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ium_464903/dockerfiles/Biblioteka_DL_trenowanie.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "12c11089-7d45-4e24-b797-83d3ae4841fa",
"metadata": {},
"source": [
"## Import bibliotek"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "64cea583-a329-4df4-be36-18d94f15966d",
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout\n",
"from keras import regularizers\n",
"import numpy as np\n",
"import pandas as pd\n",
"import opendatasets as od\n",
"import chardet\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "f7a16131-7e52-43b8-96a6-694e3da8ccb1",
"metadata": {},
"source": [
"## Pobranie zbioru danych z kaggle"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "975e8b6a-4392-4fca-a4b8-8483c8782643",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skipping, found downloaded files in \".\\lettuce-growth-days\" (use force=True to force download)\n"
]
}
],
"source": [
"od.download('https://www.kaggle.com/datasets/jjayfabor/lettuce-growth-days')\n",
"#{\"username\":\"jakubbg\",\"key\":\"e42b293c818e4ecd7b9365ee037af428\"}"
]
},
{
"cell_type": "markdown",
"id": "8ac9c0ef-250d-4d6c-93e2-fc533e665836",
"metadata": {},
"source": [
"## Załadowanie zbioru danych"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "45cd2f54-f973-4a02-b40a-369e45c51522",
"metadata": {},
"outputs": [],
"source": [
"with open('./lettuce-growth-days/lettuce_dataset_updated.csv', 'rb') as f:\n",
" result = chardet.detect(f.read())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "57e8a928-a7a6-4e83-92b4-ffa19b04265e",
"metadata": {},
"outputs": [],
"source": [
"dataset = pd.read_csv('./lettuce-growth-days/lettuce_dataset_updated.csv', encoding=result['encoding'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "add14d7c-dc88-42dd-9113-b9dc0e9b5c83",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3169\n",
" Plant_ID Date Temperature (°C) Humidity (%) TDS Value (ppm) \\\n",
"0 1 8/3/2023 33.4 53 582 \n",
"1 1 8/4/2023 33.5 53 451 \n",
"2 1 8/5/2023 33.4 59 678 \n",
"3 1 8/6/2023 33.4 68 420 \n",
"4 1 8/7/2023 33.4 74 637 \n",
"\n",
" pH Level Growth Days Temperature (F) Humidity \n",
"0 6.4 1 92.12 0.53 \n",
"1 6.1 2 92.30 0.53 \n",
"2 6.4 3 92.12 0.59 \n",
"3 6.4 4 92.12 0.68 \n",
"4 6.5 5 92.12 0.74 \n"
]
}
],
"source": [
"print(len(dataset))\n",
"print(dataset[:5])"
]
},
{
"cell_type": "markdown",
"id": "e9e15b0b-4f5d-4135-a4aa-e7160cfd8292",
"metadata": {},
"source": [
"## Wyciąganie ze zbioru wybranych kolumn"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e9a90813-275d-466a-936f-0d427d442319",
"metadata": {},
"outputs": [],
"source": [
"ph_level = dataset['pH Level'].values.tolist()\n",
"temp_F = dataset['Temperature (F)'].values.tolist()\n",
"humid = dataset['Humidity'].values.tolist()\n",
"days = dataset['Growth Days'].values.tolist()\n",
"plant_id = dataset['Plant_ID'].values.tolist()"
]
},
{
"cell_type": "markdown",
"id": "636b9e9c-fa7d-4408-932c-c7a1648b251e",
"metadata": {},
"source": [
"## Przetwarzanie danych do postaci zbioru X i Y"
]
},
{
"cell_type": "markdown",
"id": "b9135091-4559-4ed7-b2ba-36dc82529019",
"metadata": {},
"source": [
"Każda próbka składać się będzie ze średniej temperatury (F), średniej wilgotności oraz średniego ph gleby dla danej rośliny oraz z przypisanej jej klasy będącej ilością dni, jakie były wymagane do całkowitego wyrośnięcia rośliny."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c91849f4-2e8e-4eba-9b65-824f045e7ddb",
"metadata": {},
"outputs": [],
"source": [
"X = []\n",
"Y = []\n",
"\n",
"id = plant_id[0]\n",
"temp_sum = 0\n",
"humid_sum = 0\n",
"ph_level_sum = 0\n",
"day = 1\n",
"\n",
"for i in range(0, len(plant_id)):\n",
" if plant_id[i] == id:\n",
" temp_sum += temp_F[i]\n",
" humid_sum += humid[i]\n",
" ph_level_sum += ph_level[i]\n",
" day = days[i]\n",
" else:\n",
" temp = []\n",
" temp.append(temp_sum/day)\n",
" temp.append(humid_sum/day)\n",
" temp.append(ph_level_sum/day)\n",
" X.append(temp)\n",
" Y.append(day)\n",
" temp_sum = 0\n",
" humid_sum = 0\n",
" ph_level_sum = 0\n",
" day = 1\n",
" id = plant_id[i]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d8e7682d-3469-4d68-98e0-f34b1d57ae27",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[87.13199999999998, 0.6395555555555558, 6.382222222222223], [85.08488888888886, 0.6295555555555553, 6.311111111111113], [85.13148936170211, 0.6759574468085107, 6.27659574468085], [85.24333333333331, 0.6206250000000001, 6.293749999999999], [85.08488888888886, 0.6357777777777776, 6.264444444444444], [85.08488888888886, 0.6295555555555555, 6.297777777777776], [85.10851063829786, 0.625531914893617, 6.285106382978723], [85.08488888888886, 0.6457777777777778, 6.266666666666666], [85.15565217391303, 0.6530434782608696, 6.2195652173913025], [85.08488888888886, 0.6435555555555555, 6.262222222222222]]\n"
]
}
],
"source": [
"print(X[:10])"
]
},
{
"cell_type": "markdown",
"id": "a6e7e9a2-7521-4aea-9e37-36256800e064",
"metadata": {},
"source": [
"## Normalizacja danych "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ca14eeb3-6616-405b-98a9-373e3ee8e07d",
"metadata": {},
"outputs": [],
"source": [
"scaler = MinMaxScaler()\n",
"X = scaler.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "adfcfa06-d323-4c76-aaaa-cd2b98aeb18a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41, 3) (28, 3) (41, 4) (28, 4)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\obses\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
" warnings.warn(\n"
]
}
],
"source": [
"X = np.array(X)\n",
"Y = np.array(Y)\n",
"\n",
"encoder = OneHotEncoder(sparse=False)\n",
"y_onehot = encoder.fit_transform(Y.reshape(-1,1))\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y_onehot, test_size=0.4, random_state=42)\n",
"print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
]
},
{
"cell_type": "markdown",
"id": "83689322-9d41-46db-9fbd-6f3040b3249c",
"metadata": {},
"source": [
"## Budowanie modelu sieci neuronowej"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "df2e1035-ae75-4fbf-9216-2738ed4cadd0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\obses\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
}
],
"source": [
"model = Sequential([\n",
" Dense(8, activation='relu', input_dim=3, kernel_regularizer=regularizers.l2(0.04)),\n",
" Dropout(0.5),\n",
" Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.04)),\n",
" Dropout(0.5),\n",
" Dense(4, activation='softmax', kernel_regularizer=regularizers.l2(0.04)),\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0ff7bb18-e65f-4891-9ace-d3ab02b2325d",
"metadata": {},
"outputs": [],
"source": [
"model.compile(optimizer='sgd',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"id": "2ac98bdc-1851-439d-8306-4fe651a70069",
"metadata": {},
"source": [
"## Trenowanie modelu"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9b89b3c2-24de-4127-9aba-3e8402f09b5f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/500\n",
"2/2 - 1s - 265ms/step - accuracy: 0.3902 - loss: 2.1238 - val_accuracy: 0.1429 - val_loss: 2.1243\n",
"Epoch 2/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.2439 - loss: 2.1641 - val_accuracy: 0.2500 - val_loss: 2.1104\n",
"Epoch 3/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.3902 - loss: 2.1102 - val_accuracy: 0.5714 - val_loss: 2.0970\n",
"Epoch 4/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.4146 - loss: 2.0846 - val_accuracy: 0.7857 - val_loss: 2.0847\n",
"Epoch 5/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.5854 - loss: 2.1145 - val_accuracy: 0.8571 - val_loss: 2.0710\n",
"Epoch 6/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.6585 - loss: 2.0605 - val_accuracy: 0.8571 - val_loss: 2.0591\n",
"Epoch 7/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.6829 - loss: 2.0652 - val_accuracy: 0.8571 - val_loss: 2.0459\n",
"Epoch 8/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.6585 - loss: 2.0399 - val_accuracy: 0.8571 - val_loss: 2.0329\n",
"Epoch 9/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 2.0242 - val_accuracy: 0.8571 - val_loss: 2.0210\n",
"Epoch 10/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.6829 - loss: 2.0091 - val_accuracy: 0.8571 - val_loss: 2.0091\n",
"Epoch 11/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8049 - loss: 1.9942 - val_accuracy: 0.8571 - val_loss: 1.9976\n",
"Epoch 12/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.7561 - loss: 1.9887 - val_accuracy: 0.8571 - val_loss: 1.9867\n",
"Epoch 13/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.7805 - loss: 1.9710 - val_accuracy: 0.8571 - val_loss: 1.9748\n",
"Epoch 14/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.7805 - loss: 1.9850 - val_accuracy: 0.8571 - val_loss: 1.9635\n",
"Epoch 15/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.7805 - loss: 1.9553 - val_accuracy: 0.8571 - val_loss: 1.9527\n",
"Epoch 16/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.7805 - loss: 1.9349 - val_accuracy: 0.8571 - val_loss: 1.9419\n",
"Epoch 17/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.7561 - loss: 1.9401 - val_accuracy: 0.8571 - val_loss: 1.9307\n",
"Epoch 18/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.7805 - loss: 1.9213 - val_accuracy: 0.8571 - val_loss: 1.9209\n",
"Epoch 19/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.9173 - val_accuracy: 0.8571 - val_loss: 1.9100\n",
"Epoch 20/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.8995 - val_accuracy: 0.8571 - val_loss: 1.8995\n",
"Epoch 21/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8780 - loss: 1.8758 - val_accuracy: 0.8571 - val_loss: 1.8900\n",
"Epoch 22/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.8806 - val_accuracy: 0.8571 - val_loss: 1.8796\n",
"Epoch 23/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.8815 - val_accuracy: 0.8571 - val_loss: 1.8693\n",
"Epoch 24/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.8560 - val_accuracy: 0.8571 - val_loss: 1.8589\n",
"Epoch 25/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.7805 - loss: 1.8495 - val_accuracy: 0.8571 - val_loss: 1.8491\n",
"Epoch 26/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.8245 - val_accuracy: 0.8571 - val_loss: 1.8394\n",
"Epoch 27/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8049 - loss: 1.8372 - val_accuracy: 0.8571 - val_loss: 1.8294\n",
"Epoch 28/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.7805 - loss: 1.8236 - val_accuracy: 0.8571 - val_loss: 1.8191\n",
"Epoch 29/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.8232 - val_accuracy: 0.8571 - val_loss: 1.8090\n",
"Epoch 30/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.7873 - val_accuracy: 0.8571 - val_loss: 1.8008\n",
"Epoch 31/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.7862 - val_accuracy: 0.8571 - val_loss: 1.7918\n",
"Epoch 32/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.8029 - val_accuracy: 0.8571 - val_loss: 1.7828\n",
"Epoch 33/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.7658 - val_accuracy: 0.8571 - val_loss: 1.7734\n",
"Epoch 34/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.7646 - val_accuracy: 0.8571 - val_loss: 1.7637\n",
"Epoch 35/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.7255 - val_accuracy: 0.8571 - val_loss: 1.7539\n",
"Epoch 36/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.7385 - val_accuracy: 0.8571 - val_loss: 1.7438\n",
"Epoch 37/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.7172 - val_accuracy: 0.8571 - val_loss: 1.7340\n",
"Epoch 38/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.7158 - val_accuracy: 0.8571 - val_loss: 1.7246\n",
"Epoch 39/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.7122 - val_accuracy: 0.8571 - val_loss: 1.7162\n",
"Epoch 40/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.7141 - val_accuracy: 0.8571 - val_loss: 1.7086\n",
"Epoch 41/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6996 - val_accuracy: 0.8571 - val_loss: 1.6996\n",
"Epoch 42/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.6795 - val_accuracy: 0.8571 - val_loss: 1.6900\n",
"Epoch 43/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6662 - val_accuracy: 0.8571 - val_loss: 1.6833\n",
"Epoch 44/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.6729 - val_accuracy: 0.8571 - val_loss: 1.6753\n",
"Epoch 45/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6760 - val_accuracy: 0.8571 - val_loss: 1.6665\n",
"Epoch 46/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6566 - val_accuracy: 0.8571 - val_loss: 1.6581\n",
"Epoch 47/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6475 - val_accuracy: 0.8571 - val_loss: 1.6501\n",
"Epoch 48/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6537 - val_accuracy: 0.8571 - val_loss: 1.6430\n",
"Epoch 49/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.6250 - val_accuracy: 0.8571 - val_loss: 1.6356\n",
"Epoch 50/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6135 - val_accuracy: 0.8571 - val_loss: 1.6266\n",
"Epoch 51/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6359 - val_accuracy: 0.8571 - val_loss: 1.6192\n",
"Epoch 52/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.5970 - val_accuracy: 0.8571 - val_loss: 1.6120\n",
"Epoch 53/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6041 - val_accuracy: 0.8571 - val_loss: 1.6046\n",
"Epoch 54/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5999 - val_accuracy: 0.8571 - val_loss: 1.5963\n",
"Epoch 55/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 1.5691 - val_accuracy: 0.8571 - val_loss: 1.5882\n",
"Epoch 56/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5940 - val_accuracy: 0.8571 - val_loss: 1.5820\n",
"Epoch 57/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5604 - val_accuracy: 0.8571 - val_loss: 1.5755\n",
"Epoch 58/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5783 - val_accuracy: 0.8571 - val_loss: 1.5676\n",
"Epoch 59/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5775 - val_accuracy: 0.8571 - val_loss: 1.5591\n",
"Epoch 60/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5195 - val_accuracy: 0.8571 - val_loss: 1.5509\n",
"Epoch 61/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5291 - val_accuracy: 0.8571 - val_loss: 1.5442\n",
"Epoch 62/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5216 - val_accuracy: 0.8571 - val_loss: 1.5362\n",
"Epoch 63/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5330 - val_accuracy: 0.8571 - val_loss: 1.5293\n",
"Epoch 64/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5344 - val_accuracy: 0.8571 - val_loss: 1.5228\n",
"Epoch 65/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5161 - val_accuracy: 0.8571 - val_loss: 1.5158\n",
"Epoch 66/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4849 - val_accuracy: 0.8571 - val_loss: 1.5081\n",
"Epoch 67/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5029 - val_accuracy: 0.8571 - val_loss: 1.5009\n",
"Epoch 68/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4785 - val_accuracy: 0.8571 - val_loss: 1.4936\n",
"Epoch 69/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5226 - val_accuracy: 0.8571 - val_loss: 1.4871\n",
"Epoch 70/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4863 - val_accuracy: 0.8571 - val_loss: 1.4801\n",
"Epoch 71/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4651 - val_accuracy: 0.8571 - val_loss: 1.4741\n",
"Epoch 72/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4743 - val_accuracy: 0.8571 - val_loss: 1.4673\n",
"Epoch 73/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4487 - val_accuracy: 0.8571 - val_loss: 1.4599\n",
"Epoch 74/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4422 - val_accuracy: 0.8571 - val_loss: 1.4536\n",
"Epoch 75/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4413 - val_accuracy: 0.8571 - val_loss: 1.4475\n",
"Epoch 76/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4394 - val_accuracy: 0.8571 - val_loss: 1.4413\n",
"Epoch 77/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4215 - val_accuracy: 0.8571 - val_loss: 1.4355\n",
"Epoch 78/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4270 - val_accuracy: 0.8571 - val_loss: 1.4285\n",
"Epoch 79/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4395 - val_accuracy: 0.8571 - val_loss: 1.4219\n",
"Epoch 80/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.4311 - val_accuracy: 0.8571 - val_loss: 1.4154\n",
"Epoch 81/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4002 - val_accuracy: 0.8571 - val_loss: 1.4086\n",
"Epoch 82/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4201 - val_accuracy: 0.8571 - val_loss: 1.4030\n",
"Epoch 83/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3839 - val_accuracy: 0.8571 - val_loss: 1.3973\n",
"Epoch 84/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4089 - val_accuracy: 0.8571 - val_loss: 1.3913\n",
"Epoch 85/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3913 - val_accuracy: 0.8571 - val_loss: 1.3845\n",
"Epoch 86/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3826 - val_accuracy: 0.8571 - val_loss: 1.3788\n",
"Epoch 87/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3724 - val_accuracy: 0.8571 - val_loss: 1.3733\n",
"Epoch 88/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3838 - val_accuracy: 0.8571 - val_loss: 1.3679\n",
"Epoch 89/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3502 - val_accuracy: 0.8571 - val_loss: 1.3624\n",
"Epoch 90/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3619 - val_accuracy: 0.8571 - val_loss: 1.3573\n",
"Epoch 91/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3734 - val_accuracy: 0.8571 - val_loss: 1.3538\n",
"Epoch 92/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3499 - val_accuracy: 0.8571 - val_loss: 1.3479\n",
"Epoch 93/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3392 - val_accuracy: 0.8571 - val_loss: 1.3426\n",
"Epoch 94/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3334 - val_accuracy: 0.8571 - val_loss: 1.3370\n",
"Epoch 95/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3347 - val_accuracy: 0.8571 - val_loss: 1.3315\n",
"Epoch 96/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3316 - val_accuracy: 0.8571 - val_loss: 1.3265\n",
"Epoch 97/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3638 - val_accuracy: 0.8571 - val_loss: 1.3228\n",
"Epoch 98/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3231 - val_accuracy: 0.8571 - val_loss: 1.3187\n",
"Epoch 99/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3332 - val_accuracy: 0.8571 - val_loss: 1.3138\n",
"Epoch 100/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3142 - val_accuracy: 0.8571 - val_loss: 1.3088\n",
"Epoch 101/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3083 - val_accuracy: 0.8571 - val_loss: 1.3047\n",
"Epoch 102/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2839 - val_accuracy: 0.8571 - val_loss: 1.2998\n",
"Epoch 103/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2872 - val_accuracy: 0.8571 - val_loss: 1.2956\n",
"Epoch 104/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3188 - val_accuracy: 0.8571 - val_loss: 1.2922\n",
"Epoch 105/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2763 - val_accuracy: 0.8571 - val_loss: 1.2873\n",
"Epoch 106/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2957 - val_accuracy: 0.8571 - val_loss: 1.2831\n",
"Epoch 107/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 1.2953 - val_accuracy: 0.8571 - val_loss: 1.2787\n",
"Epoch 108/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2710 - val_accuracy: 0.8571 - val_loss: 1.2737\n",
"Epoch 109/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2953 - val_accuracy: 0.8571 - val_loss: 1.2693\n",
"Epoch 110/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2716 - val_accuracy: 0.8571 - val_loss: 1.2660\n",
"Epoch 111/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2748 - val_accuracy: 0.8571 - val_loss: 1.2631\n",
"Epoch 112/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2488 - val_accuracy: 0.8571 - val_loss: 1.2585\n",
"Epoch 113/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2603 - val_accuracy: 0.8571 - val_loss: 1.2538\n",
"Epoch 114/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2413 - val_accuracy: 0.8571 - val_loss: 1.2496\n",
"Epoch 115/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2690 - val_accuracy: 0.8571 - val_loss: 1.2452\n",
"Epoch 116/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2510 - val_accuracy: 0.8571 - val_loss: 1.2411\n",
"Epoch 117/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2378 - val_accuracy: 0.8571 - val_loss: 1.2377\n",
"Epoch 118/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2281 - val_accuracy: 0.8571 - val_loss: 1.2339\n",
"Epoch 119/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2639 - val_accuracy: 0.8571 - val_loss: 1.2304\n",
"Epoch 120/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2622 - val_accuracy: 0.8571 - val_loss: 1.2262\n",
"Epoch 121/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2354 - val_accuracy: 0.8571 - val_loss: 1.2230\n",
"Epoch 122/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2722 - val_accuracy: 0.8571 - val_loss: 1.2196\n",
"Epoch 123/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2421 - val_accuracy: 0.8571 - val_loss: 1.2153\n",
"Epoch 124/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2292 - val_accuracy: 0.8571 - val_loss: 1.2126\n",
"Epoch 125/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2138 - val_accuracy: 0.8571 - val_loss: 1.2092\n",
"Epoch 126/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1895 - val_accuracy: 0.8571 - val_loss: 1.2055\n",
"Epoch 127/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2061 - val_accuracy: 0.8571 - val_loss: 1.2014\n",
"Epoch 128/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1968 - val_accuracy: 0.8571 - val_loss: 1.1979\n",
"Epoch 129/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2231 - val_accuracy: 0.8571 - val_loss: 1.1943\n",
"Epoch 130/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2190 - val_accuracy: 0.8571 - val_loss: 1.1910\n",
"Epoch 131/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2191 - val_accuracy: 0.8571 - val_loss: 1.1880\n",
"Epoch 132/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2025 - val_accuracy: 0.8571 - val_loss: 1.1843\n",
"Epoch 133/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.1829 - val_accuracy: 0.8571 - val_loss: 1.1806\n",
"Epoch 134/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 1.1836 - val_accuracy: 0.8571 - val_loss: 1.1770\n",
"Epoch 135/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.1937 - val_accuracy: 0.8571 - val_loss: 1.1741\n",
"Epoch 136/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1785 - val_accuracy: 0.8571 - val_loss: 1.1710\n",
"Epoch 137/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1702 - val_accuracy: 0.8571 - val_loss: 1.1680\n",
"Epoch 138/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.1911 - val_accuracy: 0.8571 - val_loss: 1.1646\n",
"Epoch 139/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.1821 - val_accuracy: 0.8571 - val_loss: 1.1611\n",
"Epoch 140/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1455 - val_accuracy: 0.8571 - val_loss: 1.1576\n",
"Epoch 141/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1711 - val_accuracy: 0.8571 - val_loss: 1.1544\n",
"Epoch 142/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1591 - val_accuracy: 0.8571 - val_loss: 1.1509\n",
"Epoch 143/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.1675 - val_accuracy: 0.8571 - val_loss: 1.1474\n",
"Epoch 144/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1860 - val_accuracy: 0.8571 - val_loss: 1.1444\n",
"Epoch 145/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1656 - val_accuracy: 0.8571 - val_loss: 1.1419\n",
"Epoch 146/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1621 - val_accuracy: 0.8571 - val_loss: 1.1386\n",
"Epoch 147/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1650 - val_accuracy: 0.8571 - val_loss: 1.1365\n",
"Epoch 148/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1870 - val_accuracy: 0.8571 - val_loss: 1.1336\n",
"Epoch 149/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1472 - val_accuracy: 0.8571 - val_loss: 1.1304\n",
"Epoch 150/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.1646 - val_accuracy: 0.8571 - val_loss: 1.1277\n",
"Epoch 151/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1335 - val_accuracy: 0.8571 - val_loss: 1.1259\n",
"Epoch 152/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1459 - val_accuracy: 0.8571 - val_loss: 1.1237\n",
"Epoch 153/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1264 - val_accuracy: 0.8571 - val_loss: 1.1209\n",
"Epoch 154/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1382 - val_accuracy: 0.8571 - val_loss: 1.1182\n",
"Epoch 155/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1280 - val_accuracy: 0.8571 - val_loss: 1.1153\n",
"Epoch 156/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1313 - val_accuracy: 0.8571 - val_loss: 1.1123\n",
"Epoch 157/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1102 - val_accuracy: 0.8571 - val_loss: 1.1094\n",
"Epoch 158/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.1071 - val_accuracy: 0.8571 - val_loss: 1.1069\n",
"Epoch 159/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0845 - val_accuracy: 0.8571 - val_loss: 1.1038\n",
"Epoch 160/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1140 - val_accuracy: 0.8571 - val_loss: 1.1009\n",
"Epoch 161/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1390 - val_accuracy: 0.8571 - val_loss: 1.0985\n",
"Epoch 162/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0967 - val_accuracy: 0.8571 - val_loss: 1.0959\n",
"Epoch 163/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0894 - val_accuracy: 0.8571 - val_loss: 1.0935\n",
"Epoch 164/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0937 - val_accuracy: 0.8571 - val_loss: 1.0908\n",
"Epoch 165/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1170 - val_accuracy: 0.8571 - val_loss: 1.0882\n",
"Epoch 166/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1016 - val_accuracy: 0.8571 - val_loss: 1.0855\n",
"Epoch 167/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1365 - val_accuracy: 0.8571 - val_loss: 1.0842\n",
"Epoch 168/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1163 - val_accuracy: 0.8571 - val_loss: 1.0821\n",
"Epoch 169/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0915 - val_accuracy: 0.8571 - val_loss: 1.0794\n",
"Epoch 170/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0816 - val_accuracy: 0.8571 - val_loss: 1.0777\n",
"Epoch 171/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1195 - val_accuracy: 0.8571 - val_loss: 1.0762\n",
"Epoch 172/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0741 - val_accuracy: 0.8571 - val_loss: 1.0741\n",
"Epoch 173/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1333 - val_accuracy: 0.8571 - val_loss: 1.0718\n",
"Epoch 174/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0593 - val_accuracy: 0.8571 - val_loss: 1.0693\n",
"Epoch 175/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0957 - val_accuracy: 0.8571 - val_loss: 1.0670\n",
"Epoch 176/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0722 - val_accuracy: 0.8571 - val_loss: 1.0650\n",
"Epoch 177/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0786 - val_accuracy: 0.8571 - val_loss: 1.0626\n",
"Epoch 178/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0939 - val_accuracy: 0.8571 - val_loss: 1.0609\n",
"Epoch 179/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0600 - val_accuracy: 0.8571 - val_loss: 1.0586\n",
"Epoch 180/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0768 - val_accuracy: 0.8571 - val_loss: 1.0561\n",
"Epoch 181/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0888 - val_accuracy: 0.8571 - val_loss: 1.0545\n",
"Epoch 182/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0809 - val_accuracy: 0.8571 - val_loss: 1.0526\n",
"Epoch 183/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0803 - val_accuracy: 0.8571 - val_loss: 1.0508\n",
"Epoch 184/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0355 - val_accuracy: 0.8571 - val_loss: 1.0486\n",
"Epoch 185/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0581 - val_accuracy: 0.8571 - val_loss: 1.0463\n",
"Epoch 186/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0827 - val_accuracy: 0.8571 - val_loss: 1.0447\n",
"Epoch 187/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0457 - val_accuracy: 0.8571 - val_loss: 1.0424\n",
"Epoch 188/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0647 - val_accuracy: 0.8571 - val_loss: 1.0413\n",
"Epoch 189/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0579 - val_accuracy: 0.8571 - val_loss: 1.0397\n",
"Epoch 190/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0724 - val_accuracy: 0.8571 - val_loss: 1.0374\n",
"Epoch 191/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0497 - val_accuracy: 0.8571 - val_loss: 1.0353\n",
"Epoch 192/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0767 - val_accuracy: 0.8571 - val_loss: 1.0335\n",
"Epoch 193/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0515 - val_accuracy: 0.8571 - val_loss: 1.0314\n",
"Epoch 194/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0273 - val_accuracy: 0.8571 - val_loss: 1.0289\n",
"Epoch 195/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0470 - val_accuracy: 0.8571 - val_loss: 1.0268\n",
"Epoch 196/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0527 - val_accuracy: 0.8571 - val_loss: 1.0246\n",
"Epoch 197/500\n",
"2/2 - 0s - 18ms/step - accuracy: 0.8537 - loss: 1.0460 - val_accuracy: 0.8571 - val_loss: 1.0228\n",
"Epoch 198/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0396 - val_accuracy: 0.8571 - val_loss: 1.0212\n",
"Epoch 199/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0383 - val_accuracy: 0.8571 - val_loss: 1.0199\n",
"Epoch 200/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0257 - val_accuracy: 0.8571 - val_loss: 1.0177\n",
"Epoch 201/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0097 - val_accuracy: 0.8571 - val_loss: 1.0159\n",
"Epoch 202/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9978 - val_accuracy: 0.8571 - val_loss: 1.0139\n",
"Epoch 203/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0471 - val_accuracy: 0.8571 - val_loss: 1.0119\n",
"Epoch 204/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0409 - val_accuracy: 0.8571 - val_loss: 1.0102\n",
"Epoch 205/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0357 - val_accuracy: 0.8571 - val_loss: 1.0086\n",
"Epoch 206/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0442 - val_accuracy: 0.8571 - val_loss: 1.0075\n",
"Epoch 207/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0219 - val_accuracy: 0.8571 - val_loss: 1.0058\n",
"Epoch 208/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0257 - val_accuracy: 0.8571 - val_loss: 1.0042\n",
"Epoch 209/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9953 - val_accuracy: 0.8571 - val_loss: 1.0021\n",
"Epoch 210/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0650 - val_accuracy: 0.8571 - val_loss: 1.0003\n",
"Epoch 211/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9909 - val_accuracy: 0.8571 - val_loss: 0.9984\n",
"Epoch 212/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9847 - val_accuracy: 0.8571 - val_loss: 0.9963\n",
"Epoch 213/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9929 - val_accuracy: 0.8571 - val_loss: 0.9942\n",
"Epoch 214/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9696 - val_accuracy: 0.8571 - val_loss: 0.9922\n",
"Epoch 215/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0115 - val_accuracy: 0.8571 - val_loss: 0.9909\n",
"Epoch 216/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0426 - val_accuracy: 0.8571 - val_loss: 0.9896\n",
"Epoch 217/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0355 - val_accuracy: 0.8571 - val_loss: 0.9878\n",
"Epoch 218/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0111 - val_accuracy: 0.8571 - val_loss: 0.9862\n",
"Epoch 219/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0047 - val_accuracy: 0.8571 - val_loss: 0.9850\n",
"Epoch 220/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9901 - val_accuracy: 0.8571 - val_loss: 0.9831\n",
"Epoch 221/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.9875 - val_accuracy: 0.8571 - val_loss: 0.9815\n",
"Epoch 222/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9783 - val_accuracy: 0.8571 - val_loss: 0.9796\n",
"Epoch 223/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0097 - val_accuracy: 0.8571 - val_loss: 0.9780\n",
"Epoch 224/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9895 - val_accuracy: 0.8571 - val_loss: 0.9764\n",
"Epoch 225/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0003 - val_accuracy: 0.8571 - val_loss: 0.9752\n",
"Epoch 226/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0074 - val_accuracy: 0.8571 - val_loss: 0.9736\n",
"Epoch 227/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.9722 - val_accuracy: 0.8571 - val_loss: 0.9722\n",
"Epoch 228/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9806 - val_accuracy: 0.8571 - val_loss: 0.9703\n",
"Epoch 229/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9936 - val_accuracy: 0.8571 - val_loss: 0.9686\n",
"Epoch 230/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9889 - val_accuracy: 0.8571 - val_loss: 0.9668\n",
"Epoch 231/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9880 - val_accuracy: 0.8571 - val_loss: 0.9654\n",
"Epoch 232/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9783 - val_accuracy: 0.8571 - val_loss: 0.9638\n",
"Epoch 233/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9825 - val_accuracy: 0.8571 - val_loss: 0.9623\n",
"Epoch 234/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9876 - val_accuracy: 0.8571 - val_loss: 0.9615\n",
"Epoch 235/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0037 - val_accuracy: 0.8571 - val_loss: 0.9605\n",
"Epoch 236/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9587 - val_accuracy: 0.8571 - val_loss: 0.9587\n",
"Epoch 237/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9992 - val_accuracy: 0.8571 - val_loss: 0.9576\n",
"Epoch 238/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.9783 - val_accuracy: 0.8571 - val_loss: 0.9562\n",
"Epoch 239/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0140 - val_accuracy: 0.8571 - val_loss: 0.9549\n",
"Epoch 240/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9395 - val_accuracy: 0.8571 - val_loss: 0.9534\n",
"Epoch 241/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9933 - val_accuracy: 0.8571 - val_loss: 0.9526\n",
"Epoch 242/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9540 - val_accuracy: 0.8571 - val_loss: 0.9511\n",
"Epoch 243/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9409 - val_accuracy: 0.8571 - val_loss: 0.9498\n",
"Epoch 244/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9850 - val_accuracy: 0.8571 - val_loss: 0.9487\n",
"Epoch 245/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9520 - val_accuracy: 0.8571 - val_loss: 0.9474\n",
"Epoch 246/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9655 - val_accuracy: 0.8571 - val_loss: 0.9462\n",
"Epoch 247/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9951 - val_accuracy: 0.8571 - val_loss: 0.9448\n",
"Epoch 248/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9752 - val_accuracy: 0.8571 - val_loss: 0.9434\n",
"Epoch 249/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9690 - val_accuracy: 0.8571 - val_loss: 0.9419\n",
"Epoch 250/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9625 - val_accuracy: 0.8571 - val_loss: 0.9407\n",
"Epoch 251/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9777 - val_accuracy: 0.8571 - val_loss: 0.9396\n",
"Epoch 252/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9895 - val_accuracy: 0.8571 - val_loss: 0.9383\n",
"Epoch 253/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9370 - val_accuracy: 0.8571 - val_loss: 0.9368\n",
"Epoch 254/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.9518 - val_accuracy: 0.8571 - val_loss: 0.9352\n",
"Epoch 255/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.9588 - val_accuracy: 0.8571 - val_loss: 0.9337\n",
"Epoch 256/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9775 - val_accuracy: 0.8571 - val_loss: 0.9326\n",
"Epoch 257/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.9639 - val_accuracy: 0.8571 - val_loss: 0.9314\n",
"Epoch 258/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9272 - val_accuracy: 0.8571 - val_loss: 0.9299\n",
"Epoch 259/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9601 - val_accuracy: 0.8571 - val_loss: 0.9284\n",
"Epoch 260/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9253 - val_accuracy: 0.8571 - val_loss: 0.9271\n",
"Epoch 261/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9673 - val_accuracy: 0.8571 - val_loss: 0.9258\n",
"Epoch 262/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9492 - val_accuracy: 0.8571 - val_loss: 0.9246\n",
"Epoch 263/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9503 - val_accuracy: 0.8571 - val_loss: 0.9232\n",
"Epoch 264/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.9502 - val_accuracy: 0.8571 - val_loss: 0.9223\n",
"Epoch 265/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8984 - val_accuracy: 0.8571 - val_loss: 0.9208\n",
"Epoch 266/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.9395 - val_accuracy: 0.8571 - val_loss: 0.9195\n",
"Epoch 267/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9419 - val_accuracy: 0.8571 - val_loss: 0.9182\n",
"Epoch 268/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9278 - val_accuracy: 0.8571 - val_loss: 0.9171\n",
"Epoch 269/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9086 - val_accuracy: 0.8571 - val_loss: 0.9159\n",
"Epoch 270/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9250 - val_accuracy: 0.8571 - val_loss: 0.9145\n",
"Epoch 271/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9115 - val_accuracy: 0.8571 - val_loss: 0.9132\n",
"Epoch 272/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9435 - val_accuracy: 0.8571 - val_loss: 0.9118\n",
"Epoch 273/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9561 - val_accuracy: 0.8571 - val_loss: 0.9108\n",
"Epoch 274/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9403 - val_accuracy: 0.8571 - val_loss: 0.9099\n",
"Epoch 275/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9595 - val_accuracy: 0.8571 - val_loss: 0.9086\n",
"Epoch 276/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9120 - val_accuracy: 0.8571 - val_loss: 0.9076\n",
"Epoch 277/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9327 - val_accuracy: 0.8571 - val_loss: 0.9065\n",
"Epoch 278/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9364 - val_accuracy: 0.8571 - val_loss: 0.9056\n",
"Epoch 279/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9078 - val_accuracy: 0.8571 - val_loss: 0.9044\n",
"Epoch 280/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8879 - val_accuracy: 0.8571 - val_loss: 0.9031\n",
"Epoch 281/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9402 - val_accuracy: 0.8571 - val_loss: 0.9023\n",
"Epoch 282/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9134 - val_accuracy: 0.8571 - val_loss: 0.9009\n",
"Epoch 283/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9364 - val_accuracy: 0.8571 - val_loss: 0.8997\n",
"Epoch 284/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8979 - val_accuracy: 0.8571 - val_loss: 0.8985\n",
"Epoch 285/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9045 - val_accuracy: 0.8571 - val_loss: 0.8975\n",
"Epoch 286/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9414 - val_accuracy: 0.8571 - val_loss: 0.8964\n",
"Epoch 287/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8925 - val_accuracy: 0.8571 - val_loss: 0.8951\n",
"Epoch 288/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.9091 - val_accuracy: 0.8571 - val_loss: 0.8944\n",
"Epoch 289/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9149 - val_accuracy: 0.8571 - val_loss: 0.8936\n",
"Epoch 290/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8909 - val_accuracy: 0.8571 - val_loss: 0.8925\n",
"Epoch 291/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8932 - val_accuracy: 0.8571 - val_loss: 0.8912\n",
"Epoch 292/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9086 - val_accuracy: 0.8571 - val_loss: 0.8901\n",
"Epoch 293/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9072 - val_accuracy: 0.8571 - val_loss: 0.8890\n",
"Epoch 294/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9204 - val_accuracy: 0.8571 - val_loss: 0.8879\n",
"Epoch 295/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8734 - val_accuracy: 0.8571 - val_loss: 0.8866\n",
"Epoch 296/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9355 - val_accuracy: 0.8571 - val_loss: 0.8856\n",
"Epoch 297/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8829 - val_accuracy: 0.8571 - val_loss: 0.8843\n",
"Epoch 298/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8882 - val_accuracy: 0.8571 - val_loss: 0.8831\n",
"Epoch 299/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9170 - val_accuracy: 0.8571 - val_loss: 0.8821\n",
"Epoch 300/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8941 - val_accuracy: 0.8571 - val_loss: 0.8812\n",
"Epoch 301/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8746 - val_accuracy: 0.8571 - val_loss: 0.8801\n",
"Epoch 302/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8817 - val_accuracy: 0.8571 - val_loss: 0.8789\n",
"Epoch 303/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9101 - val_accuracy: 0.8571 - val_loss: 0.8777\n",
"Epoch 304/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8715 - val_accuracy: 0.8571 - val_loss: 0.8766\n",
"Epoch 305/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.8919 - val_accuracy: 0.8571 - val_loss: 0.8756\n",
"Epoch 306/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9159 - val_accuracy: 0.8571 - val_loss: 0.8745\n",
"Epoch 307/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9122 - val_accuracy: 0.8571 - val_loss: 0.8736\n",
"Epoch 308/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8950 - val_accuracy: 0.8571 - val_loss: 0.8726\n",
"Epoch 309/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8599 - val_accuracy: 0.8571 - val_loss: 0.8716\n",
"Epoch 310/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9009 - val_accuracy: 0.8571 - val_loss: 0.8705\n",
"Epoch 311/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8738 - val_accuracy: 0.8571 - val_loss: 0.8694\n",
"Epoch 312/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8587 - val_accuracy: 0.8571 - val_loss: 0.8682\n",
"Epoch 313/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8846 - val_accuracy: 0.8571 - val_loss: 0.8674\n",
"Epoch 314/500\n",
"2/2 - 0s - 28ms/step - accuracy: 0.8537 - loss: 0.8935 - val_accuracy: 0.8571 - val_loss: 0.8664\n",
"Epoch 315/500\n",
"2/2 - 0s - 30ms/step - accuracy: 0.8537 - loss: 0.8979 - val_accuracy: 0.8571 - val_loss: 0.8655\n",
"Epoch 316/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8693 - val_accuracy: 0.8571 - val_loss: 0.8644\n",
"Epoch 317/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8792 - val_accuracy: 0.8571 - val_loss: 0.8633\n",
"Epoch 318/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8820 - val_accuracy: 0.8571 - val_loss: 0.8626\n",
"Epoch 319/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8652 - val_accuracy: 0.8571 - val_loss: 0.8614\n",
"Epoch 320/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8814 - val_accuracy: 0.8571 - val_loss: 0.8605\n",
"Epoch 321/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8621 - val_accuracy: 0.8571 - val_loss: 0.8594\n",
"Epoch 322/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8744 - val_accuracy: 0.8571 - val_loss: 0.8585\n",
"Epoch 323/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8805 - val_accuracy: 0.8571 - val_loss: 0.8575\n",
"Epoch 324/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8818 - val_accuracy: 0.8571 - val_loss: 0.8568\n",
"Epoch 325/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8989 - val_accuracy: 0.8571 - val_loss: 0.8560\n",
"Epoch 326/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8706 - val_accuracy: 0.8571 - val_loss: 0.8548\n",
"Epoch 327/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8623 - val_accuracy: 0.8571 - val_loss: 0.8539\n",
"Epoch 328/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8570 - val_accuracy: 0.8571 - val_loss: 0.8529\n",
"Epoch 329/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8858 - val_accuracy: 0.8571 - val_loss: 0.8519\n",
"Epoch 330/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8571 - val_accuracy: 0.8571 - val_loss: 0.8512\n",
"Epoch 331/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8839 - val_accuracy: 0.8571 - val_loss: 0.8503\n",
"Epoch 332/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8556 - val_accuracy: 0.8571 - val_loss: 0.8492\n",
"Epoch 333/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8764 - val_accuracy: 0.8571 - val_loss: 0.8482\n",
"Epoch 334/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8353 - val_accuracy: 0.8571 - val_loss: 0.8470\n",
"Epoch 335/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8659 - val_accuracy: 0.8571 - val_loss: 0.8461\n",
"Epoch 336/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8745 - val_accuracy: 0.8571 - val_loss: 0.8452\n",
"Epoch 337/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8465 - val_accuracy: 0.8571 - val_loss: 0.8444\n",
"Epoch 338/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8740 - val_accuracy: 0.8571 - val_loss: 0.8434\n",
"Epoch 339/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8760 - val_accuracy: 0.8571 - val_loss: 0.8423\n",
"Epoch 340/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8553 - val_accuracy: 0.8571 - val_loss: 0.8415\n",
"Epoch 341/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8460 - val_accuracy: 0.8571 - val_loss: 0.8406\n",
"Epoch 342/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8487 - val_accuracy: 0.8571 - val_loss: 0.8397\n",
"Epoch 343/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8667 - val_accuracy: 0.8571 - val_loss: 0.8387\n",
"Epoch 344/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8513 - val_accuracy: 0.8571 - val_loss: 0.8378\n",
"Epoch 345/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8692 - val_accuracy: 0.8571 - val_loss: 0.8371\n",
"Epoch 346/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8271 - val_accuracy: 0.8571 - val_loss: 0.8360\n",
"Epoch 347/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8738 - val_accuracy: 0.8571 - val_loss: 0.8353\n",
"Epoch 348/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8613 - val_accuracy: 0.8571 - val_loss: 0.8344\n",
"Epoch 349/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8322 - val_accuracy: 0.8571 - val_loss: 0.8334\n",
"Epoch 350/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8745 - val_accuracy: 0.8571 - val_loss: 0.8325\n",
"Epoch 351/500\n",
"2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 0.8528 - val_accuracy: 0.8571 - val_loss: 0.8316\n",
"Epoch 352/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8418 - val_accuracy: 0.8571 - val_loss: 0.8306\n",
"Epoch 353/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8466 - val_accuracy: 0.8571 - val_loss: 0.8296\n",
"Epoch 354/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8299 - val_accuracy: 0.8571 - val_loss: 0.8287\n",
"Epoch 355/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8493 - val_accuracy: 0.8571 - val_loss: 0.8277\n",
"Epoch 356/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8468 - val_accuracy: 0.8571 - val_loss: 0.8267\n",
"Epoch 357/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8561 - val_accuracy: 0.8571 - val_loss: 0.8261\n",
"Epoch 358/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8319 - val_accuracy: 0.8571 - val_loss: 0.8252\n",
"Epoch 359/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8462 - val_accuracy: 0.8571 - val_loss: 0.8244\n",
"Epoch 360/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8342 - val_accuracy: 0.8571 - val_loss: 0.8235\n",
"Epoch 361/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8424 - val_accuracy: 0.8571 - val_loss: 0.8225\n",
"Epoch 362/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8315 - val_accuracy: 0.8571 - val_loss: 0.8216\n",
"Epoch 363/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8650 - val_accuracy: 0.8571 - val_loss: 0.8210\n",
"Epoch 364/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8644 - val_accuracy: 0.8571 - val_loss: 0.8201\n",
"Epoch 365/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8452 - val_accuracy: 0.8571 - val_loss: 0.8193\n",
"Epoch 366/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8463 - val_accuracy: 0.8571 - val_loss: 0.8186\n",
"Epoch 367/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8374 - val_accuracy: 0.8571 - val_loss: 0.8176\n",
"Epoch 368/500\n",
"2/2 - 0s - 17ms/step - accuracy: 0.8537 - loss: 0.8477 - val_accuracy: 0.8571 - val_loss: 0.8168\n",
"Epoch 369/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8179 - val_accuracy: 0.8571 - val_loss: 0.8159\n",
"Epoch 370/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7949 - val_accuracy: 0.8571 - val_loss: 0.8150\n",
"Epoch 371/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8133 - val_accuracy: 0.8571 - val_loss: 0.8141\n",
"Epoch 372/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8204 - val_accuracy: 0.8571 - val_loss: 0.8132\n",
"Epoch 373/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8575 - val_accuracy: 0.8571 - val_loss: 0.8124\n",
"Epoch 374/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8066 - val_accuracy: 0.8571 - val_loss: 0.8115\n",
"Epoch 375/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8284 - val_accuracy: 0.8571 - val_loss: 0.8106\n",
"Epoch 376/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8040 - val_accuracy: 0.8571 - val_loss: 0.8097\n",
"Epoch 377/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8243 - val_accuracy: 0.8571 - val_loss: 0.8091\n",
"Epoch 378/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8145 - val_accuracy: 0.8571 - val_loss: 0.8084\n",
"Epoch 379/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8263 - val_accuracy: 0.8571 - val_loss: 0.8075\n",
"Epoch 380/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8289 - val_accuracy: 0.8571 - val_loss: 0.8068\n",
"Epoch 381/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8228 - val_accuracy: 0.8571 - val_loss: 0.8059\n",
"Epoch 382/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8157 - val_accuracy: 0.8571 - val_loss: 0.8050\n",
"Epoch 383/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8155 - val_accuracy: 0.8571 - val_loss: 0.8045\n",
"Epoch 384/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8240 - val_accuracy: 0.8571 - val_loss: 0.8036\n",
"Epoch 385/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8237 - val_accuracy: 0.8571 - val_loss: 0.8027\n",
"Epoch 386/500\n",
"2/2 - 0s - 18ms/step - accuracy: 0.8537 - loss: 0.8163 - val_accuracy: 0.8571 - val_loss: 0.8020\n",
"Epoch 387/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8028 - val_accuracy: 0.8571 - val_loss: 0.8012\n",
"Epoch 388/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7948 - val_accuracy: 0.8571 - val_loss: 0.8004\n",
"Epoch 389/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7891 - val_accuracy: 0.8571 - val_loss: 0.7996\n",
"Epoch 390/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8254 - val_accuracy: 0.8571 - val_loss: 0.7987\n",
"Epoch 391/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8277 - val_accuracy: 0.8571 - val_loss: 0.7981\n",
"Epoch 392/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8004 - val_accuracy: 0.8571 - val_loss: 0.7973\n",
"Epoch 393/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8248 - val_accuracy: 0.8571 - val_loss: 0.7966\n",
"Epoch 394/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8204 - val_accuracy: 0.8571 - val_loss: 0.7958\n",
"Epoch 395/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8073 - val_accuracy: 0.8571 - val_loss: 0.7950\n",
"Epoch 396/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7913 - val_accuracy: 0.8571 - val_loss: 0.7942\n",
"Epoch 397/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8046 - val_accuracy: 0.8571 - val_loss: 0.7934\n",
"Epoch 398/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8163 - val_accuracy: 0.8571 - val_loss: 0.7927\n",
"Epoch 399/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.7983 - val_accuracy: 0.8571 - val_loss: 0.7918\n",
"Epoch 400/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8062 - val_accuracy: 0.8571 - val_loss: 0.7912\n",
"Epoch 401/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8047 - val_accuracy: 0.8571 - val_loss: 0.7904\n",
"Epoch 402/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8145 - val_accuracy: 0.8571 - val_loss: 0.7898\n",
"Epoch 403/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8128 - val_accuracy: 0.8571 - val_loss: 0.7892\n",
"Epoch 404/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8163 - val_accuracy: 0.8571 - val_loss: 0.7885\n",
"Epoch 405/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8054 - val_accuracy: 0.8571 - val_loss: 0.7877\n",
"Epoch 406/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8282 - val_accuracy: 0.8571 - val_loss: 0.7870\n",
"Epoch 407/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7878 - val_accuracy: 0.8571 - val_loss: 0.7862\n",
"Epoch 408/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7720 - val_accuracy: 0.8571 - val_loss: 0.7854\n",
"Epoch 409/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8026 - val_accuracy: 0.8571 - val_loss: 0.7848\n",
"Epoch 410/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7872 - val_accuracy: 0.8571 - val_loss: 0.7840\n",
"Epoch 411/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7907 - val_accuracy: 0.8571 - val_loss: 0.7833\n",
"Epoch 412/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8070 - val_accuracy: 0.8571 - val_loss: 0.7828\n",
"Epoch 413/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7948 - val_accuracy: 0.8571 - val_loss: 0.7821\n",
"Epoch 414/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8079 - val_accuracy: 0.8571 - val_loss: 0.7815\n",
"Epoch 415/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8118 - val_accuracy: 0.8571 - val_loss: 0.7811\n",
"Epoch 416/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7934 - val_accuracy: 0.8571 - val_loss: 0.7804\n",
"Epoch 417/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7853 - val_accuracy: 0.8571 - val_loss: 0.7797\n",
"Epoch 418/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7973 - val_accuracy: 0.8571 - val_loss: 0.7792\n",
"Epoch 419/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7896 - val_accuracy: 0.8571 - val_loss: 0.7785\n",
"Epoch 420/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8097 - val_accuracy: 0.8571 - val_loss: 0.7779\n",
"Epoch 421/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8039 - val_accuracy: 0.8571 - val_loss: 0.7773\n",
"Epoch 422/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8143 - val_accuracy: 0.8571 - val_loss: 0.7769\n",
"Epoch 423/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7784 - val_accuracy: 0.8571 - val_loss: 0.7763\n",
"Epoch 424/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7868 - val_accuracy: 0.8571 - val_loss: 0.7756\n",
"Epoch 425/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7817 - val_accuracy: 0.8571 - val_loss: 0.7748\n",
"Epoch 426/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7935 - val_accuracy: 0.8571 - val_loss: 0.7741\n",
"Epoch 427/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7944 - val_accuracy: 0.8571 - val_loss: 0.7734\n",
"Epoch 428/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7773 - val_accuracy: 0.8571 - val_loss: 0.7727\n",
"Epoch 429/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8049 - val_accuracy: 0.8571 - val_loss: 0.7720\n",
"Epoch 430/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.8097 - val_accuracy: 0.8571 - val_loss: 0.7714\n",
"Epoch 431/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7760 - val_accuracy: 0.8571 - val_loss: 0.7707\n",
"Epoch 432/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7626 - val_accuracy: 0.8571 - val_loss: 0.7701\n",
"Epoch 433/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7663 - val_accuracy: 0.8571 - val_loss: 0.7694\n",
"Epoch 434/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8070 - val_accuracy: 0.8571 - val_loss: 0.7687\n",
"Epoch 435/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7924 - val_accuracy: 0.8571 - val_loss: 0.7680\n",
"Epoch 436/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7837 - val_accuracy: 0.8571 - val_loss: 0.7674\n",
"Epoch 437/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7776 - val_accuracy: 0.8571 - val_loss: 0.7668\n",
"Epoch 438/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7733 - val_accuracy: 0.8571 - val_loss: 0.7662\n",
"Epoch 439/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7763 - val_accuracy: 0.8571 - val_loss: 0.7655\n",
"Epoch 440/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7831 - val_accuracy: 0.8571 - val_loss: 0.7649\n",
"Epoch 441/500\n",
"2/2 - 0s - 18ms/step - accuracy: 0.8537 - loss: 0.7754 - val_accuracy: 0.8571 - val_loss: 0.7642\n",
"Epoch 442/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7793 - val_accuracy: 0.8571 - val_loss: 0.7636\n",
"Epoch 443/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7956 - val_accuracy: 0.8571 - val_loss: 0.7630\n",
"Epoch 444/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7908 - val_accuracy: 0.8571 - val_loss: 0.7623\n",
"Epoch 445/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7548 - val_accuracy: 0.8571 - val_loss: 0.7617\n",
"Epoch 446/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7658 - val_accuracy: 0.8571 - val_loss: 0.7610\n",
"Epoch 447/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7624 - val_accuracy: 0.8571 - val_loss: 0.7606\n",
"Epoch 448/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7858 - val_accuracy: 0.8571 - val_loss: 0.7600\n",
"Epoch 449/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7509 - val_accuracy: 0.8571 - val_loss: 0.7593\n",
"Epoch 450/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7449 - val_accuracy: 0.8571 - val_loss: 0.7585\n",
"Epoch 451/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7845 - val_accuracy: 0.8571 - val_loss: 0.7581\n",
"Epoch 452/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7731 - val_accuracy: 0.8571 - val_loss: 0.7575\n",
"Epoch 453/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7696 - val_accuracy: 0.8571 - val_loss: 0.7568\n",
"Epoch 454/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7762 - val_accuracy: 0.8571 - val_loss: 0.7562\n",
"Epoch 455/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7623 - val_accuracy: 0.8571 - val_loss: 0.7558\n",
"Epoch 456/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7681 - val_accuracy: 0.8571 - val_loss: 0.7552\n",
"Epoch 457/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.7775 - val_accuracy: 0.8571 - val_loss: 0.7546\n",
"Epoch 458/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7442 - val_accuracy: 0.8571 - val_loss: 0.7539\n",
"Epoch 459/500\n",
"2/2 - 0s - 17ms/step - accuracy: 0.8537 - loss: 0.7491 - val_accuracy: 0.8571 - val_loss: 0.7533\n",
"Epoch 460/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7569 - val_accuracy: 0.8571 - val_loss: 0.7526\n",
"Epoch 461/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7578 - val_accuracy: 0.8571 - val_loss: 0.7520\n",
"Epoch 462/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7868 - val_accuracy: 0.8571 - val_loss: 0.7514\n",
"Epoch 463/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7384 - val_accuracy: 0.8571 - val_loss: 0.7507\n",
"Epoch 464/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.7416 - val_accuracy: 0.8571 - val_loss: 0.7501\n",
"Epoch 465/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7469 - val_accuracy: 0.8571 - val_loss: 0.7495\n",
"Epoch 466/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7544 - val_accuracy: 0.8571 - val_loss: 0.7489\n",
"Epoch 467/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7512 - val_accuracy: 0.8571 - val_loss: 0.7483\n",
"Epoch 468/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7596 - val_accuracy: 0.8571 - val_loss: 0.7478\n",
"Epoch 469/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7349 - val_accuracy: 0.8571 - val_loss: 0.7473\n",
"Epoch 470/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7470 - val_accuracy: 0.8571 - val_loss: 0.7467\n",
"Epoch 471/500\n",
"2/2 - 0s - 16ms/step - accuracy: 0.8537 - loss: 0.7698 - val_accuracy: 0.8571 - val_loss: 0.7461\n",
"Epoch 472/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7768 - val_accuracy: 0.8571 - val_loss: 0.7455\n",
"Epoch 473/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7659 - val_accuracy: 0.8571 - val_loss: 0.7449\n",
"Epoch 474/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7718 - val_accuracy: 0.8571 - val_loss: 0.7443\n",
"Epoch 475/500\n",
"2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.7533 - val_accuracy: 0.8571 - val_loss: 0.7437\n",
"Epoch 476/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7394 - val_accuracy: 0.8571 - val_loss: 0.7432\n",
"Epoch 477/500\n",
"2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7536 - val_accuracy: 0.8571 - val_loss: 0.7426\n",
"Epoch 478/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7421 - val_accuracy: 0.8571 - val_loss: 0.7421\n",
"Epoch 479/500\n",
"2/2 - 0s - 18ms/step - accuracy: 0.8537 - loss: 0.7407 - val_accuracy: 0.8571 - val_loss: 0.7414\n",
"Epoch 480/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7582 - val_accuracy: 0.8571 - val_loss: 0.7409\n",
"Epoch 481/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7463 - val_accuracy: 0.8571 - val_loss: 0.7403\n",
"Epoch 482/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7320 - val_accuracy: 0.8571 - val_loss: 0.7397\n",
"Epoch 483/500\n",
"2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.7533 - val_accuracy: 0.8571 - val_loss: 0.7391\n",
"Epoch 484/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7584 - val_accuracy: 0.8571 - val_loss: 0.7385\n",
"Epoch 485/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7506 - val_accuracy: 0.8571 - val_loss: 0.7379\n",
"Epoch 486/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7710 - val_accuracy: 0.8571 - val_loss: 0.7375\n",
"Epoch 487/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7334 - val_accuracy: 0.8571 - val_loss: 0.7369\n",
"Epoch 488/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7446 - val_accuracy: 0.8571 - val_loss: 0.7365\n",
"Epoch 489/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7415 - val_accuracy: 0.8571 - val_loss: 0.7360\n",
"Epoch 490/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7383 - val_accuracy: 0.8571 - val_loss: 0.7354\n",
"Epoch 491/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7500 - val_accuracy: 0.8571 - val_loss: 0.7348\n",
"Epoch 492/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7453 - val_accuracy: 0.8571 - val_loss: 0.7343\n",
"Epoch 493/500\n",
"2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7379 - val_accuracy: 0.8571 - val_loss: 0.7337\n",
"Epoch 494/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7510 - val_accuracy: 0.8571 - val_loss: 0.7333\n",
"Epoch 495/500\n",
"2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7632 - val_accuracy: 0.8571 - val_loss: 0.7327\n",
"Epoch 496/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7321 - val_accuracy: 0.8571 - val_loss: 0.7323\n",
"Epoch 497/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7605 - val_accuracy: 0.8571 - val_loss: 0.7317\n",
"Epoch 498/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7427 - val_accuracy: 0.8571 - val_loss: 0.7311\n",
"Epoch 499/500\n",
"2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7268 - val_accuracy: 0.8571 - val_loss: 0.7305\n",
"Epoch 500/500\n",
"2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7267 - val_accuracy: 0.8571 - val_loss: 0.7300\n",
"1/1 - 0s - 16ms/step - accuracy: 0.8571 - loss: 0.7300\n",
"Dokładność testowa: 85.71%\n"
]
}
],
"source": [
"history = model.fit(X_train, y_train, epochs=500, validation_data=(X_test, y_test), verbose=2)\n",
"\n",
"test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=2)\n",
"print(f\"Dokładność testowa: {test_accuracy:.2%}\")"
]
},
{
"cell_type": "markdown",
"id": "84409c40-7973-4e65-b81f-72d0837e8781",
"metadata": {},
"source": [
"## Efekty uczenia"
]
},
{
"cell_type": "markdown",
"id": "ef8129a5-7c32-4559-a717-c4737b9abe76",
"metadata": {},
"source": [
"Wytrenowany model osiąga skuteczność predykcji na poziomie 85,7%"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8ddb08bb-0f04-420e-9749-8c7218bb729a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.8571 - loss: 0.7300\n"
]
},
{
"data": {
"text/plain": [
"0.8571428656578064"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(X_test, y_test)[1]"
]
},
{
"cell_type": "markdown",
"id": "cb9ef13b-39d7-4526-be9c-15e165f8c50a",
"metadata": {},
"source": [
"## Wykresy"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "5f24c9b1-002c-43d3-962c-089671824e92",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('Model loss')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['Train', 'Val'], loc='upper right')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d6ff63d4-57ad-48a3-8104-bb4a47d14dcc",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])\n",
"plt.title('Model accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['Train', 'Val'], loc='lower right')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "75484222-6113-4e1e-a0e9-d371355ba02b",
"metadata": {},
"source": [
"## Zapisanie modelu do pliku"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "df41cdc5-73f1-472e-bd5c-1eb5324849b6",
"metadata": {},
"outputs": [],
"source": [
"model.save('./model.keras') "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a0aa450e-81be-4987-8125-8a53556e83de",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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