From 90693959aeb7d41c73f315b1b42e2d73294866df Mon Sep 17 00:00:00 2001 From: jihyeon baek Date: Wed, 13 Nov 2024 00:55:27 +0000 Subject: [PATCH] =?UTF-8?q?[=EB=B0=B1=EC=A7=80=ED=98=84]=20week6=5F2024=5F?= =?UTF-8?q?GDG=5FML=EC=9E=85=EB=AC=B8=EC=8A=A4=ED=84=B0=EB=94=94?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5 6\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb" | 1 + ...5 6\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb" | 1 + ...5 6\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb" | 1 + 3 files changed, 3 insertions(+) create mode 100644 "week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb" create mode 100644 "week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb" create mode 100644 "week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb" diff --git "a/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb" "b/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb" new file mode 100644 index 0000000..8ad486e --- /dev/null +++ "b/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -1.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"FuoCYvFP3rz2"},"source":["# 인공 신경망"]},{"cell_type":"markdown","metadata":{"id":"Abp3s6mi3rz9"},"source":["\n"," \n","
\n"," 구글 코랩에서 실행하기\n","
"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C23oqM6GsYQF"},"outputs":[],"source":["# 실행마다 동일한 결과를 얻기 위해 케라스에 랜덤 시드를 사용하고 텐서플로 연산을 결정적으로 만듭니다.\n","import tensorflow as tf\n","\n","tf.keras.utils.set_random_seed(42)\n","tf.config.experimental.enable_op_determinism()"]},{"cell_type":"markdown","metadata":{"id":"u5msa4prlifV"},"source":["## 패션 MNIST"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fMTyGYIMqUE9","outputId":"082e548e-85fb-498a-f021-e4b1ea0ff6cc","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n"]}],"source":["from tensorflow import keras\n","\n","(train_input, train_target), (test_input, test_target) = keras.datasets.fashion_mnist.load_data()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2-r-EgKgqYkK","outputId":"a875d4d6-f550-4737-eb6b-0de63117717f"},"outputs":[{"output_type":"stream","name":"stdout","text":["(60000, 28, 28) (60000,)\n"]}],"source":["print(train_input.shape, train_target.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LAJGYTkFrS6p","outputId":"c321a608-1247-47ba-d92a-17c8e585f930"},"outputs":[{"output_type":"stream","name":"stdout","text":["(10000, 28, 28) (10000,)\n"]}],"source":["print(test_input.shape, test_target.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":102},"id":"lAYiLboglZIz","outputId":"f1c2fd09-6914-4342-f3b0-a2ac5bccf387"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","fig, axs = plt.subplots(1, 10, figsize=(10,10))\n","for i in range(10):\n"," axs[i].imshow(train_input[i], cmap='gray_r')\n"," axs[i].axis('off')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SZjVIoyqmVy-","outputId":"7a3d4d33-69f0-4c3d-a2e5-820d4800db16"},"outputs":[{"output_type":"stream","name":"stdout","text":["[9, 0, 0, 3, 0, 2, 7, 2, 5, 5]\n"]}],"source":["print([train_target[i] for i in range(10)])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rx32OX7NqNnD","outputId":"d0895226-e100-4dd5-f7c4-3810aa34ac8a"},"outputs":[{"output_type":"stream","name":"stdout","text":["(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8), array([6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000]))\n"]}],"source":["import numpy as np\n","\n","print(np.unique(train_target, return_counts=True))"]},{"cell_type":"markdown","metadata":{"id":"lrhnk0zXYj5e"},"source":["## 로지스틱 회귀로 패션 아이템 분류하기"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2HSAg_UFNH52"},"outputs":[],"source":["train_scaled = train_input / 255.0\n","train_scaled = train_scaled.reshape(-1, 28*28)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kwOFVfpxxuLU","outputId":"f4f4e59b-a1c2-4485-fa1e-d3b99059e9e3"},"outputs":[{"output_type":"stream","name":"stdout","text":["(60000, 784)\n"]}],"source":["print(train_scaled.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ui3iJlfLNLzr","outputId":"ea89c20c-ec78-40b2-8986-09542d74f7f4"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/joblib/externals/loky/backend/fork_exec.py:38: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," pid = os.fork()\n","/usr/local/lib/python3.10/dist-packages/joblib/externals/loky/backend/fork_exec.py:38: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n"," pid = os.fork()\n"]},{"output_type":"stream","name":"stdout","text":["0.8196000000000001\n"]}],"source":["from sklearn.model_selection import cross_validate\n","from sklearn.linear_model import SGDClassifier\n","\n","sc = SGDClassifier(loss='log_loss', max_iter=5, random_state=42)\n","\n","scores = cross_validate(sc, train_scaled, train_target, n_jobs=-1)\n","print(np.mean(scores['test_score']))"]},{"cell_type":"markdown","metadata":{"id":"Y0W7D4ED3r0B","tags":[]},"source":["## 인공신경망"]},{"cell_type":"markdown","metadata":{"id":"zaFtnKmzcQeJ"},"source":["### 텐서플로와 케라스"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_WOZURhzcTAi"},"outputs":[],"source":["import tensorflow as tf"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rbKFPAUucVkV"},"outputs":[],"source":["from tensorflow import keras"]},{"cell_type":"markdown","metadata":{"id":"YdNdad0mcoGD"},"source":["## 인공신경망으로 모델 만들기"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4smUJC1hOWTF"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","train_scaled, val_scaled, train_target, val_target = train_test_split(\n"," train_scaled, train_target, test_size=0.2, random_state=42)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G9OwZTRorHWQ","outputId":"52f71885-3a00-4115-a939-cb1fc2f28c58"},"outputs":[{"output_type":"stream","name":"stdout","text":["(48000, 784) (48000,)\n"]}],"source":["print(train_scaled.shape, train_target.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Van_eJ7BOo-y","outputId":"2e44e405-d852-41a9-a124-15bd10a2805d"},"outputs":[{"output_type":"stream","name":"stdout","text":["(12000, 784) (12000,)\n"]}],"source":["print(val_scaled.shape, val_target.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DEXlpF_8gmJv","outputId":"40a9b169-ad46-4e3f-8843-39e25c3c4c50","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]}],"source":["dense = keras.layers.Dense(10, activation='softmax', input_shape=(784,))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k8mtAypNPIz7"},"outputs":[],"source":["model = keras.Sequential([dense])"]},{"cell_type":"markdown","metadata":{"id":"xPTmHpMe3r0D"},"source":["## 인공신경망으로 패션 아이템 분류하기"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VqJ5dpwwPWPr"},"outputs":[],"source":["model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JLLytXw7UcvB","outputId":"4ef62477-0ad0-4e0e-e127-8656ea9d97da"},"outputs":[{"output_type":"stream","name":"stdout","text":["[7 3 5 8 6 9 3 3 9 9]\n"]}],"source":["print(train_target[:10])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiZnrOgQP4ps","outputId":"16ea05f8-4e26-4eb7-a590-2e7b8c7d580a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.7370 - loss: 0.7853\n","Epoch 2/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8346 - loss: 0.4845\n","Epoch 3/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8452 - loss: 0.4564\n","Epoch 4/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8504 - loss: 0.4425\n","Epoch 5/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.8537 - loss: 0.4337\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":21}],"source":["model.fit(train_scaled, train_target, epochs=5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OdXPQEXOQIFm","outputId":"f48df514-bca9-472c-dc5b-90a56dca2628"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8462 - loss: 0.4364\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.44444453716278076, 0.8458333611488342]"]},"metadata":{},"execution_count":22}],"source":["model.evaluate(val_scaled, val_target)"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"https://github.com/rickiepark/hg-mldl/blob/master/7-1.ipynb","timestamp":1731458714179}],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","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":"3.7.3"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git "a/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb" "b/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb" new file mode 100644 index 0000000..038ee91 --- /dev/null +++ "b/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -2.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"iNFOeMfl3tIu"},"source":["# 심층 신경망"]},{"cell_type":"markdown","metadata":{"id":"zKfwb8gS3tI2"},"source":["\n"," \n","
\n"," 구글 코랩에서 실행하기\n","
"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZT8SDtZVv41l"},"outputs":[],"source":["# 실행마다 동일한 결과를 얻기 위해 케라스에 랜덤 시드를 사용하고 텐서플로 연산을 결정적으로 만듭니다.\n","import tensorflow as tf\n","\n","tf.keras.utils.set_random_seed(42)\n","tf.config.experimental.enable_op_determinism()"]},{"cell_type":"markdown","metadata":{"id":"dPE5XsFhcfVD"},"source":["## 2개의 층"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4sNOMcdaFVKa","outputId":"172da922-8d45-4894-a309-141325c36a46"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"]}],"source":["from tensorflow import keras\n","\n","(train_input, train_target), (test_input, test_target) = keras.datasets.fashion_mnist.load_data()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aJJiRMa6FkWx"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","train_scaled = train_input / 255.0\n","train_scaled = train_scaled.reshape(-1, 28*28)\n","\n","train_scaled, val_scaled, train_target, val_target = train_test_split(\n"," train_scaled, train_target, test_size=0.2, random_state=42)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MCZWQiEwF6MD","outputId":"fbe50704-5d7f-47de-dd27-d1b3b610511f","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]}],"source":["dense1 = keras.layers.Dense(100, activation='sigmoid', input_shape=(784,))\n","dense2 = keras.layers.Dense(10, activation='softmax')"]},{"cell_type":"markdown","metadata":{"id":"Agy5gCVhcrm-"},"source":["## 심층 신경망 만들기"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xmWL7kOoGB4P"},"outputs":[],"source":["model = keras.Sequential([dense1, dense2])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":197},"id":"em0xDl8qa12J","outputId":"31e599be-a83e-41f0-bf73-759741557e5d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"],"text/html":["
Model: \"sequential\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m78,500\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,010\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ dense (Dense)                        │ (None, 100)                 │          78,500 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (Dense)                      │ (None, 10)                  │           1,010 │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Total params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Trainable params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}}],"source":["model.summary()"]},{"cell_type":"markdown","metadata":{"id":"qAi41rBTdk7k"},"source":["## 층을 추가하는 다른 방법"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0XeV6V4ha3I8"},"outputs":[],"source":["model = keras.Sequential([\n"," keras.layers.Dense(100, activation='sigmoid', input_shape=(784,), name='hidden'),\n"," keras.layers.Dense(10, activation='softmax', name='output')\n","], name='패션 MNIST 모델')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":198},"id":"bwXDLSOWbm3L","outputId":"a14d71eb-5765-4c0a-81af-8f8d2f8e7d50"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"패션 MNIST 모델\"\u001b[0m\n"],"text/html":["
Model: \"패션 MNIST 모델\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ hidden (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m78,500\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ output (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,010\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ hidden (Dense)                       │ (None, 100)                 │          78,500 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ output (Dense)                       │ (None, 10)                  │           1,010 │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Total params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Trainable params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}}],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yZSAxgZCbax7"},"outputs":[],"source":["model = keras.Sequential()\n","model.add(keras.layers.Dense(100, activation='sigmoid', input_shape=(784,)))\n","model.add(keras.layers.Dense(10, activation='softmax'))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":197},"id":"bW2coaNQboe5","outputId":"13286f4d-4081-4ba3-cfbf-03e930749e62"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_1\"\u001b[0m\n"],"text/html":["
Model: \"sequential_1\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m78,500\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,010\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ dense_2 (Dense)                      │ (None, 100)                 │          78,500 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_3 (Dense)                      │ (None, 10)                  │           1,010 │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Total params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Trainable params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}}],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kkYrPJembpYk","outputId":"02c5131e-b695-4108-9f1b-754d419b8b39"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.7525 - loss: 0.7720\n","Epoch 2/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.8463 - loss: 0.4270\n","Epoch 3/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8604 - loss: 0.3857\n","Epoch 4/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8696 - loss: 0.3600\n","Epoch 5/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8759 - loss: 0.3410\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":12}],"source":["model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","\n","model.fit(train_scaled, train_target, epochs=5)"]},{"cell_type":"markdown","metadata":{"id":"_dfXJDhwcyAK"},"source":["## 렐루 활성화 함수"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dG7yF8g6b062","outputId":"71b3e0e9-d1b2-46bb-dd3d-eb25e8487b16","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(**kwargs)\n"]}],"source":["model = keras.Sequential()\n","model.add(keras.layers.Flatten(input_shape=(28, 28)))\n","model.add(keras.layers.Dense(100, activation='relu'))\n","model.add(keras.layers.Dense(10, activation='softmax'))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":230},"id":"zHogWhu6g90a","outputId":"2491bab6-148a-4827-cfd2-1bc16f2c907a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_2\"\u001b[0m\n"],"text/html":["
Model: \"sequential_2\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m78,500\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,010\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ flatten (Flatten)                    │ (None, 784)                 │               0 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_4 (Dense)                      │ (None, 100)                 │          78,500 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_5 (Dense)                      │ (None, 10)                  │           1,010 │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Total params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Trainable params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}}],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JfPe_ruQdhqA"},"outputs":[],"source":["(train_input, train_target), (test_input, test_target) = keras.datasets.fashion_mnist.load_data()\n","\n","train_scaled = train_input / 255.0\n","\n","train_scaled, val_scaled, train_target, val_target = train_test_split(\n"," train_scaled, train_target, test_size=0.2, random_state=42)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9PGejuuhdvvk","outputId":"4fc4dfa3-5969-4495-e6e6-92b1225a6882"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7637 - loss: 0.6723\n","Epoch 2/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8515 - loss: 0.4054\n","Epoch 3/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8676 - loss: 0.3595\n","Epoch 4/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8786 - loss: 0.3344\n","Epoch 5/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8858 - loss: 0.3177\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":17}],"source":["model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","\n","model.fit(train_scaled, train_target, epochs=5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lVYLpnjeep4y","outputId":"58a72357-71df-4dc4-cd36-8f554343a9f2"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8671 - loss: 0.3837\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.3847014605998993, 0.8665000200271606]"]},"metadata":{},"execution_count":18}],"source":["model.evaluate(val_scaled, val_target)"]},{"cell_type":"markdown","metadata":{"id":"3YtLsmGAoavz"},"source":["## 옵티마이저"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NxVj04Haocwa"},"outputs":[],"source":["model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1426O4G8Hpfi"},"outputs":[],"source":["sgd = keras.optimizers.SGD()\n","model.compile(optimizer=sgd, loss='sparse_categorical_crossentropy', metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sh-HDiULlp18"},"outputs":[],"source":["sgd = keras.optimizers.SGD(learning_rate=0.1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uF1XolBXsl3a"},"outputs":[],"source":["sgd = keras.optimizers.SGD(momentum=0.9, nesterov=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Hy2MENbL170j"},"outputs":[],"source":["adagrad = keras.optimizers.Adagrad()\n","model.compile(optimizer=adagrad, loss='sparse_categorical_crossentropy', metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KkpbSMXWtakr"},"outputs":[],"source":["rmsprop = keras.optimizers.RMSprop()\n","model.compile(optimizer=rmsprop, loss='sparse_categorical_crossentropy', metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Gdu0hQIAz4JW","outputId":"9eed5bc9-f729-47da-b3c7-1c407e470da3","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(**kwargs)\n"]}],"source":["model = keras.Sequential()\n","model.add(keras.layers.Flatten(input_shape=(28, 28)))\n","model.add(keras.layers.Dense(100, activation='relu'))\n","model.add(keras.layers.Dense(10, activation='softmax'))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2tcxIfILoi1t","outputId":"6f650dcc-2441-4df3-8e3d-45a744f2dd70"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7691 - loss: 0.6706\n","Epoch 2/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8515 - loss: 0.4134\n","Epoch 3/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8691 - loss: 0.3618\n","Epoch 4/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8793 - loss: 0.3302\n","Epoch 5/5\n","\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8873 - loss: 0.3088\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":28}],"source":["model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","\n","model.fit(train_scaled, train_target, epochs=5)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8gxAWehsv9Gi","outputId":"5659c776-a4f5-4dad-a9c1-4ac4ad36d498"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8762 - loss: 0.3506\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.35239025950431824, 0.8725833296775818]"]},"metadata":{},"execution_count":29}],"source":["model.evaluate(val_scaled, val_target)"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"https://github.com/rickiepark/hg-mldl/blob/master/7-2.ipynb","timestamp":1731458739075}]},"kernelspec":{"display_name":"default:Python","language":"python","name":"conda-env-default-py"},"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":0} \ No newline at end of file diff --git "a/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb" "b/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb" new file mode 100644 index 0000000..cbc33e6 --- /dev/null +++ "b/week6/ML\341\204\211\341\205\263\341\204\220\341\205\245\341\204\203\341\205\265 6\341\204\214\341\205\256\341\204\216\341\205\241 -3.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"pqxnslml3tu6"},"source":["# 신경망 모델 훈련"]},{"cell_type":"markdown","metadata":{"id":"fN7zF6Zy3tvE"},"source":["\n"," \n","
\n"," 구글 코랩에서 실행하기\n","
"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M54mUYlhwnex"},"outputs":[],"source":["# 실행마다 동일한 결과를 얻기 위해 케라스에 랜덤 시드를 사용하고 텐서플로 연산을 결정적으로 만듭니다.\n","import tensorflow as tf\n","\n","tf.keras.utils.set_random_seed(42)\n","tf.config.experimental.enable_op_determinism()"]},{"cell_type":"markdown","metadata":{"id":"XGP-X65EmJBg"},"source":["## 손실 곡선"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hZsGl9udlqZk","outputId":"d942bc1e-1f56-4a1a-bddc-9ab2c90d7918"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"]}],"source":["from tensorflow import keras\n","from sklearn.model_selection import train_test_split\n","\n","(train_input, train_target), (test_input, test_target) = \\\n"," keras.datasets.fashion_mnist.load_data()\n","\n","train_scaled = train_input / 255.0\n","\n","train_scaled, val_scaled, train_target, val_target = train_test_split(\n"," train_scaled, train_target, test_size=0.2, random_state=42)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iogH7o0Ll6uL"},"outputs":[],"source":["def model_fn(a_layer=None):\n"," model = keras.Sequential()\n"," model.add(keras.layers.Flatten(input_shape=(28, 28)))\n"," model.add(keras.layers.Dense(100, activation='relu'))\n"," if a_layer:\n"," model.add(a_layer)\n"," model.add(keras.layers.Dense(10, activation='softmax'))\n"," return model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"5Eh6hM4DNdzu","outputId":"f6509ba8-eff8-4a0b-c798-282ccc879637"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(**kwargs)\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"],"text/html":["
Model: \"sequential\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m78,500\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,010\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ flatten (Flatten)                    │ (None, 784)                 │               0 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense (Dense)                        │ (None, 100)                 │          78,500 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_1 (Dense)                      │ (None, 10)                  │           1,010 │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Total params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Trainable params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}}],"source":["model = model_fn()\n","\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P-UK21N_mCM0"},"outputs":[],"source":["model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","\n","history = model.fit(train_scaled, train_target, epochs=5, verbose=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1ix_EA2LpaR0","outputId":"3b9179e3-cd4f-4c14-afea-4d09331c4570"},"outputs":[{"output_type":"stream","name":"stdout","text":["dict_keys(['accuracy', 'loss'])\n"]}],"source":["print(history.history.keys())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"uz_TCdfPmG6e","outputId":"0ff3e6cd-2552-4e1f-d887-89d381bcd190"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","plt.plot(history.history['loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"CpmK9lXQcBe9","outputId":"5864caaa-ce82-48ba-e91b-f3a2977e9dbc"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["plt.plot(history.history['accuracy'])\n","plt.xlabel('epoch')\n","plt.ylabel('accuracy')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5cJlWITXqJWr"},"outputs":[],"source":["model = model_fn()\n","model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","\n","history = model.fit(train_scaled, train_target, epochs=20, verbose=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":451},"id":"YT87Fjo2qKPC","outputId":"876af6b8-13c4-47a4-81f5-2b13699f4244"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["plt.plot(history.history['loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"bGqf6ceRr3zO"},"source":["## 검증 손실"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4_iHvMxwu2D2"},"outputs":[],"source":["model = model_fn()\n","model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","\n","history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n"," validation_data=(val_scaled, val_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nJ5RGEmLu5KI","outputId":"11fedf90-e939-455f-fa81-adfb3711a4de"},"outputs":[{"output_type":"stream","name":"stdout","text":["dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss'])\n"]}],"source":["print(history.history.keys())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"zcpm7CpXu5vC","outputId":"b6a6780f-a22e-4682-a87b-dd9eb19c1a15"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAj8AAAGwCAYAAABGogSnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAByC0lEQVR4nO3dd3wUdf7H8dduei+EVAKhd4i0CGIlCoqUs4GHIujh7zz1DqOnogfY7rCdx6koHqciVs5eUBSiYKFKb4YikFCSQCAdkrA7vz+GLEQSCKTMJnk/H499MNmdmf1MlpA33/kWm2EYBiIiIiJNhN3qAkRERETqk8KPiIiINCkKPyIiItKkKPyIiIhIk6LwIyIiIk2Kwo+IiIg0KQo/IiIi0qR4Wl2AO3I6nezbt4+goCBsNpvV5YiIiEg1GIZBQUEBsbGx2O1Vt+8o/FRi3759xMfHW12GiIiInIOMjAxatGhR5esKP5UICgoCzG9ecHCwxdWIiIhIdeTn5xMfH+/6PV4VhZ9KlN/qCg4OVvgRERFpYM7UZUUdnkVERKRJUfgRERGRJkXhR0RERJoUhR8RERFpUhR+REREpElR+BEREZEmReFHREREmhSFHxEREWlSFH5ERESkSVH4ERERkSZF4UdERESaFMvDz4wZM0hISMDX15ekpCRWrFhR5b6zZ8/GZrNVePj6+lbYZ9y4cafsM2TIkLq+DBEREWkgLF3YdO7cuaSkpDBz5kySkpKYPn06gwcPJi0tjcjIyEqPCQ4OJi0tzfV1ZYuXDRkyhNdff931tY+PT+0XLyIi0liVFIBXANgtbyOpE5aGn+eee44JEyYwfvx4AGbOnMm8efN47bXXePDBBys9xmazER0dfdrz+vj4nHEfERERqUTafHjvRvCPgPaXQ/sroO2l4BtidWW1xrJIV1payqpVq0hOTj5RjN1OcnIyS5curfK4wsJCWrVqRXx8PCNGjGDTpk2n7LNo0SIiIyPp2LEjd9xxBzk5OaetpaSkhPz8/AoPERGRJsdRBl8/BIYTirJh7dvw/i3wdBt4fSj89G/I3gKGYXWlNWJZ+Dl48CAOh4OoqKgKz0dFRZGZmVnpMR07duS1117j008/5a233sLpdDJgwAD27Nnj2mfIkCHMmTOH1NRUnnrqKRYvXsyVV16Jw+GospZp06YREhLiesTHx9fORYqIiDQka96EQzvMVp8xH8D5d0Kz9uA8Brt/hAVT4KXzYXoP+CIFtn4NpcVWV33WbIZhTXzbt28fcXFxLFmyhP79+7uev//++1m8eDHLly8/4znKysro3LkzN954I48//nil+/z666+0bduWhQsXMmjQoEr3KSkpoaSkxPV1fn4+8fHx5OXlERwcfJZXJiIi0gCVFsPz50FhJgx5Cs7/44nXDv0K2xaYYWfXj+A48TsTT19IuNC8Pdb+cghvXf+1H5efn09ISMgZf39b1ucnIiICDw8PsrKyKjyflZVV7f46Xl5enHfeeWzfvr3Kfdq0aUNERATbt2+vMvz4+PioU7SIiDRty2eawSe0JfQZX/G18DaQ9H/mo7QIdn4P276Brd9A/h7YvsB8fAVEdDgehK6Alv3B09uSyzkdy8KPt7c3vXv3JjU1lZEjRwLgdDpJTU3lrrvuqtY5HA4HGzZs4Kqrrqpynz179pCTk0NMTExtlC0iItL4FB+CH6eb25f+DTxP0yDgHQAdrzQfhmH2Adr2jflIXwYHt5qPpS+CdxC0veREGApyj8FIlo72SklJ4ZZbbqFPnz7069eP6dOnU1RU5Br9NXbsWOLi4pg2bRoAjz32GOeffz7t2rUjNzeXZ555ht27d/OHP/wBMDtDP/roo1x77bVER0ezY8cO7r//ftq1a8fgwYMtu04RERG39uO/oCQPIrtC9+uqf5zNBlFdzMfAiXAkF3Z8a94i274Aig7Als/NB0B0D+gw2AxCcb3B7lEXV3NGloafUaNGceDAAaZMmUJmZiaJiYnMnz/f1Qk6PT0d+0lzDBw+fJgJEyaQmZlJWFgYvXv3ZsmSJXTp0gUADw8P1q9fzxtvvEFubi6xsbFcccUVPP7447qtJSIiUpm8vbDiP+Z28tSaBRK/UOh2jflwOmH/mhN9hfathsz15uP7Z+DC+2DQ5Fq5hLNlWYdnd1bdDlMiIiIN3md3w+o50HIAjP/SbM2pC4XZsH2heXts+7dw4zuQMLBW38LtOzyLiIiIxQ5shTVvmdvJj9Rd8AEIjITE35sPRxnYrJs9WuFHRESkqfr2MXNCw45XQcuk+ntfD6/6e69KNM5FO0REROT09qwyOyLb7DBoitXV1CuFHxERkabGMGDhVHO7540Q2dnaeuqZwo+IiEhTsyMVdv0AHt5wySSrq6l3Cj8iIiJNidMJCx8xt/tOgNCmt56lwk89Wv5rDi9+u421GblWlyIiIk3Vpo8gcwP4BMOF91pdjSU02qse/e/nPXy4eg9OAxLjQ60uR0REmppjpfDtE+b2gD9DQDNr67GIWn7qUYeoQAC2ZhVYXImIiDRJq9+AwzshIBLOv8Pqaiyj8FOPOkQFAbAtq9DiSkREpMkpKYTFT5vbF98PPoHW1mMhhZ961P54y8+vBwspczgtrkZERJqU5S9DUTaEJUCvW6yuxlIKP/UoLtSPAG8PyhwGu3OKrC5HRESaiuJD8NPz5vZlk8HT29p6LKbwU49sNhvtj9/62qpbXyIiUl9++CeU5EN0d+h6jdXVWE7hp56Vd3pOy1SnZxERqQe5GbBilrk96BGw61e/vgP1zNXpOVvhR0RE6sGiJ8FRAgkXQrtBVlfjFhR+6plue4mISL3J/gXWvWNuD5oKNpu19bgJhZ96Vn7ba9fBIkqPacSXiIjUodTHwHBCp6shvq/V1bgNhZ96Fh3sS5CPJ8ecBjsPasSXiIjUkYwVkDYPbHYYNMXqatyKwk89M0d8aaZnERGpQ4ZxYvHSxDHQvKOl5bgbhR8LnJjpWeFHRETqwLYFsPsn8PCBSx60uhq3o/BjAXV6FhGROuN0Quqj5nbS7RDSwtp63JDCjwVcC5xquLuIiNS2jR9A1kbwCYGBKVZX45YUfizQ8XjLz66DRRwtc1hcjYiINBrHSuHbJ8ztgX8B/3Br63FTCj8WaB7kQ4ifF04Dfj2gEV8iIlJLVr0OubshMAqS/mh1NW5L4ccCNpvNdetLMz2LiEitKCmAxU+b2xc/AN4B1tbjxhR+LHKi07PCj4iI1IKlL0HxQQhvA73GWl2NW1P4sUiHyPK5fjTiS0REaqjoICx53ty+7G/g4WVtPW5O4ccimutHRERqzffPQmkhxPSELr+zuhq3p/BjkfLbXrsPFWvEl4hIfXM64fBucybkhu7wbvj5VXM7+RGw61f7meg7ZJGIQG/C/L0wDNierVtfIiL1avFT8O8eMPNC2PSJGYYaqkXTwFEKrS+GtpdZXU2DoPBjEXONr+O3vjTiS0Sk/jjKYOUscztrA7x/C7x0Pqz/HziOWVvb2craBOveM7eTp1pbSwOi8GOh8uHuaZlq+RERqTfbF0JxDgQ0N4eE+4TAwTT4aALM6Aur3zQnC2wIUh8DDOgyAuJ6W11Ng6HwY6GO6vQsIlL/1r1r/tn9erj0IbhnA1w2GfzC4dCv8Nld8EIvWPlfKDtqba2ns3spbJ0PNg+zfqk2hR8Lueb60W0vEZH6ceQwpM03t3uONv/0DYGL7oN7NsIVT0BAJORlwLx74flEc/6c0mLLSq6UYcDCR8zt826CiPaWltPQKPxYqHy4e8ahIxSXNrD7zCIiDdGmT8BRApFdILpHxde8A2DA3TBxPVz5DATHQcF++HoSTO8OPzwHR/MtKfsUW+dDxjLw9IVLHrS6mgZH4cdC4QHeRAR6AxrxJSJSL8o7B/ccDTZb5ft4+UHS7fDntTDs3xDaypw5OfVRMwQtetJsQapvx0pg7ypYMQvmTzKfS/ojBMfWfy0NnKfVBTR17SODOFiYw9asQnq0CLW6HBGRxuvQr2Zric0O3W848/6e3tB7HCTeBBs/MCcSzNlmDi1f8iL0mwD974SAiNqv1emEQzvMsFP+yNxgDmkv5xcGAyfW/ns3AQo/FusQFcjSX3PU6VlEpK6tm2v+2eYSCI6p/nEenmZLUffrYfOnZgjK3gQ/PgfLZ0KfW83bZUHR515bQWbFoLN3DZTknbqfX7g5qiuuN/QcZQYgOWsKPxbTAqciIvXAME6M8up547mdw+4B3a6BLiNh61fmCur718LSF81bUb3GwgV/gdD405/naL55nCvorIb8vafu5+kLMYnHw04v88+whKpv10m1WR5+ZsyYwTPPPENmZiY9e/bkhRdeoF+/fpXuO3v2bMaPH1/hOR8fH44ePTEU0TAMpk6dyqxZs8jNzeWCCy7g5Zdfpn179+wJ38EVftTnR0SkzqQvg9zd4B0InYbW7Fx2u3mOjlfB9lT4/mnIWG5OnLhqNiTeCAPvMVdXP1ZqthKVh5y9q+BAGvDbZTVsENn5RMiJ62N+rQVK64Sl4Wfu3LmkpKQwc+ZMkpKSmD59OoMHDyYtLY3IyMhKjwkODiYtLc31te03Cfjpp5/m+eef54033qB169ZMnjyZwYMHs3nzZnx9fev0es5F+USHe3OPUFhyjEAfy/OoiEjjU97q02WEOaqrNths0D4Z2g2CXT+YLUG7foDVc2DN2xDV1Qw6jpJTjw2JPyno9DYXJPUJqp265Iws/U373HPPMWHCBFdrzsyZM5k3bx6vvfYaDz5Y+dA9m81GdHTl91UNw2D69On87W9/Y8SIEQDMmTOHqKgoPvnkE0aPHl03F1IDof7eNA/y4UBBCduyCjivpe7fiojUqrKj5hB3ODG3T22y2aD1ReYjfRl8/4w5i3TmevN135ATISeuN8T2gqCo2q9Dqs2y8FNaWsqqVauYNGmS6zm73U5ycjJLly6t8rjCwkJatWqF0+mkV69e/OMf/6Br164A7Ny5k8zMTJKTk137h4SEkJSUxNKlS6sMPyUlJZSUnEjm+fn1O49Dh6jA4+GnUOFHRKS2bf3K7Dwc3AJaDazb92p5Ptz0IexfDznbzRad8Dbqp+NmLJvn5+DBgzgcDqKiKqbfqKgoMjMzKz2mY8eOvPbaa3z66ae89dZbOJ1OBgwYwJ49ewBcx53NOQGmTZtGSEiI6xEff4bOarWsgzo9i4jUnfK5fXrcYPbXqQ8xPczO0c3aKvi4oQY1yWH//v0ZO3YsiYmJXHzxxXz00Uc0b96cV155pUbnnTRpEnl5ea5HRkZGLVVcPa7wo4kORURqV+EB2LbA3K6LW17SIFkWfiIiIvDw8CArK6vC81lZWVX26fktLy8vzjvvPLZv3w7gOu5sz+nj40NwcHCFR30q7/SsuX5ERGrZxg/AcJj9bJp3tLoacROWhR9vb2969+5Namqq6zmn00lqair9+/ev1jkcDgcbNmwgJsacrKp169ZER0dXOGd+fj7Lly+v9jmt0C7SbPnZn3eU/KNlFlcjItKI1HRuH2mULL3tlZKSwqxZs3jjjTfYsmULd9xxB0VFRa7RX2PHjq3QIfqxxx7jm2++4ddff2X16tXcdNNN7N69mz/84Q+AORJs4sSJPPHEE3z22Wds2LCBsWPHEhsby8iRI624xGoJ8fMiOtgchr9N8/2IiNSOrM2wfx3YPaHbtVZXI27E0qHuo0aN4sCBA0yZMoXMzEwSExOZP3++q8Nyeno69pM6px0+fJgJEyaQmZlJWFgYvXv3ZsmSJXTp0sW1z/33309RURG33347ubm5DBw4kPnz57vlHD8nax8VSGb+UbZlFdC7lUZ8iYjU2PrjHZ3bD4aAZtbWIm7FZhjGb6eZbPLy8/MJCQkhLy+v3vr/PP7FZl79cSe3XtCaKcO6nPkAERGpmtMB/+oKBfvhhjehy3CrK5J6UN3f3w1qtFdjVt7pWcPdRURqwc7FZvDxDYUOg62uRtyMwo+b0AKnIiK1qHwF927XgqePtbWI21H4cRPtI82Wn+yCEvKKNeJLROSclRTCls/Mbc3tI5VQ+HETQb5exIX6AbA1W60/IiLnbMvnUFZsLivRoq/V1YgbUvhxI+3V70dEpOZOnttHS0tIJRR+3Ej5Mhea60dE5Bzl7YGd35vbPW6wthZxWwo/bqS8349afkREztH6/wEGtLoAwhKsrkbclMKPGzmxurtafkREzpphnFjBXR2d5TQUftxIu+MtPwcLSzhUVGpxNSIiDcy+NXAwDTx9ocsIq6sRN6bw40YCfDxpEXZ8xJdufYmInJ3yVp9OQ8E3xNpaxK0p/LiZE52eFX5ERKrNUQYbPzC3tYK7nIHCj5s5Mdxd/X5ERKpt+0IozoGASGhzqdXViJtT+HEzHSK1zIWIyFkrn9unxw3g4WltLeL2FH7cTMfo47e9stXyIyJSLUcOQ9pX5naPUdbWIg2Cwo+bads8EJsNDhWVcrCwxOpyRETc36aPwVEKkV0hurvV1UgDoPDjZvy8PWgZ7g/o1peISLWcPLePlrOQalD4cUPtI7XMhYhIteTsgIzlYLND9+utrkYaCIUfN9RBC5yKiFTP+rnmn20uheAYa2uRBkPhxw1pgVMRkWqosJyF5vaR6lP4cUPlc/2kZRVgGIbF1YiIuKn0ZZC7G7wDzVmdRapJ4ccNtW0eiN0GeUfKOFCgEV8iIpUqn9uny0jw9re0FGlYFH7ckK+XB62aBQCa6VlEpFJlR2DTJ+a2VnCXs6Tw46baR6rTs4hIldK+gpI8CImHVhdYXY00MAo/burETM8KPyIipyjv6NzjBrDrV5mcHf2NcVPto8rX+NJtLxGRCgqzzYVMAXrolpecPYUfN3XyXD8a8SUicpINH4DhgLje0LyD1dVIA6Slb91U64gAPOw2Co4eIyu/hOgQX6tLEpHGyOmEY0fNDsRlxaf+ebrXyo5CTA9IvKl+bz2Vj/LS3D5yjhR+3JSPpwcJzfzZcaCIrVkFCj8icva2fA5r34Wyot8El5O2jx2t+fts/Ah+NxOComt+rjPJ2gSZ68HuBV2vqfv3k0ZJ4ceNdYgKcoWfizo0t7ocEWlIDm6HD/9wduHG0xe8/MDTz/zTy//4n7/dPv5wOuHn1+DX7+DlATDiJeg4pO6uCU50dO4wGAKa1e17SaOl8OPG2kcF8dXGTA13F5Gz43TCZ3eZwafVBdB7fCUhxh+8fE987el3breueo+DD2+FzA3w7ijodztc/ph5ztrmdMCG981tze0jNaDw48ZOdHrWiC8ROQsrZ0H6UnPZh9/NhNCWdfdezTvAH1Ih9TFY+iKs+A/s+hGufRWiutTue+1cDAX7wS8M2l9Ru+eWJkWjvdxY+QKn27MLNeJLRKrn8C5Y+Ii5ffmjdRt8ynn6wOC/w5gPIaA5ZG+GWZfCilnm4qO1pfyWV7drzfcUOUcKP24soVkAnnYbhSXH2JdXC50SRaRxMwz47G6zM3PChdD71vp9//bJcMcSaHe5ecvty/vg3RuhKKfm5y4pMDtwg0Z5SY0p/Lgxb087bZqXr/Glfj8icgarZsPO783+O8Oft2bm48BIGPM+DHkKPLxh61dmZ+hfF9XsvFs+N0Nds3bm/D4iNaDw4+bKZ3repvAjIqeTmwHfTDa3B02B8DbW1WKzwfl/hAnfQkRHKMyEOSNhwRQ4Vnpu5yyf26fHaPP8IjWg8OPmOkRqmQsRyxRkwX8uhYWP1m7fldpmGPDFRCgtgPgkSPo/qysyRXeH2xdBn1sBA376N7x6uTkM/2zkZsDOH8ztHjfUdpXSBCn8uLnyEV9q+RGxwNIXYN9q+PE5WPay1dVUbe075lpXHj4w/EWwe1hd0Qne/nD1v2DU2+Yorf1r4ZWLYM1b1Q+UG/4HGNBqIIS1qstqpYlQ+HFzJy9w6nS68f88RRqbo3nw8+wTX3/zMGz9xrJyqpS/H76eZG5f+pD7rnXV+WqzM3TCheaM05/eCR/cCkdyT3+cYZwY5aW5faSWKPy4uYRm/nh72DlS5mBv7hGryxFpOlbPMW8jRXSE824Gw2n+ss7abHVlJxgGzEsxg1psL+h/l9UVnV5wLIz9FAZNBbsnbPoIZg6E3UurPmbfGji41Zx9usuI+qtVGjXLw8+MGTNISEjA19eXpKQkVqxYUa3j3nvvPWw2GyNHjqzw/Lhx47DZbBUeQ4bU8XTrdcjTQyO+ROqdo+zEba4Bd8HQ58xbLqUF5izGhQesra/cxg8h7UtznasRM8CjAcxba/eAC1Pg1m8grDXkZcDsq+C7aeA4dur+5a0+na4G3+D6rVUaLUvDz9y5c0lJSWHq1KmsXr2anj17MnjwYLKzs0973K5du7jvvvu48MILK319yJAh7N+/3/V4991366L8enPyrS8RqQebPob8vRAQCd1vAE9vGPWmOYIqNx3m3gTHSqytsTAbvvyruX3x/bU/m3Jda9Eb/viDOWeP4YTFT8Lsoeb3t9yxUtj4gbmtuX2kFlkafp577jkmTJjA+PHj6dKlCzNnzsTf35/XXnutymMcDgdjxozh0UcfpU2byody+vj4EB0d7XqEhYXV1SXUiw6R6vQsUm8MA5Y8b24n/Z+5/hWAfzjcOBd8QiBjGXz2Z2tHgH35VzhyyBxRNfAe6+qoCZ8gc/mNa/4LPsHm9/XlgWaLFpiduItzIDAK2lxiaanSuFgWfkpLS1m1ahXJycknirHbSU5OZunSqu//PvbYY0RGRnLbbbdVuc+iRYuIjIykY8eO3HHHHeTknH520ZKSEvLz8ys83Imr5Sdb4Uekzu1cbC7S6eV/fIj2SZp3gBtmg80D1r9njgKzwuZPYfMnZr+ZETPAw8uaOmpLj+vNVqAWfaEkz+xb9cmfYNXr5uvdr28Yt/SkwbAs/Bw8eBCHw0FUVFSF56OiosjMzKz0mB9//JFXX32VWbNmVXneIUOGMGfOHFJTU3nqqadYvHgxV155JQ6Ho8pjpk2bRkhIiOsRHx9/bhdVRzpGn1jjSyO+pMEpKYT9662uovqWvGD+ed7NZmvPb7W9DK58ytxOfQw2f1Z/tQEUH4J595rbA++BmJ71+/51JSwBxs+Hi+4Hmx3Wvg3bjo+u0y0vqWWWd3iuroKCAm6++WZmzZpFRERElfuNHj2a4cOH0717d0aOHMkXX3zBypUrWbRoUZXHTJo0iby8PNcjIyOjDq7g3LUM98fH087RMicZh4utLkek+vL2mKN5XrkQ1s21upozy9ps3mqx2eH8O6rer98E6He7uf3x/8H+dfVTH8BXD0DRAWjeGS76a/29b33w8ITLHoZbvoDgFuZzUd0hupu1dUmjY1k7YkREBB4eHmRlZVV4Pisri+jo6FP237FjB7t27WLYsGGu55xOJwCenp6kpaXRtm3bU45r06YNERERbN++nUGDBlVai4+PDz4+7rtCsIfdRtvmgWzen8/WrEJaNQuwuiSRM8vNgDeuNlcZB0h9FLoMBy8/S8s6rfJWn87DIbz16fcdPA1ydsCOVHhntLmUQ3BM3daX9pU54Z/Nbt7uaqwrmydcAHf8CGvfhQ6Dra5GGiHLWn68vb3p3bs3qampruecTiepqan079//lP07derEhg0bWLt2resxfPhwLr30UtauXVvlrao9e/aQk5NDTEwd/6NUx8pnetZwd2kQctPNkTuHd5m3M4LjzNFTy1+xurKq5e+DDe+b2wPuPvP+Hp5w/evmPEAF++C9G6G0Dltmj+TCF8c7Nve/yxwt1Zj5hUH/P0GzU/9TK1JTlt72SklJYdasWbzxxhts2bKFO+64g6KiIsaPHw/A2LFjmTTJnLnU19eXbt26VXiEhoYSFBREt27d8Pb2prCwkL/+9a8sW7aMXbt2kZqayogRI2jXrh2DBzfs/z2cGO6u8CNu7vBueH0o5O4253EZ9yVc9jfztR+fM/usuKPlr4CzDFoOgBZ9qneMbwj8/j3wCzcn4/vkDjjeIl3rvnkYCvabq5pf+lDdvIdIE2Fp+Bk1ahTPPvssU6ZMITExkbVr1zJ//nxXJ+j09HT2799f7fN5eHiwfv16hg8fTocOHbjtttvo3bs3P/zwg1vf1qqODprrRxqCw7vMFp+8dAhvC+O/hJA46DEKIruaMxFbNULqdEoK4OfjI4uq0+pzsvA2MOotc6LBzZ+Y89XUtu0LzbWwsJm3u9z51qFIA2AzDHdeqtga+fn5hISEkJeXR3Cwe8woujuniIufWYS3p50tjw3Bw26zuiSRig79CrOHQf4es3Xili8q9oHZtgDevs5cfPPuVRDqRqMql75kro/VrB3cuRLs5/D/wjVvmetVAVz7KnS/rnZqO5oPL/U3v69Jd8CVdRCuRBqJ6v7+bjCjvZq6+DB/fL3slB5zsjunyOpyRCrK2QGzrz4efNrDuHmndv5tl2wuaukoge/+bk2dlXEcg2Uvmdv97zq34ANw3k0w4M/m9id/goyVtVPfwqnm9zUsAQZNrp1zijRxCj8NhN1uo11keadn3foSN+IKPnvNzr/j5kHQqSM2sdng8kfN7XXvmRMJuoPNn5jrS/lH1HzV8ORHoONVZsB77/fmiLea2Pk9/Hx8xvvhL4C3RnqK1AaFnwakQ6TZ70fLXIjbOLjN7ONTsA+ad4JxX0BQVNX7x/WGrr8DDFj4aL2VWaWTl7Lod3vN+9LYPeCaWebcNEXZ8O5oc5LHc1FaBJ8d73/U51ZofVHNahMRF4Wf+pa355xHg3SILl/mQi0/4gYObDVbfAr2mxPu3fIFBEae+bjLJpvLMmxfYLZsWGnXj+YEhZ6+0PcPtXNOn0C48V1zUdSsjfDRBHBWPcN8lVIfNzuQh8TD5Y/VTm0iAij81K+vH4Z/94Rfvjinw8vn+lHLj1juQJo5gWFhpjmKa9wXENi8esc2a3tizawFU+puaHh1lE9qmDgGAprV3nlD42H0O2bn7rQvYeEjZ3d8+jJYPtPcHjbdXABURGqNwk998vID5zH4/ulzWg26/fHbXr8eKKLMYeEvDGnasn8xW3wKsyCqG9zyOQRUveRMpS66H7wDzblxNn9cN3WeSfYvsO1rwAb976z988f3hZHHO1Ivef74UPVqKDtyfNSYYXaibpd8xkNE5Owo/NSn8/9k/oOfuQG2zj/rw+NC/fD39qDUoRFfYpGszWaLT1E2RHc/HnzOocUksPmJkVGpj8Gx0tqtszqWvmj+2Wlo3c0i3P06uPgBc/vzibDrpzMf890/IGc7BMXAFW40Kk6kEVH4qU/+4Sf6FSw++9Yfu91Ge434EqtkbYI3hpmLakb3gLGfVb7qeXX1v9PsF3N4F6yaXVtVVk9BFqw/vtDqBX+p2/e6+EGzk7ezDObeZM6HVJU9P58IZVdPB7/Quq1NpIlS+Klv/e8CTz/Ytxq2p555/9/QMhdiicyNZvApPggxiTD205oFHzA7Bl/yoLm9+ClzMr/6suI/4CiF+CSI71e372W3w4iXIPY8OHLIXAT1aN6p+x0rMW93GU7ofgN0HFK3dYk0YQo/9S2wOfS9zdxe/NRZt/6c6PSslh+pJ/vXHw8+OeYv8LGf1Dz4lOs11pxVufjgic7Hda20CFb+19w+26UszpW3P9z4HgTFwsE0eH+cObniyRY/DQd+gYDmcOVT9VOXSBOl8GOFAXebo0D2rICdi8/qULX8SL3avw7mDDdbLOJ6w82fmKtt1xYPLxg0xdxe+iIUZNbeuauy5m04mmuuydXxqrp/v3JB0eYiqF7+sONbczmNcvvXwY//MreH/rP2wqWIVErhxwpB0dB7nLm9+OmzOrR8gdOdB4soPaYRX1KH9q2FN4bDkcMQ1wdu/rhu+qB0Hg4t+kJZsdkaWpecjhN9avrfaU5KWJ9iesI1/zG3V/wHVswyO3t/cicYDugywnyISJ1S+LHKBX8BD2/Y/ZM50Vo1xYb4EujjyTGnwS6N+JK6sne12eJzNBda9DODj29I3byXzXZiEr9Vb5izRteVLZ9D7m7wC4eev6+79zmdzsNg0FRz+6sH4MNbIWuDWdNVz1pTk0gTo/BjlZA4cw4POKvWH5vNRvuo8hFfuvUldWDvKpgz0uyUG58EN30IvlWvjlwrWg2ADlearR+pdbTsRYWlLCaY/XCsMvAe6Hmjeb1bPjefu/Lp6s2QLSI1pvBjpYH3mNP871wM6curfVj5Gl8a7i61bs/PZvApyYOW/esn+JRLngo2uxkGMlbU/vnTl5rBzsMH+k6o/fOfDZsNhv0b4s83v+54lTknkIjUC4UfK4W2NP/3B+asz9XkavnJVMuP1KKMFfDm76AkH1pdAGM+qN9lFSI7Q+LxW1ELppzTLOin5VrK4sbqL8VRlzx94KYP4PrZcN1rZiASkXqh8GO1C1PA5gHbF5r/K62G8k7PW7MVfqSWpC+HN68xg0/ChTDmfXMenvp2yUPmIqPpS89pFvQqHdxmrrEF5lxb7sInyJwAsaaryYvIWfG0uoAmL7wN9LgB1r0Li58xh8KeQXn42Z1TTMkxBz6e9TxiReqXYcD6/8HhnebacE6H+afhrPh1Zc8ZjpNer+I4w2GGg7JiM/j8fi54B1hzrSFxcP4d5rDvhY9Au8vBoxb+mSof4dXxKohoX/PziUiDpvDjDi6815xqf+tX5nwfMT1Pu3tUsA9Bvp4UHD3GrweK6BxTT30yxBrr3oVP7qj792l9sTkRn5UdgQEumGgud3HgF1j3jjkRYk0UHoC175rb9TWpoYi4NYUfdxDRHrpeAxs/gO+fgVGnX/3ZZrPRISqIVbsPszWrQOGnMcvfB18dXwKi/WCzn5jd05yfxu5hbts8Tn3O9XxVz3lWPN47wFzmob7nvamMXyhceB9887C5yGe362oWyFbOAkeJOUljy/61VqaINFwKP+7iovtg44fmSJeszRDV5bS7d4gKZNXuw1rmojEzDHMl8JI8iO0Fo9+pnVtADUG/CbD8FchLh+Uvm62j56K02JxIEMxWH3UqFhHU4dl9RHaGLsPN7e+fOePu7SO1zEWjt34ubPvanAxzxIymE3zAHAl12d/M7R+nQ/GhczvPunfMpTlCW0GnYbVWnog0bAo/7uSiv5p/bvoYDmw97a7lnZ63Zavlp1EqyISv7je3L37gjC2BjVL36yG6uzkC7ftzmPnY6YClM8zt/nc2rfAoIqel8ONOortDx6GAAT+c/h/78tXdd+cUcbTMUQ/FSb0xDPjiHnOG5ZhEswNwU2S3Q/Lx2Z5XzoLDu8/u+LQv4dCv4BsKiWNqvTwRabgUftzNxcdbfza8Dzk7qtyteZAPof5eOA3YrtafxmXDB+YvbrsXjHypabdYtL3MHIXmKIVvnzi7Y8snNez7B2vmLBIRt6Xw425iz4P2V5hzsfzwXJW72Ww21zIX2zTZYeNRkAVfHQ/AFz8AUV2trcdqNhtcfrz1Z8P/zKkgqiN9OWQsN/tL9bu97uoTkQZJ4ccdXXS8r8e6d+Hwrip3O7HAqVp+GgXDgHkpcOQwRPeAgROtrsg9xJ5nDncHc+LD6lh6vNWnxygIiqqTskSk4VL4cUfxfaHNpebMuz/+q8rdXJ2eNeKrcdj4IfzyhTn3zsiXwcPL6orcx6DJ5m3AHd/Cju9Ov2/ODtjyhbntTktZiIjbUPhxVxc/YP655m3Izah0F7X8NCKF2fDl8dtdF/0VortZW4+7CUsw++6Aueip01n1vsteAgxzUsjITvVRnYg0MAo/7qpVf3OdJWcZ/PTvSncpb/nJOFzMkVKN+GrQvrzPnI8mqjsMTLG6Gvd00X3gHQSZ681WssoU5Zj/YQAtZSEiVVL4cWcXH+/7s3oO5O8/5eWIQB/CA7wxNOKrYdv0MWz+9PjtrpfA09vqitxTQAQM/Iu5/e1jcKzk1H1+fhWOHTGnCEgYWK/liUjDofDjzhIuNNcicpTAkucr3aV9ZPmtL/X7aZCKDsK840s3XHgvxPSwth53d/6fIDAactPh59cqvlZ2xFwSA7SUhYiclsKPO7PZTsz6/PNrZr+Q3yi/9bVVw90bpi/vg+IciOxqLuYpp+cdAJdOMrcXP21OBFlu3XtQfBBC4qHLSEvKE5GGQeHH3bW9DOL6wLGjJyZtO0n5TM9a4LQB2vSJecvL5qHbXWcj8SaI6GD2kSrvD+d0wtIXze3z/9S0J4YUkTNS+HF3NtuJvj8rXzU7dJ6kvOUnLVMtPw1KUc5Jt7tSIDbR0nIaFA9PGDTV3F76EuTvg63zIWc7+IRAr5utrU9E3J7CT0PQ/gqI6QllRbBsRoWXysPP3twjFJUcs6I6ORdf/dW8RdO884lbm1J9nYZCfJLZuXnRkydaRfuMB58ga2sTEben8NMQ2GwnZn1e/h8oPuR6KSzAm4hAH0ArvDcYWz43h2q7bnf5WF1Rw2OzweWPm9ur50D6EnMSxKT/s7YuEWkQFH4aio5XQVQ3KC04MaLluA5RGvHVYBQfgi+Oz+NzwV8grpe19TRkLZOg09WAYX7d/XoIjrW0JBFpGBR+Ggq73ZzkDWDZyxVGuWiZiwbkqwegKBuad4JLHrS6moZv0BSzBQ1ggJayEJHqsTz8zJgxg4SEBHx9fUlKSmLFihXVOu69997DZrMxcuTICs8bhsGUKVOIiYnBz8+P5ORktm3bVgeVW6DzCPOXZkmeefvrOC1z0UD8Ms9cmdxmhxG63VUrmneEMe/Dje9BVFerqxGRBsLS8DN37lxSUlKYOnUqq1evpmfPngwePJjs7FPnsznZrl27uO+++7jwwgtPee3pp5/m+eefZ+bMmSxfvpyAgAAGDx7M0aNH6+oy6o/dfmIumGUzoMRs6VHLzxk4jsG6ufD5X2DvamtqKD4EX9xjbg/4M7TobU0djVG7QdDxSqurEJEGxNLw89xzzzFhwgTGjx9Ply5dmDlzJv7+/rz22mtVHuNwOBgzZgyPPvoobdq0qfCaYRhMnz6dv/3tb4wYMYIePXowZ84c9u3bxyeffFLHV1NPul0DzdrBkcOw8r8AdIg0w8++vKMUHC2zsjr3UnbU/B69cB58fDusmg2zLoUPboVDO+u3lvmToDDLnJ/mkkn1+94iIlKBZeGntLSUVatWkZycfKIYu53k5GSWLl1a5XGPPfYYkZGR3Hbbbae8tnPnTjIzMyucMyQkhKSkpNOes6SkhPz8/AoPt2X3MJdBAFjyIpQWEeLvRWSQRny5lBSYk9/9u4c5l05uOvhHmJ3GsZkjrV7sawaSk0bO1Zm0+bD+vRO3u7x86/49RUSkSpaFn4MHD+JwOIiKiqrwfFRUFJmZmZUe8+OPP/Lqq68ya9asSl8vP+5szgkwbdo0QkJCXI/4+PizuZT61/16CEsw54n5+XXgpGUumvJkh8WH4Lt/wL+6woIpZktLcAu48hmYuAFufBf+73tz1mxnGSx7Cf6dCD88Z64LVReOHIYvJprb/e+E+L518z4iIlJtlnd4rq6CggJuvvlmZs2aRURERK2ee9KkSeTl5bkeGRkZtXr+WufhdVLrz/NQduRE+GmKnZ7z98HXD8O/usHip8yRcM3am60sf14DSbeDt7+5b0wPuPljuOkjiOpudh5PfRRe6A1r3gano3Zr+/phKNhv3qq89OHaPbeIiJwTyxbAiYiIwMPDg6ysrArPZ2VlER0dfcr+O3bsYNeuXQwbNsz1nNPpBMDT05O0tDTXcVlZWcTExFQ4Z2JiYpW1+Pj44OPTwEbe9BhtLuyYlwGr59Ahyuzwua0pLXCas8O8vbXuXXCUms/F9DSDYaerzVuEVWk3CNpcChveh28fN7+Pn/4Jls6Ayx+Fdsk1XxV86zew9m3Advx2l1/NziciIrXCspYfb29vevfuTWpqqus5p9NJamoq/fv3P2X/Tp06sWHDBtauXet6DB8+nEsvvZS1a9cSHx9P69atiY6OrnDO/Px8li9fXuk5GzRPbxh4fPTQj9PpEGEuitkkJjrM3Agf3AYv9oHVb5jBp9UFcNOHcPti6DLi9MGnnN0OPUfBXT/DFU+Abwhkb4K3r4M5w2HfmnOv8UiuOboMzNtdLZPO/VwiIlKrLF36OCUlhVtuuYU+ffrQr18/pk+fTlFREePHjwdg7NixxMXFMW3aNHx9fenWrVuF40NDQwEqPD9x4kSeeOIJ2rdvT+vWrZk8eTKxsbGnzAfUKJx3E3z/LBTso3PWZ0AcWfkl5B0pI8TPy+rqal/GCvjhn+YiluXaXwEDU6BVDcKtly8MuBsSx8CPz5kzaO/8Hv5zCXS7DgZNNvtYnY1vHoaCfRDeVre7RETcjKXhZ9SoURw4cIApU6aQmZlJYmIi8+fPd3VYTk9Px24/u8ap+++/n6KiIm6//XZyc3MZOHAg8+fPx9e3EY6w8fSBgRPhq/vxW/Y8rUL+ye68Y3y9MZMb+rp5p+3qMgz49TuzU/KuH44/aYOuI83QE9Oj9t7LP9xsAep3O3z7d1g/FzZ+AJs/hX4TzAVI/cPPfJ5tC2HNW2adI2ac6G8kIiJuwWYYhmF1Ee4mPz+fkJAQ8vLyCA4Otrqc0ys7AtN7QFE2izpOYdy6TjQL8Obbey8hxL8Bt/44nZA2z2zpKb/9ZPeCnqPhgokQ0a7ua9i/DhZMNcMXgE8IXHgPJP2x6v47R/Pgpf6QvxfO/xMMmVb3dYqICFD9398NZrSXVMHLz1wgE7g4ew4dm/uRU1TKM9/8YnFh58hRBuveg5fOh7k3mcHH0w+S7oC/rIURL9ZP8AGz8/TYTyqODFv4yOlHhn0z2Qw+Ya3hssn1U6eIiJyVcwo/b7zxBvPmzXN9ff/99xMaGsqAAQPYvXt3rRUn1dRnPPg3w3Z4Fy923wHA28vTWb8n19q6zsbRPFgxC17oBR//HxxMM1taLvor3LMRrnwSQlpYU1u7Qeb8QL97xZw3KH+vOTJs5oXmLa7yxtPtqWYHbNDtLhERN3ZO4ecf//gHfn5ms//SpUuZMWMGTz/9NBEREdxzzz21WqBUg3eA2WEXaP/LS9zZqQjDMJj8yUYcTje+q1l2FDZ/ZrbwPNMevrzPnI05oDkkP2KGnsv+BgG1O6/TObHbzVtud6+Cyx8/aWTYtebIsF0/nhjd1e//IOECa+sVEZEqnVOfH39/f3755RdatmzJAw88wP79+5kzZw6bNm3ikksu4cCBA3VRa71pUH1+ypUUwPTu5ozCwB4jki8dfWl5wSiGDB5m/vJ2B06HGRQ2/A82f27eSirXvBP0uQ163ez+c+IUHzoxMqx8jiEwR4XdscQMpCIiUq+q+/v7nEZ7BQYGkpOTQ8uWLfnmm29ISUkBwNfXlyNH6miZADk9nyCzb8oP/4TtqbQ4ls3tnvNg+TycGyOxd74aOg+DhAvNGaLrk2GYnYc3vG+uq1Ww/8RrwXHQ7VrocQNEdav5xIL1pXxkWN8J8N3xkWHYYPiLCj4iIm7unFp+xowZwy+//MJ5553Hu+++S3p6Os2aNeOzzz7joYceYuPGjXVRa71pkC0/JystwrF1IYs/e40+JcsJtp0USH1DoeOVZhBqe1ndtrAc+hU2fADr/wc52yrW0HWkuUZZywHu0ypVEwfSzJF3sYlWVyIi0mTVacvPjBkz+Nvf/kZGRgYffvghzZo1A2DVqlXceOON51ax1B7vADy6jSAk6CJ6v/w9A+ybeLZbOs33LoSiA+ZyEOveBS9/aH85dB5u/ukbUvP3LsyGjR+ZrTx7fz7xvKevGbq6X28uHeHZwJYTOZPmHa2uQEREqknz/FSiwbf8nOSBD9Yz9+cMOkUH8cWd/fHctxK2fG4+8k5awNXuBW0uMVuEOg09u07GJQWw5Qsz8Py6CIzjQ8BtdvOc3a8319rybdjfSxERcW/V/f19TuFn/vz5BAYGMnDgQMBsCZo1axZdunRhxowZhIWFnXvlbqAxhZ9DRaVc9s9F5BaXMeXqLtw6sLX5gmHA/rVmCNr8WcXbUja7eTuq8zDofHXlQ8yPlcL2hWbgSfsKjp10ay2utxl4ul4DQVF1en0iIiLl6jT8dO/enaeeeoqrrrqKDRs20LdvX1JSUvjuu+/o1KkTr7/+eo2Kt1pjCj8A7yxP56GPNxDo40nqvRcTFVzJUh8H0mDLZ2YY2r+u4mux5x1vERpm3jbb8D5s/sQ1sgww17DqcYMZepq1rdPrERERqUydhp/AwEA2btxIQkICjzzyCBs3buSDDz5g9erVXHXVVWRmZtaoeKs1tvDjdBr87uUlrMvIZXjPWJ6/8bzTH3B4N/zyhXkrK30pUMVfkcAoc6RW9+vNgNRQRmqJiEijVKcdnr29vSkuLgZg4cKFjB07FoDw8HDy8/PP5ZRSh+x2G0+M6MaIGT/y2bp9jO4bz4B2p+nTE9YK+t9pPgqz4Zd5ZovQzsVmJ+nOw6H7ddD6IrB71N+FiIiI1IJzCj8DBw4kJSWFCy64gBUrVjB37lwAtm7dSosWFi1BIKfVvUUIN53fijlLdzP504189ZeL8PasxhDzwEhz+Yw+482h3DYP8PSu+4JFRETqyDlNsPLiiy/i6enJBx98wMsvv0xcXBwAX331FUOGDKnVAqX23HtFRyICvdlxoIhXf9x59ifw8lPwERGRBk9D3SvR2Pr8nOzDVXu49/11+Hl5sPDei4kLdfNlJERERKqpTvv8ADgcDj755BO2bNkCQNeuXRk+fDgeHuoD4s6u6RXH3JUZrNh1iMc+38QrN/exuiQREZF6dU63vbZv307nzp0ZO3YsH330ER999BE33XQTXbt2ZceOHbVdo9Qim83GYyO74mG38fWmLL5Ly7a6JBERkXp1TuHnz3/+M23btiUjI4PVq1ezevVq0tPTad26NX/+859ru0apZZ2ig7n1ggQAHvlsE0fLHNYWJCIiUo/OKfwsXryYp59+mvDwcNdzzZo148knn2Tx4sW1VpzUnb8kdyAq2IfdOcXMXKzWOhERaTrOKfz4+PhQUFBwyvOFhYV4e2s0UEMQ6OPJ5Ku7APDSoh3szimyuCIREZH6cU7h5+qrr+b2229n+fLlGIaBYRgsW7aMP/7xjwwfPry2a5Q6MrR7DAPbRVB6zMkjn21CA/9ERKQpOKfw8/zzz9O2bVv69++Pr68vvr6+DBgwgHbt2jF9+vRaLlHqis1m49ERXfHysPFd2gG+3pRldUkiIiJ1rkbz/Gzfvt011L1z5860a9eu1gqzUmOe56cyz36dxovfbSc2xJeF916Mv/c5z4AgIiJimVqf5yclJeW0r3/33Xeu7eeee666pxU3cOel7fh4zV725h7hhW+388CQTlaXJCIiUmeqHX7WrFlTrf1sWtm7wfHz9uCR4V2ZMOdn/vvDr1zbqwXtIgOtLktERKROVDv8nNyyI43P5V2iGNQpktRfspny6Ube/kOSgqyIiDRK59ThWRqnR4Z3xcfTzpIdOXy+fr/V5YiIiNQJhR9xiQ/3585LzU7rT3yxmYKjZRZXJCIiUvsUfqSC2y9qQ+uIALILSvjXgm1WlyMiIlLrFH6kAl8vDx4d3hWAN5buYsv+fIsrEhERqV0KP3KKizo056ru0TicBpM/2YjTqZmfRUSk8VD4kUpNvroL/t4e/Lz7MB+u3mN1OSIiIrVG4UcqFRPix18GtQfgya9+Ibe41OKKREREaofCj1Tp1oGtaR8ZSE5RKc98nWZ1OSIiIrVC4Ueq5OVh5/GR3QB4Z0U66zJyrS1IRESkFij8yGmd36YZvzsvDsOAyZ9uxKHOzyIi0sAp/MgZTbqqE0E+nqzfk8e7K9KtLkdERKRGFH7kjCKDfLn3ig4A/OPLLazYecjiikRERM6dwo9Uy03nt+LC9hEUlzoY//oKft6lACQiIg2T5eFnxowZJCQk4OvrS1JSEitWrKhy348++og+ffoQGhpKQEAAiYmJvPnmmxX2GTduHDabrcJjyJAhdX0ZjZ6nh51ZY/swsF0ERaUObnltBat2KwCJiEjDY2n4mTt3LikpKUydOpXVq1fTs2dPBg8eTHZ2dqX7h4eH8/DDD7N06VLWr1/P+PHjGT9+PF9//XWF/YYMGcL+/ftdj3fffbc+LqfR8/XyYNbYPgxo2+x4AFrJ6vTDVpclIiJyVmyGYVg2fCcpKYm+ffvy4osvAuB0OomPj+fuu+/mwQcfrNY5evXqxdChQ3n88ccBs+UnNzeXTz755Jzrys/PJyQkhLy8PIKDg8/5PI3VkVIHt85eydJfcwjy8eTNPySRGB9qdVkiItLEVff3t2UtP6WlpaxatYrk5OQTxdjtJCcns3Tp0jMebxgGqamppKWlcdFFF1V4bdGiRURGRtKxY0fuuOMOcnJyTnuukpIS8vPzKzykan7eHrw6rg9JrcMpKDnGza8u1xxAIiLSYFgWfg4ePIjD4SAqKqrC81FRUWRmZlZ5XF5eHoGBgXh7ezN06FBeeOEFLr/8ctfrQ4YMYc6cOaSmpvLUU0+xePFirrzyShwOR5XnnDZtGiEhIa5HfHx8zS+wkfP39uS1cX3plxBOwdFj3PTqctbvybW6LBERkTOyvMPz2QoKCmLt2rWsXLmSv//976SkpLBo0SLX66NHj2b48OF0796dkSNH8sUXX7By5coK+/zWpEmTyMvLcz0yMjLq/kIagQAfT14f35e+CWFmAPrvcjbuzbO6LBERkdOyLPxERETg4eFBVlZWheezsrKIjo6u8ji73U67du1ITEzk3nvv5brrrmPatGlV7t+mTRsiIiLYvn17lfv4+PgQHBxc4SHVYwagfvRuFUb+0WOMUQASERE3Z1n48fb2pnfv3qSmprqeczqdpKam0r9//2qfx+l0UlJSUuXre/bsIScnh5iYmBrVK1UL9PFk9vi+9GoZSt6RMm56dTmb96nflIiIuCdLb3ulpKQwa9Ys3njjDbZs2cIdd9xBUVER48ePB2Ds2LFMmjTJtf+0adNYsGABv/76K1u2bOGf//wnb775JjfddBMAhYWF/PWvf2XZsmXs2rWL1NRURowYQbt27Rg8eLAl19hUBPl68cat/UiMDyW3uIwx/13Glv0KQCIi4n48rXzzUaNGceDAAaZMmUJmZiaJiYnMnz/f1Qk6PT0du/1EPisqKuJPf/oTe/bswc/Pj06dOvHWW28xatQoADw8PFi/fj1vvPEGubm5xMbGcsUVV/D444/j4+NjyTU2JUG+Xsy5rR83v7qCdRm5jPnvct6ZkESnaN1GFBER92HpPD/uSvP81EzekTJufnU56/fkER7gzbsTzqdjdJDVZYmISCPn9vP8SOMV4ufFm7cm0T0uhENFpfx+1jK2ZRVYXZaIiAig8CN1JMTfizdv60fX2GByikq5cdZytmcrAImIiPUUfqTOhPp78/YfkugSE8zBwhJG/2c527MLrS5LRESaOIUfqVPlAajz8QB046xl7DigACQiItZR+JE6FxZgBqBO0UEcKCjhxv8sY+fBIqvLEhGRJkrhR+pF+PEA1DEqiOzjAWiXApCIiFhA4UfqTbNAH96ekET7yEAy849y46xl7M5RABIRkfql8CP1KiLQh3cmnE+7yED25x3lxv8sIz2n2OqyRESkCVH4kXrXPMiHdyYk0bZ5APvyzBagjEMKQCIiUj8UfsQSkUG+vDvhfNo0D2Bv7hFG/0cBSERE6ofCj1gmMtiX9yacT5sIMwCpBUhEROqDwo9YKjLYl3cmnE9CM3/2HD7CVc//wIer9qAl50REpK4o/IjlokN8eff28+kZH0rB0WPc+/46bn9zFQcKSqwuTUREGiGFH3ELMSF+fPjH/vx1cEe8PGws2JzFFf9azJcb9ltdmoiINDIKP+I2PD3s3HlpOz69cyCdY4I5XFzGn95ezZ/fXUNucanV5YmISCOh8CNup0tsMJ/eeQF3XdoOuw0+W7ePK/71Pd/+kmV1aSIi0ggo/Ihb8va0c9/gjnx4xwDaNA8gu6CEW2f/zAMfrKfgaJnV5YmISAOm8CNu7byWYXz55wu5bWBrbDaY+3MGQ6b/wJLtB60uTUREGiiFH3F7vl4eTL66C+9OOJ/4cD/25h7h9/9dziOfbeJIqcPq8kREpIFR+JEG4/w2zfjqLxfx+6SWAMxesournv+BVbsPW1yZiIg0JAo/0qAE+njyj991541b+xEd7MvOg0VcP3MJT371CyXH1AokIiJnpvAjDdLFHZrz9T0XcU2vOJwGzFy8g+Ev/MTGvXlWlyYiIm5O4UcarBA/L567IZFXbu5NRKA3aVkFjJzxE/9euI0yh9Pq8kRExE0p/EiDN7hrNF9PvIgru0VzzGnwr4VbuealJWzLKrC6NBERcUMKP9IoNAv04aUxvfj36ERC/LzYsDePoS/8yH++34HDqUVSRUTkBIUfaTRsNhsjEuP45p6LuKRjc0qPOfnHl78w6pWl7DpYZHV5IiLiJhR+pNGJCvbl9XF9eera7gR4e/Dz7sNc+e8feHPpLpxqBRIRafIUfqRRstlsjOrbkvkTL6J/m2YcKXMw+dNN/O7lJaxJ17xAIiJNmcKPNGrx4f68/YckHhnWhQBvD9Zl5PK7l5aQMnctWflHrS5PREQsYDMMQ/cBfiM/P5+QkBDy8vIIDg62uhypJdkFR3l6fhofrNoDgL+3B3dd1o7bBrbGx9PD4upERKSmqvv7W+GnEgo/jdu6jFwe+XwTa9JzAWjVzJ+Hr+rM5V2isNls1hYnIiLnTOGnBhR+Gj+n0+CTtXt58qtfyC4oAeDC9hFMuboL7aOCLK5ORETOhcJPDSj8NB1FJceY8d12/vvDTkodTjzsNm4+vxX3JHcgxN/L6vJEROQsKPzUgMJP07M7p4i/z9vCN5uzAAjz9+LeKzpyY7+WeNh1K0xEpCFQ+KkBhZ+m68dtB3n0801syy4EoHNMMFOHdeH8Ns0srkxERM5E4acGFH6atmMOJ28t281zC7aSf/QYAEO7xzDpqk60CPO3uDoREamKwk8NKPwIwKGiUp5bkMY7y9NxGuDjaeePF7fljxe3xc9bQ+NFRNyNwk8NKPzIyTbvy+fRzzexfOchAGJDfJl0VWeu7hGjofEiIm5E4acGFH7ktwzD4KuNmfx93hb25h4BoF9COFOGdaFbXIjF1YmICFT/97fly1vMmDGDhIQEfH19SUpKYsWKFVXu+9FHH9GnTx9CQ0MJCAggMTGRN998s8I+hmEwZcoUYmJi8PPzIzk5mW3bttX1ZUgjZ7PZuKp7DKn3XkzK5R3w9bKzYtchhr34I5M+2kBOYYnVJYqISDVZGn7mzp1LSkoKU6dOZfXq1fTs2ZPBgweTnZ1d6f7h4eE8/PDDLF26lPXr1zN+/HjGjx/P119/7drn6aef5vnnn2fmzJksX76cgIAABg8ezNGjWsdJas7Xy4M/D2rPt/dewvCesRgGvLsinUueXcSs73+l4GiZ1SWKiMgZWHrbKykpib59+/Liiy8C4HQ6iY+P5+677+bBBx+s1jl69erF0KFDefzxxzEMg9jYWO69917uu+8+APLy8oiKimL27NmMHj26WufUbS+prpW7DvHIZ5vYtC8fgABvD67r3YKb+yfQLjLQ4upERJoWt7/tVVpayqpVq0hOTj5RjN1OcnIyS5cuPePxhmGQmppKWloaF110EQA7d+4kMzOzwjlDQkJISko67TlLSkrIz8+v8BCpjr4J4Xx210CevrYH7SIDKSp18MbS3SQ/t5ibX13Ogs1ZOJzqVici4k48rXrjgwcP4nA4iIqKqvB8VFQUv/zyS5XH5eXlERcXR0lJCR4eHrz00ktcfvnlAGRmZrrO8dtzlr9WmWnTpvHoo4+e66VIE+dht3FD33iu79OCJTtymL1kF6lbsvhh20F+2HaQ+HA/bj6/FTf0iSfU39vqckVEmjzLws+5CgoKYu3atRQWFpKamkpKSgpt2rThkksuOedzTpo0iZSUFNfX+fn5xMfH10K10pTYbDYuaBfBBe0iyDhUzFvLdzN3ZQYZh47wjy9/4bkFWxmZGMfY/gl0idXtVBERq1gWfiIiIvDw8CArK6vC81lZWURHR1d5nN1up127dgAkJiayZcsWpk2bxiWXXOI6Lisri5iYmArnTExMrPKcPj4++Pj41OBqRCqKD/dn0pWdmTioA5+t28vsJbvZsj+f91Zm8N7KDPolhHPLgASu6BqFl4flgy5FRJoUy/7V9fb2pnfv3qSmprqeczqdpKam0r9//2qfx+l0UlJiDjNu3bo10dHRFc6Zn5/P8uXLz+qcIrXFz9uDUX1b8uWfB/L+H/sztEcMHnYbK3Yd4s53VnPhU9/xQuo2DmqovIhIvbH0tldKSgq33HILffr0oV+/fkyfPp2ioiLGjx8PwNixY4mLi2PatGmA2TenT58+tG3blpKSEr788kvefPNNXn75ZcC87TBx4kSeeOIJ2rdvT+vWrZk8eTKxsbGMHDnSqssUwWaz0TchnL4J4WTmHeWd5bt5Z0U6mflH+eeCrbzw7XaG9ojhlgEJJMaHWl2uiEijZmn4GTVqFAcOHGDKlClkZmaSmJjI/PnzXR2W09PTsdtPNE4VFRXxpz/9iT179uDn50enTp146623GDVqlGuf+++/n6KiIm6//XZyc3MZOHAg8+fPx9fXt96vT6Qy0SG+pFzRkTsva8dXGzKZvWQXazNy+XjNXj5es5eeLUK4ZUACQ3vE4OOpNcRERGqblreohOb5kfq2LiOXN5bu4ot1+yl1OAFoFuDNjf1aMub8lsSE+FlcoYiI+9PaXjWg8CNWOVhYwnsr0nlrmXlLDMyh9EO6RnPT+a04v024FlMVEamCwk8NKPyI1Y45nHyzOYvZS3ax4vhq8gAJzfwZ1bcl1/aOIzJIt3JFRE6m8FMDCj/iTrbsz2fO0t18tnYvRaUOwGwNGtQpkhv7teSiDs3xsKs1SERE4acGFH7EHRWVHGPe+v28tzKd1em5rudjQny5vncLru8TT3y4v3UFiohYTOGnBhR+xN1tzSpg7soMPlq9h8PF5kryNhsMbBfB6L4tSe4SqZFiItLkKPzUgMKPNBQlxxx8symLuSsz+HH7Qdfz4QHeXHNeHKP7xdMuMsjCCkVE6o/CTw0o/EhDlJ5TzP9+zuD9VRlk5Z+YMbpPqzBG9Y1naI8Y/L0b3HJ+IiLVpvBTAwo/0pAdczhZvPUA767I4Lu0bBxO80c8yMeT4YmxjO7bku4tQiyuUkSk9in81IDCjzQWWflH+WDVHuauzCD9ULHr+a6xwYzuG8/wxDhC/LwsrFBEpPYo/NSAwo80Nk6nwbJfc3hvZQbzN2a6ZpH28bQztHsMo/rG06+1JlAUkYZN4acGFH6kMTtcVMona/fy3ooM0rIKXM/HhfoxtEcMV/eIoXtciIKQiDQ4Cj81oPAjTYFhGKzNyGXuygw+X7fPNYEiQMtwf4b2iGFo9xi6xgYrCIlIg6DwUwMKP9LUHC1zsCgtm8/X7+fbLdkcKTsRhFpHBDC0ewxX94yhY1SQgpCIuC2FnxpQ+JGmrLj0GN/+ks289fv59pdsSo45Xa+1bR7A1T1iubpHDO2jNH+QiLgXhZ8aUPgRMRWVHGPhliy+WL+fxWkHXB2lATpGBZm3xnrE0LZ5oIVVioiYFH5qQOFH5FT5R8tYuDmLeev38/22A5Q5TvzT0TkmmKuP9xFKiAiwsEoRacoUfmpA4Ufk9PKKy/hmcybzNuznx20HOeY88c9It7hgru4Ry9DuMVpoVUTqlcJPDSj8iFTf4aJSvt5kBqElO3JcM0oD9IwP5eruMVzZPZoWYQpCIlK3FH5qQOFH5NzkFJYwf1MmX6zbz/KdOZyUg4gP96Nvq3D6JITTJyGMds0Dsds1ckxEao/CTw0o/IjUXHbBUb7emMnn6/fz865DFYIQQIifF31ahdEnIZy+CWF0iwvB18vDmmJFpFFQ+KkBhR+R2lVwtIw16bn8vOsQK3cdZk3GYY6WOSvs4+1hp0eLEFcY6t0qjFB/b4sqFpGGSOGnBhR+ROpWmcPJpn35/LzrED/vOszPuw9xsLD0lP3aRwa6wlDfhHBahPlpkkURqZLCTw0o/IjUL8Mw2JVTzMpdh8xAtPswvx4oOmW/qGAfs89QKzMMdYoOwtPDbkHFIuKOFH5qQOFHxHoHC0tYtfuw61bZxr15FYbUAwR4e9CrVRjnt2nG4K7RtIvUZIsiTZnCTw0o/Ii4nyOlDtbtOdFvaPXuwxSUHKuwT/vIQK7sFs3gbtF0idGCrCJNjcJPDSj8iLg/h9MgLbOAn3cfInVLNkt2HKww63TLcH9XEEpsEaph9SJNgMJPDSj8iDQ8eUfK+PaXLL7akMnirQcqLMgaHezLkG7RDO4aTb/W4XgoCIk0Sgo/NaDwI9KwFZUcY/HWA3y1MZNvt2RRVOpwvdYswJsrukYxuGs0A9pG4O2pDtMijYXCTw0o/Ig0HkfLHPy0/SBfbcxkweYs8o6UuV4L8vXk8s5RDO4WzcUdmmuSRZEGTuGnBhR+RBqnMoeT5b8e4quN+/l6UxYHC0tcr/l7e3Bpx0gGd4vmsk6RBPp4WlipiJwLhZ8aUPgRafwcToPV6Yf5akMmX2/KZG/uEddr3p52LmofweCu0VzeJUozTYs0EAo/NaDwI9K0GIbBhr15fLUxk/kbM9l58MQEix52GwPaNmNYj1gGd40mxN/LwkpF5HQUfmpA4Uek6TIMg61ZhXy1cT/zN2byS2aB6zUvDxsXtm/O1T1iuLxLFEG+CkIi7kThpwYUfkSk3M6DRcxbv48v1u+vEIS8Pe1c3MEMQsmdowhQHyERyyn81IDCj4hUZnt2AZ+v288X6/ex46S1x3y97FzWKZKre8RyacdI/Lw1akzECgo/NaDwIyKnYxgGv2QWMG+9GYR25RS7XvP39iC5cxRDe8Ro+LxIPVP4qQGFHxGpLsMw2LQvn8/X7+OLdfsrjBoL8vHk8i5RXN0zhoHtmmtCRZE6pvBTAwo/InIuDMNgbUYuX6zfz7z1+8nMP+p6LcTPi8Fdo7i6Ryz92zbDy0NBSKS2KfzUgMKPiNSU02mwKv0wX6zbx5cbMzlQcGJCxTB/L4Z0i2FYjxiS2jTTWmMitaS6v78t/6/HjBkzSEhIwNfXl6SkJFasWFHlvrNmzeLCCy8kLCyMsLAwkpOTT9l/3Lhx2Gy2Co8hQ4bU9WWIiFRgt9vomxDOoyO6sWzSIN6dcD5jkloSHuDN4eIy3l2Rzu//u5ykf6SSMnct7/+cUeGWmYjUHUtbfubOncvYsWOZOXMmSUlJTJ8+nffff5+0tDQiIyNP2X/MmDFccMEFDBgwAF9fX5566ik+/vhjNm3aRFxcHGCGn6ysLF5//XXXcT4+PoSFhVW7LrX8iEhdOeZwsvTXHL5Yt5/5mzIrrDUGkNDMn/5tIxjQthn92zYjItDHokpFGp4GcdsrKSmJvn378uKLLwLgdDqJj4/n7rvv5sEHHzzj8Q6Hg7CwMF588UXGjh0LmOEnNzeXTz755Jzrqu43z+FwUFZWVuXrUjVvb2/sdssbHkUsVXrMycpdh/hp+0GW7Mhh/Z5cnL/5F7ljVBAD2jVjQNsI+rUOJ8RPEyuKVKW6v78tm5WrtLSUVatWMWnSJNdzdrud5ORkli5dWq1zFBcXU1ZWRnh4eIXnFy1aRGRkJGFhYVx22WU88cQTNGvWrMrzlJSUUFJy4n58fn7+ad/XMAwyMzPJzc2tVp1yKrvdTuvWrfH21ppJ0nR5e9q5oF0EF7SLACD/aBkrdx5iyY4cftp+kF8yC0jLMh+v/7QLuw26twhlQNtmDGjbjD6twjWnkMg5sCz8HDx4EIfDQVRUVIXno6Ki+OWXX6p1jgceeIDY2FiSk5Ndzw0ZMoRrrrmG1q1bs2PHDh566CGuvPJKli5diodH5f9ITJs2jUcffbTatZcHn8jISPz9/bHZ1FnxbDidTvbt28f+/ftp2bKlvn8ixwX7ejGocxSDOpv/LuYUlrDs10Ms2XGQpTty+PVgEesyclmXkcvLi3bg7WHnvJahDGgbwYB2zejZIlTD6UWqocHOx/7kk0/y3nvvsWjRInx9fV3Pjx492rXdvXt3evToQdu2bVm0aBGDBg2q9FyTJk0iJSXF9XV+fj7x8fGV7utwOFzB53StSXJ6zZs3Z9++fRw7dgwvLzXji1SmWaAPQ3vEMLRHDAD7co+wdEcOS3bksGTHQfbnHWX5zkMs33mIfy00J1jsmxB+vGUogi6xwRpJJlIJy8JPREQEHh4eZGVlVXg+KyuL6Ojo0x777LPP8uSTT7Jw4UJ69Ohx2n3btGlDREQE27dvrzL8+Pj44ONTvU6F5X18/P39q7W/VK78dpfD4VD4Eamm2FA/ru3dgmt7t8AwDHbnFPPTDrO/0NIdORwqKmXx1gMs3noAgGBfT/q3bcagzlFc1ilSnadFjrMs/Hh7e9O7d29SU1MZOXIkYN4OSU1N5a677qryuKeffpq///3vfP311/Tp0+eM77Nnzx5ycnKIiYmprdIBdKumhvT9E6kZm81GQkQACREBjElqhdNpsDW7gCXbzVah5b8eIv/oMb7elMXXm7Kw2aB3yzCSu0SR3DmKts0D9HMoTZalt71SUlK45ZZb6NOnD/369WP69OkUFRUxfvx4AMaOHUtcXBzTpk0D4KmnnmLKlCm88847JCQkkJmZCUBgYCCBgYEUFhby6KOPcu211xIdHc2OHTu4//77adeuHYMHD7bsOkVE6prdbqNTdDCdooO5dWBrjjmcbNibx+KtB1i4JYuNe/P5efdhft59mCe/+oXWEQEkd44kuXMUvVuF4akZp6UJsTT8jBo1igMHDjBlyhQyMzNJTExk/vz5rk7Q6enpFYZDv/zyy5SWlnLddddVOM/UqVN55JFH8PDwYP369bzxxhvk5uYSGxvLFVdcweOPP17t21pSPQkJCUycOJGJEydaXYqIVMLTw855LcM4r2UYE5M7sC/3CKm/ZLNwcxZLd+Sw82ARs37YyawfdhLq78VlHSNJ7hLFRR2aE+jTYLuDilSLlreoxOnmCTh69Cg7d+6kdevWFTpaNwSXXHIJiYmJTJ8+vcbnOnDgAAEBAefc96khfx9FGrrCkmP8sPUACzZn8W1aNrnFJ+Yr8/awc37bZlze2QxDMSF+FlYqcnbcfp4fcT+GYeBwOPD0PPNfi+bNm9dDRSJSFwJ9PLmyewxXdo/hmMPJqt2HWbgliwWbs9iVU8z3Ww/w/dYDTP50E93igknubPYT6hobrH5C0ijoJm8NGYZBcekxSx5n02g3btw4Fi9ezL///W/XmmezZ8/GZrPx1Vdf0bt3b3x8fPjxxx/ZsWMHI0aMICoqisDAQPr27cvChQsrnC8hIaFCC5LNZuO///0vv/vd7/D396d9+/Z89tlntfVtFpE64ulhJ6lNMx4e2oXv7ruEhSkX8+CVnejTKgybDTbuzWf6wm1c/cKPDHjyWyZ/spHFWw9Qcsxhdeki50wtPzV0pMxBlylfW/Lemx8bjL939T7Cf//732zdupVu3brx2GOPAbBp0yYAHnzwQZ599lnatGlDWFgYGRkZXHXVVfz973/Hx8eHOXPmMGzYMNLS0mjZsmWV7/Hoo4/y9NNP88wzz/DCCy8wZswYdu/efcoM3CLinmw2G+0iA2kXGcgfL27LwcISvj3eT+iHbea8Qm8u282by3YT4O3BxR2bc2H75vRoEUKHqCC81GlaGgiFnyYiJCQEb29v/P39XfMolc+k/dhjj3H55Ze79g0PD6dnz56urx9//HE+/vhjPvvss9NOQzBu3DhuvPFGAP7xj3/w/PPPs2LFCoYMGVIXlyQidSwi0Icb+sRzQ594jpY5WLLjIAs2Z5O6JYvsghK+3JDJlxvMUbfenna6xATTPS6E7i1C6NEihHbNAzWKTNySwk8N+Xl5sPkxa4bR+3nVzpo+v50vqbCwkEceeYR58+axf/9+jh07xpEjR0hPTz/teU6ecDIgIIDg4GCys7NrpUYRsZavlweXdYrisk5ROJ3d2LA3j4Vbslidfpj1e/IoOHqMtRm5rM3IPekYO11jQ+geZ4ahHi1CaB0RqFmnxXIKPzVks9mqfevJXQUEBFT4+r777mPBggU8++yztGvXDj8/P6677jpKS0tPe57fztRss9lwOp21Xq+IWMtut9EzPpSe8aEAOJ0G6YeKWb83jw17clm/J4+Ne/MoKnWwavdhVu0+7Do2wNvDDETHw1D3uBASmgVgVyCSetSwf2vLWfH29sbhOHMnxZ9++olx48bxu9/9DjBbgnbt2lXH1YlIQ2W3n5htenjPWMAMRDtzitiwJ4/1e/LYsDeXjXvzKSp1sGLXIVbsOuQ6PsjHk27HW4e6twihR1wo8eF+GlkmdUbhpwlJSEhg+fLl7Nq1i8DAwCpbZdq3b89HH33EsGHDsNlsTJ48WS04InJW7HYbbZsH0rZ5ICPPiwPA4TTYcaDQDEN7clm/N4/N+/IpKDnG0l9zWPprjuv4ED8vuseFkBgfynktQ0mMD6WZ1iaTWqLw04Tcd9993HLLLXTp0oUjR47w+uuvV7rfc889x6233sqAAQOIiIjggQceID8/v56rFZHGxsNuo0NUEB2igriudwsAyhxOtmcXmi1Ee3PZsCePLfsLyDtSxo/bD/Lj9oOu41s18+e8+FDOaxlGr5ZhdIrRCDM5N5rhuRKNdYZnd6Lvo4hUpfSYk61ZBazbk8va9FzWZOSyPbvwlP18PO30aBFiLuMRH0qvVmFEBevfk6ZMMzyLiEiD5O1pp1tcCN3iQhiT1AqAvCNlrMvIZXX6Ydak57Im/TD5R4+xctdhVu460aE6NsT3+Jpm5u2yrrEh+NbSyFhpPBR+RETE7YX4eXFRh+Zc1MFcWqe8Q3V5EFqdnktaZj778o6yb8N+5m3YD4CXh40uMcGuQNSrZRgtwtSZuqlT+BERkQbn5A7V5f2HikqOsX5PHmsyTrQOHSwsZd2ePNbtyWP2EvPYiEBvEuPNMNQxKoj2UYG0CPPX/ENNiMKPiIg0CgE+nvRv24z+bZsB5tqLew4fOXGrLCOXzfvyOFhYysItWSzckuU61sfTTpvmgbSPNB/tIgNpHxVIq2YB6lTdCCn8iIhIo2Sz2YgP9yc+3J8RieZw+6NlDjbty2fN8Zmpt2cXsuNAISXHnGzZn8+W/RVHtnoen8PIFYqigmjXPJA2zQPUl6gBU/gREZEmw9fLg96twujdKsz1nMNpsOdwMduyCtl+oND8M7uA7dmFFJU62J5dyPbsQr466Tx2G7QM9z++EGyQGY6izNtwAT761eru9AmJiEiT5mG30apZAK2aBZBMlOt5wzDYn3eUbdmFbMsqYMfxYLQtu5C8I2XsyilmV04xC7dUXMMwLtSPdpGBdIwOcs1LFB2iIfjuROFHRESkEjabjdhQP2JD/bj4+CgzMEPRgcISV4uQ2VJkhqKDhSXszT3C3twjLN56wHVMxSH4YXSLC8bHU7fNrKLwIyIichZsNhuRQb5EBvkyoG1EhdcOF5W6bp1t2pfHmvRcfqlkCL63h50uscGu4ffntQwlLlRD8OuLwo9UW0JCAhMnTmTixIlWlyIi4pbCArzpGxBO34Rw13PlQ/BPnqAxp6iUtRm5rM3I5fWfdgEQGeRzUhgKo3tcCH7eah2qCwo/IiIidaiyIfgZh8qH4B8+PgQ/n+yCEr7elMXXm8wh+J52G51jKrYOtQz3V+tQLVD4ERERqUc2m42Wzfxp2czfteL9kVIHG/flsXq32Tq0Ov0w2QUlbNibx4a9ecxZuhuAZgHern5DXWKDaRsRSFyYnyZoPEsKPzVlGFBWbM17e/lDNf8H8J///IdHHnmEPXv2YLefmLBrxIgRNGvWjIcffpiUlBSWLVtGUVERnTt3Ztq0aSQnJ9dV9SIicpyftwd9E07cLjMMg315R11haE3GYTbtzSenqJSFW7IrjDDz9rDTspk/rSMCaBMRYP7ZPJDWEQFEBHqrpagSCj81VVYM/4i15r0f2gfeAdXa9frrr+fuu+/mu+++Y9CgQQAcOnSI+fPn8+WXX1JYWMhVV13F3//+d3x8fJgzZw7Dhg0jLS2Nli1b1uVViIjIb9hsNuJC/YgL9WNYT/N3zNEyB5v355uBKCOX7VmF7MwpovSY0zXy7LeCfDxp3dwMROWhqE1EAAkRAQQ24fmImu6VNzFhYWFceeWVvPPOO67w88EHHxAREcGll16K3W6nZ8+erv0ff/xxPv74Yz777DPuuusuq8oWEZHjfL086NUyjF4tK07QuD/vCL8eKGLnQfPx68Eidh4sZM/hIxQc72y9fk/eKeeLDPKpEIhaRwTQunkA8WH+eHs27iU9FH5qysvfbIGx6r3PwpgxY5gwYQIvvfQSPj4+vP3224wePRq73U5hYSGPPPII8+bNY//+/Rw7dowjR46Qnp5eR8WLiEhNedhttAjzp0WYv2vF+3JHyxykHyo+KRgVmuHoQBE5RaVkF5SQXVDC8p2HKjmnH80DfQj19ybU34tQPy/CArwJ8fMi1N+LMH9zOyzAm1A/L/y9PRrU7TWFn5qy2ap968lqw4YNwzAM5s2bR9++ffnhhx/417/+BcB9993HggULePbZZ2nXrh1+fn5cd911lJaWWly1iIicC18vDzpEBdEhKuiU1/KKy9iZczwQHShix8Eidh4PSUfKHOzOKWZ3TvX7s3p72Ak5HpJC/b3M0HTytr8XoX7ehPl7mfv5e9MswNuy9dEUfpoQX19frrnmGt5++222b99Ox44d6dWrFwA//fQT48aN43e/+x0AhYWF7Nq1y8JqRUSkroT4e5HoH0pifGiF5w3DICu/hF05RRwqKiW3uIzcI8f/LC7lcHEZecefO3z8uTKHQanDyYGCEg4UlFS7hilXd+HWga1r+cqqR+GniRkzZgxXX301mzZt4qabbnI93759ez766COGDRuGzWZj8uTJOJ1OCysVEZH6ZrPZiA7xrfZaZIZhcKTM4QpCecVl5vZJgckMUKduhwV41fHVVE3hp4m57LLLCA8PJy0tjd///veu55977jluvfVWBgwYQEREBA888AD5+fkWVioiIu7OZrPh7+2Jv7cncaF+1T7OMAwMow4LOwOFnybGbrezb9+pHbQTEhL49ttvKzx35513Vvhat8FERKQ22Gy26k5TVyca91g2ERERkd9Q+BEREZEmReFHREREmhSFHxEREWlSFH7OkWFlN/VGQN8/ERGxisLPWfLyMuclKC62aCX3RqJ85mgPD2tm9xQRkaZLQ93PkoeHB6GhoWRnZwPg7+/foNYzcQdOp5MDBw7g7++Pp6f+CoqISP3Sb55zEB0dDeAKQHL27HY7LVu2VHAUEZF6Z3n4mTFjBs888wyZmZn07NmTF154gX79+lW676xZs5gzZw4bN24EoHfv3vzjH/+osL9hGEydOpVZs2aRm5vLBRdcwMsvv0z79u1rrWabzUZMTAyRkZGUlZXV2nmbEm9vb+x23XUVEZH6Z2n4mTt3LikpKcycOZOkpCSmT5/O4MGDSUtLIzIy8pT9Fy1axI033siAAQPw9fXlqaee4oorrmDTpk3ExcUB8PTTT/P888/zxhtv0Lp1ayZPnszgwYPZvHkzvr7VW6ukujw8PNRnRUREpIGxGRYOu0lKSqJv3768+OKLgNkXJD4+nrvvvpsHH3zwjMc7HA7CwsJ48cUXGTt2LIZhEBsby7333st9990HQF5eHlFRUcyePZvRo0dXq678/HxCQkLIy8sjODj43C9QRERE6k11f39bdt+htLSUVatWkZycfKIYu53k5GSWLl1arXMUFxdTVlZGeHg4ADt37iQzM7PCOUNCQkhKSjrtOUtKSsjPz6/wEBERkcbJsvBz8OBBHA4HUVFRFZ6PiooiMzOzWud44IEHiI2NdYWd8uPO9pzTpk0jJCTE9YiPjz+bSxEREZEGxPIOz+fqySef5L333mPRokU17sszadIkUlJSXF/n5eXRsmVLtQCJiIg0IOW/t8/Uo8ey8BMREYGHhwdZWVkVns/KynINJa/Ks88+y5NPPsnChQvp0aOH6/ny47KysoiJialwzsTExCrP5+Pjg4+Pj+vr8m+eWoBEREQanoKCAkJCQqp83bLw4+3tTe/evUlNTWXkyJGA2eE5NTWVu+66q8rjnn76af7+97/z9ddf06dPnwqvtW7dmujoaFJTU11hJz8/n+XLl3PHHXdUu7bY2FgyMjIICgqq1Xlo8vPziY+PJyMjo0l0pG5K16trbbya0vXqWhuvpnK9hmFQUFBAbGzsafez9LZXSkoKt9xyC3369KFfv35Mnz6doqIixo8fD8DYsWOJi4tj2rRpADz11FNMmTKFd955h4SEBFc/nsDAQAIDA7HZbEycOJEnnniC9u3bu4a6x8bGugJWddjtdlq0aFHr11suODi4Uf/l+62mdL261sarKV2vrrXxagrXe7oWn3KWhp9Ro0Zx4MABpkyZQmZmJomJicyfP9/VYTk9Pb3CRHgvv/wypaWlXHfddRXOM3XqVB555BEA7r//foqKirj99tvJzc1l4MCBzJ8/v9bn+BEREZGGydJ5fpqapjZ/UFO6Xl1r49WUrlfX2ng1tes9E60vUI98fHyYOnVqhc7VjVlTul5da+PVlK5X19p4NbXrPRO1/IiIiEiTopYfERERaVIUfkRERKRJUfgRERGRJkXhR0RERJoUhZ9aNmPGDBISEvD19SUpKYkVK1acdv/333+fTp064evrS/fu3fnyyy/rqdKamTZtGn379iUoKIjIyEhGjhxJWlraaY+ZPXs2NputwqMhzL/0yCOPnFJ3p06dTntMQ/1cARISEk65XpvNxp133lnp/g3pc/3+++8ZNmwYsbGx2Gw2PvnkkwqvG4bBlClTiImJwc/Pj+TkZLZt23bG857tz319ON21lpWV8cADD9C9e3cCAgKIjY1l7Nix7Nu377TnPJefhfpyps923Lhxp9Q+ZMiQM563oX22QKU/vzabjWeeeabKc7rzZ1sXFH5q0dy5c0lJSWHq1KmsXr2anj17MnjwYLKzsyvdf8mSJdx4443cdtttrFmzhpEjRzJy5Eg2btxYz5WfvcWLF3PnnXeybNkyFixYQFlZGVdccQVFRUWnPS44OJj9+/e7Hrt3766nimuma9euFer+8ccfq9y3IX+uACtXrqxwrQsWLADg+uuvr/KYhvK5FhUV0bNnT2bMmFHp608//TTPP/88M2fOZPny5QQEBDB48GCOHj1a5TnP9ue+vpzuWouLi1m9ejWTJ09m9erVfPTRR6SlpTF8+PAznvdsfhbq05k+W4AhQ4ZUqP3dd9897Tkb4mcLVLjG/fv389prr2Gz2bj22mtPe153/WzrhCG1pl+/fsadd97p+trhcBixsbHGtGnTKt3/hhtuMIYOHVrhuaSkJOP//u//6rTOupCdnW0AxuLFi6vc5/XXXzdCQkLqr6haMnXqVKNnz57V3r8xfa6GYRh/+ctfjLZt2xpOp7PS1xvq5woYH3/8setrp9NpREdHG88884zrudzcXMPHx8d49913qzzP2f7cW+G311qZFStWGICxe/fuKvc5258Fq1R2vbfccosxYsSIszpPY/lsR4wYYVx22WWn3aehfLa1RS0/taS0tJRVq1aRnJzses5ut5OcnMzSpUsrPWbp0qUV9gcYPHhwlfu7s7y8PADCw8NPu19hYSGtWrUiPj6eESNGsGnTpvoor8a2bdtGbGwsbdq0YcyYMaSnp1e5b2P6XEtLS3nrrbe49dZbT7vIb0P9XE+2c+dOMjMzK3x2ISEhJCUlVfnZncvPvbvKy8vDZrMRGhp62v3O5mfB3SxatIjIyEg6duzIHXfcQU5OTpX7NpbPNisri3nz5nHbbbedcd+G/NmeLYWfWnLw4EEcDodrXbJyUVFRrgVYfyszM/Os9ndXTqeTiRMncsEFF9CtW7cq9+vYsSOvvfYan376KW+99RZOp5MBAwawZ8+eeqz27CUlJTF79mzmz5/Pyy+/zM6dO7nwwgspKCiodP/G8rkCfPLJJ+Tm5jJu3Lgq92mon+tvlX8+Z/PZncvPvTs6evQoDzzwADfeeONplz44258FdzJkyBDmzJlDamoqTz31FIsXL+bKK6/E4XBUun9j+WzfeOMNgoKCuOaaa067X0P+bM+FpQubSuNw5513snHjxjPeH+7fvz/9+/d3fT1gwAA6d+7MK6+8wuOPP17XZZ6zK6+80rXdo0cPkpKSaNWqFf/73/+q9b+phuzVV1/lyiuvJDY2tsp9GurnKqaysjJuuOEGDMPg5ZdfPu2+DflnYfTo0a7t7t2706NHD9q2bcuiRYsYNGiQhZXVrddee40xY8accRBCQ/5sz4VafmpJREQEHh4eZGVlVXg+KyuL6OjoSo+Jjo4+q/3d0V133cUXX3zBd999R4sWLc7qWC8vL8477zy2b99eR9XVjdDQUDp06FBl3Y3hcwXYvXs3Cxcu5A9/+MNZHddQP9fyz+dsPrtz+bl3J+XBZ/fu3SxYsOCsF7w808+CO2vTpg0RERFV1t7QP1uAH374gbS0tLP+GYaG/dlWh8JPLfH29qZ3796kpqa6nnM6naSmplb4X/HJ+vfvX2F/gAULFlS5vzsxDIO77rqLjz/+mG+//ZbWrVuf9TkcDgcbNmwgJiamDiqsO4WFhezYsaPKuhvy53qy119/ncjISIYOHXpWxzXUz7V169ZER0dX+Ozy8/NZvnx5lZ/dufzcu4vy4LNt2zYWLlxIs2bNzvocZ/pZcGd79uwhJyenytob8mdb7tVXX6V379707NnzrI9tyJ9ttVjd47oxee+99wwfHx9j9uzZxubNm43bb7/dCA0NNTIzMw3DMIybb77ZePDBB137//TTT4anp6fx7LPPGlu2bDGmTp1qeHl5GRs2bLDqEqrtjjvuMEJCQoxFixYZ+/fvdz2Ki4td+/z2eh999FHj66+/Nnbs2GGsWrXKGD16tOHr62ts2rTJikuotnvvvddYtGiRsXPnTuOnn34ykpOTjYiICCM7O9swjMb1uZZzOBxGy5YtjQceeOCU1xry51pQUGCsWbPGWLNmjQEYzz33nLFmzRrXCKcnn3zSCA0NNT799FNj/fr1xogRI4zWrVsbR44ccZ3jsssuM1544QXX12f6ubfK6a61tLTUGD58uNGiRQtj7dq1FX6GS0pKXOf47bWe6WfBSqe73oKCAuO+++4zli5dauzcudNYuHCh0atXL6N9+/bG0aNHXedoDJ9tuby8PMPf3994+eWXKz1HQ/ps64LCTy174YUXjJYtWxre3t5Gv379jGXLlrleu/jii41bbrmlwv7/+9//jA4dOhje3t5G165djXnz5tVzxecGqPTx+uuvu/b57fVOnDjR9b2JiooyrrrqKmP16tX1X/xZGjVqlBETE2N4e3sbcXFxxqhRo4zt27e7Xm9Mn2u5r7/+2gCMtLS0U15ryJ/rd999V+nf2/LrcTqdxuTJk42oqCjDx8fHGDRo0Cnfg1atWhlTp06t8Nzpfu6tcrpr3blzZ5U/w999953rHL+91jP9LFjpdNdbXFxsXHHFFUbz5s0NLy8vo1WrVsaECRNOCTGN4bMt98orrxh+fn5Gbm5upedoSJ9tXbAZhmHUadOSiIiIiBtRnx8RERFpUhR+REREpElR+BEREZEmReFHREREmhSFHxEREWlSFH5ERESkSVH4ERERkSZF4UdERESaFIUfEZFqWLRoETabjdzcXKtLEZEaUvgRERGRJkXhR0RERJoUhR8RaRCcTifTpk2jdevW+Pn50bNnTz744APgxC2pefPm0aNHD3x9fTn//PPZuHFjhXN8+OGHdO3aFR8fHxISEvjnP/9Z4fWSkhIeeOAB4uPj8fHxoV27drz66qsV9lm1ahV9+vTB39+fAQMGkJaWVrcXLiK1TuFHRBqEadOmMWfOHGbOnMmmTZu45557uOmmm1i8eLFrn7/+9a/885//ZOXKlTRv3pxhw4ZRVlYGmKHlhhtuYPTo0WzYsIFHHnmEyZMnM3v2bNfxY8eO5d133+X5559ny5YtvPLKKwQGBlao4+GHH+af//wnP//8M56entx66631cv0iUnu0qruIuL2SkhLCw8NZuHAh/fv3dz3/hz/8geLiYm6//XYuvfRS3nvvPUaNGgXAoUOHaNGiBbNnz+aGG25gzJgxHDhwgG+++cZ1/P3338+8efPYtGkTW7dupWPHjixYsIDk5ORTali0aBGXXnopCxcuZNCgQQB8+eWXDB06lCNHjuDr61vH3wURqS1q+RERt7d9+3aKi4u5/PLLCQwMdD3mzJnDjh07XPudHIzCw8Pp2LEjW7ZsAWDLli1ccMEFFc57wQUXsG3bNhwOB2vXrsXDw4OLL774tLX06NHDtR0TEwNAdnZ2ja9RROqPp9UFiIicSWFhIQDz5s0jLi6uwms+Pj4VAtC58vPzq9Z+Xl5erm2bzQaY/ZFEpOFQy4+IuL0uXbrg4+NDeno67dq1q/CIj4937bds2TLX9uHDh9m6dSudO3cGoHPnzvz0008VzvvTTz/RoUMHPDw86N69O06ns0IfIhFpnNTyIyJuLygoiPvuu4977rkHp9PJwIEDycvL46effiI4OJhWrVoB8Nhjj9GsWTOioqJ4+OGHiYiIYOTIkQDce++99O3bl8cff5xRo0axdOlSXnzxRV566SUAEhISuOWWW7j11lt5/vnn6dmzJ7t37yY7O5sbbrjBqksXkTqg8CMiDcLjjz9O8+bNmTZtGr/++iuhoaH06tWLhx56yHXb6cknn+Qvf/kL27ZtIzExkc8//xxvb28AevXqxf/+9z+mTJnC448/TkxMDI899hjjxo1zvcfLL7/MQw89xJ/+9CdycnJo2bIlDz30kBWXKyJ1SKO9RKTBKx+JdfjwYUJDQ60uR0TcnPr8iIiISJOi8CMiIiJNim57iYiISJOilh8RERFpUhR+REREpElR+BEREZEmReFHREREmhSFHxEREWlSFH5ERESkSVH4ERERkSZF4UdERESalP8HhUG3NarQi9EAAAAASUVORK5CYII=\n"},"metadata":{}}],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train', 'val'])\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qC8gDwo3qcJv"},"outputs":[],"source":["model = model_fn()\n","model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])\n","\n","history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n"," validation_data=(val_scaled, val_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"k8wWnyFzsLKb","outputId":"ecbce503-d4d2-4daf-a802-e89ee31cc0da"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train', 'val'])\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"F-dFY8lrYXm3"},"source":["## 드롭아웃"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":262},"id":"AppFtFKgsk--","outputId":"c8f1362b-85ec-4d7e-8c08-3878c3b5ca88"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_4\"\u001b[0m\n"],"text/html":["
Model: \"sequential_4\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m78,500\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,010\u001b[0m │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n","│ flatten_4 (Flatten)                  │ (None, 784)                 │               0 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_8 (Dense)                      │ (None, 100)                 │          78,500 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dropout (Dropout)                    │ (None, 100)                 │               0 │\n","├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n","│ dense_9 (Dense)                      │ (None, 10)                  │           1,010 │\n","└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Total params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["
 Trainable params: 79,510 (310.59 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}}],"source":["model = model_fn(keras.layers.Dropout(0.3))\n","\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_TSe7oM9v1lW"},"outputs":[],"source":["model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])\n","\n","history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n"," validation_data=(val_scaled, val_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"Rj_syB_iv30l","outputId":"1f3a4f5d-72cd-4e78-fb26-b51b72579417"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train', 'val'])\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"hQi91PCQorHr"},"source":["## 모델 저장과 복원"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"npsYdX3rv6Oa"},"outputs":[],"source":["model = model_fn(keras.layers.Dropout(0.3))\n","model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])\n","\n","history = model.fit(train_scaled, train_target, epochs=10, verbose=0,\n"," validation_data=(val_scaled, val_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hWVYzt0Y2FPm"},"outputs":[],"source":["model.save('model-whole.keras')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NissHzcq3xbN"},"outputs":[],"source":["model.save_weights('model.weights.h5')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2vljkGGu3AUl","outputId":"1fa87796-0ceb-467e-c61f-b990a789199f","scrolled":true},"outputs":[{"output_type":"stream","name":"stdout","text":["-rw-r--r-- 1 root root 971928 Aug 6 06:42 model.weights.h5\n","-rw-r--r-- 1 root root 975720 Aug 6 06:42 model-whole.keras\n"]}],"source":["!ls -al model*"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7-t6gC5Z3GCM"},"outputs":[],"source":["model = model_fn(keras.layers.Dropout(0.3))\n","\n","model.load_weights('model.weights.h5')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pHV9tBnzi8St","outputId":"4fe46add-02d5-4816-e6d3-7215d80fee57"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n","0.878\n"]}],"source":["import numpy as np\n","\n","val_labels = np.argmax(model.predict(val_scaled), axis=-1)\n","print(np.mean(val_labels == val_target))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sRM3Vpki4QyH","outputId":"33b8f762-fa44-47a3-a64d-738fd112e4c8"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8831 - loss: 0.3368\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.33735886216163635, 0.878000020980835]"]},"metadata":{},"execution_count":25}],"source":["model = keras.models.load_model('model-whole.keras')\n","\n","model.evaluate(val_scaled, val_target)"]},{"cell_type":"markdown","metadata":{"id":"4NTCF3YD3EyA"},"source":["## 콜백"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L2lKN_934VB4","outputId":"87af097f-7c30-4aee-9429-d242211901fc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":26}],"source":["model = model_fn(keras.layers.Dropout(0.3))\n","model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])\n","\n","checkpoint_cb = keras.callbacks.ModelCheckpoint('best-model.keras',\n"," save_best_only=True)\n","\n","model.fit(train_scaled, train_target, epochs=20, verbose=0,\n"," validation_data=(val_scaled, val_target),\n"," callbacks=[checkpoint_cb])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qYISeH6U5oh9","outputId":"b85ff524-cbb9-4cd1-ae6e-e3685ca14f00"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8875 - loss: 0.3102\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.31200945377349854, 0.8870833516120911]"]},"metadata":{},"execution_count":27}],"source":["model = keras.models.load_model('best-model.keras')\n","\n","model.evaluate(val_scaled, val_target)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HLLlkR0s5Nd8"},"outputs":[],"source":["model = model_fn(keras.layers.Dropout(0.3))\n","model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n"," metrics=['accuracy'])\n","\n","checkpoint_cb = keras.callbacks.ModelCheckpoint('best-model.keras',\n"," save_best_only=True)\n","early_stopping_cb = keras.callbacks.EarlyStopping(patience=2,\n"," restore_best_weights=True)\n","\n","history = model.fit(train_scaled, train_target, epochs=20, verbose=0,\n"," validation_data=(val_scaled, val_target),\n"," callbacks=[checkpoint_cb, early_stopping_cb])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b6TazMcDxqXD","outputId":"8400394d-b62b-4d4d-c2cd-030aa5ab61f7"},"outputs":[{"output_type":"stream","name":"stdout","text":["16\n"]}],"source":["print(early_stopping_cb.stopped_epoch)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"QrUNYGPB6Kq7","outputId":"7c03f6cf-18a8-433f-cf19-031171331bc2"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.xlabel('epoch')\n","plt.ylabel('loss')\n","plt.legend(['train', 'val'])\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H-7y1qlg5yqO","outputId":"4a272196-10e9-449d-c3ca-96a1e6e7e65e"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8868 - loss: 0.3207\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.32281607389450073, 0.8859166502952576]"]},"metadata":{},"execution_count":31}],"source":["model.evaluate(val_scaled, val_target)"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"https://github.com/rickiepark/hg-mldl/blob/master/7-3.ipynb","timestamp":1731458760737}]},"kernelspec":{"display_name":"default:Python","language":"python","name":"conda-env-default-py"},"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":0} \ No newline at end of file