diff --git a/src/classes/Changelog.py b/src/classes/Changelog.py index b24752a5..4cc05ea6 100644 --- a/src/classes/Changelog.py +++ b/src/classes/Changelog.py @@ -7,7 +7,7 @@ from classes.ColorText import colorText -VERSION = "2.07" +VERSION = "2.08" changelog = colorText.BOLD + '[ChangeLog]\n' + colorText.END + colorText.BLUE + ''' [1.00 - Beta] @@ -242,4 +242,7 @@ 1. US S&P 500 Index added - Try Index `15 > US S&P 500` 2. Minor improvemnets +[2.08] +1. Nifty Prediction enhanced - New AI model uses Crude and Gold data for Gap Prediction + ''' + colorText.END diff --git a/src/classes/Fetcher.py b/src/classes/Fetcher.py index 56128912..8c244827 100644 --- a/src/classes/Fetcher.py +++ b/src/classes/Fetcher.py @@ -159,6 +159,21 @@ def fetchLatestNiftyDaily(self, proxyServer=None): progress=False, timeout=10 ) + gold = yf.download( + tickers="GC=F", + period='5d', + interval='1d', + progress=False, + timeout=10 + ).add_prefix(prefix='gold_') + crude = yf.download( + tickers="CL=F", + period='5d', + interval='1d', + progress=False, + timeout=10 + ).add_prefix(prefix='crude_') + data = pd.concat([data, gold, crude], axis=1) return data # Get Data for Five EMA strategy diff --git a/src/classes/Screener.py b/src/classes/Screener.py index ecc3830e..b6585ea5 100644 --- a/src/classes/Screener.py +++ b/src/classes/Screener.py @@ -583,10 +583,8 @@ def getNiftyPrediction(self, data, proxyServer): with SuppressOutput(suppress_stderr=True, suppress_stdout=True): data = data[pkl['columns']] ### v2 Preprocessing - data['High'] = data['High'].pct_change() * 100 - data['Low'] = data['Low'].pct_change() * 100 - data['Open'] = data['Open'].pct_change() * 100 - data['Close'] = data['Close'].pct_change() * 100 + for col in pkl['columns']: + data[col] = data[col].pct_change() * 100 data = data.iloc[-1] ### data = pkl['scaler'].transform([data]) diff --git a/src/classes/Utility.py b/src/classes/Utility.py index 42789412..ae2a1bd8 100644 --- a/src/classes/Utility.py +++ b/src/classes/Utility.py @@ -309,10 +309,10 @@ def getProgressbarStyle(): return bar, spinner def getNiftyModel(proxyServer=None): - files = ['nifty_model_v2.h5', 'nifty_model_v2.pkl'] + files = ['nifty_model_v3.h5', 'nifty_model_v3.pkl'] urls = [ - "https://raw.github.com/pranjal-joshi/Screeni-py/new-features/src/ml/nifty_model_v2.h5", - "https://raw.github.com/pranjal-joshi/Screeni-py/new-features/src/ml/nifty_model_v2.pkl" + f"https://raw.github.com/pranjal-joshi/Screeni-py/new-features/src/ml/{files[0]}", + f"https://raw.github.com/pranjal-joshi/Screeni-py/new-features/src/ml/{files[1]}" ] if os.path.isfile(files[0]) and os.path.isfile(files[1]): file_age = (time.time() - os.path.getmtime(files[0]))/604800 @@ -332,7 +332,7 @@ def getNiftyModel(proxyServer=None): resp = requests.get(file_url, stream=True) if resp.status_code == 200: print(colorText.BOLD + colorText.GREEN + - "[+] Downloading AI model (v2) for Nifty predictions, Please Wait.." + colorText.END) + "[+] Downloading AI model (v3) for Nifty predictions, Please Wait.." + colorText.END) try: chunksize = 1024*1024*1 filesize = int(int(resp.headers.get('content-length'))/chunksize) diff --git a/src/ml/experiment.ipynb b/src/ml/experiment.ipynb index ac446d86..a6b1a206 100644 --- a/src/ml/experiment.ipynb +++ b/src/ml/experiment.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -18,16 +18,17 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "TEST_DAYS = 40" + "TEST_DAYS = 50\n", + "PERIOD = '5y'" ] }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -36,32 +37,46 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9c/8m67hqg13wd179_xl1xrnn2c0000gp/T/ipykernel_58100/1703223587.py:24: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " d.dropna(inplace=True)\n" - ] - } - ], + "outputs": [], + "source": [ + "INCLUDE_COMMODITIES = True" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ "if INDICATOR_DATASET:\n", " d = joblib.load('nifty_data.pkl')\n", "else:\n", " d = yf.download(\n", " tickers=\"^NSEI\",\n", - " period='max',\n", + " period=PERIOD,\n", " interval='1d',\n", " progress=False,\n", " timeout=10\n", " )\n", + " if INCLUDE_COMMODITIES:\n", + " gold = yf.download(\n", + " tickers=\"GC=F\",\n", + " period=PERIOD,\n", + " interval='1d',\n", + " progress=False,\n", + " timeout=10\n", + " ).add_prefix(prefix='gold_')\n", + " crude = yf.download(\n", + " tickers=\"CL=F\",\n", + " period=PERIOD,\n", + " interval='1d',\n", + " progress=False,\n", + " timeout=10\n", + " ).add_prefix(prefix='crude_')\n", + " d = pd.concat([d, gold, crude], axis=1)\n", + " \n", " d['target'] = d.Open/d.Close.shift(-1)\n", " d.target = d.target.apply(np.floor)\n", "\n", @@ -71,17 +86,28 @@ " d['Low'] = d['Low'].pct_change() * 100\n", " d['Open'] = d['Open'].pct_change() * 100\n", " d['Close'] = d['Close'].pct_change() * 100 \n", + "\n", + " if INCLUDE_COMMODITIES:\n", + " d['gold_High'] = d['gold_High'].pct_change() * 100\n", + " d['gold_Low'] = d['gold_Low'].pct_change() * 100\n", + " d['gold_Open'] = d['gold_Open'].pct_change() * 100\n", + " d['gold_Close'] = d['gold_Close'].pct_change() * 100\n", + "\n", + " d['crude_High'] = d['crude_High'].pct_change() * 100\n", + " d['crude_Low'] = d['crude_Low'].pct_change() * 100\n", + " d['crude_Open'] = d['crude_Open'].pct_change() * 100\n", + " d['crude_Close'] = d['crude_Close'].pct_change() * 100\n", " # d.rename(columns = {'HighNew':'High','LowNew':'Low','OpenNew':'Open','CloseNew':'Close'}, inplace = True)\n", "\n", " # Remove outliers when Market closes +- 3.5%\n", - " d = d[d['change'] < 3.5]\n", + " d = d[d['change'] < 3]\n", " d.dropna(inplace=True)\n", " d.tail()" ] }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -90,12 +116,23 @@ " df['Low'] = df['Low'].pct_change() * 100\n", " df['Open'] = df['Open'].pct_change() * 100\n", " df['Close'] = df['Close'].pct_change() * 100 \n", + "\n", + " if INCLUDE_COMMODITIES:\n", + " df['gold_High'] = df['gold_High'].pct_change() * 100\n", + " df['gold_Low'] = df['gold_Low'].pct_change() * 100\n", + " df['gold_Open'] = df['gold_Open'].pct_change() * 100\n", + " df['gold_Close'] = df['gold_Close'].pct_change() * 100\n", + "\n", + " df['crude_High'] = df['crude_High'].pct_change() * 100\n", + " df['crude_Low'] = df['crude_Low'].pct_change() * 100\n", + " df['crude_Open'] = df['crude_Open'].pct_change() * 100\n", + " df['crude_Close'] = df['crude_Close'].pct_change() * 100\n", " return df" ] }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -121,13 +158,17 @@ " x = d.drop(columns=['target'])\n", " y = d.target\n", "else:\n", - " x = d.drop(columns=['target', 'Adj Close', 'Volume', 'change'], errors='ignore')\n", + " if INCLUDE_COMMODITIES:\n", + " # x = d.drop(columns=['target', 'Adj Close', 'Volume', 'change', 'gold_Adj Close', 'gold_Volume', 'crude_Adj Close', 'crude_Volume'], errors='ignore')\n", + " x = d.drop(columns=['target', 'Adj Close', 'Volume', 'change', 'gold_Open', 'gold_High', 'gold_Low', 'gold_Adj Close', 'gold_Volume', 'crude_Open', 'crude_High', 'crude_Low', 'crude_Adj Close', 'crude_Volume'], errors='ignore')\n", + " else:\n", + " x = d.drop(columns=['target', 'Adj Close', 'Volume', 'change'], errors='ignore')\n", " y = d.target" ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -155,6 +196,8 @@ " High\n", " Low\n", " Close\n", + " gold_Close\n", + " crude_Close\n", " \n", " \n", " Date\n", @@ -162,43 +205,55 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 2007-09-18\n", - " -0.538904\n", - " 0.060452\n", - " -0.029006\n", - " 1.146926\n", + " 2018-10-23\n", + " -2.433727\n", + " -1.791318\n", + " -1.189851\n", + " -0.960935\n", + " 0.999023\n", + " -3.961252\n", " \n", " \n", - " 2007-09-20\n", - " 4.056922\n", - " 0.461070\n", - " 3.755835\n", - " 0.321187\n", + " 2018-10-24\n", + " 1.236637\n", + " 0.670614\n", + " 0.241039\n", + " 0.768224\n", + " -0.454028\n", + " 0.587083\n", " \n", " \n", - " 2007-09-21\n", - " 0.382274\n", - " 1.992293\n", - " 0.265831\n", - " 1.895715\n", + " 2018-10-25\n", + " -1.392280\n", + " -1.205471\n", + " -0.468073\n", + " -0.976548\n", + " 0.105874\n", + " 0.763248\n", " \n", " \n", - " 2007-09-24\n", - " 1.771525\n", - " 1.759781\n", - " 2.185388\n", - " 1.956577\n", + " 2018-10-26\n", + " -0.125310\n", + " -0.371314\n", + " -0.741619\n", + " -0.937297\n", + " 0.276627\n", + " 0.386150\n", " \n", " \n", - " 2007-09-25\n", - " 2.107650\n", - " 0.258037\n", - " 0.847607\n", - " 0.134826\n", + " 2018-10-29\n", + " -0.437151\n", + " 1.445872\n", + " 0.157926\n", + " 2.201890\n", + " -0.649087\n", + " -0.813723\n", " \n", " \n", " ...\n", @@ -206,66 +261,78 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 2022-12-02\n", - " -0.633474\n", - " -0.559364\n", - " -0.740220\n", - " -0.618740\n", + " 2023-07-25\n", + " -0.096714\n", + " -0.269934\n", + " -0.215439\n", + " 0.041937\n", + " 0.091819\n", + " 1.130302\n", " \n", " \n", - " 2022-12-05\n", - " -0.175175\n", - " -0.284047\n", - " -0.256715\n", - " 0.026482\n", + " 2023-07-26\n", + " 0.020274\n", + " 0.487852\n", + " 0.513613\n", + " 0.496434\n", + " 0.346570\n", + " -1.067435\n", " \n", " \n", - " 2022-12-06\n", - " -0.635167\n", - " -0.393512\n", - " -0.072341\n", - " -0.311751\n", + " 2023-07-27\n", + " 0.595696\n", + " 0.211601\n", + " -0.573871\n", + " -0.598638\n", + " -1.193560\n", + " 1.662856\n", " \n", " \n", - " 2022-12-07\n", - " 0.205365\n", - " 0.071833\n", - " -0.266446\n", - " -0.441190\n", + " 2023-07-28\n", + " -0.962931\n", + " -0.863974\n", + " -0.206346\n", + " -0.070446\n", + " 0.771050\n", + " 0.611819\n", " \n", " \n", - " 2022-12-08\n", - " -0.364829\n", - " -0.231948\n", - " 0.046139\n", - " 0.263191\n", + " 2023-07-31\n", + " 0.033569\n", + " 0.390181\n", + " 0.176352\n", + " 0.548456\n", + " 0.515200\n", + " 1.514025\n", " \n", " \n", "\n", - "

3632 rows × 4 columns

\n", + "

1059 rows × 6 columns

\n", "" ], "text/plain": [ - " Open High Low Close\n", - "Date \n", - "2007-09-18 -0.538904 0.060452 -0.029006 1.146926\n", - "2007-09-20 4.056922 0.461070 3.755835 0.321187\n", - "2007-09-21 0.382274 1.992293 0.265831 1.895715\n", - "2007-09-24 1.771525 1.759781 2.185388 1.956577\n", - "2007-09-25 2.107650 0.258037 0.847607 0.134826\n", - "... ... ... ... ...\n", - "2022-12-02 -0.633474 -0.559364 -0.740220 -0.618740\n", - "2022-12-05 -0.175175 -0.284047 -0.256715 0.026482\n", - "2022-12-06 -0.635167 -0.393512 -0.072341 -0.311751\n", - "2022-12-07 0.205365 0.071833 -0.266446 -0.441190\n", - "2022-12-08 -0.364829 -0.231948 0.046139 0.263191\n", + " Open High Low Close gold_Close crude_Close\n", + "Date \n", + "2018-10-23 -2.433727 -1.791318 -1.189851 -0.960935 0.999023 -3.961252\n", + "2018-10-24 1.236637 0.670614 0.241039 0.768224 -0.454028 0.587083\n", + "2018-10-25 -1.392280 -1.205471 -0.468073 -0.976548 0.105874 0.763248\n", + "2018-10-26 -0.125310 -0.371314 -0.741619 -0.937297 0.276627 0.386150\n", + "2018-10-29 -0.437151 1.445872 0.157926 2.201890 -0.649087 -0.813723\n", + "... ... ... ... ... ... ...\n", + "2023-07-25 -0.096714 -0.269934 -0.215439 0.041937 0.091819 1.130302\n", + "2023-07-26 0.020274 0.487852 0.513613 0.496434 0.346570 -1.067435\n", + "2023-07-27 0.595696 0.211601 -0.573871 -0.598638 -1.193560 1.662856\n", + "2023-07-28 -0.962931 -0.863974 -0.206346 -0.070446 0.771050 0.611819\n", + "2023-07-31 0.033569 0.390181 0.176352 0.548456 0.515200 1.514025\n", "\n", - "[3632 rows x 4 columns]" + "[1059 rows x 6 columns]" ] }, - "execution_count": 92, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -276,28 +343,28 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Date\n", - "2007-09-18 0.0\n", - "2007-09-20 0.0\n", - "2007-09-21 0.0\n", - "2007-09-24 0.0\n", - "2007-09-25 0.0\n", + "2018-10-23 0.0\n", + "2018-10-24 1.0\n", + "2018-10-25 1.0\n", + "2018-10-26 0.0\n", + "2018-10-29 0.0\n", " ... \n", - "2022-12-02 1.0\n", - "2022-12-05 1.0\n", - "2022-12-06 1.0\n", - "2022-12-07 1.0\n", - "2022-12-08 1.0\n", - "Name: target, Length: 3632, dtype: float64" + "2023-07-25 0.0\n", + "2023-07-26 1.0\n", + "2023-07-27 1.0\n", + "2023-07-28 0.0\n", + "2023-07-31 0.0\n", + "Name: target, Length: 1059, dtype: float64" ] }, - "execution_count": 93, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -308,15 +375,15 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No. of Bullish samples: 1853\n", - "No. of Bearish samples: 1779\n" + "No. of Bullish samples: 554\n", + "No. of Bearish samples: 505\n" ] } ], @@ -327,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -340,16 +407,22 @@ { "data": { "text/plain": [ - "array([[-0.44397101, 0.03341403, -0.07956986, 1.02006975],\n", - " [ 2.99061278, 0.42381322, 2.99941387, 0.25208989],\n", - " [ 0.24444976, 1.91598031, 0.16028141, 1.71648272],\n", + "array([[-2.20749184, -2.15630367, -1.28386843, -1.12858003, 1.00255906,\n", + " -0.38714857],\n", + " [ 1.02520233, 0.70696761, 0.15712183, 0.76864741, -0.54208534,\n", + " 0.07144068],\n", + " [-1.29023079, -1.4749529 , -0.5569958 , -1.14571072, 0.05311023,\n", + " 0.0892026 ],\n", " ...,\n", - " [-0.51591104, -0.40897068, -0.11482332, -0.33657615],\n", - " [ 0.11224095, 0.04450465, -0.27272798, -0.4569611 ],\n", - " [-0.31388057, -0.25152816, -0.0184391 , 0.19815053]])" + " [ 0.4606901 , 0.17312764, -0.66354028, -0.73106887, -1.32823429,\n", + " 0.17990622],\n", + " [-0.91207923, -1.07778541, -0.2934211 , -0.1515387 , 0.76021492,\n", + " 0.07393465],\n", + " [-0.03440639, 0.38081913, 0.09197882, 0.5275186 , 0.48823743,\n", + " 0.16490025]])" ] }, - "execution_count": 95, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -373,35 +446,35 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential_5\"\n", + "Model: \"sequential_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", - " dense_35 (Dense) (None, 64) 320 \n", + " dense_7 (Dense) (None, 128) 896 \n", " \n", - " dense_36 (Dense) (None, 32) 2080 \n", + " dense_8 (Dense) (None, 64) 8256 \n", " \n", - " dense_37 (Dense) (None, 16) 528 \n", + " dense_9 (Dense) (None, 32) 2080 \n", " \n", - " dense_38 (Dense) (None, 8) 136 \n", + " dense_10 (Dense) (None, 16) 528 \n", " \n", - " dense_39 (Dense) (None, 4) 36 \n", + " dense_11 (Dense) (None, 8) 136 \n", " \n", - " dense_40 (Dense) (None, 2) 10 \n", + " dense_12 (Dense) (None, 4) 36 \n", " \n", - " dense_41 (Dense) (None, 1) 3 \n", + " dense_13 (Dense) (None, 1) 5 \n", " \n", "=================================================================\n", - "Total params: 3,113\n", - "Trainable params: 3,113\n", - "Non-trainable params: 0\n", + "Total params: 11937 (46.63 KB)\n", + "Trainable params: 11937 (46.63 KB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", "_________________________________________________________________\n" ] } @@ -411,21 +484,21 @@ "from keras import Sequential\n", "from keras import Model\n", "from keras.layers import Dense\n", - "from keras.optimizers import SGD\n", + "from keras.optimizers import legacy\n", "import keras\n", "\n", "lr_list = []\n", "def scheduler(epoch, lr):\n", - " if epoch < 15:\n", + " if epoch < 10:\n", " lr = lr\n", " else:\n", " lr = lr * tf.math.exp(-0.01)\n", " lr_list.append(lr)\n", " return lr\n", "\n", - "units = 64 #1024\n", + "units = 128 #1024\n", "# sgd = SGD(learning_rate=0.0001, momentum=0.0, nesterov=True)\n", - "sgd = SGD(learning_rate=0.001, momentum=0.45, nesterov=True)\n", + "sgd = legacy.SGD(learning_rate=0.001, momentum=0.45, nesterov=True)\n", "kernel_init = 'he_uniform'\n", "activation = 'relu'\n", "\n", @@ -460,38 +533,35 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/500\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-07 21:32:56.887556: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "BATCH SIZE = 160\n", + "Epoch 1/750\n", + "\n", + "Epoch 1: val_accuracy improved from -inf to 0.40881, saving model to best_model.h5\n", + "6/6 - 5s - loss: 2.2815 - accuracy: 0.3682 - val_loss: 1.2598 - val_accuracy: 0.4088 - lr: 0.0010 - 5s/epoch - 765ms/step\n", + "Epoch 2/750\n", + "\n", + "Epoch 2: val_accuracy improved from 0.40881 to 0.44654, saving model to best_model.h5\n", + "6/6 - 0s - loss: 1.7582 - accuracy: 0.4060 - val_loss: 1.1155 - val_accuracy: 0.4465 - lr: 0.0010 - 88ms/epoch - 15ms/step\n", + "Epoch 3/750\n", "\n", - "Epoch 1: val_accuracy improved from -inf to 0.56514, saving model to best_model.h5\n", - "25/25 - 1s - loss: 0.7395 - accuracy: 0.5190 - val_loss: 0.6813 - val_accuracy: 0.5651 - lr: 0.0010 - 798ms/epoch - 32ms/step\n", - "Epoch 2/500\n" + "Epoch 3: val_accuracy improved from 0.44654 to 0.47170, saving model to best_model.h5\n", + "6/6 - 0s - loss: 1.4263 - accuracy: 0.4449 - val_loss: 0.9977 - val_accuracy: 0.4717 - lr: 0.0010 - 85ms/epoch - 14ms/step\n", + "Epoch 4/750\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-02-07 21:32:57.345736: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n" + "/Users/pranjaljoshi/miniforge3/envs/screenipy/lib/python3.10/site-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" ] }, { @@ -499,1210 +569,1139 @@ "output_type": "stream", "text": [ "\n", - "Epoch 2: val_accuracy improved from 0.56514 to 0.56881, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.7237 - accuracy: 0.5238 - val_loss: 0.6748 - val_accuracy: 0.5688 - lr: 0.0010 - 227ms/epoch - 9ms/step\n", - "Epoch 3/500\n", + "Epoch 4: val_accuracy did not improve from 0.47170\n", + "6/6 - 0s - loss: 1.2121 - accuracy: 0.4883 - val_loss: 0.8805 - val_accuracy: 0.4654 - lr: 0.0010 - 66ms/epoch - 11ms/step\n", + "Epoch 5/750\n", "\n", - "Epoch 3: val_accuracy improved from 0.56881 to 0.57064, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.7119 - accuracy: 0.5287 - val_loss: 0.6682 - val_accuracy: 0.5706 - lr: 0.0010 - 232ms/epoch - 9ms/step\n", - "Epoch 4/500\n", + "Epoch 5: val_accuracy improved from 0.47170 to 0.52830, saving model to best_model.h5\n", + "6/6 - 0s - loss: 1.0301 - accuracy: 0.5106 - val_loss: 0.7915 - val_accuracy: 0.5283 - lr: 0.0010 - 82ms/epoch - 14ms/step\n", + "Epoch 6/750\n", "\n", - "Epoch 4: val_accuracy improved from 0.57064 to 0.57798, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.7027 - accuracy: 0.5368 - val_loss: 0.6624 - val_accuracy: 0.5780 - lr: 0.0010 - 228ms/epoch - 9ms/step\n", - "Epoch 5/500\n", + "Epoch 6: val_accuracy improved from 0.52830 to 0.59119, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.9162 - accuracy: 0.5328 - val_loss: 0.7282 - val_accuracy: 0.5912 - lr: 0.0010 - 80ms/epoch - 13ms/step\n", + "Epoch 7/750\n", "\n", - "Epoch 5: val_accuracy improved from 0.57798 to 0.59266, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6922 - accuracy: 0.5465 - val_loss: 0.6567 - val_accuracy: 0.5927 - lr: 0.0010 - 205ms/epoch - 8ms/step\n", - "Epoch 6/500\n", + "Epoch 7: val_accuracy improved from 0.59119 to 0.63522, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.8414 - accuracy: 0.5940 - val_loss: 0.6987 - val_accuracy: 0.6352 - lr: 0.0010 - 81ms/epoch - 14ms/step\n", + "Epoch 8/750\n", "\n", - "Epoch 6: val_accuracy improved from 0.59266 to 0.60734, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6815 - accuracy: 0.5640 - val_loss: 0.6493 - val_accuracy: 0.6073 - lr: 0.0010 - 205ms/epoch - 8ms/step\n", - "Epoch 7/500\n", + "Epoch 8: val_accuracy improved from 0.63522 to 0.64780, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.8066 - accuracy: 0.6151 - val_loss: 0.6838 - val_accuracy: 0.6478 - lr: 0.0010 - 80ms/epoch - 13ms/step\n", + "Epoch 9/750\n", "\n", - "Epoch 7: val_accuracy improved from 0.60734 to 0.66055, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6670 - accuracy: 0.6158 - val_loss: 0.6347 - val_accuracy: 0.6606 - lr: 0.0010 - 203ms/epoch - 8ms/step\n", - "Epoch 8/500\n", + "Epoch 9: val_accuracy improved from 0.64780 to 0.65409, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.7914 - accuracy: 0.6218 - val_loss: 0.6749 - val_accuracy: 0.6541 - lr: 0.0010 - 85ms/epoch - 14ms/step\n", + "Epoch 10/750\n", "\n", - "Epoch 8: val_accuracy improved from 0.66055 to 0.68257, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6458 - accuracy: 0.6683 - val_loss: 0.6256 - val_accuracy: 0.6826 - lr: 0.0010 - 214ms/epoch - 9ms/step\n", - "Epoch 9/500\n", + "Epoch 10: val_accuracy improved from 0.65409 to 0.66038, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.7745 - accuracy: 0.6318 - val_loss: 0.6685 - val_accuracy: 0.6604 - lr: 0.0010 - 81ms/epoch - 13ms/step\n", + "Epoch 11/750\n", "\n", - "Epoch 9: val_accuracy improved from 0.68257 to 0.69358, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6340 - accuracy: 0.6796 - val_loss: 0.6224 - val_accuracy: 0.6936 - lr: 0.0010 - 210ms/epoch - 8ms/step\n", - "Epoch 10/500\n", + "Epoch 11: val_accuracy did not improve from 0.66038\n", + "6/6 - 0s - loss: 0.7525 - accuracy: 0.6452 - val_loss: 0.6647 - val_accuracy: 0.6541 - lr: 9.9005e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 12/750\n", "\n", - "Epoch 10: val_accuracy improved from 0.69358 to 0.70642, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6285 - accuracy: 0.6884 - val_loss: 0.6199 - val_accuracy: 0.7064 - lr: 0.0010 - 208ms/epoch - 8ms/step\n", - "Epoch 11/500\n", + "Epoch 12: val_accuracy did not improve from 0.66038\n", + "6/6 - 0s - loss: 0.7431 - accuracy: 0.6407 - val_loss: 0.6572 - val_accuracy: 0.6604 - lr: 9.8020e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 13/750\n", "\n", - "Epoch 11: val_accuracy improved from 0.70642 to 0.71376, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6239 - accuracy: 0.6916 - val_loss: 0.6174 - val_accuracy: 0.7138 - lr: 0.0010 - 207ms/epoch - 8ms/step\n", - "Epoch 12/500\n", + "Epoch 13: val_accuracy improved from 0.66038 to 0.66667, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.7312 - accuracy: 0.6440 - val_loss: 0.6517 - val_accuracy: 0.6667 - lr: 9.7045e-04 - 80ms/epoch - 13ms/step\n", + "Epoch 14/750\n", "\n", - "Epoch 12: val_accuracy improved from 0.71376 to 0.71927, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6202 - accuracy: 0.6910 - val_loss: 0.6154 - val_accuracy: 0.7193 - lr: 0.0010 - 206ms/epoch - 8ms/step\n", - "Epoch 13/500\n", + "Epoch 14: val_accuracy did not improve from 0.66667\n", + "6/6 - 0s - loss: 0.7195 - accuracy: 0.6507 - val_loss: 0.6460 - val_accuracy: 0.6541 - lr: 9.6079e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 15/750\n", "\n", - "Epoch 13: val_accuracy improved from 0.71927 to 0.72661, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6181 - accuracy: 0.6923 - val_loss: 0.6136 - val_accuracy: 0.7266 - lr: 0.0010 - 214ms/epoch - 9ms/step\n", - "Epoch 14/500\n", + "Epoch 15: val_accuracy did not improve from 0.66667\n", + "6/6 - 0s - loss: 0.7093 - accuracy: 0.6541 - val_loss: 0.6421 - val_accuracy: 0.6541 - lr: 9.5123e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 16/750\n", "\n", - "Epoch 14: val_accuracy improved from 0.72661 to 0.72844, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6157 - accuracy: 0.6929 - val_loss: 0.6117 - val_accuracy: 0.7284 - lr: 0.0010 - 212ms/epoch - 8ms/step\n", - "Epoch 15/500\n", + "Epoch 16: val_accuracy did not improve from 0.66667\n", + "6/6 - 0s - loss: 0.6963 - accuracy: 0.6685 - val_loss: 0.6391 - val_accuracy: 0.6604 - lr: 9.4176e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 17/750\n", "\n", - "Epoch 15: val_accuracy did not improve from 0.72844\n", - "25/25 - 0s - loss: 0.6137 - accuracy: 0.6955 - val_loss: 0.6098 - val_accuracy: 0.7284 - lr: 0.0010 - 186ms/epoch - 7ms/step\n", - "Epoch 16/500\n", + "Epoch 17: val_accuracy did not improve from 0.66667\n", + "6/6 - 0s - loss: 0.6889 - accuracy: 0.6663 - val_loss: 0.6333 - val_accuracy: 0.6541 - lr: 9.3239e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 18/750\n", "\n", - "Epoch 16: val_accuracy improved from 0.72844 to 0.73028, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6117 - accuracy: 0.6945 - val_loss: 0.6080 - val_accuracy: 0.7303 - lr: 9.9005e-04 - 256ms/epoch - 10ms/step\n", - "Epoch 17/500\n", + "Epoch 18: val_accuracy did not improve from 0.66667\n", + "6/6 - 0s - loss: 0.6785 - accuracy: 0.6696 - val_loss: 0.6289 - val_accuracy: 0.6541 - lr: 9.2312e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 19/750\n", "\n", - "Epoch 17: val_accuracy improved from 0.73028 to 0.73578, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6098 - accuracy: 0.6952 - val_loss: 0.6064 - val_accuracy: 0.7358 - lr: 9.8020e-04 - 212ms/epoch - 8ms/step\n", - "Epoch 18/500\n", + "Epoch 19: val_accuracy did not improve from 0.66667\n", + "6/6 - 0s - loss: 0.6721 - accuracy: 0.6696 - val_loss: 0.6256 - val_accuracy: 0.6667 - lr: 9.1393e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 20/750\n", "\n", - "Epoch 18: val_accuracy did not improve from 0.73578\n", - "25/25 - 0s - loss: 0.6079 - accuracy: 0.6948 - val_loss: 0.6044 - val_accuracy: 0.7339 - lr: 9.7045e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 19/500\n", + "Epoch 20: val_accuracy improved from 0.66667 to 0.68553, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.6641 - accuracy: 0.6774 - val_loss: 0.6227 - val_accuracy: 0.6855 - lr: 9.0484e-04 - 80ms/epoch - 13ms/step\n", + "Epoch 21/750\n", "\n", - "Epoch 19: val_accuracy did not improve from 0.73578\n", - "25/25 - 0s - loss: 0.6063 - accuracy: 0.6991 - val_loss: 0.6025 - val_accuracy: 0.7321 - lr: 9.6079e-04 - 195ms/epoch - 8ms/step\n", - "Epoch 20/500\n", + "Epoch 21: val_accuracy improved from 0.68553 to 0.69811, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.6559 - accuracy: 0.6785 - val_loss: 0.6188 - val_accuracy: 0.6981 - lr: 8.9583e-04 - 79ms/epoch - 13ms/step\n", + "Epoch 22/750\n", "\n", - "Epoch 20: val_accuracy did not improve from 0.73578\n", - "25/25 - 0s - loss: 0.6049 - accuracy: 0.6965 - val_loss: 0.6008 - val_accuracy: 0.7321 - lr: 9.5123e-04 - 185ms/epoch - 7ms/step\n", - "Epoch 21/500\n", + "Epoch 22: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6487 - accuracy: 0.6852 - val_loss: 0.6172 - val_accuracy: 0.6604 - lr: 8.8692e-04 - 62ms/epoch - 10ms/step\n", + "Epoch 23/750\n", "\n", - "Epoch 21: val_accuracy did not improve from 0.73578\n", - "25/25 - 0s - loss: 0.6032 - accuracy: 0.6968 - val_loss: 0.5987 - val_accuracy: 0.7358 - lr: 9.4176e-04 - 186ms/epoch - 7ms/step\n", - "Epoch 22/500\n", + "Epoch 23: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6430 - accuracy: 0.6863 - val_loss: 0.6169 - val_accuracy: 0.6730 - lr: 8.7809e-04 - 62ms/epoch - 10ms/step\n", + "Epoch 24/750\n", "\n", - "Epoch 22: val_accuracy improved from 0.73578 to 0.73761, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.6015 - accuracy: 0.6994 - val_loss: 0.5973 - val_accuracy: 0.7376 - lr: 9.3239e-04 - 224ms/epoch - 9ms/step\n", - "Epoch 23/500\n", + "Epoch 24: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6394 - accuracy: 0.6819 - val_loss: 0.6132 - val_accuracy: 0.6604 - lr: 8.6936e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 25/750\n", "\n", - "Epoch 23: val_accuracy did not improve from 0.73761\n", - "25/25 - 0s - loss: 0.6001 - accuracy: 0.6994 - val_loss: 0.5964 - val_accuracy: 0.7358 - lr: 9.2312e-04 - 187ms/epoch - 7ms/step\n", - "Epoch 24/500\n", + "Epoch 25: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6290 - accuracy: 0.6919 - val_loss: 0.6114 - val_accuracy: 0.6667 - lr: 8.6071e-04 - 62ms/epoch - 10ms/step\n", + "Epoch 26/750\n", "\n", - "Epoch 24: val_accuracy did not improve from 0.73761\n", - "25/25 - 0s - loss: 0.5990 - accuracy: 0.7007 - val_loss: 0.5959 - val_accuracy: 0.7358 - lr: 9.1393e-04 - 188ms/epoch - 8ms/step\n", - "Epoch 25/500\n", + "Epoch 26: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6244 - accuracy: 0.6897 - val_loss: 0.6090 - val_accuracy: 0.6792 - lr: 8.5214e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 27/750\n", "\n", - "Epoch 25: val_accuracy improved from 0.73761 to 0.73945, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.5980 - accuracy: 0.7007 - val_loss: 0.5947 - val_accuracy: 0.7394 - lr: 9.0484e-04 - 234ms/epoch - 9ms/step\n", - "Epoch 26/500\n", + "Epoch 27: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6228 - accuracy: 0.6874 - val_loss: 0.6074 - val_accuracy: 0.6541 - lr: 8.4366e-04 - 62ms/epoch - 10ms/step\n", + "Epoch 28/750\n", "\n", - "Epoch 26: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5968 - accuracy: 0.7017 - val_loss: 0.5935 - val_accuracy: 0.7394 - lr: 8.9583e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 27/500\n", + "Epoch 28: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6155 - accuracy: 0.6897 - val_loss: 0.6069 - val_accuracy: 0.6541 - lr: 8.3527e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 29/750\n", "\n", - "Epoch 27: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5957 - accuracy: 0.7033 - val_loss: 0.5924 - val_accuracy: 0.7394 - lr: 8.8692e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 28/500\n", + "Epoch 29: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6108 - accuracy: 0.6930 - val_loss: 0.6047 - val_accuracy: 0.6667 - lr: 8.2696e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 30/750\n", "\n", - "Epoch 28: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5947 - accuracy: 0.7026 - val_loss: 0.5912 - val_accuracy: 0.7376 - lr: 8.7809e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 29/500\n", + "Epoch 30: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6079 - accuracy: 0.6908 - val_loss: 0.6016 - val_accuracy: 0.6792 - lr: 8.1873e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 31/750\n", "\n", - "Epoch 29: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5938 - accuracy: 0.7020 - val_loss: 0.5902 - val_accuracy: 0.7376 - lr: 8.6936e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 30/500\n", + "Epoch 31: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6047 - accuracy: 0.6908 - val_loss: 0.6000 - val_accuracy: 0.6918 - lr: 8.1058e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 32/750\n", "\n", - "Epoch 30: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5929 - accuracy: 0.7026 - val_loss: 0.5892 - val_accuracy: 0.7394 - lr: 8.6071e-04 - 201ms/epoch - 8ms/step\n", - "Epoch 31/500\n", + "Epoch 32: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.6027 - accuracy: 0.6930 - val_loss: 0.5990 - val_accuracy: 0.6855 - lr: 8.0252e-04 - 62ms/epoch - 10ms/step\n", + "Epoch 33/750\n", "\n", - "Epoch 31: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5920 - accuracy: 0.7023 - val_loss: 0.5884 - val_accuracy: 0.7358 - lr: 8.5214e-04 - 181ms/epoch - 7ms/step\n", - "Epoch 32/500\n", + "Epoch 33: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5986 - accuracy: 0.6974 - val_loss: 0.5981 - val_accuracy: 0.6855 - lr: 7.9453e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 34/750\n", "\n", - "Epoch 32: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5911 - accuracy: 0.7049 - val_loss: 0.5877 - val_accuracy: 0.7358 - lr: 8.4366e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 33/500\n", + "Epoch 34: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5949 - accuracy: 0.6930 - val_loss: 0.5985 - val_accuracy: 0.6918 - lr: 7.8663e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 35/750\n", "\n", - "Epoch 33: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5904 - accuracy: 0.7059 - val_loss: 0.5866 - val_accuracy: 0.7376 - lr: 8.3527e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 34/500\n", + "Epoch 35: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5925 - accuracy: 0.6974 - val_loss: 0.5967 - val_accuracy: 0.6855 - lr: 7.7880e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 36/750\n", "\n", - "Epoch 34: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5896 - accuracy: 0.7049 - val_loss: 0.5854 - val_accuracy: 0.7376 - lr: 8.2696e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 35/500\n", + "Epoch 36: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5899 - accuracy: 0.7052 - val_loss: 0.5956 - val_accuracy: 0.6855 - lr: 7.7105e-04 - 62ms/epoch - 10ms/step\n", + "Epoch 37/750\n", "\n", - "Epoch 35: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5892 - accuracy: 0.7033 - val_loss: 0.5848 - val_accuracy: 0.7339 - lr: 8.1873e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 36/500\n", + "Epoch 37: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5891 - accuracy: 0.7052 - val_loss: 0.5980 - val_accuracy: 0.6855 - lr: 7.6338e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 38/750\n", "\n", - "Epoch 36: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5883 - accuracy: 0.7049 - val_loss: 0.5847 - val_accuracy: 0.7339 - lr: 8.1058e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 37/500\n", + "Epoch 38: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5836 - accuracy: 0.7041 - val_loss: 0.5989 - val_accuracy: 0.6981 - lr: 7.5578e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 39/750\n", "\n", - "Epoch 37: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5877 - accuracy: 0.7033 - val_loss: 0.5838 - val_accuracy: 0.7358 - lr: 8.0252e-04 - 194ms/epoch - 8ms/step\n", - "Epoch 38/500\n", + "Epoch 39: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5849 - accuracy: 0.7052 - val_loss: 0.5965 - val_accuracy: 0.6855 - lr: 7.4826e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 40/750\n", "\n", - "Epoch 38: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5869 - accuracy: 0.7039 - val_loss: 0.5832 - val_accuracy: 0.7358 - lr: 7.9453e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 39/500\n", + "Epoch 40: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5819 - accuracy: 0.7075 - val_loss: 0.5963 - val_accuracy: 0.6981 - lr: 7.4082e-04 - 68ms/epoch - 11ms/step\n", + "Epoch 41/750\n", "\n", - "Epoch 39: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5863 - accuracy: 0.7039 - val_loss: 0.5825 - val_accuracy: 0.7358 - lr: 7.8663e-04 - 182ms/epoch - 7ms/step\n", - "Epoch 40/500\n", + "Epoch 41: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5837 - accuracy: 0.7086 - val_loss: 0.5950 - val_accuracy: 0.6792 - lr: 7.3345e-04 - 86ms/epoch - 14ms/step\n", + "Epoch 42/750\n", "\n", - "Epoch 40: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5857 - accuracy: 0.7052 - val_loss: 0.5821 - val_accuracy: 0.7358 - lr: 7.7880e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 41/500\n", + "Epoch 42: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5779 - accuracy: 0.7108 - val_loss: 0.5961 - val_accuracy: 0.6918 - lr: 7.2615e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 43/750\n", "\n", - "Epoch 41: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5849 - accuracy: 0.7046 - val_loss: 0.5817 - val_accuracy: 0.7339 - lr: 7.7105e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 42/500\n", + "Epoch 43: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5765 - accuracy: 0.7108 - val_loss: 0.5962 - val_accuracy: 0.6981 - lr: 7.1892e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 44/750\n", "\n", - "Epoch 42: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5844 - accuracy: 0.7039 - val_loss: 0.5811 - val_accuracy: 0.7358 - lr: 7.6338e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 43/500\n", + "Epoch 44: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5793 - accuracy: 0.7097 - val_loss: 0.5968 - val_accuracy: 0.6918 - lr: 7.1177e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 45/750\n", "\n", - "Epoch 43: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5837 - accuracy: 0.7042 - val_loss: 0.5806 - val_accuracy: 0.7358 - lr: 7.5578e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 44/500\n", + "Epoch 45: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5743 - accuracy: 0.7075 - val_loss: 0.5966 - val_accuracy: 0.6855 - lr: 7.0469e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 46/750\n", "\n", - "Epoch 44: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5831 - accuracy: 0.7046 - val_loss: 0.5797 - val_accuracy: 0.7339 - lr: 7.4826e-04 - 176ms/epoch - 7ms/step\n", - "Epoch 45/500\n", + "Epoch 46: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5726 - accuracy: 0.7164 - val_loss: 0.5958 - val_accuracy: 0.6918 - lr: 6.9767e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 47/750\n", "\n", - "Epoch 45: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5826 - accuracy: 0.7042 - val_loss: 0.5794 - val_accuracy: 0.7358 - lr: 7.4082e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 46/500\n", + "Epoch 47: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5715 - accuracy: 0.7141 - val_loss: 0.5965 - val_accuracy: 0.6918 - lr: 6.9073e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 48/750\n", "\n", - "Epoch 46: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5819 - accuracy: 0.7046 - val_loss: 0.5790 - val_accuracy: 0.7358 - lr: 7.3345e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 47/500\n", + "Epoch 48: val_accuracy did not improve from 0.69811\n", + "6/6 - 0s - loss: 0.5716 - accuracy: 0.7141 - val_loss: 0.5967 - val_accuracy: 0.6918 - lr: 6.8386e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 49/750\n", "\n", - "Epoch 47: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5813 - accuracy: 0.7055 - val_loss: 0.5788 - val_accuracy: 0.7358 - lr: 7.2615e-04 - 176ms/epoch - 7ms/step\n", - "Epoch 48/500\n", + "Epoch 49: val_accuracy improved from 0.69811 to 0.70440, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.5691 - accuracy: 0.7175 - val_loss: 0.5951 - val_accuracy: 0.7044 - lr: 6.7706e-04 - 82ms/epoch - 14ms/step\n", + "Epoch 50/750\n", "\n", - "Epoch 48: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5810 - accuracy: 0.7059 - val_loss: 0.5785 - val_accuracy: 0.7376 - lr: 7.1892e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 49/500\n", + "Epoch 50: val_accuracy did not improve from 0.70440\n", + "6/6 - 0s - loss: 0.5714 - accuracy: 0.7152 - val_loss: 0.5971 - val_accuracy: 0.6918 - lr: 6.7032e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 51/750\n", "\n", - "Epoch 49: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5804 - accuracy: 0.7062 - val_loss: 0.5779 - val_accuracy: 0.7394 - lr: 7.1177e-04 - 182ms/epoch - 7ms/step\n", - "Epoch 50/500\n", + "Epoch 51: val_accuracy did not improve from 0.70440\n", + "6/6 - 0s - loss: 0.5682 - accuracy: 0.7175 - val_loss: 0.5957 - val_accuracy: 0.7044 - lr: 6.6365e-04 - 77ms/epoch - 13ms/step\n", + "Epoch 52/750\n", "\n", - "Epoch 50: val_accuracy did not improve from 0.73945\n", - "25/25 - 0s - loss: 0.5798 - accuracy: 0.7065 - val_loss: 0.5776 - val_accuracy: 0.7394 - lr: 7.0469e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 51/500\n", + "Epoch 52: val_accuracy did not improve from 0.70440\n", + "6/6 - 0s - loss: 0.5665 - accuracy: 0.7175 - val_loss: 0.5982 - val_accuracy: 0.7044 - lr: 6.5705e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 53/750\n", "\n", - "Epoch 51: val_accuracy improved from 0.73945 to 0.74128, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.5792 - accuracy: 0.7065 - val_loss: 0.5768 - val_accuracy: 0.7413 - lr: 6.9767e-04 - 208ms/epoch - 8ms/step\n", - "Epoch 52/500\n", + "Epoch 53: val_accuracy improved from 0.70440 to 0.71069, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.5684 - accuracy: 0.7152 - val_loss: 0.5971 - val_accuracy: 0.7107 - lr: 6.5051e-04 - 97ms/epoch - 16ms/step\n", + "Epoch 54/750\n", "\n", - "Epoch 52: val_accuracy did not improve from 0.74128\n", - "25/25 - 0s - loss: 0.5786 - accuracy: 0.7081 - val_loss: 0.5762 - val_accuracy: 0.7394 - lr: 6.9073e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 53/500\n", + "Epoch 54: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5685 - accuracy: 0.7230 - val_loss: 0.5968 - val_accuracy: 0.7044 - lr: 6.4403e-04 - 81ms/epoch - 14ms/step\n", + "Epoch 55/750\n", "\n", - "Epoch 53: val_accuracy did not improve from 0.74128\n", - "25/25 - 0s - loss: 0.5780 - accuracy: 0.7065 - val_loss: 0.5758 - val_accuracy: 0.7394 - lr: 6.8386e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 54/500\n", + "Epoch 55: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5656 - accuracy: 0.7175 - val_loss: 0.5969 - val_accuracy: 0.7107 - lr: 6.3763e-04 - 76ms/epoch - 13ms/step\n", + "Epoch 56/750\n", "\n", - "Epoch 54: val_accuracy did not improve from 0.74128\n", - "25/25 - 0s - loss: 0.5777 - accuracy: 0.7085 - val_loss: 0.5753 - val_accuracy: 0.7394 - lr: 6.7706e-04 - 185ms/epoch - 7ms/step\n", - "Epoch 55/500\n", + "Epoch 56: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5670 - accuracy: 0.7197 - val_loss: 0.5984 - val_accuracy: 0.7044 - lr: 6.3128e-04 - 75ms/epoch - 13ms/step\n", + "Epoch 57/750\n", "\n", - "Epoch 55: val_accuracy did not improve from 0.74128\n", - "25/25 - 0s - loss: 0.5773 - accuracy: 0.7088 - val_loss: 0.5746 - val_accuracy: 0.7413 - lr: 6.7032e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 56/500\n", + "Epoch 57: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5678 - accuracy: 0.7119 - val_loss: 0.5997 - val_accuracy: 0.6981 - lr: 6.2500e-04 - 70ms/epoch - 12ms/step\n", + "Epoch 58/750\n", "\n", - "Epoch 56: val_accuracy did not improve from 0.74128\n", - "25/25 - 0s - loss: 0.5768 - accuracy: 0.7088 - val_loss: 0.5739 - val_accuracy: 0.7413 - lr: 6.6365e-04 - 185ms/epoch - 7ms/step\n", - "Epoch 57/500\n", + "Epoch 58: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5644 - accuracy: 0.7175 - val_loss: 0.5986 - val_accuracy: 0.7044 - lr: 6.1878e-04 - 70ms/epoch - 12ms/step\n", + "Epoch 59/750\n", "\n", - "Epoch 57: val_accuracy did not improve from 0.74128\n", - "25/25 - 0s - loss: 0.5764 - accuracy: 0.7101 - val_loss: 0.5735 - val_accuracy: 0.7394 - lr: 6.5705e-04 - 182ms/epoch - 7ms/step\n", - "Epoch 58/500\n", + "Epoch 59: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5667 - accuracy: 0.7186 - val_loss: 0.5975 - val_accuracy: 0.7044 - lr: 6.1262e-04 - 75ms/epoch - 12ms/step\n", + "Epoch 60/750\n", "\n", - "Epoch 58: val_accuracy did not improve from 0.74128\n", - "25/25 - 0s - loss: 0.5760 - accuracy: 0.7088 - val_loss: 0.5731 - val_accuracy: 0.7394 - lr: 6.5051e-04 - 187ms/epoch - 7ms/step\n", - "Epoch 59/500\n", + "Epoch 60: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5639 - accuracy: 0.7186 - val_loss: 0.5985 - val_accuracy: 0.7107 - lr: 6.0653e-04 - 79ms/epoch - 13ms/step\n", + "Epoch 61/750\n", "\n", - "Epoch 59: val_accuracy improved from 0.74128 to 0.74312, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.5757 - accuracy: 0.7091 - val_loss: 0.5722 - val_accuracy: 0.7431 - lr: 6.4403e-04 - 206ms/epoch - 8ms/step\n", - "Epoch 60/500\n", + "Epoch 61: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5672 - accuracy: 0.7108 - val_loss: 0.5997 - val_accuracy: 0.7107 - lr: 6.0049e-04 - 79ms/epoch - 13ms/step\n", + "Epoch 62/750\n", "\n", - "Epoch 60: val_accuracy did not improve from 0.74312\n", - "25/25 - 0s - loss: 0.5753 - accuracy: 0.7101 - val_loss: 0.5717 - val_accuracy: 0.7413 - lr: 6.3763e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 61/500\n", + "Epoch 62: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5634 - accuracy: 0.7208 - val_loss: 0.5974 - val_accuracy: 0.7044 - lr: 5.9452e-04 - 78ms/epoch - 13ms/step\n", + "Epoch 63/750\n", "\n", - "Epoch 61: val_accuracy did not improve from 0.74312\n", - "25/25 - 0s - loss: 0.5749 - accuracy: 0.7101 - val_loss: 0.5711 - val_accuracy: 0.7431 - lr: 6.3128e-04 - 181ms/epoch - 7ms/step\n", - "Epoch 62/500\n", + "Epoch 63: val_accuracy did not improve from 0.71069\n", + "6/6 - 0s - loss: 0.5633 - accuracy: 0.7219 - val_loss: 0.5989 - val_accuracy: 0.6981 - lr: 5.8860e-04 - 77ms/epoch - 13ms/step\n", + "Epoch 64/750\n", "\n", - "Epoch 62: val_accuracy did not improve from 0.74312\n", - "25/25 - 0s - loss: 0.5745 - accuracy: 0.7094 - val_loss: 0.5708 - val_accuracy: 0.7431 - lr: 6.2500e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 63/500\n", + "Epoch 64: val_accuracy improved from 0.71069 to 0.71698, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.5639 - accuracy: 0.7275 - val_loss: 0.5986 - val_accuracy: 0.7170 - lr: 5.8275e-04 - 96ms/epoch - 16ms/step\n", + "Epoch 65/750\n", "\n", - "Epoch 63: val_accuracy did not improve from 0.74312\n", - "25/25 - 0s - loss: 0.5741 - accuracy: 0.7101 - val_loss: 0.5704 - val_accuracy: 0.7413 - lr: 6.1878e-04 - 190ms/epoch - 8ms/step\n", - "Epoch 64/500\n", + "Epoch 65: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5633 - accuracy: 0.7297 - val_loss: 0.6001 - val_accuracy: 0.7170 - lr: 5.7695e-04 - 81ms/epoch - 14ms/step\n", + "Epoch 66/750\n", "\n", - "Epoch 64: val_accuracy did not improve from 0.74312\n", - "25/25 - 0s - loss: 0.5737 - accuracy: 0.7104 - val_loss: 0.5698 - val_accuracy: 0.7431 - lr: 6.1262e-04 - 194ms/epoch - 8ms/step\n", - "Epoch 65/500\n", + "Epoch 66: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5643 - accuracy: 0.7219 - val_loss: 0.5978 - val_accuracy: 0.7170 - lr: 5.7121e-04 - 81ms/epoch - 13ms/step\n", + "Epoch 67/750\n", "\n", - "Epoch 65: val_accuracy improved from 0.74312 to 0.74495, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.5734 - accuracy: 0.7098 - val_loss: 0.5694 - val_accuracy: 0.7450 - lr: 6.0653e-04 - 210ms/epoch - 8ms/step\n", - "Epoch 66/500\n", + "Epoch 67: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5616 - accuracy: 0.7130 - val_loss: 0.5998 - val_accuracy: 0.6855 - lr: 5.6552e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 68/750\n", "\n", - "Epoch 66: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5730 - accuracy: 0.7098 - val_loss: 0.5691 - val_accuracy: 0.7450 - lr: 6.0049e-04 - 190ms/epoch - 8ms/step\n", - "Epoch 67/500\n", + "Epoch 68: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5653 - accuracy: 0.7219 - val_loss: 0.6005 - val_accuracy: 0.6918 - lr: 5.5990e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 69/750\n", "\n", - "Epoch 67: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5727 - accuracy: 0.7098 - val_loss: 0.5688 - val_accuracy: 0.7431 - lr: 5.9452e-04 - 189ms/epoch - 8ms/step\n", - "Epoch 68/500\n", + "Epoch 69: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5620 - accuracy: 0.7186 - val_loss: 0.5992 - val_accuracy: 0.6981 - lr: 5.5433e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 70/750\n", "\n", - "Epoch 68: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5724 - accuracy: 0.7088 - val_loss: 0.5684 - val_accuracy: 0.7431 - lr: 5.8860e-04 - 186ms/epoch - 7ms/step\n", - "Epoch 69/500\n", + "Epoch 70: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5621 - accuracy: 0.7219 - val_loss: 0.5986 - val_accuracy: 0.6918 - lr: 5.4881e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 71/750\n", "\n", - "Epoch 69: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5720 - accuracy: 0.7104 - val_loss: 0.5681 - val_accuracy: 0.7413 - lr: 5.8275e-04 - 185ms/epoch - 7ms/step\n", - "Epoch 70/500\n", + "Epoch 71: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5610 - accuracy: 0.7241 - val_loss: 0.5995 - val_accuracy: 0.6981 - lr: 5.4335e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 72/750\n", "\n", - "Epoch 70: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5717 - accuracy: 0.7110 - val_loss: 0.5679 - val_accuracy: 0.7413 - lr: 5.7695e-04 - 186ms/epoch - 7ms/step\n", - "Epoch 71/500\n", + "Epoch 72: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5652 - accuracy: 0.7264 - val_loss: 0.6028 - val_accuracy: 0.6855 - lr: 5.3794e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 73/750\n", "\n", - "Epoch 71: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5715 - accuracy: 0.7110 - val_loss: 0.5673 - val_accuracy: 0.7413 - lr: 5.7121e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 72/500\n", + "Epoch 73: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5602 - accuracy: 0.7286 - val_loss: 0.6050 - val_accuracy: 0.7107 - lr: 5.3259e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 74/750\n", "\n", - "Epoch 72: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5711 - accuracy: 0.7117 - val_loss: 0.5672 - val_accuracy: 0.7413 - lr: 5.6552e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 73/500\n", + "Epoch 74: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5617 - accuracy: 0.7230 - val_loss: 0.5998 - val_accuracy: 0.7044 - lr: 5.2729e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 75/750\n", "\n", - "Epoch 73: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5710 - accuracy: 0.7104 - val_loss: 0.5670 - val_accuracy: 0.7431 - lr: 5.5990e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 74/500\n", + "Epoch 75: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5598 - accuracy: 0.7253 - val_loss: 0.5999 - val_accuracy: 0.7044 - lr: 5.2204e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 76/750\n", "\n", - "Epoch 74: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5707 - accuracy: 0.7114 - val_loss: 0.5662 - val_accuracy: 0.7431 - lr: 5.5433e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 75/500\n", + "Epoch 76: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5670 - accuracy: 0.7208 - val_loss: 0.6006 - val_accuracy: 0.7044 - lr: 5.1685e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 77/750\n", "\n", - "Epoch 75: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5703 - accuracy: 0.7114 - val_loss: 0.5661 - val_accuracy: 0.7413 - lr: 5.4881e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 76/500\n", + "Epoch 77: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5597 - accuracy: 0.7230 - val_loss: 0.6061 - val_accuracy: 0.6730 - lr: 5.1171e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 78/750\n", "\n", - "Epoch 76: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5701 - accuracy: 0.7117 - val_loss: 0.5660 - val_accuracy: 0.7450 - lr: 5.4335e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 77/500\n", + "Epoch 78: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5620 - accuracy: 0.7297 - val_loss: 0.6025 - val_accuracy: 0.6918 - lr: 5.0661e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 79/750\n", "\n", - "Epoch 77: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5699 - accuracy: 0.7114 - val_loss: 0.5657 - val_accuracy: 0.7450 - lr: 5.3794e-04 - 186ms/epoch - 7ms/step\n", - "Epoch 78/500\n", + "Epoch 79: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5628 - accuracy: 0.7175 - val_loss: 0.6041 - val_accuracy: 0.6730 - lr: 5.0157e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 80/750\n", "\n", - "Epoch 78: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5697 - accuracy: 0.7123 - val_loss: 0.5655 - val_accuracy: 0.7450 - lr: 5.3259e-04 - 194ms/epoch - 8ms/step\n", - "Epoch 79/500\n", + "Epoch 80: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5626 - accuracy: 0.7230 - val_loss: 0.6046 - val_accuracy: 0.6855 - lr: 4.9658e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 81/750\n", "\n", - "Epoch 79: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5694 - accuracy: 0.7117 - val_loss: 0.5654 - val_accuracy: 0.7450 - lr: 5.2729e-04 - 185ms/epoch - 7ms/step\n", - "Epoch 80/500\n", + "Epoch 81: val_accuracy did not improve from 0.71698\n", + "6/6 - 0s - loss: 0.5618 - accuracy: 0.7230 - val_loss: 0.6011 - val_accuracy: 0.7044 - lr: 4.9164e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 82/750\n", "\n", - "Epoch 80: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5692 - accuracy: 0.7120 - val_loss: 0.5655 - val_accuracy: 0.7450 - lr: 5.2204e-04 - 187ms/epoch - 7ms/step\n", - "Epoch 81/500\n", + "Epoch 82: val_accuracy improved from 0.71698 to 0.72956, saving model to best_model.h5\n", + "6/6 - 0s - loss: 0.5600 - accuracy: 0.7275 - val_loss: 0.6033 - val_accuracy: 0.7296 - lr: 4.8675e-04 - 80ms/epoch - 13ms/step\n", + "Epoch 83/750\n", "\n", - "Epoch 81: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5691 - accuracy: 0.7117 - val_loss: 0.5657 - val_accuracy: 0.7450 - lr: 5.1685e-04 - 188ms/epoch - 8ms/step\n", - "Epoch 82/500\n", + "Epoch 83: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5625 - accuracy: 0.7253 - val_loss: 0.6057 - val_accuracy: 0.7170 - lr: 4.8191e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 84/750\n", "\n", - "Epoch 82: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5690 - accuracy: 0.7130 - val_loss: 0.5653 - val_accuracy: 0.7450 - lr: 5.1171e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 83/500\n", + "Epoch 84: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5626 - accuracy: 0.7275 - val_loss: 0.6040 - val_accuracy: 0.6981 - lr: 4.7711e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 85/750\n", "\n", - "Epoch 83: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5687 - accuracy: 0.7127 - val_loss: 0.5647 - val_accuracy: 0.7450 - lr: 5.0661e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 84/500\n", + "Epoch 85: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5646 - accuracy: 0.7253 - val_loss: 0.6027 - val_accuracy: 0.6855 - lr: 4.7236e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 86/750\n", "\n", - "Epoch 84: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5685 - accuracy: 0.7123 - val_loss: 0.5645 - val_accuracy: 0.7450 - lr: 5.0157e-04 - 182ms/epoch - 7ms/step\n", - "Epoch 85/500\n", + "Epoch 86: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5609 - accuracy: 0.7253 - val_loss: 0.6045 - val_accuracy: 0.7107 - lr: 4.6766e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 87/750\n", "\n", - "Epoch 85: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5683 - accuracy: 0.7133 - val_loss: 0.5643 - val_accuracy: 0.7450 - lr: 4.9658e-04 - 189ms/epoch - 8ms/step\n", - "Epoch 86/500\n", + "Epoch 87: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5595 - accuracy: 0.7297 - val_loss: 0.6020 - val_accuracy: 0.6981 - lr: 4.6301e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 88/750\n", "\n", - "Epoch 86: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5681 - accuracy: 0.7123 - val_loss: 0.5642 - val_accuracy: 0.7450 - lr: 4.9164e-04 - 215ms/epoch - 9ms/step\n", - "Epoch 87/500\n", + "Epoch 88: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5609 - accuracy: 0.7241 - val_loss: 0.6038 - val_accuracy: 0.6981 - lr: 4.5840e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 89/750\n", "\n", - "Epoch 87: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5680 - accuracy: 0.7117 - val_loss: 0.5639 - val_accuracy: 0.7450 - lr: 4.8675e-04 - 188ms/epoch - 8ms/step\n", - "Epoch 88/500\n", + "Epoch 89: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5598 - accuracy: 0.7253 - val_loss: 0.6068 - val_accuracy: 0.6981 - lr: 4.5384e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 90/750\n", "\n", - "Epoch 88: val_accuracy did not improve from 0.74495\n", - "25/25 - 0s - loss: 0.5678 - accuracy: 0.7117 - val_loss: 0.5638 - val_accuracy: 0.7450 - lr: 4.8191e-04 - 189ms/epoch - 8ms/step\n", - "Epoch 89/500\n", + "Epoch 90: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5576 - accuracy: 0.7219 - val_loss: 0.6098 - val_accuracy: 0.7044 - lr: 4.4933e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 91/750\n", "\n", - "Epoch 89: val_accuracy improved from 0.74495 to 0.74679, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.5676 - accuracy: 0.7123 - val_loss: 0.5635 - val_accuracy: 0.7468 - lr: 4.7711e-04 - 219ms/epoch - 9ms/step\n", - "Epoch 90/500\n", + "Epoch 91: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5630 - accuracy: 0.7264 - val_loss: 0.6033 - val_accuracy: 0.7044 - lr: 4.4486e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 92/750\n", "\n", - "Epoch 90: val_accuracy did not improve from 0.74679\n", - "25/25 - 0s - loss: 0.5674 - accuracy: 0.7117 - val_loss: 0.5633 - val_accuracy: 0.7468 - lr: 4.7236e-04 - 185ms/epoch - 7ms/step\n", - "Epoch 91/500\n", + "Epoch 92: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5595 - accuracy: 0.7308 - val_loss: 0.6046 - val_accuracy: 0.7044 - lr: 4.4043e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 93/750\n", "\n", - "Epoch 91: val_accuracy improved from 0.74679 to 0.75046, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.5673 - accuracy: 0.7120 - val_loss: 0.5630 - val_accuracy: 0.7505 - lr: 4.6766e-04 - 223ms/epoch - 9ms/step\n", - "Epoch 92/500\n", + "Epoch 93: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5594 - accuracy: 0.7297 - val_loss: 0.6047 - val_accuracy: 0.6981 - lr: 4.3605e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 94/750\n", "\n", - "Epoch 92: val_accuracy did not improve from 0.75046\n", - "25/25 - 0s - loss: 0.5671 - accuracy: 0.7130 - val_loss: 0.5628 - val_accuracy: 0.7468 - lr: 4.6301e-04 - 183ms/epoch - 7ms/step\n", - "Epoch 93/500\n", + "Epoch 94: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5598 - accuracy: 0.7230 - val_loss: 0.6097 - val_accuracy: 0.6730 - lr: 4.3171e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 95/750\n", "\n", - "Epoch 93: val_accuracy did not improve from 0.75046\n", - "25/25 - 0s - loss: 0.5670 - accuracy: 0.7123 - val_loss: 0.5626 - val_accuracy: 0.7468 - lr: 4.5840e-04 - 186ms/epoch - 7ms/step\n", - "Epoch 94/500\n", + "Epoch 95: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5623 - accuracy: 0.7264 - val_loss: 0.6054 - val_accuracy: 0.6981 - lr: 4.2741e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 96/750\n", "\n", - "Epoch 94: val_accuracy did not improve from 0.75046\n", - "25/25 - 0s - loss: 0.5668 - accuracy: 0.7117 - val_loss: 0.5624 - val_accuracy: 0.7486 - lr: 4.5384e-04 - 183ms/epoch - 7ms/step\n", - "Epoch 95/500\n", + "Epoch 96: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5621 - accuracy: 0.7297 - val_loss: 0.6055 - val_accuracy: 0.6981 - lr: 4.2316e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 97/750\n", "\n", - "Epoch 95: val_accuracy did not improve from 0.75046\n", - "25/25 - 0s - loss: 0.5666 - accuracy: 0.7127 - val_loss: 0.5621 - val_accuracy: 0.7468 - lr: 4.4933e-04 - 183ms/epoch - 7ms/step\n", - "Epoch 96/500\n", + "Epoch 97: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5578 - accuracy: 0.7253 - val_loss: 0.6111 - val_accuracy: 0.6918 - lr: 4.1895e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 98/750\n", "\n", - "Epoch 96: val_accuracy did not improve from 0.75046\n", - "25/25 - 0s - loss: 0.5665 - accuracy: 0.7123 - val_loss: 0.5617 - val_accuracy: 0.7486 - lr: 4.4486e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 97/500\n", + "Epoch 98: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5594 - accuracy: 0.7319 - val_loss: 0.6060 - val_accuracy: 0.6981 - lr: 4.1478e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 99/750\n", "\n", - "Epoch 97: val_accuracy did not improve from 0.75046\n", - "25/25 - 0s - loss: 0.5664 - accuracy: 0.7130 - val_loss: 0.5616 - val_accuracy: 0.7505 - lr: 4.4043e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 98/500\n", + "Epoch 99: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5596 - accuracy: 0.7308 - val_loss: 0.6111 - val_accuracy: 0.6918 - lr: 4.1065e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 100/750\n", "\n", - "Epoch 98: val_accuracy improved from 0.75046 to 0.75229, saving model to best_model.h5\n", - "25/25 - 0s - loss: 0.5662 - accuracy: 0.7120 - val_loss: 0.5615 - val_accuracy: 0.7523 - lr: 4.3605e-04 - 195ms/epoch - 8ms/step\n", - "Epoch 99/500\n", + "Epoch 100: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5623 - accuracy: 0.7253 - val_loss: 0.6151 - val_accuracy: 0.6918 - lr: 4.0657e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 101/750\n", "\n", - "Epoch 99: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5661 - accuracy: 0.7114 - val_loss: 0.5614 - val_accuracy: 0.7505 - lr: 4.3171e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 100/500\n", + "Epoch 101: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5654 - accuracy: 0.7286 - val_loss: 0.6088 - val_accuracy: 0.6792 - lr: 4.0252e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 102/750\n", "\n", - "Epoch 100: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5660 - accuracy: 0.7114 - val_loss: 0.5612 - val_accuracy: 0.7486 - lr: 4.2741e-04 - 176ms/epoch - 7ms/step\n", - "Epoch 101/500\n", + "Epoch 102: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5604 - accuracy: 0.7241 - val_loss: 0.6057 - val_accuracy: 0.7107 - lr: 3.9852e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 103/750\n", "\n", - "Epoch 101: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5658 - accuracy: 0.7120 - val_loss: 0.5610 - val_accuracy: 0.7486 - lr: 4.2316e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 102/500\n", + "Epoch 103: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5589 - accuracy: 0.7308 - val_loss: 0.6043 - val_accuracy: 0.7044 - lr: 3.9455e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 104/750\n", "\n", - "Epoch 102: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5657 - accuracy: 0.7117 - val_loss: 0.5610 - val_accuracy: 0.7486 - lr: 4.1895e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 103/500\n", + "Epoch 104: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5624 - accuracy: 0.7341 - val_loss: 0.6103 - val_accuracy: 0.6792 - lr: 3.9063e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 105/750\n", "\n", - "Epoch 103: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5655 - accuracy: 0.7114 - val_loss: 0.5609 - val_accuracy: 0.7468 - lr: 4.1478e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 104/500\n", + "Epoch 105: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5616 - accuracy: 0.7286 - val_loss: 0.6076 - val_accuracy: 0.7044 - lr: 3.8674e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 106/750\n", "\n", - "Epoch 104: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5654 - accuracy: 0.7130 - val_loss: 0.5609 - val_accuracy: 0.7468 - lr: 4.1065e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 105/500\n", + "Epoch 106: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5588 - accuracy: 0.7253 - val_loss: 0.6069 - val_accuracy: 0.6981 - lr: 3.8289e-04 - 70ms/epoch - 12ms/step\n", + "Epoch 107/750\n", "\n", - "Epoch 105: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5653 - accuracy: 0.7130 - val_loss: 0.5609 - val_accuracy: 0.7468 - lr: 4.0657e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 106/500\n", + "Epoch 107: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5590 - accuracy: 0.7297 - val_loss: 0.6069 - val_accuracy: 0.6981 - lr: 3.7908e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 108/750\n", "\n", - "Epoch 106: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5651 - accuracy: 0.7130 - val_loss: 0.5607 - val_accuracy: 0.7468 - lr: 4.0252e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 107/500\n", + "Epoch 108: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5589 - accuracy: 0.7275 - val_loss: 0.6096 - val_accuracy: 0.6918 - lr: 3.7531e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 109/750\n", "\n", - "Epoch 107: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5649 - accuracy: 0.7127 - val_loss: 0.5606 - val_accuracy: 0.7468 - lr: 3.9852e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 108/500\n", + "Epoch 109: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5624 - accuracy: 0.7253 - val_loss: 0.6127 - val_accuracy: 0.6730 - lr: 3.7157e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 110/750\n", "\n", - "Epoch 108: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5648 - accuracy: 0.7123 - val_loss: 0.5606 - val_accuracy: 0.7468 - lr: 3.9455e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 109/500\n", + "Epoch 110: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5642 - accuracy: 0.7241 - val_loss: 0.6079 - val_accuracy: 0.6981 - lr: 3.6788e-04 - 84ms/epoch - 14ms/step\n", + "Epoch 111/750\n", "\n", - "Epoch 109: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5647 - accuracy: 0.7130 - val_loss: 0.5605 - val_accuracy: 0.7468 - lr: 3.9063e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 110/500\n", + "Epoch 111: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5619 - accuracy: 0.7175 - val_loss: 0.6099 - val_accuracy: 0.6918 - lr: 3.6422e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 112/750\n", "\n", - "Epoch 110: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5645 - accuracy: 0.7133 - val_loss: 0.5603 - val_accuracy: 0.7468 - lr: 3.8674e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 111/500\n", + "Epoch 112: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5670 - accuracy: 0.7286 - val_loss: 0.6105 - val_accuracy: 0.6981 - lr: 3.6059e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 113/750\n", "\n", - "Epoch 111: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5644 - accuracy: 0.7133 - val_loss: 0.5604 - val_accuracy: 0.7468 - lr: 3.8289e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 112/500\n", + "Epoch 113: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5583 - accuracy: 0.7308 - val_loss: 0.6110 - val_accuracy: 0.6918 - lr: 3.5700e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 114/750\n", "\n", - "Epoch 112: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5643 - accuracy: 0.7133 - val_loss: 0.5603 - val_accuracy: 0.7468 - lr: 3.7908e-04 - 182ms/epoch - 7ms/step\n", - "Epoch 113/500\n", + "Epoch 114: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5592 - accuracy: 0.7308 - val_loss: 0.6086 - val_accuracy: 0.6981 - lr: 3.5345e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 115/750\n", "\n", - "Epoch 113: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5642 - accuracy: 0.7133 - val_loss: 0.5600 - val_accuracy: 0.7486 - lr: 3.7531e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 114/500\n", + "Epoch 115: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5620 - accuracy: 0.7264 - val_loss: 0.6145 - val_accuracy: 0.6730 - lr: 3.4994e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 116/750\n", "\n", - "Epoch 114: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5640 - accuracy: 0.7123 - val_loss: 0.5600 - val_accuracy: 0.7468 - lr: 3.7157e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 115/500\n", + "Epoch 116: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5598 - accuracy: 0.7264 - val_loss: 0.6054 - val_accuracy: 0.7107 - lr: 3.4645e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 117/750\n", "\n", - "Epoch 115: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5640 - accuracy: 0.7130 - val_loss: 0.5598 - val_accuracy: 0.7505 - lr: 3.6788e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 116/500\n", + "Epoch 117: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5588 - accuracy: 0.7330 - val_loss: 0.6044 - val_accuracy: 0.7107 - lr: 3.4301e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 118/750\n", "\n", - "Epoch 116: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5639 - accuracy: 0.7127 - val_loss: 0.5597 - val_accuracy: 0.7505 - lr: 3.6422e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 117/500\n", + "Epoch 118: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5592 - accuracy: 0.7230 - val_loss: 0.6163 - val_accuracy: 0.6730 - lr: 3.3959e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 119/750\n", "\n", - "Epoch 117: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5637 - accuracy: 0.7130 - val_loss: 0.5597 - val_accuracy: 0.7486 - lr: 3.6059e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 118/500\n", + "Epoch 119: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5620 - accuracy: 0.7375 - val_loss: 0.6085 - val_accuracy: 0.7044 - lr: 3.3621e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 120/750\n", "\n", - "Epoch 118: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5636 - accuracy: 0.7133 - val_loss: 0.5596 - val_accuracy: 0.7468 - lr: 3.5700e-04 - 176ms/epoch - 7ms/step\n", - "Epoch 119/500\n", + "Epoch 120: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5612 - accuracy: 0.7308 - val_loss: 0.6129 - val_accuracy: 0.6981 - lr: 3.3287e-04 - 76ms/epoch - 13ms/step\n", + "Epoch 121/750\n", "\n", - "Epoch 119: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5635 - accuracy: 0.7133 - val_loss: 0.5594 - val_accuracy: 0.7486 - lr: 3.5345e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 120/500\n", + "Epoch 121: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5587 - accuracy: 0.7297 - val_loss: 0.6099 - val_accuracy: 0.7044 - lr: 3.2956e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 122/750\n", "\n", - "Epoch 120: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5634 - accuracy: 0.7127 - val_loss: 0.5593 - val_accuracy: 0.7468 - lr: 3.4994e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 121/500\n", + "Epoch 122: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5651 - accuracy: 0.7308 - val_loss: 0.6153 - val_accuracy: 0.6855 - lr: 3.2628e-04 - 76ms/epoch - 13ms/step\n", + "Epoch 123/750\n", "\n", - "Epoch 121: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5633 - accuracy: 0.7130 - val_loss: 0.5592 - val_accuracy: 0.7468 - lr: 3.4645e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 122/500\n", + "Epoch 123: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5724 - accuracy: 0.7264 - val_loss: 0.6062 - val_accuracy: 0.6981 - lr: 3.2303e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 124/750\n", "\n", - "Epoch 122: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5632 - accuracy: 0.7136 - val_loss: 0.5591 - val_accuracy: 0.7486 - lr: 3.4301e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 123/500\n", + "Epoch 124: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5689 - accuracy: 0.7219 - val_loss: 0.6092 - val_accuracy: 0.7044 - lr: 3.1982e-04 - 70ms/epoch - 12ms/step\n", + "Epoch 125/750\n", "\n", - "Epoch 123: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5631 - accuracy: 0.7133 - val_loss: 0.5589 - val_accuracy: 0.7505 - lr: 3.3959e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 124/500\n", + "Epoch 125: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5579 - accuracy: 0.7297 - val_loss: 0.6106 - val_accuracy: 0.6981 - lr: 3.1663e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 126/750\n", "\n", - "Epoch 124: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5630 - accuracy: 0.7133 - val_loss: 0.5588 - val_accuracy: 0.7505 - lr: 3.3621e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 125/500\n", + "Epoch 126: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5612 - accuracy: 0.7319 - val_loss: 0.6168 - val_accuracy: 0.6730 - lr: 3.1348e-04 - 79ms/epoch - 13ms/step\n", + "Epoch 127/750\n", "\n", - "Epoch 125: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5629 - accuracy: 0.7130 - val_loss: 0.5586 - val_accuracy: 0.7505 - lr: 3.3287e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 126/500\n", + "Epoch 127: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5584 - accuracy: 0.7319 - val_loss: 0.6082 - val_accuracy: 0.7044 - lr: 3.1036e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 128/750\n", "\n", - "Epoch 126: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5628 - accuracy: 0.7136 - val_loss: 0.5586 - val_accuracy: 0.7505 - lr: 3.2956e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 127/500\n", + "Epoch 128: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5582 - accuracy: 0.7286 - val_loss: 0.6163 - val_accuracy: 0.6730 - lr: 3.0728e-04 - 76ms/epoch - 13ms/step\n", + "Epoch 129/750\n", "\n", - "Epoch 127: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5628 - accuracy: 0.7130 - val_loss: 0.5586 - val_accuracy: 0.7468 - lr: 3.2628e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 128/500\n", + "Epoch 129: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5649 - accuracy: 0.7353 - val_loss: 0.6114 - val_accuracy: 0.6981 - lr: 3.0422e-04 - 78ms/epoch - 13ms/step\n", + "Epoch 130/750\n", "\n", - "Epoch 128: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5627 - accuracy: 0.7143 - val_loss: 0.5586 - val_accuracy: 0.7486 - lr: 3.2303e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 129/500\n", + "Epoch 130: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5666 - accuracy: 0.7286 - val_loss: 0.6205 - val_accuracy: 0.6667 - lr: 3.0119e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 131/750\n", "\n", - "Epoch 129: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5626 - accuracy: 0.7136 - val_loss: 0.5584 - val_accuracy: 0.7486 - lr: 3.1982e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 130/500\n", + "Epoch 131: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5636 - accuracy: 0.7253 - val_loss: 0.6097 - val_accuracy: 0.6981 - lr: 2.9820e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 132/750\n", "\n", - "Epoch 130: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5625 - accuracy: 0.7143 - val_loss: 0.5582 - val_accuracy: 0.7505 - lr: 3.1663e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 131/500\n", + "Epoch 132: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5654 - accuracy: 0.7230 - val_loss: 0.6085 - val_accuracy: 0.6981 - lr: 2.9523e-04 - 78ms/epoch - 13ms/step\n", + "Epoch 133/750\n", "\n", - "Epoch 131: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5625 - accuracy: 0.7133 - val_loss: 0.5581 - val_accuracy: 0.7505 - lr: 3.1348e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 132/500\n", + "Epoch 133: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5580 - accuracy: 0.7308 - val_loss: 0.6083 - val_accuracy: 0.6981 - lr: 2.9229e-04 - 80ms/epoch - 13ms/step\n", + "Epoch 134/750\n", "\n", - "Epoch 132: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5624 - accuracy: 0.7136 - val_loss: 0.5581 - val_accuracy: 0.7505 - lr: 3.1036e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 133/500\n", + "Epoch 134: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5567 - accuracy: 0.7319 - val_loss: 0.6079 - val_accuracy: 0.6981 - lr: 2.8938e-04 - 78ms/epoch - 13ms/step\n", + "Epoch 135/750\n", "\n", - "Epoch 133: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5623 - accuracy: 0.7136 - val_loss: 0.5579 - val_accuracy: 0.7505 - lr: 3.0728e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 134/500\n", + "Epoch 135: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5568 - accuracy: 0.7275 - val_loss: 0.6106 - val_accuracy: 0.6981 - lr: 2.8650e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 136/750\n", "\n", - "Epoch 134: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5622 - accuracy: 0.7133 - val_loss: 0.5578 - val_accuracy: 0.7505 - lr: 3.0422e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 135/500\n", + "Epoch 136: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5603 - accuracy: 0.7364 - val_loss: 0.6079 - val_accuracy: 0.7107 - lr: 2.8365e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 137/750\n", "\n", - "Epoch 135: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5621 - accuracy: 0.7133 - val_loss: 0.5578 - val_accuracy: 0.7505 - lr: 3.0119e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 136/500\n", + "Epoch 137: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5599 - accuracy: 0.7241 - val_loss: 0.6094 - val_accuracy: 0.7044 - lr: 2.8083e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 138/750\n", "\n", - "Epoch 136: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5620 - accuracy: 0.7133 - val_loss: 0.5577 - val_accuracy: 0.7505 - lr: 2.9820e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 137/500\n", + "Epoch 138: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5574 - accuracy: 0.7230 - val_loss: 0.6218 - val_accuracy: 0.6667 - lr: 2.7804e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 139/750\n", "\n", - "Epoch 137: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5620 - accuracy: 0.7136 - val_loss: 0.5574 - val_accuracy: 0.7505 - lr: 2.9523e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 138/500\n", + "Epoch 139: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5670 - accuracy: 0.7264 - val_loss: 0.6173 - val_accuracy: 0.6792 - lr: 2.7527e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 140/750\n", "\n", - "Epoch 138: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5619 - accuracy: 0.7136 - val_loss: 0.5574 - val_accuracy: 0.7505 - lr: 2.9229e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 139/500\n", + "Epoch 140: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5587 - accuracy: 0.7297 - val_loss: 0.6114 - val_accuracy: 0.6981 - lr: 2.7253e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 141/750\n", "\n", - "Epoch 139: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5618 - accuracy: 0.7136 - val_loss: 0.5574 - val_accuracy: 0.7505 - lr: 2.8938e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 140/500\n", + "Epoch 141: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5594 - accuracy: 0.7286 - val_loss: 0.6129 - val_accuracy: 0.6918 - lr: 2.6982e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 142/750\n", "\n", - "Epoch 140: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5617 - accuracy: 0.7136 - val_loss: 0.5573 - val_accuracy: 0.7505 - lr: 2.8650e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 141/500\n", + "Epoch 142: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5695 - accuracy: 0.7219 - val_loss: 0.6173 - val_accuracy: 0.6792 - lr: 2.6713e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 143/750\n", "\n", - "Epoch 141: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5616 - accuracy: 0.7133 - val_loss: 0.5572 - val_accuracy: 0.7486 - lr: 2.8365e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 142/500\n", + "Epoch 143: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5628 - accuracy: 0.7241 - val_loss: 0.6090 - val_accuracy: 0.6918 - lr: 2.6448e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 144/750\n", "\n", - "Epoch 142: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5616 - accuracy: 0.7127 - val_loss: 0.5571 - val_accuracy: 0.7486 - lr: 2.8083e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 143/500\n", + "Epoch 144: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5638 - accuracy: 0.7230 - val_loss: 0.6251 - val_accuracy: 0.6792 - lr: 2.6184e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 145/750\n", "\n", - "Epoch 143: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5615 - accuracy: 0.7127 - val_loss: 0.5570 - val_accuracy: 0.7468 - lr: 2.7804e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 144/500\n", + "Epoch 145: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5623 - accuracy: 0.7241 - val_loss: 0.6109 - val_accuracy: 0.6981 - lr: 2.5924e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 146/750\n", "\n", - "Epoch 144: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5614 - accuracy: 0.7130 - val_loss: 0.5569 - val_accuracy: 0.7468 - lr: 2.7527e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 145/500\n", + "Epoch 146: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5574 - accuracy: 0.7319 - val_loss: 0.6111 - val_accuracy: 0.6981 - lr: 2.5666e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 147/750\n", "\n", - "Epoch 145: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5613 - accuracy: 0.7127 - val_loss: 0.5568 - val_accuracy: 0.7468 - lr: 2.7253e-04 - 181ms/epoch - 7ms/step\n", - "Epoch 146/500\n", + "Epoch 147: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5580 - accuracy: 0.7330 - val_loss: 0.6110 - val_accuracy: 0.7044 - lr: 2.5411e-04 - 80ms/epoch - 13ms/step\n", + "Epoch 148/750\n", "\n", - "Epoch 146: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5613 - accuracy: 0.7123 - val_loss: 0.5567 - val_accuracy: 0.7468 - lr: 2.6982e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 147/500\n", + "Epoch 148: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5571 - accuracy: 0.7330 - val_loss: 0.6104 - val_accuracy: 0.7044 - lr: 2.5158e-04 - 75ms/epoch - 13ms/step\n", + "Epoch 149/750\n", "\n", - "Epoch 147: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5612 - accuracy: 0.7123 - val_loss: 0.5567 - val_accuracy: 0.7468 - lr: 2.6713e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 148/500\n", + "Epoch 149: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5666 - accuracy: 0.7264 - val_loss: 0.6104 - val_accuracy: 0.6981 - lr: 2.4907e-04 - 78ms/epoch - 13ms/step\n", + "Epoch 150/750\n", "\n", - "Epoch 148: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5612 - accuracy: 0.7127 - val_loss: 0.5567 - val_accuracy: 0.7468 - lr: 2.6448e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 149/500\n", + "Epoch 150: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5573 - accuracy: 0.7341 - val_loss: 0.6110 - val_accuracy: 0.7044 - lr: 2.4660e-04 - 81ms/epoch - 13ms/step\n", + "Epoch 151/750\n", "\n", - "Epoch 149: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5611 - accuracy: 0.7130 - val_loss: 0.5564 - val_accuracy: 0.7468 - lr: 2.6184e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 150/500\n", + "Epoch 151: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5645 - accuracy: 0.7197 - val_loss: 0.6107 - val_accuracy: 0.6918 - lr: 2.4414e-04 - 77ms/epoch - 13ms/step\n", + "Epoch 152/750\n", "\n", - "Epoch 150: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5610 - accuracy: 0.7117 - val_loss: 0.5563 - val_accuracy: 0.7468 - lr: 2.5924e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 151/500\n", + "Epoch 152: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5617 - accuracy: 0.7297 - val_loss: 0.6100 - val_accuracy: 0.6981 - lr: 2.4171e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 153/750\n", "\n", - "Epoch 151: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5609 - accuracy: 0.7123 - val_loss: 0.5562 - val_accuracy: 0.7468 - lr: 2.5666e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 152/500\n", + "Epoch 153: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5620 - accuracy: 0.7264 - val_loss: 0.6139 - val_accuracy: 0.6918 - lr: 2.3931e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 154/750\n", "\n", - "Epoch 152: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5609 - accuracy: 0.7136 - val_loss: 0.5562 - val_accuracy: 0.7468 - lr: 2.5411e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 153/500\n", + "Epoch 154: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5573 - accuracy: 0.7364 - val_loss: 0.6101 - val_accuracy: 0.7107 - lr: 2.3693e-04 - 78ms/epoch - 13ms/step\n", + "Epoch 155/750\n", "\n", - "Epoch 153: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5608 - accuracy: 0.7123 - val_loss: 0.5562 - val_accuracy: 0.7468 - lr: 2.5158e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 154/500\n", + "Epoch 155: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5604 - accuracy: 0.7264 - val_loss: 0.6104 - val_accuracy: 0.7107 - lr: 2.3457e-04 - 77ms/epoch - 13ms/step\n", + "Epoch 156/750\n", "\n", - "Epoch 154: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5608 - accuracy: 0.7130 - val_loss: 0.5562 - val_accuracy: 0.7468 - lr: 2.4907e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 155/500\n", + "Epoch 156: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5617 - accuracy: 0.7264 - val_loss: 0.6144 - val_accuracy: 0.6918 - lr: 2.3223e-04 - 69ms/epoch - 11ms/step\n", + "Epoch 157/750\n", "\n", - "Epoch 155: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5607 - accuracy: 0.7123 - val_loss: 0.5561 - val_accuracy: 0.7468 - lr: 2.4660e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 156/500\n", + "Epoch 157: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5569 - accuracy: 0.7319 - val_loss: 0.6129 - val_accuracy: 0.7044 - lr: 2.2992e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 158/750\n", "\n", - "Epoch 156: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5606 - accuracy: 0.7130 - val_loss: 0.5560 - val_accuracy: 0.7468 - lr: 2.4414e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 157/500\n", + "Epoch 158: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5622 - accuracy: 0.7241 - val_loss: 0.6135 - val_accuracy: 0.6918 - lr: 2.2764e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 159/750\n", "\n", - "Epoch 157: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5606 - accuracy: 0.7123 - val_loss: 0.5559 - val_accuracy: 0.7468 - lr: 2.4171e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 158/500\n", + "Epoch 159: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5627 - accuracy: 0.7286 - val_loss: 0.6125 - val_accuracy: 0.6981 - lr: 2.2537e-04 - 63ms/epoch - 10ms/step\n", + "Epoch 160/750\n", "\n", - "Epoch 158: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5605 - accuracy: 0.7127 - val_loss: 0.5558 - val_accuracy: 0.7468 - lr: 2.3931e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 159/500\n", + "Epoch 160: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5662 - accuracy: 0.7241 - val_loss: 0.6113 - val_accuracy: 0.7044 - lr: 2.2313e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 161/750\n", "\n", - "Epoch 159: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5605 - accuracy: 0.7123 - val_loss: 0.5557 - val_accuracy: 0.7468 - lr: 2.3693e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 160/500\n", + "Epoch 161: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5654 - accuracy: 0.7230 - val_loss: 0.6098 - val_accuracy: 0.6981 - lr: 2.2091e-04 - 63ms/epoch - 11ms/step\n", + "Epoch 162/750\n", "\n", - "Epoch 160: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5604 - accuracy: 0.7130 - val_loss: 0.5556 - val_accuracy: 0.7468 - lr: 2.3457e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 161/500\n", + "Epoch 162: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5671 - accuracy: 0.7152 - val_loss: 0.6118 - val_accuracy: 0.6981 - lr: 2.1871e-04 - 64ms/epoch - 11ms/step\n", + "Epoch 163/750\n", "\n", - "Epoch 161: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5603 - accuracy: 0.7123 - val_loss: 0.5556 - val_accuracy: 0.7468 - lr: 2.3223e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 162/500\n", + "Epoch 163: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5632 - accuracy: 0.7341 - val_loss: 0.6105 - val_accuracy: 0.7044 - lr: 2.1653e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 164/750\n", "\n", - "Epoch 162: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5602 - accuracy: 0.7133 - val_loss: 0.5556 - val_accuracy: 0.7468 - lr: 2.2992e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 163/500\n", + "Epoch 164: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5608 - accuracy: 0.7275 - val_loss: 0.6315 - val_accuracy: 0.6667 - lr: 2.1438e-04 - 78ms/epoch - 13ms/step\n", + "Epoch 165/750\n", "\n", - "Epoch 163: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5602 - accuracy: 0.7130 - val_loss: 0.5555 - val_accuracy: 0.7468 - lr: 2.2764e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 164/500\n", + "Epoch 165: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5730 - accuracy: 0.7230 - val_loss: 0.6153 - val_accuracy: 0.6855 - lr: 2.1225e-04 - 81ms/epoch - 14ms/step\n", + "Epoch 166/750\n", "\n", - "Epoch 164: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5601 - accuracy: 0.7123 - val_loss: 0.5553 - val_accuracy: 0.7468 - lr: 2.2537e-04 - 186ms/epoch - 7ms/step\n", - "Epoch 165/500\n", + "Epoch 166: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5695 - accuracy: 0.7208 - val_loss: 0.6513 - val_accuracy: 0.5912 - lr: 2.1013e-04 - 108ms/epoch - 18ms/step\n", + "Epoch 167/750\n", "\n", - "Epoch 165: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5600 - accuracy: 0.7127 - val_loss: 0.5554 - val_accuracy: 0.7468 - lr: 2.2313e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 166/500\n", + "Epoch 167: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5711 - accuracy: 0.7175 - val_loss: 0.6149 - val_accuracy: 0.7044 - lr: 2.0804e-04 - 83ms/epoch - 14ms/step\n", + "Epoch 168/750\n", "\n", - "Epoch 166: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5599 - accuracy: 0.7127 - val_loss: 0.5554 - val_accuracy: 0.7468 - lr: 2.2091e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 167/500\n", + "Epoch 168: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5611 - accuracy: 0.7241 - val_loss: 0.6220 - val_accuracy: 0.6730 - lr: 2.0597e-04 - 76ms/epoch - 13ms/step\n", + "Epoch 169/750\n", "\n", - "Epoch 167: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5599 - accuracy: 0.7130 - val_loss: 0.5554 - val_accuracy: 0.7468 - lr: 2.1871e-04 - 176ms/epoch - 7ms/step\n", - "Epoch 168/500\n", + "Epoch 169: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5611 - accuracy: 0.7219 - val_loss: 0.6171 - val_accuracy: 0.6981 - lr: 2.0392e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 170/750\n", "\n", - "Epoch 168: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5599 - accuracy: 0.7123 - val_loss: 0.5554 - val_accuracy: 0.7468 - lr: 2.1653e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 169/500\n", + "Epoch 170: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5623 - accuracy: 0.7319 - val_loss: 0.6199 - val_accuracy: 0.6918 - lr: 2.0189e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 171/750\n", "\n", - "Epoch 169: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5598 - accuracy: 0.7127 - val_loss: 0.5553 - val_accuracy: 0.7468 - lr: 2.1438e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 170/500\n", + "Epoch 171: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5641 - accuracy: 0.7275 - val_loss: 0.6131 - val_accuracy: 0.6918 - lr: 1.9989e-04 - 77ms/epoch - 13ms/step\n", + "Epoch 172/750\n", "\n", - "Epoch 170: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5597 - accuracy: 0.7130 - val_loss: 0.5552 - val_accuracy: 0.7468 - lr: 2.1225e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 171/500\n", + "Epoch 172: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5672 - accuracy: 0.7186 - val_loss: 0.6130 - val_accuracy: 0.6981 - lr: 1.9790e-04 - 77ms/epoch - 13ms/step\n", + "Epoch 173/750\n", "\n", - "Epoch 171: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5597 - accuracy: 0.7123 - val_loss: 0.5552 - val_accuracy: 0.7468 - lr: 2.1013e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 172/500\n", + "Epoch 173: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5732 - accuracy: 0.7141 - val_loss: 0.6179 - val_accuracy: 0.6981 - lr: 1.9593e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 174/750\n", "\n", - "Epoch 172: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5596 - accuracy: 0.7130 - val_loss: 0.5551 - val_accuracy: 0.7468 - lr: 2.0804e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 173/500\n", + "Epoch 174: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5679 - accuracy: 0.7208 - val_loss: 0.6176 - val_accuracy: 0.6981 - lr: 1.9398e-04 - 69ms/epoch - 12ms/step\n", + "Epoch 175/750\n", "\n", - "Epoch 173: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5595 - accuracy: 0.7136 - val_loss: 0.5550 - val_accuracy: 0.7468 - lr: 2.0597e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 174/500\n", + "Epoch 175: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5608 - accuracy: 0.7275 - val_loss: 0.6131 - val_accuracy: 0.6981 - lr: 1.9205e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 176/750\n", "\n", - "Epoch 174: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5595 - accuracy: 0.7133 - val_loss: 0.5550 - val_accuracy: 0.7468 - lr: 2.0392e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 175/500\n", + "Epoch 176: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5665 - accuracy: 0.7253 - val_loss: 0.7011 - val_accuracy: 0.5346 - lr: 1.9014e-04 - 77ms/epoch - 13ms/step\n", + "Epoch 177/750\n", "\n", - "Epoch 175: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5594 - accuracy: 0.7130 - val_loss: 0.5549 - val_accuracy: 0.7468 - lr: 2.0189e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 176/500\n", + "Epoch 177: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5953 - accuracy: 0.7019 - val_loss: 0.6109 - val_accuracy: 0.6981 - lr: 1.8825e-04 - 80ms/epoch - 13ms/step\n", + "Epoch 178/750\n", "\n", - "Epoch 176: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5594 - accuracy: 0.7127 - val_loss: 0.5547 - val_accuracy: 0.7468 - lr: 1.9989e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 177/500\n", + "Epoch 178: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5651 - accuracy: 0.7264 - val_loss: 0.6113 - val_accuracy: 0.6918 - lr: 1.8637e-04 - 69ms/epoch - 11ms/step\n", + "Epoch 179/750\n", "\n", - "Epoch 177: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5593 - accuracy: 0.7127 - val_loss: 0.5548 - val_accuracy: 0.7468 - lr: 1.9790e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 178/500\n", + "Epoch 179: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5595 - accuracy: 0.7297 - val_loss: 0.6133 - val_accuracy: 0.6981 - lr: 1.8452e-04 - 69ms/epoch - 11ms/step\n", + "Epoch 180/750\n", "\n", - "Epoch 178: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5593 - accuracy: 0.7130 - val_loss: 0.5547 - val_accuracy: 0.7468 - lr: 1.9593e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 179/500\n", + "Epoch 180: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5631 - accuracy: 0.7308 - val_loss: 0.6224 - val_accuracy: 0.6667 - lr: 1.8268e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 181/750\n", "\n", - "Epoch 179: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5592 - accuracy: 0.7133 - val_loss: 0.5546 - val_accuracy: 0.7468 - lr: 1.9398e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 180/500\n", + "Epoch 181: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5674 - accuracy: 0.7230 - val_loss: 0.6616 - val_accuracy: 0.6038 - lr: 1.8086e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 182/750\n", "\n", - "Epoch 180: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5592 - accuracy: 0.7133 - val_loss: 0.5545 - val_accuracy: 0.7468 - lr: 1.9205e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 181/500\n", + "Epoch 182: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5637 - accuracy: 0.7152 - val_loss: 0.6208 - val_accuracy: 0.6918 - lr: 1.7906e-04 - 68ms/epoch - 11ms/step\n", + "Epoch 183/750\n", "\n", - "Epoch 181: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5591 - accuracy: 0.7127 - val_loss: 0.5545 - val_accuracy: 0.7468 - lr: 1.9014e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 182/500\n", + "Epoch 183: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5586 - accuracy: 0.7286 - val_loss: 0.6118 - val_accuracy: 0.6981 - lr: 1.7728e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 184/750\n", "\n", - "Epoch 182: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5591 - accuracy: 0.7127 - val_loss: 0.5544 - val_accuracy: 0.7468 - lr: 1.8825e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 183/500\n", + "Epoch 184: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5697 - accuracy: 0.7152 - val_loss: 0.6124 - val_accuracy: 0.6981 - lr: 1.7552e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 185/750\n", "\n", - "Epoch 183: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5590 - accuracy: 0.7130 - val_loss: 0.5542 - val_accuracy: 0.7468 - lr: 1.8637e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 184/500\n", + "Epoch 185: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5775 - accuracy: 0.7208 - val_loss: 0.6690 - val_accuracy: 0.6541 - lr: 1.7377e-04 - 70ms/epoch - 12ms/step\n", + "Epoch 186/750\n", "\n", - "Epoch 184: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5590 - accuracy: 0.7120 - val_loss: 0.5542 - val_accuracy: 0.7468 - lr: 1.8452e-04 - 185ms/epoch - 7ms/step\n", - "Epoch 185/500\n", + "Epoch 186: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5878 - accuracy: 0.7230 - val_loss: 0.6145 - val_accuracy: 0.6667 - lr: 1.7204e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 187/750\n", "\n", - "Epoch 185: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5589 - accuracy: 0.7123 - val_loss: 0.5542 - val_accuracy: 0.7468 - lr: 1.8268e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 186/500\n", + "Epoch 187: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5570 - accuracy: 0.7264 - val_loss: 0.6119 - val_accuracy: 0.6981 - lr: 1.7033e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 188/750\n", "\n", - "Epoch 186: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5589 - accuracy: 0.7127 - val_loss: 0.5542 - val_accuracy: 0.7468 - lr: 1.8086e-04 - 183ms/epoch - 7ms/step\n", - "Epoch 187/500\n", + "Epoch 188: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5664 - accuracy: 0.7208 - val_loss: 0.6138 - val_accuracy: 0.6792 - lr: 1.6864e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 189/750\n", "\n", - "Epoch 187: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5589 - accuracy: 0.7127 - val_loss: 0.5541 - val_accuracy: 0.7468 - lr: 1.7906e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 188/500\n", + "Epoch 189: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5589 - accuracy: 0.7253 - val_loss: 0.6137 - val_accuracy: 0.6981 - lr: 1.6696e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 190/750\n", "\n", - "Epoch 188: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5588 - accuracy: 0.7127 - val_loss: 0.5540 - val_accuracy: 0.7468 - lr: 1.7728e-04 - 185ms/epoch - 7ms/step\n", - "Epoch 189/500\n", + "Epoch 190: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5567 - accuracy: 0.7275 - val_loss: 0.6259 - val_accuracy: 0.6667 - lr: 1.6530e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 191/750\n", "\n", - "Epoch 189: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5587 - accuracy: 0.7123 - val_loss: 0.5541 - val_accuracy: 0.7468 - lr: 1.7552e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 190/500\n", + "Epoch 191: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5672 - accuracy: 0.7230 - val_loss: 0.6136 - val_accuracy: 0.6792 - lr: 1.6365e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 192/750\n", "\n", - "Epoch 190: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5587 - accuracy: 0.7127 - val_loss: 0.5540 - val_accuracy: 0.7468 - lr: 1.7377e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 191/500\n", + "Epoch 192: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5576 - accuracy: 0.7286 - val_loss: 0.6150 - val_accuracy: 0.6918 - lr: 1.6202e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 193/750\n", "\n", - "Epoch 191: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5586 - accuracy: 0.7127 - val_loss: 0.5539 - val_accuracy: 0.7468 - lr: 1.7204e-04 - 187ms/epoch - 7ms/step\n", - "Epoch 192/500\n", + "Epoch 193: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5618 - accuracy: 0.7253 - val_loss: 0.6229 - val_accuracy: 0.6667 - lr: 1.6041e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 194/750\n", "\n", - "Epoch 192: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5586 - accuracy: 0.7120 - val_loss: 0.5538 - val_accuracy: 0.7468 - lr: 1.7033e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 193/500\n", + "Epoch 194: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5605 - accuracy: 0.7208 - val_loss: 0.6151 - val_accuracy: 0.6855 - lr: 1.5882e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 195/750\n", "\n", - "Epoch 193: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5586 - accuracy: 0.7120 - val_loss: 0.5539 - val_accuracy: 0.7468 - lr: 1.6864e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 194/500\n", + "Epoch 195: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5623 - accuracy: 0.7241 - val_loss: 0.6210 - val_accuracy: 0.6855 - lr: 1.5724e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 196/750\n", "\n", - "Epoch 194: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5585 - accuracy: 0.7123 - val_loss: 0.5537 - val_accuracy: 0.7468 - lr: 1.6696e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 195/500\n", + "Epoch 196: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5705 - accuracy: 0.7230 - val_loss: 0.6129 - val_accuracy: 0.6918 - lr: 1.5567e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 197/750\n", "\n", - "Epoch 195: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5585 - accuracy: 0.7120 - val_loss: 0.5536 - val_accuracy: 0.7468 - lr: 1.6530e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 196/500\n", + "Epoch 197: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5678 - accuracy: 0.7264 - val_loss: 0.6150 - val_accuracy: 0.6918 - lr: 1.5412e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 198/750\n", "\n", - "Epoch 196: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5584 - accuracy: 0.7123 - val_loss: 0.5537 - val_accuracy: 0.7468 - lr: 1.6365e-04 - 191ms/epoch - 8ms/step\n", - "Epoch 197/500\n", + "Epoch 198: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5577 - accuracy: 0.7275 - val_loss: 0.6114 - val_accuracy: 0.6981 - lr: 1.5259e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 199/750\n", "\n", - "Epoch 197: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5584 - accuracy: 0.7123 - val_loss: 0.5536 - val_accuracy: 0.7468 - lr: 1.6202e-04 - 204ms/epoch - 8ms/step\n", - "Epoch 198/500\n", + "Epoch 199: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5660 - accuracy: 0.7286 - val_loss: 0.6095 - val_accuracy: 0.6981 - lr: 1.5107e-04 - 75ms/epoch - 13ms/step\n", + "Epoch 200/750\n", "\n", - "Epoch 198: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5583 - accuracy: 0.7127 - val_loss: 0.5536 - val_accuracy: 0.7468 - lr: 1.6041e-04 - 189ms/epoch - 8ms/step\n", - "Epoch 199/500\n", + "Epoch 200: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5546 - accuracy: 0.7330 - val_loss: 0.6240 - val_accuracy: 0.6730 - lr: 1.4957e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 201/750\n", "\n", - "Epoch 199: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5583 - accuracy: 0.7130 - val_loss: 0.5536 - val_accuracy: 0.7468 - lr: 1.5882e-04 - 203ms/epoch - 8ms/step\n", - "Epoch 200/500\n", + "Epoch 201: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5727 - accuracy: 0.7186 - val_loss: 0.6156 - val_accuracy: 0.6730 - lr: 1.4808e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 202/750\n", "\n", - "Epoch 200: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5583 - accuracy: 0.7127 - val_loss: 0.5536 - val_accuracy: 0.7468 - lr: 1.5724e-04 - 192ms/epoch - 8ms/step\n", - "Epoch 201/500\n", + "Epoch 202: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5588 - accuracy: 0.7341 - val_loss: 0.6111 - val_accuracy: 0.6981 - lr: 1.4661e-04 - 70ms/epoch - 12ms/step\n", + "Epoch 203/750\n", "\n", - "Epoch 201: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5582 - accuracy: 0.7130 - val_loss: 0.5536 - val_accuracy: 0.7468 - lr: 1.5567e-04 - 189ms/epoch - 8ms/step\n", - "Epoch 202/500\n", + "Epoch 203: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5580 - accuracy: 0.7297 - val_loss: 0.6118 - val_accuracy: 0.6981 - lr: 1.4515e-04 - 70ms/epoch - 12ms/step\n", + "Epoch 204/750\n", "\n", - "Epoch 202: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5582 - accuracy: 0.7127 - val_loss: 0.5535 - val_accuracy: 0.7468 - lr: 1.5412e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 203/500\n", + "Epoch 204: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5570 - accuracy: 0.7286 - val_loss: 0.6151 - val_accuracy: 0.6918 - lr: 1.4370e-04 - 70ms/epoch - 12ms/step\n", + "Epoch 205/750\n", "\n", - "Epoch 203: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5582 - accuracy: 0.7127 - val_loss: 0.5535 - val_accuracy: 0.7468 - lr: 1.5259e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 204/500\n", + "Epoch 205: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5613 - accuracy: 0.7253 - val_loss: 0.6129 - val_accuracy: 0.6918 - lr: 1.4227e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 206/750\n", "\n", - "Epoch 204: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5581 - accuracy: 0.7127 - val_loss: 0.5535 - val_accuracy: 0.7468 - lr: 1.5107e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 205/500\n", + "Epoch 206: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5596 - accuracy: 0.7230 - val_loss: 0.6161 - val_accuracy: 0.6730 - lr: 1.4086e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 207/750\n", "\n", - "Epoch 205: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5581 - accuracy: 0.7133 - val_loss: 0.5535 - val_accuracy: 0.7468 - lr: 1.4957e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 206/500\n", + "Epoch 207: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5602 - accuracy: 0.7286 - val_loss: 0.6166 - val_accuracy: 0.6730 - lr: 1.3946e-04 - 67ms/epoch - 11ms/step\n", + "Epoch 208/750\n", "\n", - "Epoch 206: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5580 - accuracy: 0.7140 - val_loss: 0.5534 - val_accuracy: 0.7468 - lr: 1.4808e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 207/500\n", + "Epoch 208: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5648 - accuracy: 0.7208 - val_loss: 0.6142 - val_accuracy: 0.6918 - lr: 1.3807e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 209/750\n", "\n", - "Epoch 207: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5580 - accuracy: 0.7127 - val_loss: 0.5534 - val_accuracy: 0.7468 - lr: 1.4661e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 208/500\n", + "Epoch 209: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5587 - accuracy: 0.7297 - val_loss: 0.6192 - val_accuracy: 0.6667 - lr: 1.3669e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 210/750\n", "\n", - "Epoch 208: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5580 - accuracy: 0.7140 - val_loss: 0.5533 - val_accuracy: 0.7468 - lr: 1.4515e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 209/500\n", + "Epoch 210: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5593 - accuracy: 0.7264 - val_loss: 0.6123 - val_accuracy: 0.6981 - lr: 1.3533e-04 - 68ms/epoch - 11ms/step\n", + "Epoch 211/750\n", "\n", - "Epoch 209: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5579 - accuracy: 0.7143 - val_loss: 0.5533 - val_accuracy: 0.7468 - lr: 1.4370e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 210/500\n", + "Epoch 211: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5623 - accuracy: 0.7297 - val_loss: 0.6153 - val_accuracy: 0.6855 - lr: 1.3399e-04 - 68ms/epoch - 11ms/step\n", + "Epoch 212/750\n", "\n", - "Epoch 210: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5579 - accuracy: 0.7143 - val_loss: 0.5533 - val_accuracy: 0.7468 - lr: 1.4227e-04 - 189ms/epoch - 8ms/step\n", - "Epoch 211/500\n", + "Epoch 212: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5597 - accuracy: 0.7253 - val_loss: 0.6122 - val_accuracy: 0.6981 - lr: 1.3265e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 213/750\n", "\n", - "Epoch 211: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5579 - accuracy: 0.7146 - val_loss: 0.5533 - val_accuracy: 0.7468 - lr: 1.4086e-04 - 228ms/epoch - 9ms/step\n", - "Epoch 212/500\n", + "Epoch 213: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5630 - accuracy: 0.7386 - val_loss: 0.6112 - val_accuracy: 0.6981 - lr: 1.3133e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 214/750\n", "\n", - "Epoch 212: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5578 - accuracy: 0.7140 - val_loss: 0.5533 - val_accuracy: 0.7468 - lr: 1.3946e-04 - 184ms/epoch - 7ms/step\n", - "Epoch 213/500\n", + "Epoch 214: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5588 - accuracy: 0.7286 - val_loss: 0.6117 - val_accuracy: 0.6981 - lr: 1.3003e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 215/750\n", "\n", - "Epoch 213: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5578 - accuracy: 0.7143 - val_loss: 0.5532 - val_accuracy: 0.7468 - lr: 1.3807e-04 - 193ms/epoch - 8ms/step\n", - "Epoch 214/500\n", + "Epoch 215: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5583 - accuracy: 0.7241 - val_loss: 0.6104 - val_accuracy: 0.7044 - lr: 1.2873e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 216/750\n", "\n", - "Epoch 214: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5578 - accuracy: 0.7143 - val_loss: 0.5532 - val_accuracy: 0.7486 - lr: 1.3669e-04 - 186ms/epoch - 7ms/step\n", - "Epoch 215/500\n", + "Epoch 216: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5632 - accuracy: 0.7275 - val_loss: 0.6151 - val_accuracy: 0.6855 - lr: 1.2745e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 217/750\n", "\n", - "Epoch 215: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5577 - accuracy: 0.7140 - val_loss: 0.5532 - val_accuracy: 0.7486 - lr: 1.3533e-04 - 183ms/epoch - 7ms/step\n", - "Epoch 216/500\n", + "Epoch 217: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5547 - accuracy: 0.7319 - val_loss: 0.6233 - val_accuracy: 0.6667 - lr: 1.2618e-04 - 66ms/epoch - 11ms/step\n", + "Epoch 218/750\n", "\n", - "Epoch 216: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5577 - accuracy: 0.7143 - val_loss: 0.5532 - val_accuracy: 0.7486 - lr: 1.3399e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 217/500\n", + "Epoch 218: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5625 - accuracy: 0.7330 - val_loss: 0.6148 - val_accuracy: 0.6792 - lr: 1.2493e-04 - 65ms/epoch - 11ms/step\n", + "Epoch 219/750\n", "\n", - "Epoch 217: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5577 - accuracy: 0.7153 - val_loss: 0.5531 - val_accuracy: 0.7486 - lr: 1.3265e-04 - 181ms/epoch - 7ms/step\n", - "Epoch 218/500\n", + "Epoch 219: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5582 - accuracy: 0.7297 - val_loss: 0.6126 - val_accuracy: 0.6981 - lr: 1.2369e-04 - 68ms/epoch - 11ms/step\n", + "Epoch 220/750\n", "\n", - "Epoch 218: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5577 - accuracy: 0.7143 - val_loss: 0.5531 - val_accuracy: 0.7486 - lr: 1.3133e-04 - 176ms/epoch - 7ms/step\n", - "Epoch 219/500\n", + "Epoch 220: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5579 - accuracy: 0.7275 - val_loss: 0.6140 - val_accuracy: 0.7044 - lr: 1.2245e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 221/750\n", "\n", - "Epoch 219: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5576 - accuracy: 0.7156 - val_loss: 0.5531 - val_accuracy: 0.7486 - lr: 1.3003e-04 - 176ms/epoch - 7ms/step\n", - "Epoch 220/500\n", + "Epoch 221: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5637 - accuracy: 0.7319 - val_loss: 0.6131 - val_accuracy: 0.6981 - lr: 1.2124e-04 - 69ms/epoch - 11ms/step\n", + "Epoch 222/750\n", "\n", - "Epoch 220: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5576 - accuracy: 0.7156 - val_loss: 0.5531 - val_accuracy: 0.7486 - lr: 1.2873e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 221/500\n", + "Epoch 222: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5604 - accuracy: 0.7219 - val_loss: 0.6174 - val_accuracy: 0.6918 - lr: 1.2003e-04 - 93ms/epoch - 16ms/step\n", + "Epoch 223/750\n", "\n", - "Epoch 221: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5575 - accuracy: 0.7156 - val_loss: 0.5530 - val_accuracy: 0.7486 - lr: 1.2745e-04 - 177ms/epoch - 7ms/step\n", - "Epoch 222/500\n", + "Epoch 223: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5581 - accuracy: 0.7286 - val_loss: 0.6134 - val_accuracy: 0.6855 - lr: 1.1884e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 224/750\n", "\n", - "Epoch 222: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5575 - accuracy: 0.7153 - val_loss: 0.5530 - val_accuracy: 0.7486 - lr: 1.2618e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 223/500\n", + "Epoch 224: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5597 - accuracy: 0.7297 - val_loss: 0.6123 - val_accuracy: 0.6918 - lr: 1.1765e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 225/750\n", "\n", - "Epoch 223: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5575 - accuracy: 0.7156 - val_loss: 0.5530 - val_accuracy: 0.7486 - lr: 1.2493e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 224/500\n", + "Epoch 225: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5663 - accuracy: 0.7264 - val_loss: 0.6133 - val_accuracy: 0.7044 - lr: 1.1648e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 226/750\n", "\n", - "Epoch 224: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5575 - accuracy: 0.7156 - val_loss: 0.5529 - val_accuracy: 0.7486 - lr: 1.2369e-04 - 183ms/epoch - 7ms/step\n", - "Epoch 225/500\n", + "Epoch 226: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5584 - accuracy: 0.7241 - val_loss: 0.6133 - val_accuracy: 0.7044 - lr: 1.1532e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 227/750\n", "\n", - "Epoch 225: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5574 - accuracy: 0.7153 - val_loss: 0.5529 - val_accuracy: 0.7486 - lr: 1.2245e-04 - 191ms/epoch - 8ms/step\n", - "Epoch 226/500\n", + "Epoch 227: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5614 - accuracy: 0.7297 - val_loss: 0.6117 - val_accuracy: 0.6981 - lr: 1.1418e-04 - 69ms/epoch - 11ms/step\n", + "Epoch 228/750\n", "\n", - "Epoch 226: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5574 - accuracy: 0.7149 - val_loss: 0.5528 - val_accuracy: 0.7468 - lr: 1.2124e-04 - 182ms/epoch - 7ms/step\n", - "Epoch 227/500\n", + "Epoch 228: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5556 - accuracy: 0.7286 - val_loss: 0.6194 - val_accuracy: 0.6604 - lr: 1.1304e-04 - 69ms/epoch - 12ms/step\n", + "Epoch 229/750\n", "\n", - "Epoch 227: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5573 - accuracy: 0.7156 - val_loss: 0.5528 - val_accuracy: 0.7486 - lr: 1.2003e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 228/500\n", + "Epoch 229: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5621 - accuracy: 0.7341 - val_loss: 0.6187 - val_accuracy: 0.6667 - lr: 1.1192e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 230/750\n", "\n", - "Epoch 228: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5573 - accuracy: 0.7153 - val_loss: 0.5528 - val_accuracy: 0.7486 - lr: 1.1884e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 229/500\n", + "Epoch 230: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5564 - accuracy: 0.7197 - val_loss: 0.6111 - val_accuracy: 0.6981 - lr: 1.1080e-04 - 69ms/epoch - 12ms/step\n", + "Epoch 231/750\n", "\n", - "Epoch 229: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5573 - accuracy: 0.7153 - val_loss: 0.5528 - val_accuracy: 0.7486 - lr: 1.1765e-04 - 204ms/epoch - 8ms/step\n", - "Epoch 230/500\n", + "Epoch 231: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5599 - accuracy: 0.7308 - val_loss: 0.6119 - val_accuracy: 0.6981 - lr: 1.0970e-04 - 72ms/epoch - 12ms/step\n", + "Epoch 232/750\n", "\n", - "Epoch 230: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5573 - accuracy: 0.7156 - val_loss: 0.5527 - val_accuracy: 0.7486 - lr: 1.1648e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 231/500\n", + "Epoch 232: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5636 - accuracy: 0.7230 - val_loss: 0.6184 - val_accuracy: 0.6855 - lr: 1.0861e-04 - 69ms/epoch - 12ms/step\n", + "Epoch 233/750\n", "\n", - "Epoch 231: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5572 - accuracy: 0.7153 - val_loss: 0.5526 - val_accuracy: 0.7486 - lr: 1.1532e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 232/500\n", + "Epoch 233: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5608 - accuracy: 0.7286 - val_loss: 0.6118 - val_accuracy: 0.6981 - lr: 1.0753e-04 - 69ms/epoch - 11ms/step\n", + "Epoch 234/750\n", "\n", - "Epoch 232: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5572 - accuracy: 0.7153 - val_loss: 0.5526 - val_accuracy: 0.7486 - lr: 1.1418e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 233/500\n", + "Epoch 234: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5560 - accuracy: 0.7286 - val_loss: 0.6125 - val_accuracy: 0.6981 - lr: 1.0646e-04 - 71ms/epoch - 12ms/step\n", + "Epoch 235/750\n", "\n", - "Epoch 233: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5572 - accuracy: 0.7153 - val_loss: 0.5526 - val_accuracy: 0.7486 - lr: 1.1304e-04 - 176ms/epoch - 7ms/step\n", - "Epoch 234/500\n", + "Epoch 235: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5571 - accuracy: 0.7253 - val_loss: 0.6114 - val_accuracy: 0.6981 - lr: 1.0540e-04 - 78ms/epoch - 13ms/step\n", + "Epoch 236/750\n", "\n", - "Epoch 234: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5571 - accuracy: 0.7156 - val_loss: 0.5526 - val_accuracy: 0.7486 - lr: 1.1192e-04 - 175ms/epoch - 7ms/step\n", - "Epoch 235/500\n", + "Epoch 236: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5589 - accuracy: 0.7297 - val_loss: 0.6133 - val_accuracy: 0.6855 - lr: 1.0435e-04 - 73ms/epoch - 12ms/step\n", + "Epoch 237/750\n", "\n", - "Epoch 235: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5571 - accuracy: 0.7153 - val_loss: 0.5526 - val_accuracy: 0.7486 - lr: 1.1080e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 236/500\n", + "Epoch 237: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5635 - accuracy: 0.7230 - val_loss: 0.6103 - val_accuracy: 0.7044 - lr: 1.0331e-04 - 75ms/epoch - 13ms/step\n", + "Epoch 238/750\n", "\n", - "Epoch 236: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5571 - accuracy: 0.7153 - val_loss: 0.5526 - val_accuracy: 0.7486 - lr: 1.0970e-04 - 174ms/epoch - 7ms/step\n", - "Epoch 237/500\n", + "Epoch 238: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5623 - accuracy: 0.7230 - val_loss: 0.6318 - val_accuracy: 0.6667 - lr: 1.0228e-04 - 76ms/epoch - 13ms/step\n", + "Epoch 239/750\n", "\n", - "Epoch 237: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5571 - accuracy: 0.7149 - val_loss: 0.5525 - val_accuracy: 0.7486 - lr: 1.0861e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 238/500\n", + "Epoch 239: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5650 - accuracy: 0.7197 - val_loss: 0.6133 - val_accuracy: 0.7044 - lr: 1.0127e-04 - 76ms/epoch - 13ms/step\n", + "Epoch 240/750\n", "\n", - "Epoch 238: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5570 - accuracy: 0.7153 - val_loss: 0.5525 - val_accuracy: 0.7486 - lr: 1.0753e-04 - 171ms/epoch - 7ms/step\n", - "Epoch 239/500\n", + "Epoch 240: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5614 - accuracy: 0.7330 - val_loss: 0.6123 - val_accuracy: 0.6981 - lr: 1.0026e-04 - 74ms/epoch - 12ms/step\n", + "Epoch 241/750\n", "\n", - "Epoch 239: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5570 - accuracy: 0.7149 - val_loss: 0.5524 - val_accuracy: 0.7486 - lr: 1.0646e-04 - 180ms/epoch - 7ms/step\n", - "Epoch 240/500\n", + "Epoch 241: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5589 - accuracy: 0.7208 - val_loss: 0.6109 - val_accuracy: 0.6981 - lr: 9.9260e-05 - 74ms/epoch - 12ms/step\n", + "Epoch 242/750\n", "\n", - "Epoch 240: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5570 - accuracy: 0.7159 - val_loss: 0.5524 - val_accuracy: 0.7486 - lr: 1.0540e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 241/500\n", + "Epoch 242: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5582 - accuracy: 0.7286 - val_loss: 0.6198 - val_accuracy: 0.6604 - lr: 9.8272e-05 - 73ms/epoch - 12ms/step\n", + "Epoch 243/750\n", "\n", - "Epoch 241: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5570 - accuracy: 0.7153 - val_loss: 0.5524 - val_accuracy: 0.7486 - lr: 1.0435e-04 - 178ms/epoch - 7ms/step\n", - "Epoch 242/500\n", + "Epoch 243: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5600 - accuracy: 0.7297 - val_loss: 0.6239 - val_accuracy: 0.6730 - lr: 9.7294e-05 - 74ms/epoch - 12ms/step\n", + "Epoch 244/750\n", "\n", - "Epoch 242: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5570 - accuracy: 0.7159 - val_loss: 0.5524 - val_accuracy: 0.7486 - lr: 1.0331e-04 - 172ms/epoch - 7ms/step\n", - "Epoch 243/500\n", + "Epoch 244: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5597 - accuracy: 0.7264 - val_loss: 0.6187 - val_accuracy: 0.6792 - lr: 9.6326e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 245/750\n", "\n", - "Epoch 243: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5569 - accuracy: 0.7153 - val_loss: 0.5524 - val_accuracy: 0.7486 - lr: 1.0228e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 244/500\n", + "Epoch 245: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5614 - accuracy: 0.7230 - val_loss: 0.6111 - val_accuracy: 0.7107 - lr: 9.5368e-05 - 64ms/epoch - 11ms/step\n", + "Epoch 246/750\n", "\n", - "Epoch 244: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5569 - accuracy: 0.7153 - val_loss: 0.5524 - val_accuracy: 0.7486 - lr: 1.0127e-04 - 173ms/epoch - 7ms/step\n", - "Epoch 245/500\n", + "Epoch 246: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5578 - accuracy: 0.7264 - val_loss: 0.6128 - val_accuracy: 0.6981 - lr: 9.4419e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 247/750\n", "\n", - "Epoch 245: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5569 - accuracy: 0.7153 - val_loss: 0.5523 - val_accuracy: 0.7486 - lr: 1.0026e-04 - 179ms/epoch - 7ms/step\n", - "Epoch 246/500\n", + "Epoch 247: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5572 - accuracy: 0.7275 - val_loss: 0.6117 - val_accuracy: 0.6981 - lr: 9.3479e-05 - 67ms/epoch - 11ms/step\n", + "Epoch 248/750\n", "\n", - "Epoch 246: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5569 - accuracy: 0.7159 - val_loss: 0.5523 - val_accuracy: 0.7486 - lr: 9.9260e-05 - 179ms/epoch - 7ms/step\n", - "Epoch 247/500\n", + "Epoch 248: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5556 - accuracy: 0.7264 - val_loss: 0.6224 - val_accuracy: 0.6730 - lr: 9.2549e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 249/750\n", "\n", - "Epoch 247: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5568 - accuracy: 0.7156 - val_loss: 0.5523 - val_accuracy: 0.7486 - lr: 9.8272e-05 - 175ms/epoch - 7ms/step\n", - "Epoch 248/500\n", + "Epoch 249: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5639 - accuracy: 0.7253 - val_loss: 0.6115 - val_accuracy: 0.7107 - lr: 9.1628e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 250/750\n", "\n", - "Epoch 248: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5568 - accuracy: 0.7153 - val_loss: 0.5522 - val_accuracy: 0.7486 - lr: 9.7294e-05 - 174ms/epoch - 7ms/step\n", - "Epoch 249/500\n", + "Epoch 250: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5673 - accuracy: 0.7241 - val_loss: 0.6119 - val_accuracy: 0.7044 - lr: 9.0717e-05 - 64ms/epoch - 11ms/step\n", + "Epoch 251/750\n", "\n", - "Epoch 249: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5568 - accuracy: 0.7149 - val_loss: 0.5522 - val_accuracy: 0.7486 - lr: 9.6326e-05 - 175ms/epoch - 7ms/step\n", - "Epoch 250/500\n", + "Epoch 251: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5581 - accuracy: 0.7275 - val_loss: 0.6126 - val_accuracy: 0.6855 - lr: 8.9814e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 252/750\n", "\n", - "Epoch 250: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5567 - accuracy: 0.7159 - val_loss: 0.5522 - val_accuracy: 0.7486 - lr: 9.5368e-05 - 178ms/epoch - 7ms/step\n", - "Epoch 251/500\n", + "Epoch 252: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5581 - accuracy: 0.7308 - val_loss: 0.6169 - val_accuracy: 0.6730 - lr: 8.8920e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 253/750\n", "\n", - "Epoch 251: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5567 - accuracy: 0.7159 - val_loss: 0.5522 - val_accuracy: 0.7468 - lr: 9.4419e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 252/500\n", + "Epoch 253: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5616 - accuracy: 0.7275 - val_loss: 0.6111 - val_accuracy: 0.6981 - lr: 8.8036e-05 - 70ms/epoch - 12ms/step\n", + "Epoch 254/750\n", "\n", - "Epoch 252: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5567 - accuracy: 0.7156 - val_loss: 0.5522 - val_accuracy: 0.7468 - lr: 9.3479e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 253/500\n", + "Epoch 254: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5587 - accuracy: 0.7253 - val_loss: 0.6123 - val_accuracy: 0.6918 - lr: 8.7160e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 255/750\n", "\n", - "Epoch 253: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5567 - accuracy: 0.7159 - val_loss: 0.5521 - val_accuracy: 0.7486 - lr: 9.2549e-05 - 181ms/epoch - 7ms/step\n", - "Epoch 254/500\n", + "Epoch 255: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5610 - accuracy: 0.7330 - val_loss: 0.6133 - val_accuracy: 0.6855 - lr: 8.6292e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 256/750\n", "\n", - "Epoch 254: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5567 - accuracy: 0.7162 - val_loss: 0.5521 - val_accuracy: 0.7486 - lr: 9.1628e-05 - 179ms/epoch - 7ms/step\n", - "Epoch 255/500\n", + "Epoch 256: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5584 - accuracy: 0.7241 - val_loss: 0.6126 - val_accuracy: 0.6918 - lr: 8.5434e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 257/750\n", "\n", - "Epoch 255: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5566 - accuracy: 0.7159 - val_loss: 0.5521 - val_accuracy: 0.7468 - lr: 9.0717e-05 - 174ms/epoch - 7ms/step\n", - "Epoch 256/500\n", + "Epoch 257: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5595 - accuracy: 0.7319 - val_loss: 0.6121 - val_accuracy: 0.6981 - lr: 8.4584e-05 - 63ms/epoch - 11ms/step\n", + "Epoch 258/750\n", "\n", - "Epoch 256: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5566 - accuracy: 0.7153 - val_loss: 0.5520 - val_accuracy: 0.7468 - lr: 8.9814e-05 - 174ms/epoch - 7ms/step\n", - "Epoch 257/500\n", + "Epoch 258: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5596 - accuracy: 0.7275 - val_loss: 0.6116 - val_accuracy: 0.6981 - lr: 8.3742e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 259/750\n", "\n", - "Epoch 257: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5566 - accuracy: 0.7153 - val_loss: 0.5520 - val_accuracy: 0.7468 - lr: 8.8920e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 258/500\n", + "Epoch 259: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5601 - accuracy: 0.7308 - val_loss: 0.6120 - val_accuracy: 0.6981 - lr: 8.2909e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 260/750\n", "\n", - "Epoch 258: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5566 - accuracy: 0.7149 - val_loss: 0.5520 - val_accuracy: 0.7468 - lr: 8.8036e-05 - 177ms/epoch - 7ms/step\n", - "Epoch 259/500\n", + "Epoch 260: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5572 - accuracy: 0.7241 - val_loss: 0.6137 - val_accuracy: 0.6981 - lr: 8.2084e-05 - 64ms/epoch - 11ms/step\n", + "Epoch 261/750\n", "\n", - "Epoch 259: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5566 - accuracy: 0.7159 - val_loss: 0.5520 - val_accuracy: 0.7486 - lr: 8.7160e-05 - 171ms/epoch - 7ms/step\n", - "Epoch 260/500\n", + "Epoch 261: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5576 - accuracy: 0.7330 - val_loss: 0.6177 - val_accuracy: 0.7044 - lr: 8.1267e-05 - 64ms/epoch - 11ms/step\n", + "Epoch 262/750\n", "\n", - "Epoch 260: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5565 - accuracy: 0.7159 - val_loss: 0.5520 - val_accuracy: 0.7486 - lr: 8.6292e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 261/500\n", + "Epoch 262: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5627 - accuracy: 0.7286 - val_loss: 0.6155 - val_accuracy: 0.6792 - lr: 8.0458e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 263/750\n", "\n", - "Epoch 261: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5565 - accuracy: 0.7156 - val_loss: 0.5520 - val_accuracy: 0.7486 - lr: 8.5434e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 262/500\n", + "Epoch 263: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5611 - accuracy: 0.7275 - val_loss: 0.6161 - val_accuracy: 0.6981 - lr: 7.9658e-05 - 67ms/epoch - 11ms/step\n", + "Epoch 264/750\n", "\n", - "Epoch 262: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5565 - accuracy: 0.7159 - val_loss: 0.5520 - val_accuracy: 0.7486 - lr: 8.4584e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 263/500\n", + "Epoch 264: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5679 - accuracy: 0.7286 - val_loss: 0.6136 - val_accuracy: 0.6792 - lr: 7.8865e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 265/750\n", "\n", - "Epoch 263: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5565 - accuracy: 0.7153 - val_loss: 0.5520 - val_accuracy: 0.7486 - lr: 8.3742e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 264/500\n", + "Epoch 265: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5595 - accuracy: 0.7275 - val_loss: 0.6245 - val_accuracy: 0.6792 - lr: 7.8081e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 266/750\n", "\n", - "Epoch 264: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5565 - accuracy: 0.7149 - val_loss: 0.5519 - val_accuracy: 0.7486 - lr: 8.2909e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 265/500\n", + "Epoch 266: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5630 - accuracy: 0.7208 - val_loss: 0.6121 - val_accuracy: 0.6918 - lr: 7.7304e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 267/750\n", "\n", - "Epoch 265: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5564 - accuracy: 0.7162 - val_loss: 0.5519 - val_accuracy: 0.7486 - lr: 8.2084e-05 - 183ms/epoch - 7ms/step\n", - "Epoch 266/500\n", + "Epoch 267: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5611 - accuracy: 0.7275 - val_loss: 0.6114 - val_accuracy: 0.6981 - lr: 7.6534e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 268/750\n", "\n", - "Epoch 266: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5564 - accuracy: 0.7159 - val_loss: 0.5519 - val_accuracy: 0.7486 - lr: 8.1267e-05 - 179ms/epoch - 7ms/step\n", - "Epoch 267/500\n", + "Epoch 268: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5588 - accuracy: 0.7230 - val_loss: 0.6283 - val_accuracy: 0.6667 - lr: 7.5773e-05 - 69ms/epoch - 12ms/step\n", + "Epoch 269/750\n", "\n", - "Epoch 267: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5564 - accuracy: 0.7156 - val_loss: 0.5519 - val_accuracy: 0.7486 - lr: 8.0458e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 268/500\n", + "Epoch 269: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5662 - accuracy: 0.7275 - val_loss: 0.6122 - val_accuracy: 0.6918 - lr: 7.5019e-05 - 68ms/epoch - 11ms/step\n", + "Epoch 270/750\n", "\n", - "Epoch 268: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5564 - accuracy: 0.7156 - val_loss: 0.5519 - val_accuracy: 0.7486 - lr: 7.9658e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 269/500\n", + "Epoch 270: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5592 - accuracy: 0.7319 - val_loss: 0.6162 - val_accuracy: 0.6981 - lr: 7.4272e-05 - 69ms/epoch - 12ms/step\n", + "Epoch 271/750\n", "\n", - "Epoch 269: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5564 - accuracy: 0.7143 - val_loss: 0.5518 - val_accuracy: 0.7486 - lr: 7.8865e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 270/500\n", + "Epoch 271: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5611 - accuracy: 0.7286 - val_loss: 0.6120 - val_accuracy: 0.6981 - lr: 7.3533e-05 - 64ms/epoch - 11ms/step\n", + "Epoch 272/750\n", "\n", - "Epoch 270: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5564 - accuracy: 0.7149 - val_loss: 0.5518 - val_accuracy: 0.7486 - lr: 7.8081e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 271/500\n", + "Epoch 272: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5580 - accuracy: 0.7275 - val_loss: 0.6137 - val_accuracy: 0.6855 - lr: 7.2802e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 273/750\n", "\n", - "Epoch 271: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5563 - accuracy: 0.7153 - val_loss: 0.5518 - val_accuracy: 0.7486 - lr: 7.7304e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 272/500\n", + "Epoch 273: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5591 - accuracy: 0.7286 - val_loss: 0.6122 - val_accuracy: 0.6981 - lr: 7.2077e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 274/750\n", "\n", - "Epoch 272: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5563 - accuracy: 0.7149 - val_loss: 0.5518 - val_accuracy: 0.7486 - lr: 7.6534e-05 - 177ms/epoch - 7ms/step\n", - "Epoch 273/500\n", + "Epoch 274: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5587 - accuracy: 0.7286 - val_loss: 0.6115 - val_accuracy: 0.6981 - lr: 7.1360e-05 - 85ms/epoch - 14ms/step\n", + "Epoch 275/750\n", "\n", - "Epoch 273: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5563 - accuracy: 0.7156 - val_loss: 0.5518 - val_accuracy: 0.7486 - lr: 7.5773e-05 - 201ms/epoch - 8ms/step\n", - "Epoch 274/500\n", + "Epoch 275: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5586 - accuracy: 0.7275 - val_loss: 0.6143 - val_accuracy: 0.6981 - lr: 7.0650e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 276/750\n", "\n", - "Epoch 274: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5563 - accuracy: 0.7149 - val_loss: 0.5517 - val_accuracy: 0.7486 - lr: 7.5019e-05 - 175ms/epoch - 7ms/step\n", - "Epoch 275/500\n", + "Epoch 276: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5531 - accuracy: 0.7241 - val_loss: 0.6166 - val_accuracy: 0.6730 - lr: 6.9947e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 277/750\n", "\n", - "Epoch 275: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5563 - accuracy: 0.7149 - val_loss: 0.5517 - val_accuracy: 0.7486 - lr: 7.4272e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 276/500\n", + "Epoch 277: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5632 - accuracy: 0.7330 - val_loss: 0.6119 - val_accuracy: 0.6918 - lr: 6.9251e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 278/750\n", "\n", - "Epoch 276: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5562 - accuracy: 0.7156 - val_loss: 0.5517 - val_accuracy: 0.7486 - lr: 7.3533e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 277/500\n", + "Epoch 278: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5578 - accuracy: 0.7275 - val_loss: 0.6122 - val_accuracy: 0.6918 - lr: 6.8562e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 279/750\n", "\n", - "Epoch 277: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5562 - accuracy: 0.7149 - val_loss: 0.5517 - val_accuracy: 0.7486 - lr: 7.2802e-05 - 181ms/epoch - 7ms/step\n", - "Epoch 278/500\n", + "Epoch 279: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5603 - accuracy: 0.7241 - val_loss: 0.6126 - val_accuracy: 0.6981 - lr: 6.7880e-05 - 65ms/epoch - 11ms/step\n", + "Epoch 280/750\n", "\n", - "Epoch 278: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5562 - accuracy: 0.7156 - val_loss: 0.5517 - val_accuracy: 0.7486 - lr: 7.2077e-05 - 174ms/epoch - 7ms/step\n", - "Epoch 279/500\n", + "Epoch 280: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5643 - accuracy: 0.7208 - val_loss: 0.6123 - val_accuracy: 0.6918 - lr: 6.7204e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 281/750\n", "\n", - "Epoch 279: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5562 - accuracy: 0.7153 - val_loss: 0.5517 - val_accuracy: 0.7486 - lr: 7.1360e-05 - 176ms/epoch - 7ms/step\n", - "Epoch 280/500\n", + "Epoch 281: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5577 - accuracy: 0.7297 - val_loss: 0.6125 - val_accuracy: 0.6981 - lr: 6.6536e-05 - 66ms/epoch - 11ms/step\n", + "Epoch 282/750\n", "\n", - "Epoch 280: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5562 - accuracy: 0.7153 - val_loss: 0.5517 - val_accuracy: 0.7486 - lr: 7.0650e-05 - 179ms/epoch - 7ms/step\n", - "Epoch 281/500\n", - "\n", - "Epoch 281: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5562 - accuracy: 0.7149 - val_loss: 0.5516 - val_accuracy: 0.7486 - lr: 6.9947e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 282/500\n", - "\n", - "Epoch 282: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5562 - accuracy: 0.7149 - val_loss: 0.5515 - val_accuracy: 0.7486 - lr: 6.9251e-05 - 179ms/epoch - 7ms/step\n", - "Epoch 283/500\n", - "\n", - "Epoch 283: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5561 - accuracy: 0.7149 - val_loss: 0.5515 - val_accuracy: 0.7486 - lr: 6.8562e-05 - 177ms/epoch - 7ms/step\n", - "Epoch 284/500\n", - "\n", - "Epoch 284: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5561 - accuracy: 0.7146 - val_loss: 0.5515 - val_accuracy: 0.7486 - lr: 6.7880e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 285/500\n", - "\n", - "Epoch 285: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5561 - accuracy: 0.7149 - val_loss: 0.5515 - val_accuracy: 0.7486 - lr: 6.7204e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 286/500\n", - "\n", - "Epoch 286: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5561 - accuracy: 0.7153 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.6536e-05 - 170ms/epoch - 7ms/step\n", - "Epoch 287/500\n", - "\n", - "Epoch 287: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5561 - accuracy: 0.7149 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.5874e-05 - 170ms/epoch - 7ms/step\n", - "Epoch 288/500\n", - "\n", - "Epoch 288: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5561 - accuracy: 0.7149 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.5218e-05 - 174ms/epoch - 7ms/step\n", - "Epoch 289/500\n", - "\n", - "Epoch 289: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5561 - accuracy: 0.7149 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.4569e-05 - 172ms/epoch - 7ms/step\n", - "Epoch 290/500\n", - "\n", - "Epoch 290: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5560 - accuracy: 0.7149 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.3927e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 291/500\n", - "\n", - "Epoch 291: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5560 - accuracy: 0.7149 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.3291e-05 - 170ms/epoch - 7ms/step\n", - "Epoch 292/500\n", - "\n", - "Epoch 292: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5560 - accuracy: 0.7153 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.2661e-05 - 169ms/epoch - 7ms/step\n", - "Epoch 293/500\n", - "\n", - "Epoch 293: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5560 - accuracy: 0.7153 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.2038e-05 - 168ms/epoch - 7ms/step\n", - "Epoch 294/500\n", - "\n", - "Epoch 294: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5560 - accuracy: 0.7149 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.1420e-05 - 171ms/epoch - 7ms/step\n", - "Epoch 295/500\n", - "\n", - "Epoch 295: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5560 - accuracy: 0.7153 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.0809e-05 - 174ms/epoch - 7ms/step\n", - "Epoch 296/500\n", - "\n", - "Epoch 296: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5560 - accuracy: 0.7153 - val_loss: 0.5514 - val_accuracy: 0.7486 - lr: 6.0204e-05 - 173ms/epoch - 7ms/step\n", - "Epoch 297/500\n", - "\n", - "Epoch 297: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5559 - accuracy: 0.7153 - val_loss: 0.5513 - val_accuracy: 0.7486 - lr: 5.9605e-05 - 170ms/epoch - 7ms/step\n", - "Epoch 298/500\n", - "\n", - "Epoch 298: val_accuracy did not improve from 0.75229\n", - "25/25 - 0s - loss: 0.5559 - accuracy: 0.7156 - val_loss: 0.5513 - val_accuracy: 0.7486 - lr: 5.9012e-05 - 168ms/epoch - 7ms/step\n" + "Epoch 282: val_accuracy did not improve from 0.72956\n", + "6/6 - 0s - loss: 0.5567 - accuracy: 0.7308 - val_loss: 0.6119 - val_accuracy: 0.6918 - lr: 6.5874e-05 - 66ms/epoch - 11ms/step\n" ] } ], "source": [ - "BATCH_SIZE = 128 #24 #4\n", - "history = model.fit(x, y, callbacks=[callback_mc, callback_es, callback_lr], batch_size=BATCH_SIZE, epochs=500, validation_split=0.15, verbose=2)" + "BATCH_SIZE = int(len(y)/6.6125) #128 #24 #4\n", + "print(f'BATCH SIZE = {BATCH_SIZE}')\n", + "history = model.fit(x, y, callbacks=[callback_mc, callback_es, callback_lr], batch_size=BATCH_SIZE, epochs=750, validation_split=0.15, verbose=2)" ] }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] }, "metadata": {}, @@ -1787,7 +1786,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1798,67 +1797,82 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2023-02-07 21:41:16.408838: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n" + "2023-10-20 15:46:29.966870: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n", + "2023-10-20 15:46:29.966900: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n", + "2023-10-20 15:46:29.966905: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n", + "2023-10-20 15:46:29.967252: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2023-10-20 15:46:29.967459: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n", + "2023-10-20 15:46:32.474441: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "13-12-2022 Nifty Prediction -> Market may Close BEARISH on 14-12-2022! Actual -> BEARISH, Prediction -> Correct, Pred = 0.57\n", - "14-12-2022 Nifty Prediction -> Market may Close BULLISH on 15-12-2022! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.4\n", - "15-12-2022 Nifty Prediction -> Market may Close BEARISH on 16-12-2022! Actual -> BEARISH, Prediction -> Correct, Pred = 0.86\n", - "16-12-2022 Nifty Prediction -> Market may Close BEARISH on 17-12-2022! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.67\n", - "19-12-2022 Nifty Prediction -> Market may Close BULLISH on 20-12-2022! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.35\n", - "20-12-2022 Nifty Prediction -> Market may Close BEARISH on 21-12-2022! Actual -> BEARISH, Prediction -> Correct, Pred = 0.59\n", - "21-12-2022 Nifty Prediction -> Market may Close BEARISH on 22-12-2022! Actual -> BEARISH, Prediction -> Correct, Pred = 0.8\n", - "22-12-2022 Nifty Prediction -> Market may Close BEARISH on 23-12-2022! Actual -> BEARISH, Prediction -> Correct, Pred = 0.65\n", - "23-12-2022 Nifty Prediction -> Market may Close BEARISH on 24-12-2022! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.86\n", - "26-12-2022 Nifty Prediction -> Market may Close BULLISH on 27-12-2022! Actual -> BULLISH, Prediction -> Correct, Pred = 0.28\n", - "27-12-2022 Nifty Prediction -> Market may Close BULLISH on 28-12-2022! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.27\n", - "28-12-2022 Nifty Prediction -> Market may Close BEARISH on 29-12-2022! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.56\n", - "29-12-2022 Nifty Prediction -> Market may Close BULLISH on 30-12-2022! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.43\n", - "30-12-2022 Nifty Prediction -> Market may Close BEARISH on 31-12-2022! Actual -> BEARISH, Prediction -> Correct, Pred = 0.62\n", - "02-01-2023 Nifty Prediction -> Market may Close BULLISH on 03-01-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.36\n", - "03-01-2023 Nifty Prediction -> Market may Close BULLISH on 04-01-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.43\n", - "04-01-2023 Nifty Prediction -> Market may Close BEARISH on 05-01-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.86\n", - "05-01-2023 Nifty Prediction -> Market may Close BEARISH on 06-01-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.54\n", - "06-01-2023 Nifty Prediction -> Market may Close BEARISH on 07-01-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.74\n", - "09-01-2023 Nifty Prediction -> Market may Close BULLISH on 10-01-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.2\n", - "10-01-2023 Nifty Prediction -> Market may Close BEARISH on 11-01-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.85\n", - "11-01-2023 Nifty Prediction -> Market may Close BULLISH on 12-01-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.48\n", - "12-01-2023 Nifty Prediction -> Market may Close BEARISH on 13-01-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.62\n", - "13-01-2023 Nifty Prediction -> Market may Close BULLISH on 14-01-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.39\n", - "16-01-2023 Nifty Prediction -> Market may Close BEARISH on 17-01-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.63\n", - "17-01-2023 Nifty Prediction -> Market may Close BULLISH on 18-01-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.32\n", - "18-01-2023 Nifty Prediction -> Market may Close BULLISH on 19-01-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.31\n", - "19-01-2023 Nifty Prediction -> Market may Close BEARISH on 20-01-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.59\n", - "20-01-2023 Nifty Prediction -> Market may Close BEARISH on 21-01-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.71\n", - "23-01-2023 Nifty Prediction -> Market may Close BULLISH on 24-01-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.36\n", - "24-01-2023 Nifty Prediction -> Market may Close BEARISH on 25-01-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.6\n", - "25-01-2023 Nifty Prediction -> Market may Close BEARISH on 26-01-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.82\n", - "27-01-2023 Nifty Prediction -> Market may Close BEARISH on 28-01-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.82\n", - "30-01-2023 Nifty Prediction -> Market may Close BULLISH on 31-01-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.34\n", - "31-01-2023 Nifty Prediction -> Market may Close BEARISH on 01-02-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.57\n", - "01-02-2023 Nifty Prediction -> Market may Close BEARISH on 02-02-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.51\n", - "02-02-2023 Nifty Prediction -> Market may Close BULLISH on 03-02-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.39\n", - "03-02-2023 Nifty Prediction -> Market may Close BULLISH on 04-02-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.21\n", - "06-02-2023 Nifty Prediction -> Market may Close BEARISH on 07-02-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.6\n", - "07-02-2023 Nifty Prediction -> Market may Close BEARISH on 08-02-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.66\n", - "Correct: 111, Wrong: 17, Accuracy: 0.8671875\n", - "{'TP': 12, 'FP': 14, 'TN': 34, 'FN': 20}\n" + "11-08-2023 Nifty Prediction -> Market may Close BULLISH on 12-08-2023! Actual -> BULLISH, Prediction -> Correct, Pred = nan\n", + "14-08-2023 Nifty Prediction -> Market may Close BULLISH on 15-08-2023! Actual -> BULLISH, Prediction -> Correct, Pred = nan\n", + "16-08-2023 Nifty Prediction -> Market may Close BULLISH on 17-08-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = nan\n", + "17-08-2023 Nifty Prediction -> Market may Close BULLISH on 18-08-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = nan\n", + "18-08-2023 Nifty Prediction -> Market may Close BULLISH on 19-08-2023! Actual -> BULLISH, Prediction -> Correct, Pred = nan\n", + "21-08-2023 Nifty Prediction -> Market may Close BULLISH on 22-08-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = nan\n", + "22-08-2023 Nifty Prediction -> Market may Close BULLISH on 23-08-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = nan\n", + "23-08-2023 Nifty Prediction -> Market may Close BULLISH on 24-08-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = nan\n", + "24-08-2023 Nifty Prediction -> Market may Close BEARISH on 25-08-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.61\n", + "25-08-2023 Nifty Prediction -> Market may Close BEARISH on 26-08-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.57\n", + "28-08-2023 Nifty Prediction -> Market may Close BULLISH on 29-08-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.44\n", + "29-08-2023 Nifty Prediction -> Market may Close BULLISH on 30-08-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.49\n", + "30-08-2023 Nifty Prediction -> Market may Close BEARISH on 31-08-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.51\n", + "31-08-2023 Nifty Prediction -> Market may Close BEARISH on 01-09-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.59\n", + "01-09-2023 Nifty Prediction -> Market may Close BULLISH on 02-09-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.18\n", + "04-09-2023 Nifty Prediction -> Market may Close BEARISH on 05-09-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.52\n", + "05-09-2023 Nifty Prediction -> Market may Close BULLISH on 06-09-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.42\n", + "06-09-2023 Nifty Prediction -> Market may Close BULLISH on 07-09-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.44\n", + "07-09-2023 Nifty Prediction -> Market may Close BULLISH on 08-09-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.34\n", + "08-09-2023 Nifty Prediction -> Market may Close BULLISH on 09-09-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.44\n", + "11-09-2023 Nifty Prediction -> Market may Close BULLISH on 12-09-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.31\n", + "12-09-2023 Nifty Prediction -> Market may Close BEARISH on 13-09-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.6\n", + "13-09-2023 Nifty Prediction -> Market may Close BULLISH on 14-09-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.33\n", + "14-09-2023 Nifty Prediction -> Market may Close BEARISH on 15-09-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.5\n", + "15-09-2023 Nifty Prediction -> Market may Close BULLISH on 16-09-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.38\n", + "18-09-2023 Nifty Prediction -> Market may Close BEARISH on 19-09-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.58\n", + "19-09-2023 Nifty Prediction -> Market may Close BEARISH on 20-09-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.5\n", + "20-09-2023 Nifty Prediction -> Market may Close BEARISH on 21-09-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.75\n", + "21-09-2023 Nifty Prediction -> Market may Close BEARISH on 22-09-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.68\n", + "22-09-2023 Nifty Prediction -> Market may Close BEARISH on 23-09-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.54\n", + "25-09-2023 Nifty Prediction -> Market may Close BULLISH on 26-09-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.48\n", + "26-09-2023 Nifty Prediction -> Market may Close BEARISH on 27-09-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.51\n", + "27-09-2023 Nifty Prediction -> Market may Close BULLISH on 28-09-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.35\n", + "28-09-2023 Nifty Prediction -> Market may Close BEARISH on 29-09-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.81\n", + "29-09-2023 Nifty Prediction -> Market may Close BULLISH on 30-09-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.25\n", + "02-10-2023 Nifty Prediction -> Market may Close BEARISH on 03-10-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.51\n", + "03-10-2023 Nifty Prediction -> Market may Close BEARISH on 04-10-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.7\n", + "04-10-2023 Nifty Prediction -> Market may Close BEARISH on 05-10-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.62\n", + "05-10-2023 Nifty Prediction -> Market may Close BULLISH on 06-10-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.38\n", + "06-10-2023 Nifty Prediction -> Market may Close BULLISH on 07-10-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.39\n", + "09-10-2023 Nifty Prediction -> Market may Close BEARISH on 10-10-2023! Actual -> BULLISH, Prediction -> Wrong, Pred = 0.63\n", + "10-10-2023 Nifty Prediction -> Market may Close BULLISH on 11-10-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.26\n", + "11-10-2023 Nifty Prediction -> Market may Close BULLISH on 12-10-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.46\n", + "12-10-2023 Nifty Prediction -> Market may Close BEARISH on 13-10-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.57\n", + "13-10-2023 Nifty Prediction -> Market may Close BULLISH on 14-10-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.44\n", + "16-10-2023 Nifty Prediction -> Market may Close BEARISH on 17-10-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.59\n", + "17-10-2023 Nifty Prediction -> Market may Close BULLISH on 18-10-2023! Actual -> BEARISH, Prediction -> Wrong, Pred = 0.44\n", + "18-10-2023 Nifty Prediction -> Market may Close BEARISH on 19-10-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.68\n", + "19-10-2023 Nifty Prediction -> Market may Close BULLISH on 20-10-2023! Actual -> BULLISH, Prediction -> Correct, Pred = 0.43\n", + "20-10-2023 Nifty Prediction -> Market may Close BEARISH on 21-10-2023! Actual -> BEARISH, Prediction -> Correct, Pred = 0.63\n", + "Correct: 27, Wrong: 23, Accuracy: 0.54\n", + "{'TP': 14, 'FP': 9, 'TN': 13, 'FN': 14}\n" ] } ], "source": [ - "endpoint = keras.models.load_model('best_model.h5')\n", + "endpoint = keras.models.load_model('nifty_model_v3.h5')\n", "try:\n", " scaler\n", "except NameError:\n", @@ -1871,13 +1885,33 @@ " progress=False,\n", " timeout=10\n", " )\n", - "today = today.drop(columns=['Adj Close', 'Volume'])\n", + "if INCLUDE_COMMODITIES:\n", + " gold = yf.download(\n", + " tickers=\"GC=F\",\n", + " period=f'{TEST_DAYS}d',\n", + " interval='1d',\n", + " progress=False,\n", + " timeout=10\n", + " ).add_prefix(prefix='gold_')\n", + " crude = yf.download(\n", + " tickers=\"CL=F\",\n", + " period=f'{TEST_DAYS}d',\n", + " interval='1d',\n", + " progress=False,\n", + " timeout=10\n", + " ).add_prefix(prefix='crude_')\n", + "\n", + " today = pd.concat([today, gold, crude], axis=1)\n", + " today = today.drop(columns=['Adj Close', 'Volume', 'gold_Adj Close', 'gold_Volume', 'crude_Adj Close', 'crude_Volume'])\n", + "else:\n", + " today = today.drop(columns=['Adj Close', 'Volume'])\n", "\n", "###\n", "today = preprocessBeforeScaling(today)\n", + "today = today.drop(columns=['gold_Open', 'gold_High', 'gold_Low', 'crude_Open', 'crude_High', 'crude_Low'])\n", "###\n", "\n", - "cnt_corrct, cnt_wrong = 0, 0\n", + "cnt_correct, cnt_wrong = 0, 0\n", "for i in range(-TEST_DAYS,0):\n", " df = today.iloc[i]\n", " twr = today.iloc[i+1]['Close']\n", @@ -1931,7 +1965,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1940,7 +1974,7 @@ "['nifty_model.pkl']" ] }, - "execution_count": 102, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1949,7 +1983,7 @@ "pkl = {\n", " # 'model': model,\n", " 'scaler': scaler,\n", - " 'columns': ['Open', 'Close', 'High', 'Low']\n", + " 'columns': ['Open', 'Close', 'High', 'Low', 'gold_Close', 'crude_Close']\n", "}\n", "\n", "joblib.dump(pkl, 'nifty_model.pkl')" @@ -1957,18 +1991,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Open -0.015599\n", + "Close -0.418090\n", + "High -0.447114\n", + "Low 0.032541\n", + "gold_Close 1.325949\n", + "crude_Close 1.242028\n", + "Name: 2023-10-20 00:00:00, dtype: float64\n", + "1/1 [==============================] - 0s 15ms/step\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0.52346516]], dtype=float32)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pkl = joblib.load('nifty_model.pkl')\n", "z = yf.download(\n", " tickers=\"^NSEI\",\n", - " period='1d',\n", + " period='5d',\n", " interval='1d',\n", " progress=False,\n", " timeout=10\n", " )\n", + "if INCLUDE_COMMODITIES:\n", + " gold = yf.download(\n", + " tickers=\"GC=F\",\n", + " period='5d',\n", + " interval='1d',\n", + " progress=False,\n", + " timeout=10\n", + " ).add_prefix(prefix='gold_')\n", + " crude = yf.download(\n", + " tickers=\"CL=F\",\n", + " period='5d',\n", + " interval='1d',\n", + " progress=False,\n", + " timeout=10\n", + " ).add_prefix(prefix='crude_')\n", + " z = pd.concat([z, gold, crude], axis=1)\n", "z = preprocessBeforeScaling(z)\n", "z = z.iloc[-1]\n", "z = z[pkl['columns']]\n", @@ -1979,112 +2054,9 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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OpenHighLowCloseAdj CloseVolume
Date
2023-02-01NaNNaNNaNNaN17616.300781512900
2023-02-02-1.653417-1.7710620.533318-0.03349417610.400391490100
2023-02-031.1682891.2257940.7924481.38356017854.050781424100
2023-02-060.546226-0.2607770.649165-0.50101317764.599609282500
2023-02-07-0.159672-0.070405-0.258775-0.24261517721.5000000
\n", - "
" - ], - "text/plain": [ - " Open High Low Close Adj Close Volume\n", - "Date \n", - "2023-02-01 NaN NaN NaN NaN 17616.300781 512900\n", - "2023-02-02 -1.653417 -1.771062 0.533318 -0.033494 17610.400391 490100\n", - "2023-02-03 1.168289 1.225794 0.792448 1.383560 17854.050781 424100\n", - "2023-02-06 0.546226 -0.260777 0.649165 -0.501013 17764.599609 282500\n", - "2023-02-07 -0.159672 -0.070405 -0.258775 -0.242615 17721.500000 0" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "z = yf.download(\n", " tickers=\"^NSEI\",\n", @@ -2135,20 +2107,9 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "92.66973999999999" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def getSigmoidConfidence(x):\n", " out_min, out_max = 0, 100\n", @@ -2187,7 +2148,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.6" }, "orig_nbformat": 4, "vscode": { diff --git a/src/ml/nifty_model.pkl b/src/ml/nifty_model.pkl deleted file mode 100644 index 89564227..00000000 Binary files a/src/ml/nifty_model.pkl and /dev/null differ diff --git a/src/ml/nifty_model_v3.h5 b/src/ml/nifty_model_v3.h5 new file mode 100644 index 00000000..eccf5acd Binary files /dev/null and b/src/ml/nifty_model_v3.h5 differ diff --git a/src/ml/nifty_model_v3.pkl b/src/ml/nifty_model_v3.pkl new file mode 100644 index 00000000..7f8927af Binary files /dev/null and b/src/ml/nifty_model_v3.pkl differ diff --git a/src/release.md b/src/release.md index 479b1f86..6308d79b 100644 --- a/src/release.md +++ b/src/release.md @@ -7,11 +7,11 @@ Screeni-py is now on **YouTube** for additional help! - Thank You for your suppo ⚠️ **Executable files (.exe, .bin and .run) are now DEPRECATED! Please Switch to Docker** -1. **US S&P 500** Index added for scanning US markets. -2. **Search Similar Stocks** Added using Vector Similarity search - Try `Search Similar Stocks`. -3. Progressbar added for Screener in GUI mode! -4. New Index - **F&O Stocks Only** Added for F&O traders with modified screening criterias. -5. **Artificial Intelligence v2 for Nifty 50 Prediction** - Predict Next day Gap-up/down - Try `Select Index for Screening > N` +1. **Artificial Intelligence v3 for Nifty 50 Prediction** - Predict Next day Gap-up/down using Nifty, Gold and Crude prices! - Try `Select Index for Screening > N` +2. **US S&P 500** Index added for scanning US markets. +3. **Search Similar Stocks** Added using Vector Similarity search - Try `Search Similar Stocks`. +4. Progressbar added for Screener in GUI mode! +5. New Index - **F&O Stocks Only** Added for F&O traders with modified screening criterias. 6. New Screener **Buy at Trendline** added for Swing/Mid/Long term traders - Try `Option > 7 > 5`. ## Installation Guide diff --git a/src/streamlit_app.py b/src/streamlit_app.py index 8c8aa6d4..74edc4d3 100644 --- a/src/streamlit_app.py +++ b/src/streamlit_app.py @@ -148,6 +148,7 @@ def nifty_predict(col): else: col.info("Couldn't determine the Trend. Try again later!") col.warning('The AI prediction should be executed After 3 PM or Around the Closing hours as the Prediction Accuracy is based on the Closing price!\n\nThis is Just a Statistical Prediction and There are Chances of **False** Predictions!', icon='⚠️') + col.info("What's New in **v3**?\n\nMachine Learning model (v3) now uses Nifty, Crude and Gold Historical prices to Predict the Gap!", icon='🆕') def find_similar_stocks(stockCode:str, candles:int): global execute_inputs @@ -231,7 +232,8 @@ def get_extra_inputs(tickerOption, executeOption, c_index=None, c_criteria=None, ac, bc = st.columns([13,1]) ac.title('📈 Screeni-py') -ac.subheader('in Beta Release 🚧 (Scan QR to Report Bugs / Request Features)') +if guiUpdateMessage == "": + ac.subheader('Find Breakouts, Just in Time!') if isDevVersion: ac.warning(guiUpdateMessage, icon='⚠️')