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"source": [ "Modelo de clasificación de imágenes\n", "===================================" ] }, { "cell_type": "markdown", "metadata": { "id": "j3576kEYCwpp" }, "source": [ "## Introducción" ] }, { "cell_type": "markdown", "metadata": { "id": "o-aoxMmlCwpp" }, "source": [ "La clasificación de objetos es la tarea por la cual un modelo de aprendizaje automático asigna una o varias etiquetas a diferentes imágenes dependiendo del contenido de la misma. Esta tarea se volvió muy popular en 2012 en el contexto de aprendizaje automático ya que fué la primera tarea en alcanzar una performance sustancialmente mayor que con técnicas clásicas.\n", "\n", "Desde aquel entonces, el progreso en este campo ha sido tan grande que hoy en día disponemos de modelos de aprendizaje automático con performance superiores a las de un humano. Un ejemplo clásico es el de clasificación de razas de perror donde los modelos alcanzan a distinguir razas que resultan imposibles distinguir para incluso personas altamente experimentadas en esto." ] }, { "cell_type": "markdown", "metadata": { "id": "TghjzJl0wKId" }, "source": [ "### Preparación del ambiente" ] }, { "cell_type": "markdown", "metadata": { "id": "Ez9d-jEjDfeV" }, "source": [ "Intalamos las librerias necesarias" ] }, { "cell_type": "code", "metadata": { "id": "mrs7T2yDDfeW", "outputId": "9ed68cdc-a0ec-4d00-aabd-654cae529785", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "!wget https://raw.githubusercontent.com/santiagxf/M72109/refs/heads/master/docs/vision/neural/cnn/classification/code/cnn_class.txt \\\n", " --quiet --no-clobber\n", "!pip install -r cnn_class.txt --quiet" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[31mERROR: Could not open requirements file: [Errno 2] No such file or directory: 'cnn_class.txt'\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "avCIgEVTCwpt" }, "source": [ "### Sobre el conjunto de datos de este ejemplo" ] }, { "cell_type": "markdown", "metadata": { "id": "ZJ1mR6CBCwpt" }, "source": [ "Para ejemplificar esta técnica utilizaremos un conjunto de datos muy popular llamado CIFAR-10. CIFAR-10 es un conjunto de datos que consiste en 60.000 imagenes a color de 32x32 agrupadas en 10 clases, con 6000 imagenes cada una. Hay alrededor de 50000 imagenes para entrenamiento y 10000 para testing.\n", "\n", "Las categorias son:\n", " - airplane\n", " - automobile\n", " - bird\n", " - cat\n", " - deer\n", " - dog\n", " - frog\n", " - horse\n", " - ship\n", " - truck" ] }, { "cell_type": "markdown", "metadata": { "id": "Ehq4pYRGCwpu" }, "source": [ "Podemos cargar este conjunto de datos facilmente utilizando `tensorflow-datasets`:" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 148, "referenced_widgets": [ "66c36b34f0d94cd691b76e920bcd72dc", "f9aea0e78ae74316a82575c09d4d0bd9", "888a66dd95ee4489aa348620c18aa90d", "9b3c70e9f0404a9fa58e19668fa3ef00", "ef6e5846493240598d58a28f3b13165c", "1cbbef39c42b4c419d1e6ff8f161747b", "74b175a8519343eb899d614e617a9849", "23f0aaa29f8d49df87ac7f462500b816", "59741ad84dcf4c0a82a4e2bc0afb1149", "ff30b3fdad3b48fc8870ed66b08b8b93", "5864a9a5033644859e303877907ba498", "615514a6b0614d9aae43e618f087166b", "eb9aef413d6641b59c966884da1e3e61", "4ed34fb6d85840f1a704a567533bfe25", "5ea136443b954f5997019ae6d30b0649", "49765402e3e3467c84556d378d5f9676", "62e90afc75ae482e8fe3f726a0584b05", "9dba7396a824471ca248b643e675586a", "85729949c9784df389f5ffbeae3a1abb", "b9b05de7f9dc4eefb605c76a6958e494", "b24e57e774d24fe7a8112083f4a7793a", "81ad4e6096f94b3884bae98c4dc70e88", "6b3a699d44b94d3ba049e393783e1b46", "6579ed7a24c6445fa74c29fbdd3ee1db", "57a270a7d352418f9bb3d4fd79e9f50f", "01a449e9ca31434d9b3cda24b456ec4f", "8822643a209b45f3afd3cd225711f21d", "c98602e49a2d4d06b1f0920dfd3d20a7", "3352617a4bba4a9893f14ff6d551f67b", "39f0bb4fcc5b48189fd6b25b7d2f56fa", "39c71c03db58424187fbf1023dd0dd8e", "63f378b1688646d4bd2a0eb08a50c53f", "f509beeb98a841baab20346075d62da7", "12e640b20c9b400c819a39b9d6badce2", "866431bb4dcb4c9793811352fa7d4e16", "b5bfca1cbeae4bd1b17e9b67ba6f96db", "7003835493fc4da9a567a0e8f8c47437", "62cb730497ff484c85ce101d51857a04", "7ebc051c372c4142b1714eea19a8ab21", "93c8ac6430db4841b26a1e1b171aa6e9", "412426dafed2455d95f17173beec645a", "5f29590b7a5148629e663d3941343acd", "3aa9fdc36fee419fa8c59647fec21b4a", "480ec58f34044b7ba14f406c380b9407", "35ec8a0478bb4d6882932159330cbed8", "468e7f09add94aea83b03240c348b8f0", "762622503d7d461988e4a1c637a7d70d", "650bb0467a44476ba43f8ba684f54afc", "6c54d06b6364452693b1a9d1e1514691", "9fb1de492ed245f4b4e82e54c6b9c655", "493fd43f7df344eca75b6f5577f0a6e2", "e5af53e1a3b44f49b8cab4aa1dd3da9e", "f0e41d735c6747a0a2eb85e8f71c4959", "c2d36754c8d14d079731de5e17aee390", "4a00911305f349f4945c599c9fdd1b07", "c905a876bf5c424f8b467aeba0f48401", "7fa7efd315df400f8a599fb55fefad9e", "db05e6ca03b846e6b4b21a9a1ef9aa18", "71822c374e9f4e06b5b2b703f9c2b796", "2cefbb83d07b4ab5b73e2e4e6c9ac5cb", "172175ac1f474b0aa79538c4febea53b", "ba0182a605e94b16b8112290f6d9167c", "deedc419140140a7b437394e65b534a8", "f39162261f64448d95354874a1876164", "58b897ee6b3d4c29a10e019d56d35e36", "3ce097a357a34371a9fbb74be9b9d41e", "473e79010cce491491a317f5aa5b2968", "811b01936a994f7c90e69acdae828e90", "cf8613baa2b3419fae384ca77d912b3c", "a1a460600e7c413588b8957070cbbcd2", "c8e01a263b514b798c93e5738c8a7134", "a85d579561b6468cacf5eb03e1df8ac3", "2c3ee37303dd4a53bda1bcc47290e980", "a1ebd86a65ea4beb8dcefeb7388e0e92", "40fd17bf0b2e4eb2a41e5ba854c64d67", "4f58afb3c457413793b6085002eb99a0", "631d8ce03aba4dee91a5ec5dc863bcad", "3444a216754f47c8875ebeda8c2d1cff", "0f4c20dcac0b495ab8e0a43e7bc45459", "12c8512aaad247389cf2b4f57811048c", "86a5012389724edca51199a403c75e1a", "88299148c9fd48128276d7305237cd97", "3c9adb534927476fa7d25954d7c0cb45", "93b7cfe5573a496b8feda8033483ceed", "ec500ba7a06443d9ac2f94df4d48d424", "4b880937d9b347e1931ba5f7f175451e", "24dcd47b75214ad8bac4235dc1f0d376", "ef1e94bc09a94526804c3d6453dc4c3b" ] }, "id": "-ToQd6ybDfef", "outputId": "34dda09a-0174-4302-c8e4-f63ea815feeb" }, "source": [ "import tensorflow as tf\n", "import tensorflow.keras as keras\n", "import tensorflow_datasets as tfds\n", "\n", "ds, info = tfds.load('cifar10', split='train', shuffle_files=True, with_info=True)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset 162.17 MiB (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Dl Completed...: 0 url [00:00, ? url/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "66c36b34f0d94cd691b76e920bcd72dc" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Dl Size...: 0 MiB [00:00, ? MiB/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "615514a6b0614d9aae43e618f087166b" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Extraction completed...: 0 file [00:00, ? file/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6b3a699d44b94d3ba049e393783e1b46" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating splits...: 0%| | 0/2 [00:00" ], "image/png": 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\n" }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "
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\n" }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "markdown", "metadata": { "id": "2U9-305aDfeq" }, "source": [ "## Construcción de una red neuronal convolucional" ] }, { "cell_type": "markdown", "metadata": { "id": "c2ydVcyxCwpx" }, "source": [ "Antes de comenzar necesitaremos verificar que tenemos el runtime correcto en nuestro ambiente. Esta tarea se beneficiará mucho de una GPU." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ABQ6LdkQDfeZ", "outputId": "93c0d760-296b-4d0c-ddc4-06699f53ee7e" }, "source": [ "import tensorflow as tf\n", "print(\"GPUs disponibles: \", len(tf.config.experimental.list_physical_devices('GPU')))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPUs disponibles: 1\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "PziBXrSoDfeq" }, "source": [ "### Entendiendo una estructura basada en CNN estandar" ] }, { "cell_type": "markdown", "metadata": { "id": "-vp215uiDfer" }, "source": [ "En este primer intento de solución del problema, generaremos una red basada en CNNs típica. En este caso la misma constará de:\n", " - 2 capas de CNN\n", " - 1 capas de Pooling\n", " - 1 capa de regularización\n", " - Una red de 2 capas fully-connected" ] }, { "cell_type": "markdown", "metadata": { "id": "ABF9i2EzDfev" }, "source": [ "Instanciamos nuestro modelo y verificamos su arquitectura" ] }, { "cell_type": "code", "metadata": { "id": "4QC17MxkDfes" }, "source": [ "model = keras.models.Sequential()\n", "model.add(keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)))\n", "model.add(keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu'))\n", "model.add(keras.layers.MaxPooling2D((2, 2)))\n", "model.add(keras.layers.Flatten())\n", "model.add(keras.layers.Dropout(0.5))\n", "model.add(keras.layers.Dense(64, activation='relu'))\n", "model.add(keras.layers.Dense(10, activation='softmax'))\n", "\n", "model.compile(optimizer='adam',\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n", " metrics=['accuracy'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X46DBEQnDfev", "outputId": "7269aea6-32f9-4b7f-fd9a-d15249fb2f1d" }, "source": [ "model.summary()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " conv2d (Conv2D) (None, 32, 32, 32) 896 \n", " \n", " conv2d_1 (Conv2D) (None, 32, 32, 32) 9248 \n", " \n", " max_pooling2d (MaxPooling2 (None, 16, 16, 32) 0 \n", " D) \n", " \n", " flatten (Flatten) (None, 8192) 0 \n", " \n", " dropout (Dropout) (None, 8192) 0 \n", " \n", " dense (Dense) (None, 64) 524352 \n", " \n", " dense_1 (Dense) (None, 10) 650 \n", " \n", "=================================================================\n", "Total params: 535146 (2.04 MB)\n", "Trainable params: 535146 (2.04 MB)\n", "Non-trainable params: 0 (0.00 Byte)\n", "_________________________________________________________________\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "cfrHfmyPDfey" }, "source": [ "Antes de comenzar el entrenamiento, configuraremos nuestro conjunto de datos para una tarea supervisada. Esto lo hacemos especificando el parametro *as_supervised=True* lo cual nos da la posibilidad de acceder a las anotaciones del conjunto de datos." ] }, { "cell_type": "code", "metadata": { "id": "rF2k-8vvDfey" }, "source": [ "(training_set, validation_set) = tfds.load('cifar10', split=['train', 'test'], as_supervised=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "rzxxC_l2Cwp0" }, "source": [ "Configuramos los parametros de entrenamiento:" ] }, { "cell_type": "code", "metadata": { "id": "9ejNMk7MDfe1" }, "source": [ "BATCH_SIZE=64\n", "EPOCHS = 3\n", "\n", "training_set_batch = training_set.batch(batch_size=BATCH_SIZE).cache().repeat()\n", "validation_set_batch = validation_set.batch(batch_size=BATCH_SIZE).cache().repeat()\n", "train_size = info.splits['train'].num_examples\n", "test_size = info.splits['test'].num_examples" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "wUr_78wVDfe4" }, "source": [ "Comenzamos el entrenamiento" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GKNXfxsGDfe7", "scrolled": true, "outputId": "bca6ae0e-33f8-423a-d0c1-69e13c73cd45" }, "source": [ "history = model.fit(training_set_batch,\n", " epochs=EPOCHS,\n", " steps_per_epoch=train_size,\n", " validation_data=validation_set_batch,\n", " validation_steps=test_size)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/3\n", "50000/50000 [==============================] - 507s 10ms/step - loss: 0.6684 - accuracy: 0.7716 - val_loss: 1.2468 - val_accuracy: 0.6616\n", "Epoch 2/3\n", "50000/50000 [==============================] - 463s 9ms/step - loss: 0.3743 - accuracy: 0.8786 - val_loss: 1.3201 - val_accuracy: 0.6591\n", "Epoch 3/3\n", "50000/50000 [==============================] - 465s 9ms/step - loss: 0.3305 - accuracy: 0.8969 - val_loss: 1.4154 - val_accuracy: 0.6689\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "6z33mGnmCwp1" }, "source": [ "> Dado que estamos utilizando un objeto de tipo `tf.Dataset` como argumento de `validation_data` de `Model.fit`, Keras no sabe por cuantos etapas hay que validar. Por este motivo el argumento `validation_steps` fue configurado." ] }, { "cell_type": "code", "metadata": { "id": "p1-69LFhCwp1", "colab": { "base_uri": "https://localhost:8080/", "height": 573 }, "outputId": "1c77b898-4c52-406b-a3e7-644ed0a9566e" }, "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot_history(history):\n", " # summarize history for accuracy\n", " plt.plot(history.history['accuracy'])\n", " plt.plot(history.history['val_accuracy'])\n", " plt.title('model accuracy')\n", " plt.ylabel('accuracy')\n", " plt.xlabel('epoch')\n", " plt.legend(['train', 'test'], loc='upper left')\n", " plt.show()\n", "\n", " # summarize history for loss\n", " plt.plot(history.history['loss'])\n", " plt.plot(history.history['val_loss'])\n", " plt.title('model loss')\n", " plt.ylabel('loss')\n", " plt.xlabel('epoch')\n", " plt.legend(['train', 'test'], loc='upper left')\n", " plt.show()\n", "\n", "plot_history(history)" ], "execution_count": null, "outputs": [ { "output_type": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "BMmIgUQXCwp1" }, "source": [ "### Apilando estructuras de CNN" ] }, { "cell_type": "markdown", "metadata": { "id": "PSxGn9IPCwp1" }, "source": [ "Verifiquemos los resultados si repetimos la estructura básica de la red para generar características de mayor nivel. En este segundo intento de solución del problema, generaremos una red basada en CNNs de 3 bloques concatenados. En este caso la misma constará de:\n", " - 3 bloques de:\n", " - 2 capas de CNN\n", " - 1 capas de Pooling\n", " - 1 capa de regularización\n", " - Una red de 2 capas fully-connected" ] }, { "cell_type": "markdown", "metadata": { "id": "TNNPNMtiCwp2" }, "source": [ "Instanciamos nuestro modelo y verificamos su arquitectura" ] }, { "cell_type": "code", "metadata": { "id": "_uHKE3woCwp1" }, "source": [ "model = keras.models.Sequential()\n", "# BLOQUE 1\n", "model.add(keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)))\n", "model.add(keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu'))\n", "model.add(keras.layers.MaxPooling2D((2, 2))) # Me quedo con 1/4\n", "\n", "# BLOQUE 2\n", "model.add(keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu'))\n", "model.add(keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu'))\n", "model.add(keras.layers.MaxPooling2D((2, 2)))\n", "\n", "# BLOQUE 3\n", "model.add(keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n", "model.add(keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n", "model.add(keras.layers.MaxPooling2D((2, 2)))\n", "\n", "# CLASIFICADOR NORMAL\n", "model.add(keras.layers.Flatten())\n", "model.add(keras.layers.Dropout(0.5))\n", "model.add(keras.layers.Dense(128, activation='relu'))\n", "model.add(keras.layers.Dense(10, activation='softmax'))\n", "\n", "model.compile(optimizer='adam',\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n", " metrics=['accuracy'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "scrolled": true, "id": "ulNPrl0UCwp2", "outputId": "d63be8cb-c452-4211-c954-cb1e86775545" }, "source": [ "model.summary()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential_1\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_2 (Conv2D) (None, 32, 32, 32) 896 \n", "_________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 32, 32, 32) 9248 \n", "_________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32) 0 \n", "_________________________________________________________________\n", "conv2d_4 (Conv2D) (None, 16, 16, 64) 18496 \n", "_________________________________________________________________\n", "conv2d_5 (Conv2D) (None, 16, 16, 64) 36928 \n", "_________________________________________________________________\n", "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64) 0 \n", "_________________________________________________________________\n", "conv2d_6 (Conv2D) (None, 8, 8, 128) 73856 \n", "_________________________________________________________________\n", "conv2d_7 (Conv2D) (None, 8, 8, 128) 147584 \n", "_________________________________________________________________\n", "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128) 0 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 2048) 0 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 2048) 0 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 128) 262272 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 10) 1290 \n", "=================================================================\n", "Total params: 550,570\n", "Trainable params: 550,570\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "zOjZ-VSiCwp2" }, "source": [ "Comenzamos el entrenamiento" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "scrolled": true, "id": "A63IzL1rCwp2", "outputId": "998e337f-1dba-45b0-c3a1-ccc2b4a734f8" }, "source": [ "history = model.fit(training_set_batch,\n", " epochs=EPOCHS,\n", " steps_per_epoch=train_size,\n", " validation_data=validation_set_batch,\n", " validation_steps=test_size)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/3\n", "50000/50000 [==============================] - 950s 19ms/step - loss: 0.4996 - accuracy: 0.8294 - val_loss: 0.9502 - val_accuracy: 0.7566\n", "Epoch 2/3\n", "50000/50000 [==============================] - 939s 19ms/step - loss: 0.3650 - accuracy: 0.8892 - val_loss: 1.0295 - val_accuracy: 0.7693\n", "Epoch 3/3\n", "50000/50000 [==============================] - 947s 19ms/step - loss: 0.3559 - accuracy: 0.9006 - val_loss: 1.2571 - val_accuracy: 0.7666\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "_OtiXeEXCwp2", "colab": { "base_uri": "https://localhost:8080/", "height": 573 }, "outputId": "7cf972e9-dd91-4e13-80d0-2223e6ecb9eb" }, "source": [ "plot_history(history)" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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A9Uvhkf9SqK+GrFw45SK48KtBn/4avF0GACUCkf6UaA4e7Gqp/A++FwzefupH4JI7g9G8ho1Id5Qi7SgRiLwfzY1wYHvwZG99DdRVw57NwUAuh3YFg7dPuTTo12fKZRq8XQY0JQKRjhIJOLw7qNzrqtsq+tbpmuBI3xPt18sbETT7TF8Ip16qwdtl0FAikHhxh4a6tgq9PqzgW4/st0F9bdBPf1ROHhRXQFF5UNkXlQevi8uDLp2Ly2FoYXo+k8gJUiKQzNJ4JKjI67a1r9xbp2vg2IH261g2FJUFlXt5JUyPVO7FFcF0/ih13iYZK2mJwMweAK4Edrr7zC7mG/DvwOXAYeAmd1+TrHgkAzQ3Be3yXTXV1G0Lpg/v6bze8DFBpV5yKkye13ZkX1wR/BSM1ahcEmvJPCP4EfBd4MFu5i8ApoQ/Hwa+H/6WOHKHQ7vbKvRo5d5yNH9ge+d2+aHFbUfu5R8Kp8eHFX158DtnaHo+k8ggkbRE4O7PmdnEHhZZCDzo7g6sMrMRZjbO3bcnKyZJo4a6bppqWo7sa6H5aPt1cvLaKvTJ8yKVe0Vb+7za5UVOWDqvEZQD2yKvq8MyJYLBprGhrVLvstmmuut2+cJx4ZH8mUG3CsXj247iiysgv0Tt8iIpMCguFpvZYmAxwIQJ6n0xpZqbglslu6rc68O7bQ7v7rze8NFBhV5yStCNQsvRfEuzTcFYyB4UXz+RjJfO/8QaYHzkdUVY1om73wfcB1BZWenJDy0mWtrlWyr0aOXecmTfU7t8UTmUzW67s6alrKgccvPS85lEpM/SmQiWAl8ys4cILhLX6fpAP2uob99c01WzTcd2+eyhbRdfJ10YaaqJNNvoKVmRjJLM20eXAPOAUjOrBr4B5AK4+73AMoJbRzcT3D766WTFkpFa2uVbK/eaDkf2NXC0vv06re3y5TBuVtjXfUX7Zhu1y4vETjLvGrrhOPMd+GKytj+otbbLd1G5txzRd9Uun18aVOwlp8CkC9o/EFVcDgUnqV1eRDpRrZBq7sFDTx37sYke2R/YDt7cfr2hRW1H7mWzOjz5qnZ5EXn/lAj6W0N9N/3YRO6Xb2pov05Lu3xROUw6v/1Try2Vf15xej6PiGQ8JYK+aDrac5t8XQ0crWu/jmUF7fJFLe3yV7R/IKqoAoaXql1eRNJGiaBFohkOvNfFnTWR6UO7Oq+XXxpU6CMnwcTz2z8QVVyhdnkRGfDiU0MdPRgMHNJd98P1tZ3b5YcUtrXDjzujQ3NNRdBjZe6w9HweEZF+Ep9EsPG38OjNba+zh7RV6BPP67ofG7XLi0gMxCcRnHwOfPynkX5sSiErK91RiYikXXwSQVFZ8CMiIu3okFhEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmepUIzOzLZlZkgf80szVmdlmygxMRkeTr7RnBX7t7PXAZMBL4JHBX0qISEZGU6W0isPD35cBP3H1dpExERAax3iaCV8xsOUEieNrMCoFE8sISEZFU6W0iuBm4DZjj7oeBXODTx1vJzOab2QYz22xmt3Uxf4KZrTCzP5vZ62Z2eZ+iFxGRE9bbRHA2sMHd95vZIuAOoK6nFcwsG7gHWABMB24ws+kdFrsDeNjdZwPXA9/rS/AiInLiepsIvg8cNrMzgFuALcCDx1lnLrDZ3be6+zHgIWBhh2UcKAqni4HaXsYjIiL9pLeJoMndnaAi/6673wMUHmedcmBb5HV1WBb1j8AiM6sGlgF/28t4RESkn/Q2ERwws9sJbht90syyCK4TnKgbgB+5ewXhHUnhe7djZovNbLWZrd61a1c/bFZERFr0NhF8HDhK8DzBe0AF8K/HWacGGB95XRGWRd0MPAzg7n8C8oDSjm/k7ve5e6W7V44ePbqXIYuISG/0KhGElf/PgGIzuxJocPfjXSN4GZhiZpPMbAjBxeClHZZ5F7gEwMymESQCHfKLiKRQb7uYuA54Cfgr4DrgRTP7y57Wcfcm4EvA08CbBHcHrTOzb5nZ1eFitwCfNbPXgCXATeG1CBERSRHrTb0bVtSXuvvO8PVo4Bl3PyPJ8XVSWVnpq1evTvVmRUQGNTN7xd0ru5rX22sEWS1JILSnD+uKiMgAltPL5X5rZk8TNN9AcPF4WXJCEhGRVOpVInD3W83sWuDcsOg+d38seWGJiEiq9PaMAHd/FHg0ibGIiEga9JgIzOwAQTcQnWYB7u5FXcwTEZFBpMdE4O7H60ZCREQGOd35IyISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNJTQRmNt/MNpjZZjO7rZtlrjOzKjNbZ2Y/T2Y8IiLSWU6y3tjMsoF7gEuBauBlM1vq7lWRZaYAtwPnuvs+MxuTrHhERKRryTwjmAtsdvet7n4MeAhY2GGZzwL3uPs+AHffmcR4RESkC8lMBOXAtsjr6rAsaiow1cyeN7NVZja/qzcys8VmttrMVu/atStJ4YqIxFO6LxbnAFOAecANwA/NbETHhdz9PnevdPfK0aNHpzhEEZHMlsxEUAOMj7yuCMuiqoGl7t7o7m8BGwkSg4iIpEgyE8HLwBQzm2RmQ4DrgaUdlnmc4GwAMyslaCramsSYRESkg6QlAndvAr4EPA28CTzs7uvM7FtmdnW42NPAHjOrAlYAt7r7nmTFJCIinZm7pzuGPqmsrPTVq1enOwwRkUHFzF5x98qu5qX7YrGIiKSZEoGISMwpEYiIxJwSgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMwpEYiIxJwSgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMwpEYiIxFxsEsH2uiM89udqNu04QHNicI3BICKSTDnpDiBVnt+8h6/88jUA8nKzmDauiJllxcwoK2JmeTFTxhYwNCc7zVGKiKRebBLBNbPK+EB5Metq61hbU8/a2joe/3MNP1n1DgC52caUMYXMLA8Sw4yyIqaNKyJ/SGx2kYjEVKyHqkwknHf3HmZtbR3rautZWxP83nvoGABZBpNHFwRnDWXFzCgvYsa4Yorzc/tl+yIiqdLTUJWxPtzNyjImlg5nYulwrvxgGQDuznv1DcFZQ5gYXnprL0+8Wtu63vhRw1qblWaUFzOzrJjRhUPT9TFERE5IrBNBV8yMccXDGFc8jEunj20t33PwaHDWUFvHupp61tXW8dTa91rnjy0ayoyyYmaGyWFGWRHlI4ZhZun4GCIivaZE0EslBUO5YOpoLpg6urWsvqGRqtp61tXWs66mjrW1dazcsJOWm5JG5Od2OHMoYmLJcLKylBxEZOBQIjgBRXm5nDW5hLMml7SWHTnWzPr36lkbJod1tfX81/Nvc6w5AcDwIdlMLytiRuSOpVPHFJCbHZs7eUVkgFEi6GfDhmQze8JIZk8Y2Vp2rCnBpp0HImcO9Ty8ehuHjzUDMCQni9NPKgyalsqDJHH6SYXk5ep2VhFJvljfNZROzQnnrd2HWBe5Y2ltTR31DU0AZGcZU8YUMD28Y2lmeTHTy4ooGKrcLSJ919NdQ0oEA4i7U73vSOuzDutq63ijpp7dB4+2LjOpdHhrk9KMsIlp1PAhaYxaRAYD3T46SJgZ40flM35UPvNnjmst31nf0HbWUFvHq9v285vXt7fOLx8xrPXMoSVJjC0aqjuWRKRXlAgGgTFFeYwpyuOi08e0lu0/fCy45hB5UvqZN3fQcoJXWjCk3QXpmWXFjB+l21lFpDMlgkFqRP4Qzj21lHNPLW0tO3S0iTe3t5w5BLe1Pv/cVprC+1kL83LaPSU9s6yYyaMLyNbtrCKxpkSQQYYPzaFy4igqJ45qLWtobGbjjgORpqV6frLqHY42BbezDsvN5vRxheEF6eCagzrgE4kXXSyOoabmBFt2HWrtQmNtbR1VtfUcPBrcsZSbbUwdWxi5KF3MtHGF6oBPZBDTXUNyXImE887ew+3uWFpbU8e+w41AWwd8M1sehgvPHoqHqQM+kcFAdw3JcWVlGZNKhzOpQwd82+saWs8c1tXWsWrrXh7vogO+6O2s6oBPZHBRIpBumRllI4ZRNmIYl804qbV8d0sHfDVBk9LaLjrgi/axpA74RAY2JQLps9KCoVw4dTQXdtEBX/TsYUVXHfCFTUrqgE9k4EhqIjCz+cC/A9nA/e5+VzfLXQs8Asxxd10AGIS664Dvzffa9876X3/sugO+lqYldcAnknpJu1hsZtnARuBSoBp4GbjB3as6LFcIPAkMAb50vESgi8WDW2sHfOFDcOtq66mqredIY1sHfNNOKmR6eDvrzLJiTlMHfCInLF0Xi+cCm919axjEQ8BCoKrDcv8T+N/ArUmMRQaIITlZ4RPPxVzHeKClA76Dkc736nny9VqWvPQu0NYBX/RJaXXAJ9J/kvmfVA5si7yuBj4cXcDMzgTGu/uTZtZtIjCzxcBigAkTJiQhVEmn7Czj1DGFnDqmkIWzyoG2Dviizzr8YeMuHl1T3bqeOuAT6R9pO6Qysyzg28BNx1vW3e8D7oOgaSi5kclAEO2Ab8EH2nfAtzbyrMOf3+2+A76Z5UGSGFOoDvhEepLMRFAD4bl/oCIsa1EIzARWhv+kJwFLzexqXTCW7owpyuPiojwuPr1tPOl9h45R1a6PJXXAJ9IXyUwELwNTzGwSQQK4HvhEy0x3rwNae0wzs5XAV5QEpK9GDu/cAd/BSAd8Ldce/rh5N81ddMDX0rSkDvgkrpKWCNy9ycy+BDxNcPvoA+6+zsy+Bax296XJ2rZIwdAc5kwcxZwuOuBbG7lj6cFV73As0gHftHHthwydOraQITm6nVUym/oaklhrbE6wZdfBtttZa+qp2t65A77ow3DqgE8GI3U6J9IHLR3wRZ+S7q4DvpZbWdUBnwx06nROpA+iHfBddUZbB3y1dQ3hE9L1VHXRAV/5iGEU5uWQZUZOtpFlRnZW+BOd7qYsy4ycLCMry8jOgpysrPA9IDsrK/ht1jYd/m5ZLztcNyerw7Z7sf2O67XGEVmmXVl2h/c0U3chg5gSgUgvmBnlI4ZR3kMHfBveO0BDYzMJd5oSTnPCg+lmpymR4GhTUNbsTnMCmhOJcBloSiRIJILfzQnC9RIkPHjgrm29gX0GH00WOdEE00Myar9MmACzCJfJItsiiTBS1rJepyQaSVQ9Jq8u1mtJ4L1dr3U6u3MSPV5CzjIGzJ1rSgQiJ6CrDviSyd1bk0M04TRHE0/CSYSvm8Ky6DLNkdeJcJlmd5qbg9+JDuu1vl/H90l0WD7yXolu1wsToBMuk+iyLJEIrt80JZpbP0vH7XX8vNHtRcsGsrZk2HIGCDnZbWeCrUkxTCA3zJ3AZ86f3O9xKBGIDCJmFh4hD4wjycGgUzLqKnl1TKLhmVyPSTSyTMczwESHs75mb5mmdZmOSTe6vS6TqDulBckZ60OJQEQyWlaWkYWhfgu7pxukRURiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmBl3vo2a2C3jnfa5eCuzux3D6y0CNCwZubIqrbxRX32RiXCe7e5d9oQy6RHAizGx1d92wptNAjQsGbmyKq28UV9/ELS41DYmIxJwSgYhIzMUtEdyX7gC6MVDjgoEbm+LqG8XVN7GKK1bXCEREpLO4nRGIiEgHSgQiIjGXMYnAzOab2QYz22xmt3Uxf6iZ/SKc/6KZTYzMuz0s32BmH01xXP/DzKrM7HUz+72ZnRyZ12xmr4Y/S1Mc101mtiuy/c9E5n3KzDaFP59KcVz/Folpo5ntj8xL5v56wMx2mtnabuabmX0njPt1MzszMi+Z+0+ME40AAAYkSURBVOt4cd0YxvOGmb1gZmdE5r0dlr9qZqtTHNc8M6uL/L2+HpnX43cgyXHdGolpbfidGhXOS8r+MrPxZrYirAfWmdmXu1gmud8vdx/0P0A2sAWYDAwBXgOmd1jmC8C94fT1wC/C6enh8kOBSeH7ZKcwrouA/HD6b1riCl8fTOP+ugn4bhfrjgK2hr9HhtMjUxVXh+X/Fngg2fsrfO8LgDOBtd3Mvxx4CjDgLODFZO+vXsZ1Tsv2gAUtcYWv3wZK07S/5gG/OdHvQH/H1WHZq4Bnk72/gHHAmeF0IbCxi//HpH6/MuWMYC6w2d23uvsx4CFgYYdlFgI/DqcfAS4xMwvLH3L3o+7+FrA5fL+UxOXuK9z9cPhyFVDRT9s+obh68FHgd+6+1933Ab8D5qcprhuAJf207R65+3PA3h4WWQg86IFVwAgzG0dy99dx43L3F8LtQuq+X73ZX905ke9mf8eVku+Xu2939zXh9AHgTaC8w2JJ/X5lSiIoB7ZFXlfTeUe2LuPuTUAdUNLLdZMZV9TNBFm/RZ6ZrTazVWZ2TT/F1Je4rg1PQx8xs/F9XDeZcRE2oU0Cno0UJ2t/9UZ3sSdzf/VVx++XA8vN7BUzW5yGeM42s9fM7CkzmxGWDYj9ZWb5BBXqo5HipO8vC5qsZwMvdpiV1O+XBq8fIMxsEVAJXBgpPtnda8xsMvCsmb3h7ltSFNKvgSXuftTMPkdwNnVxirbdG9cDj7h7c6QsnftrQDOziwgSwXmR4vPC/TUG+J2ZrQ+PmFNhDcHf66CZXQ48DkxJ0bZ74yrgeXePnj0kdX+ZWQFB4vl7d6/vr/ftjUw5I6gBxkdeV4RlXS5jZjlAMbCnl+smMy7M7CPA14Cr3f1oS7m714S/twIrCY4UUhKXu++JxHI/8KHerpvMuCKup8NpexL3V290F3sy91evmNkHCf6GC919T0t5ZH/tBB6j/5pEj8vd6939YDi9DMg1s1IGwP4K9fT96vf9ZWa5BEngZ+7+qy4WSe73q78vfKTjh+DMZitBU0HLBaYZHZb5Iu0vFj8cTs+g/cXirfTfxeLexDWb4OLYlA7lI4Gh4XQpsIl+umjWy7jGRaY/BqzytotTb4XxjQynR6UqrnC50wku3Fkq9ldkGxPp/uLnFbS/mPdSsvdXL+OaQHDd65wO5cOBwsj0C8D8FMZ1Usvfj6BCfTfcd736DiQrrnB+McF1hOGp2F/h534QuLuHZZL6/eq3nZvuH4Kr6hsJKtWvhWXfIjjKBsgDfhn+U7wETI6s+7VwvQ3AghTH9QywA3g1/Fkalp8DvBH+I7wB3JziuP4FWBdufwVwemTdvw7342bg06mMK3z9j8BdHdZL9v5aAmwHGgnaYW8GPg98PpxvwD1h3G8AlSnaX8eL635gX+T7tTosnxzuq9fCv/PXUhzXlyLfr1VEElVX34FUxRUucxPBDSTR9ZK2vwia6xx4PfJ3ujyV3y91MSEiEnOZco1ARETeJyUCEZGYUyIQEYk5JQIRkZhTIhARiTklApEUCnvd/E264xCJUiIQEYk5JQKRLpjZIjN7Kex7/gdmlm1mB8PxENZZMHbE6HDZWWFHd6+b2WNmNjIsP9XMngk7VltjZqeEb18QduS33sx+FvaCK5I2SgQiHZjZNODjwLnuPgtoBm4k6FpgtbvPAP4AfCNc5UHgq+7+QYKnPlvKfwbc4+5nEDz5vD0snw38PcFYGJOBc5P+oUR6oN5HRTq7hKCTvZfDg/VhwE4gAfwiXOanwK/MrBgY4e5/CMt/DPzSzAqBcnd/DMDdGwDC93vJ3avD168S9H3zx+R/LJGuKRGIdGbAj9399naFZnd2WO799s9yNDLdjP4PJc3UNCTS2e+Bvwz7ncfMRoUD4WQBfxku8wngj+5eB+wzs/PD8k8Cf/BgpKnqlgFyLBgzOz+ln0Kkl3QkItKBu1eZ2R0Eo1FlEfRU+UXgEDA3nLeT4DoCwKeAe8OKfivw6bD8k8APzOxb4Xv8VQo/hkivqfdRkV4ys4PuXpDuOET6m5qGRERiTmcEIiIxpzMCEZGYUyIQEYk5JQIRkZhTIhARiTklAhGRmPv/cEa5SXf5PcEAAAAASUVORK5CYII=\n", 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