test-the-script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "245ae981-705a-4929-aef0-f3bca1d82beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0419f33-eb4a-4b4b-b857-d24ef1bb157d",
   "metadata": {},
   "outputs": [],
   "source": [
    "exec(open(\"configuration.py\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "696a908a-c5af-4a0a-ade7-12442854950e",
   "metadata": {},
   "outputs": [],
   "source": [
    "original_stdout = sys.stdout\n",
    "if LogFile:\n",
    "    if os.path.exists(LogFile):\n",
    "        os.remove(LogFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b629ff36-70d3-413f-b1f8-b59f6e40b611",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16 files have been deleted.\n",
      "in: ziffer_raw\\0.0_ROI0_20211125-030625.jpg  -  out: ziffer_resize/0.0_ROI0_20211125-030625.jpg\n",
      "in: ziffer_raw\\0_0_digit1_20210322-100645.jpg  -  out: ziffer_resize/0_0_digit1_20210322-100645.jpg\n",
      "in: ziffer_raw\\0_0_digit7_20210406-115029.jpg  -  out: ziffer_resize/0_0_digit7_20210406-115029.jpg\n",
      "in: ziffer_raw\\1_1_3.jpg  -  out: ziffer_resize/1_1_3.jpg\n",
      "in: ziffer_raw\\1_1_digit5_20210527-191335.jpg  -  out: ziffer_resize/1_1_digit5_20210527-191335.jpg\n",
      "in: ziffer_raw\\2_2_digit3_20210627-211437.jpg  -  out: ziffer_resize/2_2_digit3_20210627-211437.jpg\n",
      "in: ziffer_raw\\2_2_digit4_20210406-110843.jpg  -  out: ziffer_resize/2_2_digit4_20210406-110843.jpg\n",
      "in: ziffer_raw\\2_2_digit4_20210406-111334.jpg  -  out: ziffer_resize/2_2_digit4_20210406-111334.jpg\n",
      "in: ziffer_raw\\2_2_digit4_20210413-094554(1).jpg  -  out: ziffer_resize/2_2_digit4_20210413-094554(1).jpg\n",
      "in: ziffer_raw\\2_2_digit5_20210526-115910.jpg  -  out: ziffer_resize/2_2_digit5_20210526-115910.jpg\n",
      "in: ziffer_raw\\2_2_digit5_20210526-120410.jpg  -  out: ziffer_resize/2_2_digit5_20210526-120410.jpg\n",
      "in: ziffer_raw\\2_2_digit6_20210412-164714.jpg  -  out: ziffer_resize/2_2_digit6_20210412-164714.jpg\n",
      "in: ziffer_raw\\2_2_digit7_20210406-123018.jpg  -  out: ziffer_resize/2_2_digit7_20210406-123018.jpg\n",
      "in: ziffer_raw\\2_2_digit7_20210526-025315.jpg  -  out: ziffer_resize/2_2_digit7_20210526-025315.jpg\n",
      "in: ziffer_raw\\2_2_digit8_20210526-105627.jpg  -  out: ziffer_resize/2_2_digit8_20210526-105627.jpg\n",
      "in: ziffer_raw\\2_2_ht6_20210626-080221.jpg  -  out: ziffer_resize/2_2_ht6_20210626-080221.jpg\n"
     ]
    }
   ],
   "source": [
    "if not ReportOnly:\n",
    "    exec(open(\"c_scripts/image_preparation.py\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27c6383d-5d22-4c3f-8281-38fc5b990e78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dig-s0\n",
      "a_output_actual/dig-s0\n",
      "File Exists\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization (BatchNo (None, 32, 20, 3)         12        \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 32, 20, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 16, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 10, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 8, 5, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 11)                5643      \n",
      "=================================================================\n",
      "Total params: 324,631\n",
      "Trainable params: 324,625\n",
      "Non-trainable params: 6\n",
      "_________________________________________________________________\n",
      "16\n",
      "(16, 32, 20, 3)\n",
      "(16, 11)\n",
      "(12, 32, 20, 3)\n",
      "(4, 32, 20, 3)\n",
      "3/3 [==============================] - 1s 365ms/step - loss: 1.1708 - accuracy: 0.7500 - val_loss: 2.8656 - val_accuracy: 0.5000\n",
      "INFO:tensorflow:Assets written to: a_output_actual/dig-s0\\assets\n",
      "INFO:tensorflow:Assets written to: b_output_historic/saved_model/20211227-111956_dig-s0\\assets\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp8jvfpqzd\\assets\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpkhmvzs5q\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpkhmvzs5q\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_output_actual/dig-s0-q.tflite\n",
      "20211227\t16\t1\t1.3340073823928833\t2.8655853271484375\n",
      "dig-s0\n",
      "File Exists\n",
      "ziffer_resize\\0.0_ROI0_20211125-030625.jpg 0 2\n",
      "ziffer_resize\\0_0_digit1_20210322-100645.jpg 0 2\n",
      "ziffer_resize\\0_0_digit7_20210406-115029.jpg 0 2\n",
      "ziffer_resize\\1_1_3.jpg 1 2\n",
      "ziffer_resize\\1_1_digit5_20210527-191335.jpg 1 2\n",
      "dig-s1\n",
      "a_output_actual/dig-s1\n",
      "File Exists\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization_1 (Batch (None, 32, 20, 3)         12        \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 32, 20, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 16, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 10, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 8, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 5, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 4, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 11)                2827      \n",
      "=================================================================\n",
      "Total params: 190,487\n",
      "Trainable params: 190,481\n",
      "Non-trainable params: 6\n",
      "_________________________________________________________________\n",
      "16\n",
      "(16, 32, 20, 3)\n",
      "(16, 11)\n",
      "(12, 32, 20, 3)\n",
      "(4, 32, 20, 3)\n",
      "3/3 [==============================] - 1s 110ms/step - loss: 0.7011 - accuracy: 0.6667 - val_loss: 9.5889 - val_accuracy: 0.5000\n",
      "INFO:tensorflow:Assets written to: a_output_actual/dig-s1\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: a_output_actual/dig-s1\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: b_output_historic/saved_model/20211227-112005_dig-s1\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: b_output_historic/saved_model/20211227-112005_dig-s1\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpshba2fkh\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpshba2fkh\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpvc928eyl\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpvc928eyl\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_output_actual/dig-s1-q.tflite\n",
      "20211227\t16\t1\t0.741168200969696\t9.58890151977539\n",
      "dig-s1\n",
      "File Exists\n",
      "ziffer_resize\\0.0_ROI0_20211125-030625.jpg 0 2\n",
      "ziffer_resize\\0_0_digit1_20210322-100645.jpg 0 2\n",
      "ziffer_resize\\0_0_digit7_20210406-115029.jpg 0 2\n",
      "ziffer_resize\\1_1_3.jpg 1 2\n",
      "ziffer_resize\\1_1_digit5_20210527-191335.jpg 1 2\n",
      "dig-s2\n",
      "a_output_actual/dig-s2\n",
      "File Exists\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization (BatchNo (None, 32, 20, 3)         12        \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 32, 20, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 16, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 10, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 5, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 8, 5, 32)          9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 2, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 11)                2827      \n",
      "=================================================================\n",
      "Total params: 88,023\n",
      "Trainable params: 88,017\n",
      "Non-trainable params: 6\n",
      "_________________________________________________________________\n",
      "16\n",
      "(16, 32, 20, 3)\n",
      "(16, 11)\n",
      "(12, 32, 20, 3)\n",
      "(4, 32, 20, 3)\n",
      "3/3 [==============================] - 1s 94ms/step - loss: 1.0572 - accuracy: 0.8021 - val_loss: 0.6074 - val_accuracy: 0.7500\n",
      "INFO:tensorflow:Assets written to: a_output_actual/dig-s2\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: a_output_actual/dig-s2\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: b_output_historic/saved_model/20211227-112013_dig-s2\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: b_output_historic/saved_model/20211227-112013_dig-s2\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpgxnfdkm_\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpgxnfdkm_\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpt7d7om0h\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpt7d7om0h\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_output_actual/dig-s2-q.tflite\n",
      "20211227\t16\t1\t1.3887602090835571\t0.6073781251907349\n",
      "dig-s2\n",
      "File Exists\n",
      "ziffer_resize\\0.0_ROI0_20211125-030625.jpg 0 2\n",
      "ziffer_resize\\0_0_digit1_20210322-100645.jpg 0 2\n",
      "ziffer_resize\\0_0_digit7_20210406-115029.jpg 0 2\n",
      "ziffer_resize\\1_1_3.jpg 1 2\n",
      "ziffer_resize\\1_1_digit5_20210527-191335.jpg 1 2\n",
      "dig-s3\n",
      "a_output_actual/dig-s3\n",
      "File Exists\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization_1 (Batch (None, 32, 20, 3)         12        \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 32, 20, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 16, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 10, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 8, 5, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 8, 5, 32)          9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 4, 2, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 11)                1419      \n",
      "=================================================================\n",
      "Total params: 53,719\n",
      "Trainable params: 53,713\n",
      "Non-trainable params: 6\n",
      "_________________________________________________________________\n",
      "16\n",
      "(16, 32, 20, 3)\n",
      "(16, 11)\n",
      "(12, 32, 20, 3)\n",
      "(4, 32, 20, 3)\n",
      "3/3 [==============================] - 1s 91ms/step - loss: 0.5685 - accuracy: 0.8854 - val_loss: 11.1960 - val_accuracy: 0.2500\n",
      "INFO:tensorflow:Assets written to: a_output_actual/dig-s3\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: a_output_actual/dig-s3\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: b_output_historic/saved_model/20211227-112021_dig-s3\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: b_output_historic/saved_model/20211227-112021_dig-s3\\assets\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpekaa0tgu\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpekaa0tgu\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpef9qnibg\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpef9qnibg\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_output_actual/dig-s3-q.tflite\n",
      "20211227\t16\t1\t0.7058742046356201\t11.19602108001709\n",
      "dig-s3\n",
      "File Exists\n",
      "ziffer_resize\\0.0_ROI0_20211125-030625.jpg 0 2\n",
      "ziffer_resize\\0_0_digit1_20210322-100645.jpg 0 2\n",
      "ziffer_resize\\0_0_digit7_20210406-115029.jpg 0 2\n",
      "ziffer_resize\\1_1_3.jpg 1 2\n",
      "ziffer_resize\\1_1_digit5_20210527-191335.jpg 1 2\n"
     ]
    },
    {
     "data": {
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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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PGKmygmCnxyR9sWoOfUIxx/9hSf8bEStVuepO3ZYlRU6cgaOvrZD0oCtXWfl+jY9xxPZzqlz+7M+LbXepckWaF2xfIOmXOjUl0a3imp5rJD1dbLovIt7N9Mk5RcQh2ytVWQ7116pMg3T6nKSVtk9I+omk1zrHocr0y7PFD6QDkm5QZSXL220fU+XaqtWvC2AQYjVCoCS239d5PU7bX1XlQtG3ljwsJMIZOFCe62x/TZW/hy9JWljucJANZ+AAkBQvYgJAUgQcAJIi4ACQFAEHgKQIOAAk9f+ZEpmoFrZLaQAAAABJRU5ErkJggg==\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"
    },
    {
     "data": {
      "image/png": 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T7Jc0wXaHpL9XsebFj22v0JvnPZQlLYuIe950hX2miiP7NbY/HxFfbdREkA+nUNBqz0qaX15ecoj9DuVSSbJ9hopTDS9JukfSsprzy3Pr+DwbJX3U9qTydMtF5bZGOhDrF22/U+Wco1jne6D8ASMVq/UdcI+kq2vOoc8uz/EfK+mnEbFS0pdVnBpCG+MIHK12s6Q7bH9S0mjfbWev7YclTZT0++W2GyT9raRHXbwhxDN6/ZTEsCLiB7bXqFjVUpK+HBGHc/pkRBGx2/ZKSdsk/Y+K0yAHXClppe39kr6r4t2lpCLOnZJ+UP5A2inpoyqWm73W9qCkPXrj8wJoQ6xGCFTE9jsjYk95+TOSpkfEH1U8LCTCEThQnQ/b/nMV34fPqb3eQg4NwBE4ACTFk5gAkBQBB4CkCDgAJEXAASApAg4ASf0/sNzzCdWXeV8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y5A5qs5rhAmFDoiXpoD7BBcKsZrhA2JBYm2/nmBkTmDyuLusoZlYmFwgbEjk/QW1Wc1wgLHXPd+zh+Y6XWOgnqM1qiguEpS6Xdwe1WS1ygbDUteTbGCWYf9TkrKOY2QFwgbDUrc2385rDJzH+kNSeyzSzFLhAWKoigpZkDmozqy0uEJaq/Au72b5rL02z6rOOYmYHyAXCUuUOarPa5QJhqcq1tlE3Whx7xKSso5jZAXKBsFTlNrZz/JGTGTtmdNZRzOwADVggJL1G0t2SHkmWmyT9bfrRrNZ1dwePuIParGaVcwbxz8BlwD6AiMhRmOWtX5JWStrcU1hKrH+fpJykFkm/k7SoaN2GpP1hSavL+1Gs2jy9bRc7XupkkZ+gNqtJ5RSI8RHxYK+2zjL2uxZY2s/6p4E3RsRC4CpgRa/1b4qIEyOiuYz3sirUknRQew5qs9pUzpNLWyW9CggASe8BNg20U0TcJ2lOP+t/V7T4ANBYRharIWvzbYyrG8W8wyZmHcXMBqGcAvExCp/uj5PUSuGT//srnONDwM+LlgO4U1IA346I3mcXL5O0HFgOMHv27ArHsoPRkm9nwVFTGDPa90KY1aIBC0REPAW8RdIEYFRE7KhkAElvolAgTi9qPj0iWiUdBtwl6Q8RcV8f+VaQXJ5qbm6OSmazwevs6uaRZ9tZdrKLtlmtGrBASLq81zIAEXHlwb65pCbgO8DZEbGtpz0iWpPvmyXdBpwMlCwQVp2e3LyTPfu63UFtVsPKOfffVfTVBZwNzDnYN5Y0G/gx8IGIeKKofYKkST2vgSVAyTuhrHq5g9qs9pVzienvi5clfRW4Y6D9JN0EnAnMkJQHrgDqkmNeA1wOTAf+KTkr6UzuWDocuC1pGwPcGBG/KP9HsmqwNt/GpLFjmDt9QtZRzGyQBjP+8njKuOMoIpYNsP5i4OIS7U8Bi/bfw2pJS2s7JzRMYdQoZR3FzAapnD6IFpJbXIHRwEzgoPsfbPh6qbOLdZs6+ODpc7OOYmYHoZwziHcWve4Eno+Ich6UsxHq8ed2sK8raGqozzqKmR2EPguEpGnJy963tU6WRERsTy+W1bK1SQd1kzuozWpaf2cQayhcWip1ETmAY1JJZDWvJd/G1PF1NE49NOsoZnYQ+iwQEeELyDYouXw7TY31Lz8zY2a1qay7mCRNBeYB43ra+nqy2Ua2F/d28sTzO1gy//Cso5jZQSrnLqaLgUsp3Nr6MHAKcD/w5lSTWU167NkOugMW+glqs5pXzpPUlwInAc9ExJuAxUBbmqGsdrmD2mz4KKdA7ImIPQCSxkbEH4Bj041ltaol38bhk8dy+ORxA29sZlWtnD6IvKR64CcURlZ9AXgmzVBWu3o6qM2s9vX3HMRngJsi4k+Tps9J+hUwBfDYSLafjj37eGrrLv50cUPWUcysAvo7gzgKuF/SBuAm4EcR8eshSWU16ZGe/odZ9dkGMbOK6LMPIiI+CcwG/hZYCOQk/ULShT3DcZsVy7UmQ3w3uIPabDjot5M6Cn4dER+hcJvr14BPAM8PQTarMbl8G7OmHcq0CYdkHcXMKqDcB+UWAucD7wW2ApelGcpq09qN7Zw4uz7rGGZWIf11Us+jUBTOpzCT3M3AkmS+BrNX2LbzJVrbdnPBqUdnHcXMKqS/S0y/AMYC742Ipoj44oEWB0krJW2WVHLKUBV8Q9J6STlJry1ad6GkJ5OvCw/kfW3o9fQ/+BZXs+Gjv8H6XlWB418LfBP4Xh/rz6YwxtM84PXAt4DXJ0ONXwE0Uxg5do2kVRHxQgUyWQpa8u1IcELD5KyjmFmFDGbK0bJFxH2S5vSzybnA9yIigAck1Us6ksJc1nf1zDkh6S5gKYXbbe0gdXUHV/30MZ7v2FOxY+by7RwzYwKTxtVV7Jhmlq1UC0QZGoCNRcv5pK2v9v1IWg4sB5g9e3Y6KYeZdZs6uPZ3G2ioP5QJY0dX5JgTxo7mz5tnVeRYZlYdsi4QBy0iVgArAJqbm2OAzQ1oSfoLbvyvr+fo6RMyTmNm1aq/u5haKFz/328VhUckmirw/q1A8cfOxqStlcJlpuL2eyvwfkbheYUph9Yxe9r4rKOYWRXr7wzinUPw/quASyTdTKGTuj0iNkm6A/hiMlERwBL87EXFFAbUm+IZ38ysX/3dxXTQI7ZKuonCmcAMSXkKdybVJce/BrgdeDuwHngR+Mtk3XZJVwEPJYe6sqfD2g7Onn1dPP7cDpaf4SnFzax/5cwodwrwj8DxwCHAaGBXRAx4P2NELBtgfQAf62PdSmDlQO9hB+axTR10doefVzCzAZUzYdA3gWXAk8ChwMXA1WmGsvS0eMY3MytTOQWCiFgPjI6Iroj4FwrPJFgNWptvY8bEsRw5xTO+mVn/yrnN9UVJhwAPS/o7YBNlFharPi3uoDazMpXzh/4DyXaXALso3Jb6X9IMZenY+VIn67fs9OUlMytLOQXivIjYExEdEfH5iPgUQ3MLrFXYo63tRMAid1CbWRnKKRClRlK9qMI5bAjkkg7qhT6DMLMy9Pck9TLgL4C5klYVrZoE+JmEGpRrbaeh/lBmTBybdRQzqwH9dVL/jkKH9Azg74vadwC5NENZOnL5Ns8XbWZlG+hJ6meAU4cujqWl7cW9PLPtRd57kkdcNbPy9HeJ6TcRcbqkHbxy0L6ewfo8M0wN6RnBtamhPtsgZlYz+juDOD35Pmno4lhaXu6g9iUmMytTOWMxTSvRvCMi9qWQx1KSy7cxZ/p4poz3jG9mVp5ybnP9PbAFeILCeExbgA2Sfi/pdWmGs8opDPFdn3UMM6sh5RSIu4C3R8SMiJgOnA38FPgo8E9phrPK2LxjD5va9/gJajM7IOUUiFMi4o6ehYi4Ezg1Ih4AfEN9DfjjCK712QYxs5pSzmB9myT9D+DmZPm9wPOSRgPdqSWzisnl2xklWHCUbzwzs/KVcwbxFxTmhP5J8jU7aRsN/Hl/O0paKulxSeslfbbE+q9Jejj5ekJSW9G6rqJ1q3rva+XL5dt49WETmTC2nM8DZmYFA/7FiIitwMf7WL2+r/2SM4yrgbcCeeAhSasi4rGiY3+yaPuPA4uLDrE7Ik4cKJ/1LyLI5dt503GHZR3FzGpMfw/KfT0iPiHpX3nlg3IARMQ5Axz7ZGB9RDyVHO9m4FzgsT62X0ZhzmqroGfb97Bt1153UJvZAevvDOL7yfevDvLYDcDGouU88PpSG0o6GpgL3FPUPE7SaqAT+HJE/KSPfZcDywFmz549yKjDV25jG+AOajM7cP09Sb0m+f5rSTOT11tSynE+cEtEdBW1HR0RrZKOAe6R1BIR/1Ei5wpgBUBzc/N+ZzojXa61nTGjxHFH+IF4Mzsw/XZSS/qcpK3A48ATkrZIurzMY7dSmH2uR2PSVsr5wE3FDRHRmnx/CriXV/ZPWJly+TaOO3IS4+pGZx3FzGpMnwVC0qeA04CTImJaREylcInoNEmf7Gu/Ig8B8yTNTea0Ph/Y724kSccBU4H7i9qmShqbvJ6R5Oir78L60NNB7ctLZjYY/Z1BfABYFhFP9zQkn+bfD1ww0IEjopPCPNZ3AOuAH0bEo5KulFTcwX0+cHNEFF8eOh5YLWkt8CsKfRAuEAdow7YX2bGnkyYP0Gdmg9BfJ3VdcovrK0TEFklljfgWEbcDt/dqu7zX8udK7Pc7YGE572F9y+XbAHdQm9ng9HcGsXeQ66xK5PLtjB0zinmHT8w6ipnVoP7OIBZJ6ijRLmBcSnmsgnL5NhYcNZm60eU8MG9m9kr93ebq215qWFd38Ehrh6cYNbNB80fLYWr95p3s3tflJ6jNbNBcIIapP3ZQu0CY2eC4QAxTuXw7E8eO4ZgZ7qA2s8FxgRimcq3tnNAwmVGjlHUUM6tRLhDD0N7ObtY92+HnH8zsoLhADENPPL+DvV3d7n8ws4PiAjEMre3poG6ozzSHmdU2F4hhKLexnfrxdcyadmjWUcyshrlADEO51nYWNkxBcge1mQ2eC8Qws3tvF088v4NF7qA2s4PkAjHMPLapg67uYKE7qM3sILlADDM9T1D7DMLMDlaqBULSUkmPS1ov6bMl1l+UTGP6cPJ1cdG6CyU9mXxdmGbO4aQl385hk8ZyxBQPuGtmB6e/4b4PiqTRwNXAW4E88JCkVSVmhvtBRFzSa99pwBVAMxDAmmTfF9LKO1yszbf5+Qczq4g0zyBOBtZHxFMRsRe4GTi3zH3fBtwVEduTonAXsDSlnMPGjj37eGrrLj9BbWYVkWaBaAA2Fi3nk7be3i0pJ+kWST2TF5S7L5KWS1otafWWLVsqkbtmPdLaQQTuoDazisi6k/pfgTkR0UThLOG6Az1ARKyIiOaIaJ45c2bFA9aSl4f4bnCBMLODl2aBaAWKpzNrTNpeFhHbIuKlZPE7wOvK3df2l2ttp6H+UKZPHJt1FDMbBtIsEA8B8yTNlXQIcD6wqngDSUcWLZ4DrEte3wEskTRV0lRgSdJm/cjl21g0y2cPZlYZqd3FFBGdki6h8Id9NLAyIh6VdCWwOiJWAX8l6RygE9gOXJTsu13SVRSKDMCVEbE9razDwQu79rJx+27+4uSjs45iZsNEagUCICJuB27v1XZ50evLgMv62HclsDLNfMNJrrUdgEXuoDazCsm6k9oqpCXpoD7BBcLMKsQFYphYm2/nmBkTmDyuLusoZjZMuEAMEy35dj9BbWYV5QIxDGzu2MNzHXtY6CeozayCXCCGgbV5d1CbWeW5QAwDLfk2RgnmHzU56yhmNoy4QAwDa/PtvObwSYw/JNW7ls1shHGBqHERQUsyB7WZWSW5QNS4/Au72b5rL02z6rOOYmbDjAtEjWvxE9RmlhIXiBq3Nt9G3Whx7BGTso5iZsOMC0SNa8m3c/yRkxk7ZnTWUcxsmHGBqGHd3UFL3h3UZpYOF4ga9vS2Xex4qZNFfoLazFLgAlHDWpInqD0HtZmlwQWihq3NtzGubhTzDpuYdRQzG4ZSLRCSlkp6XNJ6SZ8tsf5Tkh6TlJN0t6Sji9Z1SXo4+VrVe18rnEEsOGoKY0a7zptZ5aX2l0XSaOBq4GxgPrBM0vxem/070BwRTcAtwN8VrdsdEScmX+eklbNWdXZ188izHuLbzNKT5kfPk4H1EfFUROwFbgbOLd4gIn4VES8miw8AjSnmGVbWb9nJnn3d7qA2s9SkWSAagI1Fy/mkrS8fAn5etDxO0mpJD0g6r6+dJC1Ptlu9ZcuWgwpcS3Ib3UFtZumqiuE/Jb0faAbeWNR8dES0SjoGuEdSS0T8R+99I2IFsAKgubk5hiRwFci1tjFp7BjmTp+QdRQzG6bSPINoBWYVLTcmba8g6S3A/wTOiYiXetojojX5/hRwL7A4xaw1J5dv54SGKYwapayjmNkwlWaBeAiYJ2mupEOA84FX3I0kaTHwbQrFYXNR+1RJY5PXM4DTgMdSzFpTXursYt2mDppm+fKSmaUntUtMEdEp6RLgDmA0sDIiHpV0JbA6IlYBXwEmAj+SBPCfyR1LxwPfltRNoYh9OSJcIBKPP7eDfV1BU0N91lHMbBhLtQ8iIm4Hbu/VdnnR67f0sd/vgIVpZqtlPXNQ+xZXM0uTn7CqQS35NqaOr6Nx6qFZRzGzYcwFogbl8u00NdaTXJYzM0uFC0SN2b23iyee3+EZ5MwsdS4QNebRZ9vpDljoJ6jNLGUuEDUm5w5qMxsiLhA1Jpdv4/DJYzl88riso5jZMOcCUWN6OqjNzNLmAlFDOvbs46mtu2jyHNRmNgRcIGrIIz39D7Pqsw1iZiOCC0QNybUmQ3z7DMLMhoALRA3J5duYNe1Qpk04JOsoZjYCuEDUEHdQm9lQcoGoEdt2vkT+hd3uoDazIeMCUSNaWnsekKvPNoiZjRguEDUil29HghMaJmcdxcxGCBeIGpHLt3HMjAlMGleXdRQzGyFSLRCSlkp6XNJ6SZ8tsX6spB8k6/9N0pyidZcl7Y9LeluaOWuBO6jNbKilViAkjQauBs4G5gPLJM3vtdmHgBci4tXA14D/new7n8Ic1guApcA/JccbkZ5r38PmHS95gD4zG1JpTjl6MrA+Ip4CkHQzcC5QPLf0ucDnkte3AN9UYRacc4GbI+Il4GlJ65Pj3Z9G0Hf942/Ys68rjUNXxO4kmwuEmQ2lNAtEA7CxaDkPvL6vbSKiU1I7MD1pf6DXvg2l3kTScmA5wOzZswcV9FUzJ7C3q3tQ+w6VNx17mC8xmdmQSrNADImIWAGsAGhubo7BHOPr5y+uaCYzs+EgzU7qVmBW0XJj0lZyG0ljgCnAtjL3NTOzFKVZIB4C5kmaK+kQCp3Oq3ptswq4MHn9HuCeiIik/fzkLqe5wDzgwRSzmplZL6ldYkr6FC4B7gBGAysj4lFJVwKrI2IV8F3g+0kn9HYKRYRkux9S6NDuBD4WEdXbi2xmNgyp8IF9eGhubo7Vq1dnHcPMrGZIWhMRzaXW+UlqMzMryQXCzMxKcoEwM7OSXCDMzKykYdVJLWkL8Mwgd58BbK1gnEqr9nzgjJVQ7fmg+jNWez6oroxHR8TMUiuGVYE4GJJW99WTXw2qPR84YyVUez6o/ozVng9qIyP4EpOZmfXBBcLMzEpygfijFVkHGEC15wNnrIRqzwfVn7Ha80FtZHQfhJmZleYzCDMzK8kFwszMShrxBULSUkmPS1ov6bNZ5+lN0ixJv5L0mKRHJV2adaZSJI2W9O+Sfpp1llIk1Uu6RdIfJK2TdGrWmXqT9Mnkv/Ejkm6SNK4KMq2UtFnSI0Vt0yTdJenJ5PvUKsv3leS/c07SbZLqs8qX5NkvY9G6v5YUkmZkkW0gI7pASBoNXA2cDcwHlkman22q/XQCfx0R84FTgI9VYUaAS4F1WYfox/8BfhERxwGLqLKskhqAvwKaI+IECkPkn59tKgCuBZb2avsscHdEzAPuTpazci3757sLOCEimoAngMuGOlQv17J/RiTNApYA/znUgco1ogsEcDKwPiKeioi9wM3AuRlneoWI2BQRv09e76Dwh63k/NxZkdQIvAP4TtZZSpE0BTiDwvwjRMTeiGjLNFRpY4BDk9kVxwPPZpyHiLiPwlwtxc4FrkteXwecN5SZipXKFxF3RkRnsvgAhRkpM9PHvyHA14D/DlTtnUIjvUA0ABuLlvNU2R/fYpLmAIuBf8s4Sm9fp/A/enfGOfoyF9gC/EtyGew7kiZkHapYRLQCX6XwaXIT0B4Rd2abqk+HR8Sm5PVzwOFZhhnAB4GfZx2iN0nnAq0RsTbrLP0Z6QWiZkiaCNwKfCIiOrLO00PSO4HNEbEm6yz9GAO8FvhWRCwGdpHtZZH9JNfxz6VQzI4CJkh6f7apBpZMEVyVn4Al/U8Kl2hvyDpLMUnjgb8BLs86y0BGeoFoBWYVLTcmbVVFUh2F4nBDRPw46zy9nAacI2kDhUt0b5Z0fbaR9pMH8hHRc+Z1C4WCUU3eAjwdEVsiYh/wY+ANGWfqy/OSjgRIvm/OOM9+JF0EvBN4X1Tfw16vovBBYG3ye9MI/F7SEZmmKmGkF4iHgHmS5ko6hEKn4KqMM72CJFG4dr4uIv4h6zy9RcRlEdEYEXMo/PvdExFV9ck3Ip4DNko6Nmk6i8J859XkP4FTJI1P/pufRZV1pBdZBVyYvL4Q+L8ZZtmPpKUULnmeExEvZp2nt4hoiYjDImJO8nuTB16b/H9aVUZ0gUg6si4B7qDwy/jDiHg021T7OQ34AIVP5g8nX2/POlQN+jhwg6QccCLwxWzjvFJydnML8HughcLvZubDMUi6CbgfOFZSXtKHgC8Db5X0JIUzny9XWb5vApOAu5Lfl2uyytdPxprgoTbMzKykEX0GYWZmfXOBMDOzklwgzMysJBcIMzMryQXCzMxKcoEw64ekL0l6k6TzJF2WtF0r6enkFsrf94wMK+n2/kYOTY5RjQMtmpXkAmHWv9dTGPDtjcB9Re2fiYgTKQzZ8W2AiHj7AIMAnkdh1OCyJQP3mWXCBcKshGROgRxwEoWHnC4GviWp9/g59wGvTvbZ0DOuv6QLkvkI1kr6vqQ3AOcAX0nOPF4l6V5Jzcn2M5JhF5B0kaRVku4B7pY0IZlT4MFksMGqGnHYhi9/OjErISI+I+mHwAXAp4B7I+I0KFxiKtr0XRSefH6ZpAXA3wJviIitkqZFxHZJq4CfRsQtyXb9RXgt0JTs90UKQ5h8MLmE9aCkX0bEror8sGZ9cIEw69trgbXAcew/LtJXJP0thWHEew+d8GbgRxGxFSAiSs0FMJC7ivZbQmFAxE8ny+OA2SUymVWUC4RZL5JOpDALWCOwlcLkPZL0MNAzVelnes4EDkInf7zM23t60eKzAwHvjojHD/L9zA6I+yDMeomIh5MO6CcodCrfA7wtIk6MiN1lHOIe4M8kTYfCHM5J+w4Kg8j12AC8Lnn9nn6Odwfw8WSUVyQtLvNHMTsoLhBmJUiaCbwQEd3AcRFR9vDgyYjA/wv4taS1QM8w7TcDn0k6ml9FYQa5j0j6d6C/SeuvAuqAnKRHk2Wz1Hk0VzMzK8lnEGZmVpILhJmZleQCYWZmJblAmJlZSS4QZmZWkguEmZmV5AJhZmYl/X9zarPIQMyvwwAAAABJRU5ErkJggg==\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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T7Jc0wXaHpL9XsebFj22v0JvnPZQlLYuIe950hX2miiP7NbY/HxFfbdREkA+nUNBqz0qaX15ecoj9DuVSSbJ9hopTDS9JukfSsprzy3Pr+DwbJX3U9qTydMtF5bZGOhDrF22/U+Wco1jne6D8ASMVq/UdcI+kq2vOoc8uz/EfK+mnEbFS0pdVnBpCG+MIHK12s6Q7bH9S0mjfbWev7YclTZT0++W2GyT9raRHXbwhxDN6/ZTEsCLiB7bXqFjVUpK+HBGHc/pkRBGx2/ZKSdsk/Y+K0yAHXClppe39kr6r4t2lpCLOnZJ+UP5A2inpoyqWm73W9qCkPXrj8wJoQ6xGCFTE9jsjYk95+TOSpkfEH1U8LCTCEThQnQ/b/nMV34fPqb3eQg4NwBE4ACTFk5gAkBQBB4CkCDgAJEXAASApAg4ASf0/sNzzCdWXeV8AAAAASUVORK5CYII=\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 x in configurations:\n",
    "    arg1 = x\n",
    "    arg2 = x\n",
    "    if not ReportOnly:\n",
    "#        print(\"Das ist in Script - Teil 1: \" + str(arg1))\n",
    "        a_script = open(\"c_scripts//doTraining.py\").read()\n",
    "        sys.argv = [\"c_scripts//doTraining.py\", arg1]\n",
    "        exec(a_script)\n",
    "#    print(\"Das ist in Script - Teil 2: \" + str(arg2))\n",
    "    b_script = open(\"c_scripts//doReport.py\").read()\n",
    "    sys.argv = [\"c_scripts//doReport.py\", arg2]\n",
    "    exec(b_script)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00452155",
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.stdout = original_stdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18755765-8322-49bd-ad7d-0acbcc18025b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89e4ffe0-3327-48f2-af4d-601872042d65",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}