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--git a/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb b/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb deleted file mode 100644 index 6ff0c90bd..000000000 --- a/.ipynb_checkpoints/Train_CNN_Digital-Readout-Small-v2-checkpoint.ipynb +++ /dev/null @@ -1,1704 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CNN Training\n", - "\n", - "Target of this code is to train a CNN network to classify images of a digital readout to the digits 0 to 9. Additionally a category \"NaN\" is introduced, to mark images that are not amibiguous.\n", - "\n", - "### Preparing the training\n", - "* First all libraries are loaded\n", - " * It is assumed, that they are installed during the Python setup\n", - "* matplotlib is set to print the output inline in the jupyter notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "########### Basic Parameters for Running: ################################\n", - " \n", - "TFlite_Version = \"1800\" \n", - "TFlite_MainType = \"dig-class11\"\n", - "TFlite_Size = \"s2\"\n", - "Training_Percentage = 0.0 # 0.0 = Use all Images for Training\n", - "Epoch_Anz = 500\n", - "\n", - "##########################################################################\n", - "\n", - "## 2024-03-30: Code adapted to TF 2.16 ###################################\n", - "\n", - "\n", - "import tensorflow as tf\n", - "import matplotlib.pyplot as plt\n", - "import glob\n", - "import os\n", - "import numpy as np\n", - "from sklearn.utils import shuffle\n", - "from tensorflow.python import keras\n", - "from tensorflow.python.keras import Sequential\n", - "from tensorflow.keras.layers import Dense, InputLayer, Conv2D, MaxPool2D, Flatten, BatchNormalization\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", - "from sklearn.model_selection import train_test_split\n", - "from tensorflow.keras.callbacks import History \n", - "from tensorflow.keras.utils import to_categorical\n", - "from PIL import Image \n", - "from pathlib import Path\n", - "\n", - "loss_ges = np.array([])\n", - "val_loss_ges = np.array([])\n", - "\n", - "%matplotlib inline\n", - "np.set_printoptions(precision=4)\n", - "np.set_printoptions(suppress=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load training data\n", - "* The data is expected in the \"Input_dir\"\n", - "* Inside subdirectories are expected from -1, 0, 1, ... 9 in which the pictures are sorted according to their values (=category)\n", - "* Picture size must be 20x32 with 3 color channels (RGB)\n", - "* The filename can be arbitrary\n", - "\n", - "* The images are stored in the x_data[]\n", - "* The expected category for each image in the corresponding y_data[]\n", - "\n", - "* The last step is a shuffle (from sklearn.utils) and split the data into training and validation data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1300, 32, 20, 3)\n", - "(1300, 11)\n" - ] - } - ], - "source": [ - "Input_dir='ziffer_sortiert_resize'\n", - "\n", - "files = glob.glob(Input_dir + '/*.jpg')\n", - "x_data = []\n", - "y_data = []\n", - "\n", - "for aktfile in files:\n", - " base = os.path.basename(aktfile)\n", - " target = base[0:1]\n", - " if target == \"N\":\n", - " category = 10 # NaN does not work --> convert to 10\n", - " else:\n", - " category = int(target)\n", - " test_image = Image.open(aktfile)\n", - " test_image = np.array(test_image, dtype=\"float32\")\n", - " x_data.append(test_image)\n", - " y_data.append(np.array([category]))\n", - "\n", - "x_data = np.array(x_data)\n", - "y_data = np.array(y_data)\n", - "y_data = to_categorical(y_data, 11)\n", - "print(x_data.shape)\n", - "print(y_data.shape)\n", - "\n", - "x_data, y_data = shuffle(x_data, y_data)\n", - "\n", - "if (Training_Percentage > 0):\n", - " X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=Training_Percentage)\n", - "else:\n", - " X_train = x_data\n", - " y_train = y_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define the model\n", - "\n", - "The layout of the network ist a typcial CNN network with alternating **Conv2D** and **MaxPool2D** layers. Finished after **flattening** with additional **Dense** layer.\n", - "\n", - "#### Important\n", - "* Shape of the input layer: (32, 20, 3)\n", - "* Number of output layers: 11\n", - "* As loss function \"categorical_crossentropy\" is choosen, as it is a categories task" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
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\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m6\u001b[0m (24.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inputs = tf.keras.Input(shape=(32, 20, 3))\n", - "inputs2 = tf.keras.layers.BatchNormalization()(inputs)\n", - "inputs3 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs2)\n", - "inputs4 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs3)\n", - "inputs5 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs4)\n", - "inputs6 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs5)\n", - "inputs7 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs6)\n", - "inputs8 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs7)\n", - "inputs9 = tf.keras.layers.Flatten()(inputs8)\n", - "inputs10 = tf.keras.layers.Dense(256,activation=\"relu\")(inputs9)\n", - "output = tf.keras.layers.Dense(11, activation='softmax')(inputs10)\n", - "\n", - "model = tf.keras.Model(inputs=inputs, outputs=output)\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n", - "\n", - "model.summary()\n", - "\n", - "#model = Sequential()\n", - "#model.add(BatchNormalization(input_shape=(32,20,3)))\n", - "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n", - "#model.add(MaxPool2D(pool_size=(2,2)))\n", - "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n", - "#model.add(MaxPool2D(pool_size=(2,2)))\n", - "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n", - "#model.add(MaxPool2D(pool_size=(2,2)))\n", - "#model.add(Flatten())\n", - "#model.add(Dense(256,activation=\"relu\"))\n", - "#model.add(Dense(11, activation = \"softmax\"))\n", - "\n", - "#model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n", - "\n", - "#model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Training\n", - "The input pictures are randomly scattered for brightness, pixel shift variations and rotation angle. This is implemented with a ImageDataGenerator.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/500\n", - "\u001b[1m 51/325\u001b[0m \u001b[32mâ”â”â”\u001b[0m\u001b[37mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2469 - loss: 2.3019" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Muell\\anaconda3\\envs\\py311-tf216-opencv\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n", - " self._warn_if_super_not_called()\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.2822 - loss: 2.2734\n", - "Epoch 2/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.3882 - loss: 1.8318\n", - "Epoch 3/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5754 - loss: 1.2654\n", - "Epoch 4/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7161 - loss: 0.8801\n", - "Epoch 5/500\n", - "\u001b[1m325/325\u001b[0m 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\u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0097\n", - "Epoch 491/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9890 - loss: 0.0315\n", - "Epoch 492/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0207\n", - "Epoch 493/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0615\n", - "Epoch 494/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9925 - loss: 0.0141\n", - "Epoch 495/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9893 - loss: 0.0557\n", - "Epoch 496/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9929 - loss: 0.0229\n", - "Epoch 497/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9878 - loss: 0.0459\n", - "Epoch 498/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9938 - loss: 0.0249\n", - "Epoch 499/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9929 - loss: 0.0390\n", - "Epoch 500/500\n", - "\u001b[1m325/325\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9964 - loss: 0.0116\n" - ] - } - ], - "source": [ - "Batch_Size = 4\n", - "Shift_Range = 1\n", - "Brightness_Range = 0.2\n", - "Rotation_Angle = 5\n", - "ZoomRange = 0.3\n", - "\n", - "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], \n", - " height_shift_range=[-Shift_Range,Shift_Range],\n", - " brightness_range=[1-Brightness_Range,1+Brightness_Range],\n", - " zoom_range=[1-ZoomRange, 1+ZoomRange],\n", - " rotation_range=Rotation_Angle)\n", - "\n", - "if (Training_Percentage > 0):\n", - " train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n", - " validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n", - " history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n", - "else:\n", - " train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n", - " history = model.fit(train_iterator, epochs = Epoch_Anz)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Learing result\n", - " \n", - "* Visualization of the training and validation results" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "loss_ges = np.append(loss_ges, history.history['loss'])\n", - "plt.semilogy(history.history['loss'])\n", - "\n", - "if (Training_Percentage > 0):\n", - " val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n", - " plt.semilogy(history.history['val_loss'])\n", - "\n", - "plt.title('model loss')\n", - "plt.ylabel('loss')\n", - "plt.xlabel('epoch')\n", - "plt.legend(['train','eval'], loc='upper left')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check the model by hand\n", - "\n", - "* The following code uses the trained model to check the deviation for each picture.\n", - "* x-axis walks through each pixel, y-axis shows the result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check each image for expected and deviation\n", - "* setting the switch \"only_deviation = true\" will only print the images for which the classification and the CNN-result deviates\n", - "\n", - "The output contains the following information:\n", - "\n", - "| Filename | Expected Category | Predicted Category |\n", - "|------------- |:-----------------------------:|--------------|\n", - "| ziffer_sortiert_resize_NaN/5\\Ziffer_4_0034.jpg | 4 | -1 |\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ziffer_sortiert_resize\\0_litre3_20220312-060001.jpg 0 8\n" - ] - }, - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Input_dir='ziffer_sortiert_resize'\n", - "res = []\n", - "only_deviation = True\n", - "show_wrong_image = True\n", - "\n", - "files = glob.glob(Input_dir + '/*.jpg')\n", - "\n", - "for aktfile in files:\n", - " base = os.path.basename(aktfile)\n", - " target = base[0:1]\n", - " if target == \"N\":\n", - " zw1 = -1\n", - " else:\n", - " zw1 = int(target)\n", - " expected_class = zw1\n", - " image_in = Image.open(aktfile)\n", - " test_image = np.array(image_in, dtype=\"float32\")\n", - " img = np.reshape(test_image,[1,32,20,3])\n", - " classes = np.argmax(model.predict(img, verbose=0), axis=-1)\n", - " classes = classes[0]\n", - " if classes == 10: \n", - " classes = -1\n", - " zw2 = classes\n", - " zw3 = zw2 - zw1\n", - " res.append(np.array([zw1, zw2, zw3]))\n", - " if only_deviation == True:\n", - " if str(classes) != str(expected_class):\n", - " print(aktfile + \" \" + str(expected_class) + \" \" + str(classes))\n", - " if show_wrong_image == True:\n", - " display(image_in)\n", - " else:\n", - " print(aktfile + \" \" + aktsubdir + \" \" + str(classes))\n", - " \n", - "\n", - "res = np.asarray(res)\n", - "\n", - "\n", - "plt.plot(res[:,0])\n", - "plt.plot(res[:,1])\n", - "plt.title('Result')\n", - "plt.ylabel('Digital Value')\n", - "plt.xlabel('#Picture')\n", - "plt.legend(['real','model'], loc='upper left')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save the model\n", - "\n", - "* Save the model to the file with the \"h5\" file format" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: test\\assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: test\\assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved artifact at 'test'. The following endpoints are available:\n", - "\n", - "* Endpoint 'serve'\n", - " args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 32, 20, 3), dtype=tf.float32, name='keras_tensor')\n", - "Output Type:\n", - " TensorSpec(shape=(None, 11), dtype=tf.float32, name=None)\n", - "Captures:\n", - " 1786040602288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040602464: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040601408: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040602112: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040602992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040603696: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040603872: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040604224: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040604048: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040604752: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040600528: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040603344: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040605104: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 1786040604928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n" - ] - } - ], - "source": [ - "model.export(\"test\", \"tf_saved_model\") # replace tf.saved_model.save with this line" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dig-class11_1800_s2.tflite\n" - ] - }, - { - "data": { - "text/plain": [ - "356676" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \".tflite\"\n", - "\n", - "converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n", - "tflite_model = converter.convert()\n", - "open(FileName, \"wb\").write(tflite_model)\n", - "print(FileName)\n", - "Path(FileName).stat().st_size" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dig-class11_1800_s2_q.tflite\n" - ] - }, - { - "data": { - "text/plain": [ - "97240" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \"_q.tflite\"\n", - "\n", - "import tensorflow as tf\n", - "\n", - "def representative_dataset():\n", - " for n in range(x_data[0].size):\n", - " data = np.expand_dims(x_data[5], axis=0)\n", - " yield [data.astype(np.float32)]\n", - " \n", - "converter2 = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n", - "converter2.representative_dataset = representative_dataset\n", - "converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n", - "converter2.representative_dataset = representative_dataset\n", - "tflite_quant_model = converter2.convert()\n", - "\n", - "open(FileName, \"wb\").write(tflite_quant_model)\n", - "print(FileName)\n", - "Path(FileName).stat().st_size" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Checking the images shows, that this are border line images, which can be interpreted as a good digit or a faulty one." - ] - } - ], - "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": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/01 - Clean Images.ipynb b/01 - Clean Images.ipynb index 882f0ce93..e246648c8 100644 --- a/01 - Clean Images.ipynb +++ b/01 - Clean Images.ipynb @@ -25,7 +25,8 @@ "metadata": {}, "outputs": [], "source": [ - "target_raw_path = \"./ziffer_sortiert_raw\" # here all raw images are stored" + "target_raw_path = \"./ziffer_sortiert_raw\" # here all raw images are stored\n", + "target_raw_path = './ziffer_sortiert_resize' # remove after resize -- reduktion um fast 70% --> schnelleres Training" ] }, { @@ -38,9 +39,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Find similar images now in 1519 images ...\n", + "Find similar images now in 1377 images ...\n", "...1000\n", - "273 duplicates will be removed.\n" + "969 duplicates will be removed.\n" ] } ], @@ -74,7 +75,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/02 - Image_Preparation.ipynb b/02 - Image_Preparation.ipynb index f70f2e26f..ba69a23e0 100644 --- a/02 - Image_Preparation.ipynb +++ b/02 - Image_Preparation.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -34,14 +34,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1379 files have been deleted.\n" + "1377 files have been deleted.\n" ] } ], @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -68,7 +68,7 @@ "750 ...\n", "1000 ...\n", "1250 ...\n", - "1379\n" + "1377\n" ] } ], @@ -116,7 +116,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/03 - Train_CNN_Digital-Readout-Small-v2.ipynb b/03 - Train_CNN_Digital-Readout-Small-v2.ipynb new file mode 100644 index 000000000..6ecdf472e --- /dev/null +++ b/03 - Train_CNN_Digital-Readout-Small-v2.ipynb @@ -0,0 +1,1652 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CNN Training\n", + "\n", + "Target of this code is to train a CNN network to classify images of a digital readout to the digits 0 to 9. Additionally a category \"NaN\" is introduced, to mark images that are not amibiguous.\n", + "\n", + "### Preparing the training\n", + "* First all libraries are loaded\n", + " * It is assumed, that they are installed during the Python setup\n", + "* matplotlib is set to print the output inline in the jupyter notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "########### Basic Parameters for Running: ################################\n", + " \n", + "TFlite_Version = \"1900\" \n", + "TFlite_MainType = \"dig-class11\"\n", + "TFlite_Size = \"s2\"\n", + "Training_Percentage = 0.0 # 0.0 = Use all Images for Training\n", + "Epoch_Anz = 250 # x2 !!!\n", + "\n", + "##########################################################################\n", + "\n", + "## 2024-03-30: Code adapted to TF 2.16 ###################################\n", + "\n", + "\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import glob\n", + "import os\n", + "import numpy as np\n", + "from sklearn.utils import shuffle\n", + "from tensorflow.python import keras\n", + "from tensorflow.python.keras import Sequential\n", + "from tensorflow.keras.layers import Dense, InputLayer, Conv2D, MaxPool2D, Flatten, BatchNormalization\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.callbacks import History \n", + "from tensorflow.keras.utils import to_categorical\n", + "from PIL import Image \n", + "from pathlib import Path\n", + "\n", + "loss_ges = np.array([])\n", + "val_loss_ges = np.array([])\n", + "\n", + "%matplotlib inline\n", + "np.set_printoptions(precision=4)\n", + "np.set_printoptions(suppress=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load training data\n", + "* The data is expected in the \"Input_dir\"\n", + "* Inside subdirectories are expected from -1, 0, 1, ... 9 in which the pictures are sorted according to their values (=category)\n", + "* Picture size must be 20x32 with 3 color channels (RGB)\n", + "* The filename can be arbitrary\n", + "\n", + "* The images are stored in the x_data[]\n", + "* The expected category for each image in the corresponding y_data[]\n", + "\n", + "* The last step is a shuffle (from sklearn.utils) and split the data into training and validation data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(408, 32, 20, 3)\n", + "(408, 11)\n" + ] + } + ], + "source": [ + "Input_dir='ziffer_sortiert_resize'\n", + "\n", + "files = glob.glob(Input_dir + '/*.jpg')\n", + "x_data = []\n", + "y_data = []\n", + "\n", + "for aktfile in files:\n", + " base = os.path.basename(aktfile)\n", + " target = base[0:1]\n", + " if target == \"N\":\n", + " category = 10 # NaN does not work --> convert to 10\n", + " else:\n", + " category = int(target)\n", + " test_image = Image.open(aktfile)\n", + " test_image = np.array(test_image, dtype=\"float32\")\n", + " x_data.append(test_image)\n", + " y_data.append(np.array([category]))\n", + "\n", + "x_data = np.array(x_data)\n", + "y_data = np.array(y_data)\n", + "y_data = to_categorical(y_data, 11)\n", + "print(x_data.shape)\n", + "print(y_data.shape)\n", + "\n", + "x_data, y_data = shuffle(x_data, y_data)\n", + "\n", + "if (Training_Percentage > 0):\n", + " X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=Training_Percentage)\n", + "else:\n", + " X_train = x_data\n", + " y_train = y_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the model\n", + "\n", + "The layout of the network ist a typcial CNN network with alternating **Conv2D** and **MaxPool2D** layers. Finished after **flattening** with additional **Dense** layer.\n", + "\n", + "#### Important\n", + "* Shape of the input layer: (32, 20, 3)\n", + "* Number of output layers: 11\n", + "* As loss function \"categorical_crossentropy\" is choosen, as it is a categories task" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
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This is implemented with a ImageDataGenerator.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Muell\\anaconda3\\envs\\py311-tf218\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n", + " self._warn_if_super_not_called()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.1004 - loss: 2.4207\n", + "Epoch 2/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1724 - loss: 2.3588\n", + "Epoch 3/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.1526 - loss: 2.3444\n", + "Epoch 4/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.2418 - loss: 2.1446\n", + "Epoch 5/250\n", + "\u001b[1m102/102\u001b[0m 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\u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9900 - loss: 0.0323\n", + "Epoch 241/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9756 - loss: 0.0534\n", + "Epoch 242/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.0731\n", + "Epoch 243/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9954 - loss: 0.0307\n", + "Epoch 244/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9829 - loss: 0.0677\n", + "Epoch 245/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9954 - loss: 0.0240\n", + "Epoch 246/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9972 - loss: 0.0088\n", + "Epoch 247/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9774 - loss: 0.0673\n", + "Epoch 248/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9782 - loss: 0.0720\n", + "Epoch 249/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9949 - loss: 0.0197\n", + "Epoch 250/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9745 - loss: 0.2399\n" + ] + } + ], + "source": [ + "Batch_Size = 4\n", + "Shift_Range = 1\n", + "Brightness_Range = 0.2\n", + "Rotation_Angle = 5\n", + "ZoomRange = 0.3\n", + "\n", + "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], \n", + " height_shift_range=[-Shift_Range,Shift_Range],\n", + " brightness_range=[1-Brightness_Range,1+Brightness_Range],\n", + " zoom_range=[1-ZoomRange, 1+ZoomRange],\n", + " rotation_range=Rotation_Angle)\n", + "\n", + "if (Training_Percentage > 0):\n", + " train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n", + " validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n", + " history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n", + "else:\n", + " train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n", + " history = model.fit(train_iterator, epochs = Epoch_Anz)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9789 - loss: 0.0764\n", + "Epoch 2/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9974 - loss: 0.0123\n", + "Epoch 3/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9933 - loss: 0.0161\n", + "Epoch 4/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9825 - loss: 0.0260\n", + "Epoch 5/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9917 - loss: 0.0418\n", + "Epoch 6/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9892 - loss: 0.0210\n", + "Epoch 7/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9812 - loss: 0.0630\n", + "Epoch 8/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9559 - loss: 0.1137\n", + "Epoch 9/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9850 - loss: 0.0427\n", + "Epoch 10/250\n", + "\u001b[1m102/102\u001b[0m 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\u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9920 - loss: 0.0503\n", + "Epoch 206/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0047\n", + "Epoch 207/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9979 - loss: 0.0044\n", + "Epoch 208/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9947 - loss: 0.0167\n", + "Epoch 209/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9932 - loss: 0.0416\n", + "Epoch 210/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9998 - loss: 0.0016\n", + "Epoch 211/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9606 - loss: 0.1202\n", + "Epoch 212/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9989 - loss: 0.0242\n", + "Epoch 213/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9948 - loss: 0.0130\n", + "Epoch 214/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9902 - loss: 0.0557\n", + "Epoch 215/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9857 - loss: 0.0236\n", + "Epoch 216/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9908 - loss: 0.0191\n", + "Epoch 217/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9882 - loss: 0.0318\n", + "Epoch 218/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9979 - loss: 0.0043\n", + "Epoch 219/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9832 - loss: 0.0961\n", + "Epoch 220/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9936 - loss: 0.0234\n", + "Epoch 221/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9868 - loss: 0.0732\n", + "Epoch 222/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9902 - loss: 0.0270\n", + "Epoch 223/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9999 - loss: 0.0063\n", + "Epoch 224/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0034\n", + "Epoch 225/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9871 - loss: 0.0895\n", + "Epoch 226/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9944 - loss: 0.0233\n", + "Epoch 227/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9985 - loss: 0.0055\n", + "Epoch 228/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9941 - loss: 0.0276\n", + "Epoch 229/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9974 - loss: 0.0041\n", + "Epoch 230/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9931 - loss: 0.0435\n", + "Epoch 231/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9970 - loss: 0.0039\n", + "Epoch 232/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9977 - loss: 0.0216\n", + "Epoch 233/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9867 - loss: 0.0443\n", + "Epoch 234/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9936 - loss: 0.0176\n", + "Epoch 235/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9877 - loss: 0.0127\n", + "Epoch 236/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9890 - loss: 0.0171\n", + "Epoch 237/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9781 - loss: 0.0560\n", + "Epoch 238/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9761 - loss: 0.0869\n", + "Epoch 239/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9955 - loss: 0.0069\n", + "Epoch 240/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9966 - loss: 0.0082\n", + "Epoch 241/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9906 - loss: 0.0552\n", + "Epoch 242/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9873 - loss: 0.0099\n", + "Epoch 243/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9852 - loss: 0.0617\n", + "Epoch 244/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9932 - loss: 0.0197\n", + "Epoch 245/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9951 - loss: 0.0262\n", + "Epoch 246/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0019\n", + "Epoch 247/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9978 - loss: 0.0040\n", + "Epoch 248/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9944 - loss: 0.0376\n", + "Epoch 249/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9946 - loss: 0.0195\n", + "Epoch 250/250\n", + "\u001b[1m102/102\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9904 - loss: 0.0599\n" + ] + } + ], + "source": [ + "loss_ges = np.append(loss_ges, history.history['loss'])\n", + "plt.semilogy(history.history['loss'])\n", + "\n", + "if (Training_Percentage > 0):\n", + " val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n", + " plt.semilogy(history.history['val_loss'])\n", + "\n", + "plt.title('model loss')\n", + "plt.ylabel('loss')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train','eval'], loc='upper left')\n", + "plt.show()\n", + "\n", + "if (Training_Percentage > 0):\n", + " history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n", + "else:\n", + " history = model.fit(train_iterator, epochs = Epoch_Anz)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Learing result\n", + " \n", + "* Visualization of the training and validation results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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hhpmysCFUA5ZlYcGCBVi3bh3Wr1/f7OFMWnp7ezF//vxmD4NhFBxOn2eYaQEbQjWSzWZx4IEHsnusSjKZDCtBTEuiFlRk1xjDTFXYEKoDtm2jra2t2cNgGKaOKC022BBimCkLB7UwDMMYKHGMEMNMC9gQYhiGMUCzxjhGiGGmLmwIMQzDGOAWGwwzPWBDiGEYxgA3XWWY6QEbQgzDMAaoITQ8zooQw0xV2BBiGIYxQF1jowVWhBhmqsKGEMMwjAFVEWJDiGGmKmwIMQzDGCB2EBdUZJgpDBtCDMMwBvQWG/R3hmGmDmwIMQzDGKAFFQFgrMjuMYaZirAhxDAMY0BXgDhOiGGmJmwIMQzDGNAVIY4TYqYjdz67Fcd+5Vbc9dy2Zg+lYbAhxDAMY6DEihDD4J7nt2Pb4Dj+woYQwzDM9MLRFKHRAitCzPSjWHkgKE7hZAE2hBiGYQywIsQw3n1QoF2IpxhsCDEMwxjQ532OEWKmIyJWjg0hhmGYaYbuGmNFiJmOlEoV11iJXWMMwzDTCt01xooQMx0RilCeFSGGYZjphVCEUrYFoFxdmmGmG+KBgBWhSc473/lOzJw5E+95z3uaPRSGYSYJwhDqyqUBAMNsCDHTEA6WniJ86lOfwk9/+tNmD4NhmEmEWABmtJUNoZFxdo0x0w9pCHH6/OTmpJNOwowZM5o9DIZhJhFO5QFYKEIjBVaEmOlHsXIjFFkRahx33303zjrrLCxcuBCWZeH666/3bdPf34999tkHbW1tOP744/Hggw9O/EAZPPnqHnzht09g6+BYs4fCMA1HBIl2VgyhMTaEmGmIsH/YNdZAhoeHsXTpUvT39xv/ft111+GSSy7B5ZdfjkceeQRLly7Fqaeeiq1bt8ptli1bhsMPP9z3b+PGjRN1GNOCH927Dr948GXc+PimZg+FYRqOcAl0ZFMA6m8IjRdLeHnHSF33ybQmdzy7FW//9j14bstgs4eSmFJFESpM4WDpdLMHcPrpp+P0008P/PvVV1+NCy64AB/5yEcAANdeey1uvPFG/OhHP8Kll14KAHjsscfqNp7x8XGMj4/L3wcGBuq278nO0Fg5RmKsMHWfDBhGIIKlO7NCEarvdf/5Xz+O6x/biM++9SBc/OYD67pvprX4379uxOOv7MEta7bgoHmTK0xD2D+sCDWJfD6P1atXY8WKFfI127axYsUK3HfffQ35zCuvvBI9PT3y3+LFixvyOZOR0coT8VT2FTOMQCpCucYoQjev2QIA+I+bn8MvHny5rvtmWgunci1NRvdqScYITV1FqKUNoe3bt6NUKmHevHnK6/PmzcPmzZtj72fFihV473vfiz/+8Y9YtGhRqBH1hS98AXv27JH/NmzYUPX4JwMbd4/ii9c/iRe2DUVuO155Ip7KzfcYRiAMIU8Rqu8itvesTvnzZTc8iT0jhbrufzqzfscw7nhma/SGE4SwISZjLarpkD7fdNfYRHDrrbfG3jaXyyGXyzVwNK3F3656CM9sHsQdz27FPZ9/c+i2UhFypu4NwTACxxcsXd/rni4shZKLXSN59HRk6voZ05VPX/cYHn15N65feSKWLe5t9nDktTQ6KRUhkT4/def9llaEZs+ejVQqhS1btiivb9myBfPnz2/SqKYWz2wuB++9sms0clvPNTY5FSFniilZWwbGsGWAM/iS8vBLO7F+x3Dkdp4i1BjXWL6oLiystNaPTbvL98X/vbC9ySMpI+ae0UmoCInrslCcutdnSxtC2WwWRx99NG677Tb5muM4uO2227B8+fImjmx6MiYVocl3Q/zh8Y04/Et/xi1rtkRvPAkolhyc8Y2/4Ixv/GVKS9b1ZtOeUbzve/fhgp8+HLmt6Lna0aD0ef1703ubMdUzXOkLt/qlXU0eSZnSJDaEhBE3lT0BTTeEhoaG8Nhjj8nMr3Xr1uGxxx7Dyy+XgwcvueQSfP/738dPfvITPP3007joooswPDwss8iY+iCKxoUx1qLB0k++ugeXXPcYXt0drGr96YnNGMmXWuYJsVaGx0vYMZzHjuE8tg+NR7+BAQBs3D0GxwW2DESfM1lHSChCxfpe97oixIZQfXBdF8OVKuCrX94F123+eRVf7WQsyikVoUnqCYhD02OEHn74YZx88sny90suuQQAcN5552HVqlU455xzsG3bNlx22WXYvHkzli1bhptuuskXQM3URk97dGyCeJppNUXo5/evx28ffRUHzpuBi07a37jNs5X6HYNjU6NNwnjJm1B3DOWxoKe9iaOZPIgFMo4x72WNNUYR0rt5syFUH8YKjjQ8do8U8MK2YRwwt6upYxIxQmOTUBGaDsHSTVeETjrpJLiu6/u3atUquc3FF1+M9evXY3x8HA888ACOP/74ho+rv78fhx12GI499tiGf1Yr0B1hCLmuK5+IWy1GSBg3I3mzkTNeLGHd9nJMyMDo1MjMoWrCjuG87+/fuu15nPTvd2DbIKtFFGEIxemb5BhihOqpLojv0Co3t5/0roeh8SLe9Z178b27XmjqOIa1eeCR9c13jwlDaKTgjc11XfzonnW45/nWVqm5+/w0ZuXKlVizZg0eeuihZg+lYdDg4d4IQ6hQcls2e0AYQEFP7Gu3DsmxTxlFiBhCO4f9xs6fntyMl3aM4JGXm78ItBJDwhCKowi5orJ0WRFyXL+KUwtiDO2ZsqE12RWhxzfsxiMv78avVr/S1HEMa81xH16/s0kj8TDFCL2wbRhf/sMa/PP1TzRrWLEQ90G+5LSEm7ERsCHUJEqOi/FiqamZTNQo6G4P95KOFb0buNUmbJHNNh4Qw0HL2g+MTUFFaMivCIlFdqoYfvVCGEKuG30dC5uns1JQEahfCn3JcaX7RrTwaDWXc1KEkdjs7MwhzRBa3UKKEDWExDj18bYa9D5ptbm/XrAh1CSO+ddbcPA/34S1MQoZNoqdI94CmrbDLwXq2241iVRMLkGKkCgRAEwdw4AafdtDDKGhKWL41QuqFkSpQmLxasukpPtqvE5xQtSQbZsiipAYv9Nk1UAULZxZqcn0wrbhpmdrCRGd1hGaLD286Hzf6mOtFjaEmkSqYng0c/LbSWJLoiYvegNPZCzDloEx/PKhDaGBqiPSEApQhDZPbUXI5BoTE9ZUMfzqxdC4dx1FGULi3rQtS7qv6qUIURfbVFGExDVXarIhJBQWmkAQFD84UYhzQitLi0ug1bJwdeja0GphEfWCDaEmkaqc+TiG0BX/+xT+5Q9r6j6GXcQQihoHXQAmUhH6+s3P4h9+8zj++ERwx3vPNWY2lp7VFKGp4OemxxrmGmt12X2ioYpQ1HUsXDwp25KqzVjANZYUasjm0kIRat1FxnFc/Hr1KzLpwIR4QGr2YYjvuKstjWxlog1ym08UYs4ZLzq+ujxxAvebCTXQC00+j42CDaEmkY6pCA2OFfDje1/CD+9Zpyg49YC6xpIoQqYbdyRfxLdvfx5rt9bX1ScW+bBjHw1RhPaMFrBxj1d9ueS4de/304wnOrqQbjecG2EIDbAipKC4xiJWbPEUn7IstKXL92u9XCzi+8mmbKRTZb9bq7mcKfev24HP/uqvuOyGJwO3EeNvtotvpKL6deXSyKVbwxCi50TMpV42VmsbFzTma7KrlkGwIRRAo9PnRUhO1IVFJ96NIQUDq2GX4hoL33Y07/dtU/74xGb8x83P4Ru3PV+38QHeBBY2kQnDxqQIPV8JlF7Q04aUXV5w6uku+qffPYFjvnIrtg56xtaru0fxwe/fj5ueDFaxaiUqa0y4KVgRUhlSYoSigqUrrjHbi+OpVy0hYchm0zbSleuy2QZEGJsrDxNh5RiEcdfsGCHxHXfm0shlhCHU3BgheqmNahX6Hbf5AeZhKIpQixtt1cKGUACNTp8XilDUpEFVjk176ttXiipCka6xIo2t8G8rqhvvqXOdnrGIjDDXdeXEYlKEXqxI+QfM7UJ3WzkzbrDKOCGTS+3u57dh90gBT2/y3G8/+MuL+L8XduDjP3+kqs+JQ/yssakRE1UvhhTXWLxg6ZRtIZcJry791MY9eMPXbseP710Xaxzi+8mkLGmgNzu2JgxRfyusaWixRYKlherXmU1Jt+N4nRvmJoXOHeKhslSaHEqLoxhCrTvOWmBDqEnYoohaxIVFJ55NexqpCEUYQooi5N92qKKy1CurRiAMIL0dgRwXmeBMxpI4xtldOcxoK2eRVBMwvWs4jzd87Q5c9adnlNcHRv3HLQJr6efXG/qEO5Iv+Vw2XtYYK0KU4QSKkLjMU5aF9oqyEKQI3bJmCzbsHMUV/7sGNz+1OXIcIli6rAg1P3EiCq9oabQhVK/jGC+W0H/HWjz56p5E7xuujFFVhFrPNVZUXE6tq7Qo42RFiKkncRUhagiF9dKqhl0jnkGQKGvMcDMI5aGeBecAqgiZJ2CaDWIywsQxzuzIylpJ1cTNPLVxAK/sGlUWOcdx5XFTpaC3wytOef+LOxJ/Vhx0w3AHcY/RGjWcNaYylCB93nONWZGuMfrU/OnrHouMlRPfXyZlw7ZbP0ZosHLewlpEiHmhXobQX57bjn//87P4t5ueid6YMExdY0IRarJrjJ4SYUyWJonSQsdZ7/m9VWBDqEkIOTxJjNCm3fV1jVWbNWa6acVEWW8JOkoRogZamCI0syODGbmKIlSF+86U/jqUL8oJji6Q9Pz83wuNMYT0Y6XuMbrAs2tMZXicGvTB1zw1bFJWtCFEr4uRfAk/+MuLoeMQ18hkiRES19FISJsRcT7r5RnbXblPk7rbVddYRRFqsmuMXk9ez0aaidu6BgZ12baysV4LbAg1CRkXECGJjjXQNaZkjUXch2ohMIMhVFEe6v3EIJ7kAg0hsgCZFqldlWPs7fQUoWpUEjGRUQWKGlTUMKE/39ugbve6IUSz6hRDaAKCpW9ZswWnXXM3ntk80PDPqhXqGgu7Vunkb1sW2qRrLNwgP2H/WQCAPzy+KTTDTAZLp+zYD0XNRLiAS44bqF6ILLx6xTqJ+zlppp7oNdbZQlljVHEfK/gVoVb97l3X1ZSr1jXYaoENoSbhGULh29HFfWMDFaHooG2aPh/sGqu3BC0WnqCJbCTCENpdcY31dWRrihFyDIoQfVIdVxQhb6wvbhuWGTf1RD8fIli9/Pnedzk0Xmx4RsqNj2/EM5sHcfNTWxr6ObXiui6G8vGCpenkb9tAWzpcERKL9RsPmoPFfe0YGi/izyGxQl6wNFWEWneRofdMkGFS7/R5aQgljDscpunzmdZwjVHj0Owaa83vXv8qW9mFVwtsCDWJuIoQnQQ2D4zVbZIpOa6UnoHop7ixCEVoqGGusQhFKMI1JlSvmR0ZdFcMoWoUIVnzw3HlWMRTsv7ZetGx/2uAKuSPETIrQq5bdmc0EvE0S42xVmQkX1LcNmFP4fTBIGVbaMuGV5YWi1tnNoV3v3YRAOA3jwQ3Hx0n6fOTQRGi9wztoE4p1jl9XpyjpCULxFzUQV1jzVaEQuoIAclcTpv2jE5YUVg9iLtVDbZaYUOoScRVhHSXVFgdjyTsGS0oi0KUakCfAk03bSNcY67ryoUnaL/KuBzX95S/WxhCnVnMqKTPVxMj5ChPdOVjpYoQnaz1sYZV460W/QmXusZ0I6nRcULieqjXtdko9K7koa4xqghZlqcIBQXtV77/9mxaGkL3rN0e6M6m6fOTKUYICFGESF2celCta0zcn0pBxQY/DERBz8loZXzVZI39evUrWH7l7fjpfevrOr4g9GG1cnZbLbAhFECjCyqmLPEUGKEIaZPAxjrFCemVmqMmL7WOkH/MXvp8/W4UulAFZ42pr9MnP9d1tayx6hUhU9YHdReMBbjGgGA1qxbEPkWqPlVjdGWh0Sn04vNa3RDSi0uGB0t7P5dbbISnz4vFrSObwuK+DhwwtwuuCzy/xZw95hVUTMm+g62sCNFMy6AU+nqnz1PXWBIFRLjG1Kyx1kufryZG6IVKk+7ntw5GbFkf/IpQ616jtcCGUAANL6hYKaufJDYHqF916V0jqiEUNXmN5r0bIixYup6+eDp5xXGNAer5GhgryrH2dmQ8RagKhYQes3jijAqWFk/6jZiExT4X9rYBCM4aAxrfZkO4d1vVNfbLhzbgb370IDYPqLFaoTFC1DUWI2tsVCpC5e2E4RTkcvZabEw+RSjoHNDrrh5xaUINdtxkSrNXWTol6wjVq1lutbiGGKFiFa4xcY004uEq7PME7Bpj6optxasdot/A9Uqh36EtWomCpbWbYbxYkhOV49YvFXQsIv4H8J7ETdsJt1hHNoW2TKqmGCHHMJENBLjGxFNTV8XwijOJbx0Ywx+f2BT73OWlIVTusB3mGmt0m41WV4R+dO863P3cNl/j3tiuMTs6a0xcEx0Vg0moPKWgDCtSUDGVam1DqFBylOMOVITIsVID8DerX8HHf7Y6sYuL3lNJDJkRY9ZY6wRLe4oQLUkS7/jEOW6WIcTp80xdEU+BSQoZAvVzjb1aMaj6OrOxxhEWLK0bFvVSQKibLejG1ydlOs6dsoZQ+Ri7a4gRUhWh8mfsCVCE8kUROJsOHTvly39Yg//3X4/gzme3xRqPmNjndOUAqCqXLrNPVIzQcL6klBdoFcR18Nxm1U0V6hoj7TUAzwUZlTUmFKF0RAC0uF4yKZu4yVtzkdHvb3rP/fjedbjwpw8jX3QUNwqdT77/lxdx01Ob8fD6nYk+d1QxhOIZMuPFknwQaS3XmPfzqEkRivndC+NpolxU+lzPBRWZuhK7oGJlAphVMVjqpQi9uqtsUC3u6wAQwzUW0n1enyjr9bRCn+LiusZURahsAIhKz/WLEaq4xsh+TIqQcMXFOR+ij9y2mO4lsU/xGTRTTX+6bHSMEL12tg82pqVItZTjxMpj0uschcXnSUOoYqTkYhZU7MgKRShc5RHXSIbUEWrV9Hn9wYGegx/esw43r9mCNZsGlMWZHoq4VpO6p+j2cdUkWjCzI5NqGUUoqtdYXEVIzL0TZdjprl1WhJi6EjVRCkRJ+/3mdAKopyI0AgBYPLPsWomKRaSTn+6+0Rfaet2kUX3EAP8ESccpFkChetUSI+REKEJjBvWqKxffEEraq02cD5P7TU/fb3SbDWpQbBuqf82kWhgaL8oFWo+VyodM6rTzPEC7z4dfh+0VFdBThMzbm7rPT0ZFSCzg44WSMpfRBVRcm0mNEbp93FpCIjOwLWMjnbK9XmNNjhFS6ggJ11gVFZuF8TRRyow+Lo4RYupKXENITAB7zyobQkniMNZuHcQl1z1mTN8WfcuWCEUowBIaGi+iWHIwSiYSx1UNA931Uq+nL7qfOAUVAXWhEi6RXukay8j3JI1jUiaycX+MkKJelYKNlCBkHaaYRqRYSDsNxpb+eY12jdFruNXihHYNBx972DUg7BehCLVVlAXTglwsOfKcezFCUYqQCJYmMUIt+rStXz9qSY/y/4WSqwZLGxb5pMYIfaiJbQiR1HnAK4TZbNcYnS/HDAUV46alC2M5HzDHbh0cq6uxoodMsCHE1JXYilBlAtirEhS7YzgfO5X0Fw9uwG8ffRXfu+sF39+Ea2xJiGts3fZhHP+VW/HZX/3V5xKgT696G4e6ucYURSg8W8e0nVdVumwACcMESK6SmOoIqenzfteUyUgJwqvMHe/cie1mVD6DuiX0pzj9+3FdFy/vGKlbUTZ6LWwbqr9rbKxQqnoC3jkSPJ6wfQrDVyQ1hGWN0WtQjxEKur/V7vOtrQjpCipNUPBiVhzluqMLv1SNEs4L9J4Ka/ZKGZbFFMv3hdd9vnXqCJmyxuLG/IjzbZpTXto+jNd99TZ8+rrHqh+ohn5Ncvo8U1eSKkLCEMoXHQwnnBT0xp8j+aKsryNihExr4h+f2IThfAn3vrDDtwCUFEWoQcHScdLnQxQh2WesoghlUraM4UhsCFHXWMHkGvPHM82IaQi5rksUoXjfbV5zjdFFXV/g9WP9+f3r8cZ/vwP/89CGWJ8VBV0A660I5YsOTv6PO3H2t+9VXt+4exTX3vVCZBDtruEwQyiOa6wSLJ0NVhbENWhZkDEpUfe3133e8jLMDNs++eoe/Pufn/EVg5xIdJciVWGlQlFSg6VNHcuTGiNVKUKkhhCAFgqWDq8jFDdjkJ5vnZd2DMNxgRe2mmtXVYM/a4wVoWnFxBVUjDKEyhfezM6snGTDJnf1veUb7uWdI9iwc0S+LtSgGW1p9FQCiE034l3PlTOYtg2O+wImab+xoQa5xvRqzSYFQ89Sop+9i7TXEFQbJ6RkjUnXGGngaQhWFpPxeMTkMVooySfGuO6DcS0zrei40ljTJ0k9huu5SpG/ek2YSoxQnQ2h7UPj2LRnrBKM633OF377BK760zP4/WMbQ9+vFw6lJMkaC+s1RlPnrcp9HZUM4bnGUqGK0Dduex79d7yAW9Y0r4+bbkibGjAXSo5iWFJXctWKELmXxwoOrnvoZZzy9TuxfkdwpXZhMHblyt9Xy3SfNwRLF6sIlg6rIyQ+o57Koq+OUIuqlrXChlAAE1ZQMWawdEc2JTPHdsQ0hOikfR9RhV6pxAft1dsupX8xcd27djt+8JcXsWe0gEfW75Lv0VUoehObFKFb12zB3//yr7HSqccKJWMxPrVKtPkJ3ucao4pQJT5kZuW8AV6cUGJDSJO280UnML03LH7HBDVUksYIUXefMID08zQ4bo7xqNdTspI1VueiinSMNEj93rXl/m3bh8M/Ty8cCpSVGyDCNSYUIekaC64sPaIFSgNAOkTlAYgilLZCs8aE6hj3nm8EvhghgyJU0BQhpadb1TFCJGusUMINj23EC9uGcf+LOwLfM6S7xuqcNXb7M1uwmsyLcXEi6gjFDZYuhhlClZfqWY+KCyoyDSVp+nxbJoW+rvKCvjNi8hfQiYQ2/hSK0KKZ7XIcQm258KcP419vfBrnfO++0LHRSU8v2DdedPCdO9fiN4+8gnuej244+qEfPIDX/9vtPqVLX3RMcrCYlLOGCc9ThDxDyOs3lszV4GoxQrohpTRdFenz0hAKn4RpDE9s15gIyM55i6+YpIR8XflqfYqQZwjVZ3FQYoTqrAjRMQqj+vZntsjPjEqrNilCQgUthASoigUgpWWNmVw0owWvvYYgviIUHiMk7oFGB7yHIe6VrCFgXCpCRVctqCj6jjmuPK5aXWOyn2GIAS+MUnFfeN3na1/Ad4/k8bGfPIwLf/pw4vdGxQjFDZb2FDj/tVKSilD9jBV2jTENRbjG4hYybMvYckHfGZIJQ6ET1v+9sEMu5q8qilD57+KCF8rPM5vDe9nQSU+PIRgvOHLSitPeYe22IYwVHF9pAH3yMk2AYlLpq5wbU4wQNYS6KopQ0mrLekHFPSG1VcZ1RShi8qhGERJP153EEBLnRyyy4rh1xU6ojKbz6bou7nthh6/yeBiNjBEaN1Q0/vOTW3yvBSFi4YRhAnjnJZZrTNYR8ipL6y5a0X6GGkLpiNpANFhajM2kDnuGUPNihIQRNndGuXinOOeu63qumpKjLOxywSbHn9QYGdeCpeNkVtL2GgDq2n1+cKwIxy2rc0lVF7r9mCFGKCxerVBy5LXhGZXmexeob60fDpZmGkrcRouyh1HGc43FVYRoTZqtg+PSPSYUob1mtstgUHFT0ngaAJhdqVwsEE+FqmtMNQryJUdOlnGCPMW+9MlFf4JU0+nLjRjF+RFFE8UkozRc7fSOScQOJA0+LemKUMUQylYkgzFDjFBXzIKK1CiLHSNU+Yy2TAqZipu1IGuMVL7LyvWiZ42FucYeeXk3PvD9+3Hpb5+INQ5AvYa3D43XLRsN0FSBfAljhZKMXQNiGEIVRejAuV3yNakIxXGNaZWlAf95E0pVWya+IpQvll+nBRVN2462gCIkjLB53eW+dkKFo8MtZ4350+fpwplEESo5rvIAUVaEojMrfVljdXSNmYyZOOj3w0i+CNd1VUUo4FocK5Twpq/dgfNXPVQZg8ga83++2EU9Y4Q4fZ5pKEJyj5s+355Noa+zbJTEjRcQk6goKHjuDx/AF69/Ei9XAqf36u2QT7zieqejac+kcPayhfL3bMqWE0uoa6zgtVqIo7xIl452LvTidcKg2DY4jmP/9VZ8+rrH5KQsnvLFJCnieOjfAC+4OKkiROcDqgjNqTwllxxXTmZeQcWU8nsQg2PJXGOu68p95tI2MpWLqSBjhMr/90lFKH6M0NZKY1JhLAt+eM86/N3PHjYeS0l76tcNr1rQY4T+8vz2RK0XRPr8kYt65GvC2A97uvUFS1NDSLsuxXgURSiiNlDe4BozzQXiHkjqyq0nIsZsXnf5WhfHW9R6ZSmVpSs/0gU+SYyQ/r2OFkpSXU7kGhNZY3UIljb1C4v1Pu17FU1kqQIYZLxs3D2KjXvG8PBLO8vbhRRUFNdsPWOEuKAi01DCUmYFdHJpz6QwS8QIxazVIibRq951BM5ethCuC/zs/vV4bMNuABVFSAuWFuO56l1H4L8vOF55ks5lzPEMYiEXQahJFSEvtiVKESpv9+TGPRgYK+LWNVvk5wjVR0ygwi2WTdvKAiVcSUl7YumuMTEpC0MI8FQhqQjlymMyTdw7h/P48A8fwP/+daOqCMWQ8OkkmCWGUF6ex4oh1Om5C+kENhriGhNZIfpC9MO/vIg/P7UFj77sDxTVJ/F6usfo+RjOF/H0pnKbDHGtRX2PQhE6YlGvfM1zjQWfa3FI4kGBKjdj2nWpt9cAYsQIyWBpO1Qdlq6x8ebHCM2dUVaExPHqrh1T+jy9VpO4p/Trb89oQV6vYa5mzzWm1hFKouAEoaTAJ2ggaypWO5ovxaojJD5TV83Dssbqaaxw01WmocRRhOjN25ZJyQnclAkT9v69ZrbjG+8/Ct/+4FHK3/fqbZctBGhwIwAs338WjloyU3Y3B8rGWDrld42JGBehQIzmS3LSizKESo7rPT1q8RRBitCOiiE4nC8R15iqCAnjrLstLVOaAe9JkfYkioNqCBV9ihBQVsJocKiIUzBN3Dc/tRl/eX47fnzvOqX8QJzFgm6TS9vSXektFKprDFDjkMakImSS18v70J94hZG31WDkiGtBuOi2V7a545mtWFtjir7SZoHEiczqVONVghD3yuELu+Vr4lpJ4hoDSHXpvNkQSpQ1FlsRap0YIeEaG5OKkDfefFErqFija2xMuw+ocR2mCNGYSqC+MUKmWkBxMHmKR/WWJAGxZDIrz1FVc8f1G/KNUIR0I46brjJ1JY4iJAwBUaitL2H6PM04A4C3HbkQf/+WgwCUDYLZXVmSNVZ+j7jRhFK0sLdN7q89mzL2UBITpVCsREVnABiKMDjoYhRXETKlaHvB0mqNDqGWCMSTYnLXmKYIVQyh3vaMNETGio4yUXSRqs96IOz6inty+1BeU4SiJ1i6EGRTtoxT0l1jbRnvb3TiDnONifOmGxgi3mzLgL+XmLiGZQuTQgkvbR/GR1Y9hAt/ljzDRv1c1TUmzpUwQMOezB3HixNb2NuOC96wL85ethBL+srGfVhNFPF9pYgRLatLa9+RuOY6DDFCQa1rZNYYSZ/XFaEiUYSbawgJRUgYn+Xf9aahpmDpYp0UobiGkDjd4pzSgoq1xq5VrQiR94n7cSRf0lyL4YqQ65avSVOhSm/b8v/1rSOkfsZUVYTS0ZswjSCsoOLmPWO489mtOGafmQDKxdwsy/JcYzENIbGI0EDPi998AGZ2ZrGwtw2WZflcY3psxIIeTxFqS6eMT3qDylP6kKJYRSlCpslTH7/8vbIAmTKaRLC0mGzFJCNiNQRxgqV3Dufx8Es78eZD5koFrBRgCHW3Z5BL28gXHV8rCJrani85aLO970EUhds+NK6mz8eIZRDHmE3bsCyLBEurrrFsykYmZSFfQmzXmExL97UuCVGEKue6I5fCjuHyMQhj9cVtwxgcK2BGW8b3vjiMKYpQUX5vc2bk8PSmcEVocKwor6nejgz+6czDAJQrawPhrjHxfRM7KLDxqjAM2o1ZY0HB0uI7SiGdqhgWuiJKvp9mBUu7rusLlja3iAgKlq5PjFBcQ8iR35ua7QeU70FhGFWDqRZQ0vd15lLIjzgYzauKUFDKu3KOHcenwpHwx8YoQtqw6pma30qwItQkwgoqXn3Ls7j0t0/glw+/AsCbYL30+WhDiGZd0EBPy7LwodftjTcfMg+Ap/yIm8ern1J+vTOXlkZGWzaFjKZkOY7XHmJ25YmRKkLDETEctFO6L1g6IH1+hxYjlUvb8hzpsr1wUQg6YgRLX/nHp3Hhz1bjZlLNl04ItI5Qd1tGnt/xgqNM0p05f7FDwfodI5V9lbBtwJvkY8UIiUDpipEmVC/x3gJRw3S3GeAt5EZFiMQg0GtCvL5VU4Qc4toUgej5kqPsW1SyDsJ1Xdy7drvxuvYpQpo6EbYgiUDprlxaWQA9BS14wdDvAyC4qKLnGjPECEUES5dbbJi3pZ8TpwxFIxgnKuf8npwyLrrAF0quorCJ9VJkx5X3lcA1phlNiiEUGttVMYQqvwvXWPnza1vEddfY4FgB//XA+sgiotR2EHPCWEGNEQq6Tkpa3BX9XT8PLjGE6pW5qRvn+SmqCLEh1CTCgim3VxZ6ERgqFB2RPj84VozMRKKTKFWEgsYBqIsafV2oQm2k5ol4+hsplKQcLcaXRBEqKNVVNQUoQJXYpk08HdmUr6eQmFjocQDeRBQ2rk17yov9S6SUP530xwoOtlSMl76urLdAFktycU3bljIJ0+/LdV1pCOmfE2exENuIp11h7BS0jJJMyhxILXs/GYyIUokaTH7laMuAeu6pUiaChccLJeU9z28Jr0l134s7cO4PHsAXb3jScKw0WNpzjYmyDmHB0sKwouUTAO8hJCxGSFdGgeDGq0JhM7rGAp6gxWdn0sExQnq18kY1Dn119yjO/OZf8D8Pvqy8/uK2IVxW+U4sy4vLEp3m6dw1XlQVoZJJEUpgiOjXphp0HXwexOUoHvCyKVuqerVmjumusese2oB/+t2T+P7dL4a/j9wjQiXWY4SCjPJCSd1GV4TU8Xk/18s95lOEOEZoejFRvcZMBRXFBCjS3MWC19OekQUQowKm6SRKF2QdaidQo4TGRuxViRNqz5JgaUfELpSVkbRtobtSn2UXVYQiY4SoPOxiYKyAD/x/9+O/H3g5tiLUkU37ntZLjmeQUOIESwsVi2bn6crdc5XFfdHMdiVFV7o8Km4roT7QSWvnsBoX9BIxipIoQllNERLqmvg/nbJ8ihA9p2GKEOApHXTh2TqoKkL0SVaobePaov1shCH0cuX4t+zxxx+N666xvBojFOYaExljfdR/AMhrODxYuvy/bYoRSqIIRbrGgrPG9M9pVJzQD/7yIp7aOOCrHXX+jx+SqvTJB89FR847vtFCyR8jZKgsXYxZUPGpjXtw7V0vkOs0+HsNd42V/xe3vWVZdaslpBtCwk2sF1f1j4nECKW9a6+ozH3C9azeA4r7rOQov+vngX5OvdxjuiuM0+enGY3uNRY2UYoJ8JVKLReh6Ni2Fds9JlwG2bStZL7o0L9Ro8Q2KELtGX+wtJicZ7R5xshuYqRFBSXTJ4yi42D1S7tw34s78JP/e8n/VCgMIa2gZDtRhGQKe0CMkMjkChuXaKpKz7Ee9Cq+m8Uz2xVFiKoxAIyuKWr46J+TJEZItA/Qg6XFNaUGUldif/KqyqBDr0cvu8zbbqumCNGJUihCZfWCusbCDSFxDZkmWb2OkDBg4wRLC9cYzZ4DgGwq3G0FRLnG1HF6dYRo1lh4jJD4PrJEEdIfivTPaZQhROO3xDXhOC5e2VW+Tn943jH44XnHVIy28lhHfcG+jvIgJVwzcV1j//KHNbjqT8/IVkD6sVPCDCrxudSArVcHer2OkFCVo4wO8RBlW951USi5WmNaFz+77yUc95XbcP2jr8rX9RhKes3qrjHVVVkfg8Xfa4xdY0wdCZPORcd5cRFS11ZfZzxDaMwQKG0cB5kwaLwOVVL2ntUBoKxIpbUqxn+t1CSaO6NNLrqKaywqRog+FZW8uKYdw3lp1IihiMwPoQiJ19szKblICeOpJF1j6iUuFaGQcYm/0ey8oOa4e/V2yM7k1CUkDCBpCJGJ6eWdwd2zRcXsMHRFSP8MGn/iU4QMrUAophRhapwNjhcVdxTdXpzb8aKjvOfZzeExQkJVFPEHruvK61uvLK1nMBUdN1AhCFSEKtdEaNaY1mIDCO5AL11jiiIUXjmefoc2iRESLU52DecNilBjAqbnd3uZoS9sK39XQ/miVFdOPGA2LMuCZVlyPhnJ664dVa2gXekFYUb+hp3lBwth6Ipjn9Hmz+eJFyztvVavDvR0qh6jhlDE/UrDDWjxU13tebrS1ujpzQPydbo+FBy1VpNPEQpJPKkWsZ84jYonM2wINYlUyBOjroRQyT1uCr1eTyMI+uREL3L6JPzeoxfjk28+AH/3pv19wdL/XYkrePuyhVKhoE9zkTFCNK6APPHsGsnLnljiiTVfLFd0FovLoQu6AaiKkJc1VgkYriJGSLg6qLFpmldmd2XLny2MsKJXuFAaKQbX2EvbRxCE40b79/UYIZE1JnuNkWJ9XoxQ+T00uFivbguo34fJNQaoqhAda5uIESqWlPdsHxoP7V0mAoGFOvjVPz6No//1Fjz68i5fQcVhLX0eCFaFghQhacyHLKheHSHvNbmIaefM1GIjMmssIEbogXU78YHv349/uv4JXyB4oxQhupCLuMQ9Ffd2W8ZWjkvMRXpBwHxRrSxtagAapMi4riuDocX7xBwyUzNigahg6fL/iiIk788aXWNa5qgoDRL0kKS/z7Y8Q6ioxfsUHVcaatRgU9yNJTfUNUbFmvrFCJX3Ix4C6pE+/+zmQVx+w5M+N2AzYUOoSYQZQvqTYJtJEYrIVJCtOSIUITrR0wmGTiQ9HRlc8taDse/sTjnuQsnBmo0DePTl3UjbFt57zCKZxUQplNzQCYjeWPSJp+S4Mhuju93LRhKB5DPa0jhkftkQ6sj6FSGxn6Bg6bBxicU2zDUGAItmlpUyqhR49WFUtYZ+loj9ygbEbkUFwgfGCJXUp/CMTbPG/K4xwFSLRI2DKI9d3YbWEhLfHw0O111jQHjmmMjAE+N+etMgXLf8/7hiVJMCmu0ZaUCMFMwGglSEfK4xf5sYHVOwdCog0zNpZWnaIoW6m4qOg82VOKkNO0cNrjFVEdo9ksc3bn0ez0Y0SI6CBsiv2VgxhGSdLPXciWMcLRSVa0Ufa5Kssd0jBXkdinMrvme992F5n0kVoTq5xsj1orjGImwDzzXmlbrIlxxlf0XHm4/o/K+obob0eYrbgBghMe+J+bUeitCq/1uHn9y3Hjc8urHmfdULNoSaRKghpF3gRkNoJFwmFxNTWxLXGLmjdQNCIHsoOS7++8FyPZZTXzMfc2e0KTU7KMPjJTz80k68uM2/GNKFuKT1KxKqlyjUN15wpLIwpyuH/ed2AiinbXvF7lS3oi9GiCxWpoDpIkn93hnhGls0sxw7RTuTi8lJTHhZQ2VbUUNoKel/RYmasL0YoYohJA0QtXRAJk2CtSvnOag+kMAcI6QpQoNUEfIMTrrg+A2h4MVatHDQs95G8kXls2kKdVdbWqoTQQHTO4crTXcDgqXjxAjRBwJTexkgoteYwdii79crS4tjHx4v+r4rPYX+Vw+/gv+89Tmc3X8PfvfoK4HHEgU9DcItI0pg9GqGiHiwGs2rrh09e89YRyigqOEWogw4UhFSK8ZT4hg0aoxQfapLUxtgNO/1U4xShKhRTa89JViazDvUENJT7GmA+njIQ0y9Y4TE/FoIeXiIi3jAitshYSJgQ6hJhBVU1J/a24mBEbcDvZhEc1GKUIBrLCi+Wkza+aIjLfoPHr+k/FkBCse67UM45/+7H3+7yh94rgZLu8b0TGEIUUVoVlcW737tIpx5xAKcd8I+JA5ArSyt1xFKp2z5dGNyj41oFZjFd2EyWHVFaNwULG1wjYnU+dfuPdO3T7GfMMY1RSinKUKeMWZLI0m4gfyGkPp70egaC1aEaHYeVb/09wiXi4lBTREqSEOopCgNQkrPpMpGVwdx05gQE22flj5Pn8qDMJWRCIrrG01YR4jeZxmtsrT47gbHi5FZY+J8jBUcfOa6v+KBF3cEHk8Y9HjKapyL3aPlc9fTrhlC0vgsGjMM5T4NhpDrmoNtqatVXE/iPtbVPKCWGKH6ZY2NFbxSDlHqC42zyQbECJUVamEIOb73AuWHDqXAoi9rzDzWWtDjVOvhGvMyjptXLV2HDaEmIQsqak9Iruv6Ukepe2umrNUTpQgJ11hEjBCZ6MUEk7ItpT+XOu7y/obGi7Ii8lFLegEEu3qe2jiAkuPK+jwUPX3eFMAqAibHi47MGJvVmcO87jb0n/taHLdvn08RkpWlDRZdV0ibjRFNJRKfZ5pXPEXIi40KDJYWC9xYQSpdRy9RDSGvDk9MRahigOm1ggrEGPMpQrprLEQRCmrFsW3QHyOUTtmaa6z83v1ml1W76x97FRt2mmOjZIyQFmA7kldjjcQ1L9ybIksrqKiiMIR0RSgTQxEytdgIenjxgqX9WWOm8hh6ixTal6xAFCF94fa7xtTfn9oYbGyGQY9n53AeWwfHoxUhrQ6O/h2I8xfVNgdQDWupCFXOkf75QIQhVPlTI7LGHC1GSCjKSYKlaQ0r3cgR3zed//UWRGEtNuj46h0jJOa4eihNJWkINa+RsA4bQk0i6IkxX3J8TfrayJOmWCzHQtKGARosHV1Snsb9AOrkryOepqk1LxSRoPL1IjjYJI1TqbXkuEq8gkDUJ8oXVUWIIhbhUkVVEhOBycUnFiyTIqRnkwn3mGlBk4YQUUJoVWfAn9H143tfku/dp2IkAGWXnTSE4sYIpYVrTGuxIVxjKQtZ7W+6ITRedPAPv/4r3vatvyjVpAFz1hgQrAhRF4R4z2mHz8fx+/ZhrODgshueNLpGpCIkg71FPFPReC6EIUszmEwExQiJxSgsRkgGuJLrR7q7yD3ruq5UEduVgorBWWPiWrCs8vVJFSFqBA7nwxUhvX5NkvYHz20ZxKMv7wLgd+2s2TgQHSOkpc/r15Wp+zxgvrapq1VsLrPGcmnfw0ycytLmYOlaXWPqvSHmj9iuMctS4vl0t9eYVITMMULFBFlj9eoJJsboxQixIsTUkaCCiqb6GXSCbSNPZGHEDZYGPDeYuMjtkKtCTPDiIs6ROkVBrrH1SuVkTYEIadwokDFCRa+HlagsLKAG3xhZ0PWmqwDJHDMsoPqELtSbUNcYVYQq2VniXFCVZO3WQXz79rUAgM+derByDDPaMuTJNco1pn5GRnO/UdeY7przucYKDv7w+CY8+eoAXt45rEx0oxWjMCxGSBrPtiWfGscLXvXqtkwKX3nnEcimbNzx7Dbc/sxW3/GIvm3CKJaqSN7vYgM8Q8hblP0TaslxsbuyXz1rTD9fJrwYIe81UyPVPHmyj9trjAZKW5alxN3RMemZdgOa4SOOT8S9JVEBzv3BAzjn/7sfg2MF3/vWbBqQtcB8ilDlIUJPn9fvG/EnXUEwGkLUsNZihHKZlO9hLk7TVZNrTHc1JkU/XvHQFK0ICdeYFyytF0cslDxFiJ4jvZ8b/ar8BRW9n+vVE0zPGqurIpSw8XUjYUOoSQRllZj82G0GQyjqpo4bLA14T0/iItfjaigiHX1ovDwJ08k/yDVGW0jo49bT501PHCJrbLxYkovD7C5zJpD4jKAWG0B441X9NVFdOkwRolWthZohFltqCF3++6eQLzk46eA5ePvShehtz8jxdbWlYwd1Brnf9BgbU4sN/fyPFUtSUSmU1F5Go3lHGY84zkBFiHyWUIRyaRsHzO3CO45aCAB49OXdyue7rterTg+WHs2XjPeDMGTDgqX3jBbkotirxblkImr8AOassbRWOkKMUUCDpe3QGKHya+KapW1raC8noX6K7XyKUMV9JXr8lWI+rTtOOV09X3SUxrSCV3aNSNdYj881Vh7LqNYra0R3jRlihADz/EbbtrjSEPLmrySGkKcIea/VL2vMO95dI3lpeMSNEUrZagkGXe3xan2ZY4T0e9eX8dmIrDHpGot2J8eFFSFGEpQ1ZlJ6qKrj+ejDb+pR6RqL/orFWMSNFVKIWm4rFq824g4Lco2JYmmAX/FSfOABwdJeHSFHFlOcpSlCthKs64TGCHWGxQhpi6ruGhPnc3ZXTk7QSraUXkeIuMbuf3EnAOCfzjgUlmXBti3ptunKpb3xJ4wR0qtHhzVd1Z/c95A4E73svx4jtKSvrICZ6gilU7ZSr0WoSOLzRdCtvjAO50vKguI4XjbNcIBrrFNThEyG0E6ZcZiWcW2CdILK0tTFYhtihMQ5yqQsRX2MpQhVzo2nDquLvFA/Rc2kwXFdEarcC5VrKK4i5HNHV94nlKVNe8ak2uR3jVXisvKl0AJ+XkFFPUbI5BrzG9a0Dlp7Vv3+TPWvBOJVy5g1Vr86QrTRqukhiULjlmQxT039pllj1FjU+7lRwtLnG1VHqBy6Udu+xcMWxwgxiiE0mi/hkusew01Pbo7tGotWhOLHCMkJPkRFEaS1p1OqCOmusbRmYJnGrVaWdnzB0pmUJY2PfIhrDCg3hRWf4S3QwYZQnBgh3TUmjDKhBgG6IlRRY4RaUzlfe0YKch8Ler33iuOY0Zb2XEsRE7YvRkjrPl+UipBayRbwG9q0RIAetyBdY5X3LOkrxzQNjhe9AnimGKGC4zfWDBW2Ab+7p+A4SpyMSUGYoQdLGwwhL2PMn3VEVbKgSd3UYoO6sASyz5h2n9HaQDpUsdO3pYaiOMdC8dGfoIVqIx4KwlSAofEibn9mi6/wYZEYQsLVu3nPmDSQ9ayxNhKXFUdRi+Ma22LIGhPxMm3plNG9HxQnZIoRaiMu21qgxhftdxjZYoMWVExT15j6EGiuIxQ8d+rnUq1UXWdDiDxQ16o2ibGxIsQohtD963bgt4++iv471hoNHBos3d4Q11j5f5o1FkRaV4Qywa4xk7GiZ8RFpc+3pdWq0TsCgqUBKDEqQS02AKArJFhazxoTZQrEsES7BtF2BFAVIV9l6co5EQaVZaldyoWLryuXlq6l6DpCaoyQ7hrLE0UopytC2nVDa3nocQtiW7Ho9HVmcNC8LgDA6vW7lM9MEUUuX/Ky5/Q4Jn1h1CdD2mZlJFARKp+/tpBgaa/zvMkQ8q7voEnd1GLDlOBgSp0HIhQhregmNbDo+RGusTldfkNorODFT4lrKKzGy7duex5/u+ph/Gr1BiXtukRSsveqGPdlRSggRkhmZ5ZCF8S4rjFaVZq+jz7IyV6LZFoKNoTg21Z3OZccF2u3DiVWNvRK0PIzI+wrL/Dec8uagqWFoUbryCVRhBoSI+T6499qDZgW183QeLFmdalesCEUQMO7z5OJUkwOA2MFOQHMIhN4GzEw2oiP3sT6HcPYNZz3XGMB7irTWOiiFoSYtIfGhCHkjU1XhGgbBEGYa4zK9HKfGc+9MzhWkAF2szsNihBpfiqUpXDXmP8c+rPGyk/G4oZ9x1F74e/euB8+8eYD/J+r9BpTCyqKhbkrm1YykYSx2JVLx24FEKQIhcYIGTJSAM0Q0hZi4X714n1SOGafPgDA6vVlNx9VTqRBWPDS5702IOYA5QFNHi+UNEUojmvMUFk6qM8YAMVVFjSpi9OgZI0ZUuJHDKnzQHhlab3opqmOEOAZ4XO7hSHknSuhBqVtS2ZVhsUIPf7KHgBlt6bujvYUobIhtGe0IEtd6IpQmgT7hilC4iP86fPq90mrStP3jRPXmDB4aRmEoDghY9NV7b767p1rseLqu/A70tw0DkEusKhgaZcY1UG9xgqOuaCiUkAxIkaoEd3nZYwQWUdqLaoorpuS40Ym/UwUbAgF0Oju82rtkEpMBKkmO7fba2JKrXGZNWZ4Cr7jma04+T/uxPk/flDeNLp/3YQeLB2ePu/VEQJUl4CuCM01GkLBrrFCyR8snUunpIH1ciXWKJe2ZQC1vi1QUYQCus8D4cHSYmETC4BUhCqTzOyuLL5wxqE4YO4M+R4qvetuj2wqVdmP1xqEMq/S8LK3IxO7OaSutmRT6vfnjYGqNOXx69eNMPTE+8JabOTSNo6pFIF86KWyIkRdkDQWQ3eN5dLekzBFjxOg18DIuBdrRI1zf9aYQREK6DMGqIpQ0KQeqghprRYAv/JqCqyWn6ldI2Jb11UNBfFWoQjRytJUsYkT/L1uezlhIV9Sq37T2jS97RkZJyTUJ10RypD4KlPVbDn2mOnzW7R+U+K80/Mq5r/u9oyxQKnp/QgJlhb1lkSrmx/85UVc9adnItWJIOMiOli6/L+t1RHS22XQhAYxFrXSu/YQGZY+XydDSBiytGtArW43er5axT3GhlCTEA+lJdd7Ch8aL5Ku8TYW9pYXSZE+DnhG0XhRDRhcu3UIn/zFo3DccnVYsaDHco1JRchVfjePW1eEgoOlzYpQWNaY45N0qSIk4oMW9LQZCz5SRagYQxEKyxpb3Fd+Ot6pxQiZ1DLFAIhwjXVphtC5xy/BuccvwQeP3zt2dst4gNtJLA5F4hrzBUvritAwdY1pMUIFNX0+l7FxbEURemrjHowVSvKJNU36mtE6Qrpq5Y8RUr+DceJyGRovymuSZn51xcgaC6ohBHjuCXHMJrymq/6CiqZMHj0pIY4iJL4/ek2ZjDqhCNFClbtJDE9Yux6gfE1vrmT6FYqOT4X1am7ZmN/TprxXb3Ehg30dN3RBDI4R0koxDKglAsSc5rn2bfmwNaMt7buefZ9LApMF+gOGcMWJ8f/rjU/j2rtekKpZEFFu1Kj32ZbX8kYvjkivYcf15mK1aW38rLF6xQiJY8vYts9zUC1FxRBqjYBpNoSahCy4RnrOjBUc2b+mPZvCFWcfjk+vOBBH7NUj30cND7pgfup/HpVuo3zJwbaK0RCroKIvfT5EERKGUN6vCGVSllK/I45rTPeThylCAn2yFqQN59QUIxQna2xxJXBUGDBijrGNBphXR0imz2vxO0JZEou4YHFfB77yziOw7+zOyOyWV3eP4mf3r5fj9lWvLrnlhp4G15gXLK2e/52Ka0w1RP2KUAqLZrZjXncOhZKLv27YrRicwpCjC3aQsSbQJ0JqCNDvhyoT0jUWoo4G9RkDysZN1KTupc97r6UM2WZjAS5oU2C1wK8IedeUnoYOlKuoC8QTtFf5ORvYA00g1CCgPDfowdLC+EinLCzo8QL507al9OYrj7ly3opOqAEgW2wU1W3CinPS98k6QmkvfZ4aQoGd7Cv/m2OEyvsUc2PBUYPlX9we3BgYCHGNRagv1DWWJu1d6Pv0uUjEUoYpQv6sMTqm+sQI0V6CmVR9DCE6NlaEpjm0oCK17IUC0ZZO4U0HzcGnVxykPJXSeCHxdF9yXCn3inVatDNIEiwtLvBwRciT8QFVMrXIEw9gNoT0RT4qfb4tY/sMITpZU0x9fDLGrLGKa8xQiE8YoosrqeKDY0Wl4rLp3FAlShRUlH3A9BihNn/LAIGMZQhwjV1+w1P44vVP4i/Pb6/sW22xUSiqcnvWoAiN5VUFYzcxhPJaI0g9RiibLhcAPGbvsir08PpdWtNVb5ES13RQQLdAbyQaVCWaGjRdWtaY3vATCO4zJkhHGUKOt3jp76FP3uNEuaCEqTSyV5xBETJVjO/MpeQxP1apw7RHuMbaM8RAMx8LNYRoDJYYH63CTh8yejsyPuVVNg11wmOExPnTXY+6AUOLc9L30WBpaQjlPPdxshghVWndThQhahRu3O1vAUQJOl79O/7Z/evxy4c2eH8n/c/SAQkR+vGI4y8ZjO6g9+h1ieqBuFTStqUEeteCqgixITStodI5ncCilJw0qRYsbgx6gyyoxJyICaYtHf0Vi8VdLF5hMUJ6zI2e2kqNFmPWWERBRf0GzqVtGWcjCFSEFP97cOB3ZzYsWLr82oKeNvneXSN5OZmZzg2NTRJPwFKt0Wr8zMj5Y5t8+wmY5G99eovyuylYmp7PdMryxQ8J41nUh6E963xZY1plafHdHrNPOU7o4Zd2eunzKa3pKgmwLo/RbHjowdIm4zRtW0psVZyCijJrzKAIlcfjqYcmTC02Uoa4H/Hk7o8RClOEPNcl3RYwJ0G0ZVI4sJKt97GfPoz/+POzSsHDTEg8EqAaQtRIBcoGjYynsy0sIPeWHihNx1wo+RMbKMF1hHTXWIAiVLkH2rMps2usZDaYQ5uuFh0MjxflPV4sqQroq7u9emfGfcdwjQ2NF3HZDU/i0t8+7rXgqPw5ZXv3Y1TWr7h/ioZrTW4TEixdv/R57+E4HWFwx98nG0JMBbHIOo76VCLSw3MhhRBzWuYYnTxFLRCBntYbNhaxiIdljekKi88QynhF/vSKvoDBNRbRYqMtk/KdiwUBhpDSx0fGrpiCpcPS54tym5kVd8yOobynEBi+FlURMscI6Z9tIso1tmxxr3F7+ZSsuT2UGCHNEBKLnB4jROs4yfR5LfD54PnlQPGXd44oLkgaEC3eK767oCBXfSI0ubnaMiklK0sPljYtKmF1hIBgw0wgA1yVpqvl/1V3RfI6Qr6CilGGUDqFH513LD5w3BIAwLfvWCsX7d72rC8e6fktg3huy6B8/4vbPJdPoeRq6fMuxCVjW7oiFBxoXnSciBihyudp37cvWLoSIySuj5JTnhPFOWpL25hXiZFa2Nsutwt6WHDIsQi8B5WSUgix4OiKULghFLT+q7E+RbhueRwbdpVVeW/u8Aoqxq0Dp8ajhatIcZuuDowVfEbsbx95BR/7ycO+BxPq+g6K80sKvW4GDWNpBmwINQml0SKZLHfEiO1p12IjPH+67XNHVdNiI8wQ0mNu9P3TTLdOw6IfViZeNEyllBUhLUaoO8gQ8qf26lWFgaiCil46tDi2fMkB7RekQxUhLz5HTZ8X6MHS6n7CJ3n9dV8gshYISwsq6pWlResEpZeRoxZ5M2WNAVDOi3RBkl5jgBfz4K+wrU56ekFFU/+3XNpWDPo4wdJhdYQA77oIkvlNLTZSYrEu+RenXIAhFBYjJM6NZVnSPW0yBNuzNmZ2ZnHlu46Q9/fDlay93o6Mrw7RW/7zbrz1P++Wi5oSI0QaAwMifb6iCKUs5d4yPcikiXskTh0hXZnV3b6iqrQwwBzXVa7ztkwKH16+N771gaPwsTfsGx0sLe5T8hq9r2jNolJJnW9e3RVhCAXECNHTQI/v5R1lQ0icJ8uyZOxgVNq4LI6apI4Q+TUoRmjb4DiO/8pt+LufrZavua6Lf//zs7j16S24/4Ud2j69+yBKRY2LHht17FduxQlX3ibDOZoBG0JNgk6U9MISwblhzVK9zDFhCHkyst6DK1nT1WhDyKcIZXVFqHxJdQQaQsGKUJE8oWWl2mFShMwxQjSjhVY81gkPli6/1pFLEfeGI5+aja4xqggFVJYWhCpCERVwdcNNGLBU3aCp8zRmS+81ZlrkaJA5QFtsBKs7BbLQ0WPVY8hoHBPFrwj5v5Nc2lb6eIkYrw7SAJRSKDlyv6Y6QoAX9B9UeM7UYsMUlByUNRYWwFzQjOXy9sFKAc3GFAUtn95cjgns7cgogd/0/U++sgeu6+JFJUZIrRVVUhILVEVI7zMGJKkjVP6bMHzFda+rnUIRkoaQ4yrHINTAs5YuxIy2TKQhJJMayH1Pu89TQ6igxTlt2DUSmkIf5BqjCzt9sNtQMay8UgzedWfqIEDxFCHH95r8rCoUoZd2DGO0UMIzlesHAF7ZNSrrRumu6aJiCNU/a+yVXaPYOZzHxj1jxiK5EwUbQk2CxhAo1WRFbE+Ia0xkqIimmDRzxa8IRX/F/l5j0enzAj2QWSyGHdmUsuiL2Bjdz60GS3s++4WVibEzl/YrQkGusbS32IYZdWJcI/mSb+ITlaU7SeHDYslVnox0hELiup4hFeQa0+sIUaJcY2LfH3rdEpy1dCGO27cctJwh7i/PJWgrf9NjhEzxH7prUiyaQTWBaBB5OmX5sgbpewLT5zUp3qTu5EgtGcBT1WQdoYAikbYFWWxQh54X0+JHG2UKpDvbpYaQCJbWFSG/eiTQg6Xpvk3HT4/9wEr9KjGEnvaMMpdQQ/bpzYPYMZxXjE1arwaoZI2R+Df6kKH3GQOoayy8jpCXNVbexjOEvPfQqtLifi+5rpwjMinLd7/phr2OFyztvUZdY9uIa0wvFzFWUA0lnTjB0tQ4EQqH0mKjMn6T8kcZM8UIhcRXAvHS58X3QY26B9ftlD/rcZNUGY1SUeNCr5s1lSSf2V05X1HSiYQNoSaRUgwh78LaTrLGgmjTYiPoU6keoBzUCJWSxDWW0VxjfkXIe1oXT+4AsKiShaWPWe015k3k5xy7BO8/djHOO2FvZcHIpCyl6rY6Nu8p31ugTa6x8rhKTlmGd10XdzyzFZv2jMonoo5sSllgTEGYAvpd7am4evTUdkG8GCHzJC8UrL974/741geOIkUbhQHo+lxzOaLeuK4Xu6MXygPUKsOCsQINfNbcXEVHiRGyLMtnGEdljemKkNEQStsywB0grjHZYsPcg4vW2NER3+2Nj2/G0ituxu3PqIHopoKKJpUnqIJ7PEXI9m0fFCwtOGBul/K3cvq8yORS3exPbxrAi9uGle1p+xPAnzU2s8NTXUzXiOoa8/1ZItZkcawmRYhWlZ5HFCE90J4S1ZhYnG7qwhYq+cY9o0q6fjnOSd3P+hD3TFD6PH193GgIlX+nAcdR1eNNMUI+15g2djq8IKNNvIcaTdQQGtFUZ6oWStdYnSpLA+VrFPDqtjULNoSahDSEXFURkkGCIS6tNs3PTJ9KdUMoTrC0NIRiBEvrWWP6AiAWvvasGuAqyvePFRzc+PgmHH75n3HDY68qNxVt8bBoZjuueveROGR+tzIhzutuC0zv97LGvMnd6Boj4xoaL+KXD2/AR1Y9hM//5glPEcqlvVpPxEAwnZts2pb1VjbvUYM/k8UIBWeNlRxXfs+6y5EaGTL+xGC0lI2+8ntMgbDl9Hv1s0fz/k7yNACbxgjR4/aOSTfWNEWoYjiK7Uyp8LlMSqo/tuUZQO3ygUAtLhoVHwR4RsitT2/BwFgRD7y4U/m7saAicZUKxgNcY2ExQnqwNODVKDJBMz8P1A2hdjVGiCoBT766B89WgqaFXaDHkakPDWV3qkhGMBpCxD0SqgjJ9PmKa6xy3VMDRlSVntmRkfNIyVUNMx1Zqyqi/hNVtfeeVa7RNVZwZGxV+Rj8dcvW7wg2hOJUllYUoV1qjFCKuKojXWOijlBYi42Q9Pmg70YcLz19D77kXft6jJ44n+mYrrFdw3nc/NTm0MwyJWusYngt6esI2nxCYEOoSaiKkP+iaQsxYNo1lwAtR19NsLSv11gC15hPEapM2p3ZFFK2hb1ndaAtY8vYhvFCCQ+9tBNFx8Uj63cF9j2i8RP056CMsfJ2nkFQDFG3bFIo7pH1u/CftzwPoLxwCEWokypCrmtUCChzK0GmIitFjCWXKEYo2DVGffcdviJ3JGtMFHRMabE5JTX2wuQyMlULHi2U/DWByD7z2nmmQcPZlC2fzEXXbX0BE4qQUPmCFCFxnXVm03Kf9DxQJSWsz5hAnBehEOhP0HGbrkalz4d1n88aFCH/OC1F1Txw3gzl7zRGSM/ken7rEH73yCsAICuCm+oI6fFQYlEytcjxAmbjxQiFucZEfNC87jYyH3rvNZ2T6DpCqByL91rKtnBQ5bw98rJnCOnp8wDw8g5VQTMdU9jr9N7dsHMULpk7bNtTqINiywSea4wY3ZFNV1W3tgnx3Ytttw6MKcH0ehxikVwbmRiusa/9+Rlc+LPVuOmpzYHbmNx2bAhNU+gEa5J5w+r/tMugWoNrTDeE4tQREsHSIU9igoy2sAfVERJq0K/+bjn++Mk3yJ5aY8WSdPGMFtQMlpLjkGap3udQl8v8gEBpOjbq+zcVVASAM45YAAD4+M9Xy/YDO4fzUsbuyHkxQiWSIROkRukGaJBrLFaMkOF6EBNU2va7n8Qx0uDltJa1Nl50pLGQSfkrBgPmxW204K8JRI9JXHvi8+jY6M+m9PlCyRuTUG9GDLWdaLA0VdSoGkkNKFEt26R6CTwXRcVVoB236fs21QYaiyio6Lj+IFuv6ao/RkhHV1z7OrNKQkRvO6ksXXKVhbPkuHikUoDxPUcvkp9NM/eKDlVPy+P5pzMPxedPOwQnHzLXN564dYT0FhtSESKGgqghNJcYQq4bfq/FzxpT3ytKPugZc/qiXI1rjLqZ6LhGCyVsH8prMUKqy7T8sODfpzl9XqsjFGIIBX034vsQf6dqEOBXZKlaGFWEFAC2V8q/hGXgmR4OFrMhND2hUrgeQAxEuMYyZkWoPaNmjelPk0HIgoqGBpe+cevB0toCIBZL8QQ/t7sN+83pkhP6WMGRbRXGCppMT9JZdRecmADDFSEq23uxKyauOPs1OHRBN4Lm8vZMSonzCGuxAfifnjNBrrFcSGXpENfYMHHZ6Sn8imtMW2SpSjZK+s+Z4i9MMUKjpAO8zBojxyTGJa4L+jd6bZieJml8kKj3Y2oxkUun0J5Jy+MX2LblKyUBeIZkmFtYj3XTJ3dxGpT0eREA7foXp6Cmq/r2AOlYT2Lo0gHXqZ6WD3hxQpZVNqxpjJBJpTlm75nYb3YnAPUaAVRFSBzrIfO7cdFJ+xuvEap0hVaW1pqGdmX9ipAo+jp3Rk4aAzQez6S+RgVLB8XyHTJ/hm/bYsmvxoe5xpIGSwPlWlu0JpX+IJlO2UblS1xXphYbtG4YhdoXgTFCWrD0I+t3A/DuWz1YmqqFcdLnxX5NGbni76ahLZ7JhtC0hN7kppTZWIZQJWtsvKAucN2Vp6+wgGvTWAoldUI0EVVQMUtcYxSZYl7wFKGxQsmXPk8bhirvF4pQQA0hQK0NE1ZQESgrVv/fh4/Gwp42HLWkV2ZgAeWn+5RtKe5LWVk64I6ZO0MdV02KkMk1Nu657HTE4uC4XkVevcVHnihC7Rl//zbAn34PqDFCumsM8BqzisWYLp70Z72wI+D1GevMeoaZKX2+LWPjkPkzkLYtvGZht/I3oRSNFLz3hblWBMJVp79HYGqxIRslG57SdaOBPujo+/bcr961EHTPtWf935PIHOtpz5T7pml1hHTOPmovxRANckeH3fcCuhiGV5Yu/y8WXlOMkFCE5nXnvIa2bnQ8HhDSayzggeWQ+d2+bU3GXFh16cDK0iEBza/sGlEyr/R5jRZZpJiUSnEfimveFyOkKEIRMUKVbcW8IuZVX7A0UQtNcW8j+SI+9T+P4qYnNyv7DaoYHVSLacksNoSmJUp/IZNrLCTtXRgfY1odIWEgCfdYWJwRRc8aC0uf129a3WDzgqXTxu3GCiV5k9D4E6AykQe0xhALTagiRKRbIb+GLYaL+zpw9z+cjN9edIIShCoWKBojZKorQxEdwgUyWFqPEQozhDLBk7xcPA0xRnRyFROZSRGSVZCzKZ+BBqiL24xKT7QRqghVvgNan0ioG+JcRbnGqAIhOs93t2ek4WWOEUphn9mdePCfVuDq9y1T/mYqqliI8d3r17G+IIa12FCbrprVJ/rZPkOIqHum7QFP0TA9zIh2G6IWFFUu9af1tG3hzCMWKAaEWkfIiWU4yv0ZVFcTPteYIWtMxAjNneG5xhwnPFg6G/KwUP7c8v+6bXGwQRGiRqG4/sIqPntzgPZ6gGsMKBdVdMj7dKWbNmKlSEXIdK1lzIYQLQMRmD6vucbEue5urxSZ1R5EaEV9k2u4/461uOGxjfj4z1crfwtShEzXTEYr5NkM2BBqEmnFEDLUDglVhMpfm3AHjGry/JxK5licGkKAN2l46fPxxm0ap5Dt9ewW2qF9qGIIjRfU9NUCqYOjK0+idpBYBEzQ9E7q2w4jXQno3W+Ot1/hsqBZQoldY2m/qwhQVQAdWqFaRyyeHQZDSHFVCcNEixEqOq7cR5AiRJ+QxcI1OF6Qx04/R/wsDJBUyn+8StkD8rO4zoQiNKMtLb87Y2XpynXc15n1LY6il9jOIa9ViKjdE/bd60/mgTFCZBfGGCERLK2dTzpO3cgyqXt6PIw4LpN7TzS9FfcavU7FA8CcGTmcsP8srDz5APR1ZhWDOChGKJYiFOGGE3i9xkKCpQc9RciWipC5qregmqarQPl86GU3aHC5mMfC4l+EwaM/jFDxRe//tWHXiNL2Q38wMqlEQECMUNF7kDGNNajp6kMv7cT37nqh0s5JDZYW/8+ouOyHNdeYd23YXl01su/ntwwp24v9DQUoQqZrZtHMjljXXiNpXgWjFqe/vx/9/f0oBTT3qxVFETLc1HFabJjqCAGeIhSnqjQdS0G6k4ItIT3mSB/nR1+/L059zXyZLi+3q0xgY8WS7Dg+ViwptS9Kjgu3clr0MfR/8LXYsGsEB8z1P9npY6Pp80ExQjoihgLwjBWaJRTlPvC5xgwxQiKTLog4rrGunP87pcapCHb0FCHvb6J4Ybl/m38/+aIXoyVceLtJU9acbuSMe58XpQjRcRRKDtoyKTme7raMHK8uzev70VnQ04YnXt2DTaQ+jCngXkc3tP0xQv7v29Q/bDyooCJZiHUjSzwthylCMzsy2DmcNypChy3sxi2feSMW9rZX3uspVeIe7m5L478veJ18DzUg9KyxsGKhOl5l6fCCijJGSFSWbjPECAlFqLtNqkOOksXm32/cYGnTew9ZMAP3rvVaSNDg8vZsCgNjxdCMKIc8JFDXD1WERJhCyrZQclxs2DmKIxb1ytd04zydspAumRQhf9aY+BiRiFKsfHfCQHG0uVRw+Q1PYc2mARy/3yySPq/+H6gIkfR5qpAL9mgtciIVIcP5bXagNMCKUCArV67EmjVr8NBDDzVk/7S/kF4fAohwjfkKKqqTsacIVekaSxAsrRtblmVhcV+HL6BXjGW84GBoXARLl5RJmUrV+oQxv6dNpgAHQYOlpbIU80ljvzmeISR88FQBKAU8aQp015iXPu+viBxEaLC0Ia5EQF1V4onOZIiJSSssRkjMn5GGkOYaS0XFCBEDWixiwiAuK0LhrrEghKt0E4ntKAW4Vym6QR+sCHn7SBsWnKBgadv2qmzrWTLiGKkhFKR0BTVfPnDeDPn+NMlECoqxUxQhcn2VlZ3o8yUQn0WrmJsQRkM+wDVGq0rPnZGTc6Hj0vY4/mM3GULUJeR9N/5jOXheOU6I9nnUFSHqutURD1fivNMAb4E4XnFdbh0cI64xc4yQKXtQnCeTgkJVQhpaoKbPe6+L+35wrEAUoUqGnisMZ6EImQsq2jRm0rBvbwzl/wcDDCF6L4hzvnhmc4spAmwINRVxYUX1F/L9LSRrDPBSueMGS4vJXkwuYd4k/Um6zRDMaUIsFINjBWm4jRZKyo1eIq4Zk1wchZo+H39yB4C9etvlsYmJjk6YUnIPDJbWK3r7DZGwGkJARIyQQUWgeIZEUfmdZkcJo6YjIEaIXociu41WyqbGrXj/aFiMEFnELYsWZBMxQsI1RhShgGDpIEQ5hc17SMXgAPcqRf+bL0ao8hXQ68c2KEJh9WBMrjSAfpcp37biM0WdpziqrtKuJ+C6py5SqjjShT9OjBC9nnQ3EKWkxwhpwdK0qvScGTmvVIUTkT6vZY1tHRzD8V+9DVf96RkA4YrQ6/YrP0iJgHtaLoLGNAa5xxzNNTaD3IvC2BHHJ+512sLEti1ftmJaU4lEootUhAzGJq2fReeKoPR5ca50NdBxvXHLmMCArLE0MYTovaI3TRb7G9Ra5+j7sy1PhWp2DSGADaGm4hlC/hsvLPW3ncTbAP7qtqJmT1iGkmkccRQhtb6PPxg4CDG2ASIp+9LniQ87zqTsGxt5Wo0bI+S918bes8qqkKcIiWws4hoLUIR62jNqTIxBkelqC06dBzwjolwhWL0mvABb83UhPkdsJxQP2/YMkB2VOJoZbWmjIkRdtGJCFn27fD3ltBihqDpCgKpKAF5mSXd7Wr5fqiVZs7Kks7C3oghRQyiGW1RfkHxZYyEtNoS8T1uWmNRXUwFGgLjGArLGMilLLqRJCqLSmBdd8aKGH13silTtTKAIAeZYtqy8hkUqfPl1GiN0+zNb8OdKwb2ZHRnk0il5nh03PENTjxF66tUBbB0cx13PbStvEBLL95bD5uHPn34jLnvbYQBUBZoaF0GGkEwkEIYQuZ/FmIXRIYwl6lY3Bkvbavq8qH1lihESUOOYKmNB6fNiG73PHDU6qWuMKmxUCTf12hvQYoFKMWOE0rYtzx+7xqY54uZPXkeo8jQuXGNaddvTDp+Pj75+X3zilANjjUPch16MUJhLwftbeyblc4EFj9l/PL70eSXtvRZFyIkV76Qj4oSED542XY0qqGhZlnRJAt6CQNPwZ0QpQmnzkx5AA2yDFCFVUaEGqvh553DZFdHVljYaF2PELaW7xvTtA7PGiDKiq05ijOPSNeYpQlntb7QpbFiMkMg22bTHc40JIzJMEdIXpKDAU1OLDTGZF0reQm9SX8W1pwSxkia2XUqMkPp9CQUlTsIDjRESbkHdJUy/Cxq/UXIcL7g8Qfo8YI5lE5XUXa11kDjWPaMF/O2qh3Hpb58A4D20UUXIVLpAPw5xDsVniMXb1GJDYFkWDp4/Q85F1HBUDSGza0wczjH7zMReve1425ELyN8qhlBRNayKJNEiZaojpLnGhBIoHkpMxQfTKdtYT6kUpAgJQ4hUngfUBzxhlDiu+mBuKqhIjSw9FiiqjhCNtfzgcUtw9N4zceIBs43bTiRsCDWRMNdYnMrS4n20UB5QnnS++LbDsGxxb6xxJGm6SifLuDFIgHmhGC/4n1Bk2ntMJYeidsaOHwAqEFk4PVpaMn2yDWs/QuOE6OQmFvIo1xhdrHyGUEj6PP08kXWltCip7HfnCDE8jIoQNYTK50D07QpWhMrjiooRou/xKUJtGd/33UOqQgfFyQCQ3dI37RmTi2GcLKiorDHHoEpQhRBQz5dpjCZXAs2KowUV6VizaVsazbFcY+S6lw8A2vmkChiNw0qcNUZOiJh/6NuyRNU0GUI6Ihs0RbLG4qTP56UhpBpA4lSHPZ+ZXOjZlO1TxnXEZyzsace9l74ZnyQPmuJvwjgUDyxKfCFRZwX0QQnw5p4wRShtW5GxUkr/ysrPvtIJZGz0IY0GTNNYOS9GKDg2TOxvJF8yjp32gPzb1++L31x0gvLQ0yw4a6yJBLnGyn7j+IaQHixd7TiS1hGKm5UGmBeKfMlRAsWLjiOU7SoNIW9yKDrmBSGMDy/fG8PjRfzN8r0BqAtZ2JOmgMYJ0Qkvm7Yxki9FBkunKhNloeT6nrZNtWco0jARrTjCFKFcgGuMfBeiG/jLlZYDQYaQUCWF0ZgNcY1lfa4xL31eP96ednMrDZ15PeVzPl50sGukgL7ObGDAMCU6Rsj/feuGjThflmVWrUwxQsPjXgwXNRTpdZpJ2XjbkQvx6Mu78fZlewUeg+lzxMKuH7tNri1FESqRSs4xDKFUJQjcJcpBRzYt9yndu67a0JQaQrYF/Pgjx+GPj2/Ce49ZpHy2E5HFJpuuaoqJOMVODDdfkOGYSZUzvYIy0mTfrcq+6fiCFKFCyVXiliyrrKxQg8AleoQwCsS8aAqWThGDio5Vbbrqyv/p2AqagkQfGDuzKQznSxgeL8rm3VExQjrUhhwaL/qMHJnIUMX83kjYEGoiQTEMUQaGHiwdVMskLr5g6Ziusbh1ioDyBCkmUAqdlOn9pcdwxEFtNZA81mhBTzuuOPtwsj8StyAnweD3C5mfNhsVvwPRihBQnugLpaIv/iKssjT9jGEtfZ7+vDMiRoj2IltUSc2mwdLqOIULpPy7bLoaGiOkXmeioOKMtrQv+0RxjYVcZ7l0ua3M9qE8Nu0ZLRtCMRSOqKyxsPR58UQ8TloemFzEQZV4Ab9BqytCRyzqwS8/vjxw/Kb3Fh0n1L2dSdkolEpKQLraayzevZKxbeRJgc62jI1Kr2F5nTgkzs221JjH9xy9CG86aA7edNAc+Zo4fTRGyPTQIV2oJABYvA/wrsewI/F6s3l1zNIpG5mU7YtbpNDigvr4hAdLKLlC7SuWHJ9hl05ZilFFTQXxACCMzCSKEN20qBlmgKghRWOKPCMtnbLQkUtXDCE1mF6MXVdE6Xmi37vAZAglvdYmCnaNNZGgi8FU44Xi9VeqZF/l1UJbSRFPOHFabKiGUPzPo41TKUEPF7W4xgolGvdQ/SVOg13jPDULRUhXG8QkESd43asllNA1llaDjbNkDGKfOypurq5KvzKxqIhDEtdTyrawoFeti6Rfk3qQvDhmNWvM7BoTk/FgpYwCrSwtUGOEwq+zBVrmWBwjWFdM9OB0sbCZgqV1RSjowcWkCIleTnqsFx1r0oxJGosUFCxN96svdCV538f7XHFv6rGJgPddOa73PWdSNnJpW9YW+9SKg3z7VNrZhKi5ugEgvgthAMVRbmW9MWIEZmyLKJZBMULqeVIUIVc1PDpJ1pi4tISxTL9ff9aYiBEKV4RMLWtoEHNRusO877qsCKmqEVU+xUPWsGYoi88U51S8RktrCNcaHYMpYLoYY41pBqwINZHAjtMRSosweIR8KhbNql1jMlhapM/X3zUGlMdnypAzUUv6vFpQsfobjlbsFfNRuGusoggFuJHiKULCEDK7xoL24S1ywYqQuE5E/E8uXX6y727PYPdIQV5PaduWxfr0cenHJBCLg1JZOrCWTflkyhYbpLK0IG6wNFCOM3ni1T3YKA2h6KdOPZg4XosN1bCJckmnpAvGu+ZHDKnzgHpdJb32aUFUzzUWbEToC53M0oqZ+CDOq97yAfDUO+oaEwrpDStPRNFxpXKqHAPNGjO4JfVjEA2i9ZYR4lsMu+0z8gGHKGgVRYjuU0c/T/QzdNdYJy16qL1PrxuUIvpV7BihlGoQApohZFCE8kWt4a7rega/bUnjbVgLphefKQy2kjSEvGrudJ8CUwp9WI2oZsKGUBMx1frIF51Ig8aXNSYk6ph1g3R0S78RihAgxmeuL+H7nBrS54u011gNvmjqE4/qNQYAc7qFImSOjYmKEQI8FSVIEeqIcI156ezEINEMCWFM5TI2BsfLk+/ukYIX75Oy0N2WwYxcWhZGizKEvDpCVB0wG0Jich4klaV1BWNGW0a6UqMMIVG8bnMlc4y6O4IIco3d/NTmSsdw//etV9Y1KSLKZxiyxoQruENXhLSYsiRQo0e460zqTlYzlgGtt1fMe0V8j7R3nSBHXCR6cdRZXWqtLYqpjlBosHQpKFi6/H9YNqv47h3XuxYzKUuqqlHp8+LSEUVxHZcGS6sxQqrqAvlZcizaMXqGkJoVR0nZNrIiVkoJfvaPlc4jeTIvAmXVk7ohhfFmco0pBRUrr4lECoCcf3Ktm4oq1uMBtRGwIdRE/NVkM9gyMB6tCMn0z/JkIwsqxixuqKMHFjYia6y8ffzxVXOj0Ce6Yh2ePEzpomHjOmjeDKRsy1cXIyddY9HZEVIRCooRigiWFttR15iuDggXnTBaxOSrKykLetswWOklFJQ+LzDGCGXMxlOh5MB1XVJZOqOMV2zbkSkHb0ZdZzRzDFADPIMIarHxT9c/iW2D4/Kc0MOki7XruqTzvPkaM2eNiVYpeowQVdKSXftqA+dKnFeIEUHdI9TIj/vwIQwbU3sRGSvi0oam0fcgzRoz1XCS+0+p94eXPl/OmvJ6AkaPH/CMWVrhOco1phrHtlK3zAuW9hdoFNcPnZNStq1kuMVShFLRWWNSESqpihDtM1dyVeVcqJSmrDGlxUbltV3ENSaDsyNcY0mvtYmitfSpaYa+qPa2VxotRipC3t/HCiUyIVfrGktiCHmXTBLDprx9vPGlbSt2fSIKVRzE/VjLDScWJzrZhLkP9uptx58//Ub86LxjldePWjITubSNwysVbcOIco3FTZ+PowiJ1/WARvH9U/dYlCIkDAs1a8xsPAnjnRZz0w3WtG3JzLVZXWqzTB2vzYbmGgvNGjMrQmLyFsHbJkVIbB/pGrPUhQMILoxZjxghwFvYTUqoyV1GGxSHqZ2mzxOLLJ2vxHdccjyDIs7xiENw3XC3dk5ThIT65xAjCIhQhAyGY9q2fVmNOjSwWB+3VGBkQUV/TTBxPdB7JG2rKfUya6zoKOeCkrItWa8p0DWmBZOLfVLXmJqh5zV0pv3+qGLkeQ7K+9g1QhUhsU9vnKZaQkkr/k8UrAg1EX1R7e0o3wRRBgPNwBrNl2pOn9dFk/D0ee9vSWOEwoLAhcQMVO/OEmMbJWngtaRpivmbPlVZEXO6qEVEufysw/D50w6JFcxu6jfmui4JljbvI6P57+fMMNc0AjwXnVhUujVDSCx0SQwhcx0hs7E0XnRkfFDKttCeSSnd6cX+v3Pua/HqrlEsmhleeVbUotlcabxaDMmc8saiBUtXzptewM6UNSa2HyVZUyaMilBAYUw9aywJ9L1CKTEZgab90kU/7kODvp92Y7A0VYSi92sTozFJ+nyeuMaoIRCqCFHDsUBcY4YihRRjSQUS2wR4cZv0+xX3sqcIkfenLGV/9F4cLzpmRUgJlva7sejPetaYr44QOaYu4RrL+/dJCyqKXVDXmN7EFQhXhNgQYiQ+RahDBLGGL5iWZaEtncJooaSkHVedNZZAEbJtzy+e9POC0vstqzy5ykmpSneWXtumln0ByRWhICzLin2uvH5j3jGMFkryaTcoWDqrXTMHzfMMMt0gmaEZQroiJAzRhT1eUGuQm0u+x+AaCwoaL5QcpYaQZVk+V04mZeOQ+d04ZH60irZQusZGK0/R0fFh4m+ipotwd+lukZTmBhGUFaGIGCFpnHrXT5CLszZFyO/qMbnGTPulLti4Dw26wUQNQRks7SR0jRlihIy9xnwFFb06QvSbC1OEqGEmFaGU7WWdBtQRMi3i1F0KeEYULZYpxirmWT1rjF5j9F4cL5gNoaA6QlQRC3KN6XWEvO7ythyzGiztHXNKu55psLRQjqgxGhYjVEvsZiNg11gTCeo4HcflJLahftpq6wjp44iy1sXTZvIYoSA1w1YWmVoVIZqZVsuTh9gfnTwa/SRjihES7hTLClbh9KfuA+bMkD/rmVzC0E7mGlM/NxcQIxTmGpMxGEVHxgeJdOGgbulxmD1D9GdyMJwvxVKERJ2kQxaUz1OhZO46bsoaA8oT/3hEkoKp19iwofO8vu+4/fvoGMXbRQkEoyJkeI0ulLGzxrT9UCNffIZDjMokMUKOG17XiDaPpbWKaE0cIFwRskiVZFF6JJMgRoieJ73/1rghk06cY1OwdMpS0+e7cmm5zyFDA2JAV4TUmB+BuObyWrA03d5x1PpoXVrWWPmceuNMSdeYCJYuKPui5wEIUIQSlmqYKFprNNMMvyIUL0aIbiP8tFHVqMNIogiJzwJqC5amH5Gx1cmg2uMwTbi1xQj5DaEqBKFEmFxj1J0S9KRLF7m5M3Lo6fCMG3peaObamw+Zhzkzcjh+3z5lX5kqXGPG7vMBlaXzJYf0GSuPR3eNxXGneJ9D+kQV4wXKL99/Fn5/8Yn4yjuOAFB+yg2KxzD9XCrRGCHz54RVltYLY9LrNKlrrPz+8nvCYoRM+x2v4qFB/27aDOnzjutKZUX/bk0ovcbCgqXJvvIk/V2PEYqKd9Ld6OmU7WsBo2MsqSBdet6YxDg91cYLyAb86fN0vstlbPlAOxzQrytl28pDhUBNn/fHCBkrS5Nz3aG5xqhhlba9FiTC6DEpQmqGpD9DmAsqMj70i+HMIxZg6aIevH3Zwsj3tlUmUnExJo3XofgMoZiTSC3B0jSVNpNWOzBXe5P42gpY8TpqByFu/FpdY0kwBUt7KdfB3zFdIPQ4JWokUdfaRSftjwf/8RTsP0fdXipCPQkMocpn5AyLov6eQslV+owBhiahCYxh0fYBAAqOlyIc5uqxLAtHLuqVMRlFxzUugPTeoEMsxnCNhfUa8ytC3vEmMQL1z/JqQRmMCJNrjFxnsbPGtO1MMUKKayzGfj1lxTMqjK4x2vSVVIGmKexAtCEk5gpaMiIqRsgxLOI+11hlrsilU775Q4xJfehT3cK5dEpeT0GNS1VFSE2HF3jp83pBRTVGiMZjifhDUXmcGjW27c+i3UkMIZG1R98zyDFCTBzojW5ZwOF7deOGi18f671Cjheusahq1GHo82OU8SAmjMQFFcmT+5yuHLYNluvyl582vO2qdo1p76u1aJe48ak603DXmIgRIk/qojZQWEFGagQeqBlC9Ilcr25tmTpiV87jvJ6cV8snorK0dI3Rp9sA11i+6GBgNEoRSvbdZVK2rJwre43F+P6VPl0Gl0hKuUe9PlElx41dR4gu0MMBBRXp4VZTTFR3C5uufWOMUBXXtq7YUgPda7VQrtwc9Lk64qNp0LPpoUOpmVQqEUPIc+MA0cqtbjhmbO9hrBpFSG+6mkvbyNg2xuB4wdIBBRXF+cykyi47cT0FK0KkCnZEQUW9jpCaNab2TxMB3qL6OTVqqCIk0+eH1YKKtDl1eT/BWWOsCDESJUDSNvcrCkL45YVrLKk6Q9GfnqIuUnFD1OIam02ymrIpS5m4qw1w1ifcWo0W3TVmWeFBmPUgZyiUFlVDCFCP/YB5M5S/BSlCAr8BKdxcKcypKHe64aMHZ8v30MDZAOOmHCxdUYQqiox+zSU1hmm14CRF22gcT8HxL4BBpSWKjhOdPm+IERoK+C6VOkLVuMZSqqsnrLI0xVMr4l/bYa4xJUYoiWuMZI3JNgyGY6CtevJFR27raopQ1KFktPOVTllynEHB0qa2K9Q4cF2XKEK2/E68Ho7is2mwtLeduPfFPRRkCNE6QrrCIzDFCI0b6ghJRcgiilDlc6lrzLa9a9RUR0hsP1kVITaEmgi9GJLK4UKN2V0JWKvJNaZdlA1ThDKqIiRIp2yfXFwN+jmsNTPBe0JMVmelFky9xqKqSgNqET5dEcqGKEKA3/CkRumCSpxQVNaYqaBiUNZYvmiIEQqoxh2XDFkYSlKJiP6+vE7kjlER0m1yL04CStNR474NMUJBTVeVGKEqFCGxSNEsKJ0wRSiJeqpva4oRKrlVusac6JYf9DrKE0UoWYyQ7hqjdYTMwdKmGjiyjlAlyFt81dm07WWdygepimtMizsT95+4d4RBZDIkxNhl2xyl1xgdqz9rzJg+T3ooimtSGOulkqoIqeqp42uU7DhasHRY1hgbQoyA3lBJA4S9rDGhCNXgGksYI1StIiTcK5YFzCZF8miNCqB6l5apKF8t6OnzjY4PAmjWmOfbF0+GYa4xanT4DKGEihC9Lo/Yq5y+vlir5eOPEbJ8r/uDpT2FzUufD8gaS6iKiO++UPIW4DhPnZ77Sn16FgRlVCZShEwFFUPqCNXiGvMMm3iKkCz2l+Be0Q1MYx2hhOnzUhFyw+sIlT/DMzCE8SoUGX1/QUgFLe8ZazK4ObCOkH/f0jXmuMr1owZLq+eYXttp2/I9RLRFKUIkRqhQJFlg5PhLAcHSRcVw8hrCpmwvWHrEECxN4y2LjuszgsT+IrPG5HfbWqYHxwg1EbqwJp38xOQrOgDX5hrTxhWxKzGJJA+WLm/flU375HT6NFdNsKjpfbXebLKgoiyIVtPuYkF7jY0XS/jXPzyN+1/cASCea2xmR8bX0ykoa8z0d0A1jP75zMPw/mOX4DVaVWxdtUjLp1qqDgTECJVcOcl3S0XIMm4bF2pkeYpQ9D6U9hRaNW8g2G2sxAgFGG2mOkJDATFCtWaNieOQLTaM6fP++2pcy2iKg77vdkOMEG26Gseo9VxM0f2oaMNR2mLDURSheMcgzpfaYiPANWasLO1dD4ohlPJiavQHKaqQ0ZpA4n4RsZQisN621GOjY6UFFdXK0ob0eUP3eYcoQnr6PHVjifg48fqIYWy6a4wVISYWtbjGxBPltqFywHEtipC/11j4ZbHvrE5YFrDv7M5EnyNu8BltqiGUTlnKuajWf0xvVqB6g8obhzoxTrRr7IEXd+Jn96/H81vL/b6W9AVXWBYT44FzZ/j+prrG/P3OfC5Fcg7bMikcvlePL35Ed5XFSp8nLg3acJWOP2hMUaSJW6NQCl9ITeMG1PpTAn9pifLnFB0XY/morDFvW8FIgLpH42GqUYTE+ZLB0nHT56tQhHx1hBRFqKKwOUjkGhPnWQmWDjKEyD0iArJ1NSIq3slkOEZ2nw+pLF1yXeVc0m724nXxNr2gosy41BQhYUjo11c6ZXmGf1E1bATG7vMlR8syUytLC9e7cMXrBqmp6CV98KF1h8T4aRNWACgJtbbFCiqyItRE0jVMfotmlmM31u8YBlBn11jEUPrPfS22D41Htj7QEWPsakujnSykmZQNxyL+6CrrCJXfa9Wtw7FYJMXkMaGusWJJxtHsP6cT/3DaIXjTQXMC33fsPn3o68ziLEPpBWpUmFxjosBcEtk6qKBiLm3j8L26MTJeQq9WqNEcLF2nGCFFEXKU18Kg9+Bo3q8I6deQUREK7DUGuS1QXijEU76v+3yNBrxvYY+ZNZYPcaUFoRs2bSZDKGnT1comjusaDQ4KNahFYDM1hOLcpnoquBqAbI4RMrnsaMwYDZSmn6Ebm3R+s23L9xAhzqcwmsvnj7QNss3B0lRV1ytdA2pXecDvhhSK81ilojUNpKbjLzle9fZs2pZxVqbzNpwvKg9f4nzHMY4nEjaEAujv70d/fz9KJf/kWC+Uho4JJ7/9KrVfhMFdT0UoSvloy6QSG0Hl95Vv3hltGWW8GdtGEd4NW4uSk0nZJIW4VkVIlbZrqUkUF1lQseDIhXlxXwdOfc380PcdsagHq/95hfFJOKcoQuZbPk0MoTjnPyhGyLIsXP//ToQLv0FLA5oHfDFCtbnGqJFVTFC91tSwlBKUNaY2XTV/jq4I0dYvYd3ng7rZh5HWgqVNDwFhLrck17Y+V1HXmBh7uY6QcI3FV4So2hB0CSiGEG2x4ar7CsPnDrb9MT06Rc0wUMbtutJNJcbnzR8lZVvqokwTN5e49706Ql4GYCZlyfNJ3zOuxfx4Yy2/birMKigbNP6sMaBswOhuLBofR6uGixIbJiVtcEw1hFo1Rqi1RtNCrFy5EmvWrMFDDz3UsM+oJVNEd0u11zV9vjGXxTF792Fedw5vOWyeagil1fT5Wj5fkZ1rUJYAMpElCL6tFa/XmCMXzrBsMUpg1WmyAAYFXOu1TaIIyhoDoLgFKLRjtl5QUf+uEqfPV95fLIW3aNChm9AAdfl37TBoALQwOoJqeOlZY2Ihsi2/8VRLrzF9XID5/IXtN8lDg/5d0WNRY4SSKEJCWXEjF0tafV24xgDPEIhzKPr5oU1X9ca7Aq+OkHncwjAWc7leoFE2XVXuNZI+n1FdY+J6oTV8xHu8YGlz+rzj+gO4R7Xr2yGVpW27PG5hDA6NFX093+T17JJmrLbXeoMaQuIc6HFCHCPE+LCVxSPZhaEbQjW5xnyTfdW7CmXJrA7c/4VT8PE37a9MnrSWBlCrIqQ+bdWCrCNEaq00GuoaEwGJ7ZnahFu6EJlihACt2m0VhlCc2k9CGciX/AUVfXWKqnSN5UteZek49xSNKzPFCIW6xqJ6jYnU/MrT81BIq5Ras8biKGphSlOiYGlt2w5yfQojhSoESbPGItPniYFBDQFxnuPUQ/KVjKAxQiTuhuJVllZdW0AlWLpyvMKg8VeWrny2FiM0b0a5ufG87vL/ubQaq0NT7MV7TFWwXW3YRcdVFCGdkqMek2VZsu/g7pGCYuzQ4yk5nhKXTlnyHFDXmGjxM6Bll0kjl2OEGEEtT4Ht2RQW9rRh454xADW6xrSJo5FBwWKSok/RmZStyLq1KEL0vfWKEZrYOkLe065wjbVna7NM4yhCatPb6M/zVZaOMbGJa3x4vChjZXo7hCJUm2ssbVSE4u1DxJXpT8xAvIKK7QGKXZosHICXlmzK/lOartaQNaZ/NiXsnNYSLD2zM4N3LFuotIdQXGMxrg0aa+MkcI1R9UZWSY5xKPrxZmxSrTmysjTZj+X9bdynCFWOyRXbWsrrYhwnHzIXqz5yLJYt7gXgzeVCNU2n1F6MqYCxljRLSM9k8x2P4/qOqbcji+1DeewezaO3PSs/j/6vq64mRWhWZxbbBsd9RRdbVRFiQ6iJKE+BVSz++83paoghNBEuoHbFELLguHQhrpMiNBldY6T7vOcaq6ciZN6XHrcQRVDT1dD3VMaxtdJaxbaIa0xfmBIaA2Lf48WSfDKO3zvLBuBIhYfiz6j0FuxxGSwdFCPkqRwA6RmX89+rtdcR0hS6mAUVTZ8f+VkGo/Wa9x8FAHhsw24AWq+xBIoQ7T4f5BrLEtVUTQePn93pq6ZO3EKmOkKO4xVsNHafp4pQ5WFG/07EtaS678uJCicdPFe+prvGUralnIs4dYSAsrEeVBMJELWX1GOa2eEpQjNyGeUYvX5wXvXvNCkTQA2hOTNyeGbzoKxz533mxM2lSWDXWBNRJr8YAYU61D1WSx2hoKJxjURNn1d94LU8Leiycy3o52FCFKGMN8mLKsS1VA0HYipCdYwRCkIYN1sHyobQzI6sXBwsy1JUpuTp8+XtqaoT16AWY69WEQpyjemZSWGFMdU6QsmvM58iFDN93vT5UQT1mQOg9N5KZAhVNlEzmQI+3xAsDXjnOc6RmGpnZQyZWAKqtiiVpWmwdNHLpBL7pJiarpruG73XWNkVpr5HD5Z2tcraQFm5yRsSAOTfiZomxtHbUVaBdo3kfZW0hWFXJFljaZs0PNYUIcDfj6xVFSE2hJoInWSrcQftN4cYQgGTcRz0a3JiDCF10VMqS9eUPl8/15jPEJqAu0V1jYW7XuKiVJYOyhpLWMohl9KKJcY4OTS2A/DcYsYxJDzZsps4SYGP7RoTMUKG9HldETLGCEXUERKtCmTneYPCp7jGUsm/7zgNh+liqhvXSYx8fd90HhM/Om5C11jlja5LXVzm99Gge8UQSuDC9imQNq0j5I8RUjux+w2ZkuNKhVAaQr4HzPL/2YiHNaEKD0lFSHtQpKn+snyA/xiLEa4xGj8kjokqQno9JzFs2g+O1oDLE3VKFHXdqStCCTI6J5LWGs00g14M1cjhVBGqZbH0yf8ToHxQwy1j24oBU0uwdFZZ0Gs7jrAJv1HQgoqjhegeY3GI6jUGqIZHUkXIsuKlX+uKRF9nVvld3ANp20pcqkA20cwnV4TEdmMJWmyMFbzmrlG9xsR2ooikKUZIaTpcxXXrW9gN+6DB0vp8kcQdTbfVv3taIVoqJAkrfEe1SBGqKW26CiBRHSGTIhQWI6TGMBpcY1rD1fLfNNeYodeYyWiTilDlWqbB0WK/uhuvZLCEaAC3CXqcYm6TitBwnpShEIaQLfdL4/D0GCHbIgbVMMcIMRHUmim132yvp1RNrjE9WHoiYoTIRJxJW0iXalPH5Hu1CaMW/IrQRBhCoo4QzRqrzRASk2g2ZSuVYCm1ZI3FndT0xUdMut7f/a6DuEhFiLrGEsUIwRwjZJm3FRk9QHSvMbFICZfgnBm5wG2B5PFR5fdHuyrp+dfbgiS5V8IyMxXDQLjG4nSfNxhCQd8fVRbVpqJquncYfgWNZGIZDGJFETLVESKVtIUhpM/pYlul15jhWhdzOW1xofZitORneHWU/IZQoeTIAG4TNOPOc42VDZhdIwVf9p7edFUco9eDzDNgZ1YecnyKUJ2K3dYbVoSaCL2hqlGE9prZLieF2lxjZvm/kdDx6nUyagmW1ieMWtDfP7ExQo5nCNWoCHVW3t/dbk6dB1QDMs75LwdwWvLnOOgLQ5/PELKV/5OgG0KiP1Ic9KrMFF9GZWVoVHkKSkvXFSERJD7XYAjV2n3erwj590GN1zbtmkpyy4VlZopfacZSnOOh5zkfYdAoWWNa7yx9X0H4Y6o8lcWoCJGXzJWlqSKUkvs0faaqvvrPjT6X08asYj+60aa2Fyn/H60I+eOeZlbuyT2jeZ/RIs5r0XFl/aaUTbPGvPPf1zG5YoRYEWoi6RoX/5RtYb85nXhm82DoIheFr7L0RCgfWhG2TLE2o5DuS1DvYOmJdI0VHVd2b67VNXbA3C783Rv3wyEL/H3IBGojyHjnP5uyMeqUYit4usHQ22mOEarGEPB6bSVvIiquk9FYdYRURSibtgMNLq/GTHm/2wbLGZ5zuw2KELn/q0qf92VBhStCusqYRIVVFSGz+4cGS8c5HnpvCaUisI4Q7TWmBEvHr/flqyNk02Bpv7qiBBYHVZbWg6UDjMQo9dXUW8yfBKIrQt722ZSN8aITGSOUN7jGZlJFSCvOKf4vF730AuH1rLFwRYh7jTEa1OCoZvIHgMvPeg3uWbsNx+3bV/U4fAUVJ2jBF6XZTU881VKrcUlphmuMLhp7KsXIai2oaFkWvnDGoaHbJHWNAV6fobjnWTdwG6II5UWvrQSGkGZEUYKyxkYq7Q/0nmvKfgMVobbAbYEqDcEAg41Cry3dEKq2jlBQDJWTVBEim8hYk0DXmEgoKCkNbT33VXLXWCZlh8YI0awxNSbK+2wZLJ0KMoQs+Vne+/1j9Tc0tpGyVTeWGINJEcqmy4ZQVB0hepxifzRrTCpCWq+xIqkRVY7nU/eXsiypLO2eJHWE2DXWROqxaC/ffxY+d+ohNakozagjZFkW8aXbarBoLYZQHWOEgp7oGgldNEQNjlpdY3HQa5vEIejJN85nAJ4ML/9e+b6qKSUhrp/RKhShlBYjRLPZgrLGhCKkL1rqfr2YCsCLETK5xuodI2SKOaTXln5NVaOg6T8DqkJStSIUFSNU2Z/eRDRJQUVTsDRtsvzKrhH86x/W4NXdowA811hoTJRwjVWuiaD0+ajq97oiVHaFqSqcnoFJu7zT+KGwytJivPS7pwaMr/u8sR8cCZYuei7NmZ0i+yyvxFdxrzHGR61F1OpFMwwhwHsqrWf6vFKLpt6usQk4L+mULc/FiOxUPsGGUFxFKCWyY6o0hPSssbT/iTku4r3CvZVkH7JLeOW9IpvNdFzCMBLKU1DwOd1vsdLJe/tQxRAyucaUeMFqDEHdNRYRI1SDIkTPbZBb3XG99OwkvcYAL0YoaEziOEbyah8rJ0GMkDF9nhgQP7//ZfzgnnX4/t0vAqBVpc3GTbHkypo+niIUECMUoQiZYoSUuCyaPl85V7prDIifNUavvd4Oz4DxZ415hpApWDqvZI1l5bhom40ppQj95Cc/wY033ih//4d/+Af09vbihBNOwPr16+s2uKlOqxhCzVjwAW8yTqdsxWdcU7B0RLGyJDSjoCLgj6WpNWssDmpKcLKYn7jxJf70eTVGSLrGqnhaFO8RtYCSKULCiCq/VxSDM7mIdSM1tH9X5Xgcx8XOkTyKjgvLAmZ3GYKlyXWr12iKdQy6q8dw/HSO0Y3rJAtTmNEsCyo6yRQhy/IK8+nd2nX0OjsCL1g38uOMMVXSNVZ0sWe0rMau2TQAgLT9CHhodEiLjSBFSLxVSUwwBUtn/AaU7rrOEGOHKjSApyjSGCHTHOJl2XmvCUPIcYHdlXMgCyqmPEOoQIwkPX1eBHPPqJSJoNWlSxFGbrOoavX96le/ivb2dgDAfffdh/7+fnzta1/D7Nmz8ZnPfKauA5zKqL3Gmndh6NfkRC34bVIRspXFr5qFUL63ChdPEM0yEPVu5hPtGot7LYoFLu550WNFfOnzNbjG9KyxJA8WsgZR5b1L+jrRlUtjcV+7b1sZI0SCpYOgipBwi83qzBrHptQUq8o1GH2thsUIJYl/C3vYoBWiZfp8zO/ClgtquGrgKUKqa0wYA9U1XfWMi0LJwVDF7fb0pgG4StsPs+FH1RcRwxT0ndD7yzTVmYKl9dpF9LsslBy4rmcEyliekiMNIVMh1bxBEcqlU9JI3lK5ZoWxJrYrOo4815mUrahi5TGUfxeKLzWEWlURqioKc8OGDTjggAMAANdffz3e/e5348ILL8SJJ56Ik046qZ7jm9JQg6OW2jn1HAcwcRcprbdRr/T5sIyWpPgzYmraXWx0lWEiXGPVKGlBrQSCiEyfr8E1prfYqEYRojFCt/39m4z1gWSMkAiWDlHrqCthayVjbI4hUJruF6hPrzGTa4yef5qsoH9+FKF1hEiFaL3AYBQpy0IJbmSwdJAiJLPGYnycL1haK1I4Utn34FgRr+4eJan56n5kZiBtuirvC03ZMQRLmxUh3W1pI2WrBR3pd5AvOZ7rzvKMJhrAPSOXxrZKsL6gYIgRAspurZH8qOwbt1dvu7Kd43pGFC1+qvdknNmZxcs7R7CTFFWcUr3Gurq6sGPHDgDAzTffjLe85S0AgLa2NoyOjtZvdFMcejNWkzJbL5qlfAilI0PiYoDaDLF6dp9vFddYLTWi4qJ0n0+o8MQuXJiy5UJiWf66RmIM1RgCYiwididRpWQZLO3I987rbkOPoSSFUG6EIhQ3ayyshlB5v+VtLau669+fBeXfB3W56fdcsmDp4HuM3iNjhfiuMQDG7CMT4rse1gwhqQjFyBpT4pysskEjXiuWXMXIenrToMzKCjJu1Kar5vvCMhhC5l5jmlFrG3qNke8gX3RkjJBtew1ax8nrJkUoqIK3cI+tfmknAODg+TMq4/A+U8R/lVtsqPuTipBIxR82KEJTIX3+LW95Cz72sY/hqKOOwnPPPYczzjgDAPDUU09hn332qef4pjSqItRM15gub0+QayztSchqQb86ucYmYbA0oAbgtmXsCfk+qlHSPNdYEjdUObW3tz3jO5+0AnZS9DpCSb57PX0+zDUrhiZjhGJljTnyaTzIEBLjzaSC6xKFEVTviEJdbpl0uf5LoYqYjXTItUKv1bGEbkphVJiymSj1yBozJWdkSbA0dbs9vWkAi2a2V/ZtnivLLUX0XmPmYOmohz49AN83P9p2xXArf3+FkkMCxb17gR6DqdFv0HcvAp1Fiw9hCNG4KqE00RYbenB1X4ffNTalssb6+/uxfPlybNu2Db/5zW8wa9YsAMDq1avxgQ98oK4DnMrUKofXi2b0GgOAE/afha5cGkcu6q2bIqQs6DWe06YpQmRx7TA06GwEyuLWoPR5wDNy9NR5wPvuqsqa0mKEkrhFZdPVGAu3pwhFB0vLbt0lF1sHgospAp47pFo3aJxeY0pGpVayol5ZY3Q/48WkilD5vVHGmTSEaskaMzwwSUXI0RWhAbKAq/uRdYRcVx5vLsBlLA6Hng/TMfrS5Q0xQnS8ZUXIC+bW49gAsyEkxqufrx6tGbI0hMh240Q99b43v2sMUIsqTqkYod7eXnz729/2vX7FFVfUPKDphK0YQs27MIIyIRrNJ045EBedtD/SKRv3rt0uX6/FKKynIuSrkTLBsVPAxGSMAdFyvYmk6fNAZREY96fO0zHU5BorJHeN0UJxUe9Na4tMWPq8GiMUXEwRABbNbMenTjkQe8/qiD1udVzq92dSlcqvl+N3snpcXiLXWPD7TA9Rcec2U4ViE+K71ttrFWSwdPRnmY6BjpOqGIohpM+V1DUWVVnapAgFnJu2dAqFkug+7+81Jj5nJF9CoeRIY4bGCAlj3bLMBnbQeZ5JDKFZnVmZ5agauZ7yKpW8yv7EKRJlKKhrbEr1Grvppptwzz33yN/7+/uxbNkyfPCDH8SuXbvqNripTssoQto1OZEXaVoufskVCfP+/E9O1eLPEKlpd7Ghi+tEZIwB0QGcJrJp/3cX93Nmdvjjb2oxhMT3Xk3wr368YccjronhWIqQZ2BtEYpQgGvMsix85i0H4V2vXRR73MpnxagMblleingtMUJhRrPJCIlbDoD2sqK/+/YX8HBQKpkVDhNqw2v/dbeH1L5Zv3MEg5V2N0F1k8yKUJxg6ehjTNu23JdleZ/pKUKuFiOkGkLZlG1U5fSYHgFVaw+a57XloWP1FCHPdS9dY7KTvdeuQ9CqilBVq+/nPvc5DAwMAACeeOIJ/P3f/z3OOOMMrFu3DpdcckldBziVUTOlpp9rTPlMO/lCbKKaNPAg4qQkNwK6uE5ExhgQHbdgImn6PODFqdTbNearFJzENebrRB5t3OhP/yaMilCAa6xW4tYko4ZQtW1twoyuqLT9MHzZqwHXQVAMWZQBRaFxYOmUalgAnkGdTdtw3bIqBAQrYMamqwGuddWQNB8LDZimipCpFUu+5LnGyjFCInFAqJa28ZoIDpb27k3hFgPUdUIWy7TNdYQAGBuvtmrWWFWusXXr1uGwww4DAPzmN7/B2972Nnz1q1/FI488IgOnmWjUyauJrjHfU87Ej0F9Qqv+XKjZFfWNEaomiLUaaIzQRLnGlNYkMc9/0oKKgDd599XZNaZfM9W4xoL2FbZtaEFF4uqJco3VSlx1J1NxTWbSeqZmsoD3oM+qzTWm/h5k0AQZVqUkrjFDwHeqoqbQ4oSHLujGXzfsxovbh8tjCjD8SibXmB4jVBl2VIsNQE2hT9sWnMp76Pmmwd2Oqx4D4KmW2XTKeM7yAfWaqFpLDSGxbdFxSXNjz6DO61ljphgh+ZlTIFg6m81iZGQEAHDrrbfirW99KwCgr69PKkVMNK3qGmvGRVqtTO/fT/1ihCzLUs7NRCllTXGN0WsxQfd5oDq3il5MESBP5lWUktDvn2Tp4LoRFW3cCMLqCIn97h4pyEVyToBrrFbiPlSJ7yynVXNP9B0a1BSBfovYVny1O26sYpDxWZSGUBzXmFk5pj9blldlXMSE6WNUXWOqu1S/j4SBQD87KO4wWBHyjztfdGQvNEuJEfIUIaNrTARL+xShYENIfCc0fT4oyH2mQREKKkzZbKpShF7/+tfjkksuwYknnogHH3wQ1113HQDgueeew6JF1fm4pyP0iaeVeo01w1ivV7uResUayX3Ytq9QWKNpimusmmDpKmKExHv09hqAt0hXkz6vL7ZJriFdOQwzoPW/xVGENlfig7rb0sYijfUgqm2DQBatTGv9q6p0jelzh3h4EKJKku8hros+SBFyakyfB8rjFfWPOjIpqciOBLRuMbnGMgEPCOL3rPJdBRhCaVURcuHfpzgP1DWWsrzvVcYIpW2l3hVNu6fHIOgNiBGin0+DpcWQdMNKNl4dLaDkuIra1mp1hKpacb797W8jnU7j17/+Nb773e9ir732AgD86U9/wmmnnVbXAU5lqklZbgRx5O1GU69sr3pmjQHquZmo06JmjU1M+nw1BmQ1MULiCXvRTH921OF79cCyyv8nRTfGkqlUumsszLhR/xYaLK3td0GPv2VHvYiTiQQ0PkZI31eSQrFxmz8HGcqFJOnzATFxdN+dubT8foW6ElhHyHWlqynQNSYVoejzTg3mlG1LxdbkRaDp87blubaDgqWFkZUPqOC93+xOdGRTWLa415d271Vh94Kl9RghsTtR+sN1vb8Vp1KM0JIlS/CHP/zB9/p//ud/1jyg6QS9qap5Cm7EOIDmXKT1ChxXlY3az2m1i0UtUHdLe3Zirovqus+LoND4Y/zy2Yfj0Q27sXy/Wb6/nbV0Id58yFx0GmqeRI9FV4Tif1dBhR3N26q/h6fPqxuLonyNIK6iSuOwqq3dlbHD77HyfFJemOO21yjvK6ZrLKCIpQjEjXMoGU0FMv3cmUvLe3EkoGK512ssuqCiGFfatrB0UQ8GxoroNVQvB1TXWPkz/caVbBJbcpQ+a7prLKu5xnKZFAbHi6TFhvrZvR1Z3PP5NxvVaLFvoQjR7vMFrcQAfaAW4ytF9JFrFlU/bpZKJVx//fV4+umnAQCvec1r8Pa3vx2pKjonT1fqGc9SC9QQsq2JCwqm1KsBbb1dY4oh1IQWG00pqJi4snT887K4rwOL+4Jr5VRjBAGGVOUqCip6+wo+Hn2/cZquCvZqoCEU16iZ292GZzYPYs6MnPK9JSkWGqUIVevy13cV9NaorLE40m2QGk+rb3fmUp4iVKli7X9oLP/vkI7ssvVMgEppWRZ++/9OhOO6gQ99OS1Y2vvZ214NlvY+w5c+n7aVcyaMrLyW7k4xJTPQYxgn1b+lIqS5xuitInqhTakYobVr1+KMM87Aq6++ioMPPhgAcOWVV2Lx4sW48cYbsf/++9d1kFMVeqFUEyBaL5qheujEjXGIot6uMbqPKV1QsYp4keP2nYkZuTSW7+9XdyYan3srwXelGzdhhng1MUKCRipCcVvU/Nu7j8CajQM4anFv9YpQRIZhta4xvyJkfq/oWUeSuwB4akO8GCHzPEGPrSPrxXSJKtb+DFvqGlOzxvTriBpRKdtCKqQnGo0RCpqfZfq85hoT9/IocY3R9UUcU1RzWxPSECqI9HnPxSrdXgZFSMRvyRihFssaq8oQ+uQnP4n9998f999/P/r6+gAAO3bswIc+9CF88pOfxI033ljXQU5VFOu+RbLGJqqNhE7cGIck+6mHUac+Nde8u1g0I2usmvIFR+/dh79e/tYJMxDDqCVrzJd6H9prTDOEQnqN+RSh3uqqRschrqK6oKddxiopC2yiJrVRipD3czJFyOx2MpFNe0HNgkR1hAIUUPpA0EVihIRRoY9JHD91T3nZlJprLME1Sa+rtO1VCjfGCJVcL1DctuR3OUxdYyn/w5UwhKopPjpW9FyFXvd5Ve2h15c4N1NKEbrrrrsUIwgAZs2ahauuugonnnhi3QY31aFzRDODpe2IiW0iqLbcvw598qlHJl69Das4qL3GWjdrDJg4lSwKX0HFRFlj8V1j+t/itNgQTFSMUNzvT8kaS/AARFt1mN5nUi3i7pcSJhpkU35DiKoiST4ryDXWkU35FCF9TMLoEnV1yvuoKEL68SS4VRRFKGXDdv0GhPicfNGRrifaYmOUusYURchzqdFjiIM4fq+OkCUr7uuuMcvyrhMxvtJUqiydy+UwODjoe31oaAjZrNm3yPihTwzNTJ+nk1mzFja1lkcNrrEqU4KDoOdjwpquNsU1Zn5Cniz44nwSPeVqbrWQ60+/BuI0XRVMVIxQ3DpQ1brELcuSn2F6n5IE0oCssfJ+Tb2z4tcRihMsTRUhmSUV0FGeGmVB9bWSGJttmiJkUllosLSohE2brg6Pk/R5o2ssuTojjn+clArQm67S3XnlBcq/t2rWWFUz3tve9jZceOGFeOCBB+C6LlzXxf3334+Pf/zjePvb317vMU5Zqpm8GoHuu24GSouNegVL1ztGaAoXVFRjTFprkoqDvuAmqyytvTfkuvHHCAV/P3S3bRlblg5oBEEKRxhx+pNFvdf0WbZhsY5DWCd7HZMBmiRrLOjY9Rgh3fUZNMZRqgjJc1O9a0xNn/cMTyVrrKJeFYqOUlVbttgoeAUelawx7ZpNMueLbWlPP1+LDcODtU8RarE5pqrV95vf/Cb2339/LF++HG1tbWhra8MJJ5yAAw44ANdcc02dhzh1UbIr0s27MOiE3YwaQkBwZkTi/cQMGo1LU9Lnm1JQsfnu0VpIouroJKsjpG4bnjXm/W3RzI6GZmNmqrjua7m2xfk2vS9VpSKkr41hc5HJEJKVpUOCkAXpADU+qyhCKcVFZRqjmMPHC15gsview4KlowhWhPxjVQoqkm0FubQtS13o+046LlOGpddKxotTEtCCk8AUixHq7e3FDTfcgLVr18r0+UMPPRQHHHBAXQc31akmZbkRtEbWGH0qayVFyPteJkwRUnqNTUz6fLbKGKFWQc+6rOYpV+4rxJDwxwjFM5r26m2cW0z/rLgZc2r8W7L5R5wj07ylBktX/z2Eu8ZMipBYiKM/K6jMBn29I+dXhILGKNQXtddhLa4xkj6fsuG6/tiaDDGEhEJDY4QEekHFWhQhX2Nc2zP8TIqQ2HfJKQd0e0pSa7nfY8+yUV3l77jjDvnz1VdfXf2IphGtWFCxaYZQnYyxsIaQ1aA83UzQV9QU15jiWmmtSSoOvoKKNcQIhcn2vhihmFljCxtsCFWTdVlLgoL4DGOMEHWN1RAjFOZKMu03SdaYeuxmdagzl/YpQkGZbSJGKBuSrGEluK30Fhs97WW3Kq3vowRLk6wx3cjIpm3FQKtJEfJlWFpyXjR1sxc/llzXq/OE1nvYim0IPfroo7G2a0YxvslKvVLGa0UtqNgsQ6g+geNBDRSrpTkxQs3NGpsKrrEkCoevjlDIe/VFJjRrjFx/87sb03VeflYVhqyidiZ2jQlFyOAaUwyh+NevX20IMYQMx+g41QZL0xAFYghlUz5D199RXleEgh/EkihCOa3p6vL9ZuG7574Wy5b0ytdpsDTNmNPH6M8aU7+TJPe7P8OSttgwuMZszzVWIoZQq80xsQ0hqvhMJjZs2IAPf/jD2Lp1K9LpNL74xS/ive99b7OHBUCTs5uZNdYCrrF6pc+rjSTrGyM0cQUViSI0UVljVXYibxVoSjeQ7MHC95QbWlk6vmuMXscLehprCCkLe8zvr5Z7TlwvpnuCLvi1uMbCHjyM3dQTFVQ0P4TqvcZ0oyGosvRYXm2vAfgN6iT3VZtWWdq2LZx+xAJlG1lZuugSQ8jCnBk5dbtUSssaq8GNrBurSh0h4Rrz77usCDm+11uFyaeBJySdTuOaa67BmjVrcPPNN+PTn/40hoeHmz0sALoh1ExFyPu5WZZ6nEaEcaBPdPXOGpuwFhs0RmjCXGOtUcqhWmhKN1Bb+nxoHaEEhhC9juc12BBSH2biKkLV33PpVLAiRG+TJL3GkmSNmWOE4tfFCapkT+fhzmzaN/4gY22s6DeE9OsoyfShZ42ZUIOlK9taFs5ethBHEeXIcV01WFp399WiCJGssaKhLpEt/9baitDkm/ESsmDBAixbtgwAMH/+fMyePRs7d+5s7qAqdLWlkUlZ6OvMNtWlqNTKadIFOm9GG048YBbeedReNZ2LTMCTXrWodYRq3l0smuEaowtAi81RsVEDYJO4xtQDDnON6RWY42aN7TOrcVWly5+V/KGqFiVYGMvGrLEqle76ZY1FE5RUocYIpXyKkD4mPWMqG+JiThQsnTYbahRxDPmSmj6fS6dw7YeOltvNmZELdY0lmSb9MUJeiw1h5xhdYy0eI9R0Q+juu+/GWWedhYULF8KyLFx//fW+bfr7+7HPPvugra0Nxx9/PB588MGqPmv16tUolUpYvHhxjaOuD91tGaz6yHH48fnHNnUcSpR/kwwy27bwXx97Hf7znGU17Wcq9BoTE5Vl+Z/eGgV9wp+scX7VxjnpC02oa8xXUDG8svS/vuNw/POZh2LvWZ2xx1MNtdYRSm4IlbeParpabWVpy4oKlvaf91KCGCGl11hY93nN4PKpVtpnqYpQ9UHJsRShyjmgvcbEtvO623DzZ96Ii08+AGctXWisLC3HVUPWGO0+L8drUIRKJEYo1YJzzMTk5oYwPDyMpUuX4m//9m/xrne9y/f36667DpdccgmuvfZaHH/88bjmmmtw6qmn4tlnn8XcuXMBAMuWLUOxWPS99+abb8bChQsBADt37sTf/M3f4Pvf/35jDyghJx4wu9lDaImssXqhTu51riM0QTfv3Bk5vP/YxZjVlZ0w42tmRwZp28Lsrlz0xi2KWksngSGUoI5QWluso9SXD71u79jjqAW1iWjyOkKJs8Yq25vuMVMbiDjYCR7ITAaWzJyKcShBCho1GDpz6cDeYnLMupoYEixdda+xgGtMjFsNlva2PWjeDHz21HJT9KFxb33MRahcYfiTEizf+6MUoVZcY5puCJ1++uk4/fTTA/9+9dVX44ILLsBHPvIRAMC1116LG2+8ET/60Y9w6aWXAgAee+yx0M8YHx/HO97xDlx66aU44YQTIrcdHx+Xvw8MDMQ8ksmLUlCxBS/SJEwFRciyLFz17iMn5LMEvR1Z/PcFr0N3e9OnhKqptoZUkvYc9P7Ipe2WebKtpg5XTVljITFC1SpCSmxJxHhC6wjF+E5s25Id7INjhPyqU6QiFJCNlnTq0NPnTchg6ZIjW1gEnbf2TAp9nVkUSw662zLK35LV3NIyLEmLDW8b/75LDlAqtWafMaAFXGNh5PN5rF69GitWrJCv2baNFStW4L777ou1D9d1cf755+PNb34zPvzhD0duf+WVV6Knp0f+axU3WiNphV5j9aJaVSAIeuM3q7TARHHcvn04ZH53s4dRNZmAANgodJdM2MJAr6kwt9hEU00dqEZljVVbRyjJeEwxQoUEBRUBYswF9DnsyKb9xQcj+qFlAmJ7kj5gqllj5gOSwdJK01Xz/lK2hRtWnoj//cTrI4tEhqFfWumUXxFSXWPl/0uO27J9xoAWN4S2b9+OUqmEefPmKa/PmzcPmzdvjrWPe++9F9dddx2uv/56LFu2DMuWLcMTTzwRuP0XvvAF7NmzR/7bsGFDTccwGVAl6SYOpA6kbAszcmmkbAsd2drVDfXppubdMQ0kqFpwFIoiYIerPNU2FG00tVeWTuoaC6kjRF6qttdYpGssJGssrkonzpOp4bOoxhyZNab9HhQsnfQhqi0TbUR5laVdWXk67Lwt7uvA3rM6I425MExNZ/W3W4ZQC8d1W7bzPNACrrFG8/rXvx4OqV8QRS6XQy43eeMkqsFWnsRaZ3Kvlu99+GgMjhfR056J3jiCZrTYYKqjWkUoncCAovtNkhreaKqpn1VL/FvcrLFqe41FusYMBlaxFD9rDBCKUEk5d2K/nbmUHEc2ZcsaOUGVpeX7Sc9I6n5LbgjRFhvhrrFysHT5tThGYJJ6TVHvzdgm15jfACwrQiJGqHXuG0FLG0KzZ89GKpXCli1blNe3bNmC+fPnN2lUU5OUbaHkuLFl5VbmhDoGoJtuaqY1qba9itrwN/x9eoxQq5DEmJPbVZFpJnjP0YuwaySPNx00x/c3uhgnSZ+3E3wP9NyLQppJYoToZ5iq0VM1OZfxDCF/ZWl1n7qBlrbL702quNHji1KECiR9Ps7p9heFrN4QSqX8TV5NGYylFleEWudONpDNZnH00Ufjtttuk685joPbbrsNy5cvb+LIph7i2mxF/20zqVcPNKbxVNu4N0ndm1aNEaqmOGuqChVJcNrh8/Gbi07A4j5/faRqu89XGywtjI+oOBkd8V0qZRcqP3fliCFEvudoRchciiHpM5RlWdI9FlTXiipCriFrLIgkzW2j3puxbX/WmCH7z3E4ayyUoaEhrF27Vv6+bt06PPbYY+jr68OSJUtwySWX4LzzzsMxxxyD4447Dtdccw2Gh4dlFhlTH8oXr8uqh0YrFJtk4lG9IhQ/wF5RhEIark40qqoVb1xKqYk63vfVu8aqixHKpW2M08ajsRUhf5yTMKo6cqQSsxKvo405JH2e7ruaxf/CN+yHF7cPY9FMc8PerEERimcIqb8narqqZ1ia6giR/Yu/lesImVW1VqDphtDDDz+Mk08+Wf4uutyfd955WLVqFc455xxs27YNl112GTZv3oxly5bhpptu8gVQ15v+/n709/ejVCo19HNaBXGjtqJs2UzUgMcmDoSJxBT0GgfFrRRhRNAFupVcY9UpQo1RO+m6mk2w6JnqzwShGEKZFDBW9GKE4gZLp4RrzNtXT0c5rnAOqaeluKlCaubo4wI8hakaQ/OStx4c+vdMJR6pQFpsxHlYC+qXFgdziw1tG5Mi5Lry+2FFyMBJJ50kZb0gLr74Ylx88cUTNKIyK1euxMqVKzEwMICenp4J/exmIG6OVrxIm0kzCioy1VGPGKEoI6LazuqNxrIspG0LRcdN0H2++hihMKpWhBLUM6OxOMJQKcpeY/E+L20I+D754Ln48tmvwRsO9GKfFNdYgjpCgHeOG1FvSnzWeFHtPh+F3zVWXYkDUSE6LJPOU4TQ0jFCTTeEmNZAXJvsGlNpRkFFpjqCCuNFUX2MUOsoQkD5OIqOG3uhaVQigNp9vkrXWBJFqPIz7bcVB1OwdDZt42+W76NsR11jpsrKQeOi2zei9AYNlnZipM8LouKcwjDVetLnRVOMUMlt7ayx1hsR0xRS8oblxZ6SpLYJ01xo6nKSyTao75SJVs0aA+jCXoUiVMf7XimoWGXWWNRw6LkXylwxYYyQOE9R5ytMEfL33jK7xhrxgJkjwdKVpLaq0ueTGGmmayasLpGsI+S0dtYYK0IMAHaNBaEqQk0cCBNJte1VkmSbqXWEWsc1BiR/mFGzxupoCNEYoSqzxqJitei59ytC8Y7l/cctRvujKRyzd1/odkqwdETqeVDWWCMMIXG9Oy5QLImqzdHvq6UHmlLiQBh5IcYhDZbmrDGm5bFZETIynVpsTHaSZH+p74uvjCjB0i2UNQZ4C1P8YGn63sbECCVyjSVwQ4e5xuJOYecevzfOPT66KS41uqLUlKAYoUbMq/QcjBXLST2x+qzV4BozxdP5jUO67/L/5TpCjvK+VqK17mSmaYiLmd0/KlxHaPJAXWPV9hqLdI21cIyQuFbjd58PVjpqgS60Sc6RElsS8TZf1hiSu8biQhUhfd8+15gvRki4xuo6pPJnkZM0VqgEisf4oNrqCPlVRP1yU79HzzXWyopQa93JTNMQ12YrXqTNhCtLTx4URajKOkLRrjE1sLaVkIZQFZWl63nfV68IkZ+j6giRjcXPxVKyrLG4UEVIP7f6ecv5YoTMAcX1gF6rYwWhCEW/L6wAYpL3ekZe8DmRLTaUytKtdd8AbAgF0t/fj8MOOwzHHntss4cyIbBrzAwbQpMHJUYoiWssSR0hJVi6xWKEDHVxQre34x93EqptTFt11ljGi5UB6p+qnkwRUn+XmVUNmDssy5JGoFCEYmWNaV9J1fdKyrxmBCpCLVxHiA2hAFauXIk1a9bgoYceavZQJgRP5my9i7SZNKroHFN/MgkMGkqSYoStWlAR8JSRuONqVCJAtYZQ1QUVNcOv3jZHLhMWI6QFS6dU47iWgopxENfreCVGaCKbrgZljZnrCHHWGDMJkFljrHooqO6DJg6EiaRqRSiBK8e2Ldnks9UMoYtOOgD3PL8NRy7qjbV9oxQhegoTNV2l2UYR81DOoAjFfW9SwipL+1tsmBWhRk2r2bSN4XzJU4TixAjV0nTVUCMqrMik+LnY4jFCbAgxADhGKAh6PhpRHZapH6qLq8qn3BgLd9q2UCi5ilLQCrzn6EV4z9GLYm+v9BprUIxQEmMxifoqusNnUpYvU6veU1hbiCKkG12BLTYaNK9mpGssfoxQVHXsMEzfUWjWmHCNudxrjJkEcEFFM9xiY/KgKkLxF2DaniITM+umUHJbThFKSqPqCFkG1SAOptiSIPo6s/jcqQejuz2DF7cNBe6nHiiKUKRrbOLS5wFPDRstxE+fj+qXFoYaI2Q28kzKXskBV5ZmWh8uqGiG0+cnD3QRShqHIJsOx3haFW6kyW4INaqhcIrMJclSs+nP0e9befIB+PDr9vYt/vVWbqnyF9lrLKDFRqPUZBGwP5ovGcdnIqxbfOR7qZEbEFeqqkbl/x23tWOEJvedzNQNcYFzZpQKfXrhU9PaVOsao9vHUZLErie7ISSNP9uq60It9pu0cJ6pR1W896m/1z1YOrT7vLqtbghlZLB0fcckaPMpQtHvqSVY2vRgGJaOL7Zp9crSk/tOZuoGnRQZD+XGZ0uopam2+zzdPo5rLC2zs1orRigpQU0za0XcJkn6jAHJXGNB7yv/nuhjI1FjhNS/+YOlbePfG/WAqStCceYofZvqMyxFsLS6jdozzjOEWBGahEy7OkKVa5PT51WSpPQyzUUoENUoHGJSj6MIietgKilCdd1v5dxnExqK1Zaq0L/rxsYIhWeoBfYaa1SMUOXzRvJFAPFccLrhkqjpqqGOUFjLDhoszXWEJiHTrY6QHSBzTnfUWit8blqZTA0ZOslihCqGUIv1GkuKUALqvTCJ/WWTusaqvNf8rrFGGkLq32IHSzdMEdIKKlaRPp/EcFQb45rjSukpUBUhR3lfKzG572SmbngBjk0eSIvBlaUnD17T0eQXcVou3tHv3XtWB9K2hUUzOxJ/TivRqExRYcQkbUFiqj8T632+mJdEHxsJdY1FNSwNSp9vVKKUcI3lE7QXqaXXmNrGxtxiwxgj5LotnTXG6fMMAJo11noXaTPhGKHJQzag5H8cUtKtFn39/+j8Y7F7pIB53W2JP6eVmDMjh5RtYX6dj0Oc/qQGKd08iWqgK0AWGqkI6cHS4TFCMrOqUYqQXkwyxnmzLK8oaNz3CBT3ZcD9ZnJxOo4L8bVwHSGmZRHzPytCKooixOempRFGTNJsJQDI2CJGKPq9Hdm0LOg3mZkzI4ffX3wiZnXm6rpfL0ao+mDpWlxjDQ2WNhg0KduSgcD6MacCVJN6ocepxf2clGWhWLGEqlXfhJEX2mKD1BFyrNaNEZr8dzNTFxqd3TBZYdfY5CGTrj1GqBojajLzmoU9dd9nta6x6tPnw1WaWglThIDyWEuoGEK6IlSDShlvbGpAetzzZtuW7FKbzDVGg6UDssYMcUSO66JyiloyRogNIQaAd/G24kXaTLjp6uQhk8C9peNlULHsVytiMU7uGvO7XeLQ6DpCYS02gIohUM5e9xnS3gNmfcfkja26hrOmzK5Y77P9Ro7PNWZQ9kqOCxesCDEtDleWNkMXRlaEWhuxYFWTzSVcYtNNEWoEYvFLWl5AKVWR4F6byPR5k9pUniMcZFO2byxSNWlwHSE5vriusSof8NS+fEGuMbK9cI25Lly3desIsSHEACBPLi14kTYTVoQmD0fu1YNzj1+C4/eblfi9aRkjxIpQrdjVKkJ1UCnKn5/oYyOJihHygsNNRlKjXWPmAo5R0M2SGGmqIhSj+7xosUEKKrZiQg4bQgwAYGZHFgDQV/mfKaPGCDVxIEwk6ZSNr7zziOre26DigtMRsfglrSxtqj8TB59rbAKzxuhrppiooKKDdRubnjUW1zVW5QOeEiMUECytJph4rrFWrizNhlAA/f396O/vR6lUavZQJoTPn3Yw3njQbKw4bF6zh9JScLD09MALlm69p9XJhjCA2rPJKktTt1KSFGt/09VEHxtJOmXLzLAwQ8h07WQCVJN64XONxfwcxRBKYnSagqVDgtWpa4x7jU1Cpltl6bndbTh72V68EGhw9/npwUkHz8XsriyWLe5t9lAmPacePh9vO3IBPvS6vRO9TwmybaEYIQBoC8lIFJ9nUoReu3cvOrIpHLdvX93HBFSfPq+WKoj/eXQ+zMj2IcH7pnWEpCLUgnF4rAgxTAisCE0PLjppf3z8TfvVvT3DdGRBTzu+/cHXJn6f6q6J/75G1xECgH3ndOK5LUOYO8Nfc8lrKeIf9NF79+Hxy9/asNgz3TUWV92pR7B0UGVyk0FbcoGiE78NyETDhhDDhECfXlowxo+pI2wENRe1wXH8m63RdYQA4L8veB0Gx4roNcRQhilCQGMD8HXXWNxL2FTrJw6m7vOhWWMmRYgNIYaZXFTb/4hhmGRUe6/phk8jDNrutgy62zLGv4UFSzeaarPGqlW6TaECvqyxgGDpQql1s8Zab0QM00Jw+jzDTAyqkpDgfb6ssYmlmYH2NLUfmIg6QrTpakDWmMGgLba4IsSGEMOEQAsqsuuEYRpH9b3GGh8sHf755f+TlguoB75g6QbXEUobXGP6Z6ouzvL/juu2dIwQG0IMEwIt9d+KNzDDTBVM7RviMBHB0mFIRagprjFdEYr3vmoVIdvwvljB0qwIMczkhWOEGGZisA0LaBwmIn0+DBks3QxFyFdQMXn6fLUFFTNBLTZM6fNcR4hhJi/0pmU7iGEaR9VxKw0uqBj5+TJYeuIniGrrCNWl15gsFqluY8oaK7V4HSE2hBgmBC6oyDATAzVoErnGQgr6TQRhdYQaTV1cY0l6jRmqf+vn25SRVnJcFFs4a4zT5wOYbi02GDO9HRmcccR8dGbTXHWbYRoIXU9rCZaeaEWo2iaz9aD6pqvVBabTmEmhCIVmjRHXWCvHCLEhFMDKlSuxcuVKDAwMoKenp9nDYZqEZVn4zrlHN3sYDDPlqValaHaMUFPrCFUZI1SXpquyxUZw1piiCHHWGMMwDMMEYyrEFwdfHaGJjhGKqCzdSKpuulpt8coA92WQEStjhFy0tCLEhhDDMAzTdKpt+9D0OkKVVbQZMUIp25LZW0D8GCEaplNt01XaOiSoYa6sI+Rw1hjDMAzDhFJ1bZtp7BoDVFWoqqarSYKlA2o9BRlW5jpCrWd2tN6IGIZhmGlHkKoQRbMLKjYzWBpQA6bjVr+vto6QZVny/KYj3GH0Z64jxDAMwzAR0DW8FkWoeXWEmm8IJW26alvJWwcJRYe6xkwB0oBnIHEdIYZhGIaJoPq2D+rvE90TMNVsRYg0Xo1dR6gy5mrUGfGewGBpU/d510WxxFljDMMwDBPIZE2fFwqHXtNnoqjKNSYVoRoMoZT5+zIVxnRavNcY1xFiGIZhmk61cSv+YOm6DSkW7z16MfaMFnDSwXMm9oMrVOUaq0EREgYQzZKjBpgdpAi1cIwQG0IMwzBM06lXr7GJVoRWHDYPKw6bN6GfSanKNSY6x1dxrj564r54auMA9pvTRfZn/nyxf8dBS2eNsSHEMAzDNJ2gzKMoml1QsdlQRSh293nhGqtCnfnEKQf6XgtSmGjT1VZWhFrPNGMYhmGmHZah/kys9/myxlpvoW0ktI5Q7BYbhhT4WgiKORK/FyqB0vX8zHrChhDDMAzTdOqlCLXgOttQaL+x2E1Xa1CETEhXW4AilC96hlCK0+cnD/39/TjssMNw7LHHNnsoDMMwU57q0+ebGyPUbFTXWLz3SFdWnc5V0P5E7NA4K0KTk5UrV2LNmjV46KGHmj0UhmGYKU/1WWPq79PMDlJdYwkLKtYrXidIYRLfqaIIsSHEMAzDMH6qrSPU7F5jzaa2YOn6jEF8X76sMYPR04pZY603IoZhGGbaQdfMmlps1GtAkwQlRih2sHR9XWN2gMJkMsxaUBBiQ4hhGIZpPpZlSbdWTQUVW3GlbSBtxDUW165J1T1Yuvy//l3o32Patloyq48NIYZhGKYl8OrRxH+Pvq5OMztIUYTiGjbCYKlb+nxEHaGg31sFNoQYhmGYlqCaHlj+7vOtudg2ChosHds1FqDgVIttmb83/fdWzBgD2BBiGIZhWoT2SruI9mwqYksPPfZ2egdLx3tPUExPtUTVEQr6vVXgFhsMwzBMS/Dls1+DjbvHsKCnPfZ7/L3G6j2q1kYxhCag6WrY/oJ6jQnSSXyeEwgbQgzDMExLcPayvRK/x9diY5rljalNV5PVEaqba0y42vSsMc3uaVVFqDXNM4ZhGIaJARdUTJ4+HxTcXC1B3ez1/WfYEGIYhmGY+sIFFb1l3Iq5ogcZLtUSZFj50ulbsM8YwIYQwzAMM4nx1xFq0kCaRHVZY41RhKLrCLXml9Oao2IYhmGYGPjrCLWm6tAo2jJVtNhoULC0L0sswjBqFdgQYhiGYSYtfndMkwbSJNSmq/HekwoIbq6WwKarhsrSrQgbQgzDMMykxa+CtOZi2yhytShCdTpVwr4x2TlKM102hBiGYRimvuhra4uutQ2jmu7zjYoRMsUo0ddYEWIYhmGYOqPXEZpuMUJduXI5wGzKjm0E1r2OkGV2jZVf839uq8EFFQPo7+9Hf38/SqVSs4fCMAzDBOBXhFpzsW0UvR1Z/POZh2JGWzp2n7Uj9upBLm3j6L1n1mUMYYpQOVPMIT+3HmwIBbBy5UqsXLkSAwMD6OnpafZwGIZhGAP+pqtNGkgT+dgb9ku0/VFLZuKJL52KbLo+hklYyw76UqsqQq1pnjEMwzBMDKIylRgz9TKCgOCsMUA1ftJcUJFhGIZh6ouvxUZzhjGtCWq6CnDWGMMwDMM0lOneYqMVsENihGzOGmMYhmGYxuE3hJo0kGlMWIFGVoQYhmEYpoHoIkTczCmmfshg6UhFqDVNjtYcFcMwDMPEgBWh5mOFZI2xIsQwDMMwDcTfa6w1F9upTCpu1hgbQgzDMAxTX3xZY6251k5pvIKK/r9xHSGGYRiGaSB6TBDHCE08ssWGqdcY1xFiGIZhmMZChYYWFR2mNGFZY9Q4YkWIYRiGYRoAXWw5RmjiCcsaU2OEWtPkaM1RMQzDMExM2BBqLnFbbLAixDAMwzANgAoNbAdNPG86aA72m92Jtxw21/e3yVBZmrvPMwzDMJMautiyITTxHLVkJm7/7EnGv7EixDAMwzANhl1jrUtqEihCbAgxDMMwkxpLyRprzcV2ukLdlikOlmYYhmGY+qMqQk0cCOOD6wgxDMMwTIOhxg8XVGwtuI4QwzAMwzQY2euqNdfZaQ33GmMYhmGYBiNUIFaDWo8UK0IMwzAM01jE+tqi6+y0xmZFiGEYhmEai82KUMuiKkKtaXK05qgYhmEYJiZe9/MmD4TxwTFCDMMwDNNghNDANYRaD64sPYnp7+/HYYcdhmOPPbbZQ2EYhmFCkK6xJo+D8cN1hCYxK1euxJo1a/DQQw81eygMwzBMCJ5rrDUX2ukM1xFiGIZhmAYj1lq2g1qPFLEyOEaIYRiGYRqAVIRadKGdzqgxQq1pcrTmqBiGYRgmJl4dITaEWg2bu88zDMMwTGPh9PnWhbPGGIZhGKbBeKpDay600xlWhBiGYRimwXh1hJo7DsYPK0IMwzAM02A4fb514TpCDMMwDNNgLI4Ralls7jXGMAzDMI3FlnWE2BJqNbiOEMMwDMM0mJSsI9TkgTA+UlxZmmEYhmEai9drrDUX2umMzd3nGYZhGKaxWLKgYnPHwfhhRYhhGIZhGgxnjbUuqiLUmiZHa46KYRiGYWIi1le2g1oPpY4Qp88zDMMwTP1hRah1SXFlaYZhGIZpLGwItS42V5ZmGIZhmMbi1RFq7jgYP9QbxooQwzAMwzQAmT7PllDLwb3GGIZhGKbBcIuN1oWzxhiGYRimwdiyjhBbQq0G1xFiGIZhmAZjsyLUsnBlaYZhGIZpMEJp4Bih1kMYP7alGkWtBBtCDMMwzKTG4qyxlkUYqa0aHwSwIcQwDMNMcriOUOsivpNWjQ8C2BBiGIZhJjlesHRzx8H48RSh1v1y2BBiGIZhJjVcR6h1kYpQi/YZA9gQYhiGYSY5XEeodWFFiGEYhmEaTKqyknGMUOshvhuOEWIYhmGYBuG5xpo8EMaH+G44a4xhGIZhGoTFWWMti1CCWBFiGIZhmAbhdZ9v3cV2upKyOEaIYRiGYRoKt9hoXZbM6kDKtrDv7M5mDyWQdLMHwDAMwzC1wE1XW5dFMztw36VvRk9HptlDCYQNIYZhGGZSY9usCLUyc7vbmj2EUNg1xjAMw0xqPCWILSEmOVPeENq9ezeOOeYYLFu2DIcffji+//3vN3tIDMMwTB3hFhtMLUx519iMGTNw9913o6OjA8PDwzj88MPxrne9C7NmzWr20BiGYZg6wE1XmVqY8opQKpVCR0cHAGB8fByu68J13SaPimEYhqkXso7QlF/RmEbQ9Mvm7rvvxllnnYWFCxfCsixcf/31vm36+/uxzz77oK2tDccffzwefPDBRJ+xe/duLF26FIsWLcLnPvc5zJ49u06jZxiGYZoN1xFiaqHphtDw8DCWLl2K/v5+49+vu+46XHLJJbj88svxyCOPYOnSpTj11FOxdetWuY2I/9H/bdy4EQDQ29uLv/71r1i3bh3++7//G1u2bJmQY2MYhmEaj6hazGYQUw1NjxE6/fTTcfrppwf+/eqrr8YFF1yAj3zkIwCAa6+9FjfeeCN+9KMf4dJLLwUAPPbYY7E+a968eVi6dCn+8pe/4D3veY9xm/HxcYyPj8vfBwYGYh4JwzAM0wy4xQZTC01XhMLI5/NYvXo1VqxYIV+zbRsrVqzAfffdF2sfW7ZsweDgIABgz549uPvuu3HwwQcHbn/llVeip6dH/lu8eHFtB8EwDMM0FM4aY2qhpQ2h7du3o1QqYd68ecrr8+bNw+bNm2PtY/369XjDG96ApUuX4g1veAM+8YlP4Igjjgjc/gtf+AL27Nkj/23YsKGmY2AYhmEaC2eNMbXQdNdYoznuuONiu84AIJfLIZfLNW5ADMMwTF3hYGmmFlpaEZo9ezZSqZQvuHnLli2YP39+k0bFMAzDtBLCAGI7iKmGljaEstksjj76aNx2223yNcdxcNttt2H58uVNHBnDMAzTKrzhwNnYe1YHVhw6t9lDYSYhTXeNDQ0NYe3atfL3devW4bHHHkNfXx+WLFmCSy65BOeddx6OOeYYHHfccbjmmmswPDwss8gYhmGY6c2Ri3px1+dObvYwmElK0w2hhx9+GCef7F3Al1xyCQDgvPPOw6pVq3DOOedg27ZtuOyyy7B582YsW7YMN910ky+AmmEYhmEYJimWy/0mjPT396O/vx+lUgnPPfcc9uzZg+7u7mYPi2EYhmGYGAwMDKCnpydy/WZDKIK4J5JhGIZhmNYh7vrd0sHSDMMwDMMwjYQNIYZhGIZhpi1sCDEMwzAMM21hQ4hhGIZhmGkLG0IMwzAMw0xb2BAKoL+/H4cddhiOPfbYZg+FYRiGYZgGwenzEXD6PMMwDMNMPjh9nmEYhmEYJgI2hBiGYRiGmbawIcQwDMMwzLSFDSGGYRiGYaYtTe8+3+qIWPKBgYEmj4RhGIZhmLiIdTsqJ4wNoQgGBwcBAIsXL27ySBiGYRiGScrg4CB6enoC/87p8xE4joONGzdixowZsCyrbvsdGBjA4sWLsWHDBk7LbzB8ricGPs8TB5/riYHP88TRiHPtui4GBwexcOFC2HZwJBArQhHYto1FixY1bP/d3d18g00QfK4nBj7PEwef64mBz/PEUe9zHaYECThYmmEYhmGYaQsbQgzDMAzDTFvYEGoSuVwOl19+OXK5XLOHMuXhcz0x8HmeOPhcTwx8nieOZp5rDpZmGIZhGGbawooQwzAMwzDTFjaEGIZhGIaZtrAhxDAMwzDMtIUNIYZhGIZhpi1sCDWJ/v5+7LPPPmhra8Pxxx+PBx98sNlDmtR86UtfgmVZyr9DDjlE/n1sbAwrV67ErFmz0NXVhXe/+93YsmVLE0c8ebj77rtx1llnYeHChbAsC9dff73yd9d1cdlll2HBggVob2/HihUr8Pzzzyvb7Ny5E+eeey66u7vR29uLj370oxgaGprAo2h9os7z+eef77vGTzvtNGUbPs/RXHnllTj22GMxY8YMzJ07F+94xzvw7LPPKtvEmS9efvllnHnmmejo6MDcuXPxuc99DsVicSIPpeWJc65POukk33X98Y9/XNmm0eeaDaEmcN111+GSSy7B5ZdfjkceeQRLly7Fqaeeiq1btzZ7aJOa17zmNdi0aZP8d88998i/feYzn8H//u//4le/+hXuuusubNy4Ee9617uaONrJw/DwMJYuXYr+/n7j37/2ta/hm9/8Jq699lo88MAD6OzsxKmnnoqxsTG5zbnnnounnnoKt9xyC/7whz/g7rvvxoUXXjhRhzApiDrPAHDaaacp1/gvfvEL5e98nqO56667sHLlStx///245ZZbUCgU8Na3vhXDw8Nym6j5olQq4cwzz0Q+n8f//d//4Sc/+QlWrVqFyy67rBmH1LLEOdcAcMEFFyjX9de+9jX5twk51y4z4Rx33HHuypUr5e+lUslduHChe+WVVzZxVJObyy+/3F26dKnxb7t373YzmYz7q1/9Sr729NNPuwDc++67b4JGODUA4P7ud7+TvzuO486fP9/993//d/na7t273Vwu5/7iF79wXdd116xZ4wJwH3roIbnNn/70J9eyLPfVV1+dsLFPJvTz7Lque95557lnn3124Hv4PFfH1q1bXQDuXXfd5bpuvPnij3/8o2vbtrt582a5zXe/+123u7vbHR8fn9gDmETo59p1XfdNb3qT+6lPfSrwPRNxrlkRmmDy+TxWr16NFStWyNds28aKFStw3333NXFkk5/nn38eCxcuxH777Ydzzz0XL7/8MgBg9erVKBQKyjk/5JBDsGTJEj7nNbJu3Tps3rxZObc9PT04/vjj5bm977770Nvbi2OOOUZus2LFCti2jQceeGDCxzyZufPOOzF37lwcfPDBuOiii7Bjxw75Nz7P1bFnzx4AQF9fH4B488V9992HI444AvPmzZPbnHrqqRgYGMBTTz01gaOfXOjnWvBf//VfmD17Ng4//HB84QtfwMjIiPzbRJxrbro6wWzfvh2lUkn5UgFg3rx5eOaZZ5o0qsnP8ccfj1WrVuHggw/Gpk2bcMUVV+ANb3gDnnzySWzevBnZbBa9vb3Ke+bNm4fNmzc3Z8BTBHH+TNez+NvmzZsxd+5c5e/pdBp9fX18/hNw2mmn4V3vehf23XdfvPDCC/jHf/xHnH766bjvvvuQSqX4PFeB4zj49Kc/jRNPPBGHH344AMSaLzZv3my85sXfGD+mcw0AH/zgB7H33ntj4cKFePzxx/H5z38ezz77LH77298CmJhzzYYQMyU4/fTT5c9HHnkkjj/+eOy999745S9/ifb29iaOjGHqw/vf/3758xFHHIEjjzwS+++/P+68806ccsopTRzZ5GXlypV48sknlXhCpjEEnWsaw3bEEUdgwYIFOOWUU/DCCy9g//33n5CxsWtsgpk9ezZSqZQvA2HLli2YP39+k0Y19ejt7cVBBx2EtWvXYv78+cjn89i9e7eyDZ/z2hHnL+x6nj9/vi8RoFgsYufOnXz+a2C//fbD7NmzsXbtWgB8npNy8cUX4w9/+APuuOMOLFq0SL4eZ76YP3++8ZoXf2NUgs61ieOPPx4AlOu60eeaDaEJJpvN4uijj8Ztt90mX3McB7fddhuWL1/exJFNLYaGhvDCCy9gwYIFOProo5HJZJRz/uyzz+Lll1/mc14j++67L+bPn6+c24GBATzwwAPy3C5fvhy7d+/G6tWr5Ta33347HMeRkx6TnFdeeQU7duzAggULAPB5jovrurj44ovxu9/9Drfffjv23Xdf5e9x5ovly5fjiSeeUAzPW265Bd3d3TjssMMm5kAmAVHn2sRjjz0GAMp13fBzXZeQayYR//M//+Pmcjl31apV7po1a9wLL7zQ7e3tVaLimWT8/d//vXvnnXe669atc++99153xYoV7uzZs92tW7e6ruu6H//4x90lS5a4t99+u/vwww+7y5cvd5cvX97kUU8OBgcH3UcffdR99NFHXQDu1Vdf7T766KPu+vXrXdd13auuusrt7e11b7jhBvfxxx93zz77bHffffd1R0dH5T5OO+0096ijjnIfeOAB95577nEPPPBA9wMf+ECzDqklCTvPg4OD7mc/+1n3vvvuc9etW+feeuut7mtf+1r3wAMPdMfGxuQ++DxHc9FFF7k9PT3unXfe6W7atEn+GxkZkdtEzRfFYtE9/PDD3be+9a3uY4895t50003unDlz3C984QvNOKSWJepcr1271v3yl7/sPvzww+66devcG264wd1vv/3cN77xjXIfE3Gu2RBqEt/61rfcJUuWuNls1j3uuOPc+++/v9lDmtScc8457oIFC9xsNuvutdde7jnnnOOuXbtW/n10dNT9f//v/7kzZ850Ozo63He+853upk2bmjjiycMdd9zhAvD9O++881zXLafQf/GLX3TnzZvn5nI595RTTnGfffZZZR87duxwP/CBD7hdXV1ud3e3+5GPfMQdHBxswtG0LmHneWRkxH3rW9/qzpkzx81kMu7ee+/tXnDBBb6HJz7P0ZjOMQD3xz/+sdwmznzx0ksvuaeffrrb3t7uzp492/37v/97t1AoTPDRtDZR5/rll1923/jGN7p9fX1uLpdzDzjgAPdzn/ucu2fPHmU/jT7XVmWwDMMwDMMw0w6OEWIYhmEYZtrChhDDMAzDMNMWNoQYhmEYhpm2sCHEMAzDMMy0hQ0hhmEYhmGmLWwIMQzDMAwzbWFDiGEYhmGYaQsbQgzDMAm48847YVmWrxcVwzCTEzaEGIZhGIaZtrAhxDAMwzDMtIUNIYZhJhWO4+DKK6/Evvvui/b2dixduhS//vWvAXhuqxtvvBFHHnkk2tra8LrXvQ5PPvmkso/f/OY3eM1rXoNcLod99tkHX//615W/j4+P4/Of/zwWL16MXC6HAw44AD/84Q+VbVavXo1jjjkGHR0dOOGEE/Dss8829sAZhmkIbAgxDDOpuPLKK/HTn/4U1157LZ566il85jOfwYc+9CHcddddcpvPfe5z+PrXv46HHnoIc+bMwVlnnYVCoQCgbMC8733vw/vf/3488cQT+NKXvoQvfvGLWLVqlXz/3/zN3+AXv/gFvvnNb+Lpp5/G9773PXR1dSnj+Kd/+id8/etfx8MPP4x0Oo2//du/nZDjZximvnDTVYZhJg3j4+Po6+vDrbfeiuXLl8vXP/axj2FkZAQXXnghTj75ZPzP//wPzjnnHADAzp07sWjRIqxatQrve9/7cO6552Lbtm24+eab5fv/4R/+ATfeeCOeeuopPPfcczj44INxyy23YMWKFb4x3HnnnTj55JNx66234pRTTgEA/PGPf8SZZ56J0dFRtLW1NfgsMAxTT1gRYhhm0rB27VqMjIzgLW95C7q6uuS/n/70p3jhhRfkdtRI6uvrw8EHH4ynn34aAPD000/jxBNPVPZ74okn4vnnn0epVPr/27d/l9TCOI7j7/CmOCSCRYj0Y4jAwAyhTehfaMrRaGyJyOkEDZ6hZhH7H1ylP+FQY3NBUKMgIjjLHS5XcLnEvXTFzvsFB75wHs55nu3D83wfnp+fSSQSHB0d/XEu+/v70zqfzwPQ7/f/eY2S/q8f856AJH3WeDwG4OHhgUKhMPMulUrNhKG/lU6nPzVueXl5Wi8tLQG/+pckLRZ3hCQtjL29PVKpFB8fH+zs7Mw8Gxsb03FPT0/Tejgc8vLyQrFYBKBYLBJF0cx3oyhid3eXRCJBqVRiMpnM9BxJ+r7cEZK0MFZWVmg0GlxeXjKZTKhWq4xGI6IoIpPJsLW1BUCz2SSXy7G+vs719TWrq6scHx8DcHV1xeHhIWEYUqvVeHx8pN1u0+l0ANje3qZer3N2dkar1aJcLvP+/k6/3+fk5GReS5f0RQxCkhZKGIasra1xe3vL29sb2WyWSqVCEATTo6m7uzsuLi54fX3l4OCAXq9HMpkEoFKp0O12ubm5IQxD8vk8zWaT09PT6T/u7+8JgoDz83MGgwGbm5sEQTCP5Ur6Yt4ak/Rt/L7RNRwOyWaz856OpAVgj5AkSYotg5AkSYotj8YkSVJsuSMkSZJiyyAkSZJiyyAkSZJiyyAkSZJiyyAkSZJiyyAkSZJiyyAkSZJiyyAkSZJiyyAkSZJi6yepzotI3eHVTgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loss_ges = np.append(loss_ges, history.history['loss'])\n", + "plt.semilogy(history.history['loss'])\n", + "\n", + "if (Training_Percentage > 0):\n", + " val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n", + " plt.semilogy(history.history['val_loss'])\n", + "\n", + "plt.title('model loss')\n", + "plt.ylabel('loss')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train','eval'], loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check the model by hand\n", + "\n", + "* The following code uses the trained model to check the deviation for each picture.\n", + "* x-axis walks through each pixel, y-axis shows the result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check each image for expected and deviation\n", + "* setting the switch \"only_deviation = true\" will only print the images for which the classification and the CNN-result deviates\n", + "\n", + "The output contains the following information:\n", + "\n", + "| Filename | Expected Category | Predicted Category |\n", + "|------------- |:-----------------------------:|--------------|\n", + "| ziffer_sortiert_resize_NaN/5\\Ziffer_4_0034.jpg | 4 | -1 |\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Input_dir='ziffer_sortiert_resize'\n", + "res = []\n", + "only_deviation = True\n", + "show_wrong_image = True\n", + "\n", + "files = glob.glob(Input_dir + '/*.jpg')\n", + "\n", + "for aktfile in files:\n", + " base = os.path.basename(aktfile)\n", + " target = base[0:1]\n", + " if target == \"N\":\n", + " zw1 = -1\n", + " else:\n", + " zw1 = int(target)\n", + " expected_class = zw1\n", + " image_in = Image.open(aktfile)\n", + " test_image = np.array(image_in, dtype=\"float32\")\n", + " img = np.reshape(test_image,[1,32,20,3])\n", + " classes = np.argmax(model.predict(img, verbose=0), axis=-1)\n", + " classes = classes[0]\n", + " if classes == 10: \n", + " classes = -1\n", + " zw2 = classes\n", + " zw3 = zw2 - zw1\n", + " res.append(np.array([zw1, zw2, zw3]))\n", + " if only_deviation == True:\n", + " if str(classes) != str(expected_class):\n", + " print(aktfile + \" \" + str(expected_class) + \" \" + str(classes))\n", + " if show_wrong_image == True:\n", + " display(image_in)\n", + " else:\n", + " print(aktfile + \" \" + aktsubdir + \" \" + str(classes))\n", + " \n", + "\n", + "res = np.asarray(res)\n", + "\n", + "\n", + "plt.plot(res[:,0])\n", + "plt.plot(res[:,1])\n", + "plt.title('Result')\n", + "plt.ylabel('Digital Value')\n", + "plt.xlabel('#Picture')\n", + "plt.legend(['real','model'], loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save the model\n", + "\n", + "* Save the model to the file with the \"h5\" file format" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#model.export(\"test\", \"tf_saved_model\") # replace tf.saved_model.save with this line" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dig-class11_1900_s2.tflite\n" + ] + }, + { + "data": { + "text/plain": [ + "356636" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \".tflite\"\n", + "\n", + "converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n", + "tflite_model = converter.convert()\n", + "open(FileName, \"wb\").write(tflite_model)\n", + "print(FileName)\n", + "Path(FileName).stat().st_size" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dig-class11_1900_s2_q.tflite\n" + ] + }, + { + "data": { + "text/plain": [ + "97192" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \"_q.tflite\"\n", + "\n", + "import tensorflow as tf\n", + "\n", + "def representative_dataset():\n", + " for n in range(x_data[0].size):\n", + " data = np.expand_dims(x_data[5], axis=0)\n", + " yield [data.astype(np.float32)]\n", + " \n", + "converter2 = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n", + "converter2.representative_dataset = representative_dataset\n", + "converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n", + "converter2.representative_dataset = representative_dataset\n", + "converter2._experimental_disable_per_channel_quantization_for_dense_layers = True\n", + "\n", + "tflite_quant_model = converter2.convert()\n", + "\n", + "open(FileName, \"wb\").write(tflite_quant_model)\n", + "print(FileName)\n", + "Path(FileName).stat().st_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checking the images shows, that this are border line images, which can be interpreted as a good digit or a faulty one." + ] + } + ], + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Train_CNN_Digital-Readout-Small-v2.ipynb b/Train_CNN_Digital-Readout-Small-v2.ipynb deleted file mode 100644 index bcd2b333c..000000000 --- a/Train_CNN_Digital-Readout-Small-v2.ipynb +++ /dev/null @@ -1,1598 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CNN Training\n", - "\n", - "Target of this code is to train a CNN network to classify images of a digital readout to the digits 0 to 9. Additionally a category \"NaN\" is introduced, to mark images that are not amibiguous.\n", - "\n", - "### Preparing the training\n", - "* First all libraries are loaded\n", - " * It is assumed, that they are installed during the Python setup\n", - "* matplotlib is set to print the output inline in the jupyter notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "########### Basic Parameters for Running: ################################\n", - " \n", - "TFlite_Version = \"1801\" \n", - "TFlite_MainType = \"dig-class11\"\n", - "TFlite_Size = \"s2\"\n", - "Training_Percentage = 0.0 # 0.0 = Use all Images for Training\n", - "Epoch_Anz = 500\n", - "\n", - "##########################################################################\n", - "\n", - "## 2024-03-30: Code adapted to TF 2.16 ###################################\n", - "\n", - "\n", - "import tensorflow as tf\n", - "import matplotlib.pyplot as plt\n", - "import glob\n", - "import os\n", - "import numpy as np\n", - "from sklearn.utils import shuffle\n", - "from tensorflow.python import keras\n", - "from tensorflow.python.keras import Sequential\n", - "from tensorflow.keras.layers import Dense, InputLayer, Conv2D, MaxPool2D, Flatten, BatchNormalization\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", - "from sklearn.model_selection import train_test_split\n", - "from tensorflow.keras.callbacks import History \n", - "from tensorflow.keras.utils import to_categorical\n", - "from PIL import Image \n", - "from pathlib import Path\n", - "\n", - "loss_ges = np.array([])\n", - "val_loss_ges = np.array([])\n", - "\n", - "%matplotlib inline\n", - "np.set_printoptions(precision=4)\n", - "np.set_printoptions(suppress=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load training data\n", - "* The data is expected in the \"Input_dir\"\n", - "* Inside subdirectories are expected from -1, 0, 1, ... 9 in which the pictures are sorted according to their values (=category)\n", - "* Picture size must be 20x32 with 3 color channels (RGB)\n", - "* The filename can be arbitrary\n", - "\n", - "* The images are stored in the x_data[]\n", - "* The expected category for each image in the corresponding y_data[]\n", - "\n", - "* The last step is a shuffle (from sklearn.utils) and split the data into training and validation data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1379, 32, 20, 3)\n", - "(1379, 11)\n" - ] - } - ], - "source": [ - "Input_dir='ziffer_sortiert_resize'\n", - "\n", - "files = glob.glob(Input_dir + '/*.jpg')\n", - "x_data = []\n", - "y_data = []\n", - "\n", - "for aktfile in files:\n", - " base = os.path.basename(aktfile)\n", - " target = base[0:1]\n", - " if target == \"N\":\n", - " category = 10 # NaN does not work --> convert to 10\n", - " else:\n", - " category = int(target)\n", - " test_image = Image.open(aktfile)\n", - " test_image = np.array(test_image, dtype=\"float32\")\n", - " x_data.append(test_image)\n", - " y_data.append(np.array([category]))\n", - "\n", - "x_data = np.array(x_data)\n", - "y_data = np.array(y_data)\n", - "y_data = to_categorical(y_data, 11)\n", - "print(x_data.shape)\n", - "print(y_data.shape)\n", - "\n", - "x_data, y_data = shuffle(x_data, y_data)\n", - "\n", - "if (Training_Percentage > 0):\n", - " X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=Training_Percentage)\n", - "else:\n", - " X_train = x_data\n", - " y_train = y_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define the model\n", - "\n", - "The layout of the network ist a typcial CNN network with alternating **Conv2D** and **MaxPool2D** layers. Finished after **flattening** with additional **Dense** layer.\n", - "\n", - "#### Important\n", - "* Shape of the input layer: (32, 20, 3)\n", - "* Number of output layers: 11\n", - "* As loss function \"categorical_crossentropy\" is choosen, as it is a categories task" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adadelta` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adadelta`.\n", - "WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adadelta`.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_2 (InputLayer) [(None, 32, 20, 3)] 0 \n", - " \n", - " batch_normalization_1 (Bat (None, 32, 20, 3) 12 \n", - " chNormalization) \n", - " \n", - " conv2d_3 (Conv2D) (None, 32, 20, 32) 896 \n", - " \n", - " max_pooling2d_3 (MaxPoolin (None, 16, 10, 32) 0 \n", - " g2D) \n", - " \n", - " conv2d_4 (Conv2D) (None, 16, 10, 32) 9248 \n", - " \n", - " max_pooling2d_4 (MaxPoolin (None, 8, 5, 32) 0 \n", - " g2D) \n", - " \n", - " conv2d_5 (Conv2D) (None, 8, 5, 32) 9248 \n", - " \n", - " max_pooling2d_5 (MaxPoolin (None, 4, 2, 32) 0 \n", - " g2D) \n", - " \n", - " flatten_1 (Flatten) (None, 256) 0 \n", - " \n", - " dense_2 (Dense) (None, 256) 65792 \n", - " \n", - " dense_3 (Dense) (None, 11) 2827 \n", - " \n", - "=================================================================\n", - "Total params: 88023 (343.84 KB)\n", - "Trainable params: 88017 (343.82 KB)\n", - "Non-trainable params: 6 (24.00 Byte)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "inputs = tf.keras.Input(shape=(32, 20, 3))\n", - "inputs2 = tf.keras.layers.BatchNormalization()(inputs)\n", - "inputs3 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs2)\n", - "inputs4 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs3)\n", - "inputs5 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs4)\n", - "inputs6 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs5)\n", - "inputs7 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs6)\n", - "inputs8 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs7)\n", - "inputs9 = tf.keras.layers.Flatten()(inputs8)\n", - "inputs10 = tf.keras.layers.Dense(256,activation=\"relu\")(inputs9)\n", - "output = tf.keras.layers.Dense(11, activation='softmax')(inputs10)\n", - "\n", - "model = tf.keras.Model(inputs=inputs, outputs=output)\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n", - "\n", - "model.summary()\n", - "\n", - "#model = Sequential()\n", - "#model.add(BatchNormalization(input_shape=(32,20,3)))\n", - "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n", - "#model.add(MaxPool2D(pool_size=(2,2)))\n", - "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n", - "#model.add(MaxPool2D(pool_size=(2,2)))\n", - "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n", - "#model.add(MaxPool2D(pool_size=(2,2)))\n", - "#model.add(Flatten())\n", - "#model.add(Dense(256,activation=\"relu\"))\n", - "#model.add(Dense(11, activation = \"softmax\"))\n", - "\n", - "#model.compile(loss=keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n", - "\n", - "#model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Training\n", - "The input pictures are randomly scattered for brightness, pixel shift variations and rotation angle. This is implemented with a ImageDataGenerator.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/500\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-21 16:17:55.310101: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "345/345 [==============================] - 6s 16ms/step - loss: 2.2587 - accuracy: 0.2676\n", - "Epoch 2/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 1.6624 - accuracy: 0.4540\n", - "Epoch 3/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 1.1131 - accuracy: 0.6381\n", - "Epoch 4/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.9123 - accuracy: 0.7143\n", - "Epoch 5/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.7362 - accuracy: 0.7774\n", - "Epoch 6/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.6626 - accuracy: 0.7977\n", - "Epoch 7/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.6025 - accuracy: 0.8332\n", - "Epoch 8/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.5084 - accuracy: 0.8434\n", - "Epoch 9/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.4649 - accuracy: 0.8651\n", - "Epoch 10/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.4486 - accuracy: 0.8687\n", - "Epoch 11/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.4169 - accuracy: 0.8782\n", - "Epoch 12/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.3897 - accuracy: 0.8832\n", - "Epoch 13/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.3731 - accuracy: 0.8840\n", - "Epoch 14/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.3419 - accuracy: 0.8912\n", - "Epoch 15/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.3429 - accuracy: 0.8891\n", - "Epoch 16/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.2951 - accuracy: 0.9072\n", - "Epoch 17/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2877 - accuracy: 0.9065\n", - "Epoch 18/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2931 - accuracy: 0.9173\n", - "Epoch 19/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2538 - accuracy: 0.9275\n", - "Epoch 20/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2595 - accuracy: 0.9195\n", - "Epoch 21/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2368 - accuracy: 0.9347\n", - "Epoch 22/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.3047 - accuracy: 0.9137\n", - "Epoch 23/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2463 - accuracy: 0.9188\n", - "Epoch 24/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2186 - accuracy: 0.9347\n", - "Epoch 25/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2351 - accuracy: 0.9376\n", - "Epoch 26/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2188 - accuracy: 0.9333\n", - "Epoch 27/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2268 - accuracy: 0.9391\n", - "Epoch 28/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1979 - accuracy: 0.9456\n", - "Epoch 29/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2398 - accuracy: 0.9289\n", - "Epoch 30/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1811 - accuracy: 0.9471\n", - "Epoch 31/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2299 - accuracy: 0.9463\n", - "Epoch 32/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2021 - accuracy: 0.9434\n", - "Epoch 33/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1988 - accuracy: 0.9398\n", - "Epoch 34/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1881 - accuracy: 0.9456\n", - "Epoch 35/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.2109 - accuracy: 0.9478\n", - "Epoch 36/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1667 - accuracy: 0.9463\n", - "Epoch 37/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1550 - accuracy: 0.9543\n", - "Epoch 38/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1859 - accuracy: 0.9485\n", - "Epoch 39/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1455 - accuracy: 0.9543\n", - "Epoch 40/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1829 - accuracy: 0.9536\n", - "Epoch 41/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1656 - accuracy: 0.9485\n", - "Epoch 42/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1474 - accuracy: 0.9558\n", - "Epoch 43/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1556 - accuracy: 0.9572\n", - "Epoch 44/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1578 - accuracy: 0.9608\n", - "Epoch 45/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1368 - accuracy: 0.9616\n", - "Epoch 46/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1374 - accuracy: 0.9594\n", - "Epoch 47/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1666 - accuracy: 0.9507\n", - "Epoch 48/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1566 - accuracy: 0.9572\n", - "Epoch 49/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1490 - accuracy: 0.9543\n", - "Epoch 50/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1251 - accuracy: 0.9608\n", - "Epoch 51/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1358 - accuracy: 0.9543\n", - "Epoch 52/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1156 - accuracy: 0.9616\n", - "Epoch 53/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1369 - accuracy: 0.9616\n", - "Epoch 54/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1085 - accuracy: 0.9659\n", - "Epoch 55/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1403 - accuracy: 0.9630\n", - "Epoch 56/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1010 - accuracy: 0.9637\n", - "Epoch 57/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1305 - accuracy: 0.9630\n", - "Epoch 58/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1114 - accuracy: 0.9695\n", - "Epoch 59/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1204 - accuracy: 0.9630\n", - "Epoch 60/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1268 - accuracy: 0.9616\n", - "Epoch 61/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1217 - accuracy: 0.9703\n", - "Epoch 62/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1055 - accuracy: 0.9674\n", - "Epoch 63/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1329 - accuracy: 0.9630\n", - "Epoch 64/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0915 - accuracy: 0.9717\n", - "Epoch 65/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1249 - accuracy: 0.9637\n", - "Epoch 66/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1218 - accuracy: 0.9637\n", - "Epoch 67/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1248 - accuracy: 0.9674\n", - "Epoch 68/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1230 - accuracy: 0.9688\n", - "Epoch 69/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1060 - accuracy: 0.9674\n", - "Epoch 70/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1341 - accuracy: 0.9623\n", - "Epoch 71/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0862 - accuracy: 0.9717\n", - "Epoch 72/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0860 - accuracy: 0.9768\n", - "Epoch 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15ms/step - loss: 0.1034 - accuracy: 0.9717\n", - "Epoch 82/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1043 - accuracy: 0.9652\n", - "Epoch 83/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1158 - accuracy: 0.9645\n", - "Epoch 84/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1170 - accuracy: 0.9732\n", - "Epoch 85/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.1251 - accuracy: 0.9674\n", - "Epoch 86/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.1296 - accuracy: 0.9710\n", - "Epoch 87/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.0903 - accuracy: 0.9710\n", - "Epoch 88/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.0887 - accuracy: 0.9732\n", - "Epoch 89/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0966 - accuracy: 0.9710\n", - "Epoch 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14ms/step - loss: 0.0810 - accuracy: 0.9761\n", - "Epoch 99/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0983 - accuracy: 0.9732\n", - "Epoch 100/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1125 - accuracy: 0.9688\n", - "Epoch 101/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0883 - accuracy: 0.9761\n", - "Epoch 102/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0799 - accuracy: 0.9775\n", - "Epoch 103/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.0833 - accuracy: 0.9768\n", - "Epoch 104/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0931 - accuracy: 0.9739\n", - "Epoch 105/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.1013 - accuracy: 0.9717\n", - "Epoch 106/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0715 - accuracy: 0.9768\n", - "Epoch 107/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0996 - accuracy: 0.9695\n", - "Epoch 108/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.0921 - accuracy: 0.9703\n", - "Epoch 109/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.0634 - accuracy: 0.9775\n", - "Epoch 110/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0753 - accuracy: 0.9790\n", - "Epoch 111/500\n", - "345/345 [==============================] - 5s 15ms/step - loss: 0.0703 - accuracy: 0.9819\n", - "Epoch 112/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0672 - accuracy: 0.9768\n", - "Epoch 113/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0846 - accuracy: 0.9732\n", - "Epoch 114/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0731 - accuracy: 0.9797\n", - "Epoch 115/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0785 - accuracy: 0.9775\n", - "Epoch 116/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0895 - accuracy: 0.9768\n", - "Epoch 117/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0749 - accuracy: 0.9840\n", - "Epoch 118/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0976 - accuracy: 0.9724\n", - "Epoch 119/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0725 - accuracy: 0.9790\n", - "Epoch 120/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0937 - accuracy: 0.9703\n", - "Epoch 121/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0928 - accuracy: 0.9746\n", - "Epoch 122/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0719 - accuracy: 0.9746\n", - "Epoch 123/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0932 - accuracy: 0.9732\n", - 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[==============================] - 5s 13ms/step - loss: 0.0544 - accuracy: 0.9811\n", - "Epoch 133/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0677 - accuracy: 0.9790\n", - "Epoch 134/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0513 - accuracy: 0.9833\n", - "Epoch 135/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1071 - accuracy: 0.9724\n", - "Epoch 136/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0655 - accuracy: 0.9797\n", - "Epoch 137/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0518 - accuracy: 0.9891\n", - "Epoch 138/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0936 - accuracy: 0.9753\n", - "Epoch 139/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0799 - accuracy: 0.9826\n", - "Epoch 140/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 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- loss: 0.0583 - accuracy: 0.9833\n", - "Epoch 158/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0494 - accuracy: 0.9848\n", - "Epoch 159/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0415 - accuracy: 0.9877\n", - "Epoch 160/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0649 - accuracy: 0.9753\n", - "Epoch 161/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0649 - accuracy: 0.9819\n", - "Epoch 162/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0677 - accuracy: 0.9819\n", - "Epoch 163/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0728 - accuracy: 0.9811\n", - "Epoch 164/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0615 - accuracy: 0.9790\n", - "Epoch 165/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0640 - accuracy: 0.9826\n", - "Epoch 166/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0748 - accuracy: 0.9797\n", - "Epoch 167/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0720 - accuracy: 0.9833\n", - "Epoch 168/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.1208 - accuracy: 0.9739\n", - "Epoch 169/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0476 - accuracy: 0.9855\n", - "Epoch 170/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0697 - accuracy: 0.9826\n", - "Epoch 171/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0495 - accuracy: 0.9833\n", - "Epoch 172/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0356 - accuracy: 0.9869\n", - "Epoch 173/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0575 - accuracy: 0.9898\n", - "Epoch 174/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0519 - accuracy: 0.9826\n", - "Epoch 175/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0521 - accuracy: 0.9855\n", - "Epoch 176/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0938 - accuracy: 0.9826\n", - "Epoch 177/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0509 - accuracy: 0.9869\n", - "Epoch 178/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0698 - accuracy: 0.9819\n", - "Epoch 179/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0612 - accuracy: 0.9826\n", - "Epoch 180/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0514 - accuracy: 0.9819\n", - "Epoch 181/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0383 - accuracy: 0.9891\n", - "Epoch 182/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0850 - accuracy: 0.9761\n", - 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[==============================] - 5s 13ms/step - loss: 0.0602 - accuracy: 0.9840\n", - "Epoch 192/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0489 - accuracy: 0.9891\n", - "Epoch 193/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0250 - accuracy: 0.9913\n", - "Epoch 194/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0433 - accuracy: 0.9855\n", - "Epoch 195/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0456 - accuracy: 0.9840\n", - "Epoch 196/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0595 - accuracy: 0.9848\n", - "Epoch 197/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0638 - accuracy: 0.9826\n", - "Epoch 198/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0873 - accuracy: 0.9775\n", - "Epoch 199/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 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- loss: 0.0540 - accuracy: 0.9884\n", - "Epoch 217/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0478 - accuracy: 0.9869\n", - "Epoch 218/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0548 - accuracy: 0.9804\n", - "Epoch 219/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0749 - accuracy: 0.9775\n", - "Epoch 220/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0743 - accuracy: 0.9811\n", - "Epoch 221/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0570 - accuracy: 0.9855\n", - "Epoch 222/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0583 - accuracy: 0.9826\n", - "Epoch 223/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0478 - accuracy: 0.9862\n", - "Epoch 224/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0741 - accuracy: 0.9833\n", - "Epoch 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- 4s 13ms/step - loss: 0.0591 - accuracy: 0.9782\n", - "Epoch 234/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0463 - accuracy: 0.9848\n", - "Epoch 235/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0504 - accuracy: 0.9848\n", - "Epoch 236/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0587 - accuracy: 0.9848\n", - "Epoch 237/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0615 - accuracy: 0.9848\n", - "Epoch 238/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0484 - accuracy: 0.9877\n", - "Epoch 239/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0481 - accuracy: 0.9877\n", - "Epoch 240/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0553 - accuracy: 0.9869\n", - "Epoch 241/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0776 - accuracy: 0.9790\n", - "Epoch 242/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0368 - accuracy: 0.9877\n", - "Epoch 243/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0857 - accuracy: 0.9811\n", - "Epoch 244/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0612 - accuracy: 0.9840\n", - "Epoch 245/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0400 - accuracy: 0.9898\n", - "Epoch 246/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0391 - accuracy: 0.9906\n", - "Epoch 247/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0814 - accuracy: 0.9848\n", - "Epoch 248/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0732 - accuracy: 0.9826\n", - "Epoch 249/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0519 - accuracy: 0.9826\n", - "Epoch 250/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0559 - accuracy: 0.9833\n", - "Epoch 251/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0589 - accuracy: 0.9869\n", - "Epoch 252/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0576 - accuracy: 0.9811\n", - "Epoch 253/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0632 - accuracy: 0.9848\n", - "Epoch 254/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0356 - accuracy: 0.9898\n", - "Epoch 255/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0310 - accuracy: 0.9913\n", - "Epoch 256/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0559 - accuracy: 0.9884\n", - "Epoch 257/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0810 - accuracy: 0.9790\n", - "Epoch 258/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0618 - accuracy: 0.9840\n", - "Epoch 259/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0390 - accuracy: 0.9920\n", - "Epoch 260/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0354 - accuracy: 0.9891\n", - "Epoch 261/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0477 - accuracy: 0.9869\n", - "Epoch 262/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0404 - accuracy: 0.9869\n", - "Epoch 263/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0425 - accuracy: 0.9913\n", - "Epoch 264/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0792 - accuracy: 0.9804\n", - "Epoch 265/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0593 - accuracy: 0.9826\n", - "Epoch 266/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0505 - accuracy: 0.9891\n", - "Epoch 267/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0663 - accuracy: 0.9826\n", - "Epoch 268/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0548 - accuracy: 0.9877\n", - "Epoch 269/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0324 - accuracy: 0.9862\n", - "Epoch 270/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0437 - accuracy: 0.9884\n", - "Epoch 271/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0599 - accuracy: 0.9862\n", - "Epoch 272/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0423 - accuracy: 0.9862\n", - "Epoch 273/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0651 - accuracy: 0.9848\n", - "Epoch 274/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0573 - accuracy: 0.9848\n", - "Epoch 275/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0605 - accuracy: 0.9869\n", - "Epoch 276/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0304 - accuracy: 0.9913\n", - "Epoch 277/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0367 - accuracy: 0.9891\n", - "Epoch 278/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0369 - accuracy: 0.9913\n", - "Epoch 279/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0544 - accuracy: 0.9848\n", - "Epoch 280/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0333 - accuracy: 0.9884\n", - "Epoch 281/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0589 - accuracy: 0.9819\n", - "Epoch 282/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0391 - accuracy: 0.9913\n", - "Epoch 283/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0678 - accuracy: 0.9869\n", - "Epoch 284/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0513 - accuracy: 0.9862\n", - "Epoch 285/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0411 - accuracy: 0.9869\n", - "Epoch 286/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0563 - accuracy: 0.9819\n", - "Epoch 287/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0379 - accuracy: 0.9877\n", - "Epoch 288/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0427 - accuracy: 0.9848\n", - "Epoch 289/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0746 - accuracy: 0.9884\n", - "Epoch 290/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0598 - accuracy: 0.9877\n", - "Epoch 291/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0594 - accuracy: 0.9877\n", - "Epoch 292/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0508 - accuracy: 0.9877\n", - "Epoch 293/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0444 - accuracy: 0.9869\n", - "Epoch 294/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0340 - accuracy: 0.9913\n", - "Epoch 295/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0303 - accuracy: 0.9927\n", - "Epoch 296/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0508 - accuracy: 0.9869\n", - "Epoch 297/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0616 - accuracy: 0.9811\n", - "Epoch 298/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0751 - accuracy: 0.9790\n", - "Epoch 299/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0544 - accuracy: 0.9797\n", - "Epoch 300/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0521 - accuracy: 0.9826\n", - "Epoch 301/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0377 - accuracy: 0.9906\n", - "Epoch 302/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0369 - accuracy: 0.9913\n", - "Epoch 303/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0368 - accuracy: 0.9906\n", - "Epoch 304/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0715 - accuracy: 0.9833\n", - "Epoch 305/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0370 - accuracy: 0.9840\n", - "Epoch 306/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0426 - accuracy: 0.9891\n", - "Epoch 307/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0449 - accuracy: 0.9869\n", - "Epoch 308/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0694 - accuracy: 0.9935\n", - "Epoch 309/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0277 - accuracy: 0.9906\n", - "Epoch 310/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0451 - accuracy: 0.9869\n", - "Epoch 311/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0449 - accuracy: 0.9862\n", - "Epoch 312/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0514 - accuracy: 0.9862\n", - "Epoch 313/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0251 - accuracy: 0.9935\n", - "Epoch 314/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0474 - accuracy: 0.9884\n", - "Epoch 315/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0222 - accuracy: 0.9949\n", - "Epoch 316/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0435 - accuracy: 0.9891\n", - "Epoch 317/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0464 - accuracy: 0.9898\n", - "Epoch 318/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0566 - accuracy: 0.9891\n", - "Epoch 319/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0369 - accuracy: 0.9869\n", - "Epoch 320/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0373 - accuracy: 0.9913\n", - "Epoch 321/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0727 - accuracy: 0.9848\n", - "Epoch 322/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0527 - accuracy: 0.9877\n", - "Epoch 323/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0502 - accuracy: 0.9855\n", - "Epoch 324/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0682 - accuracy: 0.9848\n", - "Epoch 325/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0374 - accuracy: 0.9891\n", - "Epoch 326/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0595 - accuracy: 0.9869\n", - "Epoch 327/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0449 - accuracy: 0.9869\n", - "Epoch 328/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0596 - accuracy: 0.9840\n", - "Epoch 329/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0501 - accuracy: 0.9862\n", - "Epoch 330/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0471 - accuracy: 0.9833\n", - "Epoch 331/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0402 - accuracy: 0.9877\n", - "Epoch 332/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0313 - accuracy: 0.9884\n", - "Epoch 333/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0428 - accuracy: 0.9884\n", - "Epoch 334/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0545 - accuracy: 0.9906\n", - "Epoch 335/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0428 - accuracy: 0.9898\n", - "Epoch 336/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0398 - accuracy: 0.9906\n", - "Epoch 337/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0511 - accuracy: 0.9891\n", - "Epoch 338/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0591 - accuracy: 0.9848\n", - "Epoch 339/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0614 - accuracy: 0.9840\n", - "Epoch 340/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0460 - accuracy: 0.9920\n", - "Epoch 341/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0394 - accuracy: 0.9869\n", - "Epoch 342/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0384 - accuracy: 0.9884\n", - "Epoch 343/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0785 - accuracy: 0.9826\n", - "Epoch 344/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0477 - accuracy: 0.9898\n", - "Epoch 345/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0410 - accuracy: 0.9877\n", - "Epoch 346/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0566 - accuracy: 0.9913\n", - "Epoch 347/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0240 - accuracy: 0.9949\n", - "Epoch 348/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0483 - accuracy: 0.9906\n", - "Epoch 349/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0694 - accuracy: 0.9833\n", - "Epoch 350/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0597 - accuracy: 0.9855\n", - "Epoch 351/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0414 - accuracy: 0.9898\n", - "Epoch 352/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0459 - accuracy: 0.9877\n", - "Epoch 353/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0360 - accuracy: 0.9884\n", - "Epoch 354/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0451 - accuracy: 0.9848\n", - "Epoch 355/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0499 - accuracy: 0.9855\n", - "Epoch 356/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0388 - accuracy: 0.9906\n", - "Epoch 357/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0530 - accuracy: 0.9898\n", - "Epoch 358/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0581 - accuracy: 0.9862\n", - "Epoch 359/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0409 - accuracy: 0.9855\n", - "Epoch 360/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0514 - accuracy: 0.9906\n", - "Epoch 361/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0604 - accuracy: 0.9869\n", - "Epoch 362/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0501 - accuracy: 0.9862\n", - "Epoch 363/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0581 - accuracy: 0.9848\n", - "Epoch 364/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0421 - accuracy: 0.9833\n", - "Epoch 365/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0429 - accuracy: 0.9884\n", - "Epoch 366/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0494 - accuracy: 0.9840\n", - "Epoch 367/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0387 - accuracy: 0.9862\n", - "Epoch 368/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0400 - accuracy: 0.9906\n", - "Epoch 369/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0612 - accuracy: 0.9898\n", - "Epoch 370/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0354 - accuracy: 0.9869\n", - "Epoch 371/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0548 - accuracy: 0.9855\n", - "Epoch 372/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0484 - accuracy: 0.9848\n", - "Epoch 373/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0286 - accuracy: 0.9898\n", - "Epoch 374/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0433 - accuracy: 0.9877\n", - "Epoch 375/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0243 - accuracy: 0.9920\n", - "Epoch 376/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0343 - accuracy: 0.9906\n", - "Epoch 377/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0470 - accuracy: 0.9833\n", - "Epoch 378/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0390 - accuracy: 0.9898\n", - "Epoch 379/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0291 - accuracy: 0.9891\n", - "Epoch 380/500\n", - "345/345 [==============================] - 5s 14ms/step - loss: 0.0982 - accuracy: 0.9862\n", - "Epoch 381/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0584 - accuracy: 0.9826\n", - "Epoch 382/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0480 - accuracy: 0.9884\n", - "Epoch 383/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0501 - accuracy: 0.9884\n", - "Epoch 384/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0619 - accuracy: 0.9855\n", - "Epoch 385/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0244 - accuracy: 0.9927\n", - "Epoch 386/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0527 - accuracy: 0.9877\n", - "Epoch 387/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0684 - accuracy: 0.9833\n", - "Epoch 388/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0304 - accuracy: 0.9891\n", - "Epoch 389/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0476 - accuracy: 0.9877\n", - "Epoch 390/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0688 - accuracy: 0.9869\n", - "Epoch 391/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0428 - accuracy: 0.9862\n", - "Epoch 392/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0323 - accuracy: 0.9920\n", - "Epoch 393/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0374 - accuracy: 0.9913\n", - "Epoch 394/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0479 - accuracy: 0.9906\n", - "Epoch 395/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0425 - accuracy: 0.9884\n", - "Epoch 396/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0283 - accuracy: 0.9913\n", - "Epoch 397/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0345 - accuracy: 0.9891\n", - "Epoch 398/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0500 - accuracy: 0.9855\n", - "Epoch 399/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0418 - accuracy: 0.9898\n", - "Epoch 400/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0260 - accuracy: 0.9927\n", - "Epoch 401/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0414 - accuracy: 0.9920\n", - "Epoch 402/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0496 - accuracy: 0.9913\n", - "Epoch 403/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0603 - accuracy: 0.9862\n", - "Epoch 404/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0551 - accuracy: 0.9869\n", - "Epoch 405/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0447 - accuracy: 0.9913\n", - "Epoch 406/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0531 - accuracy: 0.9869\n", - "Epoch 407/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0558 - accuracy: 0.9877\n", - "Epoch 408/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0438 - accuracy: 0.9906\n", - "Epoch 409/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0380 - accuracy: 0.9877\n", - "Epoch 410/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0451 - accuracy: 0.9927\n", - "Epoch 411/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0216 - accuracy: 0.9927\n", - "Epoch 412/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0228 - accuracy: 0.9920\n", - "Epoch 413/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0721 - accuracy: 0.9862\n", - "Epoch 414/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0600 - accuracy: 0.9891\n", - "Epoch 415/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0401 - accuracy: 0.9898\n", - "Epoch 416/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0429 - accuracy: 0.9884\n", - "Epoch 417/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0374 - accuracy: 0.9891\n", - "Epoch 418/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0248 - accuracy: 0.9913\n", - "Epoch 419/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0264 - accuracy: 0.9906\n", - "Epoch 420/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0773 - accuracy: 0.9804\n", - "Epoch 421/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0392 - accuracy: 0.9891\n", - "Epoch 422/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0453 - accuracy: 0.9898\n", - "Epoch 423/500\n", - "345/345 [==============================] - 4s 12ms/step - loss: 0.0418 - accuracy: 0.9855\n", - "Epoch 424/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0367 - accuracy: 0.9906\n", - "Epoch 425/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0413 - accuracy: 0.9898\n", - "Epoch 426/500\n", - "345/345 [==============================] - 5s 13ms/step - loss: 0.0634 - accuracy: 0.9877\n", - "Epoch 427/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0246 - accuracy: 0.9920\n", - "Epoch 428/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0326 - accuracy: 0.9920\n", - "Epoch 429/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0350 - accuracy: 0.9877\n", - "Epoch 430/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0374 - accuracy: 0.9877\n", - "Epoch 431/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0493 - accuracy: 0.9884\n", - "Epoch 432/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0426 - accuracy: 0.9891\n", - "Epoch 433/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0344 - accuracy: 0.9935\n", - "Epoch 434/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0473 - accuracy: 0.9891\n", - "Epoch 435/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0584 - accuracy: 0.9884\n", - "Epoch 436/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0469 - accuracy: 0.9906\n", - "Epoch 437/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0295 - accuracy: 0.9913\n", - "Epoch 438/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0438 - accuracy: 0.9898\n", - "Epoch 439/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0395 - accuracy: 0.9913\n", - "Epoch 440/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0452 - accuracy: 0.9891\n", - "Epoch 441/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0375 - accuracy: 0.9906\n", - "Epoch 442/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0380 - accuracy: 0.9920\n", - "Epoch 443/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0517 - accuracy: 0.9840\n", - "Epoch 444/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0439 - accuracy: 0.9898\n", - "Epoch 445/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0395 - accuracy: 0.9877\n", - "Epoch 446/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0421 - accuracy: 0.9869\n", - "Epoch 447/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0416 - accuracy: 0.9884\n", - "Epoch 448/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0268 - accuracy: 0.9942\n", - "Epoch 449/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0396 - accuracy: 0.9884\n", - "Epoch 450/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0680 - accuracy: 0.9840\n", - "Epoch 451/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0608 - accuracy: 0.9855\n", - "Epoch 452/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0504 - accuracy: 0.9913\n", - "Epoch 453/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0352 - accuracy: 0.9891\n", - "Epoch 454/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0314 - accuracy: 0.9906\n", - "Epoch 455/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0230 - accuracy: 0.9927\n", - "Epoch 456/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0459 - accuracy: 0.9891\n", - "Epoch 457/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0344 - accuracy: 0.9884\n", - "Epoch 458/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0632 - accuracy: 0.9869\n", - "Epoch 459/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0311 - accuracy: 0.9927\n", - "Epoch 460/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0499 - accuracy: 0.9898\n", - "Epoch 461/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0686 - accuracy: 0.9855\n", - "Epoch 462/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0527 - accuracy: 0.9877\n", - "Epoch 463/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0168 - accuracy: 0.9942\n", - "Epoch 464/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0298 - accuracy: 0.9935\n", - "Epoch 465/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0466 - accuracy: 0.9869\n", - "Epoch 466/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0384 - accuracy: 0.9935\n", - "Epoch 467/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0321 - accuracy: 0.9920\n", - "Epoch 468/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0462 - accuracy: 0.9869\n", - "Epoch 469/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0362 - accuracy: 0.9913\n", - "Epoch 470/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0364 - accuracy: 0.9927\n", - "Epoch 471/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0224 - accuracy: 0.9913\n", - "Epoch 472/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0195 - accuracy: 0.9978\n", - "Epoch 473/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0651 - accuracy: 0.9855\n", - "Epoch 474/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0331 - accuracy: 0.9891\n", - "Epoch 475/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0609 - accuracy: 0.9877\n", - "Epoch 476/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0417 - accuracy: 0.9906\n", - "Epoch 477/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0382 - accuracy: 0.9927\n", - "Epoch 478/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0395 - accuracy: 0.9927\n", - "Epoch 479/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0332 - accuracy: 0.9927\n", - "Epoch 480/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0367 - accuracy: 0.9884\n", - "Epoch 481/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0277 - accuracy: 0.9906\n", - "Epoch 482/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0381 - accuracy: 0.9906\n", - "Epoch 483/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0330 - accuracy: 0.9956\n", - "Epoch 484/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0673 - accuracy: 0.9833\n", - "Epoch 485/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0403 - accuracy: 0.9942\n", - "Epoch 486/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0552 - accuracy: 0.9855\n", - "Epoch 487/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0249 - accuracy: 0.9898\n", - "Epoch 488/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0204 - accuracy: 0.9906\n", - "Epoch 489/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0577 - accuracy: 0.9862\n", - "Epoch 490/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0462 - accuracy: 0.9869\n", - "Epoch 491/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0247 - accuracy: 0.9891\n", - "Epoch 492/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0381 - accuracy: 0.9898\n", - "Epoch 493/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0337 - accuracy: 0.9913\n", - "Epoch 494/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0269 - accuracy: 0.9935\n", - "Epoch 495/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0278 - accuracy: 0.9935\n", - "Epoch 496/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0301 - accuracy: 0.9913\n", - "Epoch 497/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0622 - accuracy: 0.9898\n", - "Epoch 498/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0512 - accuracy: 0.9891\n", - "Epoch 499/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0301 - accuracy: 0.9927\n", - "Epoch 500/500\n", - "345/345 [==============================] - 4s 13ms/step - loss: 0.0362 - accuracy: 0.9913\n" - ] - } - ], - "source": [ - "Batch_Size = 4\n", - "Shift_Range = 1\n", - "Brightness_Range = 0.2\n", - "Rotation_Angle = 5\n", - "ZoomRange = 0.3\n", - "\n", - "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], \n", - " height_shift_range=[-Shift_Range,Shift_Range],\n", - " brightness_range=[1-Brightness_Range,1+Brightness_Range],\n", - " zoom_range=[1-ZoomRange, 1+ZoomRange],\n", - " rotation_range=Rotation_Angle)\n", - "\n", - "if (Training_Percentage > 0):\n", - " train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n", - " validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n", - " history = model.fit(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n", - "else:\n", - " train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n", - " history = model.fit(train_iterator, epochs = Epoch_Anz)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Learing result\n", - " \n", - "* Visualization of the training and validation results" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "loss_ges = np.append(loss_ges, history.history['loss'])\n", - "plt.semilogy(history.history['loss'])\n", - "\n", - "if (Training_Percentage > 0):\n", - " val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n", - " plt.semilogy(history.history['val_loss'])\n", - "\n", - "plt.title('model loss')\n", - "plt.ylabel('loss')\n", - "plt.xlabel('epoch')\n", - "plt.legend(['train','eval'], loc='upper left')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check the model by hand\n", - "\n", - "* The following code uses the trained model to check the deviation for each picture.\n", - "* x-axis walks through each pixel, y-axis shows the result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check each image for expected and deviation\n", - "* setting the switch \"only_deviation = true\" will only print the images for which the classification and the CNN-result deviates\n", - "\n", - "The output contains the following information:\n", - "\n", - "| Filename | Expected Category | Predicted Category |\n", - "|------------- |:-----------------------------:|--------------|\n", - "| ziffer_sortiert_resize_NaN/5\\Ziffer_4_0034.jpg | 4 | -1 |\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-21 16:55:57.926647: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Input_dir='ziffer_sortiert_resize'\n", - "res = []\n", - "only_deviation = True\n", - "show_wrong_image = True\n", - "\n", - "files = glob.glob(Input_dir + '/*.jpg')\n", - "\n", - "for aktfile in files:\n", - " base = os.path.basename(aktfile)\n", - " target = base[0:1]\n", - " if target == \"N\":\n", - " zw1 = -1\n", - " else:\n", - " zw1 = int(target)\n", - " expected_class = zw1\n", - " image_in = Image.open(aktfile)\n", - " test_image = np.array(image_in, dtype=\"float32\")\n", - " img = np.reshape(test_image,[1,32,20,3])\n", - " classes = np.argmax(model.predict(img, verbose=0), axis=-1)\n", - " classes = classes[0]\n", - " if classes == 10: \n", - " classes = -1\n", - " zw2 = classes\n", - " zw3 = zw2 - zw1\n", - " res.append(np.array([zw1, zw2, zw3]))\n", - " if only_deviation == True:\n", - " if str(classes) != str(expected_class):\n", - " print(aktfile + \" \" + str(expected_class) + \" \" + str(classes))\n", - " if show_wrong_image == True:\n", - " display(image_in)\n", - " else:\n", - " print(aktfile + \" \" + aktsubdir + \" \" + str(classes))\n", - " \n", - "\n", - "res = np.asarray(res)\n", - "\n", - "\n", - "plt.plot(res[:,0])\n", - "plt.plot(res[:,1])\n", - "plt.title('Result')\n", - "plt.ylabel('Digital Value')\n", - "plt.xlabel('#Picture')\n", - "plt.legend(['real','model'], loc='upper left')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save the model\n", - "\n", - "* Save the model to the file with the \"h5\" file format" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "export() takes 2 positional arguments but 3 were given", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtf_saved_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# replace tf.saved_model.save with this line\u001b[39;00m\n", - "\u001b[0;31mTypeError\u001b[0m: export() takes 2 positional arguments but 3 were given" - ] - } - ], - "source": [ - "#model.export(\"test\", \"tf_saved_model\") # replace tf.saved_model.save with this line" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dig-class11_1801_s2.tflite\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-21 17:22:44.265619: E tensorflow/core/framework/node_def_util.cc:676] NodeDef mentions attribute debug_name which is not in the op definition: Op resource:resource; attr=container:string,default=\"\"; attr=shared_name:string,default=\"\"; attr=dtype:type; attr=shape:shape; attr=allowed_devices:list(string),default=[]; is_stateful=true> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node batch_normalization/moving_mean}}\n", - "2024-07-21 17:22:44.276177: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format.\n", - "2024-07-21 17:22:44.276186: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency.\n", - "2024-07-21 17:22:44.276496: I tensorflow/cc/saved_model/reader.cc:45] Reading SavedModel from: test\n", - "2024-07-21 17:22:44.277069: I tensorflow/cc/saved_model/reader.cc:91] Reading meta graph with tags { serve }\n", - "2024-07-21 17:22:44.277073: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: test\n", - "2024-07-21 17:22:44.278151: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:375] MLIR V1 optimization pass is not enabled\n", - "2024-07-21 17:22:44.279102: I tensorflow/cc/saved_model/loader.cc:231] Restoring SavedModel bundle.\n", - "2024-07-21 17:22:44.309888: E tensorflow/core/framework/node_def_util.cc:676] NodeDef mentions attribute debug_name which is not in the op definition: Op resource:resource; attr=container:string,default=\"\"; attr=shared_name:string,default=\"\"; attr=dtype:type; attr=shape:shape; attr=allowed_devices:list(string),default=[]; is_stateful=true> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node Variable}}\n", - "2024-07-21 17:22:44.311639: I tensorflow/cc/saved_model/loader.cc:215] Running initialization op on SavedModel bundle at path: test\n", - "2024-07-21 17:22:44.321600: I tensorflow/cc/saved_model/loader.cc:314] SavedModel load for tags { serve }; Status: success: OK. Took 45103 microseconds.\n", - "2024-07-21 17:22:44.332106: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n" - ] - }, - { - "data": { - "text/plain": [ - "357348" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \".tflite\"\n", - "\n", - "converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n", - "tflite_model = converter.convert()\n", - "open(FileName, \"wb\").write(tflite_model)\n", - "print(FileName)\n", - "Path(FileName).stat().st_size" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-21 17:22:47.808765: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format.\n", - "2024-07-21 17:22:47.808778: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency.\n", - "2024-07-21 17:22:47.808930: I tensorflow/cc/saved_model/reader.cc:45] Reading SavedModel from: test\n", - "2024-07-21 17:22:47.809442: I tensorflow/cc/saved_model/reader.cc:91] Reading meta graph with tags { serve }\n", - "2024-07-21 17:22:47.809447: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: test\n", - "2024-07-21 17:22:47.811249: I tensorflow/cc/saved_model/loader.cc:231] Restoring SavedModel bundle.\n", - "2024-07-21 17:22:47.844302: I tensorflow/cc/saved_model/loader.cc:215] Running initialization op on SavedModel bundle at path: test\n", - "2024-07-21 17:22:47.854692: I tensorflow/cc/saved_model/loader.cc:314] SavedModel load for tags { serve }; Status: success: OK. Took 45764 microseconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dig-class11_1801_s2_q.tflite\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "fully_quantize: 0, inference_type: 6, input_inference_type: FLOAT32, output_inference_type: FLOAT32\n" - ] - }, - { - "data": { - "text/plain": [ - "97856" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \"_q.tflite\"\n", - "\n", - "import tensorflow as tf\n", - "\n", - "def representative_dataset():\n", - " for n in range(x_data[0].size):\n", - " data = np.expand_dims(x_data[5], axis=0)\n", - " yield [data.astype(np.float32)]\n", - " \n", - "converter2 = tf.lite.TFLiteConverter.from_saved_model(\"test\")\n", - "converter2.representative_dataset = representative_dataset\n", - "converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n", - "converter2.representative_dataset = representative_dataset\n", - "tflite_quant_model = converter2.convert()\n", - "\n", - "open(FileName, \"wb\").write(tflite_quant_model)\n", - "print(FileName)\n", - "Path(FileName).stat().st_size" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Checking the images shows, that this are border line images, which can be interpreted as a good digit or a faulty one." - ] - } - ], - "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": "3.9.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Train_CNN_Digital-Readout-Small-v0.ipynb b/Unused Models/Train_CNN_Digital-Readout-Small-v0.ipynb similarity index 100% rename from Train_CNN_Digital-Readout-Small-v0.ipynb rename to Unused Models/Train_CNN_Digital-Readout-Small-v0.ipynb diff --git a/Train_CNN_Digital-Readout-Small-v1.ipynb b/Unused Models/Train_CNN_Digital-Readout-Small-v1.ipynb similarity index 100% rename from Train_CNN_Digital-Readout-Small-v1.ipynb rename to Unused Models/Train_CNN_Digital-Readout-Small-v1.ipynb diff --git a/Train_CNN_Digital-Readout-Small-v3.ipynb b/Unused Models/Train_CNN_Digital-Readout-Small-v3.ipynb similarity index 100% rename from Train_CNN_Digital-Readout-Small-v3.ipynb rename to Unused Models/Train_CNN_Digital-Readout-Small-v3.ipynb diff --git a/Versions.md b/Versions.md index b3d773d78..bf672e30d 100644 --- a/Versions.md +++ b/Versions.md @@ -1,4 +1,9 @@ ## Version +#### 19.0.0 Current Version (2024-12-27) + +* Remove similar images **after** resize --> significantly reduced number of images +* Update Tensorflow training environment to v2.18 + #### 13.3.0 Current Version (2021-11-23) * New images for 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