v12.0.9

jomjol · Jan 14, 2024 · 04bd619 · 04bd619
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diff --git a/Image_Preparation.ipynb b/Image_Preparation.ipynb
@@ -40,7 +40,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 files have been deleted.\n"
+      "231 files have been deleted.\n"
      ]
     }
    ],
@@ -53,41 +53,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 data_raw_all/9.4_bdeab982fbac878494bf669d45b91020.jpg\n"
+      "0 data_raw_all\\0.0_0.0.jpg\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_530/2001317934.py:14: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n",
+      "C:\\Users\\Muell\\AppData\\Local\\Temp\\ipykernel_11928\\2001317934.py:14: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.\n",
       "  test_image = test_image.resize((target_size_x, target_size_y), Image.NEAREST)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+      "2000 data_raw_all\\2.6_697ad8d9b31ecfa8ee5d8f2eb67bac87.jpg\n",
+      "2500 data_raw_all\\3.3_1628_zeiger3_2019-06-01T184019.jpg\n",
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      ]
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@@ -112,7 +113,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -121,9 +121,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['data_raw_all\\\\0.3_f38f2eb5263c3f8159ae2d2a7168d5d5.jpg', 'data_raw_all\\\\0.4_f38f2eb5263c3f8159ae2d2a7168d5d5.jpg']\n",
+      "['data_raw_all\\\\1.1_e7f7d50ef31f40cf0287bf392636fff4.jpg', 'data_raw_all\\\\1.2_e7f7d50ef31f40cf0287bf392636fff4.jpg']\n",
+      "['data_raw_all\\\\3.4_bb2454a9079fb538291cb22e4a1294ad.jpg', 'data_raw_all\\\\3.5_dc83d3e47e3e8bdf992e23fa31ff4093.jpg']\n",
+      "['data_raw_all\\\\5.3_e046159c358ff9fdc11dc68ec08f8d42.jpg', 'data_raw_all\\\\5.4_96345da8a67b01f07d974c488425c3e5.jpg']\n"
+     ]
+    }
+   ],
    "source": [
     "# duplicate files are a risk to the metrics, they pollute the validation dataset\n",
     "for hash in hashes:\n",
@@ -158,7 +169,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.9.18"
   },
   "vscode": {
    "interpreter": {
@@ -167,5 +178,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/README.md b/README.md
@@ -5,6 +5,10 @@ The readout is used in a water meter measurement system. An overview can be foun
 
 ### Versions
 
+### 12.0.9 new images
+
+* new images
+
 
 ### 11.0.5 better quanization
 * new images

diff --git a/Train_CNN_Ana-Cont-S2.ipynb b/Train_CNN_Ana-Cont-S2.ipynb
diff --git a/Train_CNN_Analog-Readout_Version-Small2.ipynb b/Train_CNN_Analog-Readout_Version-Small2.ipynb
diff --git a/Train_CNN_Analog-Readout_Version-Small3-long.ipynb b/Train_CNN_Analog-Readout_Version-Small3-long.ipynb
@@ -376,13 +376,7 @@
       "721/721 [==============================] - 25s 35ms/step - loss: 4.2921e-04 - accuracy: 0.9969 - val_loss: 3.3614e-04 - val_accuracy: 0.9983\n",
       "Epoch 54/100\n",
       "721/721 [==============================] - 27s 37ms/step - loss: 4.2520e-04 - accuracy: 0.9950 - val_loss: 3.3577e-04 - val_accuracy: 0.9991\n",
-      "Epoch 55/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 55/100\n",
       "721/721 [==============================] - 26s 36ms/step - loss: 4.2422e-04 - accuracy: 0.9970 - val_loss: 3.8911e-04 - val_accuracy: 0.9974\n",
       "Epoch 56/100\n",
       "721/721 [==============================] - 25s 35ms/step - loss: 4.0279e-04 - accuracy: 0.9965 - val_loss: 3.3301e-04 - val_accuracy: 0.9957\n",
@@ -672,13 +666,7 @@
       "721/721 [==============================] - 27s 37ms/step - loss: 9.3518e-04 - accuracy: 0.9918 - val_loss: 8.6367e-04 - val_accuracy: 0.9957\n",
       "Epoch 55/600\n",
       "721/721 [==============================] - 26s 37ms/step - loss: 8.8195e-04 - accuracy: 0.9927 - val_loss: 9.1022e-04 - val_accuracy: 0.9896\n",
-      "Epoch 56/600\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 56/600\n",
       "721/721 [==============================] - 27s 37ms/step - loss: 9.0884e-04 - accuracy: 0.9913 - val_loss: 8.0402e-04 - val_accuracy: 0.9939\n",
       "Epoch 57/600\n",
       "721/721 [==============================] - 27s 38ms/step - loss: 8.5254e-04 - accuracy: 0.9939 - val_loss: 8.7878e-04 - val_accuracy: 0.9922\n",
@@ -1008,13 +996,7 @@
       "721/721 [==============================] - 9s 13ms/step - loss: 4.5002e-04 - accuracy: 0.9965 - val_loss: 5.2617e-04 - val_accuracy: 0.9931\n",
       "Epoch 214/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 4.4357e-04 - accuracy: 0.9957 - val_loss: 3.0985e-04 - val_accuracy: 0.9983\n",
-      "Epoch 215/600\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 215/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 4.0733e-04 - accuracy: 0.9964 - val_loss: 4.3648e-04 - val_accuracy: 0.9974\n",
       "Epoch 216/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 4.3433e-04 - accuracy: 0.9967 - val_loss: 5.1075e-04 - val_accuracy: 0.9948\n",
@@ -1232,13 +1214,7 @@
       "721/721 [==============================] - 9s 12ms/step - loss: 3.6516e-04 - accuracy: 0.9960 - val_loss: 3.3159e-04 - val_accuracy: 0.9965\n",
       "Epoch 320/600\n",
       "721/721 [==============================] - 10s 14ms/step - loss: 3.6608e-04 - accuracy: 0.9958 - val_loss: 3.4875e-04 - val_accuracy: 0.9948\n",
-      "Epoch 321/600\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 321/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 3.5782e-04 - accuracy: 0.9969 - val_loss: 3.4687e-04 - val_accuracy: 0.9974\n",
       "Epoch 322/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 3.6038e-04 - accuracy: 0.9977 - val_loss: 3.4564e-04 - val_accuracy: 0.9957\n",
@@ -1456,13 +1432,7 @@
       "721/721 [==============================] - 10s 13ms/step - loss: 3.2225e-04 - accuracy: 0.9970 - val_loss: 2.9804e-04 - val_accuracy: 0.9991\n",
       "Epoch 426/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 3.2054e-04 - accuracy: 0.9974 - val_loss: 3.0168e-04 - val_accuracy: 0.9983\n",
-      "Epoch 427/600\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 427/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 3.0530e-04 - accuracy: 0.9969 - val_loss: 2.6515e-04 - val_accuracy: 0.9983\n",
       "Epoch 428/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 3.2771e-04 - accuracy: 0.9972 - val_loss: 2.7787e-04 - val_accuracy: 0.9991\n",
@@ -1680,13 +1650,7 @@
       "721/721 [==============================] - 9s 13ms/step - loss: 3.0840e-04 - accuracy: 0.9970 - val_loss: 2.6101e-04 - val_accuracy: 0.9974\n",
       "Epoch 532/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 2.8530e-04 - accuracy: 0.9986 - val_loss: 2.7290e-04 - val_accuracy: 0.9965\n",
-      "Epoch 533/600\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 533/600\n",
       "721/721 [==============================] - 9s 13ms/step - loss: 2.9441e-04 - accuracy: 0.9969 - val_loss: 2.5800e-04 - val_accuracy: 0.9991\n",
       "Epoch 534/600\n",
       "721/721 [==============================] - 10s 14ms/step - loss: 2.9657e-04 - accuracy: 0.9974 - val_loss: 3.3103e-04 - val_accuracy: 0.9965\n",
@@ -2422,7 +2386,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.9.17"
   },
   "vscode": {
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