1434 lines
350 KiB
Plaintext
1434 lines
350 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Lab24"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 79,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import imageio.v3 as imageio\n",
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"import numpy as np\n",
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"from tensorflow import keras"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.\n",
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"Pakeisti modelių hiperparametrus, optimizavimo funkcijas, tinklo architektūras, aktyvacijos funkcijas."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model: \"sequential_29\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" dense_41 (Dense) (None, 20) 15700 \n",
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" \n",
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" dense_42 (Dense) (None, 10) 210 \n",
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" \n",
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"=================================================================\n",
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"Total params: 15910 (62.15 KB)\n",
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"Trainable params: 15910 (62.15 KB)\n",
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"Non-trainable params: 0 (0.00 Byte)\n",
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"_________________________________________________________________\n",
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"Epoch 1/200\n",
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"1/1 [==============================] - 2s 2s/step - loss: 0.1158 - sparse_categorical_accuracy: 0.1325 - val_loss: 0.1145 - val_sparse_categorical_accuracy: 0.1317\n",
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"Epoch 2/200\n",
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"1/1 [==============================] - 0s 99ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.1294 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.1297\n",
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"Epoch 3/200\n",
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"1/1 [==============================] - 0s 105ms/step - loss: 0.1146 - sparse_categorical_accuracy: 0.1279 - val_loss: 0.1135 - val_sparse_categorical_accuracy: 0.1270\n",
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"Epoch 4/200\n",
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"1/1 [==============================] - 0s 140ms/step - loss: 0.1142 - sparse_categorical_accuracy: 0.1258 - val_loss: 0.1131 - val_sparse_categorical_accuracy: 0.1266\n",
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"Epoch 5/200\n",
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"1/1 [==============================] - 0s 108ms/step - loss: 0.1138 - sparse_categorical_accuracy: 0.1235 - val_loss: 0.1128 - val_sparse_categorical_accuracy: 0.1255\n",
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"Epoch 6/200\n",
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"1/1 [==============================] - 0s 104ms/step - loss: 0.1135 - sparse_categorical_accuracy: 0.1222 - val_loss: 0.1125 - val_sparse_categorical_accuracy: 0.1228\n",
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"Epoch 7/200\n",
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"1/1 [==============================] - 0s 99ms/step - loss: 0.1133 - sparse_categorical_accuracy: 0.1203 - val_loss: 0.1123 - val_sparse_categorical_accuracy: 0.1217\n",
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"Epoch 8/200\n",
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"1/1 [==============================] - 0s 99ms/step - loss: 0.1131 - sparse_categorical_accuracy: 0.1185 - val_loss: 0.1121 - val_sparse_categorical_accuracy: 0.1203\n",
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"Epoch 9/200\n",
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"1/1 [==============================] - 0s 98ms/step - loss: 0.1129 - sparse_categorical_accuracy: 0.1167 - val_loss: 0.1120 - val_sparse_categorical_accuracy: 0.1182\n",
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"Epoch 10/200\n",
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"1/1 [==============================] - 0s 104ms/step - loss: 0.1128 - sparse_categorical_accuracy: 0.1156 - val_loss: 0.1119 - val_sparse_categorical_accuracy: 0.1181\n",
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"Epoch 11/200\n",
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"1/1 [==============================] - 0s 96ms/step - loss: 0.1126 - sparse_categorical_accuracy: 0.1148 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.1166\n",
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"Epoch 12/200\n",
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"1/1 [==============================] - 0s 147ms/step - loss: 0.1125 - sparse_categorical_accuracy: 0.1134 - val_loss: 0.1117 - val_sparse_categorical_accuracy: 0.1161\n",
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"Epoch 13/200\n",
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"1/1 [==============================] - 0s 157ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.1123 - val_loss: 0.1116 - val_sparse_categorical_accuracy: 0.1150\n",
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"Epoch 14/200\n",
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"1/1 [==============================] - 0s 133ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.1116 - val_loss: 0.1115 - val_sparse_categorical_accuracy: 0.1143\n",
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"Epoch 15/200\n",
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"1/1 [==============================] - 0s 132ms/step - loss: 0.1123 - sparse_categorical_accuracy: 0.1108 - val_loss: 0.1114 - val_sparse_categorical_accuracy: 0.1141\n",
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"Epoch 16/200\n",
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"1/1 [==============================] - 0s 133ms/step - loss: 0.1122 - sparse_categorical_accuracy: 0.1104 - val_loss: 0.1114 - val_sparse_categorical_accuracy: 0.1136\n",
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"Epoch 17/200\n",
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"1/1 [==============================] - 0s 127ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.1098 - val_loss: 0.1113 - val_sparse_categorical_accuracy: 0.1132\n",
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"Epoch 18/200\n",
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"1/1 [==============================] - 0s 136ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.1095 - val_loss: 0.1112 - val_sparse_categorical_accuracy: 0.1126\n",
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"Epoch 19/200\n",
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"1/1 [==============================] - 0s 126ms/step - loss: 0.1120 - sparse_categorical_accuracy: 0.1089 - val_loss: 0.1112 - val_sparse_categorical_accuracy: 0.1127\n",
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"Epoch 20/200\n",
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"1/1 [==============================] - 0s 131ms/step - loss: 0.1120 - sparse_categorical_accuracy: 0.1085 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.1127\n",
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"Epoch 21/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1119 - sparse_categorical_accuracy: 0.1082 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.1119\n",
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"Epoch 22/200\n",
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"1/1 [==============================] - 0s 141ms/step - loss: 0.1118 - sparse_categorical_accuracy: 0.1075 - val_loss: 0.1110 - val_sparse_categorical_accuracy: 0.1108\n",
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"Epoch 23/200\n",
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"1/1 [==============================] - 0s 143ms/step - loss: 0.1118 - sparse_categorical_accuracy: 0.1073 - val_loss: 0.1110 - val_sparse_categorical_accuracy: 0.1098\n",
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"Epoch 24/200\n",
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"1/1 [==============================] - 0s 134ms/step - loss: 0.1117 - sparse_categorical_accuracy: 0.1071 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.1096\n",
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"Epoch 25/200\n",
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"1/1 [==============================] - 0s 130ms/step - loss: 0.1117 - sparse_categorical_accuracy: 0.1065 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.1085\n",
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"Epoch 26/200\n",
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"1/1 [==============================] - 0s 125ms/step - loss: 0.1117 - sparse_categorical_accuracy: 0.1064 - val_loss: 0.1108 - val_sparse_categorical_accuracy: 0.1079\n",
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"Epoch 27/200\n",
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"1/1 [==============================] - 0s 129ms/step - loss: 0.1116 - sparse_categorical_accuracy: 0.1062 - val_loss: 0.1108 - val_sparse_categorical_accuracy: 0.1078\n",
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"Epoch 28/200\n",
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"1/1 [==============================] - 0s 130ms/step - loss: 0.1116 - sparse_categorical_accuracy: 0.1061 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.1078\n",
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"Epoch 29/200\n",
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"1/1 [==============================] - 0s 127ms/step - loss: 0.1115 - sparse_categorical_accuracy: 0.1058 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.1074\n",
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"Epoch 30/200\n",
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"1/1 [==============================] - 0s 124ms/step - loss: 0.1115 - sparse_categorical_accuracy: 0.1055 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.1066\n",
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"Epoch 31/200\n",
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"1/1 [==============================] - 0s 129ms/step - loss: 0.1114 - sparse_categorical_accuracy: 0.1051 - val_loss: 0.1106 - val_sparse_categorical_accuracy: 0.1067\n",
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"Epoch 32/200\n",
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"1/1 [==============================] - 0s 130ms/step - loss: 0.1114 - sparse_categorical_accuracy: 0.1047 - val_loss: 0.1106 - val_sparse_categorical_accuracy: 0.1071\n",
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"Epoch 33/200\n",
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"1/1 [==============================] - 0s 131ms/step - loss: 0.1114 - sparse_categorical_accuracy: 0.1045 - val_loss: 0.1106 - val_sparse_categorical_accuracy: 0.1072\n",
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"Epoch 34/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1113 - sparse_categorical_accuracy: 0.1041 - val_loss: 0.1105 - val_sparse_categorical_accuracy: 0.1068\n",
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"Epoch 35/200\n",
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"1/1 [==============================] - 0s 143ms/step - loss: 0.1113 - sparse_categorical_accuracy: 0.1039 - val_loss: 0.1105 - val_sparse_categorical_accuracy: 0.1064\n",
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"Epoch 36/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1113 - sparse_categorical_accuracy: 0.1035 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.1061\n",
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"Epoch 37/200\n",
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"1/1 [==============================] - 0s 127ms/step - loss: 0.1112 - sparse_categorical_accuracy: 0.1034 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.1067\n",
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"Epoch 38/200\n",
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"1/1 [==============================] - 0s 125ms/step - loss: 0.1112 - sparse_categorical_accuracy: 0.1032 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.1065\n",
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"Epoch 39/200\n",
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"1/1 [==============================] - 0s 129ms/step - loss: 0.1112 - sparse_categorical_accuracy: 0.1029 - val_loss: 0.1103 - val_sparse_categorical_accuracy: 0.1060\n",
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"Epoch 40/200\n",
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"1/1 [==============================] - 0s 130ms/step - loss: 0.1111 - sparse_categorical_accuracy: 0.1027 - val_loss: 0.1103 - val_sparse_categorical_accuracy: 0.1057\n",
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"Epoch 41/200\n",
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"1/1 [==============================] - 0s 134ms/step - loss: 0.1111 - sparse_categorical_accuracy: 0.1024 - val_loss: 0.1103 - val_sparse_categorical_accuracy: 0.1050\n",
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"Epoch 42/200\n",
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"1/1 [==============================] - 0s 135ms/step - loss: 0.1111 - sparse_categorical_accuracy: 0.1022 - val_loss: 0.1103 - val_sparse_categorical_accuracy: 0.1051\n",
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"Epoch 43/200\n",
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"1/1 [==============================] - 0s 129ms/step - loss: 0.1110 - sparse_categorical_accuracy: 0.1020 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.1043\n",
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"Epoch 44/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1110 - sparse_categorical_accuracy: 0.1017 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.1037\n",
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"Epoch 45/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1110 - sparse_categorical_accuracy: 0.1012 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.1026\n",
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"Epoch 46/200\n",
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"1/1 [==============================] - 0s 129ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.1010 - val_loss: 0.1101 - val_sparse_categorical_accuracy: 0.1027\n",
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"Epoch 47/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.1009 - val_loss: 0.1101 - val_sparse_categorical_accuracy: 0.1020\n",
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"Epoch 48/200\n",
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"1/1 [==============================] - 0s 131ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.1006 - val_loss: 0.1101 - val_sparse_categorical_accuracy: 0.1017\n",
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"Epoch 49/200\n",
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"1/1 [==============================] - 0s 126ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.1005 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.1018\n",
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"Epoch 50/200\n",
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"1/1 [==============================] - 0s 130ms/step - loss: 0.1108 - sparse_categorical_accuracy: 0.1005 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.1013\n",
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"Epoch 51/200\n",
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"1/1 [==============================] - 0s 126ms/step - loss: 0.1108 - sparse_categorical_accuracy: 0.1003 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.1015\n",
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"Epoch 52/200\n",
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"1/1 [==============================] - 0s 124ms/step - loss: 0.1108 - sparse_categorical_accuracy: 0.1001 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.1014\n",
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"Epoch 53/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.0997 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.1018\n",
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"Epoch 54/200\n",
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"1/1 [==============================] - 0s 127ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.0994 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.1016\n",
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"Epoch 55/200\n",
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"1/1 [==============================] - 0s 130ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.0994 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.1017\n",
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"Epoch 56/200\n",
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"1/1 [==============================] - 0s 124ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.0994 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.1014\n",
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"Epoch 57/200\n",
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"1/1 [==============================] - 0s 126ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.0992 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.1012\n",
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"Epoch 58/200\n",
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"1/1 [==============================] - 0s 131ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.0990 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.1010\n",
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"Epoch 59/200\n",
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"1/1 [==============================] - 0s 138ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.0988 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.1007\n",
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"Epoch 60/200\n",
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"1/1 [==============================] - 0s 139ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.0986 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.1009\n",
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"Epoch 61/200\n",
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"1/1 [==============================] - 0s 127ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.0985 - val_loss: 0.1097 - val_sparse_categorical_accuracy: 0.1004\n",
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"Epoch 62/200\n",
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"1/1 [==============================] - 0s 126ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.0983 - val_loss: 0.1097 - val_sparse_categorical_accuracy: 0.1001\n",
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"Epoch 63/200\n",
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"1/1 [==============================] - 0s 134ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.0981 - val_loss: 0.1097 - val_sparse_categorical_accuracy: 0.0993\n",
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"Epoch 64/200\n",
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"1/1 [==============================] - 0s 126ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.0976 - val_loss: 0.1097 - val_sparse_categorical_accuracy: 0.0991\n",
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"Epoch 65/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.0972 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.0991\n",
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"Epoch 66/200\n",
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"1/1 [==============================] - 0s 121ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.0969 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.0989\n",
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"Epoch 67/200\n",
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"1/1 [==============================] - 0s 131ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.0967 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.0981\n",
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"Epoch 68/200\n",
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"1/1 [==============================] - 0s 127ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.0967 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.0981\n",
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"Epoch 69/200\n",
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"1/1 [==============================] - 0s 124ms/step - loss: 0.1103 - sparse_categorical_accuracy: 0.0965 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.0981\n",
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"Epoch 70/200\n",
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"1/1 [==============================] - 0s 124ms/step - loss: 0.1103 - sparse_categorical_accuracy: 0.0961 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.0977\n",
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"Epoch 71/200\n",
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"1/1 [==============================] - 0s 127ms/step - loss: 0.1103 - sparse_categorical_accuracy: 0.0957 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.0973\n",
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"Epoch 72/200\n",
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"1/1 [==============================] - 0s 143ms/step - loss: 0.1103 - sparse_categorical_accuracy: 0.0955 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.0972\n",
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"Epoch 73/200\n",
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"1/1 [==============================] - 0s 146ms/step - loss: 0.1102 - sparse_categorical_accuracy: 0.0954 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0972\n",
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"Epoch 74/200\n",
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"1/1 [==============================] - 0s 126ms/step - loss: 0.1102 - sparse_categorical_accuracy: 0.0951 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0977\n",
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"Epoch 75/200\n",
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"1/1 [==============================] - 0s 126ms/step - loss: 0.1102 - sparse_categorical_accuracy: 0.0948 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0975\n",
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"Epoch 76/200\n",
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"1/1 [==============================] - 0s 134ms/step - loss: 0.1102 - sparse_categorical_accuracy: 0.0948 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0976\n",
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"Epoch 77/200\n",
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"1/1 [==============================] - 0s 131ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0946 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.0974\n",
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"Epoch 78/200\n",
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"1/1 [==============================] - 0s 135ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0945 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0970\n",
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"Epoch 79/200\n",
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"1/1 [==============================] - 0s 130ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0946 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0968\n",
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"Epoch 80/200\n",
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"1/1 [==============================] - 0s 140ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0946 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0967\n",
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"Epoch 81/200\n",
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"1/1 [==============================] - 0s 142ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.0943 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0964\n",
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"Epoch 82/200\n",
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"1/1 [==============================] - 0s 128ms/step - loss: 0.1100 - sparse_categorical_accuracy: 0.0938 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.0958\n",
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"Epoch 83/200\n",
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"1/1 [==============================] - 0s 124ms/step - loss: 0.1100 - sparse_categorical_accuracy: 0.0934 - val_loss: 0.1092 - val_sparse_categorical_accuracy: 0.0957\n",
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"Epoch 84/200\n",
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"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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",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
|
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},
|
|
"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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"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"
|
|
},
|
|
{
|
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"data": {
|
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"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",
|
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"Epoch 4/300\n",
|
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"1/1 [==============================] - 0s 206ms/step - loss: 104.5925 - sparse_categorical_accuracy: 0.1804 - val_loss: 95.5190 - val_sparse_categorical_accuracy: 0.2200\n",
|
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"Epoch 5/300\n",
|
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"1/1 [==============================] - 0s 190ms/step - loss: 95.9215 - sparse_categorical_accuracy: 0.2210 - val_loss: 87.2152 - val_sparse_categorical_accuracy: 0.2655\n",
|
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"Epoch 6/300\n",
|
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"1/1 [==============================] - 0s 187ms/step - loss: 87.7617 - sparse_categorical_accuracy: 0.2698 - val_loss: 77.3627 - val_sparse_categorical_accuracy: 0.3068\n",
|
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"Epoch 7/300\n",
|
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"1/1 [==============================] - 0s 167ms/step - loss: 78.1472 - sparse_categorical_accuracy: 0.3109 - val_loss: 66.3837 - val_sparse_categorical_accuracy: 0.3583\n",
|
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"Epoch 8/300\n",
|
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"1/1 [==============================] - 0s 216ms/step - loss: 67.2765 - sparse_categorical_accuracy: 0.3551 - val_loss: 56.1239 - val_sparse_categorical_accuracy: 0.4027\n",
|
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"Epoch 9/300\n",
|
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"1/1 [==============================] - 0s 236ms/step - loss: 57.0718 - sparse_categorical_accuracy: 0.4009 - val_loss: 48.2395 - val_sparse_categorical_accuracy: 0.4350\n",
|
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"Epoch 10/300\n",
|
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"1/1 [==============================] - 0s 198ms/step - loss: 49.2051 - sparse_categorical_accuracy: 0.4325 - val_loss: 44.2210 - val_sparse_categorical_accuracy: 0.4495\n",
|
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"Epoch 11/300\n",
|
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"1/1 [==============================] - 0s 278ms/step - loss: 45.0963 - sparse_categorical_accuracy: 0.4487 - val_loss: 43.6507 - val_sparse_categorical_accuracy: 0.4584\n",
|
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"Epoch 12/300\n",
|
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"1/1 [==============================] - 0s 251ms/step - loss: 44.3950 - sparse_categorical_accuracy: 0.4548 - val_loss: 43.7409 - val_sparse_categorical_accuracy: 0.4641\n",
|
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"Epoch 13/300\n",
|
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"1/1 [==============================] - 0s 467ms/step - loss: 44.4352 - sparse_categorical_accuracy: 0.4608 - val_loss: 41.6169 - val_sparse_categorical_accuracy: 0.4838\n",
|
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"Epoch 14/300\n",
|
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"1/1 [==============================] - 0s 224ms/step - loss: 42.2697 - sparse_categorical_accuracy: 0.4777 - val_loss: 37.5434 - val_sparse_categorical_accuracy: 0.5103\n",
|
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"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",
|
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"Epoch 16/300\n",
|
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"1/1 [==============================] - 0s 329ms/step - loss: 34.7490 - sparse_categorical_accuracy: 0.5330 - val_loss: 32.4519 - val_sparse_categorical_accuracy: 0.5513\n",
|
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"Epoch 17/300\n",
|
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"1/1 [==============================] - 0s 309ms/step - loss: 33.1500 - sparse_categorical_accuracy: 0.5514 - val_loss: 31.9879 - val_sparse_categorical_accuracy: 0.5625\n",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"Epoch 95/300\n",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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"
|
|
},
|
|
{
|
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"data": {
|
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"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": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|