From d9b50419497d02a672cd1344101257d53be3669c Mon Sep 17 00:00:00 2001 From: "Heneka, Dr. Caroline" <caroline.heneka@uni-hamburg.de> Date: Wed, 2 Mar 2022 09:46:36 +0000 Subject: [PATCH] Upload New File --- .../spectral_classifier_from_csv.ipynb | 5061 +++++++++++++++++ 1 file changed, 5061 insertions(+) create mode 100644 spectral_classifier_full/spectral_classifier_from_csv.ipynb diff --git a/spectral_classifier_full/spectral_classifier_from_csv.ipynb b/spectral_classifier_full/spectral_classifier_from_csv.ipynb new file mode 100644 index 0000000..ae91fc3 --- /dev/null +++ b/spectral_classifier_full/spectral_classifier_from_csv.ipynb @@ -0,0 +1,5061 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Daten aus CSV-Datei auslesen" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "import numpy as np\n", + "import os\n", + "import csv\n", + "# import tensorflow as tf\n", + "\n", + "########## Input ##########\n", + "\n", + "data_path = 'F:\\\\data\\\\'\n", + "samples_per_class = 1000\n", + "\n", + "\n", + "########## Program ##########\n", + "\n", + "# Listen mit den flux Werten, Labels und Wellenlängen erstellen\n", + "data = np.zeros(shape=(4000,3522))\n", + "labels = np.zeros(shape=(4000,), dtype='int')\n", + "wavelengths = np.zeros(shape=(3522,))\n", + "\n", + "# Daten in numpy arrays data und labels einlesen\n", + "i=0\n", + "file = open(data_path + 'spectral_data.csv', 'r') \n", + "\n", + "with file:\n", + " reader = csv.reader(file, delimiter=',') \n", + " header_row = next(reader) \n", + " \n", + " for row in reader:\n", + " wavelengths[reader.line_num-2] = row[0]\n", + " \n", + " for t in range(4000):\n", + " data[t][i] = row[t+1] \n", + " i += 1\n", + " file.close()\n", + "\n", + "# Labels in die Liste labels eintragen\n", + "for i in range(4):\n", + " for t in range(samples_per_class):\n", + " labels[i*1000+t] = i\n", + " \n", + "# if i==0:\n", + "# labels[i*1000+t] = 0 #AGN\n", + "# if i==1:\n", + "# labels[i*1000+t] = 1 #galaxy\n", + "# if i==2:\n", + "# labels[i*1000+t] = 2 #QSO\n", + "# if i==3:\n", + "# labels[i*1000+t] = 3 #star" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "print(labels[2345])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Normierung" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16.179667472839355\n", + "42.20051193237305\n", + "17.748315811157227\n", + "9.19969129562378\n", + "12.269596576690674\n", + "12.973103046417236\n", + "21.66347312927246\n", + "22.901838302612305\n", + "12.559422969818115\n", + 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"28.283116340637207\n", + "65.16987991333008\n", + "-0.597342811524868\n", + "-0.061799004673957825\n", + "-0.0663379654288292\n", + "62.874229431152344\n", + "51.32086372375488\n", + "53.62981414794922\n", + "158.0717544555664\n", + "57.085960388183594\n", + "2.066745340824127\n", + "0.6592031121253967\n", + "167.5723648071289\n", + "0.36433338560163975\n", + "270.2336120605469\n", + "22.187089920043945\n", + "28.166865348815918\n", + "76.34662246704102\n", + "0.13698923191986978\n", + "-0.7586703635752201\n", + "16.763632774353027\n", + "0.8014624416828156\n", + "46.632362365722656\n", + "3.1332067251205444\n", + "0.7147399038076401\n", + "21.525383949279785\n", + "0.35499662160873413\n" + ] + } + ], + "source": [ + "data_normalized = np.zeros(shape=(4000,3522))\n", + "\n", + "# Normierungs-Test: median normalization\n", + "for i in range(len(data)):\n", + " sorted = np.sort(data[i])\n", + " median = 0.5* (data[i][1760] + data[i][1761])\n", + " data_normalized[i] = data[i]/median\n", + " print(median)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Ohne Normierung**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(4): \n", + " plt.plot(wavelengths, data[i]) \n", + " plt.xlabel('wavelength(Å)') \n", + " plt.ylabel('flux (10-17 ergs/s/cm2/Å)') \n", + " plt.grid(True) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Mit Normierung**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in [1,2,3,1001,1002,1003]:\n", + " plt.plot(wavelengths, data_normalized[i]) \n", + " plt.xlabel('wavelength(Å)') \n", + " plt.ylabel('flux / median') \n", + " plt.grid(True)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) Trainings- und Test-Datensatz erstellen" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "# z = list(zip(data_normalized, labels)) # Mit Median-Normierung\n", + "z = list(zip(data, labels)) # Ohne Median-Normierung\n", + "random.shuffle(z)\n", + "data_shuffled, labels_shuffled = zip(*z)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4000, 3522)\n", + "(4000,)\n" + ] + } + ], + "source": [ + "print(np.array(data_shuffled).shape)\n", + "print(np.array(labels_shuffled).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [], + "source": [ + "split_index = int(len(data_shuffled)*0.9)\n", + "\n", + "# Daten\n", + "data_training = data_shuffled[:split_index]\n", + "data_test = data_shuffled[split_index:]\n", + "\n", + "# Labels\n", + "labels_training = labels_shuffled[:split_index]\n", + "labels_test = labels_shuffled[split_index:]" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3600, 3522)\n", + "(400, 3522)\n", + "(3600,)\n", + "(400,)\n" + ] + } + ], + "source": [ + "data_training = np.asarray(data_training)\n", + "data_test = np.asarray(data_test)\n", + "\n", + "labels_training = np.asarray(labels_training)\n", + "labels_test = np.asarray(labels_test)\n", + "\n", + "print(data_training.shape)\n", + "print(data_test.shape)\n", + "\n", + "print(labels_training.shape)\n", + "print(labels_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4) Netzwerk erstellen" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Activation, Dense, Flatten, Conv1D, MaxPooling1D, Dropout, InputLayer, GlobalAveragePooling1D\n", + "from tensorflow.keras.metrics import Accuracy\n", + "\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [], + "source": [ + "input_shape = (3522,1)\n", + "\n", + "data_training_r = np.reshape(data_training, newshape=(len(data_training), input_shape[0], input_shape[1]))\n", + "data_test_r = np.reshape(data_test, newshape=(len(data_test), input_shape[0], input_shape[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3600, 3522, 1)\n", + "(400, 3522, 1)\n" + ] + } + ], + "source": [ + "print(data_training_r.shape)\n", + "print(data_test_r.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [], + "source": [ + "# Conv1D, maxPool1D, categorical_crossentropy\n", + "# model = Sequential([\n", + "# #Conv1D(filters=30, kernel_size=10, activation='relu', input_shape=(3522,1)),\n", + "# Conv1D(filters=64, kernel_size=5, activation='relu'), #mehr Filtern\n", + "# MaxPooling1D(4), # Größere Region\n", + "# Dropout(0.1),\n", + "# #Conv1D(filters=60, kernel_size=10, activation='relu'),\n", + "# Conv1D(filters=64, kernel_size=5, activation='relu'),\n", + "# MaxPooling1D(4),\n", + "# # GlobalAveragePooling1D(),\n", + "# Dropout(0.1),\n", + "# Flatten(),\n", + " \n", + "# Dense(units=64, activation='relu'),\n", + "# #Extra Dense\n", + " \n", + "# Dense(units=4, activation='softmax')\n", + "# ])\n", + "\n", + "# model = Sequential([\n", + "# Conv1D(filters=64, kernel_size=80, strides=10, activation='relu', input_shape=(3522,1)), # stride\n", + "# #MaxPooling1D(3),\n", + "# Dropout(0.35),\n", + "# Conv1D(filters=128, kernel_size=40, strides=10, activation='relu'),\n", + "# MaxPooling1D(3),\n", + "# Dropout(0.35),\n", + "# Flatten(),\n", + "# Dense(units=128, activation='relu'), # Droput, weniger neuronen\n", + "# Dropout(0.35),\n", + "# Dense(units=4, activation='softmax')\n", + "# ])\n", + "\n", + "model = Sequential([\n", + " Conv1D(filters=64, kernel_size=40, strides=10, activation='relu', input_shape=(3522,1)), # stride\n", + " Dropout(0.2),\n", + " Conv1D(filters=128, kernel_size=20, strides=5, activation='relu'), # stride\n", + " Dropout(0.2),\n", + " Flatten(),\n", + " Dense(units=4, activation='softmax')\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": {}, + "outputs": [], + "source": [ + "x_train = data_training_r\n", + "x_test = data_test_r\n", + "\n", + "# x_train = tf.keras.utils.normalize(data_training_r, axis=1)\n", + "# x_test = tf.keras.utils.normalize(data_test_r, axis=1)\n", + "\n", + "y_train = labels_training\n", + "y_test = labels_test" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "17/17 [==============================] - 2s 112ms/step - loss: 2.0856 - accuracy: 0.3256 - val_loss: 1.2606 - val_accuracy: 0.3806\n", + "Epoch 2/100\n", + "17/17 [==============================] - 2s 104ms/step - loss: 1.2148 - accuracy: 0.4333 - val_loss: 1.1641 - val_accuracy: 0.4667\n", + "Epoch 3/100\n", + "17/17 [==============================] - 2s 104ms/step - loss: 1.1089 - accuracy: 0.5352 - val_loss: 1.1137 - val_accuracy: 0.4778\n", + "Epoch 4/100\n", + "17/17 [==============================] - 2s 104ms/step - loss: 1.0981 - accuracy: 0.5426 - val_loss: 0.9655 - val_accuracy: 0.6583\n", + "Epoch 5/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.9724 - accuracy: 0.6336 - val_loss: 1.0990 - val_accuracy: 0.5472\n", + "Epoch 6/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.9519 - accuracy: 0.6404 - val_loss: 0.9050 - val_accuracy: 0.6583\n", + "Epoch 7/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.9087 - accuracy: 0.6593 - val_loss: 0.8785 - val_accuracy: 0.6972\n", + "Epoch 8/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.7747 - accuracy: 0.7114 - val_loss: 0.8163 - val_accuracy: 0.7028\n", + "Epoch 9/100\n", + "17/17 [==============================] - 2s 109ms/step - loss: 0.7204 - accuracy: 0.7176 - val_loss: 0.7206 - val_accuracy: 0.7611\n", + "Epoch 10/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.6547 - accuracy: 0.7574 - val_loss: 0.7962 - val_accuracy: 0.6917\n", + "Epoch 11/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.6068 - accuracy: 0.7701 - val_loss: 0.7306 - val_accuracy: 0.7639\n", + "Epoch 12/100\n", + "17/17 [==============================] - 2s 109ms/step - loss: 0.5999 - accuracy: 0.7738 - val_loss: 0.7341 - val_accuracy: 0.7472\n", + "Epoch 13/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.5551 - accuracy: 0.7994 - val_loss: 0.7240 - val_accuracy: 0.7667\n", + "Epoch 14/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.5173 - accuracy: 0.8194 - val_loss: 0.7383 - val_accuracy: 0.7944\n", + "Epoch 15/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.4966 - accuracy: 0.8315 - val_loss: 0.7023 - val_accuracy: 0.8028\n", + "Epoch 16/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.4521 - accuracy: 0.8407 - val_loss: 0.6617 - val_accuracy: 0.8222\n", + "Epoch 17/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.4373 - accuracy: 0.8509 - val_loss: 0.6597 - val_accuracy: 0.7750\n", + "Epoch 18/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.4312 - accuracy: 0.8512 - val_loss: 0.6717 - val_accuracy: 0.8111\n", + "Epoch 19/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.4172 - accuracy: 0.8605 - val_loss: 0.7807 - val_accuracy: 0.7611\n", + "Epoch 20/100\n", + "17/17 [==============================] - 2s 110ms/step - loss: 0.3792 - accuracy: 0.8781 - val_loss: 0.7497 - val_accuracy: 0.7583\n", + "Epoch 21/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.3685 - accuracy: 0.8830 - val_loss: 0.7037 - val_accuracy: 0.8083\n", + "Epoch 22/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.3282 - accuracy: 0.8978 - val_loss: 0.6473 - val_accuracy: 0.8194\n", + "Epoch 23/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.3154 - accuracy: 0.9062 - val_loss: 0.6869 - val_accuracy: 0.8167\n", + "Epoch 24/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.2956 - accuracy: 0.9086 - val_loss: 0.7059 - val_accuracy: 0.8028\n", + "Epoch 25/100\n", + "17/17 [==============================] - 2s 109ms/step - loss: 0.2887 - accuracy: 0.9188 - val_loss: 0.7045 - val_accuracy: 0.8278\n", + "Epoch 26/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.2683 - accuracy: 0.9204 - val_loss: 0.7757 - val_accuracy: 0.7889\n", + "Epoch 27/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.2574 - accuracy: 0.9269 - val_loss: 0.7604 - val_accuracy: 0.8000\n", + "Epoch 28/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.2500 - accuracy: 0.9287 - val_loss: 0.7599 - val_accuracy: 0.7917\n", + "Epoch 29/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.2598 - accuracy: 0.9256 - val_loss: 0.8611 - val_accuracy: 0.8083\n", + "Epoch 30/100\n", + "17/17 [==============================] - 2s 109ms/step - loss: 0.2601 - accuracy: 0.9284 - val_loss: 0.7808 - val_accuracy: 0.8139\n", + "Epoch 31/100\n", + "17/17 [==============================] - 2s 112ms/step - loss: 0.2250 - accuracy: 0.9370 - val_loss: 0.8355 - val_accuracy: 0.8111\n", + "Epoch 32/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.2130 - accuracy: 0.9370 - val_loss: 0.8204 - val_accuracy: 0.8083\n", + "Epoch 33/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.2137 - accuracy: 0.9358 - val_loss: 0.8654 - val_accuracy: 0.7972\n", + "Epoch 34/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.2084 - accuracy: 0.9407 - val_loss: 0.7976 - val_accuracy: 0.8139\n", + "Epoch 35/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.1929 - accuracy: 0.9478 - val_loss: 0.9358 - val_accuracy: 0.7806\n", + "Epoch 36/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1749 - accuracy: 0.9509 - val_loss: 0.8970 - val_accuracy: 0.7972\n", + "Epoch 37/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.1788 - accuracy: 0.9559 - val_loss: 0.8906 - val_accuracy: 0.8167\n", + "Epoch 38/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1629 - accuracy: 0.9562 - val_loss: 1.0327 - val_accuracy: 0.7889\n", + "Epoch 39/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1529 - accuracy: 0.9574 - val_loss: 0.9837 - val_accuracy: 0.8028\n", + "Epoch 40/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.1439 - accuracy: 0.9645 - val_loss: 1.0637 - val_accuracy: 0.7972\n", + "Epoch 41/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.1661 - accuracy: 0.9590 - val_loss: 0.9137 - val_accuracy: 0.8028\n", + "Epoch 42/100\n", + "17/17 [==============================] - 2s 109ms/step - loss: 0.1497 - accuracy: 0.9623 - val_loss: 0.9358 - val_accuracy: 0.7972\n", + "Epoch 43/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1476 - accuracy: 0.9688 - val_loss: 1.0958 - val_accuracy: 0.7944\n", + "Epoch 44/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.1280 - accuracy: 0.9670 - val_loss: 0.9156 - val_accuracy: 0.8111\n", + "Epoch 45/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1323 - accuracy: 0.9679 - val_loss: 1.1786 - val_accuracy: 0.7972\n", + "Epoch 46/100\n", + "17/17 [==============================] - 2s 109ms/step - loss: 0.1416 - accuracy: 0.9694 - val_loss: 1.1062 - val_accuracy: 0.8000\n", + "Epoch 47/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1313 - accuracy: 0.9716 - val_loss: 0.9745 - val_accuracy: 0.8056\n", + "Epoch 48/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1119 - accuracy: 0.9728 - val_loss: 1.0336 - val_accuracy: 0.8083\n", + "Epoch 49/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1040 - accuracy: 0.9747 - val_loss: 1.1659 - val_accuracy: 0.8083\n", + "Epoch 50/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.1516 - accuracy: 0.9639 - val_loss: 1.1835 - val_accuracy: 0.8083\n", + "Epoch 51/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.1329 - accuracy: 0.9667 - val_loss: 1.1922 - val_accuracy: 0.8000\n", + "Epoch 52/100\n", + "17/17 [==============================] - 2s 110ms/step - loss: 0.1093 - accuracy: 0.9701 - val_loss: 1.2481 - val_accuracy: 0.8056\n", + "Epoch 53/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.1127 - accuracy: 0.9759 - val_loss: 1.2923 - val_accuracy: 0.8028\n", + "Epoch 54/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1069 - accuracy: 0.9741 - val_loss: 1.2932 - val_accuracy: 0.8111\n", + "Epoch 55/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.0931 - accuracy: 0.9756 - val_loss: 1.3498 - val_accuracy: 0.7750\n", + "Epoch 56/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.1325 - accuracy: 0.9633 - val_loss: 1.2621 - val_accuracy: 0.7889\n", + "Epoch 57/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.2440 - accuracy: 0.9327 - val_loss: 1.1242 - val_accuracy: 0.7778\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 58/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.2458 - accuracy: 0.9336 - val_loss: 1.1163 - val_accuracy: 0.7889\n", + "Epoch 59/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1713 - accuracy: 0.9491 - val_loss: 1.2440 - val_accuracy: 0.7611\n", + "Epoch 60/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.1614 - accuracy: 0.9522 - val_loss: 1.2123 - val_accuracy: 0.7750\n", + "Epoch 61/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1360 - accuracy: 0.9596 - val_loss: 1.1881 - val_accuracy: 0.7639\n", + "Epoch 62/100\n", + "17/17 [==============================] - 2s 110ms/step - loss: 0.1316 - accuracy: 0.9617 - val_loss: 1.1149 - val_accuracy: 0.8000\n", + "Epoch 63/100\n", + "17/17 [==============================] - 2s 112ms/step - loss: 0.1081 - accuracy: 0.9698 - val_loss: 1.1906 - val_accuracy: 0.7944\n", + "Epoch 64/100\n", + "17/17 [==============================] - 2s 111ms/step - loss: 0.1579 - accuracy: 0.9512 - val_loss: 1.2332 - val_accuracy: 0.7806\n", + "Epoch 65/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.1178 - accuracy: 0.9719 - val_loss: 1.2328 - val_accuracy: 0.7944\n", + "Epoch 66/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1164 - accuracy: 0.9707 - val_loss: 1.3515 - val_accuracy: 0.7722\n", + "Epoch 67/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.1038 - accuracy: 0.9698 - val_loss: 1.4081 - val_accuracy: 0.8028\n", + "Epoch 68/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.0830 - accuracy: 0.9793 - val_loss: 1.3278 - val_accuracy: 0.7833\n", + "Epoch 69/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.1125 - accuracy: 0.9747 - val_loss: 1.4150 - val_accuracy: 0.8056\n", + "Epoch 70/100\n", + "17/17 [==============================] - 2s 110ms/step - loss: 0.1001 - accuracy: 0.9716 - val_loss: 1.3690 - val_accuracy: 0.7944\n", + "Epoch 71/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0711 - accuracy: 0.9830 - val_loss: 1.3889 - val_accuracy: 0.7944\n", + "Epoch 72/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0961 - accuracy: 0.9741 - val_loss: 1.4582 - val_accuracy: 0.8111\n", + "Epoch 73/100\n", + "17/17 [==============================] - 2s 109ms/step - loss: 0.0935 - accuracy: 0.9787 - val_loss: 1.4692 - val_accuracy: 0.7972\n", + "Epoch 74/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.0743 - accuracy: 0.9772 - val_loss: 1.4012 - val_accuracy: 0.7722\n", + "Epoch 75/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.0807 - accuracy: 0.9775 - val_loss: 1.3488 - val_accuracy: 0.7889\n", + "Epoch 76/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0789 - accuracy: 0.9809 - val_loss: 1.4130 - val_accuracy: 0.7833\n", + "Epoch 77/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0596 - accuracy: 0.9849 - val_loss: 1.4535 - val_accuracy: 0.7944\n", + "Epoch 78/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.0528 - accuracy: 0.9892 - val_loss: 1.5247 - val_accuracy: 0.7944\n", + "Epoch 79/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.0696 - accuracy: 0.9830 - val_loss: 1.4776 - val_accuracy: 0.7889\n", + "Epoch 80/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0630 - accuracy: 0.9836 - val_loss: 1.4731 - val_accuracy: 0.7806\n", + "Epoch 81/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0430 - accuracy: 0.9907 - val_loss: 1.4123 - val_accuracy: 0.8000\n", + "Epoch 82/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0522 - accuracy: 0.9883 - val_loss: 1.4712 - val_accuracy: 0.7889\n", + "Epoch 83/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.0510 - accuracy: 0.9873 - val_loss: 1.4718 - val_accuracy: 0.8056\n", + "Epoch 84/100\n", + "17/17 [==============================] - 2s 109ms/step - loss: 0.0729 - accuracy: 0.9815 - val_loss: 1.6164 - val_accuracy: 0.7889\n", + "Epoch 85/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.0510 - accuracy: 0.9864 - val_loss: 1.5057 - val_accuracy: 0.8056\n", + "Epoch 86/100\n", + "17/17 [==============================] - 2s 105ms/step - loss: 0.0643 - accuracy: 0.9833 - val_loss: 1.4542 - val_accuracy: 0.8056\n", + "Epoch 87/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0682 - accuracy: 0.9799 - val_loss: 1.4877 - val_accuracy: 0.8028\n", + "Epoch 88/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0508 - accuracy: 0.9877 - val_loss: 1.5127 - val_accuracy: 0.7944\n", + "Epoch 89/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.0603 - accuracy: 0.9883 - val_loss: 1.5198 - val_accuracy: 0.7694\n", + "Epoch 90/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.0740 - accuracy: 0.9815 - val_loss: 1.5206 - val_accuracy: 0.7889\n", + "Epoch 91/100\n", + "17/17 [==============================] - 2s 108ms/step - loss: 0.0972 - accuracy: 0.9738 - val_loss: 1.6373 - val_accuracy: 0.7889\n", + "Epoch 92/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.0563 - accuracy: 0.9852 - val_loss: 1.4448 - val_accuracy: 0.7972\n", + "Epoch 93/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0968 - accuracy: 0.9815 - val_loss: 1.4285 - val_accuracy: 0.7833\n", + "Epoch 94/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.0755 - accuracy: 0.9818 - val_loss: 1.4840 - val_accuracy: 0.8056\n", + "Epoch 95/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0497 - accuracy: 0.9873 - val_loss: 1.5756 - val_accuracy: 0.7806\n", + "Epoch 96/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0431 - accuracy: 0.9880 - val_loss: 1.5057 - val_accuracy: 0.7972\n", + "Epoch 97/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.0417 - accuracy: 0.9917 - val_loss: 1.4868 - val_accuracy: 0.8028\n", + "Epoch 98/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0331 - accuracy: 0.9904 - val_loss: 1.6449 - val_accuracy: 0.7972\n", + "Epoch 99/100\n", + "17/17 [==============================] - 2s 106ms/step - loss: 0.0403 - accuracy: 0.9889 - val_loss: 1.6417 - val_accuracy: 0.7944\n", + "Epoch 100/100\n", + "17/17 [==============================] - 2s 107ms/step - loss: 0.0503 - accuracy: 0.9852 - val_loss: 1.5776 - val_accuracy: 0.7833\n" + ] + } + ], + "source": [ + "history = model.fit(x_train, y_train,\n", + " epochs=100, validation_split=0.1,\n", + " shuffle=True, batch_size=200,\n", + " verbose=1)\n", + "\n", + "model.save('network_h5\\spectral_classifier_v2.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5) Auswertungen" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_accuracy(name, ylim=[0,1.01]):\n", + " plt.plot(history.history['accuracy'])\n", + " plt.plot(history.history['val_accuracy'])\n", + " plt.title('model accuracy')\n", + " plt.ylabel('accuracy')\n", + " plt.xlabel('epoch')\n", + " plt.ylim(ylim[0], ylim[1])\n", + " plt.legend(['training', 'validation'], loc='upper left')\n", + " plt.savefig(name)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_loss(name, ylim=[-0.5,2]):\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.ylim(ylim[0],ylim[1])\n", + " plt.legend(['training', 'validation'], loc='upper left')\n", + " plt.savefig(name)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "def plot_confusion_matrix(cm, classes,\n", + " normalize=False,\n", + " title='Confusion Matrix',\n", + " cmap=plt.cm.Blues):\n", + " \"\"\"\n", + " Diese Funktion printet und plottet die Confusion Matrix\n", + " \"\"\"\n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(classes))\n", + " plt.xticks(tick_marks, classes, rotation=45)\n", + " plt.yticks(tick_marks, classes)\n", + " \n", + " if normalize:\n", + " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + " print(\"Normierte Confusion Matrix\")\n", + " else:\n", + " print(\"Confusion Matrix ohne Normierung\")\n", + " \n", + " print(cm)\n", + " \n", + " thresh = cm.max() / 2\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j, i, cm[i, j],\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + " \n", + " plt.tight_layout()\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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SCg0UJ+s4DgCPHd/j7+1B0rEcu3TqV3fCR5dD/Fo3F1AppWqeBoriGneAkLYQu4CwQF8+XrGPiU+9BetmUrhzMUwfDrNvgpT9p557dA/ELqjhAiulVPXTQFGcCJwzFnb/wkfXd+U/l3fjkZAlZOPNudnPsyjseszWefDZtaee+92D8MlVsOT5mi+3UkpVIw0UJXUcAwW5hCcv56ou/vRN+xHvXtdw0bkDmbJvLO/43ggH10PilqJzMpNh1yLwbwI//wMW/8d95VdKqSqmgaKkVgPBJ8g2I615D/KzcQy4g0fGduLVa3ryflpPCnCwf0mxRfm2zIXCfLj2c+hxNSz+pwYLpVSdoYGiJA8v6DAati2AlW9D2+EQ1hmAi7q34J27LmStR3cKN87mlYXbyCso5NjqWaT6tWJWXCiFF78K3a60wSJ5l3vvRSmlqoAGitJ0HAeZhyE9HvrfcdKuc5oG0G3MzbSWRH76aT5DH/+UBgd+47203jw0ZyNTPlhDyuC/gXjA6vfcU36llKpCGihK036U/aAPjoQOF5yy26fbeIyHD8932sbf2+7AQwxXTb6XZy+L4vedRxg3YwcprUbD2o9sWhCllDqLaaAoTYNguOCfcOH/gaOUX5FvEHLO+bRP/IELza/QpDPN2vfk2v6t+WLqIBwO4YFdvSDzCGz5pubLr5RSVUgDRVkG3GFrFmXpdgVkJELcSoi6vGhzyyDmTB3Eeq+exDuaUfjHuzVQWKVUvXEsETIO1+hbaqCorA7ng0+g/Tlqwkm7wgJ9ee7KaN7PGYFj3zJIinVDAZVSdUpeFiz+N7zYDWaMhYKay0WngaKyvBpArxug/WgIbXfK7pGdmuLZ61pyjQf7f3zNDQVUStVKBfnw8zOQeqD852z7AV7rB4v/BS16wuFtsPbD6itjCRoozsQFz8J1X5S5++5LBvGb92CCts0mPeXIGb/dqj3JpGTmnvF1lFJn6OheWD7NPuVX1P4VsOS/5fugNwZ+eQ4+vgK8/OHGb2DKfIjob2sXuZkVf/9K0EBRjXy9PGg+7iEamkz2znn8jK51LCefSdOXc/+s9VVUuko6FAOv9Iat89xbDqXcadE/YcHDNv/bwQ0VO3f3Evt9/wrXx+VmwueTYdGz0H0S3LYYIs+1qYZGPwnHDsGKaRUveyVooKhmHXsO5Se/C+i072MKDm2u9HU2xKWQX2j4eWsii2ITq7CEFZCXBV/cDEd2wJd3QPJu95RDKXfKy4bYeRAxALJS4K2RsOxl+/RfHnt+td/jVkFhwan7C/JhwyyYPgw2fw3nPQ2XTQMv36JjWg+yyyIsfdGmEAJbloPV8yCpgaIGOEY/wTHTgJQv/lz+/0wlrN+fCkB4owY88+1m8goKq7KI5fPj45C0FS560b6ePUXniaj6Z+fPkJMG5/4F7vwdzrkAfvw77Pzp9OfmZsL+lRAUYa9RPGcc2ADxSk+Yc6udy3XtbBh8r61FlDTqcXuNjybAq33hP63ho4mV/oxxRQNFDRjeszPvel9DaNJy+4RQCev3HeYrv2eZ1mMXO5My+PD3vVVcytPY9j2snA4D7oI+U+DS1+36HD/8vWbLoeqf/BzY8RPkpJfv+MxkmDbUPul/dad96l75Fqx40/YrHN5xZuXZ/BX4NoK2w8AvBCbOgOA28OOTpdcQitu/HArz7Ic/nNz8lJ4AX94ODULg6k9h6m82nVBZmnaFfrfZTvGQdjDybzBhugaKs5WHQ2g09Ha2FLYid96jtupaQVn71hJduImo/R8xtENj/rdwG0eO1dDTfGay/YNr2g1GP2G3db7IBo2Vb8J/IuHZFvCPMFj3Sc2USdUf6z62T83/bQ+fXQ9bvnX9YfjzM5CwCbz8YMdCWPgEzHsQ5j9k+xWmDbHpdSrzgZqXbfvnOl9s88IBeHrDyL9DwkZbIzguKRbm3g3Hkoq27V4CDk+bPNQ/zNYujtv6DZhC28zUcWzpk31LGvcc/GU7XPOpreG0G1G+8ypIA0UNuaJfG16Vq/HOiC9qoyyng6lZdM1aA4DEr+UfQ3zJzC3gibmb7Ap81W3vMpv7atxz4OlTtH30kzD8MTvhsM8UCGxuax2qbsjPtZ22X99lP6A/vgqSttV8OeLXgm+QHY6+b7ldD2bRs6Ufe2gjrJ4BfW+Byd/Cg9vgkX3w4A54aDfcux5aDYBv7oVZN0BGBUcj7vwJctOh62Unb+86AZpH23LlZdtBHzPGwZoP7Ain43YvgfA+4NMQWvW3NYzjNn0Fjc+BJp0qVqYaoIGihgT4etGy91iyjDcHVlUsrcf6/SkMcWwk178FILQ5uID7zzuHbzcc5PXFO6unwMUdcb5H065k5ubz6JwNxKdk2Sep4Q/Dhc/bocJ9b4X4NXB4e/WXSVW/XYvgl//YMfxJsbb5cdOcmi9HQgw06w7j/gsPbLUBY8l/bTNSccbA/Idts9CIR4u2+wZBwya2mSi4DVw3x3YQx86DFzrbVSt3/FR6s9FPT8MbQ4r+BjZ9aVP8RJ578nEOh71m6n5be3n/IvDwth3Oq9+zzUPZqTboRQ6150T0tytjpifYWsfeZdDl0tL7I9xMA0UNum1kFzZ6dSN36/fMXR9f7vM27jlEH8c2HN0ugzZDYOPn3DmsLZf0aMHzP8SyY/aT9mmkuiTvAr/G4BvEkm1JfLJyPzNXlNJH0m0iiAM2fFZ9ZVE1Z89ScHjBfRvgTyvtk+6B1TVbhoJ824zUrLt97fCAC/8HnS6yzUgbPi86dtOX9sN25N/sh3lZHA7bR3DHUuh9ow0SH02wHcjFZafZYJSwEd4eZY+LnX9ys1NxbYfZCbhrPwTvAJgyD8Y+Z5uTlr4Ae3+3Px8PMhH97ff9K4qanbqMr/zvqhppoKhBoQ196DZ8IpFyiOc/XcCMZeUbXpq3cyneko9n+5G2mefIdiRhI89N7M6toTG0j/kf2YuqcQnW5F0nZp//ttNW1edvPHRqs1dAM7t+x4bPoNANo7JU1dr7G4T3tlkIwP4ct6paOkvLlLwT8rOhWbeibR6ecPk70GYofHkbvNwLZlwI8/5i+9F6Ty7ftcM621rKg9vscgIxX5w8CmnjLMjLsO/l19gGk9xjpzY7FTfmP3bOw5R5EBIJwa2h1/Ww+n1YNxM8fKBlP3ts8x729f4VdpBLaHvbQV0LaaCoYQ0627Tlt7fYzVPfbGbaLyWajrJS4J0LbFssUFBoaJH8O/niBa0G2ScOhyds/Bzf3KM8XPAmhQjeh2PsudXhyE47qgJYtuMwXh7CrsMZbEs4duqx3a+ClH2nn0ykarecY3BwnR2vf1zL3pCVbJtLasqhjfZ7s6iTt3v5wqSPYcj90CIaMNAwDC76n611VISnD5z7EHg2gN9esduMgVXv2QAVdTnc8iNEDrNNV23OLftajdvDhDehUUTRtqEP2OakLXNtv8Tx+RCePjYdx/YfYPev9m+7FjY7gQaKmhfSFoLbcHVILBf3aMG/5289uWYRO892cH37ZyjIZ3tiOv3NBpJDe4G3n21nbT8aYubAdw/gkZPGwtb348CQsb1ineTlkptpF3AKaUtCWjY7kzKYPKgNIjA/5uCpx3e6yI420eans1vcSru8b5vBRdvCe9vvJZuf0uJtx3d1OLTRNn817njqPt9AGPV3mPiufYK/83eI6Fu59/EPtX0fG2bZ/oS4VbbJqc9N9sO7QTDcOBfu+sPWaCoiqGVRLadkkGnV3+ZtMgW2f6KW0kBR00Sg/Xk49vzKCxM6cUHXpjz1zeaiNv+t39lOsMTNsOZ9tm7fQWfHfjw7jCy6RtRESDtgx3MPf4SwYbeSY7w4sO7Hqi/vUWcQC23L785mp/HR4fRtHcL8jYdOPd6noQ0Wm77UyXhns72/2f6m4+3oAGFdwNMXDqwp2paTDq/1t6kmXDVJHUuE9FL+v5xOQoztG/H0rvi5FTXwLttPsPx1O3LKu6FdTqC4ypZj6IPQbtQpmaZP/H6DI09uXqtlNFC4Q/vRkJeJ14EVvHJ1L0Z2CuOvX8Zw2UsLyd22kANtJ1LYerAdardlLgDBUcVW2us41iYIa9ETBt9H9zbNiHF0xCfut6ov6/HRHiHt+G3nYYIaeNGleSBjopoRm5DOrqQymp+yU2yVWp2d9v5m29B9Aoq2eXjZIaDFaxSxC+zs4Njv7IS20hTk2bTYL/e0w0Ur0sdxaGPNfYAGt7Yf5Kvfs/0V3SaefP9nIqApXD/n1EzTEf3tDOyoCbW22Qk0ULhH5FBba9j+I96eDl6/thcPjenIAGLwLszm0U0RXB83HpOZzNj41zjmCECady8636ch3LQArvkcPDxxOIS0Zv2JyNnBsZQqXtAk2QYKExLJsh1HGNg2FIdDGBPVDID5MaU8JbYdbocobq+GGs7ZJi/bTqraOLsoJ09tl5dtm15aDz51X3hv23dRkGdfx3wBAS3sOvM//O3k2sZxaz+0+cEatbYT0GbdUL7fxbFEOJZQs0/ag++1Hdb52bbZqbr5N4Zbf7I1jlpMA4U7ePvbTsIdNjeMr5cHdw5vz8OROzHeDbnx6uvxb92bWQXD8SGXA8H9T+2ga97djg13atptNA4xbFm+oGrLmrwL/JuwP8OLAylZDGofCkCLRg2IjmhUej+Fh6f9406sfBLEUqUegN9fq9lRN5WVFAtvnwf/joB3zrPJFF/obJMpxq1yd+lcO7AaCnLKCBS97Ido4mbIOmpnPne9DMa/Bg2b2vxf2alFx+dmwuL/QKuBMHWZnaQZOw/+F2Wbq2LmQG5G6eUoqyO7OjXrZptOWw+xNaqa0KKn7X+sxdwaKETkXRFJFJGYMvaLiLwsIjtEZIOI9KrpMlab9qMhaUvR5LTCQohdgHQ4j1HdIph+Qx9G3vky6X6taDH0+tNermOfEeTgRdrWRVVbziO7TjQ7AQxqF3pi17huzYg5kMb+5FJy4od1sUMNq3KY7Jr34fvHbOdfbbflG9shPGAqXDUTbv4Roq+1298eBdsXuruEZdv7GyB2BnNJxTu0t35n8xZFXe7MefQupOyHjycVzXhe8YZNhz36SfuwM+TPcOsi6H6lnacxewq8FG1/Lul4oGhag4EC4MoP4IbK5WSrq9xdo3gPGONi/1igg/PrNuCNGihTzeg6wc4YnT3FPnUdWGXX4O544YlDmjRvRcBDGwmIvvS0l/PwbkBcw240P7qK7LzTJCariOSdENKWZTuPEBbgQ7smDU/sGhvVHID3f9tz6nlNu9gqfEoVJi9M2GS/l9a8UdskxdoMoec9bfNiRfSDi16wM4uDIk5O63BcDS5t6dLeZXY8v1/IqfuC24BfqA0UMXNsc1K48/mtVX+blO7Aanh7pJ1gtvQlOGfsyUGneXe4+EV4IBZumGtHL71/yam1xYQYCGxZejmqk8Oj4iOb6ji3BgpjzBLAVWPleOADYy0HGolI85opXTULCocJb9ucMN/ca5/OHJ7Q4bxKX9Kz7VA6sZffYs4wO+ZxuRmQfhAT0pbfdx5mULtQpFiHW0SIH5P6RjDjtz3EHiqR2TPMOXGoKpufEpwVz/izIFAcjoUmpQzp9AmAQXfbIdB7iw0+SIqF/7SBF7vD13+yM46z02qsuCcU5Nk+leLzJ4oTsbWKnYth1+JTO2G7TbRDVfOyYMYY29E9qoxFuxwedjbzrYvsAI3vH4MvbikaLVeTHdnKJXfXKE4nHNhf7HWcc9spROQ2EVklIquSkpJKO6T2Oed8GPFXOwN0xTTbJtygUaUvFx59Hg4xrF82v2rWq3AuTHTQM5zDx3IZ1K7xKYc8NKYTAb6e/P2rmJNnaoc5E5slVFGgyEkvmugVv7ZqrlldCgtt8ryykrv1vN4+lS/9n32dn2vTR3h42Q/GLXNhzi22T2PeQxVPi11YWPmhyTsX2dnIZQUKsIEiLc6O/Y+6/NT9LfvArT/bCaID7rS1S1d8A+Gqj2xAiZkNM6+AjMO2iVEDRa1Q2wNFaePFSu3JNMZMN8b0Mcb0adKkSWmH1E5DH7DNTfnZ0OnC0x/vgmdEX/IdPrQ4tIib3l1OWnbemZXNOeJpQ6at+veNPLUJIMTfm4fHdGLlnmTmrCm2WLxPgG2mSNx0ZmU47nhqhZB29kmz4Azv7Uwc3mGfpsuSug/ys0qvUYDtuOw/1Q4fPrQRljxnVya7+CWYNNNmOb3pB9upuupdeLU3rChnVt6kWHhzKLzUo+JLdKbsg6+m2lQS7V3UbMP72O+hHcruPwhqCTfNhzH/LN97i9i/hcvetP0V00fYOQ012ZGtylTbA0UcUGwuPC2B8mfTOxs4HDb//Ii/Qo9JZ3YtL188O1/IVZ6LeSZuMh+++BgHEs6gdpW8C4BlyUEE+3nRJrT0kRlX9YkgOqIR/5q/hdSsYh/gYV2L+hXO1PFmp57XFY26cZcf/mrTbpfVUZ8Ua7+7Shfd7xY7oWvuPfDr/0GPa6DLJXafw8PZ3v8m/HmTrWn+8h/bnFMWY2w+oTeH2Ylt4rBzF3aUs9M8NxM+vQYKcu2iOT4Nyz42vJcd3t39qqof+99jkg2WGc7lfrVGUSvU9kAxF7jBOfppAJBqjCllPOZZzjcQhj1kO7fP1IS3YOIMgpu04K7s6RydfgmFhZUcTnpkJ/iHsSI+lx4RjU7qnyjO4RCeuTSK5IxcXvmpWIrxpl3sNSqxUNMpEjaBT2BRdk13dWgX5MOeZbbtPWVP6cckbbXfG59T9nUaBNs1POLXQGA4jP136ccFNIXhj9r1QNZ9XPoxedkw5zb45h7baT51Gdyy0NboZl5Z9nnHGWPXnDgUYxPgNe7g+ni/ELhzOQy5z/VxldVxLNz4jV0MKDiyet5DVYi7h8d+AvwOdBSROBG5WUTuEJE7nIfMA3YBO4C3gDvdVNSzh4cnRE0g8E+L2dTlfqIKNrN8RSlDD8sjeRf5wZFsTzxGzwgXaZuBqPAgLu0ZzofL95KQ5gwMYV1sO/bh2Mq9f3EJm+xInJC2djKfu/op4tfYhWug7KadpFgIaH76/qaBd0PbEXD5264fEtoMgRa94PdXT10zIeMwfDDe9nON+Btc/5XN4hvYAqbMt30N39xrjyuNMbDwSbvOxOgnbL9ZeYS2Kz3VdlWJ6AfnPlirZyvXJ+4e9XS1Maa5McbLGNPSGPOOMWaaMWaac78xxtxljGlnjOlmjKnlM5Vql45j7iAfDxJ+fa9yF0jexRHvlhgD0a0anfbwe0d1oKDQ8NoiZ+fr8ZTJZ9qhbUxRoBCxE5TcNfJp9y/2u3gUjfMvKWmr69rEcQFN4YavSp+vUJwIDL7HNgVu/a5o++Ht8PZoO1P6ivdg2F9OXgbTNxDGPW+bk9bNLP3aS/4Ly16E3lNg8H2nL7Oql2p705M6A56BTTnYZAgDM37ij10V7KtwDo3dXWhTdUS3bHTaU1qH+nNFnwg+WbmPuKOZtuPZw6eof6GyUvfbpp7jgadFTxt8irfZu2q/r0q7frHt5k06wqFSahTGuB7xVFmdL7FNSb+9bPtGVrwJb55r56rc+G3ZaySEdbJ9HKvfO7VPZdnLNp9Yj2vgwhf06V2VSQNFHRc25EaayVF++f6Lip3o7MhelxFM2yb+BPmVr5nh7pHtEYRXf95hm8GadDzzjufjHeLHR9iE97JNWsef6Dd8bucg7KziWekl5WXZOQaRw2ywKK1GkRZvm6bKGvFUWQ4PGPgniPsDpg2B+Q/ZZqXbFp8+tXbvKfbf83htCGDdJ/Dj3+3Ez/GvnlwTUaoE/d9Rx/l0uZAcj4ZExn9z6qS4shgDf7wDwM/JoURHNCr3+7Vo1IBr+rfi89Vx7DmcYWsBZ9r0dMhZIwnr7HwT50zg+LU2ZcR399uRUPMfqt5hs/tX2BxIkcPs0pzpB+1ax8Ud78iu6hoF2BQgfo3tHIZL34BrZ9thqKfT5RI7b2PVu/Z14lb7O2sz1M6kruhCP6re0UBR13n5QtfLGOv4gxmLnU/AGUfsuP3SkusZAwufgNUzSOs1lZUZTenZynVHdkl3jmiHl4fY1fvCuthcP8dz/1RGQoxtdjme8jmwhU1AF7fKjvs3hXYJysPbYKWL+QYJm+zEvcra9YudPd96YNGwzZLNT+UZGltZ3n62BnH3Woi+pvxNRZ4+9vjYebZm8flkm5jy8rert0Na1RkaKOoBn97X4ic5+G78lMSv/gYvdrPt26/2te3UKfvtB2hBHix5Hpa9BH1uZnGruwHoWYEaBUBYgC/je4Tz9bp4MoKdTTBnMvEuYdPJE7uOd2jHfAF7foUL/gn9b7eJFhf/+9SnfLBNRm8MsgnoVr5VuZrH7l/srGSfgGKBokTzU9JW+9TvH3rq+VWhUUTlrt17il2x7t2xtowTptvRUUqVgwaK+qDVAAqDWvOk1/uErXsF0+F8u7awX6htp34xCv7VEv7RGBY9YxeHH/c86/an4uPpoGOzii/ect2A1mTlFTAv0Tmb+9DGyqUHz820M8RLLjrfwtlPcc5Yu4SlCIz5N+Rlwk9PnXxsYQHMe9AOWW3Syf78+sCKzcXITrVNXZHD7Gu/EJvc75RAEVs9tYkzFdrOlv3YIRh6P7QbefpzlHLSFIn1gQiO0Y+zb/kcbt81hBvbXMSkPq3swiyJW2zKhPxs++XbyD59Ohys23+U7i2D8PKo+PNEt5ZBdG8ZxFtrMpjo1xj5/jH44e92xm9wG7uyV8t+dpGjhi5SriRttU1LJQNFl0tsxt1LXi5qgmncAfrfYbOQdhwHncbZ7avfs01tl79jcxNtWwDfPWgX0Lljafnya+1ZZssRWWzN42bdTm56MsaWt+Ryl7XFBc/Cpq/sBD6lKkADRX3RbSIRUZcTMH05/1mwlQu6NiPY39t2EId1xhjD4m1JrNiVzIAdyfRtE0JMfBo3Dmxd6be8tn8rHv5iI7GXvkKnvK12KGdOuv0wXTvT9id4+dlVxQbdbdvNizOmaJ2CkjmFwjrDtZ+f+qbDHrbnfHatrTV1vgR+/oftuI263AaVjmPBvwm8c76tXVz+tj03P8emymgQYteRON7JW1hgm7k8G9iJYMc16w6x8+1QYm9/uyJbdkrtrFGADWyaEkNVggaKekRE+Mf4KMa9/Ct/mb2B8dEtaBLgw6HUbN5csostB21a62m/7MTXy0FufiHRp5mR7crFPVrwzLdbmLYnjBcnjTt5Z0G+fRpf9iIs/pd96u93q515HRRhM8X+/pqdWBfa3tZCysM3ECZ/Zztsv7kXfn/dpuse+9zJnb8t+9gn60XPQIfzbSCZdYNdbAhg67c2B5cxtsN83++2tuLpU3SNZt0AY0d1RfQtmoFe1UNjlXIzDRT1TMdmAdw9sj0vLtzOwi0JJ7a3D2vIfyd2Z0xUM1buTuaHTQnsSDp20op2FeXn7cmEXuF8snI/j1+cS4i/d9FOD087H+LKD+wCNz/8DX56+uQLhLSzM4ujr6nYEE6fhnD1J/DNfbDuo7JTXQ+93ybN++4B8GoAOcdg4gw7k3neX+CNIba5yeEJl023q7IVd3wd80MbbODZPNe+bqyBQtUtYsrRwSgi9wIzgHTgbaAn8Igx5ofqLV7l9OnTx6xapdk+XEnJzCUpPYek9BxEhP6RITgcVT8zd1tCOuf/bwm3n9uWR8Z2KjOxIMbYZpvUA5AaZ5/cI4ed2UQwY+ziQC37gqd36ccc3QvThtqRRFfNLAooKftsoHF42Cas0uYrGAP/aQ2dLwYE1n5os9uOf63yZVbKTURktTGmT6n7yhko1htjeojIBcBdwN+BGcaYWrmGtQaK2uWeT9Yyd308F3Vvzr8mdCPAt5aN3U9PsENeK7PA/XsX2SG6AOc+BCMe01QY6qzkKlCU93Ht+P/8cdgAsZ7SFxVS6hQvXhXNQ2M6Mj/mEBe/spSNcanuLtLJAppWLkiAHb0lHnDJqzDyrxokVJ1U3hrFDOwSpJFAD8ADWGyM6V29xascrVHUTit3J3PPJ2tJTM9m8qBI7j//HBr6nOXdZHnZkHnEroGu1FmsKpqeHEA0sMsYkyIiIUBLY0wF11qsGRooaq/UzDz++8NWZq7YR1iAD/+8rBujOjd1d7GUqveqoulpIBDrDBLXAX8Daln7gTobBPl58cyl3ZgzdRAh/j7c+sEqvllft1a3VaquKW+geAPIFJEewEPAXuCDaiuVqvN6tgpm9h0D6d06mPs+W8d3G+reCrdK1RXlDRT5xrZRjQdeMsa8BFQ8AZBSxfj7eDJjSj96RjTink/XsiCmcsGisNCwMS6V8jSjKqUqrryBIl1EHgWuB74TEQ+glo1xVGejhj6evHdTP7q3DOL+Wes5mFrxlere+nUXF7+6lOlLdlVDCZVS5Q0UVwE5wE3GmEPYEVD/rbZSqXqloY8nL0/qSUGh4dnvtlTo3IOpWbz003a8PR389/tY1u47Wk2lVKr+KlegcAaHmUCQiFwEZBtjtI9CVZmIED/uHN6ebzccZNmOw+U+75nvtlBQaJgzdRBNA325+5O1pGVX4yp3StVD5QoUInIlsBK4ArgSWCEiE6uzYKr+uX1YW1qF+PH41zHk5hee9vil2w/z3YaD3Dm8PVHhQbx8dU8Opmbz6JyN2l+hVBUqb9PTX4G+xpgbjTE3AP2waTyUqjK+Xh48eUkXdiZl8M7S3S6Pzc0v5PG5MbQO9eP2YW0B6N06mPvPO4fvNhzkw+V7a6LIStUL5Q0UDmNMYrHXRypwrlLlNrJTU87r0pQXfozl2w2lz69Yvfcol72+jF1JGTx5cVd8vYoyy04d1o5RncJ4+pvNrNh1But0K6VOKO+H/QIR+V5EJovIZOA7YF71FUvVZ89f0YMeLRtx9ydrmbnC1gyMMWxPSOeh2eu5/I3fOHIsl9ev7cWITmEnnetwCP+bFE2rUD/unLmG+JSKj6JSSp2sXCk8AETkcmAwNhngEmPMl9VZsDOhKTzOflm5Bdw5czWLYpMYG9WMzQfT2HskE0+HcPPQSO4Z2QF/F3midiQe49LXltGmsR+jOzdlU3waO5OO8dAFHRkT1bwG70Sps8MZ53o622igqBvyCgp5dM5Gvt0QT//IUM7rYpulmgb6luv8HzcncPuHqzBAZGN/cvIKycor4Kf7h9llYJVSJ1Q6UIhIOlDaAQIYY0xg1RSxammgqFuMMWUveHQaR47l4Ovlgb+PJ1sPpXHRy0sZHx3O/13Zo4pLqdTZrdJJAY0xAcaYwFK+AmprkFB1T2WDBEBoQ58TTVSdmgVy+7C2fLEmrkJzNZSq787yxQCUqpi7R3Zg3sZDPPblRmZM7ktyRi4JaTkcTM3iYGo2h1KzOb9rU8ZH6/oSSh2ngULVK75eHjx7WRTXvLWCkf/3y0n7Gnh54Ovl4KetCURHNKJ1qL+bSqlU7aKBQtU7g9o15s3re5OalUfTQF/CAnxoHuRLUAMvEtNzGP1/v/DonI3MvKX/GTV7KVVXaKBQ9dIFXZuVur1poC+PjuvMY19u5PPVcVzZJ6KGS6ZU7aOzq5UqYVLfCPpFhvDsd1tITM92d3GUcjsNFEqV4HAI/5rQjay8Av7xbcXSnitVF7k1UIjIGBGJFZEdIvJIKfuHi0iqiKxzfj3ujnKq+qddk4ZMHdaOb9bH88eeZHcXRym3clugcK6S9xowFugCXC0iXUo59FdjTLTz6+kaLaSq1+4Y1o7mQb48/c1mCgvrXgYDpcrLnTWKfsAOY8wuY0wu8Cl2TW6laoUG3h48PKYTGw+k8sWauNMeX1BoSM3SRZNU3ePOUU/hwP5ir+OA/qUcN1BE1gPxwIPGmE2lXUxEbgNuA2jVqlUVF1XVV+OjW/D+73t47vtYxnZrzh97kpm2eCd7j2TSu3Uw/SJDaOTnxeLYJBbHJpKVV8DMW/rTu3WIu4uuVJVxW1JAEbkCuMAYc4vz9fVAP2PM3cWOCQQKjTHHRGQc8JIxpsPprq25nlRVWrPvKBNe/40Qf2+SM3JpHuRL79bBrNl7lPhUOyoqxN+b4ec0Yc2+o6Rk5fHF1EG0a9LQzSVXqvxc5XpyZ40iDig+SL0lttZwgjEmrdjP80TkdRFpbIzRRD2qxvRqFcyNA1uzYncyj47txPjocLw9HRhjiDuaxdHMXLq2CMLDIew9ksGE139j8oyVfDF1EGEB5ct0q1Rt5s4ahSewDRgFHAD+AK4p3rQkIs2ABGOMEZF+wGygtTlNobVGodxp3f4Urp6+nHZh/rw3pR+NG/q4u0hKnVals8dWJ2NMPvAn4HtgCzDLGLNJRO4QkTuch00EYpx9FC8Dk04XJJRyt+iIRrx2bU+2JxzjopeXsmbfUXcXSakzogsXKVVNNsWncsdHqzmUms1fx3Xmmv6t8fbUOa6qdtIV7pRyk9TMPP48ax0/b00kwNeT8zo3ZWy35ozo2ARPDw0aqvaorZ3ZStV5QX5evH1DHxbFJjJv4yF+3HyIOWsP0CLIlxsHtWFSv1YENfAq8/zM3Hxu/3A1IzuFMWVwZA2WXKkiWqNQqgblFRSyaGsi7y7bzfJdyfh7e/DqNb0Y0Sms1OMfnbORT1buw8MhzJk6iB4RjWq2wKreqJWd2UrVR14eDs7v2oxPbxvIt3cPoU1jf+76eA0xB1JPOXZBzCE+WbmP6wa0ommAD3/+bB2ZufluKLWq7zRQKOUmUeFBzJjcl0YNvLj5/T84mJp1Yt+h1GwembOBbuFBPH5RV56/sge7Dmfwr3lb3VhiVV9poFDKjcICfXl3Sl8ycwqYMuMP3lu2m/9+v5Wb3vuDnLxCXpoUjbeng0HtGnPLkEg+XL6XRVsT3V1sVc9ooFDKzTo1C+T163qxKymDJ7/ZzLRfdnE0M5fnJnanbbE0IA9e0JFOzQK477N17D2S4cYSq/pGO7OVqiVSM/MoMIZGDbxwOEpfq3vvkQzGv7aMJg19mHPnIAJ8yx4xpVRFaGe2UmeBID8vQvy9ywwSAK1D/Xn9ml7sOpzBfZ+uo8C5TkZGTj65+YU1VVRVz+g8CqXOMoPaN+aJi7vw+NebGP78IlIz80jLzieogReT+kVw/YDWtAz2AyA7r4BCY/DzLvtPPSUzl9V7j9IvMsStNZSs3AIS07NpHervtjKo0mmgUOosdP2A1mTmFrB671GaB/nSLMiXjXGpvLVkF28t2UVkY38OH8slNSsPh0C38CAGtAulf2QIUS2CaBLgQ3ZeITN+2820xTtJy86ngZcHF3ZvziU9WpCbX8jBtGyS0nMQwNvTgb+3B5f2DKeRn3eZ5TLGYAwua0WlScnM5dq3VxB7KJ13J/fl3HOanOFvSFUl7aNQqg45kJLFzOV72Zl0jKaBvjQN9CU7r4Dlu46wbn8KeQX2771xQ/thf/hYLiM7hTGpbwSLYhOZuy6ejNyCE9cTgeIfER3CGvLhzf1pFnRq+vTVe49y98dryMgtoFerRvRpE8K5HZoQFR6ISNmB42iGDRI7ko4R3qgBiWnZfHb7QKLCg6rot6LKQ3M9KaXIzM1nQ1wqWw6msTk+jbTsPG4e0pZ+kUWr8WXk5PPHnmQa+XnTPMiXxg19cAjkFRhW7Unmtg9X08jPi49u7k+bxkVNRJ+s3MfjX8fQPKgBA9uGsnrfUXYkHgOgbRN/xvcIp20Tf+KOZrH/aCaFhYZWoX60CvHj1Z93sOtwBm/d0IdOzQKY8Ppv5OQXMGfqYFqF+lX4PnPyC3jm2y00DfRhVOemdGoW4DJQKUsDhVKqSmyIS+HGd1fi4XAwJqopxkBCWjYLtyRy7jlNeHlS9ImmqeSMXL7fdIiv1h5gxe7kE9cI9vPCwyEcPpYLgI+ng7du6HOiuWlH4jEmTvuNoAZefHBTvwr3Wby2aAf//T72xOsWQb7cPLQtNw5srYkYXdBAoZSqMjsS0/nTx2tJTM/BIeDhEK7oHcGfzzsHjzL6Jg6lZpOSlUvLYD8a+tiu0WM5+ew9kkGwnzctGjU46fg1+45y03t/ADDtut4MaBtarrIdSMli9P/9wtAOjXnm0igWxybx9foDLNtxhKjwQP51WXc6NQ9gV1IG2xLS6RYedFLNqD7TQKGUOuvsOZzBze//wd4jmTx4QUeCGnhxMDWbY9n5DD2nMUPaN8arRA3hzpmr+WlLIgvvH0ZEiG22MsYwb+MhnvxmE0eO5eDpcJBbYIcS+3l78MKV0YyJalbj91fbaKBQSp2VUrPy+NPHa/h1+2HAdq57eTjIzS8k2M+Lsd2ac2G35vSPDGH5rmSue2cF9593DveM6lDqtd5asou8wkI6NwskIqQBT3+7hfX7U3jgvHO4eWgk6/ansGbvUTw9HFzUvfmJYcb1gQYKpdRZq6DQEHsonUZ+XjQJ8KHQGJZsO8zc9fEs3JxAVl4Bjfy88HQ48Pfx4Pv7zsXXy6Nc187OK+CRLzbw1br4U0Z4AfRrE8KozmFENvandag/rUP9yn3ts40GCqVUnZSVW8Av25L4ftMhlu86wnMTuzO0Q8XmYBhjmLVqP/uTs+jdOpherYJJy87j63UH+Gpd/InRWwABPp5MGdyGm4ZEupxPcjbSQKGUUpWUkpnL3iOZ7DmSwYKYQ8yPOYS/twc3DGrDTYMjaRLgU+a52XkFrNidzKKtiazZd5T2YQ0Z2DaUnq2C2ZaQzq/bk1i99yiD2zfmvtHnuFztEGxQ2xCXSmZuAb1bB1fpGuwaKJRSqopsPZTGKz/tYF7MQbw8HFzRuyXjo8OJTUhn1Z5kthxMIyOngKy8AtKz88grMPh6OejeshE7Eo+RnJF74loBPp50aRHIyj3JhPp789AFnbi0Z/hJAcAYQ0pmHvNiDvLR8n1sOZgGQEMfT4a0b8wl0S0YG9XsjOeKaKBQSqkqtjPpGG8t2cWcNQdOjKJqEuBDj5ZBBDXwpoG3gwBfL/pHhjCgbSi+Xh4UFhq2Jaazfn8K7Zo0JDqiEZ4eDmIOpPL41zGs2ZcCQKi/N00DfcnJL+BgajaZztnyXZoHct2A1jRu6M3ibUks2prIwdRs+rYJ5omLu57RbHYNFEopVU0S07JZvfcoXVsEERHSoNJP9oWFhh+3JLD1YDqH0rJJSMvGx9NB86AGtGjkS6/WwfSMaHTS9QsLDZ+v3s9zC2JJzszl6n6tePyiLpXqcNdAoZRSdVhqVh4vLdxOTHwqn946oMJJGcF1oNDssUopdZYLauDF4xd3Ib+gsFJB4nQ08YlSStUR1ZXLSgOFUkoplzRQKKWUckkDhVJKKZc0UCillHJJA4VSSimXNFAopZRySQOFUkoplzRQKKWUcsmtgUJExohIrIjsEJFHStkvIvKyc/8GEenljnIqpVR95rZAISIewGvAWKALcLWIdClx2Figg/PrNuCNGi2kUkopt9Yo+gE7jDG7jDG5wKfA+BLHjAc+MNZyoJGINK/pgiqlVH3mzkARDuwv9jrOua2ixwAgIreJyCoRWZWUlFSlBVVKqfrMnYGitBSHJXOel+cYu9GY6caYPsaYPk2aVGzNXKWUUmVzZ6CIAyKKvW4JxFfiGKWUUtXInYHiD6CDiESKiDcwCZhb4pi5wA3O0U8DgFRjzMGaLqhSStVnblu4yBiTLyJ/Ar4HPIB3jTGbROQO5/5pwDxgHLADyASmuKu8SilVX7l1hTtjzDxsMCi+bVqxnw1wV02XSymlVBGdma2UUsolDRRKKaVc0kChlFLKJQ0USimlXNJAoZRSyiUNFEoppVzSQKGUUsolDRRKKaVc0kChlFLKJQ0USimlXNJAoZRSyiUNFEoppVzSQKGUUsolDRRKKaVc0kChlFLKJQ0USimlXNJAoZRSyiUNFEoppVzSQKGUUsolDRRKKaVc0kChlFLKJQ0USimlXNJAoZRSyiUNFEoppVzSQKGUUsolDRRKKaVc0kChlFLKJQ0USimlXNJAoZRSyiUNFEoppVzSQKGUUsolDRRKKaVc0kChlFLKJQ0USimlXNJAoZRSyiVPd7ypiIQAnwFtgD3AlcaYo6UctwdIBwqAfGNMn5orpVJKKXBfjeIR4CdjTAfgJ+frsowwxkRrkFBKKfdwV6AYD7zv/Pl94FI3lUMppdRpuKXpCWhqjDkIYIw5KCJhZRxngB9ExABvGmOml3VBEbkNuM358piIxFaybI2Bw5U892xVH+8Z6ud918d7hvp53xW959Zl7RBjzJkXp7QLiywEmpWy66/A+8aYRsWOPWqMCS7lGi2MMfHOQPIjcLcxZkm1FLjoPVfVt2au+njPUD/vuz7eM9TP+67Ke662GoUxZnRZ+0QkQUSaO2sTzYHEMq4R7/yeKCJfAv2Aag0USimlTuauPoq5wI3On28Evi55gIj4i0jA8Z+B84GYGiuhUkopwH2B4t/AeSKyHTjP+RoRaSEi85zHNAWWish6YCXwnTFmQQ2Urcx+kDqsPt4z1M/7ro/3DPXzvqvsnqutj0IppVTdoDOzlVJKuaSBQimllEsaKJxEZIyIxIrIDhFxNVP8rCYiESKySES2iMgmEbnXuT1ERH4Uke3O76cMVz7biYiHiKwVkW+dr+vDPTcSkdkistX5bz6wrt+3iPzZ+X87RkQ+ERHfunjPIvKuiCSKSEyxbWXep4g86vx8ixWRCyryXhoosB8gwGvAWKALcLWIdHFvqapNPvCAMaYzMAC4y3mvFUmrcra6F9hS7HV9uOeXgAXGmE5AD+z919n7FpFw4B6gjzEmCvAAJlE37/k9YEyJbaXep/NvfBLQ1XnO687PvXLRQGH1A3YYY3YZY3KBT7FpRuocY8xBY8wa58/p2A+OcOp4WhURaQlcCLxdbHNdv+dA4FzgHQBjTK4xJoU6ft/Y+WENRMQT8APiqYP37Jx8nFxic1n3OR741BiTY4zZDezAfu6ViwYKKxzYX+x1nHNbnSYibYCewApKpFUBykqrcrZ6EXgIKCy2ra7fc1sgCZjhbHJ72zknqc7etzHmAPA8sA84CKQaY36gDt9zCWXd5xl9xmmgsKSUbXV63LCINAS+AO4zxqS5uzzVSUQuAhKNMavdXZYa5gn0At4wxvQEMqgbTS5lcrbJjwcigRaAv4hc595S1Qpn9BmngcKKAyKKvW6Jra7WSSLihQ0SM40xc5ybE5zpVHCVVuUsNRi4xLm+yafASBH5iLp9z2D/X8cZY1Y4X8/GBo66fN+jgd3GmCRjTB4wBxhE3b7n4sq6zzP6jNNAYf0BdBCRSBHxxnb6zHVzmaqFiAi2zXqLMeaFYrtOm1blbGWMedQY09IY0wb7b/uzMeY66vA9AxhjDgH7RaSjc9MoYDN1+773AQNExM/5f30Uth+uLt9zcWXd51xgkoj4iEgk0AGb8aJcdGa2k4iMw7ZjewDvGmOedW+JqoeIDAF+BTZS1F7/GLafYhbQCvvHdoUxpmRH2VlPRIYDDxpjLhKRUOr4PYtINLYD3xvYBUzBPiDW2fsWkaeAq7Aj/NYCtwANqWP3LCKfAMOx6cQTgCeAryjjPkXkr8BN2N/LfcaY+eV+Lw0USimlXNGmJ6WUUi5poFBKKeWSBgqllFIuaaBQSinlkgYKpZRSLmmgUKoWEZHhx7PbKlVbaKBQSinlkgYKpSpBRK4TkZUisk5E3nSudXFMRP5PRNaIyE8i0sR5bLSILBeRDSLy5fE1AkSkvYgsFJH1znPaOS/fsNgaEjOdM4yVchsNFEpVkIh0xs78HWyMiQYKgGsBf2CNMaYX8At2pizAB8DDxpju2Bnxx7fPBF4zxvTA5iM66NzeE7gPuzZKW2yuKqXcxtPdBVDqLDQK6A384XzYb4BNvlYIfOY85iNgjogEAY2MMb84t78PfC4iAUC4MeZLAGNMNoDzeiuNMXHO1+uANsDSar8rpcqggUKpihPgfWPMoydtFPl7ieNc5cdx1ZyUU+znAvTvVLmZNj0pVXE/ARNFJAxOrFPcGvv3NNF5zDXAUmNMKnBURIY6t18P/OJcAyRORC51XsNHRPxq8iaUKi99UlGqgowxm0Xkb8APIuIA8oC7sAsDdRWR1UAqth8DbLrnac5AcDyDK9ig8aaIPO28xhU1eBtKlZtmj1WqiojIMWNMQ3eXQ6mqpk1PSimlXNIahVJKKZe0RqGUUsolDRRKKaVc0kChlFLKJQ0USimlXNJAoZRSyqX/B5cyLZHmKmdHAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_accuracy(\"accuracy_graph_v2.png\")\n", + "plot_loss(\"loss_graph_v2.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "def plot_confusion_matrix(cm, classes,\n", + " normalize=False,\n", + " title='Confusion Matrix',\n", + " cmap=plt.cm.Blues):\n", + " \"\"\"\n", + " Diese Funktion printet und plottet die Confusion Matrix\n", + " \"\"\"\n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(classes))\n", + " plt.xticks(tick_marks, classes, rotation=45)\n", + " plt.yticks(tick_marks, classes)\n", + " \n", + " if normalize:\n", + " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + " print(\"Normierte Confusion Matrix\")\n", + " else:\n", + " print(\"Confusion Matrix ohne Normierung\")\n", + " \n", + " print(cm)\n", + " \n", + " thresh = cm.max() / 2\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j, i, cm[i, j],\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + " \n", + " plt.tight_layout()\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix ohne Normierung\n", + "[[97 4 6 2]\n", + " [40 13 39 1]\n", + " [12 1 62 12]\n", + " [ 3 3 58 47]]\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'itertools' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_300/163190581.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mcm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrounded_predictions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mplot_confusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcm_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Confusion Matrix'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_300/585287180.py\u001b[0m in \u001b[0;36mplot_confusion_matrix\u001b[1;34m(cm, classes, normalize, title, cmap)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[0mthresh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m 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+ "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "predictions = model.predict(x=x_test)\n", + "rounded_predictions = np.argmax(predictions, axis=-1)\n", + "\n", + "cm_labels = range(4)\n", + "cm = confusion_matrix(y_true=y_test, y_pred=rounded_predictions)\n", + "\n", + "plot_confusion_matrix(cm=cm, classes=cm_labels, title='Confusion Matrix', normalize=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.32" + ] + }, + "execution_count": 294, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "clf = svm.SVC(gamma=0.001, C=100.)\n", + "clf.fit(data_training, labels_training)\n", + "clf.score(data_test, labels_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Josh\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: 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", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.6775" + ] + }, + "execution_count": 296, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "clf1 = LogisticRegression(max_iter=1000, random_state=123)\n", + "clf1.fit(data_training, labels_training)\n", + "clf1.score(data_test, labels_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7225" + ] + }, + "execution_count": 299, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "clf2 = RandomForestClassifier(n_estimators=100, random_state=123)\n", + "clf2.fit(data_training, labels_training)\n", + "clf2.score(data_test, labels_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.4225" + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "clf3 = GaussianNB()\n", + "clf3.fit(data_training, labels_training)\n", + "clf3.score(data_test, labels_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} -- GitLab