{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "LR_vs_LSTM_on_PIMA_without_skin.ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true, "authorship_tag": "ABX9TyNe4S5uNRpxMSdW51WlML+l", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "sYD4qX7ik4xw", "colab_type": "text" }, "source": [ "# Overview #\n", "\n", "Two diabetic datasets can be explored:\n", "\n", "1. UCI\n", "\n", "2. PIMA: \"This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes.\"\n", "\n", "Adopted from:\n", "\n", "- [Collab notebook](https://github.com/1UC1F3R616/myGoogleCollabNotebooks/blob/master/Pima_Indians_Diabetes.ipynb)\n", "\n", "- [MDPI 2019](https://www.mdpi.com/2076-3417/9/17/3532/pdf)\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "BtG7k2Y0k80_", "colab_type": "text" }, "source": [ "# A) Mount and download datasets #" ] }, { "cell_type": "code", "metadata": { "id": "_rup1_Ybj5jh", "colab_type": "code", "outputId": "0a1bd4f9-6713-462e-ed71-715cbb2daf23", "colab": { "base_uri": "https://localhost:8080/", "height": 122 } }, "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')\n", "\n", "from pydrive.auth import GoogleAuth\n", "from pydrive.drive import GoogleDrive\n", "from google.colab import auth\n", "from oauth2client.client import GoogleCredentials" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n", "\n", "Enter your authorization code:\n", "··········\n", "Mounted at /content/drive\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "2d2ACaXRIjsD", "colab_type": "text" }, "source": [ "## Download UCI-Diabetes ##" ] }, { "cell_type": "code", "metadata": { "id": "QwGqLC0I8nsQ", "colab_type": "code", "colab": {} }, "source": [ "import os\n", "if os.path.isdir('/content/drive/My Drive/Colab Notebooks/opensource_datasets/' )==False:\n", " try:\n", " ! mkdir '/content/drive/My Drive/Colab Notebooks/opensource_datasets/'\n", " except e as Exception:\n", " pass \n", "\n", "if os.path.isdir( '/content/drive/My Drive/Colab Notebooks/opensource_datasets/UCI-diabetes' )==False:\n", " try:\n", " ! mkdir '/content/drive/My Drive/Colab Notebooks/opensource_datasets/UCI-diabetes'\n", " except e as Exception:\n", " pass \n", " \n", "os.chdir('/content/drive/My Drive/Colab Notebooks/opensource_datasets/UCI-diabetes')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "xqOOoKP38mrZ", "colab_type": "code", "colab": {} }, "source": [ "! wget -O diabetes2.Z https://archive.ics.uci.edu/ml/machine-learning-databases/diabetes/diabetes-data.tar.Z\n", "! tar xvf diabetes2.Z" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "4PLAuZJL9WcK", "colab_type": "text" }, "source": [ "## Download PIMA ##" ] }, { "cell_type": "code", "metadata": { "id": "lADZ3VR7kENs", "colab_type": "code", "colab": {} }, "source": [ "import os\n", "if os.path.isdir('/content/drive/My Drive/Colab Notebooks/opensource_datasets/' )==False:\n", " try:\n", " ! mkdir '/content/drive/My Drive/Colab Notebooks/opensource_datasets/'\n", " except e as Exception:\n", " pass \n", "\n", "if os.path.isdir( '/content/drive/My Drive/Colab Notebooks/opensource_datasets/PIMA' )==False:\n", " try:\n", " ! mkdir '/content/drive/My Drive/Colab Notebooks/opensource_datasets/PIMA'\n", " except e as Exception:\n", " pass \n", " \n", "os.chdir('/content/drive/My Drive/Colab Notebooks/opensource_datasets/PIMA')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "sP9ypL6QjsXL", "colab_type": "code", "outputId": "b35cfea7-5280-4a0e-9707-2087c2b89cc2", "colab": { "base_uri": "https://localhost:8080/", "height": 102 } }, "source": [ "! git clone https://github.com/lisatwyw/GlucoseLevels.git\n", "! ls" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'GlucoseLevels'...\n", "remote: Enumerating objects: 42, done.\u001b[K\n", "remote: Total 42 (delta 0), reused 0 (delta 0), pack-reused 42\u001b[K\n", "Unpacking objects: 100% (42/42), done.\n", "GlucoseLevels\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Q8ooVLDdlJLC", "colab_type": "code", "outputId": "822b7ae6-2979-4159-9f22-f835c3ba3fb3", "colab": { "base_uri": "https://localhost:8080/", "height": 51 } }, "source": [ "os.chdir('/content/drive/My Drive/Colab Notebooks/opensource_datasets/PIMA/GlucoseLevels')\n", "! ls" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "ann_BGL.ipynb diabetes3.csv diabetes.csv README.md\n", "diabetes2.csv diabetes4.csv glucose_RF.R\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "2fhi-8rSIqY4", "colab_type": "text" }, "source": [ "# B) Load data #" ] }, { "cell_type": "code", "metadata": { "id": "DQO8W9hklSn_", "colab_type": "code", "outputId": "1bffdeaa-ed1e-449d-a9a1-46aff9654555", "colab": { "base_uri": "https://localhost:8080/", "height": 238 } }, "source": [ "import pandas as pd\n", "col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']\n", "feature_cols=['pregnant','insulin', 'bmi', 'age','glucose','bp','pedigree']\n", "\n", "pima = pd.read_csv('diabetes.csv', header=None, names=col_names)\n", "print(pima.shape)\n", "pima.drop(pima.index[0], inplace=True)\n", "print(pima.shape)\n", "pima.head()" ], "execution_count": 7, "outputs": [ { "output_type": "stream", "text": [ "(769, 9)\n", "(768, 9)\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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pregnantglucosebpskininsulinbmipedigreeagelabel
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" ], "text/plain": [ " pregnant glucose bp skin insulin bmi pedigree age label\n", "1 6 148 72 35 0 33.6 0.627 50 1\n", "2 1 85 66 29 0 26.6 0.351 31 0\n", "3 8 183 64 0 0 23.3 0.672 32 1\n", "4 1 89 66 23 94 28.1 0.167 21 0\n", "5 0 137 40 35 168 43.1 2.288 33 1" ] }, "metadata": { "tags": [] }, "execution_count": 7 } ] }, { "cell_type": "markdown", "metadata": { "id": "0apfhWS-LFeS", "colab_type": "text" }, "source": [ "# C) Setup machine learning experiments #" ] }, { "cell_type": "code", "metadata": { "id": "7lCD_2cTlJKL", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "c2bedfbf-ec26-4362-f082-3d2a9bb36a26" }, "source": [ "from keras.models import Sequential\n", "from keras.layers import LSTM\n", "from keras.layers import Dense\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn import preprocessing\n", "from sklearn.metrics import roc_curve\n", "from sklearn.metrics import roc_auc_score\n", "from matplotlib import pyplot\n", "\n", "seed = 42\n", "np.random.seed(seed)\n", "\n", "import keras\n", "keras.__version__" ], "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'2.3.1'" ] }, "metadata": { "tags": [] }, "execution_count": 11 } ] }, { "cell_type": "code", "metadata": { "id": "M1u6DM81mNjy", "colab_type": "code", "outputId": "d82748eb-aed4-4397-f1b2-c641f8c1e41d", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "X = pima[feature_cols] # Features\n", "y = pima.label # Target variable\n", "\n", "X=X.to_numpy('float')\n", "y=y.to_numpy('int')\n", "\n", "X.shape" ], "execution_count": 12, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(768, 7)" ] }, "metadata": { "tags": [] }, "execution_count": 12 } ] }, { "cell_type": "code", "metadata": { "id": "bIZfLiTw3-UN", "colab_type": "code", "colab": {} }, "source": [ "from sklearn.model_selection import train_test_split\n", "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.5,random_state=0)\n", "\n", "X_train0=X_train.copy()\n", "X_test0=X_test.copy()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "N9gx7koH3_ky", "colab_type": "code", "outputId": "c0593bb9-b3ef-4eb8-8c2b-7221873a4631", "colab": { "base_uri": "https://localhost:8080/", "height": 323 } }, "source": [ "y_test" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n", " 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n", " 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n", " 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n", " 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n", " 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n", " 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n", " 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1,\n", " 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,\n", " 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1,\n", " 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n", " 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n", " 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n", " 0, 1, 1, 0, 0, 1, 1, 0, 0, 0])" ] }, "metadata": { "tags": [] }, "execution_count": 258 } ] }, { "cell_type": "markdown", "metadata": { "id": "yaa1EQE3KeBH", "colab_type": "text" }, "source": [ "## Try classical methods, like logistic regression ##" ] }, { "cell_type": "code", "metadata": { "id": "rnv-03bPl8CC", "colab_type": "code", "outputId": "30f3d725-e4b8-47f5-e038-b02fc4841727", "colab": { "base_uri": "https://localhost:8080/", "height": 523 } }, "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "# instantiate the model (using the default parameters)\n", "logreg = LogisticRegression(verbose=False ) \n", "\n", "# fit the model with data\n", "logreg.fit(X_train0,y_train)\n", "\n", "# Predict the response for test dataset \n", "y_pred=logreg.predict(X_test0)\n", "yp = logreg.predict_proba(X_test0)[:,1]\n", "\n", "# import the metrics class\n", "from sklearn import metrics\n", "cmat = metrics.confusion_matrix(y_test, y_pred)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred, pos_label=1))\n", "\n", "auc = roc_auc_score(y_test, yp)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, yp)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n" ], "execution_count": 14, "outputs": [ { "output_type": "stream", "text": [ "[[228 25]\n", " [ 56 75]]\n", "Accuracy: 0.7890625\n", "Precision: 0.75\n", "Recall: 0.5725190839694656\n", "AUC: 0.849\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "rqiuJ3mnKWiH", "colab_type": "text" }, "source": [ "## Standardize input variables using training set only ##" ] }, { "cell_type": "code", "metadata": { "id": "PlJx3VxiqZ34", "colab_type": "code", "outputId": "d2e6346c-1daf-493b-de39-080d270a85ef", "colab": { "base_uri": "https://localhost:8080/", "height": 68 } }, "source": [ "# raw data\n", "X_trn0 = np.expand_dims(X_train0,2)\n", "X_tst0 = np.expand_dims(X_test0,2)\n", "\n", "# after standardizing\n", "scaler = preprocessing.StandardScaler().fit(X_train0)\n", "X_train=scaler.transform(X_train)\n", "X_test=scaler.transform(X_test)\n", "X_trn = np.expand_dims(X_train,2)\n", "X_tst = np.expand_dims(X_test,2)\n", "\n", "print( scaler.n_samples_seen_,scaler.mean_, scaler.var_ )" ], "execution_count": 15, "outputs": [ { "output_type": "stream", "text": [ "384 [ 3.77083333 88.0390625 32.14453125 33.390625 121.73958333\n", " 70.5546875 0.46521875] [1.14162326e+01 1.51816834e+04 6.53255951e+01 1.43758870e+02\n", " 1.02317177e+03 3.45611593e+02 1.17655426e-01]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "ca7fC6pEOP-p", "colab_type": "code", "outputId": "8bfc1f23-d2e0-4030-b487-ffb83b6d8d56", "colab": { "base_uri": "https://localhost:8080/", "height": 289 } }, "source": [ "transformer = preprocessing.Normalizer().fit(X_train0) \n", "X_train2 = transformer.transform(X_train0)\n", "X_test2 = transformer.transform(X_test0)\n", "X_trn2 = np.expand_dims(X_train2,2)\n", "X_tst2 = np.expand_dims(X_test2,2)\n", "\n", "transformer = preprocessing.RobustScaler().fit(X_train0) \n", "X_train3 = transformer.transform(X_train0)\n", "X_test3 = transformer.transform(X_test0)\n", "X_trn3 = np.expand_dims(X_train3,2)\n", "X_tst3 = np.expand_dims(X_test3,2)\n", "\n", "\n", "\n", "df=pd.DataFrame( {'Raw':np.max(X_test0,0), '1':np.max(X_test,0), '2':np.max(X_test2,0), '3':np.max(X_test3,0) } ) \n", "print( df)\n", "\n", "df=pd.DataFrame( {'Raw':np.min(X_test0,0), '1':np.min(X_test,0), '2':np.min(X_test2,0),'3':np.min(X_test3,0) } ) \n", "print( df)\n", "\n" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "text": [ " Raw 1 2 3\n", "0 17.000 3.915354 0.117208 2.800000\n", "1 680.000 4.804333 0.960715 4.505338\n", "2 57.300 3.112366 0.423657 2.706522\n", "3 72.000 3.220145 0.512539 2.687500\n", "4 199.000 2.415365 0.973682 1.940828\n", "5 114.000 2.336947 0.868295 2.625000\n", "6 1.893 4.162514 0.013588 4.310078\n", " Raw 1 2 3\n", "0 0.000 -1.116030 0.000000 -0.600000\n", "1 0.000 -0.714522 0.000000 -0.334520\n", "2 0.000 -3.977090 0.000000 -3.521739\n", "3 21.000 -1.033418 0.032495 -0.500000\n", "4 0.000 -3.805901 0.000000 -2.769231\n", "5 0.000 -3.795174 0.000000 -4.500000\n", "6 0.078 -1.128887 0.000250 -0.806202\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "lVJMbkkGLTGM", "colab_type": "text" }, "source": [ "## Try logistic regression with the standardized input data ##" ] }, { "cell_type": "code", "metadata": { "id": "BdTBD2_gLLj8", "colab_type": "code", "outputId": "662605e3-4aee-4ed7-afa0-7e97ffb6f5a0", "colab": { "base_uri": "https://localhost:8080/", "height": 724 } }, "source": [ "# instantiate the model (using the default parameters)\n", "logreg = LogisticRegression() \n", "\n", "# fit the model with data\n", "logreg.fit(X_train,y_train)\n", "\n", "# Predict the response for test dataset \n", "y_pred=logreg.predict(X_test)\n", "\n", "\n", "# import the metrics class\n", "from sklearn import metrics\n", "cmat = metrics.confusion_matrix(y_test, y_pred)\n", "\n", "print('\\nResults using Standard normalizer:')\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred, pos_label=1))\n", "\n", "\n", "# instantiate the model (using the default parameters)\n", "logreg = LogisticRegression() \n", "\n", "# fit the model with data\n", "logreg.fit(X_train2,y_train)\n", "\n", "# Predict the response for test dataset \n", "y_pred=logreg.predict(X_test2)\n", "\n", "# import the metrics class\n", "from sklearn import metrics\n", "cmat = metrics.confusion_matrix(y_test, y_pred)\n", "print('\\nResults using data Normalizer:')\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred, pos_label=1))\n", "\n", "\n", "\n", "# instantiate the model (using the default parameters)\n", "logreg = LogisticRegression() \n", "\n", "# fit the model with data\n", "logreg.fit(X_train3,y_train)\n", "\n", "# Predict the response for test dataset \n", "y_pred=logreg.predict(X_test3)\n", "\n", "cmat = metrics.confusion_matrix(y_test, y_pred)\n", "print('\\nResults using robust normalization:')\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred, pos_label=1))\n", "\n", "\n", "\n", "yp = logreg.predict_proba(X_test3)[:,1]\n", "cmat = metrics.confusion_matrix(y_test, y_pred)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred, pos_label=1))\n", "\n", "auc = roc_auc_score(y_test, yp)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, yp)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n" ], "execution_count": 17, "outputs": [ { "output_type": "stream", "text": [ "\n", "Results using Standard normalizer:\n", "[[229 24]\n", " [ 56 75]]\n", "Accuracy: 0.7916666666666666\n", "Precision: 0.7575757575757576\n", "Recall: 0.5725190839694656\n", "\n", "Results using data Normalizer:\n", "[[245 8]\n", " [119 12]]\n", "Accuracy: 0.6692708333333334\n", "Precision: 0.6\n", "Recall: 0.0916030534351145\n", "\n", "Results using robust normalization:\n", "[[229 24]\n", " [ 56 75]]\n", "Accuracy: 0.7916666666666666\n", "Precision: 0.7575757575757576\n", "Recall: 0.5725190839694656\n", "[[229 24]\n", " [ 56 75]]\n", "Accuracy: 0.7916666666666666\n", "Precision: 0.7575757575757576\n", "Recall: 0.5725190839694656\n", "AUC: 0.850\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "l09uquYDKWfz", "colab_type": "text" }, "source": [ "## Train LSTM with raw input data ## " ] }, { "cell_type": "code", "metadata": { "id": "1wHn35l7qZ20", "colab_type": "code", "colab": {} }, "source": [ "from keras.callbacks import ModelCheckpoint\n", "C = [ModelCheckpoint(filepath='best.h5',monitor='val_accuracy',save_best_only=True)]" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "-Z5VjIh7q_FE", "colab_type": "code", "outputId": "a56240c1-6ffa-425e-c881-ff5e6430416d", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "model = Sequential()\n", "model.add(LSTM(32, input_shape = (7,1), return_sequences = True, kernel_initializer = 'uniform', activation ='relu'))\n", "model.add(LSTM(64, kernel_initializer = 'uniform', return_sequences = True, activation = 'relu'))\n", "model.add(LSTM(128, kernel_initializer = 'uniform', activation = 'relu'))\n", "model.add(Dense(256, activation = 'relu'))\n", "model.add(Dense(128, activation = 'relu'))\n", "model.add(Dense(64, activation = 'relu'))\n", "model.add(Dense(16, activation = 'relu'))\n", "model.add(Dense(1, activation = 'sigmoid'))\n", "\n", "from keras import optimizers \n", " \n", "lr=0.002 \n", "b1=0.9; b2=0.999; ep=1e-08; dd=0.004\n", "opt = optimizers.Nadam()#(lr=lr, beta_1=b1, beta_2=b2, epsilon=ep, schedule_decay=dd) \n", "\n", "model.compile(loss = 'binary_crossentropy', optimizer ='NADAM', metrics = ['accuracy'])\n", "model.summary()\n", "history = model.fit(X_trn0, y_train, validation_split = 0.33, epochs = 500, batch_size = 64, verbose =1,callbacks=C)" ], "execution_count": 21, "outputs": [ { "output_type": "stream", "text": [ "Model: \"sequential_2\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_4 (LSTM) (None, 7, 32) 4352 \n", "_________________________________________________________________\n", "lstm_5 (LSTM) (None, 7, 64) 24832 \n", "_________________________________________________________________\n", "lstm_6 (LSTM) (None, 128) 98816 \n", "_________________________________________________________________\n", "dense_6 (Dense) (None, 256) 33024 \n", "_________________________________________________________________\n", "dense_7 (Dense) (None, 128) 32896 \n", "_________________________________________________________________\n", "dense_8 (Dense) (None, 64) 8256 \n", "_________________________________________________________________\n", "dense_9 (Dense) (None, 16) 1040 \n", "_________________________________________________________________\n", "dense_10 (Dense) (None, 1) 17 \n", "=================================================================\n", "Total params: 203,233\n", "Trainable params: 203,233\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Train on 257 samples, validate on 127 samples\n", "Epoch 1/500\n", "257/257 [==============================] - 2s 6ms/step - loss: 0.6905 - accuracy: 0.6148 - val_loss: 0.6690 - val_accuracy: 0.6929\n", "Epoch 2/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6742 - accuracy: 0.6187 - val_loss: 0.6873 - val_accuracy: 0.7165\n", "Epoch 3/500\n", "257/257 [==============================] - 0s 539us/step - loss: 0.6833 - accuracy: 0.6498 - val_loss: 0.7424 - val_accuracy: 0.6929\n", "Epoch 4/500\n", "257/257 [==============================] - 0s 621us/step - loss: 0.7351 - accuracy: 0.6109 - val_loss: 0.6368 - val_accuracy: 0.6929\n", "Epoch 5/500\n", "257/257 [==============================] - 0s 606us/step - loss: 0.6623 - accuracy: 0.6187 - val_loss: 0.6150 - val_accuracy: 0.6929\n", "Epoch 6/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6565 - accuracy: 0.6187 - val_loss: 0.6038 - val_accuracy: 0.6929\n", "Epoch 7/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6578 - accuracy: 0.6187 - val_loss: 0.6242 - val_accuracy: 0.6929\n", "Epoch 8/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6697 - accuracy: 0.6265 - val_loss: 0.6587 - val_accuracy: 0.6929\n", "Epoch 9/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.6780 - accuracy: 0.6226 - val_loss: 0.6403 - val_accuracy: 0.6929\n", "Epoch 10/500\n", "257/257 [==============================] - 0s 606us/step - loss: 0.6617 - accuracy: 0.6187 - val_loss: 0.6322 - val_accuracy: 0.6929\n", "Epoch 11/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.6600 - accuracy: 0.6187 - val_loss: 0.6438 - val_accuracy: 0.6929\n", "Epoch 12/500\n", "257/257 [==============================] - 0s 580us/step - loss: 0.6660 - accuracy: 0.6187 - val_loss: 0.6341 - val_accuracy: 0.6929\n", "Epoch 13/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6615 - accuracy: 0.6187 - val_loss: 0.6200 - val_accuracy: 0.6929\n", "Epoch 14/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.6575 - accuracy: 0.6187 - val_loss: 0.6074 - val_accuracy: 0.6929\n", "Epoch 15/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6574 - accuracy: 0.6187 - val_loss: 0.6011 - val_accuracy: 0.6929\n", "Epoch 16/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6619 - accuracy: 0.6187 - val_loss: 0.6012 - val_accuracy: 0.6929\n", "Epoch 17/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6546 - accuracy: 0.6187 - val_loss: 0.5908 - val_accuracy: 0.6929\n", "Epoch 18/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.6381 - accuracy: 0.6187 - val_loss: 0.6853 - val_accuracy: 0.6929\n", "Epoch 19/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.6703 - accuracy: 0.6187 - val_loss: 0.6046 - val_accuracy: 0.7087\n", "Epoch 20/500\n", "257/257 [==============================] - 0s 650us/step - loss: 0.6296 - accuracy: 0.6342 - val_loss: 0.6841 - val_accuracy: 0.5591\n", "Epoch 21/500\n", "257/257 [==============================] - 0s 568us/step - loss: 0.6826 - accuracy: 0.6226 - val_loss: 0.6636 - val_accuracy: 0.6929\n", "Epoch 22/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.6780 - accuracy: 0.6187 - val_loss: 0.6544 - val_accuracy: 0.6929\n", "Epoch 23/500\n", "257/257 [==============================] - 0s 559us/step - loss: 0.6727 - accuracy: 0.6187 - val_loss: 0.6455 - val_accuracy: 0.6929\n", "Epoch 24/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6697 - accuracy: 0.6187 - val_loss: 0.6369 - val_accuracy: 0.6929\n", "Epoch 25/500\n", "257/257 [==============================] - 0s 588us/step - loss: 0.6695 - accuracy: 0.6187 - val_loss: 0.6373 - val_accuracy: 0.6929\n", "Epoch 26/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.6840 - accuracy: 0.6187 - val_loss: 0.6403 - val_accuracy: 0.6929\n", "Epoch 27/500\n", "257/257 [==============================] - 0s 558us/step - loss: 0.6650 - accuracy: 0.6187 - val_loss: 0.6293 - val_accuracy: 0.6929\n", "Epoch 28/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6574 - accuracy: 0.6187 - val_loss: 0.6099 - val_accuracy: 0.6929\n", "Epoch 29/500\n", "257/257 [==============================] - 0s 568us/step - loss: 0.6551 - accuracy: 0.6187 - val_loss: 0.6347 - val_accuracy: 0.6929\n", "Epoch 30/500\n", "257/257 [==============================] - 0s 586us/step - loss: 0.6625 - accuracy: 0.6187 - val_loss: 0.6280 - val_accuracy: 0.6929\n", "Epoch 31/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6588 - accuracy: 0.6187 - val_loss: 0.6179 - val_accuracy: 0.6929\n", "Epoch 32/500\n", "257/257 [==============================] - 0s 610us/step - loss: 0.6567 - accuracy: 0.6187 - val_loss: 0.6136 - val_accuracy: 0.6929\n", "Epoch 33/500\n", "257/257 [==============================] - 0s 582us/step - loss: 0.6648 - accuracy: 0.6187 - val_loss: 0.6108 - val_accuracy: 0.6929\n", "Epoch 34/500\n", "257/257 [==============================] - 0s 596us/step - loss: 0.6650 - accuracy: 0.6187 - val_loss: 0.6009 - val_accuracy: 0.6929\n", "Epoch 35/500\n", "257/257 [==============================] - 0s 569us/step - loss: 0.6539 - accuracy: 0.6187 - val_loss: 0.6286 - val_accuracy: 0.6929\n", "Epoch 36/500\n", "257/257 [==============================] - 0s 594us/step - loss: 0.6502 - accuracy: 0.6187 - val_loss: 0.6153 - val_accuracy: 0.6929\n", "Epoch 37/500\n", "257/257 [==============================] - 0s 555us/step - loss: 0.6602 - accuracy: 0.6187 - val_loss: 0.6129 - val_accuracy: 0.6929\n", "Epoch 38/500\n", "257/257 [==============================] - 0s 549us/step - loss: 0.6615 - accuracy: 0.6187 - val_loss: 0.6086 - val_accuracy: 0.6929\n", "Epoch 39/500\n", "257/257 [==============================] - 0s 589us/step - loss: 0.6622 - accuracy: 0.6187 - val_loss: 0.6168 - val_accuracy: 0.6929\n", "Epoch 40/500\n", "257/257 [==============================] - 0s 585us/step - loss: 0.6545 - accuracy: 0.6187 - val_loss: 0.6107 - val_accuracy: 0.6929\n", "Epoch 41/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.6497 - accuracy: 0.6342 - val_loss: 0.6054 - val_accuracy: 0.7008\n", "Epoch 42/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6666 - accuracy: 0.6420 - val_loss: 0.6155 - val_accuracy: 0.7008\n", "Epoch 43/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6476 - accuracy: 0.6265 - val_loss: 0.6359 - val_accuracy: 0.7087\n", "Epoch 44/500\n", "257/257 [==============================] - 0s 566us/step - loss: 0.6542 - accuracy: 0.6226 - val_loss: 0.6131 - val_accuracy: 0.6929\n", "Epoch 45/500\n", "257/257 [==============================] - 0s 577us/step - loss: 0.6563 - accuracy: 0.6226 - val_loss: 0.6105 - val_accuracy: 0.6929\n", "Epoch 46/500\n", "257/257 [==============================] - 0s 601us/step - loss: 0.6573 - accuracy: 0.6187 - val_loss: 0.6157 - val_accuracy: 0.7008\n", "Epoch 47/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.6452 - accuracy: 0.6304 - val_loss: 0.6100 - val_accuracy: 0.7008\n", "Epoch 48/500\n", "257/257 [==============================] - 0s 586us/step - loss: 0.6455 - accuracy: 0.6304 - val_loss: 0.6042 - val_accuracy: 0.7008\n", "Epoch 49/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.6398 - accuracy: 0.6304 - val_loss: 0.6191 - val_accuracy: 0.7087\n", "Epoch 50/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6431 - accuracy: 0.6420 - val_loss: 0.6109 - val_accuracy: 0.7087\n", "Epoch 51/500\n", "257/257 [==============================] - 0s 549us/step - loss: 0.6404 - accuracy: 0.6381 - val_loss: 0.6022 - val_accuracy: 0.7087\n", "Epoch 52/500\n", "257/257 [==============================] - 0s 622us/step - loss: 0.6402 - accuracy: 0.6420 - val_loss: 0.6035 - val_accuracy: 0.6929\n", "Epoch 53/500\n", "257/257 [==============================] - 0s 588us/step - loss: 0.6745 - accuracy: 0.6187 - val_loss: 0.6063 - val_accuracy: 0.6929\n", "Epoch 54/500\n", "257/257 [==============================] - 0s 581us/step - loss: 0.6752 - accuracy: 0.6226 - val_loss: 0.6057 - val_accuracy: 0.6929\n", "Epoch 55/500\n", "257/257 [==============================] - 0s 544us/step - loss: 0.6544 - accuracy: 0.6187 - val_loss: 0.5971 - val_accuracy: 0.6929\n", "Epoch 56/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6501 - accuracy: 0.6226 - val_loss: 0.6211 - val_accuracy: 0.7008\n", "Epoch 57/500\n", "257/257 [==============================] - 0s 530us/step - loss: 0.6554 - accuracy: 0.6265 - val_loss: 0.6307 - val_accuracy: 0.7087\n", "Epoch 58/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.6546 - accuracy: 0.6342 - val_loss: 0.6274 - val_accuracy: 0.7087\n", "Epoch 59/500\n", "257/257 [==============================] - 0s 611us/step - loss: 0.6527 - accuracy: 0.6304 - val_loss: 0.6433 - val_accuracy: 0.6693\n", "Epoch 60/500\n", "257/257 [==============================] - 0s 574us/step - loss: 0.6633 - accuracy: 0.6420 - val_loss: 0.6230 - val_accuracy: 0.7087\n", "Epoch 61/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6479 - accuracy: 0.6420 - val_loss: 0.6119 - val_accuracy: 0.7165\n", "Epoch 62/500\n", "257/257 [==============================] - 0s 577us/step - loss: 0.6432 - accuracy: 0.6498 - val_loss: 0.6922 - val_accuracy: 0.5669\n", "Epoch 63/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6781 - accuracy: 0.5953 - val_loss: 0.6607 - val_accuracy: 0.5906\n", "Epoch 64/500\n", "257/257 [==============================] - 0s 590us/step - loss: 0.6691 - accuracy: 0.6265 - val_loss: 0.6481 - val_accuracy: 0.5827\n", "Epoch 65/500\n", "257/257 [==============================] - 0s 570us/step - loss: 0.6570 - accuracy: 0.6187 - val_loss: 0.6641 - val_accuracy: 0.5748\n", "Epoch 66/500\n", "257/257 [==============================] - 0s 598us/step - loss: 0.6690 - accuracy: 0.6381 - val_loss: 0.6008 - val_accuracy: 0.6929\n", "Epoch 67/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6555 - accuracy: 0.6187 - val_loss: 0.6123 - val_accuracy: 0.6929\n", "Epoch 68/500\n", "257/257 [==============================] - 0s 655us/step - loss: 0.6504 - accuracy: 0.6265 - val_loss: 0.6099 - val_accuracy: 0.6929\n", "Epoch 69/500\n", "257/257 [==============================] - 0s 600us/step - loss: 0.6541 - accuracy: 0.6187 - val_loss: 0.6168 - val_accuracy: 0.6929\n", "Epoch 70/500\n", "257/257 [==============================] - 0s 581us/step - loss: 0.6530 - accuracy: 0.6187 - val_loss: 0.6128 - val_accuracy: 0.6929\n", "Epoch 71/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6479 - accuracy: 0.6187 - val_loss: 0.6285 - val_accuracy: 0.7008\n", "Epoch 72/500\n", "257/257 [==============================] - 0s 578us/step - loss: 0.6498 - accuracy: 0.6265 - val_loss: 0.6170 - val_accuracy: 0.7087\n", "Epoch 73/500\n", "257/257 [==============================] - 0s 592us/step - loss: 0.6460 - accuracy: 0.6342 - val_loss: 0.6355 - val_accuracy: 0.6850\n", "Epoch 74/500\n", "257/257 [==============================] - 0s 602us/step - loss: 0.6684 - accuracy: 0.6459 - val_loss: 0.6138 - val_accuracy: 0.7165\n", "Epoch 75/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.6438 - accuracy: 0.6381 - val_loss: 0.6116 - val_accuracy: 0.6929\n", "Epoch 76/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6550 - accuracy: 0.6187 - val_loss: 0.6031 - val_accuracy: 0.6929\n", "Epoch 77/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6548 - accuracy: 0.6187 - val_loss: 0.6122 - val_accuracy: 0.6929\n", "Epoch 78/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.6522 - accuracy: 0.6187 - val_loss: 0.6114 - val_accuracy: 0.6929\n", "Epoch 79/500\n", "257/257 [==============================] - 0s 599us/step - loss: 0.6526 - accuracy: 0.6187 - val_loss: 49.5778 - val_accuracy: 0.6929\n", "Epoch 80/500\n", "257/257 [==============================] - 0s 586us/step - loss: 21.1260 - accuracy: 0.6226 - val_loss: 0.6365 - val_accuracy: 0.7008\n", "Epoch 81/500\n", "257/257 [==============================] - 0s 583us/step - loss: 0.6566 - accuracy: 0.6342 - val_loss: 0.6724 - val_accuracy: 0.5669\n", "Epoch 82/500\n", "257/257 [==============================] - 0s 574us/step - loss: 0.6665 - accuracy: 0.6226 - val_loss: 0.6645 - val_accuracy: 0.5906\n", "Epoch 83/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6694 - accuracy: 0.6265 - val_loss: 0.6480 - val_accuracy: 0.6929\n", "Epoch 84/500\n", "257/257 [==============================] - 0s 557us/step - loss: 0.6589 - accuracy: 0.6537 - val_loss: 0.6411 - val_accuracy: 0.7008\n", "Epoch 85/500\n", "257/257 [==============================] - 0s 549us/step - loss: 0.6559 - accuracy: 0.6381 - val_loss: 0.6444 - val_accuracy: 0.6929\n", "Epoch 86/500\n", "257/257 [==============================] - 0s 618us/step - loss: 0.6565 - accuracy: 0.6537 - val_loss: 0.6290 - val_accuracy: 0.7008\n", "Epoch 87/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6535 - accuracy: 0.6304 - val_loss: 0.6250 - val_accuracy: 0.7008\n", "Epoch 88/500\n", "257/257 [==============================] - 0s 601us/step - loss: 0.6526 - accuracy: 0.6304 - val_loss: 0.6273 - val_accuracy: 0.7008\n", "Epoch 89/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.6525 - accuracy: 0.6304 - val_loss: 0.6251 - val_accuracy: 0.7008\n", "Epoch 90/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.6509 - accuracy: 0.6226 - val_loss: 0.6285 - val_accuracy: 0.7087\n", "Epoch 91/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.6513 - accuracy: 0.6304 - val_loss: 0.6245 - val_accuracy: 0.7008\n", "Epoch 92/500\n", "257/257 [==============================] - 0s 578us/step - loss: 0.6501 - accuracy: 0.6304 - val_loss: 0.6403 - val_accuracy: 0.7087\n", "Epoch 93/500\n", "257/257 [==============================] - 0s 583us/step - loss: 0.6565 - accuracy: 0.6342 - val_loss: 0.6231 - val_accuracy: 0.7008\n", "Epoch 94/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6483 - accuracy: 0.6265 - val_loss: 0.6171 - val_accuracy: 0.7008\n", "Epoch 95/500\n", "257/257 [==============================] - 0s 592us/step - loss: 0.6477 - accuracy: 0.6265 - val_loss: 0.6133 - val_accuracy: 0.7008\n", "Epoch 96/500\n", "257/257 [==============================] - 0s 540us/step - loss: 0.6446 - accuracy: 0.6265 - val_loss: 0.6190 - val_accuracy: 0.7008\n", "Epoch 97/500\n", "257/257 [==============================] - 0s 559us/step - loss: 0.6458 - accuracy: 0.6304 - val_loss: 0.6274 - val_accuracy: 0.7008\n", "Epoch 98/500\n", "257/257 [==============================] - 0s 554us/step - loss: 0.6480 - accuracy: 0.6342 - val_loss: 0.6237 - val_accuracy: 0.7008\n", "Epoch 99/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.6462 - accuracy: 0.6381 - val_loss: 0.6236 - val_accuracy: 0.7008\n", "Epoch 100/500\n", "257/257 [==============================] - 0s 582us/step - loss: 0.6470 - accuracy: 0.6342 - val_loss: 0.6197 - val_accuracy: 0.7087\n", "Epoch 101/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6455 - accuracy: 0.6342 - val_loss: 0.6166 - val_accuracy: 0.7008\n", "Epoch 102/500\n", "257/257 [==============================] - 0s 561us/step - loss: 0.6448 - accuracy: 0.6381 - val_loss: 0.6142 - val_accuracy: 0.7087\n", "Epoch 103/500\n", "257/257 [==============================] - 0s 592us/step - loss: 0.6428 - accuracy: 0.6342 - val_loss: 0.6103 - val_accuracy: 0.7087\n", "Epoch 104/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.6416 - accuracy: 0.6304 - val_loss: 0.6036 - val_accuracy: 0.7087\n", "Epoch 105/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6403 - accuracy: 0.6342 - val_loss: 0.5968 - val_accuracy: 0.7008\n", "Epoch 106/500\n", "257/257 [==============================] - 0s 540us/step - loss: 0.6477 - accuracy: 0.6342 - val_loss: 0.6195 - val_accuracy: 0.6929\n", "Epoch 107/500\n", "257/257 [==============================] - 0s 595us/step - loss: 0.6772 - accuracy: 0.6226 - val_loss: 0.6065 - val_accuracy: 0.7008\n", "Epoch 108/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.6664 - accuracy: 0.6226 - val_loss: 0.6020 - val_accuracy: 0.7008\n", "Epoch 109/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6626 - accuracy: 0.6304 - val_loss: 0.5968 - val_accuracy: 0.7008\n", "Epoch 110/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.6525 - accuracy: 0.6304 - val_loss: 0.6201 - val_accuracy: 0.6850\n", "Epoch 111/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6429 - accuracy: 0.6537 - val_loss: 0.5995 - val_accuracy: 0.7087\n", "Epoch 112/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.6399 - accuracy: 0.6381 - val_loss: 0.6197 - val_accuracy: 0.6535\n", "Epoch 113/500\n", "257/257 [==============================] - 0s 569us/step - loss: 0.6455 - accuracy: 0.6537 - val_loss: 0.5922 - val_accuracy: 0.7165\n", "Epoch 114/500\n", "257/257 [==============================] - 0s 590us/step - loss: 0.6458 - accuracy: 0.6381 - val_loss: 0.5907 - val_accuracy: 0.7087\n", "Epoch 115/500\n", "257/257 [==============================] - 0s 570us/step - loss: 0.6441 - accuracy: 0.6381 - val_loss: 0.5913 - val_accuracy: 0.7165\n", "Epoch 116/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.6408 - accuracy: 0.6304 - val_loss: 0.6159 - val_accuracy: 0.7165\n", "Epoch 117/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.6416 - accuracy: 0.6459 - val_loss: 0.6126 - val_accuracy: 0.7165\n", "Epoch 118/500\n", "257/257 [==============================] - 0s 578us/step - loss: 0.6395 - accuracy: 0.6381 - val_loss: 0.6040 - val_accuracy: 0.7165\n", "Epoch 119/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6369 - accuracy: 0.6342 - val_loss: 0.5991 - val_accuracy: 0.7087\n", "Epoch 120/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.6367 - accuracy: 0.6381 - val_loss: 0.6062 - val_accuracy: 0.7087\n", "Epoch 121/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.6364 - accuracy: 0.6576 - val_loss: 0.5981 - val_accuracy: 0.7008\n", "Epoch 122/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.6376 - accuracy: 0.6459 - val_loss: 0.5899 - val_accuracy: 0.7087\n", "Epoch 123/500\n", "257/257 [==============================] - 0s 543us/step - loss: 0.6371 - accuracy: 0.6420 - val_loss: 0.6076 - val_accuracy: 0.7087\n", "Epoch 124/500\n", "257/257 [==============================] - 0s 589us/step - loss: 0.6369 - accuracy: 0.6537 - val_loss: 0.6039 - val_accuracy: 0.7008\n", "Epoch 125/500\n", "257/257 [==============================] - 0s 568us/step - loss: 0.6357 - accuracy: 0.6459 - val_loss: 0.5979 - val_accuracy: 0.7087\n", "Epoch 126/500\n", "257/257 [==============================] - 0s 608us/step - loss: 0.6374 - accuracy: 0.6420 - val_loss: 0.5935 - val_accuracy: 0.7165\n", "Epoch 127/500\n", "257/257 [==============================] - 0s 588us/step - loss: 0.6333 - accuracy: 0.6498 - val_loss: 0.5883 - val_accuracy: 0.7087\n", "Epoch 128/500\n", "257/257 [==============================] - 0s 582us/step - loss: 0.6376 - accuracy: 0.6498 - val_loss: 0.5979 - val_accuracy: 0.7165\n", "Epoch 129/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.6350 - accuracy: 0.6459 - val_loss: 0.5893 - val_accuracy: 0.7008\n", "Epoch 130/500\n", "257/257 [==============================] - 0s 577us/step - loss: 0.6350 - accuracy: 0.6459 - val_loss: 0.5864 - val_accuracy: 0.7008\n", "Epoch 131/500\n", "257/257 [==============================] - 0s 585us/step - loss: 0.6362 - accuracy: 0.6420 - val_loss: 0.5882 - val_accuracy: 0.7087\n", "Epoch 132/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.6401 - accuracy: 0.6459 - val_loss: 0.5846 - val_accuracy: 0.7087\n", "Epoch 133/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.6401 - accuracy: 0.6498 - val_loss: 0.5941 - val_accuracy: 0.6772\n", "Epoch 134/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.6284 - accuracy: 0.6654 - val_loss: 0.5804 - val_accuracy: 0.7165\n", "Epoch 135/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.6242 - accuracy: 0.6498 - val_loss: 0.6232 - val_accuracy: 0.6929\n", "Epoch 136/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.6569 - accuracy: 0.6342 - val_loss: 0.5926 - val_accuracy: 0.6850\n", "Epoch 137/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6244 - accuracy: 0.6693 - val_loss: 0.6441 - val_accuracy: 0.6220\n", "Epoch 138/500\n", "257/257 [==============================] - 0s 558us/step - loss: 0.6514 - accuracy: 0.6459 - val_loss: 0.5989 - val_accuracy: 0.7244\n", "Epoch 139/500\n", "257/257 [==============================] - 0s 577us/step - loss: 0.6229 - accuracy: 0.6693 - val_loss: 0.5842 - val_accuracy: 0.7087\n", "Epoch 140/500\n", "257/257 [==============================] - 0s 609us/step - loss: 0.6303 - accuracy: 0.6304 - val_loss: 0.5878 - val_accuracy: 0.7087\n", "Epoch 141/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.6564 - accuracy: 0.6304 - val_loss: 0.5870 - val_accuracy: 0.7008\n", "Epoch 142/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6525 - accuracy: 0.6304 - val_loss: 0.6052 - val_accuracy: 0.6772\n", "Epoch 143/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6333 - accuracy: 0.6459 - val_loss: 0.6282 - val_accuracy: 0.5906\n", "Epoch 144/500\n", "257/257 [==============================] - 0s 574us/step - loss: 0.6487 - accuracy: 0.6226 - val_loss: 0.6023 - val_accuracy: 0.6378\n", "Epoch 145/500\n", "257/257 [==============================] - 0s 546us/step - loss: 0.6296 - accuracy: 0.6420 - val_loss: 0.6145 - val_accuracy: 0.6142\n", "Epoch 146/500\n", "257/257 [==============================] - 0s 611us/step - loss: 0.6360 - accuracy: 0.6109 - val_loss: 0.5846 - val_accuracy: 0.7008\n", "Epoch 147/500\n", "257/257 [==============================] - 0s 607us/step - loss: 0.6168 - accuracy: 0.6615 - val_loss: 0.5818 - val_accuracy: 0.6850\n", "Epoch 148/500\n", "257/257 [==============================] - 0s 578us/step - loss: 0.6126 - accuracy: 0.6459 - val_loss: 0.6161 - val_accuracy: 0.6063\n", "Epoch 149/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6347 - accuracy: 0.6498 - val_loss: 0.5897 - val_accuracy: 0.7165\n", "Epoch 150/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6165 - accuracy: 0.6654 - val_loss: 0.5742 - val_accuracy: 0.7165\n", "Epoch 151/500\n", "257/257 [==============================] - 0s 538us/step - loss: 0.6195 - accuracy: 0.6342 - val_loss: 0.5662 - val_accuracy: 0.7165\n", "Epoch 152/500\n", "257/257 [==============================] - 0s 581us/step - loss: 0.6061 - accuracy: 0.6459 - val_loss: 0.6923 - val_accuracy: 0.7008\n", "Epoch 153/500\n", "257/257 [==============================] - 0s 590us/step - loss: 0.7095 - accuracy: 0.6265 - val_loss: 0.5693 - val_accuracy: 0.7165\n", "Epoch 154/500\n", "257/257 [==============================] - 0s 608us/step - loss: 0.6062 - accuracy: 0.6693 - val_loss: 0.5679 - val_accuracy: 0.7165\n", "Epoch 155/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6109 - accuracy: 0.6576 - val_loss: 0.5624 - val_accuracy: 0.7244\n", "Epoch 156/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.5949 - accuracy: 0.6654 - val_loss: 0.5572 - val_accuracy: 0.6929\n", "Epoch 157/500\n", "257/257 [==============================] - 0s 574us/step - loss: 0.5820 - accuracy: 0.6887 - val_loss: 0.5446 - val_accuracy: 0.7244\n", "Epoch 158/500\n", "257/257 [==============================] - 0s 546us/step - loss: 0.5683 - accuracy: 0.7004 - val_loss: 0.6192 - val_accuracy: 0.7244\n", "Epoch 159/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6595 - accuracy: 0.6615 - val_loss: 0.8159 - val_accuracy: 0.3150\n", "Epoch 160/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.7525 - accuracy: 0.3813 - val_loss: 0.6854 - val_accuracy: 0.6457\n", "Epoch 161/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.6832 - accuracy: 0.6615 - val_loss: 0.6925 - val_accuracy: 0.5433\n", "Epoch 162/500\n", "257/257 [==============================] - 0s 568us/step - loss: 0.6880 - accuracy: 0.5564 - val_loss: 0.6941 - val_accuracy: 0.4961\n", "Epoch 163/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6889 - accuracy: 0.5409 - val_loss: 0.6965 - val_accuracy: 0.4409\n", "Epoch 164/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.6902 - accuracy: 0.4942 - val_loss: 0.6842 - val_accuracy: 0.6457\n", "Epoch 165/500\n", "257/257 [==============================] - 0s 600us/step - loss: 0.6828 - accuracy: 0.6537 - val_loss: 0.6737 - val_accuracy: 0.7244\n", "Epoch 166/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6783 - accuracy: 0.6459 - val_loss: 0.6758 - val_accuracy: 0.7008\n", "Epoch 167/500\n", "257/257 [==============================] - 0s 596us/step - loss: 0.6788 - accuracy: 0.6732 - val_loss: 0.6723 - val_accuracy: 0.7165\n", "Epoch 168/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.6770 - accuracy: 0.6615 - val_loss: 0.6732 - val_accuracy: 0.7087\n", "Epoch 169/500\n", "257/257 [==============================] - 0s 584us/step - loss: 0.6776 - accuracy: 0.6732 - val_loss: 0.6699 - val_accuracy: 0.7165\n", "Epoch 170/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.6751 - accuracy: 0.6693 - val_loss: 0.6656 - val_accuracy: 0.7165\n", "Epoch 171/500\n", "257/257 [==============================] - 0s 599us/step - loss: 0.6738 - accuracy: 0.6381 - val_loss: 0.6620 - val_accuracy: 0.7008\n", "Epoch 172/500\n", "257/257 [==============================] - 0s 613us/step - loss: 0.6732 - accuracy: 0.6265 - val_loss: 0.6603 - val_accuracy: 0.7008\n", "Epoch 173/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.6724 - accuracy: 0.6304 - val_loss: 0.6587 - val_accuracy: 0.7008\n", "Epoch 174/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.6717 - accuracy: 0.6304 - val_loss: 0.6568 - val_accuracy: 0.7087\n", "Epoch 175/500\n", "257/257 [==============================] - 0s 566us/step - loss: 0.6685 - accuracy: 0.6342 - val_loss: 0.6497 - val_accuracy: 0.7008\n", "Epoch 176/500\n", "257/257 [==============================] - 0s 554us/step - loss: 0.6637 - accuracy: 0.6226 - val_loss: 0.6431 - val_accuracy: 0.7008\n", "Epoch 177/500\n", "257/257 [==============================] - 0s 569us/step - loss: 0.6619 - accuracy: 0.6265 - val_loss: 0.6357 - val_accuracy: 0.7244\n", "Epoch 178/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.6550 - accuracy: 0.6381 - val_loss: 0.6249 - val_accuracy: 0.7165\n", "Epoch 179/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6514 - accuracy: 0.6342 - val_loss: 0.6083 - val_accuracy: 0.7087\n", "Epoch 180/500\n", "257/257 [==============================] - 0s 555us/step - loss: 0.6237 - accuracy: 0.6381 - val_loss: 0.6176 - val_accuracy: 0.6850\n", "Epoch 181/500\n", "257/257 [==============================] - 0s 610us/step - loss: 0.8449 - accuracy: 0.6770 - val_loss: 0.8898 - val_accuracy: 0.4016\n", "Epoch 182/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.7508 - accuracy: 0.5681 - val_loss: 0.6600 - val_accuracy: 0.6220\n", "Epoch 183/500\n", "257/257 [==============================] - 0s 585us/step - loss: 0.6657 - accuracy: 0.6576 - val_loss: 0.6267 - val_accuracy: 0.6929\n", "Epoch 184/500\n", "257/257 [==============================] - 0s 543us/step - loss: 0.6678 - accuracy: 0.6070 - val_loss: 0.6430 - val_accuracy: 0.6535\n", "Epoch 185/500\n", "257/257 [==============================] - 0s 594us/step - loss: 0.6526 - accuracy: 0.6654 - val_loss: 0.6863 - val_accuracy: 0.6929\n", "Epoch 186/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.6802 - accuracy: 0.6265 - val_loss: 0.5882 - val_accuracy: 0.7244\n", "Epoch 187/500\n", "257/257 [==============================] - 0s 609us/step - loss: 0.6132 - accuracy: 0.6654 - val_loss: 0.5561 - val_accuracy: 0.7165\n", "Epoch 188/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.5934 - accuracy: 0.6342 - val_loss: 0.5429 - val_accuracy: 0.7165\n", "Epoch 189/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.5845 - accuracy: 0.6537 - val_loss: 0.5453 - val_accuracy: 0.7008\n", "Epoch 190/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.5740 - accuracy: 0.6693 - val_loss: 0.5596 - val_accuracy: 0.7008\n", "Epoch 191/500\n", "257/257 [==============================] - 0s 587us/step - loss: 0.6095 - accuracy: 0.6732 - val_loss: 0.5781 - val_accuracy: 0.7244\n", "Epoch 192/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.5795 - accuracy: 0.6693 - val_loss: 0.5755 - val_accuracy: 0.7087\n", "Epoch 193/500\n", "257/257 [==============================] - 0s 597us/step - loss: 0.5789 - accuracy: 0.6381 - val_loss: 0.5399 - val_accuracy: 0.7087\n", "Epoch 194/500\n", "257/257 [==============================] - 0s 606us/step - loss: 0.5829 - accuracy: 0.6265 - val_loss: 0.6077 - val_accuracy: 0.7087\n", "Epoch 195/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.6318 - accuracy: 0.6654 - val_loss: 0.5606 - val_accuracy: 0.7165\n", "Epoch 196/500\n", "257/257 [==============================] - 0s 580us/step - loss: 0.5919 - accuracy: 0.6381 - val_loss: 0.5287 - val_accuracy: 0.7165\n", "Epoch 197/500\n", "257/257 [==============================] - 0s 561us/step - loss: 0.5597 - accuracy: 0.6381 - val_loss: 0.6338 - val_accuracy: 0.7087\n", "Epoch 198/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.5741 - accuracy: 0.6265 - val_loss: 0.7381 - val_accuracy: 0.7087\n", "Epoch 199/500\n", "257/257 [==============================] - 0s 566us/step - loss: 0.6333 - accuracy: 0.6498 - val_loss: 0.5273 - val_accuracy: 0.7244\n", "Epoch 200/500\n", "257/257 [==============================] - 0s 555us/step - loss: 0.5717 - accuracy: 0.6459 - val_loss: 0.7064 - val_accuracy: 0.5433\n", "Epoch 201/500\n", "257/257 [==============================] - 0s 602us/step - loss: 0.7005 - accuracy: 0.5136 - val_loss: 0.7232 - val_accuracy: 0.4173\n", "Epoch 202/500\n", "257/257 [==============================] - 0s 570us/step - loss: 0.7018 - accuracy: 0.5136 - val_loss: 0.6658 - val_accuracy: 0.7008\n", "Epoch 203/500\n", "257/257 [==============================] - 0s 593us/step - loss: 0.6751 - accuracy: 0.6265 - val_loss: 0.6705 - val_accuracy: 0.7008\n", "Epoch 204/500\n", "257/257 [==============================] - 0s 605us/step - loss: 0.6760 - accuracy: 0.6420 - val_loss: 0.6589 - val_accuracy: 0.6929\n", "Epoch 205/500\n", "257/257 [==============================] - 0s 581us/step - loss: 0.6758 - accuracy: 0.6187 - val_loss: 0.6574 - val_accuracy: 0.6929\n", "Epoch 206/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.6754 - accuracy: 0.6187 - val_loss: 0.6560 - val_accuracy: 0.6929\n", "Epoch 207/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6766 - accuracy: 0.6187 - val_loss: 0.6567 - val_accuracy: 0.6929\n", "Epoch 208/500\n", "257/257 [==============================] - 0s 577us/step - loss: 0.6747 - accuracy: 0.6187 - val_loss: 0.6564 - val_accuracy: 0.6929\n", "Epoch 209/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6744 - accuracy: 0.6187 - val_loss: 0.6555 - val_accuracy: 0.6929\n", "Epoch 210/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.6742 - accuracy: 0.6187 - val_loss: 0.6559 - val_accuracy: 0.6929\n", "Epoch 211/500\n", "257/257 [==============================] - 0s 576us/step - loss: 0.6737 - accuracy: 0.6187 - val_loss: 0.6567 - val_accuracy: 0.6929\n", "Epoch 212/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6732 - accuracy: 0.6187 - val_loss: 0.6572 - val_accuracy: 0.6929\n", "Epoch 213/500\n", "257/257 [==============================] - 0s 593us/step - loss: 0.6730 - accuracy: 0.6187 - val_loss: 0.6550 - val_accuracy: 0.6929\n", "Epoch 214/500\n", "257/257 [==============================] - 0s 584us/step - loss: 0.6704 - accuracy: 0.6187 - val_loss: 0.6391 - val_accuracy: 0.6929\n", "Epoch 215/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6676 - accuracy: 0.6342 - val_loss: 0.6440 - val_accuracy: 0.6929\n", "Epoch 216/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.6801 - accuracy: 0.6226 - val_loss: 0.6367 - val_accuracy: 0.6929\n", "Epoch 217/500\n", "257/257 [==============================] - 0s 555us/step - loss: 0.6716 - accuracy: 0.6187 - val_loss: 0.6246 - val_accuracy: 0.6929\n", "Epoch 218/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.6546 - accuracy: 0.6187 - val_loss: 0.6267 - val_accuracy: 0.6929\n", "Epoch 219/500\n", "257/257 [==============================] - 0s 604us/step - loss: 0.6537 - accuracy: 0.6187 - val_loss: 0.6330 - val_accuracy: 0.6929\n", "Epoch 220/500\n", "257/257 [==============================] - 0s 557us/step - loss: 0.6568 - accuracy: 0.6187 - val_loss: 0.6326 - val_accuracy: 0.6929\n", "Epoch 221/500\n", "257/257 [==============================] - 0s 600us/step - loss: 0.6544 - accuracy: 0.6187 - val_loss: 0.6120 - val_accuracy: 0.6929\n", "Epoch 222/500\n", "257/257 [==============================] - 0s 582us/step - loss: 0.6393 - accuracy: 0.6187 - val_loss: 0.6146 - val_accuracy: 0.6929\n", "Epoch 223/500\n", "257/257 [==============================] - 0s 587us/step - loss: 0.6410 - accuracy: 0.6187 - val_loss: 0.5948 - val_accuracy: 0.6929\n", "Epoch 224/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6301 - accuracy: 0.6187 - val_loss: 0.6054 - val_accuracy: 0.6929\n", "Epoch 225/500\n", "257/257 [==============================] - 0s 555us/step - loss: 0.6348 - accuracy: 0.6187 - val_loss: 0.6225 - val_accuracy: 0.6929\n", "Epoch 226/500\n", "257/257 [==============================] - 0s 602us/step - loss: 0.6491 - accuracy: 0.6187 - val_loss: 0.6140 - val_accuracy: 0.6929\n", "Epoch 227/500\n", "257/257 [==============================] - 0s 542us/step - loss: 0.6398 - accuracy: 0.6187 - val_loss: 0.5974 - val_accuracy: 0.6929\n", "Epoch 228/500\n", "257/257 [==============================] - 0s 606us/step - loss: 0.6282 - accuracy: 0.6187 - val_loss: 0.5884 - val_accuracy: 0.6929\n", "Epoch 229/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.6222 - accuracy: 0.6187 - val_loss: 0.5790 - val_accuracy: 0.6929\n", "Epoch 230/500\n", "257/257 [==============================] - 0s 590us/step - loss: 0.6186 - accuracy: 0.6187 - val_loss: 0.5696 - val_accuracy: 0.6929\n", "Epoch 231/500\n", "257/257 [==============================] - 0s 535us/step - loss: 0.6211 - accuracy: 0.6187 - val_loss: 0.5717 - val_accuracy: 0.6929\n", "Epoch 232/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.6087 - accuracy: 0.6187 - val_loss: 0.5564 - val_accuracy: 0.6929\n", "Epoch 233/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.6014 - accuracy: 0.6187 - val_loss: 0.5470 - val_accuracy: 0.6929\n", "Epoch 234/500\n", "257/257 [==============================] - 0s 539us/step - loss: 0.5962 - accuracy: 0.6187 - val_loss: 0.5714 - val_accuracy: 0.6929\n", "Epoch 235/500\n", "257/257 [==============================] - 0s 602us/step - loss: 0.6061 - accuracy: 0.6187 - val_loss: 0.5283 - val_accuracy: 0.6929\n", "Epoch 236/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.5650 - accuracy: 0.6187 - val_loss: 0.6178 - val_accuracy: 0.6929\n", "Epoch 237/500\n", "257/257 [==============================] - 0s 557us/step - loss: 0.5845 - accuracy: 0.6148 - val_loss: 0.8423 - val_accuracy: 0.6929\n", "Epoch 238/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.6556 - accuracy: 0.6148 - val_loss: 0.5719 - val_accuracy: 0.6929\n", "Epoch 239/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.5852 - accuracy: 0.6187 - val_loss: 0.5493 - val_accuracy: 0.6929\n", "Epoch 240/500\n", "257/257 [==============================] - 0s 591us/step - loss: 0.5593 - accuracy: 0.6226 - val_loss: 0.6113 - val_accuracy: 0.6929\n", "Epoch 241/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6037 - accuracy: 0.6265 - val_loss: 0.5200 - val_accuracy: 0.7008\n", "Epoch 242/500\n", "257/257 [==============================] - 0s 598us/step - loss: 0.5460 - accuracy: 0.6304 - val_loss: 0.5227 - val_accuracy: 0.7008\n", "Epoch 243/500\n", "257/257 [==============================] - 0s 570us/step - loss: 0.5443 - accuracy: 0.6265 - val_loss: 0.5284 - val_accuracy: 0.7008\n", "Epoch 244/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.5435 - accuracy: 0.6381 - val_loss: 0.5412 - val_accuracy: 0.7165\n", "Epoch 245/500\n", "257/257 [==============================] - 0s 537us/step - loss: 0.5465 - accuracy: 0.6381 - val_loss: 0.5571 - val_accuracy: 0.7244\n", "Epoch 246/500\n", "257/257 [==============================] - 0s 557us/step - loss: 0.5406 - accuracy: 0.6498 - val_loss: 0.5575 - val_accuracy: 0.7244\n", "Epoch 247/500\n", "257/257 [==============================] - 0s 585us/step - loss: 0.5382 - accuracy: 0.6693 - val_loss: 0.5517 - val_accuracy: 0.7638\n", "Epoch 248/500\n", "257/257 [==============================] - 0s 599us/step - loss: 0.5306 - accuracy: 0.7004 - val_loss: 0.5493 - val_accuracy: 0.7165\n", "Epoch 249/500\n", "257/257 [==============================] - 0s 555us/step - loss: 0.5237 - accuracy: 0.7160 - val_loss: 0.6103 - val_accuracy: 0.7244\n", "Epoch 250/500\n", "257/257 [==============================] - 0s 586us/step - loss: 0.5349 - accuracy: 0.7160 - val_loss: 0.6087 - val_accuracy: 0.7244\n", "Epoch 251/500\n", "257/257 [==============================] - 0s 538us/step - loss: 0.5498 - accuracy: 0.7121 - val_loss: 1.0508 - val_accuracy: 0.7480\n", "Epoch 252/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6311 - accuracy: 0.6926 - val_loss: 0.6931 - val_accuracy: 0.7165\n", "Epoch 253/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.5819 - accuracy: 0.6576 - val_loss: 0.5318 - val_accuracy: 0.7087\n", "Epoch 254/500\n", "257/257 [==============================] - 0s 623us/step - loss: 0.5238 - accuracy: 0.6926 - val_loss: 0.5303 - val_accuracy: 0.7087\n", "Epoch 255/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.5213 - accuracy: 0.6848 - val_loss: 0.5258 - val_accuracy: 0.7244\n", "Epoch 256/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.5161 - accuracy: 0.7121 - val_loss: 0.5699 - val_accuracy: 0.7402\n", "Epoch 257/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.5285 - accuracy: 0.7198 - val_loss: 0.6264 - val_accuracy: 0.7480\n", "Epoch 258/500\n", "257/257 [==============================] - 0s 586us/step - loss: 0.5586 - accuracy: 0.7004 - val_loss: 0.5478 - val_accuracy: 0.7638\n", "Epoch 259/500\n", "257/257 [==============================] - 0s 597us/step - loss: 0.5088 - accuracy: 0.7237 - val_loss: 0.5805 - val_accuracy: 0.7323\n", "Epoch 260/500\n", "257/257 [==============================] - 0s 576us/step - loss: 0.5182 - accuracy: 0.7276 - val_loss: 0.5533 - val_accuracy: 0.7323\n", "Epoch 261/500\n", "257/257 [==============================] - 0s 596us/step - loss: 0.5137 - accuracy: 0.7393 - val_loss: 0.5473 - val_accuracy: 0.6929\n", "Epoch 262/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.5185 - accuracy: 0.7121 - val_loss: 0.6598 - val_accuracy: 0.7244\n", "Epoch 263/500\n", "257/257 [==============================] - 0s 549us/step - loss: 0.5319 - accuracy: 0.7354 - val_loss: 0.8149 - val_accuracy: 0.3307\n", "Epoch 264/500\n", "257/257 [==============================] - 0s 568us/step - loss: 0.7722 - accuracy: 0.4086 - val_loss: 0.6871 - val_accuracy: 0.4724\n", "Epoch 265/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.6879 - accuracy: 0.5409 - val_loss: 0.6804 - val_accuracy: 0.5354\n", "Epoch 266/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.6809 - accuracy: 0.5798 - val_loss: 0.6757 - val_accuracy: 0.6299\n", "Epoch 267/500\n", "257/257 [==============================] - 0s 625us/step - loss: 0.6694 - accuracy: 0.6342 - val_loss: 0.6687 - val_accuracy: 0.6457\n", "Epoch 268/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6602 - accuracy: 0.6381 - val_loss: 0.6337 - val_accuracy: 0.7323\n", "Epoch 269/500\n", "257/257 [==============================] - 0s 540us/step - loss: 0.8554 - accuracy: 0.6732 - val_loss: 0.6898 - val_accuracy: 0.4961\n", "Epoch 270/500\n", "257/257 [==============================] - 0s 559us/step - loss: 0.6901 - accuracy: 0.4981 - val_loss: 0.6964 - val_accuracy: 0.4567\n", "Epoch 271/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.6941 - accuracy: 0.4786 - val_loss: 0.6812 - val_accuracy: 0.6220\n", "Epoch 272/500\n", "257/257 [==============================] - 0s 566us/step - loss: 0.6837 - accuracy: 0.6070 - val_loss: 0.6786 - val_accuracy: 0.6378\n", "Epoch 273/500\n", "257/257 [==============================] - 0s 539us/step - loss: 0.6816 - accuracy: 0.5798 - val_loss: 0.6680 - val_accuracy: 0.6929\n", "Epoch 274/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.6742 - accuracy: 0.7043 - val_loss: 0.6592 - val_accuracy: 0.7402\n", "Epoch 275/500\n", "257/257 [==============================] - 0s 538us/step - loss: 0.6692 - accuracy: 0.6576 - val_loss: 0.5723 - val_accuracy: 0.6929\n", "Epoch 276/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.6186 - accuracy: 0.6187 - val_loss: 1.4489 - val_accuracy: 0.6929\n", "Epoch 277/500\n", "257/257 [==============================] - 0s 567us/step - loss: 1.0376 - accuracy: 0.6265 - val_loss: 0.6266 - val_accuracy: 0.7008\n", "Epoch 278/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.6521 - accuracy: 0.6226 - val_loss: 0.6218 - val_accuracy: 0.7008\n", "Epoch 279/500\n", "257/257 [==============================] - 0s 582us/step - loss: 0.6430 - accuracy: 0.6304 - val_loss: 0.5991 - val_accuracy: 0.6929\n", "Epoch 280/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.6194 - accuracy: 0.6265 - val_loss: 0.5987 - val_accuracy: 0.7087\n", "Epoch 281/500\n", "257/257 [==============================] - 0s 644us/step - loss: 0.6022 - accuracy: 0.7043 - val_loss: 1.0590 - val_accuracy: 0.6929\n", "Epoch 282/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.7993 - accuracy: 0.6265 - val_loss: 0.6115 - val_accuracy: 0.7087\n", "Epoch 283/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.6322 - accuracy: 0.6887 - val_loss: 0.5827 - val_accuracy: 0.7008\n", "Epoch 284/500\n", "257/257 [==============================] - 0s 546us/step - loss: 0.6216 - accuracy: 0.6304 - val_loss: 0.5901 - val_accuracy: 0.7244\n", "Epoch 285/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.6359 - accuracy: 0.7004 - val_loss: 0.5739 - val_accuracy: 0.7244\n", "Epoch 286/500\n", "257/257 [==============================] - 0s 539us/step - loss: 0.6112 - accuracy: 0.6693 - val_loss: 0.5888 - val_accuracy: 0.7559\n", "Epoch 287/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.6176 - accuracy: 0.6926 - val_loss: 0.6379 - val_accuracy: 0.6457\n", "Epoch 288/500\n", "257/257 [==============================] - 0s 610us/step - loss: 0.6394 - accuracy: 0.6537 - val_loss: 0.5704 - val_accuracy: 0.7323\n", "Epoch 289/500\n", "257/257 [==============================] - 0s 542us/step - loss: 0.6012 - accuracy: 0.7004 - val_loss: 0.5793 - val_accuracy: 0.7559\n", "Epoch 290/500\n", "257/257 [==============================] - 0s 569us/step - loss: 0.6033 - accuracy: 0.7043 - val_loss: 0.5866 - val_accuracy: 0.7323\n", "Epoch 291/500\n", "257/257 [==============================] - 0s 529us/step - loss: 0.6004 - accuracy: 0.6848 - val_loss: 0.5181 - val_accuracy: 0.7638\n", "Epoch 292/500\n", "257/257 [==============================] - 0s 554us/step - loss: 0.5795 - accuracy: 0.7043 - val_loss: 0.7909 - val_accuracy: 0.5276\n", "Epoch 293/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.6830 - accuracy: 0.6148 - val_loss: 0.5538 - val_accuracy: 0.7480\n", "Epoch 294/500\n", "257/257 [==============================] - 0s 580us/step - loss: 0.5920 - accuracy: 0.6926 - val_loss: 0.5462 - val_accuracy: 0.7480\n", "Epoch 295/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.5838 - accuracy: 0.6926 - val_loss: 0.5225 - val_accuracy: 0.7638\n", "Epoch 296/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.5686 - accuracy: 0.6732 - val_loss: 0.5508 - val_accuracy: 0.7795\n", "Epoch 297/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.5769 - accuracy: 0.7276 - val_loss: 0.5322 - val_accuracy: 0.7638\n", "Epoch 298/500\n", "257/257 [==============================] - 0s 561us/step - loss: 0.5542 - accuracy: 0.6848 - val_loss: 0.5312 - val_accuracy: 0.7717\n", "Epoch 299/500\n", "257/257 [==============================] - 0s 569us/step - loss: 0.5481 - accuracy: 0.7160 - val_loss: 1.2192 - val_accuracy: 0.7087\n", "Epoch 300/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.7314 - accuracy: 0.7043 - val_loss: 0.5016 - val_accuracy: 0.7480\n", "Epoch 301/500\n", "257/257 [==============================] - 0s 623us/step - loss: 0.5437 - accuracy: 0.7315 - val_loss: 0.4918 - val_accuracy: 0.7402\n", "Epoch 302/500\n", "257/257 [==============================] - 0s 557us/step - loss: 0.5542 - accuracy: 0.7198 - val_loss: 0.5962 - val_accuracy: 0.6535\n", "Epoch 303/500\n", "257/257 [==============================] - 0s 550us/step - loss: 0.5862 - accuracy: 0.6576 - val_loss: 0.5422 - val_accuracy: 0.7087\n", "Epoch 304/500\n", "257/257 [==============================] - 0s 542us/step - loss: 0.5463 - accuracy: 0.7082 - val_loss: 0.6857 - val_accuracy: 0.5433\n", "Epoch 305/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6579 - accuracy: 0.5875 - val_loss: 0.5977 - val_accuracy: 0.6378\n", "Epoch 306/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.5956 - accuracy: 0.6615 - val_loss: 0.4991 - val_accuracy: 0.7638\n", "Epoch 307/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.5644 - accuracy: 0.6848 - val_loss: 0.4955 - val_accuracy: 0.7638\n", "Epoch 308/500\n", "257/257 [==============================] - 0s 582us/step - loss: 0.5494 - accuracy: 0.6926 - val_loss: 0.4940 - val_accuracy: 0.7717\n", "Epoch 309/500\n", "257/257 [==============================] - 0s 558us/step - loss: 0.5342 - accuracy: 0.7082 - val_loss: 0.4899 - val_accuracy: 0.7559\n", "Epoch 310/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.5223 - accuracy: 0.7237 - val_loss: 0.6847 - val_accuracy: 0.7008\n", "Epoch 311/500\n", "257/257 [==============================] - 0s 535us/step - loss: 0.6063 - accuracy: 0.7198 - val_loss: 0.5180 - val_accuracy: 0.7323\n", "Epoch 312/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.5281 - accuracy: 0.7315 - val_loss: 0.4954 - val_accuracy: 0.7638\n", "Epoch 313/500\n", "257/257 [==============================] - 0s 545us/step - loss: 0.5402 - accuracy: 0.7082 - val_loss: 0.5781 - val_accuracy: 0.6614\n", "Epoch 314/500\n", "257/257 [==============================] - 0s 581us/step - loss: 0.5547 - accuracy: 0.6926 - val_loss: 0.5847 - val_accuracy: 0.6535\n", "Epoch 315/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.5489 - accuracy: 0.6809 - val_loss: 0.4972 - val_accuracy: 0.7717\n", "Epoch 316/500\n", "257/257 [==============================] - 0s 529us/step - loss: 0.5011 - accuracy: 0.7432 - val_loss: 0.5317 - val_accuracy: 0.7323\n", "Epoch 317/500\n", "257/257 [==============================] - 0s 543us/step - loss: 0.5063 - accuracy: 0.7549 - val_loss: 0.5371 - val_accuracy: 0.7402\n", "Epoch 318/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.5102 - accuracy: 0.7549 - val_loss: 0.5219 - val_accuracy: 0.7795\n", "Epoch 319/500\n", "257/257 [==============================] - 0s 544us/step - loss: 0.5089 - accuracy: 0.7626 - val_loss: 1.8617 - val_accuracy: 0.3071\n", "Epoch 320/500\n", "257/257 [==============================] - 0s 617us/step - loss: 1.0266 - accuracy: 0.4008 - val_loss: 0.7056 - val_accuracy: 0.4488\n", "Epoch 321/500\n", "257/257 [==============================] - 0s 540us/step - loss: 0.6885 - accuracy: 0.5681 - val_loss: 0.6863 - val_accuracy: 0.5906\n", "Epoch 322/500\n", "257/257 [==============================] - 0s 598us/step - loss: 0.6829 - accuracy: 0.5798 - val_loss: 0.6732 - val_accuracy: 0.6457\n", "Epoch 323/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.6772 - accuracy: 0.6732 - val_loss: 0.6729 - val_accuracy: 0.6614\n", "Epoch 324/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.6765 - accuracy: 0.6693 - val_loss: 0.6651 - val_accuracy: 0.7323\n", "Epoch 325/500\n", "257/257 [==============================] - 0s 561us/step - loss: 0.6732 - accuracy: 0.6304 - val_loss: 0.6645 - val_accuracy: 0.7244\n", "Epoch 326/500\n", "257/257 [==============================] - 0s 559us/step - loss: 0.6730 - accuracy: 0.6498 - val_loss: 0.6654 - val_accuracy: 0.7402\n", "Epoch 327/500\n", "257/257 [==============================] - 0s 568us/step - loss: 0.6733 - accuracy: 0.6537 - val_loss: 0.6635 - val_accuracy: 0.7323\n", "Epoch 328/500\n", "257/257 [==============================] - 0s 557us/step - loss: 0.6725 - accuracy: 0.6381 - val_loss: 0.6605 - val_accuracy: 0.7087\n", "Epoch 329/500\n", "257/257 [==============================] - 0s 594us/step - loss: 0.6713 - accuracy: 0.6148 - val_loss: 0.6568 - val_accuracy: 0.6929\n", "Epoch 330/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.6706 - accuracy: 0.6187 - val_loss: 0.6542 - val_accuracy: 0.6929\n", "Epoch 331/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.6698 - accuracy: 0.6187 - val_loss: 0.6525 - val_accuracy: 0.6929\n", "Epoch 332/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6695 - accuracy: 0.6187 - val_loss: 0.6509 - val_accuracy: 0.6929\n", "Epoch 333/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6693 - accuracy: 0.6187 - val_loss: 0.6494 - val_accuracy: 0.6929\n", "Epoch 334/500\n", "257/257 [==============================] - 0s 543us/step - loss: 0.6696 - accuracy: 0.6187 - val_loss: 0.6500 - val_accuracy: 0.6929\n", "Epoch 335/500\n", "257/257 [==============================] - 0s 561us/step - loss: 0.6687 - accuracy: 0.6187 - val_loss: 0.6495 - val_accuracy: 0.6929\n", "Epoch 336/500\n", "257/257 [==============================] - 0s 595us/step - loss: 0.6683 - accuracy: 0.6187 - val_loss: 0.6489 - val_accuracy: 0.6929\n", "Epoch 337/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.6679 - accuracy: 0.6187 - val_loss: 0.6478 - val_accuracy: 0.6929\n", "Epoch 338/500\n", "257/257 [==============================] - 0s 537us/step - loss: 0.6677 - accuracy: 0.6187 - val_loss: 0.6469 - val_accuracy: 0.6929\n", "Epoch 339/500\n", "257/257 [==============================] - 0s 536us/step - loss: 0.6675 - accuracy: 0.6187 - val_loss: 0.6471 - val_accuracy: 0.6929\n", "Epoch 340/500\n", "257/257 [==============================] - 0s 580us/step - loss: 0.6670 - accuracy: 0.6187 - val_loss: 0.6462 - val_accuracy: 0.6929\n", "Epoch 341/500\n", "257/257 [==============================] - 0s 545us/step - loss: 0.6670 - accuracy: 0.6187 - val_loss: 0.6453 - val_accuracy: 0.6929\n", "Epoch 342/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.6670 - accuracy: 0.6187 - val_loss: 0.6455 - val_accuracy: 0.6929\n", "Epoch 343/500\n", "257/257 [==============================] - 0s 592us/step - loss: 0.6666 - accuracy: 0.6187 - val_loss: 0.6449 - val_accuracy: 0.6929\n", "Epoch 344/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.6664 - accuracy: 0.6187 - val_loss: 0.6455 - val_accuracy: 0.6929\n", "Epoch 345/500\n", "257/257 [==============================] - 0s 566us/step - loss: 0.6657 - accuracy: 0.6187 - val_loss: 0.6449 - val_accuracy: 0.6929\n", "Epoch 346/500\n", "257/257 [==============================] - 0s 583us/step - loss: 0.6656 - accuracy: 0.6187 - val_loss: 0.6435 - val_accuracy: 0.6929\n", "Epoch 347/500\n", "257/257 [==============================] - 0s 539us/step - loss: 0.6648 - accuracy: 0.6187 - val_loss: 0.6436 - val_accuracy: 0.6929\n", "Epoch 348/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6638 - accuracy: 0.6187 - val_loss: 0.6441 - val_accuracy: 0.6929\n", "Epoch 349/500\n", "257/257 [==============================] - 0s 596us/step - loss: 0.6630 - accuracy: 0.6187 - val_loss: 0.6384 - val_accuracy: 0.6929\n", "Epoch 350/500\n", "257/257 [==============================] - 0s 531us/step - loss: 0.6590 - accuracy: 0.6187 - val_loss: 0.6358 - val_accuracy: 0.6929\n", "Epoch 351/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.6552 - accuracy: 0.6187 - val_loss: 0.6312 - val_accuracy: 0.6929\n", "Epoch 352/500\n", "257/257 [==============================] - 0s 538us/step - loss: 0.6668 - accuracy: 0.6187 - val_loss: 0.6298 - val_accuracy: 0.6929\n", "Epoch 353/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.6655 - accuracy: 0.6187 - val_loss: 0.6394 - val_accuracy: 0.7087\n", "Epoch 354/500\n", "257/257 [==============================] - 0s 554us/step - loss: 0.6590 - accuracy: 0.6265 - val_loss: 0.6388 - val_accuracy: 0.7008\n", "Epoch 355/500\n", "257/257 [==============================] - 0s 543us/step - loss: 0.6585 - accuracy: 0.6187 - val_loss: 0.6393 - val_accuracy: 0.7244\n", "Epoch 356/500\n", "257/257 [==============================] - 0s 549us/step - loss: 0.6578 - accuracy: 0.6304 - val_loss: 0.6421 - val_accuracy: 0.7323\n", "Epoch 357/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.6591 - accuracy: 0.6381 - val_loss: 0.6455 - val_accuracy: 0.7244\n", "Epoch 358/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.6597 - accuracy: 0.6615 - val_loss: 0.6424 - val_accuracy: 0.7244\n", "Epoch 359/500\n", "257/257 [==============================] - 0s 549us/step - loss: 0.6576 - accuracy: 0.6693 - val_loss: 0.6448 - val_accuracy: 0.7244\n", "Epoch 360/500\n", "257/257 [==============================] - 0s 569us/step - loss: 0.6577 - accuracy: 0.6732 - val_loss: 0.6477 - val_accuracy: 0.6614\n", "Epoch 361/500\n", "257/257 [==============================] - 0s 544us/step - loss: 0.6564 - accuracy: 0.6615 - val_loss: 0.6347 - val_accuracy: 0.7244\n", "Epoch 362/500\n", "257/257 [==============================] - 0s 537us/step - loss: 0.6493 - accuracy: 0.6459 - val_loss: 0.6456 - val_accuracy: 0.6614\n", "Epoch 363/500\n", "257/257 [==============================] - 0s 539us/step - loss: 0.6571 - accuracy: 0.6654 - val_loss: 0.6368 - val_accuracy: 0.7165\n", "Epoch 364/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.6523 - accuracy: 0.6615 - val_loss: 0.6344 - val_accuracy: 0.7165\n", "Epoch 365/500\n", "257/257 [==============================] - 0s 523us/step - loss: 0.6510 - accuracy: 0.6615 - val_loss: 0.6318 - val_accuracy: 0.7165\n", "Epoch 366/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.6485 - accuracy: 0.6654 - val_loss: 0.6212 - val_accuracy: 0.7244\n", "Epoch 367/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.6404 - accuracy: 0.6615 - val_loss: 0.6222 - val_accuracy: 0.7244\n", "Epoch 368/500\n", "257/257 [==============================] - 0s 549us/step - loss: 0.6406 - accuracy: 0.6693 - val_loss: 0.6273 - val_accuracy: 0.7165\n", "Epoch 369/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6435 - accuracy: 0.6732 - val_loss: 0.6143 - val_accuracy: 0.7244\n", "Epoch 370/500\n", "257/257 [==============================] - 0s 601us/step - loss: 0.6320 - accuracy: 0.6615 - val_loss: 0.6049 - val_accuracy: 0.7165\n", "Epoch 371/500\n", "257/257 [==============================] - 0s 600us/step - loss: 0.6229 - accuracy: 0.6693 - val_loss: 0.6001 - val_accuracy: 0.7165\n", "Epoch 372/500\n", "257/257 [==============================] - 0s 530us/step - loss: 0.6212 - accuracy: 0.6732 - val_loss: 0.6008 - val_accuracy: 0.6850\n", "Epoch 373/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.6179 - accuracy: 0.6654 - val_loss: 0.5922 - val_accuracy: 0.6614\n", "Epoch 374/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6116 - accuracy: 0.6732 - val_loss: 0.5838 - val_accuracy: 0.6299\n", "Epoch 375/500\n", "257/257 [==============================] - 0s 586us/step - loss: 0.6110 - accuracy: 0.6615 - val_loss: 0.5727 - val_accuracy: 0.6378\n", "Epoch 376/500\n", "257/257 [==============================] - 0s 566us/step - loss: 0.6049 - accuracy: 0.6498 - val_loss: 0.5310 - val_accuracy: 0.7323\n", "Epoch 377/500\n", "257/257 [==============================] - 0s 552us/step - loss: 0.5757 - accuracy: 0.7004 - val_loss: 0.6362 - val_accuracy: 0.6535\n", "Epoch 378/500\n", "257/257 [==============================] - 0s 600us/step - loss: 0.6430 - accuracy: 0.6187 - val_loss: 0.5892 - val_accuracy: 0.6929\n", "Epoch 379/500\n", "257/257 [==============================] - 0s 580us/step - loss: 0.6278 - accuracy: 0.6187 - val_loss: 0.5997 - val_accuracy: 0.6929\n", "Epoch 380/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.6462 - accuracy: 0.6187 - val_loss: 0.5772 - val_accuracy: 0.7244\n", "Epoch 381/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6087 - accuracy: 0.7004 - val_loss: 0.5895 - val_accuracy: 0.6772\n", "Epoch 382/500\n", "257/257 [==============================] - 0s 569us/step - loss: 0.6131 - accuracy: 0.6770 - val_loss: 0.5869 - val_accuracy: 0.6614\n", "Epoch 383/500\n", "257/257 [==============================] - 0s 583us/step - loss: 0.6061 - accuracy: 0.6615 - val_loss: 0.5871 - val_accuracy: 0.6693\n", "Epoch 384/500\n", "257/257 [==============================] - 0s 605us/step - loss: 0.6034 - accuracy: 0.6887 - val_loss: 0.5200 - val_accuracy: 0.7323\n", "Epoch 385/500\n", "257/257 [==============================] - 0s 591us/step - loss: 0.5556 - accuracy: 0.6809 - val_loss: 0.6417 - val_accuracy: 0.6929\n", "Epoch 386/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6567 - accuracy: 0.6304 - val_loss: 0.6584 - val_accuracy: 0.5827\n", "Epoch 387/500\n", "257/257 [==============================] - 0s 591us/step - loss: 0.6671 - accuracy: 0.5992 - val_loss: 0.6258 - val_accuracy: 0.7402\n", "Epoch 388/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6492 - accuracy: 0.6576 - val_loss: 0.6271 - val_accuracy: 0.7559\n", "Epoch 389/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.6469 - accuracy: 0.6809 - val_loss: 0.6159 - val_accuracy: 0.7244\n", "Epoch 390/500\n", "257/257 [==============================] - 0s 620us/step - loss: 0.6423 - accuracy: 0.6537 - val_loss: 0.6185 - val_accuracy: 0.7717\n", "Epoch 391/500\n", "257/257 [==============================] - 0s 622us/step - loss: 0.6426 - accuracy: 0.6926 - val_loss: 0.6077 - val_accuracy: 0.7559\n", "Epoch 392/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.6336 - accuracy: 0.6732 - val_loss: 0.5914 - val_accuracy: 0.7087\n", "Epoch 393/500\n", "257/257 [==============================] - 0s 587us/step - loss: 0.6253 - accuracy: 0.6381 - val_loss: 0.5894 - val_accuracy: 0.7244\n", "Epoch 394/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6226 - accuracy: 0.6693 - val_loss: 0.5764 - val_accuracy: 0.7087\n", "Epoch 395/500\n", "257/257 [==============================] - 0s 591us/step - loss: 0.6154 - accuracy: 0.6459 - val_loss: 0.5620 - val_accuracy: 0.7008\n", "Epoch 396/500\n", "257/257 [==============================] - 0s 560us/step - loss: 0.6057 - accuracy: 0.6187 - val_loss: 0.5545 - val_accuracy: 0.6929\n", "Epoch 397/500\n", "257/257 [==============================] - 0s 577us/step - loss: 0.6033 - accuracy: 0.6187 - val_loss: 0.5477 - val_accuracy: 0.7008\n", "Epoch 398/500\n", "257/257 [==============================] - 0s 592us/step - loss: 0.5918 - accuracy: 0.6265 - val_loss: 0.5452 - val_accuracy: 0.7323\n", "Epoch 399/500\n", "257/257 [==============================] - 0s 587us/step - loss: 0.5830 - accuracy: 0.6770 - val_loss: 0.5452 - val_accuracy: 0.7480\n", "Epoch 400/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.5776 - accuracy: 0.6848 - val_loss: 0.5356 - val_accuracy: 0.7323\n", "Epoch 401/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.5604 - accuracy: 0.6887 - val_loss: 0.6732 - val_accuracy: 0.5433\n", "Epoch 402/500\n", "257/257 [==============================] - 0s 581us/step - loss: 0.6888 - accuracy: 0.5292 - val_loss: 0.6656 - val_accuracy: 0.5827\n", "Epoch 403/500\n", "257/257 [==============================] - 0s 570us/step - loss: 0.6709 - accuracy: 0.5953 - val_loss: 0.6732 - val_accuracy: 0.5354\n", "Epoch 404/500\n", "257/257 [==============================] - 0s 568us/step - loss: 0.6738 - accuracy: 0.5447 - val_loss: 0.6416 - val_accuracy: 0.7480\n", "Epoch 405/500\n", "257/257 [==============================] - 0s 651us/step - loss: 0.6524 - accuracy: 0.7160 - val_loss: 0.6232 - val_accuracy: 0.7480\n", "Epoch 406/500\n", "257/257 [==============================] - 0s 566us/step - loss: 0.6449 - accuracy: 0.6693 - val_loss: 0.6162 - val_accuracy: 0.7244\n", "Epoch 407/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.6412 - accuracy: 0.6693 - val_loss: 0.6030 - val_accuracy: 0.7008\n", "Epoch 408/500\n", "257/257 [==============================] - 0s 558us/step - loss: 0.6320 - accuracy: 0.6304 - val_loss: 0.5843 - val_accuracy: 0.6929\n", "Epoch 409/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.6227 - accuracy: 0.6187 - val_loss: 0.5711 - val_accuracy: 0.6929\n", "Epoch 410/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6167 - accuracy: 0.6187 - val_loss: 0.5661 - val_accuracy: 0.6929\n", "Epoch 411/500\n", "257/257 [==============================] - 0s 594us/step - loss: 0.6159 - accuracy: 0.6187 - val_loss: 0.5856 - val_accuracy: 0.6929\n", "Epoch 412/500\n", "257/257 [==============================] - 0s 536us/step - loss: 0.6185 - accuracy: 0.6381 - val_loss: 0.5824 - val_accuracy: 0.7165\n", "Epoch 413/500\n", "257/257 [==============================] - 0s 557us/step - loss: 0.6125 - accuracy: 0.6537 - val_loss: 0.5729 - val_accuracy: 0.7087\n", "Epoch 414/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6087 - accuracy: 0.6381 - val_loss: 0.5653 - val_accuracy: 0.6929\n", "Epoch 415/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.6024 - accuracy: 0.6420 - val_loss: 0.5756 - val_accuracy: 0.7559\n", "Epoch 416/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6028 - accuracy: 0.7004 - val_loss: 0.5604 - val_accuracy: 0.7480\n", "Epoch 417/500\n", "257/257 [==============================] - 0s 570us/step - loss: 0.5917 - accuracy: 0.6770 - val_loss: 0.5432 - val_accuracy: 0.7165\n", "Epoch 418/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.5837 - accuracy: 0.6381 - val_loss: 0.5509 - val_accuracy: 0.7638\n", "Epoch 419/500\n", "257/257 [==============================] - 0s 556us/step - loss: 0.5831 - accuracy: 0.7043 - val_loss: 0.5487 - val_accuracy: 0.7559\n", "Epoch 420/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.5754 - accuracy: 0.7354 - val_loss: 0.5237 - val_accuracy: 0.7638\n", "Epoch 421/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.5560 - accuracy: 0.7043 - val_loss: 0.5183 - val_accuracy: 0.7795\n", "Epoch 422/500\n", "257/257 [==============================] - 0s 607us/step - loss: 0.5418 - accuracy: 0.7160 - val_loss: 0.5188 - val_accuracy: 0.7559\n", "Epoch 423/500\n", "257/257 [==============================] - 0s 566us/step - loss: 0.6021 - accuracy: 0.7237 - val_loss: 0.5203 - val_accuracy: 0.7480\n", "Epoch 424/500\n", "257/257 [==============================] - 0s 608us/step - loss: 0.5582 - accuracy: 0.6848 - val_loss: 0.5167 - val_accuracy: 0.7638\n", "Epoch 425/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.5406 - accuracy: 0.7588 - val_loss: 0.5825 - val_accuracy: 0.7087\n", "Epoch 426/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.6006 - accuracy: 0.6615 - val_loss: 0.5397 - val_accuracy: 0.7874\n", "Epoch 427/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.5632 - accuracy: 0.7354 - val_loss: 0.5408 - val_accuracy: 0.7795\n", "Epoch 428/500\n", "257/257 [==============================] - 0s 683us/step - loss: 0.5607 - accuracy: 0.7393 - val_loss: 0.6656 - val_accuracy: 0.5906\n", "Epoch 429/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.6645 - accuracy: 0.5447 - val_loss: 0.6073 - val_accuracy: 0.7323\n", "Epoch 430/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.6221 - accuracy: 0.6887 - val_loss: 0.5729 - val_accuracy: 0.7480\n", "Epoch 431/500\n", "257/257 [==============================] - 0s 548us/step - loss: 0.6008 - accuracy: 0.7198 - val_loss: 0.5651 - val_accuracy: 0.7165\n", "Epoch 432/500\n", "257/257 [==============================] - 0s 568us/step - loss: 0.6073 - accuracy: 0.6381 - val_loss: 0.5721 - val_accuracy: 0.7402\n", "Epoch 433/500\n", "257/257 [==============================] - 0s 561us/step - loss: 0.6055 - accuracy: 0.6848 - val_loss: 0.5752 - val_accuracy: 0.7402\n", "Epoch 434/500\n", "257/257 [==============================] - 0s 588us/step - loss: 0.6052 - accuracy: 0.7082 - val_loss: 0.5868 - val_accuracy: 0.7402\n", "Epoch 435/500\n", "257/257 [==============================] - 0s 605us/step - loss: 0.6113 - accuracy: 0.7276 - val_loss: 0.5910 - val_accuracy: 0.7323\n", "Epoch 436/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.6117 - accuracy: 0.7160 - val_loss: 0.5900 - val_accuracy: 0.7087\n", "Epoch 437/500\n", "257/257 [==============================] - 0s 582us/step - loss: 0.6085 - accuracy: 0.7198 - val_loss: 0.5849 - val_accuracy: 0.7402\n", "Epoch 438/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.6023 - accuracy: 0.7276 - val_loss: 0.5512 - val_accuracy: 0.7717\n", "Epoch 439/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.5789 - accuracy: 0.7082 - val_loss: 0.5391 - val_accuracy: 0.7165\n", "Epoch 440/500\n", "257/257 [==============================] - 0s 570us/step - loss: 0.5890 - accuracy: 0.6381 - val_loss: 0.5332 - val_accuracy: 0.7244\n", "Epoch 441/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.5797 - accuracy: 0.6498 - val_loss: 0.5242 - val_accuracy: 0.7244\n", "Epoch 442/500\n", "257/257 [==============================] - 0s 596us/step - loss: 0.5671 - accuracy: 0.6693 - val_loss: 0.5180 - val_accuracy: 0.7874\n", "Epoch 443/500\n", "257/257 [==============================] - 0s 578us/step - loss: 0.5555 - accuracy: 0.7198 - val_loss: 0.5067 - val_accuracy: 0.7480\n", "Epoch 444/500\n", "257/257 [==============================] - 0s 565us/step - loss: 0.5468 - accuracy: 0.7004 - val_loss: 0.5113 - val_accuracy: 0.7244\n", "Epoch 445/500\n", "257/257 [==============================] - 0s 581us/step - loss: 0.5374 - accuracy: 0.6809 - val_loss: 0.5020 - val_accuracy: 0.7638\n", "Epoch 446/500\n", "257/257 [==============================] - 0s 583us/step - loss: 0.5197 - accuracy: 0.7354 - val_loss: 0.5031 - val_accuracy: 0.7323\n", "Epoch 447/500\n", "257/257 [==============================] - 0s 585us/step - loss: 0.5534 - accuracy: 0.7432 - val_loss: 0.5087 - val_accuracy: 0.7244\n", "Epoch 448/500\n", "257/257 [==============================] - 0s 574us/step - loss: 0.5195 - accuracy: 0.7471 - val_loss: 0.5127 - val_accuracy: 0.7874\n", "Epoch 449/500\n", "257/257 [==============================] - 0s 587us/step - loss: 0.5243 - accuracy: 0.7043 - val_loss: 0.5128 - val_accuracy: 0.7717\n", "Epoch 450/500\n", "257/257 [==============================] - 0s 595us/step - loss: 0.5165 - accuracy: 0.7471 - val_loss: 0.5108 - val_accuracy: 0.7795\n", "Epoch 451/500\n", "257/257 [==============================] - 0s 564us/step - loss: 0.5137 - accuracy: 0.7121 - val_loss: 0.5336 - val_accuracy: 0.7480\n", "Epoch 452/500\n", "257/257 [==============================] - 0s 580us/step - loss: 0.5206 - accuracy: 0.6926 - val_loss: 0.5146 - val_accuracy: 0.7717\n", "Epoch 453/500\n", "257/257 [==============================] - 0s 586us/step - loss: 0.5182 - accuracy: 0.7588 - val_loss: 0.5670 - val_accuracy: 0.7323\n", "Epoch 454/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.5480 - accuracy: 0.7198 - val_loss: 0.5463 - val_accuracy: 0.7165\n", "Epoch 455/500\n", "257/257 [==============================] - 0s 606us/step - loss: 0.5117 - accuracy: 0.7004 - val_loss: 1.0418 - val_accuracy: 0.6929\n", "Epoch 456/500\n", "257/257 [==============================] - 0s 554us/step - loss: 0.6746 - accuracy: 0.6537 - val_loss: 0.5736 - val_accuracy: 0.7559\n", "Epoch 457/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.7739 - accuracy: 0.7510 - val_loss: 0.4780 - val_accuracy: 0.7795\n", "Epoch 458/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.5061 - accuracy: 0.7549 - val_loss: 0.4777 - val_accuracy: 0.7559\n", "Epoch 459/500\n", "257/257 [==============================] - 0s 615us/step - loss: 0.4904 - accuracy: 0.7626 - val_loss: 0.5364 - val_accuracy: 0.7402\n", "Epoch 460/500\n", "257/257 [==============================] - 0s 582us/step - loss: 0.5464 - accuracy: 0.7276 - val_loss: 0.5880 - val_accuracy: 0.7323\n", "Epoch 461/500\n", "257/257 [==============================] - 0s 593us/step - loss: 0.5267 - accuracy: 0.7393 - val_loss: 0.6646 - val_accuracy: 0.6929\n", "Epoch 462/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.5486 - accuracy: 0.7198 - val_loss: 0.5508 - val_accuracy: 0.7717\n", "Epoch 463/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.5124 - accuracy: 0.7432 - val_loss: 0.5620 - val_accuracy: 0.7480\n", "Epoch 464/500\n", "257/257 [==============================] - 0s 598us/step - loss: 0.5130 - accuracy: 0.7393 - val_loss: 0.5991 - val_accuracy: 0.7244\n", "Epoch 465/500\n", "257/257 [==============================] - 0s 555us/step - loss: 0.5087 - accuracy: 0.7237 - val_loss: 0.5292 - val_accuracy: 0.7795\n", "Epoch 466/500\n", "257/257 [==============================] - 0s 606us/step - loss: 0.5095 - accuracy: 0.7471 - val_loss: 0.7349 - val_accuracy: 0.7402\n", "Epoch 467/500\n", "257/257 [==============================] - 0s 586us/step - loss: 0.6531 - accuracy: 0.7198 - val_loss: 0.5165 - val_accuracy: 0.7638\n", "Epoch 468/500\n", "257/257 [==============================] - 0s 589us/step - loss: 0.5252 - accuracy: 0.7510 - val_loss: 0.4883 - val_accuracy: 0.7559\n", "Epoch 469/500\n", "257/257 [==============================] - 0s 567us/step - loss: 0.4911 - accuracy: 0.7626 - val_loss: 0.5078 - val_accuracy: 0.7638\n", "Epoch 470/500\n", "257/257 [==============================] - 0s 580us/step - loss: 0.4826 - accuracy: 0.7588 - val_loss: 0.6165 - val_accuracy: 0.7244\n", "Epoch 471/500\n", "257/257 [==============================] - 0s 594us/step - loss: 0.6061 - accuracy: 0.6732 - val_loss: 0.5148 - val_accuracy: 0.7480\n", "Epoch 472/500\n", "257/257 [==============================] - 0s 580us/step - loss: 0.5192 - accuracy: 0.7082 - val_loss: 0.5201 - val_accuracy: 0.7795\n", "Epoch 473/500\n", "257/257 [==============================] - 0s 573us/step - loss: 0.4963 - accuracy: 0.7510 - val_loss: 0.5494 - val_accuracy: 0.7638\n", "Epoch 474/500\n", "257/257 [==============================] - 0s 570us/step - loss: 0.5080 - accuracy: 0.7588 - val_loss: 0.5833 - val_accuracy: 0.7402\n", "Epoch 475/500\n", "257/257 [==============================] - 0s 562us/step - loss: 0.4988 - accuracy: 0.7432 - val_loss: 0.5325 - val_accuracy: 0.7638\n", "Epoch 476/500\n", "257/257 [==============================] - 0s 553us/step - loss: 0.5182 - accuracy: 0.7393 - val_loss: 0.5130 - val_accuracy: 0.7559\n", "Epoch 477/500\n", "257/257 [==============================] - 0s 629us/step - loss: 0.5262 - accuracy: 0.7393 - val_loss: 0.5024 - val_accuracy: 0.7638\n", "Epoch 478/500\n", "257/257 [==============================] - 0s 588us/step - loss: 0.5103 - accuracy: 0.7471 - val_loss: 0.5317 - val_accuracy: 0.7638\n", "Epoch 479/500\n", "257/257 [==============================] - 0s 563us/step - loss: 0.5026 - accuracy: 0.7432 - val_loss: 0.5104 - val_accuracy: 0.7717\n", "Epoch 480/500\n", "257/257 [==============================] - 0s 579us/step - loss: 0.4814 - accuracy: 0.7588 - val_loss: 0.5568 - val_accuracy: 0.7402\n", "Epoch 481/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.4927 - accuracy: 0.7626 - val_loss: 0.5314 - val_accuracy: 0.7638\n", "Epoch 482/500\n", "257/257 [==============================] - 0s 584us/step - loss: 0.4836 - accuracy: 0.7665 - val_loss: 0.5394 - val_accuracy: 0.7323\n", "Epoch 483/500\n", "257/257 [==============================] - 0s 543us/step - loss: 0.4822 - accuracy: 0.7510 - val_loss: 0.5626 - val_accuracy: 0.7638\n", "Epoch 484/500\n", "257/257 [==============================] - 0s 585us/step - loss: 0.5355 - accuracy: 0.7237 - val_loss: 0.5660 - val_accuracy: 0.7008\n", "Epoch 485/500\n", "257/257 [==============================] - 0s 592us/step - loss: 0.6120 - accuracy: 0.6420 - val_loss: 0.5646 - val_accuracy: 0.7480\n", "Epoch 486/500\n", "257/257 [==============================] - 0s 571us/step - loss: 0.5936 - accuracy: 0.7315 - val_loss: 0.5857 - val_accuracy: 0.7402\n", "Epoch 487/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.5983 - accuracy: 0.7160 - val_loss: 0.5583 - val_accuracy: 0.7559\n", "Epoch 488/500\n", "257/257 [==============================] - 0s 575us/step - loss: 0.5772 - accuracy: 0.7276 - val_loss: 0.5366 - val_accuracy: 0.7402\n", "Epoch 489/500\n", "257/257 [==============================] - 0s 561us/step - loss: 0.5647 - accuracy: 0.6887 - val_loss: 0.5267 - val_accuracy: 0.7402\n", "Epoch 490/500\n", "257/257 [==============================] - 0s 572us/step - loss: 0.5543 - accuracy: 0.6809 - val_loss: 0.5426 - val_accuracy: 0.7402\n", "Epoch 491/500\n", "257/257 [==============================] - 0s 599us/step - loss: 0.5560 - accuracy: 0.7315 - val_loss: 0.5715 - val_accuracy: 0.7087\n", "Epoch 492/500\n", "257/257 [==============================] - 0s 547us/step - loss: 0.5605 - accuracy: 0.7082 - val_loss: 0.5055 - val_accuracy: 0.7559\n", "Epoch 493/500\n", "257/257 [==============================] - 0s 569us/step - loss: 0.5175 - accuracy: 0.7237 - val_loss: 0.5050 - val_accuracy: 0.7638\n", "Epoch 494/500\n", "257/257 [==============================] - 0s 595us/step - loss: 0.5062 - accuracy: 0.7315 - val_loss: 0.5383 - val_accuracy: 0.7480\n", "Epoch 495/500\n", "257/257 [==============================] - 0s 624us/step - loss: 0.5300 - accuracy: 0.7082 - val_loss: 0.5235 - val_accuracy: 0.7480\n", "Epoch 496/500\n", "257/257 [==============================] - 0s 639us/step - loss: 0.4962 - accuracy: 0.7315 - val_loss: 0.5482 - val_accuracy: 0.7323\n", "Epoch 497/500\n", "257/257 [==============================] - 0s 613us/step - loss: 0.5022 - accuracy: 0.7315 - val_loss: 0.5918 - val_accuracy: 0.7244\n", "Epoch 498/500\n", "257/257 [==============================] - 0s 591us/step - loss: 0.5167 - accuracy: 0.7276 - val_loss: 0.5900 - val_accuracy: 0.7165\n", "Epoch 499/500\n", "257/257 [==============================] - 0s 551us/step - loss: 0.5094 - accuracy: 0.7471 - val_loss: 0.7564 - val_accuracy: 0.5827\n", "Epoch 500/500\n", "257/257 [==============================] - 0s 617us/step - loss: 0.6176 - accuracy: 0.6342 - val_loss: 0.6187 - val_accuracy: 0.6378\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "sZDxYwOdIb0j", "colab_type": "text" }, "source": [ "## Examine convergence ##" ] }, { "cell_type": "code", "metadata": { "id": "4DH7Sb2djkWV", "colab_type": "code", "outputId": "9934e674-9b02-4098-cc15-0578a5813991", "colab": { "base_uri": "https://localhost:8080/", "height": 573 } }, "source": [ "plt.plot(history.history['accuracy'], label = 'train')\n", "plt.plot(history.history['val_accuracy'], label='validation')\n", "plt.title('model accuracy')\n", "plt.ylabel('accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'validation'], loc = 'upper left')\n", "plt.show()\n", "\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'validation'],loc = 'upper left')\n", "plt.show()" ], "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "lOwAsqvzK4JN", "colab_type": "text" }, "source": [ "## Evaluate the derived model (obtained from final epoch) ##" ] }, { "cell_type": "code", "metadata": { "id": "3p5WO16i3EHE", "colab_type": "code", "outputId": "519f7ebc-6cb7-4fec-fccf-a29361c58510", "colab": { "base_uri": "https://localhost:8080/", "height": 282 } }, "source": [ "y_pred2=model.predict(X_tst0)\n", "y_pred2=(y_pred2>.5).astype(int)\n", "\n", "# todo: use boot-strap estimation\n", "probs = model.predict_proba(X_tst0)\n", "probs = probs[:,0]\n", "auc = roc_auc_score(y_test, probs)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, probs)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()" ], "execution_count": 23, "outputs": [ { "output_type": "stream", "text": [ "AUC: 0.816\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "2LjOotjg3WAZ", "colab_type": "code", "outputId": "b21573a2-27cb-4657-a491-978f9ca7c3c8", "colab": { "base_uri": "https://localhost:8080/", "height": 136 } }, "source": [ "cmat = metrics.confusion_matrix(y_test, y_pred2)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred2, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred2, pos_label=1))\n", "\n", "! ls\n" ], "execution_count": 29, "outputs": [ { "output_type": "stream", "text": [ "[[201 52]\n", " [ 43 88]]\n", "Accuracy: 0.7526041666666666\n", "Precision: 0.6285714285714286\n", "Recall: 0.6717557251908397\n", "ann_BGL.ipynb diabetes2.csv diabetes4.csv glucose_RF.R\n", "best.h5 diabetes3.csv diabetes.csv README.md\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "AgzmnBuNKwpD", "colab_type": "text" }, "source": [ "## Evaluate using the best model ##" ] }, { "cell_type": "code", "metadata": { "id": "6Ck4i8YY3DCv", "colab_type": "code", "outputId": "c58fd815-b4b0-47cb-c6a7-b8a3cd7d9cf7", "colab": { "base_uri": "https://localhost:8080/", "height": 316 } }, "source": [ "! ls\n", "model.load_weights('best.h5')\n", "\n", "y_pred2=model.predict(X_tst0)\n", "y_pred2=(y_pred2>.5).astype(int)\n", "\n", "# todo: use boot-strap estimation\n", "probs = model.predict_proba(X_tst0)\n", "probs = probs[:,0]\n", "auc = roc_auc_score(y_test, probs)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, probs)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n" ], "execution_count": 27, "outputs": [ { "output_type": "stream", "text": [ "ann_BGL.ipynb diabetes2.csv diabetes4.csv glucose_RF.R\n", "best.h5 diabetes3.csv diabetes.csv README.md\n", "AUC: 0.825\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "yx1l183PIGy5", "colab_type": "code", "outputId": "4f5df51b-9d48-4c99-86c0-76d3da6e7606", "colab": { "base_uri": "https://localhost:8080/", "height": 102 } }, "source": [ "cmat = metrics.confusion_matrix(y_test, y_pred2)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred2, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred2, pos_label=1))\n" ], "execution_count": 28, "outputs": [ { "output_type": "stream", "text": [ "[[201 52]\n", " [ 43 88]]\n", "Accuracy: 0.7526041666666666\n", "Precision: 0.6285714285714286\n", "Recall: 0.6717557251908397\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "4WlgjQ2WL5Z8", "colab_type": "text" }, "source": [ "## Train LSTM with standardized input data ##" ] }, { "cell_type": "code", "metadata": { "id": "DrPCFs7XL3UP", "colab_type": "code", "outputId": "fc343308-5350-4502-e038-f04a809aa140", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "model = Sequential()\n", "model.add(LSTM(32, input_shape = (7,1), return_sequences = True, kernel_initializer = 'uniform', activation ='relu'))\n", "model.add(LSTM(64, kernel_initializer = 'uniform', return_sequences = True, activation = 'relu'))\n", "model.add(LSTM(128, kernel_initializer = 'uniform', activation = 'relu'))\n", "model.add(Dense(256, activation = 'relu'))\n", "model.add(Dense(128, activation = 'relu'))\n", "model.add(Dense(64, activation = 'relu'))\n", "model.add(Dense(16, activation = 'relu'))\n", "model.add(Dense(1, activation = 'sigmoid'))\n", "\n", "from keras import optimizers \n", " \n", "lr=0.002 \n", "b1=0.9; b2=0.999; ep=1e-08; dd=0.004\n", "opt = optimizers.Nadam()#lr=lr, beta_1=b1, beta_2=b2, epsilon=ep, schedule_decay=dd) \n", "model.compile(loss = 'binary_crossentropy', optimizer = opt, metrics = ['accuracy'])\n", "model.summary()\n", "history = model.fit(X_trn, y_train, validation_split = 0.3, epochs = 500, batch_size = 64, verbose = 1,callbacks=C)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Model: \"sequential_12\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_34 (LSTM) (None, 7, 32) 4352 \n", "_________________________________________________________________\n", "lstm_35 (LSTM) (None, 7, 64) 24832 \n", "_________________________________________________________________\n", "lstm_36 (LSTM) (None, 128) 98816 \n", "_________________________________________________________________\n", "dense_56 (Dense) (None, 256) 33024 \n", "_________________________________________________________________\n", "dense_57 (Dense) (None, 128) 32896 \n", "_________________________________________________________________\n", "dense_58 (Dense) (None, 64) 8256 \n", "_________________________________________________________________\n", "dense_59 (Dense) (None, 16) 1040 \n", "_________________________________________________________________\n", "dense_60 (Dense) (None, 1) 17 \n", "=================================================================\n", "Total params: 203,233\n", "Trainable params: 203,233\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Train on 268 samples, validate on 116 samples\n", "Epoch 1/500\n", "268/268 [==============================] - 1s 5ms/step - loss: 0.6914 - accuracy: 0.6269 - val_loss: 0.6847 - val_accuracy: 0.6810\n", "Epoch 2/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.6842 - accuracy: 0.6269 - val_loss: 0.6707 - val_accuracy: 0.6810\n", "Epoch 3/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6729 - accuracy: 0.6269 - val_loss: 0.6492 - val_accuracy: 0.6810\n", "Epoch 4/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6635 - accuracy: 0.6269 - val_loss: 0.6305 - val_accuracy: 0.6810\n", "Epoch 5/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6598 - accuracy: 0.6269 - val_loss: 0.6259 - val_accuracy: 0.6810\n", "Epoch 6/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.6626 - accuracy: 0.6269 - val_loss: 0.6224 - val_accuracy: 0.6810\n", "Epoch 7/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6615 - accuracy: 0.6269 - val_loss: 0.6262 - val_accuracy: 0.6810\n", "Epoch 8/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.6575 - accuracy: 0.6269 - val_loss: 0.6294 - val_accuracy: 0.6810\n", "Epoch 9/500\n", "268/268 [==============================] - 0s 618us/step - loss: 0.6580 - accuracy: 0.6269 - val_loss: 0.6303 - val_accuracy: 0.6810\n", "Epoch 10/500\n", "268/268 [==============================] - 0s 665us/step - loss: 0.6575 - accuracy: 0.6269 - val_loss: 0.6299 - val_accuracy: 0.6810\n", "Epoch 11/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6556 - accuracy: 0.6269 - val_loss: 0.6179 - val_accuracy: 0.6810\n", "Epoch 12/500\n", "268/268 [==============================] - 0s 635us/step - loss: 0.6618 - accuracy: 0.6269 - val_loss: 0.6103 - val_accuracy: 0.6810\n", "Epoch 13/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.6548 - accuracy: 0.6269 - val_loss: 0.6268 - val_accuracy: 0.6810\n", "Epoch 14/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6555 - accuracy: 0.6269 - val_loss: 0.6295 - val_accuracy: 0.6810\n", "Epoch 15/500\n", "268/268 [==============================] - 0s 558us/step - loss: 0.6545 - accuracy: 0.6269 - val_loss: 0.6176 - val_accuracy: 0.6810\n", "Epoch 16/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6501 - accuracy: 0.6269 - val_loss: 0.6126 - val_accuracy: 0.6810\n", "Epoch 17/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.6488 - accuracy: 0.6231 - val_loss: 0.6073 - val_accuracy: 0.6897\n", "Epoch 18/500\n", "268/268 [==============================] - 0s 546us/step - loss: 0.6474 - accuracy: 0.6306 - val_loss: 0.6033 - val_accuracy: 0.6897\n", "Epoch 19/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.6469 - accuracy: 0.6269 - val_loss: 0.6023 - val_accuracy: 0.6810\n", "Epoch 20/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.6454 - accuracy: 0.6269 - val_loss: 0.6083 - val_accuracy: 0.6810\n", "Epoch 21/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6460 - accuracy: 0.6269 - val_loss: 0.6004 - val_accuracy: 0.6897\n", "Epoch 22/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6451 - accuracy: 0.6306 - val_loss: 0.6027 - val_accuracy: 0.6897\n", "Epoch 23/500\n", "268/268 [==============================] - 0s 639us/step - loss: 0.6454 - accuracy: 0.6231 - val_loss: 0.6066 - val_accuracy: 0.6810\n", "Epoch 24/500\n", "268/268 [==============================] - 0s 637us/step - loss: 0.6446 - accuracy: 0.6306 - val_loss: 0.6127 - val_accuracy: 0.7155\n", "Epoch 25/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6462 - accuracy: 0.6418 - val_loss: 0.6142 - val_accuracy: 0.6897\n", "Epoch 26/500\n", "268/268 [==============================] - 0s 558us/step - loss: 0.6454 - accuracy: 0.6269 - val_loss: 0.6081 - val_accuracy: 0.6810\n", "Epoch 27/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6438 - accuracy: 0.6269 - val_loss: 0.6044 - val_accuracy: 0.6810\n", "Epoch 28/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6442 - accuracy: 0.6269 - val_loss: 0.6008 - val_accuracy: 0.6810\n", "Epoch 29/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6458 - accuracy: 0.6269 - val_loss: 0.6027 - val_accuracy: 0.6983\n", "Epoch 30/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6458 - accuracy: 0.6343 - val_loss: 0.6140 - val_accuracy: 0.7069\n", "Epoch 31/500\n", "268/268 [==============================] - 0s 600us/step - loss: 0.6453 - accuracy: 0.6418 - val_loss: 0.6035 - val_accuracy: 0.6983\n", "Epoch 32/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.6433 - accuracy: 0.6306 - val_loss: 0.6037 - val_accuracy: 0.6983\n", "Epoch 33/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6440 - accuracy: 0.6418 - val_loss: 0.6097 - val_accuracy: 0.6983\n", "Epoch 34/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.6441 - accuracy: 0.6455 - val_loss: 0.6020 - val_accuracy: 0.6983\n", "Epoch 35/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6438 - accuracy: 0.6455 - val_loss: 0.6034 - val_accuracy: 0.6983\n", "Epoch 36/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6404 - accuracy: 0.6604 - val_loss: 0.6205 - val_accuracy: 0.6724\n", "Epoch 37/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6489 - accuracy: 0.6567 - val_loss: 0.6262 - val_accuracy: 0.6724\n", "Epoch 38/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.6487 - accuracy: 0.6455 - val_loss: 0.6168 - val_accuracy: 0.6983\n", "Epoch 39/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6442 - accuracy: 0.6418 - val_loss: 0.6085 - val_accuracy: 0.6983\n", "Epoch 40/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6449 - accuracy: 0.6455 - val_loss: 0.6003 - val_accuracy: 0.6983\n", "Epoch 41/500\n", "268/268 [==============================] - 0s 602us/step - loss: 0.6441 - accuracy: 0.6455 - val_loss: 0.6034 - val_accuracy: 0.6897\n", "Epoch 42/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6421 - accuracy: 0.6530 - val_loss: 0.5983 - val_accuracy: 0.6810\n", "Epoch 43/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6440 - accuracy: 0.6567 - val_loss: 0.6016 - val_accuracy: 0.6724\n", "Epoch 44/500\n", "268/268 [==============================] - 0s 640us/step - loss: 0.6473 - accuracy: 0.6567 - val_loss: 0.6187 - val_accuracy: 0.6724\n", "Epoch 45/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.6456 - accuracy: 0.6567 - val_loss: 0.6145 - val_accuracy: 0.6810\n", "Epoch 46/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.6439 - accuracy: 0.6455 - val_loss: 0.6040 - val_accuracy: 0.6983\n", "Epoch 47/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6424 - accuracy: 0.6455 - val_loss: 0.6021 - val_accuracy: 0.6897\n", "Epoch 48/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6407 - accuracy: 0.6530 - val_loss: 0.6067 - val_accuracy: 0.6724\n", "Epoch 49/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.6407 - accuracy: 0.6530 - val_loss: 0.6103 - val_accuracy: 0.6724\n", "Epoch 50/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6425 - accuracy: 0.6530 - val_loss: 0.6113 - val_accuracy: 0.6897\n", "Epoch 51/500\n", "268/268 [==============================] - 0s 628us/step - loss: 0.6412 - accuracy: 0.6493 - val_loss: 0.6043 - val_accuracy: 0.6983\n", "Epoch 52/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6419 - accuracy: 0.6455 - val_loss: 0.6033 - val_accuracy: 0.6983\n", "Epoch 53/500\n", "268/268 [==============================] - 0s 553us/step - loss: 0.6411 - accuracy: 0.6455 - val_loss: 0.6055 - val_accuracy: 0.6897\n", "Epoch 54/500\n", "268/268 [==============================] - 0s 633us/step - loss: 0.6408 - accuracy: 0.6530 - val_loss: 0.6042 - val_accuracy: 0.6724\n", "Epoch 55/500\n", "268/268 [==============================] - 0s 602us/step - loss: 0.6403 - accuracy: 0.6530 - val_loss: 0.6033 - val_accuracy: 0.6724\n", "Epoch 56/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6409 - accuracy: 0.6567 - val_loss: 0.6023 - val_accuracy: 0.6724\n", "Epoch 57/500\n", "268/268 [==============================] - 0s 553us/step - loss: 0.6421 - accuracy: 0.6567 - val_loss: 0.6032 - val_accuracy: 0.6724\n", "Epoch 58/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6415 - accuracy: 0.6567 - val_loss: 0.6071 - val_accuracy: 0.6724\n", "Epoch 59/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6399 - accuracy: 0.6567 - val_loss: 0.6067 - val_accuracy: 0.6724\n", "Epoch 60/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6403 - accuracy: 0.6604 - val_loss: 0.6073 - val_accuracy: 0.6810\n", "Epoch 61/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6416 - accuracy: 0.6530 - val_loss: 0.6190 - val_accuracy: 0.6897\n", "Epoch 62/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.6444 - accuracy: 0.6530 - val_loss: 0.6197 - val_accuracy: 0.6897\n", "Epoch 63/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6437 - accuracy: 0.6530 - val_loss: 0.6171 - val_accuracy: 0.6810\n", "Epoch 64/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.6409 - accuracy: 0.6530 - val_loss: 0.6103 - val_accuracy: 0.6724\n", "Epoch 65/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6394 - accuracy: 0.6604 - val_loss: 0.6040 - val_accuracy: 0.6724\n", "Epoch 66/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.6417 - accuracy: 0.6567 - val_loss: 0.6033 - val_accuracy: 0.6724\n", "Epoch 67/500\n", "268/268 [==============================] - 0s 582us/step - loss: 0.6413 - accuracy: 0.6567 - val_loss: 0.6064 - val_accuracy: 0.6724\n", "Epoch 68/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6428 - accuracy: 0.6567 - val_loss: 0.6174 - val_accuracy: 0.6724\n", "Epoch 69/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6433 - accuracy: 0.6567 - val_loss: 0.6141 - val_accuracy: 0.6724\n", "Epoch 70/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.6404 - accuracy: 0.6567 - val_loss: 0.6093 - val_accuracy: 0.6724\n", "Epoch 71/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.6405 - accuracy: 0.6530 - val_loss: 0.6061 - val_accuracy: 0.6724\n", "Epoch 72/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.6410 - accuracy: 0.6567 - val_loss: 0.6104 - val_accuracy: 0.6724\n", "Epoch 73/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6405 - accuracy: 0.6567 - val_loss: 0.6110 - val_accuracy: 0.6724\n", "Epoch 74/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.6409 - accuracy: 0.6567 - val_loss: 0.6093 - val_accuracy: 0.6724\n", "Epoch 75/500\n", "268/268 [==============================] - 0s 547us/step - loss: 0.6406 - accuracy: 0.6567 - val_loss: 0.6043 - val_accuracy: 0.6724\n", "Epoch 76/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6409 - accuracy: 0.6604 - val_loss: 0.6073 - val_accuracy: 0.6724\n", "Epoch 77/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6390 - accuracy: 0.6604 - val_loss: 0.6098 - val_accuracy: 0.6724\n", "Epoch 78/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6399 - accuracy: 0.6567 - val_loss: 0.6146 - val_accuracy: 0.6724\n", "Epoch 79/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.6418 - accuracy: 0.6530 - val_loss: 0.6197 - val_accuracy: 0.6724\n", "Epoch 80/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6434 - accuracy: 0.6530 - val_loss: 0.6180 - val_accuracy: 0.6724\n", "Epoch 81/500\n", "268/268 [==============================] - 0s 651us/step - loss: 0.6411 - accuracy: 0.6530 - val_loss: 0.6120 - val_accuracy: 0.6724\n", "Epoch 82/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.6397 - accuracy: 0.6567 - val_loss: 0.6121 - val_accuracy: 0.6724\n", "Epoch 83/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6402 - accuracy: 0.6567 - val_loss: 0.6130 - val_accuracy: 0.6724\n", "Epoch 84/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6405 - accuracy: 0.6567 - val_loss: 0.6159 - val_accuracy: 0.6724\n", "Epoch 85/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.6402 - accuracy: 0.6567 - val_loss: 0.6118 - val_accuracy: 0.6724\n", "Epoch 86/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6397 - accuracy: 0.6530 - val_loss: 0.6075 - val_accuracy: 0.6724\n", "Epoch 87/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6399 - accuracy: 0.6567 - val_loss: 0.6071 - val_accuracy: 0.6724\n", "Epoch 88/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.6400 - accuracy: 0.6567 - val_loss: 0.6082 - val_accuracy: 0.6724\n", "Epoch 89/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6395 - accuracy: 0.6567 - val_loss: 0.6123 - val_accuracy: 0.6724\n", "Epoch 90/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.6397 - accuracy: 0.6567 - val_loss: 0.6102 - val_accuracy: 0.6724\n", "Epoch 91/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6394 - accuracy: 0.6567 - val_loss: 0.6088 - val_accuracy: 0.6724\n", "Epoch 92/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6394 - accuracy: 0.6567 - val_loss: 0.6102 - val_accuracy: 0.6724\n", "Epoch 93/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.6394 - accuracy: 0.6567 - val_loss: 0.6143 - val_accuracy: 0.6724\n", "Epoch 94/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6399 - accuracy: 0.6567 - val_loss: 0.6167 - val_accuracy: 0.6724\n", "Epoch 95/500\n", "268/268 [==============================] - 0s 552us/step - loss: 0.6406 - accuracy: 0.6604 - val_loss: 0.6158 - val_accuracy: 0.6724\n", "Epoch 96/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6402 - accuracy: 0.6567 - val_loss: 0.6094 - val_accuracy: 0.6724\n", "Epoch 97/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.6391 - accuracy: 0.6604 - val_loss: 0.6071 - val_accuracy: 0.6724\n", "Epoch 98/500\n", "268/268 [==============================] - 0s 558us/step - loss: 0.6401 - accuracy: 0.6567 - val_loss: 0.6066 - val_accuracy: 0.6724\n", "Epoch 99/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6431 - accuracy: 0.6567 - val_loss: 0.6054 - val_accuracy: 0.6724\n", "Epoch 100/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.6419 - accuracy: 0.6567 - val_loss: 0.6066 - val_accuracy: 0.6724\n", "Epoch 101/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6404 - accuracy: 0.6567 - val_loss: 0.6134 - val_accuracy: 0.6724\n", "Epoch 102/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6393 - accuracy: 0.6567 - val_loss: 0.6116 - val_accuracy: 0.6810\n", "Epoch 103/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6402 - accuracy: 0.6530 - val_loss: 0.6072 - val_accuracy: 0.6897\n", "Epoch 104/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6412 - accuracy: 0.6455 - val_loss: 0.6033 - val_accuracy: 0.6897\n", "Epoch 105/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.6416 - accuracy: 0.6530 - val_loss: 0.6072 - val_accuracy: 0.6810\n", "Epoch 106/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6405 - accuracy: 0.6493 - val_loss: 0.6077 - val_accuracy: 0.6724\n", "Epoch 107/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.6404 - accuracy: 0.6567 - val_loss: 0.6082 - val_accuracy: 0.6724\n", "Epoch 108/500\n", "268/268 [==============================] - 0s 552us/step - loss: 0.6393 - accuracy: 0.6567 - val_loss: 0.6082 - val_accuracy: 0.6724\n", "Epoch 109/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6399 - accuracy: 0.6567 - val_loss: 0.6062 - val_accuracy: 0.6724\n", "Epoch 110/500\n", "268/268 [==============================] - 0s 550us/step - loss: 0.6421 - accuracy: 0.6567 - val_loss: 0.6081 - val_accuracy: 0.6724\n", "Epoch 111/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.6402 - accuracy: 0.6604 - val_loss: 0.6066 - val_accuracy: 0.6724\n", "Epoch 112/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6398 - accuracy: 0.6604 - val_loss: 0.6066 - val_accuracy: 0.6724\n", "Epoch 113/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6413 - accuracy: 0.6567 - val_loss: 0.6060 - val_accuracy: 0.6724\n", "Epoch 114/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6383 - accuracy: 0.6604 - val_loss: 0.6123 - val_accuracy: 0.6724\n", "Epoch 115/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.6385 - accuracy: 0.6604 - val_loss: 0.6142 - val_accuracy: 0.6724\n", "Epoch 116/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.6397 - accuracy: 0.6604 - val_loss: 0.6157 - val_accuracy: 0.6724\n", "Epoch 117/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6387 - accuracy: 0.6604 - val_loss: 0.6122 - val_accuracy: 0.6724\n", "Epoch 118/500\n", "268/268 [==============================] - 0s 564us/step 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val_accuracy: 0.6724\n", "Epoch 125/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6386 - accuracy: 0.6567 - val_loss: 0.6099 - val_accuracy: 0.6724\n", "Epoch 126/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.6384 - accuracy: 0.6567 - val_loss: 0.6102 - val_accuracy: 0.6724\n", "Epoch 127/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6386 - accuracy: 0.6567 - val_loss: 0.6122 - val_accuracy: 0.6724\n", "Epoch 128/500\n", "268/268 [==============================] - 0s 552us/step - loss: 0.6406 - accuracy: 0.6567 - val_loss: 0.6120 - val_accuracy: 0.6724\n", "Epoch 129/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6414 - accuracy: 0.6604 - val_loss: 0.6087 - val_accuracy: 0.6724\n", "Epoch 130/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.6397 - accuracy: 0.6567 - val_loss: 0.6099 - val_accuracy: 0.6724\n", "Epoch 131/500\n", "268/268 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0.6391 - accuracy: 0.6567 - val_loss: 0.6173 - val_accuracy: 0.6724\n", "Epoch 138/500\n", "268/268 [==============================] - 0s 547us/step - loss: 0.6391 - accuracy: 0.6567 - val_loss: 0.6195 - val_accuracy: 0.6724\n", "Epoch 139/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6403 - accuracy: 0.6567 - val_loss: 0.6206 - val_accuracy: 0.6724\n", "Epoch 140/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.6407 - accuracy: 0.6567 - val_loss: 0.6215 - val_accuracy: 0.6724\n", "Epoch 141/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6400 - accuracy: 0.6567 - val_loss: 0.6183 - val_accuracy: 0.6724\n", "Epoch 142/500\n", "268/268 [==============================] - 0s 592us/step - loss: 0.6386 - accuracy: 0.6567 - val_loss: 0.6138 - val_accuracy: 0.6724\n", "Epoch 143/500\n", "268/268 [==============================] - 0s 551us/step - loss: 0.6378 - accuracy: 0.6567 - val_loss: 0.6106 - val_accuracy: 0.6724\n", "Epoch 144/500\n", "268/268 [==============================] - 0s 547us/step - loss: 0.6394 - accuracy: 0.6567 - val_loss: 0.6088 - val_accuracy: 0.6724\n", "Epoch 145/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6379 - accuracy: 0.6604 - val_loss: 0.6094 - val_accuracy: 0.6724\n", "Epoch 146/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.6388 - accuracy: 0.6567 - val_loss: 0.6099 - val_accuracy: 0.6724\n", "Epoch 147/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.6395 - accuracy: 0.6567 - val_loss: 0.6086 - val_accuracy: 0.6724\n", "Epoch 148/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6383 - accuracy: 0.6567 - val_loss: 0.6112 - val_accuracy: 0.6724\n", "Epoch 149/500\n", "268/268 [==============================] - 0s 610us/step - loss: 0.6367 - accuracy: 0.6567 - val_loss: 0.6142 - val_accuracy: 0.6724\n", "Epoch 150/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6377 - accuracy: 0.6567 - val_loss: 0.6138 - val_accuracy: 0.6724\n", "Epoch 151/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.6365 - accuracy: 0.6567 - val_loss: 0.6102 - val_accuracy: 0.6724\n", "Epoch 152/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6402 - accuracy: 0.6530 - val_loss: 0.6056 - val_accuracy: 0.6724\n", "Epoch 153/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6419 - accuracy: 0.6604 - val_loss: 0.6063 - val_accuracy: 0.6724\n", "Epoch 154/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6397 - accuracy: 0.6567 - val_loss: 0.6134 - val_accuracy: 0.6724\n", "Epoch 155/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6370 - accuracy: 0.6567 - val_loss: 0.6177 - val_accuracy: 0.6724\n", "Epoch 156/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.6382 - accuracy: 0.6567 - val_loss: 0.6225 - val_accuracy: 0.6724\n", "Epoch 157/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6402 - accuracy: 0.6567 - val_loss: 0.6234 - val_accuracy: 0.6724\n", "Epoch 158/500\n", "268/268 [==============================] - 0s 657us/step - loss: 0.6399 - accuracy: 0.6567 - val_loss: 0.6192 - val_accuracy: 0.6724\n", "Epoch 159/500\n", "268/268 [==============================] - 0s 675us/step - loss: 0.6371 - accuracy: 0.6604 - val_loss: 0.6144 - val_accuracy: 0.6724\n", "Epoch 160/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.6365 - accuracy: 0.6493 - val_loss: 0.6092 - val_accuracy: 0.6724\n", "Epoch 161/500\n", "268/268 [==============================] - 0s 619us/step - loss: 0.6392 - accuracy: 0.6567 - val_loss: 0.6081 - val_accuracy: 0.6724\n", "Epoch 162/500\n", "268/268 [==============================] - 0s 650us/step - loss: 0.6379 - accuracy: 0.6567 - val_loss: 0.6084 - val_accuracy: 0.6724\n", "Epoch 163/500\n", "268/268 [==============================] - 0s 632us/step - loss: 0.6357 - accuracy: 0.6567 - val_loss: 0.6150 - val_accuracy: 0.6724\n", "Epoch 164/500\n", "268/268 [==============================] - 0s 619us/step - loss: 0.6396 - accuracy: 0.6567 - val_loss: 0.6250 - val_accuracy: 0.6724\n", "Epoch 165/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.6398 - accuracy: 0.6567 - val_loss: 0.6185 - val_accuracy: 0.6724\n", "Epoch 166/500\n", "268/268 [==============================] - 0s 547us/step - loss: 0.6350 - accuracy: 0.6642 - val_loss: 0.6136 - val_accuracy: 0.6638\n", "Epoch 167/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6377 - accuracy: 0.6530 - val_loss: 0.6115 - val_accuracy: 0.6638\n", "Epoch 168/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6404 - accuracy: 0.6567 - val_loss: 0.6073 - val_accuracy: 0.6724\n", "Epoch 169/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6376 - accuracy: 0.6604 - val_loss: 0.6143 - val_accuracy: 0.6724\n", "Epoch 170/500\n", "268/268 [==============================] - 0s 641us/step - loss: 0.6399 - accuracy: 0.6530 - val_loss: 0.6169 - val_accuracy: 0.6724\n", "Epoch 171/500\n", "268/268 [==============================] - 0s 643us/step - loss: 0.6414 - accuracy: 0.6567 - val_loss: 0.6224 - val_accuracy: 0.6724\n", "Epoch 172/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.6405 - accuracy: 0.6567 - val_loss: 0.6200 - val_accuracy: 0.6724\n", "Epoch 173/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6387 - accuracy: 0.6567 - val_loss: 0.6173 - val_accuracy: 0.6724\n", "Epoch 174/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6394 - accuracy: 0.6567 - val_loss: 0.6113 - val_accuracy: 0.6724\n", "Epoch 175/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6406 - accuracy: 0.6567 - val_loss: 0.6081 - val_accuracy: 0.6724\n", "Epoch 176/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6446 - accuracy: 0.6567 - val_loss: 0.6174 - val_accuracy: 0.6724\n", "Epoch 177/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.6386 - accuracy: 0.6567 - val_loss: 0.6171 - val_accuracy: 0.6724\n", "Epoch 178/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.6378 - accuracy: 0.6567 - val_loss: 0.6149 - val_accuracy: 0.6724\n", "Epoch 179/500\n", "268/268 [==============================] - 0s 546us/step - loss: 0.6371 - accuracy: 0.6567 - val_loss: 0.6182 - val_accuracy: 0.6724\n", "Epoch 180/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.6377 - accuracy: 0.6604 - val_loss: 0.6171 - val_accuracy: 0.6724\n", "Epoch 181/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6371 - accuracy: 0.6567 - val_loss: 0.6146 - val_accuracy: 0.6724\n", "Epoch 182/500\n", "268/268 [==============================] - 0s 553us/step - loss: 0.6373 - accuracy: 0.6567 - val_loss: 0.6117 - val_accuracy: 0.6724\n", "Epoch 183/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6374 - accuracy: 0.6567 - val_loss: 0.6091 - val_accuracy: 0.6724\n", "Epoch 184/500\n", "268/268 [==============================] - 0s 660us/step - loss: 0.6373 - accuracy: 0.6567 - val_loss: 0.6081 - val_accuracy: 0.6724\n", "Epoch 185/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.6429 - accuracy: 0.6604 - val_loss: 0.6096 - val_accuracy: 0.6724\n", "Epoch 186/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6321 - accuracy: 0.6604 - val_loss: 0.6261 - val_accuracy: 0.6724\n", "Epoch 187/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6419 - accuracy: 0.6567 - val_loss: 0.6366 - val_accuracy: 0.6724\n", "Epoch 188/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6456 - accuracy: 0.6567 - val_loss: 0.6375 - val_accuracy: 0.6724\n", "Epoch 189/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6450 - accuracy: 0.6604 - val_loss: 0.6324 - val_accuracy: 0.6724\n", "Epoch 190/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.6430 - accuracy: 0.6567 - val_loss: 0.6240 - val_accuracy: 0.6724\n", "Epoch 191/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.6389 - accuracy: 0.6530 - val_loss: 0.6166 - val_accuracy: 0.6724\n", "Epoch 192/500\n", "268/268 [==============================] - 0s 550us/step - loss: 0.6381 - accuracy: 0.6530 - val_loss: 0.6056 - val_accuracy: 0.6897\n", "Epoch 193/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.6388 - accuracy: 0.6530 - val_loss: 0.6070 - val_accuracy: 0.6724\n", "Epoch 194/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6367 - accuracy: 0.6530 - val_loss: 0.6172 - val_accuracy: 0.6724\n", "Epoch 195/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.6385 - accuracy: 0.6567 - val_loss: 0.6218 - val_accuracy: 0.6724\n", "Epoch 196/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6391 - accuracy: 0.6530 - val_loss: 0.6181 - val_accuracy: 0.6724\n", "Epoch 197/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.6380 - accuracy: 0.6530 - val_loss: 0.6147 - val_accuracy: 0.6724\n", "Epoch 198/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.6369 - accuracy: 0.6567 - val_loss: 0.6135 - val_accuracy: 0.6724\n", "Epoch 199/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6368 - accuracy: 0.6530 - val_loss: 0.6094 - val_accuracy: 0.6724\n", "Epoch 200/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6373 - accuracy: 0.6530 - val_loss: 0.6087 - val_accuracy: 0.6724\n", "Epoch 201/500\n", "268/268 [==============================] - 0s 544us/step - loss: 0.6362 - accuracy: 0.6530 - val_loss: 0.6133 - val_accuracy: 0.6724\n", "Epoch 202/500\n", "268/268 [==============================] - 0s 547us/step - loss: 0.6361 - accuracy: 0.6567 - val_loss: 0.6143 - val_accuracy: 0.6724\n", "Epoch 203/500\n", "268/268 [==============================] - 0s 551us/step - loss: 0.6359 - accuracy: 0.6567 - val_loss: 0.6122 - val_accuracy: 0.6897\n", "Epoch 204/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.6354 - accuracy: 0.6604 - val_loss: 0.6124 - val_accuracy: 0.6897\n", "Epoch 205/500\n", "268/268 [==============================] - 0s 548us/step - loss: 0.6354 - accuracy: 0.6679 - val_loss: 0.6116 - val_accuracy: 0.6724\n", "Epoch 206/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.6349 - accuracy: 0.6604 - val_loss: 0.6100 - val_accuracy: 0.6638\n", "Epoch 207/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.6334 - accuracy: 0.6679 - val_loss: 0.6124 - val_accuracy: 0.6724\n", "Epoch 208/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.6333 - accuracy: 0.6604 - val_loss: 0.6126 - val_accuracy: 0.6724\n", "Epoch 209/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6329 - accuracy: 0.6604 - val_loss: 0.6144 - val_accuracy: 0.6724\n", "Epoch 210/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.6335 - accuracy: 0.6567 - val_loss: 0.6195 - val_accuracy: 0.6638\n", "Epoch 211/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.6372 - accuracy: 0.6493 - val_loss: 0.6155 - val_accuracy: 0.6897\n", "Epoch 212/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.6329 - accuracy: 0.6679 - val_loss: 0.6118 - val_accuracy: 0.6897\n", "Epoch 213/500\n", "268/268 [==============================] - 0s 552us/step - loss: 0.6332 - accuracy: 0.6716 - val_loss: 0.6075 - val_accuracy: 0.6724\n", "Epoch 214/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.6343 - accuracy: 0.6567 - val_loss: 0.6125 - val_accuracy: 0.6638\n", "Epoch 215/500\n", "268/268 [==============================] - 0s 582us/step - loss: 0.6324 - accuracy: 0.6604 - val_loss: 0.6107 - val_accuracy: 0.6724\n", "Epoch 216/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6318 - accuracy: 0.6567 - val_loss: 0.6104 - val_accuracy: 0.6897\n", "Epoch 217/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.6326 - accuracy: 0.6567 - val_loss: 0.6152 - val_accuracy: 0.7069\n", "Epoch 218/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6313 - accuracy: 0.6567 - val_loss: 0.6153 - val_accuracy: 0.6897\n", "Epoch 219/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6281 - accuracy: 0.6679 - val_loss: 0.6120 - val_accuracy: 0.6897\n", "Epoch 220/500\n", "268/268 [==============================] - 0s 537us/step - loss: 0.6298 - accuracy: 0.6791 - val_loss: 0.6127 - val_accuracy: 0.6897\n", "Epoch 221/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.6306 - accuracy: 0.6754 - val_loss: 0.6255 - val_accuracy: 0.6810\n", "Epoch 222/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6273 - accuracy: 0.6716 - val_loss: 0.6182 - val_accuracy: 0.6897\n", "Epoch 223/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6281 - accuracy: 0.6716 - val_loss: 0.6167 - val_accuracy: 0.6897\n", "Epoch 224/500\n", "268/268 [==============================] - 0s 548us/step - loss: 0.6263 - accuracy: 0.6754 - val_loss: 0.6247 - val_accuracy: 0.6897\n", "Epoch 225/500\n", "268/268 [==============================] - 0s 540us/step - loss: 0.6239 - accuracy: 0.6791 - val_loss: 0.6361 - val_accuracy: 0.6897\n", "Epoch 226/500\n", "268/268 [==============================] - 0s 544us/step - loss: 0.6274 - accuracy: 0.6754 - val_loss: 0.6284 - val_accuracy: 0.6897\n", "Epoch 227/500\n", "268/268 [==============================] - 0s 551us/step - loss: 0.6262 - accuracy: 0.6754 - val_loss: 0.6258 - val_accuracy: 0.6897\n", "Epoch 228/500\n", "268/268 [==============================] - 0s 547us/step - loss: 0.6240 - accuracy: 0.6791 - val_loss: 0.6375 - val_accuracy: 0.6810\n", "Epoch 229/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6303 - accuracy: 0.6754 - val_loss: 0.6216 - val_accuracy: 0.6810\n", "Epoch 230/500\n", "268/268 [==============================] - 0s 606us/step - loss: 0.6296 - accuracy: 0.6716 - val_loss: 0.6280 - val_accuracy: 0.6810\n", "Epoch 231/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6323 - accuracy: 0.6604 - val_loss: 0.6329 - val_accuracy: 0.6466\n", "Epoch 232/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.6418 - accuracy: 0.6530 - val_loss: 0.6275 - val_accuracy: 0.6552\n", "Epoch 233/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6360 - accuracy: 0.6530 - val_loss: 0.6249 - val_accuracy: 0.6638\n", "Epoch 234/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6273 - accuracy: 0.6754 - val_loss: 0.6244 - val_accuracy: 0.6810\n", "Epoch 235/500\n", "268/268 [==============================] - 0s 553us/step - loss: 0.6277 - accuracy: 0.6642 - val_loss: 0.6379 - val_accuracy: 0.6638\n", "Epoch 236/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.6268 - accuracy: 0.6604 - val_loss: 0.6319 - val_accuracy: 0.6724\n", "Epoch 237/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.6307 - accuracy: 0.6530 - val_loss: 0.6137 - val_accuracy: 0.6983\n", "Epoch 238/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.6298 - accuracy: 0.6604 - val_loss: 0.6134 - val_accuracy: 0.7069\n", "Epoch 239/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.6274 - accuracy: 0.6679 - val_loss: 0.6231 - val_accuracy: 0.7069\n", "Epoch 240/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6242 - accuracy: 0.6716 - val_loss: 0.6338 - val_accuracy: 0.6983\n", "Epoch 241/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6234 - accuracy: 0.6754 - val_loss: 0.6460 - val_accuracy: 0.6810\n", "Epoch 242/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6223 - accuracy: 0.6716 - val_loss: 0.6335 - val_accuracy: 0.6897\n", "Epoch 243/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6232 - accuracy: 0.6754 - val_loss: 0.6309 - val_accuracy: 0.6897\n", "Epoch 244/500\n", "268/268 [==============================] - 0s 610us/step - loss: 0.6210 - accuracy: 0.6754 - val_loss: 0.6389 - val_accuracy: 0.6810\n", "Epoch 245/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.6208 - accuracy: 0.6754 - val_loss: 0.6535 - val_accuracy: 0.6810\n", "Epoch 246/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.6225 - accuracy: 0.6754 - val_loss: 0.6495 - val_accuracy: 0.6897\n", "Epoch 247/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.6371 - accuracy: 0.6642 - val_loss: 0.6513 - val_accuracy: 0.6638\n", "Epoch 248/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.6383 - accuracy: 0.6604 - val_loss: 0.6476 - val_accuracy: 0.6724\n", "Epoch 249/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6288 - accuracy: 0.6716 - val_loss: 0.6493 - val_accuracy: 0.6810\n", "Epoch 250/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.6216 - accuracy: 0.6716 - val_loss: 0.6531 - val_accuracy: 0.6810\n", "Epoch 251/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6231 - accuracy: 0.6754 - val_loss: 0.6548 - val_accuracy: 0.6897\n", "Epoch 252/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.6287 - accuracy: 0.6716 - val_loss: 0.6523 - val_accuracy: 0.6897\n", "Epoch 253/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6255 - accuracy: 0.6754 - val_loss: 0.6424 - val_accuracy: 0.6810\n", "Epoch 254/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6257 - accuracy: 0.6754 - val_loss: 0.6365 - val_accuracy: 0.6983\n", "Epoch 255/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.6237 - accuracy: 0.6679 - val_loss: 0.6427 - val_accuracy: 0.6983\n", "Epoch 256/500\n", "268/268 [==============================] - 0s 648us/step - loss: 0.6300 - accuracy: 0.6642 - val_loss: 0.6497 - val_accuracy: 0.7069\n", "Epoch 257/500\n", "268/268 [==============================] - 0s 667us/step - loss: 0.6226 - accuracy: 0.6679 - val_loss: 0.6371 - val_accuracy: 0.6810\n", "Epoch 258/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.6304 - accuracy: 0.6567 - val_loss: 0.6301 - val_accuracy: 0.6638\n", "Epoch 259/500\n", "268/268 [==============================] - 0s 629us/step - loss: 0.6321 - accuracy: 0.6493 - val_loss: 0.6217 - val_accuracy: 0.6724\n", "Epoch 260/500\n", "268/268 [==============================] - 0s 642us/step - loss: 0.6310 - accuracy: 0.6530 - val_loss: 0.6267 - val_accuracy: 0.6552\n", "Epoch 261/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6287 - accuracy: 0.6567 - val_loss: 0.6255 - val_accuracy: 0.6638\n", "Epoch 262/500\n", "268/268 [==============================] - 0s 669us/step - loss: 0.6259 - accuracy: 0.6716 - val_loss: 0.6230 - val_accuracy: 0.6724\n", "Epoch 263/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.6196 - accuracy: 0.6754 - val_loss: 0.6481 - val_accuracy: 0.6897\n", "Epoch 264/500\n", "268/268 [==============================] - 0s 634us/step - loss: 0.6282 - accuracy: 0.6642 - val_loss: 0.6275 - val_accuracy: 0.6897\n", "Epoch 265/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6358 - accuracy: 0.6642 - val_loss: 0.6147 - val_accuracy: 0.6983\n", "Epoch 266/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6325 - accuracy: 0.6642 - val_loss: 0.6101 - val_accuracy: 0.6983\n", "Epoch 267/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.6299 - accuracy: 0.6642 - val_loss: 0.6167 - val_accuracy: 0.6983\n", "Epoch 268/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.6291 - accuracy: 0.6604 - val_loss: 0.6389 - val_accuracy: 0.6810\n", "Epoch 269/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6260 - accuracy: 0.6754 - val_loss: 0.6375 - val_accuracy: 0.6897\n", "Epoch 270/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.6237 - accuracy: 0.6791 - val_loss: 0.6320 - val_accuracy: 0.6897\n", "Epoch 271/500\n", "268/268 [==============================] - 0s 540us/step - loss: 0.6250 - accuracy: 0.6679 - val_loss: 0.6356 - val_accuracy: 0.6897\n", "Epoch 272/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.6234 - accuracy: 0.6679 - val_loss: 0.6351 - val_accuracy: 0.6724\n", "Epoch 273/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.6226 - accuracy: 0.6604 - val_loss: 0.6405 - val_accuracy: 0.6724\n", "Epoch 274/500\n", "268/268 [==============================] - 0s 621us/step - loss: 0.6228 - accuracy: 0.6716 - val_loss: 0.6454 - val_accuracy: 0.6897\n", "Epoch 275/500\n", "268/268 [==============================] - 0s 674us/step - loss: 0.6197 - accuracy: 0.6754 - val_loss: 0.6432 - val_accuracy: 0.6724\n", "Epoch 276/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6179 - accuracy: 0.6716 - val_loss: 0.6461 - val_accuracy: 0.6810\n", "Epoch 277/500\n", "268/268 [==============================] - 0s 632us/step - loss: 0.6226 - accuracy: 0.6754 - val_loss: 0.6510 - val_accuracy: 0.6810\n", "Epoch 278/500\n", "268/268 [==============================] - 0s 620us/step - loss: 0.6220 - accuracy: 0.6716 - val_loss: 0.6601 - val_accuracy: 0.6897\n", "Epoch 279/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6202 - accuracy: 0.6716 - val_loss: 0.6919 - val_accuracy: 0.6810\n", "Epoch 280/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6291 - accuracy: 0.6642 - val_loss: 0.6512 - val_accuracy: 0.6810\n", "Epoch 281/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6208 - accuracy: 0.6716 - val_loss: 0.6421 - val_accuracy: 0.6897\n", "Epoch 282/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.6200 - accuracy: 0.6754 - val_loss: 0.6452 - val_accuracy: 0.6897\n", "Epoch 283/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6193 - accuracy: 0.6679 - val_loss: 0.6543 - val_accuracy: 0.6897\n", "Epoch 284/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6180 - accuracy: 0.6642 - val_loss: 0.6651 - val_accuracy: 0.6810\n", "Epoch 285/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6189 - accuracy: 0.6716 - val_loss: 0.6666 - val_accuracy: 0.6897\n", "Epoch 286/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6196 - accuracy: 0.6754 - val_loss: 0.6503 - val_accuracy: 0.6897\n", "Epoch 287/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.6205 - accuracy: 0.6716 - val_loss: 0.6408 - val_accuracy: 0.6724\n", "Epoch 288/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.6213 - accuracy: 0.6716 - val_loss: 0.6461 - val_accuracy: 0.6897\n", "Epoch 289/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6232 - accuracy: 0.6754 - val_loss: 0.6525 - val_accuracy: 0.6897\n", "Epoch 290/500\n", "268/268 [==============================] - 0s 611us/step - loss: 0.6178 - accuracy: 0.6716 - val_loss: 0.6610 - val_accuracy: 0.6897\n", "Epoch 291/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6181 - accuracy: 0.6754 - val_loss: 0.6716 - val_accuracy: 0.6897\n", "Epoch 292/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.6191 - accuracy: 0.6716 - val_loss: 0.6800 - val_accuracy: 0.6897\n", "Epoch 293/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6224 - accuracy: 0.6716 - val_loss: 0.6845 - val_accuracy: 0.6897\n", "Epoch 294/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.6222 - accuracy: 0.6716 - val_loss: 0.6763 - val_accuracy: 0.6897\n", "Epoch 295/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.6205 - accuracy: 0.6716 - val_loss: 0.6805 - val_accuracy: 0.6897\n", "Epoch 296/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.6249 - accuracy: 0.6754 - val_loss: 0.6662 - val_accuracy: 0.6897\n", "Epoch 297/500\n", "268/268 [==============================] - 0s 610us/step - loss: 0.6188 - accuracy: 0.6754 - val_loss: 0.6747 - val_accuracy: 0.6897\n", "Epoch 298/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6169 - accuracy: 0.6754 - val_loss: 0.6780 - val_accuracy: 0.6897\n", "Epoch 299/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6180 - accuracy: 0.6716 - val_loss: 0.6714 - val_accuracy: 0.6897\n", "Epoch 300/500\n", "268/268 [==============================] - 0s 610us/step - loss: 0.6175 - accuracy: 0.6716 - val_loss: 0.6670 - val_accuracy: 0.6897\n", "Epoch 301/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6176 - accuracy: 0.6754 - val_loss: 0.6694 - val_accuracy: 0.6897\n", "Epoch 302/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6153 - accuracy: 0.6754 - val_loss: 0.6704 - val_accuracy: 0.6897\n", "Epoch 303/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6179 - accuracy: 0.6716 - val_loss: 0.6825 - val_accuracy: 0.6897\n", "Epoch 304/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.6208 - accuracy: 0.6791 - val_loss: 0.6838 - val_accuracy: 0.6810\n", "Epoch 305/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6213 - accuracy: 0.6791 - val_loss: 0.6886 - val_accuracy: 0.6897\n", "Epoch 306/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6196 - accuracy: 0.6828 - val_loss: 0.6944 - val_accuracy: 0.6897\n", "Epoch 307/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6200 - accuracy: 0.6791 - val_loss: 0.6995 - val_accuracy: 0.6897\n", "Epoch 308/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.6201 - accuracy: 0.6754 - val_loss: 0.6890 - val_accuracy: 0.6897\n", "Epoch 309/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6149 - accuracy: 0.6754 - val_loss: 0.6785 - val_accuracy: 0.6810\n", "Epoch 310/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6164 - accuracy: 0.6679 - val_loss: 0.6770 - val_accuracy: 0.6897\n", "Epoch 311/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.6142 - accuracy: 0.6679 - val_loss: 0.6761 - val_accuracy: 0.6897\n", "Epoch 312/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6152 - accuracy: 0.6754 - val_loss: 0.6659 - val_accuracy: 0.6724\n", "Epoch 313/500\n", "268/268 [==============================] - 0s 651us/step - loss: 0.6161 - accuracy: 0.6679 - val_loss: 0.6680 - val_accuracy: 0.6724\n", "Epoch 314/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.6165 - accuracy: 0.6791 - val_loss: 0.6709 - val_accuracy: 0.6897\n", "Epoch 315/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6155 - accuracy: 0.6754 - val_loss: 0.6753 - val_accuracy: 0.6897\n", "Epoch 316/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6158 - accuracy: 0.6754 - val_loss: 0.6825 - val_accuracy: 0.6897\n", "Epoch 317/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6164 - accuracy: 0.6679 - val_loss: 0.6798 - val_accuracy: 0.6897\n", "Epoch 318/500\n", "268/268 [==============================] - 0s 558us/step - loss: 0.6156 - accuracy: 0.6716 - val_loss: 0.6713 - val_accuracy: 0.6897\n", "Epoch 319/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.6157 - accuracy: 0.6716 - val_loss: 0.6643 - val_accuracy: 0.6897\n", "Epoch 320/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6146 - accuracy: 0.6791 - val_loss: 0.6681 - val_accuracy: 0.6897\n", "Epoch 321/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6139 - accuracy: 0.6754 - val_loss: 0.6709 - val_accuracy: 0.6897\n", "Epoch 322/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.6142 - accuracy: 0.6716 - val_loss: 0.6726 - val_accuracy: 0.6897\n", "Epoch 323/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6141 - accuracy: 0.6754 - val_loss: 0.6807 - val_accuracy: 0.6897\n", "Epoch 324/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.6171 - accuracy: 0.6754 - val_loss: 0.6862 - val_accuracy: 0.6897\n", "Epoch 325/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6182 - accuracy: 0.6754 - val_loss: 0.6921 - val_accuracy: 0.6897\n", "Epoch 326/500\n", "268/268 [==============================] - 0s 659us/step - loss: 0.6213 - accuracy: 0.6754 - val_loss: 0.6945 - val_accuracy: 0.6897\n", "Epoch 327/500\n", "268/268 [==============================] - 0s 659us/step - loss: 0.6211 - accuracy: 0.6754 - val_loss: 0.6809 - val_accuracy: 0.6810\n", "Epoch 328/500\n", "268/268 [==============================] - 0s 632us/step - loss: 0.6221 - accuracy: 0.6679 - val_loss: 0.6484 - val_accuracy: 0.6552\n", "Epoch 329/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6243 - accuracy: 0.6642 - val_loss: 0.6359 - val_accuracy: 0.6638\n", "Epoch 330/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6279 - accuracy: 0.6642 - val_loss: 0.6345 - val_accuracy: 0.6724\n", "Epoch 331/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.6250 - accuracy: 0.6679 - val_loss: 0.6415 - val_accuracy: 0.6724\n", "Epoch 332/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.6199 - accuracy: 0.6679 - val_loss: 0.6575 - val_accuracy: 0.6810\n", "Epoch 333/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.6150 - accuracy: 0.6754 - val_loss: 0.6778 - val_accuracy: 0.6810\n", "Epoch 334/500\n", "268/268 [==============================] - 0s 639us/step - loss: 0.6137 - accuracy: 0.6754 - val_loss: 0.6945 - val_accuracy: 0.6810\n", "Epoch 335/500\n", "268/268 [==============================] - 0s 630us/step - loss: 0.6141 - accuracy: 0.6791 - val_loss: 0.6811 - val_accuracy: 0.6897\n", "Epoch 336/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.6151 - accuracy: 0.6716 - val_loss: 0.6800 - val_accuracy: 0.6897\n", "Epoch 337/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.6145 - accuracy: 0.6754 - val_loss: 0.6863 - val_accuracy: 0.6897\n", "Epoch 338/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.6172 - accuracy: 0.6791 - val_loss: 0.6963 - val_accuracy: 0.6897\n", "Epoch 339/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.6185 - accuracy: 0.6791 - val_loss: 0.6905 - val_accuracy: 0.6897\n", "Epoch 340/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6221 - accuracy: 0.6754 - val_loss: 0.6568 - val_accuracy: 0.6724\n", "Epoch 341/500\n", "268/268 [==============================] - 0s 558us/step - loss: 0.6215 - accuracy: 0.6679 - val_loss: 0.6516 - val_accuracy: 0.6724\n", "Epoch 342/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.6192 - accuracy: 0.6791 - val_loss: 0.6494 - val_accuracy: 0.6897\n", "Epoch 343/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6177 - accuracy: 0.6679 - val_loss: 0.6539 - val_accuracy: 0.6897\n", "Epoch 344/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6157 - accuracy: 0.6754 - val_loss: 0.6677 - val_accuracy: 0.6897\n", "Epoch 345/500\n", "268/268 [==============================] - 0s 610us/step - loss: 0.6150 - accuracy: 0.6679 - val_loss: 0.6780 - val_accuracy: 0.6810\n", "Epoch 346/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6140 - accuracy: 0.6679 - val_loss: 0.6809 - val_accuracy: 0.6810\n", "Epoch 347/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6135 - accuracy: 0.6679 - val_loss: 0.6811 - val_accuracy: 0.6810\n", "Epoch 348/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6146 - accuracy: 0.6716 - val_loss: 0.6755 - val_accuracy: 0.6897\n", "Epoch 349/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6146 - accuracy: 0.6754 - val_loss: 0.6684 - val_accuracy: 0.6897\n", "Epoch 350/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6185 - accuracy: 0.6754 - val_loss: 0.6680 - val_accuracy: 0.6897\n", "Epoch 351/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6163 - accuracy: 0.6754 - val_loss: 0.6883 - val_accuracy: 0.6897\n", "Epoch 352/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6163 - accuracy: 0.6716 - val_loss: 0.6827 - val_accuracy: 0.6897\n", "Epoch 353/500\n", "268/268 [==============================] - 0s 535us/step - loss: 0.6180 - accuracy: 0.6754 - val_loss: 0.6782 - val_accuracy: 0.6810\n", "Epoch 354/500\n", "268/268 [==============================] - 0s 546us/step - loss: 0.6171 - accuracy: 0.6754 - val_loss: 0.6824 - val_accuracy: 0.6897\n", "Epoch 355/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6147 - accuracy: 0.6679 - val_loss: 0.6788 - val_accuracy: 0.6897\n", "Epoch 356/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.6128 - accuracy: 0.6679 - val_loss: 0.6737 - val_accuracy: 0.6897\n", "Epoch 357/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.6138 - accuracy: 0.6679 - val_loss: 0.6763 - val_accuracy: 0.6897\n", "Epoch 358/500\n", "268/268 [==============================] - 0s 656us/step - loss: 0.6132 - accuracy: 0.6754 - val_loss: 0.6778 - val_accuracy: 0.6897\n", "Epoch 359/500\n", "268/268 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val_accuracy: 0.6897\n", "Epoch 372/500\n", "268/268 [==============================] - 0s 632us/step - loss: 0.6198 - accuracy: 0.6679 - val_loss: 0.6541 - val_accuracy: 0.6552\n", "Epoch 373/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.6216 - accuracy: 0.6642 - val_loss: 0.6522 - val_accuracy: 0.6552\n", "Epoch 374/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6218 - accuracy: 0.6567 - val_loss: 0.6515 - val_accuracy: 0.6638\n", "Epoch 375/500\n", "268/268 [==============================] - 0s 542us/step - loss: 0.6207 - accuracy: 0.6642 - val_loss: 0.6624 - val_accuracy: 0.6897\n", "Epoch 376/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6170 - accuracy: 0.6791 - val_loss: 0.6705 - val_accuracy: 0.6810\n", "Epoch 377/500\n", "268/268 [==============================] - 0s 669us/step - loss: 0.6141 - accuracy: 0.6754 - val_loss: 0.6709 - val_accuracy: 0.6724\n", "Epoch 378/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6132 - accuracy: 0.6754 - val_loss: 0.6654 - val_accuracy: 0.6724\n", "Epoch 379/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6139 - accuracy: 0.6716 - val_loss: 0.6573 - val_accuracy: 0.6810\n", "Epoch 380/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6152 - accuracy: 0.6716 - val_loss: 0.6651 - val_accuracy: 0.6810\n", "Epoch 381/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6110 - accuracy: 0.6679 - val_loss: 0.6781 - val_accuracy: 0.6724\n", "Epoch 382/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6158 - accuracy: 0.6716 - val_loss: 0.6901 - val_accuracy: 0.6810\n", "Epoch 383/500\n", "268/268 [==============================] - 0s 631us/step - loss: 0.6137 - accuracy: 0.6754 - val_loss: 0.6812 - val_accuracy: 0.6810\n", "Epoch 384/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.6098 - accuracy: 0.6642 - val_loss: 0.6645 - val_accuracy: 0.6897\n", "Epoch 385/500\n", "268/268 [==============================] - 0s 549us/step - loss: 0.6136 - accuracy: 0.6791 - val_loss: 0.6608 - val_accuracy: 0.6897\n", "Epoch 386/500\n", "268/268 [==============================] - 0s 551us/step - loss: 0.6167 - accuracy: 0.6791 - val_loss: 0.6533 - val_accuracy: 0.6724\n", "Epoch 387/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6178 - accuracy: 0.6791 - val_loss: 0.6647 - val_accuracy: 0.6724\n", "Epoch 388/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.6172 - accuracy: 0.6791 - val_loss: 0.6656 - val_accuracy: 0.6724\n", "Epoch 389/500\n", "268/268 [==============================] - 0s 553us/step - loss: 0.6145 - accuracy: 0.6791 - val_loss: 0.6592 - val_accuracy: 0.6897\n", "Epoch 390/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.6157 - accuracy: 0.6716 - val_loss: 0.6593 - val_accuracy: 0.6897\n", "Epoch 391/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6221 - accuracy: 0.6679 - val_loss: 0.6678 - val_accuracy: 0.6810\n", "Epoch 392/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6147 - accuracy: 0.6754 - val_loss: 0.6748 - val_accuracy: 0.6810\n", "Epoch 393/500\n", "268/268 [==============================] - 0s 553us/step - loss: 0.6121 - accuracy: 0.6679 - val_loss: 0.6781 - val_accuracy: 0.6810\n", "Epoch 394/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6142 - accuracy: 0.6679 - val_loss: 0.6767 - val_accuracy: 0.6810\n", "Epoch 395/500\n", "268/268 [==============================] - 0s 546us/step - loss: 0.6124 - accuracy: 0.6679 - val_loss: 0.6772 - val_accuracy: 0.6810\n", "Epoch 396/500\n", "268/268 [==============================] - 0s 595us/step - loss: 0.6108 - accuracy: 0.6679 - val_loss: 0.6815 - val_accuracy: 0.6897\n", "Epoch 397/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6116 - accuracy: 0.6754 - val_loss: 0.6950 - val_accuracy: 0.6810\n", "Epoch 398/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6096 - accuracy: 0.6754 - val_loss: 0.6971 - val_accuracy: 0.6810\n", "Epoch 399/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6123 - accuracy: 0.6716 - val_loss: 0.6937 - val_accuracy: 0.6810\n", "Epoch 400/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.6112 - accuracy: 0.6716 - val_loss: 0.6858 - val_accuracy: 0.6897\n", "Epoch 401/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6130 - accuracy: 0.6716 - val_loss: 0.6765 - val_accuracy: 0.6897\n", "Epoch 402/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6112 - accuracy: 0.6791 - val_loss: 0.6835 - val_accuracy: 0.6897\n", "Epoch 403/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6094 - accuracy: 0.6754 - val_loss: 0.7023 - val_accuracy: 0.6810\n", "Epoch 404/500\n", "268/268 [==============================] - 0s 541us/step - loss: 0.6105 - accuracy: 0.6716 - val_loss: 0.6807 - val_accuracy: 0.6897\n", "Epoch 405/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.6195 - accuracy: 0.6604 - val_loss: 0.6422 - val_accuracy: 0.6983\n", "Epoch 406/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6184 - accuracy: 0.6604 - val_loss: 0.6645 - val_accuracy: 0.6897\n", "Epoch 407/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.6165 - accuracy: 0.6567 - val_loss: 0.6673 - val_accuracy: 0.6897\n", "Epoch 408/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.6162 - accuracy: 0.6604 - val_loss: 0.6680 - val_accuracy: 0.6897\n", "Epoch 409/500\n", "268/268 [==============================] - 0s 650us/step - loss: 0.6166 - accuracy: 0.6679 - val_loss: 0.6706 - val_accuracy: 0.6897\n", "Epoch 410/500\n", "268/268 [==============================] - 0s 651us/step - loss: 0.6170 - accuracy: 0.6567 - val_loss: 0.6731 - val_accuracy: 0.6897\n", "Epoch 411/500\n", "268/268 [==============================] - 0s 657us/step - loss: 0.6139 - accuracy: 0.6604 - val_loss: 0.6845 - val_accuracy: 0.6897\n", "Epoch 412/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.6144 - accuracy: 0.6642 - val_loss: 0.6969 - val_accuracy: 0.6897\n", "Epoch 413/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.6150 - accuracy: 0.6679 - val_loss: 0.6865 - val_accuracy: 0.6897\n", "Epoch 414/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6170 - accuracy: 0.6604 - val_loss: 0.6852 - val_accuracy: 0.6897\n", "Epoch 415/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6173 - accuracy: 0.6642 - val_loss: 0.6915 - val_accuracy: 0.6810\n", "Epoch 416/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6178 - accuracy: 0.6642 - val_loss: 0.6929 - val_accuracy: 0.6810\n", "Epoch 417/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.6157 - accuracy: 0.6716 - val_loss: 0.6843 - val_accuracy: 0.6897\n", "Epoch 418/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6147 - accuracy: 0.6716 - val_loss: 0.6795 - val_accuracy: 0.6897\n", "Epoch 419/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6163 - accuracy: 0.6716 - val_loss: 0.6800 - val_accuracy: 0.6897\n", "Epoch 420/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.6138 - accuracy: 0.6716 - val_loss: 0.6902 - val_accuracy: 0.6897\n", "Epoch 421/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.6196 - accuracy: 0.6754 - val_loss: 0.7165 - val_accuracy: 0.6897\n", "Epoch 422/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.6199 - accuracy: 0.6754 - val_loss: 0.7250 - val_accuracy: 0.6810\n", "Epoch 423/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6216 - accuracy: 0.6716 - val_loss: 0.6914 - val_accuracy: 0.6897\n", "Epoch 424/500\n", "268/268 [==============================] - 0s 551us/step - loss: 0.6174 - accuracy: 0.6679 - val_loss: 0.6597 - val_accuracy: 0.6897\n", "Epoch 425/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.6164 - accuracy: 0.6604 - val_loss: 0.6668 - val_accuracy: 0.6810\n", "Epoch 426/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.6176 - accuracy: 0.6791 - val_loss: 0.6825 - val_accuracy: 0.6810\n", "Epoch 427/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.6363 - accuracy: 0.6679 - val_loss: 0.6920 - val_accuracy: 0.6897\n", "Epoch 428/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.6144 - accuracy: 0.6679 - val_loss: 0.6654 - val_accuracy: 0.6897\n", "Epoch 429/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6173 - accuracy: 0.6679 - val_loss: 0.6514 - val_accuracy: 0.6897\n", "Epoch 430/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6205 - accuracy: 0.6716 - val_loss: 0.6486 - val_accuracy: 0.6897\n", "Epoch 431/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6189 - accuracy: 0.6679 - val_loss: 0.6380 - val_accuracy: 0.6897\n", "Epoch 432/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.6218 - accuracy: 0.6679 - val_loss: 0.6405 - val_accuracy: 0.6897\n", "Epoch 433/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6198 - accuracy: 0.6754 - val_loss: 0.6508 - val_accuracy: 0.6897\n", "Epoch 434/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.6219 - accuracy: 0.6679 - val_loss: 0.6533 - val_accuracy: 0.6897\n", "Epoch 435/500\n", "268/268 [==============================] - 0s 636us/step - loss: 0.6233 - accuracy: 0.6679 - val_loss: 0.6544 - val_accuracy: 0.6810\n", "Epoch 436/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.6227 - accuracy: 0.6716 - val_loss: 0.6565 - val_accuracy: 0.6810\n", "Epoch 437/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6195 - accuracy: 0.6754 - val_loss: 0.6503 - val_accuracy: 0.6810\n", "Epoch 438/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.6161 - accuracy: 0.6754 - val_loss: 0.6491 - val_accuracy: 0.6810\n", "Epoch 439/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.6160 - accuracy: 0.6679 - val_loss: 0.6481 - val_accuracy: 0.6810\n", "Epoch 440/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6166 - accuracy: 0.6679 - val_loss: 0.6523 - val_accuracy: 0.6897\n", "Epoch 441/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.6149 - accuracy: 0.6791 - val_loss: 0.6645 - val_accuracy: 0.6897\n", "Epoch 442/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6147 - accuracy: 0.6791 - val_loss: 0.6794 - val_accuracy: 0.6810\n", "Epoch 443/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.6203 - accuracy: 0.6716 - val_loss: 0.6828 - val_accuracy: 0.6724\n", "Epoch 444/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.6173 - accuracy: 0.6679 - val_loss: 0.6838 - val_accuracy: 0.6810\n", "Epoch 445/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6251 - accuracy: 0.6642 - val_loss: 0.6658 - val_accuracy: 0.6897\n", "Epoch 446/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6216 - accuracy: 0.6642 - val_loss: 0.6741 - val_accuracy: 0.6983\n", "Epoch 447/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6212 - accuracy: 0.6642 - val_loss: 0.6854 - val_accuracy: 0.6810\n", "Epoch 448/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.6208 - accuracy: 0.6642 - val_loss: 0.6878 - val_accuracy: 0.6810\n", "Epoch 449/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6154 - accuracy: 0.6642 - val_loss: 0.6832 - val_accuracy: 0.6810\n", "Epoch 450/500\n", "268/268 [==============================] - 0s 560us/step - loss: 0.6121 - accuracy: 0.6716 - val_loss: 0.6839 - val_accuracy: 0.6897\n", "Epoch 451/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6146 - accuracy: 0.6754 - val_loss: 0.6846 - val_accuracy: 0.6897\n", "Epoch 452/500\n", "268/268 [==============================] - 0s 546us/step - loss: 0.6188 - accuracy: 0.6791 - val_loss: 0.6929 - val_accuracy: 0.6897\n", "Epoch 453/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.6132 - accuracy: 0.6791 - val_loss: 0.7048 - val_accuracy: 0.6897\n", "Epoch 454/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.6137 - accuracy: 0.6791 - val_loss: 0.7089 - val_accuracy: 0.6897\n", "Epoch 455/500\n", "268/268 [==============================] - 0s 606us/step - loss: 0.6129 - accuracy: 0.6754 - val_loss: 0.7057 - val_accuracy: 0.6897\n", "Epoch 456/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6123 - accuracy: 0.6791 - val_loss: 0.7082 - val_accuracy: 0.6810\n", "Epoch 457/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.6128 - accuracy: 0.6754 - val_loss: 0.7074 - val_accuracy: 0.6897\n", "Epoch 458/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.6116 - accuracy: 0.6791 - val_loss: 0.7123 - val_accuracy: 0.6810\n", "Epoch 459/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6102 - accuracy: 0.6754 - val_loss: 0.7105 - val_accuracy: 0.6810\n", "Epoch 460/500\n", "268/268 [==============================] - 0s 558us/step - loss: 0.6158 - accuracy: 0.6679 - val_loss: 0.7089 - val_accuracy: 0.6810\n", "Epoch 461/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.6113 - accuracy: 0.6716 - val_loss: 0.7195 - val_accuracy: 0.6810\n", "Epoch 462/500\n", "268/268 [==============================] - 0s 548us/step - loss: 0.6142 - accuracy: 0.6716 - val_loss: 0.7197 - val_accuracy: 0.6810\n", "Epoch 463/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.6148 - accuracy: 0.6716 - val_loss: 0.7078 - val_accuracy: 0.6897\n", "Epoch 464/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.6132 - accuracy: 0.6716 - val_loss: 0.7112 - val_accuracy: 0.6897\n", "Epoch 465/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.6121 - accuracy: 0.6716 - val_loss: 0.7088 - val_accuracy: 0.6897\n", "Epoch 466/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6124 - accuracy: 0.6716 - val_loss: 0.6977 - val_accuracy: 0.6897\n", "Epoch 467/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6136 - accuracy: 0.6679 - val_loss: 0.6984 - val_accuracy: 0.6897\n", "Epoch 468/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.6127 - accuracy: 0.6716 - val_loss: 0.7150 - val_accuracy: 0.6897\n", "Epoch 469/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6160 - accuracy: 0.6791 - val_loss: 0.7193 - val_accuracy: 0.6897\n", "Epoch 470/500\n", "268/268 [==============================] - 0s 558us/step - loss: 0.6160 - accuracy: 0.6828 - val_loss: 0.7095 - val_accuracy: 0.6897\n", "Epoch 471/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6143 - accuracy: 0.6754 - val_loss: 0.6981 - val_accuracy: 0.6897\n", "Epoch 472/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6131 - accuracy: 0.6716 - val_loss: 0.7040 - val_accuracy: 0.6897\n", "Epoch 473/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.6089 - accuracy: 0.6716 - val_loss: 0.7209 - val_accuracy: 0.6810\n", "Epoch 474/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.6125 - accuracy: 0.6679 - val_loss: 0.7370 - val_accuracy: 0.6810\n", "Epoch 475/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6128 - accuracy: 0.6754 - val_loss: 0.7268 - val_accuracy: 0.6897\n", "Epoch 476/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.6147 - accuracy: 0.6754 - val_loss: 0.7181 - val_accuracy: 0.6897\n", "Epoch 477/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.6170 - accuracy: 0.6754 - val_loss: 0.7336 - val_accuracy: 0.6897\n", "Epoch 478/500\n", "268/268 [==============================] - 0s 554us/step - loss: 0.6129 - accuracy: 0.6791 - val_loss: 0.7275 - val_accuracy: 0.6897\n", "Epoch 479/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.6129 - accuracy: 0.6754 - val_loss: 0.7220 - val_accuracy: 0.6810\n", "Epoch 480/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.6125 - accuracy: 0.6754 - val_loss: 0.7208 - val_accuracy: 0.6897\n", "Epoch 481/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.6097 - accuracy: 0.6791 - val_loss: 0.7289 - val_accuracy: 0.6897\n", "Epoch 482/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.6098 - accuracy: 0.6754 - val_loss: 0.7359 - val_accuracy: 0.6897\n", "Epoch 483/500\n", "268/268 [==============================] - 0s 595us/step - loss: 0.6100 - accuracy: 0.6791 - val_loss: 0.7329 - val_accuracy: 0.6897\n", "Epoch 484/500\n", "268/268 [==============================] - 0s 624us/step - loss: 0.6118 - accuracy: 0.6754 - val_loss: 0.7312 - val_accuracy: 0.6810\n", "Epoch 485/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.6117 - accuracy: 0.6716 - val_loss: 0.7402 - val_accuracy: 0.6810\n", "Epoch 486/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.6085 - accuracy: 0.6754 - val_loss: 0.7546 - val_accuracy: 0.6724\n", "Epoch 487/500\n", "268/268 [==============================] - 0s 665us/step - loss: 0.6081 - accuracy: 0.6903 - val_loss: 0.7536 - val_accuracy: 0.6897\n", "Epoch 488/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6079 - accuracy: 0.6679 - val_loss: 0.7394 - val_accuracy: 0.6810\n", "Epoch 489/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6095 - accuracy: 0.6716 - val_loss: 0.7427 - val_accuracy: 0.6897\n", "Epoch 490/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6118 - accuracy: 0.6754 - val_loss: 0.7510 - val_accuracy: 0.6897\n", "Epoch 491/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.6069 - accuracy: 0.6716 - val_loss: 0.7503 - val_accuracy: 0.6810\n", "Epoch 492/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.6064 - accuracy: 0.6716 - val_loss: 0.7534 - val_accuracy: 0.6810\n", "Epoch 493/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.6078 - accuracy: 0.6791 - val_loss: 0.7640 - val_accuracy: 0.6810\n", "Epoch 494/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6074 - accuracy: 0.6754 - val_loss: 0.7810 - val_accuracy: 0.6810\n", "Epoch 495/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6157 - accuracy: 0.6679 - val_loss: 0.7634 - val_accuracy: 0.6810\n", "Epoch 496/500\n", "268/268 [==============================] - 0s 549us/step - loss: 0.6082 - accuracy: 0.6716 - val_loss: 0.7438 - val_accuracy: 0.6810\n", "Epoch 497/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.6025 - accuracy: 0.6754 - val_loss: 0.7454 - val_accuracy: 0.6897\n", "Epoch 498/500\n", "268/268 [==============================] - 0s 550us/step - loss: 0.6142 - accuracy: 0.6716 - val_loss: 0.7221 - val_accuracy: 0.6897\n", "Epoch 499/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6137 - accuracy: 0.6716 - val_loss: 0.6378 - val_accuracy: 0.6810\n", "Epoch 500/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.6374 - accuracy: 0.6567 - val_loss: 0.6363 - val_accuracy: 0.6724\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "ImgtaBkUMVyq", "colab_type": "code", "outputId": "f7417101-d22a-45aa-fc14-ca0378104f8d", "colab": { "base_uri": "https://localhost:8080/", "height": 573 } }, "source": [ "plt.plot(history.history['accuracy'], label = 'train')\n", "plt.plot(history.history['val_accuracy'], label='validation')\n", "plt.title('model accuracy')\n", "plt.ylabel('accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'validation'], loc = 'upper left')\n", "plt.show()\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'validation'],loc = 'upper left')\n", "plt.show()\n" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "KesioDjVMM1Z", "colab_type": "text" }, "source": [ "## Evaluate the derived model ##" ] }, { "cell_type": "code", "metadata": { "id": "4FQJiCA_MJX6", "colab_type": "code", "outputId": "51624adf-f5db-4c03-fc83-b7859e323366", "colab": { "base_uri": "https://localhost:8080/", "height": 384 } }, "source": [ "! ls\n", "y_pred2=model.predict(X_tst)\n", "y_pred2=(y_pred2>.5).astype(int)\n", "\n", "# todo: use boot-strap estimation\n", "probs = model.predict_proba(X_tst)\n", "probs = probs[:,0]\n", "auc = roc_auc_score(y_test, probs)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, probs)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n", "\n", "\n", "cmat = metrics.confusion_matrix(y_test, y_pred2)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred2, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred2, pos_label=1))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "accuracy.png best.h5 Diabetes-Data\n", "AUC: 0.501\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "[[233 20]\n", " [114 17]]\n", "Accuracy: 0.6510416666666666\n", "Precision: 0.4594594594594595\n", "Recall: 0.1297709923664122\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Z1lBeI-bMQMZ", "colab_type": "text" }, "source": [ "## Evaluate the best model ##" ] }, { "cell_type": "code", "metadata": { "id": "ukEMWGcxMHdT", "colab_type": "code", "outputId": "1b9f1792-b288-4b3c-c05f-c002a0f4c265", "colab": { "base_uri": "https://localhost:8080/", "height": 438 } }, "source": [ "! ls\n", "model.load_weights('best.h5')\n", "\n", "y_pred2=model.predict(X_tst)\n", "y_pred2=(y_pred2>.5).astype(int)\n", "\n", "# todo: use boot-strap estimation\n", "probs = model.predict_proba(X_tst)\n", "probs = probs[:,0]\n", "auc = roc_auc_score(y_test, probs)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, probs)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n", "\n", "\n", "cmat = metrics.confusion_matrix(y_test, y_pred2)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred2, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred2, pos_label=1))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "accuracy.png best.h5 Diabetes-Data\n", "AUC: 0.500\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "[[253 0]\n", " [131 0]]\n", "Accuracy: 0.6588541666666666\n", "Precision: 0.0\n", "Recall: 0.0\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "-RtiPUPMQFRJ", "colab_type": "code", "outputId": "ca0e72a5-5ff4-4518-a56c-c6a4a6073ae5", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "model.compile(loss = 'binary_crossentropy', optimizer = 'ADAM', metrics = ['accuracy'])\n", "model.summary()\n", "history = model.fit(X_trn2, y_train, validation_split = 0.3, epochs = 500, batch_size = 64, verbose = 1,callbacks=C)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Model: \"sequential_10\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_28 (LSTM) (None, 7, 32) 4352 \n", "_________________________________________________________________\n", "lstm_29 (LSTM) (None, 7, 64) 24832 \n", "_________________________________________________________________\n", "lstm_30 (LSTM) (None, 128) 98816 \n", "_________________________________________________________________\n", "dense_46 (Dense) (None, 256) 33024 \n", "_________________________________________________________________\n", "dense_47 (Dense) (None, 128) 32896 \n", "_________________________________________________________________\n", "dense_48 (Dense) (None, 64) 8256 \n", "_________________________________________________________________\n", "dense_49 (Dense) (None, 16) 1040 \n", "_________________________________________________________________\n", "dense_50 (Dense) (None, 1) 17 \n", "=================================================================\n", "Total params: 203,233\n", "Trainable params: 203,233\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Train on 268 samples, validate on 116 samples\n", "Epoch 1/500\n", "268/268 [==============================] - 2s 6ms/step - loss: 0.7392 - accuracy: 0.4851 - val_loss: 0.6349 - val_accuracy: 0.6810\n", "Epoch 2/500\n", "268/268 [==============================] - 0s 606us/step - loss: 0.6834 - accuracy: 0.6269 - val_loss: 0.6357 - val_accuracy: 0.6810\n", "Epoch 3/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.6624 - accuracy: 0.6269 - val_loss: 0.6321 - val_accuracy: 0.6810\n", "Epoch 4/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6616 - accuracy: 0.6269 - val_loss: 0.6367 - val_accuracy: 0.6810\n", "Epoch 5/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6612 - accuracy: 0.6269 - val_loss: 0.6401 - val_accuracy: 0.6810\n", "Epoch 6/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6612 - accuracy: 0.6269 - val_loss: 0.6397 - val_accuracy: 0.6810\n", "Epoch 7/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6609 - accuracy: 0.6269 - val_loss: 0.6382 - val_accuracy: 0.6810\n", "Epoch 8/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6610 - accuracy: 0.6269 - val_loss: 0.6332 - val_accuracy: 0.6810\n", "Epoch 9/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.6592 - accuracy: 0.6269 - val_loss: 0.6309 - val_accuracy: 0.6810\n", "Epoch 10/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.6607 - accuracy: 0.6269 - val_loss: 0.6303 - val_accuracy: 0.6810\n", "Epoch 11/500\n", "268/268 [==============================] - 0s 600us/step - loss: 0.6605 - accuracy: 0.6269 - val_loss: 0.6338 - val_accuracy: 0.6810\n", "Epoch 12/500\n", "268/268 [==============================] - 0s 631us/step - loss: 0.6597 - accuracy: 0.6269 - val_loss: 0.6330 - val_accuracy: 0.6810\n", "Epoch 13/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.6594 - accuracy: 0.6269 - val_loss: 0.6320 - val_accuracy: 0.6810\n", "Epoch 14/500\n", "268/268 [==============================] - 0s 592us/step - loss: 0.6592 - accuracy: 0.6269 - val_loss: 0.6319 - val_accuracy: 0.6810\n", "Epoch 15/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.6598 - accuracy: 0.6269 - val_loss: 0.6340 - val_accuracy: 0.6810\n", "Epoch 16/500\n", "268/268 [==============================] - 0s 643us/step - loss: 0.6593 - accuracy: 0.6269 - val_loss: 0.6331 - val_accuracy: 0.6810\n", "Epoch 17/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6583 - accuracy: 0.6269 - val_loss: 0.6358 - val_accuracy: 0.6810\n", "Epoch 18/500\n", "268/268 [==============================] - 0s 630us/step - loss: 0.6586 - accuracy: 0.6269 - val_loss: 0.6382 - val_accuracy: 0.6810\n", "Epoch 19/500\n", "268/268 [==============================] - 0s 677us/step - loss: 0.6582 - accuracy: 0.6269 - val_loss: 0.6330 - val_accuracy: 0.6810\n", "Epoch 20/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.6582 - accuracy: 0.6269 - val_loss: 0.6291 - val_accuracy: 0.6810\n", "Epoch 21/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.6592 - accuracy: 0.6269 - val_loss: 0.6309 - val_accuracy: 0.6810\n", "Epoch 22/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.6590 - accuracy: 0.6269 - val_loss: 0.6399 - val_accuracy: 0.6810\n", "Epoch 23/500\n", "268/268 [==============================] - 0s 643us/step - loss: 0.6605 - accuracy: 0.6269 - val_loss: 0.6385 - val_accuracy: 0.6810\n", "Epoch 24/500\n", "268/268 [==============================] - 0s 665us/step - loss: 0.6577 - accuracy: 0.6269 - val_loss: 0.6319 - val_accuracy: 0.6810\n", "Epoch 25/500\n", "268/268 [==============================] - 0s 657us/step - loss: 0.6586 - accuracy: 0.6269 - val_loss: 0.6274 - val_accuracy: 0.6810\n", "Epoch 26/500\n", "268/268 [==============================] - 0s 651us/step - loss: 0.6658 - accuracy: 0.6269 - val_loss: 0.6275 - val_accuracy: 0.6810\n", "Epoch 27/500\n", "268/268 [==============================] - 0s 654us/step - loss: 0.6630 - accuracy: 0.6269 - val_loss: 0.6288 - val_accuracy: 0.6810\n", "Epoch 28/500\n", "268/268 [==============================] - 0s 647us/step - loss: 0.6601 - accuracy: 0.6269 - val_loss: 0.6320 - val_accuracy: 0.6810\n", "Epoch 29/500\n", "268/268 [==============================] - 0s 642us/step - loss: 0.6597 - accuracy: 0.6269 - val_loss: 0.6357 - val_accuracy: 0.6810\n", "Epoch 30/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.6607 - accuracy: 0.6269 - val_loss: 0.6381 - val_accuracy: 0.6810\n", "Epoch 31/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6614 - accuracy: 0.6269 - val_loss: 0.6393 - val_accuracy: 0.6810\n", "Epoch 32/500\n", "268/268 [==============================] - 0s 602us/step - loss: 0.6619 - accuracy: 0.6269 - val_loss: 0.6404 - val_accuracy: 0.6810\n", "Epoch 33/500\n", "268/268 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val_accuracy: 0.6810\n", "Epoch 148/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6510 - accuracy: 0.6269 - val_loss: 0.6289 - val_accuracy: 0.6810\n", "Epoch 149/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6524 - accuracy: 0.6269 - val_loss: 0.6342 - val_accuracy: 0.6810\n", "Epoch 150/500\n", "268/268 [==============================] - 0s 629us/step - loss: 0.6528 - accuracy: 0.6269 - val_loss: 0.6299 - val_accuracy: 0.6810\n", "Epoch 151/500\n", "268/268 [==============================] - 0s 685us/step - loss: 0.6464 - accuracy: 0.6269 - val_loss: 0.6221 - val_accuracy: 0.6810\n", "Epoch 152/500\n", "268/268 [==============================] - 0s 679us/step - loss: 0.6459 - accuracy: 0.6269 - val_loss: 0.6210 - val_accuracy: 0.6810\n", "Epoch 153/500\n", "268/268 [==============================] - 0s 654us/step - loss: 0.6466 - accuracy: 0.6269 - val_loss: 0.6246 - val_accuracy: 0.6810\n", "Epoch 154/500\n", "268/268 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val_accuracy: 0.6810\n", "Epoch 167/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.6539 - accuracy: 0.6269 - val_loss: 0.6322 - val_accuracy: 0.6810\n", "Epoch 168/500\n", "268/268 [==============================] - 0s 619us/step - loss: 0.6525 - accuracy: 0.6231 - val_loss: 0.6342 - val_accuracy: 0.6810\n", "Epoch 169/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6554 - accuracy: 0.6231 - val_loss: 0.6258 - val_accuracy: 0.6724\n", "Epoch 170/500\n", "268/268 [==============================] - 0s 611us/step - loss: 0.6490 - accuracy: 0.6381 - val_loss: 0.6492 - val_accuracy: 0.6293\n", "Epoch 171/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.6705 - accuracy: 0.6157 - val_loss: 0.6641 - val_accuracy: 0.6293\n", "Epoch 172/500\n", "268/268 [==============================] - 0s 605us/step - loss: 0.6617 - accuracy: 0.6269 - val_loss: 0.6324 - val_accuracy: 0.6724\n", "Epoch 173/500\n", "268/268 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0.6137 - accuracy: 0.6269 - val_loss: 0.6172 - val_accuracy: 0.6724\n", "Epoch 180/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.6188 - accuracy: 0.6269 - val_loss: 0.6440 - val_accuracy: 0.6466\n", "Epoch 181/500\n", "268/268 [==============================] - 0s 637us/step - loss: 0.6804 - accuracy: 0.5485 - val_loss: 0.6949 - val_accuracy: 0.5690\n", "Epoch 182/500\n", "268/268 [==============================] - 0s 715us/step - loss: 0.6823 - accuracy: 0.6007 - val_loss: 0.6574 - val_accuracy: 0.6810\n", "Epoch 183/500\n", "268/268 [==============================] - 0s 655us/step - loss: 0.6678 - accuracy: 0.6269 - val_loss: 0.6461 - val_accuracy: 0.6810\n", "Epoch 184/500\n", "268/268 [==============================] - 0s 617us/step - loss: 0.6664 - accuracy: 0.6269 - val_loss: 0.6373 - val_accuracy: 0.6810\n", "Epoch 185/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6623 - accuracy: 0.6269 - val_loss: 0.6353 - val_accuracy: 0.6810\n", "Epoch 186/500\n", "268/268 [==============================] - 0s 621us/step - loss: 0.6613 - accuracy: 0.6269 - val_loss: 0.6325 - val_accuracy: 0.6810\n", "Epoch 187/500\n", "268/268 [==============================] - 0s 666us/step - loss: 0.6612 - accuracy: 0.6269 - val_loss: 0.6309 - val_accuracy: 0.6810\n", "Epoch 188/500\n", "268/268 [==============================] - 0s 644us/step - loss: 0.6573 - accuracy: 0.6269 - val_loss: 0.6332 - val_accuracy: 0.6810\n", "Epoch 189/500\n", "268/268 [==============================] - 0s 620us/step - loss: 0.6557 - accuracy: 0.6269 - val_loss: 0.6368 - val_accuracy: 0.6810\n", "Epoch 190/500\n", "268/268 [==============================] - 0s 682us/step - loss: 0.6555 - accuracy: 0.6381 - val_loss: 0.6343 - val_accuracy: 0.6810\n", "Epoch 191/500\n", "268/268 [==============================] - 0s 696us/step - loss: 0.6543 - accuracy: 0.6269 - val_loss: 0.6304 - val_accuracy: 0.6810\n", "Epoch 192/500\n", "268/268 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0.6528 - accuracy: 0.6269 - val_loss: 0.6289 - val_accuracy: 0.6810\n", "Epoch 199/500\n", "268/268 [==============================] - 0s 651us/step - loss: 0.6535 - accuracy: 0.6269 - val_loss: 0.6242 - val_accuracy: 0.6810\n", "Epoch 200/500\n", "268/268 [==============================] - 0s 661us/step - loss: 0.6575 - accuracy: 0.6269 - val_loss: 0.6231 - val_accuracy: 0.6810\n", "Epoch 201/500\n", "268/268 [==============================] - 0s 776us/step - loss: 0.6565 - accuracy: 0.6269 - val_loss: 0.6244 - val_accuracy: 0.6810\n", "Epoch 202/500\n", "268/268 [==============================] - 0s 632us/step - loss: 0.6531 - accuracy: 0.6269 - val_loss: 0.6269 - val_accuracy: 0.6810\n", "Epoch 203/500\n", "268/268 [==============================] - 0s 637us/step - loss: 0.6508 - accuracy: 0.6269 - val_loss: 0.6283 - val_accuracy: 0.6810\n", "Epoch 204/500\n", "268/268 [==============================] - 0s 647us/step - loss: 0.6495 - accuracy: 0.6269 - val_loss: 0.6315 - val_accuracy: 0.6724\n", "Epoch 205/500\n", "268/268 [==============================] - 0s 644us/step - loss: 0.6487 - accuracy: 0.6306 - val_loss: 0.6271 - val_accuracy: 0.6724\n", "Epoch 206/500\n", "268/268 [==============================] - 0s 694us/step - loss: 0.6473 - accuracy: 0.6343 - val_loss: 0.6242 - val_accuracy: 0.6810\n", "Epoch 207/500\n", "268/268 [==============================] - 0s 633us/step - loss: 0.6492 - accuracy: 0.6343 - val_loss: 0.6256 - val_accuracy: 0.6810\n", "Epoch 208/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.6541 - accuracy: 0.6269 - val_loss: 0.6403 - val_accuracy: 0.6897\n", "Epoch 209/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.6478 - accuracy: 0.6306 - val_loss: 0.6251 - val_accuracy: 0.6810\n", "Epoch 210/500\n", "268/268 [==============================] - 0s 645us/step - loss: 0.6468 - accuracy: 0.6381 - val_loss: 0.6225 - val_accuracy: 0.6810\n", "Epoch 211/500\n", "268/268 [==============================] - 0s 631us/step - loss: 0.6490 - accuracy: 0.6269 - val_loss: 0.6238 - val_accuracy: 0.6810\n", "Epoch 212/500\n", "268/268 [==============================] - 0s 642us/step - loss: 0.6442 - accuracy: 0.6269 - val_loss: 0.6235 - val_accuracy: 0.6810\n", "Epoch 213/500\n", "268/268 [==============================] - 0s 636us/step - loss: 0.6428 - accuracy: 0.6306 - val_loss: 0.6251 - val_accuracy: 0.6810\n", "Epoch 214/500\n", "268/268 [==============================] - 0s 619us/step - loss: 0.6416 - accuracy: 0.6306 - val_loss: 0.6248 - val_accuracy: 0.6724\n", "Epoch 215/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6427 - accuracy: 0.6343 - val_loss: 0.6234 - val_accuracy: 0.6810\n", "Epoch 216/500\n", "268/268 [==============================] - 0s 676us/step - loss: 0.6396 - accuracy: 0.6343 - val_loss: 0.6193 - val_accuracy: 0.6810\n", "Epoch 217/500\n", "268/268 [==============================] - 0s 644us/step - loss: 0.6400 - accuracy: 0.6306 - val_loss: 0.6221 - val_accuracy: 0.6810\n", "Epoch 218/500\n", "268/268 [==============================] - 0s 641us/step - loss: 0.6377 - accuracy: 0.6269 - val_loss: 0.6182 - val_accuracy: 0.6724\n", "Epoch 219/500\n", "268/268 [==============================] - 0s 666us/step - loss: 0.6406 - accuracy: 0.6306 - val_loss: 0.6252 - val_accuracy: 0.6810\n", "Epoch 220/500\n", "268/268 [==============================] - 0s 630us/step - loss: 0.6520 - accuracy: 0.6269 - val_loss: 0.6549 - val_accuracy: 0.6724\n", "Epoch 221/500\n", "268/268 [==============================] - 0s 611us/step - loss: 0.6631 - accuracy: 0.6343 - val_loss: 0.6360 - val_accuracy: 0.6724\n", "Epoch 222/500\n", "268/268 [==============================] - 0s 671us/step - loss: 0.6422 - accuracy: 0.6269 - val_loss: 0.6182 - val_accuracy: 0.6810\n", "Epoch 223/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6459 - accuracy: 0.6269 - val_loss: 0.6174 - val_accuracy: 0.6810\n", "Epoch 224/500\n", "268/268 [==============================] - 0s 625us/step - loss: 0.6393 - accuracy: 0.6343 - val_loss: 0.6197 - val_accuracy: 0.6724\n", "Epoch 225/500\n", "268/268 [==============================] - 0s 636us/step - loss: 0.6349 - accuracy: 0.6269 - val_loss: 0.6189 - val_accuracy: 0.6810\n", "Epoch 226/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.6300 - accuracy: 0.6231 - val_loss: 0.6248 - val_accuracy: 0.6724\n", "Epoch 227/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.6341 - accuracy: 0.6269 - val_loss: 0.6203 - val_accuracy: 0.6810\n", "Epoch 228/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.6269 - accuracy: 0.6306 - val_loss: 0.6202 - val_accuracy: 0.6810\n", "Epoch 229/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.6415 - accuracy: 0.6306 - val_loss: 0.6222 - val_accuracy: 0.6810\n", "Epoch 230/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6513 - accuracy: 0.6269 - val_loss: 0.6471 - val_accuracy: 0.6724\n", "Epoch 231/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6532 - accuracy: 0.6418 - val_loss: 0.6144 - val_accuracy: 0.6810\n", "Epoch 232/500\n", "268/268 [==============================] - 0s 626us/step - loss: 0.6530 - accuracy: 0.6269 - val_loss: 0.6134 - val_accuracy: 0.6810\n", "Epoch 233/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.6430 - accuracy: 0.6269 - val_loss: 0.6325 - val_accuracy: 0.6810\n", "Epoch 234/500\n", "268/268 [==============================] - 0s 634us/step - loss: 0.6508 - accuracy: 0.6306 - val_loss: 0.6136 - val_accuracy: 0.6810\n", "Epoch 235/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6320 - accuracy: 0.6269 - val_loss: 0.6178 - val_accuracy: 0.6810\n", "Epoch 236/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6324 - accuracy: 0.6269 - val_loss: 0.6181 - val_accuracy: 0.6810\n", "Epoch 237/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.6271 - accuracy: 0.6306 - val_loss: 0.6231 - val_accuracy: 0.6724\n", "Epoch 238/500\n", "268/268 [==============================] - 0s 637us/step - loss: 0.6573 - accuracy: 0.6231 - val_loss: 0.6606 - val_accuracy: 0.6552\n", "Epoch 239/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.6665 - accuracy: 0.6157 - val_loss: 0.6476 - val_accuracy: 0.6810\n", "Epoch 240/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.6594 - accuracy: 0.6269 - val_loss: 0.6235 - val_accuracy: 0.6810\n", "Epoch 241/500\n", "268/268 [==============================] - 0s 639us/step - loss: 0.6436 - accuracy: 0.6269 - val_loss: 0.6170 - val_accuracy: 0.6810\n", "Epoch 242/500\n", "268/268 [==============================] - 0s 606us/step - loss: 0.6345 - accuracy: 0.6269 - val_loss: 0.6144 - val_accuracy: 0.6810\n", "Epoch 243/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6341 - accuracy: 0.6269 - val_loss: 0.6141 - val_accuracy: 0.6810\n", "Epoch 244/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.6269 - accuracy: 0.6269 - val_loss: 0.6149 - val_accuracy: 0.6810\n", "Epoch 245/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.6311 - accuracy: 0.6343 - val_loss: 0.6185 - val_accuracy: 0.6810\n", "Epoch 246/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.6259 - accuracy: 0.6306 - val_loss: 0.6199 - val_accuracy: 0.6724\n", "Epoch 247/500\n", "268/268 [==============================] - 0s 610us/step - loss: 0.6343 - accuracy: 0.6269 - val_loss: 0.6220 - val_accuracy: 0.6724\n", "Epoch 248/500\n", "268/268 [==============================] - 0s 600us/step - loss: 0.6236 - accuracy: 0.6306 - val_loss: 0.6361 - val_accuracy: 0.6724\n", "Epoch 249/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.6266 - accuracy: 0.6343 - val_loss: 0.6249 - val_accuracy: 0.6810\n", "Epoch 250/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6396 - accuracy: 0.6269 - val_loss: 0.6200 - val_accuracy: 0.6724\n", "Epoch 251/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.6258 - accuracy: 0.6269 - val_loss: 0.6184 - val_accuracy: 0.6724\n", "Epoch 252/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.6244 - accuracy: 0.6306 - val_loss: 0.6225 - val_accuracy: 0.6724\n", "Epoch 253/500\n", "268/268 [==============================] - 0s 636us/step - loss: 0.6328 - accuracy: 0.6418 - val_loss: 0.6203 - val_accuracy: 0.6810\n", "Epoch 254/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.6226 - accuracy: 0.6231 - val_loss: 0.6193 - val_accuracy: 0.6810\n", "Epoch 255/500\n", "268/268 [==============================] - 0s 640us/step - loss: 0.6159 - accuracy: 0.6269 - val_loss: 0.6248 - val_accuracy: 0.6810\n", "Epoch 256/500\n", "268/268 [==============================] - 0s 665us/step - loss: 0.6187 - accuracy: 0.6231 - val_loss: 0.6223 - val_accuracy: 0.6724\n", "Epoch 257/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.6151 - accuracy: 0.6269 - val_loss: 0.6229 - val_accuracy: 0.6724\n", "Epoch 258/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6139 - accuracy: 0.6306 - val_loss: 0.6282 - val_accuracy: 0.6810\n", "Epoch 259/500\n", "268/268 [==============================] - 0s 653us/step - loss: 0.6122 - accuracy: 0.6306 - val_loss: 0.6369 - val_accuracy: 0.6724\n", "Epoch 260/500\n", "268/268 [==============================] - 0s 641us/step - loss: 0.6144 - accuracy: 0.6306 - val_loss: 0.6270 - val_accuracy: 0.6810\n", "Epoch 261/500\n", "268/268 [==============================] - 0s 675us/step - loss: 0.6072 - accuracy: 0.6269 - val_loss: 0.6356 - val_accuracy: 0.6810\n", "Epoch 262/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.6058 - accuracy: 0.6343 - val_loss: 0.6362 - val_accuracy: 0.6724\n", "Epoch 263/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.6175 - accuracy: 0.6343 - val_loss: 0.6197 - val_accuracy: 0.6724\n", "Epoch 264/500\n", "268/268 [==============================] - 0s 660us/step - loss: 0.6168 - accuracy: 0.6306 - val_loss: 0.6194 - val_accuracy: 0.6810\n", "Epoch 265/500\n", "268/268 [==============================] - 0s 633us/step - loss: 0.6037 - accuracy: 0.6306 - val_loss: 0.6761 - val_accuracy: 0.6724\n", "Epoch 266/500\n", "268/268 [==============================] - 0s 611us/step - loss: 0.6377 - accuracy: 0.6306 - val_loss: 0.6211 - val_accuracy: 0.6810\n", "Epoch 267/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.6157 - accuracy: 0.6343 - val_loss: 0.6648 - val_accuracy: 0.6810\n", "Epoch 268/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6369 - accuracy: 0.6306 - val_loss: 0.6173 - val_accuracy: 0.6724\n", "Epoch 269/500\n", "268/268 [==============================] - 0s 600us/step - loss: 0.6046 - accuracy: 0.6306 - val_loss: 0.6274 - val_accuracy: 0.6724\n", "Epoch 270/500\n", "268/268 [==============================] - 0s 592us/step - loss: 0.6049 - accuracy: 0.6269 - val_loss: 0.6375 - val_accuracy: 0.6724\n", "Epoch 271/500\n", "268/268 [==============================] - 0s 647us/step - loss: 0.6157 - accuracy: 0.6269 - val_loss: 0.6231 - val_accuracy: 0.6810\n", "Epoch 272/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.6412 - accuracy: 0.6157 - val_loss: 0.6295 - val_accuracy: 0.6724\n", "Epoch 273/500\n", "268/268 [==============================] - 0s 664us/step - loss: 0.6222 - accuracy: 0.6343 - val_loss: 0.6137 - val_accuracy: 0.6724\n", "Epoch 274/500\n", "268/268 [==============================] - 0s 666us/step - loss: 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val_accuracy: 0.6810\n", "Epoch 281/500\n", "268/268 [==============================] - 0s 618us/step - loss: 0.6367 - accuracy: 0.6269 - val_loss: 0.6166 - val_accuracy: 0.6810\n", "Epoch 282/500\n", "268/268 [==============================] - 0s 639us/step - loss: 0.6146 - accuracy: 0.6269 - val_loss: 0.6142 - val_accuracy: 0.6810\n", "Epoch 283/500\n", "268/268 [==============================] - 0s 611us/step - loss: 0.6118 - accuracy: 0.6269 - val_loss: 0.6179 - val_accuracy: 0.6810\n", "Epoch 284/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.5971 - accuracy: 0.6269 - val_loss: 0.6569 - val_accuracy: 0.6810\n", "Epoch 285/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.5994 - accuracy: 0.6269 - val_loss: 0.6146 - val_accuracy: 0.6724\n", "Epoch 286/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.6002 - accuracy: 0.6343 - val_loss: 0.6494 - val_accuracy: 0.6810\n", "Epoch 287/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.5900 - accuracy: 0.6269 - val_loss: 0.6380 - val_accuracy: 0.6724\n", "Epoch 288/500\n", "268/268 [==============================] - 0s 610us/step - loss: 0.6076 - accuracy: 0.6269 - val_loss: 0.6182 - val_accuracy: 0.6724\n", "Epoch 289/500\n", "268/268 [==============================] - 0s 643us/step - loss: 0.5837 - accuracy: 0.6306 - val_loss: 0.6967 - val_accuracy: 0.6810\n", "Epoch 290/500\n", "268/268 [==============================] - 0s 619us/step - loss: 0.6111 - accuracy: 0.6269 - val_loss: 0.6250 - val_accuracy: 0.6724\n", "Epoch 291/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.6204 - accuracy: 0.6269 - val_loss: 0.6458 - val_accuracy: 0.6724\n", "Epoch 292/500\n", "268/268 [==============================] - 0s 619us/step - loss: 0.5951 - accuracy: 0.6269 - val_loss: 0.6856 - val_accuracy: 0.6810\n", "Epoch 293/500\n", "268/268 [==============================] - 0s 611us/step - loss: 0.6127 - accuracy: 0.6269 - val_loss: 0.6335 - val_accuracy: 0.6724\n", "Epoch 294/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.5950 - accuracy: 0.6269 - val_loss: 0.7132 - val_accuracy: 0.6810\n", "Epoch 295/500\n", "268/268 [==============================] - 0s 634us/step - loss: 0.5987 - accuracy: 0.6306 - val_loss: 0.6169 - val_accuracy: 0.6724\n", "Epoch 296/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.6098 - accuracy: 0.6343 - val_loss: 0.6502 - val_accuracy: 0.6810\n", "Epoch 297/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.6044 - accuracy: 0.6306 - val_loss: 0.6197 - val_accuracy: 0.6810\n", "Epoch 298/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.5944 - accuracy: 0.6381 - val_loss: 0.6733 - val_accuracy: 0.6810\n", "Epoch 299/500\n", "268/268 [==============================] - 0s 647us/step - loss: 0.6216 - accuracy: 0.6306 - val_loss: 0.6198 - val_accuracy: 0.6810\n", "Epoch 300/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6042 - accuracy: 0.6231 - val_loss: 0.6696 - val_accuracy: 0.6810\n", "Epoch 301/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.5815 - accuracy: 0.6306 - val_loss: 0.6192 - val_accuracy: 0.6810\n", "Epoch 302/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.6123 - accuracy: 0.6306 - val_loss: 0.6897 - val_accuracy: 0.6724\n", "Epoch 303/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.5978 - accuracy: 0.6343 - val_loss: 0.6236 - val_accuracy: 0.6724\n", "Epoch 304/500\n", "268/268 [==============================] - 0s 636us/step - loss: 0.6403 - accuracy: 0.6306 - val_loss: 0.6238 - val_accuracy: 0.6724\n", "Epoch 305/500\n", "268/268 [==============================] - 0s 648us/step - loss: 0.6296 - accuracy: 0.6343 - val_loss: 0.6542 - val_accuracy: 0.6810\n", "Epoch 306/500\n", "268/268 [==============================] - 0s 660us/step - loss: 0.6178 - accuracy: 0.6157 - val_loss: 0.6196 - val_accuracy: 0.6810\n", "Epoch 307/500\n", "268/268 [==============================] - 0s 709us/step - loss: 0.5951 - accuracy: 0.6343 - val_loss: 0.7156 - val_accuracy: 0.6810\n", "Epoch 308/500\n", "268/268 [==============================] - 0s 605us/step - loss: 0.6229 - accuracy: 0.6269 - val_loss: 0.6215 - val_accuracy: 0.6810\n", "Epoch 309/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.6167 - accuracy: 0.6306 - val_loss: 0.6252 - val_accuracy: 0.6724\n", "Epoch 310/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.6181 - accuracy: 0.6269 - val_loss: 0.6295 - val_accuracy: 0.6810\n", "Epoch 311/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.6137 - accuracy: 0.6269 - val_loss: 0.6384 - val_accuracy: 0.6810\n", "Epoch 312/500\n", "268/268 [==============================] - 0s 595us/step - loss: 0.6500 - accuracy: 0.6269 - val_loss: 0.6226 - val_accuracy: 0.6810\n", "Epoch 313/500\n", "268/268 [==============================] - 0s 625us/step - loss: 0.6056 - accuracy: 0.6231 - val_loss: 0.6691 - val_accuracy: 0.6810\n", "Epoch 314/500\n", "268/268 [==============================] - 0s 634us/step - loss: 0.6027 - accuracy: 0.6269 - val_loss: 0.6203 - val_accuracy: 0.6810\n", "Epoch 315/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.5912 - accuracy: 0.6269 - val_loss: 0.6385 - val_accuracy: 0.6810\n", "Epoch 316/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.6034 - accuracy: 0.6269 - val_loss: 0.6167 - val_accuracy: 0.6724\n", "Epoch 317/500\n", "268/268 [==============================] - 0s 625us/step - loss: 0.5952 - accuracy: 0.6306 - val_loss: 0.6749 - val_accuracy: 0.6810\n", "Epoch 318/500\n", "268/268 [==============================] - 0s 676us/step - loss: 0.5809 - accuracy: 0.6306 - val_loss: 0.6329 - val_accuracy: 0.6810\n", "Epoch 319/500\n", "268/268 [==============================] - 0s 685us/step - loss: 0.5824 - accuracy: 0.6269 - val_loss: 0.6629 - val_accuracy: 0.6810\n", "Epoch 320/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.5865 - accuracy: 0.6343 - val_loss: 0.6289 - val_accuracy: 0.6810\n", "Epoch 321/500\n", "268/268 [==============================] - 0s 626us/step - loss: 0.5923 - accuracy: 0.6269 - val_loss: 0.7051 - val_accuracy: 0.6810\n", "Epoch 322/500\n", "268/268 [==============================] - 0s 707us/step - loss: 0.6164 - accuracy: 0.6306 - val_loss: 0.6465 - val_accuracy: 0.6810\n", "Epoch 323/500\n", "268/268 [==============================] - 0s 621us/step - loss: 0.5926 - accuracy: 0.6306 - val_loss: 0.6527 - val_accuracy: 0.6724\n", "Epoch 324/500\n", "268/268 [==============================] - 0s 654us/step - loss: 0.5815 - accuracy: 0.6306 - val_loss: 0.6574 - val_accuracy: 0.6724\n", "Epoch 325/500\n", "268/268 [==============================] - 0s 636us/step - loss: 0.5817 - accuracy: 0.6343 - val_loss: 0.6899 - val_accuracy: 0.6724\n", "Epoch 326/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.5898 - accuracy: 0.6269 - val_loss: 0.6382 - val_accuracy: 0.6724\n", "Epoch 327/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.5821 - accuracy: 0.6306 - val_loss: 0.6779 - val_accuracy: 0.6724\n", "Epoch 328/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.5676 - accuracy: 0.6269 - val_loss: 0.7068 - val_accuracy: 0.6810\n", "Epoch 329/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.5758 - accuracy: 0.6231 - val_loss: 0.6925 - val_accuracy: 0.6724\n", "Epoch 330/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.5826 - accuracy: 0.6343 - val_loss: 0.6632 - val_accuracy: 0.6810\n", "Epoch 331/500\n", "268/268 [==============================] - 0s 618us/step - loss: 0.5955 - accuracy: 0.6381 - val_loss: 0.6372 - val_accuracy: 0.6724\n", "Epoch 332/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6125 - accuracy: 0.6306 - val_loss: 0.6839 - val_accuracy: 0.6810\n", "Epoch 333/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.6521 - accuracy: 0.6269 - val_loss: 0.6369 - val_accuracy: 0.6810\n", "Epoch 334/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.6006 - accuracy: 0.6381 - val_loss: 0.6579 - val_accuracy: 0.6810\n", "Epoch 335/500\n", "268/268 [==============================] - 0s 611us/step - loss: 0.5800 - accuracy: 0.6269 - val_loss: 0.6762 - val_accuracy: 0.6810\n", "Epoch 336/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.5778 - accuracy: 0.6269 - val_loss: 0.6578 - val_accuracy: 0.6810\n", "Epoch 337/500\n", "268/268 [==============================] - 0s 666us/step - loss: 0.5726 - accuracy: 0.6269 - val_loss: 0.6769 - val_accuracy: 0.6810\n", "Epoch 338/500\n", "268/268 [==============================] - 0s 630us/step - loss: 0.5768 - accuracy: 0.6306 - val_loss: 0.6432 - val_accuracy: 0.6810\n", "Epoch 339/500\n", "268/268 [==============================] - 0s 673us/step - loss: 0.5808 - accuracy: 0.6343 - val_loss: 0.6788 - val_accuracy: 0.6724\n", "Epoch 340/500\n", "268/268 [==============================] - 0s 655us/step - loss: 0.5897 - accuracy: 0.6231 - val_loss: 0.6328 - val_accuracy: 0.6724\n", "Epoch 341/500\n", "268/268 [==============================] - 0s 655us/step - loss: 0.6309 - accuracy: 0.6306 - val_loss: 0.6696 - val_accuracy: 0.6552\n", "Epoch 342/500\n", "268/268 [==============================] - 0s 638us/step - loss: 0.6753 - accuracy: 0.6194 - val_loss: 0.6737 - val_accuracy: 0.6638\n", "Epoch 343/500\n", "268/268 [==============================] - 0s 647us/step - loss: 0.6727 - accuracy: 0.6157 - val_loss: 0.6561 - val_accuracy: 0.6810\n", "Epoch 344/500\n", "268/268 [==============================] - 0s 673us/step - loss: 0.6531 - accuracy: 0.6231 - val_loss: 0.6558 - val_accuracy: 0.6810\n", "Epoch 345/500\n", "268/268 [==============================] - 0s 653us/step - loss: 0.6229 - accuracy: 0.6269 - val_loss: 0.6751 - val_accuracy: 0.6810\n", "Epoch 346/500\n", "268/268 [==============================] - 0s 677us/step - loss: 0.6161 - accuracy: 0.6269 - val_loss: 0.6373 - val_accuracy: 0.6810\n", "Epoch 347/500\n", "268/268 [==============================] - 0s 658us/step - loss: 0.6041 - accuracy: 0.6306 - val_loss: 0.6346 - val_accuracy: 0.6810\n", "Epoch 348/500\n", "268/268 [==============================] - 0s 628us/step - loss: 0.5959 - accuracy: 0.6306 - val_loss: 0.6349 - val_accuracy: 0.6724\n", "Epoch 349/500\n", "268/268 [==============================] - 0s 618us/step - loss: 0.5974 - accuracy: 0.6343 - val_loss: 0.6622 - val_accuracy: 0.6810\n", "Epoch 350/500\n", "268/268 [==============================] - 0s 669us/step - loss: 0.5783 - accuracy: 0.6306 - val_loss: 0.6520 - val_accuracy: 0.6810\n", "Epoch 351/500\n", "268/268 [==============================] - 0s 663us/step - loss: 0.5751 - accuracy: 0.6306 - val_loss: 0.7426 - val_accuracy: 0.6810\n", "Epoch 352/500\n", "268/268 [==============================] - 0s 672us/step - loss: 0.5924 - accuracy: 0.6306 - val_loss: 0.6374 - val_accuracy: 0.6810\n", "Epoch 353/500\n", "268/268 [==============================] - 0s 656us/step - loss: 0.5956 - accuracy: 0.6381 - val_loss: 0.6426 - val_accuracy: 0.6810\n", "Epoch 354/500\n", "268/268 [==============================] - 0s 680us/step - loss: 0.5923 - accuracy: 0.6306 - val_loss: 0.6415 - val_accuracy: 0.6810\n", "Epoch 355/500\n", "268/268 [==============================] - 0s 676us/step - loss: 0.5782 - accuracy: 0.6269 - val_loss: 0.6747 - val_accuracy: 0.6810\n", "Epoch 356/500\n", "268/268 [==============================] - 0s 666us/step - loss: 0.5846 - accuracy: 0.6343 - val_loss: 0.6559 - val_accuracy: 0.6724\n", "Epoch 357/500\n", "268/268 [==============================] - 0s 666us/step - loss: 0.5724 - accuracy: 0.6306 - val_loss: 0.7843 - val_accuracy: 0.6724\n", "Epoch 358/500\n", "268/268 [==============================] - 0s 661us/step - loss: 0.6162 - accuracy: 0.6269 - val_loss: 0.6316 - val_accuracy: 0.6724\n", "Epoch 359/500\n", "268/268 [==============================] - 0s 617us/step - loss: 0.6222 - accuracy: 0.6306 - val_loss: 0.6590 - val_accuracy: 0.6810\n", "Epoch 360/500\n", "268/268 [==============================] - 0s 658us/step - loss: 0.5985 - accuracy: 0.6306 - val_loss: 0.6934 - val_accuracy: 0.6810\n", "Epoch 361/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.5735 - accuracy: 0.6306 - val_loss: 0.6567 - val_accuracy: 0.6810\n", "Epoch 362/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.5773 - accuracy: 0.6306 - val_loss: 0.6486 - val_accuracy: 0.6810\n", "Epoch 363/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.5907 - accuracy: 0.6306 - val_loss: 0.6363 - val_accuracy: 0.6810\n", "Epoch 364/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.5747 - accuracy: 0.6269 - val_loss: 0.7583 - val_accuracy: 0.6810\n", "Epoch 365/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.5952 - accuracy: 0.6269 - val_loss: 0.6278 - val_accuracy: 0.6810\n", "Epoch 366/500\n", "268/268 [==============================] - 0s 628us/step - loss: 0.6392 - accuracy: 0.6269 - val_loss: 0.6559 - val_accuracy: 0.6724\n", "Epoch 367/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6611 - accuracy: 0.6306 - val_loss: 0.6462 - val_accuracy: 0.6724\n", "Epoch 368/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.6326 - accuracy: 0.6343 - val_loss: 0.7056 - val_accuracy: 0.6810\n", "Epoch 369/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.6195 - accuracy: 0.6231 - val_loss: 0.6396 - val_accuracy: 0.6810\n", "Epoch 370/500\n", "268/268 [==============================] - 0s 582us/step - loss: 0.6111 - accuracy: 0.6343 - val_loss: 0.6203 - val_accuracy: 0.6810\n", "Epoch 371/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.5997 - accuracy: 0.6343 - val_loss: 0.6497 - val_accuracy: 0.6810\n", "Epoch 372/500\n", "268/268 [==============================] - 0s 649us/step - loss: 0.5900 - accuracy: 0.6343 - val_loss: 0.6353 - val_accuracy: 0.6810\n", "Epoch 373/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.6034 - accuracy: 0.6306 - val_loss: 0.6330 - val_accuracy: 0.6724\n", "Epoch 374/500\n", "268/268 [==============================] - 0s 674us/step - loss: 0.5954 - accuracy: 0.6306 - val_loss: 0.6700 - val_accuracy: 0.6810\n", "Epoch 375/500\n", "268/268 [==============================] - 0s 667us/step - loss: 0.5891 - accuracy: 0.6306 - val_loss: 0.6370 - val_accuracy: 0.6810\n", "Epoch 376/500\n", "268/268 [==============================] - 0s 652us/step - loss: 0.5884 - accuracy: 0.6306 - val_loss: 0.6815 - val_accuracy: 0.6810\n", "Epoch 377/500\n", "268/268 [==============================] - 0s 650us/step - loss: 0.5956 - accuracy: 0.6306 - val_loss: 0.6344 - val_accuracy: 0.6724\n", "Epoch 378/500\n", "268/268 [==============================] - 0s 639us/step - loss: 0.6432 - accuracy: 0.6343 - val_loss: 0.6796 - val_accuracy: 0.6466\n", "Epoch 379/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.6814 - accuracy: 0.6157 - val_loss: 0.6663 - val_accuracy: 0.6810\n", "Epoch 380/500\n", "268/268 [==============================] - 0s 633us/step - loss: 0.6719 - accuracy: 0.6306 - val_loss: 0.6493 - val_accuracy: 0.6810\n", "Epoch 381/500\n", "268/268 [==============================] - 0s 652us/step - loss: 0.6691 - accuracy: 0.6269 - val_loss: 0.6407 - val_accuracy: 0.6810\n", "Epoch 382/500\n", "268/268 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[==============================] - 0s 664us/step - loss: 0.6081 - accuracy: 0.6231 - val_loss: 0.7283 - val_accuracy: 0.6638\n", "Epoch 497/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.6041 - accuracy: 0.6082 - val_loss: 0.8699 - val_accuracy: 0.6638\n", "Epoch 498/500\n", "268/268 [==============================] - 0s 592us/step - loss: 0.6539 - accuracy: 0.6119 - val_loss: 0.6466 - val_accuracy: 0.6293\n", "Epoch 499/500\n", "268/268 [==============================] - 0s 625us/step - loss: 0.6335 - accuracy: 0.6157 - val_loss: 0.6200 - val_accuracy: 0.6810\n", "Epoch 500/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.6467 - accuracy: 0.6231 - val_loss: 0.6241 - val_accuracy: 0.6810\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "0j3ya9bFQNxa", "colab_type": "code", "outputId": "f1770173-0251-4153-a844-1609be94f6ad", "colab": { "base_uri": "https://localhost:8080/", "height": 573 } }, "source": [ "plt.plot(history.history['accuracy'], label = 'train')\n", "plt.plot(history.history['val_accuracy'], label='validation')\n", "plt.title('model accuracy')\n", "plt.ylabel('accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'validation'], loc = 'upper left')\n", "plt.show()\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'validation'],loc = 'upper left')\n", "plt.show()\n" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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q1dNeOy+xTZu2nOcHGDFTCYiaTep+8wqVuc1btnaP6U9/h9ok692ebbQ/cvyZOTm9cVL3Fpy9+aCjcQ8E1V88xchrfVzMoUHImNuR7XcNkcGrD4QS59HH+mkd+uMH1WP0M1umEkZFFdDu8U8Ulqs/77kLK5TwKSy31qUQRukodFyvqMK9HlQjmo6y0e79iyoSGpx+b3pb/a7EcS21qrebn0LIapoPOH5bgrdgQEI4OM+fDv38iirUOVsOqN/6WOd2TSg/sT2VQNP7O/eLtig/A6iG1LlfqADyit1lcN6H9jcVViS26XfQ6CMgoq1KI9bXyStUxzY7OkmhQreAa21QZcwrtOpbc2Kb9z61Jl4yWP1vb7Lqnecb1GWLtriP10JKm4K927ONFkSB3DTlRkD0FpwNqPO3CKhGuSMB4TUxOZ3KuhF1CYgMGla/Mvk19M21Ce0n3u7vkE4lIPwau2AeBENQUObWToocjVtLrRJEmQg6J35CwbleO2dTobUoZ0MZawNkQkDkF6vf2nkI6j4KK6CoPP35Ww64f7ccSDTmfveQCmf5Wqz347x+kUdQaLR26iyHa7vn+JYDqlHM045XmbiusyxNe61wYp9nHgip56q3aZNva31y+aKtqi45BVJRuWrItdZdNsItaHUZQ+Hk+ua9Ty1kSoa4yxkIujth2tTUnkpAxP23ZxstiEQH1oAuYgREb8HZeHt76elMQbpiJAkIH4HSkUkpXZlEBxqEs8embe+pzuUkXWNXVJ7QIIIFiUZI9xhlHOisBuFocFzCwlqfX5L+eC1Y9f7O8+levtZyahxO6/qdbs0oFe1NCVOGvvekRryDc4ASrqAa/IY9VlSO4/qhQv9zBYKWvb3WfwChNvc5Nbz2FsgLpy5jYXlCWHrX6//6mYF6L1rw53kifmKtal+tQYTCiePqdwJC+RoaPNpbu6VBeO/Xa0rTz15rEHb5Av7movYWd2dMawy2iclHQGRzYKatQRgB0b9x9da9AsK5LPy3uQREzF1p8dEgMiqT3l8kVNhUGoRTVfcLPU117XTmksJydy9aN85FFdZAty5oEIUpNAjdcGRqsvIz0QQLHNsr3M+hZoPbxJMObZrS2pNXiGZyDv2+iioSdv6i8kTP2/ksvWgBHEmRTdhZhmZH4+ukyKMp6Gfhp8Hp/+EBVtkCKhgDkutbkompyG3S0xqFV3trd5iYnCSZmKwG3SXILBOTXzh8NOIWAl4NItUx2UJrEJ39tjPECIjegtVDL5l4CogAu/bs4xM33qW2uXrycMYZZ2CH9OoPSMZUJRYBiEf5+QO/oLmlxT7mwgsv5GC9/4C9lOhzOxtNb4NcMjShQZSNUuv8GpZUKrBtZvCZ41o3VEm2a21iinfeB+G1uWtS9cAGjPRfr00xzsba6ScoqnA/h0id286eDp0nytYgPMd0Jo5eh0d7y+rc7kX7VVKZmfQ+uozRloRG4ndeVxl8NDj931m39H7eehGNWCYmLSDCiePrdyXMSPp6YI3xsMrovV+vtqs7Wq53aQV0+GkD7S3uBj/JxNTsf0y2iBsT0+GB08kkBMOHDmLuQz+2tjlfvqdBFCJROQJBJUyaa/n5A7+kuSViHzNv3jwGlmfQOLnK5NAgUlFUCSufVR9aOgdvRz4Iv0ZPN1TeRlI7qbvig0jV+9a9f6+JaeAo93K4zL2/S4NwCBw/M1DhwMx6/5qVz6qookxMSqnwClavM9kvMq2wQk2mpMcU+BEqUM/g1e8rE5Y2Memev/N9+pny9HVAOeHB7cPRxwS9AqJNvXN9PWejv3eVv5bw4t2qfuaFk31Ar9zrOb8lIAJ5bjOcEP49//YWtxDYtgQeuSCxzs8sFW1R43cenAUbF7i3rXoOnvhkYrl+F/zmNBWO/OAseNsaL7TgR7Dwf3JuYjJhrjlmzpw5jBo1ii9+8YsA3HPPPYRCIRYsWMCBAwdob2/n+9//PpdcconqgVsN3pY6+NgnrmXFylW0RFr53C1zWLZqHZMnH0VLS6IHcsstt/DO4v/QEonwiY+fz7333ssDD/yCXdX7OPPKm6kqH8iCZx+z04dXlY3ipw8+zCN/UoO2brjhBr785S+zZcuW5LTif5tLYWFFciNSVKni4QcMUx/S+pdUuU+8GT6crGLAKyfAuhcT8fmpVODxp8Ox18G4jygHd+URiW15heoDi7aoQVma/BK1TsbotA9i9GyY8WkVAupk4rlw6h1w8m0w9XIV/39gixpUt22x8r2Onq2E4PuPqYGJuiwap4A44QbViBZWJBz3065SvqFInRpxO2SqCjOtXqFCMI++At76rTVae5gajSwCcOwnSeLS36i4+gNb1EDC5ho18jhYoEJwNUdfocY05BXCyBNg9EmqsTztK2q7EHDhT9TgLs2smxPmnvFnKIdtKAwjZrjLMP50WP9vNY5Dv7er/wzLn1KjnDXTP6VMguEyGOIo25RL1GjkaVcnlqtXwClfUmMkQNWv6/4GHzwOK+ZaPohgwjcRzFPjPYIFalswXwkYzdjTErMG5hWp68/8L/U89MA5gFO+DG/c79YgPvZTNc5k3Omq4fb2/AvKLAHhEAI7rMFqlXoEdQqtY92LKtx482swwRGiqgdTat59VA2AfeZGdX9r56kBga/dp7af90P1v7MdpQw5fATEC3PcMdbZYOgxahRlGq6++mq+/OUv2wLiqaee4qWXXuL2229nwIAB7N+/n9mzZ3PxxRcjih2NoHaMAb/+7cMUFYZZ/drfWL7tIMed+lF7tx/84AdURPcQa23m7Gu+yPKNu7j97nv56W//yILnnqCqOOhqQ5eu2cqjj/2Ft956Cykls2bN4vTTT6e8vDw5rfizz3Hdddcl31R+MZx+V2J5+tWJ386xDgUDOhYQJYPh0gf9t+norXjUEwZs9SqjbV0Icx0Il/wyeX0wBOd8R/2edK7604w9xb3vGXMSv51OVKfJasKZ7g/fyRUpRo0DXPxA6m1O/ISGH0OmJl/vo55e84k3upcnflT9dcTJtykBAQnNpHyMu24AjDwePvH75OMHTYLLf5dYDubBOfdYv61nGQjCEWerOrBibnIUkxDquI9+F178mooocwrq836oRqVvfFUdEypQDT8o4brmX+pb++i98PbvElpCIATHfkr96XJ4sxiEByjHuJ8ZSWtofmGu7S2JKKtU4cTxmLqmvhcdQeX9joyTum8zY8YM9u7dy65du1i2bBnl5eUMHTqUr3/960ybNo1zzjmHnTt3Ul1dnfIc/3n9Da67/EIAph19NNOmTbO3PfXUUxx3zieYcd4nWbl2PatWrXIcKTz/4fXXX+eyyy6juLiYkpISLr/8chYtUqNZs55W3Gke6EoF1nmj4nH38fqjiUZy1nPKGGf0jrNhOhxw+h28TupDRT9LXYf0+4+2qneuBYQec6PNSjrnkfM8WgP2ltGOhLL+i6DS9iC5IfarZ+Ey5Wtw5qfSpBsoF40koqdSDUjU+dOCKQSCJsdhroePBtFBTz+XXHnllcydO5c9e/Zw9dVX85e//IV9+/axdOlS8vLyGDt2rG+ab18cHebNmzfzk5/8hHdeeILy4hDXf+X7mZ/Hh6S04i2H6ExLF7qb6fHxWLIGoUMtY21AB2GpucbZSB5uAiKvGwSEdlLr/zKmGmuv5qh9G+0R93sI5Se0vFQCQv93+gG9vg+/Blj7o/y0AN3j93VSNycCAFpSRIrF2qxkgp465Q1fj1nLRoPou1x99dU8+eSTzJ07lyuvvJK6ujoGDx5MXl4eCxYsYOvWrWmP/8hpp/L4sy8CsGLlapYvV0n56uvrKS4upqysjOp9Nbzwymv2MaWlpTQ06cqZ+JhOO+00nn32WZqbm2lqauLvf/87p512WnZvWHOoGoQVkaUEhFOD0CamSOdNTNkmXaPS33E2uKFw6v26gtPEBMl1KUlAWBpEtCVZg9Dl9Da2IY+AcAqBTMYiaee6buydo+TtDMo+WQXaIwmhksrEZGdm9goIjwahr200iL7L1KlTaWhoYMSIEQwbNoxrr72Wj3/84xxzzDHMnDmTyZMnpz3+lptv5HOffpWjTr+co46ayvHHHw/A9OnTmTFjBpNPOo9RwwZxyqyE4/Wmm27i/CuvZ/igChY897i9/rjjjuP666/nxBOVU/KGG25gxowZuZmlLisahJ8PQpuYWnteQDg53DQIp1DIugaR5/7vCvUOJqdzcUYneQWEFgTOsULgiIQKJ18jycTkU8+0I98e6V8ObVYouW7g/WiuSWgYqUxM0VQ+B48GoXNCmSimvs2HHyYc5FVVVSxZssR3v8ZGlTxs7NixrFih0pAXFhby5K8tE1nZKBXxYvGHP/wBajermPuiKjss87bbbuO26y62K6BTANxxxx3ccccdrus6rwdw550dpG/OBGcMe1d6ONrEJONpBEQvUoKdA+UOB7rVxNRBHrFU41GCeQlB4A1T1aYnbbK008mEkgVCJiamworEuAqvMHKiB0KGB6Yea2L7IDydDq+jvGmfv8ktS/Sir8uQmg5evv5gvB+OSHZSdyvBLGgQepIkXyd1Kz12b34c1iamLAsIu9G26k1H5kpt7nEeC0po67J5Q05tDciqQ8IhILz4XVMLiM2Wadc5/iKdBqGzyVZOUEJLpzzXWYYBtr0JC++D5v3uY70axKaFOTMvgREQfQ+/noItIFI0lj3VhrpU9q5oENYxOvZd4wz961UaxGFsYsrmLGngMDH55AATgUQY7gxr3EAgoMaBnPmNZBPT1EvV76mXua/h1XrsdDI+gt6vng0+So2FqF6lxn2MOz2xzR6T4RNEsXuZOm66Far8xv3q/8NnJfZ56W5Y+KPkuUO0gNC5tiBn5iU4DExMUkpEb7JTd4VMi59UibOvQXRqBkKXiakLZbBDG9v8TUyxLoyDyCWHm4BwPvvOjBDPhFRRTKDqefnY5JnZvmJNI7tpYWJdIKim/fSbxc0rIHQnxBta6tzmZPAUuNuTqmPkTPjDRapujj4ZPv8CrHtJzbYIcPN/1KRJmg+fdk8JrGm15qmIeEJotZM6XJZIyWI0iK4RDoepqanpXKPWKxEpfnt3y62JSUpJTU0N4XCGESt+H1pncM514TdQDuhVJqbQYSYgnGSSY6ozaAGh63CwE9qoU1Cn60B4zWKBTpqY/DoErkgonxT5Xu0kmO9vjtKagnNCo1BhQkA453sxGkTXGDlyJDt27GDfvn09XZRDIx6FeitaYV8M8j2D6loOqElRCtuhoCZ5fV4j7E3jNOsE4XCYkSNTJLDzcqgZJvXx3jBXp43ZmJh6B7nSILwTVkHHPeZMgwVSaRCZmpj83rffnCvpwmeD+dBe55MC3OrUtjoERPGghODwm5s+B/RrAZGXl8e4cb1gvulDpW4H/OxU9fvyh+EozyT2z98J7zyk5nI+9r8T6/VE7VMugav+1H3l1fh9aJ3BrzcGbg3CmJh6B7nSILT27zUxpT02w3rnncMikM7ElKmAcE725aNBeM8dzFcmplYfExi4U3WUDErMQe0cD5FDC0kv6n4ZUuKsnJ1pEO103blTQdNyqFE9qebIcJkQelEVPtyimJwcSsZZP5zzbzuXoXMmpnR4OzDpNAhfE5Pffj6mMJHOxJSnBEQmc5YXD/I3MflNL5wletHXZUiJS0B04pXZ80j3kIA41OumGrjkagCMBtErCGV5DEiSicmnZ57y2C4K6nQ+iC5pED6TbPmZmGJtqVNuOCmqTJiWXCYmo0Ec3nQkIGxbZ4rBPTmabapDDtXElImAMCam/oktcHxMTIEOmq1MhZX3u/FL62Hvq7UBx7X9ruPnKxEpzKP6HG3NsPuD9GXNL1X7tjcrv2LcMXjUmJgOczoSEDrHfsUE9/qAT6XuTrJpYnLeQ28TEKNPUv97Q1n6C7aJSbqXIQMfRIaCumSo+j/SSlGfyUC5jtLH+JmYnAItSYPIg8Y98Lw7s0ESA0er8kXq4EcjlWalnfE5NDH1ayd1v6EjAXHcZ2D4DBg2zb2+p3wPmmxFMXl/9zYfxLVzoTF1uvZ+zZ0bEimys0nAIyA6FcWUYcdk0CS4ZTFUHWldI52Jyaez5dch8OvUdBTFBCoKbNBkNWZ8PFAAACAASURBVDmVH5/6KyxxzJsiY5ZG0dR3fRBCiPOFEGuFEBuEEHNS7HOVEGKVEGKlEOJxx/qYEOID6++5XJaz15NuTmi93SscwO65PP3u9hwVrAMOWUCkclL3snEQBSUqbcLhSMkgGDA8++e151q3Gj/v1Lrp6Iypb8jURGSRSGdi8glZ9UP4+SBS1V1HWQvL1ayKfgQLVI41533HY46R7LkzMeVMgxBCBIEHgY8CO4B3hBDPSSlXOfaZCNwNnCKlPCCEGOw4RYuU8thcla9P0dUopp7WIA7ZxJRCg3CNg+gFAsKQA/R7dTR+wTyIxjIwMXXRYR7wadDtbRmaa31NTB0MlAOVODDVQEs72MRxbhlPduTngFxqECcCG6SUm6SUbcCTgHcW9BuBB6WUBwCklHtzWJ6+S5ejmPq4iSnlOIheZmIyZB/9Xl0jhq3GtaOOT1frfTofhJ+JyffaHTipvQ52XZdDBak1Hz9Bo01M0GcFxAjAadvYYa1zMgmYJIR4QwjxphDifMe2sBDiXWv9pTksZ++nqwLCqpg91snOahSTR1jYz8FoEP0SXWmdETp+Tt90x3aWtFFMHSTE9J7DeUw6geWc76JDAeHRILIdWuxDTzupQ8BE4AxgJPAfIcQxUsqDwBgp5U4hxHjgVSHEh1LKjc6DhRA3ATcBjB49untL3p10eaBc4vXG4pJgoJsb02zlYvL+BvUx9YY5qQ25wU+D0I1prt65bcpJM1CuQw3CR+tNd4xz5rxUJlk/ExN0y/wjufy6dgKjHMsjrXVOdgDPSSnbpZSbgXUogYGUcqf1fxOwEJjhvYCU8ndSyplSypmDBg3K/h30FvwcX5ng6Gm1RXOnhqa+fo5GUkPi4zA+iP6J8PFBZGpi6vI1M8jm2ikTUwYahNYCRCC5wdcTGqUa8NoNGkQuBcQ7wEQhxDghRD5wDeCNRnoWpT0ghKhCmZw2CSHKhRAFjvWnAKs4XDlEE5OU0BbrCQGRxSgmb6OQ696koWdxOm81uc4MkM5JbUcxdUFApBNotokpmGxi0pMg+fkyoG+bmKSUUSHErcBLQBB4REq5UgjxXeBdKeVz1rZzhRCrgBhwl5SyRghxMvBbIUQcJcTuc0Y/HXZkwUnd3hMCIqtRTMEU24wG0S8ZcyqccTeccENinR2OmsE3cNlvoWICb2zYz0/+vZanbz6JULAj34VVx0I+6eyd5qJPP+tOw+3azy+KKU0za6c1DyZrLuEyNYgu1Xm6wcSUUx+ElHIeMM+z7tuO3xK4w/pz7rMYOCaXZetTdDQOIuVxiX17REAcqvkn7STyGToNDX2TQADO8Ayd6oyJafo1ANzxw5eprm+lpqmNIQM6mMckkEZAOE1ME85MfQ5XZy6DcRtOH4T3vvSUpql8EN0w/4jRz/sCrkawaw1ie7QPTpqUzgdhBMThR6ZRTA50vyijWqIbaO88EeCobxlEUHn9FZk4qUUweb+wZWJK6YPIcOKuQ8AIiL6GVYmq6yO0tGU+aUhbrHsmGMkqqcZBQI+GudY2tVHX0rn0EvG4ZFtNc45KdJjQhfT1ejbJ9ngGHaSAW0C43nNn8pp5G/R0GoQzUaC3s2NrECnO0w3JIY2A6GtYFXTWD1/hs4+8nfFhbX1Sg0hnYsrQaZgDjvvefI7/3vxOHXP/K+v5yI8XsLWmKUelOgzINJLIQUwLiEyi+PRsbVbP/LjvzWfWD1/2XDuDDknA4ytJJ9D0/A4BHw1CO6lT+SD6eBSTIReIgN0rentLBpOMoOp0j/ggDpV0TuoeNjFFM+mROnhtnZr2dn9jdqZ+PSzpQhRT3HpPGdX/WJv67zAxRdq1jSpDExMkC4h05ZWO8ycJiFL3+ZIEhDExGbyIAK1dGNPQ9wVEKh9E36jC+vmHOmE/N3iwG95OCAg9tXMm30zM0iD8fBCBzggIj2DISIMIkdJcmnKgnDExGbwIQXMnfA/QveMg1uyp55HXN2e8/4GmNu57YY2/AHM2pt6PLIUP4vnlu/mP1VvPhKVbD/DXd7ZlvH9XicZUSxVNmpw+N/z5za0s257BLGUZ8s9lu1i4di9/eGMzq3fXZ3RMdX2En/57rd2LP2S6ML9JzKNBRGNxfvzSGg42t/nsbGl3oUJbS7fxmLc27G3kof9sSlFOjyBLF+aqZ4YTPj4IW7tIcd99eRyEIUeIAM1t0U4f1l0jqS998A0i7XGumz2G/FAAhhwDY05Ouf+3/rGCfy3fzazxFZx55GDXtrZ4ALuPlKEG8cXH3wNgy30XZVTeK36t8u9ffUJuU7XoBso2WeQQKSXffHYFkPlz6Ijbnnjf/i0EbP5Rx+ed88xyFqzdx+lHDub4MVmYszoTp6+HuPZBWAL61TV7eXDBRnbXRfjpVZ5k0Q4Tk1PjaGmLUeipb9f87k32N7Zy3ewxFOanGKNjm5jSCDSd4vuE/0pM+HXG12HhD2HCWbDkl47zeQSIERCGJESASHvnI5L0B5JrdAN4oNmKO7/l9bT7b9irBhzl+XxEkYwERHZ8EFJKRAbninWxN6w1uNZo7qPJmjqpYXZEQ8QdsZXpDJc6AihrnRNvw5sBuqxaQOt8ZLVNfhqEdZ95hdQ77rmmqZWRHu1FayANre3JAiIT34OmbCTcU5dY1r/P+Boc2OI+j1f77OO5mAy5QAQ6bWKC1D4IKWWXBE5rNJa2saxp9PkAfdhdFwGgsTVZK4o4VyWNpNbLXRwXEou7nonuMXb0LFq68KwgYWLyahCxuMy60KjJsiN8/d4Uo4YzxNec4yEai9PUGk027Tg5hCimNo+A8P2GotrEFKbRUflqGttSXrsx4qfNZzi5UEeELF+IPfe05xs2A+UMSXh8EJf96g3f3Z5ZuoO75i6zl3Vj+NamGsbOed7uuf/Pi2uZ/K0X+fv7O3zPU9PYytg5z/OPD1Sexcff2sbYOc9z5Ddf5HLHtReu3cvYOc/byxc+sIi7/7acsXOeJ5rG/6F7mY2tUTbsbWT6vf9my34VCtqcTkD4hLk6bd0TvzGPVETaYxz/vfnM/P7L9rqGSJSH/rOJyd960bYtT/32i9z2xPuMnfM8f3lrqyqTw7yXyra+aV8j0+55iY37GrnpT+/yg+dXOUxM7obpil8v5shvvsimfY2c9ZOF3G6ZcrbXNjN2zvMsWOs/RcqHO+oYO+d5PtxR51q/u66F03+80LVu1g9fZuyc5+2/Jh9hDHD/y+sZO+d5pJQ8vGgTY+c8T1s0btcVL5c8+AYPL0q2w5/7s9f4wl+W2ss/f3k9F9y/yLfx37K/iWPueYkjvvECU7/zEl//+4cA7DrYwtg5zzN/VTUNkXbGznmenfWWoOmgZ67v4+bH3rU7MW2eDoDvGCJbgyhydVguefANnlu+Wy1Y9U1XvwY/AWHXzUPUbrWz3J5dz1NmE8VkSEIEXJX7/W3+jsg/vbnVtaw/kBdW7AFg/io1h/Iqy+H45kb/kNm1exoAJRgAV4OwbEedXZZ5H+5OOvaJt9V0IJEUJgan4GiItLNhbwN1Le28Y4XvugVExyamJkfj3R6TKRvwDXsbqY9EXYPdGlujLNlUA8CSTTVIKWlqi/HPZbsA+NUClWne+exTaRNr9jRQH4ny1qZaFq3fz7IddQ4Tk/tZfGA5kt/dcoBN+5t4zrreyl3qvejn7mX+qj2u/5r3trrrQ0tbjOp6t0ax62CL7zl/9vI6ABpao3z/+dWAMq8c8DHHHGxuY9n2g6ze3eBaH49L1lU3Mu/DPXYe1rXVDazeXc8+H83m7S21rkZ2yUb1DhatV4EG8z7cbWujG/crbbOjnrm+j5dWJuYJ1wJad658/Xi2DyKc1PDPfV8LCHej76f52hrEoSYVzOtAgzAmJkMSGZqYCvMSr1aIhIo9sEjls9FmCP1/3d4G/NCNoLazVpa41dqN+1TvcmR5EalINUjpQLOjgY5E7Y9SmzRaYo6PMQMntfdjrY/4j3Ze73OvDZF21lWr9euqG1La8Z3PPtV70M90wdq9tLTHqGls9dUgnI3U/NWJxiwWlxQXqOedqrefim217tHaq3bXJe3T0ME5nebBmsY230ZwXbV6R42t7me8/UDi+l47//rqZE1kfXXiXQwdEGZrbTOR9hhbrVHnoyqK7GfXFu/64EivgPANFtACIlSYJCCKw4603A68/hm1T5ZMTME8dQ7bBxFL3p5jjIDoRbRF47zw4e4O7LCBpJ6r05a+bPtBttY0UZTvblB1I60bnB0HVC9Sf8TrqxuRUtLSFuPfK/ew62ALj76x2W4IiiwBUVLgPu8/PtjJhr2NhIKp1ennlu3y7c07G5Bttc28tFL1hnVD3Rx1CojEx/bvlXuI+/gevB/1/sY29jZE7F6pZp1PQ/Xyqmp2HGghPxRgx4EWHvUJ1d2yv4lfL0zMWbVgzV72NkSYv6ra1fDXWPf12tp99rLTB9HSFuOxN7fys/nr7GMWOkxJT7273XauNrVGWbmrjj8t2eJqjPTTbGiN8uqahHBxNrgA9/4zOQnyn9/c6tLemtuiPLZki738i1fW278fW7LVN62IfkfLd9Sxalci7FVrWoDdyGv+8cFOHl60iYcXbeLJt7cRi0vWOt7F8WPKkVKZpBZaz2717no2WSbHFusRr65usv0aizfsZ19Dxz6X9qjk9fX7be1pf2Mrj725lYVr97JkYw3rqxtobLE0q1B+ksm1tFA1xnGrydTvx1vn3tiwHx0PEpXCrtNdJRYqTHwHXg2iG6YUNlFMvYhfLdzAz19ezyPXz+SsyUP8dxIBWjzq8YGmNgZbmSpv+fNSpgwvc0VWSJmIYtK9w221zUgpqWlso7QgRENrlF11EX756gaeeHsbx44aaJs/AArzVFXRjcV5U4ewYM0+Hlq0mYcWbea2s45IeV/feW4l7bE4N5w23rXe6Ux9emnig1xnmbWa2x1CxeqNrdhZx02PLeU/Fa2Mtp6Hxvux1jS2cuvj77FmTwNrv38+BaGgfX59z5oHXt0AwLWzRvPoG1v4P0fjrfnxS2t53mFK++ozy+3fN542jm9cNMW6rnrGWms76NCUWqMx5n24m29ZYagAYyqLXI3pD59fzf9dNR1QWtGdTy9n9e56hBB8evYYAOqt9/DoG1t49I0tvHbXGYypLGatR0As35GsQfztvZ1MGTbAfh//Wr6bb/1jZWL7+4l5vf767vak4yEhIHbXRbjwgUVsue8i9je2ptwf4Kl33Y3u6Moil0A7dWIVL6+u5jevJYTM/FXVtjk0inp/v31tI/u2v8fDnzmBTz38FkcNG8ALXzot5XVBCcHrH33b7ty3RuOudzBtZBm/b2mmRMDBVmGbp66aOZKn3t3B1to2KIDqhjaGOc7rrHNNrVGuffgtlhSpfV5avZcvblzKs188BU9AbcbUtgVYvGY/l0CyDyJXEyc5MBpEL0I7A3Xv3heHk/onV6pGZL/VINW1tLOrLsLa6nrCoSBb4kMBWCHH2Y3Vfqt329Ieo6E1SlsszuwJlYD66PUgqA88g6zyQ6qq1DS1cfH04fz20zMZWZEYcdrcFqMoP8iq757HlvsuYnCp2z767pYDSbey3y/UENhVF6Eh0u72QeSXALDFymXUbKdASGgSXnW/tqnNNldt2pfIgbRub4N9z04+e9IYPnPSWN8ygRoEmIq9jl5sTVPqHm2kPc7a6gbyHXMT3H/NDJbfcy7rvn8Bcy6YTENrlO1WHWhsjbLNuue1jut7n92GvY3E4pINexu54dRxPHNLYuzJr689ji+dPdG1v1MgaT+Tk0Vfdae0Hj+o2LW8xucYLdgfuX4ma753PivuPc8WaJqrZ45i4Z1nAKpO6Cg2gCOHlrLsO+ey/B71F/JMkRuzBESQOO9vO2h/L5kM3KttaiMalynDvZfvqGNJfCoA6w+qfX736eP5309M57MnjbE11hipfRC2adTKe7Zxn7o32yJQNorOsl+WUSetZ6/HSWh052ho7mZGMAKiF6EbfmdjloTDBzFiYCLrJCTMC9trW6hraeMdOZnzWu/jj7FzbTNUrdV4NbdFqbUEy+zxlfbx8RTmLW1CqW1ss/0QzkZOCYiQbdqqKHb7Krb4JKmrtTSIkeXJqQ3W722kuT3O+a33cXPB/0KxFmLqI4zJjn0Q+5vaqLTKYZut2qJsr21h2oiypGtOHzWQ0RX+vpTapja2pMnGWlaYsAfXNLah2zbvNOCR9hjrqhuYMLjEXldZnM+AcB75oYBdrg93KAFdXd9q+0OcprFaTxjxuupGttc20xqNM2lIKVUOX9GQsjDhPHdv02neW1ed3Nh730lFkft9fuATHKHPc/TwMsJ5QUoKQknnmTJ8AGMqiygvyksKbCgtCBHOCzIgnMeAcB4FIXfzpN95UOjnkVzuVIEJNSk6I07uar+ZXx39V9YcUOeYar2LiuJEZyc/FKQtGrdzcTk7Jbo8jah7rrE0x/aYhC+vgFv8Iw7T8fm2r/KT6JVqYcKZcMvixMZAEL60HD73QqfPmynGxISKnLj3nys7nYAt22gH49ylO3h9w35KwyFbhdUBmZ99dCnLG0spCAUYZPXS75q7jDkXTHY1kMss08JaqUYI/3HxFv61fLedTbS6vpXrH1XZYCcMKmZQaQEPLtiY0in69/d38uHOOhpao3aj63RYt7RFbT8FJPsq1uxp4JyfvuZad6BJNaRDB4STtKYv/Pk9wnkBtsjRrKnDPra63ho30RaDADy3bDcPLFfb6j228p/PX2c3DN/71yp+8eoGW1BOHFKadI+ThpTacfJeOhr/sGJnHR/7xSIi7XG21TZzzIgylu2os/+D0sL+sHgLAJccO5zN+xuJtMddz1GX69kPdrnOP3FwCe9vO2A/B68z+jevbeTPVuTapKGlVJYkGrWq4gLCee7G9pU11Vx4/yKEUP6nQaUFLlu+d9Bgadj9PtticQpCibxguw62cM8/VzEgHLLrJcDQMnco5uDSAoQQTBxSytub3ZFzpWG309X7Neree4g4zW0x7v7bh/a2C+9fxM+uPtY3mg7UwM2OaCWfbWI4+dUNlBSEGG6VvbIkH2GVJhKFT/wm0Ujr73N/YytfnatMjvVSdTLaLYE2f9Ue/nCwhQeuGcHnfv8WXzp7IjPHVnRYHoDdeDTdIVMTv0UAyt0aWrYxAgJYurWWNXsauODooQR6cAKaI4eWMmxAmN11Ed7aXMOGverDnTy0FCwtev3eRo4/ajTHjSlnXFUxnz1pDHOX7mDh2n2uXqzXcXfyhCr7GvvqW3l7Sy1ba5u58viRHD+mnK98dBKL1u9HCBgyIExtUxsNkSgvO6Jr4nHJxdOHc/7Rygr7v5+Yzin3vcrg0gLbxKT579MncOfcZbb9fdrIMkb5RDodNayUiUNKiS7caJu1bvrIeJ59f6erx94YiXL8mHKOHFLK0LIw8bfUx7ftQAvhIQHGVCg1vKI4n8L8ILG4VA5CS3GZNS7xoZ04toJTjqjkZ1dPp6K4gPe2HuBAc5t6zijTwoc762iPSfY3trKtptnOnPvI9TN5a1Mt9ZF28oMB6iNR/v7+Tt6zetSnTxrEkUNKuebEUby/7SBnTR7Mn9/cyoRBJfxg3mq7DJOGlPKFM47g1TV7XQEFVSX5HDmk1PYlfGTSIEaVF3L5cSN49I0ttnP0yCGljCwvZHddhKqSAltwnjV5MFOHD3CZZypL8m3/i343H2w/wJub1D2dNrGK28+eyMK1ewkKwWmTBgHw409M4w+Lt7ByVz0l4TyeueVkXl1Tze6DESQqOOJf1viAV9YoJ/snZ412CZcLjh7G6tMbuOK4EcxduoOzjlLpVG7+yHgGlRQworyQ31njTko8QsirzWoTU2m+gBYlpErDIc6aPJh/fKByRb2wwi0gRgwsZOfBlg4Hbl49cxSL1u+jIRIlLiXDysL2fZw5eTC73i+DathV38ry/Qm/jo7a0sLutIlV1G8ptsqr6uhDi1TAw21nNbJo/X4272/i9a+dlbY8GdENiSqNgCARkfDAJ2eQ19G8td3EnU8vY+7SHZw3dQifO2UcPKjWxxH831XH2sLg3kuO5u0tB2iIRKmuj3DMiDLW7mlwJee7/awjuOPcI+3lX766nre31FKYF+THlh/jmhNHc82J7nxE//viGpeAuPy4Edx6VsKWPWJgIZ89aQzPfrCLlvaYy4xxzpQh/OpTx/Gph98C4HefnpnUm3Ry0oRKpt3zbwC+fuFRRNpj/GlJYizHyRMq+enVCVffO0uDINXz+PTsMb65lIoLQjzwynquP3ks91w8NWn7ZTNGAqpRd3Lu1KGcO3Wovfzhjjo+/kuVMuSUI6qSAgjqW9rtBvK+K45hWJkyMZw2cZC1bhqAS0BMHFzCkUNLOXKoW5MRQvCVcydx02NqoNlDnznebtyPH5NZr9NLUX7Q1iAGlRYw54LJbK1psgfUPXDNDMqL8znB06u9cqaymd81dzml4RDHjyl35VS657mEY/u9rQfIDwW4y1HPQGlNcy6YDMDdFx5lrz/7qCGcfZR6jlpAFHnMYF6FfvqYStgJRw8r5vPDxvHIG5s5fdIg7r9mBks21rBqdz2b9zdx2sQqFq3fD8DXLpjMnU8vS6tBzBxTzv98YhqXPPgGDdZobqewGjGwkNvOGg9PQFwmhN/QAWHb7LeuugEhVD1/+4EKaIQA7qgjbdbLNFVJugGmQLdEMfWO1rCH0e+rN01eqQXA4NKwq2ceJ5BkLlCmKBXHP3loqcu+DRD05DnSDXmwA23JO47AabbQFOaHaGmL0eLRIADyHDZkrz3ZS6nHJDXJYwLymjik9bYkIqVQ1yaCTFI9pOMIx/N09sQ1Tn+L10ySCq9gcOJsnPKz0GERQiT5IJzaXHlx6pQNupze9wPuvFLvbTvAhEElhA6hvAGPec8b7q3HIgwuSfg2ii3ta9KQUv69spr2mOQYh3+pNBwiPxhw+VzyPCHZOkS7tEB9R/WRaJKJVB/iDK+ePb6CfQ2tHGxuY111A2MqiijMD9IeUs+sPBBxnUM75ONSsjPFYEV937sOtnSYorymWQmz1bvrfQMNsoERECQkeibJ2rqL40arntoxI8ooyktUVilEUqNRWhBiV10L+xvbOGJwCZOGuAWEd4yCNmkE04xdAJgyzO3IrfRpSIryg7TF4jREoskCwlHO/A4EhH72OlrmaI8T2Wt+kJZ6nU5A6HN4z9VZdMiwDgrwogWnEMm94FSkG1hYWpAQModSJ50NoVdA68a4I8F9xOASCvOCKvGih2kjB9q/t9Y0uwRpZ0gVGODVIEqLVBlGDMhjynA129rMseV2ObWfyPm+SwtC5AWF7YsaW1nE+Cp3OfX3XxoO0RiJ0hhpZ4BH0AetbmTc0WTOsoI7Nu5rYtO+JiYMUudtz1NlG5zvFhB6DMbuugin3Peqa+yLk7c313LK/7yaFLLs5eY/v88fF2/hh/NWu0Kus4kxMQHSevm9RzzARdOGceTQ0zlicIkrG2ZeKOjrQNxeqyrf0LJwkrPd63jVDbk3jNDLJ08cxezxFXzyoTeprm9NGkUNUGg1iDVNbUzy9IqdDVRHAgLg7a+fTZHVc5s+soy/feFkfvLSWhZvrPFxYFoahEwvIBbceQZjUjRAneHtr59NQYrGX0cMlRSEknrBTt771keJtMeQJL8TJ15tqau8841z7Lrg1SAAln7znA59bkX5If79/z7C4AHJ2uM1J4xCAHMsZ/FQn30y4fnbT/XNjeRNBjm4THUeRpblM3J8Ja985XTGV6l1TgHmFDiVJQXkBQO2EPjLjbNpiLRz/s8XJV2vpCBEY2uUWFwmaRDaXCQdrYQWiPUt7TS2Rm2tP5qvBERVyC0gdnoCMbanCGffUx9BSlLmwNLECfD6hhpqGtvSmm8PBSMgcGoQPVsOL7oCuhva5Ffm7F1XFOfbjbbGKwh0jzhdIwWq9zp+UIkdVltZ7Gdi0gKiNan37NR0OhJGgD3YT1/7uNHl9ngK7werhWQcQX4o9bnHVRWn3NYZBvv0oDXaxORnhvHbryO82lJXGegITdVmSafVxs9k6MeoFAJWCMExIxO99Qqf+pEJpeG8zExz1lgYPZOa7rGDW7sd5mgsK4rz7Q5EKCAYXhamqdD/WqXhPNtJ7RXSwkpr0UBCiyy3nm99pF3NGWF9C9Gw0iwKPPVyV51bIAxI8Z7197b7YELAtMfijo6QACQxAsTicWqb2phqaVTZxggIHD6I3iYhLJzl8hMQzo+rsriA4gp3Q+3tJWoNoiMfhEaPgajw0SD0uaQkKS++U2vo6rPVdtiu+CC6C93QZup/6IhsaRBO/Hwn2cBpivHTMLPKSbeqlNwn3Ji0yXntYoegHhAO2fWwNBxCCEGxd/4Gi5JwyA4VTxLS407n/viVPNp+jr1Kv6fG1igt7Qkf3NYh5/CzNe+zfdBnELVNtlD2+h0i7THaY3F++eoGLj52OA8v2szpk6oSAsIhUJrbYizeUM3AonxOChVANEKMANG4pKapNWNh31mMgEBPFtPTpciMPB9TgbN3XVWST1VJAedOGcKWmibWVTcmxZPripzOHOLk19cez8Ovb/LtITv9DrPHu6NgstFwa/Oa10SiSx4n2SfT3ejea7Z6/rlozLUGke167myYq3IkID41a7R6x3lhOOsbKcqRaCALPB2T0ycNoi0a52Rr9LwQgjOPHERlSQFzl+6wn4mzR58k7AMBfi2vIEKcwrwgn5o12hYQ9S1KQGjNvbAgnx/FruDCvFIKQi12YsA9dW6TU3NbjOU76rj/lfWs3FXHy6v3Mu/D3dz0EZUCZZdj/5a2GLf8xZotsUwJCEmAupZ22mPS1z+YDYyAQPV++4h8SKFBJNaVF+cTCAh+95mZ/GjeatZVNybN6KXzKmVi9gEVsnrOFP/cUIWWw3tAOGSPj9BkRUBYoX5JPV3F7QAAGRtJREFUzlS7pROuaKmeoKrE3wzWm9BCJ9MQy0xxjuHwM0Fmg6+ed6TLXOaHs4H0aqv3XDw1Kcz50c+dyOIN+5m7dIf9TJzvz68zpPf7fx+dyE0fmYCUkmBAsL+x1dKg1TG60yQQFISCtoDwZv9tbovZ+ch0lFVdS7s94HOPS4NwDGC1zF0xAtRYqdxzpb2ZKCaUk7q3mpe8+PUunQLCL3LIO2OZjmrKVINIh+69+0XlZKNnr01MqZzcshdoEOXF6oPNhWkoW2QSJHCoZOpj6Sx+DnYv2WggnVqD37vUAkJ/Y0IISgpCdh4uLRic5fWGpDtpaYvZgsGZ+l7P0eL0QRx0Zgmw5qKOEaC6Qe2TKxOTERD0LQ0iL5Rs59bRE17bqm44vRpE2BIy3nC/Q0GHGzrJS+M8zhQ9lqE8RQ8y3gt8EAWhIAOL8uy5Nnojui54k+5lAz0CPdu92FFWMsiOQnEhocl0RkhrgaCd8E4ToZ+5UI/sdta3koKQPYq90FdApBZuzW0xO/zWOU5DZ+B1Zhve7kitsq1erY8jbKFlTEw5RNL7IphS4eeDOHlCFd/62BSmDHNHMuheo1dAjK4s4jfXHc/JRyRnNO0ss8dXcP81x3L+0UOTtmWj4f7+Zcdw3tShHOW5N/3CJMkDn3qCX117nG8qka7yz1tPzWqvv7w4n4c/M9M1Ejpb/PmGWXyw7WDSHCSHytz/Ppk1exoy1u7/csMsxlSqd/DUzScxpIOw22NGlvHgp47jzMlqxLvTxOTXIdHWOafGWhoO2WltbNOSo7jptNuW9ij7G9VZnXNu+M2/8d7WRDbk1ngIAhBweBdHV2av7jkxAgJLg+gjEqLA5yMM5wX5r1PHJa3XDUy7z5B9vwa9KwghuOTYEb7bMvVxpKOkIMQFxwxLuT1OoMc1CEjkusoWzvDRbJHKj3SoVJUU5OTcQwaEfQfopeKUIxLv4MRxmaUluWhaom45ndR+2pAe2e0U3KXhENutCZO8A0U7oqUtltHskIA9HS5Am9Vs56MEyfCycNLAvmzR819WL0C6hr/0bvJ8nNSpsDWIjnK65IjcCt2EBpGJCcJg6AjveCIvur+e59Ig8ux07Dr4Q+jWREDMEiresUml4RAL1+2zp+yFZBOxc70z1XsbShgUWALCLzNxtjBfFlqD6OlSZEZBXuYCYpbVizp3Sna0hd6EsE1MvUODMPR9nE5qv2CQhJM60ViMrUz4dLQPQueCunj6cKYOV7+neAaylRXmcbC5nRU7E5MdlYRDvlq3N1XM/dHLAdggleaeLq/XoWK+LKxxEH1EhzjYkplKCnDE4FK23HcRZ04enMMS9RQOH4TRIAxZINM8Ws769q2PJTLUahPT6Moittx3EedNHcr9Vx/Lsm+fy8csU1YoIOzpWb2E84K2kHEKIa+AWBg/lrGRx6lHCaeJXcyBlQnmy6JvaRCp8rccdtgahOgVTmpD3yfTsG+n49lpRvWakfQ5y4oSqUR0biw9NsJJOBS0zzHckRjy6BHp02h4Mx9nEyMgsKKYeroQGeKdVvNwJ44gL2CqsaH78EaX6WipgjRjHnSEVDoZVJAXsLUQ51StRw9PH7AwcUjuNIiMDNpCiL8BvwdekFL2jMczh/SlKKbffXpmTxehU9x3+TEpk70dGtZgPxHIyoA/gwFUfR3hM0e6E6/P64kbZ/PMezsYlGaw2gljy7l4+nBOsPyCj98wy55MSxMOBWnPVxrGlGEDGFdVTGFekCMGl3DrmUewv7GVJ9/Z7jrm7gsmZz282EmmZ/4V8DngASHE08CjUsq1OStVN6NGUvd0KTLD6+zq7XhnqcsaOlDEaA+GLJJJffWaNMcPKuGu8yanPaaypIAHPjnDXj75iComDy1lzZ4GBoRD1EeiFOQFqI8oATGotICbPjLB3v/O847kL29t5cl3tvORSYP4z7p9ANx8+gRySUZfl5TyZSnltcBxwBbgZSHEYiHE54QQvXf4aIb0pZHUBo16Y97Z8gyGXJPt1C7a31AQCqZNra9NqanShOeCjO9UCFEJXA/cALwP3I8SGPNzUrJuRGVzNSKiT2G9L6NBGLqbbIVV64F9evR3OC9gJ+XzG6gXTzGmIpdkdKdCiL8Di4Ai4ONSyoullH+VUt4G5M5D0k30pVQbBjdGgzB0N9lKgTLngsm8fMfpzLamLm2IRO2Z9fw0CD3BXkcTfWWTTO/0ASnlFCnlj6SUu50bpJQpvaZCiPOFEGuFEBuEEHNS7HOVEGKVEGKlEOJxx/rPCiHWW3+fzbCcXcKYmPoeetyK0SAM3U22NIi8YMCaQ16FqW6rbabZmpwrnQbRndaOTO90ihDCnqFcCFEuhPhCugOEEEHgQeACYArwSSHEFM8+E4G7gVOklFOBL1vrK4DvALOAE4HvCCGyn2XMoi+l+zZYWK/LaBCG7ibbPgingNCjtX1TfVgbuzNoL1Nvx41Sygf1gpTygBDiRlR0UypOBDZIKTcBCCGeBC4BVjnPCzwopTxgnXevtf48YL6UstY6dj5wPvBEhuXtFEaD6HvYGoQwAsLQvWQjjb2TqpJ8Zo4p5/pTxhKNSf60ZItvmvDzpg7llws28LlTxiLJfOT3oZCpgAgKIYS0RJilHXSUgHwE4Aza3YHSCJxMss73BhAE7pFSvpji2KSUoUKIm4CbAEaP7no4pfFB9D2kcVIbeohs5/4SQjD3lpPt5Utn+GdHHjwgzFtfV3Ni//CyY7JahlRkKiBeBP4qhPittXyztS4b158InAGMBP4jhMj4zqWUvwN+BzBz5swuT6bYlwbKGTRagzDvzdC9ZCONfV8hU1H4NWABcIv19wrw1Q6O2QmMciyPtNY52QE8J6Vsl1JuBtahBEYmx2YNlazP0KcQ9ki5ni2H4bBB+wUOp05JRhqElV7j19ZfprwDTBRCjEM17tcAn/Ls8yzwSeBRIUQVyuS0CdgI/NDhmD4X5czOCX0pWZ9BoV9XwLw4Qzfxr9tOZe2ehp4uRreSaS6micCPUNFI9hRPUsrxqY6RUkaFELcCL6H8C49IKVcKIb4LvCulfM7adq4QYhUQA+6SUtZY1/weSsgAfFc7rHOBmjDINDR9Ci0YjA/C0E0MH1joyrJ6OJCpD+JRVNjpz4AzUXmZOvwypZTzgHmedd92/JbAHdaf99hHgEcyLN8h0Sc0iJsWwv4NPV2KXoSVrK+HS2Ew9Gcy/b4KpZSvAEJKuVVKeQ9wUe6K1b30iXTfw2fAtCt7uhS9hoQC0evfnMHQZ8lUg2gVKuB8vWU22kk/SLGhMVFMfRGd7ruHi2Ew9GMy1SC+hMrDdDtwPHAdkNP0F91JX0r3bVDo1xWky9HNBoOhAzrUIKxBcVdLKe8EGlH+h/5FX/BBGNxYL8yYmAyG3JGJozkGnNoNZekx4tJEMfVVjJPaYMgdmfog3hdCPAc8DTTplVLKv+WkVN2MSbXR99A+I/PeDIbckamACAM1wFmOdRLoHwLCJOvrewg9o1wPl8Ng6MdkOpK6//kdHCgNwoiIvohxQRgMuSPTkdSPQnK4iJTy81kvUQ9gcjH1QbQG0cPFMBj6M5mamP7l+B0GLgN2Zb84PYPxQfQ9hOWeNpqfwZA7MjUxPeNcFkI8AbyekxL1BGagXJ9D6hnlhBkHYTDkiq66+CYCg7NZkJ4kbkxMfY7EjHI9XBCDoR+TqQ+iAbcPYg9qjoh+QZ9I1mdwYediMu/NYMgZmZqYSnNdkJ7EpPvui1hOavPaDIackZGJSQhxmRCizLE8UAhxae6K1b0YDaIPYg+UMy/OYMgVmfogviOlrNMLUsqDqPkh+gXGzdn3sJP1GflgMOSMTAWE336Zhsj2eqQ0U1f2NUyqDYMh92QqIN4VQvxUCDHB+vspsDSXBeteTLrvvoeZUc5gyDWZfl+3AW3AX4EngQjwxVwVqruJGx9E38NEMRkMOSfTKKYmYE6Oy9JjSJPuuw9idAeDIddkGsU0Xwgx0LFcLoR4KXfF6l5Mqo0+iND/TIiBwZArMu2GVVmRSwBIKQ/Qj0ZSm3TffQ9p5dow781gyB2ZCoi4EGK0XhBCjKUfRYdKMCpEH0NXPqNBGAy5I9NQ1W8ArwshXkN12k4DbspZqboZKaVxdvYx2oNFAMSC+T1cEoOh/5Kpk/pFIcRMlFB4H3gWaMllwbobIx/6Fm+MuokFW9sID7mIc3u6MAZDPyXTZH03AF8CRgIfALOBJbinIO2zxKU0KRv6GG2BAh6MXcpXRL8Zr2kw9Doy9UF8CTgB2CqlPBOYARxMf0jfwTip+x7Scj0YuW4w5I5MBUREShkBEEIUSCnXAEfmrljdi0nW1/ewndTmxRkMOSNT/XyHNQ7iWWC+EOIAsDV3xepeTLrvvoc0wUsGQ87J1El9mfXzHiHEAqAMeDFnpepmpMTYmPoY0tIhjAJhMOSOTnv4pJSv5aIgPYmRD30Q7YMwb85gyBkmoQ2ASffdZzGvzWDIHUZAoMNce7oUhs5gXBAGQ+4xAgKTrK8vIi0vtRkBbzDkDiMgMOm++yJx44MwGHKOERAYDaIvYgbKGQy5xwgITEx9X0QaL4TBkHOMgEBrEKYr2pdIaBDmvRkMucIICACT7rvPYl6bwZA7cioghBDnCyHWCiE2CCGS5rQWQlwvhNgnhPjA+rvBsS3mWP9cLssZN8n6+hw6iskoEAZD7shZrmQhRBB4EPgosAN4RwjxnJRylWfXv0opb/U5RYuU8thclc+JxKT77mskZpQzGAy5IpcaxInABinlJillG/AkcEkOr9dlTLrvvofxQRgMuSeXAmIEsN2xvMNa5+UKIcRyIcRcIcQox/qwEOJdIcSbQohL/S4ghLjJ2ufdffv2dbmgJt1330NHMRnfkcGQO3raSf1PYKyUchowH/ijY9sYKeVM4FPAz4UQE7wHSyl/J6WcKaWcOWjQoC4XwrJmd/l4Q/czrqoEgOEDC3u4JAZD/yWX8zXuBJwawUhrnY2Ussax+DDwv45tO63/m4QQC1Gz2G3MRUGlycXU5/jcyWOZMmwAJ02o7OmiGAz9llxqEO8AE4UQ44QQ+cA1gCsaSQgxzLF4MbDaWl8uhCiwflcBpwBe53ZWMaaKvkUgIIxwMBhyTM40CCllVAhxK/ASEAQekVKuFEJ8F3hXSvkccLsQ4mIgCtQC11uHHwX8VggRRwmx+3yin7JG3ORiMhgMhiRyaWJCSjkPmOdZ923H77uBu32OWwwck8uyua9nnNQGg8Hgpaed1L0Ck6zPYDAYkjECApPu22AwGPwwAgIrzNXIB4PBYHBhBASAGUltMBgMSRgBgdIgAsYJYTAYDC6MgMAMlDMYDAY/jIDApPs2GAwGP4yAwKT7NhgMBj+MgMCk+zYYDAY/jIDAmlvASAiDwWBwYQSEhRkoZzAYDG6MgMBEMRkMBoMfRkCgx0H0dCkMBoOhd2EEBCbdt8FgMPhhBAQm3bfBYDD4YQQEJt23wWAw+GEEBFaYqzExGQwGgwsjIAAwUUwGg8HgxQgIzEhqg8Fg8MMICEy6b4PBYPDDCAisMFcjHwwGg8GFERAYE5PBYDD4YQQEOtWGEREGg8HgxAgIlA/CYDAYDG6MgAAwI6kNBoMhCSMgsEZSGy+EwWAwuDACAuWDMNlcDQaDwY0REEDcmJgMBoMhCSMgAImJYjIYDAYvRkBgxkEYDAaDH0ZAYIW5GglhMBgMLoyAABXmaiSEwWAwuDACAu2D6OlSGAwGQ+/CCAiUD8KEuRoMBoMbIyCwsrkaE5PBYDC4MAICMye1wWAw+GEEBCbM1WAwGPwwAkJjVAiDwWBwcdgLCClVsm8jHgwGg8FNTgWEEOJ8IcTa/9/e/cZYcZVxHP/+gAItaPnTtdkAgdKSKEZEu0Fqa4I1RVobNBFjK1ZqMLxpY5uYKBu1jfjKN6ImRGmUiJFY0loikia4pQ1JX1hY2i3lT5FtgymkulgoWhNR4PHFnLud3UyTZffODnvn90kmd86Zmct5ltl97jkz94ykXknrC7bfL+m0pJ60fCO3bY2k42lZU1YbU35wB8LMbJAJZb2xpPHAJuAO4CSwX9LOiDgyaNftEfHgoGNnAI8CHWTXkA+kY882u52NhwWNc4YwMxugzB7EEqA3Il6PiP8CjwOfH+KxnwW6IuJMSgpdwIoyGnnJQ0xmZoXKTBCzgDdy5ZOpbrAvSjoo6UlJcy7nWEnrJHVL6j59+vSwGukhJjOzYlVfpP4jMC8iFpH1ErZezsER8VhEdERER1tb27AaEGmQydN9m5kNVGaCOAXMyZVnp7p+EfFWRJxPxV8CNw/12GZp9CDMzGygMhPEfmCBpBskTQTuAXbmd5DUniuuBI6m9d3AcknTJU0Hlqe60rgDYWY2UGl3MUXEBUkPkv1hHw9siYjDkjYA3RGxE/impJXABeAMcH869oykH5IlGYANEXGmnHZmr56LycxsoNISBEBEPA08Pajukdx6J9D5HsduAbaU2T7IX4Mo+18yMxtbqr5IXblGD8LTfZuZDVT7BPHu9yCcIczM8mqfIBo3MXmIycxsICcI3+ZqZlao9gmC/m9SuwthZpZX+wTRfxdTxe0wM7vSOEF4LiYzs0JOEOnV032bmQ1U+wTRf5ur84OZ2QC1TxATJ4zjcx9pZ+7MKVU3xczsilLqVBtjwfsnX8Wm1R+vuhlmZlec2vcgzMysmBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhRQt8kAESaeBv47gLa4D/tGk5owVjrkeHHM9DDfmuRHRVrShZRLESEnqjoiOqtsxmhxzPTjmeigjZg8xmZlZIScIMzMr5ATxrseqbkAFHHM9OOZ6aHrMvgZhZmaF3IMwM7NCThBmZlao9glC0gpJxyT1SlpfdXuaRdIWSX2SDuXqZkjqknQ8vU5P9ZL0s/QzOChpTD5BSdIcSc9JOiLpsKSHUn3Lxi1psqR9kl5OMf8g1d8g6YUU23ZJE1P9pFTuTdvnVdn+kZA0XtJLknalckvHLOmEpFck9UjqTnWlntu1ThCSxgObgDuBhcC9khZW26qm+TWwYlDdemBPRCwA9qQyZPEvSMs64Oej1MZmuwB8KyIWAkuBB9L/ZyvHfR64PSI+CiwGVkhaCvwI2BgRNwFngbVp/7XA2VS/Me03Vj0EHM2V6xDzpyNice77DuWe2xFR2wW4BdidK3cCnVW3q4nxzQMO5crHgPa03g4cS+ubgXuL9hvLC/AH4I66xA1cA7wIfILsG7UTUn3/eQ7sBm5J6xPSfqq67cOIdXb6g3g7sAtQDWI+AVw3qK7Uc7vWPQhgFvBGrnwy1bWq6yPizbT+N+D6tN5yP4c0jPAx4AVaPO401NID9AFdwGvA2xFxIe2Sj6s/5rT9HDBzdFvcFD8Bvg1cSuWZtH7MAfxJ0gFJ61Jdqef2hOG21Ma2iAhJLXmPs6SpwO+BhyPin5L6t7Vi3BFxEVgsaRqwA/hgxU0qlaS7gb6IOCBpWdXtGUW3RcQpSR8AuiS9mt9Yxrld9x7EKWBOrjw71bWqv0tqB0ivfam+ZX4Okq4iSw7bIuKpVN3ycQNExNvAc2TDK9MkNT4A5uPqjzltvxZ4a5SbOlK3AislnQAeJxtm+imtHTMRcSq99pF9EFhCyed23RPEfmBBuvthInAPsLPiNpVpJ7Amra8hG6Nv1H8t3fmwFDiX67aOGcq6Cr8CjkbEj3ObWjZuSW2p54Ckq8muuRwlSxSr0m6DY278LFYBz0YapB4rIqIzImZHxDyy39lnI2I1LRyzpCmS3tdYB5YDhyj73K76wkvVC3AX8BeycdvvVt2eJsb1O+BN4H9k449rycZd9wDHgWeAGWlfkd3N9RrwCtBRdfuHGfNtZOO0B4GetNzVynEDi4CXUsyHgEdS/XxgH9ALPAFMSvWTU7k3bZ9fdQwjjH8ZsKvVY06xvZyWw42/VWWf255qw8zMCtV9iMnMzN6DE4SZmRVygjAzs0JOEGZmVsgJwszMCjlBmF0BJC1rzEpqdqVwgjAzs0JOEGaXQdJX0/MXeiRtThPlvSNpY3oewx5JbWnfxZL+nObj35Gbq/8mSc+kZzi8KOnG9PZTJT0p6VVJ25SfRMqsAk4QZkMk6UPAl4FbI2IxcBFYDUwBuiPiw8Be4NF0yG+A70TEIrJvszbqtwGbInuGwyfJvvEO2eyzD5M9m2Q+2ZxDZpXxbK5mQ/cZ4GZgf/pwfzXZ5GiXgO1pn98CT0m6FpgWEXtT/VbgiTSfzqyI2AEQEf8BSO+3LyJOpnIP2fM8ni8/LLNiThBmQydga0R0DqiUvj9ov+HOX3M+t34R/35axTzEZDZ0e4BVaT7+xvOA55L9HjVmEf0K8HxEnAPOSvpUqr8P2BsR/wJOSvpCeo9Jkq4Z1SjMhsifUMyGKCKOSPoe2VO9xpHNlPsA8G9gSdrWR3adArLpl3+REsDrwNdT/X3AZkkb0nt8aRTDMBsyz+ZqNkKS3omIqVW3w6zZPMRkZmaF3IMwM7NC7kGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFfo//VOP7TvPS7QAAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "-ybrPnh4QEvC", "colab_type": "code", "outputId": "493548f1-f6a7-4619-d90e-bc5f1d2db830", "colab": { "base_uri": "https://localhost:8080/", "height": 421 } }, "source": [ "y_pred2=model.predict(X_tst2)\n", "y_pred2=(y_pred2>.5).astype(int)\n", "\n", "# todo: use boot-strap estimation\n", "probs = model.predict_proba(X_tst2)\n", "probs = probs[:,0]\n", "auc = roc_auc_score(y_test, probs)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, probs)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n", "\n", "cmat = metrics.confusion_matrix(y_test, y_pred2)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred2, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred2, pos_label=1))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "AUC: 0.696\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "[[253 0]\n", " [131 0]]\n", "Accuracy: 0.6588541666666666\n", "Precision: 0.0\n", "Recall: 0.0\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "DA-IoBQaQTkV", "colab_type": "code", "outputId": "920e0c55-c22e-4d16-e88d-1320a2c4644e", "colab": { "base_uri": "https://localhost:8080/", "height": 421 } }, "source": [ "model.load_weights('best.h5')\n", "\n", "y_pred2=model.predict(X_tst)\n", "y_pred2=(y_pred2>.5).astype(int)\n", "\n", "# todo: use boot-strap estimation\n", "probs = model.predict_proba(X_tst)\n", "probs = probs[:,0]\n", "auc = roc_auc_score(y_test, probs)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, probs)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n", "\n", "\n", "cmat = metrics.confusion_matrix(y_test, y_pred2)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred2, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred2, pos_label=1))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "AUC: 0.500\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "[[253 0]\n", " [131 0]]\n", "Accuracy: 0.6588541666666666\n", "Precision: 0.0\n", "Recall: 0.0\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "7A3prmgjS0wv", "colab_type": "text" }, "source": [ "## Train LSTM with robustly normalized input data ##" ] }, { "cell_type": "code", "metadata": { "id": "VY3k5jUbSgDL", "colab_type": "code", "outputId": "f69e7a48-845e-424c-9ab1-bf6790df503a", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "model = Sequential()\n", "model.add(LSTM(32, input_shape = (7,1), return_sequences = True, kernel_initializer = 'uniform', activation ='relu'))\n", "model.add(LSTM(64, kernel_initializer = 'uniform', return_sequences = True, activation = 'relu'))\n", "model.add(LSTM(128, kernel_initializer = 'uniform', activation = 'relu'))\n", "model.add(Dense(256, activation = 'relu'))\n", "model.add(Dense(128, activation = 'relu'))\n", "model.add(Dense(64, activation = 'relu'))\n", "model.add(Dense(16, activation = 'relu'))\n", "model.add(Dense(1, activation = 'sigmoid'))\n", "\n", "from keras import optimizers \n", " \n", "lr=0.002 \n", "b1=0.9; b2=0.999; ep=1e-08; dd=0.004\n", "opt = optimizers.Nadam() #lr=lr, beta_1=b1, beta_2=b2, epsilon=ep, schedule_decay=dd) \n", "model.compile(loss = 'binary_crossentropy', optimizer = opt, metrics = ['accuracy'])\n", "model.summary()\n", "history = model.fit(X_trn3, y_train, validation_split = 0.3, epochs = 500, batch_size = 64, verbose = 1,callbacks=C)\n", "\n", "plt.plot(history.history['accuracy'], label = 'train')\n", "plt.plot(history.history['val_accuracy'], label='validation')\n", "plt.title('model accuracy')\n", "plt.ylabel('accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'validation'], loc = 'upper left')\n", "plt.show()\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'validation'],loc = 'upper left')\n", "plt.show()\n", "\n", "y_pred2=model.predict(X_tst3)\n", "y_pred2=(y_pred2>.5).astype(int)\n", "\n", "# todo: use boot-strap estimation\n", "probs = model.predict_proba(X_tst3)\n", "probs = probs[:,0]\n", "auc = roc_auc_score(y_test, probs)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, probs)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n", "\n", "\n", "cmat = metrics.confusion_matrix(y_test, y_pred2)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred2, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred2, pos_label=1))\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Model: \"sequential_13\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_37 (LSTM) (None, 7, 32) 4352 \n", "_________________________________________________________________\n", "lstm_38 (LSTM) (None, 7, 64) 24832 \n", "_________________________________________________________________\n", "lstm_39 (LSTM) (None, 128) 98816 \n", "_________________________________________________________________\n", "dense_61 (Dense) (None, 256) 33024 \n", "_________________________________________________________________\n", "dense_62 (Dense) (None, 128) 32896 \n", "_________________________________________________________________\n", "dense_63 (Dense) (None, 64) 8256 \n", "_________________________________________________________________\n", "dense_64 (Dense) (None, 16) 1040 \n", "_________________________________________________________________\n", "dense_65 (Dense) (None, 1) 17 \n", "=================================================================\n", "Total params: 203,233\n", "Trainable params: 203,233\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Train on 268 samples, validate on 116 samples\n", "Epoch 1/500\n", "268/268 [==============================] - 2s 6ms/step - loss: 0.6834 - accuracy: 0.6269 - val_loss: 0.6467 - val_accuracy: 0.6810\n", "Epoch 2/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.6639 - accuracy: 0.6269 - val_loss: 0.6145 - val_accuracy: 0.6810\n", "Epoch 3/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.6416 - accuracy: 0.6269 - val_loss: 0.6960 - val_accuracy: 0.6810\n", "Epoch 4/500\n", "268/268 [==============================] - 0s 592us/step - loss: 0.6568 - accuracy: 0.6269 - val_loss: 0.5540 - val_accuracy: 0.6810\n", "Epoch 5/500\n", "268/268 [==============================] - 0s 606us/step - loss: 0.5992 - accuracy: 0.6269 - val_loss: 0.5707 - val_accuracy: 0.6810\n", "Epoch 6/500\n", "268/268 [==============================] - 0s 645us/step - loss: 0.5840 - accuracy: 0.6269 - val_loss: 0.5643 - val_accuracy: 0.6810\n", "Epoch 7/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.5837 - accuracy: 0.6306 - val_loss: 0.5964 - val_accuracy: 0.6724\n", "Epoch 8/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.5822 - accuracy: 0.6903 - val_loss: 0.5413 - val_accuracy: 0.7241\n", "Epoch 9/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.5833 - accuracy: 0.6866 - val_loss: 0.5265 - val_accuracy: 0.7241\n", "Epoch 10/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.5700 - accuracy: 0.6754 - val_loss: 0.6086 - val_accuracy: 0.7155\n", "Epoch 11/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.6036 - accuracy: 0.6791 - val_loss: 0.5614 - val_accuracy: 0.7241\n", "Epoch 12/500\n", "268/268 [==============================] - 0s 630us/step - loss: 0.5696 - accuracy: 0.7052 - val_loss: 0.5761 - val_accuracy: 0.6983\n", "Epoch 13/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.5715 - accuracy: 0.6866 - val_loss: 0.5233 - val_accuracy: 0.7155\n", "Epoch 14/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.5618 - accuracy: 0.7090 - val_loss: 0.5124 - val_accuracy: 0.7241\n", "Epoch 15/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.5576 - accuracy: 0.6866 - val_loss: 0.5395 - val_accuracy: 0.7069\n", "Epoch 16/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.5724 - accuracy: 0.6903 - val_loss: 0.5133 - val_accuracy: 0.7241\n", "Epoch 17/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.5481 - accuracy: 0.7127 - val_loss: 0.5180 - val_accuracy: 0.7328\n", "Epoch 18/500\n", "268/268 [==============================] - 0s 595us/step - loss: 0.5514 - accuracy: 0.7127 - val_loss: 0.5172 - val_accuracy: 0.7241\n", "Epoch 19/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.5515 - accuracy: 0.7090 - val_loss: 0.5406 - val_accuracy: 0.7241\n", "Epoch 20/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.5691 - accuracy: 0.6642 - val_loss: 0.5348 - val_accuracy: 0.7069\n", "Epoch 21/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.5622 - accuracy: 0.7090 - val_loss: 0.5177 - val_accuracy: 0.7241\n", "Epoch 22/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.5574 - accuracy: 0.7164 - val_loss: 0.5184 - val_accuracy: 0.7155\n", "Epoch 23/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.5480 - accuracy: 0.7127 - val_loss: 0.5180 - val_accuracy: 0.7155\n", "Epoch 24/500\n", "268/268 [==============================] - 0s 624us/step - loss: 0.5536 - accuracy: 0.7127 - val_loss: 0.6421 - val_accuracy: 0.7155\n", "Epoch 25/500\n", "268/268 [==============================] - 0s 619us/step - loss: 0.6176 - accuracy: 0.6567 - val_loss: 0.5195 - val_accuracy: 0.7328\n", "Epoch 26/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.5529 - accuracy: 0.6978 - val_loss: 0.5163 - val_accuracy: 0.7155\n", "Epoch 27/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.5457 - accuracy: 0.7164 - val_loss: 0.5201 - val_accuracy: 0.7241\n", "Epoch 28/500\n", "268/268 [==============================] - 0s 582us/step - loss: 0.5450 - accuracy: 0.7164 - val_loss: 0.5166 - val_accuracy: 0.7414\n", "Epoch 29/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.5454 - accuracy: 0.7164 - val_loss: 0.5725 - val_accuracy: 0.7069\n", "Epoch 30/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.5546 - accuracy: 0.7127 - val_loss: 0.5197 - val_accuracy: 0.7414\n", "Epoch 31/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.5544 - accuracy: 0.7015 - val_loss: 0.5182 - val_accuracy: 0.7155\n", "Epoch 32/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.5448 - accuracy: 0.7276 - val_loss: 0.5191 - val_accuracy: 0.7155\n", "Epoch 33/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.5597 - accuracy: 0.6940 - val_loss: 0.5198 - val_accuracy: 0.7155\n", "Epoch 34/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.5432 - accuracy: 0.7127 - val_loss: 0.5165 - val_accuracy: 0.7241\n", "Epoch 35/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.5713 - accuracy: 0.6903 - val_loss: 0.5262 - val_accuracy: 0.7155\n", "Epoch 36/500\n", "268/268 [==============================] - 0s 625us/step - loss: 0.5557 - accuracy: 0.6866 - val_loss: 0.5646 - val_accuracy: 0.6897\n", "Epoch 37/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.5620 - accuracy: 0.6828 - val_loss: 0.5190 - val_accuracy: 0.7241\n", "Epoch 38/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.5418 - accuracy: 0.6978 - val_loss: 0.5227 - val_accuracy: 0.6897\n", "Epoch 39/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.5450 - accuracy: 0.7015 - val_loss: 0.5431 - val_accuracy: 0.7069\n", "Epoch 40/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.5520 - accuracy: 0.6978 - val_loss: 0.5657 - val_accuracy: 0.6983\n", "Epoch 41/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.5483 - accuracy: 0.7052 - val_loss: 0.5306 - val_accuracy: 0.6897\n", "Epoch 42/500\n", "268/268 [==============================] - 0s 648us/step - loss: 0.5409 - accuracy: 0.7090 - val_loss: 0.5327 - val_accuracy: 0.7241\n", "Epoch 43/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.5584 - accuracy: 0.6978 - val_loss: 0.5597 - val_accuracy: 0.7328\n", "Epoch 44/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.5493 - accuracy: 0.7164 - val_loss: 0.5494 - val_accuracy: 0.7155\n", "Epoch 45/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.5564 - accuracy: 0.7052 - val_loss: 0.5389 - val_accuracy: 0.7241\n", "Epoch 46/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.5519 - accuracy: 0.7127 - val_loss: 0.5216 - val_accuracy: 0.7241\n", "Epoch 47/500\n", "268/268 [==============================] - 0s 600us/step - loss: 0.5373 - accuracy: 0.7239 - val_loss: 0.5316 - val_accuracy: 0.7069\n", "Epoch 48/500\n", "268/268 [==============================] - 0s 658us/step - loss: 0.5345 - accuracy: 0.7052 - val_loss: 0.5501 - val_accuracy: 0.6897\n", "Epoch 49/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.5396 - accuracy: 0.7127 - val_loss: 0.5195 - val_accuracy: 0.7069\n", "Epoch 50/500\n", "268/268 [==============================] - 0s 605us/step - loss: 0.5444 - accuracy: 0.6978 - val_loss: 0.5206 - val_accuracy: 0.7155\n", "Epoch 51/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.5391 - accuracy: 0.6903 - val_loss: 0.5321 - val_accuracy: 0.7069\n", "Epoch 52/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.5340 - accuracy: 0.7052 - val_loss: 0.5849 - val_accuracy: 0.7328\n", "Epoch 53/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.5667 - accuracy: 0.7015 - val_loss: 0.5213 - val_accuracy: 0.7155\n", "Epoch 54/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.5387 - accuracy: 0.6978 - val_loss: 0.5204 - val_accuracy: 0.6983\n", "Epoch 55/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.5360 - accuracy: 0.6978 - val_loss: 0.5251 - val_accuracy: 0.6810\n", "Epoch 56/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.5318 - accuracy: 0.7052 - val_loss: 0.5209 - val_accuracy: 0.6983\n", "Epoch 57/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.5400 - accuracy: 0.7127 - val_loss: 0.5345 - val_accuracy: 0.7500\n", "Epoch 58/500\n", "268/268 [==============================] - 0s 595us/step - loss: 0.5403 - accuracy: 0.7090 - val_loss: 0.5156 - val_accuracy: 0.7241\n", "Epoch 59/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.5348 - accuracy: 0.6940 - val_loss: 0.5156 - val_accuracy: 0.7328\n", "Epoch 60/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.5308 - accuracy: 0.7201 - val_loss: 0.5321 - val_accuracy: 0.6897\n", "Epoch 61/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.5369 - accuracy: 0.7015 - val_loss: 0.5305 - val_accuracy: 0.7155\n", "Epoch 62/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.5342 - accuracy: 0.6903 - val_loss: 0.5511 - val_accuracy: 0.7155\n", "Epoch 63/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.5313 - accuracy: 0.7313 - val_loss: 0.5707 - val_accuracy: 0.7414\n", "Epoch 64/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.5363 - accuracy: 0.7052 - val_loss: 0.5174 - val_accuracy: 0.7241\n", "Epoch 65/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.5246 - accuracy: 0.7239 - val_loss: 0.5282 - val_accuracy: 0.7328\n", "Epoch 66/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.5280 - accuracy: 0.7239 - val_loss: 0.5263 - val_accuracy: 0.7328\n", "Epoch 67/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.5464 - accuracy: 0.6940 - val_loss: 0.5425 - val_accuracy: 0.7155\n", "Epoch 68/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.5326 - accuracy: 0.7052 - val_loss: 0.6183 - val_accuracy: 0.7241\n", "Epoch 69/500\n", "268/268 [==============================] - 0s 636us/step - loss: 0.5424 - accuracy: 0.6828 - val_loss: 0.6057 - val_accuracy: 0.7414\n", "Epoch 70/500\n", "268/268 [==============================] - 0s 636us/step - loss: 0.5555 - accuracy: 0.7388 - val_loss: 0.5247 - val_accuracy: 0.6983\n", "Epoch 71/500\n", "268/268 [==============================] - 0s 669us/step - loss: 0.5247 - accuracy: 0.7090 - val_loss: 0.5928 - val_accuracy: 0.6466\n", "Epoch 72/500\n", "268/268 [==============================] - 0s 645us/step - loss: 0.5476 - accuracy: 0.6716 - val_loss: 0.5534 - val_accuracy: 0.7241\n", "Epoch 73/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.5266 - accuracy: 0.7164 - val_loss: 0.5225 - val_accuracy: 0.7069\n", "Epoch 74/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.5237 - accuracy: 0.7164 - val_loss: 0.5330 - val_accuracy: 0.7241\n", "Epoch 75/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.5273 - accuracy: 0.7052 - val_loss: 0.5934 - val_accuracy: 0.6810\n", "Epoch 76/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.5537 - accuracy: 0.7388 - val_loss: 0.5504 - val_accuracy: 0.6983\n", "Epoch 77/500\n", "268/268 [==============================] - 0s 624us/step - loss: 0.5612 - accuracy: 0.6567 - val_loss: 0.5496 - val_accuracy: 0.7155\n", "Epoch 78/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.5525 - accuracy: 0.6455 - val_loss: 0.5212 - val_accuracy: 0.7241\n", "Epoch 79/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.5300 - accuracy: 0.7201 - val_loss: 0.5418 - val_accuracy: 0.7328\n", "Epoch 80/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.5240 - accuracy: 0.7090 - val_loss: 0.5450 - val_accuracy: 0.7328\n", "Epoch 81/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.5273 - accuracy: 0.7015 - val_loss: 0.5260 - val_accuracy: 0.7241\n", "Epoch 82/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.5298 - accuracy: 0.7127 - val_loss: 0.5278 - val_accuracy: 0.7328\n", "Epoch 83/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.5312 - accuracy: 0.7127 - val_loss: 0.5188 - val_accuracy: 0.7241\n", "Epoch 84/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.5218 - accuracy: 0.7239 - val_loss: 0.5310 - val_accuracy: 0.7241\n", "Epoch 85/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.5228 - accuracy: 0.7201 - val_loss: 0.5300 - val_accuracy: 0.7069\n", "Epoch 86/500\n", "268/268 [==============================] - 0s 606us/step - loss: 0.5310 - accuracy: 0.6903 - val_loss: 0.5437 - val_accuracy: 0.7241\n", "Epoch 87/500\n", "268/268 [==============================] - 0s 600us/step - loss: 0.5269 - accuracy: 0.7351 - val_loss: 0.5786 - val_accuracy: 0.7414\n", "Epoch 88/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.5554 - accuracy: 0.7313 - val_loss: 0.5186 - val_accuracy: 0.7241\n", "Epoch 89/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.5243 - accuracy: 0.7164 - val_loss: 0.5641 - val_accuracy: 0.7414\n", "Epoch 90/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.5483 - accuracy: 0.6940 - val_loss: 0.5164 - val_accuracy: 0.7328\n", "Epoch 91/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.5215 - accuracy: 0.7201 - val_loss: 0.5276 - val_accuracy: 0.7155\n", "Epoch 92/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.5163 - accuracy: 0.7164 - val_loss: 0.6570 - val_accuracy: 0.7414\n", "Epoch 93/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.5414 - accuracy: 0.7090 - val_loss: 0.5255 - val_accuracy: 0.7069\n", "Epoch 94/500\n", "268/268 [==============================] - 0s 661us/step - loss: 0.5349 - accuracy: 0.7015 - val_loss: 0.5216 - val_accuracy: 0.7069\n", "Epoch 95/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.5188 - accuracy: 0.7201 - val_loss: 0.5270 - val_accuracy: 0.6983\n", "Epoch 96/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.5217 - accuracy: 0.7239 - val_loss: 0.5288 - val_accuracy: 0.7155\n", "Epoch 97/500\n", "268/268 [==============================] - 0s 611us/step - loss: 0.5168 - accuracy: 0.7164 - val_loss: 0.5763 - val_accuracy: 0.7241\n", "Epoch 98/500\n", "268/268 [==============================] - 0s 649us/step - loss: 0.5151 - accuracy: 0.7239 - val_loss: 0.5274 - val_accuracy: 0.7328\n", "Epoch 99/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.5162 - accuracy: 0.7201 - val_loss: 0.5604 - val_accuracy: 0.6983\n", "Epoch 100/500\n", "268/268 [==============================] - 0s 626us/step - loss: 0.5409 - accuracy: 0.6940 - val_loss: 0.5330 - val_accuracy: 0.7241\n", "Epoch 101/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.5227 - accuracy: 0.6866 - val_loss: 0.5204 - val_accuracy: 0.7241\n", "Epoch 102/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.5294 - accuracy: 0.7164 - val_loss: 0.5594 - val_accuracy: 0.6897\n", "Epoch 103/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.5283 - accuracy: 0.7425 - val_loss: 0.5452 - val_accuracy: 0.7328\n", "Epoch 104/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.5289 - accuracy: 0.7090 - val_loss: 0.5441 - val_accuracy: 0.7328\n", "Epoch 105/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.5153 - accuracy: 0.7201 - val_loss: 0.5248 - val_accuracy: 0.7241\n", "Epoch 106/500\n", "268/268 [==============================] - 0s 656us/step - loss: 0.5191 - accuracy: 0.7127 - val_loss: 0.5317 - val_accuracy: 0.7155\n", "Epoch 107/500\n", "268/268 [==============================] - 0s 600us/step - loss: 0.5183 - accuracy: 0.7090 - val_loss: 0.5454 - val_accuracy: 0.7328\n", "Epoch 108/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.5184 - accuracy: 0.7164 - val_loss: 0.5263 - val_accuracy: 0.7155\n", "Epoch 109/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.5183 - accuracy: 0.7201 - val_loss: 0.5764 - val_accuracy: 0.7328\n", "Epoch 110/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.5140 - accuracy: 0.7239 - val_loss: 0.5895 - val_accuracy: 0.6983\n", "Epoch 111/500\n", "268/268 [==============================] - 0s 624us/step - loss: 0.5464 - accuracy: 0.7313 - val_loss: 0.5175 - val_accuracy: 0.7241\n", "Epoch 112/500\n", "268/268 [==============================] - 0s 620us/step - loss: 0.5218 - accuracy: 0.7313 - val_loss: 0.5340 - val_accuracy: 0.7241\n", "Epoch 113/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.5131 - accuracy: 0.7239 - val_loss: 0.5358 - val_accuracy: 0.7241\n", "Epoch 114/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.5159 - accuracy: 0.7052 - val_loss: 0.5584 - val_accuracy: 0.7241\n", "Epoch 115/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.5147 - accuracy: 0.7164 - val_loss: 0.6034 - val_accuracy: 0.7500\n", "Epoch 116/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.5157 - accuracy: 0.7052 - val_loss: 0.5228 - val_accuracy: 0.7069\n", "Epoch 117/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.5142 - accuracy: 0.7015 - val_loss: 0.5348 - val_accuracy: 0.7241\n", "Epoch 118/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.5055 - accuracy: 0.7276 - val_loss: 0.5420 - val_accuracy: 0.6983\n", "Epoch 119/500\n", "268/268 [==============================] - 0s 619us/step - loss: 0.5227 - accuracy: 0.7052 - val_loss: 0.5482 - val_accuracy: 0.7241\n", "Epoch 120/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.5180 - accuracy: 0.7090 - val_loss: 0.6356 - val_accuracy: 0.7500\n", "Epoch 121/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.5118 - accuracy: 0.7388 - val_loss: 0.5394 - val_accuracy: 0.7241\n", "Epoch 122/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.5055 - accuracy: 0.7239 - val_loss: 0.5505 - val_accuracy: 0.7155\n", "Epoch 123/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.5118 - accuracy: 0.7164 - val_loss: 0.5310 - val_accuracy: 0.7155\n", "Epoch 124/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.5033 - accuracy: 0.7351 - val_loss: 0.6370 - val_accuracy: 0.7328\n", "Epoch 125/500\n", "268/268 [==============================] - 0s 634us/step - loss: 0.5121 - accuracy: 0.7201 - val_loss: 0.5441 - val_accuracy: 0.7155\n", "Epoch 126/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.5073 - accuracy: 0.7351 - val_loss: 0.5865 - val_accuracy: 0.7155\n", "Epoch 127/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.5179 - accuracy: 0.7090 - val_loss: 0.6069 - val_accuracy: 0.7500\n", "Epoch 128/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.5157 - accuracy: 0.7201 - val_loss: 0.5753 - val_accuracy: 0.7328\n", "Epoch 129/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.5116 - accuracy: 0.7090 - val_loss: 0.5290 - val_accuracy: 0.7241\n", "Epoch 130/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.5091 - accuracy: 0.7201 - val_loss: 0.5349 - val_accuracy: 0.7155\n", "Epoch 131/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.5117 - accuracy: 0.7127 - val_loss: 0.5240 - val_accuracy: 0.7069\n", "Epoch 132/500\n", "268/268 [==============================] - 0s 622us/step - loss: 0.5041 - accuracy: 0.7164 - val_loss: 0.5952 - val_accuracy: 0.7500\n", "Epoch 133/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.5077 - accuracy: 0.7239 - val_loss: 0.5354 - val_accuracy: 0.7241\n", "Epoch 134/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.5056 - accuracy: 0.7201 - val_loss: 0.5302 - val_accuracy: 0.7241\n", "Epoch 135/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.5022 - accuracy: 0.7164 - val_loss: 0.5341 - val_accuracy: 0.7069\n", "Epoch 136/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.4991 - accuracy: 0.7201 - val_loss: 0.5393 - val_accuracy: 0.7069\n", "Epoch 137/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.5016 - accuracy: 0.7164 - val_loss: 0.6486 - val_accuracy: 0.7328\n", "Epoch 138/500\n", "268/268 [==============================] - 0s 626us/step - loss: 0.5248 - accuracy: 0.7239 - val_loss: 0.5552 - val_accuracy: 0.7155\n", "Epoch 139/500\n", "268/268 [==============================] - 0s 606us/step - loss: 0.4988 - accuracy: 0.7351 - val_loss: 0.5350 - val_accuracy: 0.7069\n", "Epoch 140/500\n", "268/268 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0.5121 - accuracy: 0.7201 - val_loss: 0.5704 - val_accuracy: 0.7414\n", "Epoch 147/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.4981 - accuracy: 0.7127 - val_loss: 0.5358 - val_accuracy: 0.7069\n", "Epoch 148/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.4925 - accuracy: 0.7313 - val_loss: 0.5166 - val_accuracy: 0.7155\n", "Epoch 149/500\n", "268/268 [==============================] - 0s 654us/step - loss: 0.4980 - accuracy: 0.7239 - val_loss: 0.5257 - val_accuracy: 0.6810\n", "Epoch 150/500\n", "268/268 [==============================] - 0s 675us/step - loss: 0.4966 - accuracy: 0.7127 - val_loss: 0.6219 - val_accuracy: 0.7586\n", "Epoch 151/500\n", "268/268 [==============================] - 0s 649us/step - loss: 0.4975 - accuracy: 0.7090 - val_loss: 0.5404 - val_accuracy: 0.6724\n", "Epoch 152/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.4986 - accuracy: 0.7425 - val_loss: 0.5337 - val_accuracy: 0.6810\n", "Epoch 153/500\n", "268/268 [==============================] - 0s 600us/step - loss: 0.5004 - accuracy: 0.7276 - val_loss: 0.5443 - val_accuracy: 0.6983\n", "Epoch 154/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.4948 - accuracy: 0.7351 - val_loss: 0.5422 - val_accuracy: 0.6724\n", "Epoch 155/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.4964 - accuracy: 0.7351 - val_loss: 0.5487 - val_accuracy: 0.6897\n", "Epoch 156/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.4880 - accuracy: 0.7313 - val_loss: 0.5896 - val_accuracy: 0.7155\n", "Epoch 157/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.4982 - accuracy: 0.7201 - val_loss: 0.6700 - val_accuracy: 0.7500\n", "Epoch 158/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.5028 - accuracy: 0.7201 - val_loss: 0.5872 - val_accuracy: 0.7069\n", "Epoch 159/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.4902 - accuracy: 0.7313 - val_loss: 0.5998 - val_accuracy: 0.7069\n", "Epoch 160/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.4907 - accuracy: 0.7351 - val_loss: 0.5600 - val_accuracy: 0.6810\n", "Epoch 161/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.4958 - accuracy: 0.7388 - val_loss: 0.5412 - val_accuracy: 0.7069\n", "Epoch 162/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.4868 - accuracy: 0.7388 - val_loss: 0.5676 - val_accuracy: 0.7069\n", "Epoch 163/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.5046 - accuracy: 0.7127 - val_loss: 0.6027 - val_accuracy: 0.7155\n", "Epoch 164/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.4903 - accuracy: 0.7164 - val_loss: 0.6317 - val_accuracy: 0.7328\n", "Epoch 165/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.5022 - accuracy: 0.7201 - val_loss: 0.6134 - val_accuracy: 0.6897\n", "Epoch 166/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.4983 - accuracy: 0.7276 - val_loss: 0.5480 - val_accuracy: 0.6897\n", "Epoch 167/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.4937 - accuracy: 0.7239 - val_loss: 0.5537 - val_accuracy: 0.6810\n", "Epoch 168/500\n", "268/268 [==============================] - 0s 690us/step - loss: 0.4962 - accuracy: 0.7463 - val_loss: 0.5620 - val_accuracy: 0.7414\n", "Epoch 169/500\n", "268/268 [==============================] - 0s 635us/step - loss: 0.4827 - accuracy: 0.7388 - val_loss: 0.5451 - val_accuracy: 0.6897\n", "Epoch 170/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.4836 - accuracy: 0.7313 - val_loss: 0.5927 - val_accuracy: 0.6724\n", "Epoch 171/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.4962 - accuracy: 0.7425 - val_loss: 0.6452 - val_accuracy: 0.7069\n", "Epoch 172/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.4841 - accuracy: 0.7425 - val_loss: 0.6242 - val_accuracy: 0.7069\n", "Epoch 173/500\n", "268/268 [==============================] - 0s 595us/step - loss: 0.4815 - accuracy: 0.7388 - val_loss: 0.5619 - val_accuracy: 0.6724\n", "Epoch 174/500\n", "268/268 [==============================] - 0s 676us/step - loss: 0.4990 - accuracy: 0.7052 - val_loss: 0.5584 - val_accuracy: 0.6983\n", "Epoch 175/500\n", "268/268 [==============================] - 0s 650us/step - loss: 0.4809 - accuracy: 0.7425 - val_loss: 0.6270 - val_accuracy: 0.6983\n", "Epoch 176/500\n", "268/268 [==============================] - 0s 630us/step - loss: 0.4793 - accuracy: 0.7500 - val_loss: 0.7282 - val_accuracy: 0.7759\n", "Epoch 177/500\n", "268/268 [==============================] - 0s 632us/step - loss: 0.5054 - accuracy: 0.7351 - val_loss: 0.5426 - val_accuracy: 0.6897\n", "Epoch 178/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.4955 - accuracy: 0.7351 - val_loss: 0.5394 - val_accuracy: 0.6724\n", "Epoch 179/500\n", "268/268 [==============================] - 0s 582us/step - loss: 0.4869 - accuracy: 0.7425 - val_loss: 0.5966 - val_accuracy: 0.6638\n", "Epoch 180/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.5072 - accuracy: 0.7276 - val_loss: 0.6052 - val_accuracy: 0.6724\n", "Epoch 181/500\n", "268/268 [==============================] - 0s 631us/step - loss: 0.4724 - accuracy: 0.7500 - val_loss: 0.6930 - val_accuracy: 0.7672\n", "Epoch 182/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.4859 - accuracy: 0.7388 - val_loss: 0.7169 - val_accuracy: 0.7586\n", "Epoch 183/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.4808 - accuracy: 0.7612 - val_loss: 0.6292 - val_accuracy: 0.6466\n", "Epoch 184/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.4794 - accuracy: 0.7425 - val_loss: 0.6070 - val_accuracy: 0.7328\n", "Epoch 185/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.4747 - accuracy: 0.7463 - val_loss: 0.5692 - val_accuracy: 0.6810\n", "Epoch 186/500\n", "268/268 [==============================] - 0s 605us/step - loss: 0.4918 - accuracy: 0.7388 - val_loss: 0.5755 - val_accuracy: 0.6810\n", "Epoch 187/500\n", "268/268 [==============================] - 0s 676us/step - loss: 0.4843 - accuracy: 0.7575 - val_loss: 0.5804 - val_accuracy: 0.6724\n", "Epoch 188/500\n", "268/268 [==============================] - 0s 629us/step - loss: 0.4893 - accuracy: 0.7537 - val_loss: 0.5867 - val_accuracy: 0.6810\n", "Epoch 189/500\n", "268/268 [==============================] - 0s 608us/step - loss: 0.4992 - accuracy: 0.7239 - val_loss: 0.6218 - val_accuracy: 0.7328\n", "Epoch 190/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.4705 - accuracy: 0.7425 - val_loss: 0.5915 - val_accuracy: 0.7069\n", "Epoch 191/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.4676 - accuracy: 0.7575 - val_loss: 0.6010 - val_accuracy: 0.7500\n", "Epoch 192/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.4635 - accuracy: 0.7463 - val_loss: 0.6623 - val_accuracy: 0.7328\n", "Epoch 193/500\n", "268/268 [==============================] - 0s 634us/step - loss: 0.4738 - accuracy: 0.7500 - val_loss: 0.5909 - val_accuracy: 0.6810\n", "Epoch 194/500\n", "268/268 [==============================] - 0s 642us/step - loss: 0.4591 - accuracy: 0.7649 - val_loss: 0.6414 - val_accuracy: 0.6983\n", "Epoch 195/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.4674 - accuracy: 0.7500 - val_loss: 0.6546 - val_accuracy: 0.6466\n", "Epoch 196/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.4691 - accuracy: 0.7575 - val_loss: 0.7517 - val_accuracy: 0.7500\n", "Epoch 197/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.4801 - accuracy: 0.7500 - val_loss: 0.6288 - val_accuracy: 0.7069\n", "Epoch 198/500\n", "268/268 [==============================] - 0s 692us/step - loss: 0.4620 - accuracy: 0.7649 - val_loss: 0.6445 - val_accuracy: 0.6207\n", "Epoch 199/500\n", "268/268 [==============================] - 0s 676us/step - loss: 0.5341 - accuracy: 0.6940 - val_loss: 0.5935 - val_accuracy: 0.6552\n", "Epoch 200/500\n", "268/268 [==============================] - 0s 645us/step - loss: 0.5077 - accuracy: 0.7201 - val_loss: 0.6173 - val_accuracy: 0.7500\n", "Epoch 201/500\n", "268/268 [==============================] - 0s 617us/step - loss: 0.4770 - accuracy: 0.7537 - val_loss: 0.5618 - val_accuracy: 0.7155\n", "Epoch 202/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.4655 - accuracy: 0.7575 - val_loss: 0.5901 - val_accuracy: 0.6724\n", "Epoch 203/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.4737 - accuracy: 0.7537 - val_loss: 0.6266 - val_accuracy: 0.6983\n", "Epoch 204/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.4644 - accuracy: 0.7612 - val_loss: 0.6643 - val_accuracy: 0.7069\n", "Epoch 205/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.4605 - accuracy: 0.7649 - val_loss: 0.6888 - val_accuracy: 0.7241\n", "Epoch 206/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.4587 - accuracy: 0.7612 - val_loss: 0.6669 - val_accuracy: 0.7155\n", "Epoch 207/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.4549 - accuracy: 0.7612 - val_loss: 0.6534 - val_accuracy: 0.6293\n", "Epoch 208/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.4928 - accuracy: 0.7276 - val_loss: 0.6434 - val_accuracy: 0.6466\n", "Epoch 209/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.5403 - accuracy: 0.7127 - val_loss: 0.7471 - val_accuracy: 0.7414\n", "Epoch 210/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.4715 - accuracy: 0.7612 - val_loss: 0.6704 - val_accuracy: 0.7155\n", "Epoch 211/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.4517 - accuracy: 0.7649 - val_loss: 0.7599 - val_accuracy: 0.7155\n", "Epoch 212/500\n", "268/268 [==============================] - 0s 623us/step - loss: 0.4582 - accuracy: 0.7612 - val_loss: 0.6368 - val_accuracy: 0.6724\n", "Epoch 213/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.4501 - accuracy: 0.7724 - val_loss: 0.7377 - val_accuracy: 0.7414\n", "Epoch 214/500\n", "268/268 [==============================] - 0s 621us/step - loss: 0.4512 - accuracy: 0.7836 - val_loss: 0.6081 - val_accuracy: 0.7328\n", "Epoch 215/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.4370 - accuracy: 0.7910 - val_loss: 0.6131 - val_accuracy: 0.7241\n", "Epoch 216/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.4549 - accuracy: 0.7649 - val_loss: 0.6469 - val_accuracy: 0.7328\n", "Epoch 217/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.4424 - accuracy: 0.7985 - val_loss: 0.7263 - val_accuracy: 0.7414\n", "Epoch 218/500\n", "268/268 [==============================] - 0s 626us/step - loss: 0.4380 - accuracy: 0.7985 - val_loss: 0.6357 - val_accuracy: 0.7155\n", "Epoch 219/500\n", "268/268 [==============================] - 0s 605us/step - loss: 0.4453 - accuracy: 0.7836 - val_loss: 0.7646 - val_accuracy: 0.7414\n", "Epoch 220/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.4363 - accuracy: 0.7948 - val_loss: 0.6720 - val_accuracy: 0.6121\n", "Epoch 221/500\n", "268/268 [==============================] - 0s 624us/step - loss: 0.4782 - accuracy: 0.7649 - val_loss: 0.6124 - val_accuracy: 0.7500\n", "Epoch 222/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.4780 - accuracy: 0.7575 - val_loss: 0.5763 - val_accuracy: 0.7328\n", "Epoch 223/500\n", "268/268 [==============================] - 0s 605us/step - loss: 0.4593 - accuracy: 0.7425 - val_loss: 0.6509 - val_accuracy: 0.7845\n", "Epoch 224/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.4499 - accuracy: 0.7836 - val_loss: 0.6771 - val_accuracy: 0.7241\n", "Epoch 225/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.4380 - accuracy: 0.7910 - val_loss: 0.6089 - val_accuracy: 0.7500\n", "Epoch 226/500\n", "268/268 [==============================] - 0s 613us/step - loss: 0.4800 - accuracy: 0.7724 - val_loss: 0.6440 - val_accuracy: 0.7414\n", "Epoch 227/500\n", "268/268 [==============================] - 0s 585us/step - loss: 0.4718 - accuracy: 0.7575 - val_loss: 0.5843 - val_accuracy: 0.7500\n", "Epoch 228/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.4403 - accuracy: 0.7724 - val_loss: 0.5913 - val_accuracy: 0.7586\n", "Epoch 229/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.4351 - accuracy: 0.7948 - val_loss: 0.6905 - val_accuracy: 0.6897\n", "Epoch 230/500\n", "268/268 [==============================] - 0s 582us/step - loss: 0.4416 - accuracy: 0.7985 - val_loss: 0.7193 - val_accuracy: 0.6983\n", "Epoch 231/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.4412 - accuracy: 0.7836 - val_loss: 0.6633 - val_accuracy: 0.7069\n", "Epoch 232/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.4884 - accuracy: 0.7463 - val_loss: 0.6447 - val_accuracy: 0.6897\n", "Epoch 233/500\n", "268/268 [==============================] - 0s 609us/step - loss: 0.4390 - accuracy: 0.8022 - val_loss: 0.8208 - val_accuracy: 0.7155\n", "Epoch 234/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.4186 - accuracy: 0.7985 - val_loss: 0.7394 - val_accuracy: 0.6552\n", "Epoch 235/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.4184 - accuracy: 0.8022 - val_loss: 1.0399 - val_accuracy: 0.7672\n", "Epoch 236/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.5023 - accuracy: 0.7500 - val_loss: 0.6302 - val_accuracy: 0.6724\n", "Epoch 237/500\n", "268/268 [==============================] - 0s 605us/step - loss: 0.4250 - accuracy: 0.7948 - val_loss: 0.7121 - val_accuracy: 0.7155\n", "Epoch 238/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.4159 - accuracy: 0.7985 - val_loss: 0.7019 - val_accuracy: 0.7241\n", "Epoch 239/500\n", "268/268 [==============================] - 0s 621us/step - loss: 0.4113 - accuracy: 0.7948 - val_loss: 0.6847 - val_accuracy: 0.7500\n", "Epoch 240/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.4250 - accuracy: 0.8022 - val_loss: 0.6761 - val_accuracy: 0.6983\n", "Epoch 241/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.4023 - accuracy: 0.8022 - val_loss: 0.7586 - val_accuracy: 0.6034\n", "Epoch 242/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.4206 - accuracy: 0.7948 - val_loss: 0.7621 - val_accuracy: 0.6121\n", "Epoch 243/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.4008 - accuracy: 0.8172 - val_loss: 0.7647 - val_accuracy: 0.7155\n", "Epoch 244/500\n", "268/268 [==============================] - 0s 606us/step - loss: 0.3853 - accuracy: 0.8172 - val_loss: 0.7850 - val_accuracy: 0.6034\n", "Epoch 245/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.4659 - accuracy: 0.7575 - val_loss: 0.7549 - val_accuracy: 0.7241\n", "Epoch 246/500\n", "268/268 [==============================] - 0s 673us/step - loss: 0.4193 - accuracy: 0.8060 - val_loss: 0.8180 - val_accuracy: 0.6724\n", "Epoch 247/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.4153 - accuracy: 0.8209 - val_loss: 0.7527 - val_accuracy: 0.7672\n", "Epoch 248/500\n", "268/268 [==============================] - 0s 584us/step - loss: 0.4541 - accuracy: 0.7948 - val_loss: 0.6467 - val_accuracy: 0.7414\n", "Epoch 249/500\n", "268/268 [==============================] - 0s 580us/step - loss: 0.4141 - accuracy: 0.7836 - val_loss: 0.6824 - val_accuracy: 0.6810\n", "Epoch 250/500\n", "268/268 [==============================] - 0s 620us/step - loss: 0.3858 - accuracy: 0.7985 - val_loss: 0.7381 - val_accuracy: 0.7155\n", "Epoch 251/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.3813 - accuracy: 0.8246 - val_loss: 0.8919 - val_accuracy: 0.6552\n", "Epoch 252/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.3781 - accuracy: 0.8246 - val_loss: 0.7139 - val_accuracy: 0.6724\n", "Epoch 253/500\n", "268/268 [==============================] - 0s 582us/step - loss: 0.3575 - accuracy: 0.8396 - val_loss: 0.8204 - val_accuracy: 0.6638\n", "Epoch 254/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.3886 - accuracy: 0.8134 - val_loss: 0.9045 - val_accuracy: 0.6293\n", "Epoch 255/500\n", "268/268 [==============================] - 0s 563us/step - loss: 0.3531 - accuracy: 0.8582 - val_loss: 0.8935 - val_accuracy: 0.6379\n", "Epoch 256/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.3912 - accuracy: 0.8396 - val_loss: 1.1913 - val_accuracy: 0.6034\n", "Epoch 257/500\n", "268/268 [==============================] - 0s 630us/step - loss: 0.4706 - accuracy: 0.7687 - val_loss: 0.7300 - val_accuracy: 0.7328\n", "Epoch 258/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.4295 - accuracy: 0.7687 - val_loss: 0.7745 - val_accuracy: 0.6983\n", "Epoch 259/500\n", "268/268 [==============================] - 0s 592us/step - loss: 0.3653 - accuracy: 0.8284 - val_loss: 0.7227 - val_accuracy: 0.7069\n", "Epoch 260/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.3828 - accuracy: 0.8134 - val_loss: 0.8656 - val_accuracy: 0.7069\n", "Epoch 261/500\n", "268/268 [==============================] - 0s 568us/step - loss: 0.3603 - accuracy: 0.8582 - val_loss: 0.8869 - val_accuracy: 0.6293\n", "Epoch 262/500\n", "268/268 [==============================] - 0s 605us/step - loss: 0.5546 - accuracy: 0.7127 - val_loss: 0.5883 - val_accuracy: 0.7328\n", "Epoch 263/500\n", "268/268 [==============================] - 0s 638us/step - loss: 0.4907 - accuracy: 0.7649 - val_loss: 0.6072 - val_accuracy: 0.7069\n", "Epoch 264/500\n", "268/268 [==============================] - 0s 634us/step - loss: 0.4822 - accuracy: 0.7463 - val_loss: 0.6138 - val_accuracy: 0.6810\n", "Epoch 265/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.4621 - accuracy: 0.7724 - val_loss: 0.6077 - val_accuracy: 0.7241\n", "Epoch 266/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.4472 - accuracy: 0.7575 - val_loss: 0.7066 - val_accuracy: 0.7586\n", "Epoch 267/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.4216 - accuracy: 0.7836 - val_loss: 0.7964 - val_accuracy: 0.7328\n", "Epoch 268/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.3967 - accuracy: 0.8097 - val_loss: 0.8942 - val_accuracy: 0.7414\n", "Epoch 269/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.3785 - accuracy: 0.8172 - val_loss: 0.7874 - val_accuracy: 0.7069\n", "Epoch 270/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.3596 - accuracy: 0.8209 - val_loss: 0.8266 - val_accuracy: 0.7241\n", "Epoch 271/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.3563 - accuracy: 0.8433 - val_loss: 1.0366 - val_accuracy: 0.6121\n", "Epoch 272/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.3922 - accuracy: 0.8134 - val_loss: 0.9105 - val_accuracy: 0.6638\n", "Epoch 273/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.3432 - accuracy: 0.8507 - val_loss: 0.9312 - val_accuracy: 0.6810\n", "Epoch 274/500\n", "268/268 [==============================] - 0s 658us/step - loss: 0.3177 - accuracy: 0.8657 - val_loss: 1.0439 - val_accuracy: 0.7155\n", "Epoch 275/500\n", "268/268 [==============================] - 0s 672us/step - loss: 0.3366 - accuracy: 0.8507 - val_loss: 1.2571 - val_accuracy: 0.7241\n", "Epoch 276/500\n", "268/268 [==============================] - 0s 612us/step - loss: 0.4242 - accuracy: 0.8060 - val_loss: 0.9653 - val_accuracy: 0.6293\n", "Epoch 277/500\n", "268/268 [==============================] - 0s 617us/step - loss: 0.3400 - accuracy: 0.8284 - val_loss: 1.2089 - val_accuracy: 0.7759\n", "Epoch 278/500\n", "268/268 [==============================] - 0s 649us/step - loss: 0.3864 - accuracy: 0.8209 - val_loss: 0.7902 - val_accuracy: 0.6983\n", "Epoch 279/500\n", "268/268 [==============================] - 0s 694us/step - loss: 0.3485 - accuracy: 0.8507 - val_loss: 0.9255 - val_accuracy: 0.7069\n", "Epoch 280/500\n", "268/268 [==============================] - 0s 655us/step - loss: 0.3267 - accuracy: 0.8619 - val_loss: 1.0338 - val_accuracy: 0.6121\n", "Epoch 281/500\n", "268/268 [==============================] - 0s 648us/step - loss: 0.3514 - accuracy: 0.8507 - val_loss: 1.0676 - val_accuracy: 0.6897\n", "Epoch 282/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.4011 - accuracy: 0.8022 - val_loss: 0.7558 - val_accuracy: 0.6724\n", "Epoch 283/500\n", "268/268 [==============================] - 0s 570us/step - loss: 0.3274 - accuracy: 0.8694 - val_loss: 0.9124 - val_accuracy: 0.6897\n", "Epoch 284/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.3136 - accuracy: 0.8619 - val_loss: 1.0523 - val_accuracy: 0.5948\n", "Epoch 285/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.3023 - accuracy: 0.8731 - val_loss: 1.0162 - val_accuracy: 0.6552\n", "Epoch 286/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.3632 - accuracy: 0.8358 - val_loss: 0.9950 - val_accuracy: 0.6897\n", "Epoch 287/500\n", "268/268 [==============================] - 0s 627us/step - loss: 0.2754 - accuracy: 0.8955 - val_loss: 1.3010 - val_accuracy: 0.6724\n", "Epoch 288/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.3143 - accuracy: 0.8806 - val_loss: 1.1638 - val_accuracy: 0.6034\n", "Epoch 289/500\n", "268/268 [==============================] - 0s 601us/step - loss: 0.2768 - accuracy: 0.8843 - val_loss: 1.1909 - val_accuracy: 0.6379\n", "Epoch 290/500\n", "268/268 [==============================] - 0s 614us/step - loss: 0.3771 - accuracy: 0.8396 - val_loss: 1.0294 - val_accuracy: 0.6983\n", "Epoch 291/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.3047 - accuracy: 0.8619 - val_loss: 1.2890 - val_accuracy: 0.6466\n", "Epoch 292/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.2684 - accuracy: 0.8881 - val_loss: 1.3963 - val_accuracy: 0.6466\n", "Epoch 293/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.3241 - accuracy: 0.8731 - val_loss: 0.9954 - val_accuracy: 0.6983\n", "Epoch 294/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.2907 - accuracy: 0.8806 - val_loss: 0.9559 - val_accuracy: 0.6983\n", "Epoch 295/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.6176 - accuracy: 0.6940 - val_loss: 0.5634 - val_accuracy: 0.7155\n", "Epoch 296/500\n", "268/268 [==============================] - 0s 597us/step - loss: 0.4781 - accuracy: 0.7276 - val_loss: 0.6296 - val_accuracy: 0.7155\n", "Epoch 297/500\n", "268/268 [==============================] - 0s 587us/step - loss: 0.4758 - accuracy: 0.7351 - val_loss: 0.5747 - val_accuracy: 0.7155\n", "Epoch 298/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.4872 - accuracy: 0.7127 - val_loss: 0.7054 - val_accuracy: 0.7414\n", "Epoch 299/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.4770 - accuracy: 0.7463 - val_loss: 0.6820 - val_accuracy: 0.7414\n", "Epoch 300/500\n", "268/268 [==============================] - 0s 604us/step - loss: 0.4611 - accuracy: 0.7500 - val_loss: 0.6461 - val_accuracy: 0.7155\n", "Epoch 301/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.4686 - accuracy: 0.7351 - val_loss: 1.0583 - val_accuracy: 0.7759\n", "Epoch 302/500\n", "268/268 [==============================] - 0s 595us/step - loss: 0.5008 - accuracy: 0.7276 - val_loss: 0.6811 - val_accuracy: 0.7414\n", "Epoch 303/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.4338 - accuracy: 0.7836 - val_loss: 0.8248 - val_accuracy: 0.7241\n", "Epoch 304/500\n", "268/268 [==============================] - 0s 557us/step - loss: 0.4469 - accuracy: 0.7575 - val_loss: 0.7265 - val_accuracy: 0.7414\n", "Epoch 305/500\n", "268/268 [==============================] - 0s 582us/step - loss: 0.4320 - accuracy: 0.7799 - val_loss: 0.6072 - val_accuracy: 0.6897\n", "Epoch 306/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.4298 - accuracy: 0.7724 - val_loss: 1.0812 - val_accuracy: 0.7845\n", "Epoch 307/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.4703 - accuracy: 0.7575 - val_loss: 0.6906 - val_accuracy: 0.7414\n", "Epoch 308/500\n", "268/268 [==============================] - 0s 558us/step - loss: 0.4213 - accuracy: 0.7575 - val_loss: 0.6121 - val_accuracy: 0.7500\n", "Epoch 309/500\n", "268/268 [==============================] - 0s 607us/step - loss: 0.4161 - accuracy: 0.7724 - val_loss: 0.8476 - val_accuracy: 0.7500\n", "Epoch 310/500\n", "268/268 [==============================] - 0s 559us/step - loss: 0.3977 - accuracy: 0.7910 - val_loss: 0.7082 - val_accuracy: 0.6983\n", "Epoch 311/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.3872 - accuracy: 0.8060 - val_loss: 1.0286 - val_accuracy: 0.6724\n", "Epoch 312/500\n", "268/268 [==============================] - 0s 556us/step - loss: 0.4486 - accuracy: 0.7799 - val_loss: 0.7342 - val_accuracy: 0.7241\n", "Epoch 313/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.3683 - accuracy: 0.8134 - val_loss: 0.9175 - val_accuracy: 0.7328\n", "Epoch 314/500\n", "268/268 [==============================] - 0s 571us/step - loss: 0.3725 - accuracy: 0.8134 - val_loss: 1.1136 - val_accuracy: 0.7672\n", "Epoch 315/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.3607 - accuracy: 0.8172 - val_loss: 0.9081 - val_accuracy: 0.6552\n", "Epoch 316/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.4452 - accuracy: 0.8022 - val_loss: 0.9108 - val_accuracy: 0.6724\n", "Epoch 317/500\n", "268/268 [==============================] - 0s 588us/step - loss: 0.4475 - accuracy: 0.7649 - val_loss: 0.7249 - val_accuracy: 0.6552\n", "Epoch 318/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.4812 - accuracy: 0.7687 - val_loss: 0.7000 - val_accuracy: 0.6983\n", "Epoch 319/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.3859 - accuracy: 0.8060 - val_loss: 0.7421 - val_accuracy: 0.6983\n", "Epoch 320/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.3578 - accuracy: 0.8284 - val_loss: 0.8395 - val_accuracy: 0.6897\n", "Epoch 321/500\n", "268/268 [==============================] - 0s 616us/step - loss: 0.3548 - accuracy: 0.8246 - val_loss: 0.9053 - val_accuracy: 0.6983\n", "Epoch 322/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.3295 - accuracy: 0.8433 - val_loss: 1.0891 - val_accuracy: 0.7328\n", "Epoch 323/500\n", "268/268 [==============================] - 0s 581us/step - loss: 0.3615 - accuracy: 0.8209 - val_loss: 1.0104 - val_accuracy: 0.6466\n", "Epoch 324/500\n", "268/268 [==============================] - 0s 565us/step - loss: 0.3685 - accuracy: 0.8172 - val_loss: 0.8730 - val_accuracy: 0.7241\n", "Epoch 325/500\n", "268/268 [==============================] - 0s 603us/step - loss: 0.3432 - accuracy: 0.8433 - val_loss: 0.9479 - val_accuracy: 0.6810\n", "Epoch 326/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.3286 - accuracy: 0.8433 - val_loss: 1.0083 - val_accuracy: 0.7069\n", "Epoch 327/500\n", "268/268 [==============================] - 0s 595us/step - loss: 0.3392 - accuracy: 0.8172 - val_loss: 0.9296 - val_accuracy: 0.7241\n", "Epoch 328/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.3480 - accuracy: 0.8358 - val_loss: 1.1396 - val_accuracy: 0.7414\n", "Epoch 329/500\n", "268/268 [==============================] - 0s 637us/step - loss: 0.3128 - accuracy: 0.8396 - val_loss: 1.0642 - val_accuracy: 0.7069\n", "Epoch 330/500\n", "268/268 [==============================] - 0s 641us/step - loss: 0.3057 - accuracy: 0.8507 - val_loss: 1.0902 - val_accuracy: 0.7241\n", "Epoch 331/500\n", "268/268 [==============================] - 0s 596us/step - loss: 0.2866 - accuracy: 0.8507 - val_loss: 1.2280 - val_accuracy: 0.7328\n", "Epoch 332/500\n", "268/268 [==============================] - 0s 633us/step - loss: 0.3939 - accuracy: 0.8097 - val_loss: 1.2857 - val_accuracy: 0.7500\n", "Epoch 333/500\n", "268/268 [==============================] - 0s 598us/step - loss: 0.4863 - accuracy: 0.8060 - val_loss: 0.8613 - val_accuracy: 0.6724\n", "Epoch 334/500\n", "268/268 [==============================] - 0s 591us/step - loss: 0.3639 - accuracy: 0.8060 - val_loss: 1.0878 - val_accuracy: 0.7155\n", "Epoch 335/500\n", "268/268 [==============================] - 0s 569us/step - loss: 0.3286 - accuracy: 0.8433 - val_loss: 0.9352 - val_accuracy: 0.6724\n", "Epoch 336/500\n", "268/268 [==============================] - 0s 589us/step - loss: 0.3367 - accuracy: 0.8209 - val_loss: 1.2492 - val_accuracy: 0.6897\n", "Epoch 337/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.3952 - accuracy: 0.8246 - val_loss: 1.0331 - val_accuracy: 0.7845\n", "Epoch 338/500\n", "268/268 [==============================] - 0s 629us/step - loss: 0.3210 - accuracy: 0.8470 - val_loss: 0.8938 - val_accuracy: 0.7241\n", "Epoch 339/500\n", "268/268 [==============================] - 0s 583us/step - loss: 0.3328 - accuracy: 0.8172 - val_loss: 1.0339 - val_accuracy: 0.7328\n", "Epoch 340/500\n", "268/268 [==============================] - 0s 562us/step - loss: 0.2926 - accuracy: 0.8657 - val_loss: 1.1513 - val_accuracy: 0.7414\n", "Epoch 341/500\n", "268/268 [==============================] - 0s 592us/step - loss: 0.2746 - accuracy: 0.8545 - val_loss: 1.3227 - val_accuracy: 0.7414\n", "Epoch 342/500\n", "268/268 [==============================] - 0s 566us/step - loss: 0.2808 - accuracy: 0.8619 - val_loss: 1.6935 - val_accuracy: 0.7414\n", "Epoch 343/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.3459 - accuracy: 0.8284 - val_loss: 1.1513 - val_accuracy: 0.7069\n", "Epoch 344/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.3523 - accuracy: 0.8470 - val_loss: 1.3919 - val_accuracy: 0.7328\n", "Epoch 345/500\n", "268/268 [==============================] - 0s 646us/step - loss: 0.3155 - accuracy: 0.8470 - val_loss: 1.2708 - val_accuracy: 0.7414\n", "Epoch 346/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.3118 - accuracy: 0.8433 - val_loss: 2.2760 - val_accuracy: 0.7845\n", "Epoch 347/500\n", "268/268 [==============================] - 0s 594us/step - loss: 0.4621 - accuracy: 0.8022 - val_loss: 0.9405 - val_accuracy: 0.7500\n", "Epoch 348/500\n", "268/268 [==============================] - 0s 572us/step - loss: 0.3047 - accuracy: 0.8470 - val_loss: 0.9405 - val_accuracy: 0.7414\n", "Epoch 349/500\n", "268/268 [==============================] - 0s 555us/step - loss: 0.3096 - accuracy: 0.8321 - val_loss: 1.1548 - val_accuracy: 0.7500\n", "Epoch 350/500\n", "268/268 [==============================] - 0s 593us/step - loss: 0.3187 - accuracy: 0.8321 - val_loss: 1.0476 - val_accuracy: 0.7328\n", "Epoch 351/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.2950 - accuracy: 0.8433 - val_loss: 0.9937 - val_accuracy: 0.7155\n", "Epoch 352/500\n", "268/268 [==============================] - 0s 599us/step - loss: 0.2737 - accuracy: 0.8470 - val_loss: 0.9501 - val_accuracy: 0.6897\n", "Epoch 353/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.2615 - accuracy: 0.8582 - val_loss: 1.3080 - val_accuracy: 0.6810\n", "Epoch 354/500\n", "268/268 [==============================] - 0s 577us/step - loss: 0.2994 - accuracy: 0.8694 - val_loss: 1.1222 - val_accuracy: 0.6810\n", "Epoch 355/500\n", "268/268 [==============================] - 0s 567us/step - loss: 0.2423 - accuracy: 0.8619 - val_loss: 1.4232 - val_accuracy: 0.7328\n", "Epoch 356/500\n", "268/268 [==============================] - 0s 590us/step - loss: 0.2429 - accuracy: 0.8619 - val_loss: 1.5608 - val_accuracy: 0.7500\n", "Epoch 357/500\n", "268/268 [==============================] - 0s 625us/step - loss: 0.3639 - accuracy: 0.8172 - val_loss: 0.8504 - val_accuracy: 0.6810\n", "Epoch 358/500\n", "268/268 [==============================] - 0s 586us/step - loss: 0.3562 - accuracy: 0.8097 - val_loss: 0.9806 - val_accuracy: 0.7069\n", "Epoch 359/500\n", "268/268 [==============================] - 0s 578us/step - loss: 0.2870 - accuracy: 0.8657 - val_loss: 1.0820 - val_accuracy: 0.7241\n", "Epoch 360/500\n", "268/268 [==============================] - 0s 561us/step - loss: 0.2692 - accuracy: 0.8507 - val_loss: 1.2455 - val_accuracy: 0.7241\n", "Epoch 361/500\n", "268/268 [==============================] - 0s 579us/step - loss: 0.2487 - accuracy: 0.8619 - val_loss: 1.1002 - val_accuracy: 0.7328\n", "Epoch 362/500\n", "268/268 [==============================] - 0s 573us/step - loss: 0.2480 - accuracy: 0.8731 - val_loss: 1.3297 - val_accuracy: 0.7241\n", "Epoch 363/500\n", "268/268 [==============================] - 0s 615us/step - loss: 0.2436 - accuracy: 0.8731 - val_loss: 1.3566 - val_accuracy: 0.6810\n", "Epoch 364/500\n", "268/268 [==============================] - 0s 576us/step - loss: 0.2734 - accuracy: 0.8545 - val_loss: 1.4978 - val_accuracy: 0.7155\n", "Epoch 365/500\n", "268/268 [==============================] - 0s 564us/step - loss: 0.2271 - accuracy: 0.8881 - val_loss: 1.7161 - val_accuracy: 0.7328\n", "Epoch 366/500\n", "268/268 [==============================] - 0s 575us/step - loss: 0.2056 - accuracy: 0.8918 - val_loss: 1.1993 - val_accuracy: 0.6983\n", "Epoch 367/500\n", "268/268 [==============================] - 0s 574us/step - loss: 0.2070 - accuracy: 0.8955 - val_loss: 1.6197 - val_accuracy: 0.7155\n", "Epoch 368/500\n", "268/268 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "[[189 64]\n", " [ 67 64]]\n", "Accuracy: 0.6588541666666666\n", "Precision: 0.5\n", "Recall: 0.48854961832061067\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "CDNmUanwSwLv", "colab_type": "code", "outputId": "3d92c4aa-b884-4d68-8d48-63b87de55e85", "colab": { "base_uri": "https://localhost:8080/", "height": 367 } }, "source": [ "model.load_weights('best.h5')\n", "\n", "y_pred2=model.predict(X_tst3)\n", "y_pred2=(y_pred2>.5).astype(int)\n", "\n", "# todo: use boot-strap estimation\n", "probs = model.predict_proba(X_tst3)\n", "probs = probs[:,0]\n", "auc = roc_auc_score(y_test, probs)\n", "print('AUC: %.3f' % auc)\n", "fpr, tpr, thresholds = roc_curve(y_test, probs)\n", "pyplot.plot([0, 1], [0, 1], linestyle = '-')\n", "pyplot.plot(fpr, tpr, marker = '.')\n", "pyplot.show()\n", "\n", "cmat = metrics.confusion_matrix(y_test, y_pred2)\n", "print( cmat )\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred2))\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred2, pos_label=1)) \n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred2, pos_label=1))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "AUC: 0.805\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "[[214 39]\n", " [ 52 79]]\n", "Accuracy: 0.7630208333333334\n", "Precision: 0.6694915254237288\n", "Recall: 0.6030534351145038\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "LbvQsn3RMtWE", "colab_type": "text" }, "source": [ "\n", "## Summarize results ##\n", "\n", "### Using raw input data ###\n", "\n", "| Method | Accuracy | Precision | Recall |\n", "|-----|----------|:-------------:|------:|\n", "| Logistic regression | 78.9 | 75.0 | 57.3 |\n", "| LSTM (trial 1: best) | 76.3 | 67.0 | 60.3 |\n", "| LSTM (trial 2: final) | 72.4 | 57.1 | 77.1 |\n", "| LSTM (trial 4: best) | 65.9 | 50.0 | 93.2 |\n", "| LSTM (trial 3: final) | 75.3 | 62.9 | 67.2 |\n", "\n", "### Using robustly normalized input data ###\n", "\n", "| Method | Accuracy | Precision | Recall |\n", "|-----|----------|:-------------:|------:|\n", "| Logistic regression | 79.2 | 75.8 | 57.3 |\n", "| LSTM (best) | 76.3 | 67.0 | 60.3 |" ] } ] }