: 0.7867\n",
+      "Epoch 287/300\n",
+      "1/1 [==============================] - 0s 237ms/step - loss: 6.8408 - sparse_categorical_accuracy: 0.7900 - val_loss: 7.4022 - val_sparse_categorical_accuracy: 0.7867\n",
+      "Epoch 288/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 6.8241 - sparse_categorical_accuracy: 0.7901 - val_loss: 7.3885 - val_sparse_categorical_accuracy: 0.7869\n",
+      "Epoch 289/300\n",
+      "1/1 [==============================] - 0s 232ms/step - loss: 6.8075 - sparse_categorical_accuracy: 0.7901 - val_loss: 7.3749 - val_sparse_categorical_accuracy: 0.7869\n",
+      "Epoch 290/300\n",
+      "1/1 [==============================] - 0s 244ms/step - loss: 6.7910 - sparse_categorical_accuracy: 0.7902 - val_loss: 7.3614 - val_sparse_categorical_accuracy: 0.7871\n",
+      "Epoch 291/300\n",
+      "1/1 [==============================] - 0s 247ms/step - loss: 6.7746 - sparse_categorical_accuracy: 0.7902 - val_loss: 7.3479 - val_sparse_categorical_accuracy: 0.7873\n",
+      "Epoch 292/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 6.7582 - sparse_categorical_accuracy: 0.7904 - val_loss: 7.3346 - val_sparse_categorical_accuracy: 0.7875\n",
+      "Epoch 293/300\n",
+      "1/1 [==============================] - 0s 239ms/step - loss: 6.7419 - sparse_categorical_accuracy: 0.7904 - val_loss: 7.3213 - val_sparse_categorical_accuracy: 0.7874\n",
+      "Epoch 294/300\n",
+      "1/1 [==============================] - 0s 249ms/step - loss: 6.7257 - sparse_categorical_accuracy: 0.7906 - val_loss: 7.3081 - val_sparse_categorical_accuracy: 0.7876\n",
+      "Epoch 295/300\n",
+      "1/1 [==============================] - 0s 248ms/step - loss: 6.7095 - sparse_categorical_accuracy: 0.7907 - val_loss: 7.2949 - val_sparse_categorical_accuracy: 0.7881\n",
+      "Epoch 296/300\n",
+      "1/1 [==============================] - 0s 259ms/step - loss: 6.6934 - sparse_categorical_accuracy: 0.7908 - val_loss: 7.2819 - val_sparse_categorical_accuracy: 0.7881\n",
+      "Epoch 297/300\n",
+      "1/1 [==============================] - 0s 185ms/step - loss: 6.6774 - sparse_categorical_accuracy: 0.7908 - val_loss: 7.2688 - val_sparse_categorical_accuracy: 0.7883\n",
+      "Epoch 298/300\n",
+      "1/1 [==============================] - 0s 168ms/step - loss: 6.6615 - sparse_categorical_accuracy: 0.7909 - val_loss: 7.2559 - val_sparse_categorical_accuracy: 0.7885\n",
+      "Epoch 299/300\n",
+      "1/1 [==============================] - 0s 169ms/step - loss: 6.6456 - sparse_categorical_accuracy: 0.7911 - val_loss: 7.2429 - val_sparse_categorical_accuracy: 0.7887\n",
+      "Epoch 300/300\n",
+      "1/1 [==============================] - 0s 173ms/step - loss: 6.6297 - sparse_categorical_accuracy: 0.7912 - val_loss: 7.2301 - val_sparse_categorical_accuracy: 0.7885\n"
+     ]
+    }
+   ],
+   "source": [
+    "k_l2=0\n",
+    "keras_model = tf.keras.Sequential([\n",
+    "    #tf.keras.layers.Dense(20, activation='tanh',kernel_regularizer=keras.regularizers.l2(k_l2)),\n",
+    "    #tf.keras.layers.Dense(20, activation='tanh',kernel_regularizer=keras.regularizers.l2(k_l2)),\n",
+    "    tf.keras.layers.Dense(10, activation='softmax', kernel_regularizer=keras.regularizers.l2(k_l2))\n",
+    "])\n",
+    "\n",
+    "keras_model.build(input_shape=[None, 784])\n",
+    "keras_model.summary()\n",
+    "\n",
+    "keras_model.compile(\n",
+    "    optimizer=tf.keras.optimizers.Adam(), # Optimizer\n",
+    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(), # Loss function to minimize\n",
+    "    metrics=[keras.metrics.SparseCategoricalAccuracy()] # List of metrics to monitor\n",
+    ")\n",
+    "\n",
+    "history = keras_model.fit(\n",
+    "    train_input,\n",
+    "    train_label,\n",
+    "    batch_size=len(train_input),\n",
+    "    epochs=300,\n",
+    "    validation_data=(test_input, test_label)\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key: loss\n",
+      "Key: sparse_categorical_accuracy\n",
+      "Key: val_loss\n",
+      "Key: val_sparse_categorical_accuracy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# vizualize\n",
+    "\n",
+    "for item in history.history:\n",
+    "    print(\"Key:\",item)\n",
+    "\n",
+    "plt.plot(history.history['loss'],label=\"train\")\n",
+    "plt.plot(history.history['val_loss'],label=\"validation\")\n",
+    "plt.title('Model Loss')\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Cross Entropy')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "#print(\"history\",history.history)\n",
+    "plt.plot(history.history['sparse_categorical_accuracy'],label=\"train\")\n",
+    "plt.plot(history.history['val_sparse_categorical_accuracy'],label=\"validation\")\n",
+    "plt.title('Model Accuracy')\n",
+    "#plt.yscale('log')\n",
+    "plt.ylabel('Acc')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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+}